text stringlengths 31 243k | type stringclasses 1
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|---|---|---|---|---|---|---|---|
class OriginalOneFormerConfigToProcessorConverter:
def __call__(self, original_config: object, model_repo: str) -> OneFormerProcessor:
model = original_config.MODEL
model_input = original_config.INPUT
dataset_catalog = MetadataCatalog.get(original_config.DATASETS.TEST_PANOPTIC[0])
i... | class_definition | 7,730 | 9,139 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/oneformer/convert_to_hf_oneformer.py | null | 5,100 |
class OriginalOneFormerCheckpointToOursConverter:
def __init__(self, original_model: nn.Module, config: OneFormerConfig):
self.original_model = original_model
self.config = config
def pop_all(self, renamed_keys: List[Tuple[str, str]], dst_state_dict: StateDict, src_state_dict: StateDict):
... | class_definition | 9,142 | 41,080 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/oneformer/convert_to_hf_oneformer.py | null | 5,101 |
class OneFormerHungarianMatcher(nn.Module):
def __init__(
self, cost_class: float = 1.0, cost_mask: float = 1.0, cost_dice: float = 1.0, num_points: int = 12544
):
"""This class computes an assignment between the labels and the predictions of the network.
For efficiency reasons, the lab... | class_definition | 9,679 | 14,963 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/oneformer/modeling_oneformer.py | null | 5,102 |
class OneFormerLoss(nn.Module):
def __init__(
self,
num_classes: int,
matcher: OneFormerHungarianMatcher,
weight_dict: Dict[str, float],
eos_coef: float,
num_points: int,
oversample_ratio: float,
importance_sample_ratio: float,
contrastive_temp... | class_definition | 14,966 | 33,133 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/oneformer/modeling_oneformer.py | null | 5,103 |
class OneFormerTransformerDecoderOutput(BaseModelOutput):
"""
Base class for outputs of the Transformer decoder. This class adds attributes for class predictions, mask
predictions and contrastive logits to BaseModelOutputWithCrossAttentions.
Args:
object_logits (`torch.FloatTensor` of shape `(b... | class_definition | 33,147 | 34,539 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/oneformer/modeling_oneformer.py | null | 5,104 |
class OneFormerPixelDecoderOutput(ModelOutput):
"""
OneFormer's pixel decoder module output, practically a Multi-Scale Deformable Attention based decoder. It returns
the mask features and the multiscale features.
Args:
multi_scale_features (`tuple(torch.FloatTensor)`):
Tuple of mult... | class_definition | 34,666 | 35,857 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/oneformer/modeling_oneformer.py | null | 5,105 |
class OneFormerPixelLevelModuleOutput(ModelOutput):
"""
OneFormer's pixel level module output. It returns both the last and (optionally) the hidden states from the
`encoder` and `decoder`. By default, the `encoder` is a Swin/Dinat Backbone and the `decoder` is a Multi-Scale
Deformable Attention based de... | class_definition | 35,871 | 37,038 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/oneformer/modeling_oneformer.py | null | 5,106 |
class OneFormerModelOutput(ModelOutput):
"""
Class for outputs of [`OneFormerModel`]. This class returns all the needed hidden states to compute the logits.
Args:
encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.outpu... | class_definition | 37,052 | 41,059 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/oneformer/modeling_oneformer.py | null | 5,107 |
class OneFormerForUniversalSegmentationOutput(ModelOutput):
"""
Class for outputs of [`OneFormerForUniversalSegmentationOutput`].
This output can be directly passed to [`~OneFormerImageProcessor.post_process_semantic_segmentation`] or
[`~OneFormerImageProcessor.post_process_instance_segmentation`] or
... | class_definition | 41,073 | 46,339 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/oneformer/modeling_oneformer.py | null | 5,108 |
class OneFormerPixelDecoderFrozenBatchNorm2d(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.
"""
... | class_definition | 46,494 | 47,947 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/oneformer/modeling_oneformer.py | null | 5,109 |
class OneFormerPixelDecoderEncoderMultiscaleDeformableAttention(nn.Module):
"""
Multiscale deformable attention as proposed in Deformable DETR.
