text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
>>> inputs = image_processor([image], return_codebook_pixels=True, return_tensors="pt")
>>> inputs = dict(pixel_values=inputs.codebook_pixel_values)
>>> outputs = model.get_codebook_indices(**inputs)
```
""".format(_CHECKPOINT_FOR_CODEBOOK_DOC)
z_logits = self.blocks(pixel_values)
return torch.argmax(z_logits, axis=1)
def get_codebook_probs(self, pixel_values: torch.Tensor) -> torch.Tensor:
z_logits = self.blocks(pixel_values)
return nn.Softmax(dim=1)(z_logits)
def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Codebook pixel values can be obtained using [`AutoImageProcessor`] by passing
`return_codebook_pixels=True`. See [`FlavaImageProcessor.__call__`] for details.
Examples: | 3,124 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoImageProcessor, FlavaImageCodebook
>>> model = FlavaImageCodebook.from_pretrained("{0}")
>>> image_processor = AutoImageProcessor.from_pretrained("{0}")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> inputs = image_processor([image], return_codebook_pixels=True, return_tensors="pt")
>>> inputs = dict(pixel_values=inputs.codebook_pixel_values) | 3,124 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
>>> outputs = model(**inputs)
>>> print(outputs.shape)
(1, 196)
```
""".format(_CHECKPOINT_FOR_CODEBOOK_DOC)
if len(pixel_values.shape) != 4:
raise ValueError(f"input shape {pixel_values.shape} is not 4d")
if pixel_values.shape[1] != self.input_channels:
raise ValueError(f"input has {pixel_values.shape[1]} channels but model built for {self.input_channels}")
return self.blocks(pixel_values) | 3,124 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
class FlavaPredictionHeadTransform(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.transform_act_fn = config.hidden_act
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
def forward(self, hidden_states):
hidden_states = self.dense(hidden_states)
hidden_states = self.transform_act_fn(hidden_states)
hidden_states = self.LayerNorm(hidden_states)
return hidden_states | 3,125 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
class FlavaMaskedPredictionHead(nn.Module):
def __init__(self, config, weight=None):
super().__init__()
self.config = config
self.transform = FlavaPredictionHeadTransform(config)
self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.bias = nn.Parameter(torch.zeros(config.vocab_size))
if weight is not None:
self.decoder.weight = weight
# Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
self.decoder.bias = self.bias
def _tie_weights(self):
self.decoder.bias = self.bias
def forward(self, x):
x = self.transform(x)
x = self.decoder(x)
return x | 3,126 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
class FlavaITMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.pooler = FlavaPooler(config)
self.seq_relationship = nn.Linear(config.hidden_size, 2)
def forward(self, x):
x = self.pooler(x)
x = self.seq_relationship(x)
return x | 3,127 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
class FlavaGlobalContrastiveHead(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.global_backprop_contrastive = config.global_backprop_contrastive
def forward(self, image_embeddings, text_embeddings, logit_scale):
temperature = torch.exp(logit_scale)
if not torch.distributed.is_available() or not torch.distributed.is_initialized():
labels = torch.arange(image_embeddings.size(0), device=image_embeddings.device)
image_embeddings_all = [image_embeddings]
text_embeddings_all = [text_embeddings]
else:
local_batch_size = image_embeddings.size(0)
world_size = torch.distributed.get_world_size() | 3,128 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
if self.global_backprop_contrastive:
# `torch.distributed.nn.functional.all_gather` does backprop on all active workers
# whereas `torch.distributed.all_gather` does only backpropagates on the current worker.
image_embeddings_all = torch.distributed.nn.functional.all_gather(image_embeddings)
text_embeddings_all = torch.distributed.nn.functional.all_gather(text_embeddings)
else:
image_embeddings_all = [torch.zeros_like(text_embeddings) for _ in range(world_size)]
text_embeddings_all = [torch.zeros_like(image_embeddings) for _ in range(world_size)]
torch.distributed.all_gather(image_embeddings_all, image_embeddings)
torch.distributed.all_gather(text_embeddings_all, text_embeddings)
labels = local_batch_size * torch.distributed.get_rank() + torch.arange(
local_batch_size, device=image_embeddings.device
) | 3,128 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
image_embeddings_all = torch.cat(image_embeddings_all)
text_embeddings_all = torch.cat(text_embeddings_all)
logits_per_image = torch.matmul(image_embeddings, text_embeddings_all.transpose(0, 1)) * temperature
logits_per_text = torch.matmul(text_embeddings, image_embeddings_all.transpose(0, 1)) * temperature
return logits_per_image, logits_per_text, labels | 3,128 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
class FlavaForPreTraining(FlavaPreTrainedModel):
# Those are linked to xxx.bias
_tied_weights_keys = [
"mmm_text_head.decoder.bias",
"mmm_image_head.decoder.bias",
"mlm_head.decoder.bias",
"mim_head.decoder.bias",
]
def __init__(self, config: FlavaConfig, image_codebook: Optional[nn.Module] = None):
super().__init__(config)
self.flava = FlavaModel(config)
self.image_codebook = image_codebook
if self.image_codebook is None and config.init_codebook:
self.image_codebook = FlavaImageCodebook(config.image_codebook_config) | 3,129 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
# Levarage text and image encoder configs to create the masked
# head since it has the right vocab
self.mim_head = FlavaMaskedPredictionHead(config.image_config)
self.mlm_head = FlavaMaskedPredictionHead(config.text_config)
self.itm_head = FlavaITMHead(config)
self.mmm_image_head = FlavaMaskedPredictionHead(config.image_config)
self.mmm_text_head = FlavaMaskedPredictionHead(config.text_config)
self.global_contrastive_head = FlavaGlobalContrastiveHead(config) | 3,129 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
self.image_vocab_size = config.image_config.vocab_size
self.text_vocab_size = config.text_config.vocab_size
self.mlm_weight = config.mlm_weight
self.mim_weight = config.mim_weight
self.global_contrastive_weight = config.global_contrastive_weight
self.ce_ignore_index = config.ce_ignore_index
self.itm_weight = config.itm_weight
self.mmm_image_weight = config.mmm_image_weight
self.mmm_text_weight = config.mmm_text_weight
self.skip_unmasked_multimodal_encoder = config.skip_unmasked_multimodal_encoder
self.post_init()
def _resize_to_2d(self, x: torch.Tensor):
if x.dim() > 2:
