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- # Copyright (c) OpenMMLab. All rights reserved.
- from unittest import TestCase
- import torch
- import torch.nn as nn
- from mmcv.cnn import ConvModule
- from mmengine.utils.dl_utils.parrots_wrapper import _BatchNorm
- from mmpose.models.backbones import ResNet, ResNetV1d
- from mmpose.models.backbones.resnet import (BasicBlock, Bottleneck, ResLayer,
- get_expansion)
- class TestResnet(TestCase):
- @staticmethod
- def is_block(modules):
- """Check if is ResNet building block."""
- if isinstance(modules, (BasicBlock, Bottleneck)):
- return True
- return False
- @staticmethod
- def all_zeros(modules):
- """Check if the weight(and bias) is all zero."""
- weight_zero = torch.equal(modules.weight.data,
- torch.zeros_like(modules.weight.data))
- if hasattr(modules, 'bias'):
- bias_zero = torch.equal(modules.bias.data,
- torch.zeros_like(modules.bias.data))
- else:
- bias_zero = True
- return weight_zero and bias_zero
- @staticmethod
- def check_norm_state(modules, train_state):
- """Check if norm layer is in correct train state."""
- for mod in modules:
- if isinstance(mod, _BatchNorm):
- if mod.training != train_state:
- return False
- return True
- def test_get_expansion(self):
- self.assertEqual(get_expansion(Bottleneck, 2), 2)
- self.assertEqual(get_expansion(BasicBlock), 1)
- self.assertEqual(get_expansion(Bottleneck), 4)
- class MyResBlock(nn.Module):
- expansion = 8
- self.assertEqual(get_expansion(MyResBlock), 8)
- # expansion must be an integer or None
- with self.assertRaises(TypeError):
- get_expansion(Bottleneck, '0')
- # expansion is not specified and cannot be inferred
- with self.assertRaises(TypeError):
- class SomeModule(nn.Module):
- pass
- get_expansion(SomeModule)
- def test_basic_block(self):
- # expansion must be 1
- with self.assertRaises(AssertionError):
- BasicBlock(64, 64, expansion=2)
- # BasicBlock with stride 1, out_channels == in_channels
- block = BasicBlock(64, 64)
- self.assertEqual(block.in_channels, 64)
- self.assertEqual(block.mid_channels, 64)
- self.assertEqual(block.out_channels, 64)
- self.assertEqual(block.conv1.in_channels, 64)
- self.assertEqual(block.conv1.out_channels, 64)
- self.assertEqual(block.conv1.kernel_size, (3, 3))
- self.assertEqual(block.conv1.stride, (1, 1))
- self.assertEqual(block.conv2.in_channels, 64)
- self.assertEqual(block.conv2.out_channels, 64)
- self.assertEqual(block.conv2.kernel_size, (3, 3))
- x = torch.randn(1, 64, 56, 56)
- x_out = block(x)
- self.assertEqual(x_out.shape, torch.Size([1, 64, 56, 56]))
- # BasicBlock with stride 1 and downsample
- downsample = nn.Sequential(
- nn.Conv2d(64, 128, kernel_size=1, bias=False), nn.BatchNorm2d(128))
- block = BasicBlock(64, 128, downsample=downsample)
- self.assertEqual(block.in_channels, 64)
- self.assertEqual(block.mid_channels, 128)
- self.assertEqual(block.out_channels, 128)
- self.assertEqual(block.conv1.in_channels, 64)
- self.assertEqual(block.conv1.out_channels, 128)
- self.assertEqual(block.conv1.kernel_size, (3, 3))
- self.assertEqual(block.conv1.stride, (1, 1))
