619 lines
21 KiB
Python
619 lines
21 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import pytest
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import torch
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import torch.nn as nn
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from mmcv.cnn import ConvModule
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from mmengine.utils.parrots_wrapper import _BatchNorm
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from mmcls.models.backbones import ResNet, ResNetV1c, ResNetV1d
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from mmcls.models.backbones.resnet import (BasicBlock, Bottleneck, ResLayer,
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get_expansion)
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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_get_expansion():
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assert get_expansion(Bottleneck, 2) == 2
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assert get_expansion(BasicBlock) == 1
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assert get_expansion(Bottleneck) == 4
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class MyResBlock(nn.Module):
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expansion = 8
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assert get_expansion(MyResBlock) == 8
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# expansion must be an integer or None
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with pytest.raises(TypeError):
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get_expansion(Bottleneck, '0')
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# expansion is not specified and cannot be inferred
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with pytest.raises(TypeError):
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class SomeModule(nn.Module):
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pass
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get_expansion(SomeModule)
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def test_basic_block():
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# expansion must be 1
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with pytest.raises(AssertionError):
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BasicBlock(64, 64, expansion=2)
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# BasicBlock with stride 1, out_channels == in_channels
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block = BasicBlock(64, 64)
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assert block.in_channels == 64
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assert block.mid_channels == 64
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assert block.out_channels == 64
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assert block.conv1.in_channels == 64
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assert block.conv1.out_channels == 64
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assert block.conv1.kernel_size == (3, 3)
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assert block.conv1.stride == (1, 1)
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assert block.conv2.in_channels == 64
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assert block.conv2.out_channels == 64
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assert block.conv2.kernel_size == (3, 3)
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x = torch.randn(1, 64, 56, 56)
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x_out = block(x)
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assert x_out.shape == torch.Size([1, 64, 56, 56])
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# BasicBlock with stride 1 and downsample
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downsample = nn.Sequential(
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nn.Conv2d(64, 128, kernel_size=1, bias=False), nn.BatchNorm2d(128))
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block = BasicBlock(64, 128, downsample=downsample)
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assert block.in_channels == 64
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assert block.mid_channels == 128
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assert block.out_channels == 128
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assert block.conv1.in_channels == 64
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assert block.conv1.out_channels == 128
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assert block.conv1.kernel_size == (3, 3)
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assert block.conv1.stride == (1, 1)
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assert block.conv2.in_channels == 128
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assert block.conv2.out_channels == 128
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assert block.conv2.kernel_size == (3, 3)
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x = torch.randn(1, 64, 56, 56)
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x_out = block(x)
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assert x_out.shape == torch.Size([1, 128, 56, 56])
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# BasicBlock with stride 2 and downsample
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downsample = nn.Sequential(
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nn.Conv2d(64, 128, kernel_size=1, stride=2, bias=False),
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nn.BatchNorm2d(128))
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block = BasicBlock(64, 128, stride=2, downsample=downsample)
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assert block.in_channels == 64
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assert block.mid_channels == 128
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assert block.out_channels == 128
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assert block.conv1.in_channels == 64
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assert block.conv1.out_channels == 128
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assert block.conv1.kernel_size == (3, 3)
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assert block.conv1.stride == (2, 2)
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assert block.conv2.in_channels == 128
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assert block.conv2.out_channels == 128
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assert block.conv2.kernel_size == (3, 3)
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x = torch.randn(1, 64, 56, 56)
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x_out = block(x)
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assert x_out.shape == torch.Size([1, 128, 28, 28])
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# forward with checkpointing
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block = BasicBlock(64, 64, with_cp=True)
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assert block.with_cp
