code stringlengths 17 6.64M |
|---|
def test_ttsr_dict():
cfg = dict(type='TTSRDiscriminator', in_channels=3, in_size=160)
net = build_component(cfg)
net.init_weights(pretrained=None)
inputs = torch.rand((2, 3, 160, 160))
output = net(inputs)
assert (output.shape == (2, 1))
if torch.cuda.is_available():
net.init_weig... |
def test_patch_discriminator():
cfg = dict(type='PatchDiscriminator', in_channels=3, base_channels=64, num_conv=3, norm_cfg=dict(type='BN'), init_cfg=dict(type='normal', gain=0.02))
net = build_component(cfg)
net.init_weights(pretrained=None)
input_shape = (1, 3, 64, 64)
img = _demo_inputs(input_s... |
def test_smpatch_discriminator():
cfg = dict(type='SoftMaskPatchDiscriminator', in_channels=3, base_channels=64, num_conv=3, with_spectral_norm=True)
net = build_component(cfg)
net.init_weights(pretrained=None)
input_shape = (1, 3, 64, 64)
img = _demo_inputs(input_shape)
output = net(img)
... |
def _demo_inputs(input_shape=(1, 3, 64, 64)):
'Create a superset of inputs needed to run backbone.\n\n Args:\n input_shape (tuple): input batch dimensions.\n Default: (1, 3, 64, 64).\n\n Returns:\n imgs: (Tensor): Images in FloatTensor with desired shapes.\n '
imgs = np.rando... |
def test_max_feature():
conv2d = MaxFeature(16, 16, filter_type='conv2d')
x1 = torch.rand(3, 16, 16, 16)
y1 = conv2d(x1)
assert (y1.shape == (3, 16, 16, 16))
linear = MaxFeature(16, 16, filter_type='linear')
x2 = torch.rand(3, 16)
y2 = linear(x2)
assert (y2.shape == (3, 16))
if tor... |
def test_light_cnn():
cfg = dict(type='LightCNN', in_channels=3)
net = build_component(cfg)
net.init_weights(pretrained=None)
inputs = torch.rand((2, 3, 128, 128))
output = net(inputs)
assert (output.shape == (2, 1))
if torch.cuda.is_available():
net.init_weights(pretrained=None)
... |
def test_multi_layer_disc():
with pytest.raises(AssertionError):
multi_disc = MultiLayerDiscriminator(3, 236, fc_in_channels=(- 100), out_act_cfg=None)
with pytest.raises(TypeError):
multi_disc = MultiLayerDiscriminator(3, 256, num_convs=3, stride_list=(1, 2))
input_g = torch.randn(1, 3, 2... |
def test_unet_disc_with_spectral_norm():
disc = UNetDiscriminatorWithSpectralNorm(in_channels=3)
img = torch.randn(1, 3, 16, 16)
disc(img)
with pytest.raises(TypeError):
disc.init_weights(pretrained=233)
if torch.cuda.is_available():
disc = disc.cuda()
img = img.cuda()
... |
def assert_dict_keys_equal(dictionary, target_keys):
'Check if the keys of the dictionary is equal to the target key set.'
assert isinstance(dictionary, dict)
assert (set(dictionary.keys()) == set(target_keys))
|
def assert_tensor_with_shape(tensor, shape):
'"Check if the shape of the tensor is equal to the target shape.'
assert isinstance(tensor, torch.Tensor)
assert (tensor.shape == shape)
|
def test_plain_refiner():
'Test PlainRefiner.'
