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 FlaxResNetBasicLayer(nn.Module):
"""
A classic ResNet's residual layer composed by two `3x3` convolutions.
"""
in_channels: int
out_channels: int
stride: int = 1
activation: Optional[str] = "relu"
dtype: jnp.dtype = jnp.float32
def setup(self):
should_apply_shortcut =... | class_definition | 7,812 | 9,008 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/resnet/modeling_flax_resnet.py | null | 5,200 |
class FlaxResNetBottleNeckLayerCollection(nn.Module):
out_channels: int
stride: int = 1
activation: Optional[str] = "relu"
reduction: int = 4
dtype: jnp.dtype = jnp.float32
def setup(self):
reduces_channels = self.out_channels // self.reduction
self.layer = [
FlaxRe... | class_definition | 9,011 | 9,859 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/resnet/modeling_flax_resnet.py | null | 5,201 |
class FlaxResNetBottleNeckLayer(nn.Module):
"""
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 features ... | class_definition | 9,862 | 11,384 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/resnet/modeling_flax_resnet.py | null | 5,202 |
class FlaxResNetStageLayersCollection(nn.Module):
"""
A ResNet stage composed by stacked layers.
"""
config: ResNetConfig
in_channels: int
out_channels: int
stride: int = 2
depth: int = 2
dtype: jnp.dtype = jnp.float32
def setup(self):
layer = FlaxResNetBottleNeckLayer ... | class_definition | 11,387 | 12,733 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/resnet/modeling_flax_resnet.py | null | 5,203 |
class FlaxResNetStage(nn.Module):
"""
A ResNet stage composed by stacked layers.
"""
config: ResNetConfig
in_channels: int
out_channels: int
stride: int = 2
depth: int = 2
dtype: jnp.dtype = jnp.float32
def setup(self):
self.layers = FlaxResNetStageLayersCollection(
... | class_definition | 12,736 | 13,408 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/resnet/modeling_flax_resnet.py | null | 5,204 |
class FlaxResNetStageCollection(nn.Module):
config: ResNetConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
in_out_channels = zip(self.config.hidden_sizes, self.config.hidden_sizes[1:])
stages = [
FlaxResNetStage(
self.config,
self.config.emb... | class_definition | 13,411 | 14,842 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/resnet/modeling_flax_resnet.py | null | 5,205 |
class FlaxResNetEncoder(nn.Module):
config: ResNetConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.stages = FlaxResNetStageCollection(self.config, dtype=self.dtype)
def __call__(
self,
hidden_state: jnp.ndarray,
output_hidden_states: bool = False,
ret... | class_definition | 14,845 | 15,802 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/resnet/modeling_flax_resnet.py | null | 5,206 |
class FlaxResNetPreTrainedModel(FlaxPreTrainedModel):
"""
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"
module_class: ... | class_definition | 15,805 | 18,585 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/resnet/modeling_flax_resnet.py | null | 5,207 |
class FlaxResNetModule(nn.Module):
config: ResNetConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.embedder = FlaxResNetEmbeddings(self.config, dtype=self.dtype)
self.encoder = FlaxResNetEncoder(self.config, dtype=self.dtype)
# Adaptive ave... | class_definition | 18,588 | 20,509 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/resnet/modeling_flax_resnet.py | null | 5,208 |
class FlaxResNetModel(FlaxResNetPreTrainedModel):
module_class = FlaxResNetModule | class_definition | 20,652 | 20,737 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/resnet/modeling_flax_resnet.py | null | 5,209 |
class FlaxResNetClassifierCollection(nn.Module):
config: ResNetConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.classifier = nn.Dense(self.config.num_labels, dtype=self.dtype, name="1")
def __call__(self, x: jnp.ndarray) -> jnp.ndarray:
return self.classifier(x) | class_definition | 21,614 | 21,921 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/resnet/modeling_flax_resnet.py | null | 5,210 |
class FlaxResNetForImageClassificationModule(nn.Module):
config: ResNetConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.resnet = FlaxResNetModule(config=self.config, dtype=self.dtype)
if self.config.num_labels > 0:
self.classifier = FlaxResNetClassifierCollection(sel... | class_definition | 21,924 | 23,133 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/resnet/modeling_flax_resnet.py | null | 5,211 |
class FlaxResNetForImageClassification(FlaxResNetPreTrainedModel):
module_class = FlaxResNetForImageClassificationModule | class_definition | 23,335 | 23,459 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/resnet/modeling_flax_resnet.py | null | 5,212 |
class ResNetConvLayer(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=stride, pa... | class_definition | 1,715 | 2,460 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/resnet/modeling_resnet.py | null | 5,213 |
class ResNetEmbeddings(nn.Module):
"""
ResNet Embeddings (stem) composed of a single aggressive convolution.
