def test_vit_hybrid_backbone():
# Test VGG11+ViT-B/16 hybrid model
backbone = VGG(11, norm_eval=True)
backbone.init_weights()
model = VisionTransformer(hybrid_backbone=backbone)
model.init_weights()
model.train()
assert check_norm_state(model.modules(), True)
imgs = torch.randn(1, 3, 224, 224)
feat = model(imgs)
assert feat.shape == torch.Size((1, 768))
def test_structure(self):
# Test invalid default arch
with self.assertRaisesRegex(AssertionError, 'not in default archs'):
cfg = deepcopy(self.cfg)
cfg['arch'] = 'unknown'
VisionTransformer(**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
}
VisionTransformer(**cfg)
# Test custom arch
cfg = deepcopy(self.cfg)
cfg['arch'] = {
'embed_dims': 128,
'num_layers': 24,
'num_heads': 16,
'feedforward_channels': 1024
}
model = VisionTransformer(**cfg)
self.assertEqual(model.embed_dims, 128)
self.assertEqual(model.num_layers, 24)
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'>"):
VisionTransformer(**cfg)
cfg['out_indices'] = [0, 13]
with self.assertRaisesRegex(AssertionError, 'Invalid out_indices 13'):
VisionTransformer(**cfg)
# Test model structure
cfg = deepcopy(self.cfg)
model = VisionTransformer(**cfg)
self.assertEqual(len(model.layers), 12)
dpr_inc = 0.1 / (12 - 1)
dpr = 0
for layer in model.layers:
self.assertEqual(layer.attn.embed_dims, 768)
self.assertEqual(layer.attn.num_heads, 12)
self.assertEqual(layer.ffn.feedforward_channels, 3072)
self.assertAlmostEqual(layer.attn.out_drop.drop_prob, dpr)
self.assertAlmostEqual(layer.ffn.dropout_layer.drop_prob, dpr)
dpr += dpr_inc
def test_init_weights(self):
# test weight init cfg
cfg = deepcopy(self.cfg)
cfg['init_cfg'] = [
dict(type='Kaiming',
layer='Conv2d',
mode='fan_in',
nonlinearity='linear')
]
model = VisionTransformer(**cfg)
ori_weight = model.patch_embed.projection.weight.clone().detach()
# The pos_embed is all zero before initialize
self.assertTrue(torch.allclose(model.pos_embed, torch.tensor(0.)))
model.init_weights()
initialized_weight = model.patch_embed.projection.weight
self.assertFalse(torch.allclose(ori_weight, initialized_weight))
self.assertFalse(torch.allclose(model.pos_embed, torch.tensor(0.)))
# test load checkpoint
pretrain_pos_embed = model.pos_embed.clone().detach()
tmpdir = tempfile.gettempdir()
checkpoint = os.path.join(tmpdir, 'test.pth')
save_checkpoint(model, checkpoint)
cfg = deepcopy(self.cfg)
model = VisionTransformer(**cfg)
load_checkpoint(model, checkpoint, strict=True)
self.assertTrue(torch.allclose(model.pos_embed, pretrain_pos_embed))
# test load checkpoint with different img_size
cfg = deepcopy(self.cfg)
cfg['img_size'] = 384
model = VisionTransformer(**cfg)
load_checkpoint(model, checkpoint, strict=True)
resized_pos_embed = timm_resize_pos_embed(pretrain_pos_embed,
model.pos_embed)
self.assertTrue(torch.allclose(model.pos_embed, resized_pos_embed))
os.remove(checkpoint)
def test_vit_backbone():
with pytest.raises(TypeError):
# pretrained must be a string path
model = VisionTransformer()
model.init_weights(pretrained=0)
# Test ViT base model with input size of 224
# and patch size of 16
model = VisionTransformer()
model.init_weights()
model.train()
assert check_norm_state(model.modules(), True)
imgs = torch.randn(1, 3, 224, 224)
feat = model(imgs)
assert feat.shape == torch.Size((1, 768))
def test_vit_backbone():
cfg_ori = dict(arch='b',
img_size=224,
patch_size=16,
drop_rate=0.1,
init_cfg=[
dict(type='Kaiming',
layer='Conv2d',
mode='fan_in',
nonlinearity='linear')
])
with pytest.raises(AssertionError):
# test invalid arch
cfg = deepcopy(cfg_ori)
cfg['arch'] = 'unknown'
VisionTransformer(**cfg)
with pytest.raises(AssertionError):
# test arch without essential keys
cfg = deepcopy(cfg_ori)
cfg['arch'] = {
'num_layers': 24,
'num_heads': 16,
'feedforward_channels': 4096
}
VisionTransformer(**cfg)
# Test ViT base model with input size of 224
# and patch size of 16
model = VisionTransformer(**cfg_ori)
model.init_weights()
model.train()
assert check_norm_state(model.modules(), True)
imgs = torch.randn(3, 3, 224, 224)
patch_token, cls_token = model(imgs)[-1]
assert cls_token.shape == (3, 768)
assert patch_token.shape == (3, 768, 14, 14)
# Test custom arch ViT without output cls token
cfg = deepcopy(cfg_ori)
cfg['arch'] = {
'embed_dims': 128,
'num_layers': 24,
'num_heads': 16,
'feedforward_channels': 1024
}
cfg['output_cls_token'] = False
model = VisionTransformer(**cfg)
patch_token = model(imgs)[-1]
assert patch_token.shape == (3, 128, 14, 14)
# Test ViT with multi out indices
cfg = deepcopy(cfg_ori)
cfg['out_indices'] = [-3, -2, -1]
model = VisionTransformer(**cfg)
for out in model(imgs):
assert out[0].shape == (3, 768, 14, 14)
assert out[1].shape == (3, 768)
def test_vit_weight_init():
# test weight init cfg
pretrain_cfg = dict(
arch='b',
img_size=224,
patch_size=16,
init_cfg=[dict(type='Constant', val=1., layer='Conv2d')])
pretrain_model = VisionTransformer(**pretrain_cfg)
pretrain_model.init_weights()
assert torch.allclose(pretrain_model.patch_embed.projection.weight,
torch.tensor(1.))
