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# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import pytest
import torch
import torch.nn as nn
from mmcv.cnn.bricks import (ACTIVATION_LAYERS, CONV_LAYERS, NORM_LAYERS,
PADDING_LAYERS, PLUGIN_LAYERS,
build_activation_layer, build_conv_layer,
build_norm_layer, build_padding_layer,
build_plugin_layer, build_upsample_layer, is_norm)
from mmcv.cnn.bricks.norm import infer_abbr as infer_norm_abbr
from mmcv.cnn.bricks.plugin import infer_abbr as infer_plugin_abbr
from mmcv.cnn.bricks.upsample import PixelShufflePack
from mmcv.utils.parrots_wrapper import _BatchNorm
def test_build_conv_layer():
with pytest.raises(TypeError):
# cfg must be a dict
cfg = 'Conv2d'
build_conv_layer(cfg)
with pytest.raises(KeyError):
# `type` must be in cfg
cfg = dict(kernel_size=3)
build_conv_layer(cfg)
with pytest.raises(KeyError):
# unsupported conv type
cfg = dict(type='FancyConv')
build_conv_layer(cfg)
kwargs = dict(
in_channels=4, out_channels=8, kernel_size=3, groups=2, dilation=2)
cfg = None
layer = build_conv_layer(cfg, **kwargs)
assert isinstance(layer, nn.Conv2d)
assert layer.in_channels == kwargs['in_channels']
assert layer.out_channels == kwargs['out_channels']
assert layer.kernel_size == (kwargs['kernel_size'], kwargs['kernel_size'])
assert layer.groups == kwargs['groups']
assert layer.dilation == (kwargs['dilation'], kwargs['dilation'])
cfg = dict(type='Conv')
layer = build_conv_layer(cfg, **kwargs)
assert isinstance(layer, nn.Conv2d)
assert layer.in_channels == kwargs['in_channels']
assert layer.out_channels == kwargs['out_channels']
assert layer.kernel_size == (kwargs['kernel_size'], kwargs['kernel_size'])
assert layer.groups == kwargs['groups']
assert layer.dilation == (kwargs['dilation'], kwargs['dilation'])
cfg = dict(type='deconv')
layer = build_conv_layer(cfg, **kwargs)
assert isinstance(layer, nn.ConvTranspose2d)
assert layer.in_channels == kwargs['in_channels']
assert layer.out_channels == kwargs['out_channels']
assert layer.kernel_size == (kwargs['kernel_size'], kwargs['kernel_size'])
assert layer.groups == kwargs['groups']
assert layer.dilation == (kwargs['dilation'], kwargs['dilation'])
# sparse convs cannot support the case when groups>1
kwargs.pop('groups')
for type_name, module in CONV_LAYERS.module_dict.items():
cfg = dict(type=type_name)
# SparseInverseConv2d and SparseInverseConv3d do not have the argument
# 'dilation'
if type_name == 'SparseInverseConv2d' or type_name == \
'SparseInverseConv3d':
kwargs.pop('dilation')
layer = build_conv_layer(cfg, **kwargs)
assert isinstance(layer, module)
assert layer.in_channels == kwargs['in_channels']
assert layer.out_channels == kwargs['out_channels']
kwargs['dilation'] = 2 # recover the key
def test_infer_norm_abbr():
with pytest.raises(TypeError):
# class_type must be a class
infer_norm_abbr(0)
class MyNorm:
_abbr_ = 'mn'
assert infer_norm_abbr(MyNorm) == 'mn'
class FancyBatchNorm:
pass
assert infer_norm_abbr(FancyBatchNorm) == 'bn'
class FancyInstanceNorm:
pass
assert infer_norm_abbr(FancyInstanceNorm) == 'in'
class FancyLayerNorm:
pass
assert infer_norm_abbr(FancyLayerNorm) == 'ln'
class FancyGroupNorm:
pass
assert infer_norm_abbr(FancyGroupNorm) == 'gn'
class FancyNorm:
pass
assert infer_norm_abbr(FancyNorm) == 'norm_layer'
def test_build_norm_layer():
with pytest.raises(TypeError):
# cfg must be a dict
cfg = 'BN'
build_norm_layer(cfg, 3)
with pytest.raises(KeyError):
# `type` must be in cfg
cfg = dict()
build_norm_layer(cfg, 3)
with pytest.raises(KeyError):
# unsupported norm type
cfg = dict(type='FancyNorm')
build_norm_layer(cfg, 3)
with pytest.raises(AssertionError):
