text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
>>> inputs = processor(image, return_tensors="pt")
>>> outputs = model(**inputs)
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.out... | 2,919 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_convnextv2.py |
if not return_dict:
output = (feature_maps,)
if output_hidden_states:
output += (hidden_states,)
return output
return BackboneOutput(
feature_maps=feature_maps,
hidden_states=hidden_states if output_hidden_states else None,
... | 2,919 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_convnextv2.py |
class ConvNextV2Config(BackboneConfigMixin, PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`ConvNextV2Model`]. It is used to instantiate an
ConvNeXTV2 model according to the specified arguments, defining the model architecture. Instantiating a
configuration with... | 2,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/configuration_convnextv2.py |
Args:
num_channels (`int`, *optional*, defaults to 3):
The number of input channels.
patch_size (`int`, *optional*, defaults to 4):
Patch size to use in the patch embedding layer.
num_stages (`int`, *optional*, defaults to 4):
The number of stages in the model... | 2,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/configuration_convnextv2.py |
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
drop_path_rate (`float`, *optional*, defaults to 0.0):
The drop rate for stochastic depth.
image_size (`int`, *optional*, defaults to 224):
The size (resolutio... | 2,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/configuration_convnextv2.py |
many stages the model has). If unset and `out_features` is set, will default to the corresponding stages.
If unset and `out_features` is unset, will default to the last stage. Must be in the
same order as defined in the `stage_names` attribute. | 2,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/configuration_convnextv2.py |
Example:
```python
>>> from transformers import ConvNeXTV2Config, ConvNextV2Model
>>> # Initializing a ConvNeXTV2 convnextv2-tiny-1k-224 style configuration
>>> configuration = ConvNeXTV2Config()
>>> # Initializing a model (with random weights) from the convnextv2-tiny-1k-224 style configuration
... | 2,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/configuration_convnextv2.py |
self.num_channels = num_channels
self.patch_size = patch_size
self.num_stages = num_stages
self.hidden_sizes = [96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
self.depths = [3, 3, 9, 3] if depths is None else depths
self.hidden_act = hidden_act
self.initiali... | 2,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/configuration_convnextv2.py |
class VitPoseConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`VitPoseForPoseEstimation`]. It is used to instantiate a
VitPose model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will ... | 2,921 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose/configuration_vitpose.py |
Args:
backbone_config (`PretrainedConfig` or `dict`, *optional*, defaults to `VitPoseBackboneConfig()`):
The configuration of the backbone model. Currently, only `backbone_config` with `vitpose_backbone` as `model_type` is supported.
backbone (`str`, *optional*):
Name of backbone... | 2,921 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose/configuration_vitpose.py |
backbone_kwargs (`dict`, *optional*):
Keyword arguments to be passed to AutoBackbone when loading from a checkpoint
e.g. `{'out_indices': (0, 1, 2, 3)}`. Cannot be specified if `backbone_config` is set.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard d... | 2,921 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose/configuration_vitpose.py |
Example:
```python
>>> from transformers import VitPoseConfig, VitPoseForPoseEstimation
>>> # Initializing a VitPose configuration
>>> configuration = VitPoseConfig()
>>> # Initializing a model (with random weights) from the configuration
>>> model = VitPoseForPoseEstimation(configuration)
... | 2,921 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose/configuration_vitpose.py |
if use_pretrained_backbone:
logger.info(
"`use_pretrained_backbone` is `True`. For the pure inference purpose of VitPose weight do not set this value."
)
if use_timm_backbone:
raise ValueError("use_timm_backbone set `True` is not supported at the moment.")
