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
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|---|---|---|---|---|---|---|---|
class RTDetrMultiscaleDeformableAttention(nn.Module):
"""
Multiscale deformable attention as proposed in Deformable DETR.
"""
def __init__(self, config: RTDetrConfig, num_heads: int, n_points: int):
super().__init__()
kernel_loaded = MultiScaleDeformableAttention is not None
if... | class_definition | 39,325 | 44,990 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rt_detr/modeling_rt_detr.py | null | 4,700 |
class RTDetrMultiheadAttention(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 Deformable DETR paper).
"""
def __init__(
self,
embed_dim: int,
num_heads: int,
... | class_definition | 44,993 | 50,292 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rt_detr/modeling_rt_detr.py | null | 4,701 |
class RTDetrDecoderLayer(nn.Module):
def __init__(self, config: RTDetrConfig):
super().__init__()
# self-attention
self.self_attn = RTDetrMultiheadAttention(
embed_dim=config.d_model,
num_heads=config.decoder_attention_heads,
dropout=config.attention_dropo... | class_definition | 50,295 | 55,104 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rt_detr/modeling_rt_detr.py | null | 4,702 |
class RTDetrPreTrainedModel(PreTrainedModel):
config_class = RTDetrConfig
base_model_prefix = "rt_detr"
main_input_name = "pixel_values"
_no_split_modules = [r"RTDetrConvEncoder", r"RTDetrEncoderLayer", r"RTDetrDecoderLayer"]
def _init_weights(self, module):
"""Initalize the weights"""
... | class_definition | 55,107 | 58,351 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rt_detr/modeling_rt_detr.py | null | 4,703 |
class RTDetrEncoder(nn.Module):
def __init__(self, config: RTDetrConfig):
super().__init__()
self.layers = nn.ModuleList([RTDetrEncoderLayer(config) for _ in range(config.encoder_layers)])
def forward(self, src, src_mask=None, pos_embed=None, output_attentions: bool = False) -> torch.Tensor:
... | class_definition | 62,035 | 62,665 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rt_detr/modeling_rt_detr.py | null | 4,704 |
class RTDetrHybridEncoder(nn.Module):
"""
Decoder consisting of a projection layer, a set of `RTDetrEncoder`, a top-down Feature Pyramid Network
(FPN) and a bottom-up Path Aggregation Network (PAN). More details on the paper: https://arxiv.org/abs/2304.08069
Args:
config: RTDetrConfig
"""
... | class_definition | 62,668 | 71,165 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rt_detr/modeling_rt_detr.py | null | 4,705 |
class RTDetrDecoder(RTDetrPreTrainedModel):
def __init__(self, config: RTDetrConfig):
super().__init__(config)
self.dropout = config.dropout
self.layers = nn.ModuleList([RTDetrDecoderLayer(config) for _ in range(config.decoder_layers)])
self.query_pos_head = RTDetrMLPPredictionHead(... | class_definition | 71,168 | 78,701 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rt_detr/modeling_rt_detr.py | null | 4,706 |
class RTDetrMLPPredictionHead(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
Origin from... | class_definition | 79,641 | 80,572 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rt_detr/modeling_rt_detr.py | null | 4,707 |
class RTDetrModel(RTDetrPreTrainedModel):
def __init__(self, config: RTDetrConfig):
super().__init__(config)
# Create backbone
self.backbone = RTDetrConvEncoder(config)
intermediate_channel_sizes = self.backbone.intermediate_channel_sizes
# Create encoder input projection l... | class_definition | 80,764 | 95,533 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rt_detr/modeling_rt_detr.py | null | 4,708 |
class RTDetrForObjectDetection(RTDetrPreTrainedModel):
# When using clones, all layers > 0 will be clones, but layer 0 *is* required
_tied_weights_keys = ["bbox_embed", "class_embed"]
# We can't initialize the model on meta device as some weights are modified during the initialization
_no_split_modules ... | class_definition | 95,759 | 104,554 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rt_detr/modeling_rt_detr.py | null | 4,709 |
class RTDetrResNetConvLayer(nn.Module):
def __init__(
self, in_channels: int, out_channels: int, kernel_size: int = 3, stride: int = 1, activation: str = "relu"
):
super().__init__()
self.convolution = nn.Conv2d(
in_channels, out_channels, kernel_size=kernel_size, stride=stri... | class_definition | 1,797 | 2,548 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rt_detr/modeling_rt_detr_resnet.py | null | 4,710 |
class RTDetrResNetEmbeddings(nn.Module):
"""
ResNet Embeddings (stem) composed of a deep aggressive convolution.
