text stringlengths 31 243k | type stringclasses 1
value | start int64 36 275k | end int64 286 280k | depth int64 0 1 | filepath stringlengths 85 188 | parent_class stringclasses 3
values | class_index int64 0 10.8k |
|---|---|---|---|---|---|---|---|
class SpeechT5ForSpeechToSpeech(SpeechT5PreTrainedModel):
def __init__(self, config: SpeechT5Config):
super().__init__(config)
speech_encoder = SpeechT5EncoderWithSpeechPrenet(config)
speech_decoder = SpeechT5DecoderWithSpeechPrenet(config)
self.speecht5 = SpeechT5Model(config, spee... | class_definition | 134,225 | 146,417 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speecht5/modeling_speecht5.py | null | 8,500 |
class HifiGanResidualBlock(nn.Module):
def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5), leaky_relu_slope=0.1):
super().__init__()
self.leaky_relu_slope = leaky_relu_slope
self.convs1 = nn.ModuleList(
[
nn.Conv1d(
channels,
... | class_definition | 147,308 | 149,437 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speecht5/modeling_speecht5.py | null | 8,501 |
class SpeechT5HifiGan(PreTrainedModel):
config_class = SpeechT5HifiGanConfig
main_input_name = "spectrogram"
def __init__(self, config: SpeechT5HifiGanConfig):
super().__init__(config)
self.num_kernels = len(config.resblock_kernel_sizes)
self.num_upsamples = len(config.upsample_rate... | class_definition | 149,523 | 154,466 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speecht5/modeling_speecht5.py | null | 8,502 |
class SpeechT5Tokenizer(PreTrainedTokenizer):
"""
Construct a SpeechT5 tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information r... | class_definition | 1,042 | 8,911 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speecht5/tokenization_speecht5.py | null | 8,503 |
class SpeechT5Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`SpeechT5Model`]. It is used to instantiate a
SpeechT5 model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a s... | class_definition | 864 | 18,967 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speecht5/configuration_speecht5.py | null | 8,504 |
class SpeechT5HifiGanConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`SpeechT5HifiGanModel`]. It is used to instantiate
a SpeechT5 HiFi-GAN vocoder model according to the specified arguments, defining the model architecture.
Instantiating a configuration w... | class_definition | 18,970 | 23,377 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speecht5/configuration_speecht5.py | null | 8,505 |
class Qwen2VLCausalLMOutputWithPast(ModelOutput):
"""
Base class for Qwen2VL causal language model (or autoregressive) outputs.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss (for next-token prediction).
l... | class_definition | 2,180 | 4,642 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py | null | 8,506 |
class Qwen2VLRotaryEmbedding(nn.Module):
def __init__(
self,
dim=None,
max_position_embeddings=2048,
base=10000,
device=None,
scaling_factor=1.0,
rope_type="default",
config: Optional[Qwen2VLConfig] = None,
):
super().__init__()
# T... | class_definition | 4,645 | 8,807 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py | null | 8,507 |
class VisionRotaryEmbedding(nn.Module):
def __init__(self, dim: int, theta: float = 10000.0) -> None:
super().__init__()
inv_freq = 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=torch.float) / dim))
self.register_buffer("inv_freq", inv_freq, persistent=False)
def forward(self, seqlen: ... | class_definition | 12,401 | 12,902 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py | null | 8,508 |
class PatchEmbed(nn.Module):
def __init__(
self,
patch_size: int = 14,
temporal_patch_size: int = 2,
in_channels: int = 3,
embed_dim: int = 1152,
) -> None:
super().__init__()
self.patch_size = patch_size
self.temporal_patch_size = temporal_patch_s... | class_definition | 12,905 | 13,870 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py | null | 8,509 |
class PatchMerger(nn.Module):
def __init__(self, dim: int, context_dim: int, spatial_merge_size: int = 2) -> None:
super().__init__()
self.hidden_size = context_dim * (spatial_merge_size**2)
self.ln_q = LayerNorm(context_dim, eps=1e-6)
self.mlp = nn.Sequential(
nn.Linear(... | class_definition | 13,873 | 14,444 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py | null | 8,510 |
class VisionMlp(nn.Module):
def __init__(self, dim: int, hidden_dim: int, hidden_act: str) -> None:
super().__init__()
self.fc1 = nn.Linear(dim, hidden_dim)
self.act = ACT2FN[hidden_act]
self.fc2 = nn.Linear(hidden_dim, dim)
def forward(self, x) -> torch.Tensor:
return s... | class_definition | 14,447 | 14,797 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py | null | 8,511 |
class VisionAttention(nn.Module):
def __init__(self, dim: int, num_heads: int = 16) -> None:
super().__init__()
self.num_heads = num_heads
self.head_dim = dim // num_heads
self.qkv = nn.Linear(dim, dim * 3, bias=True)
self.proj = nn.Linear(dim, dim)
def forward(
... | class_definition | 14,800 | 16,421 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py | null | 8,512 |
class VisionFlashAttention2(nn.Module):
def __init__(self, dim: int, num_heads: int = 16) -> None:
super().__init__()
self.num_heads = num_heads
self.qkv = nn.Linear(dim, dim * 3, bias=True)
self.proj = nn.Linear(dim, dim)
def forward(