x = x.view(x.size(0), -1)
return x | 3,129 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
@add_start_docstrings_to_model_forward(
FLAVA_PRETRAINING_INPUTS_DOCSTRING.format("batch_size, text_seq_len", "batch_size, image_num_patches")
)
@replace_return_docstrings(output_type=FlavaForPreTrainingOutput, config_class=FlavaConfig)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
input_ids_masked: Optional[torch.LongTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
codebook_pixel_values: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
bool_masked_pos: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
image_attention_mask: Optional[torch.Tensor] = None,
skip_unmasked_multimodal_encoder: bool = None,
mlm_labels: Optional[torch.Tensor] = None,
mim_labels: Optional[torch.Tensor] = None,
itm_labels: Optional[torch.Tensor] = None, | 3,129 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
output_attentions: Optional[bool] = None,
output_hidden_states: bool = True,
return_dict: Optional[bool] = None,
return_loss: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], FlavaForPreTrainingOutput]:
"""
Examples:
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import FlavaForPreTraining, AutoProcessor | 3,129 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> model = FlavaForPreTraining.from_pretrained("facebook/flava-full")
>>> processor = AutoProcessor.from_pretrained("facebook/flava-full")
>>> text = ["a photo of a cat"]
>>> inputs = processor(
... images=[image],
... text=text,
... return_masks=True,
... return_codebook_pixels=True,
... padding=True,
... max_length=77,
... return_tensors="pt",
... )
>>> output = model(**inputs)
```
Return:
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
return_loss = return_loss if return_loss is not None else self.config.return_loss | 3,129 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
skip_unmasked_multimodal_encoder = (
skip_unmasked_multimodal_encoder
if skip_unmasked_multimodal_encoder is not None
else self.skip_unmasked_multimodal_encoder
)
if input_ids_masked is None and input_ids is not None:
logger.warning(
"`input_ids_masked` isn't passed which means MLM loss won't be calculated correctlySetting it to"
" `input_ids` so that model can work. Please pass it if this is unintentional. This is usually OKAY if"
" you are doing inference on unmasked text..."
)
input_ids_masked = input_ids | 3,129 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
flava_output = self.flava(
input_ids=input_ids,
pixel_values=pixel_values,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
image_attention_mask=image_attention_mask,
# Don't need unmasked multimodal embedding for anything so skip it
# NOTE: ITM uses masked version
skip_multimodal_encoder=skip_unmasked_multimodal_encoder,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
# Pass true to have deterministic outputs
return_dict=True,
) | 3,129 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
flava_masked_output = self.flava(
input_ids=input_ids_masked,
pixel_values=pixel_values,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
image_attention_mask=image_attention_mask,
bool_masked_pos=bool_masked_pos,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=True,
)
pos_mask = None
image_embeddings = flava_output.image_embeddings
text_embeddings = flava_output.text_embeddings
image_masked_embeddings = flava_masked_output.image_embeddings
text_masked_embeddings = flava_masked_output.text_embeddings
multimodal_masked_embeddings = flava_masked_output.multimodal_embeddings | 3,129 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
total_loss = mim_loss = mlm_loss = mmm_text_loss = mmm_image_loss = gc_loss = itm_loss = None
mim_logits = mlm_logits = mmm_text_logits = mmm_image_logits = None
itm_logits = logits_per_image = logits_per_text = None | 3,129 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
# Calculate mim_labels if necessary from the image_codebook
if image_masked_embeddings is not None or multimodal_masked_embeddings is not None:
if mim_labels is None and return_loss:
if self.image_codebook is None:
raise RuntimeError(
"`return_loss` is set to True but the image codebook is not initialized and no `mim_labels` "
" have been passed. Reinstantiate the model with `init_codebook` set to True or "
"pass in your custom `mim_labels`"
)
if codebook_pixel_values is None:
raise ValueError(
"`codebook_pixel_value` are required to generate `mim_labels` if loss is expected. "
"Call `AutoProcessor` with `return_codebook_pixels` set to True"
)
mim_labels = self.image_codebook.get_codebook_indices(codebook_pixel_values) | 3,129 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
# Unimodal MIM Loss
# If multimodal embeddings are present, we will calculate MMM loss
if self.mim_weight > 0 and image_masked_embeddings is not None and multimodal_masked_embeddings is None:
sequence_for_image = image_masked_embeddings | 3,129 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
if mim_labels is not None:
mim_labels = self._resize_to_2d(mim_labels)
bool_masked_pos = self._resize_to_2d(bool_masked_pos)
mim_labels[bool_masked_pos.ne(True)] = self.ce_ignore_index
sequence_for_image = sequence_for_image[:, -mim_labels.size(1) :, :]
masked_tokens = mim_labels.ne(self.ce_ignore_index)
mim_labels_filtered = mim_labels[masked_tokens]
sequence_for_image = sequence_for_image[masked_tokens, :]
mim_logits = self.mim_head(sequence_for_image)
if return_loss:
mim_loss = nn.functional.cross_entropy(
mim_logits.view(-1, self.image_vocab_size), mim_labels_filtered.view(-1)
)
mim_loss *= self.mim_weight
else:
mim_logits = self.mim_head(sequence_for_image) | 3,129 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
# Unimodal MLM Loss
if self.mlm_weight > 0 and text_masked_embeddings is not None and multimodal_masked_embeddings is None:
sequence_for_text = text_masked_embeddings
if mlm_labels is not None:
mlm_labels = self._resize_to_2d(mlm_labels)
sequence_for_text = sequence_for_text[:, -mlm_labels.size(1) :, :]
masked_tokens = mlm_labels.ne(self.ce_ignore_index)
mlm_labels_filtered = mlm_labels[masked_tokens]
sequence_for_text = sequence_for_text[masked_tokens, :]
mlm_logits = self.mlm_head(sequence_for_text)
if return_loss:
mlm_loss = nn.functional.cross_entropy(
mlm_logits.view(-1, self.text_vocab_size), mlm_labels_filtered.view(-1)
)
mlm_loss *= self.mlm_weight
else:
mlm_logits = self.mlm_head(sequence_for_text) | 3,129 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
# ITM Loss
if self.itm_weight > 0 and multimodal_masked_embeddings is not None:
itm_logits = self.itm_head(multimodal_masked_embeddings)
if itm_labels is not None:
