text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
model_input_names = [
"pixel_values",
"pixel_mask",
"pixel_values_mixed",
"pixel_mask_mixed",
] | 10,258 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/image_processing_tvlt.py |
def __init__(
self,
do_resize: bool = True,
size: Dict[str, int] = None,
patch_size: List[int] = [16, 16],
num_frames: int = 8,
resample: PILImageResampling = PILImageResampling.BILINEAR,
do_center_crop: bool = True,
crop_size: Dict[str, int] = None,
... | 10,258 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/image_processing_tvlt.py |
self.do_resize = do_resize
self.size = size
self.patch_size = patch_size
self.num_frames = num_frames
self.do_center_crop = do_center_crop
self.crop_size = crop_size
self.resample = resample
self.do_rescale = do_rescale
self.rescale_factor = rescale_factor... | 10,258 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/image_processing_tvlt.py |
def resize(
self,
image: np.ndarray,
size: Dict[str, int],
resample: PILImageResampling = PILImageResampling.BILINEAR,
data_format: Optional[Union[str, ChannelDimension]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
**kwargs,
) -> ... | 10,258 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/image_processing_tvlt.py |
Args:
image (`np.ndarray`):
Image to resize.
size (`Dict[str, int]`):
Size of the output image. If `size` is of the form `{"height": h, "width": w}`, the output image will
have the size `(h, w)`. If `size` is of the form `{"shortest_edge": s}`, the... | 10,258 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/image_processing_tvlt.py |
size = get_size_dict(size, default_to_square=False)
if "shortest_edge" in size:
output_size = get_resize_output_image_size(
image, size["shortest_edge"], default_to_square=False, input_data_format=input_data_format
)
elif "height" in size and "width" in size:
... | 10,258 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/image_processing_tvlt.py |
def _preprocess_image(
self,
image: ImageInput,
do_resize: bool = None,
size: Dict[str, int] = None,
resample: PILImageResampling = None,
do_center_crop: bool = None,
crop_size: Dict[str, int] = None,
do_rescale: bool = None,
rescale_factor: float ... | 10,258 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/image_processing_tvlt.py |
validate_preprocess_arguments(
do_rescale=do_rescale,
rescale_factor=rescale_factor,
do_normalize=do_normalize,
image_mean=image_mean,
image_std=image_std,
do_center_crop=do_center_crop,
crop_size=crop_size,
do_resize=do_res... | 10,258 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/image_processing_tvlt.py |
if do_resize:
image = self.resize(image=image, size=size, resample=resample, input_data_format=input_data_format)
if do_center_crop:
image = self.center_crop(image, size=crop_size, input_data_format=input_data_format)
if do_rescale:
image = self.rescale(image=image,... | 10,258 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/image_processing_tvlt.py |
def preprocess(
self,
videos: ImageInput,
do_resize: bool = None,
size: Dict[str, int] = None,
patch_size: List[int] = None,
num_frames: int = None,
resample: PILImageResampling = None,
do_center_crop: bool = None,
crop_size: Dict[str, int] = None,... | 10,258 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/image_processing_tvlt.py |
Args:
videos (`ImageInput`):
Images or videos to preprocess. Expects a single or batch of frames with pixel values ranging from 0 to
255. If passing in frames with pixel values between 0 and 1, set `do_rescale=False`.
do_resize (`bool`, *optional*, defaults to `se... | 10,258 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/image_processing_tvlt.py |
has an effect if `do_resize` is set to `True`.
do_center_crop (`bool`, *optional*, defaults to `self.do_centre_crop`):
Whether to centre crop the image.
crop_size (`Dict[str, int]`, *optional*, defaults to `self.crop_size`):
Size of the image after applying the ce... | 10,258 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/image_processing_tvlt.py |
Image standard deviation.
is_mixed (`bool`, *optional*):
If the input video has negative samples.
return_tensors (`str` or `TensorType`, *optional*):
The type of tensors to return. Can be one of:
- Unset: Return a list of `np.ndarray`.
