index int64 0 50.7k | project_path stringclasses 279
values | completion_path stringlengths 57 199 | signature_postion stringlengths 3 11 | class_position stringlengths 3 11 | function_name stringlengths 1 62 | class_name stringlengths 0 55 | body_position stringlengths 3 11 | body_position_with_comments stringlengths 3 11 | masked_class stringlengths 14 8.32k | masked_class_with_comment stringlengths 14 8.32k | func_body stringlengths 9 6.72k | func_body_with_comment stringlengths 9 6.72k | len_func_body int64 1 643 | len_input int64 10 2.02k | len_output int64 4 1.9k | total int64 20 2.05k | file_code stringlengths 14 442k | file_code_with_comment stringlengths 14 442k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1,267 | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning/src/lightning/pytorch/plugins/io/async_plugin.py | 40,42 | 24,66 | save_checkpoint | AsyncCheckpointIO | 43,58 | 42,58 | class AsyncCheckpointIO(_WrappingCheckpointIO):
"""``AsyncCheckpointIO`` enables saving the checkpoints asynchronously in a thread.
.. warning:: This is an :ref:`experimental <versioning:Experimental API>` feature.
Args:
checkpoint_io: A checkpoint IO plugin that is used as the basis for async ch... | class AsyncCheckpointIO(_WrappingCheckpointIO):
"""``AsyncCheckpointIO`` enables saving the checkpoints asynchronously in a thread.
.. warning:: This is an :ref:`experimental <versioning:Experimental API>` feature.
Args:
checkpoint_io: A checkpoint IO plugin that is used as the basis for async ch... |
def _save_checkpoint(*args: Any, **kwargs: Any) -> None:
try:
assert self.checkpoint_io is not None
self.checkpoint_io.save_checkpoint(*args, **kwargs)
except BaseException as ex:
self._error = ex
self._executor.submit(_save_check... | """Uses the ``ThreadPoolExecutor`` to save the checkpoints using the base ``checkpoint_io``."""
def _save_checkpoint(*args: Any, **kwargs: Any) -> None:
try:
assert self.checkpoint_io is not None
self.checkpoint_io.save_checkpoint(*args, **kwargs)
... | 50 | 310 | 160 | 470 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... |
1,268 | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning/src/lightning/pytorch/plugins/io/wrapper.py | 68,70 | 21,73 | load_checkpoint | _WrappingCheckpointIO | 71,73 | 70,73 | class _WrappingCheckpointIO(CheckpointIO):
"""``_WrappingCheckpointIO`` is a wrapper checkpoint_io that uses a base checkpoint_io to handle checkpointing.
Args:
checkpoint_io: A checkpoint IO plugin that is used as the basis.
"""
def __init__(self, checkpoint_io: Optional["CheckpointIO"] = No... | class _WrappingCheckpointIO(CheckpointIO):
"""``_WrappingCheckpointIO`` is a wrapper checkpoint_io that uses a base checkpoint_io to handle checkpointing.
Args:
checkpoint_io: A checkpoint IO plugin that is used as the basis.
"""
def __init__(self, checkpoint_io: Optional["CheckpointIO"] = No... | assert self.checkpoint_io is not None
return self.checkpoint_io.load_checkpoint(*args, **kwargs)
| """Uses the base ``checkpoint_io`` to load the checkpoint."""
assert self.checkpoint_io is not None
return self.checkpoint_io.load_checkpoint(*args, **kwargs)
| 8 | 636 | 34 | 670 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... |
1,269 | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning/src/lightning/pytorch/plugins/layer_sync.py | 59,61 | 36,94 | revert | TorchSyncBatchNorm | 70,94 | 61,94 | class TorchSyncBatchNorm(LayerSync):
"""A plugin that wraps all batch normalization layers of a model with synchronization logic for multiprocessing.
This plugin has no effect in single-device operation.
"""
@override
def apply(self, model: Module) -> Module:
"""Add global batchnorm for a... | class TorchSyncBatchNorm(LayerSync):
"""A plugin that wraps all batch normalization layers of a model with synchronization logic for multiprocessing.
This plugin has no effect in single-device operation.
"""
@override
def apply(self, model: Module) -> Module:
"""Add global batchnorm for a... | # Code adapted from https://github.com/pytorch/pytorch/issues/41081#issuecomment-783961547
# Original author: Kapil Yedidi (@kapily)
converted_module = model
if isinstance(model, torch.nn.modules.batchnorm.SyncBatchNorm):
# Unfortunately, LayerSync does not store the original... | """Convert the wrapped batchnorm layers back to regular batchnorm layers.
Args:
model: Reference to the current LightningModule
Return:
LightningModule with regular batchnorm layers that will no longer sync across processes.
"""
# Code adapted from http... | 85 | 273 | 350 | 623 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... |
1,270 | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning/src/lightning/pytorch/plugins/layer_sync.py | 95,97 | 94,102 | _check_input_dim | _BatchNormXd | 97,102 | 97,102 | class _BatchNormXd(torch.nn.modules.batchnorm._BatchNorm):
@override
def _check_input_dim(self, input: Tensor) -> None:
FILL_FUNC_BODY | class _BatchNormXd(torch.nn.modules.batchnorm._BatchNorm):
@override
def _check_input_dim(self, input: Tensor) -> None:
FILL_FUNC_BODY | # The only difference between BatchNorm1d, BatchNorm2d, BatchNorm3d, etc
# is this method that is overwritten by the subclass.
# Here, we are bypassing some tensor sanity checks and trusting that the user
# provides the right input dimensions at inference.
return
| # The only difference between BatchNorm1d, BatchNorm2d, BatchNorm3d, etc
# is this method that is overwritten by the subclass.
# Here, we are bypassing some tensor sanity checks and trusting that the user
# provides the right input dimensions at inference.
return
| 42 | 57 | 80 | 137 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... |
1,271 | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning/src/lightning/pytorch/plugins/precision/amp.py | 58,60 | 28,132 | pre_backward | MixedPrecision | 60,64 | 60,64 | class MixedPrecision(Precision):
"""Plugin for Automatic Mixed Precision (AMP) training with ``torch.autocast``.
Args:
precision: Whether to use ``torch.float16`` (``'16-mixed'``) or ``torch.bfloat16`` (``'bf16-mixed'``).
device: The device for ``torch.autocast``.
scaler: An optional :c... | class MixedPrecision(Precision):
"""Plugin for Automatic Mixed Precision (AMP) training with ``torch.autocast``.
Args:
precision: Whether to use ``torch.float16`` (``'16-mixed'``) or ``torch.bfloat16`` (``'bf16-mixed'``).
device: The device for ``torch.autocast``.
scaler: An optional :c... | if self.scaler is not None:
tensor = self.scaler.scale(tensor)
return super().pre_backward(tensor, module)
| if self.scaler is not None:
tensor = self.scaler.scale(tensor)
return super().pre_backward(tensor, module)
| 11 | 1,347 | 43 | 1,390 | # Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under th... | # Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under th... |
1,272 | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning/src/lightning/pytorch/plugins/precision/deepspeed.py | 82,84 | 40,149 | tensor_init_context | DeepSpeedPrecision | 84,88 | 84,88 | class DeepSpeedPrecision(Precision):
"""Precision plugin for DeepSpeed integration.
.. warning:: This is an :ref:`experimental <versioning:Experimental API>` feature.
Args:
precision: Full precision (32-true), half precision (16-true, bf16-true) or
mixed precision (16-mixed, bf16-mixe... | class DeepSpeedPrecision(Precision):
"""Precision plugin for DeepSpeed integration.
.. warning:: This is an :ref:`experimental <versioning:Experimental API>` feature.
Args:
precision: Full precision (32-true), half precision (16-true, bf16-true) or
mixed precision (16-mixed, bf16-mixe... | if "true" not in self.precision:
return nullcontext()
return _DtypeContextManager(self._desired_dtype)
| if "true" not in self.precision:
return nullcontext()
return _DtypeContextManager(self._desired_dtype)
| 9 | 1,197 | 41 | 1,238 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... |
1,273 | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning/src/lightning/pytorch/plugins/precision/double.py | 58,60 | 30,63 | convert_input | DoublePrecision | 60,63 | 60,63 | class DoublePrecision(Precision):
"""Plugin for training with double (``torch.float64``) precision."""
precision: Literal["64-true"] = "64-true"
@override
def convert_module(self, module: nn.Module) -> nn.Module:
return module.double()
@override
def tensor_init_context(self) -> Contex... | class DoublePrecision(Precision):
"""Plugin for training with double (``torch.float64``) precision."""
precision: Literal["64-true"] = "64-true"
@override
def convert_module(self, module: nn.Module) -> nn.Module:
return module.double()
@override
def tensor_init_context(self) -> Contex... | return apply_to_collection(data, function=_convert_fp_tensor, dtype=Tensor, dst_type=torch.double)
| return apply_to_collection(data, function=_convert_fp_tensor, dtype=Tensor, dst_type=torch.double)
| 5 | 270 | 41 | 311 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... |
1,274 | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning/src/lightning/pytorch/plugins/precision/double.py | 90,91 | 63,120 | training_step | LightningDoublePrecisionModule | 91,96 | 91,96 | class LightningDoublePrecisionModule(_DeviceDtypeModuleMixin, nn.Module):
"""LightningModule wrapper which converts incoming floating point data in ``*_step`` and ``forward`` to double
(``torch.float64``) precision.
.. deprecated:: Use :meth:`~lightning.pytorch.core.hooks.ModelHooks.configure_model` instea... | class LightningDoublePrecisionModule(_DeviceDtypeModuleMixin, nn.Module):
"""LightningModule wrapper which converts incoming floating point data in ``*_step`` and ``forward`` to double
(``torch.float64``) precision.
.. deprecated:: Use :meth:`~lightning.pytorch.core.hooks.ModelHooks.configure_model` instea... | return self.module.training_step(
*LightningDoublePrecisionModule._move_float_tensors_to_double(args),
**LightningDoublePrecisionModule._move_float_tensors_to_double(kwargs),
)
| return self.module.training_step(
*LightningDoublePrecisionModule._move_float_tensors_to_double(args),
**LightningDoublePrecisionModule._move_float_tensors_to_double(kwargs),
)
| 5 | 767 | 68 | 835 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... |
1,275 | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning/src/lightning/pytorch/plugins/precision/fsdp.py | 85,87 | 34,178 | mixed_precision_config | FSDPPrecision | 87,111 | 87,111 | class FSDPPrecision(Precision):
"""Precision plugin for training with Fully Sharded Data Parallel (FSDP).
.. warning:: This is an :ref:`experimental <versioning:Experimental API>` feature.
Args:
precision: Full precision (32-true), half precision (16-true, bf16-true) or
mixed precisio... | class FSDPPrecision(Precision):
"""Precision plugin for training with Fully Sharded Data Parallel (FSDP).
.. warning:: This is an :ref:`experimental <versioning:Experimental API>` feature.
Args:
precision: Full precision (32-true), half precision (16-true, bf16-true) or
mixed precisio... | from torch.distributed.fsdp.fully_sharded_data_parallel import MixedPrecision as TorchMixedPrecision
if self.precision == "16-mixed":
param_dtype = torch.float32
reduce_dtype = buffer_dtype = torch.float16
elif self.precision == "bf16-mixed":
param_dtype = to... | from torch.distributed.fsdp.fully_sharded_data_parallel import MixedPrecision as TorchMixedPrecision
if self.precision == "16-mixed":
param_dtype = torch.float32
reduce_dtype = buffer_dtype = torch.float16
elif self.precision == "bf16-mixed":
param_dtype = to... | 80 | 1,666 | 357 | 2,023 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... |
1,276 | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning/src/lightning/pytorch/plugins/precision/half.py | 53,56 | 27,71 | forward_context | HalfPrecision | 61,68 | 56,68 | class HalfPrecision(Precision):
"""Plugin for training with half precision.
Args:
precision: Whether to use ``torch.float16`` (``'16-true'``) or ``torch.bfloat16`` (``'bf16-true'``).
"""
precision: Literal["bf16-true", "16-true"] = "16-true"
def __init__(self, precision: Literal["bf16-tr... | class HalfPrecision(Precision):
"""Plugin for training with half precision.
Args:
precision: Whether to use ``torch.float16`` (``'16-true'``) or ``torch.bfloat16`` (``'bf16-true'``).
"""
precision: Literal["bf16-true", "16-true"] = "16-true"
def __init__(self, precision: Literal["bf16-tr... | default_dtype = torch.get_default_dtype()
torch.set_default_dtype(self._desired_input_dtype)
try:
yield
finally:
torch.set_default_dtype(default_dtype)
| """A context manager to change the default tensor type when tensors get created during the module's forward.
See: :meth:`torch.set_default_tensor_type`
"""
default_dtype = torch.get_default_dtype()
torch.set_default_dtype(self._desired_input_dtype)
try:
yiel... | 8 | 455 | 75 | 530 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... |
1,277 | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning/src/lightning/pytorch/plugins/precision/precision.py | 124,131 | 32,190 | _clip_gradients | Precision | 131,143 | 131,143 | class Precision(FabricPrecision, CheckpointHooks):
"""Base class for all plugins handling the precision-specific parts of the training.
The class attribute precision must be overwritten in child classes. The default value reflects fp32 training.
"""
def connect(
self, model: Module, optimizer... | class Precision(FabricPrecision, CheckpointHooks):
"""Base class for all plugins handling the precision-specific parts of the training.
The class attribute precision must be overwritten in child classes. The default value reflects fp32 training.
"""
def connect(
self, model: Module, optimizer... | if not isinstance(model, pl.LightningModule) or not model.automatic_optimization:
# the configuration validator disallows clipping on manual
return
call._call_lightning_module_hook(
model.trainer,
"configure_gradient_clipping",
optimizer,
... | if not isinstance(model, pl.LightningModule) or not model.automatic_optimization:
# the configuration validator disallows clipping on manual
return
call._call_lightning_module_hook(
model.trainer,
"configure_gradient_clipping",
optimizer,
... | 23 | 1,666 | 120 | 1,786 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... |
1,278 | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning/src/lightning/pytorch/profilers/advanced.py | 65,67 | 30,100 | start | AdvancedProfiler | 67,71 | 67,71 | class AdvancedProfiler(Profiler):
"""This profiler uses Python's cProfiler to record more detailed information about time spent in each function call
recorded during a given action.
The output is quite verbose and you should only use this if you want very detailed reports.
"""
def __init__(
... | class AdvancedProfiler(Profiler):
"""This profiler uses Python's cProfiler to record more detailed information about time spent in each function call
recorded during a given action.
The output is quite verbose and you should only use this if you want very detailed reports.
"""
def __init__(
... | if action_name not in self.profiled_actions:
self.profiled_actions[action_name] = cProfile.Profile()
self.profiled_actions[action_name].enable()
| if action_name not in self.profiled_actions:
self.profiled_actions[action_name] = cProfile.Profile()
self.profiled_actions[action_name].enable()
| 9 | 776 | 57 | 833 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... |
1,279 | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning/src/lightning/pytorch/profilers/profiler.py | 105,106 | 28,152 | describe | Profiler | 107,118 | 106,118 | class Profiler(ABC):
"""If you wish to write a custom profiler, you should inherit from this class."""
def __init__(
self,
dirpath: Optional[Union[str, Path]] = None,
filename: Optional[str] = None,
) -> None:
self.dirpath = dirpath
self.filename = filename
... | class Profiler(ABC):
"""If you wish to write a custom profiler, you should inherit from this class."""
def __init__(
self,
dirpath: Optional[Union[str, Path]] = None,
filename: Optional[str] = None,
) -> None:
self.dirpath = dirpath
self.filename = filename
... | # users might call `describe` directly as the profilers can be used by themselves.
# to allow this, we open and close the files within this function by calling `_prepare_streams` and `teardown`
# manually instead of letting the `Trainer` do it through `setup` and `teardown`
self._prepare... | """Logs a profile report after the conclusion of run."""
# users might call `describe` directly as the profilers can be used by themselves.
# to allow this, we open and close the files within this function by calling `_prepare_streams` and `teardown`
# manually instead of letting the `Tr... | 64 | 1,155 | 167 | 1,322 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... |
1,281 | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning/src/lightning/pytorch/profilers/pytorch.py | 70,71 | 46,102 | _start_recording_forward | RegisterRecordFunction | 71,77 | 71,77 | class RegisterRecordFunction:
"""While profiling autograd operations, this class will add labels for module names around the forward function.
The Lightning PyTorch Profiler will activate this feature automatically. It can be deactivated as follows:
Example::
from lightning.pytorch.profilers impor... | class RegisterRecordFunction:
"""While profiling autograd operations, this class will add labels for module names around the forward function.
The Lightning PyTorch Profiler will activate this feature automatically. It can be deactivated as follows:
Example::
from lightning.pytorch.profilers impor... | # Add [pl][module] in name for pytorch profiler to recognize
record = record_function("[pl][module]" + record_name)
record.__enter__()
self._records[record_name] = record
return input
| # Add [pl][module] in name for pytorch profiler to recognize
record = record_function("[pl][module]" + record_name)
record.__enter__()
self._records[record_name] = record
return input
| 21 | 597 | 68 | 665 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... |
1,282 | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning/src/lightning/pytorch/profilers/pytorch.py | 165,166 | 102,217 | _step | ScheduleWrapper | 166,180 | 166,180 | class ScheduleWrapper:
"""This class is used to override the schedule logic from the profiler and perform recording for both
`training_step`, `validation_step`."""
def __init__(self, schedule: Callable) -> None:
if not _KINETO_AVAILABLE:
raise ModuleNotFoundError("You are trying to use ... | class ScheduleWrapper:
"""This class is used to override the schedule logic from the profiler and perform recording for both
`training_step`, `validation_step`."""
def __init__(self, schedule: Callable) -> None:
if not _KINETO_AVAILABLE:
raise ModuleNotFoundError("You are trying to use ... | if self.is_training:
self._num_training_step += 1
elif self.is_validating:
assert self._start_action_name is not None
if self._start_action_name.endswith("on_fit_start"):
if self._num_training_step > 0:
self._num_validation_step += ... | if self.is_training:
self._num_training_step += 1
elif self.is_validating:
assert self._start_action_name is not None
if self._start_action_name.endswith("on_fit_start"):
if self._num_training_step > 0:
self._num_validation_step += ... | 35 | 1,138 | 160 | 1,298 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... |
1,283 | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning/src/lightning/pytorch/profilers/simple.py | 84,85 | 36,164 | _make_report_extended | SimpleProfiler | 85,100 | 85,100 | class SimpleProfiler(Profiler):
"""This profiler simply records the duration of actions (in seconds) and reports the mean duration of each action
and the total time spent over the entire training run."""
def __init__(
self,
dirpath: Optional[Union[str, Path]] = None,
filename: Optio... | class SimpleProfiler(Profiler):
"""This profiler simply records the duration of actions (in seconds) and reports the mean duration of each action
and the total time spent over the entire training run."""
def __init__(
self,
dirpath: Optional[Union[str, Path]] = None,
filename: Optio... | total_duration = time.monotonic() - self.start_time
report = []
for a, d in self.recorded_durations.items():
d_tensor = torch.tensor(d)
len_d = len(d)
sum_d = torch.sum(d_tensor).item()
percentage_d = 100.0 * sum_d / total_duration
re... | total_duration = time.monotonic() - self.start_time
report = []
for a, d in self.recorded_durations.items():
d_tensor = torch.tensor(d)
len_d = len(d)
sum_d = torch.sum(d_tensor).item()
percentage_d = 100.0 * sum_d / total_duration
re... | 51 | 1,511 | 197 | 1,708 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... |
1,284 | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning/src/lightning/pytorch/serve/servable_module.py | 74,76 | 8,95 | serve_step | ServableModule | 76,92 | 76,92 | class ServableModule(ABC, torch.nn.Module):
"""The ServableModule provides a simple API to make your model servable.
.. warning:: This is an :ref:`experimental <versioning:Experimental API>` feature.
Here is an example of how to use the ``ServableModule`` module.
.. code-block:: python
from... | class ServableModule(ABC, torch.nn.Module):
"""The ServableModule provides a simple API to make your model servable.
