576 lines
20 KiB
Python
576 lines
20 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import pytest
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import torch
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from mmcv.ops import DeformConv2dPack
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from mmcv.utils.parrots_wrapper import _BatchNorm
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from torch.nn.modules import AvgPool2d, GroupNorm
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from mmseg.models.backbones import ResNet, ResNetV1d
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from mmseg.models.backbones.resnet import BasicBlock, Bottleneck
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from mmseg.models.utils import ResLayer
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from .utils import all_zeros, check_norm_state, is_block, is_norm
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def test_resnet_basic_block():
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with pytest.raises(AssertionError):
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# Not implemented yet.
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dcn = dict(type='DCN', deform_groups=1, fallback_on_stride=False)
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BasicBlock(64, 64, dcn=dcn)
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with pytest.raises(AssertionError):
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# Not implemented yet.
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plugins = [
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dict(
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cfg=dict(type='ContextBlock', ratio=1. / 16),
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position='after_conv3')
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]
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BasicBlock(64, 64, plugins=plugins)
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with pytest.raises(AssertionError):
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# Not implemented yet
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plugins = [
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dict(
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cfg=dict(
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type='GeneralizedAttention',
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spatial_range=-1,
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num_heads=8,
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attention_type='0010',
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kv_stride=2),
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position='after_conv2')
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]
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BasicBlock(64, 64, plugins=plugins)
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# Test BasicBlock with checkpoint forward
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block = BasicBlock(16, 16, with_cp=True)
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assert block.with_cp
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x = torch.randn(1, 16, 56, 56)
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x_out = block(x)
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assert x_out.shape == torch.Size([1, 16, 56, 56])
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# test BasicBlock structure and forward
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block = BasicBlock(64, 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.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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def test_resnet_bottleneck():
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with pytest.raises(AssertionError):
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# Style must be in ['pytorch', 'caffe']
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Bottleneck(64, 64, style='tensorflow')
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with pytest.raises(AssertionError):
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# Allowed positions are 'after_conv1', 'after_conv2', 'after_conv3'
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plugins = [
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dict(
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cfg=dict(type='ContextBlock', ratio=1. / 16),
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position='after_conv4')
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]
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Bottleneck(64, 16, plugins=plugins)
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with pytest.raises(AssertionError):
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# Need to specify different postfix to avoid duplicate plugin name
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plugins = [
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dict(
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cfg=dict(type='ContextBlock', ratio=1. / 16),
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position='after_conv3'),
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dict(
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cfg=dict(type='ContextBlock', ratio=1. / 16),
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position='after_conv3')
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]
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Bottleneck(64, 16, plugins=plugins)
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with pytest.raises(KeyError):
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# Plugin type is not supported
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plugins = [dict(cfg=dict(type='WrongPlugin'), position='after_conv3')]
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Bottleneck(64, 16, plugins=plugins)
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# Test Bottleneck with checkpoint forward
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block = Bottleneck(64, 16, with_cp=True)
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assert block.with_cp
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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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# 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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# Test Bottleneck DCN
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dcn = dict(type='DCN', deform_groups=1, fallback_on_stride=False)
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with pytest.raises(AssertionError):
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Bottleneck(64, 64, dcn=dcn, conv_cfg=dict(type='Conv'))
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block = Bottleneck(64, 64, dcn=dcn)
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assert isinstance(block.conv2, DeformConv2dPack)
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# Test Bottleneck forward
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block = Bottleneck(64, 16)
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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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# Test Bottleneck with 1 ContextBlock after conv3
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plugins = [
