code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
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def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hi... |
Examples:
```python
>>> import torch
>>> from transformers import AutoConfig, AutoTokenizer, TvpModel
>>> model = TvpModel.from_pretrained("Jiqing/tiny-random-tvp")
>>> tokenizer = AutoTokenizer.from_pretrained("Jiqing/tiny-random-tvp")
>>> pixel_values = torc... | forward | python | huggingface/transformers | src/transformers/models/tvp/modeling_tvp.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/tvp/modeling_tvp.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.LongTensor] = None,
labels: Optional[Tuple[torch.Tensor]] = None,
head_mask: Optional[torch.FloatTensor] = None,
outpu... |
labels (`torch.FloatTensor` of shape `(batch_size, 3)`, *optional*):
The labels contains duration, start time, and end time of the video corresponding to the text.
Examples:
```python
>>> import torch
>>> from transformers import AutoConfig, AutoTokenizer, TvpForVid... | forward | python | huggingface/transformers | src/transformers/models/tvp/modeling_tvp.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/tvp/modeling_tvp.py | Apache-2.0 |
def __call__(self, text=None, videos=None, return_tensors=None, **kwargs):
"""
Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
and `kwargs` arguments to BertTokenizerFast's [`~BertTokenizerFast.__call__`] if `text` is not `None` to e... |
Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
and `kwargs` arguments to BertTokenizerFast's [`~BertTokenizerFast.__call__`] if `text` is not `None` to encode
the text. To prepare the image(s), this method forwards the `videos` and... | __call__ | python | huggingface/transformers | src/transformers/models/tvp/processing_tvp.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/tvp/processing_tvp.py | Apache-2.0 |
def post_process_video_grounding(self, logits, video_durations):
"""
Compute the time of the video.
Args:
logits (`torch.Tensor`):
The logits output of TvpForVideoGrounding.
video_durations (`float`):
The video's duration.
Returns... |
Compute the time of the video.
Args:
logits (`torch.Tensor`):
The logits output of TvpForVideoGrounding.
video_durations (`float`):
The video's duration.
Returns:
start (`float`):
The start time of the video.
... | post_process_video_grounding | python | huggingface/transformers | src/transformers/models/tvp/processing_tvp.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/tvp/processing_tvp.py | Apache-2.0 |
def combine_image_text_embeddings(
image_embeddings,
inputs_embeds,
bbox,
visual_bbox,
attention_mask=None,
num_patches=14,
max_len=0,
image_size=224,
patch_size=16,
):
"""
Combine the image and text embeddings for the input to the encoder/decoder of UDOP.
First, the ima... |
Combine the image and text embeddings for the input to the encoder/decoder of UDOP.
First, the image embeddings are created by checking for each visual patch if it is inside the bounding box of a
token. If it is, the visual patch is combined with the token embedding. Then, the visual bounding boxes are co... | combine_image_text_embeddings | python | huggingface/transformers | src/transformers/models/udop/modeling_udop.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/udop/modeling_udop.py | Apache-2.0 |
def __init__(self, hidden_size, eps=1e-6):
"""
Construct a layernorm module in the Udop style. No bias and no subtraction of mean.
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps |
Construct a layernorm module in the Udop style. No bias and no subtraction of mean.
| __init__ | python | huggingface/transformers | src/transformers/models/udop/modeling_udop.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/udop/modeling_udop.py | Apache-2.0 |
def __init__(self, modules: Sequence[RelativePositionBiasBase]):
"""
Class which sums up various computed biases.
Args:
modules (Sequence[RelativePositionBiasBase]):
List of relative bias modules.
"""
super().__init__()
self.biases = nn.Module... |
Class which sums up various computed biases.
Args:
modules (Sequence[RelativePositionBiasBase]):
List of relative bias modules.
| __init__ | python | huggingface/transformers | src/transformers/models/udop/modeling_udop.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/udop/modeling_udop.py | Apache-2.0 |
def create_relative_bias(config: UdopConfig) -> Sequence[RelativePositionBiasBase]:
"""
Creates empty list or one/multiple relative biases.
:param config: Model's configuration :return: Sequence with created bias modules.
"""
bias_list = []
if hasattr(config, "relative_bias_args"):
for ... |
Creates empty list or one/multiple relative biases.
:param config: Model's configuration :return: Sequence with created bias modules.
| create_relative_bias | python | huggingface/transformers | src/transformers/models/udop/modeling_udop.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/udop/modeling_udop.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[Tensor] = None,
attention_mask: Optional[Tensor] = None,
bbox: Optional[Dict[str, Any]] = None,
pixel_values: Optional[Tensor] = None,
visual_bbox: Optional[Dict[str, Any]] = None,
decoder_input_ids: Optional[Tensor] = None,
... |
bbox (`torch.LongTensor` of shape `({0}, 4)`, *optional*):
Bounding boxes of each input sequence tokens. Selected in the range `[0,
config.max_2d_position_embeddings-1]`. Each bounding box should be a normalized version in (x0, y0, x1, y1)
format, where (x0, y0) corresponds ... | forward | python | huggingface/transformers | src/transformers/models/udop/modeling_udop.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/udop/modeling_udop.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[Tensor] = None,
attention_mask: Optional[Tensor] = None,
bbox: Optional[Dict[str, Any]] = None,
pixel_values: Optional[Tensor] = None,
visual_bbox: Optional[Dict[str, Any]] = None,
decoder_input_ids: Optional[Tensor] = None,
... |
bbox (`torch.LongTensor` of shape `({0}, 4)`, *optional*):
Bounding boxes of each input sequence tokens. Selected in the range `[0,
config.max_2d_position_embeddings-1]`. Each bounding box should be a normalized version in (x0, y0, x1, y1)
format, where (x0, y0) corresponds ... | forward | python | huggingface/transformers | src/transformers/models/udop/modeling_udop.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/udop/modeling_udop.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[Tensor] = None,
bbox: Optional[Dict[str, Any]] = None,
attention_mask: Optional[Tensor] = None,
pixel_values: Optional[Tensor] = None,
visual_bbox: Optional[Dict[str, Any]] = None,
head_mask: Optional[Tensor] = None,
... |
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. T5 is a model with relative position embeddings so you
should be able to pad the inputs on both the right and the left.
Indices can be obtained using... | forward | python | huggingface/transformers | src/transformers/models/udop/modeling_udop.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/udop/modeling_udop.py | Apache-2.0 |
def __call__(
self,
images: Optional[ImageInput] = None,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
# The following is to capture `text_pair` argument that may be passed as a positional argument.
# See transformers.processing_utils... |
This method first forwards the `images` argument to [`~UdopImageProcessor.__call__`]. In case
[`UdopImageProcessor`] was initialized with `apply_ocr` set to `True`, it passes the obtained words and
bounding boxes along with the additional arguments to [`~UdopTokenizer.__call__`] and returns the... | __call__ | python | huggingface/transformers | src/transformers/models/udop/processing_udop.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/udop/processing_udop.py | Apache-2.0 |
def batch_encode_plus_boxes(
self,
batch_text_or_text_pairs: Union[
List[TextInput],
List[TextInputPair],
List[PreTokenizedInput],
],
is_pair: Optional[bool] = None,
boxes: Optional[List[List[List[int]]]] = None,
word_labels: Optional[L... |
Tokenize and prepare for the model a list of sequences or a list of pairs of sequences.
