INSTRUCTION
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Registers op functions created by `make_op_func` under
`root_namespace.module_name.[submodule_name]`,
where `submodule_name` is one of `_OP_SUBMODULE_NAME_LIST`.
Parameters
----------
root_namespace : str
Top level module name, `mxnet` in the current cases.
module_name : str
Second level module name, `ndarray` and `symbol` in the current cases.
make_op_func : function
Function for creating op functions for `ndarray` and `symbol` modules.
|
def _init_op_module(root_namespace, module_name, make_op_func):
"""
Registers op functions created by `make_op_func` under
`root_namespace.module_name.[submodule_name]`,
where `submodule_name` is one of `_OP_SUBMODULE_NAME_LIST`.
Parameters
----------
root_namespace : str
Top level module name, `mxnet` in the current cases.
module_name : str
Second level module name, `ndarray` and `symbol` in the current cases.
make_op_func : function
Function for creating op functions for `ndarray` and `symbol` modules.
"""
plist = ctypes.POINTER(ctypes.c_char_p)()
size = ctypes.c_uint()
check_call(_LIB.MXListAllOpNames(ctypes.byref(size),
ctypes.byref(plist)))
op_names = []
for i in range(size.value):
op_names.append(py_str(plist[i]))
module_op = sys.modules["%s.%s.op" % (root_namespace, module_name)]
module_internal = sys.modules["%s.%s._internal" % (root_namespace, module_name)]
# contrib module in the old format (deprecated)
# kept here for backward compatibility
# use mx.nd.contrib or mx.sym.contrib from now on
contrib_module_name_old = "%s.contrib.%s" % (root_namespace, module_name)
contrib_module_old = sys.modules[contrib_module_name_old]
submodule_dict = {}
for op_name_prefix in _OP_NAME_PREFIX_LIST:
submodule_dict[op_name_prefix] =\
sys.modules["%s.%s.%s" % (root_namespace, module_name, op_name_prefix[1:-1])]
for name in op_names:
hdl = OpHandle()
check_call(_LIB.NNGetOpHandle(c_str(name), ctypes.byref(hdl)))
op_name_prefix = _get_op_name_prefix(name)
module_name_local = module_name
if len(op_name_prefix) > 0:
if op_name_prefix != '_random_' or name.endswith('_like'):
func_name = name[len(op_name_prefix):]
cur_module = submodule_dict[op_name_prefix]
module_name_local = "%s.%s.%s" % (root_namespace, module_name, op_name_prefix[1:-1])
else:
func_name = name
cur_module = module_internal
elif name.startswith('_'):
func_name = name
cur_module = module_internal
else:
func_name = name
cur_module = module_op
function = make_op_func(hdl, name, func_name)
function.__module__ = module_name_local
setattr(cur_module, function.__name__, function)
cur_module.__all__.append(function.__name__)
if op_name_prefix == '_contrib_':
hdl = OpHandle()
check_call(_LIB.NNGetOpHandle(c_str(name), ctypes.byref(hdl)))
func_name = name[len(op_name_prefix):]
function = make_op_func(hdl, name, func_name)
function.__module__ = contrib_module_name_old
setattr(contrib_module_old, function.__name__, function)
contrib_module_old.__all__.append(function.__name__)
|
Generate op functions created by `op_code_gen_func` and write to the source file
of `root_namespace.module_name.[submodule_name]`,
where `submodule_name` is one of `_OP_SUBMODULE_NAME_LIST`.
Parameters
----------
root_namespace : str
Top level module name, `mxnet` in the current cases.
module_name : str
Second level module name, `ndarray` and `symbol` in the current cases.
op_code_gen_func : function
Function for creating op functions for `ndarray` and `symbol` modules.
|
def _generate_op_module_signature(root_namespace, module_name, op_code_gen_func):
"""
Generate op functions created by `op_code_gen_func` and write to the source file
of `root_namespace.module_name.[submodule_name]`,
where `submodule_name` is one of `_OP_SUBMODULE_NAME_LIST`.
Parameters
----------
root_namespace : str
Top level module name, `mxnet` in the current cases.
module_name : str
Second level module name, `ndarray` and `symbol` in the current cases.
op_code_gen_func : function
Function for creating op functions for `ndarray` and `symbol` modules.
"""
def get_module_file(module_name):
"""Return the generated module file based on module name."""
path = os.path.dirname(__file__)
module_path = module_name.split('.')
module_path[-1] = 'gen_' + module_path[-1]
file_name = os.path.join(path, '..', *module_path) + '.py'
module_file = open(file_name, 'w')
dependencies = {'symbol': ['from ._internal import SymbolBase',
'from ..base import _Null'],
'ndarray': ['from ._internal import NDArrayBase',
'from ..base import _Null']}
module_file.write('# File content is auto-generated. Do not modify.' + os.linesep)
module_file.write('# pylint: skip-file' + os.linesep)
module_file.write(os.linesep.join(dependencies[module_name.split('.')[1]]))
return module_file
def write_all_str(module_file, module_all_list):
"""Write the proper __all__ based on available operators."""
module_file.write(os.linesep)
module_file.write(os.linesep)
all_str = '__all__ = [' + ', '.join(["'%s'"%s for s in module_all_list]) + ']'
module_file.write(all_str)
plist = ctypes.POINTER(ctypes.c_char_p)()
size = ctypes.c_uint()
check_call(_LIB.MXListAllOpNames(ctypes.byref(size),
ctypes.byref(plist)))
op_names = []
for i in range(size.value):
op_names.append(py_str(plist[i]))
module_op_file = get_module_file("%s.%s.op" % (root_namespace, module_name))
module_op_all = []
module_internal_file = get_module_file("%s.%s._internal"%(root_namespace, module_name))
module_internal_all = []
submodule_dict = {}
for op_name_prefix in _OP_NAME_PREFIX_LIST:
submodule_dict[op_name_prefix] =\
(get_module_file("%s.%s.%s" % (root_namespace, module_name,
op_name_prefix[1:-1])), [])
for name in op_names:
hdl = OpHandle()
check_call(_LIB.NNGetOpHandle(c_str(name), ctypes.byref(hdl)))
op_name_prefix = _get_op_name_prefix(name)
if len(op_name_prefix) > 0:
func_name = name[len(op_name_prefix):]
cur_module_file, cur_module_all = submodule_dict[op_name_prefix]
elif name.startswith('_'):
func_name = name
cur_module_file = module_internal_file
cur_module_all = module_internal_all
else:
func_name = name
cur_module_file = module_op_file
cur_module_all = module_op_all
code, _ = op_code_gen_func(hdl, name, func_name, True)
cur_module_file.write(os.linesep)
cur_module_file.write(code)
cur_module_all.append(func_name)
for (submodule_f, submodule_all) in submodule_dict.values():
write_all_str(submodule_f, submodule_all)
submodule_f.close()
write_all_str(module_op_file, module_op_all)
module_op_file.close()
write_all_str(module_internal_file, module_internal_all)
module_internal_file.close()
|
Turns on/off NumPy compatibility. NumPy-compatibility is turned off by default in backend.
Parameters
----------
active : bool
Indicates whether to turn on/off NumPy compatibility.
Returns
-------
A bool value indicating the previous state of NumPy compatibility.
|
def set_np_compat(active):
"""
Turns on/off NumPy compatibility. NumPy-compatibility is turned off by default in backend.
Parameters
----------
active : bool
Indicates whether to turn on/off NumPy compatibility.
Returns
-------
A bool value indicating the previous state of NumPy compatibility.
"""
prev = ctypes.c_int()
check_call(_LIB.MXSetIsNumpyCompatible(ctypes.c_int(active), ctypes.byref(prev)))
return bool(prev.value)
|
Checks whether the NumPy compatibility is currently turned on.
NumPy-compatibility is turned off by default in backend.
Returns
-------
A bool value indicating whether the NumPy compatibility is currently on.
|
def is_np_compat():
"""
Checks whether the NumPy compatibility is currently turned on.
NumPy-compatibility is turned off by default in backend.
Returns
-------
A bool value indicating whether the NumPy compatibility is currently on.
"""
curr = ctypes.c_bool()
check_call(_LIB.MXIsNumpyCompatible(ctypes.byref(curr)))
return curr.value
|
Wraps a function with an activated NumPy-compatibility scope. This ensures
that the execution of the function is guaranteed with NumPy compatible semantics,
such as zero-dim and zero size tensors.
Example::
import mxnet as mx
@mx.use_np_compat
def scalar_one():
return mx.nd.ones(())
print(scalar_one())
Parameters
----------
func : a user-provided callable function to be scoped by the NumPy compatibility state.
Returns
-------
Function
A function for wrapping the user functions in the NumPy compatibility scope.
|
def use_np_compat(func):
"""Wraps a function with an activated NumPy-compatibility scope. This ensures
that the execution of the function is guaranteed with NumPy compatible semantics,
such as zero-dim and zero size tensors.
Example::
import mxnet as mx
@mx.use_np_compat
def scalar_one():
return mx.nd.ones(())
print(scalar_one())
Parameters
----------
func : a user-provided callable function to be scoped by the NumPy compatibility state.
Returns
-------
Function
A function for wrapping the user functions in the NumPy compatibility scope.
"""
@wraps(func)
def _with_np_compat(*args, **kwargs):
with np_compat(active=True):
return func(*args, **kwargs)
return _with_np_compat
|
computes the root relative squared error (condensed using standard deviation formula)
|
def rse(label, pred):
"""computes the root relative squared error (condensed using standard deviation formula)"""
numerator = np.sqrt(np.mean(np.square(label - pred), axis = None))
denominator = np.std(label, axis = None)
return numerator / denominator
|
computes the relative absolute error (condensed using standard deviation formula)
|
def rae(label, pred):
"""computes the relative absolute error (condensed using standard deviation formula)"""
numerator = np.mean(np.abs(label - pred), axis=None)
denominator = np.mean(np.abs(label - np.mean(label, axis=None)), axis=None)
return numerator / denominator
|
computes the empirical correlation coefficient
|
def corr(label, pred):
"""computes the empirical correlation coefficient"""
numerator1 = label - np.mean(label, axis=0)
numerator2 = pred - np.mean(pred, axis = 0)
numerator = np.mean(numerator1 * numerator2, axis=0)
denominator = np.std(label, axis=0) * np.std(pred, axis=0)
return np.mean(numerator / denominator)
|
:return: mxnet metric object
|
def get_custom_metrics():
"""
:return: mxnet metric object
"""
_rse = mx.metric.create(rse)
_rae = mx.metric.create(rae)
_corr = mx.metric.create(corr)
return mx.metric.create([_rae, _rse, _corr])
|
Get input size
|
def _get_input(proto):
"""Get input size
"""
layer = caffe_parser.get_layers(proto)
if len(proto.input_dim) > 0:
input_dim = proto.input_dim
elif len(proto.input_shape) > 0:
input_dim = proto.input_shape[0].dim
elif layer[0].type == "Input":
input_dim = layer[0].input_param.shape[0].dim
layer.pop(0)
else:
raise ValueError('Cannot find input size')
assert layer[0].type != "Input", 'only support single input'
# We assume the first bottom blob of first layer is the output from data layer
input_name = layer[0].bottom[0]
return input_name, input_dim, layer
|
Convert convolution layer parameter from Caffe to MXNet
|
def _convert_conv_param(param):
"""
Convert convolution layer parameter from Caffe to MXNet
"""
param_string = "num_filter=%d" % param.num_output
pad_w = 0
pad_h = 0
if isinstance(param.pad, int):
pad = param.pad
param_string += ", pad=(%d, %d)" % (pad, pad)
else:
if len(param.pad) > 0:
pad = param.pad[0]
param_string += ", pad=(%d, %d)" % (pad, pad)
else:
if isinstance(param.pad_w, int):
pad_w = param.pad_w
if isinstance(param.pad_h, int):
pad_h = param.pad_h
param_string += ", pad=(%d, %d)" % (pad_h, pad_w)
if isinstance(param.kernel_size, int):
kernel_size = param.kernel_size
param_string += ", kernel=(%d,%d)" % (kernel_size, kernel_size)
else:
if len(param.kernel_size) > 0:
kernel_size = param.kernel_size[0]
param_string += ", kernel=(%d,%d)" % (kernel_size, kernel_size)
else:
assert isinstance(param.kernel_w, int)
kernel_w = param.kernel_w
assert isinstance(param.kernel_h, int)
kernel_h = param.kernel_h
param_string += ", kernel=(%d,%d)" % (kernel_h, kernel_w)
stride = 1
if isinstance(param.stride, int):
stride = param.stride
else:
stride = 1 if len(param.stride) == 0 else param.stride[0]
param_string += ", stride=(%d,%d)" % (stride, stride)
dilate = 1
if hasattr(param, 'dilation'):
if isinstance(param.dilation, int):
dilate = param.dilation
else:
dilate = 1 if len(param.dilation) == 0 else param.dilation[0]
param_string += ", no_bias=%s" % (not param.bias_term)
# deal with dilation. Won't be in deconvolution
if dilate > 1:
param_string += ", dilate=(%d, %d)" % (dilate, dilate)
if isinstance(param.group, int):
if param.group != 1:
param_string += ", num_group=%d" % param.group
return param_string
|
Convert the pooling layer parameter
|
def _convert_pooling_param(param):
"""Convert the pooling layer parameter
"""
param_string = "pooling_convention='full', "
if param.global_pooling:
param_string += "global_pool=True, kernel=(1,1)"
else:
param_string += "pad=(%d,%d), kernel=(%d,%d), stride=(%d,%d)" % (
param.pad, param.pad, param.kernel_size, param.kernel_size,
param.stride, param.stride)
if param.pool == 0:
param_string += ", pool_type='max'"
elif param.pool == 1:
param_string += ", pool_type='avg'"
else:
raise ValueError("Unknown Pooling Method!")