"""
def __init__(self, config: RTDetrResNetConfig):
super().__init__()
self.embedder = nn.Sequential(
*[
RTDetrResNetConvLayer(
... | class_definition | 2,551 | 4,178 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rt_detr/modeling_rt_detr_resnet.py | null | 4,711 |
class RTDetrResNetShortCut(nn.Module):
"""
ResNet 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__()
... | class_definition | 4,277 | 4,934 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rt_detr/modeling_rt_detr_resnet.py | null | 4,712 |
class RTDetrResNetBasicLayer(nn.Module):
"""
A classic ResNet's residual layer composed by two `3x3` convolutions.
See https://github.com/lyuwenyu/RT-DETR/blob/5b628eaa0a2fc25bdafec7e6148d5296b144af85/rtdetr_pytorch/src/nn/backbone/presnet.py#L34.
"""
def __init__(
self,
config: RTD... | class_definition | 4,937 | 6,502 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rt_detr/modeling_rt_detr_resnet.py | null | 4,713 |
class RTDetrResNetBottleNeckLayer(nn.Module):
"""
A classic RTDetrResNet's bottleneck layer composed by three `3x3` convolutions.
The first `1x1` convolution reduces the input by a factor of `reduction` in order to make the second `3x3`
convolution faster. The last `1x1` convolution remaps the reduced ... | class_definition | 6,505 | 8,656 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rt_detr/modeling_rt_detr_resnet.py | null | 4,714 |
class RTDetrResNetStage(nn.Module):
"""
A RTDetrResNet stage composed by stacked layers.
"""
def __init__(
self,
config: RTDetrResNetConfig,
in_channels: int,
out_channels: int,
stride: int = 2,
depth: int = 2,
):
super().__init__()
l... | class_definition | 8,659 | 9,740 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rt_detr/modeling_rt_detr_resnet.py | null | 4,715 |
class RTDetrResNetEncoder(nn.Module):
def __init__(self, config: RTDetrResNetConfig):
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(
RTDet... | class_definition | 9,840 | 11,439 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rt_detr/modeling_rt_detr_resnet.py | null | 4,716 |
class RTDetrResNetPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = RTDetrResNetConfig
base_model_prefix = "resnet"
main_input_name = "pixel_values"
_no_split_... | class_definition | 11,547 | 12,704 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rt_detr/modeling_rt_detr_resnet.py | null | 4,717 |
class RTDetrResNetBackbone(RTDetrResNetPreTrainedModel, BackboneMixin):
def __init__(self, config):
super().__init__(config)
super()._init_backbone(config)
self.num_features = [config.embedding_size] + config.hidden_sizes
self.embedder = RTDetrResNetEmbeddings(config)
self.e... | class_definition | 14,099 | 16,382 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rt_detr/modeling_rt_detr_resnet.py | null | 4,718 |
class RTDetrConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`RTDetrModel`]. It is used to instantiate a
RT-DETR model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a simila... | class_definition | 948 | 18,040 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rt_detr/configuration_rt_detr.py | null | 4,719 |
class RTDetrImageProcessorFast(DetrImageProcessorFast, BaseImageProcessorFast):
r"""
Constructs a fast RTDetr image processor.
Args:
format (`str`, *optional*, defaults to `AnnotationFormat.COCO_DETECTION`):
Data format of the annotations. One of "coco_detection" or "coco_panoptic".
... | class_definition | 3,578 | 29,296 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rt_detr/modular_rt_detr.py | null | 4,720 |
class RTDetrImageProcessorFast(BaseImageProcessorFast):
r"""
Constructs a fast RTDetr 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`, ... | class_definition | 4,460 | 38,541 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rt_detr/image_processing_rt_detr_fast.py | null | 4,721 |
class JetMoeParallelExperts(nn.Module):
def __init__(self, num_experts: int, input_size: int, output_size: int) -> None:
"""
Initialize the JetMoeParallelExperts module.
