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|---|---|---|
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
encoder_layer.__call__,
hidden_states,
extended_attention_mask,
(head_mask[idx] if head_mask is not None else None),
... | 9,335 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
if not return_dict:
return tuple(v for v in [hidden_states, encoder_hidden_states, all_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states, hidden_states=encoder_hidden_states, attentions=all_attentions
) | 9,335 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
class ProphetNetDecoder(ProphetNetPreTrainedModel):
r"""
word_embeddings (`torch.nn.Embeddings` of shape `(config.vocab_size, config.hidden_size)`, *optional*):
The word embedding parameters. This can be used to initialize [`ProphetNetEncoder`] with pre-defined word
embeddings instead of random... | 9,336 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
self.word_embeddings = (
word_embeddings
if word_embeddings is not None
else nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
)
self.position_embeddings = ProphetNetPositionalEmbeddings(config)
self.ngram_embeddings = nn.Em... | 9,336 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
@add_start_docstrings_to_model_forward(PROPHETNET_STANDALONE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=ProphetNetDecoderModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
... | 9,336 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
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
the model is configured as a decoder.
encoder_attention_mask (`torch.Fl... | 9,336 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
Contains precomp... | 9,336 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, ProphetNetDecoder
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("microsoft/prophetnet-large... | 9,336 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
>>> last_hidden_states = outputs.last_hidden_state
```"""
use_cache = use_cache if use_cache is not None else self.config.use_cache
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden... | 9,336 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
batch_size, sequence_length = inputs_embeds.shape[:2]
main_stream_pos_embed, position_ids = self.position_embeddings(
(batch_size, sequence_length),
device=inputs_embeds.device,
past_key_values=past_key_values,
)
if past_key_values is not None:
m... | 9,336 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
# prepare attention mask
if past_key_values is not None:
assert (
hidden_states.size(1) == 1
), "At the moment `use_cache` is only supported for `decoder_input_ids` of length 1"
ngram_hidden_states = [
(ngram_embeddings[ngram - 1] + predicting... | 9,336 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
# prepare encoder attention mask
if encoder_attention_mask is not None:
extended_encoder_attention_mask = (
1.0 - encoder_attention_mask[:, None, None, :].repeat(1, self.config.num_decoder_attention_heads, 1, 1)
) * torch.finfo(self.dtype).min
extended_encoder... | 9,336 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
all_main_stream_attns = () if output_attentions else None
all_ngram_stream_attns = () if output_attentions else None
all_cross_attns = () if output_attentions and self.config.add_cross_attention else None
if self.gradient_checkpointing and self.training:
if use_cache:
... | 9,336 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
# check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired
for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]):
if attn_mask is not None:
assert attn_mask.size()[0] == (len(self.layers)), (
... | 9,336 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
decoder_layer.__call__,
hidden_states,
extended_attention_mask,
encoder_hidden_states,
extended_encoder... | 9,336 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
encoder_hidden_states=encoder_hidden_states,
encoder_attn_mask=extended_encoder_attention_mask,
layer_head_mask=(head_mask[idx] if head_mask is not None else None),
cross_attn_layer_head_mask=(
cross_attn_head_mask[idx] if cross_attn_he... | 9,336 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
hidden_states = layer_outputs[0]
if use_cache:
present_key_values += (layer_outputs[4 if output_attentions else 1],)
if output_attentions:
all_main_stream_attns += (layer_outputs[1],)
all_ngram_stream_attns += (layer_outputs[2],)
... | 9,336 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
if not return_dict:
return tuple(
v
for v in [
last_hidden_state,
last_hidden_state_ngram,
present_key_values,
all_main_stream_hidden_states,
all_ngram_stream_hidden_states,
... | 9,336 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
def compute_buffered_relative_buckets(self, position_ids):
batch_size, sequence_length = position_ids.shape
position_ids = torch.arange(1, self.max_target_positions).to(position_ids.device).repeat(1, 1)
main_relative_buckets, predict_relative_buckets = compute_all_stream_relative_buckets(
... | 9,336 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
def prepare_attention_mask(self, hidden_states, attention_mask):
batch_size, seq_length = hidden_states.shape[:2]
# get causal mask
causal_mask = torch.full(