return param_string
|
Parse Caffe prototxt into symbol string
|
def _parse_proto(prototxt_fname):
"""Parse Caffe prototxt into symbol string
"""
proto = caffe_parser.read_prototxt(prototxt_fname)
# process data layer
input_name, input_dim, layers = _get_input(proto)
# only support single input, so always use `data` as the input data
mapping = {input_name: 'data'}
need_flatten = {input_name: False}
symbol_string = "import mxnet as mx\ndata = mx.symbol.Variable(name='data')\n"
flatten_count = 0
output_name = ""
prev_name = None
# convert reset layers one by one
for i, layer in enumerate(layers):
type_string = ''
param_string = ''
skip_layer = False
bottom_order = []
name = re.sub('[-/]', '_', layer.name)
if layer.type == 'Convolution' or layer.type == 4:
type_string = 'mx.symbol.Convolution'
param_string = _convert_conv_param(layer.convolution_param)
need_flatten[name] = True
if layer.type == 'Deconvolution' or layer.type == 39:
type_string = 'mx.symbol.Deconvolution'
param_string = _convert_conv_param(layer.convolution_param)
need_flatten[name] = True
if layer.type == 'Pooling' or layer.type == 17:
type_string = 'mx.symbol.Pooling'
param_string = _convert_pooling_param(layer.pooling_param)
need_flatten[name] = True
if layer.type == 'ReLU' or layer.type == 18:
type_string = 'mx.symbol.Activation'
param_string = "act_type='relu'"
need_flatten[name] = need_flatten[mapping[layer.bottom[0]]]
if layer.type == 'TanH' or layer.type == 23:
type_string = 'mx.symbol.Activation'
param_string = "act_type='tanh'"
need_flatten[name] = need_flatten[mapping[layer.bottom[0]]]
if layer.type == 'Sigmoid' or layer.type == 19:
type_string = 'mx.symbol.Activation'
param_string = "act_type='sigmoid'"
need_flatten[name] = need_flatten[mapping[layer.bottom[0]]]
if layer.type == 'LRN' or layer.type == 15:
type_string = 'mx.symbol.LRN'
param = layer.lrn_param
param_string = "alpha=%f, beta=%f, knorm=%f, nsize=%d" % (
param.alpha, param.beta, param.k, param.local_size)
need_flatten[name] = True
if layer.type == 'InnerProduct' or layer.type == 14:
type_string = 'mx.symbol.FullyConnected'
param = layer.inner_product_param
param_string = "num_hidden=%d, no_bias=%s" % (
param.num_output, not param.bias_term)
need_flatten[name] = False
if layer.type == 'Dropout' or layer.type == 6:
type_string = 'mx.symbol.Dropout'
param = layer.dropout_param
param_string = "p=%f" % param.dropout_ratio
need_flatten[name] = need_flatten[mapping[layer.bottom[0]]]
if layer.type == 'Softmax' or layer.type == 20:
if layer.softmax_param.axis == 2:
symbol_string += "%s = mx.symbol.transpose(%s, axes=(0,2,1))\n" %\
(mapping[layer.bottom[0]], mapping[layer.bottom[0]])
type_string = 'mx.symbol.SoftmaxActivation'
param_string = "mode='channel'"
need_flatten[name] = False
else:
type_string = 'mx.symbol.SoftmaxOutput'
if layer.type == 'Flatten' or layer.type == 8:
if 'softmax' in layer.bottom[0]:
prev_name = re.sub('[-/]', '_', layers[i-1].name)
skip_layer = True
else:
type_string = 'mx.symbol.Flatten'
need_flatten[name] = False
if layer.type == 'Split' or layer.type == 22:
type_string = 'split' # will process later
if layer.type == 'Concat' or layer.type == 3:
type_string = 'mx.symbol.Concat'
need_flatten[name] = True
if layer.type == 'Crop':
type_string = 'mx.symbol.Crop'
need_flatten[name] = True
param_string = 'center_crop=True'
if layer.type == 'BatchNorm':
type_string = 'mx.symbol.BatchNorm'
param = layer.batch_norm_param
# CuDNN requires eps to be greater than 1e-05
# We compensate for this change in convert_model
epsilon = param.eps
if (epsilon <= 1e-05):
epsilon = 1e-04
# if next layer is scale, don't fix gamma
fix_gamma = layers[i+1].type != 'Scale'
param_string = 'use_global_stats=%s, fix_gamma=%s, eps=%f' % (
param.use_global_stats, fix_gamma, epsilon)
need_flatten[name] = need_flatten[mapping[layer.bottom[0]]]
if layer.type == 'Scale':
assert layers[i-1].type == 'BatchNorm'
need_flatten[name] = need_flatten[mapping[layer.bottom[0]]]
skip_layer = True
prev_name = re.sub('[-/]', '_', layers[i-1].name)
if layer.type == 'PReLU':
type_string = 'mx.symbol.LeakyReLU'
param = layer.prelu_param
param_string = "act_type='prelu', slope=%f" % param.filler.value
need_flatten[name] = need_flatten[mapping[layer.bottom[0]]]
if layer.type == 'Eltwise':
type_string = 'mx.symbol.broadcast_add'
param_string = ""
need_flatten[name] = False
if layer.type == 'Reshape':
type_string = 'mx.symbol.Reshape'
param = layer.reshape_param
param_string = 'shape=(' + ','.join([str(x) for x in list(param.shape.dim)]) + ')'
need_flatten[name] = True
if layer.type == 'AbsVal':
type_string = 'mx.symbol.abs'
need_flatten[name] = need_flatten[mapping[layer.bottom[0]]]
if layer.type == 'Normalize':
bottom = re.sub('[-/]', '_', layer.bottom[0])
conv_layer = _find_layer(layers, bottom)
assert conv_layer is not None
param = layer.norm_param
assert not param.across_spatial and not param.channel_shared
assert param.scale_filler.type == 'constant'
if conv_layer.type == 'Convolution':
scale_name = "%s_scale" % name
symbol_string += "%s=mx.sym.Variable(name='%s', shape=(1, %d, 1, 1), init=mx.init.Constant(%f))\n" % \
(scale_name, scale_name, conv_layer.convolution_param.num_output,
param.scale_filler.value)
symbol_string += "%s=mx.symbol.L2Normalization(name='%s', data=%s, mode='channel')\n" %\
(name, name, mapping[layer.bottom[0]])
symbol_string += "%s=mx.symbol.broadcast_mul(lhs=%s, rhs=%s)\n" %\
(name, scale_name, name)
type_string = 'split'
need_flatten[name] = True
else:
raise ValueError('Unknown/Invalid normalize layer!')
if layer.type == 'Permute':
type_string = 'mx.symbol.transpose'
param_string = "axes=(%s)" % (','.join([str(x) for x in layer.permute_param.order]))
need_flatten[name] = True
from_name = ''
if layer.type == 'PriorBox':
param = layer.prior_box_param
if layer.bottom[0] == 'data':
bottom_order = [1]
else:
bottom_order = [0]
try:
import math
min_size = param.min_size[0] / input_dim[2]
max_size = math.sqrt(param.min_size[0] * param.max_size[0]) / input_dim[2]
sizes = '(%f, %f)' %(min_size, max_size)
except AttributeError:
min_size = param.min_size[0] / input_dim[2]
sizes = '(%f)' %(min_size)
ars = list(param.aspect_ratio)
ratios = [1.]
for ar in ars:
ratios.append(ar)
if param.flip:
ratios.append(1. / ar)
ratios_string = '(' + ','.join(str(x) for x in ratios) + ')'
clip = param.clip
if (param.step_h > 0 or param.step_w > 0):
step_h = param.step_h
step_w = param.step_w
elif param.step > 0:
step_h = param.step
step_w = param.step
else:
step_h = -1
step_w = -1
finput_dimh = float(input_dim[2])
finput_dimw = float(input_dim[3])
step = '(%f, %f)' % (step_h / finput_dimh, step_w / finput_dimw)
assert param.offset == 0.5, "currently only support offset = 0.5"
symbol_string += '%s = mx.contrib.symbol.MultiBoxPrior(%s, sizes=%s, ratios=%s, clip=%s, steps=%s, name="%s")\n' % \
(name, mapping[layer.bottom[0]], sizes, ratios_string, clip, step, name)
symbol_string += '%s = mx.symbol.Flatten(data=%s)\n' % (name, name)
type_string = 'split'
need_flatten[name] = False
if layer.type == 'DetectionOutput':
bottom_order = [1, 0, 2]
param = layer.detection_output_param
assert param.share_location == True
assert param.background_label_id == 0
nms_param = param.nms_param
type_string = 'mx.contrib.symbol.MultiBoxDetection'
param_string = "nms_threshold=%f, nms_topk=%d, clip=False" % \
(nms_param.nms_threshold, nms_param.top_k)
if skip_layer:
assert len(layer.bottom) == 1
symbol_string += "%s = %s\n" % (name, prev_name)
elif type_string == '':
raise ValueError('Unknown layer %s!' % layer.type)
elif type_string != 'split':
bottom = layer.bottom
if param_string != "":
param_string = ", " + param_string
if len(bottom) == 1:
# print(need_flatten)
if need_flatten[mapping[bottom[0]]] and type_string == 'mx.symbol.FullyConnected':
flatten_name = "flatten_%d" % flatten_count
symbol_string += "%s=mx.symbol.Flatten(name='%s', data=%s)\n" % (
flatten_name, flatten_name, mapping[bottom[0]])
flatten_count += 1
need_flatten[flatten_name] = False
bottom[0] = flatten_name
mapping[bottom[0]] = bottom[0]
symbol_string += "%s = %s(name='%s', data=%s %s)\n" % (
name, type_string, name, mapping[bottom[0]], param_string)
else:
if not bottom_order:
bottom_order = range(len(bottom))
symbol_string += "%s = %s(name='%s', *[%s] %s)\n" % \
(name, type_string, name, ','.join([mapping[bottom[x]] for x in bottom_order]), param_string)
if layer.type == 'Concat' and layer.concat_param.axis == 2:
symbol_string += "%s = mx.symbol.Reshape(data=%s, shape=(0, -1, 4), name='%s')\n" %\
(name, name, name)
for j in range(len(layer.top)):
mapping[layer.top[j]] = name
output_name = name
return symbol_string, output_name, input_dim
|
Convert caffe model definition into Symbol
Parameters
----------
prototxt_fname : str
Filename of the prototxt file
Returns
-------
Symbol
Converted Symbol
tuple
Input shape
|
def convert_symbol(prototxt_fname):
"""Convert caffe model definition into Symbol
Parameters
----------
prototxt_fname : str
Filename of the prototxt file
Returns
-------
Symbol
Converted Symbol
tuple
Input shape
"""
sym, output_name, input_dim = _parse_proto(prototxt_fname)
exec(sym) # pylint: disable=exec-used
_locals = locals()
exec("ret = " + output_name, globals(), _locals) # pylint: disable=exec-used
ret = _locals['ret']
return ret, input_dim
|
Complete an episode's worth of training for each environment.
|
def train_episode(agent, envs, preprocessors, t_max, render):
"""Complete an episode's worth of training for each environment."""
num_envs = len(envs)
# Buffers to hold trajectories, e.g. `env_xs[i]` will hold the observations
# for environment `i`.
env_xs, env_as = _2d_list(num_envs), _2d_list(num_envs)
env_rs, env_vs = _2d_list(num_envs), _2d_list(num_envs)
episode_rs = np.zeros(num_envs, dtype=np.float)
for p in preprocessors:
p.reset()
observations = [p.preprocess(e.reset())
for p, e in zip(preprocessors, envs)]
done = np.array([False for _ in range(num_envs)])
all_done = False
t = 1
while not all_done:
if render:
envs[0].render()
# NOTE(reed): Reshape to set the data shape.
agent.model.reshape([('data', (num_envs, preprocessors[0].obs_size))])
step_xs = np.vstack([o.ravel() for o in observations])
# Get actions and values for all environments in a single forward pass.
step_xs_nd = mx.nd.array(step_xs, ctx=agent.ctx)
data_batch = mx.io.DataBatch(data=[step_xs_nd], label=None)
agent.model.forward(data_batch, is_train=False)
_, step_vs, _, step_ps = agent.model.get_outputs()
step_ps = step_ps.asnumpy()
step_vs = step_vs.asnumpy()
step_as = agent.act(step_ps)
# Step each environment whose episode has not completed.
for i, env in enumerate(envs):
if not done[i]:
obs, r, done[i], _ = env.step(step_as[i])
# Record the observation, action, value, and reward in the
# buffers.
env_xs[i].append(step_xs[i].ravel())
env_as[i].append(step_as[i])
env_vs[i].append(step_vs[i][0])
env_rs[i].append(r)
episode_rs[i] += r
# Add 0 as the state value when done.
if done[i]:
env_vs[i].append(0.0)
else:
observations[i] = preprocessors[i].preprocess(obs)
# Perform an update every `t_max` steps.
if t == t_max:
# If the episode has not finished, add current state's value. This
# will be used to 'bootstrap' the final return (see Algorithm S3
# in A3C paper).
step_xs = np.vstack([o.ravel() for o in observations])
step_xs_nd = mx.nd.array(step_xs, ctx=agent.ctx)
data_batch = mx.io.DataBatch(data=[step_xs_nd], label=None)
agent.model.forward(data_batch, is_train=False)
_, extra_vs, _, _ = agent.model.get_outputs()
extra_vs = extra_vs.asnumpy()
for i in range(num_envs):
if not done[i]:
env_vs[i].append(extra_vs[i][0])
# Perform update and clear buffers.
env_xs = np.vstack(list(chain.from_iterable(env_xs)))
agent.train_step(env_xs, env_as, env_rs, env_vs)
env_xs, env_as = _2d_list(num_envs), _2d_list(num_envs)
env_rs, env_vs = _2d_list(num_envs), _2d_list(num_envs)
t = 0
all_done = np.all(done)
t += 1
return episode_rs
|
parses the trained .caffemodel file
filepath: /path/to/trained-model.caffemodel
returns: layers
|
def parse_caffemodel(file_path):
"""
parses the trained .caffemodel file
filepath: /path/to/trained-model.caffemodel
returns: layers
"""
f = open(file_path, 'rb')
contents = f.read()
net_param = caffe_pb2.NetParameter()
net_param.ParseFromString(contents)
layers = find_layers(net_param)
return layers
|
For a given audio clip, calculate the log of its Fourier Transform
Params:
audio_clip(str): Path to the audio clip
|
def featurize(self, audio_clip, overwrite=False, save_feature_as_csvfile=False):
""" For a given audio clip, calculate the log of its Fourier Transform
Params:
audio_clip(str): Path to the audio clip
"""
return spectrogram_from_file(
audio_clip, step=self.step, window=self.window,
max_freq=self.max_freq, overwrite=overwrite,
save_feature_as_csvfile=save_feature_as_csvfile)
|
Read metadata from the description file
(possibly takes long, depending on the filesize)
Params:
desc_file (str): Path to a JSON-line file that contains labels and
paths to the audio files
partition (str): One of 'train', 'validation' or 'test'
max_duration (float): In seconds, the maximum duration of
utterances to train or test on
|
def load_metadata_from_desc_file(self, desc_file, partition='train',
max_duration=16.0,):
""" Read metadata from the description file
(possibly takes long, depending on the filesize)
Params:
desc_file (str): Path to a JSON-line file that contains labels and
paths to the audio files
partition (str): One of 'train', 'validation' or 'test'
max_duration (float): In seconds, the maximum duration of
utterances to train or test on
"""
logger = logUtil.getlogger()
logger.info('Reading description file: {} for partition: {}'
.format(desc_file, partition))
audio_paths, durations, texts = [], [], []
with open(desc_file) as json_line_file:
for line_num, json_line in enumerate(json_line_file):
try:
spec = json.loads(json_line)
if float(spec['duration']) > max_duration:
continue
audio_paths.append(spec['key'])
durations.append(float(spec['duration']))
texts.append(spec['text'])
except Exception as e:
# Change to (KeyError, ValueError) or
# (KeyError,json.decoder.JSONDecodeError), depending on
# json module version
logger.warn('Error reading line #{}: {}'
.format(line_num, json_line))
logger.warn(str(e))
if partition == 'train':
self.count = len(audio_paths)
self.train_audio_paths = audio_paths
self.train_durations = durations
self.train_texts = texts
elif partition == 'validation':
self.val_audio_paths = audio_paths
self.val_durations = durations
self.val_texts = texts
self.val_count = len(audio_paths)
elif partition == 'test':
self.test_audio_paths = audio_paths
self.test_durations = durations
self.test_texts = texts
else:
raise Exception("Invalid partition to load metadata. "
"Must be train/validation/test")
|
Featurize a minibatch of audio, zero pad them and return a dictionary
Params:
audio_paths (list(str)): List of paths to audio files
texts (list(str)): List of texts corresponding to the audio files
Returns:
dict: See below for contents
|
def prepare_minibatch(self, audio_paths, texts, overwrite=False,
is_bi_graphemes=False, seq_length=-1, save_feature_as_csvfile=False):
""" Featurize a minibatch of audio, zero pad them and return a dictionary
Params:
audio_paths (list(str)): List of paths to audio files
texts (list(str)): List of texts corresponding to the audio files
Returns:
dict: See below for contents
"""
assert len(audio_paths) == len(texts),\
"Inputs and outputs to the network must be of the same number"
# Features is a list of (timesteps, feature_dim) arrays
# Calculate the features for each audio clip, as the log of the
# Fourier Transform of the audio
features = [self.featurize(a, overwrite=overwrite, save_feature_as_csvfile=save_feature_as_csvfile) for a in audio_paths]
input_lengths = [f.shape[0] for f in features]
feature_dim = features[0].shape[1]
mb_size = len(features)
# Pad all the inputs so that they are all the same length
if seq_length == -1:
x = np.zeros((mb_size, self.max_seq_length, feature_dim))
else:
x = np.zeros((mb_size, seq_length, feature_dim))
y = np.zeros((mb_size, self.max_label_length))
labelUtil = LabelUtil.getInstance()
label_lengths = []
for i in range(mb_size):
feat = features[i]
feat = self.normalize(feat) # Center using means and std
x[i, :feat.shape[0], :] = feat
if is_bi_graphemes:
label = generate_bi_graphemes_label(texts[i])
label = labelUtil.convert_bi_graphemes_to_num(label)
y[i, :len(label)] = label
else:
label = labelUtil.convert_word_to_num(texts[i])
y[i, :len(texts[i])] = label
label_lengths.append(len(label))
return {
'x': x, # (0-padded features of shape(mb_size,timesteps,feat_dim)
'y': y, # list(int) Flattened labels (integer sequences)
'texts': texts, # list(str) Original texts
'input_lengths': input_lengths, # list(int) Length of each input
'label_lengths': label_lengths, # list(int) Length of each label
}
|
Estimate the mean and std of the features from the training set
Params:
k_samples (int): Use this number of samples for estimation
|
def sample_normalize(self, k_samples=1000, overwrite=False):
""" Estimate the mean and std of the features from the training set
Params:
k_samples (int): Use this number of samples for estimation
"""
log = logUtil.getlogger()
log.info("Calculating mean and std from samples")
# if k_samples is negative then it goes through total dataset
if k_samples < 0:
audio_paths = self.audio_paths
# using sample
else:
k_samples = min(k_samples, len(self.train_audio_paths))
samples = self.rng.sample(self.train_audio_paths, k_samples)
audio_paths = samples
manager = Manager()
return_dict = manager.dict()
jobs = []
for threadIndex in range(cpu_count()):
proc = Process(target=self.preprocess_sample_normalize, args=(threadIndex, audio_paths, overwrite, return_dict))
jobs.append(proc)
proc.start()
for proc in jobs:
proc.join()
feat = np.sum(np.vstack([item['feat'] for item in return_dict.values()]), axis=0)
count = sum([item['count'] for item in return_dict.values()])
feat_squared = np.sum(np.vstack([item['feat_squared'] for item in return_dict.values()]), axis=0)
self.feats_mean = feat / float(count)
self.feats_std = np.sqrt(feat_squared / float(count) - np.square(self.feats_mean))
np.savetxt(
generate_file_path(self.save_dir, self.model_name, 'feats_mean'), self.feats_mean)
np.savetxt(
generate_file_path(self.save_dir, self.model_name, 'feats_std'), self.feats_std)
log.info("End calculating mean and std from samples")
|
GRU Cell symbol
Reference:
* Chung, Junyoung, et al. "Empirical evaluation of gated recurrent neural
networks on sequence modeling." arXiv preprint arXiv:1412.3555 (2014).
|
def gru(num_hidden, indata, prev_state, param, seqidx, layeridx, dropout=0., is_batchnorm=False, gamma=None, beta=None, name=None):
"""
GRU Cell symbol
Reference:
* Chung, Junyoung, et al. "Empirical evaluation of gated recurrent neural
networks on sequence modeling." arXiv preprint arXiv:1412.3555 (2014).