Args:
batch_text_or_text_pairs (`List[str]`, `List[Tuple[str, str]]`, `List[List[str]]`, `List[Tuple[List[str], List[str]]]`, and for not-fast tokenizers, also `List[List[int]]`, `List[Tuple[List[int], ... | batch_encode_plus_boxes | python | huggingface/transformers | src/transformers/models/udop/tokenization_udop.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/udop/tokenization_udop.py | Apache-2.0 |
def encode_boxes(
self,
text: Union[TextInput, PreTokenizedInput, EncodedInput],
text_pair: Optional[Union[TextInput, PreTokenizedInput, EncodedInput]] = None,
boxes: Optional[List[List[int]]] = None,
word_labels: Optional[List[List[int]]] = None,
add_special_tokens: bool... |
Args:
Converts a string to a sequence of ids (integer), using the tokenizer and vocabulary. Same as doing
`self.convert_tokens_to_ids(self.tokenize(text))`.
text (`str`, `List[str]` or `List[int]`):
The first sequence to be encoded. This can be a string, a list of st... | encode_boxes | python | huggingface/transformers | src/transformers/models/udop/tokenization_udop.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/udop/tokenization_udop.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.BoolTensor] = None,
head_mask: Optional[torch.FloatTensor] = N... |
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. UMT5 is a model with relative position embeddings so
you should be able to pad the inputs on both the right and the left.
Indices can be obtained usi... | forward | python | huggingface/transformers | src/transformers/models/umt5/modeling_umt5.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/umt5/modeling_umt5.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.BoolTensor] = None,
head_mask: Optional[torch.FloatTensor] = N... |
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. UMT5 is a model with relative position embeddings so
you should be able to pad the inputs on both the right and the left.
Indices can be obtained usi... | forward | python | huggingface/transformers | src/transformers/models/umt5/modeling_umt5.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/umt5/modeling_umt5.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_... |
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. UMT5 is a model with relative position embeddings so you
should be able to pad the inputs on both the right and the left.
Indices can be obtained usi... | forward | python | huggingface/transformers | src/transformers/models/umt5/modeling_umt5.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/umt5/modeling_umt5.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
... |
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. UMT5 is a model with relative position embeddings so
you should be able to pad the inputs on both the right and the left.
Indices can be obtained usi... | forward | python | huggingface/transformers | src/transformers/models/umt5/modeling_umt5.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/umt5/modeling_umt5.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[b... |
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. UMT5 is a model with relative position embeddings so you
should be able to pad the inputs on both the right and the left.
Indices can be obtained usi... | forward | python | huggingface/transformers | src/transformers/models/umt5/modeling_umt5.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/umt5/modeling_umt5.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.BoolTensor] = None,
head_mask: Optional[torch.FloatTensor] = N... |
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. UMT5 is a model with relative position embeddings so
you should be able to pad the inputs on both the right and the left.
Indices can be obtained usi... | forward | python | huggingface/transformers | src/transformers/models/umt5/modeling_umt5.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/umt5/modeling_umt5.py | Apache-2.0 |
def forward(
self,
input_values: Optional[torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, UniSpeechForPreTraining... |
Example:
```python
>>> import torch
>>> from transformers import AutoFeatureExtractor, UniSpeechForPreTraining
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("microsoft/unispeech-large-1500h-cv")
>>> model = UniSpeechForPreTraining.from_pretrained("micros... | forward | python | huggingface/transformers | src/transformers/models/unispeech/modeling_unispeech.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/unispeech/modeling_unispeech.py | Apache-2.0 |
def __init__(self, config, target_lang: Optional[str] = None):
r"""
target_lang (`str`, *optional*):
Language id of adapter weights. Adapter weights are stored in the format adapter.<lang>.safetensors or
adapter.<lang>.bin. Only relevant when using an instance of [`UniSpeechForCT... |
target_lang (`str`, *optional*):
Language id of adapter weights. Adapter weights are stored in the format adapter.<lang>.safetensors or
adapter.<lang>.bin. Only relevant when using an instance of [`UniSpeechForCTC`] with adapters. Uses 'eng' by
default.
| __init__ | python | huggingface/transformers | src/transformers/models/unispeech/modeling_unispeech.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/unispeech/modeling_unispeech.py | Apache-2.0 |
def forward(
self,
input_values: Optional[torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[torch.Tensor] = None... |
input_values (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
Float values of input raw speech waveform. Values can be obtained by loading a `.flac` or `.wav` audio file
into an array of type `List[float]` or a `numpy.ndarray`, *e.g.* via the soundfile library (`pip insta... | forward | python | huggingface/transformers | src/transformers/models/unispeech/modeling_unispeech.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/unispeech/modeling_unispeech.py | Apache-2.0 |
def forward(
self,
input_values: Optional[torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, UniSpeechSatForPreTrain... |
Example:
```python
>>> import torch
>>> from transformers import AutoFeatureExtractor, UniSpeechSatForPreTraining
>>> from transformers.models.unispeech_sat.modeling_unispeech_sat import _compute_mask_indices
>>> feature_extractor = AutoFeatureExtractor.from_pretrained... | forward | python | huggingface/transformers | src/transformers/models/unispeech_sat/modeling_unispeech_sat.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/unispeech_sat/modeling_unispeech_sat.py | Apache-2.0 |
def __init__(self, config, target_lang: Optional[str] = None):
r"""
target_lang (`str`, *optional*):
Language id of adapter weights. Adapter weights are stored in the format adapter.<lang>.safetensors or
adapter.<lang>.bin. Only relevant when using an instance of [`UniSpeechSatFo... |
target_lang (`str`, *optional*):
Language id of adapter weights. Adapter weights are stored in the format adapter.<lang>.safetensors or
adapter.<lang>.bin. Only relevant when using an instance of [`UniSpeechSatForCTC`] with adapters. Uses 'eng' by
default.
| __init__ | python | huggingface/transformers | src/transformers/models/unispeech_sat/modeling_unispeech_sat.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/unispeech_sat/modeling_unispeech_sat.py | Apache-2.0 |
def forward(
self,
input_values: Optional[torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[torch.Tensor] = None... |
input_values (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
Float values of input raw speech waveform. Values can be obtained by loading a `.flac` or `.wav` audio file
into an array of type `List[float]` or a `numpy.ndarray`, *e.g.* via the soundfile library (`pip insta... | forward | python | huggingface/transformers | src/transformers/models/unispeech_sat/modeling_unispeech_sat.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/unispeech_sat/modeling_unispeech_sat.py | Apache-2.0 |
def mel_spectrogram(self, waveform: np.ndarray) -> np.ndarray:
"""
Calculates log MEL spectrograms from a batch of waveforms. Note that the input waveform(s) will be padded by
`int(self.n_fft - self.hop_length) / 2` on both sides using the `reflect` padding mode.
Args:
wavef... |
Calculates log MEL spectrograms from a batch of waveforms. Note that the input waveform(s) will be padded by
`int(self.n_fft - self.hop_length) / 2` on both sides using the `reflect` padding mode.
