mirror of https://github.com/open-mmlab/mmpose
240 lines
9.4 KiB
Python
240 lines
9.4 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
|
|
from unittest import TestCase
|
|
|
|
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
|
|
|
|
|
|
class TestSEResnet(TestCase):
|
|
|
|
@staticmethod
|
|
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
|
|
|
|
@staticmethod
|
|
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(self):
|
|
# Test selayer forward
|
|
layer = SELayer(64)
|
|
x = torch.randn(1, 64, 56, 56)
|
|
x_out = layer(x)
|
|
self.assertEqual(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)
|
|
self.assertEqual(x_out.shape, torch.Size([1, 64, 56, 56]))
|
|
|
|
def test_bottleneck(self):
|
|
|
|
with self.assertRaises(AssertionError):
|
|
# Style must be in ['pytorch', 'caffe']
|
|
SEBottleneck(64, 64, style='tensorflow')
|
|
|
|
# Test SEBottleneck with checkpoint forward
|
|
block = SEBottleneck(64, 64, with_cp=True)
|
|
self.assertTrue(block.with_cp)
|
|
x = torch.randn(1, 64, 56, 56)
|
|
x_out = block(x)
|
|
self.assertEqual(x_out.shape, torch.Size([1, 64, 56, 56]))
|
|
|
|
# Test Bottleneck style
|
|
block = SEBottleneck(64, 256, stride=2, style='pytorch')
|
|
self.assertEqual(block.conv1.stride, (1, 1))
|
|
self.assertEqual(block.conv2.stride, (2, 2))
|
|
block = SEBottleneck(64, 256, stride=2, style='caffe')
|
|
self.assertEqual(block.conv1.stride, (2, 2))
|
|
self.assertEqual(block.conv2.stride, (1, 1))
|
|
|
|
# Test Bottleneck forward
|
|
block = SEBottleneck(64, 64)
|
|
x = torch.randn(1, 64, 56, 56)
|
|
x_out = block(x)
|
|
self.assertEqual(x_out.shape, torch.Size([1, 64, 56, 56]))
|
|
|
|
def test_res_layer(self):
|
|
# Test ResLayer of 3 Bottleneck w\o downsample
|
|
layer = ResLayer(SEBottleneck, 3, 64, 64, se_ratio=16)
|
|
self.assertEqual(len(layer), 3)
|
|
self.assertEqual(layer[0].conv1.in_channels, 64)
|
|
self.assertEqual(layer[0].conv1.out_channels, 16)
|
|
for i in range(1, len(layer)):
|
|
self.assertEqual(layer[i].conv1.in_channels, 64)
|
|
self.assertEqual(layer[i].conv1.out_channels, 16)
|
|
for i in range(len(layer)):
|
|
self.assertIsNone(layer[i].downsample)
|
|
x = torch.randn(1, 64, 56, 56)
|
|
x_out = layer(x)
|
|
self.assertEqual(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)
|
|
self.assertEqual(layer[0].downsample[0].out_channels, 256)
|
|
for i in range(1, len(layer)):
|
|
self.assertIsNone(layer[i].downsample)
|
|
x = torch.randn(1, 64, 56, 56)
|
|
x_out = layer(x)
|
|
self.assertEqual(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)
|
|
self.assertEqual(layer[0].downsample[0].out_channels, 256)
|
|
self.assertEqual(layer[0].downsample[0].stride, (2, 2))
|
|
for i in range(1, len(layer)):
|
|
self.assertIsNone(layer[i].downsample)
|
|
x = torch.randn(1, 64, 56, 56)
|
|
x_out = layer(x)
|
|
self.assertEqual(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)
|
|
self.assertIsInstance(layer[0].downsample[0], AvgPool2d)
|
|
self.assertEqual(layer[0].downsample[1].out_channels, 256)
|
|
self.assertEqual(layer[0].downsample[1].stride, (1, 1))
|
|
for i in range(1, len(layer)):
|
|
self.assertIsNone(layer[i].downsample)
|
|
x = torch.randn(1, 64, 56, 56)
|
|
x_out = layer(x)
|
|
self.assertEqual(x_out.shape, torch.Size([1, 256, 28, 28]))
|
|
|
|
def test_seresnet(self):
|
|
"""Test resnet backbone."""
