text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimens... | 3,204 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/image_processing_videomae.py |
image_mean = image_mean if image_mean is not None else self.image_mean
image_std = image_std if image_std is not None else self.image_std | 3,204 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/image_processing_videomae.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")
if not valid_images(videos):
raise ValueError(
... | 3,204 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/image_processing_videomae.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... | 3,204 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/image_processing_videomae.py |
class VideoMAEDecoderOutput(ModelOutput):
"""
Class for VideoMAEDecoder's outputs, with potential hidden states and attentions. | 3,205 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.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... | 3,205 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None | 3,205 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
class VideoMAEForPreTrainingOutput(ModelOutput):
"""
Class for VideoMAEForPreTraining's outputs, with potential hidden states and attentions. | 3,206 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
Args:
loss (`torch.FloatTensor` of shape `(1,)`):
Pixel reconstruction loss.
logits (`torch.FloatTensor` of shape `(batch_size, patch_size ** 2 * num_channels)`):
Pixel reconstruction logits.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hid... | 3,206 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.
""" | 3,206 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None | 3,206 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
class VideoMAEEmbeddings(nn.Module):
"""
Construct the patch and position embeddings.
"""
def __init__(self, config):
super().__init__()
self.patch_embeddings = VideoMAEPatchEmbeddings(config)
self.num_patches = self.patch_embeddings.num_patches
# fixed sin-cos embeddi... | 3,207 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
# add position embeddings
embeddings = embeddings + self.position_embeddings.type_as(embeddings).to(embeddings.device).clone().detach()
# only keep visible patches
# ~bool_masked_pos means visible
if bool_masked_pos is not None:
batch_size, _, num_channels = embeddings.shape
... | 3,207 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
class VideoMAEPatchEmbeddings(nn.Module):
"""
Video to Patch Embedding. This module turns a batch of videos of shape (batch_size, num_frames, num_channels,
height, width) into a tensor of shape (batch_size, seq_len, hidden_size) to be consumed by a Transformer encoder.
The seq_len (the number of patche... | 3,208 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.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)
self.image_size = image_size
self.patch_size = patch_size
self.tubelet_size ... | 3,208 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
def forward(self, pixel_values):
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 the configuration."
... | 3,208 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
class VideoMAESelfAttention(nn.Module):
def __init__(self, config: VideoMAEConfig) -> None:
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 n... | 3,209 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
if config.qkv_bias:
self.q_bias = nn.Parameter(torch.zeros(self.all_head_size))
self.v_bias = nn.Parameter(torch.zeros(self.all_head_size))
else:
self.q_bias = None
self.v_bias = None
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
def... | 3,209 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
def forward(
self, hidden_states, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False
) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
k_bias = torch.zeros_like(self.v_bias, requires_grad=False) if self.q_bias is not None else None
keys = nn.functiona... | 3,209 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
# Normalize the attention scores to probabilities.
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dro... | 3,209 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
class VideoMAESdpaSelfAttention(VideoMAESelfAttention):
def __init__(self, config: VideoMAEConfig) -> None:
super().__init__(config)
self.attention_probs_dropout_prob = config.attention_probs_dropout_prob
def forward(
self, hidden_states, head_mask: Optional[torch.Tensor] = None, output... | 3,210 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
context_layer = torch.nn.functional.scaled_dot_product_attention(
query_layer,
key_layer,
value_layer,
head_mask,
self.attention_probs_dropout_prob if self.training else 0.0,
is_causal=False,
scale=None,
)
context_layer... | 3,210 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
class VideoMAESelfOutput(nn.Module):
"""
The residual connection is defined in VideoMAELayer instead of here (as is the case with other models), due to the
layernorm applied before each block.
