text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
self.do_resize = do_resize
self.size = size
self.size_divisor = size_divisor
self.resample = resample
self.do_rescale = do_rescale
self.rescale_factor = rescale_factor
self.do_normalize = do_normalize
self.image_mean = image_mean if image_mean is not None else IMA... | 3,764 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/image_processing_vilt.py |
@classmethod
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(chec... | 3,764 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/image_processing_vilt.py |
def resize(
self,
image: np.ndarray,
size: Dict[str, int],
size_divisor: int = 32,
resample: PILImageResampling = PILImageResampling.BICUBIC,
data_format: Optional[Union[str, ChannelDimension]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = No... | 3,764 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/image_processing_vilt.py |
Args:
image (`np.ndarray`):
Image to resize.
size (`Dict[str, int]`):
Controls the size of the output image. Should be of the form `{"shortest_edge": int}`.
size_divisor (`int`, *optional*, defaults to 32):
The image is resized to a siz... | 3,764 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/image_processing_vilt.py |
raise ValueError(f"The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}")
shorter = size["shortest_edge"]
longer = int(1333 / 800 * shorter)
output_size = get_resize_output_image_size(
image, shorter=shorter, longer=longer, size_divisor=size_divisor, input_da... | 3,764 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/image_processing_vilt.py |
def _pad_image(
self,
image: np.ndarray,
output_size: Tuple[int, int],
constant_values: Union[float, Iterable[float]] = 0,
data_format: Optional[ChannelDimension] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
) -> np.ndarray:
"""
... | 3,764 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/image_processing_vilt.py |
def pad(
self,
images: List[np.ndarray],
constant_values: Union[float, Iterable[float]] = 0,
return_pixel_mask: bool = True,
return_tensors: Optional[Union[str, TensorType]] = None,
data_format: Optional[ChannelDimension] = None,
input_data_format: Optional[Union[... | 3,764 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/image_processing_vilt.py |
Args:
image (`np.ndarray`):
Image to pad.
constant_values (`float` or `Iterable[float]`, *optional*):
The value to use for the padding if `mode` is `"constant"`.
return_pixel_mask (`bool`, *optional*, defaults to `True`):
Whether to ret... | 3,764 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/image_processing_vilt.py |
The channel dimension format of the image. If not provided, it will be the same as the input image.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format of the input image. If not provided, it will be inferred.
"""
pad_size = get_max_heigh... | 3,764 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/image_processing_vilt.py |
padded_images = [
self._pad_image(
image,
pad_size,
constant_values=constant_values,
data_format=data_format,
input_data_format=input_data_format,
)
for image in images
]
data = {"pixel_va... | 3,764 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/image_processing_vilt.py |
@filter_out_non_signature_kwargs()
def preprocess(
self,
images: ImageInput,
do_resize: Optional[bool] = None,
size: Optional[Dict[str, int]] = None,
size_divisor: Optional[int] = None,
resample: PILImageResampling = None,
do_rescale: Optional[bool] = None,
... | 3,764 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/image_processing_vilt.py |
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`.
do_resize (`bool`, *optional*, defaults to `self.do_resiz... | 3,764 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/image_processing_vilt.py |
The image is resized to a size that is a multiple of this value.
resample (`PILImageResampling`, *optional*, defaults to `self.resample`):
Resampling filter to use if resizing the image. Only has an effect if `do_resize` is set to `True`.
do_rescale (`bool`, *optional*, defaults ... | 3,764 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/image_processing_vilt.py |
Image standard deviation to normalize the image by if `do_normalize` is set to `True`.
do_pad (`bool`, *optional*, defaults to `self.do_pad`):
Whether to pad the image to the (max_height, max_width) in the batch. If `True`, a pixel mask is also
created and returned.
... | 3,764 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/image_processing_vilt.py |
The channel dimension format for the output image. Can be one of:
- `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `ChannelDimension.LAST`: image in (height, width, num_channels) format.
input_data_format (`ChannelDimension` or `str`, *opti... | 3,764 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/image_processing_vilt.py |
resample = resample if resample is not None else self.resample
do_rescale = do_rescale if do_rescale is not None else self.do_rescale
rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
do_normalize = do_normalize if do_normalize is not None else self.do_normal... | 3,764 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/image_processing_vilt.py |
size = size if size is not None else self.size
size = get_size_dict(size, default_to_square=False)
images = make_list_of_images(images)
if not valid_images(images):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
... | 3,764 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/image_processing_vilt.py |
# All transformations expect numpy arrays.
images = [to_numpy_array(image) for image in images]
if do_rescale and is_scaled_image(images[0]):
logger.warning_once(
"It looks like you are trying to rescale already rescaled images. If the input"
" images have pi... | 3,764 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/image_processing_vilt.py |
if do_rescale:
images = [
self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format)
for image in images
]
if do_normalize:
images = [
self.normalize(image=image, mean=image_mean, std=image_std, input_... | 3,764 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/image_processing_vilt.py |
class ViltProcessor(ProcessorMixin):
r"""
Constructs a ViLT processor which wraps a BERT tokenizer and ViLT image processor into a single processor.