... | 10,258 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/image_processing_tvlt.py |
- `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- Unset: Use the inferred channel dimension format of the input image.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format for the input image. If unset, the ch... | 10,258 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/image_processing_tvlt.py |
Returns:
[`BatchFeature`]: A [`BatchFeature`] with the following fields:
- **pixel_values** -- Pixel values to be fed to a model, of shape (batch_size, num_channels, height,
width).
- **pixel_mask** -- Pixel masks to be fed to a model, of shape (batch_size, num_pixel_... | 10,258 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/image_processing_tvlt.py |
- **pixel_mask_mixed** -- Pixel masks with both postive or negative to be fed to a model, of shape
(batch_size, num_pixel_patches).
"""
do_resize = do_resize if do_resize is not None else self.do_resize
resample = resample if resample is not None else self.resample
do_cente... | 10,258 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/image_processing_tvlt.py |
size = size if size is not None else self.size
size = get_size_dict(size, default_to_square=False)
crop_size = crop_size if crop_size is not None else self.crop_size
crop_size = get_size_dict(crop_size, param_name="crop_size")
patch_size = patch_size if patch_size is not None else self.p... | 10,258 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/image_processing_tvlt.py |
# Check number of frames is fewer than maximum frames
for video in videos:
if len(video) > self.num_frames:
raise ValueError(
f"number of frames must not be greater than the maximum frames of the model {self.num_frames}."
)
max_num_frames ... | 10,258 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/image_processing_tvlt.py |
videos = [
[
self._preprocess_image(
image=img,
do_resize=do_resize,
size=size,
resample=resample,
do_center_crop=do_center_crop,
crop_size=crop_size,
d... | 10,258 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/image_processing_tvlt.py |
# If videos contain both positive/negative, use mixed key for video-audio matching task
if is_mixed:
data = {"pixel_values_mixed": videos, "pixel_mask_mixed": video_masks}
else:
data = {"pixel_values": videos, "pixel_mask": video_masks}
return BatchFeature(data=data, ten... | 10,258 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/image_processing_tvlt.py |
class TvltModelOutput(ModelOutput):
"""
Class for TvltModel's outputs, with potential hidden states and attentions. | 10,259 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
last_pixel_hidden_state (`torch.FloatTensor` of shape `(batch_size, pixel_sequence_length, hidden_size)`):
... | 10,259 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
pixel_ids_restore (`torch.LongTensor` of shape `(batch_size, pixel_patch_length)`):
Tensor containing the ids permutation of pixel masking.
audio_ids_restore (`torch.LongTensor` of shape `(batch_size, audio_patch_length)`):
Tensor containing the ids permutation of audio masking.
... | 10,259 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.
""" | 10,259 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
last_hidden_state: torch.FloatTensor = None
last_pixel_hidden_state: torch.FloatTensor = None
last_audio_hidden_state: torch.FloatTensor = None
pixel_label_masks: torch.LongTensor = None
audio_label_masks: torch.LongTensor = None
pixel_ids_restore: torch.LongTensor = None
audio_ids_restore: torc... | 10,259 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
class TvltDecoderOutput(ModelOutput):
"""
Class for TvltDecoder's outputs, with potential hidden states and attentions. | 10,260 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
Args:
logits (`torch.FloatTensor` of shape `(batch_size, patch_size ** 2 * num_channels)`):
Pixel reconstruction logits.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple o... | 10,260 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
attentions: Optional[Tuple[torch.FloatTensor, ...]] = None | 10,260 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
class TvltForPreTrainingOutput(ModelOutput):
"""
Class for TvltForPreTraining's outputs, with potential hidden states and attentions. | 10,261 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
Args:
loss (`torch.FloatTensor` of shape `(1,)`):
Pixel reconstruction loss.
matching_logits (`torch.FloatTensor` of shape `(batch_size, 1)`):
Matching objective logits.
pixel_logits (`torch.FloatTensor` of shape
`(batch_size, pixel_patch_length, image_patch_s... | 10,261 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
plus the initial embedding outputs.