pos_pairs = itm_labels.ne(0)
pos_mask = torch.where(pos_pairs.any(), pos_pairs, pos_pairs.new([True]))
if return_loss:
itm_loss = nn.functional.cross_entropy(itm_logits, itm_labels)
itm_loss *= self.itm_weight
if multimodal_masked_embeddings is not None:
multimodal_masked_embeddings = multimodal_masked_embeddings[pos_mask]
if mlm_labels is not None:
mlm_labels = mlm_labels[pos_mask]
if mim_labels is not None:
mim_labels = mim_labels[pos_mask]
bool_masked_pos = bool_masked_pos[pos_mask] | 3,129 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
# MMM Image Loss
if multimodal_masked_embeddings is not None and self.mmm_image_weight > 0:
sequence_for_image = multimodal_masked_embeddings
end_index = image_masked_embeddings.size(1) - 1
sequence_for_image = sequence_for_image[:, 2 : 2 + end_index, :]
if mim_labels is not None:
mim_labels = self._resize_to_2d(mim_labels)
bool_masked_pos = self._resize_to_2d(bool_masked_pos)
mim_labels[bool_masked_pos.ne(True)] = self.ce_ignore_index | 3,129 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
masked_tokens = mim_labels.ne(self.ce_ignore_index)
mim_labels_filtered = mim_labels[masked_tokens]
sequence_for_image = sequence_for_image[masked_tokens, :]
mmm_image_logits = self.mmm_image_head(sequence_for_image)
if return_loss:
mmm_image_loss = nn.functional.cross_entropy(
mmm_image_logits.view(-1, self.image_vocab_size), mim_labels_filtered.view(-1)
)
mmm_image_loss *= self.mmm_image_weight
else:
mmm_image_logits = self.mmm_image_head(sequence_for_image)
# MMM Text Loss
if multimodal_masked_embeddings is not None and self.mmm_text_weight > 0:
sequence_for_text = multimodal_masked_embeddings
sequence_for_text = sequence_for_text[:, -text_masked_embeddings.size(1) :, :] | 3,129 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
if mlm_labels is not None:
mlm_labels = self._resize_to_2d(mlm_labels)
masked_tokens = mlm_labels.ne(self.ce_ignore_index)
mlm_labels_filtered = mlm_labels[masked_tokens]
sequence_for_text = sequence_for_text[masked_tokens, :]
mmm_text_logits = self.mmm_text_head(sequence_for_text)
if return_loss:
mmm_text_loss = nn.functional.cross_entropy(
mmm_text_logits.view(-1, self.text_vocab_size), mlm_labels_filtered.view(-1)
)
mmm_text_loss *= self.mmm_text_weight
else:
mmm_text_logits = self.mmm_text_head(sequence_for_text) | 3,129 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
# Global Contrastive Loss
if image_embeddings is not None and text_embeddings is not None and self.global_contrastive_weight > 0:
text_embedding = self.flava.text_projection(text_embeddings[:, 0, :])
text_embedding = nn.functional.normalize(text_embedding, dim=-1)
image_embedding = self.flava.image_projection(image_embeddings[:, 0, :])
image_embedding = nn.functional.normalize(image_embedding, dim=-1)
self.flava.logit_scale.data.clamp_(LOGIT_SCALE_CLAMP_MIN, LOGIT_SCALE_CLAMP_MAX)
logits_per_image, logits_per_text, gc_labels = self.global_contrastive_head(
image_embedding, text_embedding, self.flava.logit_scale
)
# Apply ITM negative mask if any
if pos_mask is not None:
logits_per_image = logits_per_image[pos_mask]
logits_per_text = logits_per_text[pos_mask]
gc_labels = gc_labels[pos_mask] | 3,129 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
if return_loss:
gc_loss_image = nn.functional.cross_entropy(logits_per_image, gc_labels)
gc_loss_text = nn.functional.cross_entropy(logits_per_text, gc_labels)
gc_loss = (gc_loss_image + gc_loss_text) / 2
gc_loss *= self.global_contrastive_weight
flava_losses = FlavaLosses(
mim=mim_loss,
mlm=mlm_loss,
itm=itm_loss,
global_contrastive=gc_loss,
mmm_image=mmm_image_loss,
mmm_text=mmm_text_loss,
)
if return_loss and not flava_losses.all_none():
total_loss = sum(loss if loss is not None else 0 for loss in flava_losses.values()) | 3,129 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
if not return_dict:
output = (
image_embeddings,
flava_output.image_output.to_tuple() if flava_output.image_output is not None else None,
text_embeddings,
flava_output.text_output.to_tuple() if flava_output.text_output is not None else None,
flava_output.multimodal_embeddings,
flava_output.multimodal_output.to_tuple() if flava_output.multimodal_output is not None else None,
image_masked_embeddings,
flava_masked_output.image_output.to_tuple() if flava_masked_output.image_output is not None else None,
text_masked_embeddings,
flava_masked_output.text_output.to_tuple() if flava_masked_output.text_output is not None else None,
multimodal_masked_embeddings,
flava_masked_output.multimodal_output.to_tuple()
if flava_masked_output.multimodal_output is not None
else None, | 3,129 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
mim_logits,
mlm_logits,
itm_logits,
logits_per_image,
logits_per_image,
mmm_image_logits,
mmm_text_logits,
)
if return_loss and not flava_losses.all_none():
output = (
total_loss,
flava_losses,
) + output | 3,129 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
# Filter None as transformer by default won't handle it
return tuple(x for x in output if x is None) | 3,129 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
return FlavaForPreTrainingOutput(
loss=total_loss,
loss_info=flava_losses,
image_embeddings=image_embeddings,
image_output=flava_output.image_output,
text_embeddings=text_embeddings,
text_output=flava_output.text_output,
multimodal_embeddings=flava_output.multimodal_embeddings,
multimodal_output=flava_output.multimodal_output,
image_masked_embeddings=image_masked_embeddings,
image_masked_output=flava_masked_output.image_output,
text_masked_embeddings=text_masked_embeddings,
text_masked_output=flava_masked_output.text_output,
multimodal_masked_embeddings=multimodal_masked_embeddings,
multimodal_masked_output=flava_masked_output.multimodal_output,
mim_logits=mim_logits,
mlm_logits=mlm_logits,
itm_logits=itm_logits,
contrastive_logits_per_image=logits_per_image, | 3,129 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
contrastive_logits_per_text=logits_per_text,
mmm_image_logits=mmm_image_logits,
mmm_text_logits=mmm_text_logits,
) | 3,129 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
class FlavaFeatureExtractor(FlavaImageProcessor):
def __init__(self, *args, **kwargs) -> None:
warnings.warn(
"The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
" use FlavaImageProcessor instead.",
FutureWarning,
)
super().__init__(*args, **kwargs) | 3,130 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/feature_extraction_flava.py |
class FlavaImageConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`FlavaImageModel`]. It is used to instantiate an
FLAVA model according to the specified arguments, defining the model architecture.