"""
def __init__(self, embed_dim: int, num_heads: int, n_levels: int, n_points: int):
super().__init__()
if embed_dim % num_heads != 0:
... | class_definition | 48,110 | 52,486 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/oneformer/modeling_oneformer.py | null | 5,110 |
class OneFormerPixelDecoderEncoderLayer(nn.Module):
def __init__(self, config: OneFormerConfig):
super().__init__()
self.embed_dim = config.conv_dim
self.self_attn = OneFormerPixelDecoderEncoderMultiscaleDeformableAttention(
embed_dim=self.embed_dim,
num_heads=config.... | class_definition | 52,489 | 56,395 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/oneformer/modeling_oneformer.py | null | 5,111 |
class OneFormerPixelDecoderEncoderOnly(nn.Module):
"""
Transformer encoder consisting of *config.encoder_layers* deformable attention layers. Each layer is a
[`OneFormerPixelDecoderEncoderLayer`].
The encoder updates the flattened multi-scale feature maps through multiple deformable attention layers.
... | class_definition | 56,552 | 62,423 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/oneformer/modeling_oneformer.py | null | 5,112 |
class OneFormerPixelDecoder(nn.Module):
def __init__(self, config: OneFormerConfig, feature_channels):
super().__init__()
self.config = config
# positional encoding
self.position_embedding = OneFormerSinePositionEmbedding(num_pos_feats=config.conv_dim // 2, normalize=True)
... | class_definition | 62,540 | 70,955 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/oneformer/modeling_oneformer.py | null | 5,113 |
class OneFormerPixelLevelModule(nn.Module):
def __init__(self, config: OneFormerConfig):
"""
Pixel Level Module proposed in [Masked-attention Mask Transformer for Universal Image
Segmentation](https://arxiv.org/abs/2112.01527). It runs the input image through a backbone and a pixel
d... | class_definition | 71,076 | 72,304 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/oneformer/modeling_oneformer.py | null | 5,114 |
class OneFormerAttention(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 =... | class_definition | 72,397 | 78,699 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/oneformer/modeling_oneformer.py | null | 5,115 |
class OneFormerTransformerDecoderSelfAttentionLayer(nn.Module):
def __init__(
self, embed_dim, num_heads, dropout=0.0, activation="relu", normalize_before=False, layer_norm_eps=1e-05
):
super().__init__()
self.self_attn = OneFormerAttention(embed_dim=embed_dim, num_heads=num_heads, dropo... | class_definition | 78,702 | 80,815 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/oneformer/modeling_oneformer.py | null | 5,116 |
class OneFormerTransformerDecoderCrossAttentionLayer(nn.Module):
def __init__(
self, embed_dim, num_heads, dropout=0.0, activation="relu", normalize_before=False, layer_norm_eps=1e-05
):
super().__init__()
self.multihead_attn = nn.MultiheadAttention(embed_dim, num_heads, dropout=dropout)... | class_definition | 80,818 | 83,315 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/oneformer/modeling_oneformer.py | null | 5,117 |
class OneFormerTransformerDecoderFFNLayer(nn.Module):
def __init__(
self,
d_model,
dim_feedforward=2048,
dropout=0.0,
activation="relu",
normalize_before=False,
layer_norm_eps=1e-05,
):
super().__init__()
# Implementation of Feedforward mod... | class_definition | 83,318 | 84,680 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/oneformer/modeling_oneformer.py | null | 5,118 |
class OneFormerMLPPredictionHead(nn.Module):
def __init__(self, input_dim: int, hidden_dim: int, output_dim: int, num_layers: int = 3):
"""
A classic Multi Layer Perceptron (MLP).
Args:
input_dim (`int`):
The input dimensions.