"""
def __init__(self, config: VideoMAEConfig) -> None:
super().__init__()
self.dense = nn... | 3,211 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
class VideoMAEAttention(nn.Module):
def __init__(self, config: VideoMAEConfig) -> None:
super().__init__()
self.attention = VideoMAESelfAttention(config)
self.output = VideoMAESelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads: Set[int]) -> None:
if... | 3,212 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.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... | 3,212 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
class VideoMAESdpaAttention(VideoMAEAttention):
def __init__(self, config: VideoMAEConfig) -> None:
super().__init__(config)
self.attention = VideoMAESdpaSelfAttention(config) | 3,213 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
class VideoMAEIntermediate(nn.Module):
def __init__(self, config: VideoMAEConfig) -> 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]
e... | 3,214 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
class VideoMAEOutput(nn.Module):
def __init__(self, config: VideoMAEConfig) -> 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:... | 3,215 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
class VideoMAELayer(nn.Module):
"""This corresponds to the Block class in the timm implementation."""
def __init__(self, config: VideoMAEConfig) -> None:
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = VIDEOM... | 3,216 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
def forward(
self,
hidden_states: torch.Tensor,
head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
self_attention_outputs = self.attention(
self.layernorm_before(hidden_sta... | 3,216 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
outputs = (layer_output,) + outputs
return outputs | 3,216 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
class VideoMAEEncoder(nn.Module):
def __init__(self, config: VideoMAEConfig) -> None:
super().__init__()
self.config = config
self.layer = nn.ModuleList([VideoMAELayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
sel... | 3,217 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
layer_module.__call__,
hidden_states,
layer_head_mask,
output_attentions,
)
else:
... | 3,217 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
class VideoMAEPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = VideoMAEConfig
base_model_prefix = "videomae"
main_input_name = "pixel_values"
supports_gradien... | 3,218 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
class VideoMAEModel(VideoMAEPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.embeddings = VideoMAEEmbeddings(config)
self.encoder = VideoMAEEncoder(config)
if config.use_mean_pooling:
self.layernorm = None
... | 3,219 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
@add_start_docstrings_to_model_forward(VIDEOMAE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=BaseModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
pixel_values: torch.FloatTensor,
bool_masked_pos: Optional[torch.BoolTensor] = None,
head_mask: Optional[torc... | 3,219 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
```python
>>> import av
>>> import numpy as np
>>> from transformers import AutoImageProcessor, VideoMAEModel
>>> from huggingface_hub import hf_hub_download
>>> np.random.seed(0) | 3,219 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
>>> def read_video_pyav(container, indices):
... '''
... Decode the video with PyAV decoder.
... Args:
... container (`av.container.input.InputContainer`): PyAV container.
... indices (`List[int]`): List of frame indices to decode.
... Retu... | 3,219 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
>>> def sample_frame_indices(clip_len, frame_sample_rate, seg_len):
... '''
... Sample a given number of frame indices from the video.
... Args:
... clip_len (`int`): Total number of frames to sample.
... frame_sample_rate (`int`): Sample every n-th fr... | 3,219 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
>>> # video clip consists of 300 frames (10 seconds at 30 FPS)
>>> file_path = hf_hub_download(
... repo_id="nielsr/video-demo", filename="eating_spaghetti.mp4", repo_type="dataset"
... )
>>> container = av.open(file_path)
>>> # sample 16 frames
>>> indices = sample_... | 3,219 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
>>> # forward pass
>>> outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidden_state
>>> list(last_hidden_states.shape)
[1, 1568, 768]
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
o... | 3,219 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
embedding_output = self.embeddings(pixel_values, bool_masked_pos)
encoder_outputs = self.encoder(
embedding_output,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)... | 3,219 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
class VideoMAEDecoder(nn.Module):
def __init__(self, config, num_patches):
super().__init__()
decoder_num_labels = config.num_channels * config.tubelet_size * config.patch_size**2
decoder_config = deepcopy(config)
decoder_config.hidden_size = config.decoder_hidden_size
deco... | 3,220 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
def forward(
self,
hidden_states,
return_token_num,
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 = () ... | 3,220 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.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,)
if return_token_num > 0:
hidden_states = hidden_states[:, -return_token_num:]
# predi... | 3,220 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
class VideoMAEForPreTraining(VideoMAEPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.videomae = VideoMAEModel(config)
self.encoder_to_decoder = nn.Linear(config.hidden_size, config.decoder_hidden_size, bias=False)
self.mask_t... | 3,221 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
@add_start_docstrings_to_model_forward(VIDEOMAE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=VideoMAEForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
pixel_values: torch.FloatTensor,
bool_masked_pos: torch.BoolTensor,
head_mask: Optional[torch.Te... | 3,221 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
Examples:
```python
>>> from transformers import AutoImageProcessor, VideoMAEForPreTraining
>>> import numpy as np
>>> import torch
>>> num_frames = 16
>>> video = list(np.random.randint(0, 256, (num_frames, 3, 224, 224)))
>>> image_processor = AutoImageProcesso... | 3,221 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
outputs = self.videomae(
pixel_values,
bool_masked_pos=bool_masked_pos,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