... | 2,921 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose/configuration_vitpose.py |
verify_backbone_config_arguments(
use_timm_backbone=use_timm_backbone,
use_pretrained_backbone=use_pretrained_backbone,
backbone=backbone,
backbone_config=backbone_config,
backbone_kwargs=backbone_kwargs,
)
self.backbone_config = backbone_conf... | 2,921 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose/configuration_vitpose.py |
class VitPoseEstimatorOutput(ModelOutput):
"""
Class for outputs of pose estimation models. | 2,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose/modeling_vitpose.py |
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Loss is not supported at this moment. See https://github.com/ViTAE-Transformer/ViTPose/tree/main/mmpose/models/losses for further detail.
heatmaps (`torch.FloatTensor` of shape `(batch_size, num... | 2,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose/modeling_vitpose.py |
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, patch_size,
sequence_length)`. | 2,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose/modeling_vitpose.py |
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
loss: Optional[torch.FloatTensor] = None
heatmaps: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
attentions: Optional[Tuple[torc... | 2,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose/modeling_vitpose.py |
class VitPosePreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = VitPoseConfig
base_model_prefix = "vit"
main_input_name = "pixel_values"
supports_gradient_check... | 2,923 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose/modeling_vitpose.py |
def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None:
"""Initialize the weights"""
if isinstance(module, (nn.Linear, nn.Conv2d)):
# Upcast the input in `fp32` and cast it back to desired `dtype` to avoid
# `trunc_normal_cpu` not implemented in `half`... | 2,923 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose/modeling_vitpose.py |
class VitPoseSimpleDecoder(nn.Module):
"""
Simple decoding head consisting of a ReLU activation, 4x upsampling and a 3x3 convolution, turning the
feature maps into heatmaps.
"""
def __init__(self, config) -> None:
super().__init__()
self.activation = nn.ReLU()
self.upsampli... | 2,924 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose/modeling_vitpose.py |
class VitPoseClassicDecoder(nn.Module):
"""
Classic decoding head consisting of a 2 deconvolutional blocks, followed by a 1x1 convolution layer,
turning the feature maps into heatmaps.
"""
def __init__(self, config: VitPoseConfig):
super().__init__()
self.deconv1 = nn.ConvTranspose... | 2,925 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose/modeling_vitpose.py |
def forward(self, hidden_state: torch.Tensor, flip_pairs: Optional[torch.Tensor] = None):
hidden_state = self.deconv1(hidden_state)
hidden_state = self.batchnorm1(hidden_state)
hidden_state = self.relu1(hidden_state)
hidden_state = self.deconv2(hidden_state)
hidden_state = self.... | 2,925 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose/modeling_vitpose.py |
class VitPoseForPoseEstimation(VitPosePreTrainedModel):
def __init__(self, config: VitPoseConfig) -> None:
super().__init__(config)
self.backbone = load_backbone(config)
# add backbone attributes
if not hasattr(self.backbone.config, "hidden_size"):
raise ValueError("The... | 2,926 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose/modeling_vitpose.py |
@add_start_docstrings_to_model_forward(VITPOSE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=VitPoseEstimatorOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
pixel_values: torch.Tensor,
dataset_index: Optional[torch.Tensor] = None,
flip_pairs: Optional[torch.Te... | 2,926 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose/modeling_vitpose.py |
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> boxes = [[[412.8, 157.61, 53.05, 138.01], [384.43, 172.21, 15.12, 35.74]]]
>>> inputs = processor(image, boxes=boxes, return_tensors="pt")
>>> with torch.no_... | 2,926 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose/modeling_vitpose.py |
outputs = self.backbone.forward_with_filtered_kwargs(
pixel_values,
dataset_index=dataset_index,
output_hidden_states=output_hidden_states,
output_attentions=output_attentions,
return_dict=return_dict,
)
# Turn output hidden states in tensor o... | 2,926 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose/modeling_vitpose.py |
if not return_dict:
if output_hidden_states:
output = (heatmaps,) + outputs[1:]
else:
output = (heatmaps,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return VitPoseEstimatorOutput(
loss=loss,
... | 2,926 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose/modeling_vitpose.py |
class VitPoseImageProcessor(BaseImageProcessor):
r"""
Constructs a VitPose image processor. | 2,927 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose/image_processing_vitpose.py |
Args:
do_affine_transform (`bool`, *optional*, defaults to `True`):
Whether to apply an affine transformation to the input images.