"""
def __init__(self, config: ResNetConfig):
super().__init__()
self.embedder = ResNetConvLayer(
config.num_channels, config.embedding_size, kernel_size=7, stride=2,... | class_definition | 2,463 | 3,368 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/resnet/modeling_resnet.py | null | 5,214 |
class ResNetShortCut(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__()
self... | class_definition | 3,371 | 4,022 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/resnet/modeling_resnet.py | null | 5,215 |
class ResNetBasicLayer(nn.Module):
"""
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"):
super().__init__()
should_apply_shortcut = in_channels != out_channels or ... | class_definition | 4,025 | 5,016 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/resnet/modeling_resnet.py | null | 5,216 |
class ResNetBottleNeckLayer(nn.Module):
"""
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 features to ... | class_definition | 5,019 | 6,737 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/resnet/modeling_resnet.py | null | 5,217 |
class ResNetStage(nn.Module):
"""
A ResNet stage composed by stacked layers.
"""
def __init__(
self,
config: ResNetConfig,
in_channels: int,
out_channels: int,
stride: int = 2,
depth: int = 2,
):
super().__init__()
layer = ResNetBottl... | class_definition | 6,740 | 7,903 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/resnet/modeling_resnet.py | null | 5,218 |
class ResNetEncoder(nn.Module):
def __init__(self, config: ResNetConfig):
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(
ResNetStage(
... | class_definition | 7,906 | 9,481 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/resnet/modeling_resnet.py | null | 5,219 |
class ResNetPreTrainedModel(PreTrainedModel):
"""
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"
_no_split_modules = ["... | class_definition | 9,484 | 10,617 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/resnet/modeling_resnet.py | null | 5,220 |
class ResNetModel(ResNetPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.embedder = ResNetEmbeddings(config)
self.encoder = ResNetEncoder(config)
self.pooler = nn.AdaptiveAvgPool2d((1, 1))
# Initialize weights and apply ... | class_definition | 11,996 | 13,736 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/resnet/modeling_resnet.py | null | 5,221 |
class ResNetForImageClassification(ResNetPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.resnet = ResNetModel(config)
# classification head
self.classifier = nn.Sequential(
nn.Flatten(),
n... | class_definition | 13,938 | 17,082 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/resnet/modeling_resnet.py | null | 5,222 |
class ResNetBackbone(ResNetPreTrainedModel, BackboneMixin):
def __init__(self, config):
super().__init__(config)
super()._init_backbone(config)
self.num_features = [config.embedding_size] + config.hidden_sizes
self.embedder = ResNetEmbeddings(config)
self.encoder = ResNetEnc... | class_definition | 17,229 | 19,787 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/resnet/modeling_resnet.py | null | 5,223 |
class ResNetConfig(BackboneConfigMixin, PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`ResNetModel`]. It is used to instantiate an
ResNet model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the default... | class_definition | 1,038 | 5,615 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/resnet/configuration_resnet.py | null | 5,224 |
class ResNetOnnxConfig(OnnxConfig):
torch_onnx_minimum_version = version.parse("1.11")
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
]
)... | class_definition | 5,618 | 6,017 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/resnet/configuration_resnet.py | null | 5,225 |
class Mask2FormerPixelDecoderOutput(ModelOutput):
"""
Mask2Former'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 ... | class_definition | 1,914 | 3,109 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mask2former/modeling_mask2former.py | null | 5,226 |
class Mask2FormerMaskedAttentionDecoderOutput(BaseModelOutputWithCrossAttentions):
"""
Base class for outputs of the Transformer decoder. This class adds two attributes to
BaseModelOutputWithCrossAttentions for mask predictions logits and a tuple of intermediate decoder activations,
i.e. the output of e... | class_definition | 3,123 | 5,187 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mask2former/modeling_mask2former.py | null | 5,227 |
class Mask2FormerPixelLevelModuleOutput(ModelOutput):
"""
Mask2Former's pixel level module output. It returns the output of the encoder (optional) and all hidden states
(multi-scale features) from the `decoder`. By default, the `encoder` is a Swin Backbone and the `decoder` is a
Multi-Scale Deformable A... | class_definition | 5,201 | 6,961 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mask2former/modeling_mask2former.py | null | 5,228 |
class Mask2FormerModelOutput(ModelOutput):
"""
Class for outputs of [`Mask2FormerModel`]. This class returns all the needed hidden states to compute the logits.
