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 BrosSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the number... | class_definition | 12,400 | 19,806 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bros/modeling_bros.py | null | 9,000 |
class BrosSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def f... | class_definition | 19,893 | 20,499 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bros/modeling_bros.py | null | 9,001 |
class BrosAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.self = BrosSelfAttention(config)
self.output = BrosSelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads):
if len(heads) == 0:
return
heads, index =... | class_definition | 20,502 | 22,693 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bros/modeling_bros.py | null | 9,002 |
class BrosIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.interm... | class_definition | 22,782 | 23,347 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bros/modeling_bros.py | null | 9,003 |
class BrosOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def... | class_definition | 23,350 | 23,958 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bros/modeling_bros.py | null | 9,004 |
class BrosLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = BrosAttention(config)
self.is_decoder = config.is_decoder
self.add_cross_attention = config.a... | class_definition | 23,961 | 27,940 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bros/modeling_bros.py | null | 9,005 |
class BrosEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([BrosLayer(config) for _ in range(config.num_hidden_layers)])
def forward(
self,
hidden_states: torch.Tensor,
bbox_pos_emb: torch.Tensor,
... | class_definition | 27,943 | 31,910 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bros/modeling_bros.py | null | 9,006 |
class BrosPooler(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
# We "pool" the model by simply taking the hidde... | class_definition | 31,993 | 32,552 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bros/modeling_bros.py | null | 9,007 |
class BrosRelationExtractor(nn.Module):
def __init__(self, config):
super().__init__()
self.n_relations = config.n_relations
self.backbone_hidden_size = config.hidden_size
self.head_hidden_size = config.hidden_size
self.classifier_dropout_prob = config.classifier_dropout_prob... | class_definition | 32,555 | 34,037 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bros/modeling_bros.py | null | 9,008 |
class BrosPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = BrosConfig
base_model_prefix = "bros"
def _init_weights(self, module):
"""Initialize the weigh... | class_definition | 34,040 | 35,101 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bros/modeling_bros.py | null | 9,009 |
class BrosModel(BrosPreTrainedModel):
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
self.config = config
self.embeddings = BrosTextEmbeddings(config)
self.bbox_embeddings = BrosBboxEmbeddings(config)
self.encoder = BrosEncoder(config)
... | class_definition | 35,257 | 43,042 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bros/modeling_bros.py | null | 9,010 |
class BrosForTokenClassification(BrosPreTrainedModel):
_keys_to_ignore_on_load_unexpected = [r"pooler"]
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.bros = BrosModel(config)
classifier_dropout = (
config.classifier_dr... | class_definition | 43,271 | 46,879 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bros/modeling_bros.py | null | 9,011 |
class BrosSpadeEEForTokenClassification(BrosPreTrainedModel):
_keys_to_ignore_on_load_unexpected = [r"pooler"]
def __init__(self, config):
super().__init__(config)
self.config = config
self.num_labels = config.num_labels
self.n_relations = config.n_relations
self.backbon... | class_definition | 47,463 | 53,410 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bros/modeling_bros.py | null | 9,012 |
class BrosSpadeELForTokenClassification(BrosPreTrainedModel):
_keys_to_ignore_on_load_unexpected = [r"pooler"]
def __init__(self, config):
super().__init__(config)
self.config = config
self.num_labels = config.num_labels
self.n_relations = config.n_relations
self.backbon... | class_definition | 53,712 | 57,837 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bros/modeling_bros.py | null | 9,013 |
class BrosProcessor(ProcessorMixin):
r"""
Constructs a Bros processor which wraps a BERT tokenizer.
[`BrosProcessor`] offers all the functionalities of [`BertTokenizerFast`]. See the docstring of
[`~BrosProcessor.__call__`] and [`~BrosProcessor.decode`] for more information.