Instantiating a configuration with the defaults will yield a similar configuration to that of the FLAVA
[facebook/flava-full](https://huggingface.co/facebook/flava-full) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information. | 3,131 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/configuration_flava.py |
Args:
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
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*, defaults to 3072):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. | 3,131 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/configuration_flava.py |
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
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 (`int`, *optional*, defaults to 224):
The size (resolution) of each image.
patch_size (`int`, *optional*, defaults to 16):
The size (resolution) of each patch.
num_channels (`int`, *optional*, defaults to 3):
The number of input channels.
qkv_bias (`bool`, *optional*, defaults to `True`):
Whether to add a bias to the queries, keys and values.
mask_token (`bool`, *optional*, defaults to `True`): | 3,131 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/configuration_flava.py |
Whether to use a mask token or not. Used in MIM (Masked Image Modeling) loss for FLAVA.
vocab_size (`int`, *optional*, defaults to 8192):
Vocabulary size of the [`FlavaImageCodebook`] used in conjunction with [`FlavaImageModel`] for MIM (Masked
Image Modeling) loss for FLAVA. | 3,131 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/configuration_flava.py |
Example:
```python
>>> from transformers import FlavaImageConfig, FlavaImageModel
>>> # Initializing a FlavaImageModel with style configuration
>>> configuration = FlavaImageConfig()
>>> # Initializing a FlavaImageModel model (with random weights) from the style configuration
>>> model = FlavaImageModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "flava_image_model"
base_config_key = "image_config" | 3,131 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/configuration_flava.py |
def __init__(
self,
hidden_size: int = 768,
num_hidden_layers: int = 12,
num_attention_heads: int = 12,
intermediate_size: int = 3072,
hidden_act: int = "gelu",
hidden_dropout_prob: float = 0.0,
attention_probs_dropout_prob: float = 0.0,
initializer_range: float = 0.02,
layer_norm_eps: float = 1e-12,
image_size: int = 224,
patch_size: int = 16,
num_channels: int = 3,
qkv_bias: bool = True,
mask_token: bool = True,
vocab_size: int = 8192,
**kwargs,
):
super().__init__(**kwargs) | 3,131 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/configuration_flava.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_prob = attention_probs_dropout_prob
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.image_size = image_size
self.patch_size = patch_size
self.num_channels = num_channels
self.qkv_bias = qkv_bias
self.mask_token = mask_token
self.vocab_size = vocab_size | 3,131 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/configuration_flava.py |
class FlavaTextConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`FlavaTextModel`]. It is used to instantiate an
FLAVA model according to the specified arguments, defining the model architecture.
Instantiating a configuration with the defaults will yield a similar configuration to that of the FLAVA
[facebook/flava-full](https://huggingface.co/facebook/flava-full) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information. | 3,132 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/configuration_flava.py |
Args:
vocab_size (`int`, *optional*, defaults to 30522):
Vocabulary size of the BERT model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`FlavaTextModel`].
type_vocab_size (`int`, *optional*, defaults to 2):
The vocabulary size of the `token_type_ids` passed when calling [`FlavaTextModel`]. Note that even though
text encoder allows `token_type_ids`'s value as 2, for text-only pretraining and fine-tuning, only 1 is
used similar to RoBERTa.
max_position_embeddings (`int`, *optional*, defaults to 512):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048). For VL, max_length passed to model is 77.
position_embedding_type (`str`, *optional*, defaults to `"absolute"`): | 3,132 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/configuration_flava.py |
Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
[Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
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. | 3,132 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/configuration_flava.py |
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
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): | 3,132 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/configuration_flava.py |
The epsilon used by the layer normalization layers.
image_size (`int`, *optional*, defaults to 224):
The size (resolution) of each image.
patch_size (`int`, *optional*, defaults to 16):
The size (resolution) of each patch.
num_channels (`int`, *optional*, defaults to 3):
The number of input channels.
qkv_bias (`bool`, *optional*, defaults to `True`):
Whether to add a bias to the queries, keys and values. | 3,132 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/configuration_flava.py |
Example:
```python
>>> from transformers import FlavaTextConfig, FlavaTextModel
>>> # Initializing a FlavaTextModel with style configuration
>>> configuration = FlavaTextConfig()
>>> # Initializing a FlavaTextModel model (with random weights) from the style configuration
>>> model = FlavaTextModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "flava_text_model"
base_config_key = "text_config" | 3,132 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/configuration_flava.py |
def __init__(
self,
vocab_size: int = 30522,
type_vocab_size: int = 2,
max_position_embeddings: int = 512,
position_embedding_type: str = "absolute",
hidden_size: int = 768,
num_hidden_layers: int = 12,
num_attention_heads: int = 12,
intermediate_size: int = 3072,
hidden_act: str = "gelu",
hidden_dropout_prob: float = 0.0,
attention_probs_dropout_prob: float = 0.0,
initializer_range: float = 0.02,
layer_norm_eps: float = 1e-12,
pad_token_id: int = 0,
qkv_bias: bool = True,
**kwargs,
):
super().__init__(**kwargs) | 3,132 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/configuration_flava.py |
self.vocab_size = vocab_size
self.type_vocab_size = type_vocab_size
self.max_position_embeddings = max_position_embeddings
self.position_embedding_type = position_embedding_type
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_prob = attention_probs_dropout_prob
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.qkv_bias = qkv_bias
self.pad_token_id = pad_token_id | 3,132 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/configuration_flava.py |
class FlavaMultimodalConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`FlavaMultimodalModel`]. It is used to instantiate
an FLAVA model according to the specified arguments, defining the model architecture.