Args:
encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`, *optional*):
Last h... | class_definition | 6,975 | 10,568 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mask2former/modeling_mask2former.py | null | 5,229 |
class Mask2FormerForUniversalSegmentationOutput(ModelOutput):
"""
Class for outputs of [`Mask2FormerForUniversalSegmentationOutput`].
This output can be directly passed to [`~Mask2FormerImageProcessor.post_process_semantic_segmentation`] or
[`~Mask2FormerImageProcessor.post_process_instance_segmentatio... | class_definition | 10,582 | 14,860 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mask2former/modeling_mask2former.py | null | 5,230 |
class Mask2FormerHungarianMatcher(nn.Module):
"""This class computes an assignment between the labels and the predictions of the network.
For efficiency reasons, the labels don't include the no_object. Because of this, in general, there are more
predictions than labels. In this case, we do a 1-to-1 matchin... | class_definition | 20,349 | 25,909 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mask2former/modeling_mask2former.py | null | 5,231 |
class Mask2FormerLoss(nn.Module):
def __init__(self, config: Mask2FormerConfig, weight_dict: Dict[str, float]):
"""
The Mask2Former Loss. The loss is computed very similar to DETR. The process happens in two steps: 1) we
compute hungarian assignment between ground truth masks and the outputs... | class_definition | 26,019 | 40,853 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mask2former/modeling_mask2former.py | null | 5,232 |
class Mask2FormerSinePositionEmbedding(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, no... | class_definition | 43,197 | 45,011 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mask2former/modeling_mask2former.py | null | 5,233 |
class Mask2FormerPixelDecoderEncoderMultiscaleDeformableAttention(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 | 45,124 | 49,646 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mask2former/modeling_mask2former.py | null | 5,234 |
class Mask2FormerPixelDecoderEncoderLayer(nn.Module):
def __init__(self, config: Mask2FormerConfig):
super().__init__()
self.embed_dim = config.feature_size
self.self_attn = Mask2FormerPixelDecoderEncoderMultiscaleDeformableAttention(
embed_dim=self.embed_dim,
num_hea... | class_definition | 49,649 | 53,495 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mask2former/modeling_mask2former.py | null | 5,235 |
class Mask2FormerPixelDecoderEncoderOnly(nn.Module):
"""
Transformer encoder consisting of *config.encoder_layers* deformable attention layers. Each layer is a
[`Mask2FormerPixelDecoderEncoderLayer`]. The encoder updates the flattened multi-scale feature maps through
multiple deformable attention layers... | class_definition | 53,654 | 59,625 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mask2former/modeling_mask2former.py | null | 5,236 |
class Mask2FormerPixelDecoder(nn.Module):
def __init__(self, config: Mask2FormerConfig, feature_channels):
super().__init__()
self.config = config
feature_dim = config.feature_size
mask_dim = config.mask_feature_size
num_pos_features = feature_dim // 2
self.positio... | class_definition | 59,769 | 67,961 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mask2former/modeling_mask2former.py | null | 5,237 |
class Mask2FormerPixelLevelModule(nn.Module):
def __init__(self, config: Mask2FormerConfig):
"""
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
... | class_definition | 67,964 | 69,300 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mask2former/modeling_mask2former.py | null | 5,238 |
class Mask2FormerAttention(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 | 69,395 | 75,699 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mask2former/modeling_mask2former.py | null | 5,239 |
class Mask2FormerMaskedAttentionDecoderLayer(nn.Module):
"""
The Mask2FormerMaskedAttentionDecoderLayer is made up of self-attention, cross (masked) attention as well as FFN