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, sequence_length,
sequence... | 10,261 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
loss: Optional[torch.FloatTensor] = None
matching_logits: torch.FloatTensor = None
pixel_logits: torch.FloatTensor = None
audio_logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
attentions: Optional[Tuple[torch.FloatTensor, ...]] = None | 10,261 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
class TvltPixelEmbeddings(nn.Module):
"""Construct the patch and position embeddings."""
def __init__(self, config):
super().__init__()
self.patch_embeddings = TvltPixelPatchEmbeddings(config)
self.num_patches_per_image = self.patch_embeddings.num_patches_per_image
self.type_e... | 10,262 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
embeddings = self.patch_embeddings(pixel_values)
embeddings += self.pos_embed_v.repeat(1, num_frames, 1)
embeddings += torch.repeat_interleave(self.temporal_embed[:, :num_frames], self.num_patches_per_image, dim=1)
embeddings += self.type_embed_v
return embeddings, attention_masks | 10,262 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
class TvltAudioEmbeddings(nn.Module):
"""Construct the patch and position embeddings."""
def __init__(self, config):
super().__init__()
self.patch_embeddings = TvltAudioPatchEmbeddings(config)
self.num_patches = self.patch_embeddings.num_patches
self.type_embed_a = nn.Paramete... | 10,263 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
num_time_patches = embeddings.size(1) // self.num_freq_patches
embeddings += self.freq_embed.repeat(1, num_time_patches, 1)
embeddings += torch.repeat_interleave(self.pos_embed_a[:, :num_time_patches], self.num_freq_patches, dim=1)
embeddings += self.type_embed_a
return embeddings, atte... | 10,263 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
class TvltPixelPatchEmbeddings(nn.Module):
"""
This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
`hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
Transformer.
"""
def __init__(self, config... | 10,264 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size)
patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size)
num_patches_per_image = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
... | 10,264 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
batch_size, num_frames, num_channels, height, width = pixel_values.shape
if num_channels != self.num_channels:
raise ValueError(
"Make sure that the channel dimension of the pixel values match with the one set in ... | 10,264 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
class TvltAudioPatchEmbeddings(nn.Module):
"""
This class turns `audio_values` of shape `(batch_size, num_channels, height, width)` into the initial
`hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
Transformer.
"""
def __init__(self, config... | 10,265 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
spectrogram_size = (spectrogram_length, frequency_length)
patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size)
num_patches = (spectrogram_size[1] // patch_size[1]) * (spectrogram_size[0] // patch_size[0])
patch_shape = (spectrogram_size[0] // ... | 10,265 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
def forward(self, audio_values: torch.Tensor) -> torch.Tensor:
batch_size, num_channels, height, width = audio_values.shape
if num_channels != self.num_channels:
raise ValueError(
"Make sure that the channel dimension of the pixel values match with the one set in the configur... | 10,265 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
class TvltSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size {config.hidden_size,} is not a multiple of the number ... | 10,266 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
def transpose_for_scores(self, x):
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(*new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(self, hidden_states, attention_mask=None, head_mask=None, output_attentions=False):
mixed_query_lay... | 10,266 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
if attention_mask is not None:
# Apply the attention m... | 10,266 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(*new_context_layer_shape)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
... | 10,266 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
class TvltSelfOutput(nn.Module):
"""
The residual connection is defined in TvltLayer instead of here (as is the case with other models), due to the
layernorm applied before each block.
