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class MultiCropTransform():
"Define multi crop transform that apply several sets of transform to the inputs.\n\n Args:\n set_transforms: List of Dictionary of sets of transforms specifying transforms and number of views per set.\n\n Example::\n\n set_transforms = [\n {'transform': [... |
class OnlyInputListTransform(Compose):
"Apply Transform to only the key ``'input'`` in a list of sample dictionary.\n\n Args:\n transform: The transform to apply.\n "
def __init__(self, transform: Callable) -> None:
transforms = [ApplyTransformInputKeyOnList(transform), DictKeepInputLabe... |
class OnlyInputTransform(Compose):
"Apply Transform to only the key ``'input'`` in a sample dictionary.\n\n Args:\n transform: The transform to apply.\n "
def __init__(self, transform: Callable) -> None:
transforms = [ApplyTransformInputKey(transform), DictKeepInputLabelIdx()]
su... |
class OnlyInputListSameTransform(Compose):
"Apply the same transform to only the key ``'input'`` in a list of sample dictionary.\n\n Args:\n transform: The transform to apply.\n "
def __init__(self, transform: Callable) -> None:
transforms = [ApplySameTransformInputKeyOnList(transform), ... |
class OnlyInputTransformWithDictTransform(Compose):
"Apply Transform to only the key ``'input'`` in a sample dictionary with a transformation on the dictionary\n afterwards.\n\n Args:\n transform: The transform to apply to the input.\n dict_transform: The transform to apply to the dictionary.\... |
class OnlyInputListTransformWithDictTransform():
"Apply Transform to only the key ``'input'`` in a list of sample dictionary with a transformation on the\n dictionary afterwards.\n\n Args:\n transform: The transform to apply to the input.\n dict_transform: The transform to apply to the diction... |
class RandomResizedCrop(transforms.RandomResizedCrop):
def __init__(self, size: Union[(int, Iterable[int])], scale: Iterable[float]=[0.08, 1.0], ratio: Iterable[float]=[(3 / 4), (4 / 3)], interpolation: Union[(str, InterpolationMode)]='bilinear', antialias: bool=True, **kwargs) -> None:
if (type(interpol... |
class RemoveKey(Module):
"Removes the given key from the input dict. Useful for removing modalities from a video clip that aren't\n needed.\n\n Args:\n key: The dictionary key to remove.\n "
def __init__(self, key: str):
super().__init__()
self._key = key
def __call__(sel... |
class RemoveInputKey(RemoveKey):
'Remove video key from sample dictionary.'
def __init__(self):
super().__init__('input')
|
class RemoveAudioKey(RemoveKey):
'Remove audio key from sample dictionary.'
def __init__(self):
super().__init__('audio')
|
class RemoveTimeDim(nn.Module):
'Remove time dimension from tensor.\n\n Suppose the tensor shape is [C,T,H,W].\n '
def __init__(self) -> None:
super().__init__()
def forward(self, tensor: Tensor):
(c, t, h, w) = tensor.shape
return tensor.view((c * t), h, w)
def __repr... |
def mix_spotting(x: Tensor, mix_value: Tensor, permutation: Tensor, labels: Tensor, has_label: Tensor, ignore_class: Tensor):
'Make mixup of the batch for action spotting.\n\n Args:\n x: The batch values to mix.\n mix_value: Value coefficients for mixing.\n permutation: Permutation to perf... |
class SpottingMixup(Module):