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x = torch.randn(1, 64, 56, 56, requires_grad=True)
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x_out = block(x)
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assert x_out.shape == torch.Size([1, 64, 56, 56])
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def test_bottleneck():
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# style must be in ['pytorch', 'caffe']
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with pytest.raises(AssertionError):
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Bottleneck(64, 64, style='tensorflow')
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# expansion must be divisible by out_channels
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with pytest.raises(AssertionError):
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Bottleneck(64, 64, expansion=3)
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# Test Bottleneck style
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block = Bottleneck(64, 64, stride=2, style='pytorch')
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assert block.conv1.stride == (1, 1)
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assert block.conv2.stride == (2, 2)
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block = Bottleneck(64, 64, stride=2, style='caffe')
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assert block.conv1.stride == (2, 2)
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assert block.conv2.stride == (1, 1)
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# Bottleneck with stride 1
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block = Bottleneck(64, 64, style='pytorch')
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assert block.in_channels == 64
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assert block.mid_channels == 16
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assert block.out_channels == 64
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assert block.conv1.in_channels == 64
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assert block.conv1.out_channels == 16
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assert block.conv1.kernel_size == (1, 1)
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assert block.conv2.in_channels == 16
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assert block.conv2.out_channels == 16
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assert block.conv2.kernel_size == (3, 3)
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assert block.conv3.in_channels == 16
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assert block.conv3.out_channels == 64
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assert block.conv3.kernel_size == (1, 1)
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x = torch.randn(1, 64, 56, 56)
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x_out = block(x)
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assert x_out.shape == (1, 64, 56, 56)
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# Bottleneck with stride 1 and downsample
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downsample = nn.Sequential(
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nn.Conv2d(64, 128, kernel_size=1), nn.BatchNorm2d(128))
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block = Bottleneck(64, 128, style='pytorch', downsample=downsample)
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assert block.in_channels == 64
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assert block.mid_channels == 32
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assert block.out_channels == 128
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assert block.conv1.in_channels == 64
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assert block.conv1.out_channels == 32
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assert block.conv1.kernel_size == (1, 1)
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assert block.conv2.in_channels == 32
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assert block.conv2.out_channels == 32
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assert block.conv2.kernel_size == (3, 3)
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assert block.conv3.in_channels == 32
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assert block.conv3.out_channels == 128
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assert block.conv3.kernel_size == (1, 1)
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x = torch.randn(1, 64, 56, 56)
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x_out = block(x)
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assert x_out.shape == (1, 128, 56, 56)
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# Bottleneck with stride 2 and downsample
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downsample = nn.Sequential(
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nn.Conv2d(64, 128, kernel_size=1, stride=2), nn.BatchNorm2d(128))
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block = Bottleneck(
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64, 128, stride=2, style='pytorch', downsample=downsample)
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x = torch.randn(1, 64, 56, 56)
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x_out = block(x)
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assert x_out.shape == (1, 128, 28, 28)
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# Bottleneck with expansion 2
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block = Bottleneck(64, 64, style='pytorch', expansion=2)
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assert block.in_channels == 64
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assert block.mid_channels == 32
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assert block.out_channels == 64
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assert block.conv1.in_channels == 64
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assert block.conv1.out_channels == 32
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assert block.conv1.kernel_size == (1, 1)
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assert block.conv2.in_channels == 32
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assert block.conv2.out_channels == 32
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assert block.conv2.kernel_size == (3, 3)
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assert block.conv3.in_channels == 32
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assert block.conv3.out_channels == 64
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assert block.conv3.kernel_size == (1, 1)
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x = torch.randn(1, 64, 56, 56)
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x_out = block(x)
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assert x_out.shape == (1, 64, 56, 56)
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# Test Bottleneck with checkpointing