Args:
waveform (`np.ndarray` of shape `(length,)`):
The input wavefor... | mel_spectrogram | python | huggingface/transformers | src/transformers/models/univnet/feature_extraction_univnet.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/univnet/feature_extraction_univnet.py | Apache-2.0 |
def generate_noise(
self,
noise_length: int,
generator: Optional[np.random.Generator] = None,
) -> np.ndarray:
"""
Generates a random noise sequence of standard Gaussian noise for use in the `noise_sequence` argument of
[`UnivNetModel.forward`].
Args:
... |
Generates a random noise sequence of standard Gaussian noise for use in the `noise_sequence` argument of
[`UnivNetModel.forward`].
Args:
spectrogram_length (`int`):
The length (dim 0) of the generated noise.
model_in_channels (`int`, *optional*, defaults... | generate_noise | python | huggingface/transformers | src/transformers/models/univnet/feature_extraction_univnet.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/univnet/feature_extraction_univnet.py | Apache-2.0 |
def batch_decode(self, waveforms, waveform_lengths=None) -> List[np.ndarray]:
r"""
Removes padding from generated audio after running [`UnivNetModel.forward`]. This returns a ragged list of 1D
audio waveform arrays and not a single tensor/array because in general the waveforms will have differen... |
Removes padding from generated audio after running [`UnivNetModel.forward`]. This returns a ragged list of 1D
audio waveform arrays and not a single tensor/array because in general the waveforms will have different
lengths after removing padding.
Args:
waveforms (`torch.Flo... | batch_decode | python | huggingface/transformers | src/transformers/models/univnet/feature_extraction_univnet.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/univnet/feature_extraction_univnet.py | Apache-2.0 |
def __call__(
self,
raw_speech: Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],
sampling_rate: Optional[int] = None,
padding: Union[bool, str, PaddingStrategy] = True,
max_length: Optional[int] = None,
truncation: bool = True,
pad_to_multiple_... |
Main method to featurize and prepare for the model one or several sequence(s).
Args:
raw_speech (`np.ndarray`, `List[float]`, `List[np.ndarray]`, `List[List[float]]`):
The sequence or batch of sequences to be padded. Each sequence can be a numpy array, a list of float
... | __call__ | python | huggingface/transformers | src/transformers/models/univnet/feature_extraction_univnet.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/univnet/feature_extraction_univnet.py | Apache-2.0 |
def forward(self, spectrogram: torch.FloatTensor):
"""
Maps a conditioning log-mel spectrogram to a tensor of convolutional kernels and biases, for use in location
variable convolutional layers. Note that the input spectrogram should have shape (batch_size, input_channels,
seq_length).
... |
Maps a conditioning log-mel spectrogram to a tensor of convolutional kernels and biases, for use in location
variable convolutional layers. Note that the input spectrogram should have shape (batch_size, input_channels,
seq_length).
Args:
spectrogram (`torch.FloatTensor` of ... | forward | python | huggingface/transformers | src/transformers/models/univnet/modeling_univnet.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/univnet/modeling_univnet.py | Apache-2.0 |
def forward(
self,
input_features: torch.FloatTensor,
noise_sequence: Optional[torch.FloatTensor] = None,
padding_mask: Optional[torch.FloatTensor] = None,
generator: Optional[torch.Generator] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.FloatTen... |
input_features (`torch.FloatTensor`):
Tensor containing the log-mel spectrograms. Can be batched and of shape `(batch_size, sequence_length,
config.num_mel_channels)`, or un-batched and of shape `(sequence_length, config.num_mel_channels)`.
noise_sequence (`torch.FloatTensor`, *... | forward | python | huggingface/transformers | src/transformers/models/univnet/modeling_univnet.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/univnet/modeling_univnet.py | Apache-2.0 |
def forward(
self,
pixel_values: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
labels: Optional[torch.Tensor] = None,
return_dict: Optional[bool] = None,
) -> Union[tuple, SemanticSegmenterOutput]... |
labels (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*):
Ground truth semantic segmentation maps for computing the loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels > 1`, a classification loss is computed (Cross-Entropy).
... | forward | python | huggingface/transformers | src/transformers/models/upernet/modeling_upernet.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/upernet/modeling_upernet.py | Apache-2.0 |
def forward(
self,
pixel_values: torch.FloatTensor,
bool_masked_pos: Optional[torch.BoolTensor] = None,
head_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = N... |
bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, sequence_length)`, *optional*):
Boolean masked positions. Indicates which patches are masked (1) and which aren't (0). Each video in the
batch must have the same number of masked patches. If `None`, then all patches are consider... | forward | python | huggingface/transformers | src/transformers/models/videomae/modeling_videomae.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/videomae/modeling_videomae.py | Apache-2.0 |
def forward(
self,
pixel_values: torch.FloatTensor,
bool_masked_pos: torch.BoolTensor,
head_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Uni... |
bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, sequence_length)`):
Boolean masked positions. Indicates which patches are masked (1) and which aren't (0). Each video in the
batch must have the same number of masked patches. Sequence length is `(num_frames // tubelet_size) *
... | forward | python | huggingface/transformers | src/transformers/models/videomae/modeling_videomae.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/videomae/modeling_videomae.py | Apache-2.0 |
def forward(
self,
pixel_values: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = No... |
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.n... | forward | python | huggingface/transformers | src/transformers/models/videomae/modeling_videomae.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/videomae/modeling_videomae.py | Apache-2.0 |
def preprocess(
self,
images: Optional[List[ImageInput]] = None,
videos: Optional[List[VideoInput]] = None,
do_resize: Optional[bool] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = None,
do_center_crop: Optional[bool] = None,
... |
Preprocess an image or batch of images.
Args:
images (`ImageInput`, *optional*):
List of images to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
passing in images with pixel values between 0 and 1, set `do_rescal... | preprocess | python | huggingface/transformers | src/transformers/models/video_llava/image_processing_video_llava.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/video_llava/image_processing_video_llava.py | Apache-2.0 |
def get_image_features(
self,
pixel_values_images: torch.FloatTensor,
vision_feature_layer: Optional[Union[int, List[int]]] = None,
vision_feature_select_strategy: Optional[str] = None,
):
"""
Obtains image last hidden states from the vision tower and apply multimodal... |
Obtains image last hidden states from the vision tower and apply multimodal projection.
Args:
pixel_values_images (`torch.FloatTensor]` of shape `(batch_size, channels, height, width)`)
The tensors corresponding to the input images.
vision_feature_layer (`Union[i... | get_image_features | python | huggingface/transformers | src/transformers/models/video_llava/modeling_video_llava.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/video_llava/modeling_video_llava.py | Apache-2.0 |
def get_video_features(
self,
pixel_values_videos: torch.FloatTensor,
vision_feature_layer: Optional[Union[int, List[int]]] = None,
):
"""
Obtains video last hidden states from the vision tower and apply multimodal projection.
Args:
pixel_values_videos (`... |
Obtains video last hidden states from the vision tower and apply multimodal projection.
