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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
17,276 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_peft/peft | /home/hieuvd/dung_repo/raw/stars_01/huggingface_peft/peft/src/peft/tuners/ln_tuning/model.py | 162,163 | 30,202 | set_adapter | LNTuningModel | 163,171 | 163,171 | class LNTuningModel(BaseTuner):
"""
Creates LayerNorm tuning from a pretrained transformer model.
The method is described in detail in https://arxiv.org/abs/2312.11420.
Args:
model ([`torch.nn.Module`]): The model to be adapted.
config ([`LNTuningConfig`]): The configuration of the Lor... | class LNTuningModel(BaseTuner):
"""
Creates LayerNorm tuning from a pretrained transformer model.
The method is described in detail in https://arxiv.org/abs/2312.11420.
Args:
model ([`torch.nn.Module`]): The model to be adapted.
config ([`LNTuningConfig`]): The configuration of the Lor... | for module in self.model.modules():
if isinstance(module, LNTuningLayer):
if module.merged:
warnings.warn("Adapter cannot be set when the model is merged. Unmerging the model first.")
module.unmerge()
module.set_adapter(adapter_... | for module in self.model.modules():
if isinstance(module, LNTuningLayer):
if module.merged:
warnings.warn("Adapter cannot be set when the model is merged. Unmerging the model first.")
module.unmerge()
module.set_adapter(adapter_... | 27 | 1,865 | 97 | 1,962 | # Copyright 2024-present the HuggingFace Inc. 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... | # Copyright 2024-present the HuggingFace Inc. 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... |
17,277 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_peft/peft | /home/hieuvd/dung_repo/raw/stars_01/huggingface_peft/peft/src/peft/tuners/loha/model.py | 89,98 | 27,115 | _create_and_replace | LoHaModel | 101,115 | 98,115 | class LoHaModel(LycorisTuner):
"""
Creates Low-Rank Hadamard Product model from a pretrained model. The method is partially described in
https://arxiv.org/abs/2108.06098 Current implementation heavily borrows from
https://github.com/KohakuBlueleaf/LyCORIS/blob/eb460098187f752a5d66406d3affade6f0a07ece/ly... | class LoHaModel(LycorisTuner):
"""
Creates Low-Rank Hadamard Product model from a pretrained model. The method is partially described in
https://arxiv.org/abs/2108.06098 Current implementation heavily borrows from
https://github.com/KohakuBlueleaf/LyCORIS/blob/eb460098187f752a5d66406d3affade6f0a07ece/ly... |
# Regexp matching - Find key which matches current target_name in patterns provided
pattern_keys = list(chain(config.rank_pattern.keys(), config.alpha_pattern.keys()))
target_name_key = next(filter(lambda key: re.match(rf"(.*\.)?{key}$", current_key), pattern_keys), target_name)
kwargs... | """
A private method to create and replace the target module with the adapter module.
"""
# Regexp matching - Find key which matches current target_name in patterns provided
pattern_keys = list(chain(config.rank_pattern.keys(), config.alpha_pattern.keys()))
target_name_k... | 52 | 888 | 247 | 1,135 | # Copyright 2023-present the HuggingFace Inc. 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... | # Copyright 2023-present the HuggingFace Inc. 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... |
17,278 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_peft/peft | /home/hieuvd/dung_repo/raw/stars_01/huggingface_peft/peft/src/peft/tuners/lokr/model.py | 90,99 | 27,116 | _create_and_replace | LoKrModel | 102,116 | 99,116 | class LoKrModel(LycorisTuner):
"""
Creates Low-Rank Kronecker Product model from a pretrained model. The original method is partially described in
https://arxiv.org/abs/2108.06098 and in https://arxiv.org/abs/2309.14859 Current implementation heavily borrows
from
https://github.com/KohakuBlueleaf/Ly... | class LoKrModel(LycorisTuner):
"""
Creates Low-Rank Kronecker Product model from a pretrained model. The original method is partially described in
https://arxiv.org/abs/2108.06098 and in https://arxiv.org/abs/2309.14859 Current implementation heavily borrows
from
https://github.com/KohakuBlueleaf/Ly... |
# Regexp matching - Find key which matches current target_name in patterns provided
pattern_keys = list(chain(config.rank_pattern.keys(), config.alpha_pattern.keys()))
target_name_key = next(filter(lambda key: re.match(rf"(.*\.)?{key}$", current_key), pattern_keys), target_name)
kwargs... | """
A private method to create and replace the target module with the adapter module.
"""
# Regexp matching - Find key which matches current target_name in patterns provided
pattern_keys = list(chain(config.rank_pattern.keys(), config.alpha_pattern.keys()))
target_name_k... | 52 | 929 | 248 | 1,177 | # Copyright 2023-present the HuggingFace Inc. 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... | # Copyright 2023-present the HuggingFace Inc. 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... |
17,281 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_peft/peft | /home/hieuvd/dung_repo/raw/stars_01/huggingface_peft/peft/src/peft/tuners/lora/gptq.py | 95,100 | 1,115 | dispatch_gptq | 100,115 | 100,115 | # Copyright 2023-present the HuggingFace Inc. 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... | # Copyright 2023-present the HuggingFace Inc. 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... | new_module = None
if isinstance(target, BaseTunerLayer):
target_base_layer = target.get_base_layer()
else:
target_base_layer = target
gptq_quantization_config = kwargs.get("gptq_quantization_config", None)
AutoGPTQQuantLinear = get_auto_gptq_quant_linear(gptq_quantization_config)
... | new_module = None
if isinstance(target, BaseTunerLayer):
target_base_layer = target.get_base_layer()
else:
target_base_layer = target
gptq_quantization_config = kwargs.get("gptq_quantization_config", None)
AutoGPTQQuantLinear = get_auto_gptq_quant_linear(gptq_quantization_config)
... | 38 | 1,072 | 195 | 1,267 | # Copyright 2023-present the HuggingFace Inc. 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... | # Copyright 2023-present the HuggingFace Inc. 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... | |
17,282 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_peft/peft | /home/hieuvd/dung_repo/raw/stars_01/huggingface_peft/peft/src/peft/tuners/lora/gptq.py | 57,58 | 24,95 | forward | QuantLinear | 58,84 | 58,84 | class QuantLinear(torch.nn.Module, LoraLayer):
def __init__(
self,
base_layer,
adapter_name: str,
r: int = 0,
lora_alpha: int = 1,
lora_dropout: float = 0.0,
init_lora_weights: bool = True,
use_rslora: bool = False,
use_dora: bool = False,
... | class QuantLinear(torch.nn.Module, LoraLayer):
def __init__(
self,
base_layer,
adapter_name: str,
r: int = 0,
lora_alpha: int = 1,
lora_dropout: float = 0.0,
init_lora_weights: bool = True,
use_rslora: bool = False,
use_dora: bool = False,
... | # note: logic differs from default Linear because merging is not supported
result = self.quant_linear_module(x)
if self.disable_adapters:
return result
for active_adapter in self.active_adapters:
if active_adapter not in self.lora_A.keys():
conti... | # note: logic differs from default Linear because merging is not supported
result = self.quant_linear_module(x)
if self.disable_adapters:
return result
for active_adapter in self.active_adapters:
if active_adapter not in self.lora_A.keys():
conti... | 71 | 523 | 296 | 819 | # Copyright 2023-present the HuggingFace Inc. 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... | # Copyright 2023-present the HuggingFace Inc. 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... |
17,284 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_peft/peft | /home/hieuvd/dung_repo/raw/stars_01/huggingface_peft/peft/src/peft/tuners/lora/tp_layer.py | 155,156 | 28,191 | forward | LoraParallelLinear | 156,191 | 156,191 | class LoraParallelLinear(nn.Module, LoraLayer):
"""
When the target layer parallel_linear is RowParallelLinear, in order to keep the input and output shapes
consistent, we need to split the lora matrix A into rows, and the lora_B at this time should be a complete linear
layer; In the same way, when the ... | class LoraParallelLinear(nn.Module, LoraLayer):
"""
When the target layer parallel_linear is RowParallelLinear, in order to keep the input and output shapes
consistent, we need to split the lora matrix A into rows, and the lora_B at this time should be a complete linear
layer; In the same way, when the ... | previous_dtype = x.dtype
# If weight is used for matrix multiplication here, the final aggregation operation of the original
# parallel_linear layer will be missing, so we need to directly call its forward function to obtain the
# output of the original parallel_linear layer.
if ... | previous_dtype = x.dtype
# If weight is used for matrix multiplication here, the final aggregation operation of the original
# parallel_linear layer will be missing, so we need to directly call its forward function to obtain the
# output of the original parallel_linear layer.
if ... | 129 | 1,552 | 459 | 2,011 | # Copyright 2023-present the HuggingFace Inc. 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... | # Copyright 2023-present the HuggingFace Inc. 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... |
17,286 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_peft/peft | /home/hieuvd/dung_repo/raw/stars_01/huggingface_peft/peft/src/peft/tuners/multitask_prompt_tuning/model.py | 107,108 | 27,120 | forward | MultitaskPromptEmbedding | 108,120 | 108,120 | class MultitaskPromptEmbedding(PromptEmbedding):
def __init__(self, config: MultitaskPromptTuningConfig, word_embeddings):
super().__init__(config, word_embeddings)
self.num_tasks = config.num_tasks
self.num_ranks = config.num_ranks
self.num_virtual_tokens = config.num_virtual_token... | class MultitaskPromptEmbedding(PromptEmbedding):
def __init__(self, config: MultitaskPromptTuningConfig, word_embeddings):
super().__init__(config, word_embeddings)
self.num_tasks = config.num_tasks
self.num_ranks = config.num_ranks
self.num_virtual_tokens = config.num_virtual_token... | if task_ids is None:
raise ValueError("task_ids cannot be None")
prompt_embeddings = self.embedding(indices)
task_cols = torch.index_select(self.prefix_task_cols, 0, task_ids)
task_rows = torch.index_select(self.prefix_task_rows, 0, task_ids)
task_prompts = torch.ma... | if task_ids is None:
raise ValueError("task_ids cannot be None")
prompt_embeddings = self.embedding(indices)
task_cols = torch.index_select(self.prefix_task_cols, 0, task_ids)
task_rows = torch.index_select(self.prefix_task_rows, 0, task_ids)
task_prompts = torch.ma... | 31 | 1,126 | 155 | 1,281 | # Copyright 2023-present the HuggingFace Inc. 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... | # Copyright 2023-present the HuggingFace Inc. 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... |
17,287 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_peft/peft | /home/hieuvd/dung_repo/raw/stars_01/huggingface_peft/peft/src/peft/tuners/oft/model.py | 82,91 | 26,107 | _create_and_replace | OFTModel | 94,107 | 91,107 | class OFTModel(LycorisTuner):
"""
Creates Orthogonal Finetuning model from a pretrained model. The method is described in
https://arxiv.org/abs/2306.07280
Args:
model (`torch.nn.Module`): The model to which the adapter tuner layers will be attached.
config ([`OFTConfig`]): The configura... | class OFTModel(LycorisTuner):
"""
Creates Orthogonal Finetuning model from a pretrained model. The method is described in
https://arxiv.org/abs/2306.07280
Args:
model (`torch.nn.Module`): The model to which the adapter tuner layers will be attached.
config ([`OFTConfig`]): The configura... |
# Regexp matching - Find key which matches current target_name in patterns provided
pattern_keys = list(config.rank_pattern.keys())
target_name_key = next(filter(lambda key: re.match(rf"(.*\.)?{key}$", current_key), pattern_keys), target_name)
kwargs = config.to_dict()
kwargs["... | """
A private method to create and replace the target module with the adapter module.
"""
# Regexp matching - Find key which matches current target_name in patterns provided
pattern_keys = list(config.rank_pattern.keys())
target_name_key = next(filter(lambda key: re.matc... | 47 | 746 | 209 | 955 | # Copyright 2023-present the HuggingFace Inc. 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... | # Copyright 2023-present the HuggingFace Inc. 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... |
17,288 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_peft/peft | /home/hieuvd/dung_repo/raw/stars_01/huggingface_peft/peft/src/peft/tuners/p_tuning/model.py | 121,122 | 24,131 | forward | PromptEncoder | 122,131 | 122,131 | class PromptEncoder(torch.nn.Module):
"""
The prompt encoder network that is used to generate the virtual token embeddings for p-tuning.
Args:
config ([`PromptEncoderConfig`]): The configuration of the prompt encoder.
Example:
```py
>>> from peft import PromptEncoder, PromptEncoderCon... | class PromptEncoder(torch.nn.Module):
"""
The prompt encoder network that is used to generate the virtual token embeddings for p-tuning.
Args:
config ([`PromptEncoderConfig`]): The configuration of the prompt encoder.
Example:
```py
>>> from peft import PromptEncoder, PromptEncoderCon... | input_embeds = self.embedding(indices)
if self.encoder_type == PromptEncoderReparameterizationType.LSTM:
output_embeds = self.mlp_head(self.lstm_head(input_embeds)[0])
elif self.encoder_type == PromptEncoderReparameterizationType.MLP:
output_embeds = self.mlp_head(input_e... | input_embeds = self.embedding(indices)
if self.encoder_type == PromptEncoderReparameterizationType.LSTM:
output_embeds = self.mlp_head(self.lstm_head(input_embeds)[0])
elif self.encoder_type == PromptEncoderReparameterizationType.MLP:
output_embeds = self.mlp_head(input_e... | 34 | 1,290 | 157 | 1,447 | # Copyright 2023-present the HuggingFace Inc. 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... | # Copyright 2023-present the HuggingFace Inc. 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... |
17,289 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_peft/peft | /home/hieuvd/dung_repo/raw/stars_01/huggingface_peft/peft/src/peft/tuners/poly/model.py | 153,154 | 33,188 | _register_pre_hooks | PolyModel | 155,171 | 154,171 | class PolyModel(BaseTuner):
prefix: str = "poly_"
def __init__(self, model, config, adapter_name) -> None:
super().__init__(model, config, adapter_name)
@staticmethod
def _check_target_module_exists(poly_config, key):
return check_target_module_exists(poly_config, key)
def _create... | class PolyModel(BaseTuner):
prefix: str = "poly_"
def __init__(self, model, config, adapter_name) -> None:
super().__init__(model, config, adapter_name)
@staticmethod
def _check_target_module_exists(poly_config, key):
return check_target_module_exists(poly_config, key)
def _create... | if task_ids is None:
return []
def pre_hook(_, args, kwargs):
kwargs["task_ids"] = task_ids
return args, kwargs
handles = []
for module in self.model.modules():
if isinstance(module, Linear):
handle = module.register_forw... | """Helper method to register pre hooks."""
if task_ids is None:
return []
def pre_hook(_, args, kwargs):
kwargs["task_ids"] = task_ids
return args, kwargs
handles = []
for module in self.model.modules():
if isinstance(module, Lin... | 33 | 1,561 | 133 | 1,694 | # Copyright 2023-present the HuggingFace Inc. 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... | # Copyright 2023-present the HuggingFace Inc. 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... |
17,290 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_peft/peft | /home/hieuvd/dung_repo/raw/stars_01/huggingface_peft/peft/src/peft/tuners/poly/router.py | 27,28 | 1,84 | get_router | 28,38 | 28,38 | # Copyright 2023-present the HuggingFace Inc. 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... | # Copyright 2023-present the HuggingFace Inc. 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... | if poly_config.poly_type == "poly":
return PolyRouter(poly_config)
else:
raise ValueError(
f"Unsupported poly_type: {poly_config.poly_type}. "
"Currently, only the following types are supported: "
"`poly`."
)
| if poly_config.poly_type == "poly":
return PolyRouter(poly_config)
else:
raise ValueError(
f"Unsupported poly_type: {poly_config.poly_type}. "
"Currently, only the following types are supported: "
"`poly`."
)
| 23 | 868 | 91 | 959 | # Copyright 2023-present the HuggingFace Inc. 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... | # Copyright 2023-present the HuggingFace Inc. 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... | |
17,291 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_peft/peft | /home/hieuvd/dung_repo/raw/stars_01/huggingface_peft/peft/src/peft/tuners/poly/router.py | 64,65 | 48,84 | forward | PolyRouter | 65,84 | 65,84 | class PolyRouter(Router):
# It's a simplified implementation of
# https://github.com/microsoft/mttl/blob/ce4ca51dbca73be656feb9b3e5233633e3c5dec7/mttl/models/poly.py#L138
def __init__(self, poly_config: PolyConfig):
super().__init__()
self.poly_type = poly_config.poly_type
self.n_ta... | class PolyRouter(Router):
# It's a simplified implementation of
# https://github.com/microsoft/mttl/blob/ce4ca51dbca73be656feb9b3e5233633e3c5dec7/mttl/models/poly.py#L138
def __init__(self, poly_config: PolyConfig):
super().__init__()
self.poly_type = poly_config.poly_type
self.n_ta... | if task_ids is None:
raise ValueError("task_ids should not be None.")
if task_ids.max().item() >= self.n_tasks:
raise ValueError(f"Only {self.n_tasks} tasks available. Found task id = {task_ids.max().item()}")
# move task id to input's device
task_ids = task_ids.... | if task_ids is None:
raise ValueError("task_ids should not be None.")
if task_ids.max().item() >= self.n_tasks:
raise ValueError(f"Only {self.n_tasks} tasks available. Found task id = {task_ids.max().item()}")
# move task id to input's device
task_ids = task_ids.... | 62 | 290 | 283 | 573 | # Copyright 2023-present the HuggingFace Inc. 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... | # Copyright 2023-present the HuggingFace Inc. 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... |
17,292 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_peft/peft | /home/hieuvd/dung_repo/raw/stars_01/huggingface_peft/peft/src/peft/tuners/poly/router.py | 39,41 | 38,48 | reset | Router | 41,43 | 41,43 | class Router(nn.Module, ABC):
@abstractmethod
def reset(self):
FILL_FUNC_BODY @abstractmethod
def forward(self, task_ids: torch.Tensor, input_ids: torch.Tensor):
...
| class Router(nn.Module, ABC):
@abstractmethod
def reset(self):
FILL_FUNC_BODY @abstractmethod
def forward(self, task_ids: torch.Tensor, input_ids: torch.Tensor):
...
| ...
| ...
| 1 | 72 | 6 | 78 | # Copyright 2023-present the HuggingFace Inc. 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... | # Copyright 2023-present the HuggingFace Inc. 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... |
17,293 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_peft/peft | /home/hieuvd/dung_repo/raw/stars_01/huggingface_peft/peft/src/peft/tuners/prefix_tuning/model.py | 74,75 | 20,81 | forward | PrefixEncoder | 75,81 | 75,81 | class PrefixEncoder(torch.nn.Module):
r"""
The `torch.nn` model to encode the prefix.