size (`Dict[str, int]` *optional*, defaults to `{"height": 256, "width": 192}`):
Resolution of the image after `affine_transform` is applied. Only ha... | 2,927 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose/image_processing_vitpose.py |
image_mean (`List[int]`, defaults to `[0.485, 0.456, 0.406]`, *optional*):
The sequence of means for each channel, to be used when normalizing images.
image_std (`List[int]`, defaults to `[0.229, 0.224, 0.225]`, *optional*):
The sequence of standard deviations for each channel, to be use... | 2,927 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose/image_processing_vitpose.py |
model_input_names = ["pixel_values"]
def __init__(
self,
do_affine_transform: bool = True,
size: Dict[str, int] = None,
do_rescale: bool = True,
rescale_factor: Union[int, float] = 1 / 255,
do_normalize: bool = True,
image_mean: Optional[Union[float, List[flo... | 2,927 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose/image_processing_vitpose.py |
def affine_transform(
self,
image: np.array,
center: Tuple[float],
scale: Tuple[float],
rotation: float,
size: Dict[str, int],
data_format: Optional[ChannelDimension] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
) -> np.arr... | 2,927 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose/image_processing_vitpose.py |
Args:
image (`np.array`):
Image to transform.
center (`Tuple[float]`):
Center of the bounding box (x, y).
scale (`Tuple[float]`):
Scale of the bounding box with respect to height/width.
rotation (`float`):
Ro... | 2,927 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose/image_processing_vitpose.py |
# one uses a pixel standard deviation of 200 pixels
transformation = get_warp_matrix(rotation, center * 2.0, np.array(size) - 1.0, scale * 200.0)
# input image requires channels last format
image = (
image
if input_data_format == ChannelDimension.LAST
else to... | 2,927 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose/image_processing_vitpose.py |
def preprocess(
self,
images: ImageInput,
boxes: Union[List[List[float]], np.ndarray],
do_affine_transform: bool = None,
size: Dict[str, int] = None,
do_rescale: bool = None,
rescale_factor: float = None,
do_normalize: bool = None,
image_mean: Opti... | 2,927 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose/image_processing_vitpose.py |
boxes (`List[List[List[float]]]` or `np.ndarray`):
List or array of bounding boxes for each image. Each box should be a list of 4 floats representing the bounding
box coordinates in COCO format (top_left_x, top_left_y, width, height). | 2,927 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose/image_processing_vitpose.py |
do_affine_transform (`bool`, *optional*, defaults to `self.do_affine_transform`):
Whether to apply an affine transformation to the input images.
size (`Dict[str, int]` *optional*, defaults to `self.size`):
Dictionary in the format `{"height": h, "width": w}` specifying the si... | 2,927 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose/image_processing_vitpose.py |
image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
Image standard deviation to use if `do_normalize` is set to `True`.
return_tensors (`str` or [`~utils.TensorType`], *optional*, defaults to `'np'`):
If set, will return tensors of a particular fra... | 2,927 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose/image_processing_vitpose.py |
- `'tf'`: Return TensorFlow `tf.constant` objects.
- `'pt'`: Return PyTorch `torch.Tensor` objects.
- `'np'`: Return NumPy `np.ndarray` objects.
- `'jax'`: Return JAX `jnp.ndarray` objects.
Returns:
[`BatchFeature`]: A [`BatchFeature`] with the follow... | 2,927 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose/image_processing_vitpose.py |
- **pixel_values** -- Pixel values to be fed to a model, of shape (batch_size, num_channels, height,
width).
