Spaces:
Build error
Build error
File size: 8,313 Bytes
d7a991a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 |
# Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from torch.nn.modules import AvgPool2d
from torch.nn.modules.batchnorm import _BatchNorm
from mmpose.models.backbones import SEResNet
from mmpose.models.backbones.resnet import ResLayer
from mmpose.models.backbones.seresnet import SEBottleneck, SELayer
def all_zeros(modules):
"""Check if the weight(and bias) is all zero."""
weight_zero = torch.equal(modules.weight.data,
torch.zeros_like(modules.weight.data))
if hasattr(modules, 'bias'):
bias_zero = torch.equal(modules.bias.data,
torch.zeros_like(modules.bias.data))
else:
bias_zero = True
return weight_zero and bias_zero
def check_norm_state(modules, train_state):
"""Check if norm layer is in correct train state."""
for mod in modules:
if isinstance(mod, _BatchNorm):
if mod.training != train_state:
return False
return True
def test_selayer():
# Test selayer forward
layer = SELayer(64)
x = torch.randn(1, 64, 56, 56)
x_out = layer(x)
assert x_out.shape == torch.Size([1, 64, 56, 56])
# Test selayer forward with different ratio
layer = SELayer(64, ratio=8)
x = torch.randn(1, 64, 56, 56)
x_out = layer(x)
assert x_out.shape == torch.Size([1, 64, 56, 56])
def test_bottleneck():
with pytest.raises(AssertionError):
# Style must be in ['pytorch', 'caffe']
SEBottleneck(64, 64, style='tensorflow')
# Test SEBottleneck with checkpoint forward
block = SEBottleneck(64, 64, with_cp=True)
assert block.with_cp
x = torch.randn(1, 64, 56, 56)
x_out = block(x)
assert x_out.shape == torch.Size([1, 64, 56, 56])
# Test Bottleneck style
block = SEBottleneck(64, 256, stride=2, style='pytorch')
assert block.conv1.stride == (1, 1)
assert block.conv2.stride == (2, 2)
block = SEBottleneck(64, 256, stride=2, style='caffe')
assert block.conv1.stride == (2, 2)
assert block.conv2.stride == (1, 1)
# Test Bottleneck forward
block = SEBottleneck(64, 64)
x = torch.randn(1, 64, 56, 56)
x_out = block(x)
assert x_out.shape == torch.Size([1, 64, 56, 56])
def test_res_layer():
# Test ResLayer of 3 Bottleneck w\o downsample
layer = ResLayer(SEBottleneck, 3, 64, 64, se_ratio=16)
assert len(layer) == 3
assert layer[0].conv1.in_channels == 64
assert layer[0].conv1.out_channels == 16
for i in range(1, len(layer)):
assert layer[i].conv1.in_channels == 64
assert layer[i].conv1.out_channels == 16
for i in range(len(layer)):
assert layer[i].downsample is None
x = torch.randn(1, 64, 56, 56)
x_out = layer(x)
assert x_out.shape == torch.Size([1, 64, 56, 56])
# Test ResLayer of 3 SEBottleneck with downsample
layer = ResLayer(SEBottleneck, 3, 64, 256, se_ratio=16)
assert layer[0].downsample[0].out_channels == 256
for i in range(1, len(layer)):
assert layer[i].downsample is None
x = torch.randn(1, 64, 56, 56)
x_out = layer(x)
assert x_out.shape == torch.Size([1, 256, 56, 56])
# Test ResLayer of 3 SEBottleneck with stride=2
layer = ResLayer(SEBottleneck, 3, 64, 256, stride=2, se_ratio=8)
assert layer[0].downsample[0].out_channels == 256
assert layer[0].downsample[0].stride == (2, 2)
for i in range(1, len(layer)):
assert layer[i].downsample is None
x = torch.randn(1, 64, 56, 56)
x_out = layer(x)
assert x_out.shape == torch.Size([1, 256, 28, 28])
# Test ResLayer of 3 SEBottleneck with stride=2 and average downsample
layer = ResLayer(
SEBottleneck, 3, 64, 256, stride=2, avg_down=True, se_ratio=8)
assert isinstance(layer[0].downsample[0], AvgPool2d)
assert layer[0].downsample[1].out_channels == 256
assert layer[0].downsample[1].stride == (1, 1)
for i in range(1, len(layer)):
assert layer[i].downsample is None
x = torch.randn(1, 64, 56, 56)
x_out = layer(x)
assert x_out.shape == torch.Size([1, 256, 28, 28])
def test_seresnet():
"""Test resnet backbone."""
