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|---|---|---|
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... | 3,864 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
return SequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=discriminator_hidden_states.hidden_states,
attentions=discriminator_hidden_states.attentions,
) | 3,864 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
class ElectraForPreTraining(ElectraPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.electra = ElectraModel(config)
self.discriminator_predictions = ElectraDiscriminatorPredictions(config)
# Initialize weights and apply final processing
self.post_in... | 3,865 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
@add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=ElectraForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[t... | 3,865 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
Labels for computing the ELECTRA loss. Input should be a sequence of tokens (see `input_ids` docstring)
Indices should be in `[0, 1]`: | 3,865 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
- 0 indicates the token is an original token,
- 1 indicates the token was replaced.
Returns:
Examples:
```python
>>> from transformers import ElectraForPreTraining, AutoTokenizer
>>> import torch
>>> discriminator = ElectraForPreTraining.from_pretrained("g... | 3,865 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
>>> fake_tokens
['[CLS]', 'the', 'quick', 'brown', 'fox', 'fake', 'over', 'the', 'lazy', 'dog', '[SEP]']
>>> predictions.squeeze().tolist()
[0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0]
```"""
return_dict = return_dict if return_dict is not None else self.config.use_re... | 3,865 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
loss = None
if labels is not None:
loss_fct = nn.BCEWithLogitsLoss()
if attention_mask is not None:
active_loss = attention_mask.view(-1, discriminator_sequence_output.shape[1]) == 1
active_logits = logits.view(-1, discriminator_sequence_output.shape[1])[a... | 3,865 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
class ElectraForMaskedLM(ElectraPreTrainedModel):
_tied_weights_keys = ["generator_lm_head.weight"]
def __init__(self, config):
super().__init__(config)
self.electra = ElectraModel(config)
self.generator_predictions = ElectraGeneratorPredictions(config)
self.generator_lm_head ... | 3,866 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
@add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint="google/electra-small-generator",
output_type=MaskedLMOutput,
config_class=_CONFIG_FOR_DOC,
mask="[MASK]",
expected_output="'paris'",... | 3,866 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.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... | 3,866 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
generator_hidden_states = self.electra(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
... | 3,866 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
if not return_dict:
output = (prediction_scores,) + generator_hidden_states[1:]
return ((loss,) + output) if loss is not None else output
return MaskedLMOutput(
loss=loss,
logits=prediction_scores,
hidden_states=generator_hidden_states.hidden_states,
... | 3,866 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
class ElectraForTokenClassification(ElectraPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.electra = ElectraModel(config)
classifier_dropout = (
config.classifier_dropout if config.classifier_dropout is not ... | 3,867 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
@add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint="bhadresh-savani/electra-base-discriminator-finetuned-conll03-english",
output_type=TokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
expe... | 3,867 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
"""
return_dict = return_... | 3,867 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
discriminator_hidden_states = self.electra(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
... | 3,867 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=discriminator_hidden_states.hidden_states,
attentions=discriminator_hidden_states.attentions,
) | 3,867 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
class ElectraForQuestionAnswering(ElectraPreTrainedModel):
config_class = ElectraConfig
base_model_prefix = "electra"
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.electra = ElectraModel(config)
self.qa_outputs = nn.Linear(con... | 3,868 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
@add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint="bhadresh-savani/electra-base-squad2",
output_type=QuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
qa_target_start_index=11,
... | 3,868 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], QuestionAnsweringModelOutput]:
r"""
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
... | 3,868 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
discriminator_hidden_states = self.electra(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
... | 3,868 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
total_loss = None
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
... | 3,868 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
if not return_dict:
output = (
start_logits,
end_logits,
) + discriminator_hidden_states[1:]
return ((total_loss,) + output) if total_loss is not None else output
return QuestionAnsweringModelOutput(
loss=total_loss,
st... | 3,868 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
class ElectraForMultipleChoice(ElectraPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.electra = ElectraModel(config)
self.sequence_summary = SequenceSummary(config)
self.classifier = nn.Linear(config.hidden_size, 1)
# Initialize weights and apply... | 3,869 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
@add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MultipleChoiceModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
i... | 3,869 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
`input_ids` above)
"""
return_dict = return_dict if return_dict is not None else self.co... | 3,869 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
... | 3,869 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
discriminator_hidden_states = self.electra(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
... | 3,869 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
return MultipleChoiceModelOutput(
