text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
output = self.itm_head(text_embeds[:, : query_tokens.size(1), :])
logits_per_image = output.mean(dim=1)
logits_per_text = logits_per_image.t()
else:
query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1)
query_outputs = self.qformer(
... | 3,099 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
query_embeds = self.embeddings(
input_ids=input_ids,
)
text_outputs = self.qformer(
query_embeds=query_embeds,
query_length=0,
attention_mask=attention_mask,
return_dict=return_dict,
)
questio... | 3,099 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
if not return_dict:
output = (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs)
return output
return Blip2ImageTextMatchingModelOutput(
logits_per_image=logits_per_image,
logits_per_text=logits_per_text,
text_... | 3,099 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
class Blip2ProcessorKwargs(ProcessingKwargs, total=False):
_defaults = {
"text_kwargs": {
"add_special_tokens": True,
"padding": False,
"stride": 0,
"return_overflowing_tokens": False,
"return_special_tokens_mask": False,
"return_offset... | 3,100 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/processing_blip_2.py |
class Blip2Processor(ProcessorMixin):
r"""
Constructs a BLIP-2 processor which wraps a BLIP image processor and an OPT/T5 tokenizer into a single processor.
[`BlipProcessor`] offers all the functionalities of [`BlipImageProcessor`] and [`AutoTokenizer`]. See the docstring
of [`~BlipProcessor.__call__`]... | 3,101 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/processing_blip_2.py |
def __init__(self, image_processor, tokenizer, num_query_tokens=None, **kwargs):
tokenizer.return_token_type_ids = False
self.current_processor = image_processor
if not hasattr(tokenizer, "image_token"):
self.image_token = AddedToken("<image>", normalized=False, special=True)
... | 3,101 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/processing_blip_2.py |
Please refer to the docstring of the above two methods for more information.
Args:
images (`ImageInput`):
The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
tensor. Both channels-first and channels-last formats are suppo... | 3,101 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/processing_blip_2.py |
- `'pt'`: Return PyTorch `torch.Tensor` objects.
- `'np'`: Return NumPy `np.ndarray` objects.
- `'jax'`: Return JAX `jnp.ndarray` objects.
"""
if images is None and text is None:
raise ValueError("You have to specify either images or text.")
ou... | 3,101 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/processing_blip_2.py |
raise ValueError("Invalid input text. Please provide a string, or a list of strings") | 3,101 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/processing_blip_2.py |
text_encoding = {}
return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
_text_encoding = self.tokenizer(text, **output_kwargs["text_kwargs"], return_tensors=None)
output_kwargs["text_kwargs"]["return_tensors"] = return_tensors | 3,101 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/processing_blip_2.py |
# if we know how many query tokens, expand text inside processor. We need this hacky manipulation
# because BLIP expects image tokens to be at the beginning even before BOS token
if self.num_query_tokens is not None:
image_tokens = self.image_token.content * self.num_query_tokens... | 3,101 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/processing_blip_2.py |
"Please follow instruction here (https://gist.github.com/zucchini-nlp/e9f20b054fa322f84ac9311d9ab67042) to update your BLIP-2 model. "
"Using processors without these attributes in the config is deprecated and will throw an error in v4.50."
) | 3,101 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/processing_blip_2.py |
# cast to desired return tensors type
encoding.update(BatchEncoding(text_encoding, tensor_type=return_tensors))
# add pixel_values encoding. If we also have text_encoding, update image encoding and return it.
# else, return the text encoding.
if images is not None:
image... | 3,101 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/processing_blip_2.py |
# Copied from transformers.models.blip.processing_blip.BlipProcessor.decode with BertTokenizerFast->PreTrainedTokenizer
def decode(self, *args, **kwargs):
"""
This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer to
the docstring of thi... | 3,101 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/processing_blip_2.py |
class FlavaModelOutput(ModelOutput):
"""
Output from FlavaModel containing embeddings and outputs from individual encoders.
Note that `image_embeddings` and `text_embeddigns` returned are similar to pooled output returned from a
transformer. If you want embeddings for contrastive loss or retrieval use ... | 3,102 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
Args:
image_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `pixel_values` are present):
The image embeddings which are basically the pooled output of [`FlavaImageModel`].
image_output (`BaseModelOutputWithPooling`, *optional*, returned when `pi... | 3,102 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
The multimodal embeddings which are basically the pooled output of [`FlavaTextModel`].
multimodal_output (`BaseModelOutputWithPooling`, returned when `input_ids` and `pixel_values` are present and `skip_multimodal_encoder` is `None` or `False`):
The output of the [`FlavaMultimodalModel`].