with pytest.raises(KeyError):
# SEResNet depth should be in [50, 101, 152]
SEResNet(20)
with pytest.raises(AssertionError):
# In SEResNet: 1 <= num_stages <= 4
SEResNet(50, num_stages=0)
with pytest.raises(AssertionError):
# In SEResNet: 1 <= num_stages <= 4
SEResNet(50, num_stages=5)
with pytest.raises(AssertionError):
# len(strides) == len(dilations) == num_stages
SEResNet(50, strides=(1, ), dilations=(1, 1), num_stages=3)
with pytest.raises(TypeError):
# pretrained must be a string path
model = SEResNet(50)
model.init_weights(pretrained=0)
with pytest.raises(AssertionError):
# Style must be in ['pytorch', 'caffe']
SEResNet(50, style='tensorflow')
# Test SEResNet50 norm_eval=True
model = SEResNet(50, norm_eval=True)
model.init_weights()
model.train()
assert check_norm_state(model.modules(), False)
# Test SEResNet50 with torchvision pretrained weight
model = SEResNet(depth=50, norm_eval=True)
model.init_weights('torchvision://resnet50')
model.train()
assert check_norm_state(model.modules(), False)
# Test SEResNet50 with first stage frozen
frozen_stages = 1
model = SEResNet(50, frozen_stages=frozen_stages)
model.init_weights()
model.train()
assert model.norm1.training is False
for layer in [model.conv1, model.norm1]:
for param in layer.parameters():
assert param.requires_grad is False
for i in range(1, frozen_stages + 1):
layer = getattr(model, f'layer{i}')
for mod in layer.modules():
if isinstance(mod, _BatchNorm):
assert mod.training is False
for param in layer.parameters():
assert param.requires_grad is False
# Test SEResNet50 with BatchNorm forward
model = SEResNet(50, out_indices=(0, 1, 2, 3))
model.init_weights()
model.train()
imgs = torch.randn(1, 3, 224, 224)
feat = model(imgs)
assert len(feat) == 4
assert feat[0].shape == torch.Size([1, 256, 56, 56])
assert feat[1].shape == torch.Size([1, 512, 28, 28])
assert feat[2].shape == torch.Size([1, 1024, 14, 14])
assert feat[3].shape == torch.Size([1, 2048, 7, 7])
# Test SEResNet50 with layers 1, 2, 3 out forward
model = SEResNet(50, out_indices=(0, 1, 2))
model.init_weights()
model.train()
imgs = torch.randn(1, 3, 224, 224)
feat = model(imgs)
assert len(feat) == 3
assert feat[0].shape == torch.Size([1, 256, 56, 56])
assert feat[1].shape == torch.Size([1, 512, 28, 28])
assert feat[2].shape == torch.Size([1, 1024, 14, 14])
# Test SEResNet50 with layers 3 (top feature maps) out forward
model = SEResNet(50, out_indices=(3, ))
model.init_weights()
model.train()
imgs = torch.randn(1, 3, 224, 224)
feat = model(imgs)
assert feat.shape == torch.Size([1, 2048, 7, 7])
# Test SEResNet50 with checkpoint forward
model = SEResNet(50, out_indices=(0, 1, 2, 3), with_cp=True)
for m in model.modules():
if isinstance(m, SEBottleneck):
assert m.with_cp
model.init_weights()
model.train()
imgs = torch.randn(1, 3, 224, 224)
feat = model(imgs)
assert len(feat) == 4
assert feat[0].shape == torch.Size([1, 256, 56, 56])
assert feat[1].shape == torch.Size([1, 512, 28, 28])
assert feat[2].shape == torch.Size([1, 1024, 14, 14])
assert feat[3].shape == torch.Size([1, 2048, 7, 7])
# Test SEResNet50 zero initialization of residual
model = SEResNet(50, out_indices=(0, 1, 2, 3), zero_init_residual=True)
model.init_weights()
for m in model.modules():
if isinstance(m, SEBottleneck):
assert all_zeros(m.norm3)
model.train()
imgs = torch.randn(1, 3, 224, 224)
feat = model(imgs)
assert len(feat) == 4
assert feat[0].shape == torch.Size([1, 256, 56, 56])
assert feat[1].shape == torch.Size([1, 512, 28, 28])
assert feat[2].shape == torch.Size([1, 1024, 14, 14])
assert feat[3].shape == torch.Size([1, 2048, 7, 7])
|