blocks. The cross attention block used as part of `Mask2FormerMaskedAttentionDecoderLayer` is actually a `masked
attention` block ... | class_definition | 75,702 | 85,001 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mask2former/modeling_mask2former.py | null | 5,240 |
class Mask2FormerMaskedAttentionDecoder(nn.Module):
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a
[`Mask2FormerMaskedAttentionDecoderLayer`]. The decoder updates the query embeddings through multiple cross
(masked) and self-attention layers. The decoder uses a new... | class_definition | 85,004 | 93,003 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mask2former/modeling_mask2former.py | null | 5,241 |
class Mask2FormerPredictionBlock(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 enumerat... | class_definition | 93,116 | 93,681 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mask2former/modeling_mask2former.py | null | 5,242 |
class Mask2FormerMLPPredictionHead(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 | 93,684 | 95,387 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mask2former/modeling_mask2former.py | null | 5,243 |
class Mask2FormerMaskPredictor(nn.Module):
def __init__(self, hidden_size: int, num_heads: int, mask_feature_size: torch.Tensor):
"""
This class is used to get the predicted mask for a given Mask2FormerMaskedAttentionDecoder layer. It also
generates the binarized attention mask associated wi... | class_definition | 95,390 | 97,916 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mask2former/modeling_mask2former.py | null | 5,244 |
class Mask2FormerTransformerModule(nn.Module):
"""
The Mask2Former's transformer module.
"""
def __init__(self, in_features: int, config: Mask2FormerConfig):
super().__init__()
hidden_dim = config.hidden_dim
self.num_feature_levels = 3
self.position_embedder = Mask2Forme... | class_definition | 97,919 | 100,853 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mask2former/modeling_mask2former.py | null | 5,245 |
class Mask2FormerPreTrainedModel(PreTrainedModel):
config_class = Mask2FormerConfig
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,... | class_definition | 102,649 | 106,152 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mask2former/modeling_mask2former.py | null | 5,246 |
class Mask2FormerModel(Mask2FormerPreTrainedModel):
main_input_name = "pixel_values"
def __init__(self, config: Mask2FormerConfig):
super().__init__(config)
self.pixel_level_module = Mask2FormerPixelLevelModule(config)
self.transformer_module = Mask2FormerTransformerModule(in_features=c... | class_definition | 106,310 | 110,801 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mask2former/modeling_mask2former.py | null | 5,247 |
class Mask2FormerForUniversalSegmentation(Mask2FormerPreTrainedModel):
main_input_name = "pixel_values"
def __init__(self, config: Mask2FormerConfig):
super().__init__(config)
self.model = Mask2FormerModel(config)
self.weight_dict: Dict[str, float] = {
"loss_cross_entropy":... | class_definition | 110,954 | 121,937 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mask2former/modeling_mask2former.py | null | 5,248 |
class Mask2FormerImageProcessor(BaseImageProcessor):
r"""
Constructs a Mask2Former image processor. The image processor can be used to prepare image(s) and optional targets
for the model.
This image processor inherits from [`BaseImageProcessor`] which contains most of the main methods. Users should
... | class_definition | 12,672 | 57,253 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mask2former/image_processing_mask2former.py | null | 5,249 |
class Mask2FormerConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Mask2FormerModel`]. It is used to instantiate a
Mask2Former model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will ... | class_definition | 956 | 12,340 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mask2former/configuration_mask2former.py | null | 5,250 |
class TrackedStateDict:
def __init__(self, to_track: Dict):
"""This class "tracks" a python dictionary by keeping track of which item is accessed.