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dict(
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cfg=dict(type='ContextBlock', ratio=1. / 16),
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position='after_conv3')
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]
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block = Bottleneck(64, 16, plugins=plugins)
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assert block.context_block.in_channels == 64
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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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# Test Bottleneck with 1 GeneralizedAttention after conv2
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plugins = [
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dict(
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cfg=dict(
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type='GeneralizedAttention',
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spatial_range=-1,
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num_heads=8,
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attention_type='0010',
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kv_stride=2),
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position='after_conv2')
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]
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block = Bottleneck(64, 16, plugins=plugins)
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assert block.gen_attention_block.in_channels == 16
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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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# Test Bottleneck with 1 GeneralizedAttention after conv2, 1 NonLocal2d
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# after conv2, 1 ContextBlock after conv3
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plugins = [
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dict(
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cfg=dict(
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type='GeneralizedAttention',
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spatial_range=-1,
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num_heads=8,
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attention_type='0010',
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kv_stride=2),
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position='after_conv2'),
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dict(cfg=dict(type='NonLocal2d'), position='after_conv2'),
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dict(
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cfg=dict(type='ContextBlock', ratio=1. / 16),
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position='after_conv3')
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]
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block = Bottleneck(64, 16, plugins=plugins)
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assert block.gen_attention_block.in_channels == 16
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assert block.nonlocal_block.in_channels == 16
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assert block.context_block.in_channels == 64
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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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# Test Bottleneck with 1 ContextBlock after conv2, 2 ContextBlock after
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# conv3
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plugins = [
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dict(
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cfg=dict(type='ContextBlock', ratio=1. / 16, postfix=1),
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position='after_conv2'),
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dict(
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cfg=dict(type='ContextBlock', ratio=1. / 16, postfix=2),
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position='after_conv3'),
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dict(
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cfg=dict(type='ContextBlock', ratio=1. / 16, postfix=3),
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position='after_conv3')
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]
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block = Bottleneck(64, 16, plugins=plugins)
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assert block.context_block1.in_channels == 16
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assert block.context_block2.in_channels == 64
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assert block.context_block3.in_channels == 64
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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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def test_resnet_res_layer():
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# Test ResLayer of 3 Bottleneck w\o downsample
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layer = ResLayer(Bottleneck, 64, 16, 3)
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assert len(layer) == 3
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assert layer[0].conv1.in_channels == 64
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assert layer[0].conv1.out_channels == 16
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for i in range(1, len(layer)):
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assert layer[i].conv1.in_channels == 64
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assert layer[i].conv1.out_channels == 16
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for i in range(len(layer)):
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assert layer[i].downsample is None
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x = torch.randn(1, 64, 56, 56)
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x_out = layer(x)
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assert x_out.shape == torch.Size([1, 64, 56, 56])
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# Test ResLayer of 3 Bottleneck with downsample
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layer = ResLayer(Bottleneck, 64, 64, 3)
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assert layer[0].downsample[0].out_channels == 256
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for i in range(1, len(layer)):
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assert layer[i].downsample is None
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x = torch.randn(1, 64, 56, 56)
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x_out = layer(x)
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assert x_out.shape == torch.Size([1, 256, 56, 56])
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# Test ResLayer of 3 Bottleneck with stride=2
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layer = ResLayer(Bottleneck, 64, 64, 3, stride=2)
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assert layer[0].downsample[0].out_channels == 256
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assert layer[0].downsample[0].stride == (2, 2)
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for i in range(1, len(layer)):
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assert layer[i].downsample is None