.. warning:: This is an :ref:`experimental <versioning:Experimental API>` feature.
Here is an example of how to use the ``ServableModule`` module.
.. code-block:: python
from... | r"""Returns the predictions of your model as a dictionary.
.. code-block:: python
def serve_step(self, x: torch.Tensor) -> Dict[str, torch.Tensor]:
return {"predictions": self(x)}
Args:
args: The output from de-serializer functions provided by the ``con... | r"""Returns the predictions of your model as a dictionary.
.. code-block:: python
def serve_step(self, x: torch.Tensor) -> Dict[str, torch.Tensor]:
return {"predictions": self(x)}
Args:
args: The output from de-serializer functions provided by the ``con... | 58 | 752 | 162 | 914 | from abc import ABC, abstractmethod
from typing import Any, Callable, Dict, Tuple
import torch
from torch import Tensor
class ServableModule(ABC, torch.nn.Module):
"""The ServableModule provides a simple API to make your model servable.
.. warning:: This is an :ref:`experimental <versioning:Experimental AP... | from abc import ABC, abstractmethod
from typing import Any, Callable, Dict, Tuple
import torch
from torch import Tensor
class ServableModule(ABC, torch.nn.Module):
"""The ServableModule provides a simple API to make your model servable.
.. warning:: This is an :ref:`experimental <versioning:Experimental AP... |
1,285 | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning/src/lightning/pytorch/serve/servable_module_validator.py | 142,144 | 28,178 | _start_server | ServableModuleValidator | 145,178 | 144,178 | class ServableModuleValidator(Callback):
"""The ServableModuleValidator validates to validate a model correctly implement the ServableModule API.
.. warning:: This is an :ref:`experimental <versioning:Experimental API>` feature.
Arguments:
optimization: The format in which the model should be tes... | class ServableModuleValidator(Callback):
"""The ServableModuleValidator validates to validate a model correctly implement the ServableModule API.
.. warning:: This is an :ref:`experimental <versioning:Experimental API>` feature.
Arguments:
optimization: The format in which the model should be tes... | from fastapi import Body, FastAPI
from uvicorn import run
app = FastAPI()
deserializers, serializers = servable_model.configure_serialization()
# Note: This isn't the original version, but a copy.
servable_model.eval()
@app.get("/ping")
def ping() -> b... | """This method starts a server with a serve and ping endpoints."""
from fastapi import Body, FastAPI
from uvicorn import run
app = FastAPI()
deserializers, serializers = servable_model.configure_serialization()
# Note: This isn't the original version, but a copy.
... | 88 | 1,490 | 318 | 1,808 | import contextlib
import logging
import time
from multiprocessing import Process
from typing import Any, Dict, Literal, Optional
import requests
import torch
from lightning_utilities.core.imports import RequirementCache
from typing_extensions import override
import lightning.pytorch as pl
from lightning.pytorch.callb... | import contextlib
import logging
import time
from multiprocessing import Process
from typing import Any, Dict, Literal, Optional
import requests
import torch
from lightning_utilities.core.imports import RequirementCache
from typing_extensions import override
import lightning.pytorch as pl
from lightning.pytorch.callb... |
1,287 | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning/src/lightning/pytorch/strategies/ddp.py | 423,425 | 422,434 | on_after_inner_forward | _DDPForwardRedirection | 425,430 | 425,430 | class _DDPForwardRedirection(_ForwardRedirection):
@override
def on_after_inner_forward(self, wrapper_module: Module, original_module: "pl.LightningModule") -> None:
FILL_FUNC_BODY @override
def on_after_outer_forward(self, wrapper_module: Module, original_module: "pl.LightningModule") -> None:
i... | class _DDPForwardRedirection(_ForwardRedirection):
@override
def on_after_inner_forward(self, wrapper_module: Module, original_module: "pl.LightningModule") -> None:
FILL_FUNC_BODY @override
def on_after_outer_forward(self, wrapper_module: Module, original_module: "pl.LightningModule") -> None:
i... | # In manual_optimization, we need to prevent DDP reducer as
# it is done manually in `LightningModule.manual_backward`
if isinstance(wrapper_module, DistributedDataParallel) and not original_module.automatic_optimization:
wrapper_module.require_backward_grad_sync = False
| # In manual_optimization, we need to prevent DDP reducer as
# it is done manually in `LightningModule.manual_backward`
if isinstance(wrapper_module, DistributedDataParallel) and not original_module.automatic_optimization:
wrapper_module.require_backward_grad_sync = False
| 26 | 147 | 87 | 234 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... |
1,289 | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning/src/lightning/pytorch/strategies/launchers/multiprocessing.py | 304,306 | 280,321 | capture | _GlobalStateSnapshot | 307,314 | 306,314 | @dataclass
class _GlobalStateSnapshot:
"""Captures a hand-selected set of (global) variables in modules and provides a way to restore them.
It facilitates and encapsulates the transfer of globals like PyTorch's deterministic flags or random generator state
across process boundaries when launching processes... | @dataclass
class _GlobalStateSnapshot:
"""Captures a hand-selected set of (global) variables in modules and provides a way to restore them.
It facilitates and encapsulates the transfer of globals like PyTorch's deterministic flags or random generator state
across process boundaries when launching processes... | return cls(
use_deterministic_algorithms=torch.are_deterministic_algorithms_enabled(),
use_deterministic_algorithms_warn_only=torch.is_deterministic_algorithms_warn_only_enabled(),
cudnn_benchmark=torch.backends.cudnn.benchmark,
rng_states=_collect_rng_states(),
... | """Capture a few global states from torch, numpy, etc., that we want to restore in a spawned worker process."""
return cls(
use_deterministic_algorithms=torch.are_deterministic_algorithms_enabled(),
use_deterministic_algorithms_warn_only=torch.is_deterministic_algorithms_warn_onl... | 7 | 379 | 112 | 491 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... |
1,291 | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning/src/lightning/pytorch/strategies/parallel.py | 81,83 | 30,134 | distributed_sampler_kwargs | ParallelStrategy | 83,88 | 83,88 | class ParallelStrategy(Strategy, ABC):
"""Strategy for training with multiple processes in parallel."""
def __init__(
self,
accelerator: Optional["pl.accelerators.Accelerator"] = None,
parallel_devices: Optional[List[torch.device]] = None,
cluster_environment: Optional[ClusterEn... | class ParallelStrategy(Strategy, ABC):
"""Strategy for training with multiple processes in parallel."""
def __init__(
self,
accelerator: Optional["pl.accelerators.Accelerator"] = None,
parallel_devices: Optional[List[torch.device]] = None,
cluster_environment: Optional[ClusterEn... | return {
"num_replicas": len(self.parallel_devices) if self.parallel_devices is not None else 0,
"rank": self.global_rank,
}
| return {
"num_replicas": len(self.parallel_devices) if self.parallel_devices is not None else 0,
"rank": self.global_rank,
}
| 14 | 1,060 | 54 | 1,114 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... |
1,292 | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning/src/lightning/pytorch/strategies/single_device.py | 76,78 | 30,102 | model_to_device | SingleDeviceStrategy | 78,81 | 78,81 | class SingleDeviceStrategy(Strategy):
"""Strategy that handles communication on a single device."""
strategy_name = "single_device"
def __init__(
self,
device: _DEVICE = "cpu",
accelerator: pl.accelerators.accelerator.Accelerator | None = None,
checkpoint_io: CheckpointIO |... | class SingleDeviceStrategy(Strategy):
"""Strategy that handles communication on a single device."""
strategy_name = "single_device"
def __init__(
self,
device: _DEVICE = "cpu",
accelerator: pl.accelerators.accelerator.Accelerator | None = None,
checkpoint_io: CheckpointIO |... | assert self.model is not None, "self.model must be set before self.model.to()"
self.model.to(self.root_device)
| assert self.model is not None, "self.model must be set before self.model.to()"
self.model.to(self.root_device)
| 12 | 627 | 42 | 669 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... |
1,294 | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning/src/lightning/pytorch/strategies/strategy.py | 627,628 | 601,649 | wrapped_forward | _ForwardRedirection | 628,638 | 628,638 | class _ForwardRedirection:
"""Implements the `forward-redirection`.
A method call to a wrapped module gets rerouted through the wrapper's `forward` method instead.
"""
def __call__(
self, wrapper_module: Module, original_module: "pl.LightningModule", method_name: str, *args: Any, **kwargs: An... | class _ForwardRedirection:
"""Implements the `forward-redirection`.
A method call to a wrapped module gets rerouted through the wrapper's `forward` method instead.
"""
def __call__(
self, wrapper_module: Module, original_module: "pl.LightningModule", method_name: str, *args: Any, **kwargs: An... | # Unpatch ourselves immediately before calling the method `method_name`
# because itself may want to call the real `forward`
original_module.forward = original_forward # type: ignore[method-assign]
# Call the actual method e.g. `.training_step(...)`
method = ... | # Unpatch ourselves immediately before calling the method `method_name`
# because itself may want to call the real `forward`
original_module.forward = original_forward # type: ignore[method-assign]
# Call the actual method e.g. `.training_step(...)`
method = ... | 60 | 477 | 156 | 633 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... |
1,301 | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning/src/lightning/pytorch/trainer/connectors/data_connector.py | 349,350 | 328,375 | get_instance | _DataHookSelector | 350,375 | 350,375 | @dataclass
class _DataHookSelector:
"""Stores the info about the shared DataHooks within ``LightningModule`` and ``LightningDataModule``.
The hook source can be:
1. the :class:`~lightning.pytorch.core.LightningModule`,
2. the :class:`~lightning.pytorch.core.datamodule.LightningDataModule`,
Argume... | @dataclass
class _DataHookSelector:
"""Stores the info about the shared DataHooks within ``LightningModule`` and ``LightningDataModule``.
The hook source can be:
1. the :class:`~lightning.pytorch.core.LightningModule`,
2. the :class:`~lightning.pytorch.core.datamodule.LightningDataModule`,
Argume... | if hook_name not in self._valid_hooks:
raise ValueError(
f"`{hook_name}` is not a shared hook within `LightningModule` and `LightningDataModule`."
f" Valid hooks are {self._valid_hooks}."
)
if self.datamodule is None:
return self.model... | if hook_name not in self._valid_hooks:
raise ValueError(
f"`{hook_name}` is not a shared hook within `LightningModule` and `LightningDataModule`."
f" Valid hooks are {self._valid_hooks}."
)
if self.datamodule is None:
return self.model... | 85 | 266 | 309 | 575 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... |
1,302 | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning/src/lightning/pytorch/trainer/connectors/data_connector.py | 282,283 | 262,313 | dataloader | _DataLoaderSource | 288,296 | 283,296 | @dataclass
class _DataLoaderSource:
"""Stores the information where the dataloaders come from.
The source can be
1. from a ``*_dataloader()`` method on the :class:`~lightning.pytorch.core.LightningModule`,
2. from a ``*_dataloader()`` method on the :class:`~lightning.pytorch.core.datamodule.LightningD... | @dataclass
class _DataLoaderSource:
"""Stores the information where the dataloaders come from.
The source can be
1. from a ``*_dataloader()`` method on the :class:`~lightning.pytorch.core.LightningModule`,
2. from a ``*_dataloader()`` method on the :class:`~lightning.pytorch.core.datamodule.LightningD... | if isinstance(self.instance, pl.LightningModule):
return call._call_lightning_module_hook(self.instance.trainer, self.name, pl_module=self.instance)
if isinstance(self.instance, pl.LightningDataModule):
assert self.instance.trainer is not None
return call._call_lightn... | """Returns the dataloader from the source.
If the source is a module, the method with the corresponding :attr:`name` gets called.
"""
if isinstance(self.instance, pl.LightningModule):
return call._call_lightning_module_hook(self.instance.trainer, self.name, pl_module=self.i... | 25 | 530 | 130 | 660 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... |
1,305 | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning/src/lightning/pytorch/trainer/connectors/logger_connector/result.py | 175,177 | 106,184 | is_min_reduction | _Metadata | 177,179 | 177,179 | @dataclass
class _Metadata:
fx: str
name: str
prog_bar: bool = False
logger: bool = True
on_step: bool = False
on_epoch: bool = True
# https://github.com/pytorch/pytorch/issues/96197
reduce_fx: Callable = "mean" if _TORCH_EQUAL_2_0 else torch.mean # type: ignore[assignment]
enable_g... | @dataclass
class _Metadata:
fx: str
name: str
prog_bar: bool = False
logger: bool = True
on_step: bool = False
on_epoch: bool = True
# https://github.com/pytorch/pytorch/issues/96197
reduce_fx: Callable = "mean" if _TORCH_EQUAL_2_0 else torch.mean # type: ignore[assignment]
enable_g... | return self.reduce_fx in (torch.min, min)
| return self.reduce_fx in (torch.min, min)
| 5 | 857 | 21 | 878 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... |
1,307 | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning/src/lightning/pytorch/trainer/connectors/logger_connector/result.py | 276,278 | 184,313 | wrapped_func | _ResultMetric | 278,292 | 278,292 | class _ResultMetric(Metric):
"""Wraps the value provided to `:meth:`~lightning.pytorch.core.LightningModule.log`"""
def __init__(self, metadata: _Metadata, is_tensor: bool) -> None:
super().__init__()
self.is_tensor = is_tensor
self.meta = metadata
self.has_reset = False
... | class _ResultMetric(Metric):
"""Wraps the value provided to `:meth:`~lightning.pytorch.core.LightningModule.log`"""
def __init__(self, metadata: _Metadata, is_tensor: bool) -> None:
super().__init__()
self.is_tensor = is_tensor
self.meta = metadata
self.has_reset = False
... | update_called = self.update_called if _TORCHMETRICS_GREATER_EQUAL_1_0_0 else self._update_called
if not update_called:
rank_zero_warn(
f"The ``compute`` method of metric {self.__class__.__name__}"
" was called before the ``update`` method w... | update_called = self.update_called if _TORCHMETRICS_GREATER_EQUAL_1_0_0 else self._update_called
if not update_called:
rank_zero_warn(
f"The ``compute`` method of metric {self.__class__.__name__}"
" was called before the ``update`` method w... | 56 | 1,469 | 170 | 1,639 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... |
1,308 | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning/src/lightning/pytorch/trainer/connectors/logger_connector/result.py | 64,66 | 49,106 | should | _Sync | 66,70 | 66,70 | @dataclass
class _Sync:
fn: Optional[Callable] = None
_should: bool = False
rank_zero_only: bool = False
_op: Optional[str] = None
_group: Optional[Any] = None
def __post_init__(self) -> None:
self._generate_sync_fn()
@property
def should(self) -> bool:
return self._sho... | @dataclass
class _Sync:
fn: Optional[Callable] = None
_should: bool = False
rank_zero_only: bool = False
_op: Optional[str] = None
_group: Optional[Any] = None
def __post_init__(self) -> None:
self._generate_sync_fn()
@property
def should(self) -> bool:
return self._sho... | self._should = should
# `self._fn` needs to be re-generated.
self._generate_sync_fn()
| self._should = should
# `self._fn` needs to be re-generated.
self._generate_sync_fn()
| 10 | 511 | 37 | 548 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... |
1,309 | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning/src/lightning/pytorch/trainer/states.py | 67,69 | 44,74 | dataloader_prefix | RunningStage | 69,74 | 69,74 | class RunningStage(LightningEnum):
"""Enum for the current running stage.
This stage complements :class:`TrainerFn` by specifying the current running stage for each function.
More than one running stage value can be set while a :class:`TrainerFn` is running:
- ``TrainerFn.FITTING`` - ``RunningStag... | class RunningStage(LightningEnum):
"""Enum for the current running stage.
This stage complements :class:`TrainerFn` by specifying the current running stage for each function.
More than one running stage value can be set while a :class:`TrainerFn` is running:
- ``TrainerFn.FITTING`` - ``RunningStag... | if self in (self.VALIDATING, self.SANITY_CHECKING):
return "val"
return self.value
| if self in (self.VALIDATING, self.SANITY_CHECKING):
return "val"
return self.value
| 9 | 315 | 40 | 355 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... |
1,310 | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning/src/lightning/pytorch/trainer/states.py | 82,84 | 74,89 | finished | TrainerState | 84,86 | 84,86 | @dataclass
class TrainerState:
"""Dataclass to encapsulate the current :class:`~lightning.pytorch.trainer.trainer.Trainer` state."""
status: TrainerStatus = TrainerStatus.INITIALIZING
fn: Optional[TrainerFn] = None
stage: Optional[RunningStage] = None
@property
def finished(self) -> bool:
FILL... | @dataclass
class TrainerState:
"""Dataclass to encapsulate the current :class:`~lightning.pytorch.trainer.trainer.Trainer` state."""
status: TrainerStatus = TrainerStatus.INITIALIZING
fn: Optional[TrainerFn] = None
stage: Optional[RunningStage] = None
@property
def finished(self) -> bool:
FILL... | return self.status == TrainerStatus.FINISHED
| return self.status == TrainerStatus.FINISHED
| 4 | 140 | 18 | 158 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... |
1,311 | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning/src/lightning/pytorch/trainer/states.py | 28,30 | 20,33 | stopped | TrainerStatus | 30,33 | 30,33 | class TrainerStatus(LightningEnum):
"""Enum for the status of the :class:`~lightning.pytorch.trainer.trainer.Trainer`"""
INITIALIZING = "initializing" # trainer creation
RUNNING = "running"
FINISHED = "finished"
INTERRUPTED = "interrupted"
@property
def stopped(self) -> bool:
FILL_FUNC_BO... | class TrainerStatus(LightningEnum):
"""Enum for the status of the :class:`~lightning.pytorch.trainer.trainer.Trainer`"""
INITIALIZING = "initializing" # trainer creation
RUNNING = "running"
FINISHED = "finished"
INTERRUPTED = "interrupted"
@property
def stopped(self) -> bool:
FILL_FUNC_BO... | return self in (self.FINISHED, self.INTERRUPTED)
| return self in (self.FINISHED, self.INTERRUPTED)
| 5 | 119 | 26 | 145 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... |
1,315 | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning/src/lightning/pytorch/utilities/_pytree.py | 6,7 | 1,40 | _is_leaf_or_primitive_container | 8,18 | 7,18 | from typing import Any, List, Tuple
from torch.utils._pytree import SUPPORTED_NODES, LeafSpec, PyTree, TreeSpec, _get_node_type, tree_unflatten
def _is_leaf_or_primitive_container(pytree: PyTree) -> bool:
FILL_FUNC_BODYdef _tree_flatten(pytree: PyTree) -> Tuple[List[Any], TreeSpec]:
"""Copy of :func:`torch.utils... | from typing import Any, List, Tuple
from torch.utils._pytree import SUPPORTED_NODES, LeafSpec, PyTree, TreeSpec, _get_node_type, tree_unflatten
def _is_leaf_or_primitive_container(pytree: PyTree) -> bool:
"""Customized :func:`torch.utils._pytree._is_leaf` to avoid flattening containers of primitives."""
FILL_FUN... | is_leaf = _get_node_type(pytree) not in SUPPORTED_NODES
if is_leaf:
return True
node_type = _get_node_type(pytree)
flatten_fn = SUPPORTED_NODES[node_type].flatten_fn
child_pytrees, _ = flatten_fn(pytree)
return all(isinstance(child, (int, float, str)) for child in child_pytrees)
| """Customized :func:`torch.utils._pytree._is_leaf` to avoid flattening containers of primitives."""
is_leaf = _get_node_type(pytree) not in SUPPORTED_NODES
if is_leaf:
return True
node_type = _get_node_type(pytree)
flatten_fn = SUPPORTED_NODES[node_type].flatten_fn
child_pytrees, _ = fl... | 29 | 415 | 128 | 543 | from typing import Any, List, Tuple
from torch.utils._pytree import SUPPORTED_NODES, LeafSpec, PyTree, TreeSpec, _get_node_type, tree_unflatten
def _is_leaf_or_primitive_container(pytree: PyTree) -> bool:
FILL_FUNC_BODYdef _tree_flatten(pytree: PyTree) -> Tuple[List[Any], TreeSpec]:
"""Copy of :func:`torch.utils... | from typing import Any, List, Tuple
from torch.utils._pytree import SUPPORTED_NODES, LeafSpec, PyTree, TreeSpec, _get_node_type, tree_unflatten
def _is_leaf_or_primitive_container(pytree: PyTree) -> bool:
"""Customized :func:`torch.utils._pytree._is_leaf` to avoid flattening containers of primitives."""