"""
def __init__(self, config: TvltConfig) -> None:
super().__init__()
self.dense = nn.Linear(conf... | 10,267 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
class TvltAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.attention = TvltSelfAttention(config)
self.output = TvltSelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads):
if len(heads) == 0:
return
heads, in... | 10,268 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
# Update hyper params and store pruned heads
self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads)
self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads)
def for... | 10,268 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
class TvltIntermediate(nn.Module):
def __init__(self, config: TvltConfig) -> None:
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
... | 10,269 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
class TvltOutput(nn.Module):
def __init__(self, config: TvltConfig) -> None:
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.T... | 10,270 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
class TvltLayer(nn.Module):
"""This corresponds to the Block class in the timm implementation."""
def __init__(self, config):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = TvltAttention(config)
self... | 10,271 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
def forward(self, hidden_states, attention_mask=None, head_mask=None, output_attentions=False):
self_attention_outputs = self.attention(
self.layernorm_before(hidden_states), # in ViLT, layernorm is applied before self-attention
attention_mask,
head_mask,
output_... | 10,271 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
class TvltEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([TvltLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
hidden_states,
... | 10,272 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
layer_module.__call__,
hidden_states,
attention_mask,
layer_head_mask,
output_attentions,
... | 10,272 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
) | 10,272 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
class TvltPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = TvltConfig
base_model_prefix = "tvlt"
main_input_name = "pixel_values"
supports_gradient_checkpoint... | 10,273 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
class TvltModel(TvltPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.pixel_embeddings = TvltPixelEmbeddings(config)
self.audio_embeddings = TvltAudioEmbeddings(config)
self.encoder = TvltEncoder(config)
self.cls_embedd... | 10,274 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_he... | 10,274 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
Examples:
```python
>>> from transformers import TvltProcessor, TvltModel
>>> import numpy as np
>>> import torch
>>> num_frames = 8
>>> images = list(np.random.randn(num_frames, 3, 224, 224))
>>> audio = list(np.random.randn(10000))
>>> processor = Tvl... | 10,274 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
pixel_embedding_output, pixel_mask = self.pixel_embeddings(pixel_values, pixel_mask)
audio_embedding_output, audio_mask = self.audio_embeddings(audio_values, audio_mask)
# Mask pixel if mask_pixel is True
pixel_label_masks = None
pixel_ids_restore = None
if mask_pixel:
... | 10,274 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
# Mask audio if mask_audio is True
audio_label_masks = None
audio_ids_restore = None
if mask_audio:
num_freq_patches = self.config.frequency_length // self.config.audio_patch_size[1]
audio_mask_noise, audio_len_keep = generate_audio_mask_noise(
audio_embed... | 10,274 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
# Prepare for encoder inputs and attention masks
batch_size = pixel_values.size(0)
embedding_output = torch.cat(
[self.cls_embedding.repeat(batch_size, 1, 1), pixel_embedding_output, audio_embedding_output], 1
)
masked_pixel_len = pixel_embedding_output.size(1)
atten... | 10,274 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
encoder_outputs = self.encoder(
embedding_output,
attention_mask=extended_attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = encoder_outputs[0]
if... | 10,274 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
return TvltModelOutput(
last_hidden_state=sequence_output,
last_pixel_hidden_state=pixel_sequence_output,
last_audio_hidden_state=audio_sequence_output,