Args:
tokenize... | class_definition | 883 | 4,192 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bros/processing_bros.py | null | 9,014 |
class BrosConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`BrosModel`] or a [`TFBrosModel`]. It is used to
instantiate a Bros model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will ... | class_definition | 834 | 6,390 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bros/configuration_bros.py | null | 9,015 |
class DetrFeatureExtractor(DetrImageProcessor):
def __init__(self, *args, **kwargs) -> None:
warnings.warn(
"The class DetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
" Please use DetrImageProcessor instead.",
FutureWarning,
)... | class_definition | 1,111 | 1,473 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/detr/feature_extraction_detr.py | null | 9,016 |
class DetrImageProcessorFast(BaseImageProcessorFast):
r"""
Constructs a fast Detr image processor.
Args:
format (`str`, *optional*, defaults to `AnnotationFormat.COCO_DETECTION`):
Data format of the annotations. One of "coco_detection" or "coco_panoptic".
do_resize (`bool`, *opt... | class_definition | 9,927 | 72,621 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/detr/image_processing_detr_fast.py | null | 9,017 |
class DetrDecoderOutput(BaseModelOutputWithCrossAttentions):
"""
Base class for outputs of the DETR decoder. This class adds one attribute to BaseModelOutputWithCrossAttentions,
namely an optional stack of intermediate decoder activations, i.e. the output of each decoder layer, each of them
gone through... | class_definition | 1,556 | 3,929 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/detr/modeling_detr.py | null | 9,018 |
class DetrModelOutput(Seq2SeqModelOutput):
"""
Base class for outputs of the DETR encoder-decoder model. This class adds one attribute to Seq2SeqModelOutput,
namely an optional stack of intermediate decoder activations, i.e. the output of each decoder layer, each of them
gone through a layernorm. This i... | class_definition | 3,943 | 7,433 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/detr/modeling_detr.py | null | 9,019 |
class DetrObjectDetectionOutput(ModelOutput):
"""
Output type of [`DetrForObjectDetection`].
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` are provided)):
Total loss as a linear combination of a negative log-likehood (cross-entropy) for class predic... | class_definition | 7,447 | 12,396 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/detr/modeling_detr.py | null | 9,020 |
class DetrSegmentationOutput(ModelOutput):
"""
Output type of [`DetrForSegmentation`].
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` are provided)):
Total loss as a linear combination of a negative log-likehood (cross-entropy) for class prediction a... | class_definition | 12,410 | 17,862 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/detr/modeling_detr.py | null | 9,021 |
class DetrFrozenBatchNorm2d(nn.Module):
"""
BatchNorm2d where the batch statistics and the affine parameters are fixed.
Copy-paste from torchvision.misc.ops with added eps before rqsrt, without which any other models than
torchvision.models.resnet[18,34,50,101] produce nans.
"""
def __init__(s... | class_definition | 17,963 | 19,475 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/detr/modeling_detr.py | null | 9,022 |
class DetrConvEncoder(nn.Module):
"""
Convolutional backbone, using either the AutoBackbone API or one from the timm library.
nn.BatchNorm2d layers are replaced by DetrFrozenBatchNorm2d as defined above.
"""
def __init__(self, config):
super().__init__()
self.config = config
... | class_definition | 20,296 | 23,451 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/detr/modeling_detr.py | null | 9,023 |
class DetrConvModel(nn.Module):
"""
This module adds 2D position embeddings to all intermediate feature maps of the convolutional encoder.
"""
def __init__(self, conv_encoder, position_embedding):
super().__init__()
self.conv_encoder = conv_encoder
self.position_embedding = posi... | class_definition | 23,454 | 24,205 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/detr/modeling_detr.py | null | 9,024 |
class DetrSinePositionEmbedding(nn.Module):
"""
This is a more standard version of the position embedding, very similar to the one used by the Attention is all you
need paper, generalized to work on images.
"""
def __init__(self, embedding_dim=64, temperature=10000, normalize=False, scale=None):
... | class_definition | 24,208 | 25,912 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/detr/modeling_detr.py | null | 9,025 |
class DetrLearnedPositionEmbedding(nn.Module):
"""
This module learns positional embeddings up to a fixed maximum size.