Args:
to_track (Dict): The dictionary we wish to track
"""
self.to_track = to_track
self._seen: Set[str] = set()
... | class_definition | 1,628 | 2,623 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mask2former/convert_mask2former_original_pytorch_checkpoint_to_pytorch.py | null | 5,251 |
class Args:
"""Fake command line arguments needed by mask2former/detectron implementation"""
config_file: str | class_definition | 2,873 | 2,991 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mask2former/convert_mask2former_original_pytorch_checkpoint_to_pytorch.py | null | 5,252 |
class OriginalMask2FormerConfigToOursConverter:
def __call__(self, original_config: object) -> Mask2FormerConfig:
model = original_config.MODEL
repo_id = "huggingface/label-files"
if model.SEM_SEG_HEAD.NUM_CLASSES == 847:
filename = "mask2former-ade20k-full-id2label.json"
... | class_definition | 3,232 | 7,248 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mask2former/convert_mask2former_original_pytorch_checkpoint_to_pytorch.py | null | 5,253 |
class OriginalMask2FormerConfigToImageProcessorConverter:
def __call__(self, original_config: object) -> Mask2FormerImageProcessor:
model = original_config.MODEL
model_input = original_config.INPUT
return Mask2FormerImageProcessor(
image_mean=(torch.tensor(model.PIXEL_MEAN) / 25... | class_definition | 7,251 | 7,902 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mask2former/convert_mask2former_original_pytorch_checkpoint_to_pytorch.py | null | 5,254 |
class OriginalMask2FormerCheckpointToOursConverter:
def __init__(self, original_model: nn.Module, config: Mask2FormerConfig):
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 | 7,905 | 38,186 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mask2former/convert_mask2former_original_pytorch_checkpoint_to_pytorch.py | null | 5,255 |
class GroundingDinoConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`GroundingDinoModel`]. It is used to instantiate a
Grounding DINO model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the default... | class_definition | 894 | 14,781 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/grounding_dino/configuration_grounding_dino.py | null | 5,256 |
class MultiScaleDeformableAttentionFunction(Function):
@staticmethod
def forward(
context,
value,
value_spatial_shapes,
value_level_start_index,
sampling_locations,
attention_weights,
im2col_step,
):
context.im2col_step = im2col_step
ou... | class_definition | 2,694 | 4,138 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/grounding_dino/modeling_grounding_dino.py | null | 5,257 |
class GroundingDinoDecoderOutput(ModelOutput):
"""
Base class for outputs of the GroundingDinoDecoder. This class adds two attributes to
BaseModelOutputWithCrossAttentions, namely:
- a stacked tensor of intermediate decoder hidden states (i.e. the output of each decoder layer)
- a stacked tensor of ... | class_definition | 4,291 | 6,544 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/grounding_dino/modeling_grounding_dino.py | null | 5,258 |
class GroundingDinoEncoderOutput(ModelOutput):
"""
Base class for outputs of the GroundingDinoEncoder. This class extends BaseModelOutput, due to:
- vision and text last hidden states
- vision and text intermediate hidden states
Args:
last_hidden_state_vision (`torch.FloatTensor` of shape `... | class_definition | 6,558 | 9,024 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/grounding_dino/modeling_grounding_dino.py | null | 5,259 |
class GroundingDinoModelOutput(ModelOutput):
"""
Base class for outputs of the Grounding DINO encoder-decoder model.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the decoder ... | class_definition | 9,038 | 14,517 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/grounding_dino/modeling_grounding_dino.py | null | 5,260 |
class GroundingDinoObjectDetectionOutput(ModelOutput):
"""
Output type of [`GroundingDinoForObjectDetection`].