loss=loss,
logits=reshaped_logits,
hidden_states=discriminator_hidden_states.hidden_states,
attentions=discriminator_hidden_states.attentions,
) | 3,869 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
class ElectraForCausalLM(ElectraPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["generator_lm_head.weight"]
def __init__(self, config):
super().__init__(config)
if not config.is_decoder:
logger.warning("If you want to use `ElectraForCausalLM` as a standalone, add `is_decod... | 3,870 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
@add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Opti... | 3,870 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]:
r"""
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
... | 3,870 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
`[-100, 0,... | 3,870 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
use_cache (`bool`... | 3,870 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)
>>> prediction_logits = outputs.logits
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if labels is not None:
use_cache = F... | 3,870 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
sequence_output = outputs[0]
prediction_scores = self.generator_lm_head(self.generator_predictions(sequence_output))
lm_loss = None
if labels is not None:
# we are doing next-token prediction; shift prediction scores and input ids by one
shifted_prediction_scores = predi... | 3,870 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
return CausalLMOutputWithCrossAttentions(
loss=lm_loss,
logits=prediction_scores,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
cross_attentions=outputs.cross_attentions,
)
... | 3,870 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
class BitImageProcessor(BaseImageProcessor):
r"""
Constructs a BiT image processor. | 3,871 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/image_processing_bit.py |
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by
`do_resize` in the `preprocess` method.
size (`Dict[str, int]` *optional*, defaults to `{"shortest_edge": 224}`):
... | 3,871 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/image_processing_bit.py |
crop_size (`Dict[str, int]` *optional*, defaults to 224):
Size of the output image after applying `center_crop`. Can be overridden by `crop_size` in the `preprocess`
method.
do_rescale (`bool`, *optional*, defaults to `True`):
Whether to rescale the image by the specified sca... | 3,871 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/image_processing_bit.py |
channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method.
image_std (`float` or `List[float]`, *optional*, defaults to `OPENAI_CLIP_MEAN`):
Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
num... | 3,871 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/image_processing_bit.py |
model_input_names = ["pixel_values"]
def __init__(
self,
do_resize: bool = True,
size: Dict[str, int] = None,
resample: PILImageResampling = PILImageResampling.BICUBIC,
do_center_crop: bool = True,
crop_size: Dict[str, int] = None,
do_rescale: bool = True,
... | 3,871 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/image_processing_bit.py |
self.do_resize = do_resize
self.size = size
self.resample = resample
self.do_center_crop = do_center_crop
self.crop_size = crop_size
self.do_rescale = do_rescale
self.rescale_factor = rescale_factor
self.do_normalize = do_normalize
self.image_mean = image_... | 3,871 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/image_processing_bit.py |
# Copied from transformers.models.clip.image_processing_clip.CLIPImageProcessor.resize
def resize(
self,
image: np.ndarray,
size: Dict[str, int],
resample: PILImageResampling = PILImageResampling.BICUBIC,
data_format: Optional[Union[str, ChannelDimension]] = None,
inp... | 3,871 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/image_processing_bit.py |
Args:
image (`np.ndarray`):
Image to resize.
size (`Dict[str, int]`):
Size of the output image.
resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):
Resampling filter to use when resiizing the image.
... | 3,871 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/image_processing_bit.py |
raise ValueError("Size must contain either 'shortest_edge' or 'height' and 'width'.") | 3,871 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/image_processing_bit.py |
output_size = get_resize_output_image_size(
image,
size=size,
default_to_square=default_to_square,
input_data_format=input_data_format,
)
return resize(
image,
size=output_size,
resample=resample,
data_format... | 3,871 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/image_processing_bit.py |
@filter_out_non_signature_kwargs()
def preprocess(
self,
images: ImageInput,
do_resize: bool = None,
size: Dict[str, int] = None,
resample: PILImageResampling = None,
do_center_crop: bool = None,
crop_size: int = None,
do_rescale: bool = None,
... | 3,871 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/image_processing_bit.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... | 3,871 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/image_processing_bit.py |
Whether to center crop the image.
crop_size (`Dict[str, int]`, *optional*, defaults to `self.crop_size`):
Size of the center crop. Only has an effect if `do_center_crop` is set to `True`.
do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
Whether to r... | 3,871 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/image_processing_bit.py |
Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to
`True`.
do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`):
Whether to convert the image to RGB.
return_tensors (`str` or `TensorType`, *optional*... | 3,871 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/image_processing_bit.py |
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- Unset: Use the channel dimension format of the input image.
input_data_format (`Ch... | 3,871 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/image_processing_bit.py |
size = get_size_dict(size, param_name="size", default_to_square=False)
resample = resample if resample is not None else self.resample
do_center_crop = do_center_crop if do_center_crop is not None else self.do_center_crop
crop_size = crop_size if crop_size is not None else self.crop_size
... | 3,871 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/image_processing_bit.py |
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, "
"torch.Tensor, tf.Tensor or jax.ndarray."