Instantiating a configuration with the defaults will yield a similar configuration to that of the FLAVA
[facebook/flava-full](https://huggingface.co/facebook/flava-full) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information. | 3,133 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/configuration_flava.py |
Args:
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 6):
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*, defaults to 3072):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. | 3,133 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/configuration_flava.py |
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
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.
qkv_bias (`bool`, *optional*, defaults to `True`):
Whether to add a bias to the queries, keys and values.
use_cls_token (`bool`, *optional*, defaults to `True`):
Whether to use an extra CLS token for multimodal settings. Usually needed by the FLAVA model. | 3,133 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/configuration_flava.py |
Example:
```python
>>> from transformers import FlavaMultimodalConfig, FlavaMultimodalModel
>>> # Initializing a FlavaMultimodalModel with style configuration
>>> configuration = FlavaMultimodalConfig()
>>> # Initializing a FlavaMultimodalModel model (with random weights) from the style configuration
>>> model = FlavaMultimodalModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "flava_multimodal_model"
base_config_key = "multimodal_config" | 3,133 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/configuration_flava.py |
def __init__(
self,
hidden_size: int = 768,
num_hidden_layers: int = 6,
num_attention_heads: int = 12,
intermediate_size: int = 3072,
hidden_act: int = "gelu",
hidden_dropout_prob: int = 0.0,
attention_probs_dropout_prob: int = 0.0,
initializer_range: float = 0.02,
layer_norm_eps: float = 1e-12,
qkv_bias: bool = True,
use_cls_token: bool = True,
**kwargs,
):
super().__init__(**kwargs) | 3,133 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/configuration_flava.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_prob = attention_probs_dropout_prob
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.qkv_bias = qkv_bias
self.use_cls_token = use_cls_token | 3,133 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/configuration_flava.py |
class FlavaImageCodebookConfig(PretrainedConfig):
model_type = "flava_image_codebook"
base_config_key = "image_codebook_config"
r"""
[`FlavaImageCodebookConfig`] is the configuration class to store the configuration of a [`FlavaImageCodebook`]. It
is used to instantiate an FLAVA model according to the specified arguments, defining the model architecture.
Instantiating a configuration with the defaults will yield a similar configuration to that of the FLAVA
[facebook/flava-image-codebook](https://huggingface.co/facebook/flava-image-codebook) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information. | 3,134 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/configuration_flava.py |
Args:
num_groups (`int`, *optional*, defaults to 4):
Number of groups to be created. This parameter as of now doesn't affect the model and is used for some
internal calculation and estimations.
input_channels (`int`, *optional*, defaults to 3):
Number of channels in the image to be passed.
num_blocks_per_group (`int`, *optional*, defaults to 2):
Number of conv-based blocks per group.
hidden_size (`int`, *optional*, defaults to 256):
Size of hidden dim for the blocks.
vocab_size (`int`, *optional*, defaults to 8192):
Size of the output vocabulary for the codebook.
freeze (`bool`, defaults to `True`):
Whether to freeze the weights of the model.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
kwargs (*optional*): | 3,134 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/configuration_flava.py |
Dictionary of keyword arguments. | 3,134 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/configuration_flava.py |
Example:
```python
>>> from transformers import FlavaImageCodebookConfig, FlavaImageCodebook
>>> # Initializing a FlavaImageCodebook with style configuration
>>> configuration = FlavaImageCodebookConfig()
>>> # Initializing a FlavaImageCodebook model (with random weights) from the style configuration
>>> model = FlavaImageCodebook(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```
""" | 3,134 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/configuration_flava.py |
def __init__(
self,
num_groups: int = 4,
input_channels: int = 3,
num_blocks_per_group: int = 2,
hidden_size: int = 256,
vocab_size: int = 8192,
freeze: int = True,
initializer_range: float = 0.02,
**kwargs,
):
super().__init__(**kwargs)
self.num_groups = num_groups
self.input_channels = input_channels
self.num_blocks_per_group = num_blocks_per_group
self.hidden_size = hidden_size
self.vocab_size = vocab_size
self.freeze = freeze
self.initializer_range = initializer_range | 3,134 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/configuration_flava.py |
class FlavaConfig(PretrainedConfig):
r"""
[`FlavaConfig`] is the configuration class to store the configuration of a [`FlavaModel`]. It is used to
instantiate FLAVA model according to the specified arguments, defining the text model, image model, image codebook
and multimodal model configs. Instantiating a configuration with the defaults will yield a similar configuration to
that of the FLAVA [facebook/flava-full](https://huggingface.co/facebook/flava-full) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information. | 3,135 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/configuration_flava.py |
Args:
text_config (`dict`, *optional*):
Dictionary of configuration options used to initialize [`FlavaTextConfig`].
image_config (`dict`, *optional*):
Dictionary of configuration options used to initialize [`FlavaImageConfig`].
multimodal_config (`dict`, *optional*):
Dictionary of configuration options used to initialize [`FlavaMultimodalConfig`].
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
projection_dim (`int`, *optional*, defaults to 512):
Dimensionality of text and image projection layers.
logit_scale_init_value (`float`, *optional*, defaults to 2.6592):
The initial value of the *logit_scale* parameter. Default is used as per the original FLAVA/CLIP
implementation. | 3,135 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/configuration_flava.py |
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
ce_ignore_index (`int`, *optional*, defaults to -100):
Cross entropy index to ignore.
mim_weight (`float`, *optional*, defaults to 1.0):
Weight to be assigned to MIM (Masked Image Modeling) unimodal loss
mlm_weight (`float`, *optional*, defaults to 1.0):
Weight to be assigned to MLM (Masked Language Modeling) unimodal loss
global_contrastive_weight (`float`, *optional*, defaults to 1.0):
Weight to be assigned to global contrastive cross-alignment loss.
itm_weight (`float`, *optional*, defaults to 1.0):
Weight to be assigned to image-text matching multimodal loss.
mmm_image_weight (`float`, *optional*, defaults to 1.0):
Weight to be assigned to MMM loss's image part. | 3,135 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/configuration_flava.py |
mmm_text_weight (`float`, *optional*, defaults to 1.0):
Weight to be assigned to MMM loss's text part.
global_backprop_contrastive (`bool`, *optional*, defaults to `True`):
Whether to use global backpropgation through all workers in contrastive loss.
skip_unmasked_multimodal_encoder (`bool`, *optional*, defaults to `True`):
Whether to skip running unmasked multimodal encoder whose outputs are not used by FLAVA losses.
return_loss (`bool`, *optional*, defaults to `True`):
Whether to return loss or not | 3,135 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/configuration_flava.py |
kwargs (*optional*):
Dictionary of keyword arguments.