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... | class_definition | 14,531 | 21,889 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/grounding_dino/modeling_grounding_dino.py | null | 5,261 |
class GroundingDinoFrozenBatchNorm2d(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 _... | class_definition | 21,992 | 23,513 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/grounding_dino/modeling_grounding_dino.py | null | 5,262 |
class GroundingDinoConvEncoder(nn.Module):
"""
Convolutional backbone, using either the AutoBackbone API or one from the timm library.
nn.BatchNorm2d layers are replaced by GroundingDinoFrozenBatchNorm2d as defined above.
"""
def __init__(self, config):
super().__init__()
self.co... | class_definition | 24,449 | 26,990 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/grounding_dino/modeling_grounding_dino.py | null | 5,263 |
class GroundingDinoConvModel(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_embeddi... | class_definition | 27,085 | 27,845 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/grounding_dino/modeling_grounding_dino.py | null | 5,264 |
class GroundingDinoSinePositionEmbedding(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, config):
super().__init__()
self.embedding_d... | class_definition | 27,848 | 29,211 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/grounding_dino/modeling_grounding_dino.py | null | 5,265 |
class GroundingDinoLearnedPositionEmbedding(nn.Module):
"""
This module learns positional embeddings up to a fixed maximum size.
"""
def __init__(self, config):
super().__init__()
embedding_dim = config.d_model // 2
self.row_embeddings = nn.Embedding(50, embedding_dim)
... | class_definition | 29,214 | 30,198 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/grounding_dino/modeling_grounding_dino.py | null | 5,266 |
class GroundingDinoMultiscaleDeformableAttention(nn.Module):
"""
Multiscale deformable attention as proposed in Deformable DETR.
"""
def __init__(self, config: GroundingDinoConfig, num_heads: int, n_points: int):
super().__init__()
kernel_loaded = MultiScaleDeformableAttention is not N... | class_definition | 33,009 | 38,590 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/grounding_dino/modeling_grounding_dino.py | null | 5,267 |
class GroundingDinoTextEnhancerLayer(nn.Module):
"""Vanilla Transformer with text embeddings as input"""
def __init__(self, config):
super().__init__()
self.self_attn = GroundingDinoMultiheadAttention(
config, num_attention_heads=config.encoder_attention_heads // 2
)
... | class_definition | 38,593 | 42,588 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/grounding_dino/modeling_grounding_dino.py | null | 5,268 |
class GroundingDinoBiMultiHeadAttention(nn.Module):
def __init__(self, config):
super().__init__()
vision_dim = text_dim = config.d_model
embed_dim = config.encoder_ffn_dim // 2
num_heads = config.encoder_attention_heads // 2
dropout = config.fusion_dropout
self.emb... | class_definition | 42,591 | 50,549 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/grounding_dino/modeling_grounding_dino.py | null | 5,269 |
class GroundingDinoDropPath(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)... | class_definition | 51,798 | 52,285 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/grounding_dino/modeling_grounding_dino.py | null | 5,270 |
class GroundingDinoFusionLayer(nn.Module):
def __init__(self, config):
super().__init__()
drop_path = config.fusion_droppath
# pre layer norm
self.layer_norm_vision = nn.LayerNorm(config.d_model, config.layer_norm_eps)
self.layer_norm_text = nn.LayerNorm(config.d_model, conf... | class_definition | 52,288 | 55,948 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/grounding_dino/modeling_grounding_dino.py | null | 5,271 |
class GroundingDinoDeformableLayer(nn.Module):
def __init__(self, config: GroundingDinoConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = GroundingDinoMultiscaleDeformableAttention(
config, num_heads=config.encoder_attention_heads, n_points=config.encode... | class_definition | 55,951 | 59,656 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/grounding_dino/modeling_grounding_dino.py | null | 5,272 |
class GroundingDinoEncoderLayer(nn.Module):
def __init__(self, config) -> None:
super().__init__()
self.d_model = config.d_model
self.text_enhancer_layer = GroundingDinoTextEnhancerLayer(config)
self.fusion_layer = GroundingDinoFusionLayer(config)
self.deformable_layer = Gr... | class_definition | 61,376 | 64,645 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/grounding_dino/modeling_grounding_dino.py | null | 5,273 |
class GroundingDinoMultiheadAttention(nn.Module):
"""Equivalent implementation of nn.MultiheadAttention with `batch_first=True`."""