assert pretrain_model.pos_embed.abs().sum() > 0
pos_embed_weight = pretrain_model.pos_embed.detach()
tmpdir = tempfile.gettempdir()
checkpoint = os.path.join(tmpdir, 'test.pth')
torch.save(pretrain_model.state_dict(), checkpoint)
# test load checkpoint
finetune_cfg = dict(arch='b',
img_size=224,
patch_size=16,
init_cfg=dict(type='Pretrained',
checkpoint=checkpoint))
finetune_model = VisionTransformer(**finetune_cfg)
finetune_model.init_weights()
assert torch.allclose(finetune_model.pos_embed, pos_embed_weight)
# test load checkpoint with different img_size
finetune_cfg = dict(arch='b',
img_size=384,
patch_size=16,
init_cfg=dict(type='Pretrained',
checkpoint=checkpoint))
finetune_model = VisionTransformer(**finetune_cfg)
finetune_model.init_weights()
resized_pos_embed = timm_resize_pos_embed(pos_embed_weight,
finetune_model.pos_embed)
assert torch.allclose(finetune_model.pos_embed, resized_pos_embed)
os.remove(checkpoint)
def test_forward(self):
imgs = torch.randn(3, 3, 224, 224)
# test with_cls_token=False
cfg = deepcopy(self.cfg)
cfg['with_cls_token'] = False
cfg['output_cls_token'] = True
with self.assertRaisesRegex(AssertionError, 'but got False'):
VisionTransformer(**cfg)
cfg = deepcopy(self.cfg)
cfg['with_cls_token'] = False
cfg['output_cls_token'] = False
model = VisionTransformer(**cfg)
outs = model(imgs)
self.assertIsInstance(outs, tuple)
self.assertEqual(len(outs), 1)
patch_token = outs[-1]
self.assertEqual(patch_token.shape, (3, 768, 14, 14))
# test with output_cls_token
cfg = deepcopy(self.cfg)
model = VisionTransformer(**cfg)
outs = model(imgs)
self.assertIsInstance(outs, tuple)
self.assertEqual(len(outs), 1)
patch_token, cls_token = outs[-1]
self.assertEqual(patch_token.shape, (3, 768, 14, 14))
self.assertEqual(cls_token.shape, (3, 768))
# test without output_cls_token
cfg = deepcopy(self.cfg)
cfg['output_cls_token'] = False
model = VisionTransformer(**cfg)
outs = model(imgs)
self.assertIsInstance(outs, tuple)
self.assertEqual(len(outs), 1)
patch_token = outs[-1]
self.assertEqual(patch_token.shape, (3, 768, 14, 14))
# Test forward with multi out indices
cfg = deepcopy(self.cfg)
cfg['out_indices'] = [-3, -2, -1]
model = VisionTransformer(**cfg)
outs = model(imgs)
self.assertIsInstance(outs, tuple)
self.assertEqual(len(outs), 3)
for out in outs:
patch_token, cls_token = out
self.assertEqual(patch_token.shape, (3, 768, 14, 14))
self.assertEqual(cls_token.shape, (3, 768))
# Test forward with dynamic input size
imgs1 = torch.randn(3, 3, 224, 224)
imgs2 = torch.randn(3, 3, 256, 256)
imgs3 = torch.randn(3, 3, 256, 309)
cfg = deepcopy(self.cfg)
model = VisionTransformer(**cfg)
for imgs in [imgs1, imgs2, imgs3]:
outs = model(imgs)
self.assertIsInstance(outs, tuple)
self.assertEqual(len(outs), 1)
patch_token, cls_token = outs[-1]
expect_feat_shape = (math.ceil(imgs.shape[2] / 16),
math.ceil(imgs.shape[3] / 16))
self.assertEqual(patch_token.shape, (3, 768, *expect_feat_shape))
self.assertEqual(cls_token.shape, (3, 768))