mmpose/tests/test_models/test_backbones/test_mobilenet_v2.py

# Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase

import torch
from torch.nn.modules import GroupNorm
from torch.nn.modules.batchnorm import _BatchNorm

from mmpose.models.backbones import MobileNetV2
from mmpose.models.backbones.mobilenet_v2 import InvertedResidual


class TestMobilenetV2(TestCase):

    @staticmethod
    def is_block(modules):
        """Check if is ResNet building block."""
        if isinstance(modules, (InvertedResidual, )):
            return True
        return False

    @staticmethod
    def is_norm(modules):
        """Check if is one of the norms."""
        if isinstance(modules, (GroupNorm, _BatchNorm)):
            return True
        return False

    @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_mobilenetv2_invertedresidual(self):

        with self.assertRaises(AssertionError):
            # stride must be in [1, 2]
            InvertedResidual(16, 24, stride=3, expand_ratio=6)

        # Test InvertedResidual with checkpoint forward, stride=1
        block = InvertedResidual(16, 24, stride=1, expand_ratio=6)
        x = torch.randn(1, 16, 56, 56)
        x_out = block(x)
        self.assertEqual(x_out.shape, torch.Size((1, 24, 56, 56)))

        # Test InvertedResidual with expand_ratio=1
        block = InvertedResidual(16, 16, stride=1, expand_ratio=1)
        self.assertEqual(len(block.conv), 2)

        # Test InvertedResidual with use_res_connect
        block = InvertedResidual(16, 16, stride=1, expand_ratio=6)
        x = torch.randn(1, 16, 56, 56)
        x_out = block(x)
        self.assertTrue(block.use_res_connect)
        self.assertEqual(x_out.shape, torch.Size((1, 16, 56, 56)))

        # Test InvertedResidual with checkpoint forward, stride=2
        block = InvertedResidual(16, 24, stride=2, expand_ratio=6)
        x = torch.randn(1, 16, 56, 56)
        x_out = block(x)
        self.assertEqual(x_out.shape, torch.Size((1, 24, 28, 28)))

        # Test InvertedResidual with checkpoint forward
        block = InvertedResidual(
            16, 24, stride=1, expand_ratio=6, with_cp=True)
        self.assertTrue(block.with_cp)
        x = torch.randn(1, 16, 56, 56)
        x_out = block(x)
        self.assertEqual(x_out.shape, torch.Size((1, 24, 56, 56)))

        # Test InvertedResidual with act_cfg=dict(type='ReLU')
        block = InvertedResidual(
            16, 24, stride=1, expand_ratio=6, act_cfg=dict(type='ReLU'))
        x = torch.randn(1, 16, 56, 56)
        x_out = block(x)
        self.assertEqual(x_out.shape, torch.Size((1, 24, 56, 56)))

    def test_mobilenetv2_backbone(self):
        with self.assertRaises(TypeError):
            # pretrained must be a string path
            model = MobileNetV2()
            model.init_weights(pretrained=0)

        with self.assertRaises(ValueError):
            # frozen_stages must in range(1, 8)
            MobileNetV2(frozen_stages=8)

        with self.assertRaises(ValueError):
            # tout_indices in range(-1, 8)
            MobileNetV2(out_indices=[8])

        # Test MobileNetV2 with first stage frozen
        frozen_stages = 1
        model = MobileNetV2(frozen_stages=frozen_stages)
        model.init_weights()
        model.train()

        for mod in model.conv1.modules():
            for param in mod.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 MobileNetV2 with norm_eval=True
        model = MobileNetV2(norm_eval=True)
        model.init_weights()
        model.train()

        self.assertTrue(self.check_norm_state(model.modules(), False))

        # Test MobileNetV2 forward with widen_factor=1.0
        model = MobileNetV2(widen_factor=1.0, out_indices=range(0, 8))
        model.init_weights()
        model.train()

        self.assertTrue(self.check_norm_state(model.modules(), True))

        imgs = torch.randn(1, 3, 224, 224)
        feat = model(imgs)
        self.assertEqual(len(feat), 8)
        self.assertEqual(feat[0].shape, torch.Size((1, 16, 112, 112)))
        self.assertEqual(feat[1].shape, torch.Size((1, 24, 56, 56)))
        self.assertEqual(feat[2].shape, torch.Size((1, 32, 28, 28)))
        self.assertEqual(feat[3].shape, torch.Size((1, 64, 14, 14)))
        self.assertEqual(feat[4].shape, torch.Size((1, 96, 14, 14)))
        self.assertEqual(feat[5].shape, torch.Size((1, 160, 7, 7)))
        self.assertEqual(feat[6].shape, torch.Size((1, 320, 7, 7)))
        self.assertEqual(feat[7].shape, torch.Size((1, 1280, 7, 7)))

        # Test MobileNetV2 forward with widen_factor=0.5
        model = MobileNetV2(widen_factor=0.5, out_indices=range(0, 7))
        model.init_weights()
        model.train()

        imgs = torch.randn(1, 3, 224, 224)
        feat = model(imgs)
        self.assertEqual(len(feat), 7)
        self.assertEqual(feat[0].shape, torch.Size((1, 8, 112, 112)))
        self.assertEqual(feat[1].shape, torch.Size((1, 16, 56, 56)))
        self.assertEqual(feat[2].shape, torch.Size((1, 16, 28, 28)))
        self.assertEqual(feat[3].shape, torch.Size((1, 32, 14, 14)))
        self.assertEqual(feat[4].shape, torch.Size((1, 48, 14, 14)))
        self.assertEqual(feat[5].shape, torch.Size((1, 80, 7, 7)))
        self.assertEqual(feat[6].shape, torch.Size((1, 160, 7, 7)))