The experts weights are stored in [num_experts, output_size, input_size] format. Such that it's comptible with
man... | class_definition | 5,356 | 7,013 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/jetmoe/modeling_jetmoe.py | null | 4,722 |
class JetMoeTopKGating(nn.Module):
def __init__(self, input_size: int, num_experts: int, top_k: int):
"""
Initialize the top-k gating mechanism.
Args:
input_size (`int`):
Size of the input.
num_experts (`int`):
Number of experts.
... | class_definition | 7,016 | 9,017 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/jetmoe/modeling_jetmoe.py | null | 4,723 |
class JetMoeMoE(nn.Module):
"""
A Sparsely gated mixture of experts layer with 1-layer Feed-Forward networks as experts.
Args:
config:
Configuration object with model hyperparameters.
"""
def __init__(self, config: JetMoeConfig):
super(JetMoeMoE, self).__init__()
... | class_definition | 9,020 | 11,252 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/jetmoe/modeling_jetmoe.py | null | 4,724 |
class JetMoeMoA(nn.Module):
"""
A Sparsely gated mixture of attention layer with pairs of query- and output-projections as experts.
Args:
config:
Configuration object with model hyperparameters.
"""
def __init__(self, config: JetMoeConfig):
super(JetMoeMoA, self).__init... | class_definition | 11,255 | 14,812 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/jetmoe/modeling_jetmoe.py | null | 4,725 |
class JetMoeRMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
JetMoeRMSNorm 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 | 14,902 | 15,624 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/jetmoe/modeling_jetmoe.py | null | 4,726 |
class JetMoeRotaryEmbedding(nn.Module):
def __init__(self, config: JetMoeConfig, device=None):
super().__init__()
# BC: "rope_type" was originally "type"
if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
self.rope_type = config.rope_scaling.get("rope_type", ... | class_definition | 15,722 | 18,919 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/jetmoe/modeling_jetmoe.py | null | 4,727 |
class JetMoeAttention(nn.Module):
"""
Multi-headed attention from 'Attention Is All You Need' paper.
"""
def __init__(self, config: JetMoeConfig, layer_idx: Optional[int] = None):
"""
Initialize the JetMoeAttention module.
Args:
config:
Configuration... | class_definition | 20,790 | 25,224 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/jetmoe/modeling_jetmoe.py | null | 4,728 |
class JetMoeSdpaAttention(JetMoeAttention):
"""
JetMoe attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
`JetMoeAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
SDPA API.
"""
# Adapted ... | class_definition | 25,227 | 29,840 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/jetmoe/modeling_jetmoe.py | null | 4,729 |
class JetMoeFlashAttention2(JetMoeAttention):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
# TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
# flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right a... | class_definition | 29,843 | 35,715 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/jetmoe/modeling_jetmoe.py | null | 4,730 |
class JetMoeBlock(nn.Module):
def __init__(self, config: JetMoeConfig, layer_idx: Optional[int] = None):
"""
Initialize the JetMoeBlock module.
Args:
config:
Configuration object with model hyperparameters.