"""
def __init__(self, embedding_dim=256):
super().__init__()
self.row_embeddings = nn.Embedding(50, embedding_dim)
self.column_embeddings = nn.Embedding(50, e... | class_definition | 25,915 | 26,856 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/detr/modeling_detr.py | null | 9,026 |
class DetrAttention(nn.Module):
"""
Multi-headed attention from 'Attention Is All You Need' paper.
Here, we add position embeddings to the queries and keys (as explained in the DETR paper).
"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0... | class_definition | 27,360 | 33,235 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/detr/modeling_detr.py | null | 9,027 |
class DetrEncoderLayer(nn.Module):
def __init__(self, config: DetrConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = DetrAttention(
embed_dim=self.embed_dim,
num_heads=config.encoder_attention_heads,
dropout=config.attention_dropo... | class_definition | 33,238 | 36,301 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/detr/modeling_detr.py | null | 9,028 |
class DetrDecoderLayer(nn.Module):
def __init__(self, config: DetrConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = DetrAttention(
embed_dim=self.embed_dim,
num_heads=config.decoder_attention_heads,
dropout=config.attention_drop... | class_definition | 36,304 | 41,008 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/detr/modeling_detr.py | null | 9,029 |
class DetrPreTrainedModel(PreTrainedModel):
config_class = DetrConfig
base_model_prefix = "model"
main_input_name = "pixel_values"
_no_split_modules = [r"DetrConvEncoder", r"DetrEncoderLayer", r"DetrDecoderLayer"]
def _init_weights(self, module):
std = self.config.init_std
xavier_st... | class_definition | 41,011 | 42,437 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/detr/modeling_detr.py | null | 9,030 |
class DetrEncoder(DetrPreTrainedModel):
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`DetrEncoderLayer`].
The encoder updates the flattened feature map through multiple self-attention layers.
Small tweak for DETR:
- object_queries are a... | class_definition | 45,747 | 50,553 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/detr/modeling_detr.py | null | 9,031 |
class DetrDecoder(DetrPreTrainedModel):
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`DetrDecoderLayer`].
The decoder updates the query embeddings through multiple self-attention and cross-attention layers.
Some small tweaks for DETR:
- object_queries and... | class_definition | 50,556 | 58,438 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/detr/modeling_detr.py | null | 9,032 |
class DetrModel(DetrPreTrainedModel):
def __init__(self, config: DetrConfig):
super().__init__(config)
# Create backbone + positional encoding
backbone = DetrConvEncoder(config)
object_queries = build_position_encoding(config)
self.backbone = DetrConvModel(backbone, object_q... | class_definition | 58,659 | 65,858 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/detr/modeling_detr.py | null | 9,033 |
class DetrMLPPredictionHead(nn.Module):
"""
Very simple multi-layer perceptron (MLP, also called FFN), used to predict the normalized center coordinates,
height and width of a bounding box w.r.t. an image.
Copied from https://github.com/facebookresearch/detr/blob/master/models/detr.py
"""
def... | class_definition | 65,942 | 66,715 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/detr/modeling_detr.py | null | 9,034 |
class DetrForObjectDetection(DetrPreTrainedModel):
def __init__(self, config: DetrConfig):
super().__init__(config)
# DETR encoder-decoder model
self.model = DetrModel(config)
# Object detection heads
self.class_labels_classifier = nn.Linear(
config.d_model, con... | class_definition | 66,934 | 73,133 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/detr/modeling_detr.py | null | 9,035 |
class DetrForSegmentation(DetrPreTrainedModel):
def __init__(self, config: DetrConfig):
super().__init__(config)
# object detection model
self.detr = DetrForObjectDetection(config)
# segmentation head
hidden_size, number_of_heads = config.d_model, config.encoder_attention_h... | class_definition | 73,349 | 83,217 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/detr/modeling_detr.py | null | 9,036 |
class DetrMaskHeadSmallConv(nn.Module):
"""
Simple convolutional head, using group norm. Upsampling is done using a FPN approach
"""
def __init__(self, dim, fpn_dims, context_dim):
super().__init__()
if dim % 8 != 0:
raise ValueError(
"The hidden_size + numb... | class_definition | 83,422 | 86,765 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/detr/modeling_detr.py | null | 9,037 |
class DetrMHAttentionMap(nn.Module):
"""This is a 2D attention module, which only returns the attention softmax (no multiplication by value)"""
def __init__(self, query_dim, hidden_dim, num_heads, dropout=0.0, bias=True, std=None):
super().__init__()
self.num_heads = num_heads
self.hidd... | class_definition | 86,768 | 88,172 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/detr/modeling_detr.py | null | 9,038 |
class DetrImageProcessor(BaseImageProcessor):
r"""
Constructs a Detr image processor.