hidden_dim (`int`):
... | class_definition | 84,683 | 85,746 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/oneformer/modeling_oneformer.py | null | 5,119 |
class OneFormerTransformerDecoderLayer(nn.Module):
def __init__(self, config: OneFormerConfig):
super().__init__()
self.embed_dim = config.hidden_dim
self.num_feature_levels = 3
self.cross_attn = OneFormerTransformerDecoderCrossAttentionLayer(
embed_dim=self.embed_dim,
... | class_definition | 85,791 | 89,136 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/oneformer/modeling_oneformer.py | null | 5,120 |
class OneFormerTransformerDecoderQueryTransformerDecoder(nn.Module):
def __init__(self, decoder_layer, num_layers, norm=None, return_intermediate=False):
super().__init__()
self.layers = _get_clones(decoder_layer, num_layers)
self.num_layers = num_layers
self.norm = norm
self... | class_definition | 89,139 | 90,696 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/oneformer/modeling_oneformer.py | null | 5,121 |
class OneFormerTransformerDecoderQueryTransformerDecoderLayer(nn.Module):
def __init__(
self,
d_model,
nhead,
dim_feedforward=2048,
dropout=0.1,
activation="relu",
normalize_before=False,
layer_norm_eps=1e-05,
):
super().__init__()
... | class_definition | 90,699 | 95,312 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/oneformer/modeling_oneformer.py | null | 5,122 |
class OneFormerTransformerDecoderQueryTransformer(nn.Module):
def __init__(
self,
d_model=512,
nhead=8,
num_decoder_layers=6,
dim_feedforward=2048,
dropout=0.1,
activation="relu",
normalize_before=False,
return_intermediate_dec=False,
l... | class_definition | 95,315 | 96,900 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/oneformer/modeling_oneformer.py | null | 5,123 |
class OneFormerTransformerDecoder(nn.Module):
"""
Transformer decoder
"""
def __init__(self, in_channels: int, config: OneFormerConfig):
super().__init__()
self.config = config
self.dropout = config.dropout
self.num_heads = config.num_attention_heads
self.is_tra... | class_definition | 96,903 | 102,989 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/oneformer/modeling_oneformer.py | null | 5,124 |
class OneFormerTransformerModule(nn.Module):
"""
The OneFormer's transformer module.
"""
def __init__(self, in_features: int, config: OneFormerConfig):
super().__init__()
hidden_dim = config.hidden_dim
self.num_feature_levels = 3
self.position_embedder = OneFormerSinePos... | class_definition | 102,992 | 105,982 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/oneformer/modeling_oneformer.py | null | 5,125 |
class OneFormerSinePositionEmbedding(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, num_pos_feats: int = 64, temperature: int = 10000, norm... | class_definition | 106,097 | 107,909 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/oneformer/modeling_oneformer.py | null | 5,126 |
class PredictionBlock(nn.Module):
def __init__(self, in_dim: int, out_dim: int, activation: nn.Module) -> None:
super().__init__()
self.layers = [nn.Linear(in_dim, out_dim), activation]
# Maintain submodule indexing as if part of a Sequential block
for i, layer in enumerate(self.laye... | class_definition | 107,993 | 108,547 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/oneformer/modeling_oneformer.py | null | 5,127 |
class OneFormerTextMapperAttention(nn.Module):
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0.0, proj_drop=0.0):
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
# NOTE scale factor was wrong in my original version, can set ma... | class_definition | 108,550 | 110,218 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/oneformer/modeling_oneformer.py | null | 5,128 |
class OneFormerTextTransformerDecoderLayer(nn.Module):
def __init__(
self,
d_model,
nhead,
dropout=0.1,
layer_norm_eps=1e-05,
):
super().__init__()
self.self_attn = OneFormerTextMapperAttention(d_model, nhead, proj_drop=dropout)
self.cross_attn = O... | class_definition | 110,221 | 111,359 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/oneformer/modeling_oneformer.py | null | 5,129 |
class OneFormerTextContextDecoder(nn.Module):
def __init__(
self,
transformer_width=256,
transformer_heads=4,
transformer_layers=6,
visual_dim=1024,
dropout=0.1,
layer_norm_eps=1e-05,
**kwargs,
):
super().__init__()
self.memory_pro... | class_definition | 111,362 | 112,709 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/oneformer/modeling_oneformer.py | null | 5,130 |
class OneFormerTextMLP(nn.Module):
def __init__(
self,
hidden_size: Optional[int] = None,
intermediate_size: Optional[int] = None,
output_size: Optional[int] = None,
):
super().__init__()
self.activation_fn = ACT2FN["quick_gelu"]
hidden_size = hidden_size
... | class_definition | 112,712 | 113,485 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/oneformer/modeling_oneformer.py | null | 5,131 |
class OneFormerTextTransformerLayer(nn.Module):
def __init__(self, width: int, heads: int, attn_mask: torch.Tensor, layer_norm_eps=1e-05):