Example:
```python
>>> from transformers import FlavaConfig, FlavaModel, FlavaForPreTraining
>>> # Initializing a FlavaConfig with style configuration
>>> configuration = FlavaConfig()
>>> # Initializing a FlavaModel and FlavaForPreTraining model (with random weights) from the style configuration
>>> model = FlavaModel(configuration)
>>> model_pre = FlavaForPreTraining(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
>>> configuration_pre = model_pre.config
```
"""
model_type = "flava"
sub_configs = {
"text_config": FlavaTextConfig,
"image_config": FlavaImageConfig,
"multimodal_config": FlavaMultimodalConfig,
"image_codebook_config": FlavaImageCodebookConfig,
} | 3,135 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/configuration_flava.py |
def __init__(
self,
image_config: Dict[str, Any] = None,
text_config: Dict[str, Any] = None,
multimodal_config: Dict[str, Any] = None,
image_codebook_config: Dict[str, Any] = None,
hidden_size: int = 768,
layer_norm_eps: float = 1e-12,
projection_dim: int = 768,
init_codebook: bool = True,
logit_scale_init_value: float = 2.6592,
initializer_range: float = 0.02,
ce_ignore_index: int = -100,
mim_weight: float = 1.0,
mlm_weight: float = 1.0,
global_contrastive_weight: float = 1.0,
itm_weight: float = 1.0,
mmm_image_weight: float = 1.0,
mmm_text_weight: float = 1.0,
global_backprop_contrastive: bool = True,
skip_unmasked_multimodal_encoder: bool = True,
return_loss: bool = True,
**kwargs,
):
# If `_config_dict` exist, we use them for the backward compatibility. | 3,135 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/configuration_flava.py |
# We pop out these 2 attributes before calling `super().__init__` to avoid them being saved (which causes a lot
# of confusion!).
text_config_dict = kwargs.pop("text_config_dict", None)
image_config_dict = kwargs.pop("image_config_dict", None)
multimodal_config_dict = kwargs.pop("multimodal_config_dict", None)
image_codebook_config_dict = kwargs.pop("image_codebook_config_dict", None) | 3,135 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/configuration_flava.py |
super().__init__(**kwargs)
# Instead of simply assigning `[text|vision]_config_dict` to `[text|vision]_config`, we use the values in
# `[text|vision]_config_dict` to update the values in `[text|vision]_config`. The values should be same in most
# cases, but we don't want to break anything regarding `_config_dict` that existed before commit `8827e1b2`.
if text_config_dict is not None:
if text_config is None:
text_config = {}
# This is the complete result when using `text_config_dict`.
_text_config_dict = FlavaTextConfig(**text_config_dict).to_dict() | 3,135 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/configuration_flava.py |
# Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different.
for key, value in _text_config_dict.items():
if key in text_config and value != text_config[key] and key not in ["transformers_version"]:
# If specified in `text_config_dict`
if key in text_config_dict:
message = (
f"`{key}` is found in both `text_config_dict` and `text_config` but with different values. "
f'The value `text_config_dict["{key}"]` will be used instead.'
)
# If inferred from default argument values (just to be super careful)
else:
message = (
f"`text_config_dict` is provided which will be used to initialize `FlavaTextConfig`. The "
f'value `text_config["{key}"]` will be overridden.' | 3,135 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/configuration_flava.py |
)
logger.info(message) | 3,135 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/configuration_flava.py |
# Update all values in `text_config` with the ones in `_text_config_dict`.
text_config.update(_text_config_dict)
if image_config_dict is not None:
if image_config is None:
image_config = {}
# This is the complete result when using `image_config_dict`.
_image_config_dict = FlavaImageConfig(**image_config_dict).to_dict()
# convert keys to string instead of integer
if "id2label" in _image_config_dict:
_image_config_dict["id2label"] = {
str(key): value for key, value in _image_config_dict["id2label"].items()
} | 3,135 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/configuration_flava.py |
# Give a warning if the values exist in both `_image_config_dict` and `image_config` but being different.
for key, value in _image_config_dict.items():
if key in image_config and value != image_config[key] and key not in ["transformers_version"]:
# If specified in `image_config_dict`
if key in image_config_dict:
message = (
f"`{key}` is found in both `image_config_dict` and `image_config` but with different "
f'values. The value `image_config_dict["{key}"]` will be used instead.'
)
# If inferred from default argument values (just to be super careful)
else:
message = (
f"`image_config_dict` is provided which will be used to initialize `FlavaImageConfig`. " | 3,135 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/configuration_flava.py |
f'The value `image_config["{key}"]` will be overridden.'
)
logger.info(message) | 3,135 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/configuration_flava.py |
# Update all values in `image_config` with the ones in `_image_config_dict`.
image_config.update(_image_config_dict)
if multimodal_config_dict is not None:
if multimodal_config is None:
multimodal_config = {}
# This is the complete result when using `multimodal_config_dict`.
_multimodal_config_dict = FlavaMultimodalConfig(**multimodal_config_dict).to_dict() | 3,135 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/configuration_flava.py |
# Give a warning if the values exist in both `_multimodal_config_dict` and `multimodal_config` but being
# different.
for key, value in _multimodal_config_dict.items():
if (
key in multimodal_config
and value != multimodal_config[key]
and key not in ["transformers_version"]
):
# If specified in `multimodal_config_dict`
if key in multimodal_config_dict:
message = (
f"`{key}` is found in both `multimodal_config_dict` and `multimodal_config` but with "
f'different values. The value `multimodal_config_dict["{key}"]` will be used instead.'
)
# If inferred from default argument values (just to be super careful)
else:
message = ( | 3,135 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/configuration_flava.py |
f"`multimodal_config_dict` is provided which will be used to initialize "
f'`FlavaMultimodalConfig`. The value `multimodal_config["{key}"]` will be overridden.'
)
logger.info(message) | 3,135 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/configuration_flava.py |
# Update all values in `multimodal_config` with the ones in `_multimodal_config_dict`.
multimodal_config.update(_multimodal_config_dict)
if image_codebook_config_dict is not None:
if image_codebook_config is None:
image_codebook_config = {}
# This is the complete result when using `image_codebook_config_dict`.
_image_codebook_config_dict = FlavaImageCodebookConfig(**image_codebook_config_dict).to_dict() | 3,135 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/configuration_flava.py |
# Give a warning if the values exist in both `_image_codebook_config_dict` and `image_codebook_config` but
# being different.
for key, value in _image_codebook_config_dict.items():
if (
key in image_codebook_config
and value != image_codebook_config[key]
and key not in ["transformers_version"]
):
# If specified in `image_codebook_config_dict`
if key in image_codebook_config_dict:
message = (
f"`{key}` is found in both `image_codebook_config_dict` and `image_codebook_config` but "
f'with different values. The value `image_codebook_config_dict["{key}"]` will be used '
"instead."
