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# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import inspect
import io
import itertools
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
from megengine.utils.naming import AutoNaming
@pytest.mark.parametrize("trace_mode", [False, True])
@pytest.mark.parametrize("return_mode", ["Value", "Tuple", "List", "Dict"])
def test_trace(trace_mode, return_mode):
@trace(symbolic=trace_mode)
def f(x):
if return_mode == "Tuple":
return (-x,)
elif return_mode == "List":
return [-x]
elif return_mode == "Dict":
return {"neg": -x}
else:
return -x
def get_numpy(y):
if return_mode == "Tuple" or return_mode == "List":
return y[0].numpy()
elif return_mode == "Dict":
return y["neg"].numpy()
return y.numpy()
x = tensor([1])
y = get_numpy(f(x))
for i in range(3):
np.testing.assert_equal(get_numpy(f(x)), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = Parameter([1.0], dtype=np.float32)
def forward(self, x):
x = x * self.a
# will result into a copy of output in grad
x = F.exp(x)
return x
ys = {False: [], True: []}
for symbolic in [False, True]:
net = Simple()
gm = GradManager().attach(net.parameters())
opt = optim.SGD(net.parameters(), 1e-3, momentum=0.9)
data = tensor(np.arange(4).reshape(2, 2), dtype="float32")
@trace(symbolic=symbolic)
def train_func(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(3):
y = train_func(data).numpy()
ys[symbolic].append(y)
for i in range(3):
np.testing.assert_equal(ys[False][i], ys[True][i])
@pytest.mark.parametrize("trace_mode", [False, True])
def test_exclude_from_trace(trace_mode):
@trace(symbolic=trace_mode)
def f(x):
x = -x
with exclude_from_trace():
if i % 2:
x = -x
x = -x
return x
x = tensor([1])
for i in range(3):
y = f(x).numpy()
np.testing.assert_equal(f(x).numpy(), y)
@pytest.mark.parametrize("trace_mode", [False, True])
def test_elemwise_fuse(trace_mode):
# explicitly declare opt_level as 2
@trace(symbolic=trace_mode, opt_level=2)
def f(a, b):
base = 0
c = b - a
_, idx = F.topk(c, 3)
# internally, biased_idx will be idx as gopt will ignore the addition
biased_idx = base + idx
return biased_idx
a = tensor(np.ones((7, 2)), dtype=np.int32)
b = tensor(2 * np.ones((7, 2)), dtype=np.float32)
for i in range(3):
y = f(a, b)
y.numpy()
@pytest.mark.parametrize("trace_mode", [False, True])
def test_elemwise_fuse_in_grad(trace_mode):
w = Parameter(np.ones([4, 6]), dtype="float32")
gm = GradManager().attach(w)
opt = optim.SGD([w], lr=0.01, momentum=0.9, weight_decay=5e-4)
# explicitly declare opt_level as 2
@trace(symbolic=trace_mode, opt_level=2)
def f():
with gm:
wm = F.sum(w ** 2, axis=1) ** 0.5
loss = wm.mean()
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(3):
y = f()
y.numpy()
def test_print_in_trace():
for symbolic in [False]: # cannot read value in symbolic mode
@trace(symbolic=symbolic)
def f(x):
nonlocal buf
x = -x
buf = x.numpy()
x = -x
return x
buf = None
x = tensor([1])
for i in range(3):
y = f(x).numpy()
z = buf
buf = None
np.testing.assert_equal(f(x).numpy(), y)
np.testing.assert_equal(z, buf)
def test_dump():
@trace(symbolic=True, capture_as_const=True)
def f(a, b):
return a + b
# prevent from remaining scope from exception test
AutoNaming.clear()
a = tensor([2])
b = tensor([4])
y = f(a, b).numpy()
for i in range(3):
np.testing.assert_equal(f(a, b).numpy(), y)
file = io.BytesIO()
dump_info = f.dump(file)
assert dump_info.nr_opr == 3
np.testing.assert_equal(dump_info.inputs, ["arg_0", "arg_1"])
np.testing.assert_equal(dump_info.outputs, ["ADD"])
file.seek(0)
infer_cg = cgtools.GraphInference(file)
result = list((infer_cg.run(a, b)).values())[0]
np.testing.assert_equal(result[0], y)
def test_capture_dump():
a = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * a
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file)
file.seek(0)
infer_cg = cgtools.GraphInference(file)
result = list((infer_cg.run(x)).values())[0]
np.testing.assert_equal(result[0], y)
def test_dump_volatile():
p = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * p
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file, optimize_for_inference=False)
file.seek(0)
cg, _, outputs = G.load_graph(file)
(out,) = outputs
assert (
cgtools.get_owner_opr_type(cgtools.get_owner_opr_inputs(out)[1])
== "ImmutableTensor"
)
@pytest.mark.parametrize("trace_mode", [False, True])
def test_trace_profiler(trace_mode):
@trace(symbolic=trace_mode, profiling=True)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
f(x)
f(x) # XXX: has to run twice
out = f.get_profile()
assert out.get("profiler")
def test_goptions():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
# directly return x / x will not trigger gopt
# since there's no way to tell the two x are the same
y = 2.0 * x
return y / y
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
y = 2.0 * x
return y / y
d = tensor(0.0)
assert not np.isfinite(f(d).numpy())
np.testing.assert_equal(g(d).numpy().item(), 1.0)
def test_goptions_log_sum_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x, y):
return log(exp(x) + exp(y))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x, y):
return log(exp(x) + exp(y))
val = 1.0e4
d = tensor(val)
o = tensor(0.0)
assert not np.isfinite(f(d, o).numpy())
np.testing.assert_almost_equal(g(d, o), val)
def test_goptions_log_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
return log(exp(x))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
return log(exp(x))
f(tensor(1.0))
_, out = mkstemp()
f.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_1 = cgtools.get_oprs_seq(outputs)
g(tensor(1.0))
g.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_2 = cgtools.get_oprs_seq(outputs)
assert len(oprs_1) - len(oprs_2) == 2
def test_optimize_for_inference():
@trace(symbolic=True, capture_as_const=True)
def f(x):
return exp(x)
_, out = mkstemp()
f(tensor(5.0))
f.dump(out, enable_io16xc32=True)
res = G.load_graph(out)
computing_input = res.output_vars_list[0].owner.inputs[0]
assert computing_input.dtype == np.float16
def test_optimize_for_inference_broadcast():
a = tensor(np.ones(1, dtype=np.float32))
@trace(capture_as_const=True, symbolic_shape=True)
def f():
return a._broadcast(tensor([1, 10], dtype=np.int32))
f()
f.dump(io.BytesIO())
def test_trace_cvt_bool():
x = tensor([0], dtype=np.int32)
@trace(symbolic=True)
def f(x):
a = x.shape
b = a[0]
assert isscalar(b)
return b == 0
for i in range(3):
np.testing.assert_equal(f(x).numpy(), False)
@pytest.mark.parametrize("trace_mode", [False, True])
def test_trace_reshape(trace_mode):
x1 = tensor(np.random.randn(2, 10, 10))
x2 = tensor(np.random.randn(4, 10, 10))
x3 = tensor(np.random.randn(8, 10, 10))
@trace(symbolic=trace_mode, capture_as_const=True)
def f(x):
y = x.reshape(x.shape[0], 100)
return y
f(x1)
f(x2)
f(x3)
def test_trace_topk():
x = tensor([5, 2, 7, 1, 0, 3, 2])
@trace(symbolic=True)
def f(x):
y = F.topk(x, 3)
np.testing.assert_equal(y[0].shape.numpy(), np.array([3,]))
return y
for i in range(3):
f(x)
def test_trace_warp_perspective():
inp_shape = (1, 1, 4, 4)
x = tensor(np.arange(16, dtype=np.float32).reshape(inp_shape))
M_shape = (1, 3, 3)
M = tensor(
np.array(
[[1.0, 0.0, 1.0], [0.0, 1.0, 1.0], [0.0, 0.0, 1.0]], dtype=np.float32
).reshape(M_shape)
)
@trace(symbolic=True)
def f(x, M):
out = F.vision.warp_perspective(x, M, (2, 2))
np.testing.assert_equal(out.shape.numpy(), np.array([1, 1, 2, 2]))
return out
for i in range(3):
f(x, M)
def test_raise_on_trace():
step_count = 0
catch_count = 0
bad_step = 10
class CatchMe(Exception):
pass
a = tensor([1, 2, 3, 4])
b = tensor([5, 6, 7, 8])
c = tensor([9, 0, 1, 2])
@trace
def add_abc(a, b, c):
ps = a + b
result = ps + c
if step_count == bad_step:
raise CatchMe("catch me")
return result
for i in range(100):
try:
d = add_abc(a, b, c)
except CatchMe as e:
catch_count += 1
else:
np.testing.assert_equal(d.numpy(), (a + b + c).numpy())
step_count += 1
assert catch_count == 1
@pytest.mark.parametrize("trace_mode", [False, True])
def test_trace_broadcast(trace_mode):
x1 = tensor(np.random.randn(3, 1, 1))
x2 = tensor(np.random.randn(1, 4, 1))
x3 = tensor(np.random.randn(1, 1, 5))
@trace(symbolic=trace_mode, capture_as_const=True)
def f(x):
y = F.broadcast_to(x, (3, 4, 5))
return y
f(x1)
f(x2)
f(x3)
def test_trace_nms():
def make_inputs(n):
boxes = np.zeros((n, 4))
boxes[:, :2] = np.random.rand(n, 2) * 100
boxes[:, 2:] = np.random.rand(n, 2) * 100 + 100
scores = np.random.rand(n)
return tensor(boxes), tensor(scores)
@trace(symbolic=False)
def f(boxes, scores):
# with tracing, max_output must be specified
results = F.vision.nms(boxes, scores=scores, iou_thresh=0.5, max_output=20)
# without tracing, max output can be inferred inside nms
with exclude_from_trace():
_ = F.vision.nms(boxes, scores=scores, iou_thresh=0.5)
return results
f(*make_inputs(10))
f(*make_inputs(20))
f(*make_inputs(30))
def test_trace_valid_broadcast():
x1 = tensor(np.random.randn(1, 1))
x2 = tensor(np.random.randn(1, 2))
shape = (tensor([2]), tensor([2]))
@trace(symbolic=False)
def f(x, shape):
y = F.broadcast_to(x, shape)
return y
f(x1, shape)
f(x2, shape)
def test_clip():
x = tensor(np.random.randn(10, 10))
@trace(symbolic=True)
def f(x, lower, upper):
y = F.clip(x, lower, upper)
return y
for i in range(3):
f(x, tensor([0]), tensor([1]))
# test returning noncontiguous tensor from trace
def test_slice():
@trace
def f(x):
return x[:, 1::2]
x = F.arange(8).reshape(2, 4)
f(x)
y = f(x)
np.testing.assert_array_equal(y.numpy(), x.numpy()[:, 1::2])
y + y
@pytest.mark.parametrize("shape_mode", [False, True])
def test_random(shape_mode):
def run_test(op):
@ | trace(symbolic=True, symbolic_shape=shape_mode) | megengine.jit.trace |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import inspect
import io
import itertools
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
from megengine.utils.naming import AutoNaming
@pytest.mark.parametrize("trace_mode", [False, True])
@pytest.mark.parametrize("return_mode", ["Value", "Tuple", "List", "Dict"])
def test_trace(trace_mode, return_mode):
@trace(symbolic=trace_mode)
def f(x):
if return_mode == "Tuple":
return (-x,)
elif return_mode == "List":
return [-x]
elif return_mode == "Dict":
return {"neg": -x}
else:
return -x
def get_numpy(y):
if return_mode == "Tuple" or return_mode == "List":
return y[0].numpy()
elif return_mode == "Dict":
return y["neg"].numpy()
return y.numpy()
x = tensor([1])
y = get_numpy(f(x))
for i in range(3):
np.testing.assert_equal(get_numpy(f(x)), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = Parameter([1.0], dtype=np.float32)
def forward(self, x):
x = x * self.a
# will result into a copy of output in grad
x = F.exp(x)
return x
ys = {False: [], True: []}
for symbolic in [False, True]:
net = Simple()
gm = GradManager().attach(net.parameters())
opt = optim.SGD(net.parameters(), 1e-3, momentum=0.9)
data = tensor(np.arange(4).reshape(2, 2), dtype="float32")
@trace(symbolic=symbolic)
def train_func(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(3):
y = train_func(data).numpy()
ys[symbolic].append(y)
for i in range(3):
np.testing.assert_equal(ys[False][i], ys[True][i])
@pytest.mark.parametrize("trace_mode", [False, True])
def test_exclude_from_trace(trace_mode):
@trace(symbolic=trace_mode)
def f(x):
x = -x
with exclude_from_trace():
if i % 2:
x = -x
x = -x
return x
x = tensor([1])
for i in range(3):
y = f(x).numpy()
np.testing.assert_equal(f(x).numpy(), y)
@pytest.mark.parametrize("trace_mode", [False, True])
def test_elemwise_fuse(trace_mode):
# explicitly declare opt_level as 2
@trace(symbolic=trace_mode, opt_level=2)
def f(a, b):
base = 0
c = b - a
_, idx = F.topk(c, 3)
# internally, biased_idx will be idx as gopt will ignore the addition
biased_idx = base + idx
return biased_idx
a = tensor(np.ones((7, 2)), dtype=np.int32)
b = tensor(2 * np.ones((7, 2)), dtype=np.float32)
for i in range(3):
y = f(a, b)
y.numpy()
@pytest.mark.parametrize("trace_mode", [False, True])
def test_elemwise_fuse_in_grad(trace_mode):
w = Parameter(np.ones([4, 6]), dtype="float32")
gm = GradManager().attach(w)
opt = optim.SGD([w], lr=0.01, momentum=0.9, weight_decay=5e-4)
# explicitly declare opt_level as 2
@trace(symbolic=trace_mode, opt_level=2)
def f():
with gm:
wm = F.sum(w ** 2, axis=1) ** 0.5
loss = wm.mean()
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(3):
y = f()
y.numpy()
def test_print_in_trace():
for symbolic in [False]: # cannot read value in symbolic mode
@trace(symbolic=symbolic)
def f(x):
nonlocal buf
x = -x
buf = x.numpy()
x = -x
return x
buf = None
x = tensor([1])
for i in range(3):
y = f(x).numpy()
z = buf
buf = None
np.testing.assert_equal(f(x).numpy(), y)
np.testing.assert_equal(z, buf)
def test_dump():
@trace(symbolic=True, capture_as_const=True)
def f(a, b):
return a + b
# prevent from remaining scope from exception test
AutoNaming.clear()
a = tensor([2])
b = tensor([4])
y = f(a, b).numpy()
for i in range(3):
np.testing.assert_equal(f(a, b).numpy(), y)
file = io.BytesIO()
dump_info = f.dump(file)
assert dump_info.nr_opr == 3
np.testing.assert_equal(dump_info.inputs, ["arg_0", "arg_1"])
np.testing.assert_equal(dump_info.outputs, ["ADD"])
file.seek(0)
infer_cg = cgtools.GraphInference(file)
result = list((infer_cg.run(a, b)).values())[0]
np.testing.assert_equal(result[0], y)
def test_capture_dump():
a = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * a
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file)
file.seek(0)
infer_cg = cgtools.GraphInference(file)
result = list((infer_cg.run(x)).values())[0]
np.testing.assert_equal(result[0], y)
def test_dump_volatile():
p = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * p
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file, optimize_for_inference=False)
file.seek(0)
cg, _, outputs = G.load_graph(file)
(out,) = outputs
assert (
cgtools.get_owner_opr_type(cgtools.get_owner_opr_inputs(out)[1])
== "ImmutableTensor"
)
@pytest.mark.parametrize("trace_mode", [False, True])
def test_trace_profiler(trace_mode):
@trace(symbolic=trace_mode, profiling=True)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
f(x)
f(x) # XXX: has to run twice
out = f.get_profile()
assert out.get("profiler")
def test_goptions():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
# directly return x / x will not trigger gopt
# since there's no way to tell the two x are the same
y = 2.0 * x
return y / y
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
y = 2.0 * x
return y / y
d = tensor(0.0)
assert not np.isfinite(f(d).numpy())
np.testing.assert_equal(g(d).numpy().item(), 1.0)
def test_goptions_log_sum_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x, y):
return log(exp(x) + exp(y))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x, y):
return log(exp(x) + exp(y))
val = 1.0e4
d = tensor(val)
o = tensor(0.0)
assert not np.isfinite(f(d, o).numpy())
np.testing.assert_almost_equal(g(d, o), val)
def test_goptions_log_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
return log(exp(x))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
return log(exp(x))
f(tensor(1.0))
_, out = mkstemp()
f.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_1 = cgtools.get_oprs_seq(outputs)
g(tensor(1.0))
g.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_2 = cgtools.get_oprs_seq(outputs)
assert len(oprs_1) - len(oprs_2) == 2
def test_optimize_for_inference():
@trace(symbolic=True, capture_as_const=True)
def f(x):
return exp(x)
_, out = mkstemp()
f(tensor(5.0))
f.dump(out, enable_io16xc32=True)
res = G.load_graph(out)
computing_input = res.output_vars_list[0].owner.inputs[0]
assert computing_input.dtype == np.float16
def test_optimize_for_inference_broadcast():
a = tensor(np.ones(1, dtype=np.float32))
@trace(capture_as_const=True, symbolic_shape=True)
def f():
return a._broadcast(tensor([1, 10], dtype=np.int32))
f()
f.dump(io.BytesIO())
def test_trace_cvt_bool():
x = tensor([0], dtype=np.int32)
@trace(symbolic=True)
def f(x):
a = x.shape
b = a[0]
assert isscalar(b)
return b == 0
for i in range(3):
np.testing.assert_equal(f(x).numpy(), False)
@pytest.mark.parametrize("trace_mode", [False, True])
def test_trace_reshape(trace_mode):
x1 = tensor(np.random.randn(2, 10, 10))
x2 = tensor(np.random.randn(4, 10, 10))
x3 = tensor(np.random.randn(8, 10, 10))
@trace(symbolic=trace_mode, capture_as_const=True)
def f(x):
y = x.reshape(x.shape[0], 100)
return y
f(x1)
f(x2)
f(x3)
def test_trace_topk():
x = tensor([5, 2, 7, 1, 0, 3, 2])
@trace(symbolic=True)
def f(x):
y = F.topk(x, 3)
np.testing.assert_equal(y[0].shape.numpy(), np.array([3,]))
return y
for i in range(3):
f(x)
def test_trace_warp_perspective():
inp_shape = (1, 1, 4, 4)
x = tensor(np.arange(16, dtype=np.float32).reshape(inp_shape))
M_shape = (1, 3, 3)
M = tensor(
np.array(
[[1.0, 0.0, 1.0], [0.0, 1.0, 1.0], [0.0, 0.0, 1.0]], dtype=np.float32
).reshape(M_shape)
)
@trace(symbolic=True)
def f(x, M):
out = F.vision.warp_perspective(x, M, (2, 2))
np.testing.assert_equal(out.shape.numpy(), np.array([1, 1, 2, 2]))
return out
for i in range(3):
f(x, M)
def test_raise_on_trace():
step_count = 0
catch_count = 0
bad_step = 10
class CatchMe(Exception):
pass
a = tensor([1, 2, 3, 4])
b = tensor([5, 6, 7, 8])
c = tensor([9, 0, 1, 2])
@trace
def add_abc(a, b, c):
ps = a + b
result = ps + c
if step_count == bad_step:
raise CatchMe("catch me")
return result
for i in range(100):
try:
d = add_abc(a, b, c)
except CatchMe as e:
catch_count += 1
else:
np.testing.assert_equal(d.numpy(), (a + b + c).numpy())
step_count += 1
assert catch_count == 1
@pytest.mark.parametrize("trace_mode", [False, True])
def test_trace_broadcast(trace_mode):
x1 = tensor(np.random.randn(3, 1, 1))
x2 = tensor(np.random.randn(1, 4, 1))
x3 = tensor(np.random.randn(1, 1, 5))
@trace(symbolic=trace_mode, capture_as_const=True)
def f(x):
y = F.broadcast_to(x, (3, 4, 5))
return y
f(x1)
f(x2)
f(x3)
def test_trace_nms():
def make_inputs(n):
boxes = np.zeros((n, 4))
boxes[:, :2] = np.random.rand(n, 2) * 100
boxes[:, 2:] = np.random.rand(n, 2) * 100 + 100
scores = np.random.rand(n)
return tensor(boxes), tensor(scores)
@trace(symbolic=False)
def f(boxes, scores):
# with tracing, max_output must be specified
results = F.vision.nms(boxes, scores=scores, iou_thresh=0.5, max_output=20)
# without tracing, max output can be inferred inside nms
with exclude_from_trace():
_ = F.vision.nms(boxes, scores=scores, iou_thresh=0.5)
return results
f(*make_inputs(10))
f(*make_inputs(20))
f(*make_inputs(30))
def test_trace_valid_broadcast():
x1 = tensor(np.random.randn(1, 1))
x2 = tensor(np.random.randn(1, 2))
shape = (tensor([2]), tensor([2]))
@trace(symbolic=False)
def f(x, shape):
y = F.broadcast_to(x, shape)
return y
f(x1, shape)
f(x2, shape)
def test_clip():
x = tensor(np.random.randn(10, 10))
@trace(symbolic=True)
def f(x, lower, upper):
y = F.clip(x, lower, upper)
return y
for i in range(3):
f(x, tensor([0]), tensor([1]))
# test returning noncontiguous tensor from trace
def test_slice():
@trace
def f(x):
return x[:, 1::2]
x = F.arange(8).reshape(2, 4)
f(x)
y = f(x)
np.testing.assert_array_equal(y.numpy(), x.numpy()[:, 1::2])
y + y
@pytest.mark.parametrize("shape_mode", [False, True])
def test_random(shape_mode):
def run_test(op):
@trace(symbolic=True, symbolic_shape=shape_mode)
def f():
out = op(size=[10, 10])
out_shape = out.shape
assert out_shape is not None
if not isinstance(out_shape, tuple):
assert out.shape.numpy() is not None
return out
for _ in range(3):
f()
run_test(uniform)
run_test(normal)
@pytest.mark.parametrize("shape_mode", [False, True])
def test_trace_advance_indexing(shape_mode):
funcs = [
lambda x, i: x[i],
# lambda x, i, j: x[i, j], # FIXME
lambda x, i, j: x[i, :, j, ...],
# lambda x, start, end: x[start:end], # FIXME
lambda x, start, end: x[:, 0, start:end, ..., 1],
lambda x, vec: x[vec],
lambda x, vec: x[vec, ..., 0, 1:3],
lambda x, vec: x[vec, vec[0], vec[1]],
# lambda x, i, start, end, vec: x[i, ..., :, vec, start:end], # FIXME
lambda x, mask: x[mask],
]
inputs = {
"x": np.random.randn(5, 5, 5, 5, 5).astype("float32"),
"i": 0,
"j": 2,
"start": 1,
"end": 3,
"vec": [1, 2, 3],
"mask": np.random.randn(5, 5, 5, 5, 5) >= 0,
}
for f in funcs:
sig = inspect.signature(f)
param_names = list(sig._parameters.keys())
params = {}
params_np = {}
f_traced = | trace(f, symbolic=False, symbolic_shape=shape_mode) | megengine.jit.trace |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import inspect
import io
import itertools
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
from megengine.utils.naming import AutoNaming
@pytest.mark.parametrize("trace_mode", [False, True])
@pytest.mark.parametrize("return_mode", ["Value", "Tuple", "List", "Dict"])
def test_trace(trace_mode, return_mode):
@trace(symbolic=trace_mode)
def f(x):
if return_mode == "Tuple":
return (-x,)
elif return_mode == "List":
return [-x]
elif return_mode == "Dict":
return {"neg": -x}
else:
return -x
def get_numpy(y):
if return_mode == "Tuple" or return_mode == "List":
return y[0].numpy()
elif return_mode == "Dict":
return y["neg"].numpy()
return y.numpy()
x = tensor([1])
y = get_numpy(f(x))
for i in range(3):
np.testing.assert_equal(get_numpy(f(x)), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = | Parameter([1.0], dtype=np.float32) | megengine.Parameter |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import inspect
import io
import itertools
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
from megengine.utils.naming import AutoNaming
@pytest.mark.parametrize("trace_mode", [False, True])
@pytest.mark.parametrize("return_mode", ["Value", "Tuple", "List", "Dict"])
def test_trace(trace_mode, return_mode):
@trace(symbolic=trace_mode)
def f(x):
if return_mode == "Tuple":
return (-x,)
elif return_mode == "List":
return [-x]
elif return_mode == "Dict":
return {"neg": -x}
else:
return -x
def get_numpy(y):
if return_mode == "Tuple" or return_mode == "List":
return y[0].numpy()
elif return_mode == "Dict":
return y["neg"].numpy()
return y.numpy()
x = tensor([1])
y = get_numpy(f(x))
for i in range(3):
np.testing.assert_equal(get_numpy(f(x)), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = Parameter([1.0], dtype=np.float32)
def forward(self, x):
x = x * self.a
# will result into a copy of output in grad
x = | F.exp(x) | megengine.functional.exp |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import inspect
import io
import itertools
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
from megengine.utils.naming import AutoNaming
@pytest.mark.parametrize("trace_mode", [False, True])
@pytest.mark.parametrize("return_mode", ["Value", "Tuple", "List", "Dict"])
def test_trace(trace_mode, return_mode):
@trace(symbolic=trace_mode)
def f(x):
if return_mode == "Tuple":
return (-x,)
elif return_mode == "List":
return [-x]
elif return_mode == "Dict":
return {"neg": -x}
else:
return -x
def get_numpy(y):
if return_mode == "Tuple" or return_mode == "List":
return y[0].numpy()
elif return_mode == "Dict":
return y["neg"].numpy()
return y.numpy()
x = tensor([1])
y = get_numpy(f(x))
for i in range(3):
np.testing.assert_equal(get_numpy(f(x)), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = Parameter([1.0], dtype=np.float32)
def forward(self, x):
x = x * self.a
# will result into a copy of output in grad
x = F.exp(x)
return x
ys = {False: [], True: []}
for symbolic in [False, True]:
net = Simple()
gm = GradManager().attach(net.parameters())
opt = optim.SGD(net.parameters(), 1e-3, momentum=0.9)
data = tensor(np.arange(4).reshape(2, 2), dtype="float32")
@trace(symbolic=symbolic)
def train_func(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(3):
y = train_func(data).numpy()
ys[symbolic].append(y)
for i in range(3):
np.testing.assert_equal(ys[False][i], ys[True][i])
@pytest.mark.parametrize("trace_mode", [False, True])
def test_exclude_from_trace(trace_mode):
@trace(symbolic=trace_mode)
def f(x):
x = -x
with | exclude_from_trace() | megengine.jit.exclude_from_trace |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import inspect
import io
import itertools
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
from megengine.utils.naming import AutoNaming
@pytest.mark.parametrize("trace_mode", [False, True])
@pytest.mark.parametrize("return_mode", ["Value", "Tuple", "List", "Dict"])
def test_trace(trace_mode, return_mode):
@trace(symbolic=trace_mode)
def f(x):
if return_mode == "Tuple":
return (-x,)
elif return_mode == "List":
return [-x]
elif return_mode == "Dict":
return {"neg": -x}
else:
return -x
def get_numpy(y):
if return_mode == "Tuple" or return_mode == "List":
return y[0].numpy()
elif return_mode == "Dict":
return y["neg"].numpy()
return y.numpy()
x = tensor([1])
y = get_numpy(f(x))
for i in range(3):
np.testing.assert_equal(get_numpy(f(x)), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = Parameter([1.0], dtype=np.float32)
def forward(self, x):
x = x * self.a
# will result into a copy of output in grad
x = F.exp(x)
return x
ys = {False: [], True: []}
for symbolic in [False, True]:
net = Simple()
gm = GradManager().attach(net.parameters())
opt = optim.SGD(net.parameters(), 1e-3, momentum=0.9)
data = tensor(np.arange(4).reshape(2, 2), dtype="float32")
@trace(symbolic=symbolic)
def train_func(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(3):
y = train_func(data).numpy()
ys[symbolic].append(y)
for i in range(3):
np.testing.assert_equal(ys[False][i], ys[True][i])
@pytest.mark.parametrize("trace_mode", [False, True])
def test_exclude_from_trace(trace_mode):
@trace(symbolic=trace_mode)
def f(x):
x = -x
with exclude_from_trace():
if i % 2:
x = -x
x = -x
return x
x = tensor([1])
for i in range(3):
y = f(x).numpy()
np.testing.assert_equal(f(x).numpy(), y)
@pytest.mark.parametrize("trace_mode", [False, True])
def test_elemwise_fuse(trace_mode):
# explicitly declare opt_level as 2
@trace(symbolic=trace_mode, opt_level=2)
def f(a, b):
base = 0
c = b - a
_, idx = F.topk(c, 3)
# internally, biased_idx will be idx as gopt will ignore the addition
biased_idx = base + idx
return biased_idx
a = tensor(np.ones((7, 2)), dtype=np.int32)
b = tensor(2 * np.ones((7, 2)), dtype=np.float32)
for i in range(3):
y = f(a, b)
y.numpy()
@pytest.mark.parametrize("trace_mode", [False, True])
def test_elemwise_fuse_in_grad(trace_mode):
w = Parameter(np.ones([4, 6]), dtype="float32")
gm = | GradManager() | megengine.autodiff.GradManager |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import inspect
import io
import itertools
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
from megengine.utils.naming import AutoNaming
@pytest.mark.parametrize("trace_mode", [False, True])
@pytest.mark.parametrize("return_mode", ["Value", "Tuple", "List", "Dict"])
def test_trace(trace_mode, return_mode):
@trace(symbolic=trace_mode)
def f(x):
if return_mode == "Tuple":
return (-x,)
elif return_mode == "List":
return [-x]
elif return_mode == "Dict":
return {"neg": -x}
else:
return -x
def get_numpy(y):
if return_mode == "Tuple" or return_mode == "List":
return y[0].numpy()
elif return_mode == "Dict":
return y["neg"].numpy()
return y.numpy()
x = tensor([1])
y = get_numpy(f(x))
for i in range(3):
np.testing.assert_equal(get_numpy(f(x)), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = Parameter([1.0], dtype=np.float32)
def forward(self, x):
x = x * self.a
# will result into a copy of output in grad
x = F.exp(x)
return x
ys = {False: [], True: []}
for symbolic in [False, True]:
net = Simple()
gm = GradManager().attach(net.parameters())
opt = optim.SGD(net.parameters(), 1e-3, momentum=0.9)
data = tensor(np.arange(4).reshape(2, 2), dtype="float32")
@trace(symbolic=symbolic)
def train_func(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(3):
y = train_func(data).numpy()
ys[symbolic].append(y)
for i in range(3):
np.testing.assert_equal(ys[False][i], ys[True][i])
@pytest.mark.parametrize("trace_mode", [False, True])
def test_exclude_from_trace(trace_mode):
@trace(symbolic=trace_mode)
def f(x):
x = -x
with exclude_from_trace():
if i % 2:
x = -x
x = -x
return x
x = tensor([1])
for i in range(3):
y = f(x).numpy()
np.testing.assert_equal(f(x).numpy(), y)
@pytest.mark.parametrize("trace_mode", [False, True])
def test_elemwise_fuse(trace_mode):
# explicitly declare opt_level as 2
@trace(symbolic=trace_mode, opt_level=2)
def f(a, b):
base = 0
c = b - a
_, idx = F.topk(c, 3)
# internally, biased_idx will be idx as gopt will ignore the addition
biased_idx = base + idx
return biased_idx
a = tensor(np.ones((7, 2)), dtype=np.int32)
b = tensor(2 * np.ones((7, 2)), dtype=np.float32)
for i in range(3):
y = f(a, b)
y.numpy()
@pytest.mark.parametrize("trace_mode", [False, True])
def test_elemwise_fuse_in_grad(trace_mode):
w = Parameter(np.ones([4, 6]), dtype="float32")
gm = GradManager().attach(w)
opt = optim.SGD([w], lr=0.01, momentum=0.9, weight_decay=5e-4)
# explicitly declare opt_level as 2
@trace(symbolic=trace_mode, opt_level=2)
def f():
with gm:
wm = F.sum(w ** 2, axis=1) ** 0.5
loss = wm.mean()
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(3):
y = f()
y.numpy()
def test_print_in_trace():
for symbolic in [False]: # cannot read value in symbolic mode
@trace(symbolic=symbolic)
def f(x):
nonlocal buf
x = -x
buf = x.numpy()
x = -x
return x
buf = None
x = tensor([1])
for i in range(3):
y = f(x).numpy()
z = buf
buf = None
np.testing.assert_equal(f(x).numpy(), y)
np.testing.assert_equal(z, buf)
def test_dump():
@trace(symbolic=True, capture_as_const=True)
def f(a, b):
return a + b
# prevent from remaining scope from exception test
AutoNaming.clear()
a = tensor([2])
b = tensor([4])
y = f(a, b).numpy()
for i in range(3):
np.testing.assert_equal(f(a, b).numpy(), y)
file = io.BytesIO()
dump_info = f.dump(file)
assert dump_info.nr_opr == 3
np.testing.assert_equal(dump_info.inputs, ["arg_0", "arg_1"])
np.testing.assert_equal(dump_info.outputs, ["ADD"])
file.seek(0)
infer_cg = cgtools.GraphInference(file)
result = list((infer_cg.run(a, b)).values())[0]
np.testing.assert_equal(result[0], y)
def test_capture_dump():
a = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * a
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file)
file.seek(0)
infer_cg = cgtools.GraphInference(file)
result = list((infer_cg.run(x)).values())[0]
np.testing.assert_equal(result[0], y)
def test_dump_volatile():
p = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * p
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file, optimize_for_inference=False)
file.seek(0)
cg, _, outputs = G.load_graph(file)
(out,) = outputs
assert (
cgtools.get_owner_opr_type(cgtools.get_owner_opr_inputs(out)[1])
== "ImmutableTensor"
)
@pytest.mark.parametrize("trace_mode", [False, True])
def test_trace_profiler(trace_mode):
@trace(symbolic=trace_mode, profiling=True)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
f(x)
f(x) # XXX: has to run twice
out = f.get_profile()
assert out.get("profiler")
def test_goptions():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
# directly return x / x will not trigger gopt
# since there's no way to tell the two x are the same
y = 2.0 * x
return y / y
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
y = 2.0 * x
return y / y
d = tensor(0.0)
assert not np.isfinite(f(d).numpy())
np.testing.assert_equal(g(d).numpy().item(), 1.0)
def test_goptions_log_sum_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x, y):
return log(exp(x) + exp(y))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x, y):
return log(exp(x) + exp(y))
val = 1.0e4
d = tensor(val)
o = tensor(0.0)
assert not np.isfinite(f(d, o).numpy())
np.testing.assert_almost_equal(g(d, o), val)
def test_goptions_log_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
return log( | exp(x) | megengine.functional.exp |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import inspect
import io
import itertools
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
from megengine.utils.naming import AutoNaming
@pytest.mark.parametrize("trace_mode", [False, True])
@pytest.mark.parametrize("return_mode", ["Value", "Tuple", "List", "Dict"])
def test_trace(trace_mode, return_mode):
@trace(symbolic=trace_mode)
def f(x):
if return_mode == "Tuple":
return (-x,)
elif return_mode == "List":
return [-x]
elif return_mode == "Dict":
return {"neg": -x}
else:
return -x
def get_numpy(y):
if return_mode == "Tuple" or return_mode == "List":
return y[0].numpy()
elif return_mode == "Dict":
return y["neg"].numpy()
return y.numpy()
x = tensor([1])
y = get_numpy(f(x))
for i in range(3):
np.testing.assert_equal(get_numpy(f(x)), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = Parameter([1.0], dtype=np.float32)
def forward(self, x):
x = x * self.a
# will result into a copy of output in grad
x = F.exp(x)
return x
ys = {False: [], True: []}
for symbolic in [False, True]:
net = Simple()
gm = GradManager().attach(net.parameters())
opt = optim.SGD(net.parameters(), 1e-3, momentum=0.9)
data = tensor(np.arange(4).reshape(2, 2), dtype="float32")
@trace(symbolic=symbolic)
def train_func(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(3):
y = train_func(data).numpy()
ys[symbolic].append(y)
for i in range(3):
np.testing.assert_equal(ys[False][i], ys[True][i])
@pytest.mark.parametrize("trace_mode", [False, True])
def test_exclude_from_trace(trace_mode):
@trace(symbolic=trace_mode)
def f(x):
x = -x
with exclude_from_trace():
if i % 2:
x = -x
x = -x
return x
x = tensor([1])
for i in range(3):
y = f(x).numpy()
np.testing.assert_equal(f(x).numpy(), y)
@pytest.mark.parametrize("trace_mode", [False, True])
def test_elemwise_fuse(trace_mode):
# explicitly declare opt_level as 2
@trace(symbolic=trace_mode, opt_level=2)
def f(a, b):
base = 0
c = b - a
_, idx = F.topk(c, 3)
# internally, biased_idx will be idx as gopt will ignore the addition
biased_idx = base + idx
return biased_idx
a = tensor(np.ones((7, 2)), dtype=np.int32)
b = tensor(2 * np.ones((7, 2)), dtype=np.float32)
for i in range(3):
y = f(a, b)
y.numpy()
@pytest.mark.parametrize("trace_mode", [False, True])
def test_elemwise_fuse_in_grad(trace_mode):
w = Parameter(np.ones([4, 6]), dtype="float32")
gm = GradManager().attach(w)
opt = optim.SGD([w], lr=0.01, momentum=0.9, weight_decay=5e-4)
# explicitly declare opt_level as 2
@trace(symbolic=trace_mode, opt_level=2)
def f():
with gm:
wm = F.sum(w ** 2, axis=1) ** 0.5
loss = wm.mean()
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(3):
y = f()
y.numpy()
def test_print_in_trace():
for symbolic in [False]: # cannot read value in symbolic mode
@trace(symbolic=symbolic)
def f(x):
nonlocal buf
x = -x
buf = x.numpy()
x = -x
return x
buf = None
x = tensor([1])
for i in range(3):
y = f(x).numpy()
z = buf
buf = None
np.testing.assert_equal(f(x).numpy(), y)
np.testing.assert_equal(z, buf)
def test_dump():
@trace(symbolic=True, capture_as_const=True)
def f(a, b):
return a + b
# prevent from remaining scope from exception test
AutoNaming.clear()
a = tensor([2])
b = tensor([4])
y = f(a, b).numpy()
for i in range(3):
np.testing.assert_equal(f(a, b).numpy(), y)
file = io.BytesIO()
dump_info = f.dump(file)
assert dump_info.nr_opr == 3
np.testing.assert_equal(dump_info.inputs, ["arg_0", "arg_1"])
np.testing.assert_equal(dump_info.outputs, ["ADD"])
file.seek(0)
infer_cg = cgtools.GraphInference(file)
result = list((infer_cg.run(a, b)).values())[0]
np.testing.assert_equal(result[0], y)
def test_capture_dump():
a = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * a
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file)
file.seek(0)
infer_cg = cgtools.GraphInference(file)
result = list((infer_cg.run(x)).values())[0]
np.testing.assert_equal(result[0], y)
def test_dump_volatile():
p = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * p
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file, optimize_for_inference=False)
file.seek(0)
cg, _, outputs = G.load_graph(file)
(out,) = outputs
assert (
cgtools.get_owner_opr_type(cgtools.get_owner_opr_inputs(out)[1])
== "ImmutableTensor"
)
@pytest.mark.parametrize("trace_mode", [False, True])
def test_trace_profiler(trace_mode):
@trace(symbolic=trace_mode, profiling=True)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
f(x)
f(x) # XXX: has to run twice
out = f.get_profile()
assert out.get("profiler")
def test_goptions():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
# directly return x / x will not trigger gopt
# since there's no way to tell the two x are the same
y = 2.0 * x
return y / y
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
y = 2.0 * x
return y / y
d = tensor(0.0)
assert not np.isfinite(f(d).numpy())
np.testing.assert_equal(g(d).numpy().item(), 1.0)
def test_goptions_log_sum_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x, y):
return log(exp(x) + exp(y))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x, y):
return log(exp(x) + exp(y))
val = 1.0e4
d = tensor(val)
o = tensor(0.0)
assert not np.isfinite(f(d, o).numpy())
np.testing.assert_almost_equal(g(d, o), val)
def test_goptions_log_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
return log(exp(x))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
return log( | exp(x) | megengine.functional.exp |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import inspect
import io
import itertools
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
from megengine.utils.naming import AutoNaming
@pytest.mark.parametrize("trace_mode", [False, True])
@pytest.mark.parametrize("return_mode", ["Value", "Tuple", "List", "Dict"])
def test_trace(trace_mode, return_mode):
@trace(symbolic=trace_mode)
def f(x):
if return_mode == "Tuple":
return (-x,)
elif return_mode == "List":
return [-x]
elif return_mode == "Dict":
return {"neg": -x}
else:
return -x
def get_numpy(y):
if return_mode == "Tuple" or return_mode == "List":
return y[0].numpy()
elif return_mode == "Dict":
return y["neg"].numpy()
return y.numpy()
x = tensor([1])
y = get_numpy(f(x))
for i in range(3):