'Make mixup for spotting for labels.\n\n Args:\n alpha: Alpha value for the beta distribution of mixup.\n '
def __init__(self, alpha: float=0.5) -> None:
super().__init__()
self.alpha = alpha
self.mix_sampler = torch.distributions.Beta(alpha, ... |
def get_matching_files_in_dir(dir: str, file_pattern: str) -> List[Path]:
'Retrieve files in directory matching a pattern.\n\n Args:\n dir: Directory path.\n file_pattern: Pattern for the files.\n\n Raises:\n NotADirectoryError: If `dir` does not exist or is not a directory.\n\n Retu... |
def get_ckpts_in_dir(dir: str, ckpt_pattern: str='*.ckpt') -> List[Path]:
'Get all checkpoints in a directory.\n\n Args:\n dir: Directory path containing the checkpoints.\n ckpt_pattern: Checkpoint glob pattern.\n\n Returns:\n List of checkpoints paths in directory.\n '
try:
... |
def get_last_ckpt_in_dir(dir: str, ckpt_pattern: str='*.ckpt', key_sort: Callable=(lambda x: x.stat().st_mtime)) -> Optional[Path]:
'Get last ckpt in directory following a sorting function.\n\n Args:\n dir: Directory path containing the checkpoints.\n ckpt_pattern: Checkpoint glob pattern.\n ... |
def get_last_ckpt_in_path_or_dir(checkpoint_file: Optional[str]=None, checkpoint_dir: Optional[str]=None, ckpt_pattern: str='*.ckpt', key_sort: Callable=(lambda x: x.stat().st_mtime)) -> Optional[Path]:
'Get checkpoint from file or from last checkpoint in directory following a sorting function.\n\n Args:\n ... |
def get_ckpt_by_callback_mode(checkpoint_path: str, checkpoint_mode: str) -> List[Path]:
"Get checkpoint from ModelCheckpoint callback based on the mode: ``'best'``, ``'last'``, or ``'both'``.\n\n Args:\n checkpoint_path: Checkpoint file path containing the callback checkpoint.\n checkpoint_mode:... |
def get_sub_state_dict_from_pl_ckpt(checkpoint_path: str, pattern: str='^(trunk\\.)') -> Dict[(Any, Any)]:
'Retrieve sub state dict from a pytorch lightning checkpoint.\n\n Args:\n checkpoint_path: Pytorch lightning checkpoint path.\n pattern: Pattern to filter the keys for the sub state dictiona... |
def remove_pattern_in_keys_from_dict(d: Dict[(Any, Any)], pattern: str) -> Dict[(Any, Any)]:
'Remove the pattern from keys in a dictionary.\n\n Args:\n d: The dictionary.\n pattern: Pattern to remove from the keys.\n If value is ``""`` keep all keys.\n\n Returns:\n Input dict... |
def mask_tube_in_sequence(mask_ratio: float, tube_size: int, len_sequence: int, device: (str | torch.device)='cpu'):
'Generate indices to mask tubes from a sequence.\n\n Args:\n mask_ratio: Ratio for the masking.\n tube_size: Tube size for the masking.\n len_sequence (int): Length of the s... |
def batch_mask_tube_in_sequence(mask_ratio: float, tube_size: int, len_sequence: int, batch_size: int, device: (str | torch.device)='cpu'):
'Generate indices to mask tubes from a batch of sequences.\n\n Args:\n mask_ratio: Ratio for the masking.\n tube_size: Tube size for the masking.\n le... |
def get_global_batch_size_in_trainer(local_batch_size: int, trainer: Trainer) -> int:
'Get global batch size used by a trainer based on the local batch size.\n\n Args:\n local_batch_size: The local batch size used by the trainer.\n trainer: The trainer used.\n\n Raises:\n AttributeError... |
def get_local_batch_size_in_trainer(global_batch_size: int, trainer: Trainer) -> int:
'Get local batch size used by a trainer based on the global batch size.\n\n Args:\n global_batch_size: The global batch size used by the trainer.\n strategy: The trainer used.\n\n Raises:\n AttributeEr... |
def get_num_devices_in_trainer(trainer: Trainer) -> int:
'Get the number of devices used by the trainer.\n\n Args:\n trainer: The trainer.\n\n Raises:\n AttributeError: The strategy used by trainer is not supported\n\n Returns:\n The number of devices used by trainer.\n '
stra... |
def get_trainer_strategy(trainer: Trainer) -> Any:
'Retrieve the strategy from a trainer.\n\n Args:\n trainer: The trainer.\n\n Returns:\n The strategy.\n '
if (pl.__version__ < '1.6.0'):
return trainer.training_type_plugin
else:
return trainer.strategy
|
def is_strategy_ddp(strategy: Any) -> bool:
'Test if strategy is ddp.\n\n Args:\n strategy: The strategy.\n\n Returns:\n ``True`` if strategy is ddp.\n '
return any([isinstance(strategy, process_strategy) for process_strategy in process_independent_strategies])
|
def is_strategy_tpu(strategy: Any) -> bool:
'Test if strategy is tpu.\n\n Args:\n strategy: The strategy.\n\n Returns:\n ``True`` if strategy is tpu.\n '
return any([isinstance(strategy, tpu_strategy) for tpu_strategy in tpu_strategies])
|
def compile_model(model: LightningModule, do_compile: bool=False, fullgraph: bool=False, dynamic: bool=False, backend: Union[(str, Callable)]='inductor', mode: Union[(str, None)]=None, options: Optional[Dict[(str, Union[(str, int, bool)])]]=None, disable: bool=False):
"If torch version is greater than `'2.0.0'` a... |
def get_default_seed(default_seed: int=0) -> int:
'Get the default seed if pytorch lightning did not initialize one.\n\n Args:\n default_seed: The default seed.\n\n Returns:\n Pytorch lightning seed or the default one.\n '
return int(os.getenv('PL_GLOBAL_SEED', default_seed))
|
def get_global_rank() -> int:
'Get global rank of the process.'
if (dist.is_available() and dist.is_initialized()):
return dist.get_rank()
if (('RANK' in os.environ) and ('WORLD_SIZE' in os.environ)):
rank = int(os.environ['RANK'])
elif (int(os.environ.get('SLURM_NPROCS', 1)) > 1):
... |
def get_local_rank() -> int:
'Get local rank of the process.'
if (('RANK' in os.environ) and ('WORLD_SIZE' in os.environ)):
local_rank = int(os.environ['LOCAL_RANK'])
elif (int(os.environ.get('SLURM_NPROCS', 1)) > 1):
local_rank = int(os.environ['SLURM_LOCALID'])
else:
local_ra... |
def get_world_size() -> int:
'Get world size or number of the processes.'
if (dist.is_available() and dist.is_initialized()):
return dist.get_world_size()
if (('RANK' in os.environ) and ('WORLD_SIZE' in os.environ)):
world_size = int(os.environ['WORLD_SIZE'])
elif (int(os.environ.get('... |
def get_local_world_size() -> int:
'Get local world size or number of processes on the node.'
if (dist.is_available() and dist.is_initialized()):
return torch.cuda.device_count()
else:
return 1
|
def is_only_one_condition_true(*conditions: List[bool]) -> bool:
'Test if only one of the conditions is True.'
a = conditions[0]
b = conditions[0]
for condition in conditions[1:]:
a = (a ^ condition)
b = (b & condition)
return (a & (~ b))
|
def all_false(*conditions: List[bool]) -> bool:
'Test that all conditions are False.'