Args:
config ([`PrefixTuningConfig`]): The configuration of the prefix encoder.
Example:
```py
>>> from peft import PrefixEncoder, PrefixTuningConfig
>>> config = PrefixTuningConfig(
... ... | class PrefixEncoder(torch.nn.Module):
r"""
The `torch.nn` model to encode the prefix.
Args:
config ([`PrefixTuningConfig`]): The configuration of the prefix encoder.
Example:
```py
>>> from peft import PrefixEncoder, PrefixTuningConfig
>>> config = PrefixTuningConfig(
... ... | if self.prefix_projection:
prefix_tokens = self.embedding(prefix)
past_key_values = self.transform(prefix_tokens)
else:
past_key_values = self.embedding(prefix)
return past_key_values
| if self.prefix_projection:
prefix_tokens = self.embedding(prefix)
past_key_values = self.transform(prefix_tokens)
else:
past_key_values = self.embedding(prefix)
return past_key_values
| 14 | 679 | 74 | 753 | # Copyright 2023-present the HuggingFace Inc. 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... | # Copyright 2023-present the HuggingFace Inc. 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... |
17,294 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_peft/peft | /home/hieuvd/dung_repo/raw/stars_01/huggingface_peft/peft/src/peft/tuners/prompt_tuning/model.py | 88,89 | 24,92 | forward | PromptEmbedding | 89,92 | 89,92 | class PromptEmbedding(torch.nn.Module):
"""
The model to encode virtual tokens into prompt embeddings.
Args:
config ([`PromptTuningConfig`]): The configuration of the prompt embedding.
word_embeddings (`torch.nn.Module`): The word embeddings of the base transformer model.
**Attributes*... | class PromptEmbedding(torch.nn.Module):
"""
The model to encode virtual tokens into prompt embeddings.
Args:
config ([`PromptTuningConfig`]): The configuration of the prompt embedding.
word_embeddings (`torch.nn.Module`): The word embeddings of the base transformer model.
**Attributes*... | # Just get embeddings
prompt_embeddings = self.embedding(indices)
return prompt_embeddings
| # Just get embeddings
prompt_embeddings = self.embedding(indices)
return prompt_embeddings
| 9 | 933 | 32 | 965 | # Copyright 2023-present the HuggingFace Inc. 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... | # Copyright 2023-present the HuggingFace Inc. 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... |
17,295 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_peft/peft | /home/hieuvd/dung_repo/raw/stars_01/huggingface_peft/peft/src/peft/tuners/vera/buffer_dict.py | 100,101 | 19,161 | items | BufferDict | 101,104 | 101,104 | class BufferDict(Module):
r"""
Holds buffers in a dictionary.
BufferDict can be indexed like a regular Python dictionary, but buffers it contains are properly registered, and
will be visible by all Module methods. `torch.nn.BufferDict` is an **ordered** dictionary that respects
* the order of inse... | class BufferDict(Module):
r"""
Holds buffers in a dictionary.
BufferDict can be indexed like a regular Python dictionary, but buffers it contains are properly registered, and
will be visible by all Module methods. `torch.nn.BufferDict` is an **ordered** dictionary that respects
* the order of inse... | r"""Return an iterable of the BufferDict key/value pairs."""
return self._buffers.items()
| r"""Return an iterable of the BufferDict key/value pairs."""
return self._buffers.items()
| 10 | 1,456 | 33 | 1,489 | # Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# Adapted from https://botorch.org/api/_modules/botorch/utils/torch.html
# TODO: To be removed once (if) https://github.com/pytorch/pytorch... | # Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# Adapted from https://botorch.org/api/_modules/botorch/utils/torch.html
# TODO: To be removed once (if) https://github.com/pytorch/pytorch... |
17,301 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/avg_checkpoints.py | 46,47 | 1,153 | checkpoint_metric | 47,61 | 47,61 | #!/usr/bin/env python3
""" Checkpoint Averaging Script
This script averages all model weights for checkpoints in specified path that match
the specified filter wildcard. All checkpoints must be from the exact same model.
For any hope of decent results, the checkpoints should be from the same or child
(via resumes) tr... | #!/usr/bin/env python3
""" Checkpoint Averaging Script
This script averages all model weights for checkpoints in specified path that match
the specified filter wildcard. All checkpoints must be from the exact same model.
For any hope of decent results, the checkpoints should be from the same or child
(via resumes) tr... | if not checkpoint_path or not os.path.isfile(checkpoint_path):
return {}
print("=> Extracting metric from checkpoint '{}'".format(checkpoint_path))
checkpoint = torch.load(checkpoint_path, map_location='cpu')
metric = None
if 'metric' in checkpoint:
metric = checkpoint['metric']
... | if not checkpoint_path or not os.path.isfile(checkpoint_path):
return {}
print("=> Extracting metric from checkpoint '{}'".format(checkpoint_path))
checkpoint = torch.load(checkpoint_path, map_location='cpu')
metric = None
if 'metric' in checkpoint:
metric = checkpoint['metric']
... | 45 | 1,733 | 165 | 1,898 | #!/usr/bin/env python3
""" Checkpoint Averaging Script
This script averages all model weights for checkpoints in specified path that match
the specified filter wildcard. All checkpoints must be from the exact same model.
For any hope of decent results, the checkpoints should be from the same or child
(via resumes) tr... | #!/usr/bin/env python3
""" Checkpoint Averaging Script
This script averages all model weights for checkpoints in specified path that match
the specified filter wildcard. All checkpoints must be from the exact same model.
For any hope of decent results, the checkpoints should be from the same or child
(via resumes) tr... | |
17,302 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/clean_checkpoint.py | 55,63 | 1,116 | clean_checkpoint | 63,114 | 63,114 | #!/usr/bin/env python3
""" Checkpoint Cleaning Script
Takes training checkpoints with GPU tensors, optimizer state, extra dict keys, etc.
and outputs a CPU tensor checkpoint with only the `state_dict` along with SHA256
calculation for model zoo compatibility.
Hacked together by / Copyright 2020 Ross Wightman (https:... | #!/usr/bin/env python3
""" Checkpoint Cleaning Script
Takes training checkpoints with GPU tensors, optimizer state, extra dict keys, etc.
and outputs a CPU tensor checkpoint with only the `state_dict` along with SHA256
calculation for model zoo compatibility.
Hacked together by / Copyright 2020 Ross Wightman (https:... | # Load an existing checkpoint to CPU, strip everything but the state_dict and re-save
if checkpoint and os.path.isfile(checkpoint):
print("=> Loading checkpoint '{}'".format(checkpoint))
state_dict = load_state_dict(checkpoint, use_ema=use_ema)
new_state_dict = {}
for k, v in sta... | # Load an existing checkpoint to CPU, strip everything but the state_dict and re-save
if checkpoint and os.path.isfile(checkpoint):
print("=> Loading checkpoint '{}'".format(checkpoint))
state_dict = load_state_dict(checkpoint, use_ema=use_ema)
new_state_dict = {}
for k, v in sta... | 179 | 658 | 695 | 1,353 | #!/usr/bin/env python3
""" Checkpoint Cleaning Script
Takes training checkpoints with GPU tensors, optimizer state, extra dict keys, etc.
and outputs a CPU tensor checkpoint with only the `state_dict` along with SHA256
calculation for model zoo compatibility.
Hacked together by / Copyright 2020 Ross Wightman (https:... | #!/usr/bin/env python3
""" Checkpoint Cleaning Script
Takes training checkpoints with GPU tensors, optimizer state, extra dict keys, etc.
and outputs a CPU tensor checkpoint with only the `state_dict` along with SHA256
calculation for model zoo compatibility.
Hacked together by / Copyright 2020 Ross Wightman (https:... | |
17,303 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/onnx_export.py | 63,64 | 1,103 | main | 64,101 | 64,101 | """ ONNX export script
Export PyTorch models as ONNX graphs.
This export script originally started as an adaptation of code snippets found at
https://pytorch.org/tutorials/advanced/super_resolution_with_onnxruntime.html
The default parameters work with PyTorch 1.6 and ONNX 1.7 and produce an optimal ONNX graph
for h... | """ ONNX export script
Export PyTorch models as ONNX graphs.
This export script originally started as an adaptation of code snippets found at
https://pytorch.org/tutorials/advanced/super_resolution_with_onnxruntime.html
The default parameters work with PyTorch 1.6 and ONNX 1.7 and produce an optimal ONNX graph
for h... | args = parser.parse_args()
args.pretrained = True
if args.checkpoint:
args.pretrained = False
print("==> Creating PyTorch {} model".format(args.model))
# NOTE exportable=True flag disables autofn/jit scripted activations and uses Conv2dSameExport layers
# for models using SAME padding
... | args = parser.parse_args()
args.pretrained = True
if args.checkpoint:
args.pretrained = False
print("==> Creating PyTorch {} model".format(args.model))
# NOTE exportable=True flag disables autofn/jit scripted activations and uses Conv2dSameExport layers
# for models using SAME padding
... | 65 | 1,112 | 376 | 1,488 | """ ONNX export script
Export PyTorch models as ONNX graphs.
This export script originally started as an adaptation of code snippets found at
https://pytorch.org/tutorials/advanced/super_resolution_with_onnxruntime.html
The default parameters work with PyTorch 1.6 and ONNX 1.7 and produce an optimal ONNX graph
for h... | """ ONNX export script
Export PyTorch models as ONNX graphs.
This export script originally started as an adaptation of code snippets found at
https://pytorch.org/tutorials/advanced/super_resolution_with_onnxruntime.html
The default parameters work with PyTorch 1.6 and ONNX 1.7 and produce an optimal ONNX graph
for h... | |
17,304 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/data/config.py | 8,15 | 1,130 | resolve_data_config | 15,103 | 15,103 | import logging
from .constants import *
_logger = logging.getLogger(__name__)
def resolve_data_config(
args=None,
pretrained_cfg=None,
model=None,
use_test_size=False,
verbose=False
):
FILL_FUNC_BODYdef resolve_model_data_config(
model,
args=None,
pret... | import logging
from .constants import *
_logger = logging.getLogger(__name__)
def resolve_data_config(
args=None,
pretrained_cfg=None,
model=None,
use_test_size=False,
verbose=False
):
FILL_FUNC_BODYdef resolve_model_data_config(
model,
args=None,
pret... | assert model or args or pretrained_cfg, "At least one of model, args, or pretrained_cfg required for data config."
args = args or {}
pretrained_cfg = pretrained_cfg or {}
if not pretrained_cfg and model is not None and hasattr(model, 'pretrained_cfg'):
pretrained_cfg = model.pretrained_cfg
d... | assert model or args or pretrained_cfg, "At least one of model, args, or pretrained_cfg required for data config."
args = args or {}
pretrained_cfg = pretrained_cfg or {}
if not pretrained_cfg and model is not None and hasattr(model, 'pretrained_cfg'):
pretrained_cfg = model.pretrained_cfg
d... | 320 | 337 | 1,209 | 1,546 | import logging
from .constants import *
_logger = logging.getLogger(__name__)
def resolve_data_config(
args=None,
pretrained_cfg=None,
model=None,
use_test_size=False,
verbose=False
):
FILL_FUNC_BODYdef resolve_model_data_config(
model,
args=None,
pret... | import logging
from .constants import *
_logger = logging.getLogger(__name__)
def resolve_data_config(
args=None,
pretrained_cfg=None,
model=None,
use_test_size=False,
verbose=False
):
FILL_FUNC_BODYdef resolve_model_data_config(
model,
args=None,
pret... | |
17,305 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/data/dataset.py | 174,175 | 163,201 | _set_transforms | AugMixDataset | 175,180 | 175,180 | class AugMixDataset(torch.utils.data.Dataset):
"""Dataset wrapper to perform AugMix or other clean/augmentation mixes"""
def __init__(self, dataset, num_splits=2):
self.augmentation = None
self.normalize = None
self.dataset = dataset
if self.dataset.transform is not None:
... | class AugMixDataset(torch.utils.data.Dataset):
"""Dataset wrapper to perform AugMix or other clean/augmentation mixes"""
def __init__(self, dataset, num_splits=2):
self.augmentation = None
self.normalize = None
self.dataset = dataset
if self.dataset.transform is not None:
... | assert isinstance(x, (list, tuple)) and len(x) == 3, 'Expecting a tuple/list of 3 transforms'
self.dataset.transform = x[0]
self.augmentation = x[1]
self.normalize = x[2]
| assert isinstance(x, (list, tuple)) and len(x) == 3, 'Expecting a tuple/list of 3 transforms'
self.dataset.transform = x[0]
self.augmentation = x[1]
self.normalize = x[2]
| 23 | 365 | 76 | 441 | """ Quick n Simple Image Folder, Tarfile based DataSet
Hacked together by / Copyright 2019, Ross Wightman
"""
import io
import logging
from typing import Optional
import torch
import torch.utils.data as data
from PIL import Image
from .readers import create_reader
_logger = logging.getLogger(__name__)
_ERROR_RETR... | """ Quick n Simple Image Folder, Tarfile based DataSet
Hacked together by / Copyright 2019, Ross Wightman
"""
import io
import logging
from typing import Optional
import torch
import torch.utils.data as data
from PIL import Image
from .readers import create_reader
_logger = logging.getLogger(__name__)
_ERROR_RETR... |
17,306 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/data/dataset.py | 80,81 | 21,84 | filenames | ImageDataset | 81,84 | 81,84 | class ImageDataset(data.Dataset):
def __init__(
self,
root,
reader=None,
split='train',
class_map=None,
load_bytes=False,
input_img_mode='RGB',
transform=None,
target_transform=None,
):
if reader... | class ImageDataset(data.Dataset):
def __init__(
self,
root,
reader=None,
split='train',
class_map=None,
load_bytes=False,
input_img_mode='RGB',
transform=None,
target_transform=None,
):
if reader... | return self.reader.filenames(basename, absolute)
| return self.reader.filenames(basename, absolute)
| 3 | 538 | 21 | 559 | """ Quick n Simple Image Folder, Tarfile based DataSet
Hacked together by / Copyright 2019, Ross Wightman
"""
import io
import logging
from typing import Optional
import torch
import torch.utils.data as data
from PIL import Image
from .readers import create_reader
_logger = logging.getLogger(__name__)
_ERROR_RETR... | """ Quick n Simple Image Folder, Tarfile based DataSet
Hacked together by / Copyright 2019, Ross Wightman
"""
import io
import logging
from typing import Optional
import torch
import torch.utils.data as data
from PIL import Image
from .readers import create_reader
_logger = logging.getLogger(__name__)
_ERROR_RETR... |
17,307 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/data/dataset.py | 148,152 | 84,163 | set_loader_cfg | IterableImageDataset | 152,156 | 152,156 | class IterableImageDataset(data.IterableDataset):
def __init__(
self,
root,
reader=None,
split='train',
class_map=None,
is_training=False,
batch_size=1,
num_samples=None,
seed=42,
repeats=0,
... | class IterableImageDataset(data.IterableDataset):
def __init__(
self,
root,
reader=None,
split='train',
class_map=None,
is_training=False,
batch_size=1,
num_samples=None,
seed=42,
repeats=0,
... | # TFDS and WDS readers need # workers for correct # samples estimate before loader processes created
if hasattr(self.reader, 'set_loader_cfg'):
self.reader.set_loader_cfg(num_workers=num_workers)
| # TFDS and WDS readers need # workers for correct # samples estimate before loader processes created
if hasattr(self.reader, 'set_loader_cfg'):
self.reader.set_loader_cfg(num_workers=num_workers)
| 21 | 636 | 67 | 703 | """ Quick n Simple Image Folder, Tarfile based DataSet
Hacked together by / Copyright 2019, Ross Wightman
"""
import io
import logging
from typing import Optional
import torch
import torch.utils.data as data
from PIL import Image
from .readers import create_reader
_logger = logging.getLogger(__name__)
_ERROR_RETR... | """ Quick n Simple Image Folder, Tarfile based DataSet
Hacked together by / Copyright 2019, Ross Wightman
"""
import io
import logging
from typing import Optional
import torch
import torch.utils.data as data
from PIL import Image
from .readers import create_reader
_logger = logging.getLogger(__name__)
_ERROR_RETR... |
17,309 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/data/dataset_info.py | 62,63 | 35,74 | label_name_to_description | CustomDatasetInfo | 63,67 | 63,67 | class CustomDatasetInfo(DatasetInfo):
""" DatasetInfo that wraps passed values for custom datasets."""
def __init__(
self,
label_names: Union[List[str], Dict[int, str]],
label_descriptions: Optional[Dict[str, str]] = None
):
super().__init__()
assert len(... | class CustomDatasetInfo(DatasetInfo):
""" DatasetInfo that wraps passed values for custom datasets."""
def __init__(
self,
label_names: Union[List[str], Dict[int, str]],
label_descriptions: Optional[Dict[str, str]] = None
):
super().__init__()
assert len(... | if self._label_descriptions:
return self._label_descriptions[label]
return label # return label name itself if a descriptions is not present
| if self._label_descriptions:
return self._label_descriptions[label]
return label # return label name itself if a descriptions is not present
| 17 | 441 | 45 | 486 | from abc import ABC, abstractmethod
from typing import Dict, List, Optional, Union
class DatasetInfo(ABC):
def __init__(self):
pass
@abstractmethod
def num_classes(self):
pass
@abstractmethod
def label_names(self):
pass
@abstractmethod
def label_descriptions(sel... | from abc import ABC, abstractmethod
from typing import Dict, List, Optional, Union
class DatasetInfo(ABC):
def __init__(self):
pass
@abstractmethod
def num_classes(self):
pass
@abstractmethod
def label_names(self):
pass
@abstractmethod
def label_descriptions(sel... |
17,310 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/data/distributed_sampler.py | 134,135 | 54,136 | set_epoch | RepeatAugSampler | 135,136 | 135,136 | class RepeatAugSampler(Sampler):
"""Sampler that restricts data loading to a subset of the dataset for distributed,
with repeated augmentation.
It ensures that different each augmented version of a sample will be visible to a
different process (GPU). Heavily based on torch.utils.data.DistributedSampler
... | class RepeatAugSampler(Sampler):
"""Sampler that restricts data loading to a subset of the dataset for distributed,
with repeated augmentation.
It ensures that different each augmented version of a sample will be visible to a
different process (GPU). Heavily based on torch.utils.data.DistributedSampler
... | self.epoch = epoch
| self.epoch = epoch
| 3 | 1,066 | 9 | 1,075 | import math
import torch
from torch.utils.data import Sampler
import torch.distributed as dist
class OrderedDistributedSampler(Sampler):
"""Sampler that restricts data loading to a subset of the dataset.