self, hidden_states: torch.Tensor, ... | class_definition | 16,424 | 17,443 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py | null | 8,513 |
class VisionSdpaAttention(nn.Module):
def __init__(self, dim: int, num_heads: int = 16) -> None:
super().__init__()
self.num_heads = num_heads
self.qkv = nn.Linear(dim, dim * 3, bias=True)
self.proj = nn.Linear(dim, dim)
def forward(
self, hidden_states: torch.Tensor, cu... | class_definition | 17,446 | 18,791 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py | null | 8,514 |
class Qwen2VLVisionBlock(nn.Module):
def __init__(self, config, attn_implementation: str = "sdpa") -> None:
super().__init__()
self.norm1 = LayerNorm(config.embed_dim, eps=1e-6)
self.norm2 = LayerNorm(config.embed_dim, eps=1e-6)
mlp_hidden_dim = int(config.embed_dim * config.mlp_rati... | class_definition | 18,947 | 19,862 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py | null | 8,515 |
class Qwen2RMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
Qwen2RMSNorm is equivalent to T5LayerNorm
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
... | class_definition | 19,933 | 20,653 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py | null | 8,516 |
class Qwen2MLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.intermediate_size = config.intermediate_size
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
self... | class_definition | 20,720 | 21,388 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py | null | 8,517 |
class Qwen2VLAttention(nn.Module):
"""
Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer
and "Generating Long Sequences with Sparse Transformers".
"""
def __init__(self, config: Qwen2VLConfig, layer_idx: Optional[int] = None):
... | class_definition | 22,065 | 27,413 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py | null | 8,518 |
class Qwen2VLFlashAttention2(Qwen2VLAttention):
"""
Qwen2VL flash attention module, following Qwen2VL attention module. This module inherits from `Qwen2VLAttention`
as the weights of the module stays untouched. The only required change would be on the forward pass
where it needs to correctly call the pu... | class_definition | 27,416 | 32,776 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py | null | 8,519 |
class Qwen2VLSdpaAttention(Qwen2VLAttention):
"""
Qwen2 attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
`Qwen2Attention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
SDPA API.
"""
# Adapted ... | class_definition | 32,779 | 37,491 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py | null | 8,520 |
class Qwen2VLDecoderLayer(nn.Module):
def __init__(self, config: Qwen2VLConfig, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
if config.use_sliding_window and config._attn_implementation != "flash_attention_2":
logger.warning_once(
f"S... | class_definition | 37,643 | 41,599 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py | null | 8,521 |
class Qwen2VLPreTrainedModel(PreTrainedModel):
config_class = Qwen2VLConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["Qwen2VLDecoderLayer", "Qwen2VLVisionBlock"]
_skip_keys_device_placement = "past_key_values"
_supports_flash_attn_2 = True
_supp... | class_definition | 42,629 | 43,523 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py | null | 8,522 |
class Qwen2VisionTransformerPretrainedModel(Qwen2VLPreTrainedModel):
config_class = Qwen2VLVisionConfig
_no_split_modules = ["Qwen2VLVisionBlock"]
def __init__(self, config) -> None:
super().__init__(config)
self.spatial_merge_size = config.spatial_merge_size
self.patch_embed = Pat... | class_definition | 43,526 | 47,118 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py | null | 8,523 |
class Qwen2VLModel(Qwen2VLPreTrainedModel):
def __init__(self, config: Qwen2VLConfig):
super().__init__(config)
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
... | class_definition | 47,268 | 61,041 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py | null | 8,524 |
class Qwen2VLForConditionalGeneration(Qwen2VLPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
super().__init__(config)
self.visual = Qwen2VisionTransformerPretrainedModel._from_config(config.vision_config)
self.model = Qwen2VLModel(c... | class_definition | 66,265 | 87,175 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py | null | 8,525 |
class Qwen2VLVisionConfig(PretrainedConfig):
model_type = "qwen2_vl"
base_config_key = "vision_config"
def __init__(
self,
depth=32,
embed_dim=1280,
hidden_size=3584,
hidden_act="quick_gelu",
mlp_ratio=4,
num_heads=16,
in_channels=3,
p... | class_definition | 875 | 1,722 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_vl/configuration_qwen2_vl.py | null | 8,526 |
class Qwen2VLConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Qwen2VLModel`]. It is used to instantiate a
Qwen2-VL model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a sim... | class_definition | 1,725 | 12,149 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_vl/configuration_qwen2_vl.py | null | 8,527 |
class Qwen2VLProcessorKwargs(ProcessingKwargs, total=False):
_defaults = {
"text_kwargs": {
"padding": False,
},
} | class_definition | 1,335 | 1,485 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_vl/processing_qwen2_vl.py | null | 8,528 |
class Qwen2VLProcessor(ProcessorMixin):
r"""
Constructs a Qwen2-VL processor which wraps a Qwen2-VL image processor and a Qwen2 tokenizer into a single processor.