"""
if dropout > 0.:
indata = mx.sym.Dropout(data=indata, p=dropout)
i2h = mx.sym.FullyConnected(data=indata,
weight=param.gates_i2h_weight,
bias=param.gates_i2h_bias,
num_hidden=num_hidden * 2,
name="t%d_l%d_gates_i2h" % (seqidx, layeridx))
if is_batchnorm:
if name is not None:
i2h = batchnorm(net=i2h, gamma=gamma, beta=beta, name="%s_batchnorm" % name)
else:
i2h = batchnorm(net=i2h, gamma=gamma, beta=beta)
h2h = mx.sym.FullyConnected(data=prev_state.h,
weight=param.gates_h2h_weight,
bias=param.gates_h2h_bias,
num_hidden=num_hidden * 2,
name="t%d_l%d_gates_h2h" % (seqidx, layeridx))
gates = i2h + h2h
slice_gates = mx.sym.SliceChannel(gates, num_outputs=2,
name="t%d_l%d_slice" % (seqidx, layeridx))
update_gate = mx.sym.Activation(slice_gates[0], act_type="sigmoid")
reset_gate = mx.sym.Activation(slice_gates[1], act_type="sigmoid")
# The transform part of GRU is a little magic
htrans_i2h = mx.sym.FullyConnected(data=indata,
weight=param.trans_i2h_weight,
bias=param.trans_i2h_bias,
num_hidden=num_hidden,
name="t%d_l%d_trans_i2h" % (seqidx, layeridx))
h_after_reset = prev_state.h * reset_gate
htrans_h2h = mx.sym.FullyConnected(data=h_after_reset,
weight=param.trans_h2h_weight,
bias=param.trans_h2h_bias,
num_hidden=num_hidden,
name="t%d_l%d_trans_h2h" % (seqidx, layeridx))
h_trans = htrans_i2h + htrans_h2h
h_trans_active = mx.sym.Activation(h_trans, act_type="tanh")
next_h = prev_state.h + update_gate * (h_trans_active - prev_state.h)
return GRUState(h=next_h)
|
save image
|
def save_image(data, epoch, image_size, batch_size, output_dir, padding=2):
""" save image """
data = data.asnumpy().transpose((0, 2, 3, 1))
datanp = np.clip(
(data - np.min(data))*(255.0/(np.max(data) - np.min(data))), 0, 255).astype(np.uint8)
x_dim = min(8, batch_size)
y_dim = int(math.ceil(float(batch_size) / x_dim))
height, width = int(image_size + padding), int(image_size + padding)
grid = np.zeros((height * y_dim + 1 + padding // 2, width *
x_dim + 1 + padding // 2, 3), dtype=np.uint8)
k = 0
for y in range(y_dim):
for x in range(x_dim):
if k >= batch_size:
break
start_y = y * height + 1 + padding // 2
end_y = start_y + height - padding
start_x = x * width + 1 + padding // 2
end_x = start_x + width - padding
np.copyto(grid[start_y:end_y, start_x:end_x, :], datanp[k])
k += 1
imageio.imwrite(
'{}/fake_samples_epoch_{}.png'.format(output_dir, epoch), grid)
|
Traverses the root of directory that contains images and
generates image list iterator.
Parameters
----------
root: string
recursive: bool
exts: string
Returns
-------
image iterator that contains all the image under the specified path
|
def list_image(root, recursive, exts):
"""Traverses the root of directory that contains images and
generates image list iterator.
Parameters
----------
root: string
recursive: bool
exts: string
Returns
-------
image iterator that contains all the image under the specified path
"""
i = 0
if recursive:
cat = {}
for path, dirs, files in os.walk(root, followlinks=True):
dirs.sort()
files.sort()
for fname in files:
fpath = os.path.join(path, fname)
suffix = os.path.splitext(fname)[1].lower()
if os.path.isfile(fpath) and (suffix in exts):
if path not in cat:
cat[path] = len(cat)
yield (i, os.path.relpath(fpath, root), cat[path])
i += 1
for k, v in sorted(cat.items(), key=lambda x: x[1]):
print(os.path.relpath(k, root), v)
else:
for fname in sorted(os.listdir(root)):
fpath = os.path.join(root, fname)
suffix = os.path.splitext(fname)[1].lower()
if os.path.isfile(fpath) and (suffix in exts):
yield (i, os.path.relpath(fpath, root), 0)
i += 1
|
Hepler function to write image list into the file.
The format is as below,
integer_image_index \t float_label_index \t path_to_image
Note that the blank between number and tab is only used for readability.
Parameters
----------
path_out: string
image_list: list
|
def write_list(path_out, image_list):
"""Hepler function to write image list into the file.
The format is as below,
integer_image_index \t float_label_index \t path_to_image
Note that the blank between number and tab is only used for readability.
Parameters
----------
path_out: string
image_list: list
"""
with open(path_out, 'w') as fout:
for i, item in enumerate(image_list):
line = '%d\t' % item[0]
for j in item[2:]:
line += '%f\t' % j
line += '%s\n' % item[1]
fout.write(line)
|
Generates .lst file.
Parameters
----------
args: object that contains all the arguments
|
def make_list(args):
"""Generates .lst file.
Parameters
----------
args: object that contains all the arguments
"""
image_list = list_image(args.root, args.recursive, args.exts)
image_list = list(image_list)
if args.shuffle is True:
random.seed(100)
random.shuffle(image_list)
N = len(image_list)
chunk_size = (N + args.chunks - 1) // args.chunks
for i in range(args.chunks):
chunk = image_list[i * chunk_size:(i + 1) * chunk_size]
if args.chunks > 1:
str_chunk = '_%d' % i
else:
str_chunk = ''
sep = int(chunk_size * args.train_ratio)
sep_test = int(chunk_size * args.test_ratio)
if args.train_ratio == 1.0:
write_list(args.prefix + str_chunk + '.lst', chunk)
else:
if args.test_ratio:
write_list(args.prefix + str_chunk + '_test.lst', chunk[:sep_test])
if args.train_ratio + args.test_ratio < 1.0:
write_list(args.prefix + str_chunk + '_val.lst', chunk[sep_test + sep:])
write_list(args.prefix + str_chunk + '_train.lst', chunk[sep_test:sep_test + sep])
|
Reads the .lst file and generates corresponding iterator.
Parameters
----------
path_in: string
Returns
-------
item iterator that contains information in .lst file
|
def read_list(path_in):
"""Reads the .lst file and generates corresponding iterator.
Parameters
----------
path_in: string
Returns
-------
item iterator that contains information in .lst file
"""
with open(path_in) as fin:
while True:
line = fin.readline()
if not line:
break
line = [i.strip() for i in line.strip().split('\t')]
line_len = len(line)
# check the data format of .lst file
if line_len < 3:
print('lst should have at least has three parts, but only has %s parts for %s' % (line_len, line))
continue
try:
item = [int(line[0])] + [line[-1]] + [float(i) for i in line[1:-1]]
except Exception as e:
print('Parsing lst met error for %s, detail: %s' % (line, e))
continue
yield item
|
Reads, preprocesses, packs the image and put it back in output queue.
Parameters
----------
args: object
i: int
item: list
q_out: queue
|
def image_encode(args, i, item, q_out):
"""Reads, preprocesses, packs the image and put it back in output queue.
Parameters
----------
args: object
i: int
item: list
q_out: queue
"""
fullpath = os.path.join(args.root, item[1])
if len(item) > 3 and args.pack_label:
header = mx.recordio.IRHeader(0, item[2:], item[0], 0)
else:
header = mx.recordio.IRHeader(0, item[2], item[0], 0)
if args.pass_through:
try:
with open(fullpath, 'rb') as fin:
img = fin.read()
s = mx.recordio.pack(header, img)
q_out.put((i, s, item))
except Exception as e:
traceback.print_exc()
print('pack_img error:', item[1], e)
q_out.put((i, None, item))
return
try:
img = cv2.imread(fullpath, args.color)
except:
traceback.print_exc()
print('imread error trying to load file: %s ' % fullpath)
q_out.put((i, None, item))
return
if img is None:
print('imread read blank (None) image for file: %s' % fullpath)
q_out.put((i, None, item))
return
if args.center_crop:
if img.shape[0] > img.shape[1]:
margin = (img.shape[0] - img.shape[1]) // 2
img = img[margin:margin + img.shape[1], :]
else:
margin = (img.shape[1] - img.shape[0]) // 2
img = img[:, margin:margin + img.shape[0]]
if args.resize:
if img.shape[0] > img.shape[1]:
newsize = (args.resize, img.shape[0] * args.resize // img.shape[1])
else:
newsize = (img.shape[1] * args.resize // img.shape[0], args.resize)
img = cv2.resize(img, newsize)
try:
s = mx.recordio.pack_img(header, img, quality=args.quality, img_fmt=args.encoding)
q_out.put((i, s, item))
except Exception as e:
traceback.print_exc()
print('pack_img error on file: %s' % fullpath, e)
q_out.put((i, None, item))
return
|
Function that will be spawned to fetch the image
from the input queue and put it back to output queue.
Parameters
----------
args: object
q_in: queue
q_out: queue
|
def read_worker(args, q_in, q_out):
"""Function that will be spawned to fetch the image
from the input queue and put it back to output queue.
Parameters
----------
args: object
q_in: queue
q_out: queue
"""
while True:
deq = q_in.get()
if deq is None:
break
i, item = deq
image_encode(args, i, item, q_out)
|
Function that will be spawned to fetch processed image
from the output queue and write to the .rec file.
Parameters
----------
q_out: queue
fname: string
working_dir: string
|
def write_worker(q_out, fname, working_dir):
"""Function that will be spawned to fetch processed image
from the output queue and write to the .rec file.
Parameters
----------
q_out: queue
fname: string
working_dir: string
"""
pre_time = time.time()
count = 0
fname = os.path.basename(fname)
fname_rec = os.path.splitext(fname)[0] + '.rec'
fname_idx = os.path.splitext(fname)[0] + '.idx'
record = mx.recordio.MXIndexedRecordIO(os.path.join(working_dir, fname_idx),
os.path.join(working_dir, fname_rec), 'w')
buf = {}
more = True
while more:
deq = q_out.get()
if deq is not None:
i, s, item = deq
buf[i] = (s, item)
else:
more = False
while count in buf:
s, item = buf[count]
del buf[count]
if s is not None:
record.write_idx(item[0], s)
if count % 1000 == 0:
cur_time = time.time()
print('time:', cur_time - pre_time, ' count:', count)
pre_time = cur_time
count += 1
|
Defines all arguments.
Returns
-------
args object that contains all the params
|
def parse_args():
"""Defines all arguments.
Returns
-------
args object that contains all the params
"""
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description='Create an image list or \
make a record database by reading from an image list')
parser.add_argument('prefix', help='prefix of input/output lst and rec files.')
parser.add_argument('root', help='path to folder containing images.')
cgroup = parser.add_argument_group('Options for creating image lists')
cgroup.add_argument('--list', action='store_true',
help='If this is set im2rec will create image list(s) by traversing root folder\
and output to <prefix>.lst.\
Otherwise im2rec will read <prefix>.lst and create a database at <prefix>.rec')
cgroup.add_argument('--exts', nargs='+', default=['.jpeg', '.jpg', '.png'],
help='list of acceptable image extensions.')
cgroup.add_argument('--chunks', type=int, default=1, help='number of chunks.')
cgroup.add_argument('--train-ratio', type=float, default=1.0,
help='Ratio of images to use for training.')
cgroup.add_argument('--test-ratio', type=float, default=0,
help='Ratio of images to use for testing.')
cgroup.add_argument('--recursive', action='store_true',
help='If true recursively walk through subdirs and assign an unique label\
to images in each folder. Otherwise only include images in the root folder\
and give them label 0.')
cgroup.add_argument('--no-shuffle', dest='shuffle', action='store_false',
help='If this is passed, \
im2rec will not randomize the image order in <prefix>.lst')
rgroup = parser.add_argument_group('Options for creating database')
rgroup.add_argument('--pass-through', action='store_true',
help='whether to skip transformation and save image as is')
rgroup.add_argument('--resize', type=int, default=0,
help='resize the shorter edge of image to the newsize, original images will\
be packed by default.')
rgroup.add_argument('--center-crop', action='store_true',
help='specify whether to crop the center image to make it rectangular.')
rgroup.add_argument('--quality', type=int, default=95,
help='JPEG quality for encoding, 1-100; or PNG compression for encoding, 1-9')
rgroup.add_argument('--num-thread', type=int, default=1,
help='number of thread to use for encoding. order of images will be different\
from the input list if >1. the input list will be modified to match the\
resulting order.')
rgroup.add_argument('--color', type=int, default=1, choices=[-1, 0, 1],
help='specify the color mode of the loaded image.\
1: Loads a color image. Any transparency of image will be neglected. It is the default flag.\
0: Loads image in grayscale mode.\
-1:Loads image as such including alpha channel.')
rgroup.add_argument('--encoding', type=str, default='.jpg', choices=['.jpg', '.png'],
help='specify the encoding of the images.')
rgroup.add_argument('--pack-label', action='store_true',
help='Whether to also pack multi dimensional label in the record file')
args = parser.parse_args()
args.prefix = os.path.abspath(args.prefix)
args.root = os.path.abspath(args.root)
return args
|
Crop and normnalize an image nd array.
|
def transform(data, target_wd, target_ht, is_train, box):
"""Crop and normnalize an image nd array."""
if box is not None:
x, y, w, h = box
data = data[y:min(y+h, data.shape[0]), x:min(x+w, data.shape[1])]
# Resize to target_wd * target_ht.
data = mx.image.imresize(data, target_wd, target_ht)
# Normalize in the same way as the pre-trained model.
data = data.astype(np.float32) / 255.0
data = (data - mx.nd.array([0.485, 0.456, 0.406])) / mx.nd.array([0.229, 0.224, 0.225])
if is_train:
if random.random() < 0.5:
data = nd.flip(data, axis=1)
data, _ = mx.image.random_crop(data, (224, 224))
else:
data, _ = mx.image.center_crop(data, (224, 224))
# Transpose from (target_wd, target_ht, 3)
# to (3, target_wd, target_ht).
data = nd.transpose(data, (2, 0, 1))
# If image is greyscale, repeat 3 times to get RGB image.
if data.shape[0] == 1:
data = nd.tile(data, (3, 1, 1))
return data.reshape((1,) + data.shape)
|
Return training and testing iterator for the CUB200-2011 dataset.