super().__init__()
self.self_attn = nn.MultiheadAttention(width, heads)
self.layer_norm1 = nn.LayerNorm(width, eps=layer_norm_eps)
self.mlp = One... | class_definition | 113,488 | 114,671 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/oneformer/modeling_oneformer.py | null | 5,132 |
class OneFormerTextTransformer(nn.Module):
def __init__(
self,
width: int,
layers: int,
heads: int,
attn_mask: torch.Tensor = None,
use_checkpoint=False,
layer_norm_eps=1e-05,
):
super().__init__()
self.width = width
self.num_layers... | class_definition | 114,674 | 115,515 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/oneformer/modeling_oneformer.py | null | 5,133 |
class OneFormerTextEncoder(nn.Module):
def __init__(
self,
context_length: int,
width: int,
layers: int,
vocab_size,
use_checkpoint=False,
layer_norm_eps=1e-05,
):
super().__init__()
heads = width // 64
self.context_length = context... | class_definition | 115,518 | 117,214 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/oneformer/modeling_oneformer.py | null | 5,134 |
class OneFormerTextMapper(nn.Module):
def __init__(self, config: OneFormerConfig):
super().__init__()
self.text_encoder = OneFormerTextEncoder(
context_length=config.text_encoder_context_length,
width=config.text_encoder_width,
layers=config.text_encoder_num_layer... | class_definition | 117,217 | 119,316 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/oneformer/modeling_oneformer.py | null | 5,135 |
class OneFormerTaskModel(nn.Module):
def __init__(self, config: OneFormerConfig):
super().__init__()
self.task_mlp = OneFormerMLPPredictionHead(
config.task_seq_len,
config.hidden_dim,
config.hidden_dim,
2,
)
def forward(self, inputs: Tens... | class_definition | 119,319 | 119,724 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/oneformer/modeling_oneformer.py | null | 5,136 |
class OneFormerPreTrainedModel(PreTrainedModel):
config_class = OneFormerConfig
base_model_prefix = "model"
main_input_name = "pixel_values"
def _init_weights(self, module: nn.Module):
xavier_std = self.config.init_xavier_std
std = self.config.init_std
if isinstance(module, OneF... | class_definition | 121,728 | 128,231 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/oneformer/modeling_oneformer.py | null | 5,137 |
class OneFormerModel(OneFormerPreTrainedModel):
main_input_name = ["pixel_values", "task_inputs"]
def __init__(self, config: OneFormerConfig):
super().__init__(config)
self.pixel_level_module = OneFormerPixelLevelModule(config)
self.transformer_module = OneFormerTransformerModule(in_fea... | class_definition | 128,385 | 133,662 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/oneformer/modeling_oneformer.py | null | 5,138 |
class OneFormerForUniversalSegmentation(OneFormerPreTrainedModel):
main_input_name = ["pixel_values", "task_inputs"]
def __init__(self, config: OneFormerConfig):
super().__init__(config)
self.model = OneFormerModel(config)
self.matcher = OneFormerHungarianMatcher(
cost_clas... | class_definition | 133,800 | 143,591 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/oneformer/modeling_oneformer.py | null | 5,139 |
class OneFormerProcessor(ProcessorMixin):
r"""
Constructs an OneFormer processor which wraps [`OneFormerImageProcessor`] and
[`CLIPTokenizer`]/[`CLIPTokenizerFast`] into a single processor that inherits both the image processor and
tokenizer functionalities.
Args:
image_processor ([`OneForm... | class_definition | 826 | 9,376 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/oneformer/processing_oneformer.py | null | 5,140 |
class OneFormerConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`OneFormerModel`]. It is used to instantiate a
OneFormer model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield ... | class_definition | 937 | 13,435 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/oneformer/configuration_oneformer.py | null | 5,141 |
class LlavaNextVideoConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`LlavaNextVideoForConditionalGeneration`]. It is used to instantiate an
Llava-NeXT model according to the specified arguments, defining the model architecture. Instantiating a configuration
... | class_definition | 1,571 | 8,209 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_next_video/configuration_llava_next_video.py | null | 5,142 |
class LlavaNextVideoConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`LlavaNextVideoForConditionalGeneration`]. It is used to instantiate an
Llava-NeXT model according to the specified arguments, defining the model architecture. Instantiating a configuration
... | class_definition | 1,166 | 7,804 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_next_video/modular_llava_next_video.py | null | 5,143 |
class LlavaNextVideoCausalLMOutputWithPast(LlavaNextCausalLMOutputWithPast):
"""
video_hidden_states (`torch.FloatTensor`, *optional*):
A `torch.FloatTensor` of size `(batch_size * num_frames, num_videos, sequence_length, hidden_size)`.