model = PlainRefiner()
model.init_weights()
model.train()
(merged, alpha, trimap, raw_alpha) = _demo_inputs_pair()
prediction = model(torch.cat([merged, raw_alpha.sigmoid()], 1), raw_alpha)
assert_tensor_with_shape(prediction, torch.Size([1, 1... |
def _demo_inputs_pair(img_shape=(64, 64), batch_size=1, cuda=False):
'\n Create a superset of inputs needed to run refiner.\n\n Args:\n img_shape (tuple): shape of the input image.\n batch_size (int): batch size of the input batch.\n cuda (bool): whether transfer input into gpu.\n '
... |
def test_mlp_refiner():
model_cfg = dict(type='MLPRefiner', in_dim=8, out_dim=3, hidden_list=[8, 8, 8, 8])
mlp = build_component(model_cfg)
assert (mlp.__class__.__name__ == 'MLPRefiner')
inputs = torch.rand(2, 8)
targets = torch.rand(2, 3)
if torch.cuda.is_available():
inputs = inputs... |
class TestBlur():
@classmethod
def setup_class(cls):
cls.kernel = [1, 3, 3, 1]
cls.pad = (1, 1)
@pytest.mark.skipif((not torch.cuda.is_available()), reason='requires cuda')
def test_blur_cuda(self):
blur = Blur(self.kernel, self.pad)
x = torch.randn((2, 3, 8, 8))
... |
class TestModStyleConv():
@classmethod
def setup_class(cls):
cls.default_cfg = dict(in_channels=3, out_channels=1, kernel_size=3, style_channels=5, upsample=True)
def test_mod_styleconv_cpu(self):
conv = ModulatedStyleConv(**self.default_cfg)
input_x = torch.randn((2, 3, 4, 4))
... |
class TestToRGB():
@classmethod
def setup_class(cls):
cls.default_cfg = dict(in_channels=5, style_channels=5, out_channels=3)
def test_torgb_cpu(self):
model = ModulatedToRGB(**self.default_cfg)
input_x = torch.randn((2, 5, 4, 4))
style = torch.randn((2, 5))
res =... |
class TestStyleGAN2Generator():
@classmethod
def setup_class(cls):
cls.default_cfg = dict(out_size=64, style_channels=16, num_mlps=4, channel_multiplier=1)
def test_stylegan2_g_cpu(self):
g = StyleGANv2Generator(**self.default_cfg)
res = g(None, num_batches=2)
assert (res... |
class TestStyleGANv2Disc():
@classmethod
def setup_class(cls):
cls.default_cfg = dict(in_size=64, channel_multiplier=1)
def test_stylegan2_disc_cpu(self):
d = StyleGAN2Discriminator(**self.default_cfg)
img = torch.randn((2, 3, 64, 64))
score = d(img)
assert (score... |
def test_get_module_device_cpu():
device = get_module_device(nn.Conv2d(3, 3, 3, 1, 1))
assert (device == torch.device('cpu'))
with pytest.raises(ValueError):
get_module_device(nn.Flatten())
|
@pytest.mark.skipif((not torch.cuda.is_available()), reason='requires cuda')
def test_get_module_device_cuda():
module = nn.Conv2d(3, 3, 3, 1, 1).cuda()
device = get_module_device(module)
assert (device == next(module.parameters()).get_device())
with pytest.raises(ValueError):
get_module_devic... |
def test_res_block():
res_block = ResBlock(16, 32)
x = torch.rand(2, 16, 64, 64)
y = res_block(x)
assert (y.shape == (2, 32, 64, 64))
res_block = ResBlock(16, 16)
x = torch.rand(2, 16, 64, 64)
y = res_block(x)
assert (y.shape == (2, 16, 64, 64))
|
def test_hour_glass():
hour_glass = Hourglass(2, 16)
x = torch.rand(2, 16, 64, 64)
y = hour_glass(x)
assert (y.shape == x.shape)
|
def test_feedback_hour_glass():
model_cfg = dict(type='FeedbackHourglass', mid_channels=16, num_keypoints=20)
fhg = build_component(model_cfg)
assert (fhg.__class__.__name__ == 'FeedbackHourglass')
x = torch.rand(2, 3, 64, 64)
(heatmap, last_hidden) = fhg.forward(x)
assert (heatmap.shape == (2... |
def test_reduce_to_five_heatmaps():
heatmap = torch.rand((2, 5, 64, 64))