Args:
pixel_values_videos (`torch.FloatTensor]` of shape `(batch_size, num_frames, channels, height, width)`)
The tensors corresponding to the input videos.
vision_feature_lay... | get_video_features | python | huggingface/transformers | src/transformers/models/video_llava/modeling_video_llava.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/video_llava/modeling_video_llava.py | Apache-2.0 |
def forward(
self,
input_ids: torch.LongTensor = None,
pixel_values_images: torch.FloatTensor = None,
pixel_values_videos: torch.FloatTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Op... |
pixel_values_images (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)):
The tensors corresponding to the input images. Pixel values can be obtained using
[`AutoImageProcessor`]. See [`VideoLlavaImageProcessor.__call__`] for details ([]`LlavaProcessor`] us... | forward | python | huggingface/transformers | src/transformers/models/video_llava/modeling_video_llava.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/video_llava/modeling_video_llava.py | Apache-2.0 |
def __call__(
self,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
images: ImageInput = None,
videos: ImageInput = None,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy] = Non... |
Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
and `kwargs` arguments to LlamaTokenizerFast's [`~LlamaTokenizerFast.__call__`] if `text` is not `None` to encode
the text. To prepare the image(s), this method forwards the `images` a... | __call__ | python | huggingface/transformers | src/transformers/models/video_llava/processing_video_llava.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/video_llava/processing_video_llava.py | Apache-2.0 |
def from_dict(cls, image_processor_dict: Dict[str, Any], **kwargs):
"""
Overrides the `from_dict` method from the base class to make sure `pad_and_return_pixel_mask` is updated if image processor
is created using from_dict and kwargs e.g. `ViltImageProcessor.from_pretrained(checkpoint,
p... |
Overrides the `from_dict` method from the base class to make sure `pad_and_return_pixel_mask` is updated if image processor
is created using from_dict and kwargs e.g. `ViltImageProcessor.from_pretrained(checkpoint,
pad_and_return_pixel_mask=False)`
| from_dict | python | huggingface/transformers | src/transformers/models/vilt/image_processing_vilt.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/vilt/image_processing_vilt.py | Apache-2.0 |
def _preprocess(
self,
images: list["torch.Tensor"],
do_resize: bool,
size: SizeDict,
interpolation: Optional["F.InterpolationMode"],
size_divisor: Optional[int],
do_pad: bool,
do_rescale: bool,
rescale_factor: float,
do_normalize: bool,
... |
Preprocess an image or batch of images.
This method overrides the base class method to include padding and pixel mask generation.
| _preprocess | python | huggingface/transformers | src/transformers/models/vilt/image_processing_vilt_fast.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/vilt/image_processing_vilt_fast.py | Apache-2.0 |
def resize(
self,
images: "torch.Tensor",
size: SizeDict,
interpolation: Optional["F.InterpolationMode"] = None,
size_divisor: Optional[int] = None,
) -> "torch.Tensor":
"""
Resize an image or batch of images to specified size.
Args:
image... |
Resize an image or batch of images to specified size.
Args:
images (`torch.Tensor`): Image or batch of images to resize.
size (`Dict[str, int]`): Size dictionary with shortest_edge key.
interpolation (`F.InterpolationMode`, *optional*): Interpolation method to use.
... | resize | python | huggingface/transformers | src/transformers/models/vilt/image_processing_vilt_fast.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/vilt/image_processing_vilt_fast.py | Apache-2.0 |
def _pad_batch(
self,
images: list["torch.Tensor"],
return_tensors: Optional[Union[str, TensorType]],
) -> tuple:
"""
Pad a batch of images to the same size based on the maximum dimensions.
Args:
images (`list[torch.Tensor]`): List of images to pad.
... |
Pad a batch of images to the same size based on the maximum dimensions.
Args:
images (`list[torch.Tensor]`): List of images to pad.
return_tensors (`str` or `TensorType`, *optional*): The type of tensors to return.
Returns:
`tuple`: Tuple containing padded ... | _pad_batch | python | huggingface/transformers | src/transformers/models/vilt/image_processing_vilt_fast.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/vilt/image_processing_vilt_fast.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
pixel_mask: Optional[torch.LongTensor] = None,
... |
image_embeds (`torch.FloatTensor` of shape `(batch_size, num_patches, hidden_size)`, *optional*):
Optionally, instead of passing `pixel_values`, you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `pixel_values` into pa... | forward | python | huggingface/transformers | src/transformers/models/vilt/modeling_vilt.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/vilt/modeling_vilt.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
pixel_mask: Optional[torch.LongTensor] = None,
... |
image_embeds (`torch.FloatTensor` of shape `(batch_size, num_patches, hidden_size)`, *optional*):
Optionally, instead of passing `pixel_values`, you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `pixel_values` into pa... | forward | python | huggingface/transformers | src/transformers/models/vilt/modeling_vilt.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/vilt/modeling_vilt.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
pixel_mask: Optional[torch.LongTensor] = None,
... |
image_embeds (`torch.FloatTensor` of shape `(batch_size, num_patches, hidden_size)`, *optional*):
Optionally, instead of passing `pixel_values`, you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `pixel_values` into pa... | forward | python | huggingface/transformers | src/transformers/models/vilt/modeling_vilt.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/vilt/modeling_vilt.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
pixel_mask: Optional[torch.LongTensor] = None,
... |
image_embeds (`torch.FloatTensor` of shape `(batch_size, num_patches, hidden_size)`, *optional*):
Optionally, instead of passing `pixel_values`, you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `pixel_values` into pa... | forward | python | huggingface/transformers | src/transformers/models/vilt/modeling_vilt.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/vilt/modeling_vilt.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
pixel_mask: Optional[torch.LongTensor] = None,
... |
image_embeds (`torch.FloatTensor` of shape `(batch_size, num_patches, hidden_size)`, *optional*):
Optionally, instead of passing `pixel_values`, you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `pixel_values` into pa... | forward | python | huggingface/transformers | src/transformers/models/vilt/modeling_vilt.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/vilt/modeling_vilt.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
pixel_mask: Optional[torch.LongTensor] = None,
... |
image_embeds (`torch.FloatTensor` of shape `(batch_size, num_patches, hidden_size)`, *optional*):
Optionally, instead of passing `pixel_values`, you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `pixel_values` into pa... | forward | python | huggingface/transformers | src/transformers/models/vilt/modeling_vilt.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/vilt/modeling_vilt.py | Apache-2.0 |
def get_image_features(
self, pixel_values: torch.FloatTensor, vision_feature_layers: Optional[Union[int, List[int]]] = None
):
"""
Obtains image last hidden states from the vision tower and apply multimodal projection.
Args:
pixel_values (`torch.FloatTensor]` of shape `... |
Obtains image last hidden states from the vision tower and apply multimodal projection.