return all([(~ condition) for condition in conditions])
|
def warmup_value(initial_value: float, final_value: float, step: int=0, max_step: int=0) -> float:
'Apply warmup to a value.\n\n Args:\n initial_value: Initial value.\n final_value: Final value.\n step: Current step.\n max_step: Max step for warming up.\n\n Returns:\n The ... |
def scheduler_value(scheduler: Optional[str], initial_value: float, final_value: float, step: int=0, max_step: int=0) -> float:
'Apply scheduler to a value.\n\n Args:\n scheduler: The type of the scheduler.\n initial_value: The initial value.\n final_value: The final value.\n step: ... |
def apply_several_transforms(images: Iterable[Tensor], transforms: Iterable[Module]) -> List[List[Tensor]]:
'Apply several transformations to a list of images.\n\n Args:\n images: The images.\n transforms: The transformations to apply to the images.\n\n Returns:\n List of list of transf... |
def apply_several_video_transforms(videos: Iterable[Dict[(str, Any)]], transforms: Iterable[Module]) -> List[List[Tensor]]:
'Apply several transformations to a list of videos.\n\n Args:\n videos: The videos.\n transforms: The transformations to apply to the videos.\n\n Returns:\n List o... |
def make_grid_from_several_transforms(sets_images: Iterable[Iterable[Tensor]], n_images_per_row: int=8) -> Tensor:
'Make a grid of images by aligning images from several transformations vertically.\n\n Args:\n sets_images: Sets of transformed images aligned, base_image(sets_images[0][?]) == ... == base_... |
def make_several_transforms_from_config(cfg_transforms: Mapping[(Any, Any)]) -> List[Module]:
"Make several transformations from a configuration dictionary.\n\n Args:\n cfg_transforms: Configuration of transformations in the form {'transform1': {...}, 'transform2': {...}}.\n\n Returns:\n List ... |
def show_images(imgs: Union[(Iterable[Tensor], Tensor)], figsize: Iterable[float]=[6.4, 4.8]):
'Show images from a tensor or a list of tensor.\n\n Args:\n imgs: Images to display.\n figsize: Figure size for the images.\n '
if (not isinstance(imgs, list)):
imgs = [imgs]
(_, axs)... |
def show_video(video: Tensor) -> animation.ArtistAnimation:
'Show a video thanks to animation from matplotlib.\n\n Args:\n video: The raw video to display.\n\n Returns:\n The animation to show.\n '
video = video.long()
video = np.asarray(video.permute(1, 2, 3, 0))
(fig, ax) = pl... |
def main():
args = parser.parse_args()
soccernet_dataset(data_path=args.data_path, transform=None, video_path_prefix=args.path_prefix, decoder_args={'fps': args.fps}, features_args=None, label_args={'radius_label': args.radius_label, 'cache_dir': args.cache_dir}, task=SoccerNetTask(args.task))
|
def get_video_duration(video_file):
cmd = ['ffmpeg', '-i', str(video_file), '-f', 'null', '-']
try:
output = subprocess.check_output(cmd, stderr=subprocess.STDOUT)
except subprocess.CalledProcessError as err:
print(video_file, err.output)
return (- 1)
try:
output_decode... |
def has_video_stream(video_file):
cmd = ['ffprobe', '-i', str(video_file), '-show_streams', '-select_streams', 'v', '-loglevel', 'error']
try:
output = subprocess.check_output(cmd, stderr=subprocess.STDOUT)
except subprocess.CalledProcessError as err:
print(video_file, err.output)
... |
def is_video_empty(video_file):
return ((get_video_duration(video_file) <= 0) or (not has_video_stream(video_file)))
|
def process(row, folder_path, output_path, args):
classname = row[0]
videoname = row[1]
videostem = row[2]
inname = ((folder_path / classname) / videoname)
if is_video_empty(inname):
print(f'{inname} is empty.')
return (False, f'{inname} is empty.')