It is especially useful in conjunction with
:class:`torch.nn.parallel.DistributedDataParallel`. In suc... | import math
import torch
from torch.utils.data import Sampler
import torch.distributed as dist
class OrderedDistributedSampler(Sampler):
"""Sampler that restricts data loading to a subset of the dataset.
It is especially useful in conjunction with
:class:`torch.nn.parallel.DistributedDataParallel`. In suc... |
17,311 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/data/imagenet_info.py | 33,34 | 1,96 | infer_imagenet_subset | 34,48 | 34,48 | import csv
import os
import pkgutil
import re
from typing import Dict, List, Optional, Union
from .dataset_info import DatasetInfo
# NOTE no ambiguity wrt to mapping from # classes to ImageNet subset so far, but likely to change
_NUM_CLASSES_TO_SUBSET = {
1000: 'imagenet-1k',
11221: 'imagenet-21k-miil', # m... | import csv
import os
import pkgutil
import re
from typing import Dict, List, Optional, Union
from .dataset_info import DatasetInfo
# NOTE no ambiguity wrt to mapping from # classes to ImageNet subset so far, but likely to change
_NUM_CLASSES_TO_SUBSET = {
1000: 'imagenet-1k',
11221: 'imagenet-21k-miil', # m... | if isinstance(model_or_cfg, dict):
num_classes = model_or_cfg.get('num_classes', None)
else:
num_classes = getattr(model_or_cfg, 'num_classes', None)
if not num_classes:
pretrained_cfg = getattr(model_or_cfg, 'pretrained_cfg', {})
# FIXME at some point pretrained_... | if isinstance(model_or_cfg, dict):
num_classes = model_or_cfg.get('num_classes', None)
else:
num_classes = getattr(model_or_cfg, 'num_classes', None)
if not num_classes:
pretrained_cfg = getattr(model_or_cfg, 'pretrained_cfg', {})
# FIXME at some point pretrained_... | 58 | 1,258 | 212 | 1,470 | import csv
import os
import pkgutil
import re
from typing import Dict, List, Optional, Union
from .dataset_info import DatasetInfo
# NOTE no ambiguity wrt to mapping from # classes to ImageNet subset so far, but likely to change
_NUM_CLASSES_TO_SUBSET = {
1000: 'imagenet-1k',
11221: 'imagenet-21k-miil', # m... | import csv
import os
import pkgutil
import re
from typing import Dict, List, Optional, Union
from .dataset_info import DatasetInfo
# NOTE no ambiguity wrt to mapping from # classes to ImageNet subset so far, but likely to change
_NUM_CLASSES_TO_SUBSET = {
1000: 'imagenet-1k',
11221: 'imagenet-21k-miil', # m... | |
17,312 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/data/imagenet_info.py | 86,87 | 48,96 | index_to_description | ImageNetInfo | 87,90 | 87,90 | class ImageNetInfo(DatasetInfo):
def __init__(self, subset: str = 'imagenet-1k'):
super().__init__()
subset = re.sub(r'[-_\s]', '', subset.lower())
assert subset in _SUBSETS, f'Unknown imagenet subset {subset}.'
# WordNet synsets (part-of-speach + offset) are the unique class label... | class ImageNetInfo(DatasetInfo):
def __init__(self, subset: str = 'imagenet-1k'):
super().__init__()
subset = re.sub(r'[-_\s]', '', subset.lower())
assert subset in _SUBSETS, f'Unknown imagenet subset {subset}.'
# WordNet synsets (part-of-speach + offset) are the unique class label... | label = self.index_to_label_name(index)
return self.label_name_to_description(label, detailed=detailed)
| label = self.index_to_label_name(index)
return self.label_name_to_description(label, detailed=detailed)
| 6 | 703 | 40 | 743 | import csv
import os
import pkgutil
import re
from typing import Dict, List, Optional, Union
from .dataset_info import DatasetInfo
# NOTE no ambiguity wrt to mapping from # classes to ImageNet subset so far, but likely to change
_NUM_CLASSES_TO_SUBSET = {
1000: 'imagenet-1k',
11221: 'imagenet-21k-miil', # m... | import csv
import os
import pkgutil
import re
from typing import Dict, List, Optional, Union
from .dataset_info import DatasetInfo
# NOTE no ambiguity wrt to mapping from # classes to ImageNet subset so far, but likely to change
_NUM_CLASSES_TO_SUBSET = {
1000: 'imagenet-1k',
11221: 'imagenet-21k-miil', # m... |
17,313 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/data/random_erasing.py | 14,15 | 1,118 | _get_pixels | 15,26 | 15,26 | """ Random Erasing (Cutout)
Originally inspired by impl at https://github.com/zhunzhong07/Random-Erasing, Apache 2.0
Copyright Zhun Zhong & Liang Zheng
Hacked together by / Copyright 2019, Ross Wightman
"""
import random
import math
import torch
def _get_pixels(per_pixel, rand_color, patch_size, dtype=torch.float3... | """ Random Erasing (Cutout)
Originally inspired by impl at https://github.com/zhunzhong07/Random-Erasing, Apache 2.0
Copyright Zhun Zhong & Liang Zheng
Hacked together by / Copyright 2019, Ross Wightman
"""
import random
import math
import torch
def _get_pixels(per_pixel, rand_color, patch_size, dtype=torch.float3... | # NOTE I've seen CUDA illegal memory access errors being caused by the normal_()
# paths, flip the order so normal is run on CPU if this becomes a problem
# Issue has been fixed in master https://github.com/pytorch/pytorch/issues/19508
if per_pixel:
return torch.empty(patch_size, dtype=dtype, de... | # NOTE I've seen CUDA illegal memory access errors being caused by the normal_()
# paths, flip the order so normal is run on CPU if this becomes a problem
# Issue has been fixed in master https://github.com/pytorch/pytorch/issues/19508
if per_pixel:
return torch.empty(patch_size, dtype=dtype, de... | 59 | 1,332 | 194 | 1,526 | """ Random Erasing (Cutout)
Originally inspired by impl at https://github.com/zhunzhong07/Random-Erasing, Apache 2.0
Copyright Zhun Zhong & Liang Zheng
Hacked together by / Copyright 2019, Ross Wightman
"""
import random
import math
import torch
def _get_pixels(per_pixel, rand_color, patch_size, dtype=torch.float3... | """ Random Erasing (Cutout)
Originally inspired by impl at https://github.com/zhunzhong07/Random-Erasing, Apache 2.0
Copyright Zhun Zhong & Liang Zheng
Hacked together by / Copyright 2019, Ross Wightman
"""
import random
import math
import torch
def _get_pixels(per_pixel, rand_color, patch_size, dtype=torch.float3... | |
17,314 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/data/random_erasing.py | 78,79 | 26,118 | _erase | RandomErasing | 79,102 | 79,102 | class RandomErasing:
""" Randomly selects a rectangle region in an image and erases its pixels.
'Random Erasing Data Augmentation' by Zhong et al.
See https://arxiv.org/pdf/1708.04896.pdf
This variant of RandomErasing is intended to be applied to either a batch
or single image tenso... | class RandomErasing:
""" Randomly selects a rectangle region in an image and erases its pixels.
'Random Erasing Data Augmentation' by Zhong et al.
See https://arxiv.org/pdf/1708.04896.pdf
This variant of RandomErasing is intended to be applied to either a batch
or single image tenso... | if random.random() > self.probability:
return
area = img_h * img_w
count = self.min_count if self.min_count == self.max_count else \
random.randint(self.min_count, self.max_count)
for _ in range(count):
for attempt in range(10):
target_... | if random.random() > self.probability:
return
area = img_h * img_w
count = self.min_count if self.min_count == self.max_count else \
random.randint(self.min_count, self.max_count)
for _ in range(count):
for attempt in range(10):
target_... | 88 | 902 | 323 | 1,225 | """ Random Erasing (Cutout)
Originally inspired by impl at https://github.com/zhunzhong07/Random-Erasing, Apache 2.0
Copyright Zhun Zhong & Liang Zheng
Hacked together by / Copyright 2019, Ross Wightman
"""
import random
import math
import torch
def _get_pixels(per_pixel, rand_color, patch_size, dtype=torch.float3... | """ Random Erasing (Cutout)
Originally inspired by impl at https://github.com/zhunzhong07/Random-Erasing, Apache 2.0
Copyright Zhun Zhong & Liang Zheng
Hacked together by / Copyright 2019, Ross Wightman
"""
import random
import math
import torch
def _get_pixels(per_pixel, rand_color, patch_size, dtype=torch.float3... |
17,315 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/data/readers/class_map.py | 5,6 | 1,24 | load_class_map | 6,24 | 6,24 | import os
import pickle
def load_class_map(map_or_filename, root=''):
FILL_FUNC_BODY | import os
import pickle
def load_class_map(map_or_filename, root=''):
FILL_FUNC_BODY | if isinstance(map_or_filename, dict):
assert dict, 'class_map dict must be non-empty'
return map_or_filename
class_map_path = map_or_filename
if not os.path.exists(class_map_path):
class_map_path = os.path.join(root, class_map_path)
assert os.path.exists(class_map_path), 'Can... | if isinstance(map_or_filename, dict):
assert dict, 'class_map dict must be non-empty'
return map_or_filename
class_map_path = map_or_filename
if not os.path.exists(class_map_path):
class_map_path = os.path.join(root, class_map_path)
assert os.path.exists(class_map_path), 'Can... | 76 | 37 | 296 | 333 | import os
import pickle
def load_class_map(map_or_filename, root=''):
FILL_FUNC_BODY | import os
import pickle
def load_class_map(map_or_filename, root=''):
FILL_FUNC_BODY | |
17,316 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/data/readers/img_extensions.py | 10,11 | 1,51 | _set_extensions | 11,18 | 11,18 | from copy import deepcopy
__all__ = ['get_img_extensions', 'is_img_extension', 'set_img_extensions', 'add_img_extensions', 'del_img_extensions']
IMG_EXTENSIONS = ('.png', '.jpg', '.jpeg') # singleton, kept public for bwd compat use
_IMG_EXTENSIONS_SET = set(IMG_EXTENSIONS) # set version, private, kept in sync
de... | from copy import deepcopy
__all__ = ['get_img_extensions', 'is_img_extension', 'set_img_extensions', 'add_img_extensions', 'del_img_extensions']
IMG_EXTENSIONS = ('.png', '.jpg', '.jpeg') # singleton, kept public for bwd compat use
_IMG_EXTENSIONS_SET = set(IMG_EXTENSIONS) # set version, private, kept in sync
de... | global IMG_EXTENSIONS
global _IMG_EXTENSIONS_SET
dedupe = set() # NOTE de-duping tuple while keeping original order
IMG_EXTENSIONS = tuple(x for x in extensions if x not in dedupe and not dedupe.add(x))
_IMG_EXTENSIONS_SET = set(extensions)
| global IMG_EXTENSIONS
global _IMG_EXTENSIONS_SET
dedupe = set() # NOTE de-duping tuple while keeping original order
IMG_EXTENSIONS = tuple(x for x in extensions if x not in dedupe and not dedupe.add(x))
_IMG_EXTENSIONS_SET = set(extensions)
| 33 | 434 | 105 | 539 | from copy import deepcopy
__all__ = ['get_img_extensions', 'is_img_extension', 'set_img_extensions', 'add_img_extensions', 'del_img_extensions']
IMG_EXTENSIONS = ('.png', '.jpg', '.jpeg') # singleton, kept public for bwd compat use
_IMG_EXTENSIONS_SET = set(IMG_EXTENSIONS) # set version, private, kept in sync
de... | from copy import deepcopy
__all__ = ['get_img_extensions', 'is_img_extension', 'set_img_extensions', 'add_img_extensions', 'del_img_extensions']
IMG_EXTENSIONS = ('.png', '.jpg', '.jpeg') # singleton, kept public for bwd compat use
_IMG_EXTENSIONS_SET = set(IMG_EXTENSIONS) # set version, private, kept in sync
de... | |
17,317 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/data/readers/reader.py | 15,16 | 4,18 | filenames | Reader | 16,18 | 16,18 | class Reader:
def __init__(self):
pass
@abstractmethod
def _filename(self, index, basename=False, absolute=False):
pass
def filename(self, index, basename=False, absolute=False):
return self._filename(index, basename=basename, absolute=absolute)
def filenames(self, basenam... | class Reader:
def __init__(self):
pass
@abstractmethod
def _filename(self, index, basename=False, absolute=False):
pass
def filename(self, index, basename=False, absolute=False):
return self._filename(index, basename=basename, absolute=absolute)
def filenames(self, basenam... | return [self._filename(index, basename=basename, absolute=absolute) for index in range(len(self))]
| return [self._filename(index, basename=basename, absolute=absolute) for index in range(len(self))]
| 8 | 112 | 34 | 146 | from abc import abstractmethod
class Reader:
def __init__(self):
pass
@abstractmethod
def _filename(self, index, basename=False, absolute=False):
pass
def filename(self, index, basename=False, absolute=False):
return self._filename(index, basename=basename, absolute=absolute)... | from abc import abstractmethod
class Reader:
def __init__(self):
pass
@abstractmethod
def _filename(self, index, basename=False, absolute=False):
pass
def filename(self, index, basename=False, absolute=False):
return self._filename(index, basename=basename, absolute=absolute)... |
17,318 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/data/readers/reader_factory.py | 8,14 | 1,46 | create_reader | 14,46 | 14,46 | import os
from typing import Optional
from .reader_image_folder import ReaderImageFolder
from .reader_image_in_tar import ReaderImageInTar
def create_reader(
name: str,
root: Optional[str] = None,
split: str = 'train',
**kwargs,
):
FILL_FUNC_BODY | import os
from typing import Optional
from .reader_image_folder import ReaderImageFolder
from .reader_image_in_tar import ReaderImageInTar
def create_reader(
name: str,
root: Optional[str] = None,
split: str = 'train',
**kwargs,
):
FILL_FUNC_BODY | kwargs = {k: v for k, v in kwargs.items() if v is not None}
name = name.lower()
name = name.split('/', 1)
prefix = ''
if len(name) > 1:
prefix = name[0]
name = name[-1]
# FIXME improve the selection right now just tfds prefix or fallback path, will need options to
# explicitly s... | kwargs = {k: v for k, v in kwargs.items() if v is not None}
name = name.lower()
name = name.split('/', 1)
prefix = ''
if len(name) > 1:
prefix = name[0]
name = name[-1]
# FIXME improve the selection right now just tfds prefix or fallback path, will need options to
# explicitly s... | 175 | 88 | 484 | 572 | import os
from typing import Optional
from .reader_image_folder import ReaderImageFolder
from .reader_image_in_tar import ReaderImageInTar
def create_reader(
name: str,
root: Optional[str] = None,
split: str = 'train',
**kwargs,
):
FILL_FUNC_BODY | import os
from typing import Optional
from .reader_image_folder import ReaderImageFolder
from .reader_image_in_tar import ReaderImageInTar
def create_reader(
name: str,
root: Optional[str] = None,
split: str = 'train',
**kwargs,
):
FILL_FUNC_BODY | |
17,319 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/data/readers/reader_hfds.py | 22,23 | 1,85 | get_class_labels | 23,30 | 23,30 | """ Dataset reader that wraps Hugging Face datasets
Hacked together by / Copyright 2022 Ross Wightman
"""
import io
import math
from typing import Optional
import torch
import torch.distributed as dist
from PIL import Image
try:
import datasets
except ImportError as e:
print("Please install Hugging Face data... | """ Dataset reader that wraps Hugging Face datasets
Hacked together by / Copyright 2022 Ross Wightman
"""
import io
import math
from typing import Optional
import torch
import torch.distributed as dist
from PIL import Image
try:
import datasets
except ImportError as e:
print("Please install Hugging Face data... | if 'label' not in info.features:
return {}
class_label = info.features[label_key]
class_to_idx = {n: class_label.str2int(n) for n in class_label.names}
return class_to_idx
| if 'label' not in info.features:
return {}
class_label = info.features[label_key]
class_to_idx = {n: class_label.str2int(n) for n in class_label.names}
return class_to_idx
| 20 | 710 | 77 | 787 | """ Dataset reader that wraps Hugging Face datasets
Hacked together by / Copyright 2022 Ross Wightman
"""
import io
import math
from typing import Optional
import torch
import torch.distributed as dist
from PIL import Image
try:
import datasets
except ImportError as e:
print("Please install Hugging Face data... | """ Dataset reader that wraps Hugging Face datasets
Hacked together by / Copyright 2022 Ross Wightman
"""
import io
import math
from typing import Optional
import torch
import torch.distributed as dist
from PIL import Image
try:
import datasets
except ImportError as e:
print("Please install Hugging Face data... | |
17,320 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/data/readers/reader_hfds.py | 82,83 | 30,85 | _filename | ReaderHfds | 83,85 | 83,85 | class ReaderHfds(Reader):
def __init__(
self,
name: str,
root: Optional[str] = None,
split: str = 'train',
class_map: dict = None,
image_key: str = 'image',
target_key: str = 'label',
download: bool = False,
):
... | class ReaderHfds(Reader):
def __init__(
self,
name: str,
root: Optional[str] = None,
split: str = 'train',
class_map: dict = None,
image_key: str = 'image',
target_key: str = 'label',
download: bool = False,
):
... | item = self.dataset[index]
return item[self.image_key]['path']
| item = self.dataset[index]
return item[self.image_key]['path']
| 5 | 549 | 26 | 575 | """ Dataset reader that wraps Hugging Face datasets
Hacked together by / Copyright 2022 Ross Wightman
"""
import io
import math
from typing import Optional
import torch
import torch.distributed as dist
from PIL import Image
try:
import datasets
except ImportError as e:
print("Please install Hugging Face data... | """ Dataset reader that wraps Hugging Face datasets
Hacked together by / Copyright 2022 Ross Wightman
"""
import io
import math
from typing import Optional
import torch
import torch.distributed as dist
from PIL import Image
try:
import datasets
except ImportError as e:
print("Please install Hugging Face data... |
17,322 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/data/readers/reader_image_folder.py | 18,25 | 1,100 | find_images_and_targets | 37,59 | 25,59 | """ A dataset reader that extracts images from folders
Folders are scanned recursively to find image files. Labels are based
on the folder hierarchy, just leaf folders by default.
Hacked together by / Copyright 2020 Ross Wightman
"""
import os
from typing import Dict, List, Optional, Set, Tuple, Union
from timm.util... | """ A dataset reader that extracts images from folders
Folders are scanned recursively to find image files. Labels are based
on the folder hierarchy, just leaf folders by default.