np.testing.assert_equal(get_numpy(f(x)), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = Parameter([1.0], dtype=np.float32)
def forward(self, x):
x = x * self.a
# will result into a copy of output in grad
x = F.exp(x)
return x
ys = {False: [], True: []}
for symbolic in [False, True]:
net = Simple()
gm = GradManager().attach(net.parameters())
opt = optim.SGD(net.parameters(), 1e-3, momentum=0.9)
data = tensor(np.arange(4).reshape(2, 2), dtype="float32")
@trace(symbolic=symbolic)
def train_func(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(3):
y = train_func(data).numpy()
ys[symbolic].append(y)
for i in range(3):
np.testing.assert_equal(ys[False][i], ys[True][i])
@pytest.mark.parametrize("trace_mode", [False, True])
def test_exclude_from_trace(trace_mode):
@trace(symbolic=trace_mode)
def f(x):
x = -x
with exclude_from_trace():
if i % 2:
x = -x
x = -x
return x
x = tensor([1])
for i in range(3):
y = f(x).numpy()
np.testing.assert_equal(f(x).numpy(), y)
@pytest.mark.parametrize("trace_mode", [False, True])
def test_elemwise_fuse(trace_mode):
# explicitly declare opt_level as 2
@trace(symbolic=trace_mode, opt_level=2)
def f(a, b):
base = 0
c = b - a
_, idx = F.topk(c, 3)
# internally, biased_idx will be idx as gopt will ignore the addition
biased_idx = base + idx
return biased_idx
a = tensor(np.ones((7, 2)), dtype=np.int32)
b = tensor(2 * np.ones((7, 2)), dtype=np.float32)
for i in range(3):
y = f(a, b)
y.numpy()
@pytest.mark.parametrize("trace_mode", [False, True])
def test_elemwise_fuse_in_grad(trace_mode):
w = Parameter(np.ones([4, 6]), dtype="float32")
gm = GradManager().attach(w)
opt = optim.SGD([w], lr=0.01, momentum=0.9, weight_decay=5e-4)
# explicitly declare opt_level as 2
@trace(symbolic=trace_mode, opt_level=2)
def f():
with gm:
wm = F.sum(w ** 2, axis=1) ** 0.5
loss = wm.mean()
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(3):
y = f()
y.numpy()
def test_print_in_trace():
for symbolic in [False]: # cannot read value in symbolic mode
@trace(symbolic=symbolic)
def f(x):
nonlocal buf
x = -x
buf = x.numpy()
x = -x
return x
buf = None
x = tensor([1])
for i in range(3):
y = f(x).numpy()
z = buf
buf = None
np.testing.assert_equal(f(x).numpy(), y)
np.testing.assert_equal(z, buf)
def test_dump():
@trace(symbolic=True, capture_as_const=True)
def f(a, b):
return a + b
# prevent from remaining scope from exception test
AutoNaming.clear()
a = tensor([2])
b = tensor([4])
y = f(a, b).numpy()
for i in range(3):
np.testing.assert_equal(f(a, b).numpy(), y)
file = io.BytesIO()
dump_info = f.dump(file)
assert dump_info.nr_opr == 3
np.testing.assert_equal(dump_info.inputs, ["arg_0", "arg_1"])
np.testing.assert_equal(dump_info.outputs, ["ADD"])
file.seek(0)
infer_cg = cgtools.GraphInference(file)
result = list((infer_cg.run(a, b)).values())[0]
np.testing.assert_equal(result[0], y)
def test_capture_dump():
a = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * a
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file)
file.seek(0)
infer_cg = cgtools.GraphInference(file)
result = list((infer_cg.run(x)).values())[0]
np.testing.assert_equal(result[0], y)
def test_dump_volatile():
p = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * p
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file, optimize_for_inference=False)
file.seek(0)
cg, _, outputs = G.load_graph(file)
(out,) = outputs
assert (
cgtools.get_owner_opr_type(cgtools.get_owner_opr_inputs(out)[1])
== "ImmutableTensor"
)
@pytest.mark.parametrize("trace_mode", [False, True])
def test_trace_profiler(trace_mode):
@trace(symbolic=trace_mode, profiling=True)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
f(x)
f(x) # XXX: has to run twice
out = f.get_profile()
assert out.get("profiler")
def test_goptions():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
# directly return x / x will not trigger gopt
# since there's no way to tell the two x are the same
y = 2.0 * x
return y / y
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
y = 2.0 * x
return y / y
d = tensor(0.0)
assert not np.isfinite(f(d).numpy())
np.testing.assert_equal(g(d).numpy().item(), 1.0)
def test_goptions_log_sum_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x, y):
return log(exp(x) + exp(y))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x, y):
return log(exp(x) + exp(y))
val = 1.0e4
d = tensor(val)
o = tensor(0.0)
assert not np.isfinite(f(d, o).numpy())
np.testing.assert_almost_equal(g(d, o), val)
def test_goptions_log_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
return log(exp(x))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
return log(exp(x))
f(tensor(1.0))
_, out = mkstemp()
f.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_1 = cgtools.get_oprs_seq(outputs)
g(tensor(1.0))
g.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_2 = cgtools.get_oprs_seq(outputs)
assert len(oprs_1) - len(oprs_2) == 2
def test_optimize_for_inference():
@trace(symbolic=True, capture_as_const=True)
def f(x):
return exp(x)
_, out = mkstemp()
f(tensor(5.0))
f.dump(out, enable_io16xc32=True)
res = G.load_graph(out)
computing_input = res.output_vars_list[0].owner.inputs[0]
assert computing_input.dtype == np.float16
def test_optimize_for_inference_broadcast():
a = tensor(np.ones(1, dtype=np.float32))
@trace(capture_as_const=True, symbolic_shape=True)
def f():
return a._broadcast( | tensor([1, 10], dtype=np.int32) | megengine.tensor |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import inspect
import io
import itertools
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
from megengine.utils.naming import AutoNaming
@pytest.mark.parametrize("trace_mode", [False, True])
@pytest.mark.parametrize("return_mode", ["Value", "Tuple", "List", "Dict"])
def test_trace(trace_mode, return_mode):
@trace(symbolic=trace_mode)
def f(x):
if return_mode == "Tuple":
return (-x,)
elif return_mode == "List":
return [-x]
elif return_mode == "Dict":
return {"neg": -x}
else:
return -x
def get_numpy(y):
if return_mode == "Tuple" or return_mode == "List":
return y[0].numpy()
elif return_mode == "Dict":
return y["neg"].numpy()
return y.numpy()
x = tensor([1])
y = get_numpy(f(x))
for i in range(3):
np.testing.assert_equal(get_numpy(f(x)), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = Parameter([1.0], dtype=np.float32)
def forward(self, x):
x = x * self.a
# will result into a copy of output in grad
x = F.exp(x)
return x
ys = {False: [], True: []}
for symbolic in [False, True]:
net = Simple()
gm = GradManager().attach(net.parameters())
opt = optim.SGD(net.parameters(), 1e-3, momentum=0.9)
data = tensor(np.arange(4).reshape(2, 2), dtype="float32")
@trace(symbolic=symbolic)
def train_func(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(3):
y = train_func(data).numpy()
ys[symbolic].append(y)
for i in range(3):
np.testing.assert_equal(ys[False][i], ys[True][i])
@pytest.mark.parametrize("trace_mode", [False, True])
def test_exclude_from_trace(trace_mode):
@trace(symbolic=trace_mode)
def f(x):
x = -x
with exclude_from_trace():
if i % 2:
x = -x
x = -x
return x
x = tensor([1])
for i in range(3):
y = f(x).numpy()
np.testing.assert_equal(f(x).numpy(), y)
@pytest.mark.parametrize("trace_mode", [False, True])
def test_elemwise_fuse(trace_mode):
# explicitly declare opt_level as 2
@trace(symbolic=trace_mode, opt_level=2)
def f(a, b):
base = 0
c = b - a
_, idx = F.topk(c, 3)
# internally, biased_idx will be idx as gopt will ignore the addition
biased_idx = base + idx
return biased_idx
a = tensor(np.ones((7, 2)), dtype=np.int32)
b = tensor(2 * np.ones((7, 2)), dtype=np.float32)
for i in range(3):
y = f(a, b)
y.numpy()
@pytest.mark.parametrize("trace_mode", [False, True])
def test_elemwise_fuse_in_grad(trace_mode):
w = Parameter(np.ones([4, 6]), dtype="float32")
gm = GradManager().attach(w)
opt = optim.SGD([w], lr=0.01, momentum=0.9, weight_decay=5e-4)
# explicitly declare opt_level as 2
@trace(symbolic=trace_mode, opt_level=2)
def f():
with gm:
wm = F.sum(w ** 2, axis=1) ** 0.5
loss = wm.mean()
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(3):
y = f()
y.numpy()
def test_print_in_trace():
for symbolic in [False]: # cannot read value in symbolic mode
@trace(symbolic=symbolic)
def f(x):
nonlocal buf
x = -x
buf = x.numpy()
x = -x
return x
buf = None
x = tensor([1])
for i in range(3):
y = f(x).numpy()
z = buf
buf = None
np.testing.assert_equal(f(x).numpy(), y)
np.testing.assert_equal(z, buf)
def test_dump():
@trace(symbolic=True, capture_as_const=True)
def f(a, b):
return a + b
# prevent from remaining scope from exception test
AutoNaming.clear()
a = tensor([2])
b = tensor([4])
y = f(a, b).numpy()
for i in range(3):
np.testing.assert_equal(f(a, b).numpy(), y)
file = io.BytesIO()
dump_info = f.dump(file)
assert dump_info.nr_opr == 3
np.testing.assert_equal(dump_info.inputs, ["arg_0", "arg_1"])
np.testing.assert_equal(dump_info.outputs, ["ADD"])
file.seek(0)
infer_cg = cgtools.GraphInference(file)
result = list((infer_cg.run(a, b)).values())[0]
np.testing.assert_equal(result[0], y)
def test_capture_dump():
a = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * a
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file)
file.seek(0)
infer_cg = cgtools.GraphInference(file)
result = list((infer_cg.run(x)).values())[0]
np.testing.assert_equal(result[0], y)
def test_dump_volatile():
p = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * p
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file, optimize_for_inference=False)
file.seek(0)
cg, _, outputs = G.load_graph(file)
(out,) = outputs
assert (
cgtools.get_owner_opr_type(cgtools.get_owner_opr_inputs(out)[1])
== "ImmutableTensor"
)
@pytest.mark.parametrize("trace_mode", [False, True])
def test_trace_profiler(trace_mode):
@trace(symbolic=trace_mode, profiling=True)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
f(x)
f(x) # XXX: has to run twice
out = f.get_profile()
assert out.get("profiler")
def test_goptions():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
# directly return x / x will not trigger gopt
# since there's no way to tell the two x are the same
y = 2.0 * x
return y / y
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
y = 2.0 * x
return y / y
d = tensor(0.0)
assert not np.isfinite(f(d).numpy())
np.testing.assert_equal(g(d).numpy().item(), 1.0)
def test_goptions_log_sum_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x, y):
return log(exp(x) + exp(y))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x, y):
return log(exp(x) + exp(y))
val = 1.0e4
d = tensor(val)
o = tensor(0.0)
assert not np.isfinite(f(d, o).numpy())
np.testing.assert_almost_equal(g(d, o), val)
def test_goptions_log_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
return log(exp(x))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
return log(exp(x))
f(tensor(1.0))
_, out = mkstemp()
f.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_1 = cgtools.get_oprs_seq(outputs)
g(tensor(1.0))
g.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_2 = cgtools.get_oprs_seq(outputs)
assert len(oprs_1) - len(oprs_2) == 2
def test_optimize_for_inference():
@trace(symbolic=True, capture_as_const=True)
def f(x):
return exp(x)
_, out = mkstemp()
f(tensor(5.0))
f.dump(out, enable_io16xc32=True)
res = G.load_graph(out)
computing_input = res.output_vars_list[0].owner.inputs[0]
assert computing_input.dtype == np.float16
def test_optimize_for_inference_broadcast():
a = tensor(np.ones(1, dtype=np.float32))
@trace(capture_as_const=True, symbolic_shape=True)
def f():
return a._broadcast(tensor([1, 10], dtype=np.int32))
f()
f.dump(io.BytesIO())
def test_trace_cvt_bool():
x = tensor([0], dtype=np.int32)
@trace(symbolic=True)
def f(x):
a = x.shape
b = a[0]
assert isscalar(b)
return b == 0
for i in range(3):
np.testing.assert_equal(f(x).numpy(), False)
@pytest.mark.parametrize("trace_mode", [False, True])
def test_trace_reshape(trace_mode):
x1 = tensor(np.random.randn(2, 10, 10))
x2 = tensor(np.random.randn(4, 10, 10))
x3 = tensor(np.random.randn(8, 10, 10))
@trace(symbolic=trace_mode, capture_as_const=True)
def f(x):
y = x.reshape(x.shape[0], 100)
return y
f(x1)
f(x2)
f(x3)
def test_trace_topk():
x = tensor([5, 2, 7, 1, 0, 3, 2])
@trace(symbolic=True)
def f(x):
y = F.topk(x, 3)
np.testing.assert_equal(y[0].shape.numpy(), np.array([3,]))
return y
for i in range(3):
f(x)
def test_trace_warp_perspective():
inp_shape = (1, 1, 4, 4)
x = tensor(np.arange(16, dtype=np.float32).reshape(inp_shape))
M_shape = (1, 3, 3)
M = tensor(
np.array(
[[1.0, 0.0, 1.0], [0.0, 1.0, 1.0], [0.0, 0.0, 1.0]], dtype=np.float32
).reshape(M_shape)
)
@trace(symbolic=True)
def f(x, M):
out = F.vision.warp_perspective(x, M, (2, 2))
np.testing.assert_equal(out.shape.numpy(), np.array([1, 1, 2, 2]))
return out
for i in range(3):
f(x, M)
def test_raise_on_trace():
step_count = 0
catch_count = 0
bad_step = 10
class CatchMe(Exception):
pass
a = tensor([1, 2, 3, 4])
b = tensor([5, 6, 7, 8])
c = tensor([9, 0, 1, 2])
@trace
def add_abc(a, b, c):
ps = a + b
result = ps + c
if step_count == bad_step:
raise CatchMe("catch me")
return result
for i in range(100):
try:
d = add_abc(a, b, c)
except CatchMe as e:
catch_count += 1
else:
np.testing.assert_equal(d.numpy(), (a + b + c).numpy())
step_count += 1
assert catch_count == 1
@pytest.mark.parametrize("trace_mode", [False, True])
def test_trace_broadcast(trace_mode):
x1 = tensor(np.random.randn(3, 1, 1))
x2 = tensor(np.random.randn(1, 4, 1))
x3 = tensor(np.random.randn(1, 1, 5))
@trace(symbolic=trace_mode, capture_as_const=True)
def f(x):
y = F.broadcast_to(x, (3, 4, 5))
return y
f(x1)
f(x2)
f(x3)
def test_trace_nms():
def make_inputs(n):
boxes = np.zeros((n, 4))
boxes[:, :2] = np.random.rand(n, 2) * 100
boxes[:, 2:] = np.random.rand(n, 2) * 100 + 100
scores = np.random.rand(n)
return | tensor(boxes) | megengine.tensor |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import inspect
import io
import itertools
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
from megengine.utils.naming import AutoNaming
@pytest.mark.parametrize("trace_mode", [False, True])
@pytest.mark.parametrize("return_mode", ["Value", "Tuple", "List", "Dict"])
def test_trace(trace_mode, return_mode):
@trace(symbolic=trace_mode)
def f(x):
if return_mode == "Tuple":
return (-x,)
elif return_mode == "List":
return [-x]
elif return_mode == "Dict":
return {"neg": -x}
else:
return -x
def get_numpy(y):
if return_mode == "Tuple" or return_mode == "List":
return y[0].numpy()
elif return_mode == "Dict":
return y["neg"].numpy()
return y.numpy()
x = tensor([1])
y = get_numpy(f(x))
for i in range(3):
np.testing.assert_equal(get_numpy(f(x)), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = Parameter([1.0], dtype=np.float32)
def forward(self, x):
x = x * self.a
# will result into a copy of output in grad
x = F.exp(x)
return x
ys = {False: [], True: []}
for symbolic in [False, True]:
net = Simple()
gm = GradManager().attach(net.parameters())
opt = optim.SGD(net.parameters(), 1e-3, momentum=0.9)
data = tensor(np.arange(4).reshape(2, 2), dtype="float32")
@trace(symbolic=symbolic)
def train_func(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(3):
y = train_func(data).numpy()
ys[symbolic].append(y)
for i in range(3):
np.testing.assert_equal(ys[False][i], ys[True][i])
@pytest.mark.parametrize("trace_mode", [False, True])
def test_exclude_from_trace(trace_mode):
@trace(symbolic=trace_mode)
def f(x):
x = -x
with exclude_from_trace():
if i % 2:
x = -x
x = -x
return x
x = tensor([1])
for i in range(3):
y = f(x).numpy()
np.testing.assert_equal(f(x).numpy(), y)
@pytest.mark.parametrize("trace_mode", [False, True])
def test_elemwise_fuse(trace_mode):
# explicitly declare opt_level as 2
@trace(symbolic=trace_mode, opt_level=2)
def f(a, b):
base = 0
c = b - a
_, idx = F.topk(c, 3)
# internally, biased_idx will be idx as gopt will ignore the addition
biased_idx = base + idx
return biased_idx
a = tensor(np.ones((7, 2)), dtype=np.int32)
b = tensor(2 * np.ones((7, 2)), dtype=np.float32)
for i in range(3):
y = f(a, b)
y.numpy()
@pytest.mark.parametrize("trace_mode", [False, True])
def test_elemwise_fuse_in_grad(trace_mode):
w = Parameter(np.ones([4, 6]), dtype="float32")
gm = GradManager().attach(w)
opt = optim.SGD([w], lr=0.01, momentum=0.9, weight_decay=5e-4)
# explicitly declare opt_level as 2
@trace(symbolic=trace_mode, opt_level=2)
def f():
with gm:
wm = F.sum(w ** 2, axis=1) ** 0.5
loss = wm.mean()
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(3):
y = f()
y.numpy()
def test_print_in_trace():
for symbolic in [False]: # cannot read value in symbolic mode
@trace(symbolic=symbolic)
def f(x):
nonlocal buf
x = -x
buf = x.numpy()
x = -x
return x
buf = None
x = tensor([1])
for i in range(3):
y = f(x).numpy()
z = buf
buf = None
np.testing.assert_equal(f(x).numpy(), y)
np.testing.assert_equal(z, buf)
def test_dump():
@trace(symbolic=True, capture_as_const=True)
def f(a, b):
return a + b
# prevent from remaining scope from exception test
AutoNaming.clear()
a = tensor([2])
b = tensor([4])
y = f(a, b).numpy()
for i in range(3):
np.testing.assert_equal(f(a, b).numpy(), y)
file = io.BytesIO()
dump_info = f.dump(file)
assert dump_info.nr_opr == 3
np.testing.assert_equal(dump_info.inputs, ["arg_0", "arg_1"])
np.testing.assert_equal(dump_info.outputs, ["ADD"])
file.seek(0)
infer_cg = cgtools.GraphInference(file)
result = list((infer_cg.run(a, b)).values())[0]
np.testing.assert_equal(result[0], y)
def test_capture_dump():
a = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * a
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file)
file.seek(0)
infer_cg = cgtools.GraphInference(file)
result = list((infer_cg.run(x)).values())[0]
np.testing.assert_equal(result[0], y)
def test_dump_volatile():
p = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * p
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file, optimize_for_inference=False)
file.seek(0)
cg, _, outputs = G.load_graph(file)
(out,) = outputs
assert (
cgtools.get_owner_opr_type(cgtools.get_owner_opr_inputs(out)[1])
== "ImmutableTensor"
)
@pytest.mark.parametrize("trace_mode", [False, True])
def test_trace_profiler(trace_mode):
@trace(symbolic=trace_mode, profiling=True)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
f(x)
f(x) # XXX: has to run twice
out = f.get_profile()
assert out.get("profiler")
def test_goptions():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
# directly return x / x will not trigger gopt
# since there's no way to tell the two x are the same
y = 2.0 * x
return y / y
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
y = 2.0 * x
return y / y
d = tensor(0.0)
assert not np.isfinite(f(d).numpy())
np.testing.assert_equal(g(d).numpy().item(), 1.0)
def test_goptions_log_sum_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x, y):
return log(exp(x) + exp(y))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x, y):
return log(exp(x) + exp(y))
val = 1.0e4
d = tensor(val)
o = tensor(0.0)
assert not np.isfinite(f(d, o).numpy())
np.testing.assert_almost_equal(g(d, o), val)
def test_goptions_log_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
return log(exp(x))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
return log(exp(x))
f(tensor(1.0))
_, out = mkstemp()
f.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_1 = cgtools.get_oprs_seq(outputs)
g(tensor(1.0))
g.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_2 = cgtools.get_oprs_seq(outputs)
assert len(oprs_1) - len(oprs_2) == 2
def test_optimize_for_inference():
@trace(symbolic=True, capture_as_const=True)
def f(x):
return exp(x)
_, out = mkstemp()
f(tensor(5.0))
f.dump(out, enable_io16xc32=True)
res = G.load_graph(out)
computing_input = res.output_vars_list[0].owner.inputs[0]
assert computing_input.dtype == np.float16
def test_optimize_for_inference_broadcast():
a = tensor(np.ones(1, dtype=np.float32))
@trace(capture_as_const=True, symbolic_shape=True)
def f():
return a._broadcast(tensor([1, 10], dtype=np.int32))
f()
f.dump(io.BytesIO())
def test_trace_cvt_bool():
x = tensor([0], dtype=np.int32)
@trace(symbolic=True)
def f(x):
a = x.shape
b = a[0]
assert isscalar(b)
return b == 0
for i in range(3):
np.testing.assert_equal(f(x).numpy(), False)
@pytest.mark.parametrize("trace_mode", [False, True])
def test_trace_reshape(trace_mode):
x1 = tensor(np.random.randn(2, 10, 10))
x2 = tensor(np.random.randn(4, 10, 10))
x3 = tensor(np.random.randn(8, 10, 10))
@trace(symbolic=trace_mode, capture_as_const=True)
def f(x):
y = x.reshape(x.shape[0], 100)
return y
f(x1)
f(x2)
f(x3)
def test_trace_topk():
x = tensor([5, 2, 7, 1, 0, 3, 2])
@trace(symbolic=True)
def f(x):
y = F.topk(x, 3)
np.testing.assert_equal(y[0].shape.numpy(), np.array([3,]))
return y
for i in range(3):
f(x)
def test_trace_warp_perspective():
inp_shape = (1, 1, 4, 4)
x = tensor(np.arange(16, dtype=np.float32).reshape(inp_shape))
M_shape = (1, 3, 3)
M = tensor(
np.array(
[[1.0, 0.0, 1.0], [0.0, 1.0, 1.0], [0.0, 0.0, 1.0]], dtype=np.float32
).reshape(M_shape)
)
@trace(symbolic=True)
def f(x, M):
out = F.vision.warp_perspective(x, M, (2, 2))
np.testing.assert_equal(out.shape.numpy(), np.array([1, 1, 2, 2]))
return out
for i in range(3):
f(x, M)
def test_raise_on_trace():
step_count = 0
catch_count = 0
bad_step = 10
class CatchMe(Exception):
pass
a = tensor([1, 2, 3, 4])
b = tensor([5, 6, 7, 8])
c = tensor([9, 0, 1, 2])
@trace
def add_abc(a, b, c):
ps = a + b
result = ps + c
if step_count == bad_step:
raise CatchMe("catch me")
return result
for i in range(100):
try:
d = add_abc(a, b, c)
except CatchMe as e:
catch_count += 1
else:
np.testing.assert_equal(d.numpy(), (a + b + c).numpy())
step_count += 1
assert catch_count == 1
@pytest.mark.parametrize("trace_mode", [False, True])
def test_trace_broadcast(trace_mode):
x1 = tensor(np.random.randn(3, 1, 1))
x2 = tensor(np.random.randn(1, 4, 1))
x3 = tensor(np.random.randn(1, 1, 5))
@trace(symbolic=trace_mode, capture_as_const=True)
def f(x):
y = F.broadcast_to(x, (3, 4, 5))
return y
f(x1)
f(x2)
f(x3)
def test_trace_nms():
def make_inputs(n):
boxes = np.zeros((n, 4))
boxes[:, :2] = np.random.rand(n, 2) * 100
boxes[:, 2:] = np.random.rand(n, 2) * 100 + 100
scores = np.random.rand(n)
return tensor(boxes), | tensor(scores) | megengine.tensor |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import inspect
import io
import itertools
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
from megengine.utils.naming import AutoNaming
@pytest.mark.parametrize("trace_mode", [False, True])
@pytest.mark.parametrize("return_mode", ["Value", "Tuple", "List", "Dict"])
def test_trace(trace_mode, return_mode):
@trace(symbolic=trace_mode)
def f(x):
if return_mode == "Tuple":
return (-x,)
elif return_mode == "List":
return [-x]
elif return_mode == "Dict":
return {"neg": -x}
else:
return -x
def get_numpy(y):
if return_mode == "Tuple" or return_mode == "List":
return y[0].numpy()
elif return_mode == "Dict":
return y["neg"].numpy()
return y.numpy()
x = tensor([1])
y = get_numpy(f(x))
for i in range(3):
np.testing.assert_equal(get_numpy(f(x)), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = Parameter([1.0], dtype=np.float32)
def forward(self, x):
x = x * self.a
# will result into a copy of output in grad
x = F.exp(x)
return x
ys = {False: [], True: []}
for symbolic in [False, True]:
net = Simple()
gm = GradManager().attach(net.parameters())
opt = optim.SGD(net.parameters(), 1e-3, momentum=0.9)
data = tensor(np.arange(4).reshape(2, 2), dtype="float32")
@trace(symbolic=symbolic)
def train_func(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(3):
y = train_func(data).numpy()
ys[symbolic].append(y)
for i in range(3):
np.testing.assert_equal(ys[False][i], ys[True][i])
@pytest.mark.parametrize("trace_mode", [False, True])
def test_exclude_from_trace(trace_mode):
@trace(symbolic=trace_mode)
def f(x):
x = -x
with exclude_from_trace():
if i % 2:
x = -x
x = -x
return x
x = tensor([1])
for i in range(3):
y = f(x).numpy()
np.testing.assert_equal(f(x).numpy(), y)
@pytest.mark.parametrize("trace_mode", [False, True])
def test_elemwise_fuse(trace_mode):
# explicitly declare opt_level as 2
@trace(symbolic=trace_mode, opt_level=2)
def f(a, b):
base = 0
c = b - a
_, idx = F.topk(c, 3)
# internally, biased_idx will be idx as gopt will ignore the addition
biased_idx = base + idx
return biased_idx
a = tensor(np.ones((7, 2)), dtype=np.int32)
b = tensor(2 * np.ones((7, 2)), dtype=np.float32)
for i in range(3):
y = f(a, b)
y.numpy()
@pytest.mark.parametrize("trace_mode", [False, True])
def test_elemwise_fuse_in_grad(trace_mode):
w = Parameter(np.ones([4, 6]), dtype="float32")
gm = GradManager().attach(w)
opt = optim.SGD([w], lr=0.01, momentum=0.9, weight_decay=5e-4)
# explicitly declare opt_level as 2
@trace(symbolic=trace_mode, opt_level=2)
def f():
with gm:
wm = F.sum(w ** 2, axis=1) ** 0.5
loss = wm.mean()
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(3):
y = f()
y.numpy()
def test_print_in_trace():
for symbolic in [False]: # cannot read value in symbolic mode
@trace(symbolic=symbolic)
def f(x):
nonlocal buf
x = -x
buf = x.numpy()
x = -x
return x
buf = None
x = tensor([1])
for i in range(3):
y = f(x).numpy()
z = buf
buf = None
np.testing.assert_equal(f(x).numpy(), y)
np.testing.assert_equal(z, buf)
def test_dump():
@trace(symbolic=True, capture_as_const=True)
def f(a, b):
return a + b
# prevent from remaining scope from exception test
AutoNaming.clear()
a = tensor([2])
b = tensor([4])
y = f(a, b).numpy()
for i in range(3):
np.testing.assert_equal(f(a, b).numpy(), y)
file = io.BytesIO()
dump_info = f.dump(file)
assert dump_info.nr_opr == 3
np.testing.assert_equal(dump_info.inputs, ["arg_0", "arg_1"])
np.testing.assert_equal(dump_info.outputs, ["ADD"])
file.seek(0)
infer_cg = cgtools.GraphInference(file)
result = list((infer_cg.run(a, b)).values())[0]
np.testing.assert_equal(result[0], y)
def test_capture_dump():
a = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * a
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file)
file.seek(0)
infer_cg = cgtools.GraphInference(file)
result = list((infer_cg.run(x)).values())[0]
np.testing.assert_equal(result[0], y)
def test_dump_volatile():
p = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * p
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file, optimize_for_inference=False)
file.seek(0)
cg, _, outputs = G.load_graph(file)
(out,) = outputs
assert (
cgtools.get_owner_opr_type(cgtools.get_owner_opr_inputs(out)[1])
== "ImmutableTensor"
)
@pytest.mark.parametrize("trace_mode", [False, True])
def test_trace_profiler(trace_mode):
@trace(symbolic=trace_mode, profiling=True)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
f(x)
f(x) # XXX: has to run twice
out = f.get_profile()
assert out.get("profiler")
def test_goptions():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
# directly return x / x will not trigger gopt
# since there's no way to tell the two x are the same
y = 2.0 * x
return y / y
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
y = 2.0 * x
return y / y
d = tensor(0.0)
assert not np.isfinite(f(d).numpy())
np.testing.assert_equal(g(d).numpy().item(), 1.0)
def test_goptions_log_sum_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x, y):
return log(exp(x) + exp(y))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x, y):
return log(exp(x) + exp(y))
val = 1.0e4
d = tensor(val)
o = tensor(0.0)
assert not np.isfinite(f(d, o).numpy())
np.testing.assert_almost_equal(g(d, o), val)
def test_goptions_log_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
return log(exp(x))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
return log(exp(x))
f(tensor(1.0))
_, out = mkstemp()
f.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_1 = cgtools.get_oprs_seq(outputs)
g(tensor(1.0))
g.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_2 = cgtools.get_oprs_seq(outputs)
assert len(oprs_1) - len(oprs_2) == 2
def test_optimize_for_inference():
@trace(symbolic=True, capture_as_const=True)
def f(x):
return exp(x)
_, out = mkstemp()
f(tensor(5.0))
f.dump(out, enable_io16xc32=True)
res = G.load_graph(out)
computing_input = res.output_vars_list[0].owner.inputs[0]
assert computing_input.dtype == np.float16
def test_optimize_for_inference_broadcast():
a = tensor(np.ones(1, dtype=np.float32))
@trace(capture_as_const=True, symbolic_shape=True)
def f():
return a._broadcast(tensor([1, 10], dtype=np.int32))
f()
f.dump(io.BytesIO())
def test_trace_cvt_bool():
x = tensor([0], dtype=np.int32)
@trace(symbolic=True)
def f(x):
a = x.shape
b = a[0]
assert isscalar(b)
return b == 0
for i in range(3):
np.testing.assert_equal(f(x).numpy(), False)
@pytest.mark.parametrize("trace_mode", [False, True])
def test_trace_reshape(trace_mode):
x1 = tensor(np.random.randn(2, 10, 10))
x2 = tensor(np.random.randn(4, 10, 10))
x3 = tensor(np.random.randn(8, 10, 10))
@trace(symbolic=trace_mode, capture_as_const=True)
def f(x):
y = x.reshape(x.shape[0], 100)
return y
f(x1)
f(x2)
f(x3)
def test_trace_topk():
x = tensor([5, 2, 7, 1, 0, 3, 2])
@trace(symbolic=True)
def f(x):
y = F.topk(x, 3)
np.testing.assert_equal(y[0].shape.numpy(), np.array([3,]))
return y
for i in range(3):
f(x)
def test_trace_warp_perspective():
inp_shape = (1, 1, 4, 4)
x = tensor(np.arange(16, dtype=np.float32).reshape(inp_shape))
M_shape = (1, 3, 3)
M = tensor(
np.array(
[[1.0, 0.0, 1.0], [0.0, 1.0, 1.0], [0.0, 0.0, 1.0]], dtype=np.float32
).reshape(M_shape)
)
@trace(symbolic=True)
def f(x, M):
out = F.vision.warp_perspective(x, M, (2, 2))
np.testing.assert_equal(out.shape.numpy(), np.array([1, 1, 2, 2]))
return out
for i in range(3):
f(x, M)
def test_raise_on_trace():
step_count = 0
catch_count = 0
bad_step = 10
class CatchMe(Exception):
pass
a = tensor([1, 2, 3, 4])
b = tensor([5, 6, 7, 8])
c = tensor([9, 0, 1, 2])
@trace
def add_abc(a, b, c):
ps = a + b
result = ps + c
if step_count == bad_step:
raise CatchMe("catch me")
return result
for i in range(100):
try:
d = add_abc(a, b, c)
except CatchMe as e:
catch_count += 1
else:
np.testing.assert_equal(d.numpy(), (a + b + c).numpy())
step_count += 1
assert catch_count == 1
@pytest.mark.parametrize("trace_mode", [False, True])
def test_trace_broadcast(trace_mode):
x1 = tensor(np.random.randn(3, 1, 1))
x2 = tensor(np.random.randn(1, 4, 1))
x3 = tensor(np.random.randn(1, 1, 5))
@trace(symbolic=trace_mode, capture_as_const=True)
def f(x):
y = F.broadcast_to(x, (3, 4, 5))
return y
f(x1)
f(x2)
f(x3)
def test_trace_nms():
def make_inputs(n):
boxes = np.zeros((n, 4))
boxes[:, :2] = np.random.rand(n, 2) * 100
boxes[:, 2:] = np.random.rand(n, 2) * 100 + 100
scores = np.random.rand(n)
return tensor(boxes), tensor(scores)
@trace(symbolic=False)
def f(boxes, scores):
# with tracing, max_output must be specified
results = F.vision.nms(boxes, scores=scores, iou_thresh=0.5, max_output=20)
# without tracing, max output can be inferred inside nms
with | exclude_from_trace() | megengine.jit.exclude_from_trace |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import inspect
import io
import itertools
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
from megengine.utils.naming import AutoNaming
@pytest.mark.parametrize("trace_mode", [False, True])
@pytest.mark.parametrize("return_mode", ["Value", "Tuple", "List", "Dict"])
def test_trace(trace_mode, return_mode):
@trace(symbolic=trace_mode)
def f(x):
if return_mode == "Tuple":
return (-x,)
elif return_mode == "List":
return [-x]
elif return_mode == "Dict":
return {"neg": -x}
else:
return -x
def get_numpy(y):
if return_mode == "Tuple" or return_mode == "List":
return y[0].numpy()
elif return_mode == "Dict":
return y["neg"].numpy()
return y.numpy()
x = tensor([1])
y = get_numpy(f(x))
for i in range(3):
np.testing.assert_equal(get_numpy(f(x)), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = Parameter([1.0], dtype=np.float32)
def forward(self, x):
x = x * self.a
# will result into a copy of output in grad
x = F.exp(x)
return x
ys = {False: [], True: []}
for symbolic in [False, True]:
net = Simple()
gm = GradManager().attach(net.parameters())
opt = optim.SGD(net.parameters(), 1e-3, momentum=0.9)
data = tensor(np.arange(4).reshape(2, 2), dtype="float32")
@trace(symbolic=symbolic)
def train_func(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(3):
y = train_func(data).numpy()
ys[symbolic].append(y)
for i in range(3):
np.testing.assert_equal(ys[False][i], ys[True][i])
@pytest.mark.parametrize("trace_mode", [False, True])
def test_exclude_from_trace(trace_mode):
@trace(symbolic=trace_mode)
def f(x):
x = -x
with exclude_from_trace():
if i % 2:
x = -x
x = -x
return x
x = tensor([1])
for i in range(3):
y = f(x).numpy()
np.testing.assert_equal(f(x).numpy(), y)
@pytest.mark.parametrize("trace_mode", [False, True])
def test_elemwise_fuse(trace_mode):
# explicitly declare opt_level as 2
@trace(symbolic=trace_mode, opt_level=2)
def f(a, b):
base = 0
c = b - a
_, idx = F.topk(c, 3)
# internally, biased_idx will be idx as gopt will ignore the addition
biased_idx = base + idx
return biased_idx
a = tensor(np.ones((7, 2)), dtype=np.int32)
b = tensor(2 * np.ones((7, 2)), dtype=np.float32)
for i in range(3):
y = f(a, b)
y.numpy()
@pytest.mark.parametrize("trace_mode", [False, True])
def test_elemwise_fuse_in_grad(trace_mode):
w = Parameter(np.ones([4, 6]), dtype="float32")
gm = GradManager().attach(w)
opt = optim.SGD([w], lr=0.01, momentum=0.9, weight_decay=5e-4)
# explicitly declare opt_level as 2
@trace(symbolic=trace_mode, opt_level=2)
def f():
with gm:
wm = F.sum(w ** 2, axis=1) ** 0.5
loss = wm.mean()
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(3):
y = f()
y.numpy()
def test_print_in_trace():
for symbolic in [False]: # cannot read value in symbolic mode
@trace(symbolic=symbolic)
def f(x):
nonlocal buf
x = -x
buf = x.numpy()
x = -x
return x
buf = None
x = tensor([1])
for i in range(3):
y = f(x).numpy()
z = buf
buf = None
np.testing.assert_equal(f(x).numpy(), y)
np.testing.assert_equal(z, buf)
def test_dump():
@trace(symbolic=True, capture_as_const=True)
def f(a, b):
return a + b
# prevent from remaining scope from exception test
AutoNaming.clear()
a = tensor([2])
b = tensor([4])
y = f(a, b).numpy()
for i in range(3):
np.testing.assert_equal(f(a, b).numpy(), y)
file = io.BytesIO()
dump_info = f.dump(file)
assert dump_info.nr_opr == 3
np.testing.assert_equal(dump_info.inputs, ["arg_0", "arg_1"])
np.testing.assert_equal(dump_info.outputs, ["ADD"])
file.seek(0)
infer_cg = cgtools.GraphInference(file)
result = list((infer_cg.run(a, b)).values())[0]
np.testing.assert_equal(result[0], y)
def test_capture_dump():
a = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * a
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file)
file.seek(0)
infer_cg = cgtools.GraphInference(file)
result = list((infer_cg.run(x)).values())[0]
np.testing.assert_equal(result[0], y)
def test_dump_volatile():
p = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * p
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file, optimize_for_inference=False)
file.seek(0)
cg, _, outputs = G.load_graph(file)
(out,) = outputs
assert (
cgtools.get_owner_opr_type(cgtools.get_owner_opr_inputs(out)[1])
== "ImmutableTensor"
)
@pytest.mark.parametrize("trace_mode", [False, True])
def test_trace_profiler(trace_mode):
@trace(symbolic=trace_mode, profiling=True)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
f(x)
f(x) # XXX: has to run twice
out = f.get_profile()
assert out.get("profiler")
def test_goptions():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
# directly return x / x will not trigger gopt
# since there's no way to tell the two x are the same
y = 2.0 * x
return y / y
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
y = 2.0 * x
return y / y
d = tensor(0.0)
assert not np.isfinite(f(d).numpy())
np.testing.assert_equal(g(d).numpy().item(), 1.0)
def test_goptions_log_sum_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x, y):
return log(exp(x) + exp(y))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x, y):
return log(exp(x) + exp(y))
val = 1.0e4
d = tensor(val)
o = tensor(0.0)
assert not np.isfinite(f(d, o).numpy())
np.testing.assert_almost_equal(g(d, o), val)
def test_goptions_log_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
return log(exp(x))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
return log(exp(x))
f(tensor(1.0))
_, out = mkstemp()
f.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_1 = cgtools.get_oprs_seq(outputs)
g(tensor(1.0))
g.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_2 = cgtools.get_oprs_seq(outputs)
assert len(oprs_1) - len(oprs_2) == 2
def test_optimize_for_inference():
@trace(symbolic=True, capture_as_const=True)
def f(x):
return exp(x)
_, out = mkstemp()
f(tensor(5.0))
f.dump(out, enable_io16xc32=True)
res = G.load_graph(out)
computing_input = res.output_vars_list[0].owner.inputs[0]
assert computing_input.dtype == np.float16
def test_optimize_for_inference_broadcast():
a = tensor(np.ones(1, dtype=np.float32))
@trace(capture_as_const=True, symbolic_shape=True)
def f():
return a._broadcast(tensor([1, 10], dtype=np.int32))
f()
f.dump(io.BytesIO())
def test_trace_cvt_bool():
x = tensor([0], dtype=np.int32)
@trace(symbolic=True)
def f(x):
a = x.shape
b = a[0]
assert isscalar(b)
return b == 0
for i in range(3):
np.testing.assert_equal(f(x).numpy(), False)
@pytest.mark.parametrize("trace_mode", [False, True])
def test_trace_reshape(trace_mode):
x1 = tensor(np.random.randn(2, 10, 10))
x2 = tensor(np.random.randn(4, 10, 10))
x3 = tensor(np.random.randn(8, 10, 10))
@trace(symbolic=trace_mode, capture_as_const=True)
def f(x):
y = x.reshape(x.shape[0], 100)
return y
f(x1)
f(x2)
f(x3)
def test_trace_topk():
x = tensor([5, 2, 7, 1, 0, 3, 2])
@trace(symbolic=True)
def f(x):
y = F.topk(x, 3)
np.testing.assert_equal(y[0].shape.numpy(), np.array([3,]))
return y
for i in range(3):
f(x)
def test_trace_warp_perspective():
inp_shape = (1, 1, 4, 4)
x = tensor(np.arange(16, dtype=np.float32).reshape(inp_shape))
