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_value... | 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.coc... | 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:
... | 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.tra... | 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.Parame... | 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)
... | 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... | 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_gath... | 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)) ... | 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:... | 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... | 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_i... | 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.Lo... | 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
... | 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... | 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(
"`inp... | 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 multimo... | 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_at... | 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(
"`ret... | 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) :, :]
... | 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 ... | 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.n... | 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_l... | 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... | 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_logi... | 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_embe... | 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
... | 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,
... | 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 = (
... | 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_embe... | 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,
... | 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 ... | 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 t... | 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.
laye... | 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 = Fla... | 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,
initialize... | 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_pro... | 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 ... | 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):
T... | 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... | 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 ... | 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):
... | 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 = FlavaTex... | 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_s... | 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_h... | 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 w... | 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... | 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.
laye... | 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 configur... | 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_ran... | 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_pro... | 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 ... | 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 t... | 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 configura... | 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... | 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. Instantiatin... | 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*):
... | 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*, defau... | 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_enco... | 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 Flava... | 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 ... | 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("multimo... | 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 regar... | 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... | 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`.
... | 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_... | 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 `multimo... | 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]
... | 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 com... | 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_codebo... | 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... | 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
... | 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
se... | 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 deriv... | 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,
mas... | 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],
... | 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_... | 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 = m... | 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... | 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... | 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
... | 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`
... | 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":... | 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` param... | 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_mea... | 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] = ... | 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: b... | 3,137 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/image_processing_flava.py |
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