(seq_length, seq_length),
torch.finfo(hidden_states.dtype).min,
dtype=hidden_states.dtype,
... | 9,336 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
def prepare_predict_attention_mask(self, hidden_states, attention_mask):
batch_size, seq_length = hidden_states.shape[:2]
# get causal mask
predict_causal_mask = ngram_attention_bias(
self.max_target_positions, self.ngram, hidden_states.device, hidden_states.dtype
)
... | 9,336 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
# add usual attention mask
if attention_mask is not None:
extended_attention_mask = (1.0 - attention_mask[:, None, None, None, :]) * torch.finfo(self.dtype).min
extended_attention_mask = extended_attention_mask.expand(
(batch_size, self.config.num_decoder_attention_heads,... | 9,336 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
class ProphetNetModel(ProphetNetPreTrainedModel):
_tied_weights_keys = ["encoder.word_embeddings.weight", "decoder.word_embeddings.weight"]
def __init__(self, config: ProphetNetConfig):
super().__init__(config)
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_i... | 9,337 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
def set_input_embeddings(self, value):
self.word_embeddings = value
self.encoder.word_embeddings = self.word_embeddings
self.decoder.word_embeddings = self.word_embeddings
def _tie_weights(self):
if self.config.tie_word_embeddings:
self._tie_or_clone_weights(self.encoder... | 9,337 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
@add_start_docstrings_to_model_forward(PROPHETNET_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=ProphetNetSeq2SeqModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
decod... | 9,337 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
return_dict: Optional[bool] = None,
) -> Union[Tuple, ProphetNetSeq2SeqModelOutput]:
r"""
Returns: | 9,337 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
Example:
```python
>>> from transformers import AutoTokenizer, ProphetNetModel
>>> tokenizer = AutoTokenizer.from_pretrained("microsoft/prophetnet-large-uncased")
>>> model = ProphetNetModel.from_pretrained("microsoft/prophetnet-large-uncased")
>>> input_ids = tokenizer(
... | 9,337 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
>>> last_hidden_states = outputs.last_hidden_state # main stream hidden states
>>> last_hidden_states_ngram = outputs.last_hidden_state_ngram # predict hidden states
```"""
use_cache = use_cache if use_cache is not None else self.config.use_cache
output_attentions = output_attentions i... | 9,337 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
if encoder_outputs is None:
encoder_outputs = self.encoder(
input_ids=input_ids,
attention_mask=attention_mask,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden... | 9,337 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
# decoder outputs consists of (dec_features, past_key_values, dec_hidden, dec_attn)
decoder_outputs = self.decoder(
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
encoder_hidden_states=encoder_outputs[0],
encoder_attention_mask=attention_mask,... | 9,337 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
if not return_dict:
return decoder_outputs + encoder_outputs
return ProphetNetSeq2SeqModelOutput(
last_hidden_state=decoder_outputs.last_hidden_state,
last_hidden_state_ngram=decoder_outputs.last_hidden_state_ngram,
past_key_values=decoder_outputs.past_key_values,... | 9,337 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
class ProphetNetForConditionalGeneration(ProphetNetPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["encoder.word_embeddings.weight", "decoder.word_embeddings.weight", "lm_head.weight"]
def __init__(self, config: ProphetNetConfig):
super().__init__(config)
self.prophetnet = ProphetNetMo... | 9,338 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
@add_start_docstrings_to_model_forward(PROPHETNET_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=ProphetNetSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
decoder_... | 9,338 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, ProphetNetSeq2SeqLMOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should b... | 9,338 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, ProphetNetForConditionalGeneration
>>> tokenizer = AutoTokenizer.from_pretrained("microsoft/prophetnet-large-uncased")
>>> model = ProphetNetForConditionalGeneration.from_pretrained("microsoft/prophetnet-l... | 9,338 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
if labels is not None and decoder_input_ids is None and decoder_inputs_embeds is None:
# get decoder inputs from shifting lm labels to the right
decoder_input_ids = self._shift_right(labels) | 9,338 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
outputs = self.prophetnet(
input_ids=input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
head_mask=head_mask,
decoder_head_mask=decoder_head_mask,
cross_attn_head... | 9,338 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
predicting_streams = outputs[1].view(batch_size, self.config.ngram, sequence_length, -1)
predict_logits = self.lm_head(predicting_streams)
logits = predict_logits[:, 0]
logits_ngram = predict_logits[:, 1:] if self.config.ngram > 1 else None
