def test_regvgg_recognizer():
data = torch.randn(1, 3, 224, 224)
for key in arch_settings.keys():
print('*' * 10, key)
model = RepVGGRecognizer(arch=key)
# print(model)
outputs = model(data)[KEY_OUTPUT]
assert outputs.shape == (1, 1000)
print('insert_regvgg_block -> fuse_regvgg_block')
insert_repvgg_block(model)
# print(model)
model.eval()
outputs_insert = model(data)[KEY_OUTPUT]
fuse_repvgg_block(model)
# print(model)
model.eval()
outputs_fuse = model(data)[KEY_OUTPUT]
# print(outputs_insert)
# print(outputs_fuse)
print(torch.sqrt(torch.sum((outputs_insert - outputs_fuse)**2)))
print(torch.allclose(outputs_insert, outputs_fuse, atol=1e-8))
assert torch.allclose(outputs_insert, outputs_fuse, atol=1e-8)
print(
'insert_regvgg_block -> insert_acblock -> fuse_acblock -> fuse_regvgg_block'
)
insert_repvgg_block(model)
insert_acblock(model)
# print(model)
model.eval()
outputs_insert = model(data)[KEY_OUTPUT]
fuse_acblock(model)
fuse_repvgg_block(model)
# print(model)
model.eval()
outputs_fuse = model(data)[KEY_OUTPUT]
print(torch.sqrt(torch.sum((outputs_insert - outputs_fuse)**2)))
print(torch.allclose(outputs_insert, outputs_fuse, atol=1e-7))
assert torch.allclose(outputs_insert, outputs_fuse, atol=1e-7)
print(
'insert_acblock -> insert_regvgg_block -> fuse_regvgg_block -> fuse_acblock'
)
insert_repvgg_block(model)
insert_acblock(model)
# print(model)
model.eval()
outputs_insert = model(data)[KEY_OUTPUT]
fuse_acblock(model)
fuse_repvgg_block(model)
# print(model)
model.eval()
outputs_fuse = model(data)[KEY_OUTPUT]
print(torch.sqrt(torch.sum((outputs_insert - outputs_fuse)**2)))
print(torch.allclose(outputs_insert, outputs_fuse, atol=1e-7))
assert torch.allclose(outputs_insert, outputs_fuse, atol=1e-7)
def test_resnet50_acb():
model = resnet50()
model.eval()
# print(model)
data = torch.randn(1, 3, 224, 224)
insert_acblock(model)
model.eval()
train_outputs = model(data)
# print(model)
fuse_acblock(model, eps=1e-5)
model.eval()
eval_outputs = model(data)
# print(model)
print(torch.sqrt(torch.sum((train_outputs - eval_outputs)**2)))
print(torch.allclose(train_outputs, eval_outputs, atol=1e-7))
assert torch.allclose(train_outputs, eval_outputs, atol=1e-7)
def test_acb_helper():
in_channels = 32
out_channels = 64
dilation = 1
# 下采样 + 分组卷积
kernel_size = 3
stride = 2
padding = 1
groups = 8
model = nn.Sequential(
nn.Sequential(
nn.Conv2d(in_channels,
out_channels,
kernel_size,
stride=stride,
padding=padding,
dilation=dilation,
groups=groups), nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True)), nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True))
model.eval()
print(model)
data = torch.randn(1, in_channels, 56, 56)
insert_acblock(model)
model.eval()
train_outputs = model(data)
print(model)
fuse_acblock(model, eps=1e-5)
model.eval()
eval_outputs = model(data)
print(model)
print(torch.sqrt(torch.sum((train_outputs - eval_outputs)**2)))
print(torch.allclose(train_outputs, eval_outputs, atol=1e-6))
assert torch.allclose(train_outputs, eval_outputs, atol=1e-6)
def test_config_file():
data = torch.randn(3, 3, 224, 224)
print('repvgg_b2g4_custom_cifar100_224_e100_sgd')
config_file = "configs/benchmarks/repvgg/repvgg_b2g4_cifar100_224_e100_sgd_calr.yaml"
cfg.merge_from_file(config_file)
device = torch.device('cpu')
model = build_recognizer(cfg, device)
print(model)
outputs = model(data)[KEY_OUTPUT]
assert outputs.shape == (3, 100)
fuse_repvgg_block(model)
print(model)
outputs = model(data)[KEY_OUTPUT]
assert outputs.shape == (3, 100)
print('repvgg_b2g4_acb_custom_cifar100_224_e100_sgd')
config_file = "configs/benchmarks/repvgg/repvgg_b2g4_acb_cifar100_224_e100_sgd_calr.yaml"
cfg.merge_from_file(config_file)