|
def cub200_iterator(data_path, batch_k, batch_size, data_shape):
"""Return training and testing iterator for the CUB200-2011 dataset."""
return (CUB200Iter(data_path, batch_k, batch_size, data_shape, is_train=True),
CUB200Iter(data_path, batch_k, batch_size, data_shape, is_train=False))
|
Load and transform an image.
|
def get_image(self, img, is_train):
"""Load and transform an image."""
img_arr = mx.image.imread(img)
img_arr = transform(img_arr, 256, 256, is_train, self.boxes[img])
return img_arr
|
Sample a training batch (data and label).
|
def sample_train_batch(self):
"""Sample a training batch (data and label)."""
batch = []
labels = []
num_groups = self.batch_size // self.batch_k
# For CUB200, we use the first 100 classes for training.
sampled_classes = np.random.choice(100, num_groups, replace=False)
for i in range(num_groups):
img_fnames = np.random.choice(self.train_image_files[sampled_classes[i]],
self.batch_k, replace=False)
batch += [self.get_image(img_fname, is_train=True) for img_fname in img_fnames]
labels += [sampled_classes[i] for _ in range(self.batch_k)]
return nd.concatenate(batch, axis=0), labels
|
Return a batch.
|
def next(self):
"""Return a batch."""
if self.is_train:
data, labels = self.sample_train_batch()
else:
if self.test_count * self.batch_size < len(self.test_image_files):
data, labels = self.get_test_batch()
self.test_count += 1
else:
self.test_count = 0
raise StopIteration
return mx.io.DataBatch(data=[data], label=[labels])
|
Load mnist dataset
|
def load_mnist(training_num=50000):
"""Load mnist dataset"""
data_path = os.path.join(os.path.dirname(os.path.realpath('__file__')), 'mnist.npz')
if not os.path.isfile(data_path):
from six.moves import urllib
origin = (
'https://github.com/sxjscience/mxnet/raw/master/example/bayesian-methods/mnist.npz'
)
print('Downloading data from %s to %s' % (origin, data_path))
ctx = ssl._create_unverified_context()
with urllib.request.urlopen(origin, context=ctx) as u, open(data_path, 'wb') as f:
f.write(u.read())
print('Done!')
dat = numpy.load(data_path)
X = (dat['X'][:training_num] / 126.0).astype('float32')
Y = dat['Y'][:training_num]
X_test = (dat['X_test'] / 126.0).astype('float32')
Y_test = dat['Y_test']
Y = Y.reshape((Y.shape[0],))
Y_test = Y_test.reshape((Y_test.shape[0],))
return X, Y, X_test, Y_test
|
Check the library for compile-time features. The list of features are maintained in libinfo.h and libinfo.cc
Returns
-------
list
List of :class:`.Feature` objects
|
def feature_list():
"""
Check the library for compile-time features. The list of features are maintained in libinfo.h and libinfo.cc
Returns
-------
list
List of :class:`.Feature` objects
"""
lib_features_c_array = ctypes.POINTER(Feature)()
lib_features_size = ctypes.c_size_t()
check_call(_LIB.MXLibInfoFeatures(ctypes.byref(lib_features_c_array), ctypes.byref(lib_features_size)))
features = [lib_features_c_array[i] for i in range(lib_features_size.value)]
return features
|
Check for a particular feature by name
Parameters
----------
feature_name: str
The name of a valid feature as string for example 'CUDA'
Returns
-------
Boolean
True if it's enabled, False if it's disabled, RuntimeError if the feature is not known
|
def is_enabled(self, feature_name):
"""
Check for a particular feature by name
Parameters
----------
feature_name: str
The name of a valid feature as string for example 'CUDA'
Returns
-------
Boolean
True if it's enabled, False if it's disabled, RuntimeError if the feature is not known
"""
feature_name = feature_name.upper()
if feature_name not in self:
raise RuntimeError("Feature '{}' is unknown, known features are: {}".format(
feature_name, list(self.keys())))
return self[feature_name].enabled
|
make a directory to store all caches
Returns:
---------
cache path
|
def cache_path(self):
"""
make a directory to store all caches
Returns:
---------
cache path
"""
cache_path = os.path.join(os.path.dirname(__file__), '..', 'cache')
if not os.path.exists(cache_path):
os.mkdir(cache_path)
return cache_path
|
find out which indexes correspond to given image set (train or val)
Parameters:
----------
shuffle : boolean
whether to shuffle the image list
Returns:
----------
entire list of images specified in the setting
|
def _load_image_set_index(self, shuffle):
"""
find out which indexes correspond to given image set (train or val)
Parameters:
----------
shuffle : boolean
whether to shuffle the image list
Returns:
----------
entire list of images specified in the setting
"""
image_set_index_file = os.path.join(self.data_path, 'ImageSets', 'Main', self.image_set + '.txt')
assert os.path.exists(image_set_index_file), 'Path does not exist: {}'.format(image_set_index_file)
with open(image_set_index_file) as f:
image_set_index = [x.strip() for x in f.readlines()]
if shuffle:
np.random.shuffle(image_set_index)
return image_set_index
|
given image index, find out full path
Parameters:
----------
index: int
index of a specific image
Returns:
----------
full path of this image
|
def image_path_from_index(self, index):
"""
given image index, find out full path
Parameters:
----------
index: int
index of a specific image
Returns:
----------
full path of this image
"""
assert self.image_set_index is not None, "Dataset not initialized"
name = self.image_set_index[index]
image_file = os.path.join(self.data_path, 'JPEGImages', name + self.extension)
assert os.path.exists(image_file), 'Path does not exist: {}'.format(image_file)
return image_file
|
given image index, find out annotation path
Parameters:
----------
index: int
index of a specific image
Returns:
----------
full path of annotation file
|
def _label_path_from_index(self, index):
"""
given image index, find out annotation path
Parameters:
----------
index: int
index of a specific image
Returns:
----------
full path of annotation file
"""
label_file = os.path.join(self.data_path, 'Annotations', index + '.xml')
assert os.path.exists(label_file), 'Path does not exist: {}'.format(label_file)
return label_file
|
preprocess all ground-truths
Returns:
----------
labels packed in [num_images x max_num_objects x 5] tensor
|
def _load_image_labels(self):
"""
preprocess all ground-truths
Returns:
----------
labels packed in [num_images x max_num_objects x 5] tensor
"""
temp = []
# load ground-truth from xml annotations
for idx in self.image_set_index:
label_file = self._label_path_from_index(idx)
tree = ET.parse(label_file)
root = tree.getroot()
size = root.find('size')
width = float(size.find('width').text)
height = float(size.find('height').text)
label = []
for obj in root.iter('object'):
difficult = int(obj.find('difficult').text)
# if not self.config['use_difficult'] and difficult == 1:
# continue
cls_name = obj.find('name').text
if cls_name not in self.classes:
continue
cls_id = self.classes.index(cls_name)
xml_box = obj.find('bndbox')
xmin = float(xml_box.find('xmin').text) / width
ymin = float(xml_box.find('ymin').text) / height
xmax = float(xml_box.find('xmax').text) / width
ymax = float(xml_box.find('ymax').text) / height
label.append([cls_id, xmin, ymin, xmax, ymax, difficult])
temp.append(np.array(label))
return temp
|
top level evaluations
Parameters:
----------
detections: list
result list, each entry is a matrix of detections
Returns:
----------
None
|
def evaluate_detections(self, detections):
"""
top level evaluations
Parameters:
----------
detections: list
result list, each entry is a matrix of detections
Returns:
----------
None
"""
# make all these folders for results
result_dir = os.path.join(self.devkit_path, 'results')
if not os.path.exists(result_dir):
os.mkdir(result_dir)
year_folder = os.path.join(self.devkit_path, 'results', 'VOC' + self.year)
if not os.path.exists(year_folder):
os.mkdir(year_folder)
res_file_folder = os.path.join(self.devkit_path, 'results', 'VOC' + self.year, 'Main')
if not os.path.exists(res_file_folder):
os.mkdir(res_file_folder)
self.write_pascal_results(detections)
self.do_python_eval()
|
this is a template
VOCdevkit/results/VOC2007/Main/<comp_id>_det_test_aeroplane.txt
Returns:
----------
a string template
|
def get_result_file_template(self):
"""
this is a template
VOCdevkit/results/VOC2007/Main/<comp_id>_det_test_aeroplane.txt
Returns:
----------
a string template
"""
res_file_folder = os.path.join(self.devkit_path, 'results', 'VOC' + self.year, 'Main')
comp_id = self.config['comp_id']
filename = comp_id + '_det_' + self.image_set + '_{:s}.txt'
path = os.path.join(res_file_folder, filename)
return path
|
write results files in pascal devkit path
Parameters:
----------
all_boxes: list
boxes to be processed [bbox, confidence]
Returns:
----------
None
|
def write_pascal_results(self, all_boxes):
"""
write results files in pascal devkit path
Parameters:
----------
all_boxes: list
boxes to be processed [bbox, confidence]
Returns:
----------
None
"""
for cls_ind, cls in enumerate(self.classes):
print('Writing {} VOC results file'.format(cls))
filename = self.get_result_file_template().format(cls)
with open(filename, 'wt') as f:
for im_ind, index in enumerate(self.image_set_index):
dets = all_boxes[im_ind]
if dets.shape[0] < 1:
continue
h, w = self._get_imsize(self.image_path_from_index(im_ind))
# the VOCdevkit expects 1-based indices
for k in range(dets.shape[0]):
if (int(dets[k, 0]) == cls_ind):
f.write('{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'.
format(index, dets[k, 1],
int(dets[k, 2] * w) + 1, int(dets[k, 3] * h) + 1,
int(dets[k, 4] * w) + 1, int(dets[k, 5] * h) + 1))
|
python evaluation wrapper
Returns:
----------
None
|
def do_python_eval(self):
"""
python evaluation wrapper
Returns:
----------
None
"""
annopath = os.path.join(self.data_path, 'Annotations', '{:s}.xml')
imageset_file = os.path.join(self.data_path, 'ImageSets', 'Main', self.image_set + '.txt')
cache_dir = os.path.join(self.cache_path, self.name)
aps = []
# The PASCAL VOC metric changed in 2010
use_07_metric = True if int(self.year) < 2010 else False
print('VOC07 metric? ' + ('Y' if use_07_metric else 'No'))
for cls_ind, cls in enumerate(self.classes):
filename = self.get_result_file_template().format(cls)
rec, prec, ap = voc_eval(filename, annopath, imageset_file, cls, cache_dir,
ovthresh=0.5, use_07_metric=use_07_metric)
aps += [ap]
print('AP for {} = {:.4f}'.format(cls, ap))
print('Mean AP = {:.4f}'.format(np.mean(aps)))
|
get image size info
Returns:
----------
tuple of (height, width)
|
def _get_imsize(self, im_name):
"""
get image size info
Returns:
----------
tuple of (height, width)
"""
img = cv2.imread(im_name)
return (img.shape[0], img.shape[1])
|
parser : argparse.ArgumentParser
return a parser added with args required by fit
|
def add_fit_args(parser):
"""
parser : argparse.ArgumentParser
return a parser added with args required by fit
"""
train = parser.add_argument_group('Training', 'model training')
train.add_argument('--network', type=str,
help='the neural network to use')
train.add_argument('--num-layers', type=int,
help='number of layers in the neural network, \
required by some networks such as resnet')
train.add_argument('--gpus', type=str,
help='list of gpus to run, e.g. 0 or 0,2,5. empty means using cpu')
train.add_argument('--kv-store', type=str, default='device',
help='key-value store type')
train.add_argument('--num-epochs', type=int, default=100,
help='max num of epochs')
train.add_argument('--lr', type=float, default=0.1,
help='initial learning rate')
train.add_argument('--lr-factor', type=float, default=0.1,
help='the ratio to reduce lr on each step')
train.add_argument('--lr-step-epochs', type=str,
help='the epochs to reduce the lr, e.g. 30,60')
train.add_argument('--initializer', type=str, default='default',
help='the initializer type')
train.add_argument('--optimizer', type=str, default='sgd',
help='the optimizer type')
train.add_argument('--mom', type=float, default=0.9,
help='momentum for sgd')
train.add_argument('--wd', type=float, default=0.0001,
help='weight decay for sgd')
train.add_argument('--batch-size', type=int, default=128,
help='the batch size')
train.add_argument('--disp-batches', type=int, default=20,
help='show progress for every n batches')
train.add_argument('--model-prefix', type=str,
help='model prefix')
train.add_argument('--save-period', type=int, default=1, help='params saving period')
parser.add_argument('--monitor', dest='monitor', type=int, default=0,
help='log network parameters every N iters if larger than 0')
train.add_argument('--load-epoch', type=int,
help='load the model on an epoch using the model-load-prefix')
train.add_argument('--top-k', type=int, default=0,
help='report the top-k accuracy. 0 means no report.')
