mirror of https://github.com/open-mmlab/mmpose
561 lines
23 KiB
Python
561 lines
23 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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from unittest import TestCase
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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.dl_utils.parrots_wrapper import _BatchNorm
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from mmpose.models.backbones import ResNet, ResNetV1d
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from mmpose.models.backbones.resnet import (BasicBlock, Bottleneck, ResLayer,
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get_expansion)
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class TestResnet(TestCase):
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@staticmethod
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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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@staticmethod
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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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@staticmethod
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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(self):
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self.assertEqual(get_expansion(Bottleneck, 2), 2)
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self.assertEqual(get_expansion(BasicBlock), 1)
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self.assertEqual(get_expansion(Bottleneck), 4)
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class MyResBlock(nn.Module):
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expansion = 8
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self.assertEqual(get_expansion(MyResBlock), 8)
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# expansion must be an integer or None
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with self.assertRaises(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 self.assertRaises(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(self):
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# expansion must be 1
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with self.assertRaises(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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self.assertEqual(block.in_channels, 64)
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self.assertEqual(block.mid_channels, 64)
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self.assertEqual(block.out_channels, 64)
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self.assertEqual(block.conv1.in_channels, 64)
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self.assertEqual(block.conv1.out_channels, 64)
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self.assertEqual(block.conv1.kernel_size, (3, 3))
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self.assertEqual(block.conv1.stride, (1, 1))
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self.assertEqual(block.conv2.in_channels, 64)
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self.assertEqual(block.conv2.out_channels, 64)
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self.assertEqual(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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self.assertEqual(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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self.assertEqual(block.in_channels, 64)
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self.assertEqual(block.mid_channels, 128)
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self.assertEqual(block.out_channels, 128)
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self.assertEqual(block.conv1.in_channels, 64)
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self.assertEqual(block.conv1.out_channels, 128)
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self.assertEqual(block.conv1.kernel_size, (3, 3))
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self.assertEqual(block.conv1.stride, (1, 1))
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self.assertEqual(block.conv2.in_channels, 128)
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self.assertEqual(block.conv2.out_channels, 128)
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self.assertEqual(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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self.assertEqual(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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self.assertEqual(block.in_channels, 64)
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self.assertEqual(block.mid_channels, 128)
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self.assertEqual(block.out_channels, 128)
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self.assertEqual(block.conv1.in_channels, 64)
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self.assertEqual(block.conv1.out_channels, 128)
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self.assertEqual(block.conv1.kernel_size, (3, 3))
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self.assertEqual(block.conv1.stride, (2, 2))
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self.assertEqual(block.conv2.in_channels, 128)
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self.assertEqual(block.conv2.out_channels, 128)
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self.assertEqual(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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self.assertEqual(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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self.assertTrue(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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self.assertEqual(x_out.shape, torch.Size([1, 64, 56, 56]))
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def test_bottleneck(self):
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# style must be in ['pytorch', 'caffe']
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with self.assertRaises(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 self.assertRaises(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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self.assertEqual(block.conv1.stride, (1, 1))
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self.assertEqual(block.conv2.stride, (2, 2))
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block = Bottleneck(64, 64, stride=2, style='caffe')
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self.assertEqual(block.conv1.stride, (2, 2))
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self.assertEqual(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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self.assertEqual(block.in_channels, 64)
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self.assertEqual(block.mid_channels, 16)
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self.assertEqual(block.out_channels, 64)
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self.assertEqual(block.conv1.in_channels, 64)
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self.assertEqual(block.conv1.out_channels, 16)
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self.assertEqual(block.conv1.kernel_size, (1, 1))
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self.assertEqual(block.conv2.in_channels, 16)
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self.assertEqual(block.conv2.out_channels, 16)
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self.assertEqual(block.conv2.kernel_size, (3, 3))
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self.assertEqual(block.conv3.in_channels, 16)