"""
super().__init__()
self.inpu... | class_definition | 35,862 | 37,948 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/jetmoe/modeling_jetmoe.py | null | 4,731 |
class JetMoePreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = JetMoeConfig
base_model_prefix = "transformer"
supports_gradient_checkpointing = False
_no_split_... | class_definition | 37,951 | 39,566 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/jetmoe/modeling_jetmoe.py | null | 4,732 |
class JetMoeModel(JetMoePreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`JetMoeBlock`]
Args:
config:
JetMoeConfig
"""
def __init__(self, config: JetMoeConfig):
super().__init__(config)
self.padding_idx ... | class_definition | 42,858 | 56,238 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/jetmoe/modeling_jetmoe.py | null | 4,733 |
class JetMoeForCausalLM(JetMoePreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
super().__init__(config)
self.model = JetMoeModel(config)
self.vocab_size = config.vocab_size
self.aux_loss_coef = config.aux_loss_coef
se... | class_definition | 56,241 | 62,443 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/jetmoe/modeling_jetmoe.py | null | 4,734 |
class JetMoeForSequenceClassification(JetMoePreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = JetMoeModel(config)
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
# Initialize weight... | class_definition | 63,359 | 67,175 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/jetmoe/modeling_jetmoe.py | null | 4,735 |
class JetMoeConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`JetMoeModel`]. It is used to instantiate a
JetMoe model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a configu... | class_definition | 797 | 6,774 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/jetmoe/configuration_jetmoe.py | null | 4,736 |
class LxmertConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`LxmertModel`] or a [`TFLxmertModel`]. It is used
to instantiate a LXMERT model according to the specified arguments, defining the model architecture. Instantiating
a configuration with the defaul... | class_definition | 758 | 8,904 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lxmert/configuration_lxmert.py | null | 4,737 |
class GeLU(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
return gelu(x) | class_definition | 1,311 | 1,434 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lxmert/modeling_lxmert.py | null | 4,738 |
class LxmertModelOutput(ModelOutput):
"""
Lxmert's outputs that contain the last hidden states, pooled outputs, and attention probabilities for the language,
visual, and, cross-modality encoders. (note: the visual encoder in Lxmert is referred to as the "relation-ship"
encoder")
Args:
lang... | class_definition | 1,448 | 4,926 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lxmert/modeling_lxmert.py | null | 4,739 |
class LxmertForQuestionAnsweringOutput(ModelOutput):
"""
Output type of [`LxmertForQuestionAnswering`].
Args:
loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
Total loss as the sum of the masked language modeling loss and the next sequence pred... | class_definition | 4,940 | 7,945 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lxmert/modeling_lxmert.py | null | 4,740 |
class LxmertForPreTrainingOutput(ModelOutput):
"""
Output type of [`LxmertForPreTraining`].
Args:
loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
Total loss as the sum of the masked language modeling loss and the next sequence prediction
... | class_definition | 7,959 | 11,515 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lxmert/modeling_lxmert.py | null | 4,741 |
class LxmertEmbeddings(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=0)
self.position_embeddings = nn.Embeddi... | class_definition | 14,482 | 16,330 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lxmert/modeling_lxmert.py | null | 4,742 |
class LxmertAttention(nn.Module):
def __init__(self, config, ctx_dim=None):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
... | class_definition | 16,333 | 19,169 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lxmert/modeling_lxmert.py | null | 4,743 |
class LxmertAttentionOutput(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=1e-12)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(se... | class_definition | 19,172 | 19,725 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lxmert/modeling_lxmert.py | null | 4,744 |
class LxmertCrossAttentionLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.att = LxmertAttention(config)
self.output = LxmertAttentionOutput(config)
def forward(self, input_tensor, ctx_tensor, ctx_att_mask=None, output_attentions=False):
output = self.att(in... | class_definition | 19,728 | 20,379 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lxmert/modeling_lxmert.py | null | 4,745 |
class LxmertSelfAttentionLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.self = LxmertAttention(config)
self.output = LxmertAttentionOutput(config)
def forward(self, input_tensor, attention_mask, output_attentions=False):
# Self attention attends to itself,... | class_definition | 20,382 | 21,177 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lxmert/modeling_lxmert.py | null | 4,746 |
class LxmertIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
self.intermediate_act_fn = ACT2FN[config.hidden_act]
def forward(self, hidden_states):
hidden_states = self.dense(hidden_state... | class_definition | 21,180 | 21,595 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lxmert/modeling_lxmert.py | null | 4,747 |
class LxmertOutput(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=1e-12)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self,... | class_definition | 21,598 | 22,148 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lxmert/modeling_lxmert.py | null | 4,748 |
class LxmertLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.attention = LxmertSelfAttentionLayer(config)
self.intermediate = LxmertIntermediate(config)
self.output = LxmertOutput(config)
def forward(self, hidden_states, attention_mask=None, output_attention... | class_definition | 22,151 | 22,866 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lxmert/modeling_lxmert.py | null | 4,749 |
class LxmertXLayer(nn.Module):
def __init__(self, config):
super().__init__()
# The cross-attention Layer
self.visual_attention = LxmertCrossAttentionLayer(config)
# Self-attention Layers
self.lang_self_att = LxmertSelfAttentionLayer(config)
self.visn_self_att = Lxme... | class_definition | 22,869 | 25,962 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lxmert/modeling_lxmert.py | null | 4,750 |
class LxmertVisualFeatureEncoder(nn.Module):
def __init__(self, config):
super().__init__()
feat_dim = config.visual_feat_dim
pos_dim = config.visual_pos_dim
# Object feature encoding
self.visn_fc = nn.Linear(feat_dim, config.hidden_size)
self.visn_layer_norm = nn.La... | class_definition | 25,965 | 26,840 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lxmert/modeling_lxmert.py | null | 4,751 |
class LxmertEncoder(nn.Module):
def __init__(self, config):
super().__init__()
# Obj-level image embedding layer
self.visn_fc = LxmertVisualFeatureEncoder(config)
self.config = config
# Number of layers
self.num_l_layers = config.l_layers
self.num_x_layers =... | class_definition | 26,843 | 30,179 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lxmert/modeling_lxmert.py | null | 4,752 |
class LxmertPooler(nn.Module):
def __init__(self, config):
super(LxmertPooler, self).__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hidden_states):
# We "pool" the model by simply taking the hidden state co... | class_definition | 30,182 | 30,731 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lxmert/modeling_lxmert.py | null | 4,753 |
class LxmertPredictionHeadTransform(nn.Module):
def __init__(self, config):
super(LxmertPredictionHeadTransform, self).__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.transform_act_fn = ACT2FN[config.hidden_act]
self.LayerNorm = nn.LayerNorm(config.hidd... | class_definition | 30,734 | 31,306 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lxmert/modeling_lxmert.py | null | 4,754 |
class LxmertLMPredictionHead(nn.Module):
def __init__(self, config, lxmert_model_embedding_weights):
super(LxmertLMPredictionHead, self).__init__()
self.transform = LxmertPredictionHeadTransform(config)
# The output weights are the same as the input embeddings, but there is
# an out... | class_definition | 31,309 | 32,163 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lxmert/modeling_lxmert.py | null | 4,755 |
class LxmertVisualAnswerHead(nn.Module):
def __init__(self, config, num_labels):
super().__init__()
hid_dim = config.hidden_size
self.logit_fc = nn.Sequential(
nn.Linear(hid_dim, hid_dim * 2),
GeLU(),
nn.LayerNorm(hid_dim * 2, eps=1e-12),
nn.Li... | class_definition | 32,166 | 32,609 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lxmert/modeling_lxmert.py | null | 4,756 |
class LxmertVisualObjHead(nn.Module):
def __init__(self, config):
super().__init__()
self.transform = LxmertPredictionHeadTransform(config)
# Decide the use of visual losses
visual_losses = {}
if config.visual_obj_loss:
visual_losses["obj"] = {"shape": (-1,), "num... | class_definition | 32,612 | 33,842 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lxmert/modeling_lxmert.py | null | 4,757 |
class LxmertPreTrainingHeads(nn.Module):
def __init__(self, config, lxmert_model_embedding_weights):
super(LxmertPreTrainingHeads, self).__init__()
self.predictions = LxmertLMPredictionHead(config, lxmert_model_embedding_weights)
self.seq_relationship = nn.Linear(config.hidden_size, 2)
... | class_definition | 33,845 | 34,404 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lxmert/modeling_lxmert.py | null | 4,758 |
class LxmertPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = LxmertConfig
load_tf_weights = load_tf_weights_in_lxmert
base_model_prefix = "lxmert"
_supports_p... | class_definition | 34,407 | 35,568 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lxmert/modeling_lxmert.py | null | 4,759 |
class LxmertModel(LxmertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.embeddings = LxmertEmbeddings(config)
self.encoder = LxmertEncoder(config)
self.pooler = LxmertPooler(config)
# Initialize weights and apply final processing
self.post_... | class_definition | 40,260 | 46,587 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lxmert/modeling_lxmert.py | null | 4,760 |
class LxmertForPreTraining(LxmertPreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder.weight"]
def __init__(self, config):
super().__init__(config)
# Configuration
self.config = config
self.num_qa_labels = config.num_qa_labels
self.visual_loss_normalizer = co... | class_definition | 46,709 | 59,388 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lxmert/modeling_lxmert.py | null | 4,761 |
class LxmertForQuestionAnswering(LxmertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
# Configuration
self.config = config
self.num_qa_labels = config.num_qa_labels
self.visual_loss_normalizer = config.visual_loss_normalizer
# Lxmert backbone
... | class_definition | 59,528 | 65,841 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lxmert/modeling_lxmert.py | null | 4,762 |
class LxmertTokenizerFast(PreTrainedTokenizerFast):
r"""
Construct a "fast" Lxmert tokenizer (backed by HuggingFace's *tokenizers* library). Based on WordPiece.