)
# If inferred from default argument values (just to be super careful)
else: | 3,135 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/configuration_flava.py |
message = (
f"`image_codebook_config_dict` is provided which will be used to initialize "
f'`FlavaImageCodebookConfig`. The value `image_codebook_config["{key}"]` will be overridden.'
)
logger.info(message) | 3,135 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/configuration_flava.py |
# Update all values in `image_codebook_config` with the ones in `_image_codebook_config_dict`.
image_codebook_config.update(_image_codebook_config_dict)
if image_config is None:
image_config = {}
logger.info("`image_config` is `None`. initializing the `FlavaImageConfig` with default values.")
if text_config is None:
text_config = {}
logger.info("`text_config` is `None`. Initializing the `FlavaTextConfig` with default values.")
if multimodal_config is None:
multimodal_config = {}
logger.info("`multimodal_config` is `None`. initializing the `FlavaMultimodalConfig` with default values.")
if image_codebook_config is None:
image_codebook_config = {}
logger.info(
"`image_codebook_config` is `None`. initializing the `FlavaImageCodebookConfig` with default values."
) | 3,135 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/configuration_flava.py |
self.image_config = FlavaImageConfig(**image_config)
self.text_config = FlavaTextConfig(**text_config)
self.multimodal_config = FlavaMultimodalConfig(**multimodal_config)
self.image_codebook_config = FlavaImageCodebookConfig(**image_codebook_config)
self.projection_dim = projection_dim
self.init_codebook = init_codebook | 3,135 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/configuration_flava.py |
self.hidden_size = hidden_size
self.layer_norm_eps = layer_norm_eps
self.initializer_range = initializer_range
self.logit_scale_init_value = logit_scale_init_value
self.initializer_factor = 1.0
self.ce_ignore_index = ce_ignore_index
self.mim_weight = mim_weight
self.mlm_weight = mlm_weight
self.global_contrastive_weight = global_contrastive_weight
self.itm_weight = itm_weight
self.mmm_image_weight = mmm_image_weight
self.mmm_text_weight = mmm_text_weight
self.global_backprop_contrastive = global_backprop_contrastive
self.skip_unmasked_multimodal_encoder = skip_unmasked_multimodal_encoder
self.return_loss = return_loss | 3,135 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/configuration_flava.py |
@classmethod
def from_configs(
cls,
image_config: FlavaImageConfig,
text_config: FlavaTextConfig,
multimodal_config: FlavaMultimodalConfig,
image_codebook_config: FlavaImageCodebookConfig,
**kwargs,
):
r"""
Instantiate a [`FlavaConfig`] (or a derived class) from flava text model configuration, flava image model
configuration, flava multimodal model and flava codebook model configuration.
Returns:
[`FlavaConfig`]: An instance of a configuration object
"""
return cls(
image_config=image_config.to_dict(),
text_config=text_config.to_dict(),
multimodal_config=multimodal_config.to_dict(),
image_codebook_config=image_codebook_config.to_dict(),
**kwargs,
) | 3,135 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/configuration_flava.py |
class FlavaMaskingGenerator:
def __init__(
self,
input_size: Union[int, Tuple[int, int]] = 14,
total_mask_patches: int = 75,
mask_group_max_patches: Optional[int] = None,
mask_group_min_patches: int = 16,
mask_group_min_aspect_ratio: Optional[float] = 0.3,
mask_group_max_aspect_ratio: float = None,
):
if not isinstance(input_size, tuple):
input_size = (input_size,) * 2
self.height, self.width = input_size
self.num_patches = self.height * self.width
self.total_mask_patches = total_mask_patches
self.mask_group_min_patches = mask_group_min_patches
self.mask_group_max_patches = total_mask_patches if mask_group_max_patches is None else mask_group_max_patches
mask_group_max_aspect_ratio = mask_group_max_aspect_ratio or 1 / mask_group_min_aspect_ratio
self.log_aspect_ratio = (math.log(mask_group_min_aspect_ratio), math.log(mask_group_max_aspect_ratio)) | 3,136 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/image_processing_flava.py |
def __repr__(self):
repr_str = "MaskingGenerator(%d, %d -> [%d ~ %d], max = %d, %.3f ~ %.3f)" % (
self.height,
self.width,
self.mask_group_min_patches,
self.mask_group_max_patches,
self.total_mask_patches,
self.log_aspect_ratio[0],
self.log_aspect_ratio[1],
)
return repr_str
def get_shape(self):
return self.height, self.width | 3,136 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/image_processing_flava.py |
def _mask(self, mask, max_mask_patches):
delta = 0
for _attempt in range(10):
target_area = random.uniform(self.mask_group_min_patches, max_mask_patches)
aspect_ratio = math.exp(random.uniform(*self.log_aspect_ratio))
height = int(round(math.sqrt(target_area * aspect_ratio)))
width = int(round(math.sqrt(target_area / aspect_ratio)))
if width < self.width and height < self.height:
top = random.randint(0, self.height - height)
left = random.randint(0, self.width - width)
num_masked = mask[top : top + height, left : left + width].sum()
# Overlap
if 0 < height * width - num_masked <= max_mask_patches:
for i in range(top, top + height):
for j in range(left, left + width):
if mask[i, j] == 0:
mask[i, j] = 1
delta += 1 | 3,136 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/image_processing_flava.py |
if delta > 0:
break
return delta
def __call__(self):
mask = np.zeros(shape=self.get_shape(), dtype=int)
mask_count = 0
while mask_count < self.total_mask_patches:
max_mask_patches = self.total_mask_patches - mask_count
max_mask_patches = min(max_mask_patches, self.mask_group_max_patches)
delta = self._mask(mask, max_mask_patches)
if delta == 0:
break
else:
mask_count += delta
return mask | 3,136 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/image_processing_flava.py |
class FlavaImageProcessor(BaseImageProcessor):
r"""
Constructs a Flava image processor. | 3,137 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/image_processing_flava.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 the
`do_resize` parameter in `preprocess`.
size (`Dict[str, int]` *optional*, defaults to `{"height": 224, "width": 224}`):
Size of the image after resizing. Can be overridden by the `size` parameter in `preprocess`.
resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):
Resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in
`preprocess`.
do_center_crop (`bool`, *optional*, defaults to `True`):
Whether to center crop the images. Can be overridden by the `do_center_crop` parameter in `preprocess`.