video_hidden_states of the model produced by the vision en... | class_definition | 7,818 | 8,256 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_next_video/modular_llava_next_video.py | null | 5,144 |
class LlavaNextVideoPooler(nn.Module):
def __init__(self, config):
super().__init__()
mode = config.spatial_pool_mode
stride = config.spatial_pool_stride
out_channels = getattr(config, "spatial_pool_out_channels", config.vision_config.hidden_size)
self.image_size = (config.v... | class_definition | 8,259 | 9,748 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_next_video/modular_llava_next_video.py | null | 5,145 |
class LlavaNextVideoPreTrainedModel(LlavaNextPreTrainedModel):
pass | class_definition | 9,751 | 9,822 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_next_video/modular_llava_next_video.py | null | 5,146 |
class LlavaNextVideoForConditionalGeneration(LlavaNextForConditionalGeneration):
def __init__(self, config: LlavaNextVideoConfig, **super_kwargs):
super().__init__(config, **super_kwargs)
self.vision_resampler = LlavaNextVideoPooler(config)
self.post_init()
def get_image_features(
... | class_definition | 9,825 | 27,491 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_next_video/modular_llava_next_video.py | null | 5,147 |
class LlavaNextVideoImageProcessor(BaseImageProcessor):
r"""
Constructs a LLaVa-NeXT-Video video processor. Based on [`CLIPImageProcessor`] with incorporation of processing each video frame.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height,... | class_definition | 2,029 | 21,424 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_next_video/image_processing_llava_next_video.py | null | 5,148 |
class LlavaNextVideoProcessor(ProcessorMixin):
r"""
Constructs a LLaVa-NeXT-Video processor which wraps a LLaVa-NeXT image processor, LLaVa-NeXT-Video video processor and
a LLaMa tokenizer into a single processor.
[`LlavaNextVideoProcessor`] offers all the functionalities of [`LlavaNextImageProcessor`]... | class_definition | 1,168 | 15,080 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_next_video/processing_llava_next_video.py | null | 5,149 |
class LlavaNextVideoCausalLMOutputWithPast(ModelOutput):
"""
Base class for LlavaNextVideo 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 predicti... | class_definition | 2,174 | 5,151 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_next_video/modeling_llava_next_video.py | null | 5,150 |
class LlavaNextVideoPooler(nn.Module):
def __init__(self, config):
super().__init__()
mode = config.spatial_pool_mode
stride = config.spatial_pool_stride
out_channels = getattr(config, "spatial_pool_out_channels", config.vision_config.hidden_size)
self.image_size = (config.v... | class_definition | 5,154 | 6,643 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_next_video/modeling_llava_next_video.py | null | 5,151 |
class LlavaNextVideoPreTrainedModel(PreTrainedModel):
config_class = LlavaNextVideoConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["LlavaNextVideoVisionAttention"]
_skip_keys_device_placement = "past_key_values"
_supports_cache_class = True
_sup... | class_definition | 7,730 | 9,167 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_next_video/modeling_llava_next_video.py | null | 5,152 |
class LlavaNextVideoMultiModalProjector(nn.Module):
def __init__(self, config: LlavaNextVideoConfig):
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[... | class_definition | 9,170 | 9,901 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_next_video/modeling_llava_next_video.py | null | 5,153 |
class LlavaNextVideoForConditionalGeneration(LlavaNextVideoPreTrainedModel, GenerationMixin):
def __init__(
self,
config: LlavaNextVideoConfig,
):
super().__init__(config)
self.vision_tower = AutoModel.from_config(config.vision_config)
self.multi_modal_projector = LlavaN... | class_definition | 19,858 | 56,613 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_next_video/modeling_llava_next_video.py | null | 5,154 |
class FalconMambaConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`FalconMambaModel`]. It is used to instantiate a FALCON_MAMBA
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will ... | class_definition | 774 | 7,727 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon_mamba/configuration_falcon_mamba.py | null | 5,155 |
class FalconMambaMixer(nn.Module):
"""
Compute ∆, A, B, C, and D the state space parameters and compute the `contextualized_states`.
A, D are input independent (see FalconMamba paper [1] Section 3.5.2 "Interpretation of A" for why A isn't selective)
∆, B, C are input-dependent (this is a key difference ... | class_definition | 2,915 | 18,337 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon_mamba/modeling_falcon_mamba.py | null | 5,156 |
class FalconMambaRMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
FalconMambaRMSNorm is equivalent to T5LayerNorm and LlamaRMSNorm
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def extra_r... | class_definition | 18,432 | 19,032 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon_mamba/modeling_falcon_mamba.py | null | 5,157 |
class FalconMambaBlock(nn.Module):
def __init__(self, config, layer_idx):
super().__init__()
self.config = config
self.layer_idx = layer_idx
self.residual_in_fp32 = config.residual_in_fp32
self.norm = FalconMambaRMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
... | class_definition | 19,154 | 20,205 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon_mamba/modeling_falcon_mamba.py | null | 5,158 |
class FalconMambaPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = FalconMambaConfig
base_model_prefix = "backbone"
_no_split_modules = ["FalconMambaBlock", "Falco... | class_definition | 20,308 | 23,405 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon_mamba/modeling_falcon_mamba.py | null | 5,159 |
class FalconMambaOutput(ModelOutput):
"""
Class for the FALCONMAMBA model outputs.