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block = Bottleneck(64, 64, with_cp=True)
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block.train()
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assert block.with_cp
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x = torch.randn(1, 64, 56, 56, requires_grad=True)
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x_out = block(x)
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assert x_out.shape == torch.Size([1, 64, 56, 56])
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def test_basicblock_reslayer():
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# 3 BasicBlock w/o downsample
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layer = ResLayer(BasicBlock, 3, 32, 32)
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assert len(layer) == 3
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for i in range(3):
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assert layer[i].in_channels == 32
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assert layer[i].out_channels == 32
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assert layer[i].downsample is None
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x = torch.randn(1, 32, 56, 56)
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x_out = layer(x)
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assert x_out.shape == (1, 32, 56, 56)
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# 3 BasicBlock w/ stride 1 and downsample
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layer = ResLayer(BasicBlock, 3, 32, 64)
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assert len(layer) == 3
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assert layer[0].in_channels == 32
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assert layer[0].out_channels == 64
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assert layer[0].downsample is not None and len(layer[0].downsample) == 2
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assert isinstance(layer[0].downsample[0], nn.Conv2d)
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assert layer[0].downsample[0].stride == (1, 1)
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for i in range(1, 3):
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assert layer[i].in_channels == 64
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assert layer[i].out_channels == 64
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assert layer[i].downsample is None
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x = torch.randn(1, 32, 56, 56)
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x_out = layer(x)
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assert x_out.shape == (1, 64, 56, 56)
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# 3 BasicBlock w/ stride 2 and downsample
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layer = ResLayer(BasicBlock, 3, 32, 64, stride=2)
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assert len(layer) == 3
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assert layer[0].in_channels == 32
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assert layer[0].out_channels == 64
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assert layer[0].stride == 2
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assert layer[0].downsample is not None and len(layer[0].downsample) == 2
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assert isinstance(layer[0].downsample[0], nn.Conv2d)
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assert layer[0].downsample[0].stride == (2, 2)
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for i in range(1, 3):
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assert layer[i].in_channels == 64
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assert layer[i].out_channels == 64
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assert layer[i].stride == 1
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assert layer[i].downsample is None
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x = torch.randn(1, 32, 56, 56)
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x_out = layer(x)
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assert x_out.shape == (1, 64, 28, 28)
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# 3 BasicBlock w/ stride 2 and downsample with avg pool
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layer = ResLayer(BasicBlock, 3, 32, 64, stride=2, avg_down=True)
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assert len(layer) == 3
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assert layer[0].in_channels == 32
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assert layer[0].out_channels == 64
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assert layer[0].stride == 2
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assert layer[0].downsample is not None and len(layer[0].downsample) == 3
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assert isinstance(layer[0].downsample[0], nn.AvgPool2d)
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assert layer[0].downsample[0].stride == 2
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for i in range(1, 3):
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assert layer[i].in_channels == 64
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assert layer[i].out_channels == 64
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assert layer[i].stride == 1
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assert layer[i].downsample is None
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x = torch.randn(1, 32, 56, 56)
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x_out = layer(x)
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assert x_out.shape == (1, 64, 28, 28)
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def test_bottleneck_reslayer():
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# 3 Bottleneck w/o downsample
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layer = ResLayer(Bottleneck, 3, 32, 32)
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assert len(layer) == 3
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for i in range(3):
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assert layer[i].in_channels == 32
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assert layer[i].out_channels == 32
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assert layer[i].downsample is None
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x = torch.randn(1, 32, 56, 56)
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x_out = layer(x)
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assert x_out.shape == (1, 32, 56, 56)
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# 3 Bottleneck w/ stride 1 and downsample
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layer = ResLayer(Bottleneck, 3, 32, 64)
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assert len(layer) == 3
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assert layer[0].in_channels == 32