FILL_FUN... | |
1,316 | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning/src/lightning/pytorch/utilities/argparse.py | 27,28 | 1,73 | _parse_env_variables | 43,56 | 28,56 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | env_args = {}
for arg_name in inspect.signature(cls).parameters:
env = template % {"cls_name": cls.__name__.upper(), "cls_argument": arg_name.upper()}
val = os.environ.get(env)
if not (val is None or val == ""):
# todo: specify the possible exception
with suppress... | """Parse environment arguments if they are defined.
Examples:
>>> from lightning.pytorch import Trainer
>>> _parse_env_variables(Trainer)
Namespace()
>>> import os
>>> os.environ["PL_TRAINER_DEVICES"] = '42'
>>> os.environ["PL_TRAINER_BLABLABLA"] = '1.23'
... | 50 | 677 | 156 | 833 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | |
1,317 | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning/src/lightning/pytorch/utilities/compile.py | 25,26 | 1,131 | from_compiled | 37,70 | 26,70 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | if not isinstance(model, OptimizedModule):
raise ValueError(f"`model` is required to be a `OptimizedModule`. Found a `{type(model).__name__}` instead.")
orig_module = model._orig_mod
if not isinstance(orig_module, pl.LightningModule):
_check_mixed_imports(model)
raise ValueError(
... | """Returns an instance LightningModule from the output of ``torch.compile``.
.. warning:: This is an :ref:`experimental <versioning:Experimental API>` feature.
The ``torch.compile`` function returns a ``torch._dynamo.OptimizedModule``, which wraps the LightningModule
passed in as an argument, but doe... | 105 | 1,295 | 614 | 1,909 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | |
1,318 | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning/src/lightning/pytorch/utilities/consolidate_checkpoint.py | 10,11 | 1,31 | _format_checkpoint | 12,25 | 11,25 | import re
from typing import Any, Dict
import torch
from lightning.fabric.utilities.consolidate_checkpoint import _parse_cli_args, _process_cli_args
from lightning.fabric.utilities.load import _load_distributed_checkpoint
def _format_checkpoint(checkpoint: Dict[str, Any]) -> Dict[str, Any]:
FILL_FUNC_BODYif __name_... | import re
from typing import Any, Dict
import torch
from lightning.fabric.utilities.consolidate_checkpoint import _parse_cli_args, _process_cli_args
from lightning.fabric.utilities.load import _load_distributed_checkpoint
def _format_checkpoint(checkpoint: Dict[str, Any]) -> Dict[str, Any]:
"""Converts the spec... | # Rename the model key
checkpoint["state_dict"] = checkpoint.pop("model")
optimizer_keys = [key for key in checkpoint if re.match("optimizer_[0-9]+", key)]
if not optimizer_keys:
return checkpoint
# Optimizers are saved in special keys named `optimizer_0`, `optimizer_1`, etc.
# These n... | """Converts the special FSDP checkpoint format to the standard format the Lightning Trainer can load."""
# Rename the model key
checkpoint["state_dict"] = checkpoint.pop("model")
optimizer_keys = [key for key in checkpoint if re.match("optimizer_[0-9]+", key)]
if not optimizer_keys:
return ... | 54 | 218 | 174 | 392 | import re
from typing import Any, Dict
import torch
from lightning.fabric.utilities.consolidate_checkpoint import _parse_cli_args, _process_cli_args
from lightning.fabric.utilities.load import _load_distributed_checkpoint
def _format_checkpoint(checkpoint: Dict[str, Any]) -> Dict[str, Any]:
FILL_FUNC_BODYif __name_... | import re
from typing import Any, Dict
import torch
from lightning.fabric.utilities.consolidate_checkpoint import _parse_cli_args, _process_cli_args
from lightning.fabric.utilities.load import _load_distributed_checkpoint
def _format_checkpoint(checkpoint: Dict[str, Any]) -> Dict[str, Any]:
"""Converts the spec... | |
1,319 | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning/src/lightning/pytorch/utilities/enums.py | 34,36 | 21,41 | supported_type | GradClipAlgorithmType | 36,38 | 36,38 | class GradClipAlgorithmType(LightningEnum):
"""Define gradient_clip_algorithm types - training-tricks.
NORM type means "clipping gradients by norm". This computed over all model parameters together.
VALUE type means "clipping gradients by value". This will clip the gradient value for each parameter.
Re... | class GradClipAlgorithmType(LightningEnum):
"""Define gradient_clip_algorithm types - training-tricks.
NORM type means "clipping gradients by norm". This computed over all model parameters together.
VALUE type means "clipping gradients by value". This will clip the gradient value for each parameter.
Re... | return any(x.value == val for x in GradClipAlgorithmType)
| return any(x.value == val for x in GradClipAlgorithmType)
| 8 | 242 | 23 | 265 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... |
1,320 | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning/src/lightning/pytorch/utilities/exceptions.py | 35,36 | 1,39 | _augment_message | 36,39 | 36,39 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | exception.args = tuple(
new_message if isinstance(arg, str) and re.match(pattern, arg, re.DOTALL) else arg for arg in exception.args
)
| exception.args = tuple(
new_message if isinstance(arg, str) and re.match(pattern, arg, re.DOTALL) else arg for arg in exception.args
)
| 18 | 390 | 50 | 440 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | |
1,321 | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning/src/lightning/pytorch/utilities/grads.py | 22,23 | 1,54 | grad_norm | 41,54 | 23,54 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | norm_type = float(norm_type)
if norm_type <= 0:
raise ValueError(f"`norm_type` must be a positive number or 'inf' (infinity norm). Got {norm_type}")
norms = {
f"grad_{norm_type}_norm{group_separator}{name}": p.grad.data.norm(norm_type)
for name, p in module.named_parameters()
... | """Compute each parameter's gradient's norm and their overall norm.
The overall norm is computed over all gradients together, as if they
were concatenated into a single vector.
Args:
module: :class:`torch.nn.Module` to inspect.
norm_type: The type of the used p-norm, cast to float if n... | 46 | 424 | 190 | 614 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | |
1,322 | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning/src/lightning/pytorch/utilities/imports.py | 47,48 | 1,51 | _habana_available_and_importable | 48,51 | 48,51 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # This is defined as a function instead of a constant to avoid circular imports, because `lightning_habana`
# also imports Lightning
return bool(_LIGHTNING_HABANA_AVAILABLE) and _try_import_module("lightning_habana")
| # This is defined as a function instead of a constant to avoid circular imports, because `lightning_habana`
# also imports Lightning
return bool(_LIGHTNING_HABANA_AVAILABLE) and _try_import_module("lightning_habana")
| 25 | 661 | 72 | 733 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | |
1,323 | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning/src/lightning/pytorch/utilities/memory.py | 83,84 | 1,93 | garbage_collection_cuda | 85,93 | 84,93 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | gc.collect()
try:
# This is the last thing that should cause an OOM error, but seemingly it can.
torch.cuda.empty_cache()
except RuntimeError as exception:
if not is_oom_error(exception):
# Only handle OOM errors
raise
| """Garbage collection Torch (CUDA) memory."""
gc.collect()
try:
# This is the last thing that should cause an OOM error, but seemingly it can.
torch.cuda.empty_cache()
except RuntimeError as exception:
if not is_oom_error(exception):
# Only handle OOM errors
... | 32 | 923 | 83 | 1,006 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | |
1,326 | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning/src/lightning/pytorch/utilities/migration/utils.py | 192,194 | 184,201 | find_class | _RedirectingUnpickler | 194,201 | 194,201 | class _RedirectingUnpickler(pickle._Unpickler):
"""Redirects the unpickling of `pytorch_lightning` classes to `lightning.pytorch`.
In legacy versions of Lightning, callback classes got pickled into the checkpoint. These classes are defined in the
`pytorch_lightning` but need to be loaded from `lightning.py... | class _RedirectingUnpickler(pickle._Unpickler):
"""Redirects the unpickling of `pytorch_lightning` classes to `lightning.pytorch`.
In legacy versions of Lightning, callback classes got pickled into the checkpoint. These classes are defined in the
`pytorch_lightning` but need to be loaded from `lightning.py... | new_module = _patch_pl_to_mirror_if_necessary(module)
# this warning won't trigger for standalone as these imports are identical
if module != new_module:
warnings.warn(f"Redirecting import of {module}.{name} to {new_module}.{name}")
return super().find_class(new_module, name)... | new_module = _patch_pl_to_mirror_if_necessary(module)
# this warning won't trigger for standalone as these imports are identical
if module != new_module:
warnings.warn(f"Redirecting import of {module}.{name} to {new_module}.{name}")
return super().find_class(new_module, name)... | 28 | 143 | 103 | 246 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... |
1,327 | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning/src/lightning/pytorch/utilities/model_summary/model_summary_deepspeed.py | 33,34 | 1,120 | deepspeed_param_size | 34,38 | 34,38 | # Copyright 2020 The PyTorch Lightning team and Microsoft Corporation. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
... | # Copyright 2020 The PyTorch Lightning team and Microsoft Corporation. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
... | assert hasattr(p, "ds_numel")
return p.ds_numel
| assert hasattr(p, "ds_numel")
return p.ds_numel
| 5 | 1,392 | 29 | 1,421 | # Copyright 2020 The PyTorch Lightning team and Microsoft Corporation. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
... | # Copyright 2020 The PyTorch Lightning team and Microsoft Corporation. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
... | |
1,328 | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning/src/lightning/pytorch/utilities/model_summary/model_summary_deepspeed.py | 45,47 | 38,55 | average_shard_parameters | DeepSpeedLayerSummary | 48,55 | 47,55 | class DeepSpeedLayerSummary(LayerSummary):
@property
@override
def num_parameters(self) -> int:
"""Returns the number of parameters in this module."""
return sum(deepspeed_param_size(p) if not _is_lazy_weight_tensor(p) else 0 for p in self._module.parameters())
@property
def average... | class DeepSpeedLayerSummary(LayerSummary):
@property
@override
def num_parameters(self) -> int:
"""Returns the number of parameters in this module."""
return sum(deepspeed_param_size(p) if not _is_lazy_weight_tensor(p) else 0 for p in self._module.parameters())
@property
def average... |
def partitioned_size(p: Parameter) -> int:
return p.partitioned_size() if RequirementCache("deepspeed<0.6.6") else p.partition_numel()
return sum(partitioned_size(p) if not _is_lazy_weight_tensor(p) else 0 for p in self._module.parameters())
| """Returns the number of parameters in this module."""
def partitioned_size(p: Parameter) -> int:
return p.partitioned_size() if RequirementCache("deepspeed<0.6.6") else p.partition_numel()
return sum(partitioned_size(p) if not _is_lazy_weight_tensor(p) else 0 for p in self._module... | 22 | 129 | 101 | 230 | # Copyright 2020 The PyTorch Lightning team and Microsoft Corporation. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
... | # Copyright 2020 The PyTorch Lightning team and Microsoft Corporation. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
... |
1,329 | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning/src/lightning/pytorch/utilities/model_summary/model_summary_deepspeed.py | 89,91 | 55,120 | _get_summary_data | DeepSpeedSummary | 96,114 | 91,114 | class DeepSpeedSummary(ModelSummary):
@override
def summarize(self) -> Dict[str, DeepSpeedLayerSummary]: # type: ignore[override]
summary = OrderedDict((name, DeepSpeedLayerSummary(module)) for name, module in self.named_modules)
if self._model.example_input_array is not None:
self.... | class DeepSpeedSummary(ModelSummary):
@override
def summarize(self) -> Dict[str, DeepSpeedLayerSummary]: # type: ignore[override]
summary = OrderedDict((name, DeepSpeedLayerSummary(module)) for name, module in self.named_modules)
if self._model.example_input_array is not None:
self.... | arrays = [
(" ", list(map(str, range(len(self._layer_summary))))),
("Name", self.layer_names),
("Type", self.layer_types),
("Params", list(map(get_human_readable_count, self.param_nums))),
("Params per Device", list(map(get_human_readable_count, self.p... | """Makes a summary listing with:
Layer Name, Layer Type, Number of Parameters, Input Sizes, Output Sizes, Model Size
"""
arrays = [
(" ", list(map(str, range(len(self._layer_summary))))),
("Name", self.layer_names),
("Type", self.layer_types),
... | 62 | 582 | 286 | 868 | # Copyright 2020 The PyTorch Lightning team and Microsoft Corporation. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
... | # Copyright 2020 The PyTorch Lightning team and Microsoft Corporation. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
... |
1,330 | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning/src/lightning/pytorch/utilities/parameter_tying.py | 31,32 | 1,69 | _find_shared_parameters | 32,49 | 32,49 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | if tied_parameters is None:
tied_parameters = {}
for name, param in module._parameters.items():
param_prefix = prefix + ("." if prefix else "") + name
if param is None:
continue
if param not in tied_parameters:
tied_parameters[param] = []
tied_para... | if tied_parameters is None:
tied_parameters = {}
for name, param in module._parameters.items():
param_prefix = prefix + ("." if prefix else "") + name
if param is None:
continue
if param not in tied_parameters:
tied_parameters[param] = []
tied_para... | 71 | 597 | 192 | 789 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | |
1,331 | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning/src/lightning/pytorch/utilities/seed.py | 22,24 | 1,47 | isolate_rng | 44,47 | 24,47 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | states = _collect_rng_states(include_cuda)
yield
_set_rng_states(states)
| """A context manager that resets the global random state on exit to what it was before entering.
It supports isolating the states for PyTorch, Numpy, and Python built-in random number generators.
Args:
include_cuda: Whether to allow this function to also control the `torch.cuda` random number gene... | 5 | 497 | 35 | 532 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | |
1,332 | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning/src/lightning/pytorch/utilities/signature_utils.py | 18,21 | 1,38 | is_param_in_hook_signature | 28,38 | 21,38 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | if hasattr(hook_fx, "__wrapped__"):
# in case the hook has a decorator
hook_fx = hook_fx.__wrapped__
parameters = inspect.getfullargspec(hook_fx)
args = parameters.args[1:] # ignore `self`
return (
param in args
or (not explicit and (parameters.varargs is not None))
... | """
Args:
hook_fx: the hook callable
param: the name of the parameter to check
explicit: whether the parameter has to be explicitly declared
min_args: whether the `signature` has at least `min_args` parameters
"""
if hasattr(hook_fx, "__wrapped__"):
# in case the ... | 44 | 286 | 133 | 419 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | |
1,333 | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning/src/lightning/pytorch/utilities/types.py | 75,76 | 58,80 | no_sync | DistributedDataParallel | 76,77 | 76,77 | @runtime_checkable
class DistributedDataParallel(Protocol):
def __init__(
self,
module: torch.nn.Module,
device_ids: Optional[List[Union[int, torch.device]]] = None,
output_device: Optional[Union[int, torch.device]] = None,
dim: int = 0,
broadcast_buffers: bool = True... | @runtime_checkable
class DistributedDataParallel(Protocol):
def __init__(
self,
module: torch.nn.Module,
device_ids: Optional[List[Union[int, torch.device]]] = None,
output_device: Optional[Union[int, torch.device]] = None,
dim: int = 0,
broadcast_buffers: bool = True... | def no_sync(self) -> Generator: ...
| def no_sync(self) -> Generator: ...
| 5 | 218 | 16 | 234 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... |
1,334 | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning/src/lightning/pytorch/utilities/upgrade_checkpoint.py | 70,71 | 1,103 | main | 71,101 | 71,101 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | parser = ArgumentParser(
description=(
"A utility to upgrade old checkpoints to the format of the current Lightning version."
" This will also save a backup of the original files."
)
)
parser.add_argument(
"path",
type=str,
help="Path to a chec... | parser = ArgumentParser(
description=(
"A utility to upgrade old checkpoints to the format of the current Lightning version."
" This will also save a backup of the original files."
)
)
parser.add_argument(
"path",
type=str,
help="Path to a chec... | 101 | 729 | 275 | 1,004 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | |
1,335 | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning/src/lightning/store/store.py | 23,29 | 1,93 | upload_model | 42,58 | 29,58 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | client = _Client()
user = client.auth_service_get_user()
# TODO: Allow passing this
project_id = _get_project(client).project_id
# TODO: Post model parts if the file size is over threshold
body = V1UploadModelRequest(
name=f"{user.username}/{name}",
version=version,
proj... | """Upload a model to the lightning cloud.
Args:
name:
The model name.
path:
The path to the checkpoint to be uploaded.
version:
The version of the model to be uploaded. If not provided, default will be latest (not overridden).
progress_bar:
... | 39 | 699 | 167 | 866 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | |
1,336 | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning/src/lightning/store/utils.py | 41,42 | 1,68 | _download_file_from_url | 42,64 | 42,64 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | with requests.get(url, stream=True) as req_stream:
total_size_in_bytes = int(req_stream.headers.get("content-length", 0))
block_size = 1000 * 1000 # 1 MB
download_progress_bar = None
if progress_bar:
download_progress_bar = tqdm(
desc="Downloading",
... | with requests.get(url, stream=True) as req_stream:
total_size_in_bytes = int(req_stream.headers.get("content-length", 0))
block_size = 1000 * 1000 # 1 MB
download_progress_bar = None
if progress_bar:
download_progress_bar = tqdm(
desc="Downloading",
... | 47 | 510 | 257 | 767 | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | |
1,337 | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning/src/lightning_app/__setup__.py | 50,51 | 1,123 | _setup_args | 51,123 | 51,123 | import glob
import os
from importlib.util import module_from_spec, spec_from_file_location
from pathlib import Path
from types import ModuleType
from typing import Any, Dict
from setuptools import find_packages
_PROJECT_ROOT = "."
_SOURCE_ROOT = os.path.join(_PROJECT_ROOT, "src")
_PACKAGE_ROOT = os.path.join(_SOURCE_... | import glob
import os
from importlib.util import module_from_spec, spec_from_file_location
from pathlib import Path
from types import ModuleType
from typing import Any, Dict
from setuptools import find_packages
_PROJECT_ROOT = "."
_SOURCE_ROOT = os.path.join(_PROJECT_ROOT, "src")
_PACKAGE_ROOT = os.path.join(_SOURCE_... | assistant = _load_assistant()
about = _load_py_module("about", os.path.join(_PACKAGE_ROOT, "__about__.py"))
version = _load_py_module("version", os.path.join(_PACKAGE_ROOT, "__version__.py"))
long_description = assistant.load_readme_description(
_PACKAGE_ROOT, homepage=about.__homepage__, versio... | assistant = _load_assistant()
about = _load_py_module("about", os.path.join(_PACKAGE_ROOT, "__about__.py"))
version = _load_py_module("version", os.path.join(_PACKAGE_ROOT, "__version__.py"))
long_description = assistant.load_readme_description(
_PACKAGE_ROOT, homepage=about.__homepage__, versio... | 270 | 708 | 1,058 | 1,766 | import glob
import os
from importlib.util import module_from_spec, spec_from_file_location
from pathlib import Path
from types import ModuleType
from typing import Any, Dict
from setuptools import find_packages
_PROJECT_ROOT = "."
_SOURCE_ROOT = os.path.join(_PROJECT_ROOT, "src")
_PACKAGE_ROOT = os.path.join(_SOURCE_... | import glob
import os
from importlib.util import module_from_spec, spec_from_file_location
from pathlib import Path
from types import ModuleType
from typing import Any, Dict
from setuptools import find_packages
_PROJECT_ROOT = "."
_SOURCE_ROOT = os.path.join(_PROJECT_ROOT, "src")
_PACKAGE_ROOT = os.path.join(_SOURCE_... | |
1,338 | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning/src/lightning_fabric/__setup__.py | 52,53 | 1,114 | _setup_args | 53,114 | 53,114 | import glob
import os
from importlib.util import module_from_spec, spec_from_file_location
from pathlib import Path
from types import ModuleType
from typing import Any, Dict
from pkg_resources import parse_requirements
from setuptools import find_packages
_PROJECT_ROOT = "."