new_heatmap = reduce_to_five_heatmaps(heatmap, False)
assert (new_heatmap.shape == (2, 5, 64, 64))
new_heatmap = reduce_to_five_heatmaps(heatmap, True)
assert (new_heatmap.shape == (2, 5, 64, 64))
heatmap = torch.rand((2,... |
def test_lte():
model_cfg = dict(type='LTE', requires_grad=False, pixel_range=1.0, pretrained=None, load_pretrained_vgg=False)
lte = build_component(model_cfg)
assert (lte.__class__.__name__ == 'LTE')
x = torch.rand(2, 3, 64, 64)
(x_level3, x_level2, x_level1) = lte(x)
assert (x_level1.shape =... |
def test_light_cnn_feature_loss():
pretrained = ('https://download.openmmlab.com/mmediting/' + 'restorers/dic/light_cnn_feature.pth')
pred = torch.rand((3, 3, 128, 128))
gt = torch.rand((3, 3, 128, 128))
feature_loss = LightCNNFeatureLoss(pretrained=pretrained)
loss = feature_loss(pred, gt)
as... |
def _get_model_cfg(fname):
'\n Grab configs necessary to create a model. These are deep copied to allow\n for safe modification of parameters without influencing other tests.\n '
config_dpath = 'configs/mattors'
config_fpath = osp.join(config_dpath, fname)
if (not osp.exists(config_dpath)):
... |
def assert_dict_keys_equal(dictionary, target_keys):
'Check if the keys of the dictionary is equal to the target key set.'
assert isinstance(dictionary, dict)
assert (set(dictionary.keys()) == set(target_keys))
|
@patch.multiple(BaseMattor, __abstractmethods__=set())
def test_base_mattor():
backbone = dict(type='SimpleEncoderDecoder', encoder=dict(type='VGG16', in_channels=4), decoder=dict(type='PlainDecoder'))
refiner = dict(type='PlainRefiner')
train_cfg = mmcv.ConfigDict(train_backbone=True, train_refiner=True)... |
def test_dim():
(model_cfg, train_cfg, test_cfg) = _get_model_cfg('dim/dim_stage3_v16_pln_1x1_1000k_comp1k.py')
model_cfg['pretrained'] = None
train_cfg.train_refiner = True
test_cfg.refine = True
model = build_model(model_cfg, train_cfg=train_cfg, test_cfg=test_cfg)
input_train = _demo_input_... |
def test_indexnet():
(model_cfg, _, test_cfg) = _get_model_cfg('indexnet/indexnet_mobv2_1x16_78k_comp1k.py')
model_cfg['pretrained'] = None
with torch.no_grad():
indexnet = build_model(model_cfg, train_cfg=None, test_cfg=test_cfg)
indexnet.eval()
input_test = _demo_input_test((64, ... |
def test_gca():
(model_cfg, train_cfg, test_cfg) = _get_model_cfg('gca/gca_r34_4x10_200k_comp1k.py')
model_cfg['pretrained'] = None
model = build_model(model_cfg, train_cfg=train_cfg, test_cfg=test_cfg)
inputs = _demo_input_train((64, 64), batch_size=2)
inputs['trimap'] = inputs['trimap'].expand_a... |
def _demo_input_train(img_shape, batch_size=1, cuda=False):
'\n Create a superset of inputs needed to run backbone.\n\n Args:\n img_shape (tuple): shape of the input image.\n batch_size (int): batch size of the input batch.\n cuda (bool): whether transfer input into gpu.\n '
colo... |
def _demo_input_test(img_shape, batch_size=1, cuda=False, test_trans='resize'):
'\n Create a superset of inputs needed to run backbone.\n\n Args:\n img_shape (tuple): shape of the input image.\n batch_size (int): batch size of the input batch.\n cuda (bool): whether transfer input into ... |
def test_basic_restorer():
model_cfg = dict(type='BasicRestorer', generator=dict(type='MSRResNet', in_channels=3, out_channels=3, mid_channels=4, num_blocks=1, upscale_factor=4), pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'))
train_cfg = None
test_cfg = None
restorer = build_model(... |
