OpenMMLab Detection Toolbox and Benchmark
https://mmdetection.readthedocs.io/
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733 lines
25 KiB
733 lines
25 KiB
import pytest |
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import torch |
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from torch.nn.modules import AvgPool2d, GroupNorm |
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from torch.nn.modules.batchnorm import _BatchNorm |
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from mmdet.models.backbones import RegNet, Res2Net, ResNet, ResNetV1d, ResNeXt |
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from mmdet.models.backbones.res2net import Bottle2neck |
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from mmdet.models.backbones.resnet import BasicBlock, Bottleneck |
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from mmdet.models.backbones.resnext import Bottleneck as BottleneckX |
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from mmdet.models.utils import ResLayer |
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from mmdet.ops import DeformConvPack |
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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, BottleneckX, Bottle2neck)): |
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return True |
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return False |
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def is_norm(modules): |
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"""Check if is one of the norms.""" |
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if isinstance(modules, (GroupNorm, _BatchNorm)): |
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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.allclose(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.allclose(modules.bias.data, |
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torch.zeros_like(modules.bias.data)) |
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else: |
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bias_zero = True |
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return weight_zero and bias_zero |
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def check_norm_state(modules, train_state): |
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"""Check if norm layer is in correct train state.""" |
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for mod in modules: |
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if isinstance(mod, _BatchNorm): |
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if mod.training != train_state: |
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return False |
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return True |
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def test_resnet_basic_block(): |
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with pytest.raises(AssertionError): |
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# Not implemented yet. |
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BasicBlock(64, 64, with_cp=True) |
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with pytest.raises(AssertionError): |
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# Not implemented yet. |
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dcn = dict(type='DCN', deformable_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 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', deformable_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, DeformConvPack) |
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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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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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# with checkpoint is not implemented in BasicBlock of ResNet18 |
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ResNet(18, with_cp=True) |
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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', deformable_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) |
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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(depth=50, norm_eval=True) |
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model.init_weights('torchvision://resnet50') |
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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 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, 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) |
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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 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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|
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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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|
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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) |
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for m in model.layer1.modules(): |
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if is_block(m): |
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assert not hasattr(m, 'context_block') |
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assert not hasattr(m, 'gen_attention_block') |
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assert m.nonlocal_block.in_channels == 64 |
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for m in model.layer2.modules(): |
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if is_block(m): |
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assert m.nonlocal_block.in_channels == 128 |
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assert m.gen_attention_block.in_channels == 128 |
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assert m.context_block.in_channels == 512 |
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|
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for m in model.layer3.modules(): |
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if is_block(m): |
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assert m.nonlocal_block.in_channels == 256 |
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assert m.gen_attention_block.in_channels == 256 |
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assert m.context_block.in_channels == 1024 |
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|
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for m in model.layer4.modules(): |
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if is_block(m): |
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assert m.nonlocal_block.in_channels == 512 |
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assert m.gen_attention_block.in_channels == 512 |
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assert not hasattr(m, 'context_block') |
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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 == 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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|
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# Test ResNet50 with 1 ContextBlock after conv2, 1 ContextBlock after |