def __init__(self, config, num_attention_heads=None):
super().__init__()
if config.hidden_size % num_attention_heads != 0 and not hasattr(config, "embedding_size")... | class_definition | 64,648 | 67,711 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/grounding_dino/modeling_grounding_dino.py | null | 5,274 |
class GroundingDinoDecoderLayer(nn.Module):
def __init__(self, config: GroundingDinoConfig):
super().__init__()
self.embed_dim = config.d_model
# self-attention
self.self_attn = GroundingDinoMultiheadAttention(config, num_attention_heads=config.decoder_attention_heads)
self... | class_definition | 67,714 | 72,723 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/grounding_dino/modeling_grounding_dino.py | null | 5,275 |
class GroundingDinoContrastiveEmbedding(nn.Module):
def __init__(self, config):
super().__init__()
self.max_text_len = config.max_text_len
def forward(
self,
vision_hidden_state: torch.FloatTensor,
text_hidden_state: torch.FloatTensor,
text_token_mask: torch.Bool... | class_definition | 72,726 | 73,462 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/grounding_dino/modeling_grounding_dino.py | null | 5,276 |
class GroundingDinoPreTrainedModel(PreTrainedModel):
config_class = GroundingDinoConfig
base_model_prefix = "model"
main_input_name = "pixel_values"
def _init_weights(self, module):
std = self.config.init_std
if isinstance(module, GroundingDinoLearnedPositionEmbedding):
nn.... | class_definition | 73,465 | 77,421 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/grounding_dino/modeling_grounding_dino.py | null | 5,277 |
class GroundingDinoEncoder(GroundingDinoPreTrainedModel):
"""
Transformer encoder consisting of *config.encoder_layers* deformable attention layers. Each layer is a
[`GroundingDinoEncoderLayer`].
The encoder updates the flattened multi-scale feature maps through multiple deformable attention layers.
... | class_definition | 81,268 | 90,296 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/grounding_dino/modeling_grounding_dino.py | null | 5,278 |
class GroundingDinoDecoder(GroundingDinoPreTrainedModel):
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`GroundingDinoDecoderLayer`].
The decoder updates the query embeddings through multiple self-attention and cross-attention layers.
Some tweaks for Grounding ... | class_definition | 90,299 | 102,092 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/grounding_dino/modeling_grounding_dino.py | null | 5,279 |
class GroundingDinoModel(GroundingDinoPreTrainedModel):
def __init__(self, config: GroundingDinoConfig):
super().__init__(config)
# Create backbone + positional encoding
backbone = GroundingDinoConvEncoder(config)
position_embeddings = build_position_encoding(config)
self.ba... | class_definition | 104,338 | 123,287 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/grounding_dino/modeling_grounding_dino.py | null | 5,280 |
class GroundingDinoMLPPredictionHead(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
"""... | class_definition | 123,365 | 124,147 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/grounding_dino/modeling_grounding_dino.py | null | 5,281 |
class GroundingDinoForObjectDetection(GroundingDinoPreTrainedModel):
# When using clones, all layers > 0 will be clones, but layer 0 *is* required
# the bbox_embed in the decoder are all clones though
_tied_weights_keys = [r"bbox_embed\.[1-9]\d*", r"model\.decoder\.bbox_embed\.[0-9]\d*"]
def __init__(s... | class_definition | 124,386 | 134,116 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/grounding_dino/modeling_grounding_dino.py | null | 5,282 |
class AnnotationFormat(ExplicitEnum):
COCO_DETECTION = "coco_detection"
COCO_PANOPTIC = "coco_panoptic" | class_definition | 2,219 | 2,330 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/grounding_dino/image_processing_grounding_dino.py | null | 5,283 |
class GroundingDinoImageProcessor(BaseImageProcessor):
r"""
Constructs a Grounding DINO 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 | 31,658 | 72,240 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/grounding_dino/image_processing_grounding_dino.py | null | 5,284 |
class DictWithDeprecationWarning(dict):
message = (
"The key `labels` is will return integer ids in `GroundingDinoProcessor.post_process_grounded_object_detection` "
"output since v4.51.0. Use `text_labels` instead to retrieve string object names."