def test_basicvsr_model():
model_cfg = dict(type='BasicVSR', generator=dict(type='BasicVSRNet', mid_channels=64, num_blocks=30, spynet_pretrained=None), pixel_loss=dict(type='MSELoss', loss_weight=1.0, reduction='sum'))
train_cfg = dict(fix_iter=1)
train_cfg = mmcv.Config(train_cfg)
test_cfg = None
... |
def test_dic_model():
pretrained = ('https://download.openmmlab.com/mmediting/' + 'restorers/dic/light_cnn_feature.pth')
model_cfg_pre = dict(type='DIC', generator=dict(type='DICNet', in_channels=3, out_channels=3, mid_channels=48), pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'), align_loss... |
def test_edvr_model():
model_cfg = dict(type='EDVR', generator=dict(type='EDVRNet', in_channels=3, out_channels=3, mid_channels=8, num_frames=5, deform_groups=2, num_blocks_extraction=1, num_blocks_reconstruction=1, center_frame_idx=2, with_tsa=False), pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='su... |
def test_esrgan():
model_cfg = dict(type='ESRGAN', generator=dict(type='MSRResNet', in_channels=3, out_channels=3, mid_channels=4, num_blocks=1, upscale_factor=4), discriminator=dict(type='ModifiedVGG', in_channels=3, mid_channels=2), pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'), gan_loss=dic... |
def test_glean():
model_cfg = dict(type='GLEAN', generator=dict(type='GLEANStyleGANv2', in_size=16, out_size=64, style_channels=512), discriminator=dict(type='StyleGAN2Discriminator', in_size=64), pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'), gan_loss=dict(type='GANLoss', gan_type='vanilla', ... |
@COMPONENTS.register_module()
class BP(nn.Module):
'A simple BP network for testing LIIF.\n\n Args:\n in_dim (int): Input dimension.\n out_dim (int): Output dimension.\n '
def __init__(self, in_dim, out_dim):
super().__init__()
self.layer = nn.Linear(in_dim, out_dim)
... |
def test_liif():
model_cfg = dict(type='LIIF', generator=dict(type='LIIFEDSR', encoder=dict(type='EDSR', in_channels=3, out_channels=3, mid_channels=64, num_blocks=16), imnet=dict(type='MLPRefiner', in_dim=64, out_dim=3, hidden_list=[256, 256, 256, 256]), local_ensemble=True, feat_unfold=True, cell_decode=True, e... |
def test_real_basicvsr():
model_cfg = dict(type='RealBasicVSR', generator=dict(type='RealBasicVSRNet'), discriminator=dict(type='UNetDiscriminatorWithSpectralNorm', in_channels=3, mid_channels=64, skip_connection=True), pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'), cleaning_loss=dict(type='L1... |
def test_real_esrgan():
model_cfg = dict(type='RealESRGAN', generator=dict(type='MSRResNet', in_channels=3, out_channels=3, mid_channels=4, num_blocks=1, upscale_factor=4), discriminator=dict(type='ModifiedVGG', in_channels=3, mid_channels=2), pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'), gan... |
def test_srgan():
model_cfg = dict(type='SRGAN', generator=dict(type='MSRResNet', in_channels=3, out_channels=3, mid_channels=4, num_blocks=1, upscale_factor=4), discriminator=dict(type='ModifiedVGG', in_channels=3, mid_channels=2), pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'), gan_loss=dict(... |
def test_tdan_model():
model_cfg = dict(type='TDAN', generator=dict(type='TDANNet', in_channels=3, mid_channels=64, out_channels=3, num_blocks_before_align=5, num_blocks_after_align=10), pixel_loss=dict(type='MSELoss', loss_weight=1.0, reduction='sum'), lq_pixel_loss=dict(type='MSELoss', loss_weight=1.0, reductio... |
def test_sfe():
inputs = torch.rand(2, 3, 48, 48)