_SOURCE_ROOT = os.path.join(_PROJECT_ROOT,... | import glob
import os
from importlib.util import module_from_spec, spec_from_file_location
from pathlib import Path
from types import ModuleType
from typing import Any, Dict
from pkg_resources import parse_requirements
from setuptools import find_packages
_PROJECT_ROOT = "."
_SOURCE_ROOT = os.path.join(_PROJECT_ROOT,... | assistant = _load_assistant()
about = _load_py_module("about", os.path.join(_PACKAGE_ROOT, "__about__.py"))
version = _load_py_module("version", os.path.join(_PACKAGE_ROOT, "__version__.py"))
long_description = assistant.load_readme_description(
_PACKAGE_ROOT, homepage=about.__homepage__, versio... | assistant = _load_assistant()
about = _load_py_module("about", os.path.join(_PACKAGE_ROOT, "__about__.py"))
version = _load_py_module("version", os.path.join(_PACKAGE_ROOT, "__version__.py"))
long_description = assistant.load_readme_description(
_PACKAGE_ROOT, homepage=about.__homepage__, versio... | 227 | 729 | 874 | 1,603 | import glob
import os
from importlib.util import module_from_spec, spec_from_file_location
from pathlib import Path
from types import ModuleType
from typing import Any, Dict
from pkg_resources import parse_requirements
from setuptools import find_packages
_PROJECT_ROOT = "."
_SOURCE_ROOT = os.path.join(_PROJECT_ROOT,... | import glob
import os
from importlib.util import module_from_spec, spec_from_file_location
from pathlib import Path
from types import ModuleType
from typing import Any, Dict
from pkg_resources import parse_requirements
from setuptools import find_packages
_PROJECT_ROOT = "."
_SOURCE_ROOT = os.path.join(_PROJECT_ROOT,... | |
1,339 | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning | /home/hieuvd/dung_repo/raw/stars_01/Lightning-AI_pytorch-lightning/pytorch-lightning/src/pytorch_lightning/__setup__.py | 32,33 | 1,116 | _prepare_extras | 33,52 | 33,52 | import glob
import os.path
from importlib.util import module_from_spec, spec_from_file_location
from pathlib import Path
from types import ModuleType
from typing import Any, Dict
from pkg_resources import parse_requirements
from setuptools import find_packages
_PROJECT_ROOT = "."
_SOURCE_ROOT = os.path.join(_PROJECT_... | import glob
import os.path
from importlib.util import module_from_spec, spec_from_file_location
from pathlib import Path
from types import ModuleType
from typing import Any, Dict
from pkg_resources import parse_requirements
from setuptools import find_packages
_PROJECT_ROOT = "."
_SOURCE_ROOT = os.path.join(_PROJECT_... | assistant = _load_assistant()
# https://setuptools.readthedocs.io/en/latest/setuptools.html#declaring-extras
# Define package extras. These are only installed if you specify them.
# From remote, use like `pip install "pytorch-lightning[dev, docs]"`
# From local copy of repo, use like `PACKAGE_NAME=p... | assistant = _load_assistant()
# https://setuptools.readthedocs.io/en/latest/setuptools.html#declaring-extras
# Define package extras. These are only installed if you specify them.
# From remote, use like `pip install "pytorch-lightning[dev, docs]"`
# From local copy of repo, use like `PACKAGE_NAME=p... | 98 | 1,224 | 366 | 1,590 | import glob
import os.path
from importlib.util import module_from_spec, spec_from_file_location
from pathlib import Path
from types import ModuleType
from typing import Any, Dict
from pkg_resources import parse_requirements
from setuptools import find_packages
_PROJECT_ROOT = "."
_SOURCE_ROOT = os.path.join(_PROJECT_... | import glob
import os.path
from importlib.util import module_from_spec, spec_from_file_location
from pathlib import Path
from types import ModuleType
from typing import Any, Dict
from pkg_resources import parse_requirements
from setuptools import find_packages
_PROJECT_ROOT = "."
_SOURCE_ROOT = os.path.join(_PROJECT_... | |
2,161 | /home/hieuvd/dung_repo/raw/stars_01/Miserlou_Zappa/Zappa | /home/hieuvd/dung_repo/raw/stars_01/Miserlou_Zappa/Zappa/example/authmodule.py | 102,103 | 71,217 | _addMethod | AuthPolicy | 106,134 | 103,134 | class AuthPolicy:
awsAccountId = ""
"""The AWS account id the policy will be generated for. This is used to create the method ARNs."""
principalId = ""
"""The principal used for the policy, this should be a unique identifier for the end user."""
version = "2012-10-17"
"""The policy version used ... | class AuthPolicy:
awsAccountId = ""
"""The AWS account id the policy will be generated for. This is used to create the method ARNs."""
principalId = ""
"""The principal used for the policy, this should be a unique identifier for the end user."""
version = "2012-10-17"
"""The policy version used ... | if verb != "*" and not hasattr(HttpVerb, verb):
raise NameError("Invalid HTTP verb " + verb + ". Allowed verbs in HttpVerb class")
resourcePattern = re.compile(self.pathRegex)
if not resourcePattern.match(resource):
raise NameError("Invalid resource path: " + resource + "... | """Adds a method to the internal lists of allowed or denied methods. Each object in
the internal list contains a resource ARN and a condition statement. The condition
statement can be null."""
if verb != "*" and not hasattr(HttpVerb, verb):
raise NameError("Invalid HTTP verb ... | 99 | 1,383 | 295 | 1,678 | """
Copyright 2015-2016 Amazon.com, Inc. or its affiliates. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file except in compliance with the License. A copy of the License is located at
http://aws.amazon.com/apache2.0/
or in the "license" file accompanyin... | """
Copyright 2015-2016 Amazon.com, Inc. or its affiliates. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file except in compliance with the License. A copy of the License is located at
http://aws.amazon.com/apache2.0/
or in the "license" file accompanyin... |
2,162 | /home/hieuvd/dung_repo/raw/stars_01/Miserlou_Zappa/Zappa | /home/hieuvd/dung_repo/raw/stars_01/Miserlou_Zappa/Zappa/example/mymodule.py | 5,6 | 1,9 | myfunc_with_events | 6,9 | 6,9 | def myfunc():
print('Running my function in a schedule!')
def myfunc_with_events(event, context):
FILL_FUNC_BODY | def myfunc():
print('Running my function in a schedule!')
def myfunc_with_events(event, context):
FILL_FUNC_BODY | print('Event time was', event['time'])
print('This log is', context.log_group_name, context.log_stream_name)
print('Time left for execution:', context.get_remaining_time_in_millis())
| print('Event time was', event['time'])
print('This log is', context.log_group_name, context.log_stream_name)
print('Time left for execution:', context.get_remaining_time_in_millis())
| 14 | 42 | 63 | 105 | def myfunc():
print('Running my function in a schedule!')
def myfunc_with_events(event, context):
FILL_FUNC_BODY | def myfunc():
print('Running my function in a schedule!')
def myfunc_with_events(event, context):
FILL_FUNC_BODY | |
2,163 | /home/hieuvd/dung_repo/raw/stars_01/Miserlou_Zappa/Zappa | /home/hieuvd/dung_repo/raw/stars_01/Miserlou_Zappa/Zappa/test_settings.py | 62,63 | 1,68 | authorizer_event | 63,66 | 63,66 | APP_MODULE = 'tests.test_app'
APP_FUNCTION = 'hello_world'
DJANGO_SETTINGS = None
DEBUG = 'True'
LOG_LEVEL = 'DEBUG'
SCRIPT_NAME = 'hello_world'
DOMAIN = None
API_STAGE = 'ttt888'
PROJECT_NAME = 'ttt888'
REMOTE_ENV='s3://lmbda/test_env.json'
## test_env.json
#{
# "hello": "world"
#}
#
AWS_EVENT_MAPPING = {
'ar... | APP_MODULE = 'tests.test_app'
APP_FUNCTION = 'hello_world'
DJANGO_SETTINGS = None
DEBUG = 'True'
LOG_LEVEL = 'DEBUG'
SCRIPT_NAME = 'hello_world'
DOMAIN = None
API_STAGE = 'ttt888'
PROJECT_NAME = 'ttt888'
REMOTE_ENV='s3://lmbda/test_env.json'
## test_env.json
#{
# "hello": "world"
#}
#
AWS_EVENT_MAPPING = {
'ar... | return "AUTHORIZER_EVENT"
| return "AUTHORIZER_EVENT"
| 2 | 566 | 17 | 583 | APP_MODULE = 'tests.test_app'
APP_FUNCTION = 'hello_world'
DJANGO_SETTINGS = None
DEBUG = 'True'
LOG_LEVEL = 'DEBUG'
SCRIPT_NAME = 'hello_world'
DOMAIN = None
API_STAGE = 'ttt888'
PROJECT_NAME = 'ttt888'
REMOTE_ENV='s3://lmbda/test_env.json'
## test_env.json
#{
# "hello": "world"
#}
#
AWS_EVENT_MAPPING = {
'ar... | APP_MODULE = 'tests.test_app'
APP_FUNCTION = 'hello_world'
DJANGO_SETTINGS = None
DEBUG = 'True'
LOG_LEVEL = 'DEBUG'
SCRIPT_NAME = 'hello_world'
DOMAIN = None
API_STAGE = 'ttt888'
PROJECT_NAME = 'ttt888'
REMOTE_ENV='s3://lmbda/test_env.json'
## test_env.json
#{
# "hello": "world"
#}
#
AWS_EVENT_MAPPING = {
'ar... | |
2,191 | /home/hieuvd/dung_repo/raw/stars_01/NanmiCoder_MediaCrawler/MediaCrawler | /home/hieuvd/dung_repo/raw/stars_01/NanmiCoder_MediaCrawler/MediaCrawler/async_db.py | 40,41 | 10,97 | item_to_table | AsyncMysqlDB | 47,59 | 41,59 | class AsyncMysqlDB:
def __init__(self, pool: aiomysql.Pool) -> None:
self.__pool = pool
async def query(self, sql: str, *args: Union[str, int]) -> List[Dict[str, Any]]:
"""
从给定的 SQL 中查询记录，返回的是一个列表
:param sql: 查询的sql
:param args: sql中传递动态参数列表
:return:
"""
... | class AsyncMysqlDB:
def __init__(self, pool: aiomysql.Pool) -> None:
self.__pool = pool
async def query(self, sql: str, *args: Union[str, int]) -> List[Dict[str, Any]]:
"""
从给定的 SQL 中查询记录，返回的是一个列表
:param sql: 查询的sql
:param args: sql中传递动态参数列表
:return:
"""
... | fields = list(item.keys())
values = list(item.values())
fields = [f'`{field}`' for field in fields]
fieldstr = ','.join(fields)
valstr = ','.join(['%s'] * len(item))
sql = "INSERT INTO %s (%s) VALUES(%s)" % (table_name, fieldstr, valstr)
async with self.__pool.acq... | """
表中插入数据
:param table_name: 表名
:param item: 一条记录的字典信息
:return:
"""
fields = list(item.keys())
values = list(item.values())
fields = [f'`{field}`' for field in fields]
fieldstr = ','.join(fields)
valstr = ','.join(['%s'] * len(item... | 50 | 827 | 173 | 1,000 | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2024/4/6 14:21
# @Desc : 异步Aiomysql的增删改查封装
from typing import Any, Dict, List, Union
import aiomysql
class AsyncMysqlDB:
def __init__(self, pool: aiomysql.Pool) -> None:
self.__pool = pool
async def query(self, sql: str, *args: ... | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2024/4/6 14:21
# @Desc : 异步Aiomysql的增删改查封装
from typing import Any, Dict, List, Union
import aiomysql
class AsyncMysqlDB:
def __init__(self, pool: aiomysql.Pool) -> None:
self.__pool = pool
async def query(self, sql: str, *args: ... |
2,192 | /home/hieuvd/dung_repo/raw/stars_01/NanmiCoder_MediaCrawler/MediaCrawler | /home/hieuvd/dung_repo/raw/stars_01/NanmiCoder_MediaCrawler/MediaCrawler/base/base_crawler.py | 49,51 | 44,59 | store_comment | AbstractStore | 51,56 | 51,56 | class AbstractStore(ABC):
@abstractmethod
async def store_content(self, content_item: Dict):
pass
@abstractmethod
async def store_comment(self, comment_item: Dict):
FILL_FUNC_BODY async def store_creator(self, creator: Dict):
pass
| class AbstractStore(ABC):
@abstractmethod
async def store_content(self, content_item: Dict):
pass
@abstractmethod
async def store_comment(self, comment_item: Dict):
FILL_FUNC_BODY async def store_creator(self, creator: Dict):
pass
| pass
# TODO support all platform
# only xhs is supported, so @abstractmethod is commented
# @abstractmethod
| pass
# TODO support all platform
# only xhs is supported, so @abstractmethod is commented
# @abstractmethod
| 17 | 89 | 39 | 128 | from abc import ABC, abstractmethod
from typing import Dict, Optional
from playwright.async_api import BrowserContext, BrowserType
class AbstractCrawler(ABC):
@abstractmethod
def init_config(self, platform: str, login_type: str, crawler_type: str, start_page: int, keyword: str):
pass
@abstractme... | from abc import ABC, abstractmethod
from typing import Dict, Optional
from playwright.async_api import BrowserContext, BrowserType
class AbstractCrawler(ABC):
@abstractmethod
def init_config(self, platform: str, login_type: str, crawler_type: str, start_page: int, keyword: str):
pass
@abstractme... |
2,193 | /home/hieuvd/dung_repo/raw/stars_01/NanmiCoder_MediaCrawler/MediaCrawler | /home/hieuvd/dung_repo/raw/stars_01/NanmiCoder_MediaCrawler/MediaCrawler/db.py | 79,80 | 1,97 | init_table_schema | 85,95 | 80,95 | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2024/4/6 14:54
# @Desc : mediacrawler db 管理
import asyncio
from typing import Dict
from urllib.parse import urlparse
import aiofiles
import aiomysql
import config
from async_db import AsyncMysqlDB
from tools import utils
from var import db_conn_p... | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2024/4/6 14:54
# @Desc : mediacrawler db 管理
import asyncio
from typing import Dict
from urllib.parse import urlparse
import aiofiles
import aiomysql
import config
from async_db import AsyncMysqlDB
from tools import utils
from var import db_conn_p... | utils.logger.info("[init_table_schema] begin init mysql table schema ...")
await init_mediacrawler_db()
async_db_obj: AsyncMysqlDB = media_crawler_db_var.get()
async with aiofiles.open("schema/tables.sql", mode="r") as f:
schema_sql = await f.read()
await async_db_obj.execute(schema_sql)... | """
用来初始化数据库表结构，请在第一次需要创建表结构的时候使用，多次执行该函数会将已有的表以及数据全部删除
Returns:
"""
utils.logger.info("[init_table_schema] begin init mysql table schema ...")
await init_mediacrawler_db()
async_db_obj: AsyncMysqlDB = media_crawler_db_var.get()
async with aiofiles.open("schema/tables.sql", mode="r") as... | 33 | 812 | 155 | 967 | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2024/4/6 14:54
# @Desc : mediacrawler db 管理
import asyncio
from typing import Dict
from urllib.parse import urlparse
import aiofiles
import aiomysql
import config
from async_db import AsyncMysqlDB
from tools import utils
from var import db_conn_p... | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2024/4/6 14:54
# @Desc : mediacrawler db 管理
import asyncio
from typing import Dict
from urllib.parse import urlparse
import aiofiles
import aiomysql
import config
from async_db import AsyncMysqlDB
from tools import utils
from var import db_conn_p... | |
2,194 | /home/hieuvd/dung_repo/raw/stars_01/NanmiCoder_MediaCrawler/MediaCrawler | /home/hieuvd/dung_repo/raw/stars_01/NanmiCoder_MediaCrawler/MediaCrawler/main.py | 32,33 | 1,71 | main | 33,65 | 33,65 | import argparse
import asyncio
import sys
import config
import db
from base.base_crawler import AbstractCrawler
from media_platform.bilibili import BilibiliCrawler
from media_platform.douyin import DouYinCrawler
from media_platform.kuaishou import KuaishouCrawler
from media_platform.weibo import WeiboCrawler
from medi... | import argparse
import asyncio
import sys
import config
import db
from base.base_crawler import AbstractCrawler
from media_platform.bilibili import BilibiliCrawler
from media_platform.douyin import DouYinCrawler
from media_platform.kuaishou import KuaishouCrawler
from media_platform.weibo import WeiboCrawler
from medi... | # define command line params ...
parser = argparse.ArgumentParser(description='Media crawler program.')
parser.add_argument('--platform', type=str, help='Media platform select (xhs | dy | ks | bili | wb)',
choices=["xhs", "dy", "ks", "bili", "wb"], default=config.PLATFORM)
parser... | # define command line params ...
parser = argparse.ArgumentParser(description='Media crawler program.')
parser.add_argument('--platform', type=str, help='Media platform select (xhs | dy | ks | bili | wb)',
choices=["xhs", "dy", "ks", "bili", "wb"], default=config.PLATFORM)
parser... | 104 | 369 | 476 | 845 | import argparse
import asyncio
import sys
import config
import db
from base.base_crawler import AbstractCrawler
from media_platform.bilibili import BilibiliCrawler
from media_platform.douyin import DouYinCrawler
from media_platform.kuaishou import KuaishouCrawler
from media_platform.weibo import WeiboCrawler
from medi... | import argparse
import asyncio
import sys
import config
import db
from base.base_crawler import AbstractCrawler
from media_platform.bilibili import BilibiliCrawler
from media_platform.douyin import DouYinCrawler
from media_platform.kuaishou import KuaishouCrawler
from media_platform.weibo import WeiboCrawler
from medi... | |
2,195 | /home/hieuvd/dung_repo/raw/stars_01/NanmiCoder_MediaCrawler/MediaCrawler | /home/hieuvd/dung_repo/raw/stars_01/NanmiCoder_MediaCrawler/MediaCrawler/main.py | 24,26 | 15,32 | create_crawler | CrawlerFactory | 26,32 | 26,32 | class CrawlerFactory:
CRAWLERS = {
"xhs": XiaoHongShuCrawler,
"dy": DouYinCrawler,
"ks": KuaishouCrawler,
"bili": BilibiliCrawler,
"wb": WeiboCrawler
}
@staticmethod
def create_crawler(platform: str) -> AbstractCrawler:
FILL_FUNC_BODY | class CrawlerFactory:
CRAWLERS = {
"xhs": XiaoHongShuCrawler,
"dy": DouYinCrawler,
"ks": KuaishouCrawler,
"bili": BilibiliCrawler,
"wb": WeiboCrawler
}
@staticmethod
def create_crawler(platform: str) -> AbstractCrawler:
FILL_FUNC_BODY | crawler_class = CrawlerFactory.CRAWLERS.get(platform)
if not crawler_class:
raise ValueError("Invalid Media Platform Currently only supported xhs or dy or ks or bili ...")
return crawler_class()
| crawler_class = CrawlerFactory.CRAWLERS.get(platform)
if not crawler_class:
raise ValueError("Invalid Media Platform Currently only supported xhs or dy or ks or bili ...")