pixel_label_masks=pixel_label_masks,
audio_label_masks=audio_label_masks,
pixel_ids_restore=pix... | 10,274 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
class TvltDecoder(nn.Module):
def __init__(self, config):
super().__init__()
decoder_config = deepcopy(config)
decoder_config.hidden_size = config.decoder_hidden_size
decoder_config.num_hidden_layers = config.decoder_num_hidden_layers
decoder_config.num_attention_heads = con... | 10,275 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
def forward(
self,
hidden_states,
output_attentions=False,
output_hidden_states=False,
return_dict=True,
):
# apply Transformer layers (blocks)
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else ... | 10,275 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[1],)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
# predictor projection
logits = self.layernorm(hidden_states)
if not return_dict:
... | 10,275 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
class TvltForPreTraining(TvltPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.task_matching = config.task_matching
self.task_mae = config.task_mae
if not (self.task_matching or self.task_mae):
raise ValueError("Must... | 10,276 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
num_frames = config.num_frames
num_patches_per_image = self.tvlt.pixel_embeddings.num_patches_per_image
self.decoder_pixel_pos_embed = nn.Parameter(torch.zeros(1, num_patches_per_image, decoder_hidden_size))
self.decoder_temporal_embed = nn.Parameter(torch.zeros(1, config.num_frames,... | 10,276 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
pixel_mae_output_dim = self.config.image_patch_size[0] ** 2 * self.config.num_image_channels
self.pixel_mae_head = TvltMAEHead(config, pixel_mae_output_dim)
audio_mae_output_dim = (
self.config.audio_patch_size[0] * self.config.audio_patch_size[1] * self.config.num_audio_channels... | 10,276 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
def patchify_pixel(self, pixel_values):
"""
pixel_values: [batch_size, num_frames, 3, height, width]
"""
batch_size, num_frames, num_channels, height, width = pixel_values.shape
num_patches_height = pixel_values.shape[3] // self.image_patch_size[0]
num_patches_width = pix... | 10,276 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
self.image_patch_size[0] * self.image_patch_size[1] * num_channels,
)
)
return patchified_pixel_values | 10,276 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
def patchify_audio(self, audio_values):
"""
audio_values: [batch_size, 1, height, width]
"""
batch_size, num_channels, height, width = audio_values.shape
num_patches_height = height // self.audio_patch_size[0]
num_patches_width = width // self.audio_patch_size[1]
... | 10,276 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
return patchified_audio_values | 10,276 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
def pixel_mae_loss(self, pixel_values, pixel_predictions, mask):
patchified_pixel_values = self.patchify_pixel(pixel_values)
loss = (pixel_predictions - patchified_pixel_values) ** 2
loss = loss.mean(dim=-1) # [batch_size, pixel_pixel_length], mean loss per patch
loss = (loss * mask).su... | 10,276 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
def concatenate_mask(self, mask_token, sequence, ids_restore):
batch_size, seq_length, dim = sequence.shape
mask_tokens = mask_token.repeat(batch_size, ids_restore.shape[1] - seq_length, 1)
padded_sequence = torch.cat([sequence, mask_tokens], dim=1)
padded_sequence = torch.gather(
... | 10,276 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
@add_start_docstrings_to_model_forward(TVLT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=TvltForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
pixel_values: torch.FloatTensor,
audio_values: torch.FloatTensor,
pixel_mask: Optional[torch.FloatTensor... | 10,276 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
Pixel values that mix positive and negative samples in Tvlt vision-audio matching. Audio values can be
obtained using [`TvltProcessor`]. See [`TvltProcessor.__call__`] for details. | 10,276 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
pixel_mask_mixed (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel masks of pixel_values_mixed. Pixel values mixed can be obtained using [`TvltProcessor`]. See
[`TvltProcessor.__call__`] for details.