Hacked together by / Copyright 2020 Ross Wightman
"""
import os
from typing import Dict, List, Optional, Set, Tuple, Union
from timm.util... | types = get_img_extensions(as_set=True) if not types else set(types)
labels = []
filenames = []
for root, subdirs, files in os.walk(folder, topdown=False, followlinks=True):
rel_path = os.path.relpath(root, folder) if (root != folder) else ''
label = os.path.basename(rel_path) if leaf_na... | """ Walk folder recursively to discover images and map them to classes by folder names.
Args:
folder: root of folder to recrusively search
types: types (file extensions) to search for in path
class_to_idx: specify mapping for class (folder name) to class index if set
leaf_name_o... | 104 | 712 | 375 | 1,087 | """ A dataset reader that extracts images from folders
Folders are scanned recursively to find image files. Labels are based
on the folder hierarchy, just leaf folders by default.
Hacked together by / Copyright 2020 Ross Wightman
"""
import os
from typing import Dict, List, Optional, Set, Tuple, Union
from timm.util... | """ A dataset reader that extracts images from folders
Folders are scanned recursively to find image files. Labels are based
on the folder hierarchy, just leaf folders by default.
Hacked together by / Copyright 2020 Ross Wightman
"""
import os
from typing import Dict, List, Optional, Set, Tuple, Union
from timm.util... | |
17,323 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/data/readers/reader_image_folder.py | 93,94 | 59,100 | _filename | ReaderImageFolder | 94,100 | 94,100 | class ReaderImageFolder(Reader):
def __init__(
self,
root,
class_map='',
input_key=None,
):
super().__init__()
self.root = root
class_to_idx = None
if class_map:
class_to_idx = load_class_map(class_map, root)
f... | class ReaderImageFolder(Reader):
def __init__(
self,
root,
class_map='',
input_key=None,
):
super().__init__()
self.root = root
class_to_idx = None
if class_map:
class_to_idx = load_class_map(class_map, root)
f... | filename = self.samples[index][0]
if basename:
filename = os.path.basename(filename)
elif not absolute:
filename = os.path.relpath(filename, self.root)
return filename
| filename = self.samples[index][0]
if basename:
filename = os.path.basename(filename)
elif not absolute:
filename = os.path.relpath(filename, self.root)
return filename
| 17 | 318 | 65 | 383 | """ A dataset reader that extracts images from folders
Folders are scanned recursively to find image files. Labels are based
on the folder hierarchy, just leaf folders by default.
Hacked together by / Copyright 2020 Ross Wightman
"""
import os
from typing import Dict, List, Optional, Set, Tuple, Union
from timm.util... | """ A dataset reader that extracts images from folders
Folders are scanned recursively to find image files. Labels are based
on the folder hierarchy, just leaf folders by default.
Hacked together by / Copyright 2020 Ross Wightman
"""
import os
from typing import Dict, List, Optional, Set, Tuple, Union
from timm.util... |
17,324 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/data/readers/reader_image_tar.py | 18,19 | 1,75 | extract_tarinfo | 19,41 | 19,41 | """ A dataset reader that reads single tarfile based datasets
This reader can read datasets consisting if a single tarfile containing images.
I am planning to deprecated it in favour of ParerImageInTar.
Hacked together by / Copyright 2020 Ross Wightman
"""
import os
import tarfile
from timm.utils.misc import natural... | """ A dataset reader that reads single tarfile based datasets
This reader can read datasets consisting if a single tarfile containing images.
I am planning to deprecated it in favour of ParerImageInTar.
Hacked together by / Copyright 2020 Ross Wightman
"""
import os
import tarfile
from timm.utils.misc import natural... | extensions = get_img_extensions(as_set=True)
files = []
labels = []
for ti in tarfile.getmembers():
if not ti.isfile():
continue
dirname, basename = os.path.split(ti.path)
label = os.path.basename(dirname)
ext = os.path.splitext(basename)[1]
if ext.low... | extensions = get_img_extensions(as_set=True)
files = []
labels = []
for ti in tarfile.getmembers():
if not ti.isfile():
continue
dirname, basename = os.path.split(ti.path)
label = os.path.basename(dirname)
ext = os.path.splitext(basename)[1]
if ext.low... | 78 | 533 | 315 | 848 | """ A dataset reader that reads single tarfile based datasets
This reader can read datasets consisting if a single tarfile containing images.
I am planning to deprecated it in favour of ParerImageInTar.
Hacked together by / Copyright 2020 Ross Wightman
"""
import os
import tarfile
from timm.utils.misc import natural... | """ A dataset reader that reads single tarfile based datasets
This reader can read datasets consisting if a single tarfile containing images.
I am planning to deprecated it in favour of ParerImageInTar.
Hacked together by / Copyright 2020 Ross Wightman
"""
import os
import tarfile
from timm.utils.misc import natural... | |
17,325 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/data/readers/reader_image_tar.py | 70,71 | 41,75 | _filename | ReaderImageTar | 71,75 | 71,75 | class ReaderImageTar(Reader):
""" Single tarfile dataset where classes are mapped to folders within tar
NOTE: This class is being deprecated in favour of the more capable ReaderImageInTar that can
operate on folders of tars or tars in tars.
"""
def __init__(self, root, class_map=''):
super()... | class ReaderImageTar(Reader):
""" Single tarfile dataset where classes are mapped to folders within tar
NOTE: This class is being deprecated in favour of the more capable ReaderImageInTar that can
operate on folders of tars or tars in tars.
"""
def __init__(self, root, class_map=''):
super()... | filename = self.samples[index][0].name
if basename:
filename = os.path.basename(filename)
return filename
| filename = self.samples[index][0].name
if basename:
filename = os.path.basename(filename)
return filename
| 10 | 354 | 41 | 395 | """ A dataset reader that reads single tarfile based datasets
This reader can read datasets consisting if a single tarfile containing images.
I am planning to deprecated it in favour of ParerImageInTar.
Hacked together by / Copyright 2020 Ross Wightman
"""
import os
import tarfile
from timm.utils.misc import natural... | """ A dataset reader that reads single tarfile based datasets
This reader can read datasets consisting if a single tarfile containing images.
I am planning to deprecated it in favour of ParerImageInTar.
Hacked together by / Copyright 2020 Ross Wightman
"""
import os
import tarfile
from timm.utils.misc import natural... |
17,328 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/data/readers/reader_wds.py | 197,198 | 184,214 | run | detshuffle2 | 198,214 | 198,214 | class detshuffle2(wds.PipelineStage):
def __init__(
self,
bufsize=1000,
initial=100,
seed=0,
epoch=-1,
):
self.bufsize = bufsize
self.initial = initial
self.seed = seed
sel... | class detshuffle2(wds.PipelineStage):
def __init__(
self,
bufsize=1000,
initial=100,
seed=0,
epoch=-1,
):
self.bufsize = bufsize
self.initial = initial
self.seed = seed
sel... | if isinstance(self.epoch, SharedCount):
epoch = self.epoch.value
else:
# NOTE: this is epoch tracking is problematic in a multiprocess (dataloader workers or train)
# situation as different workers may wrap at different times (or not at all).
... | if isinstance(self.epoch, SharedCount):
epoch = self.epoch.value
else:
# NOTE: this is epoch tracking is problematic in a multiprocess (dataloader workers or train)
# situation as different workers may wrap at different times (or not at all).
... | 75 | 112 | 211 | 323 | """ Dataset reader for webdataset
Hacked together by / Copyright 2022 Ross Wightman
"""
import io
import json
import logging
import math
import os
import random
import sys
from dataclasses import dataclass
from functools import partial
from itertools import islice
from typing import Any, Callable, Dict, List, Optional... | """ Dataset reader for webdataset
Hacked together by / Copyright 2022 Ross Wightman
"""
import io
import json
import logging
import math
import os
import random
import sys
from dataclasses import dataclass
from functools import partial
from itertools import islice
from typing import Any, Callable, Dict, List, Optional... |
17,329 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/data/readers/shared_count.py | 12,14 | 4,15 | value | SharedCount | 14,15 | 14,15 | class SharedCount:
def __init__(self, epoch: int = 0):
self.shared_epoch = Value('i', epoch)
@property
def value(self):
return self.shared_epoch.value
@value.setter
def value(self, epoch):
FILL_FUNC_BODY | class SharedCount:
def __init__(self, epoch: int = 0):
self.shared_epoch = Value('i', epoch)
@property
def value(self):
return self.shared_epoch.value
@value.setter
def value(self, epoch):
FILL_FUNC_BODY | self.shared_epoch.value = epoch
| self.shared_epoch.value = epoch
| 3 | 85 | 13 | 98 | from multiprocessing import Value
class SharedCount:
def __init__(self, epoch: int = 0):
self.shared_epoch = Value('i', epoch)
@property
def value(self):
return self.shared_epoch.value
@value.setter
def value(self, epoch):
FILL_FUNC_BODY | from multiprocessing import Value
class SharedCount:
def __init__(self, epoch: int = 0):
self.shared_epoch = Value('i', epoch)
@property
def value(self):
return self.shared_epoch.value
@value.setter
def value(self, epoch):
FILL_FUNC_BODY |
17,330 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/data/real_labels.py | 30,31 | 13,48 | add_result | RealLabelsImagenet | 31,43 | 31,43 | class RealLabelsImagenet:
def __init__(self, filenames, real_json=None, topk=(1, 5)):
if real_json is not None:
with open(real_json) as real_labels:
real_labels = json.load(real_labels)
else:
real_labels = json.loads(
pkgutil.get_data(__name__... | class RealLabelsImagenet:
def __init__(self, filenames, real_json=None, topk=(1, 5)):
if real_json is not None:
with open(real_json) as real_labels:
real_labels = json.load(real_labels)
else:
real_labels = json.loads(
pkgutil.get_data(__name__... | maxk = max(self.topk)
_, pred_batch = output.topk(maxk, 1, True, True)
pred_batch = pred_batch.cpu().numpy()
for pred in pred_batch:
filename = self.filenames[self.sample_idx]
filename = os.path.basename(filename)
if self.real_labels[filename]:
... | maxk = max(self.topk)
_, pred_batch = output.topk(maxk, 1, True, True)
pred_batch = pred_batch.cpu().numpy()
for pred in pred_batch:
filename = self.filenames[self.sample_idx]
filename = os.path.basename(filename)
if self.real_labels[filename]:
... | 40 | 356 | 165 | 521 | """ Real labels evaluator for ImageNet
Paper: `Are we done with ImageNet?` - https://arxiv.org/abs/2006.07159
Based on Numpy example at https://github.com/google-research/reassessed-imagenet
Hacked together by / Copyright 2020 Ross Wightman
"""
import os
import json
import numpy as np
import pkgutil
class RealLabels... | """ Real labels evaluator for ImageNet
Paper: `Are we done with ImageNet?` - https://arxiv.org/abs/2006.07159
Based on Numpy example at https://github.com/google-research/reassessed-imagenet
Hacked together by / Copyright 2020 Ross Wightman
"""
import os
import json
import numpy as np
import pkgutil
class RealLabels... |
17,332 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/activations.py | 60,61 | 1,174 | tanh | 61,65 | 61,65 | """ Activations
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch import nn as nn
from torch.nn import functional as F
def swish(x, inplace:... | """ Activations
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch import nn as nn
from torch.nn import functional as F
def swish(x, inplace:... | return x.tanh_() if inplace else x.tanh()
# PyTorch has this, but not with a consistent inplace argmument interface
| return x.tanh_() if inplace else x.tanh()
# PyTorch has this, but not with a consistent inplace argmument interface
| 18 | 1,793 | 43 | 1,836 | """ Activations
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch import nn as nn
from torch.nn import functional as F
def swish(x, inplace:... | """ Activations
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch import nn as nn
from torch.nn import functional as F
def swish(x, inplace:... | |
17,333 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/activations.py | 144,145 | 138,148 | forward | GELU | 145,148 | 145,148 | class GELU(nn.Module):
"""Applies the Gaussian Error Linear Units function (w/ dummy inplace arg)
"""
def __init__(self, inplace: bool = False):
super(GELU, self).__init__()
def forward(self, input: torch.Tensor) -> torch.Tensor:
FILL_FUNC_BODY | class GELU(nn.Module):
"""Applies the Gaussian Error Linear Units function (w/ dummy inplace arg)
"""
def __init__(self, inplace: bool = False):
super(GELU, self).__init__()
def forward(self, input: torch.Tensor) -> torch.Tensor:
FILL_FUNC_BODY | return F.gelu(input)
| return F.gelu(input)
| 2 | 97 | 16 | 113 | """ Activations
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch import nn as nn
from torch.nn import functional as F
def swish(x, inplace:... | """ Activations
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch import nn as nn
from torch.nn import functional as F
def swish(x, inplace:... |
17,334 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/activations.py | 158,159 | 152,162 | forward | GELUTanh | 159,162 | 159,162 | class GELUTanh(nn.Module):
"""Applies the Gaussian Error Linear Units function (w/ dummy inplace arg)
"""
def __init__(self, inplace: bool = False):
super(GELUTanh, self).__init__()
def forward(self, input: torch.Tensor) -> torch.Tensor:
FILL_FUNC_BODY | class GELUTanh(nn.Module):
"""Applies the Gaussian Error Linear Units function (w/ dummy inplace arg)
"""
def __init__(self, inplace: bool = False):
super(GELUTanh, self).__init__()
def forward(self, input: torch.Tensor) -> torch.Tensor:
FILL_FUNC_BODY | return F.gelu(input, approximate='tanh')
| return F.gelu(input, approximate='tanh')
| 3 | 101 | 22 | 123 | """ Activations
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch import nn as nn
from torch.nn import functional as F
def swish(x, inplace:... | """ Activations
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch import nn as nn
from torch.nn import functional as F
def swish(x, inplace:... |
17,335 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/activations.py | 120,121 | 115,124 | forward | HardMish | 121,124 | 121,124 | class HardMish(nn.Module):
def __init__(self, inplace: bool = False):
super(HardMish, self).__init__()
self.inplace = inplace
def forward(self, x):
FILL_FUNC_BODY | class HardMish(nn.Module):
def __init__(self, inplace: bool = False):
super(HardMish, self).__init__()
self.inplace = inplace
def forward(self, x):
FILL_FUNC_BODY | return hard_mish(x, self.inplace)
| return hard_mish(x, self.inplace)
| 3 | 68 | 20 | 88 | """ Activations
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch import nn as nn
from torch.nn import functional as F
def swish(x, inplace:... | """ Activations
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch import nn as nn
from torch.nn import functional as F
def swish(x, inplace:... |
17,336 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/activations.py | 100,101 | 95,104 | forward | HardSigmoid | 101,104 | 101,104 | class HardSigmoid(nn.Module):
def __init__(self, inplace: bool = False):
super(HardSigmoid, self).__init__()
self.inplace = inplace
def forward(self, x):
FILL_FUNC_BODY | class HardSigmoid(nn.Module):
def __init__(self, inplace: bool = False):
super(HardSigmoid, self).__init__()
self.inplace = inplace
def forward(self, x):
FILL_FUNC_BODY | return hard_sigmoid(x, self.inplace)
| return hard_sigmoid(x, self.inplace)
| 3 | 70 | 21 | 91 | """ Activations
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch import nn as nn
from torch.nn import functional as F
def swish(x, inplace:... | """ Activations
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch import nn as nn
from torch.nn import functional as F
def swish(x, inplace:... |
17,337 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/activations.py | 84,85 | 79,88 | forward | HardSwish | 85,88 | 85,88 | class HardSwish(nn.Module):
def __init__(self, inplace: bool = False):
super(HardSwish, self).__init__()
self.inplace = inplace
def forward(self, x):
FILL_FUNC_BODY | class HardSwish(nn.Module):
def __init__(self, inplace: bool = False):
super(HardSwish, self).__init__()
self.inplace = inplace
def forward(self, x):
FILL_FUNC_BODY | return hard_swish(x, self.inplace)
| return hard_swish(x, self.inplace)
| 3 | 68 | 20 | 88 | """ Activations
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch import nn as nn
from torch.nn import functional as F
def swish(x, inplace:... | """ Activations
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch import nn as nn
from torch.nn import functional as F
def swish(x, inplace:... |
17,338 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/activations.py | 42,43 | 36,46 | forward | Mish | 43,46 | 43,46 | class Mish(nn.Module):
"""Mish: A Self Regularized Non-Monotonic Neural Activation Function - https://arxiv.org/abs/1908.08681
"""
def __init__(self, inplace: bool = False):
super(Mish, self).__init__()
def forward(self, x):
FILL_FUNC_BODY | class Mish(nn.Module):
"""Mish: A Self Regularized Non-Monotonic Neural Activation Function - https://arxiv.org/abs/1908.08681
"""
def __init__(self, inplace: bool = False):
super(Mish, self).__init__()
def forward(self, x):
FILL_FUNC_BODY | return mish(x)
| return mish(x)
| 2 | 102 | 13 | 115 | """ Activations
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch import nn as nn
from torch.nn import functional as F
def swish(x, inplace:... | """ Activations
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch import nn as nn
from torch.nn import functional as F
def swish(x, inplace:... |
17,339 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/activations.py | 130,131 | 124,134 | forward | PReLU | 131,134 | 131,134 | class PReLU(nn.PReLU):
"""Applies PReLU (w/ dummy inplace arg)
"""
def __init__(self, num_parameters: int = 1, init: float = 0.25, inplace: bool = False) -> None:
super(PReLU, self).__init__(num_parameters=num_parameters, init=init)
def forward(self, input: torch.Tensor) -> torch.Tensor:
FILL_F... | class PReLU(nn.PReLU):
"""Applies PReLU (w/ dummy inplace arg)
"""
def __init__(self, num_parameters: int = 1, init: float = 0.25, inplace: bool = False) -> None:
super(PReLU, self).__init__(num_parameters=num_parameters, init=init)
def forward(self, input: torch.Tensor) -> torch.Tensor:
FILL_F... | return F.prelu(input, self.weight)
| return F.prelu(input, self.weight)
| 3 | 131 | 19 | 150 | """ Activations
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch import nn as nn
from torch.nn import functional as F
def swish(x, inplace:... | """ Activations
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch import nn as nn
from torch.nn import functional as F
def swish(x, inplace:... |
17,340 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/activations.py | 172,173 | 166,174 | forward | QuickGELU | 173,174 | 173,174 | class QuickGELU(nn.Module):
"""Applies the Gaussian Error Linear Units function (w/ dummy inplace arg)
"""
def __init__(self, inplace: bool = False):
super(QuickGELU, self).__init__()
def forward(self, input: torch.Tensor) -> torch.Tensor:
FILL_FUNC_BODY | class QuickGELU(nn.Module):
"""Applies the Gaussian Error Linear Units function (w/ dummy inplace arg)
"""
def __init__(self, inplace: bool = False):
super(QuickGELU, self).__init__()
def forward(self, input: torch.Tensor) -> torch.Tensor:
FILL_FUNC_BODY | return quick_gelu(input)
| return quick_gelu(input)
| 2 | 99 | 12 | 111 | """ Activations
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch import nn as nn
from torch.nn import functional as F
def swish(x, inplace:... | """ Activations
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch import nn as nn
from torch.nn import functional as F
def swish(x, inplace:... |
17,341 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/activations.py | 56,57 | 51,60 | forward | Sigmoid | 57,60 | 57,60 | class Sigmoid(nn.Module):
def __init__(self, inplace: bool = False):
super(Sigmoid, self).__init__()
self.inplace = inplace
def forward(self, x):
FILL_FUNC_BODY | class Sigmoid(nn.Module):
def __init__(self, inplace: bool = False):
super(Sigmoid, self).__init__()
self.inplace = inplace
def forward(self, x):
FILL_FUNC_BODY | return x.sigmoid_() if self.inplace else x.sigmoid()
| return x.sigmoid_() if self.inplace else x.sigmoid()
| 6 | 68 | 27 | 95 | """ Activations
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch import nn as nn
from torch.nn import functional as F
def swish(x, inplace:... | """ Activations
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch import nn as nn
from torch.nn import functional as F
def swish(x, inplace:... |
17,342 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/activations.py | 25,26 | 20,29 | forward | Swish | 26,29 | 26,29 | class Swish(nn.Module):
def __init__(self, inplace: bool = False):
super(Swish, self).__init__()
self.inplace = inplace
def forward(self, x):
FILL_FUNC_BODY | class Swish(nn.Module):
def __init__(self, inplace: bool = False):
super(Swish, self).__init__()
self.inplace = inplace
def forward(self, x):
FILL_FUNC_BODY | return swish(x, self.inplace)
| return swish(x, self.inplace)
| 3 | 66 | 18 | 84 | """ Activations
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch import nn as nn
from torch.nn import functional as F
def swish(x, inplace:... | """ Activations
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch import nn as nn
from torch.nn import functional as F
def swish(x, inplace:... |
17,343 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/activations.py | 70,71 | 65,74 | forward | Tanh | 71,74 | 71,74 | class Tanh(nn.Module):
def __init__(self, inplace: bool = False):
super(Tanh, self).__init__()
self.inplace = inplace
def forward(self, x):
FILL_FUNC_BODY | class Tanh(nn.Module):
def __init__(self, inplace: bool = False):
super(Tanh, self).__init__()
self.inplace = inplace
def forward(self, x):
FILL_FUNC_BODY | return x.tanh_() if self.inplace else x.tanh()
| return x.tanh_() if self.inplace else x.tanh()
| 6 | 67 | 25 | 92 | """ Activations
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch import nn as nn
from torch.nn import functional as F
def swish(x, inplace:... | """ Activations
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch import nn as nn
from torch.nn import functional as F
def swish(x, inplace:... |
17,344 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/activations_jit.py | 62,64 | 1,91 | hard_swish_jit | 64,68 | 64,68 | """ Activations
A collection of jit-scripted activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
All jit scripted activations are lacking in-place variations on purpose, scripted kernel fusion does not
currently work across in-place op bou... | """ Activations
A collection of jit-scripted activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
All jit scripted activations are lacking in-place variations on purpose, scripted kernel fusion does not
currently work across in-place op bou... | # return x * (F.relu6(x + 3.) / 6)
return x * (x + 3).clamp(min=0, max=6).div(6.) # clamp seems ever so slightly faster?