train.add_argument('--loss', type=str, default='',
help='show the cross-entropy or nll loss. ce strands for cross-entropy, nll-loss stands for likelihood loss')
train.add_argument('--test-io', type=int, default=0,
help='1 means test reading speed without training')
train.add_argument('--dtype', type=str, default='float32',
help='precision: float32 or float16')
train.add_argument('--gc-type', type=str, default='none',
help='type of gradient compression to use, \
takes `2bit` or `none` for now')
train.add_argument('--gc-threshold', type=float, default=0.5,
help='threshold for 2bit gradient compression')
# additional parameters for large batch sgd
train.add_argument('--macrobatch-size', type=int, default=0,
help='distributed effective batch size')
train.add_argument('--warmup-epochs', type=int, default=5,
help='the epochs to ramp-up lr to scaled large-batch value')
train.add_argument('--warmup-strategy', type=str, default='linear',
help='the ramping-up strategy for large batch sgd')
train.add_argument('--profile-worker-suffix', type=str, default='',
help='profile workers actions into this file. During distributed training\
filename saved will be rank1_ followed by this suffix')
train.add_argument('--profile-server-suffix', type=str, default='',
help='profile server actions into a file with name like rank1_ followed by this suffix \
during distributed training')
return train
|
train a model
args : argparse returns
network : the symbol definition of the nerual network
data_loader : function that returns the train and val data iterators
|
def fit(args, network, data_loader, **kwargs):
"""
train a model
args : argparse returns
network : the symbol definition of the nerual network
data_loader : function that returns the train and val data iterators
"""
# kvstore
kv = mx.kvstore.create(args.kv_store)
if args.gc_type != 'none':
kv.set_gradient_compression({'type': args.gc_type,
'threshold': args.gc_threshold})
if args.profile_server_suffix:
mx.profiler.set_config(filename=args.profile_server_suffix, profile_all=True, profile_process='server')
mx.profiler.set_state(state='run', profile_process='server')
if args.profile_worker_suffix:
if kv.num_workers > 1:
filename = 'rank' + str(kv.rank) + '_' + args.profile_worker_suffix
else:
filename = args.profile_worker_suffix
mx.profiler.set_config(filename=filename, profile_all=True, profile_process='worker')
mx.profiler.set_state(state='run', profile_process='worker')
# logging
head = '%(asctime)-15s Node[' + str(kv.rank) + '] %(message)s'
logging.basicConfig(level=logging.DEBUG, format=head)
logging.info('start with arguments %s', args)
epoch_size = get_epoch_size(args, kv)
# data iterators
(train, val) = data_loader(args, kv)
if 'dist' in args.kv_store and not 'async' in args.kv_store:
logging.info('Resizing training data to %d batches per machine', epoch_size)
# resize train iter to ensure each machine has same number of batches per epoch
# if not, dist_sync can hang at the end with one machine waiting for other machines
train = mx.io.ResizeIter(train, epoch_size)
if args.test_io:
tic = time.time()
for i, batch in enumerate(train):
if isinstance(batch, list):
for b in batch:
for j in b.data:
j.wait_to_read()
else:
for j in batch.data:
j.wait_to_read()
if (i + 1) % args.disp_batches == 0:
logging.info('Batch [%d]\tSpeed: %.2f samples/sec', i,
args.disp_batches * args.batch_size / (time.time() - tic))
tic = time.time()
return
# load model
if 'arg_params' in kwargs and 'aux_params' in kwargs:
arg_params = kwargs['arg_params']
aux_params = kwargs['aux_params']
else:
sym, arg_params, aux_params = _load_model(args, kv.rank)
if sym is not None:
assert sym.tojson() == network.tojson()
# save model
checkpoint = _save_model(args, kv.rank)
# devices for training
devs = mx.cpu() if args.gpus is None or args.gpus == "" else [
mx.gpu(int(i)) for i in args.gpus.split(',')]
# learning rate
lr, lr_scheduler = _get_lr_scheduler(args, kv)
# create model
model = mx.mod.Module(
context=devs,
symbol=network
)
lr_scheduler = lr_scheduler
optimizer_params = {
'learning_rate': lr,
'wd': args.wd,
'lr_scheduler': lr_scheduler,
'multi_precision': True}
# Only a limited number of optimizers have 'momentum' property
has_momentum = {'sgd', 'dcasgd', 'nag', 'signum', 'lbsgd'}
if args.optimizer in has_momentum:
optimizer_params['momentum'] = args.mom
monitor = mx.mon.Monitor(
args.monitor, pattern=".*") if args.monitor > 0 else None
# A limited number of optimizers have a warmup period
has_warmup = {'lbsgd', 'lbnag'}
if args.optimizer in has_warmup:
nworkers = kv.num_workers
if epoch_size < 1:
epoch_size = 1
macrobatch_size = args.macrobatch_size
if macrobatch_size < args.batch_size * nworkers:
macrobatch_size = args.batch_size * nworkers
#batch_scale = round(float(macrobatch_size) / args.batch_size / nworkers +0.4999)
batch_scale = math.ceil(
float(macrobatch_size) / args.batch_size / nworkers)
optimizer_params['updates_per_epoch'] = epoch_size
optimizer_params['begin_epoch'] = args.load_epoch if args.load_epoch else 0
optimizer_params['batch_scale'] = batch_scale
optimizer_params['warmup_strategy'] = args.warmup_strategy
optimizer_params['warmup_epochs'] = args.warmup_epochs
optimizer_params['num_epochs'] = args.num_epochs
if args.initializer == 'default':
if args.network == 'alexnet':
# AlexNet will not converge using Xavier
initializer = mx.init.Normal()
# VGG will not trend to converge using Xavier-Gaussian
elif args.network and 'vgg' in args.network:
initializer = mx.init.Xavier()
else:
initializer = mx.init.Xavier(
rnd_type='gaussian', factor_type="in", magnitude=2)
# initializer = mx.init.Xavier(factor_type="in", magnitude=2.34),
elif args.initializer == 'xavier':
initializer = mx.init.Xavier()
elif args.initializer == 'msra':
initializer = mx.init.MSRAPrelu()
elif args.initializer == 'orthogonal':
initializer = mx.init.Orthogonal()
elif args.initializer == 'normal':
initializer = mx.init.Normal()
elif args.initializer == 'uniform':
initializer = mx.init.Uniform()
elif args.initializer == 'one':
initializer = mx.init.One()
elif args.initializer == 'zero':
initializer = mx.init.Zero()
# evaluation metrices
eval_metrics = ['accuracy']
if args.top_k > 0:
eval_metrics.append(mx.metric.create(
'top_k_accuracy', top_k=args.top_k))
supported_loss = ['ce', 'nll_loss']
if len(args.loss) > 0:
# ce or nll loss is only applicable to softmax output
loss_type_list = args.loss.split(',')
if 'softmax_output' in network.list_outputs():
for loss_type in loss_type_list:
loss_type = loss_type.strip()
if loss_type == 'nll':
loss_type = 'nll_loss'
if loss_type not in supported_loss:
logging.warning(loss_type + ' is not an valid loss type, only cross-entropy or ' \
'negative likelihood loss is supported!')
else:
eval_metrics.append(mx.metric.create(loss_type))
else:
logging.warning("The output is not softmax_output, loss argument will be skipped!")
# callbacks that run after each batch
batch_end_callbacks = [mx.callback.Speedometer(
args.batch_size, args.disp_batches)]
if 'batch_end_callback' in kwargs:
cbs = kwargs['batch_end_callback']
batch_end_callbacks += cbs if isinstance(cbs, list) else [cbs]
# run
model.fit(train,
begin_epoch=args.load_epoch if args.load_epoch else 0,
num_epoch=args.num_epochs,
eval_data=val,
eval_metric=eval_metrics,
kvstore=kv,
optimizer=args.optimizer,
optimizer_params=optimizer_params,
initializer=initializer,
arg_params=arg_params,
aux_params=aux_params,
batch_end_callback=batch_end_callbacks,
epoch_end_callback=checkpoint,
allow_missing=True,
monitor=monitor)
if args.profile_server_suffix:
mx.profiler.set_state(state='run', profile_process='server')
if args.profile_worker_suffix:
mx.profiler.set_state(state='run', profile_process='worker')
|
Helper function to create multiple random crop augmenters.
Parameters
----------
min_object_covered : float or list of float, default=0.1
The cropped area of the image must contain at least this fraction of
any bounding box supplied. The value of this parameter should be non-negative.
In the case of 0, the cropped area does not need to overlap any of the
bounding boxes supplied.
min_eject_coverage : float or list of float, default=0.3
The minimum coverage of cropped sample w.r.t its original size. With this
constraint, objects that have marginal area after crop will be discarded.
aspect_ratio_range : tuple of floats or list of tuple of floats, default=(0.75, 1.33)
The cropped area of the image must have an aspect ratio = width / height
within this range.
area_range : tuple of floats or list of tuple of floats, default=(0.05, 1.0)
The cropped area of the image must contain a fraction of the supplied
image within in this range.
max_attempts : int or list of int, default=50
Number of attempts at generating a cropped/padded region of the image of the
specified constraints. After max_attempts failures, return the original image.
Examples
--------
>>> # An example of creating multiple random crop augmenters
>>> min_object_covered = [0.1, 0.3, 0.5, 0.7, 0.9] # use 5 augmenters
>>> aspect_ratio_range = (0.75, 1.33) # use same range for all augmenters
>>> area_range = [(0.1, 1.0), (0.2, 1.0), (0.2, 1.0), (0.3, 0.9), (0.5, 1.0)]
>>> min_eject_coverage = 0.3
>>> max_attempts = 50
>>> aug = mx.image.det.CreateMultiRandCropAugmenter(min_object_covered=min_object_covered,
aspect_ratio_range=aspect_ratio_range, area_range=area_range,
min_eject_coverage=min_eject_coverage, max_attempts=max_attempts,
skip_prob=0)
>>> aug.dumps() # show some details
|
def CreateMultiRandCropAugmenter(min_object_covered=0.1, aspect_ratio_range=(0.75, 1.33),
area_range=(0.05, 1.0), min_eject_coverage=0.3,
max_attempts=50, skip_prob=0):
"""Helper function to create multiple random crop augmenters.
Parameters
----------
min_object_covered : float or list of float, default=0.1
The cropped area of the image must contain at least this fraction of
any bounding box supplied. The value of this parameter should be non-negative.
In the case of 0, the cropped area does not need to overlap any of the
bounding boxes supplied.
min_eject_coverage : float or list of float, default=0.3
The minimum coverage of cropped sample w.r.t its original size. With this
constraint, objects that have marginal area after crop will be discarded.
aspect_ratio_range : tuple of floats or list of tuple of floats, default=(0.75, 1.33)
The cropped area of the image must have an aspect ratio = width / height
within this range.
area_range : tuple of floats or list of tuple of floats, default=(0.05, 1.0)
The cropped area of the image must contain a fraction of the supplied
image within in this range.
max_attempts : int or list of int, default=50
Number of attempts at generating a cropped/padded region of the image of the
specified constraints. After max_attempts failures, return the original image.
Examples
--------
>>> # An example of creating multiple random crop augmenters
>>> min_object_covered = [0.1, 0.3, 0.5, 0.7, 0.9] # use 5 augmenters
>>> aspect_ratio_range = (0.75, 1.33) # use same range for all augmenters
>>> area_range = [(0.1, 1.0), (0.2, 1.0), (0.2, 1.0), (0.3, 0.9), (0.5, 1.0)]
>>> min_eject_coverage = 0.3
>>> max_attempts = 50
>>> aug = mx.image.det.CreateMultiRandCropAugmenter(min_object_covered=min_object_covered,
aspect_ratio_range=aspect_ratio_range, area_range=area_range,
min_eject_coverage=min_eject_coverage, max_attempts=max_attempts,
skip_prob=0)
>>> aug.dumps() # show some details
"""
def align_parameters(params):
"""Align parameters as pairs"""
out_params = []
num = 1
for p in params:
if not isinstance(p, list):
p = [p]
out_params.append(p)
num = max(num, len(p))
# align for each param
for k, p in enumerate(out_params):
if len(p) != num:
assert len(p) == 1
out_params[k] = p * num
return out_params
aligned_params = align_parameters([min_object_covered, aspect_ratio_range, area_range,
min_eject_coverage, max_attempts])
augs = []
for moc, arr, ar, mec, ma in zip(*aligned_params):
augs.append(DetRandomCropAug(min_object_covered=moc, aspect_ratio_range=arr,
area_range=ar, min_eject_coverage=mec, max_attempts=ma))
return DetRandomSelectAug(augs, skip_prob=skip_prob)
|
Create augmenters for detection.
Parameters
----------
data_shape : tuple of int
Shape for output data
resize : int
Resize shorter edge if larger than 0 at the begining
rand_crop : float
[0, 1], probability to apply random cropping
rand_pad : float
[0, 1], probability to apply random padding
rand_gray : float
[0, 1], probability to convert to grayscale for all channels
rand_mirror : bool
Whether to apply horizontal flip to image with probability 0.5
mean : np.ndarray or None
Mean pixel values for [r, g, b]
std : np.ndarray or None
Standard deviations for [r, g, b]
brightness : float
Brightness jittering range (percent)
contrast : float
Contrast jittering range (percent)
saturation : float
Saturation jittering range (percent)
hue : float
Hue jittering range (percent)
pca_noise : float
Pca noise level (percent)
inter_method : int, default=2(Area-based)
Interpolation method for all resizing operations
Possible values:
0: Nearest Neighbors Interpolation.
1: Bilinear interpolation.
2: Area-based (resampling using pixel area relation). It may be a
preferred method for image decimation, as it gives moire-free
results. But when the image is zoomed, it is similar to the Nearest
Neighbors method. (used by default).
3: Bicubic interpolation over 4x4 pixel neighborhood.
4: Lanczos interpolation over 8x8 pixel neighborhood.
9: Cubic for enlarge, area for shrink, bilinear for others
10: Random select from interpolation method metioned above.
Note:
When shrinking an image, it will generally look best with AREA-based
interpolation, whereas, when enlarging an image, it will generally look best
with Bicubic (slow) or Bilinear (faster but still looks OK).
min_object_covered : float
The cropped area of the image must contain at least this fraction of
any bounding box supplied. The value of this parameter should be non-negative.
In the case of 0, the cropped area does not need to overlap any of the
bounding boxes supplied.
min_eject_coverage : float
The minimum coverage of cropped sample w.r.t its original size. With this
constraint, objects that have marginal area after crop will be discarded.
aspect_ratio_range : tuple of floats
The cropped area of the image must have an aspect ratio = width / height
within this range.
area_range : tuple of floats
The cropped area of the image must contain a fraction of the supplied
image within in this range.
max_attempts : int
Number of attempts at generating a cropped/padded region of the image of the
specified constraints. After max_attempts failures, return the original image.
pad_val: float
Pixel value to be filled when padding is enabled. pad_val will automatically
be subtracted by mean and divided by std if applicable.
Examples
--------
>>> # An example of creating multiple augmenters
>>> augs = mx.image.CreateDetAugmenter(data_shape=(3, 300, 300), rand_crop=0.5,
... rand_pad=0.5, rand_mirror=True, mean=True, brightness=0.125, contrast=0.125,
... saturation=0.125, pca_noise=0.05, inter_method=10, min_object_covered=[0.3, 0.5, 0.9],
... area_range=(0.3, 3.0))
>>> # dump the details
>>> for aug in augs:
... aug.dumps()
|
def CreateDetAugmenter(data_shape, resize=0, rand_crop=0, rand_pad=0, rand_gray=0,
rand_mirror=False, mean=None, std=None, brightness=0, contrast=0,
saturation=0, pca_noise=0, hue=0, inter_method=2, min_object_covered=0.1,
aspect_ratio_range=(0.75, 1.33), area_range=(0.05, 3.0),
min_eject_coverage=0.3, max_attempts=50, pad_val=(127, 127, 127)):
"""Create augmenters for detection.
Parameters
----------
data_shape : tuple of int
Shape for output data
resize : int
Resize shorter edge if larger than 0 at the begining
rand_crop : float
[0, 1], probability to apply random cropping
rand_pad : float
[0, 1], probability to apply random padding
rand_gray : float
[0, 1], probability to convert to grayscale for all channels
rand_mirror : bool
Whether to apply horizontal flip to image with probability 0.5
mean : np.ndarray or None
Mean pixel values for [r, g, b]
std : np.ndarray or None
Standard deviations for [r, g, b]
brightness : float
Brightness jittering range (percent)
contrast : float
Contrast jittering range (percent)
saturation : float
Saturation jittering range (percent)
hue : float
Hue jittering range (percent)
pca_noise : float
Pca noise level (percent)
inter_method : int, default=2(Area-based)
Interpolation method for all resizing operations
Possible values:
0: Nearest Neighbors Interpolation.
1: Bilinear interpolation.
2: Area-based (resampling using pixel area relation). It may be a
preferred method for image decimation, as it gives moire-free
results. But when the image is zoomed, it is similar to the Nearest
Neighbors method. (used by default).
3: Bicubic interpolation over 4x4 pixel neighborhood.
4: Lanczos interpolation over 8x8 pixel neighborhood.
9: Cubic for enlarge, area for shrink, bilinear for others
10: Random select from interpolation method metioned above.
Note:
When shrinking an image, it will generally look best with AREA-based
interpolation, whereas, when enlarging an image, it will generally look best
with Bicubic (slow) or Bilinear (faster but still looks OK).
min_object_covered : float
The cropped area of the image must contain at least this fraction of
any bounding box supplied. The value of this parameter should be non-negative.
In the case of 0, the cropped area does not need to overlap any of the
bounding boxes supplied.
min_eject_coverage : float
The minimum coverage of cropped sample w.r.t its original size. With this
constraint, objects that have marginal area after crop will be discarded.
aspect_ratio_range : tuple of floats
The cropped area of the image must have an aspect ratio = width / height
within this range.
area_range : tuple of floats
The cropped area of the image must contain a fraction of the supplied
image within in this range.
max_attempts : int
Number of attempts at generating a cropped/padded region of the image of the
specified constraints. After max_attempts failures, return the original image.
pad_val: float
Pixel value to be filled when padding is enabled. pad_val will automatically
be subtracted by mean and divided by std if applicable.