return crawler_class()
| 23 | 122 | 71 | 193 | import argparse
import asyncio
import sys
import config
import db
from base.base_crawler import AbstractCrawler
from media_platform.bilibili import BilibiliCrawler
from media_platform.douyin import DouYinCrawler
from media_platform.kuaishou import KuaishouCrawler
from media_platform.weibo import WeiboCrawler
from medi... | import argparse
import asyncio
import sys
import config
import db
from base.base_crawler import AbstractCrawler
from media_platform.bilibili import BilibiliCrawler
from media_platform.douyin import DouYinCrawler
from media_platform.kuaishou import KuaishouCrawler
from media_platform.weibo import WeiboCrawler
from medi... |
2,196 | /home/hieuvd/dung_repo/raw/stars_01/NanmiCoder_MediaCrawler/MediaCrawler | /home/hieuvd/dung_repo/raw/stars_01/NanmiCoder_MediaCrawler/MediaCrawler/media_platform/bilibili/help.py | 35,36 | 13,58 | sign | BilibiliSign | 42,58 | 36,58 | class BilibiliSign:
def __init__(self, img_key: str, sub_key: str):
self.img_key = img_key
self.sub_key = sub_key
self.map_table = [
46, 47, 18, 2, 53, 8, 23, 32, 15, 50, 10, 31, 58, 3, 45, 35, 27, 43, 5, 49,
33, 9, 42, 19, 29, 28, 14, 39, 12, 38, 41, 13, 37, 48, 7, 1... | class BilibiliSign:
def __init__(self, img_key: str, sub_key: str):
self.img_key = img_key
self.sub_key = sub_key
self.map_table = [
46, 47, 18, 2, 53, 8, 23, 32, 15, 50, 10, 31, 58, 3, 45, 35, 27, 43, 5, 49,
33, 9, 42, 19, 29, 28, 14, 39, 12, 38, 41, 13, 37, 48, 7, 1... | current_ts = utils.get_unix_timestamp()
req_data.update({"wts": current_ts})
req_data = dict(sorted(req_data.items()))
req_data = {
# 过滤 value 中的 "!'()*" 字符
k: ''.join(filter(lambda ch: ch not in "!'()*", str(v)))
for k, v
in req_data.items... | """
请求参数中加上当前时间戳对请求参数中的key进行字典序排序
再将请求参数进行 url 编码集合 salt 进行 md5 就可以生成w_rid参数了
:param req_data:
:return:
"""
current_ts = utils.get_unix_timestamp()
req_data.update({"wts": current_ts})
req_data = dict(sorted(req_data.items()))
req_data = {
... | 50 | 497 | 209 | 706 | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2023/12/2 23:26
# @Desc : bilibili 请求参数签名
# 逆向实现参考：https://socialsisteryi.github.io/bilibili-API-collect/docs/misc/sign/wbi.html#wbi%E7%AD%BE%E5%90%8D%E7%AE%97%E6%B3%95
import urllib.parse
from hashlib import md5
from typing import Dict
from t... | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2023/12/2 23:26
# @Desc : bilibili 请求参数签名
# 逆向实现参考：https://socialsisteryi.github.io/bilibili-API-collect/docs/misc/sign/wbi.html#wbi%E7%AD%BE%E5%90%8D%E7%AE%97%E6%B3%95
import urllib.parse
from hashlib import md5
from typing import Dict
from t... |
2,197 | /home/hieuvd/dung_repo/raw/stars_01/NanmiCoder_MediaCrawler/MediaCrawler | /home/hieuvd/dung_repo/raw/stars_01/NanmiCoder_MediaCrawler/MediaCrawler/media_platform/bilibili/login.py | 59,60 | 19,107 | login_by_qrcode | BilibiliLogin | 61,95 | 60,95 | class BilibiliLogin(AbstractLogin):
def __init__(self,
login_type: str,
browser_context: BrowserContext,
context_page: Page,
login_phone: Optional[str] = "",
cookie_str: str = ""
):
self.login_type = login_... | class BilibiliLogin(AbstractLogin):
def __init__(self,
login_type: str,
browser_context: BrowserContext,
context_page: Page,
login_phone: Optional[str] = "",
cookie_str: str = ""
):
self.login_type = login_... | utils.logger.info("[BilibiliLogin.login_by_qrcode] Begin login bilibili by qrcode ...")
# click login button
login_button_ele = self.context_page.locator(
"xpath=//div[@class='right-entry__outside go-login-btn']//div"
)
await login_button_ele.click()
# find ... | """login bilibili website and keep webdriver login state"""
utils.logger.info("[BilibiliLogin.login_by_qrcode] Begin login bilibili by qrcode ...")
# click login button
login_button_ele = self.context_page.locator(
"xpath=//div[@class='right-entry__outside go-login-btn']//di... | 99 | 657 | 524 | 1,181 | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2023/12/2 18:44
# @Desc : bilibli登录实现类
import asyncio
import functools
import sys
from typing import Optional
from playwright.async_api import BrowserContext, Page
from tenacity import (RetryError, retry, retry_if_result, stop_after_attempt,
... | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2023/12/2 18:44
# @Desc : bilibli登录实现类
import asyncio
import functools
import sys
from typing import Optional
from playwright.async_api import BrowserContext, Page
from tenacity import (RetryError, retry, retry_if_result, stop_after_attempt,
... |
2,198 | /home/hieuvd/dung_repo/raw/stars_01/NanmiCoder_MediaCrawler/MediaCrawler | /home/hieuvd/dung_repo/raw/stars_01/NanmiCoder_MediaCrawler/MediaCrawler/media_platform/kuaishou/graphql.py | 13,14 | 6,23 | load_graphql_queries | KuaiShouGraphQL | 14,21 | 14,21 | class KuaiShouGraphQL:
graphql_queries: Dict[str, str]= {}
def __init__(self):
self.graphql_dir = "media_platform/kuaishou/graphql/"
self.load_graphql_queries()
def load_graphql_queries(self):
FILL_FUNC_BODY def get(self, query_name: str) -> str:
return self.graphql_queries.get(... | class KuaiShouGraphQL:
graphql_queries: Dict[str, str]= {}
def __init__(self):
self.graphql_dir = "media_platform/kuaishou/graphql/"
self.load_graphql_queries()
def load_graphql_queries(self):
FILL_FUNC_BODY def get(self, query_name: str) -> str:
return self.graphql_queries.get(... | graphql_files = ["search_query.graphql", "video_detail.graphql", "comment_list.graphql", "vision_profile.graphql"]
for file in graphql_files:
with open(self.graphql_dir + file, mode="r") as f:
query_name = file.split(".")[0]
self.graphql_queries[query_name] =... | graphql_files = ["search_query.graphql", "video_detail.graphql", "comment_list.graphql", "vision_profile.graphql"]
for file in graphql_files:
with open(self.graphql_dir + file, mode="r") as f:
query_name = file.split(".")[0]
self.graphql_queries[query_name] =... | 23 | 133 | 113 | 246 | # 快手的数据传输是基于GraphQL实现的
# 这个类负责获取一些GraphQL的schema
from typing import Dict
class KuaiShouGraphQL:
graphql_queries: Dict[str, str]= {}
def __init__(self):
self.graphql_dir = "media_platform/kuaishou/graphql/"
self.load_graphql_queries()
def load_graphql_queries(self):
FILL_FUNC_BODY def ... | # 快手的数据传输是基于GraphQL实现的
# 这个类负责获取一些GraphQL的schema
from typing import Dict
class KuaiShouGraphQL:
graphql_queries: Dict[str, str]= {}
def __init__(self):
self.graphql_dir = "media_platform/kuaishou/graphql/"
self.load_graphql_queries()
def load_graphql_queries(self):
FILL_FUNC_BODY def ... |
2,199 | /home/hieuvd/dung_repo/raw/stars_01/NanmiCoder_MediaCrawler/MediaCrawler | /home/hieuvd/dung_repo/raw/stars_01/NanmiCoder_MediaCrawler/MediaCrawler/media_platform/kuaishou/login.py | 30,31 | 16,104 | begin | KuaishouLogin | 32,42 | 31,42 | class KuaishouLogin(AbstractLogin):
def __init__(self,
login_type: str,
browser_context: BrowserContext,
context_page: Page,
login_phone: Optional[str] = "",
cookie_str: str = ""
):
self.login_type = login_... | class KuaishouLogin(AbstractLogin):
def __init__(self,
login_type: str,
browser_context: BrowserContext,
context_page: Page,
login_phone: Optional[str] = "",
cookie_str: str = ""
):
self.login_type = login_... | utils.logger.info("[KuaishouLogin.begin] Begin login kuaishou ...")
if self.login_type == "qrcode":
await self.login_by_qrcode()
elif self.login_type == "phone":
await self.login_by_mobile()
elif self.login_type == "cookie":
await self.login_by_cookies... | """Start login xiaohongshu"""
utils.logger.info("[KuaishouLogin.begin] Begin login kuaishou ...")
if self.login_type == "qrcode":
await self.login_by_qrcode()
elif self.login_type == "phone":
await self.login_by_mobile()
elif self.login_type == "cookie":
... | 38 | 1,018 | 146 | 1,164 | import asyncio
import functools
import sys
from typing import Optional
import redis
from playwright.async_api import BrowserContext, Page
from tenacity import (RetryError, retry, retry_if_result, stop_after_attempt,
wait_fixed)
import config
from base.base_crawler import AbstractLogin
from tools... | import asyncio
import functools
import sys
from typing import Optional
import redis
from playwright.async_api import BrowserContext, Page
from tenacity import (RetryError, retry, retry_if_result, stop_after_attempt,
wait_fixed)
import config
from base.base_crawler import AbstractLogin
from tools... |
2,200 | /home/hieuvd/dung_repo/raw/stars_01/NanmiCoder_MediaCrawler/MediaCrawler | /home/hieuvd/dung_repo/raw/stars_01/NanmiCoder_MediaCrawler/MediaCrawler/media_platform/weibo/help.py | 9,10 | 1,26 | filter_search_result_card | 15,26 | 10,26 | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2023/12/24 17:37
# @Desc :
from typing import Dict, List
def filter_search_result_card(card_list: List[Dict]) -> List[Dict]:
FILL_FUNC_BODY | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2023/12/24 17:37
# @Desc :
from typing import Dict, List
def filter_search_result_card(card_list: List[Dict]) -> List[Dict]:
"""
过滤微博搜索的结果，只保留card_type为9类型的数据
:param card_list:
:return:
"""
FILL_FUNC_BODY | note_list: List[Dict] = []
for card_item in card_list:
if card_item.get("card_type") == 9:
note_list.append(card_item)
if len(card_item.get("card_group", [])) > 0:
card_group = card_item.get("card_group")
for card_group_item in card_group:
if c... | """
过滤微博搜索的结果，只保留card_type为9类型的数据
:param card_list:
:return:
"""
note_list: List[Dict] = []
for card_item in card_list:
if card_item.get("card_type") == 9:
note_list.append(card_item)
if len(card_item.get("card_group", [])) > 0:
card_group = card_item.... | 32 | 141 | 151 | 292 | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2023/12/24 17:37
# @Desc :
from typing import Dict, List
def filter_search_result_card(card_list: List[Dict]) -> List[Dict]:
FILL_FUNC_BODY | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2023/12/24 17:37
# @Desc :
from typing import Dict, List
def filter_search_result_card(card_list: List[Dict]) -> List[Dict]:
"""
过滤微博搜索的结果，只保留card_type为9类型的数据
:param card_list:
:return:
"""
FILL_FUNC_BODY | |
2,201 | /home/hieuvd/dung_repo/raw/stars_01/NanmiCoder_MediaCrawler/MediaCrawler | /home/hieuvd/dung_repo/raw/stars_01/NanmiCoder_MediaCrawler/MediaCrawler/media_platform/weibo/login.py | 128,129 | 19,137 | login_by_cookies | WeiboLogin | 129,137 | 129,137 | class WeiboLogin(AbstractLogin):
def __init__(self,
login_type: str,
browser_context: BrowserContext,
context_page: Page,
login_phone: Optional[str] = "",
cookie_str: str = ""
):
self.login_type = login_typ... | class WeiboLogin(AbstractLogin):
def __init__(self,
login_type: str,
browser_context: BrowserContext,
context_page: Page,
login_phone: Optional[str] = "",
cookie_str: str = ""
):
self.login_type = login_typ... | utils.logger.info("[WeiboLogin.login_by_qrcode] Begin login weibo by cookie ...")
for key, value in utils.convert_str_cookie_to_dict(self.cookie_str).items():
await self.browser_context.add_cookies([{
'name': key,
'value': value,
'domain': ".we... | utils.logger.info("[WeiboLogin.login_by_qrcode] Begin login weibo by cookie ...")
for key, value in utils.convert_str_cookie_to_dict(self.cookie_str).items():
await self.browser_context.add_cookies([{
'name': key,
'value': value,
'domain': ".we... | 23 | 1,445 | 122 | 1,567 | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2023/12/23 15:42
# @Desc : 微博登录实现
import asyncio
import functools
import sys
from typing import Optional
from playwright.async_api import BrowserContext, Page
from tenacity import (RetryError, retry, retry_if_result, stop_after_attempt,
... | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2023/12/23 15:42
# @Desc : 微博登录实现
import asyncio
import functools
import sys
from typing import Optional
from playwright.async_api import BrowserContext, Page
from tenacity import (RetryError, retry, retry_if_result, stop_after_attempt,
... |
2,204 | /home/hieuvd/dung_repo/raw/stars_01/NanmiCoder_MediaCrawler/MediaCrawler | /home/hieuvd/dung_repo/raw/stars_01/NanmiCoder_MediaCrawler/MediaCrawler/proxy/providers/jishu_http_proxy.py | 77,78 | 1,88 | new_jisu_http_proxy | 83,88 | 78,88 | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2024/4/5 09:32
# @Desc : 极速HTTP代理提供类实现,官网地址：https://www.jisuhttp.com?pl=zG3Jna
import os
from typing import Dict, List
from urllib.parse import urlencode
import httpx
from proxy import IpGetError, ProxyProvider, RedisDbIpCache
from proxy.types im... | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2024/4/5 09:32
# @Desc : 极速HTTP代理提供类实现,官网地址：https://www.jisuhttp.com?pl=zG3Jna
import os
from typing import Dict, List
from urllib.parse import urlencode
import httpx
from proxy import IpGetError, ProxyProvider, RedisDbIpCache
from proxy.types im... | return JiSuHttpProxy(
key=os.getenv("jisu_key", ""), # 通过环境变量的方式获取极速HTTPIP提取key值
crypto=os.getenv("jisu_crypto", ""), # 通过环境变量的方式获取极速HTTPIP提取加密签名
time_validity_period=30 # 30分钟（最长时效）
)
| """
构造极速HTTP实例
Returns:
"""
return JiSuHttpProxy(
key=os.getenv("jisu_key", ""), # 通过环境变量的方式获取极速HTTPIP提取key值
crypto=os.getenv("jisu_crypto", ""), # 通过环境变量的方式获取极速HTTPIP提取加密签名
time_validity_period=30 # 30分钟（最长时效）
)
| 14 | 1,146 | 114 | 1,260 | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2024/4/5 09:32
# @Desc : 极速HTTP代理提供类实现,官网地址：https://www.jisuhttp.com?pl=zG3Jna
import os
from typing import Dict, List
from urllib.parse import urlencode
import httpx
from proxy import IpGetError, ProxyProvider, RedisDbIpCache
from proxy.types im... | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2024/4/5 09:32
# @Desc : 极速HTTP代理提供类实现,官网地址：https://www.jisuhttp.com?pl=zG3Jna
import os
from typing import Dict, List
from urllib.parse import urlencode
import httpx
from proxy import IpGetError, ProxyProvider, RedisDbIpCache
from proxy.types im... | |
2,205 | /home/hieuvd/dung_repo/raw/stars_01/NanmiCoder_MediaCrawler/MediaCrawler | /home/hieuvd/dung_repo/raw/stars_01/NanmiCoder_MediaCrawler/MediaCrawler/proxy/providers/jishu_http_proxy.py | 36,37 | 16,77 | get_proxies | JiSuHttpProxy | 41,77 | 37,77 | class JiSuHttpProxy(ProxyProvider):
def __init__(self, key: str, crypto: str, time_validity_period: int):
"""
极速HTTP 代理IP实现
:param key: 提取key值 (去官网注册后获取)
:param crypto: 加密签名 (去官网注册后获取)
"""
self.proxy_brand_name = "JISUHTTP"
self.api_path = "https://api.jisuhtt... | class JiSuHttpProxy(ProxyProvider):
def __init__(self, key: str, crypto: str, time_validity_period: int):
"""
极速HTTP 代理IP实现
:param key: 提取key值 (去官网注册后获取)
:param crypto: 加密签名 (去官网注册后获取)
"""
self.proxy_brand_name = "JISUHTTP"
self.api_path = "https://api.jisuhtt... |
# 优先从缓存中拿 IP
ip_cache_list = self.ip_cache.load_all_ip(proxy_brand_name=self.proxy_brand_name)
if len(ip_cache_list) >= num:
return ip_cache_list[:num]
# 如果缓存中的数量不够，从IP代理商获取补上，再存入缓存中
need_get_count = num - len(ip_cache_list)
self.params.update({"num": need_g... | """
:param num:
:return:
"""
# 优先从缓存中拿 IP
ip_cache_list = self.ip_cache.load_all_ip(proxy_brand_name=self.proxy_brand_name)
if len(ip_cache_list) >= num:
return ip_cache_list[:num]
# 如果缓存中的数量不够，从IP代理商获取补上，再存入缓存中
need_get_count = num -... | 100 | 381 | 611 | 992 | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2024/4/5 09:32
# @Desc : 极速HTTP代理提供类实现,官网地址：https://www.jisuhttp.com?pl=zG3Jna
import os
from typing import Dict, List
from urllib.parse import urlencode
import httpx
from proxy import IpGetError, ProxyProvider, RedisDbIpCache
from proxy.types im... | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2024/4/5 09:32
# @Desc : 极速HTTP代理提供类实现,官网地址：https://www.jisuhttp.com?pl=zG3Jna
import os
from typing import Dict, List
from urllib.parse import urlencode
import httpx
from proxy import IpGetError, ProxyProvider, RedisDbIpCache
from proxy.types im... |
2,206 | /home/hieuvd/dung_repo/raw/stars_01/NanmiCoder_MediaCrawler/MediaCrawler | /home/hieuvd/dung_repo/raw/stars_01/NanmiCoder_MediaCrawler/MediaCrawler/proxy/providers/kuaidl_proxy.py | 123,124 | 1,135 | new_kuai_daili_proxy | 129,135 | 124,135 | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2024/4/5 09:43
# @Desc : 快代理HTTP实现，官方文档：https://www.kuaidaili.com/?ref=ldwkjqipvz6c
import os
import re
from typing import Dict, List
import httpx
from pydantic import BaseModel, Field
from proxy import IpGetError, IpInfoModel, ProxyProvider, Red... | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2024/4/5 09:43
# @Desc : 快代理HTTP实现，官方文档：https://www.kuaidaili.com/?ref=ldwkjqipvz6c
import os
import re
from typing import Dict, List
import httpx
from pydantic import BaseModel, Field
from proxy import IpGetError, IpInfoModel, ProxyProvider, Red... | return KuaiDaiLiProxy(
kdl_secret_id=os.getenv("kdl_secret_id", "你的快代理secert_id"),
kdl_signature=os.getenv("kdl_signature", "你的快代理签名"),
kdl_user_name=os.getenv("kdl_user_name", "你的快代理用户名"),
kdl_user_pwd=os.getenv("kdl_user_pwd", "你的快代理密码"),
)
| """
构造快代理HTTP实例
Returns:
"""
return KuaiDaiLiProxy(
kdl_secret_id=os.getenv("kdl_secret_id", "你的快代理secert_id"),
kdl_signature=os.getenv("kdl_signature", "你的快代理签名"),
kdl_user_name=os.getenv("kdl_user_name", "你的快代理用户名"),
kdl_user_pwd=os.getenv("kdl_user_pwd", "你的快代理密码"... | 11 | 1,459 | 132 | 1,591 | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2024/4/5 09:43
