test_beit.py

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

import torch

from mmpretrain.models.backbones import BEiTViT


class TestBEiT(TestCase):

    def setUp(self):
        self.cfg = dict(
            arch='b', img_size=224, patch_size=16, drop_path_rate=0.1)

    def test_structure(self):
        # Test invalid default arch
        with self.assertRaisesRegex(AssertionError, 'not in default archs'):
            cfg = deepcopy(self.cfg)
            cfg['arch'] = 'unknown'
            BEiTViT(**cfg)

        # Test invalid custom arch
        with self.assertRaisesRegex(AssertionError, 'Custom arch needs'):
            cfg = deepcopy(self.cfg)
            cfg['arch'] = {
                'num_layers': 24,
                'num_heads': 16,
                'feedforward_channels': 4096
            }
            BEiTViT(**cfg)

        # Test custom arch
        cfg = deepcopy(self.cfg)
        cfg['arch'] = {
            'embed_dims': 128,
            'num_layers': 24,
            'num_heads': 16,
            'feedforward_channels': 1024
        }
        model = BEiTViT(**cfg)
        self.assertEqual(model.embed_dims, 128)
        self.assertEqual(model.num_layers, 24)
        self.assertIsNone(model.pos_embed)
        self.assertIsNone(model.rel_pos_bias)
        for layer in model.layers:
            self.assertEqual(layer.attn.num_heads, 16)
            self.assertEqual(layer.ffn.feedforward_channels, 1024)

        # Test out_indices
        cfg = deepcopy(self.cfg)
        cfg['out_indices'] = {1: 1}
        with self.assertRaisesRegex(AssertionError, "get <class 'dict'>"):
            BEiTViT(**cfg)
        cfg['out_indices'] = [0, 13]
        with self.assertRaisesRegex(AssertionError, 'Invalid out_indices 13'):
            BEiTViT(**cfg)

        # Test pos_embed
        cfg = deepcopy(self.cfg)
        cfg['use_abs_pos_emb'] = True
        model = BEiTViT(**cfg)
        self.assertEqual(model.pos_embed.shape, (1, 197, 768))

        # Test model structure
        cfg = deepcopy(self.cfg)
        cfg['drop_path_rate'] = 0.1
        model = BEiTViT(**cfg)
        self.assertEqual(len(model.layers), 12)
        dpr_inc = 0.1 / (12 - 1)
        dpr = 0
        for layer in model.layers:
            self.assertEqual(layer.gamma_1.shape, (768, ))
            self.assertEqual(layer.gamma_2.shape, (768, ))
            self.assertEqual(layer.attn.embed_dims, 768)
            self.assertEqual(layer.attn.num_heads, 12)
            self.assertEqual(layer.ffn.feedforward_channels, 3072)
            self.assertFalse(layer.ffn.add_identity)
            self.assertAlmostEqual(layer.ffn.dropout_layer.drop_prob, dpr)
            dpr += dpr_inc

    def test_forward(self):
        imgs = torch.randn(1, 3, 224, 224)

        cfg = deepcopy(self.cfg)
        cfg['out_type'] = 'cls_token'
        model = BEiTViT(**cfg)
        outs = model(imgs)
        self.assertIsInstance(outs, tuple)
        self.assertEqual(len(outs), 1)
        cls_token = outs[-1]
        self.assertEqual(cls_token.shape, (1, 768))

        # test without output cls_token
        cfg = deepcopy(self.cfg)
        model = BEiTViT(**cfg)
        outs = model(imgs)
        self.assertIsInstance(outs, tuple)
        self.assertEqual(len(outs), 1)
        patch_token = outs[-1]
        self.assertEqual(patch_token.shape, (1, 768))

        # test without average
        cfg = deepcopy(self.cfg)
        cfg['out_type'] = 'featmap'
        model = BEiTViT(**cfg)
        outs = model(imgs)
        self.assertIsInstance(outs, tuple)
        self.assertEqual(len(outs), 1)
        patch_token = outs[-1]
        self.assertEqual(patch_token.shape, (1, 768, 14, 14))

        # Test forward with multi out indices
        cfg = deepcopy(self.cfg)
        cfg['out_indices'] = [-3, -2, -1]
        model = BEiTViT(**cfg)
        outs = model(imgs)
        self.assertIsInstance(outs, tuple)
        self.assertEqual(len(outs), 3)
        for out in outs:
            patch_token = out
            self.assertEqual(patch_token.shape, (1, 768))