Examples
--------
>>> # An example of creating multiple augmenters
>>> augs = mx.image.CreateDetAugmenter(data_shape=(3, 300, 300), rand_crop=0.5,
... rand_pad=0.5, rand_mirror=True, mean=True, brightness=0.125, contrast=0.125,
... saturation=0.125, pca_noise=0.05, inter_method=10, min_object_covered=[0.3, 0.5, 0.9],
... area_range=(0.3, 3.0))
>>> # dump the details
>>> for aug in augs:
... aug.dumps()
"""
auglist = []
if resize > 0:
auglist.append(DetBorrowAug(ResizeAug(resize, inter_method)))
if rand_crop > 0:
crop_augs = CreateMultiRandCropAugmenter(min_object_covered, aspect_ratio_range,
area_range, min_eject_coverage,
max_attempts, skip_prob=(1 - rand_crop))
auglist.append(crop_augs)
if rand_mirror > 0:
auglist.append(DetHorizontalFlipAug(0.5))
# apply random padding as late as possible to save computation
if rand_pad > 0:
pad_aug = DetRandomPadAug(aspect_ratio_range,
(1.0, area_range[1]), max_attempts, pad_val)
auglist.append(DetRandomSelectAug([pad_aug], 1 - rand_pad))
# force resize
auglist.append(DetBorrowAug(ForceResizeAug((data_shape[2], data_shape[1]), inter_method)))
auglist.append(DetBorrowAug(CastAug()))
if brightness or contrast or saturation:
auglist.append(DetBorrowAug(ColorJitterAug(brightness, contrast, saturation)))
if hue:
auglist.append(DetBorrowAug(HueJitterAug(hue)))
if pca_noise > 0:
eigval = np.array([55.46, 4.794, 1.148])
eigvec = np.array([[-0.5675, 0.7192, 0.4009],
[-0.5808, -0.0045, -0.8140],
[-0.5836, -0.6948, 0.4203]])
auglist.append(DetBorrowAug(LightingAug(pca_noise, eigval, eigvec)))
if rand_gray > 0:
auglist.append(DetBorrowAug(RandomGrayAug(rand_gray)))
if mean is True:
mean = np.array([123.68, 116.28, 103.53])
elif mean is not None:
assert isinstance(mean, np.ndarray) and mean.shape[0] in [1, 3]
if std is True:
std = np.array([58.395, 57.12, 57.375])
elif std is not None:
assert isinstance(std, np.ndarray) and std.shape[0] in [1, 3]
if mean is not None or std is not None:
auglist.append(DetBorrowAug(ColorNormalizeAug(mean, std)))
return auglist
|
Override default.
|
def dumps(self):
"""Override default."""
return [self.__class__.__name__.lower(), [x.dumps() for x in self.aug_list]]
|
Calculate areas for multiple labels
|
def _calculate_areas(self, label):
"""Calculate areas for multiple labels"""
heights = np.maximum(0, label[:, 3] - label[:, 1])
widths = np.maximum(0, label[:, 2] - label[:, 0])
return heights * widths
|
Calculate intersect areas, normalized.
|
def _intersect(self, label, xmin, ymin, xmax, ymax):
"""Calculate intersect areas, normalized."""
left = np.maximum(label[:, 0], xmin)
right = np.minimum(label[:, 2], xmax)
top = np.maximum(label[:, 1], ymin)
bot = np.minimum(label[:, 3], ymax)
invalid = np.where(np.logical_or(left >= right, top >= bot))[0]
out = label.copy()
out[:, 0] = left
out[:, 1] = top
out[:, 2] = right
out[:, 3] = bot
out[invalid, :] = 0
return out
|
Check if constrains are satisfied
|
def _check_satisfy_constraints(self, label, xmin, ymin, xmax, ymax, width, height):
"""Check if constrains are satisfied"""
if (xmax - xmin) * (ymax - ymin) < 2:
return False # only 1 pixel
x1 = float(xmin) / width
y1 = float(ymin) / height
x2 = float(xmax) / width
y2 = float(ymax) / height
object_areas = self._calculate_areas(label[:, 1:])
valid_objects = np.where(object_areas * width * height > 2)[0]
if valid_objects.size < 1:
return False
intersects = self._intersect(label[valid_objects, 1:], x1, y1, x2, y2)
coverages = self._calculate_areas(intersects) / object_areas[valid_objects]
coverages = coverages[np.where(coverages > 0)[0]]
return coverages.size > 0 and np.amin(coverages) > self.min_object_covered
|
Convert labels according to crop box
|
def _update_labels(self, label, crop_box, height, width):
"""Convert labels according to crop box"""
xmin = float(crop_box[0]) / width
ymin = float(crop_box[1]) / height
w = float(crop_box[2]) / width
h = float(crop_box[3]) / height
out = label.copy()
out[:, (1, 3)] -= xmin
out[:, (2, 4)] -= ymin
out[:, (1, 3)] /= w
out[:, (2, 4)] /= h
out[:, 1:5] = np.maximum(0, out[:, 1:5])
out[:, 1:5] = np.minimum(1, out[:, 1:5])
coverage = self._calculate_areas(out[:, 1:]) * w * h / self._calculate_areas(label[:, 1:])
valid = np.logical_and(out[:, 3] > out[:, 1], out[:, 4] > out[:, 2])
valid = np.logical_and(valid, coverage > self.min_eject_coverage)
valid = np.where(valid)[0]
if valid.size < 1:
return None
out = out[valid, :]
return out
|
Propose cropping areas
|
def _random_crop_proposal(self, label, height, width):
"""Propose cropping areas"""
from math import sqrt
if not self.enabled or height <= 0 or width <= 0:
return ()
min_area = self.area_range[0] * height * width
max_area = self.area_range[1] * height * width
for _ in range(self.max_attempts):
ratio = random.uniform(*self.aspect_ratio_range)
if ratio <= 0:
continue
h = int(round(sqrt(min_area / ratio)))
max_h = int(round(sqrt(max_area / ratio)))
if round(max_h * ratio) > width:
# find smallest max_h satifying round(max_h * ratio) <= width
max_h = int((width + 0.4999999) / ratio)
if max_h > height:
max_h = height
if h > max_h:
h = max_h
if h < max_h:
# generate random h in range [h, max_h]
h = random.randint(h, max_h)
w = int(round(h * ratio))
assert w <= width
# trying to fix rounding problems
area = w * h
if area < min_area:
h += 1
w = int(round(h * ratio))
area = w * h
if area > max_area:
h -= 1
w = int(round(h * ratio))
area = w * h
if not (min_area <= area <= max_area and 0 <= w <= width and 0 <= h <= height):
continue
y = random.randint(0, max(0, height - h))
x = random.randint(0, max(0, width - w))
if self._check_satisfy_constraints(label, x, y, x + w, y + h, width, height):
new_label = self._update_labels(label, (x, y, w, h), height, width)
if new_label is not None:
return (x, y, w, h, new_label)
return ()
|
Update label according to padding region
|
def _update_labels(self, label, pad_box, height, width):
"""Update label according to padding region"""
out = label.copy()
out[:, (1, 3)] = (out[:, (1, 3)] * width + pad_box[0]) / pad_box[2]
out[:, (2, 4)] = (out[:, (2, 4)] * height + pad_box[1]) / pad_box[3]
return out
|
Generate random padding region
|
def _random_pad_proposal(self, label, height, width):
"""Generate random padding region"""
from math import sqrt
if not self.enabled or height <= 0 or width <= 0:
return ()
min_area = self.area_range[0] * height * width
max_area = self.area_range[1] * height * width
for _ in range(self.max_attempts):
ratio = random.uniform(*self.aspect_ratio_range)
if ratio <= 0:
continue
h = int(round(sqrt(min_area / ratio)))
max_h = int(round(sqrt(max_area / ratio)))
if round(h * ratio) < width:
h = int((width + 0.499999) / ratio)
if h < height:
h = height
if h > max_h:
h = max_h
if h < max_h:
h = random.randint(h, max_h)
w = int(round(h * ratio))
if (h - height) < 2 or (w - width) < 2:
continue # marginal padding is not helpful
y = random.randint(0, max(0, h - height))
x = random.randint(0, max(0, w - width))
new_label = self._update_labels(label, (x, y, w, h), height, width)
return (x, y, w, h, new_label)
return ()
|
Validate label and its shape.
|
def _check_valid_label(self, label):
"""Validate label and its shape."""
if len(label.shape) != 2 or label.shape[1] < 5:
msg = "Label with shape (1+, 5+) required, %s received." % str(label)
raise RuntimeError(msg)
valid_label = np.where(np.logical_and(label[:, 0] >= 0, label[:, 3] > label[:, 1],
label[:, 4] > label[:, 2]))[0]
if valid_label.size < 1:
raise RuntimeError('Invalid label occurs.')
|
Helper function to estimate label shape
|
def _estimate_label_shape(self):
"""Helper function to estimate label shape"""
max_count = 0
self.reset()
try:
while True:
label, _ = self.next_sample()
label = self._parse_label(label)
max_count = max(max_count, label.shape[0])
except StopIteration:
pass
self.reset()
return (max_count, label.shape[1])
|
Helper function to parse object detection label.
Format for raw label:
n \t k \t ... \t [id \t xmin\t ymin \t xmax \t ymax \t ...] \t [repeat]
where n is the width of header, 2 or larger
k is the width of each object annotation, can be arbitrary, at least 5
|
def _parse_label(self, label):
"""Helper function to parse object detection label.
Format for raw label:
n \t k \t ... \t [id \t xmin\t ymin \t xmax \t ymax \t ...] \t [repeat]
where n is the width of header, 2 or larger
k is the width of each object annotation, can be arbitrary, at least 5
"""
if isinstance(label, nd.NDArray):
label = label.asnumpy()
raw = label.ravel()
if raw.size < 7:
raise RuntimeError("Label shape is invalid: " + str(raw.shape))
header_width = int(raw[0])
obj_width = int(raw[1])
if (raw.size - header_width) % obj_width != 0:
msg = "Label shape %s inconsistent with annotation width %d." \
%(str(raw.shape), obj_width)
raise RuntimeError(msg)
out = np.reshape(raw[header_width:], (-1, obj_width))
# remove bad ground-truths
valid = np.where(np.logical_and(out[:, 3] > out[:, 1], out[:, 4] > out[:, 2]))[0]
if valid.size < 1:
raise RuntimeError('Encounter sample with no valid label.')
return out[valid, :]
|
Reshape iterator for data_shape or label_shape.
Parameters
----------
data_shape : tuple or None
Reshape the data_shape to the new shape if not None
label_shape : tuple or None
Reshape label shape to new shape if not None
|
def reshape(self, data_shape=None, label_shape=None):
"""Reshape iterator for data_shape or label_shape.
Parameters
----------
data_shape : tuple or None
Reshape the data_shape to the new shape if not None
label_shape : tuple or None
Reshape label shape to new shape if not None
"""
if data_shape is not None:
self.check_data_shape(data_shape)
self.provide_data = [(self.provide_data[0][0], (self.batch_size,) + data_shape)]
self.data_shape = data_shape
if label_shape is not None:
self.check_label_shape(label_shape)
self.provide_label = [(self.provide_label[0][0], (self.batch_size,) + label_shape)]
self.label_shape = label_shape
|
Override the helper function for batchifying data
|
def _batchify(self, batch_data, batch_label, start=0):
"""Override the helper function for batchifying data"""
i = start
batch_size = self.batch_size
try:
while i < batch_size:
label, s = self.next_sample()
data = self.imdecode(s)
try:
self.check_valid_image([data])
label = self._parse_label(label)
data, label = self.augmentation_transform(data, label)
self._check_valid_label(label)
except RuntimeError as e:
logging.debug('Invalid image, skipping: %s', str(e))
continue
for datum in [data]:
assert i < batch_size, 'Batch size must be multiples of augmenter output length'
batch_data[i] = self.postprocess_data(datum)
num_object = label.shape[0]
batch_label[i][0:num_object] = nd.array(label)
if num_object < batch_label[i].shape[0]:
batch_label[i][num_object:] = -1
i += 1
except StopIteration:
if not i:
raise StopIteration
return i
|
Override the function for returning next batch.
|
def next(self):
"""Override the function for returning next batch."""
batch_size = self.batch_size
c, h, w = self.data_shape
# if last batch data is rolled over
if self._cache_data is not None:
# check both the data and label have values
assert self._cache_label is not None, "_cache_label didn't have values"
assert self._cache_idx is not None, "_cache_idx didn't have values"
batch_data = self._cache_data
batch_label = self._cache_label
i = self._cache_idx
else:
batch_data = nd.zeros((batch_size, c, h, w))
batch_label = nd.empty(self.provide_label[0][1])
batch_label[:] = -1
i = self._batchify(batch_data, batch_label)
# calculate the padding
pad = batch_size - i
# handle padding for the last batch
if pad != 0:
if self.last_batch_handle == 'discard':
raise StopIteration
# if the option is 'roll_over', throw StopIteration and cache the data
elif self.last_batch_handle == 'roll_over' and \
self._cache_data is None:
self._cache_data = batch_data
self._cache_label = batch_label
self._cache_idx = i
raise StopIteration
else:
_ = self._batchify(batch_data, batch_label, i)
if self.last_batch_handle == 'pad':
self._allow_read = False
else:
self._cache_data = None
self._cache_label = None
self._cache_idx = None
return io.DataBatch([batch_data], [batch_label], pad=pad)
|
Override Transforms input data with specified augmentations.
|
def augmentation_transform(self, data, label): # pylint: disable=arguments-differ
"""Override Transforms input data with specified augmentations."""
for aug in self.auglist:
data, label = aug(data, label)
return (data, label)
|
Checks if the new label shape is valid
|
def check_label_shape(self, label_shape):
"""Checks if the new label shape is valid"""
if not len(label_shape) == 2:
raise ValueError('label_shape should have length 2')
if label_shape[0] < self.label_shape[0]:
msg = 'Attempts to reduce label count from %d to %d, not allowed.' \
% (self.label_shape[0], label_shape[0])
raise ValueError(msg)
if label_shape[1] != self.provide_label[0][1][2]:
msg = 'label_shape object width inconsistent: %d vs %d.' \
% (self.provide_label[0][1][2], label_shape[1])
raise ValueError(msg)
|
Display next image with bounding boxes drawn.
Parameters
----------
color : tuple
Bounding box color in RGB, use None for random color
thickness : int
Bounding box border thickness
mean : True or numpy.ndarray
Compensate for the mean to have better visual effect
std : True or numpy.ndarray
Revert standard deviations
clip : bool
If true, clip to [0, 255] for better visual effect
waitKey : None or int
Hold the window for waitKey milliseconds if set, skip ploting if None
window_name : str
Plot window name if waitKey is set.
id2labels : dict
Mapping of labels id to labels name.
Returns
-------
numpy.ndarray
Examples
--------
>>> # use draw_next to get images with bounding boxes drawn
>>> iterator = mx.image.ImageDetIter(1, (3, 600, 600), path_imgrec='train.rec')
>>> for image in iterator.draw_next(waitKey=None):
... # display image
>>> # or let draw_next display using cv2 module
>>> for image in iterator.draw_next(waitKey=0, window_name='disp'):
... pass
|
def draw_next(self, color=None, thickness=2, mean=None, std=None, clip=True,
waitKey=None, window_name='draw_next', id2labels=None):
"""Display next image with bounding boxes drawn.
Parameters
----------
color : tuple
Bounding box color in RGB, use None for random color
thickness : int
Bounding box border thickness
mean : True or numpy.ndarray
Compensate for the mean to have better visual effect
std : True or numpy.ndarray
Revert standard deviations
clip : bool
If true, clip to [0, 255] for better visual effect
waitKey : None or int
Hold the window for waitKey milliseconds if set, skip ploting if None
window_name : str
Plot window name if waitKey is set.
id2labels : dict
Mapping of labels id to labels name.
Returns
-------
numpy.ndarray
Examples
--------
>>> # use draw_next to get images with bounding boxes drawn
>>> iterator = mx.image.ImageDetIter(1, (3, 600, 600), path_imgrec='train.rec')
>>> for image in iterator.draw_next(waitKey=None):
... # display image
>>> # or let draw_next display using cv2 module
>>> for image in iterator.draw_next(waitKey=0, window_name='disp'):
... pass
"""
try:
import cv2
except ImportError as e:
warnings.warn('Unable to import cv2, skip drawing: %s', str(e))
return
count = 0
try:
while True:
label, s = self.next_sample()
data = self.imdecode(s)
try:
self.check_valid_image([data])
label = self._parse_label(label)
except RuntimeError as e:
logging.debug('Invalid image, skipping: %s', str(e))
continue
count += 1
data, label = self.augmentation_transform(data, label)
image = data.asnumpy()
# revert color_normalize
if std is True:
std = np.array([58.395, 57.12, 57.375])
elif std is not None:
assert isinstance(std, np.ndarray) and std.shape[0] in [1, 3]
if std is not None:
image *= std
if mean is True:
mean = np.array([123.68, 116.28, 103.53])
elif mean is not None:
assert isinstance(mean, np.ndarray) and mean.shape[0] in [1, 3]
if mean is not None:
image += mean
# swap RGB
image[:, :, (0, 1, 2)] = image[:, :, (2, 1, 0)]
if clip:
image = np.maximum(0, np.minimum(255, image))
if color:
color = color[::-1]
image = image.astype(np.uint8)
height, width, _ = image.shape
for i in range(label.shape[0]):
x1 = int(label[i, 1] * width)
if x1 < 0:
continue
y1 = int(label[i, 2] * height)
x2 = int(label[i, 3] * width)
y2 = int(label[i, 4] * height)
bc = np.random.rand(3) * 255 if not color else color
cv2.rectangle(image, (x1, y1), (x2, y2), bc, thickness)
if id2labels is not None:
cls_id = int(label[i, 0])
if cls_id in id2labels:
cls_name = id2labels[cls_id]
text = "{:s}".format(cls_name)
font = cv2.FONT_HERSHEY_SIMPLEX
font_scale = 0.5
text_height = cv2.getTextSize(text, font, font_scale, 2)[0][1]
tc = (255, 255, 255)
tpos = (x1 + 5, y1 + text_height + 5)
cv2.putText(image, text, tpos, font, font_scale, tc, 2)
if waitKey is not None:
cv2.imshow(window_name, image)
cv2.waitKey(waitKey)
yield image
except StopIteration:
if not count:
return
|
Synchronize label shape with the input iterator. This is useful when
train/validation iterators have different label padding.