M_shape = (1, 3, 3)
M = tensor(
np.array(
[[1.0, 0.0, 1.0], [0.0, 1.0, 1.0], [0.0, 0.0, 1.0]], dtype=np.float32
).reshape(M_shape)
)
@trace(symbolic=True)
def f(x, M):
out = F.vision.warp_perspective(x, M, (2, 2))
np.testing.assert_equal(out.shape.numpy(), np.array([1, 1, 2, 2]))
return out
for i in range(3):
f(x, M)
def test_raise_on_trace():
step_count = 0
catch_count = 0
bad_step = 10
class CatchMe(Exception):
pass
a = tensor([1, 2, 3, 4])
b = tensor([5, 6, 7, 8])
c = tensor([9, 0, 1, 2])
@trace
def add_abc(a, b, c):
ps = a + b
result = ps + c
if step_count == bad_step:
raise CatchMe("catch me")
return result
for i in range(100):
try:
d = add_abc(a, b, c)
except CatchMe as e:
catch_count += 1
else:
np.testing.assert_equal(d.numpy(), (a + b + c).numpy())
step_count += 1
assert catch_count == 1
@pytest.mark.parametrize("trace_mode", [False, True])
def test_trace_broadcast(trace_mode):
x1 = tensor(np.random.randn(3, 1, 1))
x2 = tensor(np.random.randn(1, 4, 1))
x3 = tensor(np.random.randn(1, 1, 5))
@trace(symbolic=trace_mode, capture_as_const=True)
def f(x):
y = F.broadcast_to(x, (3, 4, 5))
return y
f(x1)
f(x2)
f(x3)
def test_trace_nms():
def make_inputs(n):
boxes = np.zeros((n, 4))
boxes[:, :2] = np.random.rand(n, 2) * 100
boxes[:, 2:] = np.random.rand(n, 2) * 100 + 100
scores = np.random.rand(n)
return tensor(boxes), tensor(scores)
@trace(symbolic=False)
def f(boxes, scores):
# with tracing, max_output must be specified
results = F.vision.nms(boxes, scores=scores, iou_thresh=0.5, max_output=20)
# without tracing, max output can be inferred inside nms
with exclude_from_trace():
_ = | F.vision.nms(boxes, scores=scores, iou_thresh=0.5) | megengine.functional.vision.nms |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import inspect
import io
import itertools
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
from megengine.utils.naming import AutoNaming
@pytest.mark.parametrize("trace_mode", [False, True])
@pytest.mark.parametrize("return_mode", ["Value", "Tuple", "List", "Dict"])
def test_trace(trace_mode, return_mode):
@trace(symbolic=trace_mode)
def f(x):
if return_mode == "Tuple":
return (-x,)
elif return_mode == "List":
return [-x]
elif return_mode == "Dict":
return {"neg": -x}
else:
return -x
def get_numpy(y):
if return_mode == "Tuple" or return_mode == "List":
return y[0].numpy()
elif return_mode == "Dict":
return y["neg"].numpy()
return y.numpy()
x = tensor([1])
y = get_numpy(f(x))
for i in range(3):
np.testing.assert_equal(get_numpy(f(x)), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = Parameter([1.0], dtype=np.float32)
def forward(self, x):
x = x * self.a
# will result into a copy of output in grad
x = F.exp(x)
return x
ys = {False: [], True: []}
for symbolic in [False, True]:
net = Simple()
gm = GradManager().attach(net.parameters())
opt = optim.SGD(net.parameters(), 1e-3, momentum=0.9)
data = tensor(np.arange(4).reshape(2, 2), dtype="float32")
@trace(symbolic=symbolic)
def train_func(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(3):
y = train_func(data).numpy()
ys[symbolic].append(y)
for i in range(3):
np.testing.assert_equal(ys[False][i], ys[True][i])
@pytest.mark.parametrize("trace_mode", [False, True])
def test_exclude_from_trace(trace_mode):
@trace(symbolic=trace_mode)
def f(x):
x = -x
with exclude_from_trace():
if i % 2:
x = -x
x = -x
return x
x = tensor([1])
for i in range(3):
y = f(x).numpy()
np.testing.assert_equal(f(x).numpy(), y)
@pytest.mark.parametrize("trace_mode", [False, True])
def test_elemwise_fuse(trace_mode):
# explicitly declare opt_level as 2
@trace(symbolic=trace_mode, opt_level=2)
def f(a, b):
base = 0
c = b - a
_, idx = F.topk(c, 3)
# internally, biased_idx will be idx as gopt will ignore the addition
biased_idx = base + idx
return biased_idx
a = tensor(np.ones((7, 2)), dtype=np.int32)
b = tensor(2 * np.ones((7, 2)), dtype=np.float32)
for i in range(3):
y = f(a, b)
y.numpy()
@pytest.mark.parametrize("trace_mode", [False, True])
def test_elemwise_fuse_in_grad(trace_mode):
w = Parameter(np.ones([4, 6]), dtype="float32")
gm = GradManager().attach(w)
opt = optim.SGD([w], lr=0.01, momentum=0.9, weight_decay=5e-4)
# explicitly declare opt_level as 2
@trace(symbolic=trace_mode, opt_level=2)
def f():
with gm:
wm = F.sum(w ** 2, axis=1) ** 0.5
loss = wm.mean()
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(3):
y = f()
y.numpy()
def test_print_in_trace():
for symbolic in [False]: # cannot read value in symbolic mode
@trace(symbolic=symbolic)
def f(x):
nonlocal buf
x = -x
buf = x.numpy()
x = -x
return x
buf = None
x = tensor([1])
for i in range(3):
y = f(x).numpy()
z = buf
buf = None
np.testing.assert_equal(f(x).numpy(), y)
np.testing.assert_equal(z, buf)
def test_dump():
@trace(symbolic=True, capture_as_const=True)
def f(a, b):
return a + b
# prevent from remaining scope from exception test
AutoNaming.clear()
a = tensor([2])
b = tensor([4])
y = f(a, b).numpy()
for i in range(3):
np.testing.assert_equal(f(a, b).numpy(), y)
file = io.BytesIO()
dump_info = f.dump(file)
assert dump_info.nr_opr == 3
np.testing.assert_equal(dump_info.inputs, ["arg_0", "arg_1"])
np.testing.assert_equal(dump_info.outputs, ["ADD"])
file.seek(0)
infer_cg = cgtools.GraphInference(file)
result = list((infer_cg.run(a, b)).values())[0]
np.testing.assert_equal(result[0], y)
def test_capture_dump():
a = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * a
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file)
file.seek(0)
infer_cg = cgtools.GraphInference(file)
result = list((infer_cg.run(x)).values())[0]
np.testing.assert_equal(result[0], y)
def test_dump_volatile():
p = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * p
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file, optimize_for_inference=False)
file.seek(0)
cg, _, outputs = G.load_graph(file)
(out,) = outputs
assert (
cgtools.get_owner_opr_type(cgtools.get_owner_opr_inputs(out)[1])
== "ImmutableTensor"
)
@pytest.mark.parametrize("trace_mode", [False, True])
def test_trace_profiler(trace_mode):
@trace(symbolic=trace_mode, profiling=True)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
f(x)
f(x) # XXX: has to run twice
out = f.get_profile()
assert out.get("profiler")
def test_goptions():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
# directly return x / x will not trigger gopt
# since there's no way to tell the two x are the same
y = 2.0 * x
return y / y
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
y = 2.0 * x
return y / y
d = tensor(0.0)
assert not np.isfinite(f(d).numpy())
np.testing.assert_equal(g(d).numpy().item(), 1.0)
def test_goptions_log_sum_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x, y):
return log(exp(x) + exp(y))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x, y):
return log(exp(x) + exp(y))
val = 1.0e4
d = tensor(val)
o = tensor(0.0)
assert not np.isfinite(f(d, o).numpy())
np.testing.assert_almost_equal(g(d, o), val)
def test_goptions_log_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
return log(exp(x))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
return log(exp(x))
f(tensor(1.0))
_, out = mkstemp()
f.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_1 = cgtools.get_oprs_seq(outputs)
g(tensor(1.0))
g.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_2 = cgtools.get_oprs_seq(outputs)
assert len(oprs_1) - len(oprs_2) == 2
def test_optimize_for_inference():
@trace(symbolic=True, capture_as_const=True)
def f(x):
return exp(x)
_, out = mkstemp()
f(tensor(5.0))
f.dump(out, enable_io16xc32=True)
res = G.load_graph(out)
computing_input = res.output_vars_list[0].owner.inputs[0]
assert computing_input.dtype == np.float16
def test_optimize_for_inference_broadcast():
a = tensor(np.ones(1, dtype=np.float32))
@trace(capture_as_const=True, symbolic_shape=True)
def f():
return a._broadcast(tensor([1, 10], dtype=np.int32))
f()
f.dump(io.BytesIO())
def test_trace_cvt_bool():
x = tensor([0], dtype=np.int32)
@trace(symbolic=True)
def f(x):
a = x.shape
b = a[0]
assert isscalar(b)
return b == 0
for i in range(3):
np.testing.assert_equal(f(x).numpy(), False)
@pytest.mark.parametrize("trace_mode", [False, True])
def test_trace_reshape(trace_mode):
x1 = tensor(np.random.randn(2, 10, 10))
x2 = tensor(np.random.randn(4, 10, 10))
x3 = tensor(np.random.randn(8, 10, 10))
@trace(symbolic=trace_mode, capture_as_const=True)
def f(x):
y = x.reshape(x.shape[0], 100)
return y
f(x1)
f(x2)
f(x3)
def test_trace_topk():
x = tensor([5, 2, 7, 1, 0, 3, 2])
@trace(symbolic=True)
def f(x):
y = F.topk(x, 3)
np.testing.assert_equal(y[0].shape.numpy(), np.array([3,]))
return y
for i in range(3):
f(x)
def test_trace_warp_perspective():
inp_shape = (1, 1, 4, 4)
x = tensor(np.arange(16, dtype=np.float32).reshape(inp_shape))
M_shape = (1, 3, 3)
M = tensor(
np.array(
[[1.0, 0.0, 1.0], [0.0, 1.0, 1.0], [0.0, 0.0, 1.0]], dtype=np.float32
).reshape(M_shape)
)
@trace(symbolic=True)
def f(x, M):
out = F.vision.warp_perspective(x, M, (2, 2))
np.testing.assert_equal(out.shape.numpy(), np.array([1, 1, 2, 2]))
return out
for i in range(3):
f(x, M)
def test_raise_on_trace():
step_count = 0
catch_count = 0
bad_step = 10
class CatchMe(Exception):
pass
a = tensor([1, 2, 3, 4])
b = tensor([5, 6, 7, 8])
c = tensor([9, 0, 1, 2])
@trace
def add_abc(a, b, c):
ps = a + b
result = ps + c
if step_count == bad_step:
raise CatchMe("catch me")
return result
for i in range(100):
try:
d = add_abc(a, b, c)
except CatchMe as e:
catch_count += 1
else:
np.testing.assert_equal(d.numpy(), (a + b + c).numpy())
step_count += 1
assert catch_count == 1
@pytest.mark.parametrize("trace_mode", [False, True])
def test_trace_broadcast(trace_mode):
x1 = tensor(np.random.randn(3, 1, 1))
x2 = tensor(np.random.randn(1, 4, 1))
x3 = tensor(np.random.randn(1, 1, 5))
@trace(symbolic=trace_mode, capture_as_const=True)
def f(x):
y = F.broadcast_to(x, (3, 4, 5))
return y
f(x1)
f(x2)
f(x3)
def test_trace_nms():
def make_inputs(n):
boxes = np.zeros((n, 4))
boxes[:, :2] = np.random.rand(n, 2) * 100
boxes[:, 2:] = np.random.rand(n, 2) * 100 + 100
scores = np.random.rand(n)
return tensor(boxes), tensor(scores)
@trace(symbolic=False)
def f(boxes, scores):
# with tracing, max_output must be specified
results = F.vision.nms(boxes, scores=scores, iou_thresh=0.5, max_output=20)
# without tracing, max output can be inferred inside nms
with exclude_from_trace():
_ = F.vision.nms(boxes, scores=scores, iou_thresh=0.5)
return results
f(*make_inputs(10))
f(*make_inputs(20))
f(*make_inputs(30))
def test_trace_valid_broadcast():
x1 = tensor(np.random.randn(1, 1))
x2 = tensor(np.random.randn(1, 2))
shape = (tensor([2]), tensor([2]))
@trace(symbolic=False)
def f(x, shape):
y = F.broadcast_to(x, shape)
return y
f(x1, shape)
f(x2, shape)
def test_clip():
x = tensor(np.random.randn(10, 10))
@trace(symbolic=True)
def f(x, lower, upper):
y = F.clip(x, lower, upper)
return y
for i in range(3):
f(x, | tensor([0]) | megengine.tensor |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import inspect
import io
import itertools
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
from megengine.utils.naming import AutoNaming
@pytest.mark.parametrize("trace_mode", [False, True])
@pytest.mark.parametrize("return_mode", ["Value", "Tuple", "List", "Dict"])
def test_trace(trace_mode, return_mode):
@trace(symbolic=trace_mode)
def f(x):
if return_mode == "Tuple":
return (-x,)
elif return_mode == "List":
return [-x]
elif return_mode == "Dict":
return {"neg": -x}
else:
return -x
def get_numpy(y):
if return_mode == "Tuple" or return_mode == "List":
return y[0].numpy()
elif return_mode == "Dict":
return y["neg"].numpy()
return y.numpy()
x = tensor([1])
y = get_numpy(f(x))
for i in range(3):
np.testing.assert_equal(get_numpy(f(x)), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = Parameter([1.0], dtype=np.float32)
def forward(self, x):
x = x * self.a
# will result into a copy of output in grad
x = F.exp(x)
return x
ys = {False: [], True: []}
for symbolic in [False, True]:
net = Simple()
gm = GradManager().attach(net.parameters())
opt = optim.SGD(net.parameters(), 1e-3, momentum=0.9)
data = tensor(np.arange(4).reshape(2, 2), dtype="float32")
@trace(symbolic=symbolic)
def train_func(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(3):
y = train_func(data).numpy()
ys[symbolic].append(y)
for i in range(3):
np.testing.assert_equal(ys[False][i], ys[True][i])
@pytest.mark.parametrize("trace_mode", [False, True])
def test_exclude_from_trace(trace_mode):
@trace(symbolic=trace_mode)
def f(x):
x = -x
with exclude_from_trace():
if i % 2:
x = -x
x = -x
return x
x = tensor([1])
for i in range(3):
y = f(x).numpy()
np.testing.assert_equal(f(x).numpy(), y)
@pytest.mark.parametrize("trace_mode", [False, True])
def test_elemwise_fuse(trace_mode):
# explicitly declare opt_level as 2
@trace(symbolic=trace_mode, opt_level=2)
def f(a, b):
base = 0
c = b - a
_, idx = F.topk(c, 3)
# internally, biased_idx will be idx as gopt will ignore the addition
biased_idx = base + idx
return biased_idx
a = tensor(np.ones((7, 2)), dtype=np.int32)
b = tensor(2 * np.ones((7, 2)), dtype=np.float32)
for i in range(3):
y = f(a, b)
y.numpy()
@pytest.mark.parametrize("trace_mode", [False, True])
def test_elemwise_fuse_in_grad(trace_mode):
w = Parameter(np.ones([4, 6]), dtype="float32")
gm = GradManager().attach(w)
opt = optim.SGD([w], lr=0.01, momentum=0.9, weight_decay=5e-4)
# explicitly declare opt_level as 2
@trace(symbolic=trace_mode, opt_level=2)
def f():
with gm:
wm = F.sum(w ** 2, axis=1) ** 0.5
loss = wm.mean()
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(3):
y = f()
y.numpy()
def test_print_in_trace():
for symbolic in [False]: # cannot read value in symbolic mode
@trace(symbolic=symbolic)
def f(x):
nonlocal buf
x = -x
buf = x.numpy()
x = -x
return x
buf = None
x = tensor([1])
for i in range(3):
y = f(x).numpy()
z = buf
buf = None
np.testing.assert_equal(f(x).numpy(), y)
np.testing.assert_equal(z, buf)
def test_dump():
@trace(symbolic=True, capture_as_const=True)
def f(a, b):
return a + b
# prevent from remaining scope from exception test
AutoNaming.clear()
a = tensor([2])
b = tensor([4])
y = f(a, b).numpy()
for i in range(3):
np.testing.assert_equal(f(a, b).numpy(), y)
file = io.BytesIO()
dump_info = f.dump(file)
assert dump_info.nr_opr == 3
np.testing.assert_equal(dump_info.inputs, ["arg_0", "arg_1"])
np.testing.assert_equal(dump_info.outputs, ["ADD"])
file.seek(0)
infer_cg = cgtools.GraphInference(file)
result = list((infer_cg.run(a, b)).values())[0]
np.testing.assert_equal(result[0], y)
def test_capture_dump():
a = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * a
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file)
file.seek(0)
infer_cg = cgtools.GraphInference(file)
result = list((infer_cg.run(x)).values())[0]
np.testing.assert_equal(result[0], y)
def test_dump_volatile():
p = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * p
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file, optimize_for_inference=False)
file.seek(0)
cg, _, outputs = G.load_graph(file)
(out,) = outputs
assert (
cgtools.get_owner_opr_type(cgtools.get_owner_opr_inputs(out)[1])
== "ImmutableTensor"
)
@pytest.mark.parametrize("trace_mode", [False, True])
def test_trace_profiler(trace_mode):
@trace(symbolic=trace_mode, profiling=True)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
f(x)
f(x) # XXX: has to run twice
out = f.get_profile()
assert out.get("profiler")
def test_goptions():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
# directly return x / x will not trigger gopt
# since there's no way to tell the two x are the same
y = 2.0 * x
return y / y
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
y = 2.0 * x
return y / y
d = tensor(0.0)
assert not np.isfinite(f(d).numpy())
np.testing.assert_equal(g(d).numpy().item(), 1.0)
def test_goptions_log_sum_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x, y):
return log(exp(x) + exp(y))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x, y):
return log(exp(x) + exp(y))
val = 1.0e4
d = tensor(val)
o = tensor(0.0)
assert not np.isfinite(f(d, o).numpy())
np.testing.assert_almost_equal(g(d, o), val)
def test_goptions_log_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
return log(exp(x))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
return log(exp(x))
f(tensor(1.0))
_, out = mkstemp()
f.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_1 = cgtools.get_oprs_seq(outputs)
g(tensor(1.0))
g.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_2 = cgtools.get_oprs_seq(outputs)
assert len(oprs_1) - len(oprs_2) == 2
def test_optimize_for_inference():
@trace(symbolic=True, capture_as_const=True)
def f(x):
return exp(x)
_, out = mkstemp()
f(tensor(5.0))
f.dump(out, enable_io16xc32=True)
res = G.load_graph(out)
computing_input = res.output_vars_list[0].owner.inputs[0]
assert computing_input.dtype == np.float16
def test_optimize_for_inference_broadcast():
a = tensor(np.ones(1, dtype=np.float32))
@trace(capture_as_const=True, symbolic_shape=True)
def f():
return a._broadcast(tensor([1, 10], dtype=np.int32))
f()
f.dump(io.BytesIO())
def test_trace_cvt_bool():
x = tensor([0], dtype=np.int32)
@trace(symbolic=True)
def f(x):
a = x.shape
b = a[0]
assert isscalar(b)
return b == 0
for i in range(3):
np.testing.assert_equal(f(x).numpy(), False)
@pytest.mark.parametrize("trace_mode", [False, True])
def test_trace_reshape(trace_mode):
x1 = tensor(np.random.randn(2, 10, 10))
x2 = tensor(np.random.randn(4, 10, 10))
x3 = tensor(np.random.randn(8, 10, 10))
@trace(symbolic=trace_mode, capture_as_const=True)
def f(x):
y = x.reshape(x.shape[0], 100)
return y
f(x1)
f(x2)
f(x3)
def test_trace_topk():
x = tensor([5, 2, 7, 1, 0, 3, 2])
@trace(symbolic=True)
def f(x):
y = F.topk(x, 3)
np.testing.assert_equal(y[0].shape.numpy(), np.array([3,]))
return y
for i in range(3):
f(x)
def test_trace_warp_perspective():
inp_shape = (1, 1, 4, 4)
x = tensor(np.arange(16, dtype=np.float32).reshape(inp_shape))
M_shape = (1, 3, 3)
M = tensor(
np.array(
[[1.0, 0.0, 1.0], [0.0, 1.0, 1.0], [0.0, 0.0, 1.0]], dtype=np.float32
).reshape(M_shape)
)
@trace(symbolic=True)
def f(x, M):
out = F.vision.warp_perspective(x, M, (2, 2))
np.testing.assert_equal(out.shape.numpy(), np.array([1, 1, 2, 2]))
return out
for i in range(3):
f(x, M)
def test_raise_on_trace():
step_count = 0
catch_count = 0
bad_step = 10
class CatchMe(Exception):
pass
a = tensor([1, 2, 3, 4])
b = tensor([5, 6, 7, 8])
c = tensor([9, 0, 1, 2])
@trace
def add_abc(a, b, c):
ps = a + b
result = ps + c
if step_count == bad_step:
raise CatchMe("catch me")
return result
for i in range(100):
try:
d = add_abc(a, b, c)
except CatchMe as e:
catch_count += 1
else:
np.testing.assert_equal(d.numpy(), (a + b + c).numpy())
step_count += 1
assert catch_count == 1
@pytest.mark.parametrize("trace_mode", [False, True])
def test_trace_broadcast(trace_mode):
x1 = tensor(np.random.randn(3, 1, 1))
x2 = tensor(np.random.randn(1, 4, 1))
x3 = tensor(np.random.randn(1, 1, 5))
@trace(symbolic=trace_mode, capture_as_const=True)
def f(x):
y = F.broadcast_to(x, (3, 4, 5))
return y
f(x1)
f(x2)
f(x3)
def test_trace_nms():
def make_inputs(n):
boxes = np.zeros((n, 4))
boxes[:, :2] = np.random.rand(n, 2) * 100
boxes[:, 2:] = np.random.rand(n, 2) * 100 + 100
scores = np.random.rand(n)
return tensor(boxes), tensor(scores)
@trace(symbolic=False)
def f(boxes, scores):
# with tracing, max_output must be specified
results = F.vision.nms(boxes, scores=scores, iou_thresh=0.5, max_output=20)
# without tracing, max output can be inferred inside nms
with exclude_from_trace():
_ = F.vision.nms(boxes, scores=scores, iou_thresh=0.5)
return results
f(*make_inputs(10))
f(*make_inputs(20))
f(*make_inputs(30))
def test_trace_valid_broadcast():
x1 = tensor(np.random.randn(1, 1))
x2 = tensor(np.random.randn(1, 2))
shape = (tensor([2]), tensor([2]))
@trace(symbolic=False)
def f(x, shape):
y = F.broadcast_to(x, shape)
return y
f(x1, shape)
f(x2, shape)
def test_clip():
x = tensor(np.random.randn(10, 10))
@trace(symbolic=True)
def f(x, lower, upper):
y = F.clip(x, lower, upper)
return y
for i in range(3):
f(x, tensor([0]), | tensor([1]) | megengine.tensor |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import inspect
import io
import itertools
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
from megengine.utils.naming import AutoNaming
@pytest.mark.parametrize("trace_mode", [False, True])
@pytest.mark.parametrize("return_mode", ["Value", "Tuple", "List", "Dict"])
def test_trace(trace_mode, return_mode):
@trace(symbolic=trace_mode)
def f(x):
if return_mode == "Tuple":
return (-x,)
elif return_mode == "List":
return [-x]
elif return_mode == "Dict":
return {"neg": -x}
else:
return -x
def get_numpy(y):
if return_mode == "Tuple" or return_mode == "List":
return y[0].numpy()
elif return_mode == "Dict":
return y["neg"].numpy()
return y.numpy()
x = tensor([1])
y = get_numpy(f(x))
for i in range(3):
np.testing.assert_equal(get_numpy(f(x)), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = Parameter([1.0], dtype=np.float32)
def forward(self, x):
x = x * self.a
# will result into a copy of output in grad
x = F.exp(x)
return x
ys = {False: [], True: []}
for symbolic in [False, True]:
net = Simple()
gm = GradManager().attach(net.parameters())
opt = optim.SGD(net.parameters(), 1e-3, momentum=0.9)
data = tensor(np.arange(4).reshape(2, 2), dtype="float32")
@trace(symbolic=symbolic)
def train_func(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(3):
y = train_func(data).numpy()
ys[symbolic].append(y)
for i in range(3):
np.testing.assert_equal(ys[False][i], ys[True][i])
@pytest.mark.parametrize("trace_mode", [False, True])
def test_exclude_from_trace(trace_mode):
@trace(symbolic=trace_mode)
def f(x):
x = -x
with exclude_from_trace():
if i % 2:
x = -x
x = -x
return x
x = tensor([1])
for i in range(3):
y = f(x).numpy()
np.testing.assert_equal(f(x).numpy(), y)
@pytest.mark.parametrize("trace_mode", [False, True])
def test_elemwise_fuse(trace_mode):
# explicitly declare opt_level as 2
@trace(symbolic=trace_mode, opt_level=2)
def f(a, b):
base = 0
c = b - a
_, idx = F.topk(c, 3)
# internally, biased_idx will be idx as gopt will ignore the addition
biased_idx = base + idx
return biased_idx
a = tensor(np.ones((7, 2)), dtype=np.int32)
b = tensor(2 * np.ones((7, 2)), dtype=np.float32)
for i in range(3):
y = f(a, b)
y.numpy()
@pytest.mark.parametrize("trace_mode", [False, True])
def test_elemwise_fuse_in_grad(trace_mode):
w = Parameter(np.ones([4, 6]), dtype="float32")
gm = GradManager().attach(w)
opt = optim.SGD([w], lr=0.01, momentum=0.9, weight_decay=5e-4)
# explicitly declare opt_level as 2
@trace(symbolic=trace_mode, opt_level=2)
def f():
with gm:
wm = F.sum(w ** 2, axis=1) ** 0.5
loss = wm.mean()
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(3):
y = f()
y.numpy()
def test_print_in_trace():
for symbolic in [False]: # cannot read value in symbolic mode
@trace(symbolic=symbolic)
def f(x):
nonlocal buf
x = -x
buf = x.numpy()
x = -x
return x
buf = None
x = tensor([1])
for i in range(3):
y = f(x).numpy()
z = buf
buf = None
np.testing.assert_equal(f(x).numpy(), y)
np.testing.assert_equal(z, buf)
def test_dump():
@trace(symbolic=True, capture_as_const=True)
def f(a, b):
return a + b
# prevent from remaining scope from exception test
AutoNaming.clear()
a = tensor([2])
b = tensor([4])
y = f(a, b).numpy()
for i in range(3):
np.testing.assert_equal(f(a, b).numpy(), y)
file = io.BytesIO()
dump_info = f.dump(file)
assert dump_info.nr_opr == 3
np.testing.assert_equal(dump_info.inputs, ["arg_0", "arg_1"])
np.testing.assert_equal(dump_info.outputs, ["ADD"])
file.seek(0)
infer_cg = cgtools.GraphInference(file)
result = list((infer_cg.run(a, b)).values())[0]
np.testing.assert_equal(result[0], y)
def test_capture_dump():
a = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * a
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file)
file.seek(0)
infer_cg = cgtools.GraphInference(file)
result = list((infer_cg.run(x)).values())[0]
np.testing.assert_equal(result[0], y)
def test_dump_volatile():
p = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * p
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file, optimize_for_inference=False)
file.seek(0)
cg, _, outputs = G.load_graph(file)
(out,) = outputs
assert (
cgtools.get_owner_opr_type(cgtools.get_owner_opr_inputs(out)[1])
== "ImmutableTensor"
)
@pytest.mark.parametrize("trace_mode", [False, True])
def test_trace_profiler(trace_mode):
@trace(symbolic=trace_mode, profiling=True)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
f(x)
f(x) # XXX: has to run twice
out = f.get_profile()
assert out.get("profiler")
def test_goptions():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
# directly return x / x will not trigger gopt
# since there's no way to tell the two x are the same
y = 2.0 * x
return y / y
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
y = 2.0 * x
return y / y
d = tensor(0.0)
assert not np.isfinite(f(d).numpy())
np.testing.assert_equal(g(d).numpy().item(), 1.0)
def test_goptions_log_sum_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x, y):
return log(exp(x) + exp(y))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x, y):
return log(exp(x) + exp(y))
val = 1.0e4
d = tensor(val)
o = tensor(0.0)
assert not np.isfinite(f(d, o).numpy())
np.testing.assert_almost_equal(g(d, o), val)
def test_goptions_log_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
return log(exp(x))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
return log(exp(x))
f(tensor(1.0))
_, out = mkstemp()
f.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_1 = cgtools.get_oprs_seq(outputs)
g(tensor(1.0))
g.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_2 = cgtools.get_oprs_seq(outputs)
assert len(oprs_1) - len(oprs_2) == 2
def test_optimize_for_inference():
@trace(symbolic=True, capture_as_const=True)
def f(x):
return exp(x)
_, out = mkstemp()
f(tensor(5.0))
f.dump(out, enable_io16xc32=True)
res = G.load_graph(out)
computing_input = res.output_vars_list[0].owner.inputs[0]
assert computing_input.dtype == np.float16
def test_optimize_for_inference_broadcast():
a = tensor(np.ones(1, dtype=np.float32))
@trace(capture_as_const=True, symbolic_shape=True)
def f():
return a._broadcast(tensor([1, 10], dtype=np.int32))
f()
f.dump(io.BytesIO())
def test_trace_cvt_bool():
x = tensor([0], dtype=np.int32)
@trace(symbolic=True)
def f(x):
a = x.shape
b = a[0]
assert isscalar(b)
return b == 0
for i in range(3):
np.testing.assert_equal(f(x).numpy(), False)
@pytest.mark.parametrize("trace_mode", [False, True])
def test_trace_reshape(trace_mode):
x1 = tensor(np.random.randn(2, 10, 10))
x2 = tensor(np.random.randn(4, 10, 10))
x3 = tensor(np.random.randn(8, 10, 10))
@trace(symbolic=trace_mode, capture_as_const=True)
def f(x):
y = x.reshape(x.shape[0], 100)
return y
f(x1)
f(x2)
f(x3)
def test_trace_topk():
x = tensor([5, 2, 7, 1, 0, 3, 2])
@trace(symbolic=True)
def f(x):
y = F.topk(x, 3)
np.testing.assert_equal(y[0].shape.numpy(), np.array([3,]))
return y
for i in range(3):
f(x)
def test_trace_warp_perspective():
inp_shape = (1, 1, 4, 4)
x = tensor(np.arange(16, dtype=np.float32).reshape(inp_shape))
M_shape = (1, 3, 3)
M = tensor(
np.array(
[[1.0, 0.0, 1.0], [0.0, 1.0, 1.0], [0.0, 0.0, 1.0]], dtype=np.float32
).reshape(M_shape)
)
@trace(symbolic=True)
def f(x, M):
out = F.vision.warp_perspective(x, M, (2, 2))
np.testing.assert_equal(out.shape.numpy(), np.array([1, 1, 2, 2]))
return out
for i in range(3):
f(x, M)
def test_raise_on_trace():
step_count = 0
catch_count = 0
bad_step = 10
class CatchMe(Exception):
pass
a = tensor([1, 2, 3, 4])
b = tensor([5, 6, 7, 8])
c = tensor([9, 0, 1, 2])
@trace
def add_abc(a, b, c):
ps = a + b
result = ps + c
if step_count == bad_step:
raise CatchMe("catch me")
return result
for i in range(100):
try:
d = add_abc(a, b, c)
except CatchMe as e:
catch_count += 1
else:
np.testing.assert_equal(d.numpy(), (a + b + c).numpy())
step_count += 1
assert catch_count == 1
@pytest.mark.parametrize("trace_mode", [False, True])
def test_trace_broadcast(trace_mode):
x1 = tensor(np.random.randn(3, 1, 1))
x2 = tensor(np.random.randn(1, 4, 1))
x3 = tensor(np.random.randn(1, 1, 5))
@trace(symbolic=trace_mode, capture_as_const=True)
def f(x):
y = F.broadcast_to(x, (3, 4, 5))
return y
f(x1)
f(x2)
f(x3)
def test_trace_nms():
def make_inputs(n):
boxes = np.zeros((n, 4))
boxes[:, :2] = np.random.rand(n, 2) * 100
boxes[:, 2:] = np.random.rand(n, 2) * 100 + 100
scores = np.random.rand(n)
return tensor(boxes), tensor(scores)
@trace(symbolic=False)
def f(boxes, scores):
# with tracing, max_output must be specified
results = F.vision.nms(boxes, scores=scores, iou_thresh=0.5, max_output=20)
# without tracing, max output can be inferred inside nms
with exclude_from_trace():
_ = F.vision.nms(boxes, scores=scores, iou_thresh=0.5)
return results
f(*make_inputs(10))
f(*make_inputs(20))
f(*make_inputs(30))
def test_trace_valid_broadcast():
x1 = tensor(np.random.randn(1, 1))
x2 = tensor(np.random.randn(1, 2))
shape = (tensor([2]), tensor([2]))
@trace(symbolic=False)
def f(x, shape):
y = F.broadcast_to(x, shape)
return y
f(x1, shape)
f(x2, shape)
def test_clip():
x = tensor(np.random.randn(10, 10))
@trace(symbolic=True)
def f(x, lower, upper):
y = F.clip(x, lower, upper)
return y
for i in range(3):
f(x, tensor([0]), tensor([1]))
# test returning noncontiguous tensor from trace
def test_slice():
@trace
def f(x):
return x[:, 1::2]
x = | F.arange(8) | megengine.functional.arange |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import inspect
import io
import itertools
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
from megengine.utils.naming import AutoNaming
@pytest.mark.parametrize("trace_mode", [False, True])
@pytest.mark.parametrize("return_mode", ["Value", "Tuple", "List", "Dict"])
def test_trace(trace_mode, return_mode):
@trace(symbolic=trace_mode)
def f(x):
if return_mode == "Tuple":
return (-x,)
elif return_mode == "List":
return [-x]
elif return_mode == "Dict":
return {"neg": -x}
else:
return -x
def get_numpy(y):
if return_mode == "Tuple" or return_mode == "List":
return y[0].numpy()
elif return_mode == "Dict":
return y["neg"].numpy()
return y.numpy()
x = tensor([1])
y = get_numpy(f(x))
for i in range(3):
np.testing.assert_equal(get_numpy(f(x)), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = Parameter([1.0], dtype=np.float32)
def forward(self, x):
x = x * self.a
# will result into a copy of output in grad
x = F.exp(x)
return x
ys = {False: [], True: []}
for symbolic in [False, True]:
net = Simple()
gm = GradManager().attach(net.parameters())
opt = optim.SGD(net.parameters(), 1e-3, momentum=0.9)
data = tensor(np.arange(4).reshape(2, 2), dtype="float32")
@trace(symbolic=symbolic)
def train_func(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(3):
y = train_func(data).numpy()
ys[symbolic].append(y)
for i in range(3):
np.testing.assert_equal(ys[False][i], ys[True][i])
@pytest.mark.parametrize("trace_mode", [False, True])
def test_exclude_from_trace(trace_mode):
@trace(symbolic=trace_mode)
def f(x):
x = -x
with exclude_from_trace():
if i % 2:
x = -x
x = -x
return x
x = tensor([1])
for i in range(3):
y = f(x).numpy()
np.testing.assert_equal(f(x).numpy(), y)
@pytest.mark.parametrize("trace_mode", [False, True])
def test_elemwise_fuse(trace_mode):
# explicitly declare opt_level as 2
@trace(symbolic=trace_mode, opt_level=2)
def f(a, b):
base = 0
c = b - a
_, idx = F.topk(c, 3)
# internally, biased_idx will be idx as gopt will ignore the addition
biased_idx = base + idx
return biased_idx
a = tensor(np.ones((7, 2)), dtype=np.int32)
b = tensor(2 * np.ones((7, 2)), dtype=np.float32)
for i in range(3):
y = f(a, b)
y.numpy()
@pytest.mark.parametrize("trace_mode", [False, True])
def test_elemwise_fuse_in_grad(trace_mode):
w = Parameter(np.ones([4, 6]), dtype="float32")
gm = GradManager().attach(w)
opt = optim.SGD([w], lr=0.01, momentum=0.9, weight_decay=5e-4)
# explicitly declare opt_level as 2
@trace(symbolic=trace_mode, opt_level=2)
def f():
with gm:
wm = F.sum(w ** 2, axis=1) ** 0.5
loss = wm.mean()
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(3):
y = f()
y.numpy()
def test_print_in_trace():
for symbolic in [False]: # cannot read value in symbolic mode
@trace(symbolic=symbolic)
def f(x):
nonlocal buf
x = -x
buf = x.numpy()
x = -x
return x
buf = None
x = tensor([1])
for i in range(3):
y = f(x).numpy()
z = buf
buf = None
np.testing.assert_equal(f(x).numpy(), y)
np.testing.assert_equal(z, buf)
def test_dump():
@trace(symbolic=True, capture_as_const=True)
def f(a, b):
return a + b
# prevent from remaining scope from exception test
AutoNaming.clear()
a = tensor([2])
b = tensor([4])
y = f(a, b).numpy()
for i in range(3):
np.testing.assert_equal(f(a, b).numpy(), y)
file = io.BytesIO()
dump_info = f.dump(file)
assert dump_info.nr_opr == 3
np.testing.assert_equal(dump_info.inputs, ["arg_0", "arg_1"])
np.testing.assert_equal(dump_info.outputs, ["ADD"])
file.seek(0)
infer_cg = cgtools.GraphInference(file)
result = list((infer_cg.run(a, b)).values())[0]
np.testing.assert_equal(result[0], y)
def test_capture_dump():
a = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * a
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file)
file.seek(0)
infer_cg = cgtools.GraphInference(file)
result = list((infer_cg.run(x)).values())[0]
np.testing.assert_equal(result[0], y)
def test_dump_volatile():
p = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * p
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file, optimize_for_inference=False)
file.seek(0)
cg, _, outputs = G.load_graph(file)
(out,) = outputs
assert (
cgtools.get_owner_opr_type(cgtools.get_owner_opr_inputs(out)[1])
== "ImmutableTensor"
)
@pytest.mark.parametrize("trace_mode", [False, True])
def test_trace_profiler(trace_mode):
@trace(symbolic=trace_mode, profiling=True)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
f(x)
f(x) # XXX: has to run twice
out = f.get_profile()
assert out.get("profiler")
def test_goptions():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
# directly return x / x will not trigger gopt
# since there's no way to tell the two x are the same
y = 2.0 * x
return y / y
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
y = 2.0 * x
return y / y
d = tensor(0.0)
assert not np.isfinite(f(d).numpy())
np.testing.assert_equal(g(d).numpy().item(), 1.0)
def test_goptions_log_sum_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x, y):
return log(exp(x) + exp(y))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x, y):
return log(exp(x) + exp(y))
val = 1.0e4
d = tensor(val)
o = tensor(0.0)
assert not np.isfinite(f(d, o).numpy())
np.testing.assert_almost_equal(g(d, o), val)
def test_goptions_log_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
return log(exp(x))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
return log(exp(x))
f(tensor(1.0))
_, out = mkstemp()
f.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_1 = cgtools.get_oprs_seq(outputs)
g(tensor(1.0))
g.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_2 = cgtools.get_oprs_seq(outputs)
assert len(oprs_1) - len(oprs_2) == 2
def test_optimize_for_inference():
@trace(symbolic=True, capture_as_const=True)
def f(x):
return exp(x)
_, out = mkstemp()
f(tensor(5.0))
f.dump(out, enable_io16xc32=True)
res = G.load_graph(out)
computing_input = res.output_vars_list[0].owner.inputs[0]
assert computing_input.dtype == np.float16
def test_optimize_for_inference_broadcast():
a = tensor(np.ones(1, dtype=np.float32))
@trace(capture_as_const=True, symbolic_shape=True)
def f():
return a._broadcast(tensor([1, 10], dtype=np.int32))
f()
f.dump(io.BytesIO())
def test_trace_cvt_bool():
x = tensor([0], dtype=np.int32)
@trace(symbolic=True)
def f(x):
a = x.shape
b = a[0]
assert isscalar(b)
return b == 0
for i in range(3):
np.testing.assert_equal(f(x).numpy(), False)
@pytest.mark.parametrize("trace_mode", [False, True])
def test_trace_reshape(trace_mode):
x1 = tensor(np.random.randn(2, 10, 10))
x2 = tensor(np.random.randn(4, 10, 10))
x3 = tensor(np.random.randn(8, 10, 10))
@trace(symbolic=trace_mode, capture_as_const=True)
def f(x):
y = x.reshape(x.shape[0], 100)
return y
f(x1)
f(x2)
f(x3)
def test_trace_topk():
x = tensor([5, 2, 7, 1, 0, 3, 2])
@trace(symbolic=True)
def f(x):
y = F.topk(x, 3)
np.testing.assert_equal(y[0].shape.numpy(), np.array([3,]))
return y
for i in range(3):
f(x)
def test_trace_warp_perspective():
inp_shape = (1, 1, 4, 4)
x = tensor(np.arange(16, dtype=np.float32).reshape(inp_shape))
M_shape = (1, 3, 3)
M = tensor(
np.array(
[[1.0, 0.0, 1.0], [0.0, 1.0, 1.0], [0.0, 0.0, 1.0]], dtype=np.float32
).reshape(M_shape)
)
@trace(symbolic=True)
def f(x, M):
out = F.vision.warp_perspective(x, M, (2, 2))
np.testing.assert_equal(out.shape.numpy(), np.array([1, 1, 2, 2]))
return out
for i in range(3):
f(x, M)
def test_raise_on_trace():
step_count = 0
catch_count = 0
bad_step = 10
class CatchMe(Exception):
pass
a = tensor([1, 2, 3, 4])
b = tensor([5, 6, 7, 8])
c = tensor([9, 0, 1, 2])
@trace
def add_abc(a, b, c):
ps = a + b
result = ps + c
if step_count == bad_step:
raise CatchMe("catch me")
return result
for i in range(100):
try:
d = add_abc(a, b, c)
except CatchMe as e:
catch_count += 1
else:
np.testing.assert_equal(d.numpy(), (a + b + c).numpy())
step_count += 1
assert catch_count == 1
@pytest.mark.parametrize("trace_mode", [False, True])
def test_trace_broadcast(trace_mode):
x1 = tensor(np.random.randn(3, 1, 1))
x2 = tensor(np.random.randn(1, 4, 1))
x3 = tensor(np.random.randn(1, 1, 5))
@trace(symbolic=trace_mode, capture_as_const=True)
def f(x):
y = F.broadcast_to(x, (3, 4, 5))
return y
f(x1)
f(x2)
f(x3)
def test_trace_nms():
def make_inputs(n):
boxes = np.zeros((n, 4))
boxes[:, :2] = np.random.rand(n, 2) * 100
boxes[:, 2:] = np.random.rand(n, 2) * 100 + 100
scores = np.random.rand(n)