- self.assertEqual(block.conv2.in_channels, 128)
- self.assertEqual(block.conv2.out_channels, 128)
- self.assertEqual(block.conv2.kernel_size, (3, 3))
- x = torch.randn(1, 64, 56, 56)
- x_out = block(x)
- self.assertEqual(x_out.shape, torch.Size([1, 128, 56, 56]))
- # BasicBlock with stride 2 and downsample
- downsample = nn.Sequential(
- nn.Conv2d(64, 128, kernel_size=1, stride=2, bias=False),
- nn.BatchNorm2d(128))
- block = BasicBlock(64, 128, stride=2, downsample=downsample)
- self.assertEqual(block.in_channels, 64)
- self.assertEqual(block.mid_channels, 128)
- self.assertEqual(block.out_channels, 128)
- self.assertEqual(block.conv1.in_channels, 64)
- self.assertEqual(block.conv1.out_channels, 128)
- self.assertEqual(block.conv1.kernel_size, (3, 3))
- self.assertEqual(block.conv1.stride, (2, 2))
- self.assertEqual(block.conv2.in_channels, 128)
- self.assertEqual(block.conv2.out_channels, 128)
- self.assertEqual(block.conv2.kernel_size, (3, 3))
- x = torch.randn(1, 64, 56, 56)
- x_out = block(x)
- self.assertEqual(x_out.shape, torch.Size([1, 128, 28, 28]))
- # forward with checkpointing
- block = BasicBlock(64, 64, with_cp=True)
- self.assertTrue(block.with_cp)
- x = torch.randn(1, 64, 56, 56, requires_grad=True)
- x_out = block(x)
- self.assertEqual(x_out.shape, torch.Size([1, 64, 56, 56]))
- def test_bottleneck(self):
- # style must be in ['pytorch', 'caffe']
- with self.assertRaises(AssertionError):
- Bottleneck(64, 64, style='tensorflow')
- # expansion must be divisible by out_channels
- with self.assertRaises(AssertionError):
- Bottleneck(64, 64, expansion=3)
- # Test Bottleneck style
- block = Bottleneck(64, 64, stride=2, style='pytorch')
- self.assertEqual(block.conv1.stride, (1, 1))
- self.assertEqual(block.conv2.stride, (2, 2))
- block = Bottleneck(64, 64, stride=2, style='caffe')
- self.assertEqual(block.conv1.stride, (2, 2))
- self.assertEqual(block.conv2.stride, (1, 1))
- # Bottleneck with stride 1
- block = Bottleneck(64, 64, style='pytorch')
- self.assertEqual(block.in_channels, 64)
- self.assertEqual(block.mid_channels, 16)
- self.assertEqual(block.out_channels, 64)
- self.assertEqual(block.conv1.in_channels, 64)
- self.assertEqual(block.conv1.out_channels, 16)
- self.assertEqual(block.conv1.kernel_size, (1, 1))
- self.assertEqual(block.conv2.in_channels, 16)
- self.assertEqual(block.conv2.out_channels, 16)
- self.assertEqual(block.conv2.kernel_size, (3, 3))
- self.assertEqual(block.conv3.in_channels, 16)
- self.assertEqual(block.conv3.out_channels, 64)
- self.assertEqual(block.conv3.kernel_size, (1, 1))
- x = torch.randn(1, 64, 56, 56)
- x_out = block(x)
- self.assertEqual(x_out.shape, (1, 64, 56, 56))
- # Bottleneck with stride 1 and downsample
- downsample = nn.Sequential(
- nn.Conv2d(64, 128, kernel_size=1), nn.BatchNorm2d(128))
- block = Bottleneck(64, 128, style='pytorch', downsample=downsample)
- self.assertEqual(block.in_channels, 64)
- self.assertEqual(block.mid_channels, 32)
- self.assertEqual(block.out_channels, 128)
- self.assertEqual(block.conv1.in_channels, 64)
- self.assertEqual(block.conv1.out_channels, 32)