# To use .view in loss computation, make sure... | 9,338 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
if not return_dict:
all_logits = tuple(v for v in [logits, logits_ngram] if v is not None)
return (loss,) + all_logits + outputs[2:] if loss is not None else all_logits + outputs[2:]
else:
return ProphetNetSeq2SeqLMOutput(
loss=loss,
logits=log... | 9,338 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
def _compute_loss(self, logits, labels, ignore_index=-100):
expend_targets = labels.new_zeros(self.config.ngram, labels.size(0), labels.size(1)).fill_(ignore_index)
for i in range(self.config.ngram):
if i > 0 and self.disable_ngram_loss:
break
expend_targets[i, :... | 9,338 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
eps_i = self.config.eps / lprobs.size(-1)
loss = (1.0 - self.config.eps) * loss + eps_i * smooth_loss
return loss
def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
return self._shift_right(labels)
@staticmethod
# Copied from transformers.models.bart.modeli... | 9,338 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
class ProphetNetForCausalLM(ProphetNetPreTrainedModel, GenerationMixin):
_tied_weights_keys = [
"prophetnet.word_embeddings.weight",
"prophetnet.decoder.word_embeddings.weight",
"lm_head.weight",
]
def __init__(self, config: ProphetNetConfig):
# set config for CLM
co... | 9,339 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def _tie_weights(self):
if self.config.tie_word_embeddings:
self._tie_or_clone_weights(self.prophetnet.decoder.word_embeddings, self.lm_head)
... | 9,339 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
@add_start_docstrings_to_model_forward(PROPHETNET_STANDALONE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=ProphetNetDecoderLMOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
... | 9,339 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
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
the model is configured as a decoder.
encoder_attention_mask (`torch.Flo... | 9,339 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
Contains precomp... | 9,339 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.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,... | 9,339 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
>>> tokenizer = AutoTokenizer.from_pretrained("microsoft/prophetnet-large-uncased")
>>> model = ProphetNetForCausalLM.from_pretrained("microsoft/prophetnet-large-uncased")
>>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder."
>>> inputs = tokenizer("Hello, m... | 9,339 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
>>> ARTICLE = (
... "the us state department said wednesday it had received no "
... "formal word from bolivia that it was expelling the us ambassador there "
... "but said the charges made against him are `` baseless ."
... )
>>> input_ids = tokenizer_enc(ARTICLE, re... | 9,339 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
# decoder outputs consists of (dec_features, past_key_values, dec_hidden, dec_attn)
outputs = self.prophetnet.decoder(
input_ids=input_ids,
attention_mask=attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
... | 9,339 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
logits = predict_logits[:, 0]
logits_ngram = predict_logits[:, 1:] if self.config.ngram > 1 else None
loss = None
if labels is not None:
loss = self._compute_loss(predict_logits, labels)
if not return_dict:
all_logits = tuple(v for v in [logits, logits_ngram] if... | 9,339 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
def _compute_loss(self, logits, labels, ignore_index=-100):
expend_targets = labels.new_zeros(self.config.ngram, labels.size(0), labels.size(1)).fill_(ignore_index)
for i in range(self.config.ngram):
if i > 0 and self.disable_ngram_loss:
break
expend_targets[i, :... | 9,339 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
eps_i = self.config.eps / lprobs.size(-1)
loss = (1.0 - self.config.eps) * loss + eps_i * smooth_loss
return loss
def prepare_inputs_for_generation(
self,
input_ids,
past_key_values=None,
attention_mask=None,
head_mask=None,
use_cache=None,
... | 9,339 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
if past_key_values:
input_ids = input_ids[:, -1:]
# first step, decoder_cached_states are empty
return {
"input_ids": input_ids, # encoder_outputs is defined. input_ids not needed
"attention_mask": attention_mask,
"head_mask": head_mask,
"past... | 9,339 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
class ProphetNetDecoderWrapper(ProphetNetPreTrainedModel):
"""
This is a wrapper class, so that [`ProphetNetForCausalLM`] can correctly be loaded from pretrained prophetnet
classes.
"""
def __init__(self, config: ProphetNetConfig):
super().__init__(config)
self.word_embeddings = nn... | 9,340 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
class TextNetImageProcessor(BaseImageProcessor):
r"""
Constructs a TextNet image processor. | 9,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/image_processing_textnet.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": 640}`):
... | 9,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/image_processing_textnet.py |
Whether to center crop the image to the specified `crop_size`. Can be overridden by `do_center_crop` in the
`preprocess` method.