return tensor(boxes), tensor(scores)
@trace(symbolic=False)
def f(boxes, scores):
# with tracing, max_output must be specified
results = F.vision.nms(boxes, scores=scores, iou_thresh=0.5, max_output=20)
# without tracing, max output can be inferred inside nms
with exclude_from_trace():
_ = F.vision.nms(boxes, scores=scores, iou_thresh=0.5)
return results
f(*make_inputs(10))
f(*make_inputs(20))
f(*make_inputs(30))
def test_trace_valid_broadcast():
x1 = tensor(np.random.randn(1, 1))
x2 = tensor(np.random.randn(1, 2))
shape = (tensor([2]), tensor([2]))
@trace(symbolic=False)
def f(x, shape):
y = F.broadcast_to(x, shape)
return y
f(x1, shape)
f(x2, shape)
def test_clip():
x = tensor(np.random.randn(10, 10))
@trace(symbolic=True)
def f(x, lower, upper):
y = F.clip(x, lower, upper)
return y
for i in range(3):
f(x, tensor([0]), tensor([1]))
# test returning noncontiguous tensor from trace
def test_slice():
@trace
def f(x):
return x[:, 1::2]
x = F.arange(8).reshape(2, 4)
f(x)
y = f(x)
np.testing.assert_array_equal(y.numpy(), x.numpy()[:, 1::2])
y + y
@pytest.mark.parametrize("shape_mode", [False, True])
def test_random(shape_mode):
def run_test(op):
@trace(symbolic=True, symbolic_shape=shape_mode)
def f():
out = op(size=[10, 10])
out_shape = out.shape
assert out_shape is not None
if not isinstance(out_shape, tuple):
assert out.shape.numpy() is not None
return out
for _ in range(3):
f()
run_test(uniform)
run_test(normal)
@pytest.mark.parametrize("shape_mode", [False, True])
def test_trace_advance_indexing(shape_mode):
funcs = [
lambda x, i: x[i],
# lambda x, i, j: x[i, j], # FIXME
lambda x, i, j: x[i, :, j, ...],
# lambda x, start, end: x[start:end], # FIXME
lambda x, start, end: x[:, 0, start:end, ..., 1],
lambda x, vec: x[vec],
lambda x, vec: x[vec, ..., 0, 1:3],
lambda x, vec: x[vec, vec[0], vec[1]],
# lambda x, i, start, end, vec: x[i, ..., :, vec, start:end], # FIXME
lambda x, mask: x[mask],
]
inputs = {
"x": np.random.randn(5, 5, 5, 5, 5).astype("float32"),
"i": 0,
"j": 2,
"start": 1,
"end": 3,
"vec": [1, 2, 3],
"mask": np.random.randn(5, 5, 5, 5, 5) >= 0,
}
for f in funcs:
sig = inspect.signature(f)
param_names = list(sig._parameters.keys())
params = {}
params_np = {}
f_traced = trace(f, symbolic=False, symbolic_shape=shape_mode)
for name in param_names:
params[name] = | tensor(inputs[name]) | megengine.tensor |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import inspect
import io
import itertools
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
from megengine.utils.naming import AutoNaming
@pytest.mark.parametrize("trace_mode", [False, True])
@pytest.mark.parametrize("return_mode", ["Value", "Tuple", "List", "Dict"])
def test_trace(trace_mode, return_mode):
@trace(symbolic=trace_mode)
def f(x):
if return_mode == "Tuple":
return (-x,)
elif return_mode == "List":
return [-x]
elif return_mode == "Dict":
return {"neg": -x}
else:
return -x
def get_numpy(y):
if return_mode == "Tuple" or return_mode == "List":
return y[0].numpy()
elif return_mode == "Dict":
return y["neg"].numpy()
return y.numpy()
x = tensor([1])
y = get_numpy(f(x))
for i in range(3):
np.testing.assert_equal(get_numpy(f(x)), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = Parameter([1.0], dtype=np.float32)
def forward(self, x):
x = x * self.a
# will result into a copy of output in grad
x = F.exp(x)
return x
ys = {False: [], True: []}
for symbolic in [False, True]:
net = Simple()
gm = | GradManager() | megengine.autodiff.GradManager |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import inspect
import io
import itertools
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
from megengine.utils.naming import AutoNaming
@pytest.mark.parametrize("trace_mode", [False, True])
@pytest.mark.parametrize("return_mode", ["Value", "Tuple", "List", "Dict"])
def test_trace(trace_mode, return_mode):
@trace(symbolic=trace_mode)
def f(x):
if return_mode == "Tuple":
return (-x,)
elif return_mode == "List":
return [-x]
elif return_mode == "Dict":
return {"neg": -x}
else:
return -x
def get_numpy(y):
if return_mode == "Tuple" or return_mode == "List":
return y[0].numpy()
elif return_mode == "Dict":
return y["neg"].numpy()
return y.numpy()
x = tensor([1])
y = get_numpy(f(x))
for i in range(3):
np.testing.assert_equal(get_numpy(f(x)), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = Parameter([1.0], dtype=np.float32)
def forward(self, x):
x = x * self.a
# will result into a copy of output in grad
x = F.exp(x)
return x
ys = {False: [], True: []}
for symbolic in [False, True]:
net = Simple()
gm = GradManager().attach(net.parameters())
opt = optim.SGD(net.parameters(), 1e-3, momentum=0.9)
data = tensor(np.arange(4).reshape(2, 2), dtype="float32")
@trace(symbolic=symbolic)
def train_func(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(3):
y = train_func(data).numpy()
ys[symbolic].append(y)
for i in range(3):
np.testing.assert_equal(ys[False][i], ys[True][i])
@pytest.mark.parametrize("trace_mode", [False, True])
def test_exclude_from_trace(trace_mode):
@trace(symbolic=trace_mode)
def f(x):
x = -x
with exclude_from_trace():
if i % 2:
x = -x
x = -x
return x
x = tensor([1])
for i in range(3):
y = f(x).numpy()
np.testing.assert_equal(f(x).numpy(), y)
@pytest.mark.parametrize("trace_mode", [False, True])
def test_elemwise_fuse(trace_mode):
# explicitly declare opt_level as 2
@trace(symbolic=trace_mode, opt_level=2)
def f(a, b):
base = 0
c = b - a
_, idx = F.topk(c, 3)
# internally, biased_idx will be idx as gopt will ignore the addition
biased_idx = base + idx
return biased_idx
a = tensor(np.ones((7, 2)), dtype=np.int32)
b = tensor(2 * np.ones((7, 2)), dtype=np.float32)
for i in range(3):
y = f(a, b)
y.numpy()
@pytest.mark.parametrize("trace_mode", [False, True])
def test_elemwise_fuse_in_grad(trace_mode):
w = Parameter(np.ones([4, 6]), dtype="float32")
gm = GradManager().attach(w)
opt = optim.SGD([w], lr=0.01, momentum=0.9, weight_decay=5e-4)
# explicitly declare opt_level as 2
@trace(symbolic=trace_mode, opt_level=2)
def f():
with gm:
wm = | F.sum(w ** 2, axis=1) | megengine.functional.sum |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import inspect
import io
import itertools
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
from megengine.utils.naming import AutoNaming
@pytest.mark.parametrize("trace_mode", [False, True])
@pytest.mark.parametrize("return_mode", ["Value", "Tuple", "List", "Dict"])
def test_trace(trace_mode, return_mode):
@trace(symbolic=trace_mode)
def f(x):
if return_mode == "Tuple":
return (-x,)
elif return_mode == "List":
return [-x]
elif return_mode == "Dict":
return {"neg": -x}
else:
return -x
def get_numpy(y):
if return_mode == "Tuple" or return_mode == "List":
return y[0].numpy()
elif return_mode == "Dict":
return y["neg"].numpy()
return y.numpy()
x = tensor([1])
y = get_numpy(f(x))
for i in range(3):
np.testing.assert_equal(get_numpy(f(x)), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = Parameter([1.0], dtype=np.float32)
def forward(self, x):
x = x * self.a
# will result into a copy of output in grad
x = F.exp(x)
return x
ys = {False: [], True: []}
for symbolic in [False, True]:
net = Simple()
gm = GradManager().attach(net.parameters())
opt = optim.SGD(net.parameters(), 1e-3, momentum=0.9)
data = tensor(np.arange(4).reshape(2, 2), dtype="float32")
@trace(symbolic=symbolic)
def train_func(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(3):
y = train_func(data).numpy()
ys[symbolic].append(y)
for i in range(3):
np.testing.assert_equal(ys[False][i], ys[True][i])
@pytest.mark.parametrize("trace_mode", [False, True])
def test_exclude_from_trace(trace_mode):
@trace(symbolic=trace_mode)
def f(x):
x = -x
with exclude_from_trace():
if i % 2:
x = -x
x = -x
return x
x = tensor([1])
for i in range(3):
y = f(x).numpy()
np.testing.assert_equal(f(x).numpy(), y)
@pytest.mark.parametrize("trace_mode", [False, True])
def test_elemwise_fuse(trace_mode):
# explicitly declare opt_level as 2
@trace(symbolic=trace_mode, opt_level=2)
def f(a, b):
base = 0
c = b - a
_, idx = F.topk(c, 3)
# internally, biased_idx will be idx as gopt will ignore the addition
biased_idx = base + idx
return biased_idx
a = tensor(np.ones((7, 2)), dtype=np.int32)
b = tensor(2 * np.ones((7, 2)), dtype=np.float32)
for i in range(3):
y = f(a, b)
y.numpy()
@pytest.mark.parametrize("trace_mode", [False, True])
def test_elemwise_fuse_in_grad(trace_mode):
w = Parameter(np.ones([4, 6]), dtype="float32")
gm = GradManager().attach(w)
opt = optim.SGD([w], lr=0.01, momentum=0.9, weight_decay=5e-4)
# explicitly declare opt_level as 2
@trace(symbolic=trace_mode, opt_level=2)
def f():
with gm:
wm = F.sum(w ** 2, axis=1) ** 0.5
loss = wm.mean()
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(3):
y = f()
y.numpy()
def test_print_in_trace():
for symbolic in [False]: # cannot read value in symbolic mode
@trace(symbolic=symbolic)
def f(x):
nonlocal buf
x = -x
buf = x.numpy()
x = -x
return x
buf = None
x = tensor([1])
for i in range(3):
y = f(x).numpy()
z = buf
buf = None
np.testing.assert_equal(f(x).numpy(), y)
np.testing.assert_equal(z, buf)
def test_dump():
@trace(symbolic=True, capture_as_const=True)
def f(a, b):
return a + b
# prevent from remaining scope from exception test
AutoNaming.clear()
a = tensor([2])
b = tensor([4])
y = f(a, b).numpy()
for i in range(3):
np.testing.assert_equal(f(a, b).numpy(), y)
file = io.BytesIO()
dump_info = f.dump(file)
assert dump_info.nr_opr == 3
np.testing.assert_equal(dump_info.inputs, ["arg_0", "arg_1"])
np.testing.assert_equal(dump_info.outputs, ["ADD"])
file.seek(0)
infer_cg = cgtools.GraphInference(file)
result = list((infer_cg.run(a, b)).values())[0]
np.testing.assert_equal(result[0], y)
def test_capture_dump():
a = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * a
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file)
file.seek(0)
infer_cg = cgtools.GraphInference(file)
result = list((infer_cg.run(x)).values())[0]
np.testing.assert_equal(result[0], y)
def test_dump_volatile():
p = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * p
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file, optimize_for_inference=False)
file.seek(0)
cg, _, outputs = G.load_graph(file)
(out,) = outputs
assert (
cgtools.get_owner_opr_type( | cgtools.get_owner_opr_inputs(out) | megengine.utils.comp_graph_tools.get_owner_opr_inputs |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import inspect
import io
import itertools
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
from megengine.utils.naming import AutoNaming
@pytest.mark.parametrize("trace_mode", [False, True])
@pytest.mark.parametrize("return_mode", ["Value", "Tuple", "List", "Dict"])
def test_trace(trace_mode, return_mode):
@trace(symbolic=trace_mode)
def f(x):
if return_mode == "Tuple":
return (-x,)
elif return_mode == "List":
return [-x]
elif return_mode == "Dict":
return {"neg": -x}
else:
return -x
def get_numpy(y):
if return_mode == "Tuple" or return_mode == "List":
return y[0].numpy()
elif return_mode == "Dict":
return y["neg"].numpy()
return y.numpy()
x = tensor([1])
y = get_numpy(f(x))
for i in range(3):
np.testing.assert_equal(get_numpy(f(x)), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = Parameter([1.0], dtype=np.float32)
def forward(self, x):
x = x * self.a
# will result into a copy of output in grad
x = F.exp(x)
return x
ys = {False: [], True: []}
for symbolic in [False, True]:
net = Simple()
gm = GradManager().attach(net.parameters())
opt = optim.SGD(net.parameters(), 1e-3, momentum=0.9)
data = tensor(np.arange(4).reshape(2, 2), dtype="float32")
@trace(symbolic=symbolic)
def train_func(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(3):
y = train_func(data).numpy()
ys[symbolic].append(y)
for i in range(3):
np.testing.assert_equal(ys[False][i], ys[True][i])
@pytest.mark.parametrize("trace_mode", [False, True])
def test_exclude_from_trace(trace_mode):
@trace(symbolic=trace_mode)
def f(x):
x = -x
with exclude_from_trace():
if i % 2:
x = -x
x = -x
return x
x = tensor([1])
for i in range(3):
y = f(x).numpy()
np.testing.assert_equal(f(x).numpy(), y)
@pytest.mark.parametrize("trace_mode", [False, True])
def test_elemwise_fuse(trace_mode):
# explicitly declare opt_level as 2
@trace(symbolic=trace_mode, opt_level=2)
def f(a, b):
base = 0
c = b - a
_, idx = F.topk(c, 3)
# internally, biased_idx will be idx as gopt will ignore the addition
biased_idx = base + idx
return biased_idx
a = tensor(np.ones((7, 2)), dtype=np.int32)
b = tensor(2 * np.ones((7, 2)), dtype=np.float32)
for i in range(3):
y = f(a, b)
y.numpy()
@pytest.mark.parametrize("trace_mode", [False, True])
def test_elemwise_fuse_in_grad(trace_mode):
w = Parameter(np.ones([4, 6]), dtype="float32")
gm = GradManager().attach(w)
opt = optim.SGD([w], lr=0.01, momentum=0.9, weight_decay=5e-4)
# explicitly declare opt_level as 2
@trace(symbolic=trace_mode, opt_level=2)
def f():
with gm:
wm = F.sum(w ** 2, axis=1) ** 0.5
loss = wm.mean()
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(3):
y = f()
y.numpy()
def test_print_in_trace():
for symbolic in [False]: # cannot read value in symbolic mode
@trace(symbolic=symbolic)
def f(x):
nonlocal buf
x = -x
buf = x.numpy()
x = -x
return x
buf = None
x = tensor([1])
for i in range(3):
y = f(x).numpy()
z = buf
buf = None
np.testing.assert_equal(f(x).numpy(), y)
np.testing.assert_equal(z, buf)
def test_dump():
@trace(symbolic=True, capture_as_const=True)
def f(a, b):
return a + b
# prevent from remaining scope from exception test
AutoNaming.clear()
a = tensor([2])
b = tensor([4])
y = f(a, b).numpy()
for i in range(3):
np.testing.assert_equal(f(a, b).numpy(), y)
file = io.BytesIO()
dump_info = f.dump(file)
assert dump_info.nr_opr == 3
np.testing.assert_equal(dump_info.inputs, ["arg_0", "arg_1"])
np.testing.assert_equal(dump_info.outputs, ["ADD"])
file.seek(0)
infer_cg = cgtools.GraphInference(file)
result = list((infer_cg.run(a, b)).values())[0]
np.testing.assert_equal(result[0], y)
def test_capture_dump():
a = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * a
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file)
file.seek(0)
infer_cg = cgtools.GraphInference(file)
result = list((infer_cg.run(x)).values())[0]
np.testing.assert_equal(result[0], y)
def test_dump_volatile():
p = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * p
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file, optimize_for_inference=False)
file.seek(0)
cg, _, outputs = G.load_graph(file)
(out,) = outputs
assert (
cgtools.get_owner_opr_type(cgtools.get_owner_opr_inputs(out)[1])
== "ImmutableTensor"
)
@pytest.mark.parametrize("trace_mode", [False, True])
def test_trace_profiler(trace_mode):
@trace(symbolic=trace_mode, profiling=True)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
f(x)
f(x) # XXX: has to run twice
out = f.get_profile()
assert out.get("profiler")
def test_goptions():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
# directly return x / x will not trigger gopt
# since there's no way to tell the two x are the same
y = 2.0 * x
return y / y
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
y = 2.0 * x
return y / y
d = tensor(0.0)
assert not np.isfinite(f(d).numpy())
np.testing.assert_equal(g(d).numpy().item(), 1.0)
def test_goptions_log_sum_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x, y):
return log( | exp(x) | megengine.functional.exp |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import inspect
import io
import itertools
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
from megengine.utils.naming import AutoNaming
@pytest.mark.parametrize("trace_mode", [False, True])
@pytest.mark.parametrize("return_mode", ["Value", "Tuple", "List", "Dict"])
def test_trace(trace_mode, return_mode):
@trace(symbolic=trace_mode)
def f(x):
if return_mode == "Tuple":
return (-x,)
elif return_mode == "List":
return [-x]
elif return_mode == "Dict":
return {"neg": -x}
else:
return -x
def get_numpy(y):
if return_mode == "Tuple" or return_mode == "List":
return y[0].numpy()
elif return_mode == "Dict":
return y["neg"].numpy()
return y.numpy()
x = tensor([1])
y = get_numpy(f(x))
for i in range(3):
np.testing.assert_equal(get_numpy(f(x)), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = Parameter([1.0], dtype=np.float32)
def forward(self, x):
x = x * self.a
# will result into a copy of output in grad
x = F.exp(x)
return x
ys = {False: [], True: []}
for symbolic in [False, True]:
net = Simple()
gm = GradManager().attach(net.parameters())
opt = optim.SGD(net.parameters(), 1e-3, momentum=0.9)
data = tensor(np.arange(4).reshape(2, 2), dtype="float32")
@trace(symbolic=symbolic)
def train_func(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(3):
y = train_func(data).numpy()
ys[symbolic].append(y)
for i in range(3):
np.testing.assert_equal(ys[False][i], ys[True][i])
@pytest.mark.parametrize("trace_mode", [False, True])
def test_exclude_from_trace(trace_mode):
@trace(symbolic=trace_mode)
def f(x):
x = -x
with exclude_from_trace():
if i % 2:
x = -x
x = -x
return x
x = tensor([1])
for i in range(3):
y = f(x).numpy()
np.testing.assert_equal(f(x).numpy(), y)
@pytest.mark.parametrize("trace_mode", [False, True])
def test_elemwise_fuse(trace_mode):
# explicitly declare opt_level as 2
@trace(symbolic=trace_mode, opt_level=2)
def f(a, b):
base = 0
c = b - a
_, idx = F.topk(c, 3)
# internally, biased_idx will be idx as gopt will ignore the addition
biased_idx = base + idx
return biased_idx
a = tensor(np.ones((7, 2)), dtype=np.int32)
b = tensor(2 * np.ones((7, 2)), dtype=np.float32)
for i in range(3):
y = f(a, b)
y.numpy()
@pytest.mark.parametrize("trace_mode", [False, True])
def test_elemwise_fuse_in_grad(trace_mode):
w = Parameter(np.ones([4, 6]), dtype="float32")
gm = GradManager().attach(w)
opt = optim.SGD([w], lr=0.01, momentum=0.9, weight_decay=5e-4)
# explicitly declare opt_level as 2
@trace(symbolic=trace_mode, opt_level=2)
def f():
with gm:
wm = F.sum(w ** 2, axis=1) ** 0.5
loss = wm.mean()
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(3):
y = f()
y.numpy()
def test_print_in_trace():
for symbolic in [False]: # cannot read value in symbolic mode
@trace(symbolic=symbolic)
def f(x):
nonlocal buf
x = -x
buf = x.numpy()
x = -x
return x
buf = None
x = tensor([1])
for i in range(3):
y = f(x).numpy()
z = buf
buf = None
np.testing.assert_equal(f(x).numpy(), y)
np.testing.assert_equal(z, buf)
def test_dump():
@trace(symbolic=True, capture_as_const=True)
def f(a, b):
return a + b
# prevent from remaining scope from exception test
AutoNaming.clear()
a = tensor([2])
b = tensor([4])
y = f(a, b).numpy()
for i in range(3):
np.testing.assert_equal(f(a, b).numpy(), y)
file = io.BytesIO()
dump_info = f.dump(file)
assert dump_info.nr_opr == 3
np.testing.assert_equal(dump_info.inputs, ["arg_0", "arg_1"])
np.testing.assert_equal(dump_info.outputs, ["ADD"])
file.seek(0)
infer_cg = cgtools.GraphInference(file)
result = list((infer_cg.run(a, b)).values())[0]
np.testing.assert_equal(result[0], y)
def test_capture_dump():
a = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * a
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file)
file.seek(0)
infer_cg = cgtools.GraphInference(file)
result = list((infer_cg.run(x)).values())[0]
np.testing.assert_equal(result[0], y)
def test_dump_volatile():
p = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * p
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file, optimize_for_inference=False)
file.seek(0)
cg, _, outputs = G.load_graph(file)
(out,) = outputs
assert (
cgtools.get_owner_opr_type(cgtools.get_owner_opr_inputs(out)[1])
== "ImmutableTensor"
)
@pytest.mark.parametrize("trace_mode", [False, True])
def test_trace_profiler(trace_mode):
@trace(symbolic=trace_mode, profiling=True)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
f(x)
f(x) # XXX: has to run twice
out = f.get_profile()
assert out.get("profiler")
def test_goptions():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
# directly return x / x will not trigger gopt
# since there's no way to tell the two x are the same
y = 2.0 * x
return y / y
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
y = 2.0 * x
return y / y
d = tensor(0.0)
assert not np.isfinite(f(d).numpy())
np.testing.assert_equal(g(d).numpy().item(), 1.0)
def test_goptions_log_sum_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x, y):
return log(exp(x) + | exp(y) | megengine.functional.exp |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import inspect
import io
import itertools
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
from megengine.utils.naming import AutoNaming
@pytest.mark.parametrize("trace_mode", [False, True])
@pytest.mark.parametrize("return_mode", ["Value", "Tuple", "List", "Dict"])
def test_trace(trace_mode, return_mode):
@trace(symbolic=trace_mode)
def f(x):
if return_mode == "Tuple":
return (-x,)
elif return_mode == "List":
return [-x]
elif return_mode == "Dict":
return {"neg": -x}
else:
return -x
def get_numpy(y):
if return_mode == "Tuple" or return_mode == "List":
return y[0].numpy()
elif return_mode == "Dict":
return y["neg"].numpy()
return y.numpy()
x = tensor([1])
y = get_numpy(f(x))
for i in range(3):
np.testing.assert_equal(get_numpy(f(x)), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = Parameter([1.0], dtype=np.float32)
def forward(self, x):
x = x * self.a
# will result into a copy of output in grad
x = F.exp(x)
return x
ys = {False: [], True: []}
for symbolic in [False, True]:
net = Simple()
gm = GradManager().attach(net.parameters())
opt = optim.SGD(net.parameters(), 1e-3, momentum=0.9)
data = tensor(np.arange(4).reshape(2, 2), dtype="float32")
@trace(symbolic=symbolic)
def train_func(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(3):
y = train_func(data).numpy()
ys[symbolic].append(y)
for i in range(3):
np.testing.assert_equal(ys[False][i], ys[True][i])
@pytest.mark.parametrize("trace_mode", [False, True])
def test_exclude_from_trace(trace_mode):
@trace(symbolic=trace_mode)
def f(x):
x = -x
with exclude_from_trace():
if i % 2:
x = -x
x = -x
return x
x = tensor([1])
for i in range(3):
y = f(x).numpy()
np.testing.assert_equal(f(x).numpy(), y)
@pytest.mark.parametrize("trace_mode", [False, True])
def test_elemwise_fuse(trace_mode):
# explicitly declare opt_level as 2
@trace(symbolic=trace_mode, opt_level=2)
def f(a, b):
base = 0
c = b - a
_, idx = F.topk(c, 3)
# internally, biased_idx will be idx as gopt will ignore the addition
biased_idx = base + idx
return biased_idx
a = tensor(np.ones((7, 2)), dtype=np.int32)
b = tensor(2 * np.ones((7, 2)), dtype=np.float32)
for i in range(3):
y = f(a, b)
y.numpy()
@pytest.mark.parametrize("trace_mode", [False, True])
def test_elemwise_fuse_in_grad(trace_mode):
w = Parameter(np.ones([4, 6]), dtype="float32")
gm = GradManager().attach(w)
opt = optim.SGD([w], lr=0.01, momentum=0.9, weight_decay=5e-4)
# explicitly declare opt_level as 2
@trace(symbolic=trace_mode, opt_level=2)
def f():
with gm:
wm = F.sum(w ** 2, axis=1) ** 0.5
loss = wm.mean()
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(3):
y = f()
y.numpy()
def test_print_in_trace():
for symbolic in [False]: # cannot read value in symbolic mode
@trace(symbolic=symbolic)
def f(x):
nonlocal buf
x = -x
buf = x.numpy()
x = -x
return x
buf = None
x = tensor([1])
for i in range(3):
y = f(x).numpy()
z = buf
buf = None
np.testing.assert_equal(f(x).numpy(), y)
np.testing.assert_equal(z, buf)
def test_dump():
@trace(symbolic=True, capture_as_const=True)
def f(a, b):
return a + b
# prevent from remaining scope from exception test
AutoNaming.clear()
a = tensor([2])
b = tensor([4])
y = f(a, b).numpy()
for i in range(3):
np.testing.assert_equal(f(a, b).numpy(), y)
file = io.BytesIO()
dump_info = f.dump(file)
assert dump_info.nr_opr == 3
np.testing.assert_equal(dump_info.inputs, ["arg_0", "arg_1"])
np.testing.assert_equal(dump_info.outputs, ["ADD"])
file.seek(0)
infer_cg = cgtools.GraphInference(file)
result = list((infer_cg.run(a, b)).values())[0]
np.testing.assert_equal(result[0], y)
def test_capture_dump():
a = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * a
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file)
file.seek(0)
infer_cg = cgtools.GraphInference(file)
result = list((infer_cg.run(x)).values())[0]
np.testing.assert_equal(result[0], y)
def test_dump_volatile():
p = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * p
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file, optimize_for_inference=False)
file.seek(0)
cg, _, outputs = G.load_graph(file)
(out,) = outputs
assert (
cgtools.get_owner_opr_type(cgtools.get_owner_opr_inputs(out)[1])
== "ImmutableTensor"
)
@pytest.mark.parametrize("trace_mode", [False, True])
def test_trace_profiler(trace_mode):
@trace(symbolic=trace_mode, profiling=True)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
f(x)
f(x) # XXX: has to run twice
out = f.get_profile()
assert out.get("profiler")
def test_goptions():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
# directly return x / x will not trigger gopt
# since there's no way to tell the two x are the same
y = 2.0 * x
return y / y
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
y = 2.0 * x
return y / y
d = tensor(0.0)
assert not np.isfinite(f(d).numpy())
np.testing.assert_equal(g(d).numpy().item(), 1.0)
def test_goptions_log_sum_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x, y):
return log(exp(x) + exp(y))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x, y):
return log( | exp(x) | megengine.functional.exp |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import inspect
import io
import itertools
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
from megengine.utils.naming import AutoNaming
@pytest.mark.parametrize("trace_mode", [False, True])
@pytest.mark.parametrize("return_mode", ["Value", "Tuple", "List", "Dict"])
def test_trace(trace_mode, return_mode):
@trace(symbolic=trace_mode)
def f(x):
if return_mode == "Tuple":
return (-x,)
elif return_mode == "List":
return [-x]
elif return_mode == "Dict":
return {"neg": -x}
else:
return -x
def get_numpy(y):
if return_mode == "Tuple" or return_mode == "List":
return y[0].numpy()
elif return_mode == "Dict":
return y["neg"].numpy()
return y.numpy()
x = tensor([1])
y = get_numpy(f(x))
for i in range(3):
np.testing.assert_equal(get_numpy(f(x)), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = Parameter([1.0], dtype=np.float32)
def forward(self, x):
x = x * self.a
# will result into a copy of output in grad
x = F.exp(x)
return x
ys = {False: [], True: []}
for symbolic in [False, True]:
net = Simple()
gm = GradManager().attach(net.parameters())
opt = optim.SGD(net.parameters(), 1e-3, momentum=0.9)
data = tensor(np.arange(4).reshape(2, 2), dtype="float32")
@trace(symbolic=symbolic)
def train_func(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(3):
y = train_func(data).numpy()
ys[symbolic].append(y)
for i in range(3):
np.testing.assert_equal(ys[False][i], ys[True][i])
@pytest.mark.parametrize("trace_mode", [False, True])
def test_exclude_from_trace(trace_mode):
@trace(symbolic=trace_mode)
def f(x):
x = -x
with exclude_from_trace():
if i % 2:
x = -x
x = -x
return x
x = tensor([1])
for i in range(3):
y = f(x).numpy()
np.testing.assert_equal(f(x).numpy(), y)
@pytest.mark.parametrize("trace_mode", [False, True])
def test_elemwise_fuse(trace_mode):
# explicitly declare opt_level as 2
@trace(symbolic=trace_mode, opt_level=2)
def f(a, b):
base = 0
c = b - a
_, idx = F.topk(c, 3)
# internally, biased_idx will be idx as gopt will ignore the addition
biased_idx = base + idx
return biased_idx
a = tensor(np.ones((7, 2)), dtype=np.int32)
b = tensor(2 * np.ones((7, 2)), dtype=np.float32)
for i in range(3):
y = f(a, b)
y.numpy()
@pytest.mark.parametrize("trace_mode", [False, True])
def test_elemwise_fuse_in_grad(trace_mode):
w = Parameter(np.ones([4, 6]), dtype="float32")
gm = GradManager().attach(w)
opt = optim.SGD([w], lr=0.01, momentum=0.9, weight_decay=5e-4)
# explicitly declare opt_level as 2
@trace(symbolic=trace_mode, opt_level=2)
def f():
with gm:
wm = F.sum(w ** 2, axis=1) ** 0.5
loss = wm.mean()
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(3):
y = f()
y.numpy()
def test_print_in_trace():
for symbolic in [False]: # cannot read value in symbolic mode
@trace(symbolic=symbolic)
def f(x):
nonlocal buf
x = -x
buf = x.numpy()
x = -x
return x
buf = None
x = tensor([1])
for i in range(3):
y = f(x).numpy()
z = buf
buf = None
np.testing.assert_equal(f(x).numpy(), y)
np.testing.assert_equal(z, buf)
def test_dump():
@trace(symbolic=True, capture_as_const=True)
def f(a, b):
return a + b
# prevent from remaining scope from exception test
AutoNaming.clear()
a = tensor([2])
b = tensor([4])
y = f(a, b).numpy()
for i in range(3):
np.testing.assert_equal(f(a, b).numpy(), y)
file = io.BytesIO()
dump_info = f.dump(file)
assert dump_info.nr_opr == 3
np.testing.assert_equal(dump_info.inputs, ["arg_0", "arg_1"])
np.testing.assert_equal(dump_info.outputs, ["ADD"])
file.seek(0)
infer_cg = cgtools.GraphInference(file)
result = list((infer_cg.run(a, b)).values())[0]
np.testing.assert_equal(result[0], y)
def test_capture_dump():
a = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * a
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file)
file.seek(0)
infer_cg = cgtools.GraphInference(file)
result = list((infer_cg.run(x)).values())[0]
np.testing.assert_equal(result[0], y)
def test_dump_volatile():
p = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * p
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file, optimize_for_inference=False)
file.seek(0)
cg, _, outputs = G.load_graph(file)
(out,) = outputs
assert (
cgtools.get_owner_opr_type(cgtools.get_owner_opr_inputs(out)[1])
== "ImmutableTensor"
)
@pytest.mark.parametrize("trace_mode", [False, True])
def test_trace_profiler(trace_mode):
@trace(symbolic=trace_mode, profiling=True)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
f(x)
f(x) # XXX: has to run twice
out = f.get_profile()
assert out.get("profiler")
def test_goptions():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
# directly return x / x will not trigger gopt
# since there's no way to tell the two x are the same
y = 2.0 * x
return y / y
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
y = 2.0 * x
return y / y
d = tensor(0.0)
assert not np.isfinite(f(d).numpy())
np.testing.assert_equal(g(d).numpy().item(), 1.0)
def test_goptions_log_sum_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x, y):
return log(exp(x) + exp(y))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x, y):
return log(exp(x) + | exp(y) | megengine.functional.exp |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import os
import re
import subprocess
import sys
import numpy as np
import megengine as mge
import megengine.functional as F
from megengine import jit, tensor
from megengine.functional.debug_param import set_conv_execution_strategy
from megengine.module import AvgPool2d, BatchNorm2d, Conv2d, Linear, Module
from megengine.optimizer import SGD
from megengine.test import assertTensorClose
def get_gpu_name():
try:
gpu_info = subprocess.check_output(
["nvidia-smi", "--query-gpu=gpu_name", "--format=csv,noheader"]
)
gpu_info = gpu_info.decode("ascii").split("\n")[0]
except:
gpu_info = "None"
return gpu_info
def get_cpu_name():
cpu_info = "None"
try:
cpu_info = subprocess.check_output(["cat", "/proc/cpuinfo"]).decode("ascii")
for line in cpu_info.split("\n"):
if "model name" in line:
return re.sub(".*model name.*:", "", line, 1).strip()
except:
pass
return cpu_info
def get_xpu_name():
if | mge.is_cuda_available() | megengine.is_cuda_available |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import os
import re
import subprocess
import sys
import numpy as np
import megengine as mge
import megengine.functional as F
from megengine import jit, tensor
from megengine.functional.debug_param import set_conv_execution_strategy
from megengine.module import AvgPool2d, BatchNorm2d, Conv2d, Linear, Module
from megengine.optimizer import SGD
from megengine.test import assertTensorClose
def get_gpu_name():
try:
gpu_info = subprocess.check_output(
["nvidia-smi", "--query-gpu=gpu_name", "--format=csv,noheader"]
)
gpu_info = gpu_info.decode("ascii").split("\n")[0]
except:
gpu_info = "None"
return gpu_info
def get_cpu_name():
cpu_info = "None"
try:
cpu_info = subprocess.check_output(["cat", "/proc/cpuinfo"]).decode("ascii")
for line in cpu_info.split("\n"):
if "model name" in line:
return re.sub(".*model name.*:", "", line, 1).strip()
except:
pass
return cpu_info
def get_xpu_name():
if mge.is_cuda_available():
return get_gpu_name()
else:
return get_cpu_name()
class MnistNet(Module):
def __init__(self, has_bn=False):
super().__init__()
self.conv0 = Conv2d(1, 20, kernel_size=5, bias=True)
self.pool0 = AvgPool2d(2)
self.conv1 = Conv2d(20, 20, kernel_size=5, bias=True)
self.pool1 = AvgPool2d(2)
self.fc0 = Linear(20 * 4 * 4, 500, bias=True)
self.fc1 = Linear(500, 10, bias=True)
self.bn0 = None
self.bn1 = None
if has_bn:
self.bn0 = BatchNorm2d(20)
self.bn1 = BatchNorm2d(20)
def forward(self, x):
x = self.conv0(x)
if self.bn0:
x = self.bn0(x)
x = F.relu(x)
x = self.pool0(x)
x = self.conv1(x)
if self.bn1:
x = self.bn1(x)
x = F.relu(x)
x = self.pool1(x)
x = F.flatten(x, 1)
x = self.fc0(x)
x = F.relu(x)
x = self.fc1(x)
return x
def train(data, label, net, opt):
pred = net(data)
loss = | F.cross_entropy_with_softmax(pred, label) | megengine.functional.cross_entropy_with_softmax |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import os
import re
import subprocess
import sys
import numpy as np
import megengine as mge
import megengine.functional as F
from megengine import jit, tensor
from megengine.functional.debug_param import set_conv_execution_strategy
from megengine.module import AvgPool2d, BatchNorm2d, Conv2d, Linear, Module
from megengine.optimizer import SGD
from megengine.test import assertTensorClose
def get_gpu_name():
try:
gpu_info = subprocess.check_output(
["nvidia-smi", "--query-gpu=gpu_name", "--format=csv,noheader"]
)
gpu_info = gpu_info.decode("ascii").split("\n")[0]
except:
gpu_info = "None"
return gpu_info
def get_cpu_name():
cpu_info = "None"
try:
cpu_info = subprocess.check_output(["cat", "/proc/cpuinfo"]).decode("ascii")
for line in cpu_info.split("\n"):
if "model name" in line:
return re.sub(".*model name.*:", "", line, 1).strip()
except:
pass
return cpu_info
def get_xpu_name():
if mge.is_cuda_available():
return get_gpu_name()
else:
return get_cpu_name()
class MnistNet(Module):
def __init__(self, has_bn=False):
super().__init__()
self.conv0 = Conv2d(1, 20, kernel_size=5, bias=True)
self.pool0 = AvgPool2d(2)
self.conv1 = Conv2d(20, 20, kernel_size=5, bias=True)
self.pool1 = AvgPool2d(2)
self.fc0 = Linear(20 * 4 * 4, 500, bias=True)
self.fc1 = Linear(500, 10, bias=True)
self.bn0 = None
self.bn1 = None
if has_bn:
self.bn0 = BatchNorm2d(20)
self.bn1 = BatchNorm2d(20)
def forward(self, x):
x = self.conv0(x)
if self.bn0:
x = self.bn0(x)
x = F.relu(x)
x = self.pool0(x)
x = self.conv1(x)
if self.bn1:
x = self.bn1(x)
x = F.relu(x)
x = self.pool1(x)
x = F.flatten(x, 1)
x = self.fc0(x)
x = F.relu(x)
x = self.fc1(x)
return x
def train(data, label, net, opt):
pred = net(data)
loss = F.cross_entropy_with_softmax(pred, label)
opt.backward(loss)
return loss
def update_model(model_path):
"""
Update the dumped model with test cases for new reference values.
The model with pre-trained weights is trained for one iter with the test data attached.
The loss and updated net state dict is dumped.
.. code-block:: python
from test_correctness import update_model
update_model('mnist_model_with_test.mge') # for gpu
update_model('mnist_model_with_test_cpu.mge') # for cpu
"""
net = MnistNet(has_bn=True)
checkpoint = | mge.load(model_path) | megengine.load |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import os
import re
import subprocess
import sys
import numpy as np
import megengine as mge
import megengine.functional as F
from megengine import jit, tensor
from megengine.functional.debug_param import set_conv_execution_strategy
from megengine.module import AvgPool2d, BatchNorm2d, Conv2d, Linear, Module
from megengine.optimizer import SGD
from megengine.test import assertTensorClose
def get_gpu_name():
try:
gpu_info = subprocess.check_output(
["nvidia-smi", "--query-gpu=gpu_name", "--format=csv,noheader"]
)
gpu_info = gpu_info.decode("ascii").split("\n")[0]
except:
gpu_info = "None"
return gpu_info
def get_cpu_name():
cpu_info = "None"
try:
cpu_info = subprocess.check_output(["cat", "/proc/cpuinfo"]).decode("ascii")
for line in cpu_info.split("\n"):
if "model name" in line:
return re.sub(".*model name.*:", "", line, 1).strip()
except:
pass
return cpu_info
def get_xpu_name():
if mge.is_cuda_available():
return get_gpu_name()
else:
return get_cpu_name()
class MnistNet(Module):
def __init__(self, has_bn=False):
super().__init__()
self.conv0 = Conv2d(1, 20, kernel_size=5, bias=True)
self.pool0 = AvgPool2d(2)
self.conv1 = Conv2d(20, 20, kernel_size=5, bias=True)
self.pool1 = AvgPool2d(2)
self.fc0 = Linear(20 * 4 * 4, 500, bias=True)
self.fc1 = Linear(500, 10, bias=True)
self.bn0 = None
self.bn1 = None
if has_bn:
self.bn0 = BatchNorm2d(20)
self.bn1 = BatchNorm2d(20)
def forward(self, x):
x = self.conv0(x)
if self.bn0:
x = self.bn0(x)
x = F.relu(x)
x = self.pool0(x)
x = self.conv1(x)
if self.bn1:
x = self.bn1(x)
x = F.relu(x)
x = self.pool1(x)
x = F.flatten(x, 1)
x = self.fc0(x)
x = F.relu(x)
x = self.fc1(x)
return x
def train(data, label, net, opt):
pred = net(data)
loss = F.cross_entropy_with_softmax(pred, label)
opt.backward(loss)
return loss
def update_model(model_path):
"""
Update the dumped model with test cases for new reference values.
The model with pre-trained weights is trained for one iter with the test data attached.
The loss and updated net state dict is dumped.