labels (`torch.LongTensor` of shape `(batch_size, num_l... | 10,276 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
>>> num_frames = 8
>>> images = list(np.random.randn(num_frames, 3, 224, 224))
>>> images_mixed = list(np.random.randn(num_frames, 3, 224, 224))
>>> audio = list(np.random.randn(10000))
>>> processor = TvltProcessor.from_pretrained("ZinengTang/tvlt-base")
>>> model = TvltForPreTr... | 10,276 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
outputs = self.tvlt(
pixel_values_mixed,
audio_values,
pixel_mask=pixel_mask_mixed,
audio_mask=audio_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_di... | 10,276 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
pixel_logits = None
audio_logits = None
if self.task_mae and self.training:
outputs = self.tvlt(
pixel_values,
audio_values,
pixel_mask=pixel_mask,
audio_mask=audio_mask,
mask_pixel=True,
mask_aud... | 10,276 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
audio_ids_restore = outputs.audio_ids_restore if return_dict else outputs[6] | 10,276 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
pixel_decoder_input = self.encoder_to_decoder(
pixel_sequence_output
) # [batch_size, num_masked_pixel_patches, decoder_hidden_size]
audio_decoder_input = self.encoder_to_decoder(
audio_sequence_output
) # [batch_size, num_masked_audio_patches, decod... | 10,276 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
pixel_logits = self.pixel_mae_head(pixel_decoder_outputs.logits) | 10,276 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
audio_decoder_input = self.concatenate_mask(self.audio_mask_token, audio_decoder_input, audio_ids_restore)
num_time_patches = audio_decoder_input.size(1) // self.num_freq_patches
audio_decoder_input = audio_decoder_input + self.decoder_freq_embed.repeat(1, num_time_patches, 1)
audio_... | 10,276 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
if not return_dict:
output = (matching_logits, pixel_logits, audio_logits) + outputs[7:]
return ((total_loss,) + output) if loss is not None else output
return TvltForPreTrainingOutput(
loss=total_loss,
matching_logits=matching_logits,
pixel_logits=pi... | 10,276 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
class TvltPooler(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hidden_states):
first_token_tensor = hidden_states[:, 0]
pooled_output = self.dense(fi... | 10,277 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
class TvltMatchingHead(nn.Module):
def __init__(self, config):
super().__init__()
self.pooler = TvltPooler(config)
self.fc = nn.Linear(config.hidden_size, 1)
def forward(self, hidden_states):
hidden_states = self.fc(self.pooler(hidden_states))
return hidden_states | 10,278 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
class TvltMAEHead(nn.Module):
def __init__(self, config, output_dim=None):
super().__init__()
self.config = config
self.decoder = nn.Linear(config.decoder_hidden_size, output_dim)
def forward(self, hidden_states):
hidden_states = self.decoder(hidden_states)
return hidden... | 10,279 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
class TvltForAudioVisualClassification(TvltPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.tvlt = TvltModel(config)
# Classifier head
self.classifier = nn.Sequential(
nn.Linear(config.hidden_size, config.hidden_size * 2),
nn.Layer... | 10,280 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
@add_start_docstrings_to_model_forward(TVLT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
pixel_values: torch.FloatTensor,
audio_values: torch.FloatTensor,
pixel_mask: Optional[torch.FloatTensor... | 10,280 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
Examples:
```python
>>> from transformers import TvltProcessor, TvltForAudioVisualClassification
>>> import numpy as np
>>> import torch
>>> num_frames = 8
>>> images = list(np.random.randn(num_frames, 3, 224, 224))
>>> audio = list(np.random.randn(10000))
... | 10,280 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
outputs = self.tvlt(
pixel_values,
audio_values,
pixel_mask=pixel_mask,
audio_mask=audio_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = ... | 10,280 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
return SequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
) | 10,280 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py |
class TvltFeatureExtractor(SequenceFeatureExtractor):
r"""
Constructs a TVLT audio feature extractor. This feature extractor can be used to prepare audios for the model.
This feature extractor inherits from [`FeatureExtractionMixin`] which contains most of the main methods. Users
should refer to this s... | 10,281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/feature_extraction_tvlt.py |
Args:
spectrogram_length (`Dict[str, int]` *optional*, defaults to 2048):
The time length of each audio spectrogram.
num_channels (`int` *optional*, defaults to 1):
Number of audio channels.
patch_size (`List[int]` *optional*, defaults to `[16, 16]`):
The patc... | 10,281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/feature_extraction_tvlt.py |
padding_value (`float`, *optional*, defaults to 0.0):
Padding value used to pad the audio. Should correspond to silences.
""" | 10,281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/feature_extraction_tvlt.py |
model_input_names = ["audio_values", "audio_mask"]
def __init__(
self,
spectrogram_length=2048,
num_channels=1,
patch_size=[16, 16],
feature_size=128,
sampling_rate=44100,
hop_length_to_sampling_rate=86,
n_fft=2048,
padding_value=0.0,
... | 10,281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/feature_extraction_tvlt.py |
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