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 `prepr... | 9,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/image_processing_textnet.py |
image_mean (`float` or `List[float]`, *optional*, defaults to `[0.485, 0.456, 0.406]`):
Mean to use if normalizing the image. This is a float or list of floats the length of the number of
channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method.
i... | 9,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/image_processing_textnet.py |
model_input_names = ["pixel_values"]
def __init__(
self,
do_resize: bool = True,
size: Dict[str, int] = None,
size_divisor: int = 32,
resample: PILImageResampling = PILImageResampling.BILINEAR,
do_center_crop: bool = False,
crop_size: Dict[str, int] = None,
... | 9,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/image_processing_textnet.py |
self.do_resize = do_resize
self.size = size
self.size_divisor = size_divisor
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_n... | 9,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/image_processing_textnet.py |
self._valid_processor_keys = [
"images",
"do_resize",
"size",
"size_divisor",
"resample",
"do_center_crop",
"crop_size",
"do_rescale",
"rescale_factor",
"do_normalize",
"image_mean",
... | 9,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/image_processing_textnet.py |
def resize(
self,
image: np.ndarray,
size: Dict[str, int],
resample: PILImageResampling = PILImageResampling.BILINEAR,
data_format: Optional[Union[str, ChannelDimension]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
**kwargs,
) -> ... | 9,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/image_processing_textnet.py |
Args:
image (`np.ndarray`):
Image to resize.
size (`Dict[str, int]`):
Size of the output image.
size_divisor (`int`, *optional*, defaults to `32`):
Ensures height and width are rounded to a multiple of this value after resizing.
... | 9,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/image_processing_textnet.py |
The value to be passed to `get_size_dict` as `default_to_square` when computing the image size. If the
`size` argument in `get_size_dict` is an `int`, it determines whether to default to a square image or
not.Note that this attribute is not used in computing `crop_size` via calling `get_... | 9,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/image_processing_textnet.py |
height, width = get_resize_output_image_size(
image, size=size, input_data_format=input_data_format, default_to_square=False
)
if height % self.size_divisor != 0:
height += self.size_divisor - (height % self.size_divisor)
if width % self.size_divisor != 0:
wid... | 9,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/image_processing_textnet.py |
def preprocess(
self,
images: ImageInput,
do_resize: bool = None,
size: Dict[str, int] = None,
size_divisor: int = None,
resample: PILImageResampling = None,
do_center_crop: bool = None,
crop_size: int = None,
do_rescale: bool = None,
resca... | 9,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/image_processing_textnet.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... | 9,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/image_processing_textnet.py |
has an effect if `do_resize` is set to `True`.
do_center_crop (`bool`, *optional*, defaults to `self.do_center_crop`):
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 ef... | 9,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/image_processing_textnet.py |
image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
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`):
Wh... | 9,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/image_processing_textnet.py |
The channel dimension format for the output image. Can be one of:
- `"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 t... | 9,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/image_processing_textnet.py |
do_resize = do_resize if do_resize is not None else self.do_resize
size = size if size is not None else self.size
size = get_size_dict(size, param_name="size", default_to_square=False)
size_divisor = size_divisor if size_divisor is not None else self.size_divisor
resample = resample if r... | 9,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/image_processing_textnet.py |
image_std = image_std if image_std is not None else self.image_std
do_convert_rgb = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb | 9,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/image_processing_textnet.py |
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)
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.... | 9,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/image_processing_textnet.py |
if is_scaled_image(images[0]) and do_rescale:
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... | 9,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/image_processing_textnet.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_dat... | 9,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/image_processing_textnet.py |
class TextNetConvLayer(nn.Module):
def __init__(self, config: TextNetConfig):
super().__init__()
self.kernel_size = config.stem_kernel_size
self.stride = config.stem_stride
self.activation_function = config.stem_act_func
padding = (
(config.kernel_size[0] // 2, ... | 9,342 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/modeling_textnet.py |
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.conv(hidden_states)
hidden_states = self.batch_norm(hidden_states)
return self.activation(hidden_states) | 9,342 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/modeling_textnet.py |
class TextNetRepConvLayer(nn.Module):
r"""
This layer supports re-parameterization by combining multiple convolutional branches
(e.g., main convolution, vertical, horizontal, and identity branches) during training.
