mirror of
https://github.com/open-mmlab/mmselfsup.git
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20488d01b4
* [Refactor]: Refactor data flow * [Fix]: Change data sample to data samples * [Fix]: Change batch_inputs to inputs * [Fix]: Fix lint and UT * [Fix]: Fix UT * [Fix]: Fix lint * [Fix]: Fix docstring * [Fix]: Fix UT * [Refactor]: Add assert in data preprocessor * [Fix]: Fix lint
140 lines
4.4 KiB
Python
140 lines
4.4 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import torch
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from mmengine.utils.dl_utils.parrots_wrapper import _BatchNorm
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from mmselfsup.models.backbones import ResNet
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from mmselfsup.models.backbones.resnet import BasicBlock, Bottleneck
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def is_block(modules):
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"""Check if is ResNet building block."""
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if isinstance(modules, (BasicBlock, Bottleneck)):
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return True
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return False
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def all_zeros(modules):
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"""Check if the weight(and bias) is all zero."""
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weight_zero = torch.equal(modules.weight.data,
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torch.zeros_like(modules.weight.data))
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if hasattr(modules, 'bias'):
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bias_zero = torch.equal(modules.bias.data,
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torch.zeros_like(modules.bias.data))
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else:
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bias_zero = True
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return weight_zero and bias_zero
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def check_norm_state(modules, train_state):
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"""Check if norm layer is in correct train state."""
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for mod in modules:
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if isinstance(mod, _BatchNorm):
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if mod.training != train_state:
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return False
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return True
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def test_resnet():
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"""Test resnet backbone."""
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# Test ResNet50 norm_eval=True
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model = ResNet(50, norm_eval=True)
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model.init_weights()
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model.train()
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assert check_norm_state(model.modules(), False)
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# Test ResNet50 with torchvision pretrained weight
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model = ResNet(depth=50, norm_eval=True)
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model.init_weights()
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model.train()
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assert check_norm_state(model.modules(), False)
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# Test ResNet50 with first stage frozen
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frozen_stages = 1
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model = ResNet(50, frozen_stages=frozen_stages)
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model.init_weights()
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model.train()
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assert model.norm1.training is False
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for layer in [model.conv1, model.norm1]:
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for param in layer.parameters():
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assert param.requires_grad is False
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for i in range(1, frozen_stages + 1):
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layer = getattr(model, f'layer{i}')
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for mod in layer.modules():
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if isinstance(mod, _BatchNorm):
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assert mod.training is False
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for param in layer.parameters():
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assert param.requires_grad is False
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# Test ResNet18 forward
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model = ResNet(18, out_indices=(0, 1, 2, 3, 4))
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model.init_weights()
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model.train()
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imgs = torch.randn(1, 3, 224, 224)
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feat = model(imgs)
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assert len(feat) == 5
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assert feat[0].shape == (1, 64, 112, 112)
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assert feat[1].shape == (1, 64, 56, 56)
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assert feat[2].shape == (1, 128, 28, 28)
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assert feat[3].shape == (1, 256, 14, 14)
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assert feat[4].shape == (1, 512, 7, 7)
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# Test ResNet50 with BatchNorm forward
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model = ResNet(50, out_indices=(0, 1, 2, 3, 4))
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model.init_weights()
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model.train()
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imgs = torch.randn(1, 3, 224, 224)
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feat = model(imgs)
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assert len(feat) == 5
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assert feat[0].shape == (1, 64, 112, 112)
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assert feat[1].shape == (1, 256, 56, 56)
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assert feat[2].shape == (1, 512, 28, 28)
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assert feat[3].shape == (1, 1024, 14, 14)
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assert feat[4].shape == (1, 2048, 7, 7)
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# Test ResNet50 with layers 3 (top feature maps) out forward
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model = ResNet(50, out_indices=(4, ))
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model.init_weights()
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model.train()
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imgs = torch.randn(1, 3, 224, 224)
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feat = model(imgs)
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assert feat[0].shape == (1, 2048, 7, 7)
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# Test ResNet50 with checkpoint forward
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model = ResNet(50, out_indices=(0, 1, 2, 3, 4), with_cp=True)
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for m in model.modules():
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if is_block(m):
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assert m.with_cp
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model.init_weights()
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model.train()
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imgs = torch.randn(1, 3, 224, 224)
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feat = model(imgs)
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assert len(feat) == 5
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assert feat[0].shape == (1, 64, 112, 112)
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assert feat[1].shape == (1, 256, 56, 56)
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assert feat[2].shape == (1, 512, 28, 28)
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assert feat[3].shape == (1, 1024, 14, 14)
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assert feat[4].shape == (1, 2048, 7, 7)
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# zero initialization of residual blocks
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model = ResNet(50, zero_init_residual=True)
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model.init_weights()
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for m in model.modules():
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if isinstance(m, Bottleneck):
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assert all_zeros(m.norm3)
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elif isinstance(m, BasicBlock):
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assert all_zeros(m.norm2)
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# non-zero initialization of residual blocks
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model = ResNet(50, zero_init_residual=False)
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model.init_weights()
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for m in model.modules():
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if isinstance(m, Bottleneck):
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assert not all_zeros(m.norm3)
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elif isinstance(m, BasicBlock):
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assert not all_zeros(m.norm2)
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