| # return x * (F.relu6(x + 3.) / 6)
return x * (x + 3).clamp(min=0, max=6).div(6.) # clamp seems ever so slightly faster?
| 23 | 922 | 62 | 984 | """ Activations
A collection of jit-scripted activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
All jit scripted activations are lacking in-place variations on purpose, scripted kernel fusion does not
currently work across in-place op bou... | """ Activations
A collection of jit-scripted activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
All jit scripted activations are lacking in-place variations on purpose, scripted kernel fusion does not
currently work across in-place op bou... | |
17,345 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/activations_jit.py | 89,90 | 85,91 | forward | HardMishJit | 90,91 | 90,91 | class HardMishJit(nn.Module):
def __init__(self, inplace: bool = False):
super(HardMishJit, self).__init__()
def forward(self, x):
FILL_FUNC_BODY | class HardMishJit(nn.Module):
def __init__(self, inplace: bool = False):
super(HardMishJit, self).__init__()
def forward(self, x):
FILL_FUNC_BODY | return hard_mish_jit(x)
| return hard_mish_jit(x)
| 2 | 63 | 14 | 77 | """ Activations
A collection of jit-scripted activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
All jit scripted activations are lacking in-place variations on purpose, scripted kernel fusion does not
currently work across in-place op bou... | """ Activations
A collection of jit-scripted activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
All jit scripted activations are lacking in-place variations on purpose, scripted kernel fusion does not
currently work across in-place op bou... |
17,346 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/activations_jit.py | 58,59 | 54,62 | forward | HardSigmoidJit | 59,62 | 59,62 | class HardSigmoidJit(nn.Module):
def __init__(self, inplace: bool = False):
super(HardSigmoidJit, self).__init__()
def forward(self, x):
FILL_FUNC_BODY | class HardSigmoidJit(nn.Module):
def __init__(self, inplace: bool = False):
super(HardSigmoidJit, self).__init__()
def forward(self, x):
FILL_FUNC_BODY | return hard_sigmoid_jit(x)
| return hard_sigmoid_jit(x)
| 2 | 65 | 19 | 84 | """ Activations
A collection of jit-scripted activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
All jit scripted activations are lacking in-place variations on purpose, scripted kernel fusion does not
currently work across in-place op bou... | """ Activations
A collection of jit-scripted activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
All jit scripted activations are lacking in-place variations on purpose, scripted kernel fusion does not
currently work across in-place op bou... |
17,347 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/activations_jit.py | 72,73 | 68,76 | forward | HardSwishJit | 73,76 | 73,76 | class HardSwishJit(nn.Module):
def __init__(self, inplace: bool = False):
super(HardSwishJit, self).__init__()
def forward(self, x):
FILL_FUNC_BODY | class HardSwishJit(nn.Module):
def __init__(self, inplace: bool = False):
super(HardSwishJit, self).__init__()
def forward(self, x):
FILL_FUNC_BODY | return hard_swish_jit(x)
| return hard_swish_jit(x)
| 2 | 63 | 18 | 81 | """ Activations
A collection of jit-scripted activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
All jit scripted activations are lacking in-place variations on purpose, scripted kernel fusion does not
currently work across in-place op bou... | """ Activations
A collection of jit-scripted activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
All jit scripted activations are lacking in-place variations on purpose, scripted kernel fusion does not
currently work across in-place op bou... |
17,348 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/activations_jit.py | 44,45 | 40,48 | forward | MishJit | 45,48 | 45,48 | class MishJit(nn.Module):
def __init__(self, inplace: bool = False):
super(MishJit, self).__init__()
def forward(self, x):
FILL_FUNC_BODY | class MishJit(nn.Module):
def __init__(self, inplace: bool = False):
super(MishJit, self).__init__()
def forward(self, x):
FILL_FUNC_BODY | return mish_jit(x)
| return mish_jit(x)
| 2 | 61 | 16 | 77 | """ Activations
A collection of jit-scripted activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
All jit scripted activations are lacking in-place variations on purpose, scripted kernel fusion does not
currently work across in-place op bou... | """ Activations
A collection of jit-scripted activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
All jit scripted activations are lacking in-place variations on purpose, scripted kernel fusion does not
currently work across in-place op bou... |
17,349 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/activations_jit.py | 36,37 | 32,40 | forward | SwishJit | 37,40 | 37,40 | class SwishJit(nn.Module):
def __init__(self, inplace: bool = False):
super(SwishJit, self).__init__()
def forward(self, x):
FILL_FUNC_BODY | class SwishJit(nn.Module):
def __init__(self, inplace: bool = False):
super(SwishJit, self).__init__()
def forward(self, x):
FILL_FUNC_BODY | return swish_jit(x)
| return swish_jit(x)
| 2 | 61 | 16 | 77 | """ Activations
A collection of jit-scripted activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
All jit scripted activations are lacking in-place variations on purpose, scripted kernel fusion does not
currently work across in-place op bou... | """ Activations
A collection of jit-scripted activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
All jit scripted activations are lacking in-place variations on purpose, scripted kernel fusion does not
currently work across in-place op bou... |
17,351 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/activations_me.py | 200,202 | 190,206 | backward | HardMishJitAutoFn | 202,206 | 202,206 | class HardMishJitAutoFn(torch.autograd.Function):
""" A memory efficient, jit scripted variant of Hard Mish
Experimental, based on notes by Mish author Diganta Misra at
https://github.com/digantamisra98/H-Mish/blob/0da20d4bc58e696b6803f2523c58d3c8a82782d0/README.md
"""
@staticmethod
def forwar... | class HardMishJitAutoFn(torch.autograd.Function):
""" A memory efficient, jit scripted variant of Hard Mish
Experimental, based on notes by Mish author Diganta Misra at
https://github.com/digantamisra98/H-Mish/blob/0da20d4bc58e696b6803f2523c58d3c8a82782d0/README.md
"""
@staticmethod
def forwar... | x = ctx.saved_tensors[0]
return hard_mish_jit_bwd(x, grad_output)
| x = ctx.saved_tensors[0]
return hard_mish_jit_bwd(x, grad_output)
| 6 | 192 | 41 | 233 | """ Activations (memory-efficient w/ custom autograd)
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
These activations are not compatible with jit scripting or ONNX export of the model, please use either
the JIT or bas... | """ Activations (memory-efficient w/ custom autograd)
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
These activations are not compatible with jit scripting or ONNX export of the model, please use either
the JIT or bas... |
17,352 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/activations_me.py | 214,215 | 210,219 | forward | HardMishMe | 215,219 | 215,219 | class HardMishMe(nn.Module):
def __init__(self, inplace: bool = False):
super(HardMishMe, self).__init__()
def forward(self, x):
FILL_FUNC_BODY | class HardMishMe(nn.Module):
def __init__(self, inplace: bool = False):
super(HardMishMe, self).__init__()
def forward(self, x):
FILL_FUNC_BODY | return HardMishJitAutoFn.apply(x)
| return HardMishJitAutoFn.apply(x)
| 2 | 61 | 22 | 83 | """ Activations (memory-efficient w/ custom autograd)
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
These activations are not compatible with jit scripting or ONNX export of the model, please use either
the JIT or bas... | """ Activations (memory-efficient w/ custom autograd)
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
These activations are not compatible with jit scripting or ONNX export of the model, please use either
the JIT or bas... |
17,353 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/activations_me.py | 116,118 | 110,122 | backward | HardSigmoidJitAutoFn | 118,122 | 118,122 | class HardSigmoidJitAutoFn(torch.autograd.Function):
@staticmethod
def forward(ctx, x):
ctx.save_for_backward(x)
return hard_sigmoid_jit_fwd(x)
@staticmethod
def backward(ctx, grad_output):
FILL_FUNC_BODY | class HardSigmoidJitAutoFn(torch.autograd.Function):
@staticmethod
def forward(ctx, x):
ctx.save_for_backward(x)
return hard_sigmoid_jit_fwd(x)
@staticmethod
def backward(ctx, grad_output):
FILL_FUNC_BODY | x = ctx.saved_tensors[0]
return hard_sigmoid_jit_bwd(x, grad_output)
| x = ctx.saved_tensors[0]
return hard_sigmoid_jit_bwd(x, grad_output)
| 6 | 91 | 42 | 133 | """ Activations (memory-efficient w/ custom autograd)
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
These activations are not compatible with jit scripting or ONNX export of the model, please use either
the JIT or bas... | """ Activations (memory-efficient w/ custom autograd)
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
These activations are not compatible with jit scripting or ONNX export of the model, please use either
the JIT or bas... |
17,354 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/activations_me.py | 130,131 | 126,134 | forward | HardSigmoidMe | 131,134 | 131,134 | class HardSigmoidMe(nn.Module):
def __init__(self, inplace: bool = False):
super(HardSigmoidMe, self).__init__()
def forward(self, x):
FILL_FUNC_BODY | class HardSigmoidMe(nn.Module):
def __init__(self, inplace: bool = False):
super(HardSigmoidMe, self).__init__()
def forward(self, x):
FILL_FUNC_BODY | return HardSigmoidJitAutoFn.apply(x)
| return HardSigmoidJitAutoFn.apply(x)
| 2 | 63 | 21 | 84 | """ Activations (memory-efficient w/ custom autograd)
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
These activations are not compatible with jit scripting or ONNX export of the model, please use either
the JIT or bas... | """ Activations (memory-efficient w/ custom autograd)
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
These activations are not compatible with jit scripting or ONNX export of the model, please use either
the JIT or bas... |
17,355 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/activations_me.py | 158,160 | 146,166 | symbolic | HardSwishJitAutoFn | 160,166 | 160,166 | class HardSwishJitAutoFn(torch.autograd.Function):
"""A memory efficient, jit-scripted HardSwish activation"""
@staticmethod
def forward(ctx, x):
ctx.save_for_backward(x)
return hard_swish_jit_fwd(x)
@staticmethod
def backward(ctx, grad_output):
x = ctx.saved_tensors[0]
... | class HardSwishJitAutoFn(torch.autograd.Function):
"""A memory efficient, jit-scripted HardSwish activation"""
@staticmethod
def forward(ctx, x):
ctx.save_for_backward(x)
return hard_swish_jit_fwd(x)
@staticmethod
def backward(ctx, grad_output):
x = ctx.saved_tensors[0]
... | input = g.op("Add", self, g.op('Constant', value_t=torch.tensor(3, dtype=torch.float)))
hardtanh_ = g.op("Clip", input, g.op('Constant', value_t=torch.tensor(0, dtype=torch.float)), g.op('Constant', value_t=torch.tensor(6, dtype=torch.float)))
hardtanh_ = g.op("Div", hardtanh_, g.op('Constant', ... | input = g.op("Add", self, g.op('Constant', value_t=torch.tensor(3, dtype=torch.float)))
hardtanh_ = g.op("Clip", input, g.op('Constant', value_t=torch.tensor(0, dtype=torch.float)), g.op('Constant', value_t=torch.tensor(6, dtype=torch.float)))
hardtanh_ = g.op("Div", hardtanh_, g.op('Constant', ... | 28 | 156 | 169 | 325 | """ Activations (memory-efficient w/ custom autograd)
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
These activations are not compatible with jit scripting or ONNX export of the model, please use either
the JIT or bas... | """ Activations (memory-efficient w/ custom autograd)
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
These activations are not compatible with jit scripting or ONNX export of the model, please use either
the JIT or bas... |
17,356 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/activations_me.py | 174,175 | 170,178 | forward | HardSwishMe | 175,178 | 175,178 | class HardSwishMe(nn.Module):
def __init__(self, inplace: bool = False):
super(HardSwishMe, self).__init__()
def forward(self, x):
FILL_FUNC_BODY | class HardSwishMe(nn.Module):
def __init__(self, inplace: bool = False):
super(HardSwishMe, self).__init__()
def forward(self, x):
FILL_FUNC_BODY | return HardSwishJitAutoFn.apply(x)
| return HardSwishJitAutoFn.apply(x)
| 2 | 61 | 20 | 81 | """ Activations (memory-efficient w/ custom autograd)
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
These activations are not compatible with jit scripting or ONNX export of the model, please use either
the JIT or bas... | """ Activations (memory-efficient w/ custom autograd)
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
These activations are not compatible with jit scripting or ONNX export of the model, please use either
the JIT or bas... |
17,357 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/activations_me.py | 81,83 | 72,87 | backward | MishJitAutoFn | 83,87 | 83,87 | class MishJitAutoFn(torch.autograd.Function):
""" Mish: A Self Regularized Non-Monotonic Neural Activation Function - https://arxiv.org/abs/1908.08681
A memory efficient, jit scripted variant of Mish
"""
@staticmethod
def forward(ctx, x):
ctx.save_for_backward(x)
return mish_jit_fwd(... | class MishJitAutoFn(torch.autograd.Function):
""" Mish: A Self Regularized Non-Monotonic Neural Activation Function - https://arxiv.org/abs/1908.08681
A memory efficient, jit scripted variant of Mish
"""
@staticmethod
def forward(ctx, x):
ctx.save_for_backward(x)
return mish_jit_fwd(... | x = ctx.saved_tensors[0]
return mish_jit_bwd(x, grad_output)
| x = ctx.saved_tensors[0]
return mish_jit_bwd(x, grad_output)
| 6 | 145 | 39 | 184 | """ Activations (memory-efficient w/ custom autograd)
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
These activations are not compatible with jit scripting or ONNX export of the model, please use either
the JIT or bas... | """ Activations (memory-efficient w/ custom autograd)
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
These activations are not compatible with jit scripting or ONNX export of the model, please use either
the JIT or bas... |
17,358 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/activations_me.py | 95,96 | 91,99 | forward | MishMe | 96,99 | 96,99 | class MishMe(nn.Module):
def __init__(self, inplace: bool = False):
super(MishMe, self).__init__()
def forward(self, x):
FILL_FUNC_BODY | class MishMe(nn.Module):
def __init__(self, inplace: bool = False):
super(MishMe, self).__init__()
def forward(self, x):
FILL_FUNC_BODY | return MishJitAutoFn.apply(x)
| return MishJitAutoFn.apply(x)
| 2 | 59 | 19 | 78 | """ Activations (memory-efficient w/ custom autograd)
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
These activations are not compatible with jit scripting or ONNX export of the model, please use either
the JIT or bas... | """ Activations (memory-efficient w/ custom autograd)
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
These activations are not compatible with jit scripting or ONNX export of the model, please use either
the JIT or bas... |
17,359 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/activations_me.py | 42,44 | 28,48 | backward | SwishJitAutoFn | 44,48 | 44,48 | class SwishJitAutoFn(torch.autograd.Function):
""" torch.jit.script optimised Swish w/ memory-efficient checkpoint
Inspired by conversation btw Jeremy Howard & Adam Pazske
https://twitter.com/jeremyphoward/status/1188251041835315200
"""
@staticmethod
def symbolic(g, x):
return g.op("Mul"... | class SwishJitAutoFn(torch.autograd.Function):
""" torch.jit.script optimised Swish w/ memory-efficient checkpoint
Inspired by conversation btw Jeremy Howard & Adam Pazske
https://twitter.com/jeremyphoward/status/1188251041835315200
"""
@staticmethod
def symbolic(g, x):
return g.op("Mul"... | x = ctx.saved_tensors[0]
return swish_jit_bwd(x, grad_output)
| x = ctx.saved_tensors[0]
return swish_jit_bwd(x, grad_output)
| 6 | 200 | 39 | 239 | """ Activations (memory-efficient w/ custom autograd)
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
These activations are not compatible with jit scripting or ONNX export of the model, please use either