"""
do_affine_transform = do_affine_transform if do_affine_transform is not None else self.do_affine_transform
size = size if size is not None else self.size
do_resc... | 2,927 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose/image_processing_vitpose.py |
if isinstance(boxes, list) and len(images) != len(boxes):
raise ValueError(f"Batch of images and boxes mismatch : {len(images)} != {len(boxes)}")
elif isinstance(boxes, np.ndarray) and len(images) != boxes.shape[0]:
raise ValueError(f"Batch of images and boxes mismatch : {len(images)} !=... | 2,927 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose/image_processing_vitpose.py |
# transformations (affine transformation + rescaling + normalization)
if self.do_affine_transform:
new_images = []
for image, image_boxes in zip(images, boxes):
for box in image_boxes:
center, scale = box_to_center_and_scale(
bo... | 2,927 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose/image_processing_vitpose.py |
# For batch processing, the number of boxes must be consistent across all images in the batch.
# When using a list input, the number of boxes can vary dynamically per image.
# The image processor creates pixel_values of shape (batch_size*num_persons, num_channels, height, width)
all_images = []... | 2,927 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose/image_processing_vitpose.py |
return encoded_inputs
def keypoints_from_heatmaps(
self,
heatmaps: np.ndarray,
center: np.ndarray,
scale: np.ndarray,
kernel: int = 11,
):
"""
Get final keypoint predictions from heatmaps and transform them back to
the image.
Args:
... | 2,927 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose/image_processing_vitpose.py |
Returns:
tuple: A tuple containing keypoint predictions and scores.
- preds (`np.ndarray` of shape `(batch_size, num_keypoints, 2)`):
Predicted keypoint location in images.
- scores (`np.ndarray` of shape `(batch_size, num_keypoints, 1)`):
Scores (con... | 2,927 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose/image_processing_vitpose.py |
def post_process_pose_estimation(
self,
outputs: "VitPoseEstimatorOutput",
boxes: Union[List[List[List[float]]], np.ndarray],
kernel_size: int = 11,
threshold: float = None,
target_sizes: Union[TensorType, List[Tuple]] = None,
):
"""
Transform the heat... | 2,927 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose/image_processing_vitpose.py |
Args:
outputs (`VitPoseEstimatorOutput`):
VitPoseForPoseEstimation model outputs.
boxes (`List[List[List[float]]]` or `np.ndarray`):
List or array of bounding boxes for each image. Each box should be a list of 4 floats representing the bounding
box... | 2,927 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose/image_processing_vitpose.py |
`List[List[Dict]]`: A list of dictionaries, each dictionary containing the keypoints and boxes for an image
in the batch as predicted by the model.
""" | 2,927 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose/image_processing_vitpose.py |
# First compute centers and scales for each bounding box
batch_size, num_keypoints, _, _ = outputs.heatmaps.shape
if target_sizes is not None:
if batch_size != len(target_sizes):
raise ValueError(
"Make sure that you pass in as many target sizes as the ba... | 2,927 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose/image_processing_vitpose.py |
centers = np.zeros((batch_size, 2), dtype=np.float32)
scales = np.zeros((batch_size, 2), dtype=np.float32)
flattened_boxes = list(itertools.chain(*boxes))
for i in range(batch_size):
if target_sizes is not None:
image_width, image_height = target_sizes[i][0], target_s... | 2,927 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose/image_processing_vitpose.py |
all_boxes = np.zeros((batch_size, 4), dtype=np.float32)
all_boxes[:, 0:2] = centers[:, 0:2]
all_boxes[:, 2:4] = scales[:, 0:2]
poses = torch.tensor(preds)
scores = torch.tensor(scores)
labels = torch.arange(0, num_keypoints)
bboxes_xyxy = torch.tensor(coco_to_pascal_voc(... | 2,927 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose/image_processing_vitpose.py |
for image_bboxes in boxes:
image_results: List[Dict[str, torch.Tensor]] = []
for _ in image_bboxes:
# Unpack the next pose and bbox_xyxy from the iterator
pose, score, bbox_xyxy = next(pose_bbox_pairs)
score = score.squeeze()
keypoi... | 2,927 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose/image_processing_vitpose.py |
class MistralConverter:
"""
A general tiktoken converter.
