mirror of https://github.com/open-mmlab/mmpose
265 lines
10 KiB
Python
265 lines
10 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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from unittest import TestCase
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import torch
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from torch.nn.modules import GroupNorm
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from torch.nn.modules.batchnorm import _BatchNorm
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from mmpose.models.backbones import MobileNetV2
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from mmpose.models.backbones.mobilenet_v2 import InvertedResidual
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class TestMobilenetV2(TestCase):
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@staticmethod
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def is_block(modules):
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"""Check if is ResNet building block."""
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if isinstance(modules, (InvertedResidual, )):
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return True
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return False
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@staticmethod
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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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@staticmethod
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def check_norm_state(modules, train_state):
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"""Check if norm layer is in correct train state."""
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for mod in modules:
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if isinstance(mod, _BatchNorm):
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if mod.training != train_state:
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return False
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return True
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def test_mobilenetv2_invertedresidual(self):
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with self.assertRaises(AssertionError):
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# stride must be in [1, 2]
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InvertedResidual(16, 24, stride=3, expand_ratio=6)
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# Test InvertedResidual with checkpoint forward, stride=1
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block = InvertedResidual(16, 24, stride=1, expand_ratio=6)
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x = torch.randn(1, 16, 56, 56)
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x_out = block(x)
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self.assertEqual(x_out.shape, torch.Size((1, 24, 56, 56)))
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# Test InvertedResidual with expand_ratio=1
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block = InvertedResidual(16, 16, stride=1, expand_ratio=1)
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self.assertEqual(len(block.conv), 2)
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# Test InvertedResidual with use_res_connect
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block = InvertedResidual(16, 16, stride=1, expand_ratio=6)
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x = torch.randn(1, 16, 56, 56)
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x_out = block(x)
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self.assertTrue(block.use_res_connect)
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self.assertEqual(x_out.shape, torch.Size((1, 16, 56, 56)))
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# Test InvertedResidual with checkpoint forward, stride=2
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block = InvertedResidual(16, 24, stride=2, expand_ratio=6)
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x = torch.randn(1, 16, 56, 56)
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x_out = block(x)
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self.assertEqual(x_out.shape, torch.Size((1, 24, 28, 28)))
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# Test InvertedResidual with checkpoint forward
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block = InvertedResidual(
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16, 24, stride=1, expand_ratio=6, with_cp=True)
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self.assertTrue(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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self.assertEqual(x_out.shape, torch.Size((1, 24, 56, 56)))
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# Test InvertedResidual with act_cfg=dict(type='ReLU')
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block = InvertedResidual(
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16, 24, stride=1, expand_ratio=6, act_cfg=dict(type='ReLU'))
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x = torch.randn(1, 16, 56, 56)
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x_out = block(x)
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self.assertEqual(x_out.shape, torch.Size((1, 24, 56, 56)))
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def test_mobilenetv2_backbone(self):
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with self.assertRaises(TypeError):
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# pretrained must be a string path
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model = MobileNetV2()
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model.init_weights(pretrained=0)
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with self.assertRaises(ValueError):
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# frozen_stages must in range(1, 8)
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MobileNetV2(frozen_stages=8)
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with self.assertRaises(ValueError):
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# tout_indices in range(-1, 8)
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MobileNetV2(out_indices=[8])
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# Test MobileNetV2 with first stage frozen
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frozen_stages = 1
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model = MobileNetV2(frozen_stages=frozen_stages)
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model.init_weights()
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model.train()
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for mod in model.conv1.modules():
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for param in mod.parameters():
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self.assertFalse(param.requires_grad)
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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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self.assertFalse(mod.training)
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for param in layer.parameters():
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self.assertFalse(param.requires_grad)
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# Test MobileNetV2 with norm_eval=True
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model = MobileNetV2(norm_eval=True)
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model.init_weights()
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model.train()
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self.assertTrue(self.check_norm_state(model.modules(), False))
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# Test MobileNetV2 forward with widen_factor=1.0
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model = MobileNetV2(widen_factor=1.0, out_indices=range(0, 8))
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model.init_weights()
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model.train()
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self.assertTrue(self.check_norm_state(model.modules(), True))
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imgs = torch.randn(1, 3, 224, 224)
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feat = model(imgs)
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self.assertEqual(len(feat), 8)
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self.assertEqual(feat[0].shape, torch.Size((1, 16, 112, 112)))
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self.assertEqual(feat[1].shape, torch.Size((1, 24, 56, 56)))
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self.assertEqual(feat[2].shape, torch.Size((1, 32, 28, 28)))
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self.assertEqual(feat[3].shape, torch.Size((1, 64, 14, 14)))
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self.assertEqual(feat[4].shape, torch.Size((1, 96, 14, 14)))