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self.assertEqual(block.conv3.out_channels, 64)
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self.assertEqual(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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self.assertEqual(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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self.assertEqual(block.in_channels, 64)
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self.assertEqual(block.mid_channels, 32)
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self.assertEqual(block.out_channels, 128)
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self.assertEqual(block.conv1.in_channels, 64)
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self.assertEqual(block.conv1.out_channels, 32)
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self.assertEqual(block.conv1.kernel_size, (1, 1))
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self.assertEqual(block.conv2.in_channels, 32)
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self.assertEqual(block.conv2.out_channels, 32)
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self.assertEqual(block.conv2.kernel_size, (3, 3))
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self.assertEqual(block.conv3.in_channels, 32)
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self.assertEqual(block.conv3.out_channels, 128)
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self.assertEqual(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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self.assertEqual(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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self.assertEqual(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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self.assertEqual(block.in_channels, 64)
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self.assertEqual(block.mid_channels, 32)
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self.assertEqual(block.out_channels, 64)
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self.assertEqual(block.conv1.in_channels, 64)
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self.assertEqual(block.conv1.out_channels, 32)
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self.assertEqual(block.conv1.kernel_size, (1, 1))
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self.assertEqual(block.conv2.in_channels, 32)
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self.assertEqual(block.conv2.out_channels, 32)
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self.assertEqual(block.conv2.kernel_size, (3, 3))
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self.assertEqual(block.conv3.in_channels, 32)
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self.assertEqual(block.conv3.out_channels, 64)
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self.assertEqual(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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self.assertEqual(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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self.assertTrue(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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self.assertEqual(x_out.shape, torch.Size([1, 64, 56, 56]))
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def test_basicblock_reslayer(self):
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# 3 BasicBlock w/o downsample
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layer = ResLayer(BasicBlock, 3, 32, 32)
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self.assertEqual(len(layer), 3)
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for i in range(3):
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self.assertEqual(layer[i].in_channels, 32)
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self.assertEqual(layer[i].out_channels, 32)
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self.assertIsNone(layer[i].downsample)
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x = torch.randn(1, 32, 56, 56)
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x_out = layer(x)
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self.assertEqual(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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self.assertEqual(len(layer), 3)
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self.assertEqual(layer[0].in_channels, 32)
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self.assertEqual(layer[0].out_channels, 64)
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self.assertEqual(
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layer[0].downsample is not None and len(layer[0].downsample), 2)
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self.assertIsInstance(layer[0].downsample[0], nn.Conv2d)
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self.assertEqual(layer[0].downsample[0].stride, (1, 1))
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for i in range(1, 3):
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self.assertEqual(layer[i].in_channels, 64)
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self.assertEqual(layer[i].out_channels, 64)
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self.assertIsNone(layer[i].downsample)
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x = torch.randn(1, 32, 56, 56)
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x_out = layer(x)
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self.assertEqual(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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self.assertEqual(len(layer), 3)
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self.assertEqual(layer[0].in_channels, 32)
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self.assertEqual(layer[0].out_channels, 64)
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self.assertEqual(layer[0].stride, 2)
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self.assertEqual(
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layer[0].downsample is not None and len(layer[0].downsample), 2)
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self.assertIsInstance(layer[0].downsample[0], nn.Conv2d)
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self.assertEqual(layer[0].downsample[0].stride, (2, 2))
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for i in range(1, 3):
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self.assertEqual(layer[i].in_channels, 64)
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self.assertEqual(layer[i].out_channels, 64)
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self.assertEqual(layer[i].stride, 1)
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self.assertIsNone(layer[i].downsample)
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x = torch.randn(1, 32, 56, 56)
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x_out = layer(x)
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self.assertEqual(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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self.assertEqual(len(layer), 3)
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self.assertEqual(layer[0].in_channels, 32)
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self.assertEqual(layer[0].out_channels, 64)
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self.assertEqual(layer[0].stride, 2)
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self.assertEqual(
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layer[0].downsample is not None and len(layer[0].downsample), 3)
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self.assertIsInstance(layer[0].downsample[0], nn.AvgPool2d)
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self.assertEqual(layer[0].downsample[0].stride, 2)