[`ViltProcessor`] offers all the functionalities of [`ViltImageProcessor`] and [`BertTokenizerFast`]. See the
docstring of [`~ViltProcessor.__call__`] and... | 3,765 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/processing_vilt.py |
def __init__(self, image_processor=None, tokenizer=None, **kwargs):
feature_extractor = None
if "feature_extractor" in kwargs:
warnings.warn(
"The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
" instead.",
... | 3,765 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/processing_vilt.py |
def __call__(
self,
images,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy] = None,
ma... | 3,765 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/processing_vilt.py |
[`BertTokenizerFast.__call__`] to prepare text for the model. | 3,765 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/processing_vilt.py |
Please refer to the docstring of the above two methods for more information.
"""
encoding = self.tokenizer(
text=text,
add_special_tokens=add_special_tokens,
padding=padding,
truncation=truncation,
max_length=max_length,
stride=stri... | 3,765 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/processing_vilt.py |
return encoding
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to BertTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
refer to the docstring of this method for more information.
"""
return self.tokenizer.batch_decode(*args, ... | 3,765 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/processing_vilt.py |
@property
def feature_extractor_class(self):
warnings.warn(
"`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.",
FutureWarning,
)
return self.image_processor_class
@property
def feature_extractor(self):
... | 3,765 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/processing_vilt.py |
class ViltConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`ViLTModel`]. It is used to instantiate an ViLT
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar conf... | 3,766 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/configuration_vilt.py |
Args:
vocab_size (`int`, *optional*, defaults to 30522):
Vocabulary size of the text part of the model. Defines the number of different tokens that can be
represented by the `inputs_ids` passed when calling [`ViltModel`].
type_vocab_size (`int`, *optional*, defaults to 2):
... | 3,766 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/configuration_vilt.py |
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*,... | 3,766 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/configuration_vilt.py |
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
image_size (`in... | 3,766 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/configuration_vilt.py |
the encoder will sample `max_image_length` patches at maximum. If set to -1, will not be taken into
account.
num_images (`int`, *optional*, defaults to -1):
The number of images to use for natural language visual reasoning. If set to a positive integer, will be
used by [`Vilt... | 3,766 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/configuration_vilt.py |
Example:
```python
>>> from transformers import ViLTModel, ViLTConfig
>>> # Initializing a ViLT dandelin/vilt-b32-mlm style configuration
>>> configuration = ViLTConfig()
>>> # Initializing a model from the dandelin/vilt-b32-mlm style configuration
>>> model = ViLTModel(configuration)
>>... | 3,766 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/configuration_vilt.py |
def __init__(
self,
vocab_size=30522,
type_vocab_size=2,
modality_type_vocab_size=2,
max_position_embeddings=40,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropou... | 3,766 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/configuration_vilt.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... | 3,766 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/configuration_vilt.py |
class ViltFeatureExtractor(ViltImageProcessor):
def __init__(self, *args, **kwargs) -> None:
warnings.warn(
"The class ViltFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
" use ViltImageProcessor instead.",
FutureWarning,
)... | 3,767 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/feature_extraction_vilt.py |
class ViltForImagesAndTextClassificationOutput(ModelOutput):
"""
Class for outputs of [`ViltForImagesAndTextClassification`]. | 3,768 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
Classification (or regression if config.num_labe... | 3,768 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
List of tuples of `torch.FloatTensor` (one for each image-text pair, each tuple containing the attention
weights 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.... | 3,768 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
hidden_states: Optional[List[Tuple[torch.FloatTensor]]] = None
attentions: Optional[List[Tuple[torch.FloatTensor]]] = None | 3,768 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
class ViltEmbeddings(nn.Module):
"""
Construct the text and patch embeddings.
Text embeddings are equivalent to BERT embeddings.
Patch embeddings are equivalent to ViT embeddings.