- self.assertEqual(block.conv1.kernel_size, (1, 1))
- self.assertEqual(block.conv2.in_channels, 32)
- self.assertEqual(block.conv2.out_channels, 32)
- self.assertEqual(block.conv2.kernel_size, (3, 3))
- self.assertEqual(block.conv3.in_channels, 32)
- self.assertEqual(block.conv3.out_channels, 128)
- self.assertEqual(block.conv3.kernel_size, (1, 1))
- x = torch.randn(1, 64, 56, 56)
- x_out = block(x)
- self.assertEqual(x_out.shape, (1, 128, 56, 56))
- # Bottleneck with stride 2 and downsample
- downsample = nn.Sequential(
- nn.Conv2d(64, 128, kernel_size=1, stride=2), nn.BatchNorm2d(128))
- block = Bottleneck(
- 64, 128, stride=2, style='pytorch', downsample=downsample)
- x = torch.randn(1, 64, 56, 56)
- x_out = block(x)
- self.assertEqual(x_out.shape, (1, 128, 28, 28))
- # Bottleneck with expansion 2
- block = Bottleneck(64, 64, style='pytorch', expansion=2)
- self.assertEqual(block.in_channels, 64)
- self.assertEqual(block.mid_channels, 32)
- self.assertEqual(block.out_channels, 64)
- self.assertEqual(block.conv1.in_channels, 64)
- self.assertEqual(block.conv1.out_channels, 32)
- self.assertEqual(block.conv1.kernel_size, (1, 1))
- self.assertEqual(block.conv2.in_channels, 32)
- self.assertEqual(block.conv2.out_channels, 32)
- self.assertEqual(block.conv2.kernel_size, (3, 3))
- self.assertEqual(block.conv3.in_channels, 32)
- self.assertEqual(block.conv3.out_channels, 64)
- self.assertEqual(block.conv3.kernel_size, (1, 1))
- x = torch.randn(1, 64, 56, 56)
- x_out = block(x)
- self.assertEqual(x_out.shape, (1, 64, 56, 56))
- # Test Bottleneck with checkpointing
- block = Bottleneck(64, 64, with_cp=True)
- block.train()
- self.assertTrue(block.with_cp)
- x = torch.randn(1, 64, 56, 56, requires_grad=True)
- x_out = block(x)
- self.assertEqual(x_out.shape, torch.Size([1, 64, 56, 56]))
- def test_basicblock_reslayer(self):
- # 3 BasicBlock w/o downsample
- layer = ResLayer(BasicBlock, 3, 32, 32)
- self.assertEqual(len(layer), 3)
- for i in range(3):
- self.assertEqual(layer[i].in_channels, 32)
- self.assertEqual(layer[i].out_channels, 32)
- self.assertIsNone(layer[i].downsample)
- x = torch.randn(1, 32, 56, 56)
- x_out = layer(x)
- self.assertEqual(x_out.shape, (1, 32, 56, 56))
- # 3 BasicBlock w/ stride 1 and downsample
- layer = ResLayer(BasicBlock, 3, 32, 64)
- self.assertEqual(len(layer), 3)
- self.assertEqual(layer[0].in_channels, 32)
- self.assertEqual(layer[0].out_channels, 64)
- self.assertEqual(
- layer[0].downsample is not None and len(layer[0].downsample), 2)
- self.assertIsInstance(layer[0].downsample[0], nn.Conv2d)
- self.assertEqual(layer[0].downsample[0].stride, (1, 1))
- for i in range(1, 3):
- self.assertEqual(layer[i].in_channels, 64)
- self.assertEqual(layer[i].out_channels, 64)
- self.assertIsNone(layer[i].downsample)
- x = torch.randn(1, 32, 56, 56)
- x_out = layer(x)
- self.assertEqual(x_out.shape, (1, 64, 56, 56))
- # 3 BasicBlock w/ stride 2 and downsample
- layer = ResLayer(BasicBlock, 3, 32, 64, stride=2)
- self.assertEqual(len(layer), 3)
- self.assertEqual(layer[0].in_channels, 32)
- self.assertEqual(layer[0].out_channels, 64)
- self.assertEqual(layer[0].stride, 2)