sfe = SFE(3, 64, 16, 1.0)
outputs = sfe(inputs)
assert (outputs.shape == (2, 64, 48, 48))
|
def test_csfi():
inputs1 = torch.rand(2, 16, 24, 24)
inputs2 = torch.rand(2, 16, 48, 48)
inputs4 = torch.rand(2, 16, 96, 96)
csfi2 = CSFI2(mid_channels=16)
(out1, out2) = csfi2(inputs1, inputs2)
assert (out1.shape == (2, 16, 24, 24))
assert (out2.shape == (2, 16, 48, 48))
csfi3 = CSFI3... |
def test_merge_features():
inputs1 = torch.rand(2, 16, 24, 24)
inputs2 = torch.rand(2, 16, 48, 48)
inputs4 = torch.rand(2, 16, 96, 96)
merge_features = MergeFeatures(mid_channels=16, out_channels=3)
out = merge_features(inputs1, inputs2, inputs4)
assert (out.shape == (2, 3, 96, 96))
|
def test_ttsr_net():
inputs = torch.rand(2, 3, 24, 24)
soft_attention = torch.rand(2, 1, 24, 24)
t_level3 = torch.rand(2, 64, 24, 24)
t_level2 = torch.rand(2, 32, 48, 48)
t_level1 = torch.rand(2, 16, 96, 96)
ttsr_cfg = dict(type='TTSRNet', in_channels=3, out_channels=3, mid_channels=16, textur... |
def test_ttsr():
model_cfg = dict(type='TTSR', generator=dict(type='TTSRNet', in_channels=3, out_channels=3, mid_channels=64, num_blocks=(16, 16, 8, 4)), extractor=dict(type='LTE', load_pretrained_vgg=False), transformer=dict(type='SearchTransformer'), discriminator=dict(type='TTSRDiscriminator', in_size=64), pix... |
def test_cyclegan():
model_cfg = dict(type='CycleGAN', generator=dict(type='ResnetGenerator', in_channels=3, out_channels=3, base_channels=64, norm_cfg=dict(type='IN'), use_dropout=False, num_blocks=9, padding_mode='reflect', init_cfg=dict(type='normal', gain=0.02)), discriminator=dict(type='PatchDiscriminator', ... |
def test_search_transformer():
model_cfg = dict(type='SearchTransformer')
model = build_component(model_cfg)
lr_pad_level3 = torch.randn((2, 32, 32, 32))
ref_pad_level3 = torch.randn((2, 32, 32, 32))
ref_level3 = torch.randn((2, 32, 32, 32))
ref_level2 = torch.randn((2, 16, 64, 64))
ref_le... |
def test_cain():
model_cfg = dict(type='CAIN', generator=dict(type='CAINNet'), pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'))
train_cfg = None
test_cfg = None
restorer = build_model(model_cfg, train_cfg=train_cfg, test_cfg=test_cfg)
assert (restorer.__class__.__name__ == 'CAIN'... |
def test_init_random_seed():
init_random_seed(0, device='cpu')
init_random_seed(device='cpu')
if torch.cuda.is_available():
init_random_seed(0, device='cuda')
init_random_seed(device='cuda')
|
def test_set_random_seed():
set_random_seed(0, deterministic=False)
set_random_seed(0, deterministic=True)
|
@DATASETS.register_module()
class ToyDataset():
def __init__(self, ann_file=None, cnt=0):
self.ann_file = ann_file
self.cnt = cnt
def __item__(self, idx):
return idx
def __len__(self):
return 100
|
@DATASETS.register_module()
class ToyDatasetWithAnnFile():
def __init__(self, ann_file):
self.ann_file = ann_file
def __item__(self, idx):
return idx
def __len__(self):
return 100
|
def test_build_dataset():
cfg = dict(type='ToyDataset')
dataset = build_dataset(cfg)
assert isinstance(dataset, ToyDataset)
assert (dataset.cnt == 0)
dataset = build_dataset(cfg, default_args=dict(cnt=1))
assert isinstance(dataset, ToyDataset)
assert (dataset.cnt == 1)
cfg = dict(type=... |
def test_build_dataloader():
dataset = ToyDataset()
samples_per_gpu = 3
dataloader = build_dataloader(dataset, samples_per_gpu=samples_per_gpu, workers_per_gpu=2)
assert (dataloader.batch_size == samples_per_gpu)
assert (len(dataloader) == int(math.ceil((len(dataset) / samples_per_gpu))))
