mmpose/tests/test_models/test_backbones/test_shufflenet_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 ShuffleNetV2
from mmpose.models.backbones.shufflenet_v2 import InvertedResidual


class TestShufflenetV2(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_shufflenetv2_invertedresidual(self):

        with self.assertRaises(AssertionError):
            # when stride==1, in_channels should be equal to
            # out_channels // 2 * 2
            InvertedResidual(24, 32, stride=1)

        with self.assertRaises(AssertionError):
            # when in_channels !=  out_channels // 2 * 2, stride should not be
            # equal to 1.
            InvertedResidual(24, 32, stride=1)

        # Test InvertedResidual forward
        block = InvertedResidual(24, 48, stride=2)
        x = torch.randn(1, 24, 56, 56)
        x_out = block(x)
        self.assertEqual(x_out.shape, torch.Size((1, 48, 28, 28)))

        # Test InvertedResidual with checkpoint forward
        block = InvertedResidual(48, 48, stride=1, with_cp=True)
        self.assertTrue(block.with_cp)
        x = torch.randn(1, 48, 56, 56)
        x.requires_grad = True
        x_out = block(x)
        self.assertEqual(x_out.shape, torch.Size((1, 48, 56, 56)))

    def test_shufflenetv2_backbone(self):

        with self.assertRaises(ValueError):
            # groups must be in 0.5, 1.0, 1.5, 2.0]
            ShuffleNetV2(widen_factor=3.0)

        with self.assertRaises(ValueError):
            # frozen_stages must be in [0, 1, 2, 3]
            ShuffleNetV2(widen_factor=1.0, frozen_stages=4)

        with self.assertRaises(ValueError):
            # out_indices must be in [0, 1, 2, 3]
            ShuffleNetV2(widen_factor=1.0, out_indices=(4, ))

        with self.assertRaises(TypeError):
            # init_weights must have no parameter
            model = ShuffleNetV2()
            model.init_weights(pretrained=1)

        # Test ShuffleNetV2 norm state
        model = ShuffleNetV2()
        model.init_weights()
        model.train()
        self.assertTrue(self.check_norm_state(model.modules(), True))

        # Test ShuffleNetV2 with first stage frozen
        frozen_stages = 1
        model = ShuffleNetV2(frozen_stages=frozen_stages)
        model.init_weights()
        model.train()
        for param in model.conv1.parameters():
            self.assertFalse(param.requires_grad)
        for i in range(0, frozen_stages):
            layer = model.layers[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 ShuffleNetV2 with norm_eval
        model = ShuffleNetV2(norm_eval=True)
        model.init_weights()
        model.train()

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

        # Test ShuffleNetV2 forward with widen_factor=0.5
        model = ShuffleNetV2(widen_factor=0.5, out_indices=(0, 1, 2, 3))
        model.init_weights()
        model.train()

        for m in model.modules():
            if self.is_norm(m):
                self.assertIsInstance(m, _BatchNorm)

        imgs = torch.randn(1, 3, 224, 224)
        feat = model(imgs)
        self.assertEqual(len(feat), 4)
        self.assertEqual(feat[0].shape, torch.Size((1, 48, 28, 28)))
        self.assertEqual(feat[1].shape, torch.Size((1, 96, 14, 14)))
        self.assertEqual(feat[2].shape, torch.Size((1, 192, 7, 7)))

        # Test ShuffleNetV2 forward with widen_factor=1.0
        model = ShuffleNetV2(widen_factor=1.0, out_indices=(0, 1, 2, 3))
        model.init_weights()
        model.train()

        for m in model.modules():
            if self.is_norm(m):
                self.assertIsInstance(m, _BatchNorm)

        imgs = torch.randn(1, 3, 224, 224)
        feat = model(imgs)
        self.assertEqual(len(feat), 4)
        self.assertEqual(feat[0].shape, torch.Size((1, 116, 28, 28)))
        self.assertEqual(feat[1].shape, torch.Size((1, 232, 14, 14)))
        self.assertEqual(feat[2].shape, torch.Size((1, 464, 7, 7)))

        # Test ShuffleNetV2 forward with widen_factor=1.5
        model = ShuffleNetV2(widen_factor=1.5, out_indices=(0, 1, 2, 3))
        model.init_weights()
        model.train()

        for m in model.modules():
            if self.is_norm(m):
                self.assertIsInstance(m, _BatchNorm)

        imgs = torch.randn(1, 3, 224, 224)
        feat = model(imgs)
        self.assertEqual(len(feat), 4)
        self.assertEqual(feat[0].shape, torch.Size((1, 176, 28, 28)))
        self.assertEqual(feat[1].shape, torch.Size((1, 352, 14, 14)))
        self.assertEqual(feat[2].shape, torch.Size((1, 704, 7, 7)))

        # Test ShuffleNetV2 forward with widen_factor=2.0
        model = ShuffleNetV2(widen_factor=2.0, out_indices=(0, 1, 2, 3))
        model.init_weights()
        model.train()

        for m in model.modules():
            if self.is_norm(m):
                self.assertIsInstance(m, _BatchNorm)

        imgs = torch.randn(1, 3, 224, 224)
        feat = model(imgs)
        self.assertEqual(len(feat), 4)
        self.assertEqual(feat[0].shape, torch.Size((1, 244, 28, 28)))
        self.assertEqual(feat[1].shape, torch.Size((1, 488, 14, 14)))
        self.assertEqual(feat[2].shape, torch.Size((1, 976, 7, 7)))

        # Test ShuffleNetV2 forward with layers 3 forward
        model = ShuffleNetV2(widen_factor=1.0, out_indices=(2, ))
        model.init_weights()
        model.train()

        for m in model.modules():
            if self.is_norm(m):
                self.assertIsInstance(m, _BatchNorm)

        imgs = torch.randn(1, 3, 224, 224)
        feat = model(imgs)
        self.assertIsInstance(feat, tuple)
        self.assertEqual(feat[-1].shape, torch.Size((1, 464, 7, 7)))

        # Test ShuffleNetV2 forward with layers 1 2 forward
        model = ShuffleNetV2(widen_factor=1.0, out_indices=(1, 2))
        model.init_weights()
        model.train()

        for m in model.modules():
            if self.is_norm(m):
                self.assertIsInstance(m, _BatchNorm)

        imgs = torch.randn(1, 3, 224, 224)
        feat = model(imgs)
        self.assertEqual(len(feat), 2)
        self.assertEqual(feat[0].shape, torch.Size((1, 232, 14, 14)))
        self.assertEqual(feat[1].shape, torch.Size((1, 464, 7, 7)))

        # Test ShuffleNetV2 forward with checkpoint forward
        model = ShuffleNetV2(widen_factor=1.0, with_cp=True)
        for m in model.modules():
            if self.is_block(m):
                self.assertTrue(m.with_cp)