# @Desc : 快代理HTTP实现，官方文档：https://www.kuaidaili.com/?ref=ldwkjqipvz6c
import os
import re
from typing import Dict, List
import httpx
from pydantic import BaseModel, Field
from proxy import IpGetError, IpInfoModel, ProxyProvider, Red... | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2024/4/5 09:43
# @Desc : 快代理HTTP实现，官方文档：https://www.kuaidaili.com/?ref=ldwkjqipvz6c
import os
import re
from typing import Dict, List
import httpx
from pydantic import BaseModel, Field
from proxy import IpGetError, IpInfoModel, ProxyProvider, Red... | |
2,207 | /home/hieuvd/dung_repo/raw/stars_01/NanmiCoder_MediaCrawler/MediaCrawler | /home/hieuvd/dung_repo/raw/stars_01/NanmiCoder_MediaCrawler/MediaCrawler/proxy/providers/kuaidl_proxy.py | 72,73 | 48,123 | get_proxies | KuaiDaiLiProxy | 81,123 | 73,123 | class KuaiDaiLiProxy(ProxyProvider):
def __init__(self, kdl_user_name: str, kdl_user_pwd: str, kdl_secret_id: str, kdl_signature: str):
"""
Args:
kdl_user_name:
kdl_user_pwd:
"""
self.kdl_user_name = kdl_user_name
self.kdl_user_pwd = kdl_user_pwd
... | class KuaiDaiLiProxy(ProxyProvider):
def __init__(self, kdl_user_name: str, kdl_user_pwd: str, kdl_secret_id: str, kdl_signature: str):
"""
Args:
kdl_user_name:
kdl_user_pwd:
"""
self.kdl_user_name = kdl_user_name
self.kdl_user_pwd = kdl_user_pwd
... | uri = "/api/getdps/"
# 优先从缓存中拿 IP
ip_cache_list = self.ip_cache.load_all_ip(proxy_brand_name=self.proxy_brand_name)
if len(ip_cache_list) >= num:
return ip_cache_list[:num]
# 如果缓存中的数量不够，从IP代理商获取补上，再存入缓存中
need_get_count = num - len(ip_cache_list)
self... | """
快代理实现
Args:
num:
Returns:
"""
uri = "/api/getdps/"
# 优先从缓存中拿 IP
ip_cache_list = self.ip_cache.load_all_ip(proxy_brand_name=self.proxy_brand_name)
if len(ip_cache_list) >= num:
return ip_cache_list[:num]
# 如果缓... | 122 | 339 | 656 | 995 | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2024/4/5 09:43
# @Desc : 快代理HTTP实现，官方文档：https://www.kuaidaili.com/?ref=ldwkjqipvz6c
import os
import re
from typing import Dict, List
import httpx
from pydantic import BaseModel, Field
from proxy import IpGetError, IpInfoModel, ProxyProvider, Red... | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2024/4/5 09:43
# @Desc : 快代理HTTP实现，官方文档：https://www.kuaidaili.com/?ref=ldwkjqipvz6c
import os
import re
from typing import Dict, List
import httpx
from pydantic import BaseModel, Field
from proxy import IpGetError, IpInfoModel, ProxyProvider, Red... |
2,208 | /home/hieuvd/dung_repo/raw/stars_01/NanmiCoder_MediaCrawler/MediaCrawler | /home/hieuvd/dung_repo/raw/stars_01/NanmiCoder_MediaCrawler/MediaCrawler/proxy/proxy_ip_pool.py | 94,95 | 1,111 | create_ip_pool | 101,109 | 95,109 | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2023/12/2 13:45
# @Desc : ip代理池实现
import random
from typing import Dict, List
import httpx
from tenacity import retry, stop_after_attempt, wait_fixed
import config
from proxy.providers import new_jisu_http_proxy, new_kuai_daili_proxy
from tools i... | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2023/12/2 13:45
# @Desc : ip代理池实现
import random
from typing import Dict, List
import httpx
from tenacity import retry, stop_after_attempt, wait_fixed
import config
from proxy.providers import new_jisu_http_proxy, new_kuai_daili_proxy
from tools i... | pool = ProxyIpPool(ip_pool_count=ip_pool_count,
enable_validate_ip=enable_validate_ip,
ip_provider=IpProxyProvider.get(config.IP_PROXY_PROVIDER_NAME)
)
await pool.load_proxies()
return pool
| """
创建 IP 代理池
:param ip_pool_count: ip池子的数量
:param enable_validate_ip: 是否开启验证IP代理
:return:
"""
pool = ProxyIpPool(ip_pool_count=ip_pool_count,
enable_validate_ip=enable_validate_ip,
ip_provider=IpProxyProvider.get(config.IP_PROXY_PROVIDER_NAME)
... | 10 | 1,064 | 90 | 1,154 | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2023/12/2 13:45
# @Desc : ip代理池实现
import random
from typing import Dict, List
import httpx
from tenacity import retry, stop_after_attempt, wait_fixed
import config
from proxy.providers import new_jisu_http_proxy, new_kuai_daili_proxy
from tools i... | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2023/12/2 13:45
# @Desc : ip代理池实现
import random
from typing import Dict, List
import httpx
from tenacity import retry, stop_after_attempt, wait_fixed
import config
from proxy.providers import new_jisu_http_proxy, new_kuai_daili_proxy
from tools i... | |
2,209 | /home/hieuvd/dung_repo/raw/stars_01/NanmiCoder_MediaCrawler/MediaCrawler | /home/hieuvd/dung_repo/raw/stars_01/NanmiCoder_MediaCrawler/MediaCrawler/proxy/proxy_ip_pool.py | 42,43 | 19,88 | _is_valid_proxy | ProxyIpPool | 48,63 | 43,63 | class ProxyIpPool:
def __init__(self, ip_pool_count: int, enable_validate_ip: bool, ip_provider: ProxyProvider) -> None:
"""
Args:
ip_pool_count:
enable_validate_ip:
ip_provider:
"""
self.valid_ip_url = "https://httpbin.org/ip" # 验证 IP 是否有效的地址
... | class ProxyIpPool:
def __init__(self, ip_pool_count: int, enable_validate_ip: bool, ip_provider: ProxyProvider) -> None:
"""
Args:
ip_pool_count:
enable_validate_ip:
ip_provider:
"""
self.valid_ip_url = "https://httpbin.org/ip" # 验证 IP 是否有效的地址
... | utils.logger.info(f"[ProxyIpPool._is_valid_proxy] testing {proxy.ip} is it valid ")
try:
httpx_proxy = {
f"{proxy.protocol}": f"http://{proxy.user}:{proxy.password}@{proxy.ip}:{proxy.port}"
}
async with httpx.AsyncClient(proxies=httpx_proxy) as client:... | """
验证代理IP是否有效
:param proxy:
:return:
"""
utils.logger.info(f"[ProxyIpPool._is_valid_proxy] testing {proxy.ip} is it valid ")
try:
httpx_proxy = {
f"{proxy.protocol}": f"http://{proxy.user}:{proxy.password}@{proxy.ip}:{proxy.port}"
... | 43 | 536 | 208 | 744 | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2023/12/2 13:45
# @Desc : ip代理池实现
import random
from typing import Dict, List
import httpx
from tenacity import retry, stop_after_attempt, wait_fixed
import config
from proxy.providers import new_jisu_http_proxy, new_kuai_daili_proxy
from tools i... | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2023/12/2 13:45
# @Desc : ip代理池实现
import random
from typing import Dict, List
import httpx
from tenacity import retry, stop_after_attempt, wait_fixed
import config
from proxy.providers import new_jisu_http_proxy, new_kuai_daili_proxy
from tools i... |
2,210 | /home/hieuvd/dung_repo/raw/stars_01/NanmiCoder_MediaCrawler/MediaCrawler | /home/hieuvd/dung_repo/raw/stars_01/NanmiCoder_MediaCrawler/MediaCrawler/recv_sms.py | 34,36 | 1,68 | receive_sms_notification | 51,61 | 36,61 | import re
from typing import List
import redis
import uvicorn
from fastapi import FastAPI, HTTPException, status
from pydantic import BaseModel
import config
from tools import utils
app = FastAPI()
redis_client = redis.Redis(host=config.REDIS_DB_HOST, password=config.REDIS_DB_PWD)
class SmsNotification(BaseModel)... | import re
from typing import List
import redis
import uvicorn
from fastapi import FastAPI, HTTPException, status
from pydantic import BaseModel
import config
from tools import utils
app = FastAPI()
redis_client = redis.Redis(host=config.REDIS_DB_HOST, password=config.REDIS_DB_PWD)
class SmsNotification(BaseModel)... | utils.logger.info(f"Received SMS notification: {sms.platform}, {sms.current_number}")
sms_code = extract_verification_code(sms.sms_content)
if sms_code:
# Save the verification code in Redis and set the expiration time to 3 minutes.
key = f"{sms.platform}_{sms.current_number}"
redis_... | """
Receive SMS notification and send it to Redis.
Args:
sms:
{
"platform": "xhs",
"from_number": "1069421xxx134",
"sms_content": "【小红书】您的验证码是: 171959， 3分钟内有效。请勿向他人泄漏。如非本人操作，可忽略本消息。",
"timestamp": "1686720601614",
... | 36 | 517 | 148 | 665 | import re
from typing import List
import redis
import uvicorn
from fastapi import FastAPI, HTTPException, status
from pydantic import BaseModel
import config
from tools import utils
app = FastAPI()
redis_client = redis.Redis(host=config.REDIS_DB_HOST, password=config.REDIS_DB_PWD)
class SmsNotification(BaseModel)... | import re
from typing import List
import redis
import uvicorn
from fastapi import FastAPI, HTTPException, status
from pydantic import BaseModel
import config
from tools import utils
app = FastAPI()
redis_client = redis.Redis(host=config.REDIS_DB_HOST, password=config.REDIS_DB_PWD)
class SmsNotification(BaseModel)... | |
2,211 | /home/hieuvd/dung_repo/raw/stars_01/NanmiCoder_MediaCrawler/MediaCrawler | /home/hieuvd/dung_repo/raw/stars_01/NanmiCoder_MediaCrawler/MediaCrawler/store/bilibili/bilibili_store_sql.py | 43,44 | 1,103 | update_content_by_content_id | 53,59 | 44,59 | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2024/4/6 15:30
# @Desc : sql接口集合
from typing import Dict, List
from db import AsyncMysqlDB
from var import media_crawler_db_var
async def query_content_by_content_id(content_id: str) -> Dict:
"""
查询一条内容记录（xhs的帖子 ｜ 抖音的视频 ｜ 微博 ｜ 快手视频 ...）... | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2024/4/6 15:30
# @Desc : sql接口集合
from typing import Dict, List
from db import AsyncMysqlDB
from var import media_crawler_db_var
async def query_content_by_content_id(content_id: str) -> Dict:
"""
查询一条内容记录（xhs的帖子 ｜ 抖音的视频 ｜ 微博 ｜ 快手视频 ...）... | async_db_conn: AsyncMysqlDB = media_crawler_db_var.get()
effect_row: int = await async_db_conn.update_table("bilibili_video", content_item, "video_id", content_id)
return effect_row
| """
更新一条记录（xhs的帖子 ｜ 抖音的视频 ｜ 微博 ｜ 快手视频 ...）
Args:
content_id:
content_item:
Returns:
"""
async_db_conn: AsyncMysqlDB = media_crawler_db_var.get()
effect_row: int = await async_db_conn.update_table("bilibili_video", content_item, "video_id", content_id)
return effect_row
... | 14 | 964 | 80 | 1,044 | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2024/4/6 15:30
# @Desc : sql接口集合
from typing import Dict, List
from db import AsyncMysqlDB
from var import media_crawler_db_var
async def query_content_by_content_id(content_id: str) -> Dict:
"""
查询一条内容记录（xhs的帖子 ｜ 抖音的视频 ｜ 微博 ｜ 快手视频 ...）... | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2024/4/6 15:30
# @Desc : sql接口集合
from typing import Dict, List
from db import AsyncMysqlDB
from var import media_crawler_db_var
async def query_content_by_content_id(content_id: str) -> Dict:
"""
查询一条内容记录（xhs的帖子 ｜ 抖音的视频 ｜ 微博 ｜ 快手视频 ...）... | |
2,212 | /home/hieuvd/dung_repo/raw/stars_01/NanmiCoder_MediaCrawler/MediaCrawler | /home/hieuvd/dung_repo/raw/stars_01/NanmiCoder_MediaCrawler/MediaCrawler/store/douyin/douyin_store_sql.py | 59,60 | 1,103 | query_comment_by_comment_id | 68,76 | 60,76 | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2024/4/6 15:30
# @Desc : sql接口集合
from typing import Dict, List
from db import AsyncMysqlDB
from var import media_crawler_db_var
async def query_content_by_content_id(content_id: str) -> Dict:
"""
查询一条内容记录（xhs的帖子 ｜ 抖音的视频 ｜ 微博 ｜ 快手视频 ...）... | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2024/4/6 15:30
# @Desc : sql接口集合
from typing import Dict, List
from db import AsyncMysqlDB
from var import media_crawler_db_var
async def query_content_by_content_id(content_id: str) -> Dict:
"""
查询一条内容记录（xhs的帖子 ｜ 抖音的视频 ｜ 微博 ｜ 快手视频 ...）... | async_db_conn: AsyncMysqlDB = media_crawler_db_var.get()
sql: str = f"select * from douyin_aweme_comment where comment_id = '{comment_id}'"
rows: List[Dict] = await async_db_conn.query(sql)
if len(rows) > 0:
return rows[0]
return dict()
| """
查询一条评论内容
Args:
comment_id:
Returns:
"""
async_db_conn: AsyncMysqlDB = media_crawler_db_var.get()
sql: str = f"select * from douyin_aweme_comment where comment_id = '{comment_id}'"
rows: List[Dict] = await async_db_conn.query(sql)
if len(rows) > 0:
return rows[0]... | 28 | 947 | 111 | 1,058 | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2024/4/6 15:30
# @Desc : sql接口集合
from typing import Dict, List
from db import AsyncMysqlDB
from var import media_crawler_db_var
async def query_content_by_content_id(content_id: str) -> Dict:
"""
查询一条内容记录（xhs的帖子 ｜ 抖音的视频 ｜ 微博 ｜ 快手视频 ...）... | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2024/4/6 15:30
# @Desc : sql接口集合
from typing import Dict, List
from db import AsyncMysqlDB
from var import media_crawler_db_var
async def query_content_by_content_id(content_id: str) -> Dict:
"""
查询一条内容记录（xhs的帖子 ｜ 抖音的视频 ｜ 微博 ｜ 快手视频 ...）... | |
2,213 | /home/hieuvd/dung_repo/raw/stars_01/NanmiCoder_MediaCrawler/MediaCrawler | /home/hieuvd/dung_repo/raw/stars_01/NanmiCoder_MediaCrawler/MediaCrawler/store/kuaishou/kuaishou_store_sql.py | 43,44 | 1,103 | update_content_by_content_id | 53,59 | 44,59 | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2024/4/6 15:30
# @Desc : sql接口集合
from typing import Dict, List
from db import AsyncMysqlDB
from var import media_crawler_db_var
async def query_content_by_content_id(content_id: str) -> Dict:
"""
查询一条内容记录（xhs的帖子 ｜ 抖音的视频 ｜ 微博 ｜ 快手视频 ...）... | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2024/4/6 15:30
# @Desc : sql接口集合
from typing import Dict, List
from db import AsyncMysqlDB
from var import media_crawler_db_var
async def query_content_by_content_id(content_id: str) -> Dict:
"""
查询一条内容记录（xhs的帖子 ｜ 抖音的视频 ｜ 微博 ｜ 快手视频 ...）... | async_db_conn: AsyncMysqlDB = media_crawler_db_var.get()
effect_row: int = await async_db_conn.update_table("kuaishou_video", content_item, "video_id", content_id)
return effect_row
| """
更新一条记录（xhs的帖子 ｜ 抖音的视频 ｜ 微博 ｜ 快手视频 ...）
Args:
content_id:
content_item:
Returns:
"""
async_db_conn: AsyncMysqlDB = media_crawler_db_var.get()
effect_row: int = await async_db_conn.update_table("kuaishou_video", content_item, "video_id", content_id)
return effect_row
... | 14 | 969 | 81 | 1,050 | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2024/4/6 15:30
# @Desc : sql接口集合
from typing import Dict, List
from db import AsyncMysqlDB
from var import media_crawler_db_var
async def query_content_by_content_id(content_id: str) -> Dict:
"""
查询一条内容记录（xhs的帖子 ｜ 抖音的视频 ｜ 微博 ｜ 快手视频 ...）... | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2024/4/6 15:30
# @Desc : sql接口集合
from typing import Dict, List
from db import AsyncMysqlDB
from var import media_crawler_db_var
async def query_content_by_content_id(content_id: str) -> Dict:
"""
查询一条内容记录（xhs的帖子 ｜ 抖音的视频 ｜ 微博 ｜ 快手视频 ...）... | |
2,214 | /home/hieuvd/dung_repo/raw/stars_01/NanmiCoder_MediaCrawler/MediaCrawler | /home/hieuvd/dung_repo/raw/stars_01/NanmiCoder_MediaCrawler/MediaCrawler/store/weibo/weibo_store_image.py | 28,29 | 14,54 | make_save_file_name | WeiboStoreImage | 37,39 | 29,39 | class WeiboStoreImage(AbstractStoreImage):
image_store_path: str = "data/weibo/images"
async def store_image(self, image_content_item: Dict):
"""
store content
Args:
content_item:
Returns:
"""
await self.save_image(image_content_item.get("pic_id"), ... | class WeiboStoreImage(AbstractStoreImage):
image_store_path: str = "data/weibo/images"
async def store_image(self, image_content_item: Dict):
"""
store content
Args:
content_item:
Returns:
"""
await self.save_image(image_content_item.get("pic_id"), ... | return f"{self.image_store_path}/{picid}.{extension_file_name}"
| """
make save file name by store type
Args:
picid: image id
Returns:
"""
return f"{self.image_store_path}/{picid}.{extension_file_name}"
| 2 | 393 | 28 | 421 | # -*- coding: utf-8 -*-
# @Author : Erm
# @Time : 2024/4/9 17:35
# @Desc : 微博保存图片类
import pathlib
from typing import Dict
import aiofiles
from base.base_crawler import AbstractStoreImage
from tools import utils
class WeiboStoreImage(AbstractStoreImage):
image_store_path: str = "data/weibo/images"
as... | # -*- coding: utf-8 -*-
# @Author : Erm
# @Time : 2024/4/9 17:35
# @Desc : 微博保存图片类
import pathlib
from typing import Dict
import aiofiles
from base.base_crawler import AbstractStoreImage
from tools import utils
class WeiboStoreImage(AbstractStoreImage):
image_store_path: str = "data/weibo/images"
as... |
2,215 | /home/hieuvd/dung_repo/raw/stars_01/NanmiCoder_MediaCrawler/MediaCrawler | /home/hieuvd/dung_repo/raw/stars_01/NanmiCoder_MediaCrawler/MediaCrawler/store/weibo/weibo_store_sql.py | 59,60 | 1,103 | query_comment_by_comment_id | 68,76 | 60,76 | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2024/4/6 15:30
# @Desc : sql接口集合
from typing import Dict, List
from db import AsyncMysqlDB
from var import media_crawler_db_var
async def query_content_by_content_id(content_id: str) -> Dict:
"""
查询一条内容记录（xhs的帖子 ｜ 抖音的视频 ｜ 微博 ｜ 快手视频 ...）... | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2024/4/6 15:30
# @Desc : sql接口集合
from typing import Dict, List
from db import AsyncMysqlDB
from var import media_crawler_db_var
async def query_content_by_content_id(content_id: str) -> Dict:
"""
查询一条内容记录（xhs的帖子 ｜ 抖音的视频 ｜ 微博 ｜ 快手视频 ...）... | async_db_conn: AsyncMysqlDB = media_crawler_db_var.get()
sql: str = f"select * from weibo_note_comment where comment_id = '{comment_id}'"
rows: List[Dict] = await async_db_conn.query(sql)
if len(rows) > 0:
return rows[0]
return dict()
| """
查询一条评论内容
Args:
comment_id:
Returns:
"""
async_db_conn: AsyncMysqlDB = media_crawler_db_var.get()