asse... |
class SimpleModule(nn.Module):
def __init__(self):
super().__init__()
self.a = nn.Parameter(torch.tensor([1.0, 2.0]))
if (version.parse(torch.__version__) >= version.parse('1.7.0')):
self.register_buffer('b', torch.tensor([2.0, 3.0]), persistent=True)
self.register... |
class SimpleModel(nn.Module):
def __init__(self) -> None:
super().__init__()
self.module_a = SimpleModule()
self.module_b = SimpleModule()
self.module_a_ema = SimpleModule()
self.module_b_ema = SimpleModule()
|
class SimpleModelNoEMA(nn.Module):
def __init__(self) -> None:
super().__init__()
self.module_a = SimpleModule()
self.module_b = SimpleModule()
|
class SimpleRunner():
def __init__(self):
self.model = SimpleModel()
self.iter = 0
|
class TestEMA():
@classmethod
def setup_class(cls):
cls.default_config = dict(module_keys=('module_a_ema', 'module_b_ema'), interval=1, interp_cfg=dict(momentum=0.5))
cls.runner = SimpleRunner()
@torch.no_grad()
def test_ema_hook(self):
cfg_ = deepcopy(self.default_config)
... |
class ExampleDataset(Dataset):
def __getitem__(self, idx):
results = dict(imgs=torch.tensor([1]))
return results
def __len__(self):
return 1
|
class ExampleModel(nn.Module):
def __init__(self):
super().__init__()
self.test_cfg = None
self.conv = nn.Conv2d(3, 3, 3)
def forward(self, imgs, test_mode=False, **kwargs):
return imgs
def train_step(self, data_batch, optimizer):
rlt = self.forward(data_batch)
... |
def test_eval_hook():
with pytest.raises(TypeError):
test_dataset = ExampleModel()
data_loader = [DataLoader(test_dataset, batch_size=1, sampler=None, num_worker=0, shuffle=False)]
EvalIterHook(data_loader)
test_dataset = ExampleDataset()
test_dataset.evaluate = MagicMock(return_va... |
class ExampleModel(nn.Module):
def __init__(self):
super().__init__()
self.model1 = nn.Conv2d(3, 8, kernel_size=3)
self.model2 = nn.Conv2d(3, 4, kernel_size=3)
def forward(self, x):
return x
|
def test_build_optimizers():
base_lr = 0.0001
base_wd = 0.0002
momentum = 0.9
optimizer_cfg = dict(model1=dict(type='SGD', lr=base_lr, weight_decay=base_wd, momentum=momentum), model2=dict(type='SGD', lr=base_lr, weight_decay=base_wd, momentum=momentum))
model = ExampleModel()
optimizers = bui... |
def test_bbox_mask():
cfg = dict(img_shape=(256, 256), max_bbox_shape=100, max_bbox_delta=10, min_margin=10)
bbox = random_bbox(**cfg)
mask_bbox = bbox2mask(cfg['img_shape'], bbox)
assert (mask_bbox.shape == (256, 256, 1))
zero_area = np.sum((mask_bbox == 0).astype(np.uint8))
ones_area = np.su... |
def test_free_form_mask():
img_shape = (256, 256, 3)
for _ in range(10):
mask = brush_stroke_mask(img_shape)
assert (mask.shape == (256, 256, 1))
img_shape = (256, 256, 3)
mask = brush_stroke_mask(img_shape, num_vertices=8)
assert (mask.shape == (256, 256, 1))
zero_area = np.su... |
def test_irregular_mask():
img_shape = (256, 256)
for _ in range(10):
mask = get_irregular_mask(img_shape)
assert (mask.shape == (256, 256, 1))
assert (0.15 < (np.sum(mask) / (img_shape[0] * img_shape[1])) < 0.5)
zero_area = np.sum((mask == 0).astype(np.uint8))
ones_are... |
def test_modify_args():
def _parse_args():
parser = argparse.ArgumentParser(description='Generation demo')
parser.add_argument('--config-path', help='test config file path')
args = parser.parse_args()
return args
with patch('argparse._sys.argv', ['test.py', '--config_path=conf... |
@pytest.mark.skipif((torch.__version__ == 'parrots'), reason='skip parrots.')