At inference time, these branches can be collapsed into a single convolution for
effi... | 9,343 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/modeling_textnet.py |
self.main_conv = nn.Conv2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride,
padding=padding,
bias=False,
)
self.main_batch_norm = nn.BatchNorm2d(num_features=out_channels, eps=config.batch... | 9,343 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/modeling_textnet.py |
if kernel_size[0] != 1:
self.horizontal_conv = nn.Conv2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=(1, kernel_size[1]),
stride=stride,
padding=horizontal_padding,
bias=False,
)
... | 9,343 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/modeling_textnet.py |
# applies a convolution with a vertical kernel
if self.vertical_conv is not None:
vertical_outputs = self.vertical_conv(hidden_states)
vertical_outputs = self.vertical_batch_norm(vertical_outputs)
main_outputs = main_outputs + vertical_outputs
# applies a convolution... | 9,343 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/modeling_textnet.py |
class TextNetStage(nn.Module):
def __init__(self, config: TextNetConfig, depth: int):
super().__init__()
kernel_size = config.conv_layer_kernel_sizes[depth]
stride = config.conv_layer_strides[depth]
num_layers = len(kernel_size)
stage_in_channel_size = config.hidden_sizes[de... | 9,344 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/modeling_textnet.py |
class TextNetEncoder(nn.Module):
def __init__(self, config: TextNetConfig):
super().__init__()
stages = []
num_stages = len(config.conv_layer_kernel_sizes)
for stage_ix in range(num_stages):
stages.append(TextNetStage(config, stage_ix))
self.stages = nn.ModuleLi... | 9,345 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/modeling_textnet.py |
class TextNetPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = TextNetConfig
base_model_prefix = "textnet"
main_input_name = "pixel_values"
def _init_weights(... | 9,346 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/modeling_textnet.py |
class TextNetModel(TextNetPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.stem = TextNetConvLayer(config)
self.encoder = TextNetEncoder(config)
self.pooler = nn.AdaptiveAvgPool2d((2, 2))
self.post_init() | 9,347 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/modeling_textnet.py |
@add_start_docstrings_to_model_forward(TEXTNET_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,
)
... | 9,347 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/modeling_textnet.py |
last_hidden_state = encoder_outputs[0]
pooled_output = self.pooler(last_hidden_state)
if not return_dict:
output = (last_hidden_state, pooled_output)
return output + (encoder_outputs[1],) if output_hidden_states else output
return BaseModelOutputWithPoolingAndNoAttentio... | 9,347 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/modeling_textnet.py |
class TextNetForImageClassification(TextNetPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.textnet = TextNetModel(config)
self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))
self.flatten = nn.Flatten()
self.fc =... | 9,348 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/modeling_textnet.py |
@add_start_docstrings_to_model_forward(TEXTNET_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=ImageClassifierOutputWithNoAttention, config_class=_CONFIG_FOR_DOC)
def forward(
self,
pixel_values: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
... | 9,348 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/modeling_textnet.py |
Examples:
```python
>>> import torch
>>> import requests
>>> from transformers import TextNetForImageClassification, TextNetImageProcessor
>>> from PIL import Image
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests... | 9,348 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/modeling_textnet.py |
outputs = self.textnet(pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict)
last_hidden_state = outputs[0]
for layer in self.classifier:
last_hidden_state = layer(last_hidden_state)
logits = self.fc(last_hidden_state)
loss = None | 9,348 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/modeling_textnet.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... | 9,348 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/modeling_textnet.py |
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels) | 9,348 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/modeling_textnet.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) | 9,348 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/modeling_textnet.py |
class TextNetBackbone(TextNetPreTrainedModel, BackboneMixin):
def __init__(self, config):
super().__init__(config)
super()._init_backbone(config)
self.textnet = TextNetModel(config)
self.num_features = config.hidden_sizes
# initialize weights and apply final processing
... | 9,349 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/modeling_textnet.py |
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> processor = AutoImageProcessor.from_pretrained("czczup/textnet-base")
>>> model = AutoBackbone.from_pretrained("czczup/textnet-base")
>>> inputs = processor... | 9,349 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/modeling_textnet.py |
feature_maps = ()
for idx, stage in enumerate(self.stage_names):
if stage in self.out_features:
feature_maps += (hidden_states[idx],)
if not return_dict:
output = (feature_maps,)
if output_hidden_states:
hidden_states = outputs.hidden_... | 9,349 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/modeling_textnet.py |
class TextNetConfig(BackboneConfigMixin, PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`TextNextModel`]. It is used to instantiate a
TextNext model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the def... | 9,350 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/configuration_textnet.py |
Args:
stem_kernel_size (`int`, *optional*, defaults to 3):
The kernel size for the initial convolution layer.
stem_stride (`int`, *optional*, defaults to 2):
The stride for the initial convolution layer.
stem_num_channels (`int`, *optional*, defaults to 3):
Th... | 9,350 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/configuration_textnet.py |
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