[`Qwen2VLProcessor`] offers all the functionalities of [`Qwen2VLImageProcessor`] and [`Qwen2TokenizerFast`]. See the
[`~Qwen2VLProcessor.__ca... | class_definition | 1,488 | 9,482 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_vl/processing_qwen2_vl.py | null | 8,529 |
class Qwen2VLImageProcessor(BaseImageProcessor):
r"""
Constructs a Qwen2-VL image processor that dynamically resizes images based on the original images.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height, width) dimensions.
resample ... | class_definition | 4,508 | 22,359 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_vl/image_processing_qwen2_vl.py | null | 8,530 |
class GPTSw3Tokenizer(PreTrainedTokenizer):
"""
Construct an GPTSw3 tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information rega... | class_definition | 445 | 12,469 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_sw3/tokenization_gpt_sw3.py | null | 8,531 |
class TFSwiftFormerPatchEmbeddingSequential(keras.layers.Layer):
"""
The sequential component of the patch embedding layer.
Input: tensor of shape `[batch_size, in_channels, height, width]`
Output: tensor of shape `[batch_size, out_channels, height/4, width/4]`
"""
def __init__(self, config: ... | class_definition | 1,530 | 3,687 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swiftformer/modeling_tf_swiftformer.py | null | 8,532 |
class TFSwiftFormerPatchEmbedding(keras.layers.Layer):
"""
Patch Embedding Layer constructed of two 2D convolutional layers.
Input: tensor of shape `[batch_size, in_channels, height, width]`
Output: tensor of shape `[batch_size, out_channels, height/4, width/4]`
"""
def __init__(self, config:... | class_definition | 3,690 | 4,585 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swiftformer/modeling_tf_swiftformer.py | null | 8,533 |
class TFSwiftFormerDropPath(keras.layers.Layer):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
def __init__(self, config: SwiftFormerConfig, **kwargs) -> None:
super().__init__(**kwargs)
raise NotImplementedError("Drop path is not implemented in ... | class_definition | 4,588 | 5,078 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swiftformer/modeling_tf_swiftformer.py | null | 8,534 |
class TFSwiftFormerEmbeddings(keras.layers.Layer):
"""
Embeddings layer consisting of a single 2D convolutional and batch normalization layer.
Input: tensor of shape `[batch_size, channels, height, width]`
Output: tensor of shape `[batch_size, channels, height/stride, width/stride]`
"""
def _... | class_definition | 5,081 | 6,939 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swiftformer/modeling_tf_swiftformer.py | null | 8,535 |
class TFSwiftFormerConvEncoder(keras.layers.Layer):
"""
`SwiftFormerConvEncoder` with 3*3 and 1*1 convolutions.
Input: tensor of shape `[batch_size, channels, height, width]`
Output: tensor of shape `[batch_size, channels, height, width]`
"""
def __init__(self, config: SwiftFormerConfig, dim:... | class_definition | 6,942 | 9,572 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swiftformer/modeling_tf_swiftformer.py | null | 8,536 |
class TFSwiftFormerMlp(keras.layers.Layer):
"""
MLP layer with 1*1 convolutions.
Input: tensor of shape `[batch_size, channels, height, width]`
Output: tensor of shape `[batch_size, channels, height, width]`
"""
def __init__(self, config: SwiftFormerConfig, in_features: int, **kwargs):
... | class_definition | 9,575 | 11,389 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swiftformer/modeling_tf_swiftformer.py | null | 8,537 |
class TFSwiftFormerEfficientAdditiveAttention(keras.layers.Layer):
"""
Efficient Additive Attention module for SwiftFormer.
