123 lines
4.0 KiB
Python
123 lines
4.0 KiB
Python
# 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))
|