Args:
format (`str`, *optional*, defaults to `"coco_detection"`):
Data format of the annotations. One of "coco_detection" or "coco_panoptic".
do_resize (`bool`, *optional*, defaults to `True`):... | class_definition | 28,739 | 93,998 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/detr/image_processing_detr.py | null | 9,039 |
class DetrConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`DetrModel`]. It is used to instantiate a DETR
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar confi... | class_definition | 1,035 | 12,965 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/detr/configuration_detr.py | null | 9,040 |
class DetrOnnxConfig(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"}),
("pixel_m... | class_definition | 12,968 | 13,485 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/detr/configuration_detr.py | null | 9,041 |
class BartphoTokenizer(PreTrainedTokenizer):
"""
Adapted from [`XLMRobertaTokenizer`]. 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 informat... | class_definition | 1,063 | 13,522 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bartpho/tokenization_bartpho.py | null | 9,042 |
class MraConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`MraModel`]. It is used to instantiate an MRA
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configu... | class_definition | 780 | 6,509 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mra/configuration_mra.py | null | 9,043 |
class MraSampledDenseMatMul(torch.autograd.Function):
@staticmethod
def forward(ctx, dense_query, dense_key, indices, block_size):
sparse_qk_prod = mm_to_sparse(dense_query, dense_key, indices, block_size)
ctx.save_for_backward(dense_query, dense_key, indices)
ctx.block_size = block_size... | class_definition | 7,228 | 8,321 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mra/modeling_mra.py | null | 9,044 |
class MraSparseDenseMatMul(torch.autograd.Function):
@staticmethod
def forward(ctx, sparse_query, indices, dense_key, query_num_block):
sparse_qk_prod = sparse_dense_mm(sparse_query, indices, dense_key, query_num_block)
ctx.save_for_backward(sparse_query, indices, dense_key)
ctx.query_nu... | class_definition | 8,324 | 9,393 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mra/modeling_mra.py | null | 9,045 |
class MraReduceSum:
@staticmethod
def operator_call(sparse_query, indices, query_num_block, key_num_block):
batch_size, num_block, block_size, _ = sparse_query.size()
if len(sparse_query.size()) != 4:
raise ValueError("sparse_query must be a 4-dimensional tensor.")