|
|
with self.assertRaises(KeyError):
|
|
# SEResNet depth should be in [50, 101, 152]
|
|
SEResNet(20)
|
|
|
|
with self.assertRaises(AssertionError):
|
|
# In SEResNet: 1 <= num_stages <= 4
|
|
SEResNet(50, num_stages=0)
|
|
|
|
with self.assertRaises(AssertionError):
|
|
# In SEResNet: 1 <= num_stages <= 4
|
|
SEResNet(50, num_stages=5)
|
|
|
|
with self.assertRaises(AssertionError):
|
|
# len(strides) == len(dilations) == num_stages
|
|
SEResNet(50, strides=(1, ), dilations=(1, 1), num_stages=3)
|
|
|
|
with self.assertRaises(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()
|
|
self.assertTrue(self.check_norm_state(model.modules(), False))
|
|
|
|
# Test SEResNet50 with torchvision pretrained weight
|
|
init_cfg = dict(type='Pretrained', checkpoint='torchvision://resnet50')
|
|
model = SEResNet(depth=50, norm_eval=True, init_cfg=init_cfg)
|
|
model.train()
|
|
self.assertTrue(self.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()
|
|
self.assertFalse(model.norm1.training)
|
|
for layer in [model.conv1, model.norm1]:
|
|
for param in layer.parameters():
|
|
self.assertFalse(param.requires_grad)
|
|
for i in range(1, frozen_stages + 1):
|
|
layer = getattr(model, f'layer{i}')
|
|
for mod in layer.modules():
|
|
if isinstance(mod, _BatchNorm):
|
|
self.assertFalse(mod.training)
|
|
for param in layer.parameters():
|
|
self.assertFalse(param.requires_grad)
|
|
|
|
# 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)
|
|
self.assertEqual(len(feat), 4)
|
|
self.assertEqual(feat[0].shape, torch.Size([1, 256, 56, 56]))
|
|
self.assertEqual(feat[1].shape, torch.Size([1, 512, 28, 28]))
|
|
self.assertEqual(feat[2].shape, torch.Size([1, 1024, 14, 14]))
|
|
self.assertEqual(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)
|
|
self.assertEqual(len(feat), 3)
|
|
self.assertEqual(feat[0].shape, torch.Size([1, 256, 56, 56]))
|
|
self.assertEqual(feat[1].shape, torch.Size([1, 512, 28, 28]))
|
|
self.assertEqual(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)
|
|
self.assertIsInstance(feat, tuple)
|
|
self.assertEqual(feat[-1].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):
|
|
self.assertTrue(m.with_cp)
|
|
model.init_weights()
|
|
model.train()
|
|
|
|
imgs = torch.randn(1, 3, 224, 224)
|
|
feat = model(imgs)
|
|
self.assertEqual(len(feat), 4)
|
|
self.assertEqual(feat[0].shape, torch.Size([1, 256, 56, 56]))
|
|
self.assertEqual(feat[1].shape, torch.Size([1, 512, 28, 28]))
|
|
self.assertEqual(feat[2].shape, torch.Size([1, 1024, 14, 14]))
|
|
self.assertEqual(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):
|
|
self.assertTrue(self.all_zeros(m.norm3))
|
|
model.train()
|
|
|
|
imgs = torch.randn(1, 3, 224, 224)
|
|
feat = model(imgs)
|
|
self.assertEqual(len(feat), 4)
|
|
self.assertEqual(feat[0].shape, torch.Size([1, 256, 56, 56]))
|
|
self.assertEqual(feat[1].shape, torch.Size([1, 512, 28, 28]))
|
|
self.assertEqual(feat[2].shape, torch.Size([1, 1024, 14, 14]))
|
|
self.assertEqual(feat[3].shape, torch.Size([1, 2048, 7, 7]))
|