.. code-block:: python
from test_correctness import update_model
update_model('mnist_model_with_test.mge') # for gpu
update_model('mnist_model_with_test_cpu.mge') # for cpu
"""
net = MnistNet(has_bn=True)
checkpoint = mge.load(model_path)
net.load_state_dict(checkpoint["net_init"])
lr = checkpoint["sgd_lr"]
opt = SGD(net.parameters(), lr=lr)
data = | tensor(dtype=np.float32) | megengine.tensor |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import os
import re
import subprocess
import sys
import numpy as np
import megengine as mge
import megengine.functional as F
from megengine import jit, tensor
from megengine.functional.debug_param import set_conv_execution_strategy
from megengine.module import AvgPool2d, BatchNorm2d, Conv2d, Linear, Module
from megengine.optimizer import SGD
from megengine.test import assertTensorClose
def get_gpu_name():
try:
gpu_info = subprocess.check_output(
["nvidia-smi", "--query-gpu=gpu_name", "--format=csv,noheader"]
)
gpu_info = gpu_info.decode("ascii").split("\n")[0]
except:
gpu_info = "None"
return gpu_info
def get_cpu_name():
cpu_info = "None"
try:
cpu_info = subprocess.check_output(["cat", "/proc/cpuinfo"]).decode("ascii")
for line in cpu_info.split("\n"):
if "model name" in line:
return re.sub(".*model name.*:", "", line, 1).strip()
except:
pass
return cpu_info
def get_xpu_name():
if mge.is_cuda_available():
return get_gpu_name()
else:
return get_cpu_name()
class MnistNet(Module):
def __init__(self, has_bn=False):
super().__init__()
self.conv0 = Conv2d(1, 20, kernel_size=5, bias=True)
self.pool0 = AvgPool2d(2)
self.conv1 = Conv2d(20, 20, kernel_size=5, bias=True)
self.pool1 = AvgPool2d(2)
self.fc0 = Linear(20 * 4 * 4, 500, bias=True)
self.fc1 = Linear(500, 10, bias=True)
self.bn0 = None
self.bn1 = None
if has_bn:
self.bn0 = BatchNorm2d(20)
self.bn1 = BatchNorm2d(20)
def forward(self, x):
x = self.conv0(x)
if self.bn0:
x = self.bn0(x)
x = F.relu(x)
x = self.pool0(x)
x = self.conv1(x)
if self.bn1:
x = self.bn1(x)
x = F.relu(x)
x = self.pool1(x)
x = F.flatten(x, 1)
x = self.fc0(x)
x = F.relu(x)
x = self.fc1(x)
return x
def train(data, label, net, opt):
pred = net(data)
loss = F.cross_entropy_with_softmax(pred, label)
opt.backward(loss)
return loss
def update_model(model_path):
"""
Update the dumped model with test cases for new reference values.
The model with pre-trained weights is trained for one iter with the test data attached.
The loss and updated net state dict is dumped.
.. code-block:: python
from test_correctness import update_model
update_model('mnist_model_with_test.mge') # for gpu
update_model('mnist_model_with_test_cpu.mge') # for cpu
"""
net = MnistNet(has_bn=True)
checkpoint = mge.load(model_path)
net.load_state_dict(checkpoint["net_init"])
lr = checkpoint["sgd_lr"]
opt = SGD(net.parameters(), lr=lr)
data = tensor(dtype=np.float32)
label = | tensor(dtype=np.int32) | megengine.tensor |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import os
import re
import subprocess
import sys
import numpy as np
import megengine as mge
import megengine.functional as F
from megengine import jit, tensor
from megengine.functional.debug_param import set_conv_execution_strategy
from megengine.module import AvgPool2d, BatchNorm2d, Conv2d, Linear, Module
from megengine.optimizer import SGD
from megengine.test import assertTensorClose
def get_gpu_name():
try:
gpu_info = subprocess.check_output(
["nvidia-smi", "--query-gpu=gpu_name", "--format=csv,noheader"]
)
gpu_info = gpu_info.decode("ascii").split("\n")[0]
except:
gpu_info = "None"
return gpu_info
def get_cpu_name():
cpu_info = "None"
try:
cpu_info = subprocess.check_output(["cat", "/proc/cpuinfo"]).decode("ascii")
for line in cpu_info.split("\n"):
if "model name" in line:
return re.sub(".*model name.*:", "", line, 1).strip()
except:
pass
return cpu_info
def get_xpu_name():
if mge.is_cuda_available():
return get_gpu_name()
else:
return get_cpu_name()
class MnistNet(Module):
def __init__(self, has_bn=False):
super().__init__()
self.conv0 = Conv2d(1, 20, kernel_size=5, bias=True)
self.pool0 = AvgPool2d(2)
self.conv1 = Conv2d(20, 20, kernel_size=5, bias=True)
self.pool1 = AvgPool2d(2)
self.fc0 = Linear(20 * 4 * 4, 500, bias=True)
self.fc1 = Linear(500, 10, bias=True)
self.bn0 = None
self.bn1 = None
if has_bn:
self.bn0 = BatchNorm2d(20)
self.bn1 = BatchNorm2d(20)
def forward(self, x):
x = self.conv0(x)
if self.bn0:
x = self.bn0(x)
x = F.relu(x)
x = self.pool0(x)
x = self.conv1(x)
if self.bn1:
x = self.bn1(x)
x = F.relu(x)
x = self.pool1(x)
x = F.flatten(x, 1)
x = self.fc0(x)
x = F.relu(x)
x = self.fc1(x)
return x
def train(data, label, net, opt):
pred = net(data)
loss = F.cross_entropy_with_softmax(pred, label)
opt.backward(loss)
return loss
def update_model(model_path):
"""
Update the dumped model with test cases for new reference values.
The model with pre-trained weights is trained for one iter with the test data attached.
The loss and updated net state dict is dumped.
.. code-block:: python
from test_correctness import update_model
update_model('mnist_model_with_test.mge') # for gpu
update_model('mnist_model_with_test_cpu.mge') # for cpu
"""
net = MnistNet(has_bn=True)
checkpoint = mge.load(model_path)
net.load_state_dict(checkpoint["net_init"])
lr = checkpoint["sgd_lr"]
opt = SGD(net.parameters(), lr=lr)
data = tensor(dtype=np.float32)
label = tensor(dtype=np.int32)
data.set_value(checkpoint["data"])
label.set_value(checkpoint["label"])
opt.zero_grad()
loss = train(data, label, net=net, opt=opt)
opt.step()
xpu_name = get_xpu_name()
checkpoint.update(
{"net_updated": net.state_dict(), "loss": loss.numpy(), "xpu": xpu_name}
)
| mge.save(checkpoint, model_path) | megengine.save |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import os
import re
import subprocess
import sys
import numpy as np
import megengine as mge
import megengine.functional as F
from megengine import jit, tensor
from megengine.functional.debug_param import set_conv_execution_strategy
from megengine.module import AvgPool2d, BatchNorm2d, Conv2d, Linear, Module
from megengine.optimizer import SGD
from megengine.test import assertTensorClose
def get_gpu_name():
try:
gpu_info = subprocess.check_output(
["nvidia-smi", "--query-gpu=gpu_name", "--format=csv,noheader"]
)
gpu_info = gpu_info.decode("ascii").split("\n")[0]
except:
gpu_info = "None"
return gpu_info
def get_cpu_name():
cpu_info = "None"
try:
cpu_info = subprocess.check_output(["cat", "/proc/cpuinfo"]).decode("ascii")
for line in cpu_info.split("\n"):
if "model name" in line:
return re.sub(".*model name.*:", "", line, 1).strip()
except:
pass
return cpu_info
def get_xpu_name():
if mge.is_cuda_available():
return get_gpu_name()
else:
return get_cpu_name()
class MnistNet(Module):
def __init__(self, has_bn=False):
super().__init__()
self.conv0 = Conv2d(1, 20, kernel_size=5, bias=True)
self.pool0 = AvgPool2d(2)
self.conv1 = Conv2d(20, 20, kernel_size=5, bias=True)
self.pool1 = AvgPool2d(2)
self.fc0 = Linear(20 * 4 * 4, 500, bias=True)
self.fc1 = Linear(500, 10, bias=True)
self.bn0 = None
self.bn1 = None
if has_bn:
self.bn0 = BatchNorm2d(20)
self.bn1 = BatchNorm2d(20)
def forward(self, x):
x = self.conv0(x)
if self.bn0:
x = self.bn0(x)
x = F.relu(x)
x = self.pool0(x)
x = self.conv1(x)
if self.bn1:
x = self.bn1(x)
x = F.relu(x)
x = self.pool1(x)
x = F.flatten(x, 1)
x = self.fc0(x)
x = F.relu(x)
x = self.fc1(x)
return x
def train(data, label, net, opt):
pred = net(data)
loss = F.cross_entropy_with_softmax(pred, label)
opt.backward(loss)
return loss
def update_model(model_path):
"""
Update the dumped model with test cases for new reference values.
The model with pre-trained weights is trained for one iter with the test data attached.
The loss and updated net state dict is dumped.
.. code-block:: python
from test_correctness import update_model
update_model('mnist_model_with_test.mge') # for gpu
update_model('mnist_model_with_test_cpu.mge') # for cpu
"""
net = MnistNet(has_bn=True)
checkpoint = mge.load(model_path)
net.load_state_dict(checkpoint["net_init"])
lr = checkpoint["sgd_lr"]
opt = SGD(net.parameters(), lr=lr)
data = tensor(dtype=np.float32)
label = tensor(dtype=np.int32)
data.set_value(checkpoint["data"])
label.set_value(checkpoint["label"])
opt.zero_grad()
loss = train(data, label, net=net, opt=opt)
opt.step()
xpu_name = get_xpu_name()
checkpoint.update(
{"net_updated": net.state_dict(), "loss": loss.numpy(), "xpu": xpu_name}
)
mge.save(checkpoint, model_path)
def run_test(model_path, use_jit, use_symbolic):
"""
Load the model with test cases and run the training for one iter.
The loss and updated weights are compared with reference value to verify the correctness.
Dump a new file with updated result by calling update_model
if you think the test fails due to numerical rounding errors instead of bugs.
Please think twice before you do so.
"""
net = MnistNet(has_bn=True)
checkpoint = | mge.load(model_path) | megengine.load |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import os
import re
import subprocess
import sys
import numpy as np
import megengine as mge
import megengine.functional as F
from megengine import jit, tensor
from megengine.functional.debug_param import set_conv_execution_strategy
from megengine.module import AvgPool2d, BatchNorm2d, Conv2d, Linear, Module
from megengine.optimizer import SGD
from megengine.test import assertTensorClose
def get_gpu_name():
try:
gpu_info = subprocess.check_output(
["nvidia-smi", "--query-gpu=gpu_name", "--format=csv,noheader"]
)
gpu_info = gpu_info.decode("ascii").split("\n")[0]
except:
gpu_info = "None"
return gpu_info
def get_cpu_name():
cpu_info = "None"
try:
cpu_info = subprocess.check_output(["cat", "/proc/cpuinfo"]).decode("ascii")
for line in cpu_info.split("\n"):
if "model name" in line:
return re.sub(".*model name.*:", "", line, 1).strip()
except:
pass
return cpu_info
def get_xpu_name():
if mge.is_cuda_available():
return get_gpu_name()
else:
return get_cpu_name()
class MnistNet(Module):
def __init__(self, has_bn=False):
super().__init__()
self.conv0 = Conv2d(1, 20, kernel_size=5, bias=True)
self.pool0 = AvgPool2d(2)
self.conv1 = Conv2d(20, 20, kernel_size=5, bias=True)
self.pool1 = AvgPool2d(2)
self.fc0 = Linear(20 * 4 * 4, 500, bias=True)
self.fc1 = Linear(500, 10, bias=True)
self.bn0 = None
self.bn1 = None
if has_bn:
self.bn0 = BatchNorm2d(20)
self.bn1 = BatchNorm2d(20)
def forward(self, x):
x = self.conv0(x)
if self.bn0:
x = self.bn0(x)
x = F.relu(x)
x = self.pool0(x)
x = self.conv1(x)
if self.bn1:
x = self.bn1(x)
x = F.relu(x)
x = self.pool1(x)
x = F.flatten(x, 1)
x = self.fc0(x)
x = F.relu(x)
x = self.fc1(x)
return x
def train(data, label, net, opt):
pred = net(data)
loss = F.cross_entropy_with_softmax(pred, label)
opt.backward(loss)
return loss
def update_model(model_path):
"""
Update the dumped model with test cases for new reference values.
The model with pre-trained weights is trained for one iter with the test data attached.
The loss and updated net state dict is dumped.
.. code-block:: python
from test_correctness import update_model
update_model('mnist_model_with_test.mge') # for gpu
update_model('mnist_model_with_test_cpu.mge') # for cpu
"""
net = MnistNet(has_bn=True)
checkpoint = mge.load(model_path)
net.load_state_dict(checkpoint["net_init"])
lr = checkpoint["sgd_lr"]
opt = SGD(net.parameters(), lr=lr)
data = tensor(dtype=np.float32)
label = tensor(dtype=np.int32)
data.set_value(checkpoint["data"])
label.set_value(checkpoint["label"])
opt.zero_grad()
loss = train(data, label, net=net, opt=opt)
opt.step()
xpu_name = get_xpu_name()
checkpoint.update(
{"net_updated": net.state_dict(), "loss": loss.numpy(), "xpu": xpu_name}
)
mge.save(checkpoint, model_path)
def run_test(model_path, use_jit, use_symbolic):
"""
Load the model with test cases and run the training for one iter.
The loss and updated weights are compared with reference value to verify the correctness.
Dump a new file with updated result by calling update_model
if you think the test fails due to numerical rounding errors instead of bugs.
Please think twice before you do so.
"""
net = MnistNet(has_bn=True)
checkpoint = mge.load(model_path)
net.load_state_dict(checkpoint["net_init"])
lr = checkpoint["sgd_lr"]
opt = SGD(net.parameters(), lr=lr)
data = | tensor(dtype=np.float32) | megengine.tensor |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import os
import re
import subprocess
import sys
import numpy as np
import megengine as mge
import megengine.functional as F
from megengine import jit, tensor
from megengine.functional.debug_param import set_conv_execution_strategy
from megengine.module import AvgPool2d, BatchNorm2d, Conv2d, Linear, Module
from megengine.optimizer import SGD
from megengine.test import assertTensorClose
def get_gpu_name():
try:
gpu_info = subprocess.check_output(
["nvidia-smi", "--query-gpu=gpu_name", "--format=csv,noheader"]
)
gpu_info = gpu_info.decode("ascii").split("\n")[0]
except:
gpu_info = "None"
return gpu_info
def get_cpu_name():
cpu_info = "None"
try:
cpu_info = subprocess.check_output(["cat", "/proc/cpuinfo"]).decode("ascii")
for line in cpu_info.split("\n"):
if "model name" in line:
return re.sub(".*model name.*:", "", line, 1).strip()
except:
pass
return cpu_info
def get_xpu_name():
if mge.is_cuda_available():
return get_gpu_name()
else:
return get_cpu_name()
class MnistNet(Module):
def __init__(self, has_bn=False):
super().__init__()
self.conv0 = Conv2d(1, 20, kernel_size=5, bias=True)
self.pool0 = AvgPool2d(2)
self.conv1 = Conv2d(20, 20, kernel_size=5, bias=True)
self.pool1 = AvgPool2d(2)
self.fc0 = Linear(20 * 4 * 4, 500, bias=True)
self.fc1 = Linear(500, 10, bias=True)
self.bn0 = None
self.bn1 = None
if has_bn:
self.bn0 = BatchNorm2d(20)
self.bn1 = BatchNorm2d(20)
def forward(self, x):
x = self.conv0(x)
if self.bn0:
x = self.bn0(x)
x = F.relu(x)
x = self.pool0(x)
x = self.conv1(x)
if self.bn1:
x = self.bn1(x)
x = F.relu(x)
x = self.pool1(x)
x = F.flatten(x, 1)
x = self.fc0(x)
x = F.relu(x)
x = self.fc1(x)
return x
def train(data, label, net, opt):
pred = net(data)
loss = F.cross_entropy_with_softmax(pred, label)
opt.backward(loss)
return loss
def update_model(model_path):
"""
Update the dumped model with test cases for new reference values.
The model with pre-trained weights is trained for one iter with the test data attached.
The loss and updated net state dict is dumped.
.. code-block:: python
from test_correctness import update_model
update_model('mnist_model_with_test.mge') # for gpu
update_model('mnist_model_with_test_cpu.mge') # for cpu
"""
net = MnistNet(has_bn=True)
checkpoint = mge.load(model_path)
net.load_state_dict(checkpoint["net_init"])
lr = checkpoint["sgd_lr"]
opt = SGD(net.parameters(), lr=lr)
data = tensor(dtype=np.float32)
label = tensor(dtype=np.int32)
data.set_value(checkpoint["data"])
label.set_value(checkpoint["label"])
opt.zero_grad()
loss = train(data, label, net=net, opt=opt)
opt.step()
xpu_name = get_xpu_name()
checkpoint.update(
{"net_updated": net.state_dict(), "loss": loss.numpy(), "xpu": xpu_name}
)
mge.save(checkpoint, model_path)
def run_test(model_path, use_jit, use_symbolic):
"""
Load the model with test cases and run the training for one iter.
The loss and updated weights are compared with reference value to verify the correctness.
Dump a new file with updated result by calling update_model
if you think the test fails due to numerical rounding errors instead of bugs.
Please think twice before you do so.
"""
net = MnistNet(has_bn=True)
checkpoint = mge.load(model_path)
net.load_state_dict(checkpoint["net_init"])
lr = checkpoint["sgd_lr"]
opt = SGD(net.parameters(), lr=lr)
data = tensor(dtype=np.float32)
label = | tensor(dtype=np.int32) | megengine.tensor |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import os
import re
import subprocess
import sys
import numpy as np
import megengine as mge
import megengine.functional as F
from megengine import jit, tensor
from megengine.functional.debug_param import set_conv_execution_strategy
from megengine.module import AvgPool2d, BatchNorm2d, Conv2d, Linear, Module
from megengine.optimizer import SGD
from megengine.test import assertTensorClose
def get_gpu_name():
try:
gpu_info = subprocess.check_output(
["nvidia-smi", "--query-gpu=gpu_name", "--format=csv,noheader"]
)
gpu_info = gpu_info.decode("ascii").split("\n")[0]
except:
gpu_info = "None"
return gpu_info
def get_cpu_name():
cpu_info = "None"
try:
cpu_info = subprocess.check_output(["cat", "/proc/cpuinfo"]).decode("ascii")
for line in cpu_info.split("\n"):
if "model name" in line:
return re.sub(".*model name.*:", "", line, 1).strip()
except:
pass
return cpu_info
def get_xpu_name():
if mge.is_cuda_available():
return get_gpu_name()
else:
return get_cpu_name()
class MnistNet(Module):
def __init__(self, has_bn=False):
super().__init__()
self.conv0 = Conv2d(1, 20, kernel_size=5, bias=True)
self.pool0 = AvgPool2d(2)
self.conv1 = Conv2d(20, 20, kernel_size=5, bias=True)
self.pool1 = AvgPool2d(2)
self.fc0 = Linear(20 * 4 * 4, 500, bias=True)
self.fc1 = Linear(500, 10, bias=True)
self.bn0 = None
self.bn1 = None
if has_bn:
self.bn0 = BatchNorm2d(20)
self.bn1 = BatchNorm2d(20)
def forward(self, x):
x = self.conv0(x)
if self.bn0:
x = self.bn0(x)
x = F.relu(x)
x = self.pool0(x)
x = self.conv1(x)
if self.bn1:
x = self.bn1(x)
x = F.relu(x)
x = self.pool1(x)
x = F.flatten(x, 1)
x = self.fc0(x)
x = F.relu(x)
x = self.fc1(x)
return x
def train(data, label, net, opt):
pred = net(data)
loss = F.cross_entropy_with_softmax(pred, label)
opt.backward(loss)
return loss
def update_model(model_path):
"""
Update the dumped model with test cases for new reference values.
The model with pre-trained weights is trained for one iter with the test data attached.
The loss and updated net state dict is dumped.
.. code-block:: python
from test_correctness import update_model
update_model('mnist_model_with_test.mge') # for gpu
update_model('mnist_model_with_test_cpu.mge') # for cpu
"""
net = MnistNet(has_bn=True)
checkpoint = mge.load(model_path)
net.load_state_dict(checkpoint["net_init"])
lr = checkpoint["sgd_lr"]
opt = SGD(net.parameters(), lr=lr)
data = tensor(dtype=np.float32)
label = tensor(dtype=np.int32)
data.set_value(checkpoint["data"])
label.set_value(checkpoint["label"])
opt.zero_grad()
loss = train(data, label, net=net, opt=opt)
opt.step()
xpu_name = get_xpu_name()
checkpoint.update(
{"net_updated": net.state_dict(), "loss": loss.numpy(), "xpu": xpu_name}
)
mge.save(checkpoint, model_path)
def run_test(model_path, use_jit, use_symbolic):
"""
Load the model with test cases and run the training for one iter.
The loss and updated weights are compared with reference value to verify the correctness.
Dump a new file with updated result by calling update_model
if you think the test fails due to numerical rounding errors instead of bugs.
Please think twice before you do so.
"""
net = MnistNet(has_bn=True)
checkpoint = mge.load(model_path)
net.load_state_dict(checkpoint["net_init"])
lr = checkpoint["sgd_lr"]
opt = SGD(net.parameters(), lr=lr)
data = tensor(dtype=np.float32)
label = tensor(dtype=np.int32)
data.set_value(checkpoint["data"])
label.set_value(checkpoint["label"])
max_err = 1e-5
train_func = train
if use_jit:
train_func = jit.trace(train_func, symbolic=use_symbolic)
opt.zero_grad()
loss = train_func(data, label, net=net, opt=opt)
opt.step()
assertTensorClose(loss.numpy(), checkpoint["loss"], max_err=max_err)
for param, param_ref in zip(
net.state_dict().items(), checkpoint["net_updated"].items()
):
assert param[0] == param_ref[0]
assertTensorClose(param[1], param_ref[1], max_err=max_err)
def test_correctness():
if | mge.is_cuda_available() | megengine.is_cuda_available |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import os
import re
import subprocess
import sys
import numpy as np
import megengine as mge
import megengine.functional as F
from megengine import jit, tensor
from megengine.functional.debug_param import set_conv_execution_strategy
from megengine.module import AvgPool2d, BatchNorm2d, Conv2d, Linear, Module
from megengine.optimizer import SGD
from megengine.test import assertTensorClose
def get_gpu_name():
try:
gpu_info = subprocess.check_output(
["nvidia-smi", "--query-gpu=gpu_name", "--format=csv,noheader"]
)
gpu_info = gpu_info.decode("ascii").split("\n")[0]
except:
gpu_info = "None"
return gpu_info
def get_cpu_name():
cpu_info = "None"
try:
cpu_info = subprocess.check_output(["cat", "/proc/cpuinfo"]).decode("ascii")
for line in cpu_info.split("\n"):
if "model name" in line:
return re.sub(".*model name.*:", "", line, 1).strip()
except:
pass
return cpu_info
def get_xpu_name():
if mge.is_cuda_available():
return get_gpu_name()
else:
return get_cpu_name()
class MnistNet(Module):
def __init__(self, has_bn=False):
super().__init__()
self.conv0 = Conv2d(1, 20, kernel_size=5, bias=True)
self.pool0 = AvgPool2d(2)
self.conv1 = Conv2d(20, 20, kernel_size=5, bias=True)
self.pool1 = AvgPool2d(2)
self.fc0 = Linear(20 * 4 * 4, 500, bias=True)
self.fc1 = Linear(500, 10, bias=True)
self.bn0 = None
self.bn1 = None
if has_bn:
self.bn0 = BatchNorm2d(20)
self.bn1 = BatchNorm2d(20)
def forward(self, x):
x = self.conv0(x)
if self.bn0:
x = self.bn0(x)
x = F.relu(x)
x = self.pool0(x)
x = self.conv1(x)
if self.bn1:
x = self.bn1(x)
x = F.relu(x)
x = self.pool1(x)
x = F.flatten(x, 1)
x = self.fc0(x)
x = F.relu(x)
x = self.fc1(x)
return x
def train(data, label, net, opt):
pred = net(data)
loss = F.cross_entropy_with_softmax(pred, label)
opt.backward(loss)
return loss
def update_model(model_path):
"""
Update the dumped model with test cases for new reference values.
The model with pre-trained weights is trained for one iter with the test data attached.
The loss and updated net state dict is dumped.
.. code-block:: python
from test_correctness import update_model
update_model('mnist_model_with_test.mge') # for gpu
update_model('mnist_model_with_test_cpu.mge') # for cpu
"""
net = MnistNet(has_bn=True)
checkpoint = mge.load(model_path)
net.load_state_dict(checkpoint["net_init"])
lr = checkpoint["sgd_lr"]
opt = SGD(net.parameters(), lr=lr)
data = tensor(dtype=np.float32)
label = tensor(dtype=np.int32)
data.set_value(checkpoint["data"])
label.set_value(checkpoint["label"])
opt.zero_grad()
loss = train(data, label, net=net, opt=opt)
opt.step()
xpu_name = get_xpu_name()
checkpoint.update(
{"net_updated": net.state_dict(), "loss": loss.numpy(), "xpu": xpu_name}
)
mge.save(checkpoint, model_path)
def run_test(model_path, use_jit, use_symbolic):
"""
Load the model with test cases and run the training for one iter.
The loss and updated weights are compared with reference value to verify the correctness.
Dump a new file with updated result by calling update_model
if you think the test fails due to numerical rounding errors instead of bugs.
Please think twice before you do so.
"""
net = MnistNet(has_bn=True)
checkpoint = mge.load(model_path)
net.load_state_dict(checkpoint["net_init"])
lr = checkpoint["sgd_lr"]
opt = SGD(net.parameters(), lr=lr)
data = tensor(dtype=np.float32)
label = tensor(dtype=np.int32)
data.set_value(checkpoint["data"])
label.set_value(checkpoint["label"])
max_err = 1e-5
train_func = train
if use_jit:
train_func = jit.trace(train_func, symbolic=use_symbolic)
opt.zero_grad()
loss = train_func(data, label, net=net, opt=opt)
opt.step()
assertTensorClose(loss.numpy(), checkpoint["loss"], max_err=max_err)
for param, param_ref in zip(
net.state_dict().items(), checkpoint["net_updated"].items()
):
assert param[0] == param_ref[0]
assertTensorClose(param[1], param_ref[1], max_err=max_err)
def test_correctness():
if mge.is_cuda_available():
model_name = "mnist_model_with_test.mge"
else:
model_name = "mnist_model_with_test_cpu.mge"
model_path = os.path.join(os.path.dirname(__file__), model_name)
| set_conv_execution_strategy("HEURISTIC_REPRODUCIBLE") | megengine.functional.debug_param.set_conv_execution_strategy |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import os
import re
import subprocess
import sys
import numpy as np
import megengine as mge
import megengine.functional as F
from megengine import jit, tensor
from megengine.functional.debug_param import set_conv_execution_strategy
from megengine.module import AvgPool2d, BatchNorm2d, Conv2d, Linear, Module
from megengine.optimizer import SGD
from megengine.test import assertTensorClose
def get_gpu_name():
try:
gpu_info = subprocess.check_output(
["nvidia-smi", "--query-gpu=gpu_name", "--format=csv,noheader"]
)
gpu_info = gpu_info.decode("ascii").split("\n")[0]
except:
gpu_info = "None"
return gpu_info
def get_cpu_name():
cpu_info = "None"
try:
cpu_info = subprocess.check_output(["cat", "/proc/cpuinfo"]).decode("ascii")
for line in cpu_info.split("\n"):
if "model name" in line:
return re.sub(".*model name.*:", "", line, 1).strip()
except:
pass
return cpu_info
def get_xpu_name():
if mge.is_cuda_available():
return get_gpu_name()
else:
return get_cpu_name()
class MnistNet(Module):
def __init__(self, has_bn=False):
super().__init__()
self.conv0 = | Conv2d(1, 20, kernel_size=5, bias=True) | megengine.module.Conv2d |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import os
import re
import subprocess
import sys
import numpy as np
import megengine as mge
import megengine.functional as F
from megengine import jit, tensor
from megengine.functional.debug_param import set_conv_execution_strategy
from megengine.module import AvgPool2d, BatchNorm2d, Conv2d, Linear, Module
from megengine.optimizer import SGD
from megengine.test import assertTensorClose
def get_gpu_name():
try:
gpu_info = subprocess.check_output(
["nvidia-smi", "--query-gpu=gpu_name", "--format=csv,noheader"]
)
gpu_info = gpu_info.decode("ascii").split("\n")[0]
except:
gpu_info = "None"
return gpu_info
def get_cpu_name():
cpu_info = "None"
try:
cpu_info = subprocess.check_output(["cat", "/proc/cpuinfo"]).decode("ascii")
for line in cpu_info.split("\n"):
if "model name" in line:
return re.sub(".*model name.*:", "", line, 1).strip()
except:
pass
return cpu_info
def get_xpu_name():
if mge.is_cuda_available():
return get_gpu_name()
else:
return get_cpu_name()
class MnistNet(Module):
def __init__(self, has_bn=False):
super().__init__()
self.conv0 = Conv2d(1, 20, kernel_size=5, bias=True)
self.pool0 = | AvgPool2d(2) | megengine.module.AvgPool2d |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import os
import re
import subprocess
import sys
import numpy as np
import megengine as mge
import megengine.functional as F
from megengine import jit, tensor
from megengine.functional.debug_param import set_conv_execution_strategy
from megengine.module import AvgPool2d, BatchNorm2d, Conv2d, Linear, Module
from megengine.optimizer import SGD
from megengine.test import assertTensorClose
def get_gpu_name():
try:
gpu_info = subprocess.check_output(
["nvidia-smi", "--query-gpu=gpu_name", "--format=csv,noheader"]
)
gpu_info = gpu_info.decode("ascii").split("\n")[0]
except:
gpu_info = "None"
return gpu_info
def get_cpu_name():
cpu_info = "None"
try:
cpu_info = subprocess.check_output(["cat", "/proc/cpuinfo"]).decode("ascii")
for line in cpu_info.split("\n"):
if "model name" in line:
return re.sub(".*model name.*:", "", line, 1).strip()
except:
pass
return cpu_info
def get_xpu_name():
if mge.is_cuda_available():
return get_gpu_name()
else:
return get_cpu_name()
class MnistNet(Module):
def __init__(self, has_bn=False):
super().__init__()
self.conv0 = Conv2d(1, 20, kernel_size=5, bias=True)
self.pool0 = AvgPool2d(2)
self.conv1 = | Conv2d(20, 20, kernel_size=5, bias=True) | megengine.module.Conv2d |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import os
import re
import subprocess
import sys
import numpy as np
import megengine as mge
import megengine.functional as F
from megengine import jit, tensor
from megengine.functional.debug_param import set_conv_execution_strategy
from megengine.module import AvgPool2d, BatchNorm2d, Conv2d, Linear, Module
from megengine.optimizer import SGD
from megengine.test import assertTensorClose
def get_gpu_name():
try:
gpu_info = subprocess.check_output(
["nvidia-smi", "--query-gpu=gpu_name", "--format=csv,noheader"]
)
gpu_info = gpu_info.decode("ascii").split("\n")[0]
except:
gpu_info = "None"
return gpu_info
def get_cpu_name():
cpu_info = "None"
try:
cpu_info = subprocess.check_output(["cat", "/proc/cpuinfo"]).decode("ascii")
for line in cpu_info.split("\n"):
if "model name" in line:
return re.sub(".*model name.*:", "", line, 1).strip()
except:
pass
return cpu_info
def get_xpu_name():
if mge.is_cuda_available():
return get_gpu_name()
else:
return get_cpu_name()
class MnistNet(Module):
def __init__(self, has_bn=False):
super().__init__()
self.conv0 = Conv2d(1, 20, kernel_size=5, bias=True)
self.pool0 = AvgPool2d(2)
self.conv1 = Conv2d(20, 20, kernel_size=5, bias=True)
self.pool1 = | AvgPool2d(2) | megengine.module.AvgPool2d |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import os
import re
import subprocess
import sys
import numpy as np
import megengine as mge
import megengine.functional as F
from megengine import jit, tensor
from megengine.functional.debug_param import set_conv_execution_strategy
from megengine.module import AvgPool2d, BatchNorm2d, Conv2d, Linear, Module
from megengine.optimizer import SGD
from megengine.test import assertTensorClose
def get_gpu_name():
try:
gpu_info = subprocess.check_output(
["nvidia-smi", "--query-gpu=gpu_name", "--format=csv,noheader"]
)
gpu_info = gpu_info.decode("ascii").split("\n")[0]
except:
gpu_info = "None"
return gpu_info
def get_cpu_name():
cpu_info = "None"
try:
cpu_info = subprocess.check_output(["cat", "/proc/cpuinfo"]).decode("ascii")
for line in cpu_info.split("\n"):
if "model name" in line:
return re.sub(".*model name.*:", "", line, 1).strip()
except:
pass
return cpu_info
def get_xpu_name():
if mge.is_cuda_available():
return get_gpu_name()
else:
return get_cpu_name()
class MnistNet(Module):
def __init__(self, has_bn=False):
super().__init__()
self.conv0 = Conv2d(1, 20, kernel_size=5, bias=True)
self.pool0 = AvgPool2d(2)
self.conv1 = Conv2d(20, 20, kernel_size=5, bias=True)
self.pool1 = AvgPool2d(2)
self.fc0 = | Linear(20 * 4 * 4, 500, bias=True) | megengine.module.Linear |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import os
import re
import subprocess
import sys
import numpy as np
import megengine as mge
import megengine.functional as F
from megengine import jit, tensor
from megengine.functional.debug_param import set_conv_execution_strategy
from megengine.module import AvgPool2d, BatchNorm2d, Conv2d, Linear, Module
from megengine.optimizer import SGD
from megengine.test import assertTensorClose
def get_gpu_name():
try:
gpu_info = subprocess.check_output(
["nvidia-smi", "--query-gpu=gpu_name", "--format=csv,noheader"]
)
gpu_info = gpu_info.decode("ascii").split("\n")[0]
except:
gpu_info = "None"
return gpu_info
def get_cpu_name():
cpu_info = "None"
try:
cpu_info = subprocess.check_output(["cat", "/proc/cpuinfo"]).decode("ascii")
for line in cpu_info.split("\n"):
if "model name" in line:
return re.sub(".*model name.*:", "", line, 1).strip()
except:
pass
return cpu_info
def get_xpu_name():
if mge.is_cuda_available():
return get_gpu_name()
else:
return get_cpu_name()
class MnistNet(Module):
def __init__(self, has_bn=False):
super().__init__()
self.conv0 = Conv2d(1, 20, kernel_size=5, bias=True)
self.pool0 = AvgPool2d(2)
self.conv1 = Conv2d(20, 20, kernel_size=5, bias=True)
self.pool1 = AvgPool2d(2)
self.fc0 = Linear(20 * 4 * 4, 500, bias=True)
self.fc1 = | Linear(500, 10, bias=True) | megengine.module.Linear |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import os
import re
import subprocess
import sys
import numpy as np
import megengine as mge
import megengine.functional as F
from megengine import jit, tensor
from megengine.functional.debug_param import set_conv_execution_strategy
from megengine.module import AvgPool2d, BatchNorm2d, Conv2d, Linear, Module
from megengine.optimizer import SGD
from megengine.test import assertTensorClose
def get_gpu_name():
try:
gpu_info = subprocess.check_output(
["nvidia-smi", "--query-gpu=gpu_name", "--format=csv,noheader"]
)
gpu_info = gpu_info.decode("ascii").split("\n")[0]
except:
gpu_info = "None"
return gpu_info
def get_cpu_name():
cpu_info = "None"
try:
cpu_info = subprocess.check_output(["cat", "/proc/cpuinfo"]).decode("ascii")
for line in cpu_info.split("\n"):
if "model name" in line:
return re.sub(".*model name.*:", "", line, 1).strip()
except:
pass
return cpu_info
def get_xpu_name():
if mge.is_cuda_available():
return get_gpu_name()
else:
return get_cpu_name()
class MnistNet(Module):
def __init__(self, has_bn=False):
super().__init__()
self.conv0 = Conv2d(1, 20, kernel_size=5, bias=True)
self.pool0 = AvgPool2d(2)
self.conv1 = Conv2d(20, 20, kernel_size=5, bias=True)
self.pool1 = AvgPool2d(2)
self.fc0 = Linear(20 * 4 * 4, 500, bias=True)
self.fc1 = Linear(500, 10, bias=True)
self.bn0 = None
self.bn1 = None
if has_bn:
self.bn0 = BatchNorm2d(20)
self.bn1 = BatchNorm2d(20)
def forward(self, x):
x = self.conv0(x)
if self.bn0:
x = self.bn0(x)
x = | F.relu(x) | megengine.functional.relu |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import os
import re
import subprocess
import sys
import numpy as np
import megengine as mge
import megengine.functional as F
from megengine import jit, tensor
from megengine.functional.debug_param import set_conv_execution_strategy
from megengine.module import AvgPool2d, BatchNorm2d, Conv2d, Linear, Module
from megengine.optimizer import SGD
from megengine.test import assertTensorClose
def get_gpu_name():
try:
gpu_info = subprocess.check_output(
["nvidia-smi", "--query-gpu=gpu_name", "--format=csv,noheader"]
)
gpu_info = gpu_info.decode("ascii").split("\n")[0]
except:
gpu_info = "None"
return gpu_info
def get_cpu_name():
cpu_info = "None"
try:
cpu_info = subprocess.check_output(["cat", "/proc/cpuinfo"]).decode("ascii")
for line in cpu_info.split("\n"):
if "model name" in line:
return re.sub(".*model name.*:", "", line, 1).strip()
except:
pass
return cpu_info
def get_xpu_name():
if mge.is_cuda_available():
return get_gpu_name()
else:
return get_cpu_name()
class MnistNet(Module):
def __init__(self, has_bn=False):
super().__init__()
self.conv0 = Conv2d(1, 20, kernel_size=5, bias=True)
self.pool0 = AvgPool2d(2)
self.conv1 = Conv2d(20, 20, kernel_size=5, bias=True)
self.pool1 = AvgPool2d(2)
self.fc0 = Linear(20 * 4 * 4, 500, bias=True)
self.fc1 = Linear(500, 10, bias=True)
self.bn0 = None
self.bn1 = None
if has_bn:
self.bn0 = BatchNorm2d(20)
self.bn1 = BatchNorm2d(20)
def forward(self, x):
x = self.conv0(x)
if self.bn0:
x = self.bn0(x)
x = F.relu(x)
x = self.pool0(x)
x = self.conv1(x)
if self.bn1:
x = self.bn1(x)
x = | F.relu(x) | megengine.functional.relu |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import os
import re
import subprocess
import sys
import numpy as np
import megengine as mge
import megengine.functional as F
from megengine import jit, tensor
from megengine.functional.debug_param import set_conv_execution_strategy
from megengine.module import AvgPool2d, BatchNorm2d, Conv2d, Linear, Module
from megengine.optimizer import SGD
from megengine.test import assertTensorClose
def get_gpu_name():
try:
gpu_info = subprocess.check_output(
["nvidia-smi", "--query-gpu=gpu_name", "--format=csv,noheader"]
)
gpu_info = gpu_info.decode("ascii").split("\n")[0]
except:
gpu_info = "None"
return gpu_info
def get_cpu_name():
cpu_info = "None"
try:
cpu_info = subprocess.check_output(["cat", "/proc/cpuinfo"]).decode("ascii")
for line in cpu_info.split("\n"):
if "model name" in line:
return re.sub(".*model name.*:", "", line, 1).strip()
except:
pass
return cpu_info
def get_xpu_name():
if mge.is_cuda_available():
return get_gpu_name()
else:
return get_cpu_name()
class MnistNet(Module):
def __init__(self, has_bn=False):
super().__init__()
self.conv0 = Conv2d(1, 20, kernel_size=5, bias=True)
self.pool0 = AvgPool2d(2)
self.conv1 = Conv2d(20, 20, kernel_size=5, bias=True)
self.pool1 = AvgPool2d(2)
self.fc0 = Linear(20 * 4 * 4, 500, bias=True)
self.fc1 = Linear(500, 10, bias=True)
self.bn0 = None
self.bn1 = None
if has_bn:
self.bn0 = BatchNorm2d(20)
self.bn1 = BatchNorm2d(20)
def forward(self, x):
x = self.conv0(x)
if self.bn0:
x = self.bn0(x)
x = F.relu(x)
x = self.pool0(x)
x = self.conv1(x)
if self.bn1:
x = self.bn1(x)
x = F.relu(x)
x = self.pool1(x)
x = | F.flatten(x, 1) | megengine.functional.flatten |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import os
import re
import subprocess
import sys
import numpy as np
import megengine as mge
import megengine.functional as F
from megengine import jit, tensor
from megengine.functional.debug_param import set_conv_execution_strategy
from megengine.module import AvgPool2d, BatchNorm2d, Conv2d, Linear, Module
from megengine.optimizer import SGD
from megengine.test import assertTensorClose
def get_gpu_name():
try:
gpu_info = subprocess.check_output(
["nvidia-smi", "--query-gpu=gpu_name", "--format=csv,noheader"]
)
gpu_info = gpu_info.decode("ascii").split("\n")[0]
except:
gpu_info = "None"
return gpu_info
def get_cpu_name():
cpu_info = "None"
try:
cpu_info = subprocess.check_output(["cat", "/proc/cpuinfo"]).decode("ascii")
for line in cpu_info.split("\n"):
if "model name" in line:
return re.sub(".*model name.*:", "", line, 1).strip()
except:
pass
return cpu_info
def get_xpu_name():
if mge.is_cuda_available():
return get_gpu_name()
else:
return get_cpu_name()
class MnistNet(Module):
def __init__(self, has_bn=False):
super().__init__()
self.conv0 = Conv2d(1, 20, kernel_size=5, bias=True)
self.pool0 = AvgPool2d(2)
self.conv1 = Conv2d(20, 20, kernel_size=5, bias=True)
self.pool1 = AvgPool2d(2)
self.fc0 = Linear(20 * 4 * 4, 500, bias=True)
self.fc1 = Linear(500, 10, bias=True)
self.bn0 = None
self.bn1 = None
if has_bn:
self.bn0 = BatchNorm2d(20)
self.bn1 = BatchNorm2d(20)
def forward(self, x):
x = self.conv0(x)
if self.bn0:
x = self.bn0(x)
x = F.relu(x)
x = self.pool0(x)
x = self.conv1(x)
if self.bn1:
x = self.bn1(x)
x = F.relu(x)
x = self.pool1(x)
x = F.flatten(x, 1)
x = self.fc0(x)
x = | F.relu(x) | megengine.functional.relu |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import os
import re
import subprocess
import sys
import numpy as np
import megengine as mge
import megengine.functional as F
from megengine import jit, tensor
from megengine.functional.debug_param import set_conv_execution_strategy
from megengine.module import AvgPool2d, BatchNorm2d, Conv2d, Linear, Module
from megengine.optimizer import SGD
from megengine.test import assertTensorClose
def get_gpu_name():
try:
gpu_info = subprocess.check_output(
["nvidia-smi", "--query-gpu=gpu_name", "--format=csv,noheader"]
)
gpu_info = gpu_info.decode("ascii").split("\n")[0]
except:
gpu_info = "None"
return gpu_info
def get_cpu_name():
cpu_info = "None"
try:
cpu_info = subprocess.check_output(["cat", "/proc/cpuinfo"]).decode("ascii")
for line in cpu_info.split("\n"):
if "model name" in line:
return re.sub(".*model name.*:", "", line, 1).strip()
except:
pass
return cpu_info
def get_xpu_name():
if mge.is_cuda_available():
return get_gpu_name()
else:
return get_cpu_name()
class MnistNet(Module):
def __init__(self, has_bn=False):
super().__init__()
self.conv0 = Conv2d(1, 20, kernel_size=5, bias=True)
self.pool0 = AvgPool2d(2)
self.conv1 = Conv2d(20, 20, kernel_size=5, bias=True)
self.pool1 = AvgPool2d(2)
self.fc0 = Linear(20 * 4 * 4, 500, bias=True)
self.fc1 = Linear(500, 10, bias=True)
self.bn0 = None
self.bn1 = None
if has_bn:
self.bn0 = BatchNorm2d(20)
self.bn1 = BatchNorm2d(20)
def forward(self, x):
x = self.conv0(x)
if self.bn0:
x = self.bn0(x)
x = F.relu(x)
x = self.pool0(x)
x = self.conv1(x)
if self.bn1:
x = self.bn1(x)
x = F.relu(x)
x = self.pool1(x)
x = F.flatten(x, 1)
x = self.fc0(x)
x = F.relu(x)
x = self.fc1(x)
return x
def train(data, label, net, opt):
pred = net(data)
loss = F.cross_entropy_with_softmax(pred, label)
opt.backward(loss)
return loss
def update_model(model_path):
"""
Update the dumped model with test cases for new reference values.