if len(i... | class_definition | 9,396 | 10,628 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mra/modeling_mra.py | null | 9,046 |
class MraEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.position_embeddin... | class_definition | 18,536 | 21,461 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mra/modeling_mra.py | null | 9,047 |
class MraSelfAttention(nn.Module):
def __init__(self, config, position_embedding_type=None):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size ({config.hidden_size}) is... | class_definition | 21,464 | 25,721 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mra/modeling_mra.py | null | 9,048 |
class MraSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def fo... | class_definition | 25,792 | 26,397 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mra/modeling_mra.py | null | 9,049 |
class MraAttention(nn.Module):
def __init__(self, config, position_embedding_type=None):
super().__init__()
self.self = MraSelfAttention(config, position_embedding_type=position_embedding_type)
self.output = MraSelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, h... | class_definition | 26,400 | 27,856 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mra/modeling_mra.py | null | 9,050 |
class MraIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.interme... | class_definition | 27,929 | 28,493 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mra/modeling_mra.py | null | 9,051 |
class MraOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def ... | class_definition | 28,560 | 29,167 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mra/modeling_mra.py | null | 9,052 |
class MraLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = MraAttention(config)
self.add_cross_attention = config.add_cross_attention
self.intermediate =... | class_definition | 29,170 | 30,298 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mra/modeling_mra.py | null | 9,053 |
class MraEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([MraLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
hidden_states,
... | class_definition | 30,301 | 31,738 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mra/modeling_mra.py | null | 9,054 |
class MraPredictionHeadTransform(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
if isinstance(config.hidden_act, str):
self.transform_act_fn = ACT2FN[config.hidden_act]
else:
self.trans... | class_definition | 31,822 | 32,521 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mra/modeling_mra.py | null | 9,055 |
class MraLMPredictionHead(nn.Module):
def __init__(self, config):
super().__init__()
self.transform = MraPredictionHeadTransform(config)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.decoder = nn.Linear(con... | class_definition | 32,613 | 33,443 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mra/modeling_mra.py | null | 9,056 |
class MraOnlyMLMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = MraLMPredictionHead(config)
def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
prediction_scores = self.predictions(sequence_output)
return prediction_scores | class_definition | 33,530 | 33,842 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mra/modeling_mra.py | null | 9,057 |
class MraPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = MraConfig
base_model_prefix = "mra"
supports_gradient_checkpointing = True
def _init_weights(self, ... | class_definition | 33,943 | 35,044 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mra/modeling_mra.py | null | 9,058 |
class MraModel(MraPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.embeddings = MraEmbeddings(config)
self.encoder = MraEncoder(config)
# Initialize weights and apply final processing
self.post_init()
def get_inpu... | class_definition | 38,255 | 42,939 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mra/modeling_mra.py | null | 9,059 |
class MraForMaskedLM(MraPreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder.weight", "cls.predictions.decoder.bias"]
def __init__(self, config):
super().__init__(config)
self.mra = MraModel(config)
self.cls = MraOnlyMLMHead(config)
# Initialize weights and apply f... | class_definition | 43,042 | 46,031 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mra/modeling_mra.py | null | 9,060 |
class MraClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.out_proj = nn.Line... | class_definition | 46,125 | 46,800 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mra/modeling_mra.py | null | 9,061 |
class MraForSequenceClassification(MraPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.mra = MraModel(config)
self.classifier = MraClassificationHead(config)
# Initialize weights and apply final processing
... | class_definition | 47,010 | 50,579 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mra/modeling_mra.py | null | 9,062 |
class MraForMultipleChoice(MraPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.mra = MraModel(config)
self.pre_classifier = nn.Linear(config.hidden_size, config.hidden_size)
self.classifier = nn.Linear(config.hidden_size, 1)
# Initialize weights a... | class_definition | 50,798 | 54,400 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mra/modeling_mra.py | null | 9,063 |
class MraForTokenClassification(MraPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.mra = MraModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, con... | class_definition | 54,617 | 57,636 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mra/modeling_mra.py | null | 9,064 |
class MraForQuestionAnswering(MraPreTrainedModel):
def __init__(self, config):
super().__init__(config)
config.num_labels = 2
self.num_labels = config.num_labels
self.mra = MraModel(config)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
# Initia... | class_definition | 57,911 | 61,974 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mra/modeling_mra.py | null | 9,065 |
class LlavaCausalLMOutputWithPast(ModelOutput):
"""
Base class for Llava 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).