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x = torch.randn(1, 64, 56, 56)
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x_out = layer(x)
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assert x_out.shape == torch.Size([1, 256, 28, 28])
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# Test ResLayer of 3 Bottleneck with stride=2 and average downsample
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layer = ResLayer(Bottleneck, 64, 64, 3, stride=2, avg_down=True)
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assert isinstance(layer[0].downsample[0], AvgPool2d)
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assert layer[0].downsample[1].out_channels == 256
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assert layer[0].downsample[1].stride == (1, 1)
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for i in range(1, len(layer)):
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assert layer[i].downsample is None
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x = torch.randn(1, 64, 56, 56)
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x_out = layer(x)
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assert x_out.shape == torch.Size([1, 256, 28, 28])
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# Test ResLayer of 3 Bottleneck with dilation=2
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layer = ResLayer(Bottleneck, 64, 16, 3, dilation=2)
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for i in range(len(layer)):
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assert layer[i].conv2.dilation == (2, 2)
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x = torch.randn(1, 64, 56, 56)
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x_out = layer(x)
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assert x_out.shape == torch.Size([1, 64, 56, 56])
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# Test ResLayer of 3 Bottleneck with dilation=2, contract_dilation=True
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layer = ResLayer(Bottleneck, 64, 16, 3, dilation=2, contract_dilation=True)
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assert layer[0].conv2.dilation == (1, 1)
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for i in range(1, len(layer)):
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assert layer[i].conv2.dilation == (2, 2)
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x = torch.randn(1, 64, 56, 56)
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x_out = layer(x)
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assert x_out.shape == torch.Size([1, 64, 56, 56])
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# Test ResLayer of 3 Bottleneck with dilation=2, multi_grid
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layer = ResLayer(Bottleneck, 64, 16, 3, dilation=2, multi_grid=(1, 2, 4))
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assert layer[0].conv2.dilation == (1, 1)
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assert layer[1].conv2.dilation == (2, 2)
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assert layer[2].conv2.dilation == (4, 4)
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x = torch.randn(1, 64, 56, 56)
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x_out = layer(x)
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assert x_out.shape == torch.Size([1, 64, 56, 56])
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def test_resnet_backbone():
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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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# len(stage_with_dcn) == num_stages
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dcn = dict(type='DCN', deform_groups=1, fallback_on_stride=False)
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ResNet(50, dcn=dcn, stage_with_dcn=(True, ))
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with pytest.raises(AssertionError):
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# len(stage_with_plugin) == num_stages
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plugins = [
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dict(
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cfg=dict(type='ContextBlock', ratio=1. / 16),
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stages=(False, True, True),
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position='after_conv3')
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]
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ResNet(50, plugins=plugins)
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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, pretrained=0)
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model.init_weights()
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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, norm_eval=True, pretrained='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, 'layer{}'.format(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 ResNet50V1d with first stage frozen
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model = ResNetV1d(depth=50, frozen_stages=frozen_stages)
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assert len(model.stem) == 9
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model.init_weights()
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model.train()
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check_norm_state(model.stem, False)
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for param in model.stem.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, 'layer{}'.format(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)
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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 == torch.Size([1, 64, 56, 56])
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assert feat[1].shape == torch.Size([1, 128, 28, 28])
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assert feat[2].shape == torch.Size([1, 256, 14, 14])
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assert feat[3].shape == torch.Size([1, 512, 7, 7])
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# Test ResNet50 with BatchNorm forward
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model = ResNet(50)
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for m in model.modules():
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if is_norm(m):
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assert isinstance(m, _BatchNorm)
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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 == torch.Size([1, 256, 56, 56])
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assert feat[1].shape == torch.Size([1, 512, 28, 28])
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assert feat[2].shape == torch.Size([1, 1024, 14, 14])
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assert feat[3].shape == torch.Size([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()
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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) == 3
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assert feat[0].shape == torch.Size([1, 256, 56, 56])
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assert feat[1].shape == torch.Size([1, 512, 28, 28])
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assert feat[2].shape == torch.Size([1, 1024, 14, 14])