# postfix must be int or str
cfg = dict(type='BN')
build_norm_layer(cfg, 3, postfix=[1, 2])
with pytest.raises(AssertionError):
# `num_groups` must be in cfg when using 'GN'
cfg = dict(type='GN')
build_norm_layer(cfg, 3)
# test each type of norm layer in norm_cfg
abbr_mapping = {
'BN': 'bn',
'BN1d': 'bn',
'BN2d': 'bn',
'BN3d': 'bn',
'SyncBN': 'bn',
'GN': 'gn',
'LN': 'ln',
'IN': 'in',
'IN1d': 'in',
'IN2d': 'in',
'IN3d': 'in',
}
for type_name, module in NORM_LAYERS.module_dict.items():
if type_name == 'MMSyncBN': # skip MMSyncBN
continue
for postfix in ['_test', 1]:
cfg = dict(type=type_name)
if type_name == 'GN':
cfg['num_groups'] = 3
name, layer = build_norm_layer(cfg, 3, postfix=postfix)
assert name == abbr_mapping[type_name] + str(postfix)
assert isinstance(layer, module)
if type_name == 'GN':
assert layer.num_channels == 3
assert layer.num_groups == cfg['num_groups']
elif type_name != 'LN':
assert layer.num_features == 3
def test_build_activation_layer():
with pytest.raises(TypeError):
# cfg must be a dict
cfg = 'ReLU'
build_activation_layer(cfg)
with pytest.raises(KeyError):
# `type` must be in cfg
cfg = dict()
build_activation_layer(cfg)
with pytest.raises(KeyError):
# unsupported activation type
cfg = dict(type='FancyReLU')
build_activation_layer(cfg)
# test each type of activation layer in activation_cfg
for type_name, module in ACTIVATION_LAYERS.module_dict.items():
cfg['type'] = type_name
layer = build_activation_layer(cfg)
assert isinstance(layer, module)
# sanity check for Clamp
act = build_activation_layer(dict(type='Clamp'))
x = torch.randn(10) * 1000
y = act(x)
assert np.logical_and((y >= -1).numpy(), (y <= 1).numpy()).all()
act = build_activation_layer(dict(type='Clip', min=0))
y = act(x)
assert np.logical_and((y >= 0).numpy(), (y <= 1).numpy()).all()
act = build_activation_layer(dict(type='Clamp', max=0))
y = act(x)
assert np.logical_and((y >= -1).numpy(), (y <= 0).numpy()).all()
def test_build_padding_layer():
with pytest.raises(TypeError):
# cfg must be a dict
cfg = 'reflect'
build_padding_layer(cfg)
with pytest.raises(KeyError):
# `type` must be in cfg
cfg = dict()
build_padding_layer(cfg)
with pytest.raises(KeyError):
# unsupported activation type
cfg = dict(type='FancyPad')
build_padding_layer(cfg)
for type_name, module in PADDING_LAYERS.module_dict.items():
cfg['type'] = type_name
layer = build_padding_layer(cfg, 2)
assert isinstance(layer, module)
input_x = torch.randn(1, 2, 5, 5)
cfg = dict(type='reflect')
padding_layer = build_padding_layer(cfg, 2)
res = padding_layer(input_x)
assert res.shape == (1, 2, 9, 9)
def test_upsample_layer():
with pytest.raises(TypeError):
# cfg must be a dict
cfg = 'bilinear'
build_upsample_layer(cfg)
with pytest.raises(KeyError):
# `type` must be in cfg
cfg = dict()
build_upsample_layer(cfg)
with pytest.raises(KeyError):
# unsupported activation type
cfg = dict(type='FancyUpsample')
build_upsample_layer(cfg)
for type_name in ['nearest', 'bilinear']:
cfg['type'] = type_name
layer = build_upsample_layer(cfg)
assert isinstance(layer, nn.Upsample)
assert layer.mode == type_name
cfg = dict(
type='deconv', in_channels=3, out_channels=3, kernel_size=3, stride=2)
layer = build_upsample_layer(cfg)
assert isinstance(layer, nn.ConvTranspose2d)
cfg = dict(type='deconv')
kwargs = dict(in_channels=3, out_channels=3, kernel_size=3, stride=2)
layer = build_upsample_layer(cfg, **kwargs)
assert isinstance(layer, nn.ConvTranspose2d)
assert layer.in_channels == kwargs['in_channels']
assert layer.out_channels == kwargs['out_channels']
assert layer.kernel_size == (kwargs['kernel_size'], kwargs['kernel_size'])