- self.assertEqual(
- layer[0].downsample is not None and len(layer[0].downsample), 2)
- self.assertIsInstance(layer[0].downsample[0], nn.Conv2d)
- self.assertEqual(layer[0].downsample[0].stride, (2, 2))
- for i in range(1, 3):
- self.assertEqual(layer[i].in_channels, 64)
- self.assertEqual(layer[i].out_channels, 64)
- self.assertEqual(layer[i].stride, 1)
- self.assertIsNone(layer[i].downsample)
- x = torch.randn(1, 32, 56, 56)
- x_out = layer(x)
- self.assertEqual(x_out.shape, (1, 64, 28, 28))
- # 3 BasicBlock w/ stride 2 and downsample with avg pool
- layer = ResLayer(BasicBlock, 3, 32, 64, stride=2, avg_down=True)
- self.assertEqual(len(layer), 3)
- self.assertEqual(layer[0].in_channels, 32)
- self.assertEqual(layer[0].out_channels, 64)
- self.assertEqual(layer[0].stride, 2)
- self.assertEqual(
- layer[0].downsample is not None and len(layer[0].downsample), 3)
- self.assertIsInstance(layer[0].downsample[0], nn.AvgPool2d)
- self.assertEqual(layer[0].downsample[0].stride, 2)
- for i in range(1, 3):
- self.assertEqual(layer[i].in_channels, 64)
- self.assertEqual(layer[i].out_channels, 64)
- self.assertEqual(layer[i].stride, 1)
- self.assertIsNone(layer[i].downsample)
- x = torch.randn(1, 32, 56, 56)
- x_out = layer(x)
- self.assertEqual(x_out.shape, (1, 64, 28, 28))
- def test_bottleneck_reslayer(self):
- # 3 Bottleneck w/o downsample
- layer = ResLayer(Bottleneck, 3, 32, 32)
- self.assertEqual(len(layer), 3)
- for i in range(3):
- self.assertEqual(layer[i].in_channels, 32)
- self.assertEqual(layer[i].out_channels, 32)
- self.assertIsNone(layer[i].downsample)
- x = torch.randn(1, 32, 56, 56)
- x_out = layer(x)
- self.assertEqual(x_out.shape, (1, 32, 56, 56))
- # 3 Bottleneck w/ stride 1 and downsample
- layer = ResLayer(Bottleneck, 3, 32, 64)
- self.assertEqual(len(layer), 3)
- self.assertEqual(layer[0].in_channels, 32)
- self.assertEqual(layer[0].out_channels, 64)
- self.assertEqual(layer[0].stride, 1)
- self.assertEqual(layer[0].conv1.out_channels, 16)
- self.assertEqual(
- layer[0].downsample is not None and len(layer[0].downsample), 2)
- self.assertIsInstance(layer[0].downsample[0], nn.Conv2d)
- self.assertEqual(layer[0].downsample[0].stride, (1, 1))
- for i in range(1, 3):
- self.assertEqual(layer[i].in_channels, 64)
- self.assertEqual(layer[i].out_channels, 64)
- self.assertEqual(layer[i].conv1.out_channels, 16)
- self.assertEqual(layer[i].stride, 1)
- self.assertIsNone(layer[i].downsample)
- x = torch.randn(1, 32, 56, 56)
- x_out = layer(x)
- self.assertEqual(x_out.shape, (1, 64, 56, 56))
- # 3 Bottleneck w/ stride 2 and downsample
- layer = ResLayer(Bottleneck, 3, 32, 64, stride=2)
- self.assertEqual(len(layer), 3)
- self.assertEqual(layer[0].in_channels, 32)
- self.assertEqual(layer[0].out_channels, 64)
- self.assertEqual(layer[0].stride, 2)
- self.assertEqual(layer[0].conv1.out_channels, 16)
- self.assertEqual(
- layer[0].downsample is not None and len(layer[0].downsample), 2)
- self.assertIsInstance(layer[0].downsample[0], nn.Conv2d)
- self.assertEqual(layer[0].downsample[0].stride, (2, 2))
- for i in range(1, 3):