Parameters
----------
it : ImageDetIter
The other iterator to synchronize
verbose : bool
Print verbose log if true
Returns
-------
ImageDetIter
The synchronized other iterator, the internal label shape is updated as well.
Examples
--------
>>> train_iter = mx.image.ImageDetIter(32, (3, 300, 300), path_imgrec='train.rec')
>>> val_iter = mx.image.ImageDetIter(32, (3, 300, 300), path.imgrec='val.rec')
>>> train_iter.label_shape
(30, 6)
>>> val_iter.label_shape
(25, 6)
>>> val_iter = train_iter.sync_label_shape(val_iter, verbose=False)
>>> train_iter.label_shape
(30, 6)
>>> val_iter.label_shape
(30, 6)
|
def sync_label_shape(self, it, verbose=False):
"""Synchronize label shape with the input iterator. This is useful when
train/validation iterators have different label padding.
Parameters
----------
it : ImageDetIter
The other iterator to synchronize
verbose : bool
Print verbose log if true
Returns
-------
ImageDetIter
The synchronized other iterator, the internal label shape is updated as well.
Examples
--------
>>> train_iter = mx.image.ImageDetIter(32, (3, 300, 300), path_imgrec='train.rec')
>>> val_iter = mx.image.ImageDetIter(32, (3, 300, 300), path.imgrec='val.rec')
>>> train_iter.label_shape
(30, 6)
>>> val_iter.label_shape
(25, 6)
>>> val_iter = train_iter.sync_label_shape(val_iter, verbose=False)
>>> train_iter.label_shape
(30, 6)
>>> val_iter.label_shape
(30, 6)
"""
assert isinstance(it, ImageDetIter), 'Synchronize with invalid iterator.'
train_label_shape = self.label_shape
val_label_shape = it.label_shape
assert train_label_shape[1] == val_label_shape[1], "object width mismatch."
max_count = max(train_label_shape[0], val_label_shape[0])
if max_count > train_label_shape[0]:
self.reshape(None, (max_count, train_label_shape[1]))
if max_count > val_label_shape[0]:
it.reshape(None, (max_count, val_label_shape[1]))
if verbose and max_count > min(train_label_shape[0], val_label_shape[0]):
logging.info('Resized label_shape to (%d, %d).', max_count, train_label_shape[1])
return it
|
Generate anchor (reference) windows by enumerating aspect ratios X
scales wrt a reference (0, 0, 15, 15) window.
|
def _generate_base_anchors(base_size, scales, ratios):
"""
Generate anchor (reference) windows by enumerating aspect ratios X
scales wrt a reference (0, 0, 15, 15) window.
"""
base_anchor = np.array([1, 1, base_size, base_size]) - 1
ratio_anchors = AnchorGenerator._ratio_enum(base_anchor, ratios)
anchors = np.vstack([AnchorGenerator._scale_enum(ratio_anchors[i, :], scales)
for i in range(ratio_anchors.shape[0])])
return anchors
|
Return width, height, x center, and y center for an anchor (window).
|
def _whctrs(anchor):
"""
Return width, height, x center, and y center for an anchor (window).
"""
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
|
Given a vector of widths (ws) and heights (hs) around a center
(x_ctr, y_ctr), output a set of anchors (windows).
|
def _mkanchors(ws, hs, x_ctr, y_ctr):
"""
Given a vector of widths (ws) and heights (hs) around a center
(x_ctr, y_ctr), output a set of anchors (windows).
"""
ws = ws[:, np.newaxis]
hs = hs[:, np.newaxis]
anchors = np.hstack((x_ctr - 0.5 * (ws - 1),
y_ctr - 0.5 * (hs - 1),
x_ctr + 0.5 * (ws - 1),
y_ctr + 0.5 * (hs - 1)))
return anchors
|
Enumerate a set of anchors for each aspect ratio wrt an anchor.
|
def _ratio_enum(anchor, ratios):
"""
Enumerate a set of anchors for each aspect ratio wrt an anchor.
"""
w, h, x_ctr, y_ctr = AnchorGenerator._whctrs(anchor)
size = w * h
size_ratios = size / ratios
ws = np.round(np.sqrt(size_ratios))
hs = np.round(ws * ratios)
anchors = AnchorGenerator._mkanchors(ws, hs, x_ctr, y_ctr)
return anchors
|
Enumerate a set of anchors for each scale wrt an anchor.
|
def _scale_enum(anchor, scales):
"""
Enumerate a set of anchors for each scale wrt an anchor.
"""
w, h, x_ctr, y_ctr = AnchorGenerator._whctrs(anchor)
ws = w * scales
hs = h * scales
anchors = AnchorGenerator._mkanchors(ws, hs, x_ctr, y_ctr)
return anchors
|
set atual shape of data
|
def prepare_data(args):
"""
set atual shape of data
"""
rnn_type = args.config.get("arch", "rnn_type")
num_rnn_layer = args.config.getint("arch", "num_rnn_layer")
num_hidden_rnn_list = json.loads(args.config.get("arch", "num_hidden_rnn_list"))
batch_size = args.config.getint("common", "batch_size")
if rnn_type == 'lstm':
init_c = [('l%d_init_c' % l, (batch_size, num_hidden_rnn_list[l]))
for l in range(num_rnn_layer)]
init_h = [('l%d_init_h' % l, (batch_size, num_hidden_rnn_list[l]))
for l in range(num_rnn_layer)]
elif rnn_type == 'bilstm':
forward_init_c = [('forward_l%d_init_c' % l, (batch_size, num_hidden_rnn_list[l]))
for l in range(num_rnn_layer)]
backward_init_c = [('backward_l%d_init_c' % l, (batch_size, num_hidden_rnn_list[l]))
for l in range(num_rnn_layer)]
init_c = forward_init_c + backward_init_c
forward_init_h = [('forward_l%d_init_h' % l, (batch_size, num_hidden_rnn_list[l]))
for l in range(num_rnn_layer)]
backward_init_h = [('backward_l%d_init_h' % l, (batch_size, num_hidden_rnn_list[l]))
for l in range(num_rnn_layer)]
init_h = forward_init_h + backward_init_h
elif rnn_type == 'gru':
init_h = [('l%d_init_h' % l, (batch_size, num_hidden_rnn_list[l]))
for l in range(num_rnn_layer)]
elif rnn_type == 'bigru':
forward_init_h = [('forward_l%d_init_h' % l, (batch_size, num_hidden_rnn_list[l]))
for l in range(num_rnn_layer)]
backward_init_h = [('backward_l%d_init_h' % l, (batch_size, num_hidden_rnn_list[l]))
for l in range(num_rnn_layer)]
init_h = forward_init_h + backward_init_h
else:
raise Exception('network type should be one of the lstm,bilstm,gru,bigru')
if rnn_type == 'lstm' or rnn_type == 'bilstm':
init_states = init_c + init_h
elif rnn_type == 'gru' or rnn_type == 'bigru':
init_states = init_h
return init_states
|
define deep speech 2 network
|
def arch(args, seq_len=None):
"""
define deep speech 2 network
"""
if isinstance(args, argparse.Namespace):
mode = args.config.get("common", "mode")
is_bucketing = args.config.getboolean("arch", "is_bucketing")
if mode == "train" or is_bucketing:
channel_num = args.config.getint("arch", "channel_num")
conv_layer1_filter_dim = \
tuple(json.loads(args.config.get("arch", "conv_layer1_filter_dim")))
conv_layer1_stride = tuple(json.loads(args.config.get("arch", "conv_layer1_stride")))
conv_layer2_filter_dim = \
tuple(json.loads(args.config.get("arch", "conv_layer2_filter_dim")))
conv_layer2_stride = tuple(json.loads(args.config.get("arch", "conv_layer2_stride")))
rnn_type = args.config.get("arch", "rnn_type")
num_rnn_layer = args.config.getint("arch", "num_rnn_layer")
num_hidden_rnn_list = json.loads(args.config.get("arch", "num_hidden_rnn_list"))
is_batchnorm = args.config.getboolean("arch", "is_batchnorm")
if seq_len is None:
seq_len = args.config.getint('arch', 'max_t_count')
num_label = args.config.getint('arch', 'max_label_length')
num_rear_fc_layers = args.config.getint("arch", "num_rear_fc_layers")
num_hidden_rear_fc_list = json.loads(args.config.get("arch", "num_hidden_rear_fc_list"))
act_type_rear_fc_list = json.loads(args.config.get("arch", "act_type_rear_fc_list"))
# model symbol generation
# input preparation
data = mx.sym.Variable('data')
label = mx.sym.Variable('label')
net = mx.sym.Reshape(data=data, shape=(-4, -1, 1, 0, 0))
net = conv(net=net,
channels=channel_num,
filter_dimension=conv_layer1_filter_dim,
stride=conv_layer1_stride,
no_bias=is_batchnorm,
name='conv1')
if is_batchnorm:
# batch norm normalizes axis 1
net = batchnorm(net, name="conv1_batchnorm")
net = conv(net=net,
channels=channel_num,
filter_dimension=conv_layer2_filter_dim,
stride=conv_layer2_stride,
no_bias=is_batchnorm,
name='conv2')
if is_batchnorm:
# batch norm normalizes axis 1
net = batchnorm(net, name="conv2_batchnorm")
net = mx.sym.transpose(data=net, axes=(0, 2, 1, 3))
net = mx.sym.Reshape(data=net, shape=(0, 0, -3))
seq_len_after_conv_layer1 = int(
math.floor((seq_len - conv_layer1_filter_dim[0]) / conv_layer1_stride[0])) + 1
seq_len_after_conv_layer2 = int(
math.floor((seq_len_after_conv_layer1 - conv_layer2_filter_dim[0])
/ conv_layer2_stride[0])) + 1
net = slice_symbol_to_seq_symobls(net=net, seq_len=seq_len_after_conv_layer2, axis=1)
if rnn_type == "bilstm":
net = bi_lstm_unroll(net=net,
seq_len=seq_len_after_conv_layer2,
num_hidden_lstm_list=num_hidden_rnn_list,
num_lstm_layer=num_rnn_layer,
dropout=0.,
is_batchnorm=is_batchnorm,
is_bucketing=is_bucketing)
elif rnn_type == "gru":
net = gru_unroll(net=net,
seq_len=seq_len_after_conv_layer2,
num_hidden_gru_list=num_hidden_rnn_list,
num_gru_layer=num_rnn_layer,
dropout=0.,
is_batchnorm=is_batchnorm,
is_bucketing=is_bucketing)
elif rnn_type == "bigru":
net = bi_gru_unroll(net=net,
seq_len=seq_len_after_conv_layer2,
num_hidden_gru_list=num_hidden_rnn_list,
num_gru_layer=num_rnn_layer,
dropout=0.,
is_batchnorm=is_batchnorm,
is_bucketing=is_bucketing)
else:
raise Exception('rnn_type should be one of the followings, bilstm,gru,bigru')
# rear fc layers
net = sequence_fc(net=net, seq_len=seq_len_after_conv_layer2,
num_layer=num_rear_fc_layers, prefix="rear",
num_hidden_list=num_hidden_rear_fc_list,
act_type_list=act_type_rear_fc_list,
is_batchnorm=is_batchnorm)
# warpctc layer
net = warpctc_layer(net=net,
seq_len=seq_len_after_conv_layer2,
label=label,
num_label=num_label,
character_classes_count=
(args.config.getint('arch', 'n_classes') + 1))
args.config.set('arch', 'max_t_count', str(seq_len_after_conv_layer2))
return net
elif mode == 'load' or mode == 'predict':
conv_layer1_filter_dim = \
tuple(json.loads(args.config.get("arch", "conv_layer1_filter_dim")))
conv_layer1_stride = tuple(json.loads(args.config.get("arch", "conv_layer1_stride")))
conv_layer2_filter_dim = \
tuple(json.loads(args.config.get("arch", "conv_layer2_filter_dim")))
conv_layer2_stride = tuple(json.loads(args.config.get("arch", "conv_layer2_stride")))
if seq_len is None:
seq_len = args.config.getint('arch', 'max_t_count')
seq_len_after_conv_layer1 = int(
math.floor((seq_len - conv_layer1_filter_dim[0]) / conv_layer1_stride[0])) + 1
seq_len_after_conv_layer2 = int(
math.floor((seq_len_after_conv_layer1 - conv_layer2_filter_dim[0])
/ conv_layer2_stride[0])) + 1
args.config.set('arch', 'max_t_count', str(seq_len_after_conv_layer2))
else:
raise Exception('mode must be the one of the followings - train,predict,load')
|
Description : run lipnet training code using argument info
|
def main():
"""
Description : run lipnet training code using argument info
"""
parser = argparse.ArgumentParser()
parser.add_argument('--batch_size', type=int, default=64)
parser.add_argument('--epochs', type=int, default=100)
parser.add_argument('--image_path', type=str, default='./data/datasets/')
parser.add_argument('--align_path', type=str, default='./data/align/')
parser.add_argument('--dr_rate', type=float, default=0.5)
parser.add_argument('--num_gpus', type=int, default=1)
parser.add_argument('--num_workers', type=int, default=0)
parser.add_argument('--model_path', type=str, default=None)
config = parser.parse_args()
trainer = Train(config)
trainer.build_model(dr_rate=config.dr_rate, path=config.model_path)
trainer.load_dataloader()
trainer.run(epochs=config.epochs)
|
visualize [cls, conf, x1, y1, x2, y2]
|
def vis_detection(im_orig, detections, class_names, thresh=0.7):
"""visualize [cls, conf, x1, y1, x2, y2]"""
import matplotlib.pyplot as plt
import random
plt.imshow(im_orig)
colors = [(random.random(), random.random(), random.random()) for _ in class_names]
for [cls, conf, x1, y1, x2, y2] in detections:
cls = int(cls)
if cls > 0 and conf > thresh:
rect = plt.Rectangle((x1, y1), x2 - x1, y2 - y1,
fill=False, edgecolor=colors[cls], linewidth=3.5)
plt.gca().add_patch(rect)
plt.gca().text(x1, y1 - 2, '{:s} {:.3f}'.format(class_names[cls], conf),
bbox=dict(facecolor=colors[cls], alpha=0.5), fontsize=12, color='white')
plt.show()
|
Check the difference between predictions from MXNet and CoreML.
|
def check_error(model, path, shapes, output = 'softmax_output', verbose = True):
"""
Check the difference between predictions from MXNet and CoreML.