@pytest.mark.skipif((version.parse(torch.__version__) < version.parse('1.4.0')), reason='skip if torch=1.3.x')
def test_restorer_wrapper():
try:
import onnxruntime as ort
from mmedit.core.export.wrappers import ONNXRunti... |
@pytest.mark.skipif((torch.__version__ == 'parrots'), reason='skip parrots.')
@pytest.mark.skipif((version.parse(torch.__version__) < version.parse('1.4.0')), reason='skip if torch=1.3.x')
def test_mattor_wrapper():
try:
import onnxruntime as ort
from mmedit.core.export.wrappers import ONNXRuntime... |
def test_pix2pix():
model_cfg = dict(type='Pix2Pix', generator=dict(type='UnetGenerator', in_channels=3, out_channels=3, num_down=8, base_channels=64, norm_cfg=dict(type='BN'), use_dropout=True, init_cfg=dict(type='normal', gain=0.02)), discriminator=dict(type='PatchDiscriminator', in_channels=6, base_channels=64... |
def test_setup_multi_processes():
sys_start_mehod = mp.get_start_method(allow_none=True)
sys_cv_threads = cv2.getNumThreads()
sys_omp_threads = os.environ.pop('OMP_NUM_THREADS', default=None)
sys_mkl_threads = os.environ.pop('MKL_NUM_THREADS', default=None)
config = dict(data=dict(workers_per_gpu=... |
def generate_json(data_root, seg_root, bg_root, all_data):
'Generate training json list for Background Matting video dataset.\n\n Args:\n data_root (str): Background Matting video data root.\n seg_root (str): Segmentation of Background Matting video data root.\n bg_root (str): Background v... |
def parse_args():
parser = argparse.ArgumentParser(description='Prepare Background Matting video dataset', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('data_root', help='Background Matting video data root')
parser.add_argument('--seg-root', help='Segmentation of Background ... |
def main():
args = parse_args()
if (not osp.exists(args.data_root)):
raise FileNotFoundError(f'{args.data_root} does not exist!')
print('generating Background Matting dataset annotation file...')
generate_json(args.data_root, args.seg_root, args.bg_root, args.all_data)
print('annotation fi... |
def fix_png_file(filename, folder):
"Fix png files in the target filename using pngfix.\n\n pngfix is a tool to fix PNG files. It's installed on Linux or MacOS by\n default.\n\n Args:\n filename (str): png file to run pngfix.\n "
subprocess.call(f'pngfix --quiet --strip=color --prefix=fixed... |
def join_first_contain(directories, filename, data_root):
'Join the first directory that contains the file.\n\n Args:\n directories (list[str]): Directories to search for the file.\n filename (str): The target filename.\n data_root (str): Root of the data path.\n '
for directory in ... |
class ExtendFg():
def __init__(self, data_root, fg_dirs, alpha_dirs) -> None:
self.data_root = data_root
self.fg_dirs = fg_dirs
self.alpha_dirs = alpha_dirs
def extend(self, fg_name):
fg_name = fg_name.strip()
alpha_path = join_first_contain(self.alpha_dirs, fg_name, ... |
def parse_args():
parser = argparse.ArgumentParser(description='Prepare Adobe composition 1k dataset', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('data_root', help='Adobe composition 1k dataset root')
parser.add_argument('--nproc', type=int, default=4, help='number of proc... |
def main():
args = parse_args()
if (not osp.exists(args.data_root)):
raise FileNotFoundError(f'{args.data_root} does not exist!')
data_root = args.data_root
print('preparing training data...')