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.
cache_params (`MambaCac... | class_definition | 23,558 | 24,837 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon_mamba/modeling_falcon_mamba.py | null | 5,160 |
class FalconMambaCausalLMOutput(ModelOutput):
"""
Base class for 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).
logits (`torc... | class_definition | 24,979 | 26,511 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon_mamba/modeling_falcon_mamba.py | null | 5,161 |
class FalconMambaModel(FalconMambaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size)
self.layers = nn.ModuleList(
[FalconMambaBlock(config, layer_idx=idx) for idx in range(config.num_hidden... | class_definition | 29,265 | 33,743 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon_mamba/modeling_falcon_mamba.py | null | 5,162 |
class FalconMambaForCausalLM(FalconMambaPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
super().__init__(config)
self.backbone = FalconMambaModel(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
... | class_definition | 34,122 | 40,556 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon_mamba/modeling_falcon_mamba.py | null | 5,163 |
class FocalNetConfig(BackboneConfigMixin, PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`FocalNetModel`]. It is used to instantiate a
FocalNet model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the de... | class_definition | 885 | 8,025 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/focalnet/configuration_focalnet.py | null | 5,164 |
class FocalNetEncoderOutput(ModelOutput):
"""
FocalNet encoder's outputs, with potential 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 model.
hi... | class_definition | 1,687 | 3,139 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/focalnet/modeling_focalnet.py | null | 5,165 |
class FocalNetModelOutput(ModelOutput):
"""
FocalNet 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 ... | class_definition | 3,153 | 4,883 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/focalnet/modeling_focalnet.py | null | 5,166 |
class FocalNetMaskedImageModelingOutput(ModelOutput):
"""
FocalNet 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 shap... | class_definition | 4,897 | 6,488 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/focalnet/modeling_focalnet.py | null | 5,167 |
class FocalNetImageClassifierOutput(ModelOutput):
"""
FocalNet 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.FloatTen... | class_definition | 6,502 | 8,139 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/focalnet/modeling_focalnet.py | null | 5,168 |
class FocalNetEmbeddings(nn.Module):
"""
Construct the patch embeddings and layernorm. Optionally, also the mask token.
"""
def __init__(self, config, use_mask_token=False):
super().__init__()
self.patch_embeddings = FocalNetPatchEmbeddings(
config=config,
image... | class_definition | 8,142 | 9,740 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/focalnet/modeling_focalnet.py | null | 5,169 |
class FocalNetPatchEmbeddings(nn.Module):
def __init__(
self,
config,
image_size,
patch_size,
num_channels,
embed_dim,
add_norm=False,
use_conv_embed=False,
is_stem=False,
):
super().__init__()
image_size = image_size if isi... | class_definition | 9,743 | 12,657 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/focalnet/modeling_focalnet.py | null | 5,170 |
class FocalNetDropPath(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) -> t... | class_definition | 13,901 | 14,383 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/focalnet/modeling_focalnet.py | null | 5,171 |
class FocalNetModulation(nn.Module):
def __init__(self, config, index, dim, focal_factor=2, bias=True, projection_dropout=0.0):
super().__init__()
self.dim = dim
self.focal_window = config.focal_windows[index]
self.focal_level = config.focal_levels[index]
self.focal_factor =... | class_definition | 14,386 | 17,220 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/focalnet/modeling_focalnet.py | null | 5,172 |
class FocalNetMlp(nn.Module):
def __init__(self, config, in_features, hidden_features=None, out_features=None, drop=0.0):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = nn.Linear(in_features, hidden_features)
... | class_definition | 17,223 | 17,996 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/focalnet/modeling_focalnet.py | null | 5,173 |
class FocalNetLayer(nn.Module):
r"""Focal Modulation Network layer (block).