Args:
pixel_values (`torch.FloatTensor]` of shape `(batch_size, channels, height, width)`)
The tensors corresponding to the input images.
vision_feature_layers (`Union[int, Li... | get_image_features | python | huggingface/transformers | src/transformers/models/vipllava/modeling_vipllava.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/vipllava/modeling_vipllava.py | Apache-2.0 |
def forward(
self,
input_ids: torch.LongTensor = None,
pixel_values: torch.FloatTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds:... |
vision_feature_layers (`Union[int, List[int]]`, *optional*):
The vision feature layer, or the list of indexes of the layers to select
the vision feature.
| forward | python | huggingface/transformers | src/transformers/models/vipllava/modeling_vipllava.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/vipllava/modeling_vipllava.py | Apache-2.0 |
def forward(
self,
input_ids: torch.LongTensor = None,
pixel_values: torch.FloatTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds:... |
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
... | forward | python | huggingface/transformers | src/transformers/models/vipllava/modeling_vipllava.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/vipllava/modeling_vipllava.py | Apache-2.0 |
def encode(
self,
pixel_values: jnp.ndarray,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
train: bool = False,
params: Optional[dict] = None,
dropout_rng: PRNGKey = None,
):
... |
Returns:
Example:
```python
>>> from transformers import AutoImageProcessor, FlaxVisionEncoderDecoderModel
>>> from PIL import Image
>>> import requests
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.... | encode | python | huggingface/transformers | src/transformers/models/vision_encoder_decoder/modeling_flax_vision_encoder_decoder.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/vision_encoder_decoder/modeling_flax_vision_encoder_decoder.py | Apache-2.0 |
def decode(
self,
decoder_input_ids,
encoder_outputs,
decoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_position_ids: Optional[jnp.ndarray] = None,
past_key_values: Optional[dict] = None,
output_attentions: Optional[bool] = None,
output_hidden_... |
Returns:
Example:
```python
>>> from transformers import AutoImageProcessor, FlaxVisionEncoderDecoderModel
>>> import jax.numpy as jnp
>>> from PIL import Image
>>> import requests
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
... | decode | python | huggingface/transformers | src/transformers/models/vision_encoder_decoder/modeling_flax_vision_encoder_decoder.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/vision_encoder_decoder/modeling_flax_vision_encoder_decoder.py | Apache-2.0 |
def __call__(
self,
pixel_values: jnp.ndarray,
decoder_input_ids: Optional[jnp.ndarray] = None,
decoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_position_ids: Optional[jnp.ndarray] = None,
output_attentions: Optional[bool] = None,
output_hidden_states... |
Returns:
Examples:
```python
>>> from transformers import FlaxVisionEncoderDecoderModel, AutoImageProcessor, AutoTokenizer
>>> from PIL import Image
>>> import requests
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Imag... | __call__ | python | huggingface/transformers | src/transformers/models/vision_encoder_decoder/modeling_flax_vision_encoder_decoder.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/vision_encoder_decoder/modeling_flax_vision_encoder_decoder.py | Apache-2.0 |
def from_encoder_decoder_pretrained(
cls,
encoder_pretrained_model_name_or_path: Optional[Union[str, os.PathLike]] = None,
decoder_pretrained_model_name_or_path: Optional[Union[str, os.PathLike]] = None,
*model_args,
**kwargs,
) -> FlaxPreTrainedModel:
r"""
In... |
Instantiate an encoder and a decoder from one or two base classes of the library from pretrained model
checkpoints.
Params:
encoder_pretrained_model_name_or_path (`Union[str, os.PathLike]`, *optional*):
Information necessary to initiate the encoder. Can be either:
... | from_encoder_decoder_pretrained | python | huggingface/transformers | src/transformers/models/vision_encoder_decoder/modeling_flax_vision_encoder_decoder.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/vision_encoder_decoder/modeling_flax_vision_encoder_decoder.py | Apache-2.0 |
def from_encoder_decoder_pretrained(
cls,
encoder_pretrained_model_name_or_path: Optional[str] = None,
decoder_pretrained_model_name_or_path: Optional[str] = None,
*model_args,
**kwargs,
) -> TFPreTrainedModel:
r"""
Instantiate an encoder and a decoder from on... |
Instantiate an encoder and a decoder from one or two base classes of the library from pretrained model
checkpoints.
Params:
encoder_pretrained_model_name_or_path (`str`, *optional*):
Information necessary to initiate the encoder. Can be either:
... | from_encoder_decoder_pretrained | python | huggingface/transformers | src/transformers/models/vision_encoder_decoder/modeling_tf_vision_encoder_decoder.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/vision_encoder_decoder/modeling_tf_vision_encoder_decoder.py | Apache-2.0 |
def call(
self,
pixel_values: np.ndarray | tf.Tensor | None = None,
decoder_input_ids: np.ndarray | tf.Tensor | None = None,
decoder_attention_mask: np.ndarray | tf.Tensor | None = None,
encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None,
past_key_values: O... |
Returns:
Examples:
```python
>>> from transformers import AutoImageProcessor, AutoTokenizer, TFVisionEncoderDecoderModel
>>> from PIL import Image
>>> import requests
>>> image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224-in21k")... | call | python | huggingface/transformers | src/transformers/models/vision_encoder_decoder/modeling_tf_vision_encoder_decoder.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/vision_encoder_decoder/modeling_tf_vision_encoder_decoder.py | Apache-2.0 |
def from_encoder_decoder_pretrained(
cls,
encoder_pretrained_model_name_or_path: Optional[str] = None,
decoder_pretrained_model_name_or_path: Optional[str] = None,
*model_args,
**kwargs,
) -> PreTrainedModel:
r"""
Instantiate an encoder and a decoder from one ... |
Instantiate an encoder and a decoder from one or two base classes of the library from pretrained model
checkpoints.
The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated). To train
the model, you need to first set it back in training mode... | from_encoder_decoder_pretrained | python | huggingface/transformers | src/transformers/models/vision_encoder_decoder/modeling_vision_encoder_decoder.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/vision_encoder_decoder/modeling_vision_encoder_decoder.py | Apache-2.0 |
def forward(
self,
pixel_values: Optional[torch.FloatTensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.BoolTensor] = None,
encoder_outputs: Optional[Tuple[torch.FloatTensor]] = None,
past_key_values: Optional[Tupl... |
decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenize... | forward | python | huggingface/transformers | src/transformers/models/vision_encoder_decoder/modeling_vision_encoder_decoder.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/vision_encoder_decoder/modeling_vision_encoder_decoder.py | Apache-2.0 |
def from_vision_text_pretrained(
cls,
vision_model_name_or_path: Optional[str] = None,
text_model_name_or_path: Optional[str] = None,
*model_args,
**kwargs,
) -> FlaxPreTrainedModel:
"""
Params:
vision_model_name_or_path (`str`, *optional*, default... |
Params:
vision_model_name_or_path (`str`, *optional*, defaults to `None`):
Information necessary to initiate the vision model. Can be either:
- A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
- A p... | from_vision_text_pretrained | python | huggingface/transformers | src/transformers/models/vision_text_dual_encoder/modeling_flax_vision_text_dual_encoder.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/vision_text_dual_encoder/modeling_flax_vision_text_dual_encoder.py | Apache-2.0 |
def call(
self,
input_ids: tf.Tensor | None = None,
pixel_values: tf.Tensor | None = None,
attention_mask: tf.Tensor | None = None,
position_ids: tf.Tensor | None = None,
return_loss: Optional[bool] = None,
token_type_ids: tf.Tensor | None = None,
output_a... |
Returns:
Examples:
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import (
... TFVisionTextDualEncoderModel,
... VisionTextDualEncoderProcessor,
... AutoImageProcessor,
... AutoTokenizer,
... | call | python | huggingface/transformers | src/transformers/models/vision_text_dual_encoder/modeling_tf_vision_text_dual_encoder.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/vision_text_dual_encoder/modeling_tf_vision_text_dual_encoder.py | Apache-2.0 |
def from_vision_text_pretrained(
cls,
vision_model_name_or_path: Optional[str] = None,
text_model_name_or_path: Optional[str] = None,
*model_args,
**kwargs,
) -> TFPreTrainedModel:
"""
Params:
vision_model_name_or_path (`str`, *optional*, defaults ... |
Params:
vision_model_name_or_path (`str`, *optional*, defaults to `None`):
Information necessary to initiate the vision model. Can be either:
- A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
- A p... | from_vision_text_pretrained | python | huggingface/transformers | src/transformers/models/vision_text_dual_encoder/modeling_tf_vision_text_dual_encoder.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/vision_text_dual_encoder/modeling_tf_vision_text_dual_encoder.py | Apache-2.0 |
def __init__(
self,
config: Optional[VisionTextDualEncoderConfig] = None,
vision_model: Optional[PreTrainedModel] = None,
text_model: Optional[PreTrainedModel] = None,
):
r"""
vision_model (`PreTrainedModel`):
The vision model to use.