The model with pre-trained weights is trained for one iter with the test data attached.
The loss and updated net state dict is dumped.
.. code-block:: python
from test_correctness import update_model
update_model('mnist_model_with_test.mge') # for gpu
update_model('mnist_model_with_test_cpu.mge') # for cpu
"""
net = MnistNet(has_bn=True)
checkpoint = mge.load(model_path)
net.load_state_dict(checkpoint["net_init"])
lr = checkpoint["sgd_lr"]
opt = SGD(net.parameters(), lr=lr)
data = tensor(dtype=np.float32)
label = tensor(dtype=np.int32)
data.set_value(checkpoint["data"])
label.set_value(checkpoint["label"])
opt.zero_grad()
loss = train(data, label, net=net, opt=opt)
opt.step()
xpu_name = get_xpu_name()
checkpoint.update(
{"net_updated": net.state_dict(), "loss": loss.numpy(), "xpu": xpu_name}
)
mge.save(checkpoint, model_path)
def run_test(model_path, use_jit, use_symbolic):
"""
Load the model with test cases and run the training for one iter.
The loss and updated weights are compared with reference value to verify the correctness.
Dump a new file with updated result by calling update_model
if you think the test fails due to numerical rounding errors instead of bugs.
Please think twice before you do so.
"""
net = MnistNet(has_bn=True)
checkpoint = mge.load(model_path)
net.load_state_dict(checkpoint["net_init"])
lr = checkpoint["sgd_lr"]
opt = SGD(net.parameters(), lr=lr)
data = tensor(dtype=np.float32)
label = tensor(dtype=np.int32)
data.set_value(checkpoint["data"])
label.set_value(checkpoint["label"])
max_err = 1e-5
train_func = train
if use_jit:
train_func = | jit.trace(train_func, symbolic=use_symbolic) | megengine.jit.trace |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import os
import re
import subprocess
import sys
import numpy as np
import megengine as mge
import megengine.functional as F
from megengine import jit, tensor
from megengine.functional.debug_param import set_conv_execution_strategy
from megengine.module import AvgPool2d, BatchNorm2d, Conv2d, Linear, Module
from megengine.optimizer import SGD
from megengine.test import assertTensorClose
def get_gpu_name():
try:
gpu_info = subprocess.check_output(
["nvidia-smi", "--query-gpu=gpu_name", "--format=csv,noheader"]
)
gpu_info = gpu_info.decode("ascii").split("\n")[0]
except:
gpu_info = "None"
return gpu_info
def get_cpu_name():
cpu_info = "None"
try:
cpu_info = subprocess.check_output(["cat", "/proc/cpuinfo"]).decode("ascii")
for line in cpu_info.split("\n"):
if "model name" in line:
return re.sub(".*model name.*:", "", line, 1).strip()
except:
pass
return cpu_info
def get_xpu_name():
if mge.is_cuda_available():
return get_gpu_name()
else:
return get_cpu_name()
class MnistNet(Module):
def __init__(self, has_bn=False):
super().__init__()
self.conv0 = Conv2d(1, 20, kernel_size=5, bias=True)
self.pool0 = AvgPool2d(2)
self.conv1 = Conv2d(20, 20, kernel_size=5, bias=True)
self.pool1 = AvgPool2d(2)
self.fc0 = Linear(20 * 4 * 4, 500, bias=True)
self.fc1 = Linear(500, 10, bias=True)
self.bn0 = None
self.bn1 = None
if has_bn:
self.bn0 = BatchNorm2d(20)
self.bn1 = BatchNorm2d(20)
def forward(self, x):
x = self.conv0(x)
if self.bn0:
x = self.bn0(x)
x = F.relu(x)
x = self.pool0(x)
x = self.conv1(x)
if self.bn1:
x = self.bn1(x)
x = F.relu(x)
x = self.pool1(x)
x = F.flatten(x, 1)
x = self.fc0(x)
x = F.relu(x)
x = self.fc1(x)
return x
def train(data, label, net, opt):
pred = net(data)
loss = F.cross_entropy_with_softmax(pred, label)
opt.backward(loss)
return loss
def update_model(model_path):
"""
Update the dumped model with test cases for new reference values.
The model with pre-trained weights is trained for one iter with the test data attached.
The loss and updated net state dict is dumped.
.. code-block:: python
from test_correctness import update_model
update_model('mnist_model_with_test.mge') # for gpu
update_model('mnist_model_with_test_cpu.mge') # for cpu
"""
net = MnistNet(has_bn=True)
checkpoint = mge.load(model_path)
net.load_state_dict(checkpoint["net_init"])
lr = checkpoint["sgd_lr"]
opt = SGD(net.parameters(), lr=lr)
data = tensor(dtype=np.float32)
label = tensor(dtype=np.int32)
data.set_value(checkpoint["data"])
label.set_value(checkpoint["label"])
opt.zero_grad()
loss = train(data, label, net=net, opt=opt)
opt.step()
xpu_name = get_xpu_name()
checkpoint.update(
{"net_updated": net.state_dict(), "loss": loss.numpy(), "xpu": xpu_name}
)
mge.save(checkpoint, model_path)
def run_test(model_path, use_jit, use_symbolic):
"""
Load the model with test cases and run the training for one iter.
The loss and updated weights are compared with reference value to verify the correctness.
Dump a new file with updated result by calling update_model
if you think the test fails due to numerical rounding errors instead of bugs.
Please think twice before you do so.
"""
net = MnistNet(has_bn=True)
checkpoint = mge.load(model_path)
net.load_state_dict(checkpoint["net_init"])
lr = checkpoint["sgd_lr"]
opt = SGD(net.parameters(), lr=lr)
data = tensor(dtype=np.float32)
label = tensor(dtype=np.int32)
data.set_value(checkpoint["data"])
label.set_value(checkpoint["label"])
max_err = 1e-5
train_func = train
if use_jit:
train_func = jit.trace(train_func, symbolic=use_symbolic)
opt.zero_grad()
loss = train_func(data, label, net=net, opt=opt)
opt.step()
assertTensorClose(loss.numpy(), checkpoint["loss"], max_err=max_err)
for param, param_ref in zip(
net.state_dict().items(), checkpoint["net_updated"].items()
):
assert param[0] == param_ref[0]
| assertTensorClose(param[1], param_ref[1], max_err=max_err) | megengine.test.assertTensorClose |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import os
import re
import subprocess
import sys
import numpy as np
import megengine as mge
import megengine.functional as F
from megengine import jit, tensor
from megengine.functional.debug_param import set_conv_execution_strategy
from megengine.module import AvgPool2d, BatchNorm2d, Conv2d, Linear, Module
from megengine.optimizer import SGD
from megengine.test import assertTensorClose
def get_gpu_name():
try:
gpu_info = subprocess.check_output(
["nvidia-smi", "--query-gpu=gpu_name", "--format=csv,noheader"]
)
gpu_info = gpu_info.decode("ascii").split("\n")[0]
except:
gpu_info = "None"
return gpu_info
def get_cpu_name():
cpu_info = "None"
try:
cpu_info = subprocess.check_output(["cat", "/proc/cpuinfo"]).decode("ascii")
for line in cpu_info.split("\n"):
if "model name" in line:
return re.sub(".*model name.*:", "", line, 1).strip()
except:
pass
return cpu_info
def get_xpu_name():
if mge.is_cuda_available():
return get_gpu_name()
else:
return get_cpu_name()
class MnistNet(Module):
def __init__(self, has_bn=False):
super().__init__()
self.conv0 = Conv2d(1, 20, kernel_size=5, bias=True)
self.pool0 = AvgPool2d(2)
self.conv1 = Conv2d(20, 20, kernel_size=5, bias=True)
self.pool1 = AvgPool2d(2)
self.fc0 = Linear(20 * 4 * 4, 500, bias=True)
self.fc1 = Linear(500, 10, bias=True)
self.bn0 = None
self.bn1 = None
if has_bn:
self.bn0 = | BatchNorm2d(20) | megengine.module.BatchNorm2d |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import os
import re
import subprocess
import sys
import numpy as np
import megengine as mge
import megengine.functional as F
from megengine import jit, tensor
from megengine.functional.debug_param import set_conv_execution_strategy
from megengine.module import AvgPool2d, BatchNorm2d, Conv2d, Linear, Module
from megengine.optimizer import SGD
from megengine.test import assertTensorClose
def get_gpu_name():
try:
gpu_info = subprocess.check_output(
["nvidia-smi", "--query-gpu=gpu_name", "--format=csv,noheader"]
)
gpu_info = gpu_info.decode("ascii").split("\n")[0]
except:
gpu_info = "None"
return gpu_info
def get_cpu_name():
cpu_info = "None"
try:
cpu_info = subprocess.check_output(["cat", "/proc/cpuinfo"]).decode("ascii")
for line in cpu_info.split("\n"):
if "model name" in line:
return re.sub(".*model name.*:", "", line, 1).strip()
except:
pass
return cpu_info
def get_xpu_name():
if mge.is_cuda_available():
return get_gpu_name()
else:
return get_cpu_name()
class MnistNet(Module):
def __init__(self, has_bn=False):
super().__init__()
self.conv0 = Conv2d(1, 20, kernel_size=5, bias=True)
self.pool0 = AvgPool2d(2)
self.conv1 = Conv2d(20, 20, kernel_size=5, bias=True)
self.pool1 = AvgPool2d(2)
self.fc0 = Linear(20 * 4 * 4, 500, bias=True)
self.fc1 = Linear(500, 10, bias=True)
self.bn0 = None
self.bn1 = None
if has_bn:
self.bn0 = BatchNorm2d(20)
self.bn1 = | BatchNorm2d(20) | megengine.module.BatchNorm2d |
import math
import megengine.module as M
import megengine.functional as F
import megengine as mge
class PositionalEncoding(M.Module):
"""Positional encoding.
:param int d_model: embedding dim
:param float dropout_rate: dropout rate
:param int max_len: maximum input length
"""
def __init__(self, d_model, dropout_rate, max_len=5000):
"""Construct an PositionalEncoding object."""
super(PositionalEncoding, self).__init__()
self.d_model = d_model
self.xscale = math.sqrt(self.d_model)
self.dropout = | M.dropout.Dropout(dropout_rate) | megengine.module.dropout.Dropout |
import math
import megengine.module as M
import megengine.functional as F
import megengine as mge
class PositionalEncoding(M.Module):
"""Positional encoding.
:param int d_model: embedding dim
:param float dropout_rate: dropout rate
:param int max_len: maximum input length
"""
def __init__(self, d_model, dropout_rate, max_len=5000):
"""Construct an PositionalEncoding object."""
super(PositionalEncoding, self).__init__()
self.d_model = d_model
self.xscale = math.sqrt(self.d_model)
self.dropout = M.dropout.Dropout(dropout_rate)
self.pe = | mge.Tensor(0.0) | megengine.Tensor |
import math
import megengine.module as M
import megengine.functional as F
import megengine as mge
class PositionalEncoding(M.Module):
"""Positional encoding.
:param int d_model: embedding dim
:param float dropout_rate: dropout rate
:param int max_len: maximum input length
"""
def __init__(self, d_model, dropout_rate, max_len=5000):
"""Construct an PositionalEncoding object."""
super(PositionalEncoding, self).__init__()
self.d_model = d_model
self.xscale = math.sqrt(self.d_model)
self.dropout = M.dropout.Dropout(dropout_rate)
self.pe = mge.Tensor(0.0)
self.extend_pe(F.tensor.zeros([1, max_len]))
def extend_pe(self, x):
"""Reset the positional encodings."""
if len(self.pe.shape):
if self.pe.shape[1] >= x.shape[1]:
if self.pe.dtype != x.dtype or self.pe.device != x.device:
self.pe = self.pe.to(dtype=x.dtype, device=x.device)
return
pe = | F.tensor.zeros([x.shape[1], self.d_model]) | megengine.functional.tensor.zeros |
import math
import megengine.module as M
import megengine.functional as F
import megengine as mge
class PositionalEncoding(M.Module):
"""Positional encoding.
:param int d_model: embedding dim
:param float dropout_rate: dropout rate
:param int max_len: maximum input length
"""
def __init__(self, d_model, dropout_rate, max_len=5000):
"""Construct an PositionalEncoding object."""
super(PositionalEncoding, self).__init__()
self.d_model = d_model
self.xscale = math.sqrt(self.d_model)
self.dropout = M.dropout.Dropout(dropout_rate)
self.pe = mge.Tensor(0.0)
self.extend_pe(F.tensor.zeros([1, max_len]))
def extend_pe(self, x):
"""Reset the positional encodings."""
if len(self.pe.shape):
if self.pe.shape[1] >= x.shape[1]:
if self.pe.dtype != x.dtype or self.pe.device != x.device:
self.pe = self.pe.to(dtype=x.dtype, device=x.device)
return
pe = F.tensor.zeros([x.shape[1], self.d_model])
position = mge.Tensor(F.arange(0, x.shape[1], dtype="float32")).reshape(
x.shape[1], -1
)
div_term = F.exp(
mge.Tensor(F.arange(0, self.d_model, 2, dtype="float32"))
* -(math.log(10000.0) / self.d_model)
)
pe[:, 0::2] = | F.sin(position * div_term) | megengine.functional.sin |
import math
import megengine.module as M
import megengine.functional as F
import megengine as mge
class PositionalEncoding(M.Module):
"""Positional encoding.
:param int d_model: embedding dim
:param float dropout_rate: dropout rate
:param int max_len: maximum input length
"""
def __init__(self, d_model, dropout_rate, max_len=5000):
"""Construct an PositionalEncoding object."""
super(PositionalEncoding, self).__init__()
self.d_model = d_model
self.xscale = math.sqrt(self.d_model)
self.dropout = M.dropout.Dropout(dropout_rate)
self.pe = mge.Tensor(0.0)
self.extend_pe(F.tensor.zeros([1, max_len]))
def extend_pe(self, x):
"""Reset the positional encodings."""
if len(self.pe.shape):
if self.pe.shape[1] >= x.shape[1]:
if self.pe.dtype != x.dtype or self.pe.device != x.device:
self.pe = self.pe.to(dtype=x.dtype, device=x.device)
return
pe = F.tensor.zeros([x.shape[1], self.d_model])
position = mge.Tensor(F.arange(0, x.shape[1], dtype="float32")).reshape(
x.shape[1], -1
)
div_term = F.exp(
mge.Tensor(F.arange(0, self.d_model, 2, dtype="float32"))
* -(math.log(10000.0) / self.d_model)
)
pe[:, 0::2] = F.sin(position * div_term)
pe[:, 1::2] = | F.cos(position * div_term) | megengine.functional.cos |
import math
import megengine.module as M
import megengine.functional as F
import megengine as mge
class PositionalEncoding(M.Module):
"""Positional encoding.
:param int d_model: embedding dim
:param float dropout_rate: dropout rate
:param int max_len: maximum input length
"""
def __init__(self, d_model, dropout_rate, max_len=5000):
"""Construct an PositionalEncoding object."""
super(PositionalEncoding, self).__init__()
self.d_model = d_model
self.xscale = math.sqrt(self.d_model)
self.dropout = M.dropout.Dropout(dropout_rate)
self.pe = mge.Tensor(0.0)
self.extend_pe( | F.tensor.zeros([1, max_len]) | megengine.functional.tensor.zeros |
import math
import megengine.module as M
import megengine.functional as F
import megengine as mge
class PositionalEncoding(M.Module):
"""Positional encoding.
:param int d_model: embedding dim
:param float dropout_rate: dropout rate
:param int max_len: maximum input length
"""
def __init__(self, d_model, dropout_rate, max_len=5000):
"""Construct an PositionalEncoding object."""
super(PositionalEncoding, self).__init__()
self.d_model = d_model
self.xscale = math.sqrt(self.d_model)
self.dropout = M.dropout.Dropout(dropout_rate)
self.pe = mge.Tensor(0.0)
self.extend_pe(F.tensor.zeros([1, max_len]))
def extend_pe(self, x):
"""Reset the positional encodings."""
if len(self.pe.shape):
if self.pe.shape[1] >= x.shape[1]:
if self.pe.dtype != x.dtype or self.pe.device != x.device:
self.pe = self.pe.to(dtype=x.dtype, device=x.device)
return
pe = F.tensor.zeros([x.shape[1], self.d_model])
position = mge.Tensor( | F.arange(0, x.shape[1], dtype="float32") | megengine.functional.arange |
import math
import megengine.module as M
import megengine.functional as F
import megengine as mge
class PositionalEncoding(M.Module):
"""Positional encoding.
:param int d_model: embedding dim
:param float dropout_rate: dropout rate
:param int max_len: maximum input length
"""
def __init__(self, d_model, dropout_rate, max_len=5000):
"""Construct an PositionalEncoding object."""
super(PositionalEncoding, self).__init__()
self.d_model = d_model
self.xscale = math.sqrt(self.d_model)
self.dropout = M.dropout.Dropout(dropout_rate)
self.pe = mge.Tensor(0.0)
self.extend_pe(F.tensor.zeros([1, max_len]))
def extend_pe(self, x):
"""Reset the positional encodings."""
if len(self.pe.shape):
if self.pe.shape[1] >= x.shape[1]:
if self.pe.dtype != x.dtype or self.pe.device != x.device:
self.pe = self.pe.to(dtype=x.dtype, device=x.device)
return
pe = F.tensor.zeros([x.shape[1], self.d_model])
position = mge.Tensor(F.arange(0, x.shape[1], dtype="float32")).reshape(
x.shape[1], -1
)
div_term = F.exp(
mge.Tensor( | F.arange(0, self.d_model, 2, dtype="float32") | megengine.functional.arange |
#!/usr/bin/env python3
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
import copy
from typing import Any, Mapping
import megengine as mge
import megengine.functional as F
import megengine.module as M
from .modules import SE, activation, conv2d, gap2d, linear, norm2d
__all__ = ["build_head", "ClsHead", "MBV3Head", "VGGHead"]
def build_head(
w_in: int, head_args: Mapping[str, Any] = None, norm_name: str = "BN", act_name: str = "relu"
) -> M.Module:
"""The factory function to build head.
Note:
if ``head_args`` is ``None`` or ``head_args["name"]`` is ``None``, this function will do
nothing and return ``None``.
Args:
w_in: input width.
head_args: head args. Default: ``None``
norm_name: default normalization function, will be overridden by the same key in
``head_args``. Default: ``"BN"``
act_name: default activation function, will be overridden by the same key in ``head_args``.
Default: ``"relu"``
Returns:
A head.
"""
if head_args is None:
return None
head_args = copy.deepcopy(head_args)
head_name = head_args.pop("name", None)
if head_name is None:
return None
head_args["w_in"] = w_in
head_args.setdefault("norm_name", norm_name)
head_args.setdefault("act_name", act_name)
if callable(head_name):
return head_name(**head_args)
if isinstance(head_name, str):
head_funcs = {
"ClsHead": ClsHead,
"MBV3Head": MBV3Head,
"VGGHead": VGGHead,
}
if head_name in head_funcs:
return head_funcs[head_name](**head_args)
raise ValueError(f"Head '{head_name}' not supported")
class ClsHead(M.Module):
"""Cls head: Conv, BN, Act, AvgPool, FC.
Args:
w_in: input width.
w_out: output width, normally the number of classes. Default: ``1000``
width: width for first conv in head, conv will be omitted if set to 0. Default: ``0``
dropout_prob: dropout probability. Default: ``0.0``
norm_name: normalization function. Default: ``"BN"``
act_name: activation function. Default: ``"relu"``
bias: whether fc has bias. Default: ``True``
"""
def __init__(
self,
w_in: int,
w_out: int = 1000,
width: int = 0,
dropout_prob: float = 0.0,
norm_name: str = "BN",
act_name: str = "relu",
bias: bool = True,
):
super().__init__()
self.width = width
if self.width > 0:
self.conv = conv2d(w_in, self.width, 1)
self.bn = norm2d(norm_name, self.width)
self.act = activation(act_name)
w_in = self.width
self.avg_pool = gap2d()
if dropout_prob > 0.0:
self.dropout = M.Dropout(dropout_prob)
self.fc = linear(w_in, w_out, bias=bias)
def forward(self, x: mge.Tensor) -> mge.Tensor:
if self.width > 0:
x = self.conv(x)
x = self.bn(x)
x = self.act(x)
x = self.avg_pool(x)
x = | F.flatten(x, 1) | megengine.functional.flatten |
#!/usr/bin/env python3
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
import copy
from typing import Any, Mapping
import megengine as mge
import megengine.functional as F
import megengine.module as M
from .modules import SE, activation, conv2d, gap2d, linear, norm2d
__all__ = ["build_head", "ClsHead", "MBV3Head", "VGGHead"]
def build_head(
w_in: int, head_args: Mapping[str, Any] = None, norm_name: str = "BN", act_name: str = "relu"
) -> M.Module:
"""The factory function to build head.
Note:
if ``head_args`` is ``None`` or ``head_args["name"]`` is ``None``, this function will do
nothing and return ``None``.
Args:
w_in: input width.
head_args: head args. Default: ``None``
norm_name: default normalization function, will be overridden by the same key in
``head_args``. Default: ``"BN"``
act_name: default activation function, will be overridden by the same key in ``head_args``.
Default: ``"relu"``
Returns:
A head.
"""
if head_args is None:
return None
head_args = copy.deepcopy(head_args)
head_name = head_args.pop("name", None)
if head_name is None:
return None
head_args["w_in"] = w_in
head_args.setdefault("norm_name", norm_name)
head_args.setdefault("act_name", act_name)
if callable(head_name):
return head_name(**head_args)
if isinstance(head_name, str):
head_funcs = {
"ClsHead": ClsHead,
"MBV3Head": MBV3Head,
"VGGHead": VGGHead,
}
if head_name in head_funcs:
return head_funcs[head_name](**head_args)
raise ValueError(f"Head '{head_name}' not supported")
class ClsHead(M.Module):
"""Cls head: Conv, BN, Act, AvgPool, FC.
Args:
w_in: input width.
w_out: output width, normally the number of classes. Default: ``1000``
width: width for first conv in head, conv will be omitted if set to 0. Default: ``0``
dropout_prob: dropout probability. Default: ``0.0``
norm_name: normalization function. Default: ``"BN"``
act_name: activation function. Default: ``"relu"``
bias: whether fc has bias. Default: ``True``
"""
def __init__(
self,
w_in: int,
w_out: int = 1000,
width: int = 0,
dropout_prob: float = 0.0,
norm_name: str = "BN",
act_name: str = "relu",
bias: bool = True,
):
super().__init__()
self.width = width
if self.width > 0:
self.conv = conv2d(w_in, self.width, 1)
self.bn = norm2d(norm_name, self.width)
self.act = activation(act_name)
w_in = self.width
self.avg_pool = gap2d()
if dropout_prob > 0.0:
self.dropout = M.Dropout(dropout_prob)
self.fc = linear(w_in, w_out, bias=bias)
def forward(self, x: mge.Tensor) -> mge.Tensor:
if self.width > 0:
x = self.conv(x)
x = self.bn(x)
x = self.act(x)
x = self.avg_pool(x)
x = F.flatten(x, 1)
if getattr(self, "dropout", None) is not None:
x = self.dropout(x)
x = self.fc(x)
return x
class MBV3Head(M.Module):
"""MobileNet V3 head: Conv, BN, Act, AvgPool, SE, FC, Act, FC.
Args:
w_in: input width.
w_out: output width, normally the number of classes.
width: width for first conv in head.
w_h: width for first linear in head.
dropout_prob: dropout probability. Default: ``0.0``
se_r: Squeeze-and-Excitation (SE) ratio. Default: ``0.0``
norm_name: normalization function. Default: ``"BN"``
act_name: activation function. Default: ``"hswish"``
bias: whether fc has bias. Default: ``True``
"""
def __init__(
self,
w_in: int,
w_out: int = 1000,
width: int = 960,
w_h: int = 1280,
dropout_prob: float = 0.0,
se_r: float = 0.0,
norm_name: str = "BN",
act_name: str = "hswish",
bias: bool = True,
):
super().__init__()
self.conv = conv2d(w_in, width, 1)
self.bn = norm2d(norm_name, width)
self.act = activation(act_name)
self.avg_pool = gap2d()
if se_r > 0.0:
self.se = SE(width, int(se_r * width), act_name)
self.h_fc = linear(width, w_h, bias=bias)
self.h_act = activation(act_name)
if dropout_prob > 0.0:
self.dropout = M.Dropout(dropout_prob)
self.fc = linear(w_h, w_out, bias=bias)
def forward(self, x: mge.Tensor) -> mge.Tensor:
x = self.conv(x)
x = self.bn(x)
x = self.act(x)
x = self.avg_pool(x)
if getattr(self, "se", None) is not None:
x = self.se(x)
x = | F.flatten(x, 1) | megengine.functional.flatten |
#!/usr/bin/env python3
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
import copy
from typing import Any, Mapping
import megengine as mge
import megengine.functional as F
import megengine.module as M
from .modules import SE, activation, conv2d, gap2d, linear, norm2d
__all__ = ["build_head", "ClsHead", "MBV3Head", "VGGHead"]
def build_head(
w_in: int, head_args: Mapping[str, Any] = None, norm_name: str = "BN", act_name: str = "relu"
) -> M.Module:
"""The factory function to build head.
Note:
if ``head_args`` is ``None`` or ``head_args["name"]`` is ``None``, this function will do
nothing and return ``None``.
Args:
w_in: input width.
head_args: head args. Default: ``None``
norm_name: default normalization function, will be overridden by the same key in
``head_args``. Default: ``"BN"``
act_name: default activation function, will be overridden by the same key in ``head_args``.
Default: ``"relu"``
Returns:
A head.
"""
if head_args is None:
return None
head_args = copy.deepcopy(head_args)
head_name = head_args.pop("name", None)
if head_name is None:
return None
head_args["w_in"] = w_in
head_args.setdefault("norm_name", norm_name)
head_args.setdefault("act_name", act_name)
if callable(head_name):
return head_name(**head_args)
if isinstance(head_name, str):
head_funcs = {
"ClsHead": ClsHead,
"MBV3Head": MBV3Head,
"VGGHead": VGGHead,
}
if head_name in head_funcs:
return head_funcs[head_name](**head_args)
raise ValueError(f"Head '{head_name}' not supported")
class ClsHead(M.Module):
"""Cls head: Conv, BN, Act, AvgPool, FC.
Args:
w_in: input width.
w_out: output width, normally the number of classes. Default: ``1000``
width: width for first conv in head, conv will be omitted if set to 0. Default: ``0``
dropout_prob: dropout probability. Default: ``0.0``
norm_name: normalization function. Default: ``"BN"``
act_name: activation function. Default: ``"relu"``
bias: whether fc has bias. Default: ``True``
"""
def __init__(
self,
w_in: int,
w_out: int = 1000,
width: int = 0,
dropout_prob: float = 0.0,
norm_name: str = "BN",
act_name: str = "relu",
bias: bool = True,
):
super().__init__()
self.width = width
if self.width > 0:
self.conv = conv2d(w_in, self.width, 1)
self.bn = norm2d(norm_name, self.width)
self.act = activation(act_name)
w_in = self.width
self.avg_pool = gap2d()
if dropout_prob > 0.0:
self.dropout = M.Dropout(dropout_prob)
self.fc = linear(w_in, w_out, bias=bias)
def forward(self, x: mge.Tensor) -> mge.Tensor:
if self.width > 0:
x = self.conv(x)
x = self.bn(x)
x = self.act(x)
x = self.avg_pool(x)
x = F.flatten(x, 1)
if getattr(self, "dropout", None) is not None:
x = self.dropout(x)
x = self.fc(x)
return x
class MBV3Head(M.Module):
"""MobileNet V3 head: Conv, BN, Act, AvgPool, SE, FC, Act, FC.
Args:
w_in: input width.
w_out: output width, normally the number of classes.
width: width for first conv in head.
w_h: width for first linear in head.
dropout_prob: dropout probability. Default: ``0.0``
se_r: Squeeze-and-Excitation (SE) ratio. Default: ``0.0``
norm_name: normalization function. Default: ``"BN"``
act_name: activation function. Default: ``"hswish"``
bias: whether fc has bias. Default: ``True``
"""
def __init__(
self,
w_in: int,
w_out: int = 1000,
width: int = 960,
w_h: int = 1280,
dropout_prob: float = 0.0,
se_r: float = 0.0,
norm_name: str = "BN",
act_name: str = "hswish",
bias: bool = True,
):
super().__init__()
self.conv = conv2d(w_in, width, 1)
self.bn = norm2d(norm_name, width)
self.act = activation(act_name)
self.avg_pool = gap2d()
if se_r > 0.0:
self.se = SE(width, int(se_r * width), act_name)
self.h_fc = linear(width, w_h, bias=bias)
self.h_act = activation(act_name)
if dropout_prob > 0.0:
self.dropout = M.Dropout(dropout_prob)
self.fc = linear(w_h, w_out, bias=bias)
def forward(self, x: mge.Tensor) -> mge.Tensor:
x = self.conv(x)
x = self.bn(x)
x = self.act(x)
x = self.avg_pool(x)
if getattr(self, "se", None) is not None:
x = self.se(x)
x = F.flatten(x, 1)
x = self.h_fc(x)
x = self.h_act(x)
if getattr(self, "dropout", None) is not None:
x = self.dropout(x)
x = self.fc(x)
return x
class VGGHead(M.Module):
"""VGG head: AvgPool, [FC, Act, Dropout] x2, FC.
Args:
w_in: input width.
w_out: output width, normally the number of classes. Default: ``1000``
width: width for linear in head. Default: ``4096``
dropout_prob: dropout probability. Default: ``0.5``
act_name: activation function. Default: ``"relu"``
"""
def __init__(
self,
w_in: int,
w_out: int = 1000,
width: int = 4096,
dropout_prob: float = 0.5,
act_name: str = "relu",
**kwargs,
):
super().__init__()
self.avg_pool = gap2d(7)
self.classifier = M.Sequential(
linear(w_in * 7 * 7, width, bias=True),
activation(act_name),
M.Dropout(dropout_prob),
linear(width, width, bias=True),
activation(act_name),
M.Dropout(dropout_prob),
linear(width, w_out, bias=True),
)
def forward(self, x: mge.Tensor) -> mge.Tensor:
x = self.avg_pool(x)
x = | F.flatten(x, 1) | megengine.functional.flatten |
#!/usr/bin/env python3
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
import copy
from typing import Any, Mapping
import megengine as mge
import megengine.functional as F
import megengine.module as M
from .modules import SE, activation, conv2d, gap2d, linear, norm2d
__all__ = ["build_head", "ClsHead", "MBV3Head", "VGGHead"]
def build_head(
w_in: int, head_args: Mapping[str, Any] = None, norm_name: str = "BN", act_name: str = "relu"
) -> M.Module:
"""The factory function to build head.
Note:
if ``head_args`` is ``None`` or ``head_args["name"]`` is ``None``, this function will do
nothing and return ``None``.
Args:
w_in: input width.
head_args: head args. Default: ``None``
norm_name: default normalization function, will be overridden by the same key in
``head_args``. Default: ``"BN"``
act_name: default activation function, will be overridden by the same key in ``head_args``.
Default: ``"relu"``
Returns:
A head.
"""
if head_args is None:
return None
head_args = copy.deepcopy(head_args)
head_name = head_args.pop("name", None)
if head_name is None:
return None
head_args["w_in"] = w_in
head_args.setdefault("norm_name", norm_name)
head_args.setdefault("act_name", act_name)
if callable(head_name):
return head_name(**head_args)
if isinstance(head_name, str):
head_funcs = {
"ClsHead": ClsHead,
"MBV3Head": MBV3Head,
"VGGHead": VGGHead,
}
if head_name in head_funcs:
return head_funcs[head_name](**head_args)
raise ValueError(f"Head '{head_name}' not supported")
class ClsHead(M.Module):
"""Cls head: Conv, BN, Act, AvgPool, FC.
Args:
w_in: input width.
w_out: output width, normally the number of classes. Default: ``1000``
width: width for first conv in head, conv will be omitted if set to 0. Default: ``0``
dropout_prob: dropout probability. Default: ``0.0``
norm_name: normalization function. Default: ``"BN"``
act_name: activation function. Default: ``"relu"``
bias: whether fc has bias. Default: ``True``
"""
def __init__(
self,
w_in: int,
w_out: int = 1000,
width: int = 0,
dropout_prob: float = 0.0,
norm_name: str = "BN",
act_name: str = "relu",
bias: bool = True,
):
super().__init__()
self.width = width
if self.width > 0:
self.conv = conv2d(w_in, self.width, 1)
self.bn = norm2d(norm_name, self.width)
self.act = activation(act_name)
w_in = self.width
self.avg_pool = gap2d()
if dropout_prob > 0.0:
self.dropout = | M.Dropout(dropout_prob) | megengine.module.Dropout |
#!/usr/bin/env python3
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
import copy
from typing import Any, Mapping
import megengine as mge
import megengine.functional as F
import megengine.module as M
from .modules import SE, activation, conv2d, gap2d, linear, norm2d
__all__ = ["build_head", "ClsHead", "MBV3Head", "VGGHead"]
def build_head(
w_in: int, head_args: Mapping[str, Any] = None, norm_name: str = "BN", act_name: str = "relu"
) -> M.Module:
"""The factory function to build head.
Note:
if ``head_args`` is ``None`` or ``head_args["name"]`` is ``None``, this function will do
nothing and return ``None``.
Args:
w_in: input width.
head_args: head args. Default: ``None``
norm_name: default normalization function, will be overridden by the same key in
``head_args``. Default: ``"BN"``
act_name: default activation function, will be overridden by the same key in ``head_args``.
Default: ``"relu"``
Returns:
A head.
"""
if head_args is None:
return None
head_args = copy.deepcopy(head_args)
head_name = head_args.pop("name", None)
if head_name is None:
return None
head_args["w_in"] = w_in
head_args.setdefault("norm_name", norm_name)
head_args.setdefault("act_name", act_name)
if callable(head_name):
return head_name(**head_args)
if isinstance(head_name, str):
head_funcs = {
"ClsHead": ClsHead,
"MBV3Head": MBV3Head,
"VGGHead": VGGHead,
}
if head_name in head_funcs:
return head_funcs[head_name](**head_args)
raise ValueError(f"Head '{head_name}' not supported")
class ClsHead(M.Module):
"""Cls head: Conv, BN, Act, AvgPool, FC.
Args:
w_in: input width.
w_out: output width, normally the number of classes. Default: ``1000``
width: width for first conv in head, conv will be omitted if set to 0. Default: ``0``
dropout_prob: dropout probability. Default: ``0.0``
norm_name: normalization function. Default: ``"BN"``
act_name: activation function. Default: ``"relu"``
bias: whether fc has bias. Default: ``True``
"""
def __init__(
self,
w_in: int,
w_out: int = 1000,
width: int = 0,
dropout_prob: float = 0.0,
norm_name: str = "BN",
act_name: str = "relu",
bias: bool = True,
):
super().__init__()
self.width = width
if self.width > 0:
self.conv = conv2d(w_in, self.width, 1)
self.bn = norm2d(norm_name, self.width)
self.act = activation(act_name)
w_in = self.width
self.avg_pool = gap2d()
if dropout_prob > 0.0:
self.dropout = M.Dropout(dropout_prob)
self.fc = linear(w_in, w_out, bias=bias)
def forward(self, x: mge.Tensor) -> mge.Tensor:
if self.width > 0:
x = self.conv(x)
x = self.bn(x)
x = self.act(x)
x = self.avg_pool(x)
x = F.flatten(x, 1)
if getattr(self, "dropout", None) is not None:
x = self.dropout(x)
x = self.fc(x)
return x
class MBV3Head(M.Module):
"""MobileNet V3 head: Conv, BN, Act, AvgPool, SE, FC, Act, FC.
Args:
w_in: input width.
w_out: output width, normally the number of classes.
width: width for first conv in head.
w_h: width for first linear in head.
dropout_prob: dropout probability. Default: ``0.0``
se_r: Squeeze-and-Excitation (SE) ratio. Default: ``0.0``
norm_name: normalization function. Default: ``"BN"``
act_name: activation function. Default: ``"hswish"``
bias: whether fc has bias. Default: ``True``
"""
def __init__(
self,
w_in: int,
w_out: int = 1000,
width: int = 960,
w_h: int = 1280,
dropout_prob: float = 0.0,
se_r: float = 0.0,
norm_name: str = "BN",
act_name: str = "hswish",
bias: bool = True,
):
super().__init__()
self.conv = conv2d(w_in, width, 1)
self.bn = norm2d(norm_name, width)
self.act = activation(act_name)
self.avg_pool = gap2d()
if se_r > 0.0:
self.se = SE(width, int(se_r * width), act_name)
self.h_fc = linear(width, w_h, bias=bias)
self.h_act = activation(act_name)
if dropout_prob > 0.0:
self.dropout = | M.Dropout(dropout_prob) | megengine.module.Dropout |
#!/usr/bin/env python3
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
import copy
from typing import Any, Mapping
import megengine as mge
import megengine.functional as F
import megengine.module as M
from .modules import SE, activation, conv2d, gap2d, linear, norm2d
__all__ = ["build_head", "ClsHead", "MBV3Head", "VGGHead"]
def build_head(
w_in: int, head_args: Mapping[str, Any] = None, norm_name: str = "BN", act_name: str = "relu"
) -> M.Module:
"""The factory function to build head.
Note:
if ``head_args`` is ``None`` or ``head_args["name"]`` is ``None``, this function will do
nothing and return ``None``.
Args:
w_in: input width.
head_args: head args. Default: ``None``
norm_name: default normalization function, will be overridden by the same key in
``head_args``. Default: ``"BN"``
act_name: default activation function, will be overridden by the same key in ``head_args``.
Default: ``"relu"``
Returns:
A head.
"""
if head_args is None:
return None
head_args = copy.deepcopy(head_args)
head_name = head_args.pop("name", None)
if head_name is None:
return None
head_args["w_in"] = w_in
head_args.setdefault("norm_name", norm_name)
head_args.setdefault("act_name", act_name)
if callable(head_name):
return head_name(**head_args)
if isinstance(head_name, str):
head_funcs = {
"ClsHead": ClsHead,
"MBV3Head": MBV3Head,
"VGGHead": VGGHead,
}
if head_name in head_funcs:
return head_funcs[head_name](**head_args)
raise ValueError(f"Head '{head_name}' not supported")
class ClsHead(M.Module):
"""Cls head: Conv, BN, Act, AvgPool, FC.
Args:
w_in: input width.
w_out: output width, normally the number of classes. Default: ``1000``
width: width for first conv in head, conv will be omitted if set to 0. Default: ``0``
dropout_prob: dropout probability. Default: ``0.0``
norm_name: normalization function. Default: ``"BN"``
act_name: activation function. Default: ``"relu"``
bias: whether fc has bias. Default: ``True``
"""
def __init__(
self,
w_in: int,
w_out: int = 1000,
width: int = 0,
dropout_prob: float = 0.0,
norm_name: str = "BN",
act_name: str = "relu",
bias: bool = True,
):
super().__init__()
self.width = width
if self.width > 0:
self.conv = conv2d(w_in, self.width, 1)
self.bn = norm2d(norm_name, self.width)
self.act = activation(act_name)
w_in = self.width
self.avg_pool = gap2d()
if dropout_prob > 0.0:
self.dropout = M.Dropout(dropout_prob)
self.fc = linear(w_in, w_out, bias=bias)
def forward(self, x: mge.Tensor) -> mge.Tensor:
if self.width > 0:
x = self.conv(x)
x = self.bn(x)
x = self.act(x)
x = self.avg_pool(x)
x = F.flatten(x, 1)
if getattr(self, "dropout", None) is not None:
x = self.dropout(x)
x = self.fc(x)
return x
class MBV3Head(M.Module):
"""MobileNet V3 head: Conv, BN, Act, AvgPool, SE, FC, Act, FC.