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# conv3 in layers 2, 3, 4 |
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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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stages=(False, True, True, False), |
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position='after_conv3'), |
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dict( |
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cfg=dict(type='ContextBlock', ratio=1. / 16, postfix=2), |
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stages=(False, True, True, False), |
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position='after_conv3') |
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] |
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|
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model = ResNet(50, plugins=plugins) |
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for m in model.layer1.modules(): |
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if is_block(m): |
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assert not hasattr(m, 'context_block') |
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assert not hasattr(m, 'context_block1') |
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assert not hasattr(m, 'context_block2') |
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for m in model.layer2.modules(): |
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if is_block(m): |
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assert not hasattr(m, 'context_block') |
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assert m.context_block1.in_channels == 512 |
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assert m.context_block2.in_channels == 512 |
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|
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for m in model.layer3.modules(): |
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if is_block(m): |
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assert not hasattr(m, 'context_block') |
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assert m.context_block1.in_channels == 1024 |
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assert m.context_block2.in_channels == 1024 |
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|
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for m in model.layer4.modules(): |
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if is_block(m): |
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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]) |
|
|
|
|
|
def test_renext_bottleneck(): |
|
with pytest.raises(AssertionError): |
|
# Style must be in ['pytorch', 'caffe'] |
|
BottleneckX(64, 64, groups=32, base_width=4, style='tensorflow') |
|
|
|
# Test ResNeXt Bottleneck structure |
|
block = BottleneckX( |
|
64, 64, groups=32, base_width=4, stride=2, style='pytorch') |
|
assert block.conv2.stride == (2, 2) |
|
assert block.conv2.groups == 32 |
|
assert block.conv2.out_channels == 128 |
|
|
|
# Test ResNeXt Bottleneck with DCN |
|
dcn = dict(type='DCN', deformable_groups=1, fallback_on_stride=False) |
|
with pytest.raises(AssertionError): |
|
# conv_cfg must be None if dcn is not None |
|
BottleneckX( |
|
64, |
|
64, |
|
groups=32, |
|
base_width=4, |
|
dcn=dcn, |
|
conv_cfg=dict(type='Conv')) |
|
BottleneckX(64, 64, dcn=dcn) |
|
|
|
# Test ResNeXt Bottleneck forward |
|
block = BottleneckX(64, 16, groups=32, base_width=4) |
|
x = torch.randn(1, 64, 56, 56) |
|
x_out = block(x) |
|
assert x_out.shape == torch.Size([1, 64, 56, 56]) |
|
|
|
|
|
def test_resnext_backbone(): |
|
with pytest.raises(KeyError): |
|
# ResNeXt depth should be in [50, 101, 152] |
|
ResNeXt(depth=18) |
|
|
|
# Test ResNeXt with group 32, base_width 4 |
|
model = ResNeXt(depth=50, groups=32, base_width=4) |
|
for m in model.modules(): |
|
if is_block(m): |
|
assert m.conv2.groups == 32 |
|
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]) |
|
|
|
|
|
regnet_test_data = [ |
|
('regnetx_800mf', |
|
dict(w0=56, wa=35.73, wm=2.28, group_w=16, depth=16, |
|
bot_mul=1.0), [64, 128, 288, 672]), |
|
('regnetx_1.6gf', |
|
dict(w0=80, wa=34.01, wm=2.25, group_w=24, depth=18, |
|
bot_mul=1.0), [72, 168, 408, 912]), |
|
('regnetx_3.2gf', |
|
dict(w0=88, wa=26.31, wm=2.25, group_w=48, depth=25, |
|
bot_mul=1.0), [96, 192, 432, 1008]), |
|
('regnetx_4.0gf', |
|
dict(w0=96, wa=38.65, wm=2.43, group_w=40, depth=23, |
|
bot_mul=1.0), [80, 240, 560, 1360]), |
|
('regnetx_6.4gf', |
|
dict(w0=184, wa=60.83, wm=2.07, group_w=56, depth=17, |
|
bot_mul=1.0), [168, 392, 784, 1624]), |
|
('regnetx_8.0gf', |
|
dict(w0=80, wa=49.56, wm=2.88, group_w=120, depth=23, |
|
bot_mul=1.0), [80, 240, 720, 1920]), |
|
('regnetx_12gf', |
|
dict(w0=168, wa=73.36, wm=2.37, group_w=112, depth=19, |
|
bot_mul=1.0), [224, 448, 896, 2240]), |
|
] |
|
|
|
|
|
@pytest.mark.parametrize('arch_name,arch,out_channels', regnet_test_data) |
|
def test_regnet_backbone(arch_name, arch, out_channels): |
|
with pytest.raises(AssertionError): |
|
# ResNeXt depth should be in [50, 101, 152] |
|
RegNet(arch_name + '233') |
|
|
|
# Test RegNet with arch_name |
|
model = RegNet(arch_name) |
|
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, out_channels[0], 56, 56]) |
|
assert feat[1].shape == torch.Size([1, out_channels[1], 28, 28]) |
|
assert feat[2].shape == torch.Size([1, out_channels[2], 14, 14]) |
|
assert feat[3].shape == torch.Size([1, out_channels[3], 7, 7]) |
|
|
|
# Test RegNet with arch |
|
model = RegNet(arch) |
|
assert feat[0].shape == torch.Size([1, out_channels[0], 56, 56]) |
|
assert feat[1].shape == torch.Size([1, out_channels[1], 28, 28]) |
|
assert feat[2].shape == torch.Size([1, out_channels[2], 14, 14]) |
|
assert feat[3].shape == torch.Size([1, out_channels[3], 7, 7]) |
|
|
|
|
|
def test_res2net_bottle2neck(): |
|
with pytest.raises(AssertionError): |
|
# Style must be in ['pytorch', 'caffe'] |
|
Bottle2neck(64, 64, base_width=26, scales=4, style='tensorflow') |
|
|
|
with pytest.raises(AssertionError): |
|
# Scale must be larger than 1 |
|
Bottle2neck(64, 64, base_width=26, scales=1, style='pytorch') |
|
|
|
# Test Res2Net Bottle2neck structure |
|
block = Bottle2neck( |
|
64, 64, base_width=26, stride=2, scales=4, style='pytorch') |
|
assert block.scales == 4 |
|
|
|
# Test Res2Net Bottle2neck with DCN |
|
dcn = dict(type='DCN', deformable_groups=1, fallback_on_stride=False) |
|
with pytest.raises(AssertionError): |
|
# conv_cfg must be None if dcn is not None |
|
Bottle2neck( |
|
64, |
|
64, |
|
base_width=26, |
|
scales=4, |
|
dcn=dcn, |
|
conv_cfg=dict(type='Conv')) |
|
Bottle2neck(64, 64, dcn=dcn) |
|
|
|
# Test Res2Net Bottle2neck forward |
|
block = Bottle2neck(64, 16, base_width=26, scales=4) |
|
x = torch.randn(1, 64, 56, 56) |
|
x_out = block(x) |
|
assert x_out.shape == torch.Size([1, 64, 56, 56]) |
|
|
|
|
|
def test_res2net_backbone(): |
|
with pytest.raises(KeyError): |
|
# Res2Net depth should be in [50, 101, 152] |
|
Res2Net(depth=18) |
|
|
|
# Test Res2Net with scales 4, base_width 26 |
|
model = Res2Net(depth=50, scales=4, base_width=26) |
|
for m in model.modules(): |
|
if is_block(m): |
|
assert m.scales == 4 |
|
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])
|
|
|