"""
def __init__(
self,
vocab=None,
pattern=r"""(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+""",
add_prefix_space=False,
additional_sp... | 2,928 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/convert_pixtral_weights_to_hf.py |
merges = []
vocab = {}
for idx, (token, rank) in enumerate(bpe_ranks.items()):
if token not in self.additional_special_tokens:
vocab[token_bytes_to_string(token)] = idx
if len(token) == 1:
continue
local = []
... | 2,928 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/convert_pixtral_weights_to_hf.py |
def tokenizer(self):
vocab_scores, merges = self.extract_vocab_merges_from_model(self.vocab)
tokenizer = Tokenizer(BPE(vocab_scores, merges, fuse_unk=False))
if hasattr(tokenizer.model, "ignore_merges"):
tokenizer.model.ignore_merges = True
return tokenizer
def converted... | 2,928 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/convert_pixtral_weights_to_hf.py |
class PixtralVisionConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`PixtralVisionModel`]. It is used to instantiate an
Pixtral vision encoder according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defa... | 2,929 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/configuration_pixtral.py |
Args:
hidden_size (`int`, *optional*, defaults to 1024):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 4096):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 24):
Number of hi... | 2,929 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/configuration_pixtral.py |
Dropout probability for the attention layers.
rope_theta (`float`, *optional*, defaults to 10000.0):
The base period of the RoPE embeddings.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all ... | 2,929 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/configuration_pixtral.py |
Example:
```python
>>> from transformers import PixtralVisionModel, PixtralVisionConfig
>>> # Initializing a Pixtral-12B style configuration
>>> config = PixtralVisionConfig()
>>> # Initializing a model (with randomly initialized weights) from the configuration
>>> model = PixtralVisionModel(... | 2,929 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/configuration_pixtral.py |
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.num_channels = num_channels
self.patch_size = patch_size
self.image_size = image_size
self.atte... | 2,929 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/configuration_pixtral.py |
class PixtralProcessorKwargs(ProcessingKwargs, total=False):
_defaults = {
"text_kwargs": {
"padding": False,
},
"images_kwargs": {},
"common_kwargs": {
"return_tensors": "pt",
},
} | 2,930 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/processing_pixtral.py |
class BatchMixFeature(BatchFeature):
def to(self, *args, **kwargs) -> "BatchMixFeature":
"""
Send all values to device by calling `v.to(*args, **kwargs)` (PyTorch only). This should support casting in
different `dtypes` and sending the `BatchFeature` to a different `device`.
Args:
... | 2,931 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/processing_pixtral.py |
def _recursive_to(obj, device, *args, **kwargs):
# Lists can be nested, so keep digging until we hit tensors
if isinstance(obj, list):
return [_recursive_to(o, device, *args, **kwargs) for o in obj]
# We cast only floating point tensors to avoid issues with tokenizers... | 2,931 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/processing_pixtral.py |
device = kwargs.get("device")
# Check if the args are a device or a dtype
if device is None and len(args) > 0:
# device should be always the first argument
arg = args[0]
if is_torch_dtype(arg):
# The first argument is a dtype
pass
... | 2,931 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/processing_pixtral.py |
class PixtralProcessor(ProcessorMixin):
r"""
Constructs a Pixtral processor which wraps a Pixtral image processor and a Pixtral tokenizer into a single processor.