output_folder = (output_path... |
@hydra.main(config_path='../eztorch/configs/run/evaluation/feature_extractor/resnet3d50', config_name='resnet3d50_ucf101')
def main(config: DictConfig) -> None:
rundir = Path(to_absolute_path(config.dir.run))
rundir.mkdir(parents=True, exist_ok=True)
os.chdir(rundir)
rank_zero_info(f'Run directory: {r... |
@hydra.main(config_path='../eztorch/configs/run/evaluation/linear_classifier/sce/resnet50', config_name='resnet50_imagenet_mocov3')
def main(config: DictConfig) -> None:
rundir = Path(to_absolute_path(config.dir.run))
rundir.mkdir(parents=True, exist_ok=True)
os.chdir(rundir)
rank_zero_info(f'Run dire... |
@hydra.main(config_path='../eztorch/configs/run/pretrain/sce/resnet50', config_name='resnet50_imagenet')
def main(config: DictConfig) -> None:
rundir = Path(to_absolute_path(config.dir.run))
rundir.mkdir(parents=True, exist_ok=True)
os.chdir(rundir)
rank_zero_info(f'Run directory: {rundir}')
hydra... |
@hydra.main(config_path='../eztorch/configs/run/pretrain/moco', config_name='resnet18_cifar10')
def main(config: DictConfig) -> None:
rundir = Path(to_absolute_path(config.dir.run))
rundir.mkdir(parents=True, exist_ok=True)
os.chdir(rundir)
rank_zero_info(f'Run directory: {rundir}')
hydradir = (ru... |
@hydra.main(config_path='../eztorch/configs/run/evaluation/retrieval_from_bank', config_name='default')
def main(config: DictConfig) -> None:
rundir = Path(to_absolute_path(config.dir.run))
rundir.mkdir(parents=True, exist_ok=True)
os.chdir(rundir)
rank_zero_info(f'Run directory: {rundir}')
hydrad... |
@hydra.main(config_path='../eztorch/configs/run/supervised/resnet3d18', config_name='kinetics200')
def main(config: DictConfig) -> None:
rundir = Path(to_absolute_path(config.dir.run))
rundir.mkdir(parents=True, exist_ok=True)
os.chdir(rundir)
rank_zero_info(f'Run directory: {rundir}')
hydradir = ... |
@hydra.main(config_path='../eztorch/configs/run/supervised/resnet3d18', config_name='kinetics200')
def main(config: DictConfig) -> None:
rundir = Path(to_absolute_path(config.dir.run))
rundir.mkdir(parents=True, exist_ok=True)
os.chdir(rundir)
rank_zero_info(f'Run directory: {rundir}')
hydradir = ... |
@hydra.main(config_path='../eztorch/configs/run/supervised/resnet3d50', config_name='ucf101')
def main(config: DictConfig) -> None:
rundir = Path(to_absolute_path(config.dir.run))
rundir.mkdir(parents=True, exist_ok=True)
os.chdir(rundir)
rank_zero_info(f'Run directory: {rundir}')
hydradir = (rund... |
class TestFrameSoccerNetVideo(unittest.TestCase):
def setUp(self) -> None:
self.default_args = {'video_path': Path('/video/'), 'half_path': Path('/video/half1'), 'transform': None, 'video_frame_to_path_fn': get_video_to_frame_path_fn(zeros=8), 'num_threads_io': 0}
def test_same_fps_get_timestamps_in... |
class TestSoccerNetDataset(unittest.TestCase):
def test_soccernet_dataset(self):
dataset = soccernet_dataset((Path(os.path.realpath(__file__)).parent / 'small_annotations.json'), None, (Path(os.path.realpath(__file__)).parent / 'images'), label_args={'radius_label': 2, 'cache_dir': (Path(os.path.realpath... |
class TestSoccerNetPredictions(unittest.TestCase):
def test_aggregate_predictions(self):
predictions = torch.tensor([[0.5, 0.6], [0.4, 0.7], [0.7, 0.3], [0.3, 0.5], [0.8, 0.2], [0.9, 0.9]])
timestamps = torch.tensor([[0.0, 0.0], [1.0, 1.0], [1.0, 1.0], [2.0, 2.0], [8.0, 2.0], [8.0, 8.0]])
... |
class TestSCETokenMasks(unittest.TestCase):
def test_perform_hard_NMS(self):
values = torch.tensor([0.5, 1.0, 0.2, 0.3, 0.1, 0.2, 0.6])
window = 3
threshold = 0.49
keep_indexes = perform_hard_NMS(values, window, threshold)
expected_keep_indexes = torch.tensor([False, True,... |