- self.assertEqual(layer[i].in_channels, 64)
- self.assertEqual(layer[i].out_channels, 64)
- self.assertEqual(layer[i].conv1.out_channels, 16)
- self.assertEqual(layer[i].stride, 1)
- self.assertIsNone(layer[i].downsample)
- x = torch.randn(1, 32, 56, 56)
- x_out = layer(x)
- self.assertEqual(x_out.shape, (1, 64, 28, 28))
- # 3 Bottleneck w/ stride 2 and downsample with avg pool
- layer = ResLayer(Bottleneck, 3, 32, 64, stride=2, avg_down=True)
- self.assertEqual(len(layer), 3)
- self.assertEqual(layer[0].in_channels, 32)
- self.assertEqual(layer[0].out_channels, 64)
- self.assertEqual(layer[0].stride, 2)
- self.assertEqual(layer[0].conv1.out_channels, 16)
- self.assertEqual(
- layer[0].downsample is not None and len(layer[0].downsample), 3)
- self.assertIsInstance(layer[0].downsample[0], nn.AvgPool2d)
- self.assertEqual(layer[0].downsample[0].stride, 2)
- for i in range(1, 3):
- self.assertEqual(layer[i].in_channels, 64)
- self.assertEqual(layer[i].out_channels, 64)
- self.assertEqual(layer[i].conv1.out_channels, 16)
- self.assertEqual(layer[i].stride, 1)
- self.assertIsNone(layer[i].downsample)
- x = torch.randn(1, 32, 56, 56)
- x_out = layer(x)
- self.assertEqual(x_out.shape, (1, 64, 28, 28))
- # 3 Bottleneck with custom expansion
- layer = ResLayer(Bottleneck, 3, 32, 32, expansion=2)
- self.assertEqual(len(layer), 3)
- for i in range(3):
- self.assertEqual(layer[i].in_channels, 32)
- self.assertEqual(layer[i].out_channels, 32)
- self.assertEqual(layer[i].stride, 1)
- self.assertEqual(layer[i].conv1.out_channels, 16)
- self.assertIsNone(layer[i].downsample)
- x = torch.randn(1, 32, 56, 56)
- x_out = layer(x)
- self.assertEqual(x_out.shape, (1, 32, 56, 56))
- def test_resnet(self):
- """Test resnet backbone."""
- with self.assertRaises(KeyError):
- # ResNet depth should be in [18, 34, 50, 101, 152]
- ResNet(20)
- with self.assertRaises(AssertionError):
- # In ResNet: 1 <= num_stages <= 4
- ResNet(50, num_stages=0)
- with self.assertRaises(AssertionError):
- # In ResNet: 1 <= num_stages <= 4
- ResNet(50, num_stages=5)
- with self.assertRaises(AssertionError):
- # len(strides) == len(dilations) == num_stages
- ResNet(50, strides=(1, ), dilations=(1, 1), num_stages=3)
- with self.assertRaises(AssertionError):
- # Style must be in ['pytorch', 'caffe']
- ResNet(50, style='tensorflow')
- # Test ResNet50 norm_eval=True
- model = ResNet(50, norm_eval=True)
- model.init_weights()
- model.train()
- self.assertTrue(self.check_norm_state(model.modules(), False))
- # Test ResNet50 with torchvision pretrained weight
- init_cfg = dict(type='Pretrained', checkpoint='torchvision://resnet50')
- model = ResNet(depth=50, norm_eval=True, init_cfg=init_cfg)
- model.train()
- self.assertTrue(self.check_norm_state(model.modules(), False))
- # Test ResNet50 with first stage frozen
- frozen_stages = 1
- model = ResNet(50, frozen_stages=frozen_stages)
- model.init_weights()
- model.train()
- self.assertFalse(model.norm1.training)
- for layer in [model.conv1, model.norm1]:
- 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))
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