)
validate_preprocess_arguments(
do_r... | 3,871 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/image_processing_bit.py |
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 pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
)
if inpu... | 3,871 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/image_processing_bit.py |
if do_normalize:
image = self.normalize(
image=image, mean=image_mean, std=image_std, input_data_format=input_data_format
)
all_images.append(image)
images = [
to_channel_dimension_format(image, data_format, input_channel_dim=input_da... | 3,871 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/image_processing_bit.py |
class WeightStandardizedConv2d(nn.Conv2d):
"""Conv2d with Weight Standardization. Includes TensorFlow compatible SAME padding. Used for ViT Hybrid model.
Paper: [Micro-Batch Training with Batch-Channel Normalization and Weight
Standardization](https://arxiv.org/abs/1903.10520v2)
""" | 3,872 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/modeling_bit.py |
def __init__(
self,
in_channel,
out_channels,
kernel_size,
stride=1,
padding="SAME",
dilation=1,
groups=1,
bias=False,
eps=1e-6,
):
padding, is_dynamic = get_padding_value(padding, kernel_size, stride=stride, dilation=dilation)
... | 3,872 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/modeling_bit.py |
def forward(self, hidden_state):
if self.pad is not None:
hidden_state = self.pad(hidden_state)
weight = nn.functional.batch_norm(
self.weight.reshape(1, self.out_channels, -1), None, None, training=True, momentum=0.0, eps=self.eps
).reshape_as(self.weight)
hidden... | 3,872 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/modeling_bit.py |
class BitGroupNormActivation(nn.GroupNorm):
r"""
A module that combines group normalization with an activation function.
"""
def __init__(self, config, num_channels, eps=1e-5, affine=True, apply_activation=True):
super(BitGroupNormActivation, self).__init__(config.num_groups, num_channels, eps=... | 3,873 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/modeling_bit.py |
class DynamicPad2d(nn.Module):
r"""
A module that wraps dynamic padding of any input, given the parameters of the convolutional layer and the input
hidden states.
"""
def __init__(self, kernel_size, stride, dilation, value=0):
super().__init__()
# Safety checkers
if isinstan... | 3,874 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/modeling_bit.py |
# Compute the padding values
padding_height = self.compute_padding(input_height, self.kernel_size[0], self.stride[0], self.dilation[0])
padding_width = self.compute_padding(input_width, self.kernel_size[1], self.stride[1], self.dilation[1])
# apply pad
if padding_height > 0 or padding_w... | 3,874 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/modeling_bit.py |
class BitMaxPool2d(nn.MaxPool2d):
"""Tensorflow like 'SAME' wrapper for 2D max pooling"""
def __init__(
self,
kernel_size: int,
stride=None,
dilation=1,
ceil_mode=False,
padding=(0, 0),
padding_value=0,
use_dynamic_padding=True,
):
ker... | 3,875 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/modeling_bit.py |
def forward(self, hidden_states):
hidden_states = self.pad(hidden_states)
return nn.functional.max_pool2d(
hidden_states, self.kernel_size, self.stride, self.padding, self.dilation, self.ceil_mode
) | 3,875 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/modeling_bit.py |
class BitEmbeddings(nn.Module):
"""
BiT Embeddings (stem) composed of a single aggressive convolution.
"""
def __init__(self, config: BitConfig):
super().__init__()
self.convolution = WeightStandardizedConv2d(
config.num_channels,
config.embedding_size,
... | 3,876 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/modeling_bit.py |
if not config.layer_type == "preactivation":
self.norm = BitGroupNormActivation(config, num_channels=config.embedding_size)
else:
self.norm = nn.Identity()
self.num_channels = config.num_channels
def forward(self, pixel_values: Tensor) -> Tensor:
num_channels = pixe... | 3,876 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/modeling_bit.py |
class BitDropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
def __init__(self, drop_prob: Optional[float] = None) -> None:
super().__init__()
self.drop_prob = drop_prob
def forward(self, hidden_states: torch.Tensor) -> torch.... | 3,877 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/modeling_bit.py |
class BitPreActivationBottleneckLayer(nn.Module):
"""Pre-activation (v2) bottleneck block.