class BoringDataModule(LightningDataModule):
def __init__(self, data_dir: str='./', dataset=RandomDataset((32, (64 * 4))), val_dataset=RandomDataset((32, (64 * 4))), batch_size: int=1):
super().__init__()
self.data_dir = data_dir
self.non_picklable = None
self.checkpoint_state: Op... |
class RandomDataset(Dataset):
def __init__(self, size: Iterable[int]):
self.length = size[0]
self.data = torch.randn(size)
def __getitem__(self, index):
return {'input': self.data[index], 'idx': index}
def __len__(self):
return self.length
|
class RandomLabeledDataset(Dataset):
def __init__(self, size: Iterable[int], num_classes: int=10):
self.length = size[0]
self.data = torch.randn(size)
self.labels = torch.randint(num_classes, size=(size[0], 1))
def __getitem__(self, index):
return {'input': self.data[index], ... |
class RandomVisionLabeledDataset(VisionDataset):
def __init__(self, size: Iterable[int], num_classes: int=10, transform: Optional[Module]=None):
super().__init__('data/', transform=transform)
self.length = size[0]
self.data = torch.randn(size)
self.labels = torch.randint(num_class... |
class BoringModel(LightningModule):
def __init__(self):
'Testing PL Module. Use as follows:\n\n - subclass\n - modify the behavior for what you want\n class TestModel(BaseTestModel):\n def training_step(...):\n # do your own thing\n or:\n model... |
class LargeBoringModel(LightningModule):
def __init__(self):
'Testing PL Module. Use as follows:\n\n - subclass\n - modify the behavior for what you want\n class TestModel(BaseTestModel):\n def training_step(...):\n # do your own thing\n or:\n ... |
class ManualOptimBoringModel(BoringModel):
def __init__(self):
super().__init__()
self.automatic_optimization = False
def training_step(self, batch, batch_idx):
opt = self.optimizers()
output = self(batch)
loss = self.loss(batch, output)
opt.zero_grad()
... |
class TestSCELoss(unittest.TestCase):
def setUp(self) -> None:
self.coeff = 0.5
self.temp = 0.1
self.temp_m = 0.07
def test_sce_loss_without_key(self):
q = torch.arange(1.0, 9.0, 1.0).view((4, 2))
k = torch.tensor([[0, 0], [1, 1], [0, 0], [1.0, 1.0]])
queue = ... |
class TestSCETokenMasks(unittest.TestCase):
def setUp(self) -> None:
self.batch_size = 2
self.num_tokens = 8
self.num_negatives = 2
def test_one_device_zero_pos_radius_no_keys_init(self):
(mask_sim_q, mask_sim_k, mask_prob_q, mask_log_q, num_positives_per_token) = compute_sce... |
class TestSCETokenLoss(unittest.TestCase):
def setUp(self) -> None:
self.batch_size = 2
self.num_tokens = 8
self.num_negatives = 2
self.dim = 4
self.query = torch.randn(((self.batch_size * self.num_tokens), self.dim))
self.key = torch.randn(((self.batch_size * self... |
class TestMoCoModel(unittest.TestCase):
def setUp(self) -> None:
self.trunk_cfg = DictConfig({'_target_': 'eztorch.models.trunks.create_resnet', 'name': 'resnet18', 'num_classes': 0, 'small_input': True})
self.projector_cfg = DictConfig({'_target_': 'eztorch.models.heads.MLPHead', 'input_dim': 51... |
class TestReSSLModel(unittest.TestCase):
def setUp(self) -> None:
self.trunk_cfg = DictConfig({'_target_': 'eztorch.models.trunks.create_resnet', 'name': 'resnet18', 'num_classes': 0, 'small_input': True})
self.projector_cfg = DictConfig({'_target_': 'eztorch.models.heads.MLPHead', 'input_dim': 5... |
class TestSCEModel(unittest.TestCase):
def setUp(self) -> None:
self.trunk_cfg = DictConfig({'_target_': 'eztorch.models.trunks.create_resnet', 'name': 'resnet18', 'num_classes': 0, 'small_input': True})
self.projector_cfg = DictConfig({'_target_': 'eztorch.models.heads.MLPHead', 'input_dim': 512... |
class TestSimCLRModel(unittest.TestCase):