Input: tensor of shape `[batch_size, channels, height, width]`
Output: tensor of shape `[batch_size, channels, height, width]`
"""
def __init__(self, config: SwiftFormer... | class_definition | 11,392 | 13,657 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swiftformer/modeling_tf_swiftformer.py | null | 8,538 |
class TFSwiftFormerLocalRepresentation(keras.layers.Layer):
"""
Local Representation module for SwiftFormer that is implemented by 3*3 depth-wise and point-wise convolutions.
Input: tensor of shape `[batch_size, channels, height, width]`
Output: tensor of shape `[batch_size, channels, height, width]`
... | class_definition | 13,660 | 16,237 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swiftformer/modeling_tf_swiftformer.py | null | 8,539 |
class TFSwiftFormerEncoderBlock(keras.layers.Layer):
"""
SwiftFormer Encoder Block for SwiftFormer. It consists of (1) Local representation module, (2)
SwiftFormerEfficientAdditiveAttention, and (3) MLP block.
Input: tensor of shape `[batch_size, channels, height, width]`
Output: tensor of shape `... | class_definition | 16,240 | 19,125 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swiftformer/modeling_tf_swiftformer.py | null | 8,540 |
class TFSwiftFormerStage(keras.layers.Layer):
"""
A Swiftformer stage consisting of a series of `SwiftFormerConvEncoder` blocks and a final
`SwiftFormerEncoderBlock`.
Input: tensor in shape `[batch_size, channels, height, width]`
Output: tensor in shape `[batch_size, channels, height, width]`
... | class_definition | 19,128 | 20,543 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swiftformer/modeling_tf_swiftformer.py | null | 8,541 |
class TFSwiftFormerEncoder(keras.layers.Layer):
def __init__(self, config: SwiftFormerConfig, **kwargs) -> None:
super().__init__(**kwargs)
self.config = config
embed_dims = config.embed_dims
downsamples = config.downsamples
layer_depths = config.depths
# Transforme... | class_definition | 20,546 | 22,896 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swiftformer/modeling_tf_swiftformer.py | null | 8,542 |
class TFSwiftFormerPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = SwiftFormerConfig
base_model_prefix = "swiftformer"
main_input_name = "pixel_values" | class_definition | 22,899 | 23,211 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swiftformer/modeling_tf_swiftformer.py | null | 8,543 |
class TFSwiftFormerMainLayer(keras.layers.Layer):
config_class = SwiftFormerConfig
def __init__(self, config: SwiftFormerConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.patch_embed = TFSwiftFormerPatchEmbedding(config, name="patch_embed")
self.encoder = ... | class_definition | 25,871 | 28,187 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swiftformer/modeling_tf_swiftformer.py | null | 8,544 |
class TFSwiftFormerModel(TFSwiftFormerPreTrainedModel):
def __init__(self, config: SwiftFormerConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.swiftformer = TFSwiftFormerMainLayer(config, name="swiftformer")
@unpack_inputs
@add_start_docstrings_to_model_forward(... | class_definition | 28,361 | 29,485 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swiftformer/modeling_tf_swiftformer.py | null | 8,545 |
class TFSwiftFormerForImageClassification(TFSwiftFormerPreTrainedModel):
def __init__(self, config: SwiftFormerConfig, **kwargs) -> None:
super().__init__(config, **kwargs)
self.num_labels = config.num_labels
self.swiftformer = TFSwiftFormerMainLayer(config, name="swiftformer")
# C... | class_definition | 29,663 | 34,859 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swiftformer/modeling_tf_swiftformer.py | null | 8,546 |
class SwiftFormerConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`SwiftFormerModel`]. It is used to instantiate an
SwiftFormer model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will... | class_definition | 925 | 5,391 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swiftformer/configuration_swiftformer.py | null | 8,547 |
class SwiftFormerOnnxConfig(OnnxConfig):
torch_onnx_minimum_version = version.parse("1.11")
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
]
... | class_definition | 5,394 | 5,798 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swiftformer/configuration_swiftformer.py | null | 8,548 |
class SwiftFormerPatchEmbedding(nn.Module):
"""
Patch Embedding Layer constructed of two 2D convolutional layers.