... | 3,221 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
# we don't unshuffle the correct visible token order, but shuffle the position embeddings accordingly.
if bool_masked_pos is None:
raise ValueError("One must provided a boolean mask ")
expanded_position_embeddings = self.position_embeddings.expand(batch_size, -1, -1).type_as(pixel_values)
... | 3,221 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
loss = None
with torch.no_grad():
# calculate the labels to be predicted
if self.config.num_channels != 3:
# Can't unnormalize with default means/stds
frames = pixel_values
else:
# first, unnormalize the frames
d... | 3,221 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
batch_size, time, num_channels, height, width = frames.shape
tubelet_size, patch_size = self.config.tubelet_size, self.config.patch_size
if self.config.norm_pix_loss:
# step 1: split up dimensions (time by tubelet_size, height by patch_size, width by patch_size)
f... | 3,221 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
tubelet_size * patch_size * patch_size,
num_channels,
)
# step 4: normalize. The authors find that the mean is about 0.48 and standard deviation is about 0.08.
frames_norm = (frames - frames.mean(dim=-2, keepdim=True)) / (
frames.va... | 3,221 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
# step 1: split up dimensions (time by tubelet_size, height by patch_size, width by patch_size)
frames = frames.view(
batch_size,
time // tubelet_size,
tubelet_size,
num_channels,
height // patch_size,
... | 3,221 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
batch_size, _, num_channels = videos_patch.shape
labels = videos_patch[bool_masked_pos].reshape(batch_size, -1, num_channels)
loss_fct = MSELoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[1:]
return ((loss,) + output) ... | 3,221 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
class VideoMAEForVideoClassification(VideoMAEPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.videomae = VideoMAEModel(config)
# Classifier head
self.fc_norm = nn.LayerNorm(config.hidden_size) if config.use_mean... | 3,222 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
@add_start_docstrings_to_model_forward(VIDEOMAE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=ImageClassifierOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
pixel_values: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
labels: Optional... | 3,222 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
```python
>>> import av
>>> import torch
>>> import numpy as np
>>> from transformers import AutoImageProcessor, VideoMAEForVideoClassification
>>> from huggingface_hub import hf_hub_download
>>> np.random.seed(0) | 3,222 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
>>> def read_video_pyav(container, indices):
... '''
... Decode the video with PyAV decoder.
... Args:
... container (`av.container.input.InputContainer`): PyAV container.
... indices (`List[int]`): List of frame indices to decode.
... Retu... | 3,222 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
>>> def sample_frame_indices(clip_len, frame_sample_rate, seg_len):
... '''
... Sample a given number of frame indices from the video.
... Args:
... clip_len (`int`): Total number of frames to sample.