text_model (`... |
vision_model (`PreTrainedModel`):
The vision model to use.
text_model (`PreTrainedModel`):
The text model to use.
| __init__ | python | huggingface/transformers | src/transformers/models/vision_text_dual_encoder/modeling_vision_text_dual_encoder.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/vision_text_dual_encoder/modeling_vision_text_dual_encoder.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
return_loss: Optional[bool] = None,
token_type_ids: O... |
return_loss (`bool`, *optional*):
Whether or not to return the contrastive loss.
Examples:
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import (
... VisionTextDualEncoderModel,
... VisionTextDualEncod... | forward | python | huggingface/transformers | src/transformers/models/vision_text_dual_encoder/modeling_vision_text_dual_encoder.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/vision_text_dual_encoder/modeling_vision_text_dual_encoder.py | Apache-2.0 |
def from_vision_text_pretrained(
cls,
vision_model_name_or_path: Optional[str] = None,
text_model_name_or_path: Optional[str] = None,
*model_args,
**kwargs,
) -> PreTrainedModel:
"""
Params:
vision_model_name_or_path (`str`, *optional*, defaults to... |
Params:
vision_model_name_or_path (`str`, *optional*, defaults to `None`):
Information necessary to initiate the vision model. Can be either:
- A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
- A p... | from_vision_text_pretrained | python | huggingface/transformers | src/transformers/models/vision_text_dual_encoder/modeling_vision_text_dual_encoder.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/vision_text_dual_encoder/modeling_vision_text_dual_encoder.py | Apache-2.0 |
def __call__(
self,
images: Optional[ImageInput] = None,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
audio=None,
videos=None,
**kwargs: Unpack[VisionTextDualEncoderProcessorKwargs],
) -> BatchEncoding:
"""
... |
Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
and `kwargs` arguments to VisionTextDualEncoderTokenizer's [`~PreTrainedTokenizer.__call__`] if `text` is not
`None` to encode the text. To prepare the image(s), this method forwards t... | __call__ | python | huggingface/transformers | src/transformers/models/vision_text_dual_encoder/processing_vision_text_dual_encoder.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/vision_text_dual_encoder/processing_vision_text_dual_encoder.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.LongTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.LongTensor] = None,
in... |
visual_embeds (`torch.FloatTensor` of shape `(batch_size, visual_seq_length, visual_embedding_dim)`, *optional*):
The embedded representation of the visual inputs, generally derived using using an object detector.
visual_attention_mask (`torch.FloatTensor` of shape `(batch_size, visual_seq_... | forward | python | huggingface/transformers | src/transformers/models/visual_bert/modeling_visual_bert.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/visual_bert/modeling_visual_bert.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.LongTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.LongTensor] = None,
in... |
visual_embeds (`torch.FloatTensor` of shape `(batch_size, visual_seq_length, visual_embedding_dim)`, *optional*):
The embedded representation of the visual inputs, generally derived using using an object detector.
visual_attention_mask (`torch.FloatTensor` of shape `(batch_size, visual_seq_... | forward | python | huggingface/transformers | src/transformers/models/visual_bert/modeling_visual_bert.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/visual_bert/modeling_visual_bert.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.LongTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.LongTensor] = None,
in... |
input_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
... | forward | python | huggingface/transformers | src/transformers/models/visual_bert/modeling_visual_bert.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/visual_bert/modeling_visual_bert.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.LongTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.LongTensor] = None,
in... |
visual_embeds (`torch.FloatTensor` of shape `(batch_size, visual_seq_length, visual_embedding_dim)`, *optional*):
The embedded representation of the visual inputs, generally derived using using an object detector.
visual_attention_mask (`torch.FloatTensor` of shape `(batch_size, visual_seq_... | forward | python | huggingface/transformers | src/transformers/models/visual_bert/modeling_visual_bert.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/visual_bert/modeling_visual_bert.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.LongTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.LongTensor] = None,
in... |
visual_embeds (`torch.FloatTensor` of shape `(batch_size, visual_seq_length, visual_embedding_dim)`, *optional*):
The embedded representation of the visual inputs, generally derived using using an object detector.
visual_attention_mask (`torch.FloatTensor` of shape `(batch_size, visual_seq_... | forward | python | huggingface/transformers | src/transformers/models/visual_bert/modeling_visual_bert.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/visual_bert/modeling_visual_bert.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.LongTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.LongTensor] = None,
in... |
visual_embeds (`torch.FloatTensor` of shape `(batch_size, visual_seq_length, visual_embedding_dim)`, *optional*):
The embedded representation of the visual inputs, generally derived using using an object detector.
visual_attention_mask (`torch.FloatTensor` of shape `(batch_size, visual_seq_... | forward | python | huggingface/transformers | src/transformers/models/visual_bert/modeling_visual_bert.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/visual_bert/modeling_visual_bert.py | Apache-2.0 |
def preprocess(
self,
images: ImageInput,
do_resize: Optional[bool] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = None,
do_rescale: Optional[bool] = None,
rescale_factor: Optional[float] = None,
do_normalize: Optional[bool] ... |
Preprocess an image or batch of images.
Args:
images (`ImageInput`):
Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
passing in images with pixel values between 0 and 1, set `do_rescale=False`.
... | preprocess | python | huggingface/transformers | src/transformers/models/vit/image_processing_vit.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/vit/image_processing_vit.py | Apache-2.0 |
def forward(
self,
pixel_values: Optional[torch.Tensor] = None,
bool_masked_pos: Optional[torch.BoolTensor] = None,
head_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
interpolate_pos_enc... |
bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`):
Boolean masked positions. Indicates which patches are masked (1) and which aren't (0).
Examples:
```python
>>> from transformers import AutoImageProcessor, ViTForMaskedImageModeling
>>> impor... | forward | python | huggingface/transformers | src/transformers/models/vit/modeling_vit.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/vit/modeling_vit.py | Apache-2.0 |
def add_decomposed_relative_positions(attn, queries, rel_pos_h, rel_pos_w, q_size, k_size):
"""
Calculate decomposed Relative Positional Embeddings as introduced in
[MViT2](https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py).
Args:
at... |
Calculate decomposed Relative Positional Embeddings as introduced in
[MViT2](https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py).
Args:
attn (`torch.Tensor`):
Attention map.
queries (`torch.Tensor`):
Query... | add_decomposed_relative_positions | python | huggingface/transformers | src/transformers/models/vitdet/modeling_vitdet.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/vitdet/modeling_vitdet.py | Apache-2.0 |
def window_partition(hidden_state, window_size):
"""
Partition into non-overlapping windows with padding if needed.