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for i in range(1, 3):
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self.assertEqual(layer[i].in_channels, 64)
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self.assertEqual(layer[i].out_channels, 64)
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self.assertEqual(layer[i].stride, 1)
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self.assertIsNone(layer[i].downsample)
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x = torch.randn(1, 32, 56, 56)
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x_out = layer(x)
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self.assertEqual(x_out.shape, (1, 64, 28, 28))
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def test_bottleneck_reslayer(self):
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# 3 Bottleneck w/o downsample
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layer = ResLayer(Bottleneck, 3, 32, 32)
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self.assertEqual(len(layer), 3)
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for i in range(3):
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self.assertEqual(layer[i].in_channels, 32)
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self.assertEqual(layer[i].out_channels, 32)
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self.assertIsNone(layer[i].downsample)
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x = torch.randn(1, 32, 56, 56)
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x_out = layer(x)
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self.assertEqual(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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self.assertEqual(len(layer), 3)
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self.assertEqual(layer[0].in_channels, 32)
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self.assertEqual(layer[0].out_channels, 64)
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self.assertEqual(layer[0].stride, 1)
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self.assertEqual(layer[0].conv1.out_channels, 16)
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self.assertEqual(
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layer[0].downsample is not None and len(layer[0].downsample), 2)
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self.assertIsInstance(layer[0].downsample[0], nn.Conv2d)
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self.assertEqual(layer[0].downsample[0].stride, (1, 1))
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for i in range(1, 3):
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self.assertEqual(layer[i].in_channels, 64)
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self.assertEqual(layer[i].out_channels, 64)
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self.assertEqual(layer[i].conv1.out_channels, 16)
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self.assertEqual(layer[i].stride, 1)
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self.assertIsNone(layer[i].downsample)
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x = torch.randn(1, 32, 56, 56)
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x_out = layer(x)
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self.assertEqual(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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self.assertEqual(len(layer), 3)
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self.assertEqual(layer[0].in_channels, 32)
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self.assertEqual(layer[0].out_channels, 64)
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self.assertEqual(layer[0].stride, 2)
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self.assertEqual(layer[0].conv1.out_channels, 16)
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self.assertEqual(
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layer[0].downsample is not None and len(layer[0].downsample), 2)
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self.assertIsInstance(layer[0].downsample[0], nn.Conv2d)
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self.assertEqual(layer[0].downsample[0].stride, (2, 2))
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for i in range(1, 3):
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self.assertEqual(layer[i].in_channels, 64)
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self.assertEqual(layer[i].out_channels, 64)
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self.assertEqual(layer[i].conv1.out_channels, 16)
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self.assertEqual(layer[i].stride, 1)
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self.assertIsNone(layer[i].downsample)
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x = torch.randn(1, 32, 56, 56)
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x_out = layer(x)
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self.assertEqual(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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self.assertEqual(len(layer), 3)
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self.assertEqual(layer[0].in_channels, 32)
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self.assertEqual(layer[0].out_channels, 64)
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self.assertEqual(layer[0].stride, 2)
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self.assertEqual(layer[0].conv1.out_channels, 16)
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self.assertEqual(
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layer[0].downsample is not None and len(layer[0].downsample), 3)
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self.assertIsInstance(layer[0].downsample[0], nn.AvgPool2d)
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self.assertEqual(layer[0].downsample[0].stride, 2)
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for i in range(1, 3):
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self.assertEqual(layer[i].in_channels, 64)
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self.assertEqual(layer[i].out_channels, 64)
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self.assertEqual(layer[i].conv1.out_channels, 16)
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self.assertEqual(layer[i].stride, 1)
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self.assertIsNone(layer[i].downsample)
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x = torch.randn(1, 32, 56, 56)
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x_out = layer(x)
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self.assertEqual(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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self.assertEqual(len(layer), 3)
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for i in range(3):
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self.assertEqual(layer[i].in_channels, 32)
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self.assertEqual(layer[i].out_channels, 32)
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self.assertEqual(layer[i].stride, 1)
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self.assertEqual(layer[i].conv1.out_channels, 16)
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self.assertIsNone(layer[i].downsample)
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x = torch.randn(1, 32, 56, 56)
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x_out = layer(x)
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self.assertEqual(x_out.shape, (1, 32, 56, 56))
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def test_resnet(self):
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"""Test resnet backbone."""