def setUp(self) -> None:
self.trunk_cfg = DictConfig({'_target_': 'eztorch.models.trunks.create_resnet', 'name': 'resnet18', 'num_classes': 0, 'small_input': True})
self.projector_cfg = DictConfig({'_target_': 'eztorch.models.heads.MLPHead', 'input_dim': ... |
class TestResnet(unittest.TestCase):
def test_all_resnets(self):
for resnet in _ResNets:
create_resnet(resnet)
def test_resnet_small_input_with_fc(self):
resnet = create_resnet('resnet18', small_input=True)
assert isinstance(resnet.fc, nn.Linear)
assert (resnet.co... |
class TestOptimizerFactory(unittest.TestCase):
def test_init_lars(self):
model = BoringModel()
LARS(model.parameters(), lr=0.1)
|
class TestOptimizerFactory(unittest.TestCase):
def setUp(self):
self.base_model = BoringModel()
self.large_model = LargeBoringModel()
self.sgd_config = DictConfig({'name': 'sgd', 'initial_lr': 2.0, 'batch_size': None, 'num_steps_per_epoch': None, 'exclude_wd_norm': False, 'exclude_wd_bias... |
class TestFilterLearnableParmams(unittest.TestCase):
def test_filter_learnable_params(self) -> None:
boring_model = BoringModel()
large_boring_model = LargeBoringModel()
boring_model_params = list(boring_model.parameters())
filtered_boring_model_params = filter_learnable_params(bo... |
class TestLrScaler(unittest.TestCase):
def setUp(self) -> None:
self.initial_lr = 2.0
self.batch_size = 16
def test_none_scaler(self) -> None:
lr = scale_learning_rate(self.initial_lr, None, self.batch_size)
assert (lr == self.initial_lr)
lr = scale_learning_rate(self... |
class TestRetrieveModelParams(unittest.TestCase):
def setUp(self) -> None:
self.linear1 = nn.Linear(5, 5)
self.bn1 = nn.BatchNorm1d(5)
self.linear2 = nn.Linear(5, 5, bias=True)
self.model = nn.Sequential(self.linear1, self.bn1, self.linear2)
self.module_list = list(self.mo... |
class TestReducedTimestamps(unittest.TestCase):
def test_batch_reduced_timestamps(self):
x = torch.tensor([[[0.5, 0.8], [0.4, 0.6]], [[0.5, 0.8], [0.4, 0.6]], [[0.2, 0.8], [0.4, 0.6]], [[0.2, 0.8], [0.4, 0.6]], [[0.2, 0.8], [0.4, 0.6]], [[0.2, 0.8], [0.4, 0.6]]])
labels = torch.tensor([[[1.0, 0.0... |
class TestActionSpottingMixup(unittest.TestCase):
def test_mix_action_spotting(self):
x = torch.tensor([[[0.5, 0.8], [0.4, 0.6]], [[0.2, 0.8], [0.4, 0.6]], [[0.5, 0.8], [0.4, 0.5]], [[0.4, 0.2], [0.2, 0.5]]])
mix_value = torch.tensor([[[0.5]], [[0.3]], [[0.7]], [[0.2]]])
permutation = tor... |
class RSSMPrior(nn.Module):
c: Config
@nn.compact
def __call__(self, prev_state, context):
inputs = jnp.concatenate([prev_state['sample'], context], (- 1))
hl = nn.relu(nn.Dense(self.c.cell_embed_size)(inputs))
(det_state, det_out) = GRUCell()(prev_state['det_state'], hl)
... |
class RSSMPosterior(nn.Module):
c: Config
@nn.compact
def __call__(self, prior, obs_inputs):
inputs = jnp.concatenate([prior['det_out'], obs_inputs], (- 1))
hl = nn.relu(nn.Dense(self.c.cell_embed_size)(inputs))
hl = nn.relu(nn.Dense(self.c.cell_embed_size)(hl))
mean = nn.... |
class RSSMCell(nn.Module):
c: Config
@property
def state_size(self):
return dict(mean=self.c.cell_stoch_size, stddev=self.c.cell_stoch_size, sample=self.c.cell_stoch_size, det_out=self.c.cell_deter_size, det_state=self.c.cell_deter_size, output=(self.c.cell_stoch_size + self.c.cell_deter_size))
... |
class Encoder(nn.Module):
'\n Multi-level Video Encoder.\n 1. Extracts hierarchical features from a sequence of observations.\n 2. Encodes observations using Conv layers, uses them directly for the bottom-most level.\n 3. Uses dense features for each level of the hierarchy above the bottom-most level.... |
class Decoder(nn.Module):
' States to Images Decoder.'