Input: tensor of shape `[batch_size, in_channels, height, width]`
Output: tensor of shape `[batch_size, out_channels, height/4, width/4]`
"""
def __init__(self, config: SwiftForme... | class_definition | 1,598 | 2,495 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swiftformer/modeling_swiftformer.py | null | 8,549 |
class SwiftFormerDropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
def __init__(self, config: SwiftFormerConfig) -> None:
super().__init__()
self.drop_prob = config.drop_path_rate
def forward(self, hidden_states: torch.Tenso... | class_definition | 3,653 | 4,142 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swiftformer/modeling_swiftformer.py | null | 8,550 |
class SwiftFormerEmbeddings(nn.Module):
"""
Embeddings layer consisting of a single 2D convolutional and batch normalization layer.
Input: tensor of shape `[batch_size, channels, height, width]`
Output: tensor of shape `[batch_size, channels, height/stride, width/stride]`
"""
def __init__(sel... | class_definition | 4,145 | 5,351 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swiftformer/modeling_swiftformer.py | null | 8,551 |
class SwiftFormerConvEncoder(nn.Module):
"""
`SwiftFormerConvEncoder` with 3*3 and 1*1 convolutions.
Input: tensor of shape `[batch_size, channels, height, width]`
Output: tensor of shape `[batch_size, channels, height, width]`
"""
def __init__(self, config: SwiftFormerConfig, dim: int):
... | class_definition | 5,354 | 6,531 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swiftformer/modeling_swiftformer.py | null | 8,552 |
class SwiftFormerMlp(nn.Module):
"""
MLP layer with 1*1 convolutions.
Input: tensor of shape `[batch_size, channels, height, width]`
Output: tensor of shape `[batch_size, channels, height, width]`
"""
def __init__(self, config: SwiftFormerConfig, in_features: int):
super().__init__()
... | class_definition | 6,534 | 7,440 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swiftformer/modeling_swiftformer.py | null | 8,553 |
class SwiftFormerEfficientAdditiveAttention(nn.Module):
"""
Efficient Additive Attention module for SwiftFormer.
Input: tensor of shape `[batch_size, channels, height, width]`
Output: tensor of shape `[batch_size, channels, height, width]`
"""
def __init__(self, config: SwiftFormerConfig, dim... | class_definition | 7,443 | 8,698 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swiftformer/modeling_swiftformer.py | null | 8,554 |
class SwiftFormerLocalRepresentation(nn.Module):
"""
Local Representation module for SwiftFormer that is implemented by 3*3 depth-wise and point-wise convolutions.
Input: tensor of shape `[batch_size, channels, height, width]`
Output: tensor of shape `[batch_size, channels, height, width]`
"""
... | class_definition | 8,701 | 9,848 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swiftformer/modeling_swiftformer.py | null | 8,555 |
class SwiftFormerEncoderBlock(nn.Module):
"""
SwiftFormer Encoder Block for SwiftFormer. It consists of (1) Local representation module, (2)
SwiftFormerEfficientAdditiveAttention, and (3) MLP block.
Input: tensor of shape `[batch_size, channels, height, width]`
Output: tensor of shape `[batch_size... | class_definition | 9,851 | 11,745 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swiftformer/modeling_swiftformer.py | null | 8,556 |
class SwiftFormerStage(nn.Module):
"""
A Swiftformer stage consisting of a series of `SwiftFormerConvEncoder` blocks and a final
`SwiftFormerEncoderBlock`.
Input: tensor in shape `[batch_size, channels, height, width]`
Output: tensor in shape `[batch_size, channels, height, width]`
"""
de... | class_definition | 11,748 | 12,832 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swiftformer/modeling_swiftformer.py | null | 8,557 |
class SwiftFormerEncoder(nn.Module):
def __init__(self, config: SwiftFormerConfig) -> None:
super().__init__()
self.config = config
embed_dims = config.embed_dims
downsamples = config.downsamples
layer_depths = config.depths
# Transformer model
network = []
... | class_definition | 12,835 | 14,644 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swiftformer/modeling_swiftformer.py | null | 8,558 |
class SwiftFormerPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = SwiftFormerConfig
base_model_prefix = "swiftformer"
main_input_name = "pixel_values"
support... | class_definition | 14,647 | 15,525 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swiftformer/modeling_swiftformer.py | null | 8,559 |
class SwiftFormerModel(SwiftFormerPreTrainedModel):
def __init__(self, config: SwiftFormerConfig):
super().__init__(config)
self.config = config
self.patch_embed = SwiftFormerPatchEmbedding(config)
self.encoder = SwiftFormerEncoder(config)
# Initialize weights and apply fin... | class_definition | 16,941 | 18,724 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swiftformer/modeling_swiftformer.py | null | 8,560 |
class SwiftFormerForImageClassification(SwiftFormerPreTrainedModel):
def __init__(self, config: SwiftFormerConfig) -> None:
super().__init__(config)
embed_dims = config.embed_dims
self.num_labels = config.num_labels
self.swiftformer = SwiftFormerModel(config)
# Classifier ... | class_definition | 18,898 | 22,745 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swiftformer/modeling_swiftformer.py | null | 8,561 |
class NougatTokenizerFast(PreTrainedTokenizerFast):
"""
Fast tokenizer for Nougat (backed by HuggingFace tokenizers library).