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
refer to this superclass for mo... | class_definition | 1,098 | 7,719 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lxmert/tokenization_lxmert_fast.py | null | 4,763 |
class TFLxmertModelOutput(ModelOutput):
"""
Lxmert's outputs that contain the last hidden states, pooled outputs, and attention probabilities for the language,
visual, and, cross-modality encoders. (note: the visual encoder in Lxmert is referred to as the "relation-ship"
encoder")
Args:
la... | class_definition | 1,604 | 4,892 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lxmert/modeling_tf_lxmert.py | null | 4,764 |
class TFLxmertForPreTrainingOutput(ModelOutput):
"""
Output type of [`LxmertForPreTraining`].
Args:
loss (*optional*, returned when `labels` is provided, `tf.Tensor` of shape `(1,)`):
Total loss as the sum of the masked language modeling loss and the next sequence prediction
... | class_definition | 4,906 | 8,253 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lxmert/modeling_tf_lxmert.py | null | 4,765 |
class TFLxmertVisualFeatureEncoder(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
# Object feature encoding
self.visn_fc = keras.layers.Dense(
config.hidden_size,
kernel_initializer=get_initializer(config.initializer_range),
... | class_definition | 8,256 | 10,432 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lxmert/modeling_tf_lxmert.py | null | 4,766 |
class TFLxmertEmbeddings(keras.layers.Layer):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.config = config
self.hidden_size = config.hidden_size
self.max_position_embeddings =... | class_definition | 10,435 | 13,449 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lxmert/modeling_tf_lxmert.py | null | 4,767 |
class TFLxmertAttention(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "... | class_definition | 13,452 | 17,769 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lxmert/modeling_tf_lxmert.py | null | 4,768 |
class TFLxmertIntermediate(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
config.intermediate_size,
kernel_initializer=get_initializer(config.initializer_range),
name="dense",
)
... | class_definition | 17,772 | 18,769 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lxmert/modeling_tf_lxmert.py | null | 4,769 |
class TFLxmertOutput(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
config.hidden_size,
kernel_initializer=get_initializer(config.initializer_range),
name="dense",
)
self.Laye... | class_definition | 18,772 | 20,031 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lxmert/modeling_tf_lxmert.py | null | 4,770 |
class TFLxmertAttentionOutput(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
config.hidden_size,
kernel_initializer=get_initializer(config.initializer_range),
name="dense",
)
s... | class_definition | 20,034 | 21,304 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lxmert/modeling_tf_lxmert.py | null | 4,771 |
class TFLxmertSelfAttentionLayer(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.self = TFLxmertAttention(config, name="self")
self.attention_output = TFLxmertAttentionOutput(config, name="output")
def call(self, input_tensor, attention_mask, o... | class_definition | 21,307 | 22,522 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lxmert/modeling_tf_lxmert.py | null | 4,772 |
class TFLxmertCrossAttentionLayer(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.att = TFLxmertAttention(config, name="att")
self.attention_output = TFLxmertAttentionOutput(config, name="output")
def call(
self,
input_tensor,
... | class_definition | 22,525 | 23,756 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lxmert/modeling_tf_lxmert.py | null | 4,773 |
class TFLxmertLayer(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.attention = TFLxmertSelfAttentionLayer(config, name="attention")
self.intermediate = TFLxmertIntermediate(config, name="intermediate")
self.transformer_output = TFLxmertOutp... | class_definition | 23,759 | 25,242 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lxmert/modeling_tf_lxmert.py | null | 4,774 |
class TFLxmertXLayer(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.visual_attention = TFLxmertCrossAttentionLayer(config, name="visual_attention")
# Self-attention Layers
self.lang_self_att = TFLxmertSelfAttentionLayer(config, name="lang_... | class_definition | 25,245 | 30,278 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lxmert/modeling_tf_lxmert.py | null | 4,775 |