c: Config
@nn.compact
def __call__(self, bottom_layer_output):
'\n Arguments:\n bottom_layer_output : Tensor\n State tensor of shape (batch_size, timesteps, feature_dim)\n\n Returns:\n Ou... |
def must_be(value):
return field(default=value, metadata=dict(choices=[value]))
|
@dataclass
class Config():
config: str
datadir: str
logdir: str
levels: int = 3
tmp_abs_factor: int = 6
dec_stddev: float = 1.0
enc_dense_layers: int = 3
enc_dense_embed_size: int = 1000
cell_stoch_size: int = 20
cell_deter_size: int = 200
cell_embed_size: int = 200
cel... |
def parse_config(eval=False):
p = ArgumentParser()
for f in fields(Config):
kwargs = (dict(action='store_true') if ((f.type is bool) and (not f.default)) else dict(default=f.default, type=f.type))
p.add_argument(f'--{f.name}', **kwargs, **f.metadata)
c = Config(**vars(p.parse_args()))
... |
class GqnMazes(tfds.core.GeneratorBasedBuilder):
'DatasetBuilder for GQN Mazes dataset.'
VERSION = tfds.core.Version('1.0.0')
RELEASE_NOTES = {'1.0.0': 'Initial release.'}
def _info(self) -> tfds.core.DatasetInfo:
'Returns the dataset metadata.'
return tfds.core.DatasetInfo(builder=se... |
class MovingMnist_2digit(tfds.core.GeneratorBasedBuilder):
'DatasetBuilder for Moving MNIST dataset.'
VERSION = tfds.core.Version('1.0.0')
RELEASE_NOTES = {'1.0.0': 'Initial release.'}
def _info(self) -> tfds.core.DatasetInfo:
'Returns the dataset metadata.'
return tfds.core.DatasetIn... |
class SSFetcher(threading.Thread):
def __init__(self, parent, init_offset=0, init_reshuffle_count=1, eos_sym=(- 1), skip_utterance=False, skip_utterance_predict_both=False):
threading.Thread.__init__(self)
self.parent = parent
self.rng = numpy.random.RandomState(self.parent.seed)
... |
class SSIterator(object):
def __init__(self, dialogue_file, batch_size, seed, max_len=(- 1), use_infinite_loop=True, init_offset=0, init_reshuffle_count=1, eos_sym=(- 1), skip_utterance=False, skip_utterance_predict_both=False):
self.dialogue_file = dialogue_file
self.batch_size = batch_size
... |
def sharedX(value, name=None, borrow=False, dtype=None):
if (dtype is None):
dtype = theano.config.floatX
return theano.shared(theano._asarray(value, dtype=dtype), name=name, borrow=borrow)
|
def Adam(grads, lr=0.0002, b1=0.1, b2=0.001, e=1e-08):
updates = []
varlist = []
i = sharedX(0.0)
i_t = (i + 1.0)
fix1 = (1.0 - ((1.0 - b1) ** i_t))
fix2 = (1.0 - ((1.0 - b2) ** i_t))
lr_t = (lr * (T.sqrt(fix2) / fix1))
for (p, g) in grads.items():
m = sharedX((p.get_value() * ... |
def safe_pickle(obj, filename):
if os.path.isfile(filename):
logger.info(('Overwriting %s.' % filename))
else:
logger.info(('Saving to %s.' % filename))
with open(filename, 'wb') as f:
cPickle.dump(obj, f, protocol=cPickle.HIGHEST_PROTOCOL)
|
class Model(object):
def __init__(self):
self.floatX = theano.config.floatX
self.params = []
def save(self, filename):
'\n Save the model to file `filename`\n '
vals = dict([(x.name, x.get_value()) for x in self.params])
numpy.savez(filename, **vals)
... |
class Timer(object):
def __init__(self):
self.total = 0
def start(self):
self.start_time = time.time()
def finish(self):
self.total += (time.time() - self.start_time)
|
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