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assert layer[0].out_channels == 64
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assert layer[0].stride == 1
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assert layer[0].conv1.out_channels == 16
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assert layer[0].downsample is not None and len(layer[0].downsample) == 2
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assert isinstance(layer[0].downsample[0], nn.Conv2d)
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assert layer[0].downsample[0].stride == (1, 1)
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for i in range(1, 3):
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assert layer[i].in_channels == 64
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assert layer[i].out_channels == 64
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assert layer[i].conv1.out_channels == 16
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assert layer[i].stride == 1
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assert layer[i].downsample is None
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x = torch.randn(1, 32, 56, 56)
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x_out = layer(x)
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assert x_out.shape == (1, 64, 56, 56)
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# 3 Bottleneck w/ stride 2 and downsample
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layer = ResLayer(Bottleneck, 3, 32, 64, stride=2)
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assert len(layer) == 3
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assert layer[0].in_channels == 32
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assert layer[0].out_channels == 64
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assert layer[0].stride == 2
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assert layer[0].conv1.out_channels == 16
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assert layer[0].downsample is not None and len(layer[0].downsample) == 2
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assert isinstance(layer[0].downsample[0], nn.Conv2d)
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assert layer[0].downsample[0].stride == (2, 2)
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for i in range(1, 3):
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assert layer[i].in_channels == 64
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assert layer[i].out_channels == 64
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assert layer[i].conv1.out_channels == 16
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assert layer[i].stride == 1
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assert layer[i].downsample is None
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x = torch.randn(1, 32, 56, 56)
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x_out = layer(x)
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assert x_out.shape == (1, 64, 28, 28)
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# 3 Bottleneck w/ stride 2 and downsample with avg pool
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layer = ResLayer(Bottleneck, 3, 32, 64, stride=2, avg_down=True)
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assert len(layer) == 3
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assert layer[0].in_channels == 32
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assert layer[0].out_channels == 64
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assert layer[0].stride == 2
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assert layer[0].conv1.out_channels == 16
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assert layer[0].downsample is not None and len(layer[0].downsample) == 3
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assert isinstance(layer[0].downsample[0], nn.AvgPool2d)
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assert layer[0].downsample[0].stride == 2
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for i in range(1, 3):
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assert layer[i].in_channels == 64
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assert layer[i].out_channels == 64
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assert layer[i].conv1.out_channels == 16
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assert layer[i].stride == 1
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assert layer[i].downsample is None
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x = torch.randn(1, 32, 56, 56)
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x_out = layer(x)
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assert x_out.shape == (1, 64, 28, 28)
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# 3 Bottleneck with custom expansion
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layer = ResLayer(Bottleneck, 3, 32, 32, expansion=2)
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assert len(layer) == 3
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for i in range(3):
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assert layer[i].in_channels == 32
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assert layer[i].out_channels == 32
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assert layer[i].stride == 1
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assert layer[i].conv1.out_channels == 16
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assert layer[i].downsample is None
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x = torch.randn(1, 32, 56, 56)
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x_out = layer(x)
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assert x_out.shape == (1, 32, 56, 56)
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def test_resnet():
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"""Test resnet backbone."""
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with pytest.raises(KeyError):
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# ResNet depth should be in [18, 34, 50, 101, 152]
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ResNet(20)
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with pytest.raises(AssertionError):
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# In ResNet: 1 <= num_stages <= 4
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ResNet(50, num_stages=0)
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with pytest.raises(AssertionError):
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# In ResNet: 1 <= num_stages <= 4
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ResNet(50, num_stages=5)
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with pytest.raises(AssertionError):
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# len(strides) == len(dilations) == num_stages
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ResNet(50, strides=(1, ), dilations=(1, 1), num_stages=3)
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with pytest.raises(TypeError):