[`PixtralProcessor`] offers all the functionalities of [`CLIPImageProcessor`] and [`LlamaTokenizerFast`]. See the
[`~PixtralProcessor.__call... | 2,932 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/processing_pixtral.py |
Args:
image_processor ([`PixtralImageProcessor`], *optional*):
The image processor is a required input.
tokenizer ([`LlamaTokenizerFast`], *optional*):
The tokenizer is a required input.
patch_size (`int`, *optional*, defaults to 16):
Patch size from the visio... | 2,932 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/processing_pixtral.py |
attributes = ["image_processor", "tokenizer"]
valid_kwargs = [
"chat_template",
"patch_size",
"image_token",
"image_break_token",
"image_end_token",
]
image_processor_class = "AutoImageProcessor"
tokenizer_class = "AutoTokenizer"
def __init__(
self,
... | 2,932 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/processing_pixtral.py |
def __call__(
self,
images: ImageInput = None,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
audio=None,
videos=None,
**kwargs: Unpack[PixtralProcessorKwargs],
) -> BatchMixFeature:
"""
Main method to prepa... | 2,932 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/processing_pixtral.py |
Args:
images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
tensor. Both channels-first and channels-last ... | 2,932 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/processing_pixtral.py |
- `'tf'`: Return TensorFlow `tf.constant` objects.
- `'pt'`: Return PyTorch `torch.Tensor` objects.
- `'np'`: Return NumPy `np.ndarray` objects.
- `'jax'`: Return JAX `jnp.ndarray` objects.
Returns:
[`BatchFeature`]: A [`BatchFeature`] with the follow... | 2,932 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/processing_pixtral.py |
output_kwargs = self._merge_kwargs(
PixtralProcessorKwargs,
tokenizer_init_kwargs=self.tokenizer.init_kwargs,
**kwargs,
) | 2,932 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/processing_pixtral.py |
if images is not None:
if is_image_or_image_url(images):
if isinstance(text, str) or isinstance(text, list) and len(text) == 1:
# If there's a single sample, the image must belong to it
images = [[images]]
else:
rais... | 2,932 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/processing_pixtral.py |
"You have supplied multiple text samples, but `images` is not a nested list. When processing multiple samples, `images` should be a list of lists of images, one list per sample."
)
elif isinstance(images, list) and isinstance(images[0], list) and is_image_or_image_url(images[0][0]):
... | 2,932 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/processing_pixtral.py |
if isinstance(text, str):
text = [text]
elif not isinstance(text, list) and not isinstance(text[0], str):
raise ValueError("Invalid input text. Please provide a string, or a list of strings")
# try to expand inputs in processing if we have the necessary parts
prompt_stri... | 2,932 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/processing_pixtral.py |
for sample_images, sample_image_sizes, sample in zip(images, image_sizes, text):
replace_strings = []
# First calculate the number of tokens needed for each image and put in a placeholder
for image, image_size in zip(sample_images, sample_image_sizes):
... | 2,932 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/processing_pixtral.py |
sample = sample.replace(self.image_token, "<placeholder>", 1) | 2,932 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/processing_pixtral.py |
while "<placeholder>" in sample:
replace_str = replace_strings.pop(0)
sample = sample.replace("<placeholder>", replace_str, 1)
prompt_strings.append(sample)
text_inputs = self.tokenizer(prompt_strings, **output_kwargs["text_kwargs"])
return BatchM... | 2,932 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/processing_pixtral.py |
# Copied from transformers.models.clip.processing_clip.CLIPProcessor.decode with CLIP->Llama
def decode(self, *args, **kwargs):
"""
This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
the docstring of this method for more informatio... | 2,932 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/processing_pixtral.py |
class PixtralRotaryEmbedding(nn.Module):
"""
The key with pixtral embedding is just that you have a frequency for each pixel positions.
If you have height x width pixels (or embedding pixels), then the frequency used for ROPE
is given by indexing the pre_computed frequency on the width and height.