)
def __getitem__(self, key):
if k... | class_definition | 2,992 | 3,596 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/grounding_dino/processing_grounding_dino.py | null | 5,285 |
class GroundingDinoImagesKwargs(ImagesKwargs, total=False):
annotations: Optional[Union[AnnotationType, List[AnnotationType]]]
return_segmentation_masks: Optional[bool]
masks_path: Optional[Union[str, pathlib.Path]]
do_convert_annotations: Optional[bool]
format: Optional[Union[str, AnnotationFormat]... | class_definition | 3,599 | 3,920 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/grounding_dino/processing_grounding_dino.py | null | 5,286 |
class GroundingDinoProcessorKwargs(ProcessingKwargs, total=False):
images_kwargs: GroundingDinoImagesKwargs
_defaults = {
"text_kwargs": {
"add_special_tokens": True,
"padding": False,
"stride": 0,
"return_overflowing_tokens": False,
"return_sp... | class_definition | 3,923 | 4,438 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/grounding_dino/processing_grounding_dino.py | null | 5,287 |
class GroundingDinoProcessor(ProcessorMixin):
r"""
Constructs a Grounding DINO processor which wraps a Deformable DETR image processor and a BERT tokenizer into a
single processor.
[`GroundingDinoProcessor`] offers all the functionalities of [`GroundingDinoImageProcessor`] and
[`AutoTokenizer`]. Se... | class_definition | 4,441 | 14,165 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/grounding_dino/processing_grounding_dino.py | null | 5,288 |
class TableQuestionAnsweringOutput(ModelOutput):
"""
Output type of [`TapasForQuestionAnswering`].
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` (and possibly `answer`, `aggregation_labels`, `numeric_values` and `numeric_values_scale` are provided)):
... | class_definition | 1,615 | 3,606 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/tapas/modeling_tapas.py | null | 5,289 |
class TapasEmbeddings(nn.Module):
"""
Construct the embeddings from word, position and token_type embeddings. Same as BertEmbeddings but with a number of
additional token type embeddings to encode tabular structure.
"""
def __init__(self, config):
super().__init__()
# we do not incl... | class_definition | 9,568 | 13,491 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/tapas/modeling_tapas.py | null | 5,290 |
class TapasSelfAttention(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 | 13,494 | 17,834 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/tapas/modeling_tapas.py | null | 5,291 |
class TapasSelfOutput(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 ... | class_definition | 17,905 | 18,512 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/tapas/modeling_tapas.py | null | 5,292 |
class TapasAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.self = TapasSelfAttention(config)
self.output = TapasSelfOutput(config)
self.pruned_heads = set()
# Copied from transformers.models.bert.modeling_bert.BertAttention.prune_heads
def prune_hea... | class_definition | 18,515 | 20,662 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/tapas/modeling_tapas.py | null | 5,293 |
class TapasIntermediate(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.inter... | class_definition | 20,735 | 21,301 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/tapas/modeling_tapas.py | null | 5,294 |
class TapasOutput(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)
de... | class_definition | 21,368 | 21,977 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/tapas/modeling_tapas.py | null | 5,295 |
class TapasLayer(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 = TapasAttention(config)
self.is_decoder = config.is_decoder
self.add_cross_attention = config... | class_definition | 21,980 | 26,017 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/tapas/modeling_tapas.py | null | 5,296 |
class TapasEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([TapasLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
hidden_states,
... | class_definition | 26,020 | 28,331 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/tapas/modeling_tapas.py | null | 5,297 |
class TapasPooler(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 hidd... | class_definition | 28,398 | 28,958 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/tapas/modeling_tapas.py | null | 5,298 |
class TapasPredictionHeadTransform(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.tra... | class_definition | 29,059 | 29,760 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/tapas/modeling_tapas.py | null | 5,299 |
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