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with self.assertRaises(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 self.assertRaises(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 self.assertRaises(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 self.assertRaises(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 self.assertRaises(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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self.assertTrue(self.check_norm_state(model.modules(), False))
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# Test ResNet50 with torchvision pretrained weight
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init_cfg = dict(type='Pretrained', checkpoint='torchvision://resnet50')
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model = ResNet(depth=50, norm_eval=True, init_cfg=init_cfg)
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model.train()
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self.assertTrue(self.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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self.assertFalse(model.norm1.training)
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for layer in [model.conv1, model.norm1]:
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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 ResNet18 forward
|
|
model = ResNet(18, 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, (1, 64, 56, 56))
|
|
self.assertEqual(feat[1].shape, (1, 128, 28, 28))
|
|
self.assertEqual(feat[2].shape, (1, 256, 14, 14))
|
|
self.assertEqual(feat[3].shape, (1, 512, 7, 7))
|
|
|
|
# Test ResNet50 with BatchNorm forward
|
|
model = ResNet(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, (1, 256, 56, 56))
|
|
self.assertEqual(feat[1].shape, (1, 512, 28, 28))
|
|
self.assertEqual(feat[2].shape, (1, 1024, 14, 14))
|
|
self.assertEqual(feat[3].shape, (1, 2048, 7, 7))
|
|
|
|
# Test ResNet50 with layers 1, 2, 3 out forward
|
|
model = ResNet(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, (1, 256, 56, 56))
|
|
self.assertEqual(feat[1].shape, (1, 512, 28, 28))
|
|
self.assertEqual(feat[2].shape, (1, 1024, 14, 14))
|
|
|
|
# Test ResNet50 with layers 3 (top feature maps) out forward
|
|
model = ResNet(50, out_indices=(3, ))
|
|
model.init_weights()
|
|
model.train()
|
|
|
|
imgs = torch.randn(1, 3, 224, 224)
|
|
feat = model(imgs)
|
|
self.assertEqual(len(feat), 1)
|
|
self.assertEqual(feat[-1].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 self.is_block(m):
|
|
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, (1, 256, 56, 56))
|
|
self.assertEqual(feat[1].shape, (1, 512, 28, 28))
|
|
self.assertEqual(feat[2].shape, (1, 1024, 14, 14))
|
|
self.assertEqual(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):
|
|
self.assertTrue(self.all_zeros(m.norm3))
|
|
elif isinstance(m, BasicBlock):
|
|
self.assertTrue(self.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):
|
|
self.assertFalse(self.all_zeros(m.norm3))
|
|
elif isinstance(m, BasicBlock):
|
|
self.assertFalse(self.all_zeros(m.norm2))
|
|
|
|
def test_resnet_v1d(self):
|
|
model = ResNetV1d(depth=50, out_indices=(0, 1, 2, 3))
|
|
model.init_weights()
|
|
model.train()
|
|
|
|
self.assertEqual(len(model.stem), 3)
|
|
for i in range(3):
|
|
self.assertIsInstance(model.stem[i], ConvModule)
|
|
|
|
imgs = torch.randn(1, 3, 224, 224)
|
|
feat = model.stem(imgs)
|
|
self.assertEqual(feat.shape, (1, 64, 112, 112))
|
|
feat = model(imgs)
|
|
self.assertEqual(len(feat), 4)
|
|
self.assertEqual(feat[0].shape, (1, 256, 56, 56))
|
|
self.assertEqual(feat[1].shape, (1, 512, 28, 28))
|
|
self.assertEqual(feat[2].shape, (1, 1024, 14, 14))
|
|
self.assertEqual(feat[3].shape, (1, 2048, 7, 7))
|
|
|
|
# Test ResNet50V1d with first stage frozen
|
|
frozen_stages = 1
|
|
model = ResNetV1d(depth=50, frozen_stages=frozen_stages)
|
|
self.assertEqual(len(model.stem), 3)
|
|
for i in range(3):
|
|
self.assertIsInstance(model.stem[i], ConvModule)
|
|
model.init_weights()
|
|
model.train()
|
|
self.assertTrue(self.check_norm_state(model.stem, False))
|
|
for param in model.stem.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)
|
|
|
|
def test_resnet_half_channel(self):
|
|
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)
|
|
self.assertEqual(len(feat), 4)
|
|
self.assertEqual(feat[0].shape, (1, 128, 56, 56))
|
|
self.assertEqual(feat[1].shape, (1, 256, 28, 28))
|
|
self.assertEqual(feat[2].shape, (1, 512, 14, 14))
|
|
self.assertEqual(feat[3].shape, (1, 1024, 7, 7))
|