the JIT or bas... | """ Activations (memory-efficient w/ custom autograd)
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
These activations are not compatible with jit scripting or ONNX export of the model, please use either
the JIT or bas... |
17,360 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/activations_me.py | 56,57 | 52,60 | forward | SwishMe | 57,60 | 57,60 | class SwishMe(nn.Module):
def __init__(self, inplace: bool = False):
super(SwishMe, self).__init__()
def forward(self, x):
FILL_FUNC_BODY | class SwishMe(nn.Module):
def __init__(self, inplace: bool = False):
super(SwishMe, self).__init__()
def forward(self, x):
FILL_FUNC_BODY | return SwishJitAutoFn.apply(x)
| return SwishJitAutoFn.apply(x)
| 2 | 59 | 19 | 78 | """ Activations (memory-efficient w/ custom autograd)
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
These activations are not compatible with jit scripting or ONNX export of the model, please use either
the JIT or bas... | """ Activations (memory-efficient w/ custom autograd)
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
These activations are not compatible with jit scripting or ONNX export of the model, please use either
the JIT or bas... |
17,362 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/adaptive_avgmax_pool.py | 111,112 | 106,115 | forward | AdaptiveAvgMaxPool2d | 112,115 | 112,115 | class AdaptiveAvgMaxPool2d(nn.Module):
def __init__(self, output_size: _int_tuple_2_t = 1):
super(AdaptiveAvgMaxPool2d, self).__init__()
self.output_size = output_size
def forward(self, x):
FILL_FUNC_BODY | class AdaptiveAvgMaxPool2d(nn.Module):
def __init__(self, output_size: _int_tuple_2_t = 1):
super(AdaptiveAvgMaxPool2d, self).__init__()
self.output_size = output_size
def forward(self, x):
FILL_FUNC_BODY | return adaptive_avgmax_pool2d(x, self.output_size)
| return adaptive_avgmax_pool2d(x, self.output_size)
| 3 | 91 | 27 | 118 | """ PyTorch selectable adaptive pooling
Adaptive pooling with the ability to select the type of pooling from:
* 'avg' - Average pooling
* 'max' - Max pooling
* 'avgmax' - Sum of average and max pooling re-scaled by 0.5
* 'avgmaxc' - Concatenation of average and max pooling along feature dim, doubles fea... | """ PyTorch selectable adaptive pooling
Adaptive pooling with the ability to select the type of pooling from:
* 'avg' - Average pooling
* 'max' - Max pooling
* 'avgmax' - Sum of average and max pooling re-scaled by 0.5
* 'avgmaxc' - Concatenation of average and max pooling along feature dim, doubles fea... |
17,363 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/adaptive_avgmax_pool.py | 120,121 | 115,124 | forward | AdaptiveCatAvgMaxPool2d | 121,124 | 121,124 | class AdaptiveCatAvgMaxPool2d(nn.Module):
def __init__(self, output_size: _int_tuple_2_t = 1):
super(AdaptiveCatAvgMaxPool2d, self).__init__()
self.output_size = output_size
def forward(self, x):
FILL_FUNC_BODY | class AdaptiveCatAvgMaxPool2d(nn.Module):
def __init__(self, output_size: _int_tuple_2_t = 1):
super(AdaptiveCatAvgMaxPool2d, self).__init__()
self.output_size = output_size
def forward(self, x):
FILL_FUNC_BODY | return adaptive_catavgmax_pool2d(x, self.output_size)
| return adaptive_catavgmax_pool2d(x, self.output_size)
| 3 | 93 | 28 | 121 | """ PyTorch selectable adaptive pooling
Adaptive pooling with the ability to select the type of pooling from:
* 'avg' - Average pooling
* 'max' - Max pooling
* 'avgmax' - Sum of average and max pooling re-scaled by 0.5
* 'avgmaxc' - Concatenation of average and max pooling along feature dim, doubles fea... | """ PyTorch selectable adaptive pooling
Adaptive pooling with the ability to select the type of pooling from:
* 'avg' - Average pooling
* 'max' - Max pooling
* 'avgmax' - Sum of average and max pooling re-scaled by 0.5
* 'avgmaxc' - Concatenation of average and max pooling along feature dim, doubles fea... |
17,364 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/adaptive_avgmax_pool.py | 84,85 | 78,90 | forward | FastAdaptiveAvgMaxPool | 85,90 | 85,90 | class FastAdaptiveAvgMaxPool(nn.Module):
def __init__(self, flatten: bool = False, input_fmt: str = 'NCHW'):
super(FastAdaptiveAvgMaxPool, self).__init__()
self.flatten = flatten
self.dim = get_spatial_dim(input_fmt)
def forward(self, x):
FILL_FUNC_BODY | class FastAdaptiveAvgMaxPool(nn.Module):
def __init__(self, flatten: bool = False, input_fmt: str = 'NCHW'):
super(FastAdaptiveAvgMaxPool, self).__init__()
self.flatten = flatten
self.dim = get_spatial_dim(input_fmt)
def forward(self, x):
FILL_FUNC_BODY | x_avg = x.mean(self.dim, keepdim=not self.flatten)
x_max = x.amax(self.dim, keepdim=not self.flatten)
return 0.5 * x_avg + 0.5 * x_max
| x_avg = x.mean(self.dim, keepdim=not self.flatten)
x_max = x.amax(self.dim, keepdim=not self.flatten)
return 0.5 * x_avg + 0.5 * x_max
| 18 | 109 | 75 | 184 | """ PyTorch selectable adaptive pooling
Adaptive pooling with the ability to select the type of pooling from:
* 'avg' - Average pooling
* 'max' - Max pooling
* 'avgmax' - Sum of average and max pooling re-scaled by 0.5
* 'avgmaxc' - Concatenation of average and max pooling along feature dim, doubles fea... | """ PyTorch selectable adaptive pooling
Adaptive pooling with the ability to select the type of pooling from:
* 'avg' - Average pooling
* 'max' - Max pooling
* 'avgmax' - Sum of average and max pooling re-scaled by 0.5
* 'avgmaxc' - Concatenation of average and max pooling along feature dim, doubles fea... |
17,365 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/adaptive_avgmax_pool.py | 64,65 | 58,68 | forward | FastAdaptiveAvgPool | 65,68 | 65,68 | class FastAdaptiveAvgPool(nn.Module):
def __init__(self, flatten: bool = False, input_fmt: F = 'NCHW'):
super(FastAdaptiveAvgPool, self).__init__()
self.flatten = flatten
self.dim = get_spatial_dim(input_fmt)
def forward(self, x):
FILL_FUNC_BODY | class FastAdaptiveAvgPool(nn.Module):
def __init__(self, flatten: bool = False, input_fmt: F = 'NCHW'):
super(FastAdaptiveAvgPool, self).__init__()
self.flatten = flatten
self.dim = get_spatial_dim(input_fmt)
def forward(self, x):
FILL_FUNC_BODY | return x.mean(self.dim, keepdim=not self.flatten)
| return x.mean(self.dim, keepdim=not self.flatten)
| 4 | 107 | 25 | 132 | """ PyTorch selectable adaptive pooling
Adaptive pooling with the ability to select the type of pooling from:
* 'avg' - Average pooling
* 'max' - Max pooling
* 'avgmax' - Sum of average and max pooling re-scaled by 0.5
* 'avgmaxc' - Concatenation of average and max pooling along feature dim, doubles fea... | """ PyTorch selectable adaptive pooling
Adaptive pooling with the ability to select the type of pooling from:
* 'avg' - Average pooling
* 'max' - Max pooling
* 'avgmax' - Sum of average and max pooling re-scaled by 0.5
* 'avgmaxc' - Concatenation of average and max pooling along feature dim, doubles fea... |
17,366 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/adaptive_avgmax_pool.py | 100,101 | 90,106 | forward | FastAdaptiveCatAvgMaxPool | 101,106 | 101,106 | class FastAdaptiveCatAvgMaxPool(nn.Module):
def __init__(self, flatten: bool = False, input_fmt: str = 'NCHW'):
super(FastAdaptiveCatAvgMaxPool, self).__init__()
self.flatten = flatten
self.dim_reduce = get_spatial_dim(input_fmt)
if flatten:
self.dim_cat = 1
else:... | class FastAdaptiveCatAvgMaxPool(nn.Module):
def __init__(self, flatten: bool = False, input_fmt: str = 'NCHW'):
super(FastAdaptiveCatAvgMaxPool, self).__init__()
self.flatten = flatten
self.dim_reduce = get_spatial_dim(input_fmt)
if flatten:
self.dim_cat = 1
else:... | x_avg = x.mean(self.dim_reduce, keepdim=not self.flatten)
x_max = x.amax(self.dim_reduce, keepdim=not self.flatten)
return torch.cat((x_avg, x_max), self.dim_cat)
| x_avg = x.mean(self.dim_reduce, keepdim=not self.flatten)
x_max = x.amax(self.dim_reduce, keepdim=not self.flatten)
return torch.cat((x_avg, x_max), self.dim_cat)
| 14 | 152 | 84 | 236 | """ PyTorch selectable adaptive pooling
Adaptive pooling with the ability to select the type of pooling from:
* 'avg' - Average pooling
* 'max' - Max pooling
* 'avgmax' - Sum of average and max pooling re-scaled by 0.5
* 'avgmaxc' - Concatenation of average and max pooling along feature dim, doubles fea... | """ PyTorch selectable adaptive pooling
Adaptive pooling with the ability to select the type of pooling from:
* 'avg' - Average pooling
* 'max' - Max pooling
* 'avgmax' - Sum of average and max pooling re-scaled by 0.5
* 'avgmaxc' - Concatenation of average and max pooling along feature dim, doubles fea... |
17,367 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/adaptive_avgmax_pool.py | 74,75 | 68,78 | forward | FastAdaptiveMaxPool | 75,78 | 75,78 | class FastAdaptiveMaxPool(nn.Module):
def __init__(self, flatten: bool = False, input_fmt: str = 'NCHW'):
super(FastAdaptiveMaxPool, self).__init__()
self.flatten = flatten
self.dim = get_spatial_dim(input_fmt)
def forward(self, x):
FILL_FUNC_BODY | class FastAdaptiveMaxPool(nn.Module):
def __init__(self, flatten: bool = False, input_fmt: str = 'NCHW'):
super(FastAdaptiveMaxPool, self).__init__()
self.flatten = flatten
self.dim = get_spatial_dim(input_fmt)
def forward(self, x):
FILL_FUNC_BODY | return x.amax(self.dim, keepdim=not self.flatten)
| return x.amax(self.dim, keepdim=not self.flatten)
| 4 | 105 | 26 | 131 | """ PyTorch selectable adaptive pooling
Adaptive pooling with the ability to select the type of pooling from:
* 'avg' - Average pooling
* 'max' - Max pooling
* 'avgmax' - Sum of average and max pooling re-scaled by 0.5
* 'avgmaxc' - Concatenation of average and max pooling along feature dim, doubles fea... | """ PyTorch selectable adaptive pooling
Adaptive pooling with the ability to select the type of pooling from:
* 'avg' - Average pooling
* 'max' - Max pooling
* 'avgmax' - Sum of average and max pooling re-scaled by 0.5
* 'avgmaxc' - Concatenation of average and max pooling along feature dim, doubles fea... |
17,368 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/adaptive_avgmax_pool.py | 168,169 | 124,184 | is_identity | SelectAdaptivePool2d | 169,171 | 169,171 | class SelectAdaptivePool2d(nn.Module):
"""Selectable global pooling layer with dynamic input kernel size
"""
def __init__(
self,
output_size: _int_tuple_2_t = 1,
pool_type: str = 'fast',
flatten: bool = False,
input_fmt: str = 'NCHW',
):
... | class SelectAdaptivePool2d(nn.Module):
"""Selectable global pooling layer with dynamic input kernel size
"""
def __init__(
self,
output_size: _int_tuple_2_t = 1,
pool_type: str = 'fast',
flatten: bool = False,
input_fmt: str = 'NCHW',
):
... | return not self.pool_type
| return not self.pool_type
| 3 | 822 | 12 | 834 | """ PyTorch selectable adaptive pooling
Adaptive pooling with the ability to select the type of pooling from:
* 'avg' - Average pooling
* 'max' - Max pooling
* 'avgmax' - Sum of average and max pooling re-scaled by 0.5
* 'avgmaxc' - Concatenation of average and max pooling along feature dim, doubles fea... | """ PyTorch selectable adaptive pooling
Adaptive pooling with the ability to select the type of pooling from:
* 'avg' - Average pooling
* 'max' - Max pooling
* 'avgmax' - Sum of average and max pooling re-scaled by 0.5
* 'avgmaxc' - Concatenation of average and max pooling along feature dim, doubles fea... |
17,369 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/attention_pool.py | 70,71 | 12,104 | forward | AttentionPoolLatent | 71,104 | 71,104 | class AttentionPoolLatent(nn.Module):
""" Attention pooling w/ latent query
"""
fused_attn: torch.jit.Final[bool]
def __init__(
self,
in_features: int,
out_features: int = None,
embed_dim: int = None,
num_heads: int = 8,
mlp_ratio:... | class AttentionPoolLatent(nn.Module):
""" Attention pooling w/ latent query
"""
fused_attn: torch.jit.Final[bool]
def __init__(
self,
in_features: int,
out_features: int = None,
embed_dim: int = None,
num_heads: int = 8,
mlp_ratio:... | B, N, C = x.shape
if self.pos_embed is not None:
# FIXME interpolate
x = x + self.pos_embed.unsqueeze(0).to(x.dtype)
q_latent = self.latent.expand(B, -1, -1)
q = self.q(q_latent).reshape(B, self.latent_len, self.num_heads, self.head_dim).transpose(1, 2)
... | B, N, C = x.shape
if self.pos_embed is not None:
# FIXME interpolate
x = x + self.pos_embed.unsqueeze(0).to(x.dtype)
q_latent = self.latent.expand(B, -1, -1)
q = self.q(q_latent).reshape(B, self.latent_len, self.num_heads, self.head_dim).transpose(1, 2)
... | 124 | 762 | 470 | 1,232 | from typing import Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from .config import use_fused_attn
from .mlp import Mlp
from .weight_init import trunc_normal_tf_
class AttentionPoolLatent(nn.Module):
""" Attention pooling w/ latent query
"""
fused_attn: torch.jit.Final[boo... | from typing import Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from .config import use_fused_attn
from .mlp import Mlp
from .weight_init import trunc_normal_tf_
class AttentionPoolLatent(nn.Module):
""" Attention pooling w/ latent query
"""
fused_attn: torch.jit.Final[boo... |
17,370 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/attention_pool2d.py | 115,116 | 79,132 | forward | AttentionPool2d | 116,132 | 116,132 | class AttentionPool2d(nn.Module):
""" Attention based 2D feature pooling w/ learned (absolute) pos embedding.
This is a multi-head attention based replacement for (spatial) average pooling in NN architectures.
It was based on impl in CLIP by OpenAI
https://github.com/openai/CLIP/blob/3b473b0e682c091a9e... | class AttentionPool2d(nn.Module):
""" Attention based 2D feature pooling w/ learned (absolute) pos embedding.
This is a multi-head attention based replacement for (spatial) average pooling in NN architectures.
It was based on impl in CLIP by OpenAI
https://github.com/openai/CLIP/blob/3b473b0e682c091a9e... | B, _, H, W = x.shape
N = H * W
assert self.feat_size[0] == H
assert self.feat_size[1] == W
x = x.reshape(B, -1, N).permute(0, 2, 1)
x = torch.cat([x.mean(1, keepdim=True), x], dim=1)
x = x + self.pos_embed.unsqueeze(0).to(x.dtype)
x = self.qkv(x).reshape(... | B, _, H, W = x.shape
N = H * W
assert self.feat_size[0] == H
assert self.feat_size[1] == W
x = x.reshape(B, -1, N).permute(0, 2, 1)
x = torch.cat([x.mean(1, keepdim=True), x], dim=1)
x = x + self.pos_embed.unsqueeze(0).to(x.dtype)
x = self.qkv(x).reshape(... | 84 | 550 | 307 | 857 | """ Attention Pool 2D
Implementations of 2D spatial feature pooling using multi-head attention instead of average pool.
Based on idea in CLIP by OpenAI, licensed Apache 2.0
https://github.com/openai/CLIP/blob/3b473b0e682c091a9e53623eebc1ca1657385717/clip/model.py
Hacked together by / Copyright 2021 Ross Wightman
"""... | """ Attention Pool 2D
Implementations of 2D spatial feature pooling using multi-head attention instead of average pool.
Based on idea in CLIP by OpenAI, licensed Apache 2.0
https://github.com/openai/CLIP/blob/3b473b0e682c091a9e53623eebc1ca1657385717/clip/model.py
Hacked together by / Copyright 2021 Ross Wightman
"""... |
17,371 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/attention_pool2d.py | 52,53 | 20,79 | forward | RotAttentionPool2d | 53,79 | 53,79 | class RotAttentionPool2d(nn.Module):
""" Attention based 2D feature pooling w/ rotary (relative) pos embedding.
This is a multi-head attention based replacement for (spatial) average pooling in NN architectures.
Adapted from the AttentionPool2d in CLIP w/ rotary embedding instead of learned embed.
http... | class RotAttentionPool2d(nn.Module):
""" Attention based 2D feature pooling w/ rotary (relative) pos embedding.
This is a multi-head attention based replacement for (spatial) average pooling in NN architectures.
Adapted from the AttentionPool2d in CLIP w/ rotary embedding instead of learned embed.
http... | B, _, H, W = x.shape
N = H * W
x = x.reshape(B, -1, N).permute(0, 2, 1)
x = torch.cat([x.mean(1, keepdim=True), x], dim=1)
x = self.qkv(x).reshape(B, N + 1, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
q, k, v = x[0], x[1], x[2]
qc, q = q[:, :, :1],... | B, _, H, W = x.shape
N = H * W
x = x.reshape(B, -1, N).permute(0, 2, 1)
x = torch.cat([x.mean(1, keepdim=True), x], dim=1)
x = self.qkv(x).reshape(B, N + 1, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
q, k, v = x[0], x[1], x[2]
qc, q = q[:, :, :1],... | 114 | 492 | 415 | 907 | """ Attention Pool 2D
Implementations of 2D spatial feature pooling using multi-head attention instead of average pool.