Args:
config (`FocalNetConfig`):
Model config.
index (`int`):
Layer index.
dim (`int`):
Number of input channels.
input_resolution (`Tuple[int]`):
Input re... | class_definition | 17,999 | 20,584 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/focalnet/modeling_focalnet.py | null | 5,174 |
class FocalNetStage(nn.Module):
def __init__(self, config, index, input_resolution):
super().__init__()
self.config = config
self.num_stages = len(config.depths)
embed_dim = [config.embed_dim * (2**i) for i in range(self.num_stages)]
dim = embed_dim[index]
out_dim =... | class_definition | 20,587 | 23,013 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/focalnet/modeling_focalnet.py | null | 5,175 |
class FocalNetEncoder(nn.Module):
def __init__(self, config, grid_size):
super().__init__()
self.num_stages = len(config.depths)
self.config = config
self.stages = nn.ModuleList(
[
FocalNetStage(
config=config,
inde... | class_definition | 23,016 | 26,686 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/focalnet/modeling_focalnet.py | null | 5,176 |
class FocalNetPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = FocalNetConfig
base_model_prefix = "focalnet"
main_input_name = "pixel_values"
supports_gradien... | class_definition | 26,797 | 27,764 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/focalnet/modeling_focalnet.py | null | 5,177 |
class FocalNetModel(FocalNetPreTrainedModel):
def __init__(self, config, add_pooling_layer=True, use_mask_token=False):
super().__init__(config)
self.config = config
self.num_stages = len(config.depths)
self.num_features = int(config.embed_dim * 2 ** (self.num_stages - 1))
s... | class_definition | 29,155 | 32,111 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/focalnet/modeling_focalnet.py | null | 5,178 |
class FocalNetForMaskedImageModeling(FocalNetPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.focalnet = FocalNetModel(config, add_pooling_layer=False, use_mask_token=True)
self.num_stages = len(config.depths)
num_features = int(config.embed_dim * 2 ** (s... | class_definition | 32,561 | 37,005 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/focalnet/modeling_focalnet.py | null | 5,179 |
class FocalNetForImageClassification(FocalNetPreTrainedModel):
# Copied from transformers.models.swin.modeling_swin.SwinForImageClassification.__init__ with Swin->FocalNet, swin->focalnet
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.focal... | class_definition | 37,208 | 40,659 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/focalnet/modeling_focalnet.py | null | 5,180 |
class FocalNetBackbone(FocalNetPreTrainedModel, BackboneMixin):
def __init__(self, config: FocalNetConfig):
super().__init__(config)
super()._init_backbone(config)
self.num_features = [config.embed_dim] + config.hidden_sizes
self.focalnet = FocalNetModel(config)
# initializ... | class_definition | 40,800 | 43,125 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/focalnet/modeling_focalnet.py | null | 5,181 |
class TFResNetConvLayer(keras.layers.Layer):
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: int = 3,
stride: int = 1,
activation: str = "relu",
**kwargs,
) -> None:
super().__init__(**kwargs)
self.pad_value = kernel_s... | class_definition | 1,633 | 3,624 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/resnet/modeling_tf_resnet.py | null | 5,182 |
class TFResNetEmbeddings(keras.layers.Layer):
"""
ResNet Embeddings (stem) composed of a single aggressive convolution.
"""
def __init__(self, config: ResNetConfig, **kwargs) -> None:
super().__init__(**kwargs)
self.embedder = TFResNetConvLayer(
config.num_channels,
... | class_definition | 3,627 | 5,273 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/resnet/modeling_tf_resnet.py | null | 5,183 |
class TFResNetShortCut(keras.layers.Layer):
"""
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, **kwargs) -> None:
su... | class_definition | 5,276 | 6,782 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/resnet/modeling_tf_resnet.py | null | 5,184 |
class TFResNetBasicLayer(keras.layers.Layer):
"""
A classic ResNet's residual layer composed by two `3x3` convolutions.
"""
def __init__(
self, in_channels: int, out_channels: int, stride: int = 1, activation: str = "relu", **kwargs
) -> None:
super().__init__(**kwargs)
shou... | class_definition | 6,785 | 8,603 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/resnet/modeling_tf_resnet.py | null | 5,185 |
class TFResNetBottleNeckLayer(keras.layers.Layer):
"""
A classic ResNet'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 f... | class_definition | 8,606 | 11,102 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/resnet/modeling_tf_resnet.py | null | 5,186 |
class TFResNetStage(keras.layers.Layer):
"""
A ResNet stage composed of stacked layers.