... frame_sample_rate (`int`): Sample every n-th fr... | 3,222 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
>>> # video clip consists of 300 frames (10 seconds at 30 FPS)
>>> file_path = hf_hub_download(
... repo_id="nielsr/video-demo", filename="eating_spaghetti.mp4", repo_type="dataset"
... )
>>> container = av.open(file_path)
>>> # sample 16 frames
>>> indices = sample_... | 3,222 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
>>> # model predicts one of the 400 Kinetics-400 classes
>>> predicted_label = logits.argmax(-1).item()
>>> print(model.config.id2label[predicted_label])
eating spaghetti
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs... | 3,222 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
sel... | 3,222 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "singl... | 3,222 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py |
class VideoMAEConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`VideoMAEModel`]. It is used to instantiate a
VideoMAE model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a s... | 3,223 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/configuration_videomae.py |
Args:
image_size (`int`, *optional*, defaults to 224):
The size (resolution) of each image.
patch_size (`int`, *optional*, defaults to 16):
The size (resolution) of each patch.
num_channels (`int`, *optional*, defaults to 3):
The number of input channels.
... | 3,223 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/configuration_videomae.py |
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `... | 3,223 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/configuration_videomae.py |
qkv_bias (`bool`, *optional*, defaults to `True`):
Whether to add a bias to the queries, keys and values.
use_mean_pooling (`bool`, *optional*, defaults to `True`):
Whether to mean pool the final hidden states instead of using the final hidden state of the [CLS] token.
decoder_nu... | 3,223 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/configuration_videomae.py |
Example:
```python
>>> from transformers import VideoMAEConfig, VideoMAEModel
>>> # Initializing a VideoMAE videomae-base style configuration
>>> configuration = VideoMAEConfig()
>>> # Randomly initializing a model from the configuration
>>> model = VideoMAEModel(configuration)
>>> # Acc... | 3,223 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/configuration_videomae.py |
def __init__(
self,
image_size=224,
patch_size=16,
num_channels=3,
num_frames=16,
tubelet_size=2,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=... | 3,223 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/configuration_videomae.py |
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_pro... | 3,223 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/configuration_videomae.py |
class Starcoder2Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Starcoder2Model`]. It is used to instantiate a
Starcoder2 model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yie... | 3,224 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/configuration_starcoder2.py |
Args:
vocab_size (`int`, *optional*, defaults to 49152):
Vocabulary size of the Starcoder2 model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`Starcoder2Model`]
hidden_size (`int`, *optional*, defaults to 3072):
... | 3,224 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/configuration_starcoder2.py |
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be construc... | 3,224 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/configuration_starcoder2.py |
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
norm_epsilon (`float`, *optional*, defaults to 1e-05):
Epsilon value for the layer norm
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the ... | 3,224 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/configuration_starcoder2.py |
and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
accordingly.
Expected contents:
`rope_type` (`str`):
The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
... | 3,224 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/configuration_starcoder2.py |
`attention_factor` (`float`, *optional*):
Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
computation. If unspecified, it defaults to value recommended by the implementation, using the
`factor` field to infer the suggested va... | 3,224 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/configuration_starcoder2.py |
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
size divided by the number of attention heads divided by 2
`long_factor` (`List[float]`, *optional*):
Only used with 'longrope'. The scaling factor to be applied to l... | 3,224 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/configuration_starcoder2.py |
Sliding window attention window size. If not specified, will default to `None` (no sliding window).