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# Test ResNet18 with checkpoint forward
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model = ResNet(18, 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) == 4
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assert feat[0].shape == torch.Size([1, 64, 56, 56])
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assert feat[1].shape == torch.Size([1, 128, 28, 28])
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assert feat[2].shape == torch.Size([1, 256, 14, 14])
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assert feat[3].shape == torch.Size([1, 512, 7, 7])
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# Test ResNet50 with checkpoint forward
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model = ResNet(50, 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) == 4
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assert feat[0].shape == torch.Size([1, 256, 56, 56])
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assert feat[1].shape == torch.Size([1, 512, 28, 28])
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assert feat[2].shape == torch.Size([1, 1024, 14, 14])
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assert feat[3].shape == torch.Size([1, 2048, 7, 7])
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# Test ResNet50 with GroupNorm forward
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model = ResNet(
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50, norm_cfg=dict(type='GN', num_groups=32, requires_grad=True))
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for m in model.modules():
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if is_norm(m):
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assert isinstance(m, GroupNorm)
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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 == torch.Size([1, 256, 56, 56])
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assert feat[1].shape == torch.Size([1, 512, 28, 28])
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assert feat[2].shape == torch.Size([1, 1024, 14, 14])
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assert feat[3].shape == torch.Size([1, 2048, 7, 7])
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# Test ResNet50 with 1 GeneralizedAttention after conv2, 1 NonLocal2d
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# after conv2, 1 ContextBlock after conv3 in layers 2, 3, 4
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plugins = [
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dict(
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cfg=dict(
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type='GeneralizedAttention',
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spatial_range=-1,
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num_heads=8,
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attention_type='0010',
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kv_stride=2),
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stages=(False, True, True, True),
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position='after_conv2'),
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dict(cfg=dict(type='NonLocal2d'), position='after_conv2'),
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dict(
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cfg=dict(type='ContextBlock', ratio=1. / 16),
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stages=(False, True, True, False),
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position='after_conv3')
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]
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model = ResNet(50, plugins=plugins)
|
|
for m in model.layer1.modules():
|
|
if is_block(m):
|
|
assert not hasattr(m, 'context_block')
|
|
assert not hasattr(m, 'gen_attention_block')
|
|
assert m.nonlocal_block.in_channels == 64
|
|
for m in model.layer2.modules():
|
|
if is_block(m):
|
|
assert m.nonlocal_block.in_channels == 128
|
|
assert m.gen_attention_block.in_channels == 128
|
|
assert m.context_block.in_channels == 512
|
|
|
|
for m in model.layer3.modules():
|
|
if is_block(m):
|
|
assert m.nonlocal_block.in_channels == 256
|
|
assert m.gen_attention_block.in_channels == 256
|
|
assert m.context_block.in_channels == 1024
|
|
|
|
for m in model.layer4.modules():
|
|
if is_block(m):
|
|
assert m.nonlocal_block.in_channels == 512
|
|
assert m.gen_attention_block.in_channels == 512
|
|
assert not hasattr(m, 'context_block')
|
|
model.init_weights()
|
|
model.train()
|
|
|
|
imgs = torch.randn(1, 3, 224, 224)
|
|
feat = model(imgs)
|
|
assert len(feat) == 4
|
|
assert feat[0].shape == torch.Size([1, 256, 56, 56])
|
|
assert feat[1].shape == torch.Size([1, 512, 28, 28])
|
|
assert feat[2].shape == torch.Size([1, 1024, 14, 14])
|
|
assert feat[3].shape == torch.Size([1, 2048, 7, 7])
|
|
|
|
# Test ResNet50 with 1 ContextBlock after conv2, 1 ContextBlock after
|
|
# conv3 in layers 2, 3, 4
|
|
plugins = [
|
|
dict(
|
|
cfg=dict(type='ContextBlock', ratio=1. / 16, postfix=1),
|
|
stages=(False, True, True, False),
|
|
position='after_conv3'),
|
|
dict(
|
|
cfg=dict(type='ContextBlock', ratio=1. / 16, postfix=2),
|
|
stages=(False, True, True, False),
|
|
position='after_conv3')
|
|
]
|
|
|
|
model = ResNet(50, plugins=plugins)
|
|
for m in model.layer1.modules():
|
|
if is_block(m):
|
|
assert not hasattr(m, 'context_block')
|
|
assert not hasattr(m, 'context_block1')
|
|
assert not hasattr(m, 'context_block2')
|
|
for m in model.layer2.modules():
|
|
if is_block(m):
|
|
assert not hasattr(m, 'context_block')
|
|
assert m.context_block1.in_channels == 512
|
|
assert m.context_block2.in_channels == 512
|
|
|
|
for m in model.layer3.modules():
|
|
if is_block(m):
|
|
assert not hasattr(m, 'context_block')
|
|
assert m.context_block1.in_channels == 1024
|
|
assert m.context_block2.in_channels == 1024
|
|
|
|
for m in model.layer4.modules():
|
|
if is_block(m):
|
|
assert not hasattr(m, 'context_block')
|
|
assert not hasattr(m, 'context_block1')
|
|
assert not hasattr(m, 'context_block2')
|
|
model.init_weights()
|
|
model.train()
|
|
|
|
imgs = torch.randn(1, 3, 224, 224)
|
|
feat = model(imgs)
|
|
assert len(feat) == 4
|
|
assert feat[0].shape == torch.Size([1, 256, 56, 56])
|
|
assert feat[1].shape == torch.Size([1, 512, 28, 28])
|
|
assert feat[2].shape == torch.Size([1, 1024, 14, 14])
|
|
assert feat[3].shape == torch.Size([1, 2048, 7, 7])
|
|
|
|
# Test ResNet50 zero initialization of residual
|
|
model = ResNet(50, 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)
|
|
model.train()
|
|
|
|
imgs = torch.randn(1, 3, 224, 224)
|
|
feat = model(imgs)
|
|
assert len(feat) == 4
|
|
assert feat[0].shape == torch.Size([1, 256, 56, 56])
|
|
assert feat[1].shape == torch.Size([1, 512, 28, 28])
|
|
assert feat[2].shape == torch.Size([1, 1024, 14, 14])
|
|
assert feat[3].shape == torch.Size([1, 2048, 7, 7])
|
|
|
|
# Test ResNetV1d forward
|
|
model = ResNetV1d(depth=50)
|
|
model.init_weights()
|
|
model.train()
|
|
|
|
imgs = torch.randn(1, 3, 224, 224)
|
|
feat = model(imgs)
|
|
assert len(feat) == 4
|
|
assert feat[0].shape == torch.Size([1, 256, 56, 56])
|
|
assert feat[1].shape == torch.Size([1, 512, 28, 28])
|
|
assert feat[2].shape == torch.Size([1, 1024, 14, 14])
|
|
assert feat[3].shape == torch.Size([1, 2048, 7, 7])
|