Args:
hidden_state (`torch.Tensor`):
Input tokens with [batch_size, height, width, num_channels].
window_size (`int`):
Window size.
Returns:
... |
Partition into non-overlapping windows with padding if needed.
Args:
hidden_state (`torch.Tensor`):
Input tokens with [batch_size, height, width, num_channels].
window_size (`int`):
Window size.
Returns:
`tuple(torch.FloatTensor)` comprising various element... | window_partition | python | huggingface/transformers | src/transformers/models/vitdet/modeling_vitdet.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/vitdet/modeling_vitdet.py | Apache-2.0 |
def window_unpartition(windows, window_size, pad_height_width, height_width):
"""
Window unpartition into original sequences and removing padding.
Args:
windows (`torch.Tensor`):
Input tokens with [batch_size * num_windows, window_size, window_size, num_channels].
window_size (`... |
Window unpartition into original sequences and removing padding.
Args:
windows (`torch.Tensor`):
Input tokens with [batch_size * num_windows, window_size, window_size, num_channels].
window_size (`int`):
Window size.
pad_height_width (`Tuple[int]`):
... | window_unpartition | python | huggingface/transformers | src/transformers/models/vitdet/modeling_vitdet.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/vitdet/modeling_vitdet.py | Apache-2.0 |
def forward(
self,
pixel_values: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutput]:
... |
Examples:
```python
>>> from transformers import VitDetConfig, VitDetModel
>>> import torch
>>> config = VitDetConfig()
>>> model = VitDetModel(config)
>>> pixel_values = torch.randn(1, 3, 224, 224)
>>> with torch.no_grad():
... outputs = ... | forward | python | huggingface/transformers | src/transformers/models/vitdet/modeling_vitdet.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/vitdet/modeling_vitdet.py | Apache-2.0 |
def forward(
self,
pixel_values: torch.Tensor,
output_hidden_states: Optional[bool] = None,
output_attentions: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> BackboneOutput:
r"""
Examples:
```python
>>> from transformers impor... |
Examples:
```python
>>> from transformers import VitDetConfig, VitDetBackbone
>>> import torch
>>> config = VitDetConfig()
>>> model = VitDetBackbone(config)
>>> pixel_values = torch.randn(1, 3, 224, 224)
>>> with torch.no_grad():
... outp... | forward | python | huggingface/transformers | src/transformers/models/vitdet/modeling_vitdet.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/vitdet/modeling_vitdet.py | Apache-2.0 |
def preprocess(
self,
images: list["torch.Tensor"],
trimaps: list["torch.Tensor"],
**kwargs: Unpack[VitMatteFastImageProcessorKwargs],
) -> BatchFeature:
r"""
trimaps (`list[torch.Tensor]`):
The trimaps to preprocess.
"""
validate_kwargs(ca... |
trimaps (`list[torch.Tensor]`):
The trimaps to preprocess.
| preprocess | python | huggingface/transformers | src/transformers/models/vitmatte/image_processing_vitmatte_fast.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/vitmatte/image_processing_vitmatte_fast.py | Apache-2.0 |
def _prepare_input_trimaps(
self, trimaps: ImageInput, device: Optional["torch.device"] = None
) -> list["torch.Tensor"]:
"""
Prepare input trimaps for processing,m this can not yet deal with nested list
Args:
trimaps ('ImageInout):
The input trimaps to b... |
Prepare input trimaps for processing,m this can not yet deal with nested list
Args:
trimaps ('ImageInout):
The input trimaps to be process, should not be nested
device('Optional['torch.device'] defaults to 'self.device'):
The device to process th... | _prepare_input_trimaps | python | huggingface/transformers | src/transformers/models/vitmatte/image_processing_vitmatte_fast.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/vitmatte/image_processing_vitmatte_fast.py | Apache-2.0 |
def _pad_image(
self,
images: "torch.tensor",
size_divisibility: int = 32,
) -> "torch.tensor":
"""
Pads an image or batched images constantly so that width and height are divisible by size_divisibility
Args:
image (`torch,tensor`):
Image ... |
Pads an image or batched images constantly so that width and height are divisible by size_divisibility
Args:
image (`torch,tensor`):
Image to pad.
size_divisibility (`int`, *optional*, defaults to 32):
The width and height of the image will be pa... | _pad_image | python | huggingface/transformers | src/transformers/models/vitmatte/image_processing_vitmatte_fast.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/vitmatte/image_processing_vitmatte_fast.py | Apache-2.0 |
def forward(
self,
pixel_values: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
labels: Optional[torch.Tensor] = None,
return_dict: Optional[bool] = None,
):
r"""
labels (`torch.Lon... |
labels (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*):
Ground truth image matting for computing the loss.
Examples:
```python
>>> from transformers import VitMatteImageProcessor, VitMatteForImageMatting
>>> import torch
>>> from PIL... | forward | python | huggingface/transformers | src/transformers/models/vitmatte/modeling_vitmatte.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/vitmatte/modeling_vitmatte.py | Apache-2.0 |
def box_to_center_and_scale(
box: Union[Tuple, List, np.ndarray],
image_width: int,
image_height: int,
normalize_factor: float = 200.0,
padding_factor: float = 1.25,
):
"""
Encodes a bounding box in COCO format into (center, scale).
Args:
box (`Tuple`, `List`, or `np.ndarray`):
... |
Encodes a bounding box in COCO format into (center, scale).
Args:
box (`Tuple`, `List`, or `np.ndarray`):
Bounding box in COCO format (top_left_x, top_left_y, width, height).
image_width (`int`):
Image width.
image_height (`int`):
Image height.
... | box_to_center_and_scale | python | huggingface/transformers | src/transformers/models/vitpose/image_processing_vitpose.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/vitpose/image_processing_vitpose.py | Apache-2.0 |
def coco_to_pascal_voc(bboxes: np.ndarray) -> np.ndarray:
"""
Converts bounding boxes from the COCO format to the Pascal VOC format.
In other words, converts from (top_left_x, top_left_y, width, height) format
to (top_left_x, top_left_y, bottom_right_x, bottom_right_y).
Args:
bboxes (`np.n... |
Converts bounding boxes from the COCO format to the Pascal VOC format.
In other words, converts from (top_left_x, top_left_y, width, height) format
to (top_left_x, top_left_y, bottom_right_x, bottom_right_y).
Args:
bboxes (`np.ndarray` of shape `(batch_size, 4)):
Bounding boxes in... | coco_to_pascal_voc | python | huggingface/transformers | src/transformers/models/vitpose/image_processing_vitpose.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/vitpose/image_processing_vitpose.py | Apache-2.0 |
def get_keypoint_predictions(heatmaps: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
"""Get keypoint predictions from score maps.
Args:
heatmaps (`np.ndarray` of shape `(batch_size, num_keypoints, height, width)`):
Model predicted heatmaps.
Returns:
tuple: A tuple containing ag... | Get keypoint predictions from score maps.
Args:
heatmaps (`np.ndarray` of shape `(batch_size, num_keypoints, height, width)`):
Model predicted heatmaps.
Returns:
tuple: A tuple containing aggregated results.