Args:
w_in: input width.
w_out: output width, normally the number of classes.
width: width for first conv in head.
w_h: width for first linear in head.
dropout_prob: dropout probability. Default: ``0.0``
se_r: Squeeze-and-Excitation (SE) ratio. Default: ``0.0``
norm_name: normalization function. Default: ``"BN"``
act_name: activation function. Default: ``"hswish"``
bias: whether fc has bias. Default: ``True``
"""
def __init__(
self,
w_in: int,
w_out: int = 1000,
width: int = 960,
w_h: int = 1280,
dropout_prob: float = 0.0,
se_r: float = 0.0,
norm_name: str = "BN",
act_name: str = "hswish",
bias: bool = True,
):
super().__init__()
self.conv = conv2d(w_in, width, 1)
self.bn = norm2d(norm_name, width)
self.act = activation(act_name)
self.avg_pool = gap2d()
if se_r > 0.0:
self.se = SE(width, int(se_r * width), act_name)
self.h_fc = linear(width, w_h, bias=bias)
self.h_act = activation(act_name)
if dropout_prob > 0.0:
self.dropout = M.Dropout(dropout_prob)
self.fc = linear(w_h, w_out, bias=bias)
def forward(self, x: mge.Tensor) -> mge.Tensor:
x = self.conv(x)
x = self.bn(x)
x = self.act(x)
x = self.avg_pool(x)
if getattr(self, "se", None) is not None:
x = self.se(x)
x = F.flatten(x, 1)
x = self.h_fc(x)
x = self.h_act(x)
if getattr(self, "dropout", None) is not None:
x = self.dropout(x)
x = self.fc(x)
return x
class VGGHead(M.Module):
"""VGG head: AvgPool, [FC, Act, Dropout] x2, FC.
Args:
w_in: input width.
w_out: output width, normally the number of classes. Default: ``1000``
width: width for linear in head. Default: ``4096``
dropout_prob: dropout probability. Default: ``0.5``
act_name: activation function. Default: ``"relu"``
"""
def __init__(
self,
w_in: int,
w_out: int = 1000,
width: int = 4096,
dropout_prob: float = 0.5,
act_name: str = "relu",
**kwargs,
):
super().__init__()
self.avg_pool = gap2d(7)
self.classifier = M.Sequential(
linear(w_in * 7 * 7, width, bias=True),
activation(act_name),
| M.Dropout(dropout_prob) | megengine.module.Dropout |
#!/usr/bin/env python3
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
import copy
from typing import Any, Mapping
import megengine as mge
import megengine.functional as F
import megengine.module as M
from .modules import SE, activation, conv2d, gap2d, linear, norm2d
__all__ = ["build_head", "ClsHead", "MBV3Head", "VGGHead"]
def build_head(
w_in: int, head_args: Mapping[str, Any] = None, norm_name: str = "BN", act_name: str = "relu"
) -> M.Module:
"""The factory function to build head.
Note:
if ``head_args`` is ``None`` or ``head_args["name"]`` is ``None``, this function will do
nothing and return ``None``.
Args:
w_in: input width.
head_args: head args. Default: ``None``
norm_name: default normalization function, will be overridden by the same key in
``head_args``. Default: ``"BN"``
act_name: default activation function, will be overridden by the same key in ``head_args``.
Default: ``"relu"``
Returns:
A head.
"""
if head_args is None:
return None
head_args = copy.deepcopy(head_args)
head_name = head_args.pop("name", None)
if head_name is None:
return None
head_args["w_in"] = w_in
head_args.setdefault("norm_name", norm_name)
head_args.setdefault("act_name", act_name)
if callable(head_name):
return head_name(**head_args)
if isinstance(head_name, str):
head_funcs = {
"ClsHead": ClsHead,
"MBV3Head": MBV3Head,
"VGGHead": VGGHead,
}
if head_name in head_funcs:
return head_funcs[head_name](**head_args)
raise ValueError(f"Head '{head_name}' not supported")
class ClsHead(M.Module):
"""Cls head: Conv, BN, Act, AvgPool, FC.
Args:
w_in: input width.
w_out: output width, normally the number of classes. Default: ``1000``
width: width for first conv in head, conv will be omitted if set to 0. Default: ``0``
dropout_prob: dropout probability. Default: ``0.0``
norm_name: normalization function. Default: ``"BN"``
act_name: activation function. Default: ``"relu"``
bias: whether fc has bias. Default: ``True``
"""
def __init__(
self,
w_in: int,
w_out: int = 1000,
width: int = 0,
dropout_prob: float = 0.0,
norm_name: str = "BN",
act_name: str = "relu",
bias: bool = True,
):
super().__init__()
self.width = width
if self.width > 0:
self.conv = conv2d(w_in, self.width, 1)
self.bn = norm2d(norm_name, self.width)
self.act = activation(act_name)
w_in = self.width
self.avg_pool = gap2d()
if dropout_prob > 0.0:
self.dropout = M.Dropout(dropout_prob)
self.fc = linear(w_in, w_out, bias=bias)
def forward(self, x: mge.Tensor) -> mge.Tensor:
if self.width > 0:
x = self.conv(x)
x = self.bn(x)
x = self.act(x)
x = self.avg_pool(x)
x = F.flatten(x, 1)
if getattr(self, "dropout", None) is not None:
x = self.dropout(x)
x = self.fc(x)
return x
class MBV3Head(M.Module):
"""MobileNet V3 head: Conv, BN, Act, AvgPool, SE, FC, Act, FC.
Args:
w_in: input width.
w_out: output width, normally the number of classes.
width: width for first conv in head.
w_h: width for first linear in head.
dropout_prob: dropout probability. Default: ``0.0``
se_r: Squeeze-and-Excitation (SE) ratio. Default: ``0.0``
norm_name: normalization function. Default: ``"BN"``
act_name: activation function. Default: ``"hswish"``
bias: whether fc has bias. Default: ``True``
"""
def __init__(
self,
w_in: int,
w_out: int = 1000,
width: int = 960,
w_h: int = 1280,
dropout_prob: float = 0.0,
se_r: float = 0.0,
norm_name: str = "BN",
act_name: str = "hswish",
bias: bool = True,
):
super().__init__()
self.conv = conv2d(w_in, width, 1)
self.bn = norm2d(norm_name, width)
self.act = activation(act_name)
self.avg_pool = gap2d()
if se_r > 0.0:
self.se = SE(width, int(se_r * width), act_name)
self.h_fc = linear(width, w_h, bias=bias)
self.h_act = activation(act_name)
if dropout_prob > 0.0:
self.dropout = M.Dropout(dropout_prob)
self.fc = linear(w_h, w_out, bias=bias)
def forward(self, x: mge.Tensor) -> mge.Tensor:
x = self.conv(x)
x = self.bn(x)
x = self.act(x)
x = self.avg_pool(x)
if getattr(self, "se", None) is not None:
x = self.se(x)
x = F.flatten(x, 1)
x = self.h_fc(x)
x = self.h_act(x)
if getattr(self, "dropout", None) is not None:
x = self.dropout(x)
x = self.fc(x)
return x
class VGGHead(M.Module):
"""VGG head: AvgPool, [FC, Act, Dropout] x2, FC.
Args:
w_in: input width.
w_out: output width, normally the number of classes. Default: ``1000``
width: width for linear in head. Default: ``4096``
dropout_prob: dropout probability. Default: ``0.5``
act_name: activation function. Default: ``"relu"``
"""
def __init__(
self,
w_in: int,
w_out: int = 1000,
width: int = 4096,
dropout_prob: float = 0.5,
act_name: str = "relu",
**kwargs,
):
super().__init__()
self.avg_pool = gap2d(7)
self.classifier = M.Sequential(
linear(w_in * 7 * 7, width, bias=True),
activation(act_name),
M.Dropout(dropout_prob),
linear(width, width, bias=True),
activation(act_name),
| M.Dropout(dropout_prob) | megengine.module.Dropout |
import numpy as np
from megengine import Tensor
import megengine.functional as F
import pdb
class AnchorGenerator():
"""default anchor generator for fpn.
This class generate anchors by feature map in level.
"""
def __init__(self, base_size=16, ratios=[0.5, 1, 2],
base_scale=2):
self.base_size = base_size
self.base_scale = np.array(base_scale)
self.anchor_ratios = ratios
def _whctrs(self, anchor):
"""convert anchor box into (w, h, ctr_x, ctr_y)
"""
w = anchor[:, 2] - anchor[:, 0] + 1
h = anchor[:, 3] - anchor[:, 1] + 1
x_ctr = anchor[:, 0] + 0.5 * (w - 1)
y_ctr = anchor[:, 1] + 0.5 * (h - 1)
return w, h, x_ctr, y_ctr
def get_plane_anchors(self, anchor_scales: np.ndarray):
"""get anchors per location on feature map.
The anchor number is anchor_scales x anchor_ratios
"""
base_anchor = | Tensor([0, 0, self.base_size - 1, self.base_size - 1]) | megengine.Tensor |
import numpy as np
from megengine import Tensor
import megengine.functional as F
import pdb
class AnchorGenerator():
"""default anchor generator for fpn.
This class generate anchors by feature map in level.
"""
def __init__(self, base_size=16, ratios=[0.5, 1, 2],
base_scale=2):
self.base_size = base_size
self.base_scale = np.array(base_scale)
self.anchor_ratios = ratios
def _whctrs(self, anchor):
"""convert anchor box into (w, h, ctr_x, ctr_y)
"""
w = anchor[:, 2] - anchor[:, 0] + 1
h = anchor[:, 3] - anchor[:, 1] + 1
x_ctr = anchor[:, 0] + 0.5 * (w - 1)
y_ctr = anchor[:, 1] + 0.5 * (h - 1)
return w, h, x_ctr, y_ctr
def get_plane_anchors(self, anchor_scales: np.ndarray):
"""get anchors per location on feature map.
The anchor number is anchor_scales x anchor_ratios
"""
base_anchor = Tensor([0, 0, self.base_size - 1, self.base_size - 1])
base_anchor = base_anchor.reshape(1, -1)
w, h, x_ctr, y_ctr = self._whctrs(base_anchor)
# ratio enumerate
size = w * h
size_ratios = size / self.anchor_ratios
#pdb.set_trace()
ws = | F.sqrt(size_ratios) | megengine.functional.sqrt |
import numpy as np
from megengine import Tensor
import megengine.functional as F
import pdb
class AnchorGenerator():
"""default anchor generator for fpn.
This class generate anchors by feature map in level.
"""
def __init__(self, base_size=16, ratios=[0.5, 1, 2],
base_scale=2):
self.base_size = base_size
self.base_scale = np.array(base_scale)
self.anchor_ratios = ratios
def _whctrs(self, anchor):
"""convert anchor box into (w, h, ctr_x, ctr_y)
"""
w = anchor[:, 2] - anchor[:, 0] + 1
h = anchor[:, 3] - anchor[:, 1] + 1
x_ctr = anchor[:, 0] + 0.5 * (w - 1)
y_ctr = anchor[:, 1] + 0.5 * (h - 1)
return w, h, x_ctr, y_ctr
def get_plane_anchors(self, anchor_scales: np.ndarray):
"""get anchors per location on feature map.
The anchor number is anchor_scales x anchor_ratios
"""
base_anchor = Tensor([0, 0, self.base_size - 1, self.base_size - 1])
base_anchor = base_anchor.reshape(1, -1)
w, h, x_ctr, y_ctr = self._whctrs(base_anchor)
# ratio enumerate
size = w * h
size_ratios = size / self.anchor_ratios
#pdb.set_trace()
ws = F.sqrt(size_ratios)
hs = ws * self.anchor_ratios
# ws = size_ratios.sqrt().round()
# hs = (ws * self.anchor_ratios).round()
# scale enumerate
anchor_scales = anchor_scales.reshape(1, -1).astype(np.float32)
ws = | F.expand_dims(ws, 1) | megengine.functional.expand_dims |
import numpy as np
from megengine import Tensor
import megengine.functional as F
import pdb
class AnchorGenerator():
"""default anchor generator for fpn.
This class generate anchors by feature map in level.
"""
def __init__(self, base_size=16, ratios=[0.5, 1, 2],
base_scale=2):
self.base_size = base_size
self.base_scale = np.array(base_scale)
self.anchor_ratios = ratios
def _whctrs(self, anchor):
"""convert anchor box into (w, h, ctr_x, ctr_y)
"""
w = anchor[:, 2] - anchor[:, 0] + 1
h = anchor[:, 3] - anchor[:, 1] + 1
x_ctr = anchor[:, 0] + 0.5 * (w - 1)
y_ctr = anchor[:, 1] + 0.5 * (h - 1)
return w, h, x_ctr, y_ctr
def get_plane_anchors(self, anchor_scales: np.ndarray):
"""get anchors per location on feature map.
The anchor number is anchor_scales x anchor_ratios
"""
base_anchor = Tensor([0, 0, self.base_size - 1, self.base_size - 1])
base_anchor = base_anchor.reshape(1, -1)
w, h, x_ctr, y_ctr = self._whctrs(base_anchor)
# ratio enumerate
size = w * h
size_ratios = size / self.anchor_ratios
#pdb.set_trace()
ws = F.sqrt(size_ratios)
hs = ws * self.anchor_ratios
# ws = size_ratios.sqrt().round()
# hs = (ws * self.anchor_ratios).round()
# scale enumerate
anchor_scales = anchor_scales.reshape(1, -1).astype(np.float32)
ws = F.expand_dims(ws, 1)
hs = | F.expand_dims(hs, 1) | megengine.functional.expand_dims |
import numpy as np
from megengine import Tensor
import megengine.functional as F
import pdb
class AnchorGenerator():
"""default anchor generator for fpn.
This class generate anchors by feature map in level.
"""
def __init__(self, base_size=16, ratios=[0.5, 1, 2],
base_scale=2):
self.base_size = base_size
self.base_scale = np.array(base_scale)
self.anchor_ratios = ratios
def _whctrs(self, anchor):
"""convert anchor box into (w, h, ctr_x, ctr_y)
"""
w = anchor[:, 2] - anchor[:, 0] + 1
h = anchor[:, 3] - anchor[:, 1] + 1
x_ctr = anchor[:, 0] + 0.5 * (w - 1)
y_ctr = anchor[:, 1] + 0.5 * (h - 1)
return w, h, x_ctr, y_ctr
def get_plane_anchors(self, anchor_scales: np.ndarray):
"""get anchors per location on feature map.
The anchor number is anchor_scales x anchor_ratios
"""
base_anchor = Tensor([0, 0, self.base_size - 1, self.base_size - 1])
base_anchor = base_anchor.reshape(1, -1)
w, h, x_ctr, y_ctr = self._whctrs(base_anchor)
# ratio enumerate
size = w * h
size_ratios = size / self.anchor_ratios
#pdb.set_trace()
ws = F.sqrt(size_ratios)
hs = ws * self.anchor_ratios
# ws = size_ratios.sqrt().round()
# hs = (ws * self.anchor_ratios).round()
# scale enumerate
anchor_scales = anchor_scales.reshape(1, -1).astype(np.float32)
ws = F.expand_dims(ws, 1)
hs = F.expand_dims(hs, 1)
ws = (ws * anchor_scales).reshape(-1, 1)
hs = (hs * anchor_scales).reshape(-1, 1)
# make anchors
anchors = F.concat(
[
x_ctr - 0.5 * (ws - 1),
y_ctr - 0.5 * (hs - 1),
x_ctr + 0.5 * (ws - 1),
y_ctr + 0.5 * (hs - 1),
],
axis=1,
)
return anchors.astype(np.float32)
def get_center_offsets(self, featmap, stride):
# f_shp = featmap.shape
# fm_height, fm_width = f_shp[-2], f_shp[-1]
fm_height, fm_width = featmap.shape[2:]
shift_x = F.linspace(0, fm_width - 1, fm_width) * stride
shift_y = F.linspace(0, fm_height - 1, fm_height) * stride
# make the mesh grid of shift_x and shift_y
mesh_shape = (fm_height, fm_width)
broad_shift_x = F.broadcast_to(shift_x.reshape(1, -1), mesh_shape)
broad_shift_y = F.broadcast_to(shift_y.reshape(-1, 1), mesh_shape)
# broad_shift_x = shift_x.reshape(-1, shift_x.shape[0]).broadcast_to(*mesh_shape)
# broad_shift_y = shift_y.reshape(shift_y.shape[0], -1).broadcast_to(*mesh_shape)
flatten_shift_x = broad_shift_x.flatten()
flatten_shift_y = broad_shift_y.flatten()
shifts = | F.stack([flatten_shift_x, flatten_shift_y, flatten_shift_x, flatten_shift_y], axis=1) | megengine.functional.stack |
import numpy as np
from megengine import Tensor
import megengine.functional as F
import pdb
class AnchorGenerator():
"""default anchor generator for fpn.
This class generate anchors by feature map in level.
"""
def __init__(self, base_size=16, ratios=[0.5, 1, 2],
base_scale=2):
self.base_size = base_size
self.base_scale = np.array(base_scale)
self.anchor_ratios = ratios
def _whctrs(self, anchor):
"""convert anchor box into (w, h, ctr_x, ctr_y)
"""
w = anchor[:, 2] - anchor[:, 0] + 1
h = anchor[:, 3] - anchor[:, 1] + 1
x_ctr = anchor[:, 0] + 0.5 * (w - 1)
y_ctr = anchor[:, 1] + 0.5 * (h - 1)
return w, h, x_ctr, y_ctr
def get_plane_anchors(self, anchor_scales: np.ndarray):
"""get anchors per location on feature map.
The anchor number is anchor_scales x anchor_ratios
"""
base_anchor = Tensor([0, 0, self.base_size - 1, self.base_size - 1])
base_anchor = base_anchor.reshape(1, -1)
w, h, x_ctr, y_ctr = self._whctrs(base_anchor)
# ratio enumerate
size = w * h
size_ratios = size / self.anchor_ratios
#pdb.set_trace()
ws = F.sqrt(size_ratios)
hs = ws * self.anchor_ratios
# ws = size_ratios.sqrt().round()
# hs = (ws * self.anchor_ratios).round()
# scale enumerate
anchor_scales = anchor_scales.reshape(1, -1).astype(np.float32)
ws = F.expand_dims(ws, 1)
hs = F.expand_dims(hs, 1)
ws = (ws * anchor_scales).reshape(-1, 1)
hs = (hs * anchor_scales).reshape(-1, 1)
# make anchors
anchors = F.concat(
[
x_ctr - 0.5 * (ws - 1),
y_ctr - 0.5 * (hs - 1),
x_ctr + 0.5 * (ws - 1),
y_ctr + 0.5 * (hs - 1),
],
axis=1,
)
return anchors.astype(np.float32)
def get_center_offsets(self, featmap, stride):
# f_shp = featmap.shape
# fm_height, fm_width = f_shp[-2], f_shp[-1]
fm_height, fm_width = featmap.shape[2:]
shift_x = | F.linspace(0, fm_width - 1, fm_width) | megengine.functional.linspace |
import numpy as np
from megengine import Tensor
import megengine.functional as F
import pdb
class AnchorGenerator():
"""default anchor generator for fpn.
This class generate anchors by feature map in level.
"""
def __init__(self, base_size=16, ratios=[0.5, 1, 2],
base_scale=2):
self.base_size = base_size
self.base_scale = np.array(base_scale)
self.anchor_ratios = ratios
def _whctrs(self, anchor):
"""convert anchor box into (w, h, ctr_x, ctr_y)
"""
w = anchor[:, 2] - anchor[:, 0] + 1
h = anchor[:, 3] - anchor[:, 1] + 1
x_ctr = anchor[:, 0] + 0.5 * (w - 1)
y_ctr = anchor[:, 1] + 0.5 * (h - 1)
return w, h, x_ctr, y_ctr
def get_plane_anchors(self, anchor_scales: np.ndarray):
"""get anchors per location on feature map.
The anchor number is anchor_scales x anchor_ratios
"""
base_anchor = Tensor([0, 0, self.base_size - 1, self.base_size - 1])
base_anchor = base_anchor.reshape(1, -1)
w, h, x_ctr, y_ctr = self._whctrs(base_anchor)
# ratio enumerate
size = w * h
size_ratios = size / self.anchor_ratios
#pdb.set_trace()
ws = F.sqrt(size_ratios)
hs = ws * self.anchor_ratios
# ws = size_ratios.sqrt().round()
# hs = (ws * self.anchor_ratios).round()
# scale enumerate
anchor_scales = anchor_scales.reshape(1, -1).astype(np.float32)
ws = F.expand_dims(ws, 1)
hs = F.expand_dims(hs, 1)
ws = (ws * anchor_scales).reshape(-1, 1)
hs = (hs * anchor_scales).reshape(-1, 1)
# make anchors
anchors = F.concat(
[
x_ctr - 0.5 * (ws - 1),
y_ctr - 0.5 * (hs - 1),
x_ctr + 0.5 * (ws - 1),
y_ctr + 0.5 * (hs - 1),
],
axis=1,
)
return anchors.astype(np.float32)
def get_center_offsets(self, featmap, stride):
# f_shp = featmap.shape
# fm_height, fm_width = f_shp[-2], f_shp[-1]
fm_height, fm_width = featmap.shape[2:]
shift_x = F.linspace(0, fm_width - 1, fm_width) * stride
shift_y = | F.linspace(0, fm_height - 1, fm_height) | megengine.functional.linspace |
import numpy as np
from megengine import Tensor
import megengine.functional as F
import pdb
class AnchorGenerator():
"""default anchor generator for fpn.
This class generate anchors by feature map in level.
"""
def __init__(self, base_size=16, ratios=[0.5, 1, 2],
base_scale=2):
self.base_size = base_size
self.base_scale = np.array(base_scale)
self.anchor_ratios = ratios
def _whctrs(self, anchor):
"""convert anchor box into (w, h, ctr_x, ctr_y)
"""
w = anchor[:, 2] - anchor[:, 0] + 1
h = anchor[:, 3] - anchor[:, 1] + 1
x_ctr = anchor[:, 0] + 0.5 * (w - 1)
y_ctr = anchor[:, 1] + 0.5 * (h - 1)
return w, h, x_ctr, y_ctr
def get_plane_anchors(self, anchor_scales: np.ndarray):
"""get anchors per location on feature map.
The anchor number is anchor_scales x anchor_ratios
"""
base_anchor = Tensor([0, 0, self.base_size - 1, self.base_size - 1])
base_anchor = base_anchor.reshape(1, -1)
w, h, x_ctr, y_ctr = self._whctrs(base_anchor)
# ratio enumerate
size = w * h
size_ratios = size / self.anchor_ratios
#pdb.set_trace()
ws = F.sqrt(size_ratios)
hs = ws * self.anchor_ratios
# ws = size_ratios.sqrt().round()
# hs = (ws * self.anchor_ratios).round()
# scale enumerate
anchor_scales = anchor_scales.reshape(1, -1).astype(np.float32)
ws = F.expand_dims(ws, 1)
hs = F.expand_dims(hs, 1)
ws = (ws * anchor_scales).reshape(-1, 1)
hs = (hs * anchor_scales).reshape(-1, 1)
# make anchors
anchors = F.concat(
[
x_ctr - 0.5 * (ws - 1),
y_ctr - 0.5 * (hs - 1),
x_ctr + 0.5 * (ws - 1),
y_ctr + 0.5 * (hs - 1),
],
axis=1,
)
return anchors.astype(np.float32)
def get_center_offsets(self, featmap, stride):
# f_shp = featmap.shape
# fm_height, fm_width = f_shp[-2], f_shp[-1]
fm_height, fm_width = featmap.shape[2:]
shift_x = F.linspace(0, fm_width - 1, fm_width) * stride
shift_y = F.linspace(0, fm_height - 1, fm_height) * stride
# make the mesh grid of shift_x and shift_y
mesh_shape = (fm_height, fm_width)
broad_shift_x = F.broadcast_to(shift_x.reshape(1, -1), mesh_shape)
broad_shift_y = F.broadcast_to(shift_y.reshape(-1, 1), mesh_shape)
# broad_shift_x = shift_x.reshape(-1, shift_x.shape[0]).broadcast_to(*mesh_shape)
# broad_shift_y = shift_y.reshape(shift_y.shape[0], -1).broadcast_to(*mesh_shape)
flatten_shift_x = broad_shift_x.flatten()
flatten_shift_y = broad_shift_y.flatten()
shifts = F.stack([flatten_shift_x, flatten_shift_y, flatten_shift_x, flatten_shift_y], axis=1)
# flatten_shift_x = F.add_axis(broad_shift_x.reshape(-1), 1)
# flatten_shift_y = F.add_axis(broad_shift_y.reshape(-1), 1)
# shifts = F.concat(
# [flatten_shift_x, flatten_shift_y, flatten_shift_x, flatten_shift_y,],
# axis=1)
return shifts
def get_anchors_by_feature(self, featmap, stride):
# shifts shape: [A, 4]
shifts = self.get_center_offsets(featmap, stride)
# plane_anchors shape: [B, 4], e.g. B=3
plane_anchors = self.get_plane_anchors(self.base_scale * stride)
# all_anchors = shifts.repeat(1,3) + cell_anchors.flatten()
all_anchors = | F.expand_dims(plane_anchors, 0) | megengine.functional.expand_dims |
import numpy as np
from megengine import Tensor
import megengine.functional as F
import pdb
class AnchorGenerator():
"""default anchor generator for fpn.
This class generate anchors by feature map in level.
"""
def __init__(self, base_size=16, ratios=[0.5, 1, 2],
base_scale=2):
self.base_size = base_size
self.base_scale = np.array(base_scale)
self.anchor_ratios = ratios
def _whctrs(self, anchor):
"""convert anchor box into (w, h, ctr_x, ctr_y)
"""
w = anchor[:, 2] - anchor[:, 0] + 1
h = anchor[:, 3] - anchor[:, 1] + 1
x_ctr = anchor[:, 0] + 0.5 * (w - 1)
y_ctr = anchor[:, 1] + 0.5 * (h - 1)
return w, h, x_ctr, y_ctr
def get_plane_anchors(self, anchor_scales: np.ndarray):
"""get anchors per location on feature map.
The anchor number is anchor_scales x anchor_ratios
"""
base_anchor = Tensor([0, 0, self.base_size - 1, self.base_size - 1])
base_anchor = base_anchor.reshape(1, -1)
w, h, x_ctr, y_ctr = self._whctrs(base_anchor)
# ratio enumerate
size = w * h
size_ratios = size / self.anchor_ratios
#pdb.set_trace()
ws = F.sqrt(size_ratios)
hs = ws * self.anchor_ratios
# ws = size_ratios.sqrt().round()
# hs = (ws * self.anchor_ratios).round()
# scale enumerate
anchor_scales = anchor_scales.reshape(1, -1).astype(np.float32)
ws = F.expand_dims(ws, 1)
hs = F.expand_dims(hs, 1)
ws = (ws * anchor_scales).reshape(-1, 1)
hs = (hs * anchor_scales).reshape(-1, 1)
# make anchors
anchors = F.concat(
[
x_ctr - 0.5 * (ws - 1),
y_ctr - 0.5 * (hs - 1),
x_ctr + 0.5 * (ws - 1),
y_ctr + 0.5 * (hs - 1),
],
axis=1,
)
return anchors.astype(np.float32)
def get_center_offsets(self, featmap, stride):
# f_shp = featmap.shape
# fm_height, fm_width = f_shp[-2], f_shp[-1]
fm_height, fm_width = featmap.shape[2:]
shift_x = F.linspace(0, fm_width - 1, fm_width) * stride
shift_y = F.linspace(0, fm_height - 1, fm_height) * stride
# make the mesh grid of shift_x and shift_y
mesh_shape = (fm_height, fm_width)
broad_shift_x = F.broadcast_to(shift_x.reshape(1, -1), mesh_shape)
broad_shift_y = F.broadcast_to(shift_y.reshape(-1, 1), mesh_shape)
# broad_shift_x = shift_x.reshape(-1, shift_x.shape[0]).broadcast_to(*mesh_shape)
# broad_shift_y = shift_y.reshape(shift_y.shape[0], -1).broadcast_to(*mesh_shape)
flatten_shift_x = broad_shift_x.flatten()
flatten_shift_y = broad_shift_y.flatten()
shifts = F.stack([flatten_shift_x, flatten_shift_y, flatten_shift_x, flatten_shift_y], axis=1)
# flatten_shift_x = F.add_axis(broad_shift_x.reshape(-1), 1)
# flatten_shift_y = F.add_axis(broad_shift_y.reshape(-1), 1)
# shifts = F.concat(
# [flatten_shift_x, flatten_shift_y, flatten_shift_x, flatten_shift_y,],
# axis=1)
return shifts
def get_anchors_by_feature(self, featmap, stride):
# shifts shape: [A, 4]
shifts = self.get_center_offsets(featmap, stride)
# plane_anchors shape: [B, 4], e.g. B=3
plane_anchors = self.get_plane_anchors(self.base_scale * stride)
# all_anchors = shifts.repeat(1,3) + cell_anchors.flatten()
all_anchors = F.expand_dims(plane_anchors, 0) + | F.expand_dims(shifts, 1) | megengine.functional.expand_dims |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import argparse
import json
import os
from tqdm import tqdm
import megengine as mge
import megengine.distributed as dist
from megengine.data import DataLoader
from official.vision.detection.tools.utils import (
DetEvaluator,
InferenceSampler,
PseudoDetectionDataset,
import_from_file
)
logger = | mge.get_logger(__name__) | megengine.get_logger |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import argparse
import json
import os
from tqdm import tqdm
import megengine as mge
import megengine.distributed as dist
from megengine.data import DataLoader
from official.vision.detection.tools.utils import (
DetEvaluator,
InferenceSampler,
PseudoDetectionDataset,
import_from_file
)
logger = mge.get_logger(__name__)
logger.setLevel("INFO")
def make_parser():
parser = argparse.ArgumentParser()
parser.add_argument(
"-f", "--file", default="net.py", type=str, help="net description file"
)
parser.add_argument(
"-w", "--weight_file", default=None, type=str, help="weights file",
)
parser.add_argument(
"-n", "--devices", default=1, type=int, help="total number of gpus for testing",
)
parser.add_argument(
"-d", "--dataset_dir", default="/data/datasets", type=str,
)
parser.add_argument("-se", "--start_epoch", default=-1, type=int)
parser.add_argument("-ee", "--end_epoch", default=-1, type=int)
return parser
def main():
# pylint: disable=import-outside-toplevel,too-many-branches,too-many-statements
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
parser = make_parser()
args = parser.parse_args()
current_network = import_from_file(args.file)
cfg = current_network.Cfg()
if args.weight_file:
args.start_epoch = args.end_epoch = -1
else:
if args.start_epoch == -1:
args.start_epoch = cfg.max_epoch - 1
if args.end_epoch == -1:
args.end_epoch = args.start_epoch
assert 0 <= args.start_epoch <= args.end_epoch < cfg.max_epoch
for epoch_num in range(args.start_epoch, args.end_epoch + 1):
if args.weight_file:
weight_file = args.weight_file
else:
weight_file = "log-of-{}/epoch_{}.pkl".format(
os.path.basename(args.file).split(".")[0], epoch_num
)
if args.devices > 1:
dist_worker = dist.launcher(n_gpus=args.devices)(worker)
result_list = dist_worker(current_network, weight_file, args.dataset_dir)
result_list = sum(result_list, [])
else:
result_list = worker(current_network, weight_file, args.dataset_dir)
all_results = DetEvaluator.format(result_list, cfg)
if args.weight_file:
json_path = "{}_{}.json".format(
os.path.basename(args.file).split(".")[0],
os.path.basename(args.weight_file).split(".")[0],
)
else:
json_path = "log-of-{}/epoch_{}.json".format(
os.path.basename(args.file).split(".")[0], epoch_num
)
all_results = json.dumps(all_results)
with open(json_path, "w") as fo:
fo.write(all_results)
logger.info("Save results to %s, start evaluation!", json_path)
eval_gt = COCO(
os.path.join(
args.dataset_dir, cfg.test_dataset["name"], cfg.test_dataset["ann_file"]
)
)
eval_dt = eval_gt.loadRes(json_path)
cocoEval = COCOeval(eval_gt, eval_dt, iouType="bbox")
cocoEval.evaluate()
cocoEval.accumulate()
cocoEval.summarize()
metrics = [
"AP",
"[email protected]",
"[email protected]",
"APs",
"APm",
"APl",
"AR@1",
"AR@10",
"AR@100",
"ARs",
"ARm",
"ARl",
]
logger.info("mmAP".center(32, "-"))
for i, m in enumerate(metrics):
logger.info("|\t%s\t|\t%.03f\t|", m, cocoEval.stats[i])
logger.info("-" * 32)
def worker(current_network, weight_file, dataset_dir):
cfg = current_network.Cfg()
cfg.backbone_pretrained = False
model = current_network.Net(cfg)
model.eval()
state_dict = | mge.load(weight_file) | megengine.load |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import argparse
import json
import os
from tqdm import tqdm
import megengine as mge
import megengine.distributed as dist
from megengine.data import DataLoader
from official.vision.detection.tools.utils import (
DetEvaluator,
InferenceSampler,
PseudoDetectionDataset,
import_from_file
)
logger = mge.get_logger(__name__)
logger.setLevel("INFO")
def make_parser():
parser = argparse.ArgumentParser()
parser.add_argument(
"-f", "--file", default="net.py", type=str, help="net description file"
)
parser.add_argument(
"-w", "--weight_file", default=None, type=str, help="weights file",
)
parser.add_argument(
"-n", "--devices", default=1, type=int, help="total number of gpus for testing",
)
parser.add_argument(
"-d", "--dataset_dir", default="/data/datasets", type=str,
)
parser.add_argument("-se", "--start_epoch", default=-1, type=int)
parser.add_argument("-ee", "--end_epoch", default=-1, type=int)
return parser
def main():
# pylint: disable=import-outside-toplevel,too-many-branches,too-many-statements
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
parser = make_parser()
args = parser.parse_args()
current_network = import_from_file(args.file)
cfg = current_network.Cfg()
if args.weight_file:
args.start_epoch = args.end_epoch = -1
else:
if args.start_epoch == -1:
args.start_epoch = cfg.max_epoch - 1
if args.end_epoch == -1:
args.end_epoch = args.start_epoch
assert 0 <= args.start_epoch <= args.end_epoch < cfg.max_epoch
for epoch_num in range(args.start_epoch, args.end_epoch + 1):
if args.weight_file:
weight_file = args.weight_file
else:
weight_file = "log-of-{}/epoch_{}.pkl".format(
os.path.basename(args.file).split(".")[0], epoch_num
)
if args.devices > 1:
dist_worker = dist.launcher(n_gpus=args.devices)(worker)
result_list = dist_worker(current_network, weight_file, args.dataset_dir)
result_list = sum(result_list, [])
else:
result_list = worker(current_network, weight_file, args.dataset_dir)
all_results = DetEvaluator.format(result_list, cfg)
if args.weight_file:
json_path = "{}_{}.json".format(
os.path.basename(args.file).split(".")[0],
os.path.basename(args.weight_file).split(".")[0],
)
else:
json_path = "log-of-{}/epoch_{}.json".format(
os.path.basename(args.file).split(".")[0], epoch_num
)
all_results = json.dumps(all_results)
with open(json_path, "w") as fo:
fo.write(all_results)
logger.info("Save results to %s, start evaluation!", json_path)
eval_gt = COCO(
os.path.join(
args.dataset_dir, cfg.test_dataset["name"], cfg.test_dataset["ann_file"]
)
)
eval_dt = eval_gt.loadRes(json_path)
cocoEval = COCOeval(eval_gt, eval_dt, iouType="bbox")
cocoEval.evaluate()
cocoEval.accumulate()
cocoEval.summarize()
metrics = [
"AP",
"[email protected]",
"[email protected]",
"APs",
"APm",
"APl",
"AR@1",
"AR@10",
"AR@100",
"ARs",
"ARm",
"ARl",
]
logger.info("mmAP".center(32, "-"))
for i, m in enumerate(metrics):
logger.info("|\t%s\t|\t%.03f\t|", m, cocoEval.stats[i])
logger.info("-" * 32)
def worker(current_network, weight_file, dataset_dir):
cfg = current_network.Cfg()
cfg.backbone_pretrained = False
model = current_network.Net(cfg)
model.eval()
state_dict = mge.load(weight_file)
if "state_dict" in state_dict:
state_dict = state_dict["state_dict"]
model.load_state_dict(state_dict)
evaluator = DetEvaluator(model)
test_loader = build_dataloader(dataset_dir, model.cfg)
if dist.get_rank() == 0:
test_loader = tqdm(test_loader)
result_list = []
for data in test_loader:
image, im_info = DetEvaluator.process_inputs(
data[0][0],
model.cfg.test_image_short_size,
model.cfg.test_image_max_size,
)
pred_res = evaluator.predict(
image=mge.tensor(image),
im_info=mge.tensor(im_info)
)
result = {
"pred_boxes": pred_res,
"image_id": int(data[1][2][0].split(".")[0].split("_")[-1]),
}
result_list.append(result)
return result_list
# pylint: disable=unused-argument
def build_dataloader(dataset_dir, cfg):
val_dataset = PseudoDetectionDataset(length=5000, order=["image", "info"])
val_sampler = InferenceSampler(val_dataset, 1)
val_dataloader = | DataLoader(val_dataset, sampler=val_sampler, num_workers=2) | megengine.data.DataLoader |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import argparse
import json
import os
from tqdm import tqdm
import megengine as mge
import megengine.distributed as dist
from megengine.data import DataLoader
from official.vision.detection.tools.utils import (
DetEvaluator,
InferenceSampler,
PseudoDetectionDataset,
import_from_file
)
logger = mge.get_logger(__name__)
logger.setLevel("INFO")
def make_parser():
parser = argparse.ArgumentParser()
parser.add_argument(
"-f", "--file", default="net.py", type=str, help="net description file"
)
parser.add_argument(
"-w", "--weight_file", default=None, type=str, help="weights file",
)
parser.add_argument(
"-n", "--devices", default=1, type=int, help="total number of gpus for testing",
)
parser.add_argument(
"-d", "--dataset_dir", default="/data/datasets", type=str,
)
parser.add_argument("-se", "--start_epoch", default=-1, type=int)
parser.add_argument("-ee", "--end_epoch", default=-1, type=int)
return parser
def main():
# pylint: disable=import-outside-toplevel,too-many-branches,too-many-statements
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
parser = make_parser()
args = parser.parse_args()
current_network = import_from_file(args.file)
cfg = current_network.Cfg()
if args.weight_file:
args.start_epoch = args.end_epoch = -1
else:
if args.start_epoch == -1:
args.start_epoch = cfg.max_epoch - 1
if args.end_epoch == -1:
args.end_epoch = args.start_epoch
assert 0 <= args.start_epoch <= args.end_epoch < cfg.max_epoch
for epoch_num in range(args.start_epoch, args.end_epoch + 1):
if args.weight_file:
weight_file = args.weight_file
else:
weight_file = "log-of-{}/epoch_{}.pkl".format(
os.path.basename(args.file).split(".")[0], epoch_num
)
if args.devices > 1:
dist_worker = dist.launcher(n_gpus=args.devices)(worker)
result_list = dist_worker(current_network, weight_file, args.dataset_dir)
result_list = sum(result_list, [])
else:
result_list = worker(current_network, weight_file, args.dataset_dir)
all_results = DetEvaluator.format(result_list, cfg)
if args.weight_file:
json_path = "{}_{}.json".format(
os.path.basename(args.file).split(".")[0],
os.path.basename(args.weight_file).split(".")[0],
)
else:
json_path = "log-of-{}/epoch_{}.json".format(
os.path.basename(args.file).split(".")[0], epoch_num
)
all_results = json.dumps(all_results)
with open(json_path, "w") as fo:
fo.write(all_results)
logger.info("Save results to %s, start evaluation!", json_path)
eval_gt = COCO(
os.path.join(
args.dataset_dir, cfg.test_dataset["name"], cfg.test_dataset["ann_file"]
)
)
eval_dt = eval_gt.loadRes(json_path)
cocoEval = COCOeval(eval_gt, eval_dt, iouType="bbox")
cocoEval.evaluate()
cocoEval.accumulate()
cocoEval.summarize()
metrics = [
"AP",
"[email protected]",
"[email protected]",
"APs",
"APm",
"APl",
"AR@1",
"AR@10",
"AR@100",
"ARs",
"ARm",
"ARl",
]
logger.info("mmAP".center(32, "-"))
for i, m in enumerate(metrics):
logger.info("|\t%s\t|\t%.03f\t|", m, cocoEval.stats[i])
logger.info("-" * 32)
def worker(current_network, weight_file, dataset_dir):
cfg = current_network.Cfg()
cfg.backbone_pretrained = False
model = current_network.Net(cfg)
model.eval()
state_dict = mge.load(weight_file)
if "state_dict" in state_dict:
state_dict = state_dict["state_dict"]
model.load_state_dict(state_dict)
evaluator = DetEvaluator(model)
test_loader = build_dataloader(dataset_dir, model.cfg)
if | dist.get_rank() | megengine.distributed.get_rank |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import argparse
import json
import os
from tqdm import tqdm
import megengine as mge
import megengine.distributed as dist
from megengine.data import DataLoader
from official.vision.detection.tools.utils import (
DetEvaluator,
InferenceSampler,
PseudoDetectionDataset,
import_from_file
)
logger = mge.get_logger(__name__)
logger.setLevel("INFO")
def make_parser():
parser = argparse.ArgumentParser()
parser.add_argument(
"-f", "--file", default="net.py", type=str, help="net description file"
)
parser.add_argument(
"-w", "--weight_file", default=None, type=str, help="weights file",
)
parser.add_argument(
"-n", "--devices", default=1, type=int, help="total number of gpus for testing",
)
parser.add_argument(
"-d", "--dataset_dir", default="/data/datasets", type=str,
)
parser.add_argument("-se", "--start_epoch", default=-1, type=int)
parser.add_argument("-ee", "--end_epoch", default=-1, type=int)
return parser
def main():
# pylint: disable=import-outside-toplevel,too-many-branches,too-many-statements
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
parser = make_parser()
args = parser.parse_args()
current_network = import_from_file(args.file)
cfg = current_network.Cfg()
if args.weight_file:
args.start_epoch = args.end_epoch = -1
else:
if args.start_epoch == -1:
args.start_epoch = cfg.max_epoch - 1
if args.end_epoch == -1:
args.end_epoch = args.start_epoch