Follows the implementation of "Identity Mappings in Deep Residual Networks":
https://github.com/KaimingHe/resnet-1k-layers/blob/master/resnet-pre-act.lua
Except it puts the stride on 3x3 conv when available.
... | 3,878 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/modeling_bit.py |
if is_first_layer:
self.downsample = BitDownsampleConv(
config,
in_channels,
out_channels,
stride=stride,
preact=True,
)
else:
self.downsample = None
self.norm1 = BitGroupNormActivation(c... | 3,878 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/modeling_bit.py |
def forward(self, hidden_states):
hidden_states_preact = self.norm1(hidden_states)
# shortcut branch
shortcut = hidden_states
if self.downsample is not None:
shortcut = self.downsample(hidden_states_preact)
# residual branch
hidden_states = self.conv1(hidden... | 3,878 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/modeling_bit.py |
class BitBottleneckLayer(nn.Module):
"""Non Pre-activation bottleneck block, equivalent to V1.5/V1b bottleneck. Used for ViT Hybrid."""
def __init__(
self,
config,
in_channels,
out_channels=None,
bottle_ratio=0.25,
stride=1,
dilation=1,
first_dila... | 3,879 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/modeling_bit.py |
self.conv1 = WeightStandardizedConv2d(in_channels, mid_chs, 1, eps=1e-8, padding=config.global_padding)
self.norm1 = BitGroupNormActivation(config, num_channels=mid_chs)
self.conv2 = WeightStandardizedConv2d(
mid_chs,
mid_chs,
3,
stride=stride,
... | 3,879 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/modeling_bit.py |
def forward(self, hidden_states):
# shortcut branch
shortcut = hidden_states
if self.downsample is not None:
shortcut = self.downsample(hidden_states)
# residual
hidden_states = self.conv1(hidden_states)
hidden_states = self.norm1(hidden_states)
hidd... | 3,879 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/modeling_bit.py |
class BitDownsampleConv(nn.Module):
def __init__(
self,
config,
in_channels,
out_channels,
stride=1,
preact=True,
):
super().__init__()
self.conv = WeightStandardizedConv2d(
in_channels, out_channels, 1, stride=stride, eps=1e-8, padding... | 3,880 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/modeling_bit.py |
class BitStage(nn.Module):
"""
A ResNet v2 stage composed by stacked layers.
"""
def __init__(
self,
config,
in_channels,
out_channels,
stride,
dilation,
depth,
bottle_ratio=0.25,
layer_dropout=None,
):
super().__init__... | 3,881 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/modeling_bit.py |
self.layers.add_module(
str(layer_idx),
layer_cls(
config,
prev_chs,
out_channels,
stride=stride,
dilation=dilation,
bottle_ratio=bottle_ratio,
firs... | 3,881 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/modeling_bit.py |
return stride, drop_path_rate, is_first_layer
def forward(self, input: Tensor) -> Tensor:
hidden_state = input
for _, layer in enumerate(self.layers):
hidden_state = layer(hidden_state)
return hidden_state | 3,881 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/modeling_bit.py |
class BitEncoder(nn.Module):
def __init__(self, config: BitConfig):
super().__init__()
self.stages = nn.ModuleList([])
prev_chs = config.embedding_size
# These needs to stay hardcoded
current_stride = 4
dilation = 1
layer_dropouts = [
x.tolist()... | 3,882 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/modeling_bit.py |
stage = BitStage(
config,
prev_chs,
out_channels,
stride=stride,
dilation=dilation,
depth=current_depth,
layer_dropout=layer_dropout,
)
prev_chs = out_channels
current_str... | 3,882 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/modeling_bit.py |
for stage_module in self.stages:
if output_hidden_states:
hidden_states = hidden_states + (hidden_state,)
hidden_state = stage_module(hidden_state)
if output_hidden_states:
hidden_states = hidden_states + (hidden_state,)
if not return_dict:
... | 3,882 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/modeling_bit.py |
class BitPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = BitConfig
base_model_prefix = "bit"
main_input_name = "pixel_values"
_no_split_modules = ["BitEmbedd... | 3,883 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/modeling_bit.py |
def _init_weights(self, module):
if isinstance(module, nn.Conv2d):
nn.init.kaiming_normal_(module.weight, mode="fan_out", nonlinearity="relu")