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
refer to this superclass for more information regarding those meth... | class_definition | 13,076 | 24,703 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nougat/tokenization_nougat_fast.py | null | 8,562 |
class NougatProcessor(ProcessorMixin):
r"""
Constructs a Nougat processor which wraps a Nougat image processor and a Nougat tokenizer into a single processor.
[`NougatProcessor`] offers all the functionalities of [`NougatImageProcessor`] and [`NougatTokenizerFast`]. See the
[`~NougatProcessor.__call__`... | class_definition | 887 | 6,730 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nougat/processing_nougat.py | null | 8,563 |
class NougatImageProcessor(BaseImageProcessor):
r"""
Constructs a Nougat image processor.
Args:
do_crop_margin (`bool`, *optional*, defaults to `True`):
Whether to crop the image margins.
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image... | class_definition | 1,549 | 23,701 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nougat/image_processing_nougat.py | null | 8,564 |
class Swinv2Config(BackboneConfigMixin, PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Swinv2Model`]. It is used to instantiate a Swin
Transformer v2 model according to the specified arguments, defining the model architecture. Instantiating a
configuration with... | class_definition | 895 | 7,517 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swinv2/configuration_swinv2.py | null | 8,565 |
class Swinv2EncoderOutput(ModelOutput):
"""
Swinv2 encoder's outputs, with potential hidden states and attentions.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.... | class_definition | 2,124 | 4,093 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swinv2/modeling_swinv2.py | null | 8,566 |
class Swinv2ModelOutput(ModelOutput):
"""
Swinv2 model's outputs that also contains a pooling of the last hidden states.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the ... | class_definition | 4,194 | 6,427 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swinv2/modeling_swinv2.py | null | 8,567 |
class Swinv2MaskedImageModelingOutput(ModelOutput):
"""
Swinv2 masked image model outputs.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `bool_masked_pos` is provided):
Masked image modeling (MLM) loss.
reconstruction (`torch.FloatTensor` of shape `(... | class_definition | 6,542 | 8,957 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swinv2/modeling_swinv2.py | null | 8,568 |
class Swinv2ImageClassifierOutput(ModelOutput):
"""
Swinv2 outputs for image classification.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
logits (`torch.FloatTensor`... | class_definition | 9,068 | 11,208 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swinv2/modeling_swinv2.py | null | 8,569 |
class Swinv2DropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
def __init__(self, drop_prob: Optional[float] = None) -> None:
super().__init__()
self.drop_prob = drop_prob
def forward(self, hidden_states: torch.Tensor) -> tor... | class_definition | 13,456 | 13,936 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swinv2/modeling_swinv2.py | null | 8,570 |
class Swinv2Embeddings(nn.Module):
"""
Construct the patch and position embeddings. Optionally, also the mask token.
"""
def __init__(self, config, use_mask_token=False):
super().__init__()
self.patch_embeddings = Swinv2PatchEmbeddings(config)
num_patches = self.patch_embedding... | class_definition | 14,025 | 17,938 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swinv2/modeling_swinv2.py | null | 8,571 |
class Swinv2PatchEmbeddings(nn.Module):
"""
This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
`hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
Transformer.
"""
def __init__(self, config):
... | class_definition | 18,032 | 20,214 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swinv2/modeling_swinv2.py | null | 8,572 |
class Swinv2PatchMerging(nn.Module):
"""
Patch Merging Layer.