dir_prefix = 'Training_set'
fname_prefix = 'training'
fg_dirs = ['Training_set/Adobe-lice... |
def generate_json(comp1k_json_path, target_list_path, save_json_path):
data_infos = mmcv.load(comp1k_json_path)
targets = mmcv.list_from_file(target_list_path)
new_data_infos = []
for data_info in data_infos:
for target in targets:
if data_info['alpha_path'].endswith(target):
... |
def parse_args():
parser = argparse.ArgumentParser(description='Filter composition-1k annotation file')
parser.add_argument('comp1k_json_path', help='Path to the composition-1k dataset annotation file')
parser.add_argument('target_list_path', help='Path to the file name list that need to filter out')
... |
def main():
args = parse_args()
if (not osp.exists(args.comp1k_json_path)):
raise FileNotFoundError(f'{args.comp1k_json_path} does not exist!')
if (not osp.exists(args.target_list_path)):
raise FileNotFoundError(f'{args.target_list_path} does not exist!')
generate_json(args.comp1k_json... |
def fix_png_files(directory):
"Fix png files in the target directory using pngfix.\n\n pngfix is a tool to fix PNG files. It's installed on Linux or MacOS by\n default.\n\n Args:\n directory (str): Directory to run pngfix.\n "
subprocess.call('pngfix --quiet --strip=color --prefix=fixed_ *.... |
def fix_png_file(filename, folder):
"Fix png files in the target filename using pngfix.\n\n pngfix is a tool to fix PNG files. It's installed on Linux or MacOS by\n default.\n\n Args:\n filename (str): png file to run pngfix.\n "
subprocess.call(f'pngfix --quiet --strip=color --prefix=fixed... |
def join_first_contain(directories, filename, data_root):
'Join the first directory that contains the file.\n\n Args:\n directories (list[str]): Directories to search for the file.\n filename (str): The target filename.\n data_root (str): Root of the data path.\n '
for directory in ... |
def get_data_info(args):
'Function to process one piece of data.\n\n Args:\n args (tuple): Information needed to process one piece of data.\n\n Returns:\n dict: The processed data info.\n '
(name_with_postfix, source_bg_path, repeat_info, constant) = args
(alpha, fg, alpha_path, fg_... |
def generate_json(data_root, source_bg_dir, composite, nproc, mode):
'Generate training json list or test json list.\n\n It should be noted except for `source_bg_dir`, other strings are incomplete\n relative path. When using these strings to read from or write to disk, a\n data_root is added to form a co... |
def parse_args():
modify_args()
parser = argparse.ArgumentParser(description='Prepare Adobe composition 1k dataset', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('data_root', help='Adobe composition 1k dataset root')
parser.add_argument('coco_root', help='COCO2014 or COC... |
def main():
args = parse_args()
if (not osp.exists(args.data_root)):
raise FileNotFoundError(f'{args.data_root} does not exist!')
if (not osp.exists(args.coco_root)):
raise FileNotFoundError(f'{args.coco_root} does not exist!')
if (not osp.exists(args.voc_root)):
raise FileNotF... |
def make_lmdb(mode, data_path, lmdb_path, batch=5000, compress_level=1):
"Create lmdb for the REDS dataset.\n\n Contents of lmdb. The file structure is:\n example.lmdb\n ├── data.mdb\n ├── lock.mdb\n ├── meta_info.txt\n\n The data.mdb and lock.mdb are standard lmdb files and you can refer to\n ... |
def merge_train_val(train_path, val_path):
'Merge the train and val datasets of REDS.\n\n Because the EDVR uses a different validation partition, so we merge train\n and val datasets in REDS for easy switching between REDS4 partition (used\n in EDVR) and the official validation partition.\n\n The orig... |
def generate_anno_file(root_path, file_name='meta_info_REDS_GT.txt'):
'Generate anno file for REDS datasets from the ground-truth folder.\n\n Args:\n root_path (str): Root path for REDS datasets.\n '
print(f'Generate annotation files {file_name}...')
txt_file = osp.join(root_path, file_name)
... |
def unzip(zip_path):
'Unzip zip files. It will scan all zip files in zip_path and return unzip\n folder names.\n\n Args:\n zip_path (str): Path for zip files.\n\n Returns:\n list: unzip folder names.\n '
zip_files = mmcv.scandir(zip_path, suffix='zip', recursive=False)
import shu... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.