Based on idea in CLIP by OpenAI, licensed Apache 2.0
https://github.com/openai/CLIP/blob/3b473b0e682c091a9e53623eebc1ca1657385717/clip/model.py
Hacked together by / Copyright 2021 Ross Wightman
"""... | """ Attention Pool 2D
Implementations of 2D spatial feature pooling using multi-head attention instead of average pool.
Based on idea in CLIP by OpenAI, licensed Apache 2.0
https://github.com/openai/CLIP/blob/3b473b0e682c091a9e53623eebc1ca1657385717/clip/model.py
Hacked together by / Copyright 2021 Ross Wightman
"""... |
17,372 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/blur_pool.py | 40,41 | 16,43 | forward | BlurPool2d | 41,43 | 41,43 | class BlurPool2d(nn.Module):
r"""Creates a module that computes blurs and downsample a given feature map.
See :cite:`zhang2019shiftinvar` for more details.
Corresponds to the Downsample class, which does blurring and subsampling
Args:
channels = Number of input channels
filt_size (int):... | class BlurPool2d(nn.Module):
r"""Creates a module that computes blurs and downsample a given feature map.
See :cite:`zhang2019shiftinvar` for more details.
Corresponds to the Downsample class, which does blurring and subsampling
Args:
channels = Number of input channels
filt_size (int):... | x = F.pad(x, self.padding, 'reflect')
return F.conv2d(x, self.filt, stride=self.stride, groups=self.channels)
| x = F.pad(x, self.padding, 'reflect')
return F.conv2d(x, self.filt, stride=self.stride, groups=self.channels)
| 10 | 409 | 50 | 459 | """
BlurPool layer inspired by
- Kornia's Max_BlurPool2d
- Making Convolutional Networks Shift-Invariant Again :cite:`zhang2019shiftinvar`
Hacked together by Chris Ha and Ross Wightman
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from .padding import get_padding
class ... | """
BlurPool layer inspired by
- Kornia's Max_BlurPool2d
- Making Convolutional Networks Shift-Invariant Again :cite:`zhang2019shiftinvar`
Hacked together by Chris Ha and Ross Wightman
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from .padding import get_padding
class ... |
17,374 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/bottleneck_attn.py | 135,136 | 84,158 | forward | BottleneckAttn | 136,158 | 136,158 | class BottleneckAttn(nn.Module):
""" Bottleneck Attention
Paper: `Bottleneck Transformers for Visual Recognition` - https://arxiv.org/abs/2101.11605
The internal dimensions of the attention module are controlled by the interaction of several arguments.
* the output dimension of the module is specifie... | class BottleneckAttn(nn.Module):
""" Bottleneck Attention
Paper: `Bottleneck Transformers for Visual Recognition` - https://arxiv.org/abs/2101.11605
The internal dimensions of the attention module are controlled by the interaction of several arguments.
* the output dimension of the module is specifie... | B, C, H, W = x.shape
_assert(H == self.pos_embed.height, '')
_assert(W == self.pos_embed.width, '')
x = self.qkv(x) # B, (2 * dim_head_qk + dim_head_v) * num_heads, H, W
# NOTE head vs channel split ordering in qkv projection was decided before I allowed qk to differ from v
... | B, C, H, W = x.shape
_assert(H == self.pos_embed.height, '')
_assert(W == self.pos_embed.width, '')
x = self.qkv(x) # B, (2 * dim_head_qk + dim_head_v) * num_heads, H, W
# NOTE head vs channel split ordering in qkv projection was decided before I allowed qk to differ from v
... | 158 | 960 | 482 | 1,442 | """ Bottleneck Self Attention (Bottleneck Transformers)
Paper: `Bottleneck Transformers for Visual Recognition` - https://arxiv.org/abs/2101.11605
@misc{2101.11605,
Author = {Aravind Srinivas and Tsung-Yi Lin and Niki Parmar and Jonathon Shlens and Pieter Abbeel and Ashish Vaswani},
Title = {Bottleneck Transformers f... | """ Bottleneck Self Attention (Bottleneck Transformers)
Paper: `Bottleneck Transformers for Visual Recognition` - https://arxiv.org/abs/2101.11605
@misc{2101.11605,
Author = {Aravind Srinivas and Tsung-Yi Lin and Niki Parmar and Jonathon Shlens and Pieter Abbeel and Ashish Vaswani},
Title = {Bottleneck Transformers f... |
17,375 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/bottleneck_attn.py | 68,69 | 56,84 | forward | PosEmbedRel | 69,84 | 69,84 | class PosEmbedRel(nn.Module):
""" Relative Position Embedding
As per: https://gist.github.com/aravindsrinivas/56359b79f0ce4449bcb04ab4b56a57a2
Originally from: `Attention Augmented Convolutional Networks` - https://arxiv.org/abs/1904.09925
"""
def __init__(self, feat_size, dim_head, scale):
... | class PosEmbedRel(nn.Module):
""" Relative Position Embedding
As per: https://gist.github.com/aravindsrinivas/56359b79f0ce4449bcb04ab4b56a57a2
Originally from: `Attention Augmented Convolutional Networks` - https://arxiv.org/abs/1904.09925
"""
def __init__(self, feat_size, dim_head, scale):
... | B, HW, _ = q.shape
# relative logits in width dimension.
q = q.reshape(B, self.height, self.width, -1)
rel_logits_w = rel_logits_1d(q, self.width_rel, permute_mask=(0, 1, 3, 2, 4))
# relative logits in height dimension.
q = q.transpose(1, 2)
rel_logits_h = rel_l... | B, HW, _ = q.shape
# relative logits in width dimension.
q = q.reshape(B, self.height, self.width, -1)
rel_logits_w = rel_logits_1d(q, self.width_rel, permute_mask=(0, 1, 3, 2, 4))
# relative logits in height dimension.
q = q.transpose(1, 2)
rel_logits_h = rel_l... | 57 | 263 | 228 | 491 | """ Bottleneck Self Attention (Bottleneck Transformers)
Paper: `Bottleneck Transformers for Visual Recognition` - https://arxiv.org/abs/2101.11605
@misc{2101.11605,
Author = {Aravind Srinivas and Tsung-Yi Lin and Niki Parmar and Jonathon Shlens and Pieter Abbeel and Ashish Vaswani},
Title = {Bottleneck Transformers f... | """ Bottleneck Self Attention (Bottleneck Transformers)
Paper: `Bottleneck Transformers for Visual Recognition` - https://arxiv.org/abs/2101.11605
@misc{2101.11605,
Author = {Aravind Srinivas and Tsung-Yi Lin and Niki Parmar and Jonathon Shlens and Pieter Abbeel and Ashish Vaswani},
Title = {Bottleneck Transformers f... |
17,376 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/cbam.py | 92,93 | 82,98 | forward | CbamModule | 93,98 | 93,98 | class CbamModule(nn.Module):
def __init__(
self, channels, rd_ratio=1./16, rd_channels=None, rd_divisor=1,
spatial_kernel_size=7, act_layer=nn.ReLU, gate_layer='sigmoid', mlp_bias=False):
super(CbamModule, self).__init__()
self.channel = ChannelAttn(
channels, rd_... | class CbamModule(nn.Module):
def __init__(
self, channels, rd_ratio=1./16, rd_channels=None, rd_divisor=1,
spatial_kernel_size=7, act_layer=nn.ReLU, gate_layer='sigmoid', mlp_bias=False):
super(CbamModule, self).__init__()
self.channel = ChannelAttn(
channels, rd_... | x = self.channel(x)
x = self.spatial(x)
return x
| x = self.channel(x)
x = self.spatial(x)
return x
| 8 | 223 | 32 | 255 | """ CBAM (sort-of) Attention
Experimental impl of CBAM: Convolutional Block Attention Module: https://arxiv.org/abs/1807.06521
WARNING: Results with these attention layers have been mixed. They can significantly reduce performance on
some tasks, especially fine-grained it seems. I may end up removing this impl.
Hack... | """ CBAM (sort-of) Attention
Experimental impl of CBAM: Convolutional Block Attention Module: https://arxiv.org/abs/1807.06521
WARNING: Results with these attention layers have been mixed. They can significantly reduce performance on
some tasks, especially fine-grained it seems. I may end up removing this impl.
Hack... |
17,377 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/cbam.py | 33,34 | 19,39 | forward | ChannelAttn | 34,39 | 34,39 | class ChannelAttn(nn.Module):
""" Original CBAM channel attention module, currently avg + max pool variant only.
"""
def __init__(
self, channels, rd_ratio=1./16, rd_channels=None, rd_divisor=1,
act_layer=nn.ReLU, gate_layer='sigmoid', mlp_bias=False):
super(ChannelAttn, self... | class ChannelAttn(nn.Module):
""" Original CBAM channel attention module, currently avg + max pool variant only.
"""
def __init__(
self, channels, rd_ratio=1./16, rd_channels=None, rd_divisor=1,
act_layer=nn.ReLU, gate_layer='sigmoid', mlp_bias=False):
super(ChannelAttn, self... | x_avg = self.fc2(self.act(self.fc1(x.mean((2, 3), keepdim=True))))
x_max = self.fc2(self.act(self.fc1(x.amax((2, 3), keepdim=True))))
return x * self.gate(x_avg + x_max)
| x_avg = self.fc2(self.act(self.fc1(x.mean((2, 3), keepdim=True))))
x_max = self.fc2(self.act(self.fc1(x.amax((2, 3), keepdim=True))))
return x * self.gate(x_avg + x_max)
| 16 | 276 | 97 | 373 | """ CBAM (sort-of) Attention
Experimental impl of CBAM: Convolutional Block Attention Module: https://arxiv.org/abs/1807.06521
WARNING: Results with these attention layers have been mixed. They can significantly reduce performance on
some tasks, especially fine-grained it seems. I may end up removing this impl.
Hack... | """ CBAM (sort-of) Attention
Experimental impl of CBAM: Convolutional Block Attention Module: https://arxiv.org/abs/1807.06521
WARNING: Results with these attention layers have been mixed. They can significantly reduce performance on
some tasks, especially fine-grained it seems. I may end up removing this impl.
Hack... |
17,378 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/cbam.py | 108,109 | 98,113 | forward | LightCbamModule | 109,113 | 109,113 | class LightCbamModule(nn.Module):
def __init__(
self, channels, rd_ratio=1./16, rd_channels=None, rd_divisor=1,
spatial_kernel_size=7, act_layer=nn.ReLU, gate_layer='sigmoid', mlp_bias=False):
super(LightCbamModule, self).__init__()
self.channel = LightChannelAttn(
... | class LightCbamModule(nn.Module):
def __init__(
self, channels, rd_ratio=1./16, rd_channels=None, rd_divisor=1,
spatial_kernel_size=7, act_layer=nn.ReLU, gate_layer='sigmoid', mlp_bias=False):
super(LightCbamModule, self).__init__()
self.channel = LightChannelAttn(
... | x = self.channel(x)
x = self.spatial(x)
return x
| x = self.channel(x)
x = self.spatial(x)
return x
| 8 | 220 | 30 | 250 | """ CBAM (sort-of) Attention
Experimental impl of CBAM: Convolutional Block Attention Module: https://arxiv.org/abs/1807.06521
WARNING: Results with these attention layers have been mixed. They can significantly reduce performance on
some tasks, especially fine-grained it seems. I may end up removing this impl.
Hack... | """ CBAM (sort-of) Attention
Experimental impl of CBAM: Convolutional Block Attention Module: https://arxiv.org/abs/1807.06521
WARNING: Results with these attention layers have been mixed. They can significantly reduce performance on
some tasks, especially fine-grained it seems. I may end up removing this impl.
Hack... |
17,379 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/cbam.py | 48,49 | 39,54 | forward | LightChannelAttn | 49,54 | 49,54 | class LightChannelAttn(ChannelAttn):
"""An experimental 'lightweight' that sums avg + max pool first
"""
def __init__(
self, channels, rd_ratio=1./16, rd_channels=None, rd_divisor=1,
act_layer=nn.ReLU, gate_layer='sigmoid', mlp_bias=False):
super(LightChannelAttn, self).__ini... | class LightChannelAttn(ChannelAttn):
"""An experimental 'lightweight' that sums avg + max pool first
"""
def __init__(
self, channels, rd_ratio=1./16, rd_channels=None, rd_divisor=1,
act_layer=nn.ReLU, gate_layer='sigmoid', mlp_bias=False):
super(LightChannelAttn, self).__ini... | x_pool = 0.5 * x.mean((2, 3), keepdim=True) + 0.5 * x.amax((2, 3), keepdim=True)
x_attn = self.fc2(self.act(self.fc1(x_pool)))
return x * F.sigmoid(x_attn)
| x_pool = 0.5 * x.mean((2, 3), keepdim=True) + 0.5 * x.amax((2, 3), keepdim=True)
x_attn = self.fc2(self.act(self.fc1(x_pool)))
return x * F.sigmoid(x_attn)
| 20 | 167 | 92 | 259 | """ CBAM (sort-of) Attention
Experimental impl of CBAM: Convolutional Block Attention Module: https://arxiv.org/abs/1807.06521
WARNING: Results with these attention layers have been mixed. They can significantly reduce performance on
some tasks, especially fine-grained it seems. I may end up removing this impl.
Hack... | """ CBAM (sort-of) Attention
Experimental impl of CBAM: Convolutional Block Attention Module: https://arxiv.org/abs/1807.06521
WARNING: Results with these attention layers have been mixed. They can significantly reduce performance on
some tasks, especially fine-grained it seems. I may end up removing this impl.
Hack... |
17,380 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/cbam.py | 76,77 | 68,82 | forward | LightSpatialAttn | 77,82 | 77,82 | class LightSpatialAttn(nn.Module):
"""An experimental 'lightweight' variant that sums avg_pool and max_pool results.
"""
def __init__(self, kernel_size=7, gate_layer='sigmoid'):
super(LightSpatialAttn, self).__init__()
self.conv = ConvNormAct(1, 1, kernel_size, apply_act=False)
self.... | class LightSpatialAttn(nn.Module):
"""An experimental 'lightweight' variant that sums avg_pool and max_pool results.
"""
def __init__(self, kernel_size=7, gate_layer='sigmoid'):
super(LightSpatialAttn, self).__init__()
self.conv = ConvNormAct(1, 1, kernel_size, apply_act=False)
self.... | x_attn = 0.5 * x.mean(dim=1, keepdim=True) + 0.5 * x.amax(dim=1, keepdim=True)
x_attn = self.conv(x_attn)
return x * self.gate(x_attn)
| x_attn = 0.5 * x.mean(dim=1, keepdim=True) + 0.5 * x.amax(dim=1, keepdim=True)
x_attn = self.conv(x_attn)
return x * self.gate(x_attn)
| 18 | 141 | 80 | 221 | """ CBAM (sort-of) Attention
Experimental impl of CBAM: Convolutional Block Attention Module: https://arxiv.org/abs/1807.06521
WARNING: Results with these attention layers have been mixed. They can significantly reduce performance on
some tasks, especially fine-grained it seems. I may end up removing this impl.
Hack... | """ CBAM (sort-of) Attention
Experimental impl of CBAM: Convolutional Block Attention Module: https://arxiv.org/abs/1807.06521
WARNING: Results with these attention layers have been mixed. They can significantly reduce performance on
some tasks, especially fine-grained it seems. I may end up removing this impl.
Hack... |
17,381 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/cbam.py | 62,63 | 54,68 | forward | SpatialAttn | 63,68 | 63,68 | class SpatialAttn(nn.Module):
""" Original CBAM spatial attention module
"""
def __init__(self, kernel_size=7, gate_layer='sigmoid'):
super(SpatialAttn, self).__init__()
self.conv = ConvNormAct(2, 1, kernel_size, apply_act=False)
self.gate = create_act_layer(gate_layer)
def forw... | class SpatialAttn(nn.Module):
""" Original CBAM spatial attention module
"""
def __init__(self, kernel_size=7, gate_layer='sigmoid'):
super(SpatialAttn, self).__init__()
self.conv = ConvNormAct(2, 1, kernel_size, apply_act=False)
self.gate = create_act_layer(gate_layer)
def forw... | x_attn = torch.cat([x.mean(dim=1, keepdim=True), x.amax(dim=1, keepdim=True)], dim=1)
x_attn = self.conv(x_attn)
return x * self.gate(x_attn)
| x_attn = torch.cat([x.mean(dim=1, keepdim=True), x.amax(dim=1, keepdim=True)], dim=1)
x_attn = self.conv(x_attn)
return x * self.gate(x_attn)
| 14 | 126 | 81 | 207 | """ CBAM (sort-of) Attention
Experimental impl of CBAM: Convolutional Block Attention Module: https://arxiv.org/abs/1807.06521
WARNING: Results with these attention layers have been mixed. They can significantly reduce performance on
some tasks, especially fine-grained it seems. I may end up removing this impl.
Hack... | """ CBAM (sort-of) Attention
Experimental impl of CBAM: Convolutional Block Attention Module: https://arxiv.org/abs/1807.06521
WARNING: Results with these attention layers have been mixed. They can significantly reduce performance on
some tasks, especially fine-grained it seems. I may end up removing this impl.