... | 2,933 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/modeling_pixtral.py |
h = torch.arange(max_patches_per_side, device=freqs.device)
w = torch.arange(max_patches_per_side, device=freqs.device)
freqs_h = torch.outer(h, freqs[::2]).float()
freqs_w = torch.outer(w, freqs[1::2]).float()
inv_freq = torch.cat(
[
freqs_h[:, None, :].repe... | 2,933 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/modeling_pixtral.py |
@torch.no_grad()
def forward(self, x, position_ids):
if "dynamic" in self.rope_type:
self._dynamic_frequency_update(position_ids, device=x.device)
# Core RoPE block
freqs = self.inv_freq[position_ids]
# position_ids_expanded = position_ids[:, None, :].float()
# F... | 2,933 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/modeling_pixtral.py |
def _dynamic_frequency_update(self, position_ids, device):
"""
dynamic RoPE layers should recompute `inv_freq` in the following situations:
1 - growing beyond the cached sequence length (allow scaling)
2 - the current sequence length is in the original scale (avoid losing precision with ... | 2,933 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/modeling_pixtral.py |
if seq_len < self.original_max_seq_len and self.max_seq_len_cached > self.original_max_seq_len: # reset
self.register_buffer("inv_freq", self.original_inv_freq, persistent=False)
self.max_seq_len_cached = self.original_max_seq_len | 2,933 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/modeling_pixtral.py |
class PixtralAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self... | 2,934 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/modeling_pixtral.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_embeddings: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
"""Input shape: Batch x Time... | 2,934 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/modeling_pixtral.py |
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) * self.scale
if attention_mask is not None:
attn_weights = attn_weights + attention_mask
# upcast attention to fp32
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.... | 2,934 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/modeling_pixtral.py |
class PixtralMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.intermediate_size = config.intermediate_size
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
se... | 2,935 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/modeling_pixtral.py |
class PixtralRMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
PixtralRMSNorm is equivalent to T5LayerNorm
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
... | 2,936 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/modeling_pixtral.py |
class PixtralAttentionLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.attention_norm = PixtralRMSNorm(config.hidden_size, eps=1e-5)
self.feed_forward = PixtralMLP(config)
self.attention = PixtralAttention(config)
self.ffn_norm = PixtralRMSNorm(config.hid... | 2,937 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/modeling_pixtral.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor,
position_embeddings: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.FloatTensor]:
"""
Args:
hidden_states (`torch.FloatTensor`... | 2,937 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/modeling_pixtral.py |
hidden_states = self.attention_norm(hidden_states)
hidden_states, attn_weights = self.attention(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_embeddings=position_embeddings,
output_attentions=output_attentions,
)
hidden_stat... | 2,937 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/modeling_pixtral.py |
class PixtralTransformer(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layers = torch.nn.ModuleList()
for _ in range(config.num_hidden_layers):
self.layers.append(PixtralAttentionLayer(config))
self.gradient_checkpointing = F... | 2,938 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/modeling_pixtral.py |
def forward(
self,
inputs_embeds,
attention_mask: Optional[torch.Tensor] = None,
position_embeddings: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) ->... | 2,938 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/modeling_pixtral.py |
[What are attention masks?](../glossary#attention-mask)
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optiona... | 2,938 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/modeling_pixtral.py |
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
hidden_states = inputs_embeds
for encoder_layer in self.layers:
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if self.gr... | 2,938 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/modeling_pixtral.py |
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
... | 2,938 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/modeling_pixtral.py |
class PixtralPreTrainedModel(PreTrainedModel):
config_class = PixtralVisionConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["PixtralVisionAttention"]
_skip_keys_device_placement = "past_key_values"
_supports_cache_class = True
def _init_weights(... | 2,939 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/modeling_pixtral.py |
class PixtralVisionModel(PixtralPreTrainedModel):
base_model_prefix = "vision_encoder"
def __init__(self, config):
super().__init__(config)
self.config = config
self.patch_conv = nn.Conv2d(
in_channels=config.num_channels,
out_channels=config.hidden_size,
... | 2,940 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/modeling_pixtral.py |
@add_start_docstrings_to_model_forward(PIXTRAL_INPUTS_DOCSTRING)
def forward(
self,
pixel_values: List[torch.Tensor],
output_hidden_states: Optional[bool] = False,
output_attentions: Optional[bool] = None,
return_dict: Optional[bool] = None,
*args,
**kwargs,
... | 2,940 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/modeling_pixtral.py |
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