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self.assertEqual(feat[5].shape, torch.Size((1, 160, 7, 7)))
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self.assertEqual(feat[6].shape, torch.Size((1, 320, 7, 7)))
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self.assertEqual(feat[7].shape, torch.Size((1, 1280, 7, 7)))
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# Test MobileNetV2 forward with widen_factor=0.5
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model = MobileNetV2(widen_factor=0.5, out_indices=range(0, 7))
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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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self.assertEqual(len(feat), 7)
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self.assertEqual(feat[0].shape, torch.Size((1, 8, 112, 112)))
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self.assertEqual(feat[1].shape, torch.Size((1, 16, 56, 56)))
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self.assertEqual(feat[2].shape, torch.Size((1, 16, 28, 28)))
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self.assertEqual(feat[3].shape, torch.Size((1, 32, 14, 14)))
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self.assertEqual(feat[4].shape, torch.Size((1, 48, 14, 14)))
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self.assertEqual(feat[5].shape, torch.Size((1, 80, 7, 7)))
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self.assertEqual(feat[6].shape, torch.Size((1, 160, 7, 7)))
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# Test MobileNetV2 forward with widen_factor=2.0
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model = MobileNetV2(widen_factor=2.0)
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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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self.assertIsInstance(feat, tuple)
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self.assertEqual(feat[-1].shape, torch.Size((1, 2560, 7, 7)))
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# Test MobileNetV2 forward with out_indices=None
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model = MobileNetV2(widen_factor=1.0)
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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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self.assertIsInstance(feat, tuple)
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self.assertEqual(feat[-1].shape, torch.Size((1, 1280, 7, 7)))
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# Test MobileNetV2 forward with dict(type='ReLU')
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model = MobileNetV2(
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widen_factor=1.0,
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act_cfg=dict(type='ReLU'),
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out_indices=range(0, 7))
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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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self.assertEqual(len(feat), 7)
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self.assertEqual(feat[0].shape, torch.Size((1, 16, 112, 112)))
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self.assertEqual(feat[1].shape, torch.Size((1, 24, 56, 56)))
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self.assertEqual(feat[2].shape, torch.Size((1, 32, 28, 28)))
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self.assertEqual(feat[3].shape, torch.Size((1, 64, 14, 14)))
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self.assertEqual(feat[4].shape, torch.Size((1, 96, 14, 14)))
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self.assertEqual(feat[5].shape, torch.Size((1, 160, 7, 7)))
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self.assertEqual(feat[6].shape, torch.Size((1, 320, 7, 7)))
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# Test MobileNetV2 with GroupNorm forward
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model = MobileNetV2(widen_factor=1.0, out_indices=range(0, 7))
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for m in model.modules():
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if self.is_norm(m):
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self.assertIsInstance(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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self.assertEqual(len(feat), 7)
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self.assertEqual(feat[0].shape, torch.Size((1, 16, 112, 112)))
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self.assertEqual(feat[1].shape, torch.Size((1, 24, 56, 56)))
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self.assertEqual(feat[2].shape, torch.Size((1, 32, 28, 28)))
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self.assertEqual(feat[3].shape, torch.Size((1, 64, 14, 14)))
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self.assertEqual(feat[4].shape, torch.Size((1, 96, 14, 14)))
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self.assertEqual(feat[5].shape, torch.Size((1, 160, 7, 7)))
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self.assertEqual(feat[6].shape, torch.Size((1, 320, 7, 7)))
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# Test MobileNetV2 with BatchNorm forward
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model = MobileNetV2(
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widen_factor=1.0,
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norm_cfg=dict(type='GN', num_groups=2, requires_grad=True),
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out_indices=range(0, 7))
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for m in model.modules():
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if self.is_norm(m):
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self.assertIsInstance(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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self.assertEqual(len(feat), 7)
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self.assertEqual(feat[0].shape, torch.Size((1, 16, 112, 112)))
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self.assertEqual(feat[1].shape, torch.Size((1, 24, 56, 56)))
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self.assertEqual(feat[2].shape, torch.Size((1, 32, 28, 28)))
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self.assertEqual(feat[3].shape, torch.Size((1, 64, 14, 14)))
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self.assertEqual(feat[4].shape, torch.Size((1, 96, 14, 14)))
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self.assertEqual(feat[5].shape, torch.Size((1, 160, 7, 7)))
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self.assertEqual(feat[6].shape, torch.Size((1, 320, 7, 7)))
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# Test MobileNetV2 with layers 1, 3, 5 out forward
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model = MobileNetV2(widen_factor=1.0, out_indices=(0, 2, 4))
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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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self.assertEqual(len(feat), 3)
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self.assertEqual(feat[0].shape, torch.Size((1, 16, 112, 112)))
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self.assertEqual(feat[1].shape, torch.Size((1, 32, 28, 28)))
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self.assertEqual(feat[2].shape, torch.Size((1, 96, 14, 14)))
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# Test MobileNetV2 with checkpoint forward
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model = MobileNetV2(
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widen_factor=1.0, with_cp=True, out_indices=range(0, 7))
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for m in model.modules():
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if self.is_block(m):
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self.assertTrue(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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self.assertEqual(len(feat), 7)
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self.assertEqual(feat[0].shape, torch.Size((1, 16, 112, 112)))
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self.assertEqual(feat[1].shape, torch.Size((1, 24, 56, 56)))
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self.assertEqual(feat[2].shape, torch.Size((1, 32, 28, 28)))
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self.assertEqual(feat[3].shape, torch.Size((1, 64, 14, 14)))
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self.assertEqual(feat[4].shape, torch.Size((1, 96, 14, 14)))
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self.assertEqual(feat[5].shape, torch.Size((1, 160, 7, 7)))
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self.assertEqual(feat[6].shape, torch.Size((1, 320, 7, 7)))
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