Hack... |
17,383 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/classifier.py | 129,130 | 75,138 | forward | ClassifierHead | 130,138 | 130,138 | class ClassifierHead(nn.Module):
"""Classifier head w/ configurable global pooling and dropout."""
def __init__(
self,
in_features: int,
num_classes: int,
pool_type: str = 'avg',
drop_rate: float = 0.,
use_conv: bool = False,
i... | class ClassifierHead(nn.Module):
"""Classifier head w/ configurable global pooling and dropout."""
def __init__(
self,
in_features: int,
num_classes: int,
pool_type: str = 'avg',
drop_rate: float = 0.,
use_conv: bool = False,
i... | x = self.global_pool(x)
x = self.drop(x)
if pre_logits:
return self.flatten(x)
x = self.fc(x)
return self.flatten(x)
| x = self.global_pool(x)
x = self.drop(x)
if pre_logits:
return self.flatten(x)
x = self.fc(x)
return self.flatten(x)
| 15 | 598 | 70 | 668 | """ Classifier head and layer factory
Hacked together by / Copyright 2020 Ross Wightman
"""
from collections import OrderedDict
from functools import partial
from typing import Optional, Union, Callable
import torch
import torch.nn as nn
from torch.nn import functional as F
from .adaptive_avgmax_pool import SelectAd... | """ Classifier head and layer factory
Hacked together by / Copyright 2020 Ross Wightman
"""
from collections import OrderedDict
from functools import partial
from typing import Optional, Union, Callable
import torch
import torch.nn as nn
from torch.nn import functional as F
from .adaptive_avgmax_pool import SelectAd... |
17,384 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/classifier.py | 183,184 | 138,209 | reset | NormMlpClassifierHead | 184,199 | 184,199 | class NormMlpClassifierHead(nn.Module):
def __init__(
self,
in_features: int,
num_classes: int,
hidden_size: Optional[int] = None,
pool_type: str = 'avg',
drop_rate: float = 0.,
norm_layer: Union[str, Callable] = 'layernorm2d',
... | class NormMlpClassifierHead(nn.Module):
def __init__(
self,
in_features: int,
num_classes: int,
hidden_size: Optional[int] = None,
pool_type: str = 'avg',
drop_rate: float = 0.,
norm_layer: Union[str, Callable] = 'layernorm2d',
... | if pool_type is not None:
self.global_pool = SelectAdaptivePool2d(pool_type=pool_type)
self.flatten = nn.Flatten(1) if pool_type else nn.Identity()
self.use_conv = self.global_pool.is_identity()
linear_layer = partial(nn.Conv2d, kernel_size=1) if self.use_conv else nn.Lin... | if pool_type is not None:
self.global_pool = SelectAdaptivePool2d(pool_type=pool_type)
self.flatten = nn.Flatten(1) if pool_type else nn.Identity()
self.use_conv = self.global_pool.is_identity()
linear_layer = partial(nn.Conv2d, kernel_size=1) if self.use_conv else nn.Lin... | 60 | 698 | 329 | 1,027 | """ Classifier head and layer factory
Hacked together by / Copyright 2020 Ross Wightman
"""
from collections import OrderedDict
from functools import partial
from typing import Optional, Union, Callable
import torch
import torch.nn as nn
from torch.nn import functional as F
from .adaptive_avgmax_pool import SelectAd... | """ Classifier head and layer factory
Hacked together by / Copyright 2020 Ross Wightman
"""
from collections import OrderedDict
from functools import partial
from typing import Optional, Union, Callable
import torch
import torch.nn as nn
from torch.nn import functional as F
from .adaptive_avgmax_pool import SelectAd... |
17,385 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/cond_conv2d.py | 21,22 | 1,124 | get_condconv_initializer | 22,34 | 22,34 | """ PyTorch Conditionally Parameterized Convolution (CondConv)
Paper: CondConv: Conditionally Parameterized Convolutions for Efficient Inference
(https://arxiv.org/abs/1904.04971)
Hacked together by / Copyright 2020 Ross Wightman
"""
import math
from functools import partial
import numpy as np
import torch
from torc... | """ PyTorch Conditionally Parameterized Convolution (CondConv)
Paper: CondConv: Conditionally Parameterized Convolutions for Efficient Inference
(https://arxiv.org/abs/1904.04971)
Hacked together by / Copyright 2020 Ross Wightman
"""
import math
from functools import partial
import numpy as np
import torch
from torc... | def condconv_initializer(weight):
"""CondConv initializer function."""
num_params = np.prod(expert_shape)
if (len(weight.shape) != 2 or weight.shape[0] != num_experts or
weight.shape[1] != num_params):
raise (ValueError(
'CondConv variables must ha... | def condconv_initializer(weight):
"""CondConv initializer function."""
num_params = np.prod(expert_shape)
if (len(weight.shape) != 2 or weight.shape[0] != num_experts or
weight.shape[1] != num_params):
raise (ValueError(
'CondConv variables must ha... | 36 | 1,559 | 155 | 1,714 | """ PyTorch Conditionally Parameterized Convolution (CondConv)
Paper: CondConv: Conditionally Parameterized Convolutions for Efficient Inference
(https://arxiv.org/abs/1904.04971)
Hacked together by / Copyright 2020 Ross Wightman
"""
import math
from functools import partial
import numpy as np
import torch
from torc... | """ PyTorch Conditionally Parameterized Convolution (CondConv)
Paper: CondConv: Conditionally Parameterized Convolutions for Efficient Inference
(https://arxiv.org/abs/1904.04971)
Hacked together by / Copyright 2020 Ross Wightman
"""
import math
from functools import partial
import numpy as np
import torch
from torc... | |
17,386 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/cond_conv2d.py | 84,85 | 34,124 | forward | CondConv2d | 85,124 | 85,124 | class CondConv2d(nn.Module):
""" Conditionally Parameterized Convolution
Inspired by: https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/condconv/condconv_layers.py
Grouped convolution hackery for parallel execution of the per-sample kernel filters inspired by this discussion:
h... | class CondConv2d(nn.Module):
""" Conditionally Parameterized Convolution
Inspired by: https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/condconv/condconv_layers.py
Grouped convolution hackery for parallel execution of the per-sample kernel filters inspired by this discussion:
h... | B, C, H, W = x.shape
weight = torch.matmul(routing_weights, self.weight)
new_weight_shape = (B * self.out_channels, self.in_channels // self.groups) + self.kernel_size
weight = weight.view(new_weight_shape)
bias = None
if self.bias is not None:
bias = torch.ma... | B, C, H, W = x.shape
weight = torch.matmul(routing_weights, self.weight)
new_weight_shape = (B * self.out_channels, self.in_channels // self.groups) + self.kernel_size
weight = weight.view(new_weight_shape)
bias = None
if self.bias is not None:
bias = torch.ma... | 199 | 759 | 656 | 1,415 | """ PyTorch Conditionally Parameterized Convolution (CondConv)
Paper: CondConv: Conditionally Parameterized Convolutions for Efficient Inference
(https://arxiv.org/abs/1904.04971)
Hacked together by / Copyright 2020 Ross Wightman
"""
import math
from functools import partial
import numpy as np
import torch
from torc... | """ PyTorch Conditionally Parameterized Convolution (CondConv)
Paper: CondConv: Conditionally Parameterized Convolutions for Efficient Inference
(https://arxiv.org/abs/1904.04971)
Hacked together by / Copyright 2020 Ross Wightman
"""
import math
from functools import partial
import numpy as np
import torch
from torc... |
17,387 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/config.py | 130,131 | 1,150 | use_fused_attn | 131,139 | 131,139 | """ Model / Layer Config singleton state
"""
import os
import warnings
from typing import Any, Optional
import torch
__all__ = [
'is_exportable', 'is_scriptable', 'is_no_jit', 'use_fused_attn',
'set_exportable', 'set_scriptable', 'set_no_jit', 'set_layer_config', 'set_fused_attn'
]
# Set to True if prefer to... | """ Model / Layer Config singleton state
"""
import os
import warnings
from typing import Any, Optional
import torch
__all__ = [
'is_exportable', 'is_scriptable', 'is_no_jit', 'use_fused_attn',
'set_exportable', 'set_scriptable', 'set_no_jit', 'set_layer_config', 'set_fused_attn'
]
# Set to True if prefer to... | # NOTE: ONNX export cannot handle F.scaled_dot_product_attention as of pytorch 2.0
if not _HAS_FUSED_ATTN or _EXPORTABLE:
return False
if experimental:
return _USE_FUSED_ATTN > 1
return _USE_FUSED_ATTN > 0
| # NOTE: ONNX export cannot handle F.scaled_dot_product_attention as of pytorch 2.0
if not _HAS_FUSED_ATTN or _EXPORTABLE:
return False
if experimental:
return _USE_FUSED_ATTN > 1
return _USE_FUSED_ATTN > 0
| 28 | 1,343 | 101 | 1,444 | """ Model / Layer Config singleton state
"""
import os
import warnings
from typing import Any, Optional
import torch
__all__ = [
'is_exportable', 'is_scriptable', 'is_no_jit', 'use_fused_attn',
'set_exportable', 'set_scriptable', 'set_no_jit', 'set_layer_config', 'set_fused_attn'
]
# Set to True if prefer to... | """ Model / Layer Config singleton state
"""
import os
import warnings
from typing import Any, Optional
import torch
__all__ = [
'is_exportable', 'is_scriptable', 'is_no_jit', 'use_fused_attn',
'set_exportable', 'set_scriptable', 'set_no_jit', 'set_layer_config', 'set_fused_attn'
]
# Set to True if prefer to... | |
17,388 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/conv_bn_act.py | 67,68 | 1,130 | create_aa | 68,81 | 68,81 | """ Conv2d + BN + Act
Hacked together by / Copyright 2020 Ross Wightman
"""
import functools
from torch import nn as nn
from .create_conv2d import create_conv2d
from .create_norm_act import get_norm_act_layer
class ConvNormAct(nn.Module):
def __init__(
self,
in_channels,
out_... | """ Conv2d + BN + Act
Hacked together by / Copyright 2020 Ross Wightman
"""
import functools
from torch import nn as nn
from .create_conv2d import create_conv2d
from .create_norm_act import get_norm_act_layer
class ConvNormAct(nn.Module):
def __init__(
self,
in_channels,
out_... | if not aa_layer or not enable:
return nn.Identity()
if isinstance(aa_layer, functools.partial):
if issubclass(aa_layer.func, nn.AvgPool2d):
return aa_layer()
else:
return aa_layer(channels)
elif issubclass(aa_layer, nn.AvgPool2d):
return aa_layer(strid... | if not aa_layer or not enable:
return nn.Identity()
if isinstance(aa_layer, functools.partial):
if issubclass(aa_layer.func, nn.AvgPool2d):
return aa_layer()
else:
return aa_layer(channels)
elif issubclass(aa_layer, nn.AvgPool2d):
return aa_layer(strid... | 28 | 1,086 | 151 | 1,237 | """ Conv2d + BN + Act
Hacked together by / Copyright 2020 Ross Wightman
"""
import functools
from torch import nn as nn
from .create_conv2d import create_conv2d
from .create_norm_act import get_norm_act_layer
class ConvNormAct(nn.Module):
def __init__(
self,
in_channels,
out_... | """ Conv2d + BN + Act
Hacked together by / Copyright 2020 Ross Wightman
"""
import functools
from torch import nn as nn
from .create_conv2d import create_conv2d
from .create_norm_act import get_norm_act_layer
class ConvNormAct(nn.Module):
def __init__(
self,
in_channels,
out_... | |
17,389 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/conv_bn_act.py | 50,52 | 12,64 | in_channels | ConvNormAct | 52,54 | 52,54 | class ConvNormAct(nn.Module):
def __init__(
self,
in_channels,
out_channels,
kernel_size=1,
stride=1,
padding='',
dilation=1,
groups=1,
bias=False,
apply_act=True,
norm_layer=nn.BatchN... | class ConvNormAct(nn.Module):
def __init__(
self,
in_channels,
out_channels,
kernel_size=1,
stride=1,
padding='',
dilation=1,
groups=1,
bias=False,
apply_act=True,
norm_layer=nn.BatchN... | return self.conv.in_channels
| return self.conv.in_channels
| 2 | 447 | 14 | 461 | """ Conv2d + BN + Act
Hacked together by / Copyright 2020 Ross Wightman
"""
import functools
from torch import nn as nn
from .create_conv2d import create_conv2d
from .create_norm_act import get_norm_act_layer
class ConvNormAct(nn.Module):
def __init__(
self,
in_channels,
out_... | """ Conv2d + BN + Act
Hacked together by / Copyright 2020 Ross Wightman
"""
import functools
from torch import nn as nn
from .create_conv2d import create_conv2d
from .create_norm_act import get_norm_act_layer
class ConvNormAct(nn.Module):
def __init__(
self,
in_channels,
out_... |
17,390 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/conv_bn_act.py | 125,126 | 81,130 | forward | ConvNormActAa | 126,130 | 126,130 | class ConvNormActAa(nn.Module):
def __init__(
self,
in_channels,
out_channels,
kernel_size=1,
stride=1,
padding='',
dilation=1,
groups=1,
bias=False,
apply_act=True,
norm_layer=nn.Batc... | class ConvNormActAa(nn.Module):
def __init__(
self,
in_channels,
out_channels,
kernel_size=1,
stride=1,
padding='',
dilation=1,
groups=1,
bias=False,
apply_act=True,
norm_layer=nn.Batc... | x = self.conv(x)
x = self.bn(x)
x = self.aa(x)
return x
| x = self.conv(x)
x = self.bn(x)
x = self.aa(x)
return x
| 11 | 490 | 38 | 528 | """ Conv2d + BN + Act
Hacked together by / Copyright 2020 Ross Wightman
"""
import functools
from torch import nn as nn
from .create_conv2d import create_conv2d
from .create_norm_act import get_norm_act_layer
class ConvNormAct(nn.Module):
def __init__(
self,
in_channels,
out_... | """ Conv2d + BN + Act
Hacked together by / Copyright 2020 Ross Wightman
"""
import functools
from torch import nn as nn
from .create_conv2d import create_conv2d
from .create_norm_act import get_norm_act_layer
class ConvNormAct(nn.Module):
def __init__(
self,
in_channels,
out_... |
17,392 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/create_attn.py | 21,22 | 1,90 | get_attn | 22,84 | 22,84 | """ Attention Factory
Hacked together by / Copyright 2021 Ross Wightman
"""
import torch
from functools import partial
from .bottleneck_attn import BottleneckAttn
from .cbam import CbamModule, LightCbamModule
from .eca import EcaModule, CecaModule
from .gather_excite import GatherExcite
from .global_context import Gl... | """ Attention Factory
Hacked together by / Copyright 2021 Ross Wightman
"""
import torch
from functools import partial
from .bottleneck_attn import BottleneckAttn
from .cbam import CbamModule, LightCbamModule
from .eca import EcaModule, CecaModule
from .gather_excite import GatherExcite
from .global_context import Gl... | if isinstance(attn_type, torch.nn.Module):
return attn_type
module_cls = None
if attn_type:
if isinstance(attn_type, str):
attn_type = attn_type.lower()
# Lightweight attention modules (channel and/or coarse spatial).
# Typically added to existing network ... | if isinstance(attn_type, torch.nn.Module):
return attn_type
module_cls = None
if attn_type:
if isinstance(attn_type, str):
attn_type = attn_type.lower()
# Lightweight attention modules (channel and/or coarse spatial).
# Typically added to existing network ... | 253 | 298 | 740 | 1,038 | """ Attention Factory
Hacked together by / Copyright 2021 Ross Wightman
"""
import torch
from functools import partial
from .bottleneck_attn import BottleneckAttn
from .cbam import CbamModule, LightCbamModule
from .eca import EcaModule, CecaModule
from .gather_excite import GatherExcite
from .global_context import Gl... | """ Attention Factory
Hacked together by / Copyright 2021 Ross Wightman
"""
import torch
from functools import partial
from .bottleneck_attn import BottleneckAttn
from .cbam import CbamModule, LightCbamModule
from .eca import EcaModule, CecaModule
from .gather_excite import GatherExcite
from .global_context import Gl... | |
17,393 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/create_conv2d.py | 11,12 | 1,37 | create_conv2d | 17,37 | 12,37 | """ Create Conv2d Factory Method
Hacked together by / Copyright 2020 Ross Wightman
"""
from .mixed_conv2d import MixedConv2d
from .cond_conv2d import CondConv2d
from .conv2d_same import create_conv2d_pad
def create_conv2d(in_channels, out_channels, kernel_size, **kwargs):
FILL_FUNC_BODY | """ Create Conv2d Factory Method
Hacked together by / Copyright 2020 Ross Wightman
"""
from .mixed_conv2d import MixedConv2d
from .cond_conv2d import CondConv2d
from .conv2d_same import create_conv2d_pad
def create_conv2d(in_channels, out_channels, kernel_size, **kwargs):
""" Select a 2d convolution implementat... | if isinstance(kernel_size, list):
assert 'num_experts' not in kwargs # MixNet + CondConv combo not supported currently
if 'groups' in kwargs:
groups = kwargs.pop('groups')
if groups == in_channels:
kwargs['depthwise'] = True
else:
... | """ Select a 2d convolution implementation based on arguments
Creates and returns one of torch.nn.Conv2d, Conv2dSame, MixedConv2d, or CondConv2d.
Used extensively by EfficientNet, MobileNetv3 and related networks.
"""
if isinstance(kernel_size, list):
assert 'num_experts' not in kwargs # M... | 125 | 185 | 359 | 544 | """ Create Conv2d Factory Method
Hacked together by / Copyright 2020 Ross Wightman
"""
from .mixed_conv2d import MixedConv2d
from .cond_conv2d import CondConv2d
from .conv2d_same import create_conv2d_pad
def create_conv2d(in_channels, out_channels, kernel_size, **kwargs):
FILL_FUNC_BODY | """ Create Conv2d Factory Method
Hacked together by / Copyright 2020 Ross Wightman
"""
from .mixed_conv2d import MixedConv2d
from .cond_conv2d import CondConv2d
from .conv2d_same import create_conv2d_pad
def create_conv2d(in_channels, out_channels, kernel_size, **kwargs):
""" Select a 2d convolution implementat... | |
17,394 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/layers/create_norm.py | 36,37 | 1,58 | get_norm_layer | 37,58 | 37,58 | """ Norm Layer Factory
Create norm modules by string (to mirror create_act and creat_norm-act fns)
Copyright 2022 Ross Wightman
"""
import functools
import types
from typing import Type
import torch.nn as nn
from .norm import GroupNorm, GroupNorm1, LayerNorm, LayerNorm2d, RmsNorm
from torchvision.ops.misc import Fr... | """ Norm Layer Factory
Create norm modules by string (to mirror create_act and creat_norm-act fns)
Copyright 2022 Ross Wightman
"""
import functools
import types
from typing import Type
import torch.nn as nn
from .norm import GroupNorm, GroupNorm1, LayerNorm, LayerNorm2d, RmsNorm
from torchvision.ops.misc import Fr... | if norm_layer is None:
return None
assert isinstance(norm_layer, (type, str, types.FunctionType, functools.partial))
norm_kwargs = {}
# unbind partial fn, so args can be rebound later
if isinstance(norm_layer, functools.partial):
norm_kwargs.update(norm_layer.keywords)
norm_... | if norm_layer is None:
return None
assert isinstance(norm_layer, (type, str, types.FunctionType, functools.partial))
norm_kwargs = {}
# unbind partial fn, so args can be rebound later
if isinstance(norm_layer, functools.partial):
norm_kwargs.update(norm_layer.keywords)
norm_... | 62 | 331 | 237 | 568 | """ Norm Layer Factory
Create norm modules by string (to mirror create_act and creat_norm-act fns)
Copyright 2022 Ross Wightman
"""
import functools
import types
from typing import Type
import torch.nn as nn
from .norm import GroupNorm, GroupNorm1, LayerNorm, LayerNorm2d, RmsNorm
from torchvision.ops.misc import Fr... | """ Norm Layer Factory
Create norm modules by string (to mirror create_act and creat_norm-act fns)
Copyright 2022 Ross Wightman
"""
import functools
import types
from typing import Type
import torch.nn as nn
from .norm import GroupNorm, GroupNorm1, LayerNorm, LayerNorm2d, RmsNorm
from torchvision.ops.misc import Fr... |
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