"""
def __init__(
self, config: ResNetConfig, in_channels: int, out_channels: int, stride: int = 2, depth: int = 2, **kwargs
) -> None:
super().__init__(**kwargs)
layer = TFResNetBottleN... | class_definition | 11,105 | 12,327 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/resnet/modeling_tf_resnet.py | null | 5,187 |
class TFResNetEncoder(keras.layers.Layer):
def __init__(self, config: ResNetConfig, **kwargs) -> None:
super().__init__(**kwargs)
# based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
self.stages = [
TFResNetStage(
... | class_definition | 12,330 | 14,279 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/resnet/modeling_tf_resnet.py | null | 5,188 |
class TFResNetPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = ResNetConfig
base_model_prefix = "resnet"
main_input_name = "pixel_values"
@property
def... | class_definition | 14,282 | 14,740 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/resnet/modeling_tf_resnet.py | null | 5,189 |
class TFResNetMainLayer(keras.layers.Layer):
config_class = ResNetConfig
def __init__(self, config: ResNetConfig, **kwargs) -> None:
super().__init__(**kwargs)
self.config = config
self.embedder = TFResNetEmbeddings(config, name="embedder")
self.encoder = TFResNetEncoder(config,... | class_definition | 16,020 | 18,887 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/resnet/modeling_tf_resnet.py | null | 5,190 |
class TFResNetModel(TFResNetPreTrainedModel):
def __init__(self, config: ResNetConfig, **kwargs) -> None:
super().__init__(config, **kwargs)
self.resnet = TFResNetMainLayer(config=config, name="resnet")
@add_start_docstrings_to_model_forward(RESNET_INPUTS_DOCSTRING)
@add_code_sample_docstri... | class_definition | 19,030 | 20,601 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/resnet/modeling_tf_resnet.py | null | 5,191 |
class TFResNetForImageClassification(TFResNetPreTrainedModel, TFSequenceClassificationLoss):
def __init__(self, config: ResNetConfig, **kwargs) -> None:
super().__init__(config, **kwargs)
self.num_labels = config.num_labels
self.resnet = TFResNetMainLayer(config, name="resnet")
# cla... | class_definition | 20,803 | 23,649 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/resnet/modeling_tf_resnet.py | null | 5,192 |
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 | 1,117 | 1,961 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/resnet/convert_resnet_to_pytorch.py | null | 5,193 |
class ModuleTransfer:
src: nn.Module
dest: nn.Module
verbose: int = 0
src_skip: List = field(default_factory=list)
dest_skip: List = field(default_factory=list)
def __call__(self, x: Tensor):
"""
Transfer the weights of `self.src` to `self.dest` by performing a forward pass usin... | class_definition | 1,975 | 3,155 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/resnet/convert_resnet_to_pytorch.py | null | 5,194 |
class Identity(nn.Module):
"""Identity function."""
@nn.compact
def __call__(self, x, **kwargs):
return x | class_definition | 3,996 | 4,122 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/resnet/modeling_flax_resnet.py | null | 5,195 |
class FlaxResNetConvLayer(nn.Module):
out_channels: int
kernel_size: int = 3
stride: int = 1
activation: Optional[str] = "relu"
dtype: jnp.dtype = jnp.float32
def setup(self):
self.convolution = nn.Conv(
self.out_channels,
kernel_size=(self.kernel_size, self.kern... | class_definition | 4,125 | 5,213 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/resnet/modeling_flax_resnet.py | null | 5,196 |
class FlaxResNetEmbeddings(nn.Module):
"""
ResNet Embeddings (stem) composed of a single aggressive convolution.
"""
config: ResNetConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.embedder = FlaxResNetConvLayer(
self.config.embedding_size,
kernel_size... | class_definition | 5,216 | 6,262 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/resnet/modeling_flax_resnet.py | null | 5,197 |
class FlaxResNetShortCut(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`.
"""
out_channels: int
stride: int = 2
dtype: jnp.dtype = jnp.float32
def setup(self):
self.c... | class_definition | 6,265 | 7,217 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/resnet/modeling_flax_resnet.py | null | 5,198 |
class FlaxResNetBasicLayerCollection(nn.Module):
out_channels: int
stride: int = 1
dtype: jnp.dtype = jnp.float32
def setup(self):
self.layer = [
FlaxResNetConvLayer(self.out_channels, stride=self.stride, dtype=self.dtype),
FlaxResNetConvLayer(self.out_channels, activati... | class_definition | 7,220 | 7,809 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/resnet/modeling_flax_resnet.py | null | 5,199 |
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