sql: str = f"select * from weibo_note_comment where comment_id = '{comment_id}'"
rows: List[Dict] = await async_db_conn.query(sql)
if len(rows) > 0:
return rows[0]
... | 28 | 936 | 110 | 1,046 | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2024/4/6 15:30
# @Desc : sql接口集合
from typing import Dict, List
from db import AsyncMysqlDB
from var import media_crawler_db_var
async def query_content_by_content_id(content_id: str) -> Dict:
"""
查询一条内容记录（xhs的帖子 ｜ 抖音的视频 ｜ 微博 ｜ 快手视频 ...）... | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2024/4/6 15:30
# @Desc : sql接口集合
from typing import Dict, List
from db import AsyncMysqlDB
from var import media_crawler_db_var
async def query_content_by_content_id(content_id: str) -> Dict:
"""
查询一条内容记录（xhs的帖子 ｜ 抖音的视频 ｜ 微博 ｜ 快手视频 ...）... | |
2,216 | /home/hieuvd/dung_repo/raw/stars_01/NanmiCoder_MediaCrawler/MediaCrawler | /home/hieuvd/dung_repo/raw/stars_01/NanmiCoder_MediaCrawler/MediaCrawler/store/xhs/xhs_store_sql.py | 76,77 | 1,149 | add_new_comment | 85,90 | 77,90 | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2024/4/6 15:30
# @Desc : sql接口集合
from typing import Dict, List
from db import AsyncMysqlDB
from var import media_crawler_db_var
async def query_content_by_content_id(content_id: str) -> Dict:
"""
查询一条内容记录（xhs的帖子 ｜ 抖音的视频 ｜ 微博 ｜ 快手视频 ...）... | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2024/4/6 15:30
# @Desc : sql接口集合
from typing import Dict, List
from db import AsyncMysqlDB
from var import media_crawler_db_var
async def query_content_by_content_id(content_id: str) -> Dict:
"""
查询一条内容记录（xhs的帖子 ｜ 抖音的视频 ｜ 微博 ｜ 快手视频 ...）... | async_db_conn: AsyncMysqlDB = media_crawler_db_var.get()
last_row_id: int = await async_db_conn.item_to_table("xhs_note_comment", comment_item)
return last_row_id
| """
新增一条评论记录
Args:
comment_item:
Returns:
"""
async_db_conn: AsyncMysqlDB = media_crawler_db_var.get()
last_row_id: int = await async_db_conn.item_to_table("xhs_note_comment", comment_item)
return last_row_id
| 12 | 1,386 | 76 | 1,462 | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2024/4/6 15:30
# @Desc : sql接口集合
from typing import Dict, List
from db import AsyncMysqlDB
from var import media_crawler_db_var
async def query_content_by_content_id(content_id: str) -> Dict:
"""
查询一条内容记录（xhs的帖子 ｜ 抖音的视频 ｜ 微博 ｜ 快手视频 ...）... | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2024/4/6 15:30
# @Desc : sql接口集合
from typing import Dict, List
from db import AsyncMysqlDB
from var import media_crawler_db_var
async def query_content_by_content_id(content_id: str) -> Dict:
"""
查询一条内容记录（xhs的帖子 ｜ 抖音的视频 ｜ 微博 ｜ 快手视频 ...）... | |
2,217 | /home/hieuvd/dung_repo/raw/stars_01/NanmiCoder_MediaCrawler/MediaCrawler | /home/hieuvd/dung_repo/raw/stars_01/NanmiCoder_MediaCrawler/MediaCrawler/tools/easing.py | 47,48 | 1,71 | ease_out_elastic | 48,57 | 48,57 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# copy from https://github.com/aneasystone/selenium-test/blob/master/12-slider-captcha.py
# thanks to aneasystone for his great work
import math
from typing import List, Tuple
import numpy as np
# https://github.com/gdsmith/jquery.easing/blob/master/jquery.easing.js
def ... | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# copy from https://github.com/aneasystone/selenium-test/blob/master/12-slider-captcha.py
# thanks to aneasystone for his great work
import math
from typing import List, Tuple
import numpy as np
# https://github.com/gdsmith/jquery.easing/blob/master/jquery.easing.js
def ... | if x == 0:
return 0
elif x == 1:
return 1
else:
c4 = (2 * math.pi) / 3
return pow(2, -10 * x) * math.sin((x * 10 - 0.75) * c4) + 1
| if x == 0:
return 0
elif x == 1:
return 1
else:
c4 = (2 * math.pi) / 3
return pow(2, -10 * x) * math.sin((x * 10 - 0.75) * c4) + 1
| 35 | 587 | 90 | 677 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# copy from https://github.com/aneasystone/selenium-test/blob/master/12-slider-captcha.py
# thanks to aneasystone for his great work
import math
from typing import List, Tuple
import numpy as np
# https://github.com/gdsmith/jquery.easing/blob/master/jquery.easing.js
def ... | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# copy from https://github.com/aneasystone/selenium-test/blob/master/12-slider-captcha.py
# thanks to aneasystone for his great work
import math
from typing import List, Tuple
import numpy as np
# https://github.com/gdsmith/jquery.easing/blob/master/jquery.easing.js
def ... | |
2,218 | /home/hieuvd/dung_repo/raw/stars_01/NanmiCoder_MediaCrawler/MediaCrawler | /home/hieuvd/dung_repo/raw/stars_01/NanmiCoder_MediaCrawler/MediaCrawler/tools/time_util.py | 87,88 | 1,107 | rfc2822_to_timestamp | 88,103 | 88,103 | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2023/12/2 12:52
# @Desc : 时间相关的工具函数
import time
from datetime import datetime, timedelta, timezone
def get_current_timestamp() -> int:
"""
获取当前的时间戳(13 位)：1701493264496
:return:
"""
return int(time.time() * 1000)
def get_cur... | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2023/12/2 12:52
# @Desc : 时间相关的工具函数
import time
from datetime import datetime, timedelta, timezone
def get_current_timestamp() -> int:
"""
获取当前的时间戳(13 位)：1701493264496
:return:
"""
return int(time.time() * 1000)
def get_cur... | # 定义RFC 2822格式
rfc2822_format = "%a %b %d %H:%M:%S %z %Y"
# 将RFC 2822时间字符串转换为datetime对象
dt_object = datetime.strptime(rfc2822_time, rfc2822_format)
# 将datetime对象转换为UTC时间
dt_utc = dt_object.replace(tzinfo=timezone.utc)
# 计算UTC时间对应的Unix时间戳
timestamp = int(dt_utc.timestamp())
return... | # 定义RFC 2822格式
rfc2822_format = "%a %b %d %H:%M:%S %z %Y"
# 将RFC 2822时间字符串转换为datetime对象
dt_object = datetime.strptime(rfc2822_time, rfc2822_format)
# 将datetime对象转换为UTC时间
dt_utc = dt_object.replace(tzinfo=timezone.utc)
# 计算UTC时间对应的Unix时间戳
timestamp = int(dt_utc.timestamp())
return... | 30 | 1,024 | 185 | 1,209 | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2023/12/2 12:52
# @Desc : 时间相关的工具函数
import time
from datetime import datetime, timedelta, timezone
def get_current_timestamp() -> int:
"""
获取当前的时间戳(13 位)：1701493264496
:return:
"""
return int(time.time() * 1000)
def get_cur... | # -*- coding: utf-8 -*-
# @Author : relakkes@gmail.com
# @Time : 2023/12/2 12:52
# @Desc : 时间相关的工具函数
import time
from datetime import datetime, timedelta, timezone
def get_current_timestamp() -> int:
"""
获取当前的时间戳(13 位)：1701493264496
:return:
"""
return int(time.time() * 1000)
def get_cur... | |
2,219 | /home/hieuvd/dung_repo/raw/stars_01/NanmiCoder_MediaCrawler/MediaCrawler | /home/hieuvd/dung_repo/raw/stars_01/NanmiCoder_MediaCrawler/MediaCrawler/tools/utils.py | 8,9 | 1,21 | init_loging_config | 9,20 | 9,20 | import logging
from .crawler_util import *
from .slider_util import *
from .time_util import *
def init_loging_config():
FILL_FUNC_BODYlogger = init_loging_config()
| import logging
from .crawler_util import *
from .slider_util import *
from .time_util import *
def init_loging_config():
FILL_FUNC_BODYlogger = init_loging_config()
| level = logging.INFO
logging.basicConfig(
level=level,
format="%(asctime)s [%(threadName)s] %(name)s %(levelname)s (%(filename)s:%(lineno)d) - %(message)s",
datefmt='%Y-%m-%d %H:%M:%S'
)
_logger = logging.getLogger("MediaCrawler")
_logger.setLevel(level)
return _logger
| level = logging.INFO
logging.basicConfig(
level=level,
format="%(asctime)s [%(threadName)s] %(name)s %(levelname)s (%(filename)s:%(lineno)d) - %(message)s",
datefmt='%Y-%m-%d %H:%M:%S'
)
_logger = logging.getLogger("MediaCrawler")
_logger.setLevel(level)
return _logger
| 21 | 62 | 142 | 204 | import logging
from .crawler_util import *
from .slider_util import *
from .time_util import *
def init_loging_config():
FILL_FUNC_BODYlogger = init_loging_config()
| import logging
from .crawler_util import *
from .slider_util import *
from .time_util import *
def init_loging_config():
FILL_FUNC_BODYlogger = init_loging_config()
| |
4,559 | /home/hieuvd/dung_repo/raw/stars_01/OpenTalker_SadTalker/SadTalker | /home/hieuvd/dung_repo/raw/stars_01/OpenTalker_SadTalker/SadTalker/src/audio2exp_models/audio2exp.py | 13,14 | 6,42 | test | Audio2Exp | 14,42 | 14,42 | class Audio2Exp(nn.Module):
def __init__(self, netG, cfg, device, prepare_training_loss=False):
super(Audio2Exp, self).__init__()
self.cfg = cfg
self.device = device
self.netG = netG.to(device)
def test(self, batch):
FILL_FUNC_BODY | class Audio2Exp(nn.Module):
def __init__(self, netG, cfg, device, prepare_training_loss=False):
super(Audio2Exp, self).__init__()
self.cfg = cfg
self.device = device
self.netG = netG.to(device)
def test(self, batch):
FILL_FUNC_BODY |
mel_input = batch['indiv_mels'] # bs T 1 80 16
bs = mel_input.shape[0]
T = mel_input.shape[1]
exp_coeff_pred = []
for i in tqdm(range(0, T, 10),'audio2exp:'): # every 10 frames
current_mel_input = mel_input[:,i:i+10]
... |
mel_input = batch['indiv_mels'] # bs T 1 80 16
bs = mel_input.shape[0]
T = mel_input.shape[1]
exp_coeff_pred = []
for i in tqdm(range(0, T, 10),'audio2exp:'): # every 10 frames
current_mel_input = mel_input[:,i:i+10]
... | 89 | 97 | 386 | 483 | from tqdm import tqdm
import torch
from torch import nn
class Audio2Exp(nn.Module):
def __init__(self, netG, cfg, device, prepare_training_loss=False):
super(Audio2Exp, self).__init__()
self.cfg = cfg
self.device = device
self.netG = netG.to(device)
def test(self, batch):
FILL... | from tqdm import tqdm
import torch
from torch import nn
class Audio2Exp(nn.Module):
def __init__(self, netG, cfg, device, prepare_training_loss=False):
super(Audio2Exp, self).__init__()
self.cfg = cfg
self.device = device
self.netG = netG.to(device)
def test(self, batch):
FILL... |
4,560 | /home/hieuvd/dung_repo/raw/stars_01/OpenTalker_SadTalker/SadTalker | /home/hieuvd/dung_repo/raw/stars_01/OpenTalker_SadTalker/SadTalker/src/audio2exp_models/networks.py | 16,17 | 5,26 | forward | Conv2d | 17,26 | 17,26 | class Conv2d(nn.Module):
def __init__(self, cin, cout, kernel_size, stride, padding, residual=False, use_act = True, *args, **kwargs):
super().__init__(*args, **kwargs)
self.conv_block = nn.Sequential(
nn.Conv2d(cin, cout, kernel_size, stride, padding),
... | class Conv2d(nn.Module):
def __init__(self, cin, cout, kernel_size, stride, padding, residual=False, use_act = True, *args, **kwargs):
super().__init__(*args, **kwargs)
self.conv_block = nn.Sequential(
nn.Conv2d(cin, cout, kernel_size, stride, padding),
... | out = self.conv_block(x)
if self.residual:
out += x
if self.use_act:
return self.act(out)
else:
return out
| out = self.conv_block(x)
if self.residual:
out += x
if self.use_act:
return self.act(out)
else:
return out
| 15 | 167 | 63 | 230 | import torch
import torch.nn.functional as F
from torch import nn
class Conv2d(nn.Module):
def __init__(self, cin, cout, kernel_size, stride, padding, residual=False, use_act = True, *args, **kwargs):
super().__init__(*args, **kwargs)
self.conv_block = nn.Sequential(
nn.... | import torch
import torch.nn.functional as F
from torch import nn
class Conv2d(nn.Module):
def __init__(self, cin, cout, kernel_size, stride, padding, residual=False, use_act = True, *args, **kwargs):
super().__init__(*args, **kwargs)
self.conv_block = nn.Sequential(
nn.... |
4,561 | /home/hieuvd/dung_repo/raw/stars_01/OpenTalker_SadTalker/SadTalker | /home/hieuvd/dung_repo/raw/stars_01/OpenTalker_SadTalker/SadTalker/src/audio2exp_models/networks.py | 67,68 | 26,75 | forward | SimpleWrapperV2 | 68,75 | 68,75 | class SimpleWrapperV2(nn.Module):
def __init__(self) -> None:
super().__init__()
self.audio_encoder = nn.Sequential(
Conv2d(1, 32, kernel_size=3, stride=1, padding=1),
Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True),
Conv2d(32, 32, kernel_size=3,... | class SimpleWrapperV2(nn.Module):
def __init__(self) -> None:
super().__init__()
self.audio_encoder = nn.Sequential(
Conv2d(1, 32, kernel_size=3, stride=1, padding=1),
Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True),
Conv2d(32, 32, kernel_size=3,... | x = self.audio_encoder(x).view(x.size(0), -1)
ref_reshape = ref.reshape(x.size(0), -1)
ratio = ratio.reshape(x.size(0), -1)
y = self.mapping1(torch.cat([x, ref_reshape, ratio], dim=1))
out = y.reshape(ref.shape[0], ref.shape[1], -1) #+ ref # resudial
return out
| x = self.audio_encoder(x).view(x.size(0), -1)
ref_reshape = ref.reshape(x.size(0), -1)
ratio = ratio.reshape(x.size(0), -1)
y = self.mapping1(torch.cat([x, ref_reshape, ratio], dim=1))
out = y.reshape(ref.shape[0], ref.shape[1], -1) #+ ref # resudial
return out
| 29 | 792 | 135 | 927 | import torch
import torch.nn.functional as F
from torch import nn
class Conv2d(nn.Module):
def __init__(self, cin, cout, kernel_size, stride, padding, residual=False, use_act = True, *args, **kwargs):
super().__init__(*args, **kwargs)
self.conv_block = nn.Sequential(
nn.... | import torch
import torch.nn.functional as F
from torch import nn
class Conv2d(nn.Module):
def __init__(self, cin, cout, kernel_size, stride, padding, residual=False, use_act = True, *args, **kwargs):
super().__init__(*args, **kwargs)
self.conv_block = nn.Sequential(
nn.... |
4,562 | /home/hieuvd/dung_repo/raw/stars_01/OpenTalker_SadTalker/SadTalker | /home/hieuvd/dung_repo/raw/stars_01/OpenTalker_SadTalker/SadTalker/src/audio2pose_models/audio2pose.py | 48,49 | 7,95 | test | Audio2Pose | 49,95 | 49,95 | class Audio2Pose(nn.Module):
def __init__(self, cfg, wav2lip_checkpoint, device='cuda'):
super().__init__()
self.cfg = cfg
self.seq_len = cfg.MODEL.CVAE.SEQ_LEN
self.latent_dim = cfg.MODEL.CVAE.LATENT_SIZE
self.device = device
self.audio_encoder = AudioEncoder(wav2li... | class Audio2Pose(nn.Module):
def __init__(self, cfg, wav2lip_checkpoint, device='cuda'):
super().__init__()
self.cfg = cfg
self.seq_len = cfg.MODEL.CVAE.SEQ_LEN
self.latent_dim = cfg.MODEL.CVAE.LATENT_SIZE
self.device = device
self.audio_encoder = AudioEncoder(wav2li... |
batch = {}
ref = x['ref'] #bs 1 70
batch['ref'] = x['ref'][:,0,-6:]
batch['class'] = x['class']
bs = ref.shape[0]
indiv_mels= x['indiv_mels'] # bs T 1 80 16
indiv_mels_use = indiv_mels[:, 1:] # we regar... |
batch = {}
ref = x['ref'] #bs 1 70
batch['ref'] = x['ref'][:,0,-6:]
batch['class'] = x['class']
bs = ref.shape[0]
indiv_mels= x['indiv_mels'] # bs T 1 80 16
indiv_mels_use = indiv_mels[:, 1:] # we regar... | 160 | 627 | 778 | 1,405 | import torch
from torch import nn
from src.audio2pose_models.cvae import CVAE
from src.audio2pose_models.discriminator import PoseSequenceDiscriminator
from src.audio2pose_models.audio_encoder import AudioEncoder
class Audio2Pose(nn.Module):
def __init__(self, cfg, wav2lip_checkpoint, device='cuda'):
super... | import torch
from torch import nn
from src.audio2pose_models.cvae import CVAE
from src.audio2pose_models.discriminator import PoseSequenceDiscriminator
from src.audio2pose_models.audio_encoder import AudioEncoder
class Audio2Pose(nn.Module):
def __init__(self, cfg, wav2lip_checkpoint, device='cuda'):
super... |
4,563 | /home/hieuvd/dung_repo/raw/stars_01/OpenTalker_SadTalker/SadTalker | /home/hieuvd/dung_repo/raw/stars_01/OpenTalker_SadTalker/SadTalker/src/audio2pose_models/audio_encoder.py | 54,55 | 21,65 | forward | AudioEncoder | 55,65 | 55,65 | class AudioEncoder(nn.Module):
def __init__(self, wav2lip_checkpoint, device):
super(AudioEncoder, self).__init__()
self.audio_encoder = nn.Sequential(
Conv2d(1, 32, kernel_size=3, stride=1, padding=1),
Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True),
... | class AudioEncoder(nn.Module):
def __init__(self, wav2lip_checkpoint, device):
super(AudioEncoder, self).__init__()
self.audio_encoder = nn.Sequential(
Conv2d(1, 32, kernel_size=3, stride=1, padding=1),
Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True),
... | # audio_sequences = (B, T, 1, 80, 16)
B = audio_sequences.size(0)
audio_sequences = torch.cat([audio_sequences[:, i] for i in range(audio_sequences.size(1))], dim=0)
audio_embedding = self.audio_encoder(audio_sequences) # B, 512, 1, 1
dim = audio_embedding.shape[1]
audi... | # audio_sequences = (B, T, 1, 80, 16)
B = audio_sequences.size(0)
audio_sequences = torch.cat([audio_sequences[:, i] for i in range(audio_sequences.size(1))], dim=0)
audio_embedding = self.audio_encoder(audio_sequences) # B, 512, 1, 1
dim = audio_embedding.shape[1]
audi... | 43 | 685 | 195 | 880 | import torch
from torch import nn
from torch.nn import functional as F
class Conv2d(nn.Module):
def __init__(self, cin, cout, kernel_size, stride, padding, residual=False, *args, **kwargs):
super().__init__(*args, **kwargs)
self.conv_block = nn.Sequential(
nn.Conv2d(cin,... | import torch
from torch import nn
from torch.nn import functional as F
class Conv2d(nn.Module):
def __init__(self, cin, cout, kernel_size, stride, padding, residual=False, *args, **kwargs):
super().__init__(*args, **kwargs)
self.conv_block = nn.Sequential(
nn.Conv2d(cin,... |
4,564 | /home/hieuvd/dung_repo/raw/stars_01/OpenTalker_SadTalker/SadTalker | /home/hieuvd/dung_repo/raw/stars_01/OpenTalker_SadTalker/SadTalker/src/audio2pose_models/audio_encoder.py | 15,16 | 5,21 | forward | Conv2d | 16,21 | 16,21 | class Conv2d(nn.Module):