device = torch.device('cpu')
model = build_recognizer(cfg, device)
print(model)
outputs = model(data)[KEY_OUTPUT]
assert outputs.shape == (3, 100)
# 注意：如果在RepVGG中嵌入了ACBlock，融合时应该先acb再regvgg
fuse_acblock(model)
print(model)
fuse_repvgg_block(model)
print(model)
outputs = model(data)[KEY_OUTPUT]
assert outputs.shape == (3, 100)
print('acb_repvgg_b2g4_custom_cifar100_224_e100_sgd')
config_file = "configs/benchmarks/repvgg/acb_repvgg_b2g4_cifar100_224_e100_sgd_calr.yaml"
cfg.merge_from_file(config_file)
device = torch.device('cpu')
model = build_recognizer(cfg, device)
print(model)
outputs = model(data)[KEY_OUTPUT]
assert outputs.shape == (3, 100)
# 注意：如果先嵌入ACBlock再嵌入RepVGGBlock，那么融合时应该先repvgg_block再acblock
fuse_repvgg_block(model)
print(model)
fuse_acblock(model)
print(model)
outputs = model(data)[KEY_OUTPUT]
assert outputs.shape == (3, 100)
print('rxtd50_32x4d_acb_rvb_custom_cifar100_224_e100_sgd')
config_file = "configs/benchmarks/repvgg/rxtd50_32x4d_acb_rvb_cifar100_224_e100_sgd_calr.yaml"
cfg.merge_from_file(config_file)
device = torch.device('cpu')
model = build_recognizer(cfg, device)
print(model)
outputs = model(data)[KEY_OUTPUT]
assert outputs.shape == (3, 100)
# 注意：如果先嵌入ACBlock再嵌入RepVGGBlock，那么融合时应该先repvgg_block再acblock
fuse_repvgg_block(model)
print(model)
fuse_acblock(model)
print(model)
outputs = model(data)[KEY_OUTPUT]
assert outputs.shape == (3, 100)
print('rxtd50_32x4d_rvb_acb_custom_cifar100_224_e100_sgd')
config_file = "configs/benchmarks/repvgg/rxtd50_32x4d_rvb_acb_cifar100_224_e100_sgd_calr.yaml"
cfg.merge_from_file(config_file)
device = torch.device('cpu')
model = build_recognizer(cfg, device)
print(model)
outputs = model(data)[KEY_OUTPUT]
assert outputs.shape == (3, 100)
# 注意：如果先嵌入RepVGGBlock再嵌入ACBlock，那么逆序融合
fuse_acblock(model)
print(model)
fuse_repvgg_block(model)
print(model)
outputs = model(data)[KEY_OUTPUT]
assert outputs.shape == (3, 100)
def test_regvgg():
data = torch.randn(1, 3, 224, 224)
for key in arch_settings.keys():
print('*' * 10, key)
cfg.merge_from_file(
'configs/benchmarks/repvgg/repvgg_b2g4_cifar100_224_e100_sgd_calr.yaml'
)
model = RepVGG(cfg)
# print(model)
outputs = model(data)[KEY_OUTPUT]
assert outputs.shape == (1, 100)
print('insert_regvgg_block -> fuse_regvgg_block')
insert_repvgg_block(model)
# print(model)
model.eval()
outputs_insert = model(data)[KEY_OUTPUT]
fuse_repvgg_block(model)
# print(model)
model.eval()
outputs_fuse = model(data)[KEY_OUTPUT]
# print(outputs_insert)
# print(outputs_fuse)
print(torch.sqrt(torch.sum((outputs_insert - outputs_fuse)**2)))
print(torch.allclose(outputs_insert, outputs_fuse, atol=1e-8))
assert torch.allclose(outputs_insert, outputs_fuse, atol=1e-8)
print(
'insert_regvgg_block -> insert_acblock -> fuse_acblock -> fuse_regvgg_block'
)
insert_repvgg_block(model)
insert_acblock(model)
# print(model)
model.eval()
outputs_insert = model(data)[KEY_OUTPUT]
fuse_acblock(model)
fuse_repvgg_block(model)
# print(model)
model.eval()
outputs_fuse = model(data)[KEY_OUTPUT]
print(torch.sqrt(torch.sum((outputs_insert - outputs_fuse)**2)))
print(torch.allclose(outputs_insert, outputs_fuse, atol=1e-6))
assert torch.allclose(outputs_insert, outputs_fuse, atol=1e-6)
print(
'insert_acblock -> insert_regvgg_block -> fuse_regvgg_block -> fuse_acblock'
)
insert_repvgg_block(model)
insert_acblock(model)
# print(model)
model.eval()
outputs_insert = model(data)[KEY_OUTPUT]
fuse_acblock(model)
fuse_repvgg_block(model)
# print(model)
model.eval()
outputs_fuse = model(data)[KEY_OUTPUT]
print(torch.sqrt(torch.sum((outputs_insert - outputs_fuse)**2)))
print(torch.allclose(outputs_insert, outputs_fuse, atol=1e-6))
assert torch.allclose(outputs_insert, outputs_fuse, atol=1e-6)