class TFLxmertEncoder(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.visn_fc = TFLxmertVisualFeatureEncoder(config, name="visn_fc")
# Number of layers
self.num_l_layers = config.l_layers
self.num_x_layers = config.x_layers
... | class_definition | 30,281 | 34,609 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lxmert/modeling_tf_lxmert.py | null | 4,776 |
class TFLxmertMainLayer(keras.layers.Layer):
config_class = LxmertConfig
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.config = config
self.num_l_layers = config.l_layers
self.num_x_layers = config.x_layers
self.num_r_layers = config.r_layers
... | class_definition | 34,632 | 41,226 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lxmert/modeling_tf_lxmert.py | null | 4,777 |
class TFLxmertPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = LxmertConfig
base_model_prefix = "lxmert"
@property
def dummy_inputs(self):
"""
... | class_definition | 41,229 | 42,764 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lxmert/modeling_tf_lxmert.py | null | 4,778 |
class TFLxmertModel(TFLxmertPreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.lxmert = TFLxmertMainLayer(config, name="lxmert")
@unpack_inputs
@add_start_docstrings_to_model_forward(LXMERT_INPUTS_DOCSTRING)
@add_code_sample... | class_definition | 49,461 | 51,186 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lxmert/modeling_tf_lxmert.py | null | 4,779 |
class TFLxmertPooler(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
config.hidden_size,
kernel_initializer=get_initializer(config.initializer_range),
activation="tanh",
name="dense... | class_definition | 51,189 | 52,109 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lxmert/modeling_tf_lxmert.py | null | 4,780 |
class TFLxmertPredictionHeadTransform(keras.layers.Layer):
def __init__(self, config: LxmertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.hidden_size,
kernel_initializer=get_initializer(config.initializer_range),
name... | class_definition | 52,216 | 53,617 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lxmert/modeling_tf_lxmert.py | null | 4,781 |
class TFLxmertLMPredictionHead(keras.layers.Layer):
def __init__(self, config: LxmertConfig, input_embeddings: keras.layers.Layer, **kwargs):
super().__init__(**kwargs)
self.config = config
self.hidden_size = config.hidden_size
self.transform = TFLxmertPredictionHeadTransform(confi... | class_definition | 53,717 | 55,682 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lxmert/modeling_tf_lxmert.py | null | 4,782 |
class TFLxmertMLMHead(keras.layers.Layer):
def __init__(self, config: LxmertConfig, input_embeddings: keras.layers.Layer, **kwargs):
super().__init__(**kwargs)
self.predictions = TFLxmertLMPredictionHead(config, input_embeddings, name="predictions")
def call(self, sequence_output: tf.Tensor) -... | class_definition | 55,773 | 56,482 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lxmert/modeling_tf_lxmert.py | null | 4,783 |
class TFLxmertPreTrainingHeads(keras.layers.Layer):
def __init__(self, config, input_embeddings, **kwargs):
super().__init__(**kwargs)
self.predictions = TFLxmertLMPredictionHead(config, input_embeddings, name="predictions")
self.seq_relationship = keras.layers.Dense(
2,
... | class_definition | 56,485 | 57,663 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lxmert/modeling_tf_lxmert.py | null | 4,784 |
class TFLxmertVisualAnswerHead(keras.layers.Layer):
def __init__(self, config, num_labels, **kwargs):
super().__init__(**kwargs)
hid_dim = config.hidden_size
self.dense = keras.layers.Dense(
hid_dim * 2,
kernel_initializer=get_initializer(config.initializer_range),
... | class_definition | 57,666 | 59,301 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lxmert/modeling_tf_lxmert.py | null | 4,785 |
class TFLxmertVisualObjHead(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.transform = TFLxmertPredictionHeadTransform(config, name="transform")
# Decide the use of visual losses
visual_losses = {}
if config.visual_obj_loss:
... | class_definition | 59,304 | 61,177 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lxmert/modeling_tf_lxmert.py | null | 4,786 |
class TFLxmertForPreTraining(TFLxmertPreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.config = config
self.num_qa_labels = config.num_qa_labels
self.visual_loss_normalizer = config.visual_loss_normalizer
# Use... | class_definition | 61,286 | 72,617 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lxmert/modeling_tf_lxmert.py | null | 4,787 |
class LxmertTokenizer(PreTrainedTokenizer):
r"""
Construct a Lxmert tokenizer. Based on WordPiece.
