# Copyright (c) OpenMMLab. All rights reserved. from unittest import TestCase import torch import torch.nn as nn from mmengine.utils.dl_utils.parrots_wrapper import _BatchNorm from mmpose.models.backbones import ViPNAS_ResNet from mmpose.models.backbones.vipnas_resnet import (ViPNAS_Bottleneck, ViPNAS_ResLayer, get_expansion) class TestVipnasResnet(TestCase): @staticmethod def is_block(modules): """Check if is ViPNAS_ResNet building block.""" if isinstance(modules, (ViPNAS_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(ViPNAS_Bottleneck, 2), 2) self.assertEqual(get_expansion(ViPNAS_Bottleneck), 1) 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(ViPNAS_Bottleneck, '0') # expansion is not specified and cannot be inferred with self.assertRaises(TypeError): class SomeModule(nn.Module): pass get_expansion(SomeModule) def test_vipnas_bottleneck(self): # style must be in ['pytorch', 'caffe'] with self.assertRaises(AssertionError): ViPNAS_Bottleneck(64, 64, style='tensorflow') # expansion must be divisible by out_channels with self.assertRaises(AssertionError): ViPNAS_Bottleneck(64, 64, expansion=3) # Test ViPNAS_Bottleneck style block = ViPNAS_Bottleneck(64, 64, stride=2, style='pytorch') self.assertEqual(block.conv1.stride, (1, 1)) self.assertEqual(block.conv2.stride, (2, 2)) block = ViPNAS_Bottleneck(64, 64, stride=2, style='caffe') self.assertEqual(block.conv1.stride, (2, 2)) self.assertEqual(block.conv2.stride, (1, 1)) # ViPNAS_Bottleneck with stride 1 block = ViPNAS_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)) # ViPNAS_Bottleneck with stride 1 and downsample downsample = nn.Sequential( nn.Conv2d(64, 128, kernel_size=1), nn.BatchNorm2d(128)) block = ViPNAS_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)) # ViPNAS_Bottleneck with stride 2 and downsample downsample = nn.Sequential( nn.Conv2d(64, 128, kernel_size=1, stride=2), nn.BatchNorm2d(128)) block = ViPNAS_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)) # ViPNAS_Bottleneck with expansion 2 block = ViPNAS_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 ViPNAS_Bottleneck with checkpointing block = ViPNAS_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_vipnas_bottleneck_reslayer(self): # 3 Bottleneck w/o downsample layer = ViPNAS_ResLayer(ViPNAS_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 ViPNAS_Bottleneck w/ stride 1 and downsample layer = ViPNAS_ResLayer(ViPNAS_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, 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.assertEqual(layer[i].conv1.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, 56, 56)) # 3 ViPNAS_Bottleneck w/ stride 2 and downsample layer = ViPNAS_ResLayer(ViPNAS_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, 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, (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, 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 ViPNAS_Bottleneck w/ stride 2 and downsample with avg pool layer = ViPNAS_ResLayer( ViPNAS_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, 64) 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, 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 ViPNAS_Bottleneck with custom expansion layer = ViPNAS_ResLayer(ViPNAS_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 ViPNAS_ResNet backbone.""" with self.assertRaises(KeyError): # ViPNAS_ResNet depth should be in [50] ViPNAS_ResNet(20) with self.assertRaises(AssertionError): # In ViPNAS_ResNet: 1 <= num_stages <= 4 ViPNAS_ResNet(50, num_stages=0) with self.assertRaises(AssertionError): # In ViPNAS_ResNet: 1 <= num_stages <= 4 ViPNAS_ResNet(50, num_stages=5) with self.assertRaises(AssertionError): # len(strides) == len(dilations) == num_stages ViPNAS_ResNet(50, strides=(1, ), dilations=(1, 1), num_stages=3) with self.assertRaises(TypeError): # init_weights must have no parameter model = ViPNAS_ResNet(50) model.init_weights(pretrained=0) with self.assertRaises(AssertionError): # Style must be in ['pytorch', 'caffe'] ViPNAS_ResNet(50, style='tensorflow') # Test ViPNAS_ResNet50 norm_eval=True model = ViPNAS_ResNet(50, norm_eval=True) model.init_weights() model.train() self.assertTrue(self.check_norm_state(model.modules(), False)) # Test ViPNAS_ResNet50 with first stage frozen frozen_stages = 1 model = ViPNAS_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 ViPNAS_ResNet50 with BatchNorm forward model = ViPNAS_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, 80, 56, 56)) self.assertEqual(feat[1].shape, (1, 160, 28, 28)) self.assertEqual(feat[2].shape, (1, 304, 14, 14)) self.assertEqual(feat[3].shape, (1, 608, 7, 7)) # Test ViPNAS_ResNet50 with layers 1, 2, 3 out forward model = ViPNAS_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, 80, 56, 56)) self.assertEqual(feat[1].shape, (1, 160, 28, 28)) self.assertEqual(feat[2].shape, (1, 304, 14, 14)) # Test ViPNAS_ResNet50 with layers 3 (top feature maps) out forward model = ViPNAS_ResNet(50, out_indices=(3, )) model.init_weights() model.train() imgs = torch.randn(1, 3, 224, 224) feat = model(imgs) self.assertIsInstance(feat, tuple) self.assertEqual(feat[-1].shape, (1, 608, 7, 7)) # Test ViPNAS_ResNet50 with checkpoint forward model = ViPNAS_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, 80, 56, 56)) self.assertEqual(feat[1].shape, (1, 160, 28, 28)) self.assertEqual(feat[2].shape, (1, 304, 14, 14)) self.assertEqual(feat[3].shape, (1, 608, 7, 7))