crop_size (`Dict[str, int]` *optional*, defaults to `{"height": 224, "width": 224}`): | 3,137 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/image_processing_flava.py |
Size of image after the center crop `(crop_size["height"], crop_size["width"])`. Can be overridden by the
`crop_size` parameter in `preprocess`.
do_rescale (`bool`, *optional*, defaults to `True`):
Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale`
parameter in `preprocess`.
rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
Scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in
`preprocess`.
do_normalize (`bool`, *optional*, defaults to `True`):
Whether to normalize the image. Can be overridden by the `do_normalize` parameter in `preprocess`.
image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`):
Mean to use if normalizing the image. This is a float or list of floats the length of the number of | 3,137 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/image_processing_flava.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 `IMAGENET_STANDARD_STD`):
Standard deviation 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_std` parameter in the `preprocess` method.
return_image_mask (`bool`, *optional*, defaults to `False`):
Whether to return the image mask. Can be overridden by the `return_image_mask` parameter in `preprocess`.
input_size_patches (`int`, *optional*, defaults to 14):
Number of patches in the image in height and width direction. 14x14 = 196 total patches. Can be overridden
by the `input_size_patches` parameter in `preprocess`.
total_mask_patches (`int`, *optional*, defaults to 75): | 3,137 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/image_processing_flava.py |
Total number of patches that should be masked. Can be overridden by the `total_mask_patches` parameter in
`preprocess`.
mask_group_min_patches (`int`, *optional*, defaults to 16):
Minimum number of patches that should be masked. Can be overridden by the `mask_group_min_patches`
parameter in `preprocess`.
mask_group_max_patches (`int`, *optional*):
Maximum number of patches that should be masked. Can be overridden by the `mask_group_max_patches`
parameter in `preprocess`.
mask_group_min_aspect_ratio (`float`, *optional*, defaults to 0.3):
Minimum aspect ratio of the mask window. Can be overridden by the `mask_group_min_aspect_ratio` parameter
in `preprocess`.
mask_group_max_aspect_ratio (`float`, *optional*):
Maximum aspect ratio of the mask window. Can be overridden by the `mask_group_max_aspect_ratio` parameter
in `preprocess`. | 3,137 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/image_processing_flava.py |
codebook_do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the input for codebook to a certain. Can be overridden by the `codebook_do_resize`
parameter in `preprocess`. `codebook_size`.
codebook_size (`Dict[str, int]`, *optional*, defaults to `{"height": 224, "width": 224}`):
Resize the input for codebook to the given size. Can be overridden by the `codebook_size` parameter in
`preprocess`.
codebook_resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.LANCZOS`):
Resampling filter to use if resizing the codebook image. Can be overridden by the `codebook_resample`
parameter in `preprocess`.
codebook_do_center_crop (`bool`, *optional*, defaults to `True`):
Whether to crop the input for codebook at the center. If the input size is smaller than
`codebook_crop_size` along any edge, the image is padded with 0's and then center cropped. Can be | 3,137 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/image_processing_flava.py |
overridden by the `codebook_do_center_crop` parameter in `preprocess`.
codebook_crop_size (`Dict[str, int]`, *optional*, defaults to `{"height": 224, "width": 224}`):
Desired output size for codebook input when applying center-cropping. Can be overridden by the
`codebook_crop_size` parameter in `preprocess`.
codebook_do_rescale (`bool`, *optional*, defaults to `True`):
Whether to rescale the input for codebook by the specified scale `codebook_rescale_factor`. Can be
overridden by the `codebook_do_rescale` parameter in `preprocess`.
codebook_rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
Defines the scale factor to use if rescaling the codebook image. Can be overridden by the
`codebook_rescale_factor` parameter in `preprocess`.
codebook_do_map_pixels (`bool`, *optional*, defaults to `True`): | 3,137 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/image_processing_flava.py |
Whether to map the pixel values of the codebook input to (1 - 2e)x + e. Can be overridden by the
`codebook_do_map_pixels` parameter in `preprocess`.
codebook_do_normalize (`bool`, *optional*, defaults to `True`):
Whether or not to normalize the input for codebook with `codebook_image_mean` and `codebook_image_std`. Can
be overridden by the `codebook_do_normalize` parameter in `preprocess`.
codebook_image_mean (`Optional[Union[float, Iterable[float]]]`, *optional*, defaults to `[0, 0, 0]`):
The sequence of means for each channel, to be used when normalizing images for codebook. Can be overridden
by the `codebook_image_mean` parameter in `preprocess`.
codebook_image_std (`Optional[Union[float, Iterable[float]]]`, *optional*, defaults to `[0.5, 0.5, 0.5]`):
The sequence of standard deviations for each channel, to be used when normalizing images for codebook. Can | 3,137 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/image_processing_flava.py |
be overridden by the `codebook_image_std` parameter in `preprocess`.
""" | 3,137 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/image_processing_flava.py |
model_input_names = ["pixel_values"] | 3,137 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/image_processing_flava.py |
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,
rescale_factor: Union[int, float] = 1 / 255,
do_normalize: bool = True,
image_mean: Optional[Union[float, Iterable[float]]] = None,
image_std: Optional[Union[float, Iterable[float]]] = None,
# Mask related params
return_image_mask: bool = False,
input_size_patches: int = 14,
total_mask_patches: int = 75,
mask_group_min_patches: int = 16,
mask_group_max_patches: Optional[int] = None,
mask_group_min_aspect_ratio: float = 0.3,
mask_group_max_aspect_ratio: Optional[float] = None,
# Codebook related params
return_codebook_pixels: bool = False,
codebook_do_resize: bool = True,
codebook_size: bool = None, | 3,137 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/image_processing_flava.py |
codebook_resample: int = PILImageResampling.LANCZOS,
codebook_do_center_crop: bool = True,
codebook_crop_size: int = None,
codebook_do_rescale: bool = True,
codebook_rescale_factor: Union[int, float] = 1 / 255,
codebook_do_map_pixels: bool = True,
codebook_do_normalize: bool = True,
codebook_image_mean: Optional[Union[float, Iterable[float]]] = None,
codebook_image_std: Optional[Union[float, Iterable[float]]] = None,
**kwargs,
) -> None:
super().__init__(**kwargs)
size = size if size is not None else {"height": 224, "width": 224}
size = get_size_dict(size)
crop_size = crop_size if crop_size is not None else {"height": 224, "width": 224}
crop_size = get_size_dict(crop_size, param_name="crop_size") | 3,137 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/image_processing_flava.py |
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