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
residual_dropout (`float`, *optional*, defaults to 0.0):
Residual conne... | 3,224 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/configuration_starcoder2.py |
```python
>>> from transformers import Starcoder2Model, Starcoder2Config
>>> # Initializing a Starcoder2 7B style configuration
>>> configuration = Starcoder2Config()
>>> # Initializing a model from the Starcoder2 7B style configuration
>>> model = Starcoder2Model(configuration)
>>> # Accessi... | 3,224 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/configuration_starcoder2.py |
def __init__(
self,
vocab_size=49152,
hidden_size=3072,
intermediate_size=12288,
num_hidden_layers=30,
num_attention_heads=24,
num_key_value_heads=2,
hidden_act="gelu_pytorch_tanh",
max_position_embeddings=4096,
initializer_range=0.018042,
... | 3,224 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/configuration_starcoder2.py |
self.use_bias = use_bias
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.norm_epsilon = norm_epsilon
self.use_cache = use_cache
self.rope_theta = rope_theta
self.rope_scaling = rope_scalin... | 3,224 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/configuration_starcoder2.py |
super().__init__(
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
**kwargs,
) | 3,224 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/configuration_starcoder2.py |
class Starcoder2MLP(nn.Module):
def __init__(self, config: Starcoder2Config):
super().__init__()
embed_dim = config.hidden_size
self.c_fc = nn.Linear(embed_dim, config.intermediate_size, bias=config.use_bias)
self.c_proj = nn.Linear(config.intermediate_size, embed_dim, bias=config.us... | 3,225 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py |
class Starcoder2Attention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper""" | 3,226 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py |
def __init__(self, config: Starcoder2Config, layer_idx: Optional[int] = None):
super().__init__()
self.config = config
self.layer_idx = layer_idx
self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
self.num_key_value_groups = config.num_a... | 3,226 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py |
self.residual_dropout = config.residual_dropout | 3,226 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py |
def forward(
self,
hidden_states: torch.Tensor,
position_embeddings: Tuple[torch.Tensor, torch.Tensor],
attention_mask: Optional[torch.Tensor],
past_key_value: Optional[Cache] = None,
cache_position: Optional[torch.LongTensor] = None,
**kwargs: Unpack[FlashAttenti... | 3,226 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py |
if past_key_value is not None:
# sin and cos are specific to RoPE models; cache_position needed for the static cache
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, ca... | 3,226 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py |
attn_output, attn_weights = attention_interface(
self,
query_states,
key_states,
value_states,
attention_mask,
dropout=0.0 if not self.training else self.attention_dropout,
scaling=self.scaling,
sliding_window=getattr(self.c... | 3,226 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py |
class Starcoder2DecoderLayer(nn.Module):
def __init__(self, config: Starcoder2Config, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = Starcoder2Attention(config=config, layer_idx=layer_idx)
self.mlp = Starcoder2MLP(config)
self.input... | 3,227 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
... | 3,227 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py |
# Self Attention
hidden_states, self_attn_weights = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cac... | 3,227 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py |
class Starcoder2RotaryEmbedding(nn.Module):
def __init__(self, config: Starcoder2Config, device=None):
super().__init__()
# BC: "rope_type" was originally "type"
if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
self.rope_type = config.rope_scaling.get("rope... | 3,228 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py |
def _dynamic_frequency_update(self, position_ids, device):
"""
dynamic RoPE layers should recompute `inv_freq` in the following situations:
1 - growing beyond the cached sequence length (allow scaling)
2 - the current sequence length is in the original scale (avoid losing precision with ... | 3,228 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py |
if seq_len < self.original_max_seq_len and self.max_seq_len_cached > self.original_max_seq_len: # reset
# This .to() is needed if the model has been moved to a device after being initialized (because
# the buffer is automatically moved, but not the original copy)
self.original_inv_f... | 3,228 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py |
# Core RoPE block
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
position_ids_expanded = position_ids[:, None, :].float()
# Force float32 (see https://github.com/huggingface/transformers/pull/29285)
device_type = x.device.type
device... | 3,228 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py |
class Starcoder2PreTrainedModel(PreTrainedModel):
config_class = Starcoder2Config
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["Starcoder2DecoderLayer"]
_skip_keys_device_placement = ["past_key_values"]
_supports_flash_attn_2 = True
_supports_sdpa =... | 3,229 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py |
class Starcoder2Model(Starcoder2PreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Starcoder2DecoderLayer`]
Args:
config: Starcoder2Config
"""
def __init__(self, config: Starcoder2Config):
super().__init__(config)
se... | 3,230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py |
def get_input_embeddings(self):
return self.embed_tokens
def set_input_embeddings(self, value):
self.embed_tokens = value | 3,230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py |
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