assert layer.stride == (kwargs['stride'], kwargs['stride'])
layer = build_upsample_layer(cfg, 3, 3, 3, 2)
assert isinstance(layer, nn.ConvTranspose2d)
assert layer.in_channels == kwargs['in_channels']
assert layer.out_channels == kwargs['out_channels']
assert layer.kernel_size == (kwargs['kernel_size'], kwargs['kernel_size'])
assert layer.stride == (kwargs['stride'], kwargs['stride'])
cfg = dict(
type='pixel_shuffle',
in_channels=3,
out_channels=3,
scale_factor=2,
upsample_kernel=3)
layer = build_upsample_layer(cfg)
assert isinstance(layer, PixelShufflePack)
assert layer.scale_factor == 2
assert layer.upsample_kernel == 3
def test_pixel_shuffle_pack():
x_in = torch.rand(2, 3, 10, 10)
pixel_shuffle = PixelShufflePack(3, 3, scale_factor=2, upsample_kernel=3)
assert pixel_shuffle.upsample_conv.kernel_size == (3, 3)
x_out = pixel_shuffle(x_in)
assert x_out.shape == (2, 3, 20, 20)
def test_is_norm():
norm_set1 = [
nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d, nn.InstanceNorm1d,
nn.InstanceNorm2d, nn.InstanceNorm3d, nn.LayerNorm
]
norm_set2 = [nn.GroupNorm]
for norm_type in norm_set1:
layer = norm_type(3)
assert is_norm(layer)
assert not is_norm(layer, exclude=(norm_type, ))
for norm_type in norm_set2:
layer = norm_type(3, 6)
assert is_norm(layer)
assert not is_norm(layer, exclude=(norm_type, ))
class MyNorm(nn.BatchNorm2d):
pass
layer = MyNorm(3)
assert is_norm(layer)
assert not is_norm(layer, exclude=_BatchNorm)
assert not is_norm(layer, exclude=(_BatchNorm, ))
layer = nn.Conv2d(3, 8, 1)
assert not is_norm(layer)
with pytest.raises(TypeError):
layer = nn.BatchNorm1d(3)
is_norm(layer, exclude='BN')
with pytest.raises(TypeError):
layer = nn.BatchNorm1d(3)
is_norm(layer, exclude=('BN', ))
def test_infer_plugin_abbr():
with pytest.raises(TypeError):
# class_type must be a class
infer_plugin_abbr(0)
class MyPlugin:
_abbr_ = 'mp'
assert infer_plugin_abbr(MyPlugin) == 'mp'
class FancyPlugin:
pass
assert infer_plugin_abbr(FancyPlugin) == 'fancy_plugin'
def test_build_plugin_layer():
with pytest.raises(TypeError):
# cfg must be a dict
cfg = 'Plugin'
build_plugin_layer(cfg)
with pytest.raises(KeyError):
# `type` must be in cfg
cfg = dict()
build_plugin_layer(cfg)
with pytest.raises(KeyError):
# unsupported plugin type
cfg = dict(type='FancyPlugin')
build_plugin_layer(cfg)
with pytest.raises(AssertionError):
# postfix must be int or str
cfg = dict(type='ConvModule')
build_plugin_layer(cfg, postfix=[1, 2])
# test ContextBlock
for postfix in ['', '_test', 1]:
cfg = dict(type='ContextBlock')
name, layer = build_plugin_layer(
cfg, postfix=postfix, in_channels=16, ratio=1. / 4)
assert name == 'context_block' + str(postfix)
assert isinstance(layer, PLUGIN_LAYERS.module_dict['ContextBlock'])
# test GeneralizedAttention
for postfix in ['', '_test', 1]:
cfg = dict(type='GeneralizedAttention')
name, layer = build_plugin_layer(cfg, postfix=postfix, in_channels=16)
assert name == 'gen_attention_block' + str(postfix)
assert isinstance(layer,
PLUGIN_LAYERS.module_dict['GeneralizedAttention'])
# test NonLocal2d
for postfix in ['', '_test', 1]:
cfg = dict(type='NonLocal2d')
name, layer = build_plugin_layer(cfg, postfix=postfix, in_channels=16)
assert name == 'nonlocal_block' + str(postfix)
assert isinstance(layer, PLUGIN_LAYERS.module_dict['NonLocal2d'])
# test ConvModule
for postfix in ['', '_test', 1]:
cfg = dict(type='ConvModule')
name, layer = build_plugin_layer(
cfg,
postfix=postfix,
in_channels=16,
out_channels=4,
kernel_size=3)
assert name == 'conv_block' + str(postfix)
assert isinstance(layer, PLUGIN_LAYERS.module_dict['ConvModule'])
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