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# pretrained must be a string path
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model = ResNet(50)
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model.init_weights(pretrained=0)
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with pytest.raises(AssertionError):
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# Style must be in ['pytorch', 'caffe']
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ResNet(50, style='tensorflow')
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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(
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depth=50,
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norm_eval=True,
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init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'))
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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))
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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) == 4
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assert feat[0].shape == (1, 64, 56, 56)
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assert feat[1].shape == (1, 128, 28, 28)
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assert feat[2].shape == (1, 256, 14, 14)
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assert feat[3].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))
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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) == 4
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assert feat[0].shape == (1, 256, 56, 56)
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assert feat[1].shape == (1, 512, 28, 28)
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assert feat[2].shape == (1, 1024, 14, 14)
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assert feat[3].shape == (1, 2048, 7, 7)
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# Test ResNet50 with DropPath forward
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model = ResNet(50, out_indices=(0, 1, 2, 3), drop_path_rate=0.5)
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model.init_weights()
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model.train()
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|
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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) == 4
|
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assert feat[0].shape == (1, 256, 56, 56)
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|
assert feat[1].shape == (1, 512, 28, 28)
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assert feat[2].shape == (1, 1024, 14, 14)
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assert feat[3].shape == (1, 2048, 7, 7)
|
|
|
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# Test ResNet50 with layers 1, 2, 3 out forward
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model = ResNet(50, out_indices=(0, 1, 2))
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|
model.init_weights()
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model.train()
|
|
|
|
imgs = torch.randn(1, 3, 224, 224)
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|
feat = model(imgs)
|
|
assert len(feat) == 3
|
|
assert feat[0].shape == (1, 256, 56, 56)
|
|
assert feat[1].shape == (1, 512, 28, 28)
|
|
assert feat[2].shape == (1, 1024, 14, 14)
|
|
|
|
# Test ResNet50 with layers 3 (top feature maps) out forward
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|
model = ResNet(50, out_indices=(3, ))
|
|
model.init_weights()
|
|
model.train()
|
|
|
|
imgs = torch.randn(1, 3, 224, 224)
|
|
feat = model(imgs)
|
|
assert len(feat) == 1
|
|
assert feat[0].shape == (1, 2048, 7, 7)
|
|
|
|
# Test ResNet50 with checkpoint forward
|
|
model = ResNet(50, out_indices=(0, 1, 2, 3), with_cp=True)
|
|
for m in model.modules():
|
|
if is_block(m):
|
|
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 == (1, 256, 56, 56)
|
|
assert feat[1].shape == (1, 512, 28, 28)
|
|
assert feat[2].shape == (1, 1024, 14, 14)
|
|
assert feat[3].shape == (1, 2048, 7, 7)
|
|
|
|
# zero initialization of residual blocks
|
|
model = ResNet(50, out_indices=(0, 1, 2, 3), zero_init_residual=True)
|
|
model.init_weights()
|
|
for m in model.modules():
|
|
if isinstance(m, Bottleneck):
|
|
assert all_zeros(m.norm3)
|
|
elif isinstance(m, BasicBlock):
|
|
assert all_zeros(m.norm2)
|
|
|
|
# non-zero initialization of residual blocks
|
|
model = ResNet(50, out_indices=(0, 1, 2, 3), zero_init_residual=False)
|
|
model.init_weights()
|
|
for m in model.modules():
|
|
if isinstance(m, Bottleneck):
|
|
assert not all_zeros(m.norm3)
|
|
elif isinstance(m, BasicBlock):
|
|
assert not all_zeros(m.norm2)
|
|
|
|
|
|
def test_resnet_v1c():
|
|
model = ResNetV1c(depth=50, out_indices=(0, 1, 2, 3))
|
|
model.init_weights()
|
|
model.train()
|
|
|
|
assert len(model.stem) == 3
|
|
for i in range(3):
|
|
assert isinstance(model.stem[i], ConvModule)
|
|
|
|
imgs = torch.randn(1, 3, 224, 224)
|
|
feat = model.stem(imgs)
|
|
assert feat.shape == (1, 64, 112, 112)
|
|
feat = model(imgs)
|
|
assert len(feat) == 4
|
|
assert feat[0].shape == (1, 256, 56, 56)
|
|
assert feat[1].shape == (1, 512, 28, 28)
|
|
assert feat[2].shape == (1, 1024, 14, 14)
|
|
assert feat[3].shape == (1, 2048, 7, 7)
|
|
|
|
# Test ResNet50V1d with first stage frozen
|
|
frozen_stages = 1
|
|
model = ResNetV1d(depth=50, frozen_stages=frozen_stages)
|
|
assert len(model.stem) == 3
|
|
for i in range(3):
|
|
assert isinstance(model.stem[i], ConvModule)
|
|
model.init_weights()
|
|
model.train()
|
|
check_norm_state(model.stem, False)
|
|
for param in model.stem.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
|
|
|
|
|
|
def test_resnet_v1d():
|
|
model = ResNetV1d(depth=50, out_indices=(0, 1, 2, 3))
|
|
model.init_weights()
|
|
model.train()
|
|
|
|
assert len(model.stem) == 3
|
|
for i in range(3):
|
|
assert isinstance(model.stem[i], ConvModule)
|
|
|
|
imgs = torch.randn(1, 3, 224, 224)
|
|
feat = model.stem(imgs)
|
|
assert feat.shape == (1, 64, 112, 112)
|
|
feat = model(imgs)
|
|
assert len(feat) == 4
|
|
assert feat[0].shape == (1, 256, 56, 56)
|
|
assert feat[1].shape == (1, 512, 28, 28)
|
|
assert feat[2].shape == (1, 1024, 14, 14)
|
|
assert feat[3].shape == (1, 2048, 7, 7)
|
|
|
|
# Test ResNet50V1d with first stage frozen
|
|
frozen_stages = 1
|
|
model = ResNetV1d(depth=50, frozen_stages=frozen_stages)
|
|
assert len(model.stem) == 3
|
|
for i in range(3):
|
|
assert isinstance(model.stem[i], ConvModule)
|
|
model.init_weights()
|
|
model.train()
|
|
check_norm_state(model.stem, False)
|
|
for param in model.stem.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
|
|
|
|
|
|
def test_resnet_half_channel():
|
|
model = ResNet(50, base_channels=32, 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 == (1, 128, 56, 56)
|
|
assert feat[1].shape == (1, 256, 28, 28)
|
|
assert feat[2].shape == (1, 512, 14, 14)
|
|
assert feat[3].shape == (1, 1024, 7, 7)
|