assert 0 <= args.start_epoch <= args.end_epoch < cfg.max_epoch
for epoch_num in range(args.start_epoch, args.end_epoch + 1):
if args.weight_file:
weight_file = args.weight_file
else:
weight_file = "log-of-{}/epoch_{}.pkl".format(
os.path.basename(args.file).split(".")[0], epoch_num
)
if args.devices > 1:
dist_worker = | dist.launcher(n_gpus=args.devices) | megengine.distributed.launcher |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import argparse
import json
import os
from tqdm import tqdm
import megengine as mge
import megengine.distributed as dist
from megengine.data import DataLoader
from official.vision.detection.tools.utils import (
DetEvaluator,
InferenceSampler,
PseudoDetectionDataset,
import_from_file
)
logger = mge.get_logger(__name__)
logger.setLevel("INFO")
def make_parser():
parser = argparse.ArgumentParser()
parser.add_argument(
"-f", "--file", default="net.py", type=str, help="net description file"
)
parser.add_argument(
"-w", "--weight_file", default=None, type=str, help="weights file",
)
parser.add_argument(
"-n", "--devices", default=1, type=int, help="total number of gpus for testing",
)
parser.add_argument(
"-d", "--dataset_dir", default="/data/datasets", type=str,
)
parser.add_argument("-se", "--start_epoch", default=-1, type=int)
parser.add_argument("-ee", "--end_epoch", default=-1, type=int)
return parser
def main():
# pylint: disable=import-outside-toplevel,too-many-branches,too-many-statements
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
parser = make_parser()
args = parser.parse_args()
current_network = import_from_file(args.file)
cfg = current_network.Cfg()
if args.weight_file:
args.start_epoch = args.end_epoch = -1
else:
if args.start_epoch == -1:
args.start_epoch = cfg.max_epoch - 1
if args.end_epoch == -1:
args.end_epoch = args.start_epoch
assert 0 <= args.start_epoch <= args.end_epoch < cfg.max_epoch
for epoch_num in range(args.start_epoch, args.end_epoch + 1):
if args.weight_file:
weight_file = args.weight_file
else:
weight_file = "log-of-{}/epoch_{}.pkl".format(
os.path.basename(args.file).split(".")[0], epoch_num
)
if args.devices > 1:
dist_worker = dist.launcher(n_gpus=args.devices)(worker)
result_list = dist_worker(current_network, weight_file, args.dataset_dir)
result_list = sum(result_list, [])
else:
result_list = worker(current_network, weight_file, args.dataset_dir)
all_results = DetEvaluator.format(result_list, cfg)
if args.weight_file:
json_path = "{}_{}.json".format(
os.path.basename(args.file).split(".")[0],
os.path.basename(args.weight_file).split(".")[0],
)
else:
json_path = "log-of-{}/epoch_{}.json".format(
os.path.basename(args.file).split(".")[0], epoch_num
)
all_results = json.dumps(all_results)
with open(json_path, "w") as fo:
fo.write(all_results)
logger.info("Save results to %s, start evaluation!", json_path)
eval_gt = COCO(
os.path.join(
args.dataset_dir, cfg.test_dataset["name"], cfg.test_dataset["ann_file"]
)
)
eval_dt = eval_gt.loadRes(json_path)
cocoEval = COCOeval(eval_gt, eval_dt, iouType="bbox")
cocoEval.evaluate()
cocoEval.accumulate()
cocoEval.summarize()
metrics = [
"AP",
"[email protected]",
"[email protected]",
"APs",
"APm",
"APl",
"AR@1",
"AR@10",
"AR@100",
"ARs",
"ARm",
"ARl",
]
logger.info("mmAP".center(32, "-"))
for i, m in enumerate(metrics):
logger.info("|\t%s\t|\t%.03f\t|", m, cocoEval.stats[i])
logger.info("-" * 32)
def worker(current_network, weight_file, dataset_dir):
cfg = current_network.Cfg()
cfg.backbone_pretrained = False
model = current_network.Net(cfg)
model.eval()
state_dict = mge.load(weight_file)
if "state_dict" in state_dict:
state_dict = state_dict["state_dict"]
model.load_state_dict(state_dict)
evaluator = DetEvaluator(model)
test_loader = build_dataloader(dataset_dir, model.cfg)
if dist.get_rank() == 0:
test_loader = tqdm(test_loader)
result_list = []
for data in test_loader:
image, im_info = DetEvaluator.process_inputs(
data[0][0],
model.cfg.test_image_short_size,
model.cfg.test_image_max_size,
)
pred_res = evaluator.predict(
image= | mge.tensor(image) | megengine.tensor |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import argparse
import json
import os
from tqdm import tqdm
import megengine as mge
import megengine.distributed as dist
from megengine.data import DataLoader
from official.vision.detection.tools.utils import (
DetEvaluator,
InferenceSampler,
PseudoDetectionDataset,
import_from_file
)
logger = mge.get_logger(__name__)
logger.setLevel("INFO")
def make_parser():
parser = argparse.ArgumentParser()
parser.add_argument(
"-f", "--file", default="net.py", type=str, help="net description file"
)
parser.add_argument(
"-w", "--weight_file", default=None, type=str, help="weights file",
)
parser.add_argument(
"-n", "--devices", default=1, type=int, help="total number of gpus for testing",
)
parser.add_argument(
"-d", "--dataset_dir", default="/data/datasets", type=str,
)
parser.add_argument("-se", "--start_epoch", default=-1, type=int)
parser.add_argument("-ee", "--end_epoch", default=-1, type=int)
return parser
def main():
# pylint: disable=import-outside-toplevel,too-many-branches,too-many-statements
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
parser = make_parser()
args = parser.parse_args()
current_network = import_from_file(args.file)
cfg = current_network.Cfg()
if args.weight_file:
args.start_epoch = args.end_epoch = -1
else:
if args.start_epoch == -1:
args.start_epoch = cfg.max_epoch - 1
if args.end_epoch == -1:
args.end_epoch = args.start_epoch
assert 0 <= args.start_epoch <= args.end_epoch < cfg.max_epoch
for epoch_num in range(args.start_epoch, args.end_epoch + 1):
if args.weight_file:
weight_file = args.weight_file
else:
weight_file = "log-of-{}/epoch_{}.pkl".format(
os.path.basename(args.file).split(".")[0], epoch_num
)
if args.devices > 1:
dist_worker = dist.launcher(n_gpus=args.devices)(worker)
result_list = dist_worker(current_network, weight_file, args.dataset_dir)
result_list = sum(result_list, [])
else:
result_list = worker(current_network, weight_file, args.dataset_dir)
all_results = DetEvaluator.format(result_list, cfg)
if args.weight_file:
json_path = "{}_{}.json".format(
os.path.basename(args.file).split(".")[0],
os.path.basename(args.weight_file).split(".")[0],
)
else:
json_path = "log-of-{}/epoch_{}.json".format(
os.path.basename(args.file).split(".")[0], epoch_num
)
all_results = json.dumps(all_results)
with open(json_path, "w") as fo:
fo.write(all_results)
logger.info("Save results to %s, start evaluation!", json_path)
eval_gt = COCO(
os.path.join(
args.dataset_dir, cfg.test_dataset["name"], cfg.test_dataset["ann_file"]
)
)
eval_dt = eval_gt.loadRes(json_path)
cocoEval = COCOeval(eval_gt, eval_dt, iouType="bbox")
cocoEval.evaluate()
cocoEval.accumulate()
cocoEval.summarize()
metrics = [
"AP",
"[email protected]",
"[email protected]",
"APs",
"APm",
"APl",
"AR@1",
"AR@10",
"AR@100",
"ARs",
"ARm",
"ARl",
]
logger.info("mmAP".center(32, "-"))
for i, m in enumerate(metrics):
logger.info("|\t%s\t|\t%.03f\t|", m, cocoEval.stats[i])
logger.info("-" * 32)
def worker(current_network, weight_file, dataset_dir):
cfg = current_network.Cfg()
cfg.backbone_pretrained = False
model = current_network.Net(cfg)
model.eval()
state_dict = mge.load(weight_file)
if "state_dict" in state_dict:
state_dict = state_dict["state_dict"]
model.load_state_dict(state_dict)
evaluator = DetEvaluator(model)
test_loader = build_dataloader(dataset_dir, model.cfg)
if dist.get_rank() == 0:
test_loader = tqdm(test_loader)
result_list = []
for data in test_loader:
image, im_info = DetEvaluator.process_inputs(
data[0][0],
model.cfg.test_image_short_size,
model.cfg.test_image_max_size,
)
pred_res = evaluator.predict(
image=mge.tensor(image),
im_info= | mge.tensor(im_info) | megengine.tensor |
#!/usr/bin/env python3
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
import megengine as mge
import megengine.module as M
import numpy as np
import pytest
from basecls.layers import SE, DropPath, conv2d, gap2d, linear, norm2d, pool2d
@pytest.mark.parametrize("w_in", [4])
@pytest.mark.parametrize("w_out", [8])
@pytest.mark.parametrize("k", [3, 5])
@pytest.mark.parametrize("stride", [1, 2])
@pytest.mark.parametrize("dilation", [1, 2])
@pytest.mark.parametrize("groups", [1, 2])
@pytest.mark.parametrize("bias", [True, False])
def test_conv2d(w_in, w_out, k, stride, dilation, groups, bias):
m = conv2d(w_in, w_out, k, stride=stride, dilation=dilation, groups=groups, bias=bias)
assert isinstance(m, M.Conv2d)
m( | mge.random.normal(size=(2, 4, 8, 8)) | megengine.random.normal |
#!/usr/bin/env python3
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
import megengine as mge
import megengine.module as M
import numpy as np
import pytest
from basecls.layers import SE, DropPath, conv2d, gap2d, linear, norm2d, pool2d
@pytest.mark.parametrize("w_in", [4])
@pytest.mark.parametrize("w_out", [8])
@pytest.mark.parametrize("k", [3, 5])
@pytest.mark.parametrize("stride", [1, 2])
@pytest.mark.parametrize("dilation", [1, 2])
@pytest.mark.parametrize("groups", [1, 2])
@pytest.mark.parametrize("bias", [True, False])
def test_conv2d(w_in, w_out, k, stride, dilation, groups, bias):
m = conv2d(w_in, w_out, k, stride=stride, dilation=dilation, groups=groups, bias=bias)
assert isinstance(m, M.Conv2d)
m(mge.random.normal(size=(2, 4, 8, 8)))
@pytest.mark.parametrize("drop_prob", [0.0, 0.5])
def test_drop_path(drop_prob):
m = DropPath(drop_prob)
assert isinstance(m, M.Module)
m.training = True
m( | mge.random.normal(size=(2, 4, 8, 8)) | megengine.random.normal |
#!/usr/bin/env python3
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
import megengine as mge
import megengine.module as M
import numpy as np
import pytest
from basecls.layers import SE, DropPath, conv2d, gap2d, linear, norm2d, pool2d
@pytest.mark.parametrize("w_in", [4])
@pytest.mark.parametrize("w_out", [8])
@pytest.mark.parametrize("k", [3, 5])
@pytest.mark.parametrize("stride", [1, 2])
@pytest.mark.parametrize("dilation", [1, 2])
@pytest.mark.parametrize("groups", [1, 2])
@pytest.mark.parametrize("bias", [True, False])
def test_conv2d(w_in, w_out, k, stride, dilation, groups, bias):
m = conv2d(w_in, w_out, k, stride=stride, dilation=dilation, groups=groups, bias=bias)
assert isinstance(m, M.Conv2d)
m(mge.random.normal(size=(2, 4, 8, 8)))
@pytest.mark.parametrize("drop_prob", [0.0, 0.5])
def test_drop_path(drop_prob):
m = DropPath(drop_prob)
assert isinstance(m, M.Module)
m.training = True
m(mge.random.normal(size=(2, 4, 8, 8)))
m.training = False
x = np.random.rand(2, 4, 8, 8).astype("float32")
y = m(mge.Tensor(x)).numpy()
np.testing.assert_allclose(y, x, rtol=1e-4, atol=1e-6)
@pytest.mark.parametrize("shape", [1, (7, 7)])
def test_gap2d(shape):
m = gap2d(shape)
assert isinstance(m, M.AdaptiveAvgPool2d)
m( | mge.random.normal(size=(2, 4, 8, 8)) | megengine.random.normal |
#!/usr/bin/env python3
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
import megengine as mge
import megengine.module as M
import numpy as np
import pytest
from basecls.layers import SE, DropPath, conv2d, gap2d, linear, norm2d, pool2d
@pytest.mark.parametrize("w_in", [4])
@pytest.mark.parametrize("w_out", [8])
@pytest.mark.parametrize("k", [3, 5])
@pytest.mark.parametrize("stride", [1, 2])
@pytest.mark.parametrize("dilation", [1, 2])
@pytest.mark.parametrize("groups", [1, 2])
@pytest.mark.parametrize("bias", [True, False])
def test_conv2d(w_in, w_out, k, stride, dilation, groups, bias):
m = conv2d(w_in, w_out, k, stride=stride, dilation=dilation, groups=groups, bias=bias)
assert isinstance(m, M.Conv2d)
m(mge.random.normal(size=(2, 4, 8, 8)))
@pytest.mark.parametrize("drop_prob", [0.0, 0.5])
def test_drop_path(drop_prob):
m = DropPath(drop_prob)
assert isinstance(m, M.Module)
m.training = True
m(mge.random.normal(size=(2, 4, 8, 8)))
m.training = False
x = np.random.rand(2, 4, 8, 8).astype("float32")
y = m(mge.Tensor(x)).numpy()
np.testing.assert_allclose(y, x, rtol=1e-4, atol=1e-6)
@pytest.mark.parametrize("shape", [1, (7, 7)])
def test_gap2d(shape):
m = gap2d(shape)
assert isinstance(m, M.AdaptiveAvgPool2d)
m(mge.random.normal(size=(2, 4, 8, 8)))
@pytest.mark.parametrize("w_in", [4])
@pytest.mark.parametrize("w_out", [8])
@pytest.mark.parametrize("bias", [True, False])
def test_linear(w_in, w_out, bias):
m = linear(w_in, w_out, bias=bias)
assert isinstance(m, M.Linear)
m( | mge.random.normal(size=(2, 8, 4)) | megengine.random.normal |
#!/usr/bin/env python3
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
import megengine as mge
import megengine.module as M
import numpy as np
import pytest
from basecls.layers import SE, DropPath, conv2d, gap2d, linear, norm2d, pool2d
@pytest.mark.parametrize("w_in", [4])
@pytest.mark.parametrize("w_out", [8])
@pytest.mark.parametrize("k", [3, 5])
@pytest.mark.parametrize("stride", [1, 2])
@pytest.mark.parametrize("dilation", [1, 2])
@pytest.mark.parametrize("groups", [1, 2])
@pytest.mark.parametrize("bias", [True, False])
def test_conv2d(w_in, w_out, k, stride, dilation, groups, bias):
m = conv2d(w_in, w_out, k, stride=stride, dilation=dilation, groups=groups, bias=bias)
assert isinstance(m, M.Conv2d)
m(mge.random.normal(size=(2, 4, 8, 8)))
@pytest.mark.parametrize("drop_prob", [0.0, 0.5])
def test_drop_path(drop_prob):
m = DropPath(drop_prob)
assert isinstance(m, M.Module)
m.training = True
m(mge.random.normal(size=(2, 4, 8, 8)))
m.training = False
x = np.random.rand(2, 4, 8, 8).astype("float32")
y = m(mge.Tensor(x)).numpy()
np.testing.assert_allclose(y, x, rtol=1e-4, atol=1e-6)
@pytest.mark.parametrize("shape", [1, (7, 7)])
def test_gap2d(shape):
m = gap2d(shape)
assert isinstance(m, M.AdaptiveAvgPool2d)
m(mge.random.normal(size=(2, 4, 8, 8)))
@pytest.mark.parametrize("w_in", [4])
@pytest.mark.parametrize("w_out", [8])
@pytest.mark.parametrize("bias", [True, False])
def test_linear(w_in, w_out, bias):
m = linear(w_in, w_out, bias=bias)
assert isinstance(m, M.Linear)
m(mge.random.normal(size=(2, 8, 4)))
# TODO: "GN", "IN" and "LN" need different hyper-parameters
@pytest.mark.parametrize("name", [None, "BN", "SyncBN"])
@pytest.mark.parametrize("w_in", [4])
def test_norm2d(name, w_in):
m = norm2d(name, w_in)
assert isinstance(m, M.Module)
m( | mge.random.normal(size=(2, 4, 8, 8)) | megengine.random.normal |
#!/usr/bin/env python3
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
import megengine as mge
import megengine.module as M
import numpy as np
import pytest
from basecls.layers import SE, DropPath, conv2d, gap2d, linear, norm2d, pool2d
@pytest.mark.parametrize("w_in", [4])
@pytest.mark.parametrize("w_out", [8])
@pytest.mark.parametrize("k", [3, 5])
@pytest.mark.parametrize("stride", [1, 2])
@pytest.mark.parametrize("dilation", [1, 2])
@pytest.mark.parametrize("groups", [1, 2])
@pytest.mark.parametrize("bias", [True, False])
def test_conv2d(w_in, w_out, k, stride, dilation, groups, bias):
m = conv2d(w_in, w_out, k, stride=stride, dilation=dilation, groups=groups, bias=bias)
assert isinstance(m, M.Conv2d)
m(mge.random.normal(size=(2, 4, 8, 8)))
@pytest.mark.parametrize("drop_prob", [0.0, 0.5])
def test_drop_path(drop_prob):
m = DropPath(drop_prob)
assert isinstance(m, M.Module)
m.training = True
m(mge.random.normal(size=(2, 4, 8, 8)))
m.training = False
x = np.random.rand(2, 4, 8, 8).astype("float32")
y = m(mge.Tensor(x)).numpy()
np.testing.assert_allclose(y, x, rtol=1e-4, atol=1e-6)
@pytest.mark.parametrize("shape", [1, (7, 7)])
def test_gap2d(shape):
m = gap2d(shape)
assert isinstance(m, M.AdaptiveAvgPool2d)
m(mge.random.normal(size=(2, 4, 8, 8)))
@pytest.mark.parametrize("w_in", [4])
@pytest.mark.parametrize("w_out", [8])
@pytest.mark.parametrize("bias", [True, False])
def test_linear(w_in, w_out, bias):
m = linear(w_in, w_out, bias=bias)
assert isinstance(m, M.Linear)
m(mge.random.normal(size=(2, 8, 4)))
# TODO: "GN", "IN" and "LN" need different hyper-parameters
@pytest.mark.parametrize("name", [None, "BN", "SyncBN"])
@pytest.mark.parametrize("w_in", [4])
def test_norm2d(name, w_in):
m = norm2d(name, w_in)
assert isinstance(m, M.Module)
m(mge.random.normal(size=(2, 4, 8, 8)))
@pytest.mark.parametrize("k", [3, 5])
@pytest.mark.parametrize("stride", [1, 2])
@pytest.mark.parametrize("name", ["avg", "max"])
def test_pool2d(k, stride, name):
m = pool2d(k, stride=stride, name=name)
assert isinstance(m, M.Module)
m( | mge.random.normal(size=(2, 4, 8, 8)) | megengine.random.normal |
#!/usr/bin/env python3
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
import megengine as mge
import megengine.module as M
import numpy as np
import pytest
from basecls.layers import SE, DropPath, conv2d, gap2d, linear, norm2d, pool2d
@pytest.mark.parametrize("w_in", [4])
@pytest.mark.parametrize("w_out", [8])
@pytest.mark.parametrize("k", [3, 5])
@pytest.mark.parametrize("stride", [1, 2])
@pytest.mark.parametrize("dilation", [1, 2])
@pytest.mark.parametrize("groups", [1, 2])
@pytest.mark.parametrize("bias", [True, False])
def test_conv2d(w_in, w_out, k, stride, dilation, groups, bias):
m = conv2d(w_in, w_out, k, stride=stride, dilation=dilation, groups=groups, bias=bias)
assert isinstance(m, M.Conv2d)
m(mge.random.normal(size=(2, 4, 8, 8)))
@pytest.mark.parametrize("drop_prob", [0.0, 0.5])
def test_drop_path(drop_prob):
m = DropPath(drop_prob)
assert isinstance(m, M.Module)
m.training = True
m(mge.random.normal(size=(2, 4, 8, 8)))
m.training = False
x = np.random.rand(2, 4, 8, 8).astype("float32")
y = m(mge.Tensor(x)).numpy()
np.testing.assert_allclose(y, x, rtol=1e-4, atol=1e-6)
@pytest.mark.parametrize("shape", [1, (7, 7)])
def test_gap2d(shape):
m = gap2d(shape)
assert isinstance(m, M.AdaptiveAvgPool2d)
m(mge.random.normal(size=(2, 4, 8, 8)))
@pytest.mark.parametrize("w_in", [4])
@pytest.mark.parametrize("w_out", [8])
@pytest.mark.parametrize("bias", [True, False])
def test_linear(w_in, w_out, bias):
m = linear(w_in, w_out, bias=bias)
assert isinstance(m, M.Linear)
m(mge.random.normal(size=(2, 8, 4)))
# TODO: "GN", "IN" and "LN" need different hyper-parameters
@pytest.mark.parametrize("name", [None, "BN", "SyncBN"])
@pytest.mark.parametrize("w_in", [4])
def test_norm2d(name, w_in):
m = norm2d(name, w_in)
assert isinstance(m, M.Module)
m(mge.random.normal(size=(2, 4, 8, 8)))
@pytest.mark.parametrize("k", [3, 5])
@pytest.mark.parametrize("stride", [1, 2])
@pytest.mark.parametrize("name", ["avg", "max"])
def test_pool2d(k, stride, name):
m = pool2d(k, stride=stride, name=name)
assert isinstance(m, M.Module)
m(mge.random.normal(size=(2, 4, 8, 8)))
@pytest.mark.parametrize("w_in", [8])
@pytest.mark.parametrize("w_se", [4])
@pytest.mark.parametrize("act_name", ["relu", "silu"])
def test_se(w_in, w_se, act_name):
m = SE(w_in, w_se, act_name)
assert isinstance(m, M.Module)
m( | mge.random.normal(size=(2, 8, 8, 8)) | megengine.random.normal |
#!/usr/bin/env python3
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
import megengine as mge
import megengine.module as M
import numpy as np
import pytest
from basecls.layers import SE, DropPath, conv2d, gap2d, linear, norm2d, pool2d
@pytest.mark.parametrize("w_in", [4])
@pytest.mark.parametrize("w_out", [8])
@pytest.mark.parametrize("k", [3, 5])
@pytest.mark.parametrize("stride", [1, 2])
@pytest.mark.parametrize("dilation", [1, 2])
@pytest.mark.parametrize("groups", [1, 2])
@pytest.mark.parametrize("bias", [True, False])
def test_conv2d(w_in, w_out, k, stride, dilation, groups, bias):
m = conv2d(w_in, w_out, k, stride=stride, dilation=dilation, groups=groups, bias=bias)
assert isinstance(m, M.Conv2d)
m(mge.random.normal(size=(2, 4, 8, 8)))
@pytest.mark.parametrize("drop_prob", [0.0, 0.5])
def test_drop_path(drop_prob):
m = DropPath(drop_prob)
assert isinstance(m, M.Module)
m.training = True
m(mge.random.normal(size=(2, 4, 8, 8)))
m.training = False
x = np.random.rand(2, 4, 8, 8).astype("float32")
y = m( | mge.Tensor(x) | megengine.Tensor |
import platform
import numpy as np
import pytest
import megengine as mge
import megengine.distributed as dist
from megengine.distributed.helper import get_device_count_by_fork
from megengine.quantization.observer import (
ExponentialMovingAverageObserver,
MinMaxObserver,
Observer,
PassiveObserver,
SyncExponentialMovingAverageObserver,
SyncMinMaxObserver,
)
def test_observer():
with pytest.raises(TypeError):
Observer("qint8")
def test_min_max_observer():
x = np.random.rand(3, 3, 3, 3).astype("float32")
np_min, np_max = x.min(), x.max()
x = | mge.tensor(x) | megengine.tensor |
import platform
import numpy as np
import pytest
import megengine as mge
import megengine.distributed as dist
from megengine.distributed.helper import get_device_count_by_fork
from megengine.quantization.observer import (
ExponentialMovingAverageObserver,
MinMaxObserver,
Observer,
PassiveObserver,
SyncExponentialMovingAverageObserver,
SyncMinMaxObserver,
)
def test_observer():
with pytest.raises(TypeError):
Observer("qint8")
def test_min_max_observer():
x = np.random.rand(3, 3, 3, 3).astype("float32")
np_min, np_max = x.min(), x.max()
x = mge.tensor(x)
m = | MinMaxObserver() | megengine.quantization.observer.MinMaxObserver |
import platform
import numpy as np
import pytest
import megengine as mge
import megengine.distributed as dist
from megengine.distributed.helper import get_device_count_by_fork
from megengine.quantization.observer import (
ExponentialMovingAverageObserver,
MinMaxObserver,
Observer,
PassiveObserver,
SyncExponentialMovingAverageObserver,
SyncMinMaxObserver,
)
def test_observer():
with pytest.raises(TypeError):
Observer("qint8")
def test_min_max_observer():
x = np.random.rand(3, 3, 3, 3).astype("float32")
np_min, np_max = x.min(), x.max()
x = mge.tensor(x)
m = MinMaxObserver()
m(x)
np.testing.assert_allclose(m.min_val.numpy(), np_min)
np.testing.assert_allclose(m.max_val.numpy(), np_max)
def test_exponential_moving_average_observer():
t = np.random.rand()
x1 = np.random.rand(3, 3, 3, 3).astype("float32")
x2 = np.random.rand(3, 3, 3, 3).astype("float32")
expected_min = x1.min() * t + x2.min() * (1 - t)
expected_max = x1.max() * t + x2.max() * (1 - t)
m = | ExponentialMovingAverageObserver(momentum=t) | megengine.quantization.observer.ExponentialMovingAverageObserver |
import platform
import numpy as np
import pytest
import megengine as mge
import megengine.distributed as dist
from megengine.distributed.helper import get_device_count_by_fork
from megengine.quantization.observer import (
ExponentialMovingAverageObserver,
MinMaxObserver,
Observer,
PassiveObserver,
SyncExponentialMovingAverageObserver,
SyncMinMaxObserver,
)
def test_observer():
with pytest.raises(TypeError):
Observer("qint8")
def test_min_max_observer():
x = np.random.rand(3, 3, 3, 3).astype("float32")
np_min, np_max = x.min(), x.max()
x = mge.tensor(x)
m = MinMaxObserver()
m(x)
np.testing.assert_allclose(m.min_val.numpy(), np_min)
np.testing.assert_allclose(m.max_val.numpy(), np_max)
def test_exponential_moving_average_observer():
t = np.random.rand()
x1 = np.random.rand(3, 3, 3, 3).astype("float32")
x2 = np.random.rand(3, 3, 3, 3).astype("float32")
expected_min = x1.min() * t + x2.min() * (1 - t)
expected_max = x1.max() * t + x2.max() * (1 - t)
m = ExponentialMovingAverageObserver(momentum=t)
m(mge.tensor(x1, dtype=np.float32))
m(mge.tensor(x2, dtype=np.float32))
np.testing.assert_allclose(m.min_val.numpy(), expected_min)
np.testing.assert_allclose(m.max_val.numpy(), expected_max)
def test_passive_observer():
q_dict = {"scale": mge.tensor(1.0)}
m = | PassiveObserver(q_dict, "qint8") | megengine.quantization.observer.PassiveObserver |
import platform
import numpy as np
import pytest
import megengine as mge
import megengine.distributed as dist
from megengine.distributed.helper import get_device_count_by_fork
from megengine.quantization.observer import (
ExponentialMovingAverageObserver,
MinMaxObserver,
Observer,
PassiveObserver,
SyncExponentialMovingAverageObserver,
SyncMinMaxObserver,
)
def test_observer():
with pytest.raises(TypeError):
Observer("qint8")
def test_min_max_observer():
x = np.random.rand(3, 3, 3, 3).astype("float32")
np_min, np_max = x.min(), x.max()
x = mge.tensor(x)
m = MinMaxObserver()
m(x)
np.testing.assert_allclose(m.min_val.numpy(), np_min)
np.testing.assert_allclose(m.max_val.numpy(), np_max)
def test_exponential_moving_average_observer():
t = np.random.rand()
x1 = np.random.rand(3, 3, 3, 3).astype("float32")
x2 = np.random.rand(3, 3, 3, 3).astype("float32")
expected_min = x1.min() * t + x2.min() * (1 - t)
expected_max = x1.max() * t + x2.max() * (1 - t)
m = ExponentialMovingAverageObserver(momentum=t)
m(mge.tensor(x1, dtype=np.float32))
m(mge.tensor(x2, dtype=np.float32))
np.testing.assert_allclose(m.min_val.numpy(), expected_min)
np.testing.assert_allclose(m.max_val.numpy(), expected_max)
def test_passive_observer():
q_dict = {"scale": mge.tensor(1.0)}
m = PassiveObserver(q_dict, "qint8")
assert m.orig_scale == 1.0
assert m.scale == 1.0
m.scale = 2.0
assert m.scale == 2.0
assert m.get_qparams() == {"scale": mge.tensor(2.0)}
@pytest.mark.skipif(
platform.system() == "Darwin", reason="do not imp GPU mode at macos now"
)
@pytest.mark.skipif(
platform.system() == "Windows", reason="windows disable MGB_ENABLE_OPR_MM"
)
@pytest.mark.skipif(get_device_count_by_fork("gpu") < 2, reason="need more gpu device")
@pytest.mark.isolated_distributed
def test_sync_min_max_observer():
word_size = | get_device_count_by_fork("gpu") | megengine.distributed.helper.get_device_count_by_fork |
import platform
import numpy as np
import pytest
import megengine as mge
import megengine.distributed as dist
from megengine.distributed.helper import get_device_count_by_fork
from megengine.quantization.observer import (
ExponentialMovingAverageObserver,
MinMaxObserver,
Observer,
PassiveObserver,
SyncExponentialMovingAverageObserver,
SyncMinMaxObserver,
)
def test_observer():
with pytest.raises(TypeError):
Observer("qint8")
def test_min_max_observer():
x = np.random.rand(3, 3, 3, 3).astype("float32")
np_min, np_max = x.min(), x.max()
x = mge.tensor(x)
m = MinMaxObserver()
m(x)
np.testing.assert_allclose(m.min_val.numpy(), np_min)
np.testing.assert_allclose(m.max_val.numpy(), np_max)
def test_exponential_moving_average_observer():
t = np.random.rand()
x1 = np.random.rand(3, 3, 3, 3).astype("float32")
x2 = np.random.rand(3, 3, 3, 3).astype("float32")
expected_min = x1.min() * t + x2.min() * (1 - t)
expected_max = x1.max() * t + x2.max() * (1 - t)
m = ExponentialMovingAverageObserver(momentum=t)
m(mge.tensor(x1, dtype=np.float32))
m(mge.tensor(x2, dtype=np.float32))
np.testing.assert_allclose(m.min_val.numpy(), expected_min)
np.testing.assert_allclose(m.max_val.numpy(), expected_max)
def test_passive_observer():
q_dict = {"scale": mge.tensor(1.0)}
m = PassiveObserver(q_dict, "qint8")
assert m.orig_scale == 1.0
assert m.scale == 1.0
m.scale = 2.0
assert m.scale == 2.0
assert m.get_qparams() == {"scale": mge.tensor(2.0)}
@pytest.mark.skipif(
platform.system() == "Darwin", reason="do not imp GPU mode at macos now"
)
@pytest.mark.skipif(
platform.system() == "Windows", reason="windows disable MGB_ENABLE_OPR_MM"
)
@pytest.mark.skipif(get_device_count_by_fork("gpu") < 2, reason="need more gpu device")
@pytest.mark.isolated_distributed
def test_sync_min_max_observer():
word_size = get_device_count_by_fork("gpu")
x = np.random.rand(3 * word_size, 3, 3, 3).astype("float32")
np_min, np_max = x.min(), x.max()
@dist.launcher
def worker():
rank = dist.get_rank()
m = SyncMinMaxObserver()
y = mge.tensor(x[rank * 3 : (rank + 1) * 3])
m(y)
assert m.min_val == np_min and m.max_val == np_max
worker()
@pytest.mark.skipif(
platform.system() == "Darwin", reason="do not imp GPU mode at macos now"
)
@pytest.mark.skipif(
platform.system() == "Windows", reason="windows disable MGB_ENABLE_OPR_MM"
)
@pytest.mark.skipif(get_device_count_by_fork("gpu") < 2, reason="need more gpu device")
@pytest.mark.isolated_distributed
def test_sync_exponential_moving_average_observer():
word_size = | get_device_count_by_fork("gpu") | megengine.distributed.helper.get_device_count_by_fork |
import platform
import numpy as np
import pytest
import megengine as mge
import megengine.distributed as dist
from megengine.distributed.helper import get_device_count_by_fork
from megengine.quantization.observer import (
ExponentialMovingAverageObserver,
MinMaxObserver,
Observer,
PassiveObserver,
SyncExponentialMovingAverageObserver,
SyncMinMaxObserver,
)
def test_observer():
with pytest.raises(TypeError):
| Observer("qint8") | megengine.quantization.observer.Observer |
import platform
import numpy as np
import pytest
import megengine as mge
import megengine.distributed as dist
from megengine.distributed.helper import get_device_count_by_fork
from megengine.quantization.observer import (
ExponentialMovingAverageObserver,
MinMaxObserver,
Observer,
PassiveObserver,
SyncExponentialMovingAverageObserver,
SyncMinMaxObserver,
)
def test_observer():
with pytest.raises(TypeError):
Observer("qint8")
def test_min_max_observer():
x = np.random.rand(3, 3, 3, 3).astype("float32")
np_min, np_max = x.min(), x.max()
x = mge.tensor(x)
m = MinMaxObserver()
m(x)
np.testing.assert_allclose(m.min_val.numpy(), np_min)
np.testing.assert_allclose(m.max_val.numpy(), np_max)
def test_exponential_moving_average_observer():
t = np.random.rand()
x1 = np.random.rand(3, 3, 3, 3).astype("float32")
x2 = np.random.rand(3, 3, 3, 3).astype("float32")
expected_min = x1.min() * t + x2.min() * (1 - t)
expected_max = x1.max() * t + x2.max() * (1 - t)
m = ExponentialMovingAverageObserver(momentum=t)
m( | mge.tensor(x1, dtype=np.float32) | megengine.tensor |
import platform
import numpy as np
import pytest
import megengine as mge
import megengine.distributed as dist
from megengine.distributed.helper import get_device_count_by_fork
from megengine.quantization.observer import (
ExponentialMovingAverageObserver,
MinMaxObserver,
Observer,
PassiveObserver,
SyncExponentialMovingAverageObserver,
SyncMinMaxObserver,
)
def test_observer():
with pytest.raises(TypeError):
Observer("qint8")
def test_min_max_observer():
x = np.random.rand(3, 3, 3, 3).astype("float32")
np_min, np_max = x.min(), x.max()
x = mge.tensor(x)
m = MinMaxObserver()
m(x)
np.testing.assert_allclose(m.min_val.numpy(), np_min)
np.testing.assert_allclose(m.max_val.numpy(), np_max)
def test_exponential_moving_average_observer():
t = np.random.rand()
x1 = np.random.rand(3, 3, 3, 3).astype("float32")
x2 = np.random.rand(3, 3, 3, 3).astype("float32")
expected_min = x1.min() * t + x2.min() * (1 - t)
expected_max = x1.max() * t + x2.max() * (1 - t)
m = ExponentialMovingAverageObserver(momentum=t)
m(mge.tensor(x1, dtype=np.float32))
m( | mge.tensor(x2, dtype=np.float32) | megengine.tensor |
import platform
import numpy as np
import pytest
import megengine as mge
import megengine.distributed as dist
from megengine.distributed.helper import get_device_count_by_fork
from megengine.quantization.observer import (
ExponentialMovingAverageObserver,
MinMaxObserver,
Observer,
PassiveObserver,
SyncExponentialMovingAverageObserver,
SyncMinMaxObserver,
)
def test_observer():
with pytest.raises(TypeError):
Observer("qint8")
def test_min_max_observer():
x = np.random.rand(3, 3, 3, 3).astype("float32")
np_min, np_max = x.min(), x.max()
x = mge.tensor(x)
m = MinMaxObserver()
m(x)
np.testing.assert_allclose(m.min_val.numpy(), np_min)
np.testing.assert_allclose(m.max_val.numpy(), np_max)
def test_exponential_moving_average_observer():
t = np.random.rand()
x1 = np.random.rand(3, 3, 3, 3).astype("float32")
x2 = np.random.rand(3, 3, 3, 3).astype("float32")
expected_min = x1.min() * t + x2.min() * (1 - t)
expected_max = x1.max() * t + x2.max() * (1 - t)
m = ExponentialMovingAverageObserver(momentum=t)
m(mge.tensor(x1, dtype=np.float32))
m(mge.tensor(x2, dtype=np.float32))
np.testing.assert_allclose(m.min_val.numpy(), expected_min)
np.testing.assert_allclose(m.max_val.numpy(), expected_max)
def test_passive_observer():
q_dict = {"scale": | mge.tensor(1.0) | megengine.tensor |
import platform
import numpy as np
import pytest
import megengine as mge
import megengine.distributed as dist
from megengine.distributed.helper import get_device_count_by_fork
from megengine.quantization.observer import (
ExponentialMovingAverageObserver,
MinMaxObserver,
Observer,
PassiveObserver,
SyncExponentialMovingAverageObserver,
SyncMinMaxObserver,
)
def test_observer():
with pytest.raises(TypeError):
Observer("qint8")
def test_min_max_observer():
x = np.random.rand(3, 3, 3, 3).astype("float32")
np_min, np_max = x.min(), x.max()
x = mge.tensor(x)
m = MinMaxObserver()
m(x)
np.testing.assert_allclose(m.min_val.numpy(), np_min)
np.testing.assert_allclose(m.max_val.numpy(), np_max)
def test_exponential_moving_average_observer():
t = np.random.rand()
x1 = np.random.rand(3, 3, 3, 3).astype("float32")
x2 = np.random.rand(3, 3, 3, 3).astype("float32")
expected_min = x1.min() * t + x2.min() * (1 - t)
expected_max = x1.max() * t + x2.max() * (1 - t)
m = ExponentialMovingAverageObserver(momentum=t)
m(mge.tensor(x1, dtype=np.float32))
m(mge.tensor(x2, dtype=np.float32))
np.testing.assert_allclose(m.min_val.numpy(), expected_min)
np.testing.assert_allclose(m.max_val.numpy(), expected_max)
def test_passive_observer():
q_dict = {"scale": mge.tensor(1.0)}
m = PassiveObserver(q_dict, "qint8")
assert m.orig_scale == 1.0
assert m.scale == 1.0
m.scale = 2.0
assert m.scale == 2.0
assert m.get_qparams() == {"scale": mge.tensor(2.0)}
@pytest.mark.skipif(
platform.system() == "Darwin", reason="do not imp GPU mode at macos now"
)
@pytest.mark.skipif(
platform.system() == "Windows", reason="windows disable MGB_ENABLE_OPR_MM"
)
@pytest.mark.skipif(get_device_count_by_fork("gpu") < 2, reason="need more gpu device")
@pytest.mark.isolated_distributed
def test_sync_min_max_observer():
word_size = get_device_count_by_fork("gpu")
x = np.random.rand(3 * word_size, 3, 3, 3).astype("float32")
np_min, np_max = x.min(), x.max()
@dist.launcher
def worker():
rank = | dist.get_rank() | megengine.distributed.get_rank |
import platform
import numpy as np
import pytest
import megengine as mge
import megengine.distributed as dist
from megengine.distributed.helper import get_device_count_by_fork
from megengine.quantization.observer import (
ExponentialMovingAverageObserver,
MinMaxObserver,
Observer,
PassiveObserver,
SyncExponentialMovingAverageObserver,
SyncMinMaxObserver,
)
def test_observer():
with pytest.raises(TypeError):
Observer("qint8")
def test_min_max_observer():
x = np.random.rand(3, 3, 3, 3).astype("float32")
np_min, np_max = x.min(), x.max()
x = mge.tensor(x)
m = MinMaxObserver()
m(x)
np.testing.assert_allclose(m.min_val.numpy(), np_min)
np.testing.assert_allclose(m.max_val.numpy(), np_max)
def test_exponential_moving_average_observer():
t = np.random.rand()
x1 = np.random.rand(3, 3, 3, 3).astype("float32")
x2 = np.random.rand(3, 3, 3, 3).astype("float32")
expected_min = x1.min() * t + x2.min() * (1 - t)
expected_max = x1.max() * t + x2.max() * (1 - t)
m = ExponentialMovingAverageObserver(momentum=t)
m(mge.tensor(x1, dtype=np.float32))
m(mge.tensor(x2, dtype=np.float32))
np.testing.assert_allclose(m.min_val.numpy(), expected_min)
np.testing.assert_allclose(m.max_val.numpy(), expected_max)
def test_passive_observer():
q_dict = {"scale": mge.tensor(1.0)}
m = PassiveObserver(q_dict, "qint8")
assert m.orig_scale == 1.0
assert m.scale == 1.0
m.scale = 2.0
assert m.scale == 2.0
assert m.get_qparams() == {"scale": mge.tensor(2.0)}
@pytest.mark.skipif(
platform.system() == "Darwin", reason="do not imp GPU mode at macos now"
)
@pytest.mark.skipif(
platform.system() == "Windows", reason="windows disable MGB_ENABLE_OPR_MM"
)
@pytest.mark.skipif(get_device_count_by_fork("gpu") < 2, reason="need more gpu device")
@pytest.mark.isolated_distributed
def test_sync_min_max_observer():
word_size = get_device_count_by_fork("gpu")
x = np.random.rand(3 * word_size, 3, 3, 3).astype("float32")
np_min, np_max = x.min(), x.max()
@dist.launcher
def worker():
rank = dist.get_rank()
m = | SyncMinMaxObserver() | megengine.quantization.observer.SyncMinMaxObserver |
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