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
Args:
vocab_f... | class_definition | 1,824 | 12,493 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lxmert/tokenization_lxmert.py | null | 4,788 |
class BasicTokenizer:
"""
Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.).
Args:
do_lower_case (`bool`, *optional*, defaults to `True`):
Whether or not to lowercase the input when tokenizing.
never_split (`Iterable`, *opti... | class_definition | 12,568 | 19,316 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lxmert/tokenization_lxmert.py | null | 4,789 |
class WordpieceTokenizer:
"""Runs WordPiece tokenization."""
def __init__(self, vocab, unk_token, max_input_chars_per_word=100):
self.vocab = vocab
self.unk_token = unk_token
self.max_input_chars_per_word = max_input_chars_per_word
def tokenize(self, text):
"""
Toke... | class_definition | 19,395 | 21,283 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lxmert/tokenization_lxmert.py | null | 4,790 |
class CLIPTextConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`CLIPTextModel`]. It is used to instantiate a CLIP
text encoder according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield ... | class_definition | 1,013 | 6,005 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clip/configuration_clip.py | null | 4,791 |
class CLIPVisionConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`CLIPVisionModel`]. It is used to instantiate a
CLIP vision encoder according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will ... | class_definition | 6,008 | 10,261 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clip/configuration_clip.py | null | 4,792 |
class CLIPConfig(PretrainedConfig):
r"""
[`CLIPConfig`] is the configuration class to store the configuration of a [`CLIPModel`]. It is used to instantiate
a CLIP model according to the specified arguments, defining the text model and vision model configs. Instantiating
a configuration with the defaults... | class_definition | 10,264 | 17,797 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clip/configuration_clip.py | null | 4,793 |
class CLIPOnnxConfig(OnnxConfig):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("input_ids", {0: "batch", 1: "sequence"}),
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
("att... | class_definition | 17,800 | 19,269 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clip/configuration_clip.py | null | 4,794 |
class TFCLIPOutput(ModelOutput):
"""
Args:
loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`):
Contrastive loss for image-text similarity.
logits_per_image:(`tf.Tensor` of shape `(image_batch_size, text_batch_size)`):
The scaled dot prod... | class_definition | 2,731 | 4,578 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clip/modeling_tf_clip.py | null | 4,795 |
class TFCLIPVisionEmbeddings(keras.layers.Layer):
def __init__(self, config: CLIPVisionConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.hidden_size
self.image_size = config.image_size
self.patch_size = config.patch_size
self.num_patches = (self.image_si... | class_definition | 4,581 | 7,479 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clip/modeling_tf_clip.py | null | 4,796 |
class TFCLIPTextEmbeddings(keras.layers.Layer):
def __init__(self, config: CLIPTextConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.hidden_size
self.config = config
def build(self, input_shape: tf.TensorShape = None):
with tf.name_scope("token_embedding"):... | class_definition | 7,482 | 9,618 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clip/modeling_tf_clip.py | null | 4,797 |
class TFCLIPAttention(keras.layers.Layer):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: CLIPConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.hidden_size
self.num_attention_heads = config.num_attention_heads
... | class_definition | 9,621 | 15,306 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clip/modeling_tf_clip.py | null | 4,798 |
class TFCLIPMLP(keras.layers.Layer):
def __init__(self, config: CLIPConfig, **kwargs):
super().__init__(**kwargs)
self.activation_fn = get_tf_activation(config.hidden_act)
factor = config.initializer_factor
in_proj_std = (config.hidden_size**-0.5) * ((2 * config.num_hidden_layers) ... | class_definition | 15,309 | 16,737 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clip/modeling_tf_clip.py | null | 4,799 |
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