# copied from the `reset_parameters` method of `class Linear(Module)` in `torch`.
elif isinstance(module, nn.Linear):
nn.init.kai... | 3,883 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/modeling_bit.py |
class BitModel(BitPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.embedder = BitEmbeddings(config)
self.encoder = BitEncoder(config)
self.norm = (
BitGroupNormActivation(config, num_channels=config.hidden_sizes[-1... | 3,884 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/modeling_bit.py |
@add_start_docstrings_to_model_forward(BIT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPoolingAndNoAttention,
config_class=_CONFIG_FOR_DOC,
modality="vision",
expected_output=_EXPECTED_OUTPUT_SHAPE,
)
d... | 3,884 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/modeling_bit.py |
last_hidden_state = self.norm(last_hidden_state)
pooled_output = self.pooler(last_hidden_state)
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=last_hidden_state,
... | 3,884 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/modeling_bit.py |
class BitForImageClassification(BitPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.bit = BitModel(config)
# classification head
self.classifier = nn.Sequential(
nn.Flatten(),
nn.Linear(con... | 3,885 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/modeling_bit.py |
@add_start_docstrings_to_model_forward(BIT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT,
output_type=ImageClassifierOutputWithNoAttention,
config_class=_CONFIG_FOR_DOC,
expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
)
def forward(
s... | 3,885 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/modeling_bit.py |
outputs = self.bit(pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict)
pooled_output = outputs.pooler_output if return_dict else outputs[1]
logits = self.classifier(pooled_output)
loss = None | 3,885 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/modeling_bit.py |
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):
self.config.problem_typ... | 3,885 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/modeling_bit.py |
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels) | 3,885 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/modeling_bit.py |
if not return_dict:
output = (logits,) + outputs[2:]
return (loss,) + output if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=loss, logits=logits, hidden_states=outputs.hidden_states) | 3,885 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/modeling_bit.py |
class BitBackbone(BitPreTrainedModel, BackboneMixin):
def __init__(self, config):
super().__init__(config)
super()._init_backbone(config)
self.bit = BitModel(config)
self.num_features = [config.embedding_size] + config.hidden_sizes
# initialize weights and apply final proce... | 3,886 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/modeling_bit.py |
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> processor = AutoImageProcessor.from_pretrained("google/bit-50")
>>> model = AutoBackbone.from_pretrained("google/bit-50")
>>> inputs = processor(image, retu... | 3,886 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/modeling_bit.py |
if not return_dict:
output = (feature_maps,)
if output_hidden_states:
output += (outputs.hidden_states,)
return output
return BackboneOutput(
feature_maps=feature_maps,
hidden_states=outputs.hidden_states if output_hidden_states else N... | 3,886 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/modeling_bit.py |
class BitConfig(BackboneConfigMixin, PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`BitModel`]. It is used to instantiate an BiT
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yi... | 3,887 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/configuration_bit.py |
Args:
num_channels (`int`, *optional*, defaults to 3):
The number of input channels.
embedding_size (`int`, *optional*, defaults to 64):
Dimensionality (hidden size) for the embedding layer.
hidden_sizes (`List[int]`, *optional*, defaults to `[256, 512, 1024, 2048]`):
... | 3,887 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/configuration_bit.py |
num_groups (`int`, *optional*, defaults to 32):
Number of groups used for the `BitGroupNormActivation` layers.
drop_path_rate (`float`, *optional*, defaults to 0.0):
The drop path rate for the stochastic depth.
embedding_dynamic_padding (`bool`, *optional*, defaults to `False`):
... | 3,887 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/configuration_bit.py |
same order as defined in the `stage_names` attribute.
out_indices (`List[int]`, *optional*):
If used as backbone, list of indices of features to output. Can be any of 0, 1, 2, etc. (depending on how
many stages the model has). If unset and `out_features` is set, will default to the corre... | 3,887 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/configuration_bit.py |
Example:
```python
>>> from transformers import BitConfig, BitModel
>>> # Initializing a BiT bit-50 style configuration
>>> configuration = BitConfig()
>>> # Initializing a model (with random weights) from the bit-50 style configuration
>>> model = BitModel(configuration)
>>> # Accessing ... | 3,887 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/configuration_bit.py |
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