        # Test MobileNetV2 forward with widen_factor=2.0
        model = MobileNetV2(widen_factor=2.0)
        model.init_weights()
        model.train()

        imgs = torch.randn(1, 3, 224, 224)
        feat = model(imgs)
        self.assertIsInstance(feat, tuple)
        self.assertEqual(feat[-1].shape, torch.Size((1, 2560, 7, 7)))

        # Test MobileNetV2 forward with out_indices=None
        model = MobileNetV2(widen_factor=1.0)
        model.init_weights()
        model.train()

        imgs = torch.randn(1, 3, 224, 224)
        feat = model(imgs)
        self.assertIsInstance(feat, tuple)
        self.assertEqual(feat[-1].shape, torch.Size((1, 1280, 7, 7)))

        # Test MobileNetV2 forward with dict(type='ReLU')
        model = MobileNetV2(
            widen_factor=1.0,
            act_cfg=dict(type='ReLU'),
            out_indices=range(0, 7))
        model.init_weights()
        model.train()

        imgs = torch.randn(1, 3, 224, 224)
        feat = model(imgs)
        self.assertEqual(len(feat), 7)
        self.assertEqual(feat[0].shape, torch.Size((1, 16, 112, 112)))
        self.assertEqual(feat[1].shape, torch.Size((1, 24, 56, 56)))
        self.assertEqual(feat[2].shape, torch.Size((1, 32, 28, 28)))
        self.assertEqual(feat[3].shape, torch.Size((1, 64, 14, 14)))
        self.assertEqual(feat[4].shape, torch.Size((1, 96, 14, 14)))
        self.assertEqual(feat[5].shape, torch.Size((1, 160, 7, 7)))
        self.assertEqual(feat[6].shape, torch.Size((1, 320, 7, 7)))

        # Test MobileNetV2 with GroupNorm forward
        model = MobileNetV2(widen_factor=1.0, out_indices=range(0, 7))
        for m in model.modules():
            if self.is_norm(m):
                self.assertIsInstance(m, _BatchNorm)
        model.init_weights()
        model.train()

        imgs = torch.randn(1, 3, 224, 224)
        feat = model(imgs)
        self.assertEqual(len(feat), 7)
        self.assertEqual(feat[0].shape, torch.Size((1, 16, 112, 112)))
        self.assertEqual(feat[1].shape, torch.Size((1, 24, 56, 56)))
        self.assertEqual(feat[2].shape, torch.Size((1, 32, 28, 28)))
        self.assertEqual(feat[3].shape, torch.Size((1, 64, 14, 14)))
        self.assertEqual(feat[4].shape, torch.Size((1, 96, 14, 14)))
        self.assertEqual(feat[5].shape, torch.Size((1, 160, 7, 7)))
        self.assertEqual(feat[6].shape, torch.Size((1, 320, 7, 7)))

        # Test MobileNetV2 with BatchNorm forward
        model = MobileNetV2(
            widen_factor=1.0,
            norm_cfg=dict(type='GN', num_groups=2, requires_grad=True),
            out_indices=range(0, 7))
        for m in model.modules():
            if self.is_norm(m):
                self.assertIsInstance(m, GroupNorm)
        model.init_weights()
        model.train()

        imgs = torch.randn(1, 3, 224, 224)
        feat = model(imgs)
        self.assertEqual(len(feat), 7)
        self.assertEqual(feat[0].shape, torch.Size((1, 16, 112, 112)))
        self.assertEqual(feat[1].shape, torch.Size((1, 24, 56, 56)))
        self.assertEqual(feat[2].shape, torch.Size((1, 32, 28, 28)))
        self.assertEqual(feat[3].shape, torch.Size((1, 64, 14, 14)))
        self.assertEqual(feat[4].shape, torch.Size((1, 96, 14, 14)))
        self.assertEqual(feat[5].shape, torch.Size((1, 160, 7, 7)))
        self.assertEqual(feat[6].shape, torch.Size((1, 320, 7, 7)))

        # Test MobileNetV2 with layers 1, 3, 5 out forward
        model = MobileNetV2(widen_factor=1.0, out_indices=(0, 2, 4))
        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, torch.Size((1, 16, 112, 112)))
        self.assertEqual(feat[1].shape, torch.Size((1, 32, 28, 28)))
        self.assertEqual(feat[2].shape, torch.Size((1, 96, 14, 14)))

        # Test MobileNetV2 with checkpoint forward
        model = MobileNetV2(
            widen_factor=1.0, with_cp=True, out_indices=range(0, 7))
        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), 7)
        self.assertEqual(feat[0].shape, torch.Size((1, 16, 112, 112)))
        self.assertEqual(feat[1].shape, torch.Size((1, 24, 56, 56)))
        self.assertEqual(feat[2].shape, torch.Size((1, 32, 28, 28)))
        self.assertEqual(feat[3].shape, torch.Size((1, 64, 14, 14)))
        self.assertEqual(feat[4].shape, torch.Size((1, 96, 14, 14)))
        self.assertEqual(feat[5].shape, torch.Size((1, 160, 7, 7)))
        self.assertEqual(feat[6].shape, torch.Size((1, 320, 7, 7)))