logit... | class_definition | 1,355 | 3,941 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava/modeling_llava.py | null | 9,066 |
class LlavaMultiModalProjector(nn.Module):
def __init__(self, config: LlavaConfig):
super().__init__()
self.linear_1 = nn.Linear(
config.vision_config.hidden_size, config.text_config.hidden_size, bias=config.multimodal_projector_bias
)
self.act = ACT2FN[config.projector_h... | class_definition | 3,944 | 4,657 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava/modeling_llava.py | null | 9,067 |
class LlavaPreTrainedModel(PreTrainedModel):
config_class = LlavaConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["LlavaVisionAttention"]
_skip_keys_device_placement = "past_key_values"
_supports_cache_class = True
_supports_flash_attn_2 = True
... | class_definition | 5,704 | 7,094 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava/modeling_llava.py | null | 9,068 |
class LlavaForConditionalGeneration(LlavaPreTrainedModel, GenerationMixin):
def __init__(self, config: LlavaConfig):
super().__init__(config)
self.vision_tower = AutoModel.from_config(config.vision_config)
self.multi_modal_projector = LlavaMultiModalProjector(config)
self.vocab_size... | class_definition | 12,287 | 28,693 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava/modeling_llava.py | null | 9,069 |
class LlavaProcessorKwargs(ProcessingKwargs, total=False):
_defaults = {
"text_kwargs": {
"padding": False,
},
"images_kwargs": {},
} | class_definition | 1,043 | 1,220 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava/processing_llava.py | null | 9,070 |
class LlavaProcessor(ProcessorMixin):
r"""
Constructs a Llava processor which wraps a Llava image processor and a Llava tokenizer into a single processor.
[`LlavaProcessor`] offers all the functionalities of [`CLIPImageProcessor`] and [`LlamaTokenizerFast`]. See the
[`~LlavaProcessor.__call__`] and [`~... | class_definition | 1,223 | 9,271 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava/processing_llava.py | null | 9,071 |
class LlavaConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`LlavaForConditionalGeneration`]. It is used to instantiate an
Llava model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults wil... | class_definition | 883 | 5,758 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava/configuration_llava.py | null | 9,072 |
class PerceiverModelOutput(ModelOutput):
"""
Base class for Perceiver base model's outputs, with potential hidden states, attentions and cross-attentions.
Args:
logits (`torch.FloatTensor` of shape `(batch_size, num_labels)`):
Classification (or regression if config.num_labels==1) score... | class_definition | 1,863 | 4,000 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/perceiver/modeling_perceiver.py | null | 9,073 |
class PerceiverDecoderOutput(ModelOutput):
"""
Base class for Perceiver decoder outputs, with potential cross-attentions.
Args:
logits (`torch.FloatTensor` of shape `(batch_size, num_labels)`):
Output of the basic decoder.
cross_attentions (`tuple(torch.FloatTensor)`, *optional*... | class_definition | 4,014 | 4,847 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/perceiver/modeling_perceiver.py | null | 9,074 |
class PerceiverMaskedLMOutput(ModelOutput):
"""
Base class for Perceiver's masked language model outputs.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Masked language modeling (MLM) loss.
logits (`torch.FloatTensor` of shape `... | class_definition | 4,861 | 6,943 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/perceiver/modeling_perceiver.py | null | 9,075 |
class PerceiverClassifierOutput(ModelOutput):
"""
Base class for Perceiver's outputs of sequence/image classification models, optical flow and multimodal
autoencoding.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (o... | class_definition | 6,957 | 9,101 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/perceiver/modeling_perceiver.py | null | 9,076 |
class PerceiverEmbeddings(nn.Module):
"""Construct the latent embeddings."""
def __init__(self, config):
super().__init__()
self.latents = nn.Parameter(torch.randn(config.num_latents, config.d_latents))
def forward(self, batch_size: int):
return self.latents.expand(batch_size, -1, ... | class_definition | 9,104 | 9,448 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/perceiver/modeling_perceiver.py | null | 9,077 |
class PerceiverSelfAttention(nn.Module):
"""Multi-headed {cross, self}-attention. Can be used both in the encoder as well as in the decoder."""
def __init__(
self,
config,
is_cross_attention=False,
qk_channels=None,
v_channels=None,
num_heads=1,
q_dim=Non... | class_definition | 9,451 | 14,337 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/perceiver/modeling_perceiver.py | null | 9,078 |
class PerceiverSelfOutput(nn.Module):
def __init__(self, config, input_channels, output_channels):
super().__init__()
self.dense = nn.Linear(input_channels, output_channels)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
... | class_definition | 14,340 | 14,681 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/perceiver/modeling_perceiver.py | null | 9,079 |
class PerceiverAttention(nn.Module):
"""Attention module, including a dense block."""