"""
def __init__(self, config):
super().__init__()
# text embeddings
self.text_embedding... | 3,769 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
def visual_embed(self, pixel_values, pixel_mask, max_image_length=200):
_, _, ph, pw = self.patch_embeddings.projection.weight.shape
x = self.patch_embeddings(pixel_values)
x_mask = pixel_mask[:, None, :, :].float()
x_mask = nn.functional.interpolate(x_mask, size=(x.shape[2], x.shape[3]... | 3,769 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
batch_size, num_channels, height, width = x.shape
patch_dim = self.config.image_size // self.config.patch_size
spatial_pos = self.position_embeddings[:, 1:, :].transpose(1, 2).view(1, num_channels, patch_dim, patch_dim)
pos_embed = torch.cat(
[
nn.functional.pad(
... | 3,769 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
pos_embed = pos_embed.flatten(2).transpose(1, 2)
x = x.flatten(2).transpose(1, 2)
# Set `device` here, otherwise `patch_index` will always be on `CPU` and will fail near the end for torch>=1.13
patch_index = torch.stack(
meshgrid(torch.arange(x_mask.shape[-2]), torch.arange(x_mask.sh... | 3,769 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
if max_image_length < 0 or max_image_length is None or not isinstance(max_image_length, int):
# suppose aug is 800 x 1333, then, maximum effective res is 800 x 1333 (if one side gets bigger, the other will be constrained and be shrinked)
# (800 // self.patch_size) * (1333 // self.patch_size) is ... | 3,769 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
valid_idx = x_mask.nonzero(as_tuple=False)
non_valid_idx = (1 - x_mask).nonzero(as_tuple=False)
unique_rows = valid_idx[:, 0].unique()
valid_row_idx = [valid_idx[valid_idx[:, 0] == u] for u in unique_rows]
non_valid_row_idx = [non_valid_idx[non_valid_idx[:, 0] == u] for u in unique_rows]... | 3,769 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
select = torch.cat(select, dim=0)
x = x[select[:, 0], select[:, 1]].view(batch_size, -1, num_channels)
x_mask = x_mask[select[:, 0], select[:, 1]].view(batch_size, -1)
# `patch_index` should be on the same device as `select` (for torch>=1.13), which is ensured at definition time.
patch_i... | 3,769 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
def forward(
self,
input_ids,
attention_mask,
token_type_ids,
pixel_values,
pixel_mask,
inputs_embeds,
image_embeds,
image_token_type_idx=1,
):
# PART 1: text embeddings
text_embeds = self.text_embeddings(
input_ids=... | 3,769 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
# PART 3: add modality type embeddings
# 0 indicates text, 1 indicates image, 2 is optionally used when a second image is provided (NLVR2)
if image_token_type_idx is None:
image_token_type_idx = 1
text_embeds = text_embeds + self.token_type_embeddings(
torch.zeros_like(at... | 3,769 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
class TextEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.position_embeddi... | 3,770 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
# any TensorFlow checkpoint file
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
# position_ids (1, len ... | 3,770 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
seq_length = input_shape[1]
if position_ids is None:
position_ids = self.position_ids[:, :seq_length]
# Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs
# when its auto-generated, registered buffer helps users when traci... | 3,770 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embeddings = inputs_embeds + token_type_embeddings
if self.position_embedding_type == "absolute":
position_embeddings = self.posit... | 3,770 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
class ViltPatchEmbeddings(nn.Module):
"""
Image to Patch Embedding.
"""
def __init__(self, config):
super().__init__()
image_size, patch_size = config.image_size, config.patch_size
num_channels, hidden_size = config.num_channels, config.hidden_size
image_size = image_si... | 3,771 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
def forward(self, pixel_values):
batch_size, 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."
)
... | 3,771 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
class ViltSelfAttention(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 ... | 3,772 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.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... | 3,772 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.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... | 3,772 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.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,)
... | 3,772 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
class ViltSelfOutput(nn.Module):
"""
The residual connection is defined in ViltLayer instead of here (as is the case with other models), due to the
layernorm applied before each block.