- coords (`np.ndarray` of shape `(batch_size, num_keypoints, 2)`)... | get_keypoint_predictions | python | huggingface/transformers | src/transformers/models/vitpose/image_processing_vitpose.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/vitpose/image_processing_vitpose.py | Apache-2.0 |
def post_dark_unbiased_data_processing(coords: np.ndarray, batch_heatmaps: np.ndarray, kernel: int = 3) -> np.ndarray:
"""DARK post-pocessing. Implemented by unbiased_data_processing.
Paper references:
- Huang et al. The Devil is in the Details: Delving into Unbiased Data Processing for Human Pose Estimati... | DARK post-pocessing. Implemented by unbiased_data_processing.
Paper references:
- Huang et al. The Devil is in the Details: Delving into Unbiased Data Processing for Human Pose Estimation (CVPR 2020).
- Zhang et al. Distribution-Aware Coordinate Representation for Human Pose Estimation (CVPR 2020).
Ar... | post_dark_unbiased_data_processing | python | huggingface/transformers | src/transformers/models/vitpose/image_processing_vitpose.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/vitpose/image_processing_vitpose.py | Apache-2.0 |
def transform_preds(coords: np.ndarray, center: np.ndarray, scale: np.ndarray, output_size: np.ndarray) -> np.ndarray:
"""Get final keypoint predictions from heatmaps and apply scaling and
translation to map them back to the image.
Note:
num_keypoints: K
Args:
coords (`np.ndarray` of s... | Get final keypoint predictions from heatmaps and apply scaling and
translation to map them back to the image.
Note:
num_keypoints: K
Args:
coords (`np.ndarray` of shape `(num_keypoints, ndims)`):
* If ndims=2, corrds are predicted keypoint location.
* If ndims=4, c... | transform_preds | python | huggingface/transformers | src/transformers/models/vitpose/image_processing_vitpose.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/vitpose/image_processing_vitpose.py | Apache-2.0 |
def get_warp_matrix(theta: float, size_input: np.ndarray, size_dst: np.ndarray, size_target: np.ndarray):
"""
Calculate the transformation matrix under the constraint of unbiased. Paper ref: Huang et al. The Devil is in the
Details: Delving into Unbiased Data Processing for Human Pose Estimation (CVPR 2020)... |
Calculate the transformation matrix under the constraint of unbiased. Paper ref: Huang et al. The Devil is in the
Details: Delving into Unbiased Data Processing for Human Pose Estimation (CVPR 2020).
Source: https://github.com/open-mmlab/mmpose/blob/master/mmpose/core/post_processing/post_transforms.py
... | get_warp_matrix | python | huggingface/transformers | src/transformers/models/vitpose/image_processing_vitpose.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/vitpose/image_processing_vitpose.py | Apache-2.0 |
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... |
Apply an affine transformation to an image.
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 he... | affine_transform | python | huggingface/transformers | src/transformers/models/vitpose/image_processing_vitpose.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/vitpose/image_processing_vitpose.py | Apache-2.0 |
def preprocess(
self,
images: ImageInput,
boxes: Union[List[List[float]], np.ndarray],
do_affine_transform: Optional[bool] = None,
size: Optional[Dict[str, int]] = None,
do_rescale: Optional[bool] = None,
rescale_factor: Optional[float] = None,
do_normaliz... |
Preprocess an image or batch of images.
Args:
images (`ImageInput`):
Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
passing in images with pixel values between 0 and 1, set `do_rescale=False`.
... | preprocess | python | huggingface/transformers | src/transformers/models/vitpose/image_processing_vitpose.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/vitpose/image_processing_vitpose.py | Apache-2.0 |
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:
heatmaps (`np.ndarray... |
Get final keypoint predictions from heatmaps and transform them back to
the image.
Args:
heatmaps (`np.ndarray` of shape `(batch_size, num_keypoints, height, width])`):
Model predicted heatmaps.
center (`np.ndarray` of shape `(batch_size, 2)`):
... | keypoints_from_heatmaps | python | huggingface/transformers | src/transformers/models/vitpose/image_processing_vitpose.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/vitpose/image_processing_vitpose.py | Apache-2.0 |
def post_process_pose_estimation(
self,
outputs: "VitPoseEstimatorOutput",
boxes: Union[List[List[List[float]]], np.ndarray],
kernel_size: int = 11,
threshold: Optional[float] = None,
target_sizes: Union[TensorType, List[Tuple]] = None,
):
"""
Transfor... |
Transform the heatmaps into keypoint predictions and transform them back to the image.
Args:
outputs (`VitPoseEstimatorOutput`):
VitPoseForPoseEstimation model outputs.
boxes (`List[List[List[float]]]` or `np.ndarray`):
List or array of bounding ... | post_process_pose_estimation | python | huggingface/transformers | src/transformers/models/vitpose/image_processing_vitpose.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/vitpose/image_processing_vitpose.py | Apache-2.0 |
def flip_back(output_flipped, flip_pairs, target_type="gaussian-heatmap"):
"""Flip the flipped heatmaps back to the original form.
Args:
output_flipped (`torch.tensor` of shape `(batch_size, num_keypoints, height, width)`):
The output heatmaps obtained from the flipped images.
flip_... | Flip the flipped heatmaps back to the original form.
Args:
output_flipped (`torch.tensor` of shape `(batch_size, num_keypoints, height, width)`):
The output heatmaps obtained from the flipped images.
flip_pairs (`torch.Tensor` of shape `(num_keypoints, 2)`):
Pairs of keypoin... | flip_back | python | huggingface/transformers | src/transformers/models/vitpose/modeling_vitpose.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/vitpose/modeling_vitpose.py | Apache-2.0 |
def forward(
self,
pixel_values: torch.Tensor,
dataset_index: Optional[torch.Tensor] = None,
flip_pairs: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
... |
dataset_index (`torch.Tensor` of shape `(batch_size,)`):
Index to use in the Mixture-of-Experts (MoE) blocks of the backbone.
This corresponds to the dataset index used during training, e.g. For the single dataset index 0 refers to the corresponding dataset. For the multiple datasets i... | forward | python | huggingface/transformers | src/transformers/models/vitpose/modeling_vitpose.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/vitpose/modeling_vitpose.py | Apache-2.0 |
def forward(
self,
pixel_values: torch.Tensor,
dataset_index: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
):... |
dataset_index (`torch.Tensor` of shape `(batch_size,)`):
Index to use in the Mixture-of-Experts (MoE) blocks of the backbone.
This corresponds to the dataset index used during training, e.g. index 0 refers to COCO.
Examples:
```python
>>> from transformers imp... | forward | python | huggingface/transformers | src/transformers/models/vitpose_backbone/modeling_vitpose_backbone.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/vitpose_backbone/modeling_vitpose_backbone.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
speaker_id: Optional[int] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,... |
speaker_id (`int`, *optional*):
Which speaker embedding to use. Only used for multispeaker models.
labels (`torch.FloatTensor` of shape `(batch_size, config.spectrogram_bins, sequence_length)`, *optional*):
Float values of target spectrogram. Timesteps set to `-100.0` are ignore... | forward | python | huggingface/transformers | src/transformers/models/vits/modeling_vits.py | https://github.com/huggingface/transformers/blob/master/src/transformers/models/vits/modeling_vits.py | Apache-2.0 |
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