def __init__(
self,
config,
is_cross_attention=False,
qk_channels=None,
v_channels=None,
num_heads=1,
q_dim=None,
kv_dim=None,
use_query_residual=True,
... | class_definition | 14,684 | 18,366 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/perceiver/modeling_perceiver.py | null | 9,080 |
class PerceiverMLP(nn.Module):
"""A Transformer-style dense module to follow attention."""
def __init__(self, config, input_size, widening_factor):
super().__init__()
self.dense1 = nn.Linear(input_size, widening_factor * input_size)
if isinstance(config.hidden_act, str):
sel... | class_definition | 18,369 | 19,147 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/perceiver/modeling_perceiver.py | null | 9,081 |
class PerceiverLayer(nn.Module):
def __init__(
self,
config,
is_cross_attention=False,
qk_channels=None,
v_channels=None,
num_heads=1,
q_dim=None,
kv_dim=None,
widening_factor=4,
use_query_residual=True,
):
super().__init__(... | class_definition | 19,150 | 21,254 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/perceiver/modeling_perceiver.py | null | 9,082 |
class PerceiverEncoder(nn.Module):
"""The Perceiver Encoder: a scalable, fully attentional encoder."""
def __init__(self, config, kv_dim=None):
super().__init__()
self.config = config
# Check that we can use multihead-attention with these shapes.
if config.d_latents % config.nu... | class_definition | 21,257 | 25,921 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/perceiver/modeling_perceiver.py | null | 9,083 |
class PerceiverPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = PerceiverConfig
base_model_prefix = "perceiver"
main_input_name = "inputs"
def _init_weights(... | class_definition | 25,924 | 27,564 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/perceiver/modeling_perceiver.py | null | 9,084 |
class PerceiverModel(PerceiverPreTrainedModel):
def __init__(
self,
config,
decoder=None,
input_preprocessor: PreprocessorType = None,
output_postprocessor: PostprocessorType = None,
):
super().__init__(config)
self.config = config
self.input_prep... | class_definition | 32,436 | 42,407 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/perceiver/modeling_perceiver.py | null | 9,085 |
class PerceiverForMaskedLM(PerceiverPreTrainedModel):
def __init__(self, config: PerceiverConfig):
super().__init__(config)
text_preprocessor = PerceiverTextPreprocessor(config)
trainable_position_encoding_kwargs_decoder = {
"num_channels": text_preprocessor.num_channels,
... | class_definition | 42,521 | 48,019 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/perceiver/modeling_perceiver.py | null | 9,086 |
class PerceiverForSequenceClassification(PerceiverPreTrainedModel):
def __init__(self, config):
super().__init__(config)
trainable_position_encoding_kwargs_decoder = {"num_channels": config.d_latents, "index_dims": 1}
self.num_labels = config.num_labels
self.perceiver = PerceiverMo... | class_definition | 48,128 | 52,756 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/perceiver/modeling_perceiver.py | null | 9,087 |
class PerceiverForImageClassificationLearned(PerceiverPreTrainedModel):
def __init__(self, config):
super().__init__(config)
trainable_position_encoding_kwargs_preprocessor = {"num_channels": 256, "index_dims": config.image_size**2}
trainable_position_encoding_kwargs_decoder = {"num_channel... | class_definition | 53,475 | 59,202 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/perceiver/modeling_perceiver.py | null | 9,088 |
class PerceiverForImageClassificationFourier(PerceiverPreTrainedModel):
def __init__(self, config):
super().__init__(config)
fourier_position_encoding_kwargs_preprocessor = {
"concat_pos": True,
"max_resolution": (224, 224),
"num_bands": 64,
"sine_onl... | class_definition | 59,894 | 65,424 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/perceiver/modeling_perceiver.py | null | 9,089 |
class PerceiverForImageClassificationConvProcessing(PerceiverPreTrainedModel):
def __init__(self, config):
super().__init__(config)
fourier_position_encoding_kwargs_preprocessor = {
"concat_pos": True,