"""
def __init__(self, config: ViltConfig) -> None:
super().__init__()
self.dense = nn.Linear(conf... | 3,773 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
class ViltAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.attention = ViltSelfAttention(config)
self.output = ViltSelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads):
if len(heads) == 0:
return
heads, in... | 3,774 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.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... | 3,774 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
class ViltIntermediate(nn.Module):
def __init__(self, config: ViltConfig) -> 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:
... | 3,775 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
class ViltOutput(nn.Module):
def __init__(self, config: ViltConfig) -> 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... | 3,776 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
class ViltLayer(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 = ViltAttention(config)
self... | 3,777 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.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_... | 3,777 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
class ViltEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([ViltLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
hidden_states,
... | 3,778 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.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,
... | 3,778 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.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,
) | 3,778 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
class ViltPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = ViltConfig
base_model_prefix = "vilt"
supports_gradient_checkpointing = True
_no_split_modules = ["... | 3,779 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, (nn.Linear, nn.Conv2d)):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.no... | 3,779 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
class ViltModel(ViltPreTrainedModel):
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
self.config = config
self.embeddings = ViltEmbeddings(config)
self.encoder = ViltEncoder(config)
self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.l... | 3,780 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.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... | 3,780 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
@add_start_docstrings_to_model_forward(VILT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
toke... | 3,780 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
```python
>>> from transformers import ViltProcessor, ViltModel
>>> from PIL import Image
>>> import requests
>>> # prepare image and text
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
... | 3,780 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
>>> inputs = processor(image, text, return_tensors="pt")
>>> outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidden_state
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
... | 3,780 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
input_shape = input_ids.size()
... | 3,780 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
if pixel_values is not None and image_embeds is not None:
raise ValueError("You cannot specify both pixel_values and image_embeds at the same time")
elif pixel_values is None and image_embeds is None:
raise ValueError("You have to specify either pixel_values or image_embeds")
im... | 3,780 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_lengt... | 3,780 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
encoder_outputs =... | 3,780 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
return BaseModelOutputWithPooling(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
) | 3,780 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
class ViltPooler(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):
# We "pool" the model by simply taking the hidden state corresponding
... | 3,781 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
class ViltForMaskedLM(ViltPreTrainedModel):
_tied_weights_keys = ["mlm_score.decoder.weight", "mlm_score.decoder.bias"]
def __init__(self, config):
super().__init__(config)
self.vilt = ViltModel(config)
self.mlm_score = ViltMLMHead(config)
# Initialize weights and apply final ... | 3,782 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
@add_start_docstrings_to_model_forward(VILT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTe... | 3,782 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
labels (*torch.LongTensor* of shape *(batch_size, sequence_length)*, *optional*):
Labels for computing the masked language modeling loss. Indices should be in *[-100, 0, ...,
config.vocab_size]* (see *input_ids* docstring) Tokens with indices set to *-100* are ignored (masked), the
l... | 3,782 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
Returns:
Examples:
```python
>>> from transformers import ViltProcessor, ViltForMaskedLM
>>> import requests
>>> from PIL import Image
>>> import re
>>> import torch
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = ... | 3,782 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
>>> # gradually fill in the MASK tokens, one by one
>>> with torch.no_grad():
... for i in range(tl):
... encoded = processor.tokenizer(inferred_token)
... input_ids = torch.tensor(encoded.input_ids)
... encoded = encoded["input_ids"][0][1:-1]
... | 3,782 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
... inferred_token = [processor.decode(encoded)] | 3,782 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
>>> selected_token = ""
>>> encoded = processor.tokenizer(inferred_token)
>>> output = processor.decode(encoded.input_ids[0], skip_special_tokens=True)
>>> print(output)
a bunch of cats laying on a couch.
```"""
return_dict = return_dict if return_dict is not None else se... | 3,782 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
sequence_output, pooled_output = outputs[:2]
# split up final hidden states into text and image features
text_seq_len = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
text_features, _ = (sequence_output[:, :text_seq_len], sequence_output[:, text_seq_len:])
mlm_l... | 3,782 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
return MaskedLMOutput(
loss=masked_lm_loss,
logits=mlm_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
) | 3,782 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
class ViltPredictionHeadTransform(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
if isinstance(config.hidden_act, str):
self.transform_act_fn = ACT2FN[config.hidden_act]
else:
self.tran... | 3,783 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
class ViltMLMHead(nn.Module):
def __init__(self, config, weight=None):
super().__init__()
self.config = config
self.transform = ViltPredictionHeadTransform(config)
self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.bias = nn.Parameter(torch.zeros... | 3,784 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
class ViltForQuestionAnswering(ViltPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.vilt = ViltModel(config)
# Classifier head
self.classifier = nn.Sequential(
nn.Linear(config.hidden_size, config.hi... | 3,785 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
@add_start_docstrings_to_model_forward(VILT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_... | 3,785 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
Labels for computing the visual question answering loss. This tensor must be either a one-hot encoding of
all answers that are applicable for a given example in the batch, or a soft encoding indicating which
answers are applicable, where 1.0 is the highest score. | 3,785 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
Returns:
Examples:
```python
>>> from transformers import ViltProcessor, ViltForQuestionAnswering
>>> import requests
>>> from PIL import Image
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=Tr... | 3,785 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
outputs = self.vilt(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
pixel_values=pixel_values,
pixel_mask=pixel_mask,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
image_embeds=image_embeds,
... | 3,785 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
... | 3,785 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
class ViltForImageAndTextRetrieval(ViltPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.vilt = ViltModel(config)
# Classifier head
self.rank_output = nn.Linear(config.hidden_size, 1)
# Initialize weights and apply final processing
self.po... | 3,786 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
@add_start_docstrings_to_model_forward(VILT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_... | 3,786 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
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