Args:
input_resolution (`Tuple[int]`):
Resolution of input feature.
dim (`int`):
Number of input channels.
norm_layer (`nn.Module`, *optional*, defaults to `nn.LayerNorm`):
Normalizatio... | class_definition | 20,217 | 22,509 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swinv2/modeling_swinv2.py | null | 8,573 |
class Swinv2SelfAttention(nn.Module):
def __init__(self, config, dim, num_heads, window_size, pretrained_window_size=[0, 0]):
super().__init__()
if dim % num_heads != 0:
raise ValueError(
f"The hidden size ({dim}) is not a multiple of the number of attention heads ({num_h... | class_definition | 22,512 | 29,493 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swinv2/modeling_swinv2.py | null | 8,574 |
class Swinv2SelfOutput(nn.Module):
def __init__(self, config, dim):
super().__init__()
self.dense = nn.Linear(dim, dim)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
hidd... | class_definition | 29,582 | 30,021 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swinv2/modeling_swinv2.py | null | 8,575 |
class Swinv2Attention(nn.Module):
def __init__(self, config, dim, num_heads, window_size, pretrained_window_size=0):
super().__init__()
self.self = Swinv2SelfAttention(
config=config,
dim=dim,
num_heads=num_heads,
window_size=window_size,
p... | class_definition | 30,024 | 32,027 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swinv2/modeling_swinv2.py | null | 8,576 |
class Swinv2Intermediate(nn.Module):
def __init__(self, config, dim):
super().__init__()
self.dense = nn.Linear(dim, int(config.mlp_ratio * dim))
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.intermediat... | class_definition | 32,118 | 32,678 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swinv2/modeling_swinv2.py | null | 8,577 |
class Swinv2Output(nn.Module):
def __init__(self, config, dim):
super().__init__()
self.dense = nn.Linear(int(config.mlp_ratio * dim), dim)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.... | class_definition | 32,763 | 33,184 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swinv2/modeling_swinv2.py | null | 8,578 |
class Swinv2Layer(nn.Module):
def __init__(
self, config, dim, input_resolution, num_heads, drop_path_rate=0.0, shift_size=0, pretrained_window_size=0
):
super().__init__()
self.input_resolution = input_resolution
window_size, shift_size = self._compute_window_shift(
... | class_definition | 33,187 | 39,128 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swinv2/modeling_swinv2.py | null | 8,579 |
class Swinv2Stage(nn.Module):
def __init__(
self, config, dim, input_resolution, depth, num_heads, drop_path, downsample, pretrained_window_size=0
):
super().__init__()
self.config = config
self.dim = dim
blocks = []
for i in range(depth):
block = Swin... | class_definition | 39,131 | 41,424 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swinv2/modeling_swinv2.py | null | 8,580 |
class Swinv2Encoder(nn.Module):
def __init__(self, config, grid_size, pretrained_window_sizes=(0, 0, 0, 0)):
super().__init__()
self.num_layers = len(config.depths)
self.config = config
if self.config.pretrained_window_sizes is not None:
pretrained_window_sizes = config.p... | class_definition | 41,427 | 46,347 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swinv2/modeling_swinv2.py | null | 8,581 |
class Swinv2PreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = Swinv2Config
base_model_prefix = "swinv2"
main_input_name = "pixel_values"
supports_gradient_chec... | class_definition | 46,454 | 47,413 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swinv2/modeling_swinv2.py | null | 8,582 |
class Swinv2Model(Swinv2PreTrainedModel):
def __init__(self, config, add_pooling_layer=True, use_mask_token=False):
super().__init__(config)
self.config = config
self.num_layers = len(config.depths)
self.num_features = int(config.embed_dim * 2 ** (self.num_layers - 1))
self.... | class_definition | 49,563 | 53,744 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swinv2/modeling_swinv2.py | null | 8,583 |
class Swinv2ForMaskedImageModeling(Swinv2PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.swinv2 = Swinv2Model(config, add_pooling_layer=False, use_mask_token=True)
num_features = int(config.embed_dim * 2 ** (config.num_layers - 1))
self.decoder = nn.Sequ... | class_definition | 54,340 | 59,064 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swinv2/modeling_swinv2.py | null | 8,584 |
class Swinv2ForImageClassification(Swinv2PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.swinv2 = Swinv2Model(config)
# Classifier head
self.classifier = (
nn.Linear(self.swinv2.num_features, config... | class_definition | 59,742 | 63,385 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swinv2/modeling_swinv2.py | null | 8,585 |
class Swinv2Backbone(Swinv2PreTrainedModel, BackboneMixin):
def __init__(self, config):
super().__init__(config)
super()._init_backbone(config)
self.num_features = [config.embed_dim] + [int(config.embed_dim * 2**i) for i in range(len(config.depths))]
self.embeddings = Swinv2Embeddin... | class_definition | 63,532 | 66,847 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swinv2/modeling_swinv2.py | null | 8,586 |
class RegNetConvLayer(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: int = 3,
stride: int = 1,
groups: int = 1,
activation: Optional[str] = "relu",
):
super().__init__()
self.convolution = nn.Conv2d(
... | class_definition | 1,625 | 2,530 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/regnet/modeling_regnet.py | null | 8,587 |
class RegNetEmbeddings(nn.Module):
"""
RegNet Embedddings (stem) composed of a single aggressive convolution.