"max_resolution": (56, 56),
"num_bands": 64,
"sin... | class_definition | 66,054 | 71,645 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/perceiver/modeling_perceiver.py | null | 9,090 |
class PerceiverForOpticalFlow(PerceiverPreTrainedModel):
def __init__(self, config):
super().__init__(config)
fourier_position_encoding_kwargs_preprocessor = {
"num_bands": 64,
"max_resolution": config.train_size,
"sine_only": False,
"concat_pos": Tru... | class_definition | 72,492 | 76,923 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/perceiver/modeling_perceiver.py | null | 9,091 |
class PerceiverForMultimodalAutoencoding(PerceiverPreTrainedModel):
def __init__(self, config: PerceiverConfig):
super().__init__(config)
n_audio_samples = config.num_frames * config.audio_samples_per_frame
input_preprocessor = PerceiverMultimodalPreprocessor(
min_padding_size=... | class_definition | 79,000 | 87,668 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/perceiver/modeling_perceiver.py | null | 9,092 |
class PerceiverAbstractDecoder(nn.Module, metaclass=abc.ABCMeta):
"""Perceiver abstract decoder."""
@abc.abstractmethod
def decoder_query(self, inputs, modality_sizes=None, inputs_without_pos=None, subsampled_points=None):
raise NotImplementedError
@property
@abc.abstractmethod
def num... | class_definition | 89,137 | 89,622 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/perceiver/modeling_perceiver.py | null | 9,093 |
class PerceiverProjectionDecoder(PerceiverAbstractDecoder):
"""
Baseline projection decoder (no cross-attention).
Args:
config ([`PerceiverConfig`]):
Model configuration.
"""
def __init__(self, config):
super().__init__()
self.classifier = nn.Linear(config.d_lat... | class_definition | 89,625 | 90,481 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/perceiver/modeling_perceiver.py | null | 9,094 |
class PerceiverBasicDecoder(PerceiverAbstractDecoder):
"""
Cross-attention-based decoder. This class can be used to decode the final hidden states of the latents using a
cross-attention operation, in which the latents produce keys and values.
The shape of the output of this class depends on how one def... | class_definition | 90,484 | 99,636 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/perceiver/modeling_perceiver.py | null | 9,095 |
class PerceiverClassificationDecoder(PerceiverAbstractDecoder):
"""
Cross-attention based classification decoder. Light-weight wrapper of [`PerceiverBasicDecoder`] for logit output.
Will turn the output of the Perceiver encoder which is of shape (batch_size, num_latents, d_latents) to a tensor of
shape ... | class_definition | 99,639 | 101,349 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/perceiver/modeling_perceiver.py | null | 9,096 |
class PerceiverOpticalFlowDecoder(PerceiverAbstractDecoder):
"""Cross-attention based optical flow decoder."""
def __init__(self, config, output_image_shape, output_num_channels=2, rescale_factor=100.0, **decoder_kwargs):
super().__init__()
self.output_image_shape = output_image_shape
... | class_definition | 101,352 | 102,858 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/perceiver/modeling_perceiver.py | null | 9,097 |
class PerceiverBasicVideoAutoencodingDecoder(PerceiverAbstractDecoder):
"""
Cross-attention based video-autoencoding decoder. Light-weight wrapper of [*PerceiverBasicDecoder*] with video
reshaping logic.
Args:
config ([*PerceiverConfig*]):
Model configuration.
output_shape (... | class_definition | 102,861 | 104,991 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/perceiver/modeling_perceiver.py | null | 9,098 |
class PerceiverMultimodalDecoder(PerceiverAbstractDecoder):
"""
Multimodal decoding by composing uni-modal decoders. The *modalities* argument of the constructor is a dictionary
mapping modality name to the decoder of that modality. That decoder will be used to construct queries for that
modality. Modal... | class_definition | 105,681 | 110,476 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/perceiver/modeling_perceiver.py | null | 9,099 |
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