def __init__(self, cin, cout, kernel_size, stride, padding, residual=False, *args, **kwargs):
super().__init__(*args, **kwargs)
self.conv_block = nn.Sequential(
nn.Conv2d(cin, cout, kernel_size, stride, padding),
nn.Bat... | class Conv2d(nn.Module):
def __init__(self, cin, cout, kernel_size, stride, padding, residual=False, *args, **kwargs):
super().__init__(*args, **kwargs)
self.conv_block = nn.Sequential(
nn.Conv2d(cin, cout, kernel_size, stride, padding),
nn.Bat... | out = self.conv_block(x)
if self.residual:
out += x
return self.act(out)
| out = self.conv_block(x)
if self.residual:
out += x
return self.act(out)
| 10 | 150 | 40 | 190 | import torch
from torch import nn
from torch.nn import functional as F
class Conv2d(nn.Module):
def __init__(self, cin, cout, kernel_size, stride, padding, residual=False, *args, **kwargs):
super().__init__(*args, **kwargs)
self.conv_block = nn.Sequential(
nn.Conv2d(cin,... | import torch
from torch import nn
from torch.nn import functional as F
class Conv2d(nn.Module):
def __init__(self, cin, cout, kernel_size, stride, padding, residual=False, *args, **kwargs):
super().__init__(*args, **kwargs)
self.conv_block = nn.Sequential(
nn.Conv2d(cin,... |
4,566 | /home/hieuvd/dung_repo/raw/stars_01/OpenTalker_SadTalker/SadTalker | /home/hieuvd/dung_repo/raw/stars_01/OpenTalker_SadTalker/SadTalker/src/audio2pose_models/cvae.py | 30,31 | 13,51 | reparameterize | CVAE | 31,35 | 31,35 | class CVAE(nn.Module):
def __init__(self, cfg):
super().__init__()
encoder_layer_sizes = cfg.MODEL.CVAE.ENCODER_LAYER_SIZES
decoder_layer_sizes = cfg.MODEL.CVAE.DECODER_LAYER_SIZES
latent_size = cfg.MODEL.CVAE.LATENT_SIZE
num_classes = cfg.DATASET.NUM_CLASSES
audio_em... | class CVAE(nn.Module):
def __init__(self, cfg):
super().__init__()
encoder_layer_sizes = cfg.MODEL.CVAE.ENCODER_LAYER_SIZES
decoder_layer_sizes = cfg.MODEL.CVAE.DECODER_LAYER_SIZES
latent_size = cfg.MODEL.CVAE.LATENT_SIZE
num_classes = cfg.DATASET.NUM_CLASSES
audio_em... | std = torch.exp(0.5 * logvar)
eps = torch.randn_like(std)
return mu + eps * std
| std = torch.exp(0.5 * logvar)
eps = torch.randn_like(std)
return mu + eps * std
| 14 | 497 | 45 | 542 | import torch
import torch.nn.functional as F
from torch import nn
from src.audio2pose_models.res_unet import ResUnet
def class2onehot(idx, class_num):
assert torch.max(idx).item() < class_num
onehot = torch.zeros(idx.size(0), class_num).to(idx.device)
onehot.scatter_(1, idx, 1)
return onehot
class CV... | import torch
import torch.nn.functional as F
from torch import nn
from src.audio2pose_models.res_unet import ResUnet
def class2onehot(idx, class_num):
assert torch.max(idx).item() < class_num
onehot = torch.zeros(idx.size(0), class_num).to(idx.device)
onehot.scatter_(1, idx, 1)
return onehot
class CV... |
4,567 | /home/hieuvd/dung_repo/raw/stars_01/OpenTalker_SadTalker/SadTalker | /home/hieuvd/dung_repo/raw/stars_01/OpenTalker_SadTalker/SadTalker/src/audio2pose_models/cvae.py | 123,124 | 99,150 | forward | DECODER | 124,150 | 124,150 | class DECODER(nn.Module):
def __init__(self, layer_sizes, latent_size, num_classes,
audio_emb_in_size, audio_emb_out_size, seq_len):
super().__init__()
self.resunet = ResUnet()
self.num_classes = num_classes
self.seq_len = seq_len
self.MLP = nn.Sequential()... | class DECODER(nn.Module):
def __init__(self, layer_sizes, latent_size, num_classes,
audio_emb_in_size, audio_emb_out_size, seq_len):
super().__init__()
self.resunet = ResUnet()
self.num_classes = num_classes
self.seq_len = seq_len
self.MLP = nn.Sequential()... |
z = batch['z'] #bs latent_size
bs = z.shape[0]
class_id = batch['class']
ref = batch['ref'] #bs 6
audio_in = batch['audio_emb'] # bs seq_len audio_emb_in_size
#print('audio_in:... |
z = batch['z'] #bs latent_size
bs = z.shape[0]
class_id = batch['class']
ref = batch['ref'] #bs 6
audio_in = batch['audio_emb'] # bs seq_len audio_emb_in_size
#print('audio_in:... | 93 | 392 | 429 | 821 | import torch
import torch.nn.functional as F
from torch import nn
from src.audio2pose_models.res_unet import ResUnet
def class2onehot(idx, class_num):
assert torch.max(idx).item() < class_num
onehot = torch.zeros(idx.size(0), class_num).to(idx.device)
onehot.scatter_(1, idx, 1)
return onehot
class CV... | import torch
import torch.nn.functional as F
from torch import nn
from src.audio2pose_models.res_unet import ResUnet
def class2onehot(idx, class_num):
assert torch.max(idx).item() < class_num
onehot = torch.zeros(idx.size(0), class_num).to(idx.device)
onehot.scatter_(1, idx, 1)
return onehot
class CV... |
4,568 | /home/hieuvd/dung_repo/raw/stars_01/OpenTalker_SadTalker/SadTalker | /home/hieuvd/dung_repo/raw/stars_01/OpenTalker_SadTalker/SadTalker/src/audio2pose_models/cvae.py | 73,74 | 51,99 | forward | ENCODER | 74,99 | 74,99 | class ENCODER(nn.Module):
def __init__(self, layer_sizes, latent_size, num_classes,
audio_emb_in_size, audio_emb_out_size, seq_len):
super().__init__()
self.resunet = ResUnet()
self.num_classes = num_classes
self.seq_len = seq_len
self.MLP = nn.Sequential()... | class ENCODER(nn.Module):
def __init__(self, layer_sizes, latent_size, num_classes,
audio_emb_in_size, audio_emb_out_size, seq_len):
super().__init__()
self.resunet = ResUnet()
self.num_classes = num_classes
self.seq_len = seq_len
self.MLP = nn.Sequential()... | class_id = batch['class']
pose_motion_gt = batch['pose_motion_gt'] #bs seq_len 6
ref = batch['ref'] #bs 6
bs = pose_motion_gt.shape[0]
audio_in = batch['audio_emb'] # bs seq_len audio_emb_in_size
... | class_id = batch['class']
pose_motion_gt = batch['pose_motion_gt'] #bs seq_len 6
ref = batch['ref'] #bs 6
bs = pose_motion_gt.shape[0]
audio_in = batch['audio_emb'] # bs seq_len audio_emb_in_size
... | 82 | 380 | 407 | 787 | import torch
import torch.nn.functional as F
from torch import nn
from src.audio2pose_models.res_unet import ResUnet
def class2onehot(idx, class_num):
assert torch.max(idx).item() < class_num
onehot = torch.zeros(idx.size(0), class_num).to(idx.device)
onehot.scatter_(1, idx, 1)
return onehot
class CV... | import torch
import torch.nn.functional as F
from torch import nn
from src.audio2pose_models.res_unet import ResUnet
def class2onehot(idx, class_num):
assert torch.max(idx).item() < class_num
onehot = torch.zeros(idx.size(0), class_num).to(idx.device)
onehot.scatter_(1, idx, 1)
return onehot
class CV... |
4,569 | /home/hieuvd/dung_repo/raw/stars_01/OpenTalker_SadTalker/SadTalker | /home/hieuvd/dung_repo/raw/stars_01/OpenTalker_SadTalker/SadTalker/src/audio2pose_models/discriminator.py | 49,50 | 5,59 | forward | ConvNormRelu | 50,59 | 50,59 | class ConvNormRelu(nn.Module):
def __init__(self, conv_type='1d', in_channels=3, out_channels=64, downsample=False,
kernel_size=None, stride=None, padding=None, norm='BN', leaky=False):
super().__init__()
if kernel_size is None:
if downsample:
kernel_size... | class ConvNormRelu(nn.Module):
def __init__(self, conv_type='1d', in_channels=3, out_channels=64, downsample=False,
kernel_size=None, stride=None, padding=None, norm='BN', leaky=False):
super().__init__()
if kernel_size is None:
if downsample:
kernel_size... | x = self.conv(x)
if isinstance(self.norm, nn.InstanceNorm1d):
x = self.norm(x.permute((0, 2, 1))).permute((0, 2, 1)) # normalize on [C]
else:
x = self.norm(x)
x = self.act(x)
return x
| x = self.conv(x)
if isinstance(self.norm, nn.InstanceNorm1d):
x = self.norm(x.permute((0, 2, 1))).permute((0, 2, 1)) # normalize on [C]
else:
x = self.norm(x)
x = self.act(x)
return x
| 26 | 482 | 110 | 592 | import torch
import torch.nn.functional as F
from torch import nn
class ConvNormRelu(nn.Module):
def __init__(self, conv_type='1d', in_channels=3, out_channels=64, downsample=False,
kernel_size=None, stride=None, padding=None, norm='BN', leaky=False):
super().__init__()
if kernel_s... | import torch
import torch.nn.functional as F
from torch import nn
class ConvNormRelu(nn.Module):
def __init__(self, conv_type='1d', in_channels=3, out_channels=64, downsample=False,
kernel_size=None, stride=None, padding=None, norm='BN', leaky=False):
super().__init__()
if kernel_s... |
4,570 | /home/hieuvd/dung_repo/raw/stars_01/OpenTalker_SadTalker/SadTalker | /home/hieuvd/dung_repo/raw/stars_01/OpenTalker_SadTalker/SadTalker/src/audio2pose_models/discriminator.py | 72,73 | 59,77 | forward | PoseSequenceDiscriminator | 73,77 | 73,77 | class PoseSequenceDiscriminator(nn.Module):
def __init__(self, cfg):
super().__init__()
self.cfg = cfg
leaky = self.cfg.MODEL.DISCRIMINATOR.LEAKY_RELU
self.seq = nn.Sequential(
ConvNormRelu('1d', cfg.MODEL.DISCRIMINATOR.INPUT_CHANNELS, 256, downsample=True, leaky=leaky),... | class PoseSequenceDiscriminator(nn.Module):
def __init__(self, cfg):
super().__init__()
self.cfg = cfg
leaky = self.cfg.MODEL.DISCRIMINATOR.LEAKY_RELU
self.seq = nn.Sequential(
ConvNormRelu('1d', cfg.MODEL.DISCRIMINATOR.INPUT_CHANNELS, 256, downsample=True, leaky=leaky),... | x = x.reshape(x.size(0), x.size(1), -1).transpose(1, 2)
x = self.seq(x)
x = x.squeeze(1)
return x | x = x.reshape(x.size(0), x.size(1), -1).transpose(1, 2)
x = self.seq(x)
x = x.squeeze(1)
return x | 14 | 310 | 61 | 371 | import torch
import torch.nn.functional as F
from torch import nn
class ConvNormRelu(nn.Module):
def __init__(self, conv_type='1d', in_channels=3, out_channels=64, downsample=False,
kernel_size=None, stride=None, padding=None, norm='BN', leaky=False):
super().__init__()
if kernel_s... | import torch
import torch.nn.functional as F
from torch import nn
class ConvNormRelu(nn.Module):
def __init__(self, conv_type='1d', in_channels=3, out_channels=64, downsample=False,
kernel_size=None, stride=None, padding=None, norm='BN', leaky=False):
super().__init__()
if kernel_s... |
4,571 | /home/hieuvd/dung_repo/raw/stars_01/OpenTalker_SadTalker/SadTalker | /home/hieuvd/dung_repo/raw/stars_01/OpenTalker_SadTalker/SadTalker/src/audio2pose_models/networks.py | 95,96 | 59,104 | _init_weights | ASPP | 96,104 | 96,104 | class ASPP(nn.Module):
def __init__(self, in_dims, out_dims, rate=[6, 12, 18]):
super(ASPP, self).__init__()
self.aspp_block1 = nn.Sequential(
nn.Conv2d(
in_dims, out_dims, 3, stride=1, padding=rate[0], dilation=rate[0]
),
nn.ReLU(inplace=True),
... | class ASPP(nn.Module):
def __init__(self, in_dims, out_dims, rate=[6, 12, 18]):
super(ASPP, self).__init__()
self.aspp_block1 = nn.Sequential(
nn.Conv2d(
in_dims, out_dims, 3, stride=1, padding=rate[0], dilation=rate[0]
),
nn.ReLU(inplace=True),
... | for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight)
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
| for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight)
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
| 13 | 490 | 92 | 582 | import torch.nn as nn
import torch
class ResidualConv(nn.Module):
def __init__(self, input_dim, output_dim, stride, padding):
super(ResidualConv, self).__init__()
self.conv_block = nn.Sequential(
nn.BatchNorm2d(input_dim),
nn.ReLU(),
nn.Conv2d(
... | import torch.nn as nn
import torch
class ResidualConv(nn.Module):
def __init__(self, input_dim, output_dim, stride, padding):
super(ResidualConv, self).__init__()
self.conv_block = nn.Sequential(
nn.BatchNorm2d(input_dim),
nn.ReLU(),
nn.Conv2d(
... |
4,572 | /home/hieuvd/dung_repo/raw/stars_01/OpenTalker_SadTalker/SadTalker | /home/hieuvd/dung_repo/raw/stars_01/OpenTalker_SadTalker/SadTalker/src/audio2pose_models/networks.py | 137,138 | 114,141 | forward | AttentionBlock | 138,141 | 138,141 | class AttentionBlock(nn.Module):
def __init__(self, input_encoder, input_decoder, output_dim):
super(AttentionBlock, self).__init__()
self.conv_encoder = nn.Sequential(
nn.BatchNorm2d(input_encoder),
nn.ReLU(),
nn.Conv2d(input_encoder, output_dim, 3, padding=1),
... | class AttentionBlock(nn.Module):
def __init__(self, input_encoder, input_decoder, output_dim):
super(AttentionBlock, self).__init__()
self.conv_encoder = nn.Sequential(
nn.BatchNorm2d(input_encoder),
nn.ReLU(),
nn.Conv2d(input_encoder, output_dim, 3, padding=1),
... | out = self.conv_encoder(x1) + self.conv_decoder(x2)
out = self.conv_attn(out)
return out * x2 | out = self.conv_encoder(x1) + self.conv_decoder(x2)
out = self.conv_attn(out)
return out * x2 | 12 | 288 | 47 | 335 | import torch.nn as nn
import torch
class ResidualConv(nn.Module):
def __init__(self, input_dim, output_dim, stride, padding):
super(ResidualConv, self).__init__()
self.conv_block = nn.Sequential(
nn.BatchNorm2d(input_dim),
nn.ReLU(),
nn.Conv2d(
... | import torch.nn as nn
import torch
class ResidualConv(nn.Module):
def __init__(self, input_dim, output_dim, stride, padding):
super(ResidualConv, self).__init__()
self.conv_block = nn.Sequential(
nn.BatchNorm2d(input_dim),
nn.ReLU(),
nn.Conv2d(
... |
4,573 | /home/hieuvd/dung_repo/raw/stars_01/OpenTalker_SadTalker/SadTalker | /home/hieuvd/dung_repo/raw/stars_01/OpenTalker_SadTalker/SadTalker/src/audio2pose_models/networks.py | 24,25 | 5,29 | forward | ResidualConv | 25,29 | 25,29 | class ResidualConv(nn.Module):
def __init__(self, input_dim, output_dim, stride, padding):
super(ResidualConv, self).__init__()
self.conv_block = nn.Sequential(
nn.BatchNorm2d(input_dim),
nn.ReLU(),
nn.Conv2d(
input_dim, output_dim, kernel_size=3,... | class ResidualConv(nn.Module):
def __init__(self, input_dim, output_dim, stride, padding):
super(ResidualConv, self).__init__()
self.conv_block = nn.Sequential(
nn.BatchNorm2d(input_dim),
nn.ReLU(),
nn.Conv2d(
input_dim, output_dim, kernel_size=3,... |
return self.conv_block(x) + self.conv_skip(x)
|
return self.conv_block(x) + self.conv_skip(x)
| 4 | 264 | 27 | 291 | import torch.nn as nn
import torch
class ResidualConv(nn.Module):
def __init__(self, input_dim, output_dim, stride, padding):
super(ResidualConv, self).__init__()
self.conv_block = nn.Sequential(
nn.BatchNorm2d(input_dim),
nn.ReLU(),
nn.Conv2d(
... | import torch.nn as nn
import torch
class ResidualConv(nn.Module):
def __init__(self, input_dim, output_dim, stride, padding):
super(ResidualConv, self).__init__()
self.conv_block = nn.Sequential(
nn.BatchNorm2d(input_dim),
nn.ReLU(),
nn.Conv2d(
... |
4,574 | /home/hieuvd/dung_repo/raw/stars_01/OpenTalker_SadTalker/SadTalker | /home/hieuvd/dung_repo/raw/stars_01/OpenTalker_SadTalker/SadTalker/src/audio2pose_models/networks.py | 52,53 | 41,59 | forward | Squeeze_Excite_Block | 53,59 | 53,59 | class Squeeze_Excite_Block(nn.Module):
def __init__(self, channel, reduction=16):
super(Squeeze_Excite_Block, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Sequential(
nn.Linear(channel, channel // reduction, bias=False),
nn.ReLU(inplace=True),... | class Squeeze_Excite_Block(nn.Module):
def __init__(self, channel, reduction=16):
super(Squeeze_Excite_Block, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Sequential(
nn.Linear(channel, channel // reduction, bias=False),
nn.ReLU(inplace=True),... | b, c, _, _ = x.size()
y = self.avg_pool(x).view(b, c)
y = self.fc(y).view(b, c, 1, 1)
return x * y.expand_as(x)
| b, c, _, _ = x.size()
y = self.avg_pool(x).view(b, c)
y = self.fc(y).view(b, c, 1, 1)
return x * y.expand_as(x)
| 20 | 167 | 75 | 242 | import torch.nn as nn
import torch
class ResidualConv(nn.Module):
def __init__(self, input_dim, output_dim, stride, padding):
super(ResidualConv, self).__init__()
self.conv_block = nn.Sequential(
nn.BatchNorm2d(input_dim),
nn.ReLU(),
nn.Conv2d(
... | import torch.nn as nn
import torch
class ResidualConv(nn.Module):
def __init__(self, input_dim, output_dim, stride, padding):
super(ResidualConv, self).__init__()
self.conv_block = nn.Sequential(
nn.BatchNorm2d(input_dim),
nn.ReLU(),
nn.Conv2d(
... |
4,575 | /home/hieuvd/dung_repo/raw/stars_01/OpenTalker_SadTalker/SadTalker | /home/hieuvd/dung_repo/raw/stars_01/OpenTalker_SadTalker/SadTalker/src/audio2pose_models/networks.py | 37,38 | 29,41 | forward | Upsample | 38,41 | 38,41 | class Upsample(nn.Module):
def __init__(self, input_dim, output_dim, kernel, stride):
super(Upsample, self).__init__()
self.upsample = nn.ConvTranspose2d(
input_dim, output_dim, kernel_size=kernel, stride=stride
)
def forward(self, x):
FILL_FUNC_BODY | class Upsample(nn.Module):
def __init__(self, input_dim, output_dim, kernel, stride):
super(Upsample, self).__init__()
self.upsample = nn.ConvTranspose2d(
input_dim, output_dim, kernel_size=kernel, stride=stride
)
def forward(self, x):
FILL_FUNC_BODY | return self.upsample(x)
| return self.upsample(x)
| 2 | 107 | 15 | 122 | import torch.nn as nn
import torch
class ResidualConv(nn.Module):
def __init__(self, input_dim, output_dim, stride, padding):
super(ResidualConv, self).__init__()
self.conv_block = nn.Sequential(
nn.BatchNorm2d(input_dim),
nn.ReLU(),
nn.Conv2d(
... | import torch.nn as nn
import torch
class ResidualConv(nn.Module):
def __init__(self, input_dim, output_dim, stride, padding):
super(ResidualConv, self).__init__()
self.conv_block = nn.Sequential(
nn.BatchNorm2d(input_dim),
nn.ReLU(),
nn.Conv2d(
... |
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