"""
def __init__(self, config: RegNetConfig):
super().__init__()
self.embedder = RegNetConvLayer(
config.num_channels, config.embedding_size, kernel_size=3, stride=2... | class_definition | 2,533 | 3,313 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/regnet/modeling_regnet.py | null | 8,588 |
class RegNetShortCut(nn.Module):
"""
RegNet shortcut, used to project the residual features to the correct size. If needed, it is also used to
downsample the input using `stride=2`.
"""
def __init__(self, in_channels: int, out_channels: int, stride: int = 2):
super().__init__()
self... | class_definition | 3,408 | 4,059 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/regnet/modeling_regnet.py | null | 8,589 |
class RegNetSELayer(nn.Module):
"""
Squeeze and Excitation layer (SE) proposed in [Squeeze-and-Excitation Networks](https://arxiv.org/abs/1709.01507).
"""
def __init__(self, in_channels: int, reduced_channels: int):
super().__init__()
self.pooler = nn.AdaptiveAvgPool2d((1, 1))
... | class_definition | 4,062 | 4,839 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/regnet/modeling_regnet.py | null | 8,590 |
class RegNetXLayer(nn.Module):
"""
RegNet's layer composed by three `3x3` convolutions, same as a ResNet bottleneck layer with reduction = 1.
"""
def __init__(self, config: RegNetConfig, in_channels: int, out_channels: int, stride: int = 1):
super().__init__()
should_apply_shortcut = in... | class_definition | 4,842 | 6,092 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/regnet/modeling_regnet.py | null | 8,591 |
class RegNetYLayer(nn.Module):
"""
RegNet's Y layer: an X layer with Squeeze and Excitation.
"""
def __init__(self, config: RegNetConfig, in_channels: int, out_channels: int, stride: int = 1):
super().__init__()
should_apply_shortcut = in_channels != out_channels or stride != 1
... | class_definition | 6,095 | 7,383 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/regnet/modeling_regnet.py | null | 8,592 |
class RegNetStage(nn.Module):
"""
A RegNet stage composed by stacked layers.
"""
def __init__(
self,
config: RegNetConfig,
in_channels: int,
out_channels: int,
stride: int = 2,
depth: int = 2,
):
super().__init__()
layer = RegNetXLaye... | class_definition | 7,386 | 8,219 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/regnet/modeling_regnet.py | null | 8,593 |
class RegNetEncoder(nn.Module):
def __init__(self, config: RegNetConfig):
super().__init__()
self.stages = nn.ModuleList([])
# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
self.stages.append(
RegNetStage(
... | class_definition | 8,222 | 9,763 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/regnet/modeling_regnet.py | null | 8,594 |
class RegNetPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = RegNetConfig
base_model_prefix = "regnet"
main_input_name = "pixel_values"
_no_split_modules = ["... | class_definition | 9,766 | 10,975 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/regnet/modeling_regnet.py | null | 8,595 |
class RegNetModel(RegNetPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.embedder = RegNetEmbeddings(config)
self.encoder = RegNetEncoder(config)
self.pooler = nn.AdaptiveAvgPool2d((1, 1))
# Initialize weights and apply ... | class_definition | 12,464 | 14,204 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/regnet/modeling_regnet.py | null | 8,596 |
class RegNetForImageClassification(RegNetPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.regnet = RegNetModel(config)
# classification head
self.classifier = nn.Sequential(
nn.Flatten(),
n... | class_definition | 14,542 | 17,686 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/regnet/modeling_regnet.py | null | 8,597 |
class RegNetConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`RegNetModel`]. It is used to instantiate a RegNet
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar... | class_definition | 808 | 3,944 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/regnet/configuration_regnet.py | null | 8,598 |
class Tracker:
module: nn.Module
traced: List[nn.Module] = field(default_factory=list)
handles: list = field(default_factory=list)
def _forward_hook(self, m, inputs: Tensor, outputs: Tensor):
has_not_submodules = len(list(m.modules())) == 1 or isinstance(m, nn.Conv2d) or isinstance(m, nn.BatchN... | class_definition | 1,329 | 2,173 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/regnet/convert_regnet_to_pytorch.py | null | 8,599 |
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