"""
coreml_model = _coremltools.models.MLModel(path)
input_data = {}
input_data_copy = {}
for ip in shapes:
input_data[ip] = _np.random.rand(*shapes[ip]).astype('f')
input_data_copy[ip] = _np.copy(input_data[ip])
dataIter = _mxnet.io.NDArrayIter(input_data_copy)
mx_out = model.predict(dataIter).flatten()
e_out_dict = coreml_model.predict(_mxnet_remove_batch(input_data))
e_out = e_out_dict[output].flatten()
error = _np.linalg.norm(e_out - mx_out)
if verbose:
print("First few predictions from CoreML : %s" % e_out[0:10])
print("First few predictions from MXNet : %s" % e_out[0:10])
print("L2 Error on random data %s" % error)
return error
|
Description : set gpu module
|
def setting_ctx(num_gpus):
"""
Description : set gpu module
"""
if num_gpus > 0:
ctx = [mx.gpu(i) for i in range(num_gpus)]
else:
ctx = [mx.cpu()]
return ctx
|
Description : apply beam search for prediction result
|
def char_beam_search(out):
"""
Description : apply beam search for prediction result
"""
out_conv = list()
for idx in range(out.shape[0]):
probs = out[idx]
prob = probs.softmax().asnumpy()
line_string_proposals = ctcBeamSearch(prob, ALPHABET, None, k=4, beamWidth=25)
out_conv.append(line_string_proposals[0])
return out_conv
|
Description : build network
|
def build_model(self, dr_rate=0, path=None):
"""
Description : build network
"""
#set network
self.net = LipNet(dr_rate)
self.net.hybridize()
self.net.initialize(ctx=self.ctx)
if path is not None:
self.load_model(path)
#set optimizer
self.loss_fn = gluon.loss.CTCLoss()
self.trainer = gluon.Trainer(self.net.collect_params(), \
optimizer='SGD')
|
Description : save parameter of network weight
|
def save_model(self, epoch, loss):
"""
Description : save parameter of network weight
"""
prefix = 'checkpoint/epoches'
file_name = "{prefix}_{epoch}_loss_{l:.4f}".format(prefix=prefix,
epoch=str(epoch),
l=loss)
self.net.save_parameters(file_name)
|
Description : Setup the dataloader
|
def load_dataloader(self):
"""
Description : Setup the dataloader
"""
input_transform = transforms.Compose([transforms.ToTensor(), \
transforms.Normalize((0.7136, 0.4906, 0.3283), \
(0.1138, 0.1078, 0.0917))])
training_dataset = LipsDataset(self.image_path,
self.align_path,
mode='train',
transform=input_transform,
seq_len=self.seq_len)
self.train_dataloader = mx.gluon.data.DataLoader(training_dataset,
batch_size=self.batch_size,
shuffle=True,
num_workers=self.num_workers)
valid_dataset = LipsDataset(self.image_path,
self.align_path,
mode='valid',
transform=input_transform,
seq_len=self.seq_len)
self.valid_dataloader = mx.gluon.data.DataLoader(valid_dataset,
batch_size=self.batch_size,
shuffle=True,
num_workers=self.num_workers)
|
Description : training for LipNet
|
def train(self, data, label, batch_size):
"""
Description : training for LipNet
"""
# pylint: disable=no-member
sum_losses = 0
len_losses = 0
with autograd.record():
losses = [self.loss_fn(self.net(X), Y) for X, Y in zip(data, label)]
for loss in losses:
sum_losses += mx.nd.array(loss).sum().asscalar()
len_losses += len(loss)
loss.backward()
self.trainer.step(batch_size)
return sum_losses, len_losses
|
Description : Print sentence for prediction result
|
def infer(self, input_data, input_label):
"""
Description : Print sentence for prediction result
"""
sum_losses = 0
len_losses = 0
for data, label in zip(input_data, input_label):
pred = self.net(data)
sum_losses += mx.nd.array(self.loss_fn(pred, label)).sum().asscalar()
len_losses += len(data)
pred_convert = char_beam_search(pred)
label_convert = char_conv(label.asnumpy())
for target, pred in zip(label_convert, pred_convert):
print("target:{t} pred:{p}".format(t=target, p=pred))
return sum_losses, len_losses
|
Description : training for LipNet
|
def train_batch(self, dataloader):
"""
Description : training for LipNet
"""
sum_losses = 0
len_losses = 0
for input_data, input_label in tqdm(dataloader):
data = gluon.utils.split_and_load(input_data, self.ctx, even_split=False)
label = gluon.utils.split_and_load(input_label, self.ctx, even_split=False)
batch_size = input_data.shape[0]
sum_losses, len_losses = self.train(data, label, batch_size)
sum_losses += sum_losses
len_losses += len_losses
return sum_losses, len_losses
|
Description : inference for LipNet
|
def infer_batch(self, dataloader):
"""
Description : inference for LipNet
"""
sum_losses = 0
len_losses = 0
for input_data, input_label in dataloader:
data = gluon.utils.split_and_load(input_data, self.ctx, even_split=False)
label = gluon.utils.split_and_load(input_label, self.ctx, even_split=False)
sum_losses, len_losses = self.infer(data, label)
sum_losses += sum_losses
len_losses += len_losses
return sum_losses, len_losses
|
Description : Run training for LipNet
|
def run(self, epochs):
"""
Description : Run training for LipNet
"""
best_loss = sys.maxsize
for epoch in trange(epochs):
iter_no = 0
## train
sum_losses, len_losses = self.train_batch(self.train_dataloader)
if iter_no % 20 == 0:
current_loss = sum_losses / len_losses
print("[Train] epoch:{e} iter:{i} loss:{l:.4f}".format(e=epoch,
i=iter_no,
l=current_loss))
## validating
sum_val_losses, len_val_losses = self.infer_batch(self.valid_dataloader)
current_val_loss = sum_val_losses / len_val_losses
print("[Vaild] epoch:{e} iter:{i} loss:{l:.4f}".format(e=epoch,
i=iter_no,
l=current_val_loss))
if best_loss > current_val_loss:
self.save_model(epoch, current_val_loss)
best_loss = current_val_loss
iter_no += 1
|
Sample from independent categorical distributions
Each batch is an independent categorical distribution.
Parameters
----------
prob : numpy.ndarray
Probability of the categorical distribution. Shape --> (batch_num, category_num)
rng : numpy.random.RandomState
Returns
-------
ret : numpy.ndarray
Sampling result. Shape --> (batch_num,)
|
def sample_categorical(prob, rng):
"""Sample from independent categorical distributions
Each batch is an independent categorical distribution.
Parameters
----------
prob : numpy.ndarray
Probability of the categorical distribution. Shape --> (batch_num, category_num)
rng : numpy.random.RandomState
Returns
-------
ret : numpy.ndarray
Sampling result. Shape --> (batch_num,)
"""
ret = numpy.empty(prob.shape[0], dtype=numpy.float32)
for ind in range(prob.shape[0]):
ret[ind] = numpy.searchsorted(numpy.cumsum(prob[ind]), rng.rand()).clip(min=0.0,
max=prob.shape[
1] - 0.5)
return ret
|
Sample from independent normal distributions
Each element is an independent normal distribution.
Parameters
----------
mean : numpy.ndarray
Means of the normal distribution. Shape --> (batch_num, sample_dim)
var : numpy.ndarray
Variance of the normal distribution. Shape --> (batch_num, sample_dim)
rng : numpy.random.RandomState
Returns
-------
ret : numpy.ndarray
The sampling result. Shape --> (batch_num, sample_dim)
|
def sample_normal(mean, var, rng):
"""Sample from independent normal distributions
Each element is an independent normal distribution.
Parameters
----------
mean : numpy.ndarray
Means of the normal distribution. Shape --> (batch_num, sample_dim)
var : numpy.ndarray
Variance of the normal distribution. Shape --> (batch_num, sample_dim)
rng : numpy.random.RandomState
Returns
-------
ret : numpy.ndarray
The sampling result. Shape --> (batch_num, sample_dim)
"""
ret = numpy.sqrt(var) * rng.randn(*mean.shape) + mean
return ret
|
Sample from independent mixture of gaussian (MoG) distributions
Each batch is an independent MoG distribution.
Parameters
----------
prob : numpy.ndarray
mixture probability of each gaussian. Shape --> (batch_num, center_num)
mean : numpy.ndarray
mean of each gaussian. Shape --> (batch_num, center_num, sample_dim)
var : numpy.ndarray
variance of each gaussian. Shape --> (batch_num, center_num, sample_dim)
rng : numpy.random.RandomState
Returns
-------
ret : numpy.ndarray
sampling result. Shape --> (batch_num, sample_dim)
|
def sample_mog(prob, mean, var, rng):
"""Sample from independent mixture of gaussian (MoG) distributions
Each batch is an independent MoG distribution.
Parameters
----------
prob : numpy.ndarray
mixture probability of each gaussian. Shape --> (batch_num, center_num)
mean : numpy.ndarray
mean of each gaussian. Shape --> (batch_num, center_num, sample_dim)
var : numpy.ndarray
variance of each gaussian. Shape --> (batch_num, center_num, sample_dim)
rng : numpy.random.RandomState
Returns
-------
ret : numpy.ndarray
sampling result. Shape --> (batch_num, sample_dim)
"""
gaussian_inds = sample_categorical(prob, rng).astype(numpy.int32)
mean = mean[numpy.arange(mean.shape[0]), gaussian_inds, :]
var = var[numpy.arange(mean.shape[0]), gaussian_inds, :]
ret = sample_normal(mean=mean, var=var, rng=rng)
return ret
|
NCE-Loss layer under subword-units input.
|
def nce_loss_subwords(
data, label, label_mask, label_weight, embed_weight, vocab_size, num_hidden):
"""NCE-Loss layer under subword-units input.
"""
# get subword-units embedding.
label_units_embed = mx.sym.Embedding(data=label,
input_dim=vocab_size,
weight=embed_weight,
output_dim=num_hidden)
# get valid subword-units embedding with the help of label_mask
# it's achieved by multiplying zeros to useless units in order to handle variable-length input.
label_units_embed = mx.sym.broadcast_mul(lhs=label_units_embed,
rhs=label_mask,
name='label_units_embed')
# sum over them to get label word embedding.
label_embed = mx.sym.sum(label_units_embed, axis=2, name='label_embed')
# by boardcast_mul and sum you can get prediction scores in all label_embed inputs,
# which is easy to feed into LogisticRegressionOutput and make your code more concise.
data = mx.sym.Reshape(data=data, shape=(-1, 1, num_hidden))
pred = mx.sym.broadcast_mul(data, label_embed)
pred = mx.sym.sum(data=pred, axis=2)
return mx.sym.LogisticRegressionOutput(data=pred,
label=label_weight)
|
Download the BSDS500 dataset and return train and test iters.
|
def get_dataset(prefetch=False):
"""Download the BSDS500 dataset and return train and test iters."""
if path.exists(data_dir):
print(
"Directory {} already exists, skipping.\n"
"To force download and extraction, delete the directory and re-run."
"".format(data_dir),
file=sys.stderr,
)
else:
print("Downloading dataset...", file=sys.stderr)
downloaded_file = download(dataset_url, dirname=datasets_tmpdir)
print("done", file=sys.stderr)
print("Extracting files...", end="", file=sys.stderr)
os.makedirs(data_dir)
os.makedirs(tmp_dir)
with zipfile.ZipFile(downloaded_file) as archive:
archive.extractall(tmp_dir)
shutil.rmtree(datasets_tmpdir)
shutil.copytree(
path.join(tmp_dir, "BSDS500-master", "BSDS500", "data", "images"),
path.join(data_dir, "images"),
)
shutil.copytree(
path.join(tmp_dir, "BSDS500-master", "BSDS500", "data", "groundTruth"),
path.join(data_dir, "groundTruth"),
)
shutil.rmtree(tmp_dir)
print("done", file=sys.stderr)
crop_size = 256
crop_size -= crop_size % upscale_factor
input_crop_size = crop_size // upscale_factor
input_transform = [CenterCropAug((crop_size, crop_size)), ResizeAug(input_crop_size)]
target_transform = [CenterCropAug((crop_size, crop_size))]
iters = (
ImagePairIter(
path.join(data_dir, "images", "train"),
(input_crop_size, input_crop_size),
(crop_size, crop_size),
batch_size,
color_flag,
input_transform,
target_transform,
),
ImagePairIter(
path.join(data_dir, "images", "test"),
(input_crop_size, input_crop_size),
(crop_size, crop_size),
test_batch_size,
color_flag,
input_transform,
target_transform,
),
)
return [PrefetchingIter(i) for i in iters] if prefetch else iters
|
Run evaluation on cpu.
|
def evaluate(mod, data_iter, epoch, log_interval):
""" Run evaluation on cpu. """
start = time.time()
total_L = 0.0
nbatch = 0
density = 0
mod.set_states(value=0)
for batch in data_iter:
mod.forward(batch, is_train=False)
outputs = mod.get_outputs(merge_multi_context=False)
states = outputs[:-1]
total_L += outputs[-1][0]
mod.set_states(states=states)
nbatch += 1
# don't include padding data in the test perplexity
density += batch.data[1].mean()
if (nbatch + 1) % log_interval == 0:
logging.info("Eval batch %d loss : %.7f" % (nbatch, (total_L / density).asscalar()))
data_iter.reset()
loss = (total_L / density).asscalar()
ppl = math.exp(loss) if loss < 100 else 1e37
end = time.time()
logging.info('Iter[%d]\t\t CE loss %.7f, ppl %.7f. Eval duration = %.2f seconds ' % \
(epoch, loss, ppl, end - start))
return loss
|
get two list, each list contains two elements: name and nd.array value
|
def _read(self):
"""get two list, each list contains two elements: name and nd.array value"""
_, data_img_name, label_img_name = self.f.readline().strip('\n').split("\t")
data = {}
label = {}
data[self.data_name], label[self.label_name] = self._read_img(data_img_name, label_img_name)
return list(data.items()), list(label.items())
|
return one dict which contains "data" and "label"
|
def next(self):
"""return one dict which contains "data" and "label" """
if self.iter_next():
self.data, self.label = self._read()
return {self.data_name : self.data[0][1],
self.label_name : self.label[0][1]}
else:
raise StopIteration
|
Convert from onnx operator to mxnet operator.
The converter must specify conversions explicitly for incompatible name, and
apply handlers to operator attributes.
Parameters
----------
:param node_name : str
name of the node to be translated.
:param op_name : str
Operator name, such as Convolution, FullyConnected
:param attrs : dict
Dict of operator attributes
:param inputs: list
list of inputs to the operator
Returns
-------
:return mxnet_sym
Converted mxnet symbol
|
def _convert_operator(self, node_name, op_name, attrs, inputs):
"""Convert from onnx operator to mxnet operator.
The converter must specify conversions explicitly for incompatible name, and
apply handlers to operator attributes.
Parameters
----------
:param node_name : str
name of the node to be translated.
:param op_name : str
Operator name, such as Convolution, FullyConnected
:param attrs : dict
Dict of operator attributes
:param inputs: list
list of inputs to the operator
Returns
-------
:return mxnet_sym
Converted mxnet symbol
"""
if op_name in convert_map:
op_name, new_attrs, inputs = convert_map[op_name](attrs, inputs, self)
else:
raise NotImplementedError("Operator {} not implemented.".format(op_name))
if isinstance(op_name, string_types):
new_op = getattr(symbol, op_name, None)
if not new_op:
raise RuntimeError("Unable to map op_name {} to sym".format(op_name))
if node_name is None:
mxnet_sym = new_op(*inputs, **new_attrs)
else:
mxnet_sym = new_op(name=node_name, *inputs, **new_attrs)
return mxnet_sym
return op_name
|
Construct symbol from onnx graph.
Parameters
----------
graph : onnx protobuf object
The loaded onnx graph
Returns
-------
sym :symbol.Symbol
The returned mxnet symbol
params : dict
A dict of name: nd.array pairs, used as pretrained weights
|
def from_onnx(self, graph):
"""Construct symbol from onnx graph.
Parameters
----------
graph : onnx protobuf object
The loaded onnx graph
Returns
-------
sym :symbol.Symbol
The returned mxnet symbol
params : dict
A dict of name: nd.array pairs, used as pretrained weights
"""
# get input, output shapes
self.model_metadata = self.get_graph_metadata(graph)
# parse network inputs, aka parameters
for init_tensor in graph.initializer:
if not init_tensor.name.strip():
raise ValueError("Tensor's name is required.")
self._params[init_tensor.name] = self._parse_array(init_tensor)
# converting GraphProto message
for i in graph.input:
if i.name in self._params:
# i is a param instead of input
self._nodes[i.name] = symbol.Variable(name=i.name,
shape=self._params[i.name].shape)
else:
self._nodes[i.name] = symbol.Variable(name=i.name)
# constructing nodes, nodes are stored as directed acyclic graph
# converting NodeProto message
for node in graph.node:
op_name = node.op_type
node_name = node.name.strip()
node_name = node_name if node_name else None
onnx_attr = self._parse_attr(node.attribute)
inputs = [self._nodes[i] for i in node.input]
mxnet_sym = self._convert_operator(node_name, op_name, onnx_attr, inputs)
for k, i in zip(list(node.output), range(len(mxnet_sym.list_outputs()))):
self._nodes[k] = mxnet_sym[i]
# splitting params into args and aux params
for args in mxnet_sym.list_arguments():
if args in self._params:
self.arg_dict.update({args: nd.array(self._params[args])})
for aux in mxnet_sym.list_auxiliary_states():
if aux in self._params:
self.aux_dict.update({aux: nd.array(self._params[aux])})
# now return the outputs
out = [self._nodes[i.name] for i in graph.output]
if len(out) > 1:
out = symbol.Group(out)
else:
out = out[0]
return out, self.arg_dict, self.aux_dict
|
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