""" | 3,102 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
image_embeddings: Optional[torch.FloatTensor] = None
image_output: Optional[BaseModelOutputWithPooling] = None
text_embeddings: Optional[torch.FloatTensor] = None
text_output: Optional[BaseModelOutputWithPooling] = None
multimodal_embeddings: Optional[torch.FloatTensor] = None
multimodal_output: Opt... | 3,102 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
class FlavaLosses(ModelOutput):
"""Class representing pretraining losses from FLAVA model | 3,103 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
Args:
mim (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `mim_labels` and `pixel_values` are present, `input_ids_masked` is absent and `mim_weight` > 0.:
Masked Image Modeling loss as used in BeIT calculated only for unimodal image data.
mlm (`torch.FloatTensor` of shape `(1... | 3,103 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
global_contrastive (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `input_ids` and `pixel_values` are present and `global_contrastive_weight` > 0.:
Contrastive loss for image-text similarity similar to CLIP but calculated globally for paired image-text
data. This is calculated on... | 3,103 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
mim: Optional[torch.FloatTensor] = None
mlm: Optional[torch.FloatTensor] = None
itm: Optional[torch.FloatTensor] = None
global_contrastive: Optional[torch.FloatTensor] = None
mmm_image: Optional[torch.FloatTensor] = None
mmm_text: Optional[torch.FloatTensor] = None
def all_none(self) -> bool:
... | 3,103 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
class FlavaForPreTrainingOutput(ModelOutput):
"""
Output from FlavaForPreTraining containing embeddings, and outputs from individual encoders.
Note that `image_embeddings` and `text_embeddings` returned are similar to pooled output returned from a
transformer. If you want embeddings for contrastive los... | 3,104 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
Args:
loss (`torch.FloatTensor`, *optional*, returned when `return_loss` is True):
Total loss calculated for this model.
loss_info (`FlavaLosses`):
Detailed info for FLAVA Pretraining losses. Check `FlavaLosses` class description for the information on
the keys.
... | 3,104 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
text_output (`BaseModelOutputWithPooling`, *optional*, returned when `input_ids` are present):
The output of the [`FlavaTextModel`].
multimodal_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `input_ids` and `pixel_values` are present and `skip_unmasked... | 3,104 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
image_masked_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `pixel_values` are present):
The image embeddings which are basically the pooled output of [`FlavaImageModel`]. Uses `bool_masked_pos`
to create masked images.
image_masked_output ... | 3,104 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
multimodal_masked_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `input_ids` and `pixel_values` are present):
The multimodal embeddings which are basically the pooled output of [`FlavaTextModel`].
multimodal_masked_output (`BaseModelOutputWithPooling`,... | 3,104 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
mim_logits (`torch.FloatTensor` of shape `(batch_size, num_image_patches, image_vocab_size)` or of shape `(total_masked_patches, image_vocab_size)` , *optional*, returned when `pixel_values` are present and `input_ids_masked` are not):
The logits for MIM unimodal loss. Uses `book_masked_pos` to get mask... | 3,104 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
The logits for ITM loss. Note that ITM loss is calculated on masked pairs in FLAVA.
mmm_image_logits (`torch.FloatTensor` of shape `(batch_size, num_image_patches, image_vocab_size)` or of shape`(total_masked_patches, image_vocab_size)`, *optional*, returned when `pixel_values` and `input_ids_masked` are presen... | 3,104 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
contrastive_logits_per_image (`torch.FloatTensor` of shape `(image_batch_size, text_batch_size)`):
The scaled dot product scores between `image_embeddings` and `text_embeddings` but passed through FLAVA's
`image_projection` and `text_projection` layers respectively. This represents the image-tex... | 3,104 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
loss: Optional[torch.FloatTensor] = None
loss_info: FlavaLosses = None
image_embeddings: Optional[torch.FloatTensor] = None
image_output: Optional[BaseModelOutputWithPooling] = None
text_embeddings: Optional[torch.FloatTensor] = None
text_output: Optional[BaseModelOutputWithPooling] = None
multi... | 3,104 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
contrastive_logits_per_image: Optional[torch.FloatTensor] = None
contrastive_logits_per_text: Optional[torch.FloatTensor] = None
mmm_image_logits: Optional[torch.FloatTensor] = None
mmm_text_logits: Optional[torch.FloatTensor] = None | 3,104 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
def to_tuple(self) -> Tuple[Any]:
transformer_outputs = [
"text_output",
"image_output",
"multimodal_output",
"text_masked_output",
"image_masked_output",
"multimodal_masked_output",
]
return tuple(self[k] if k not in transf... | 3,104 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
class FlavaImageEmbeddings(nn.Module):
"""
Construct the CLS token, position and patch embeddings. Optionally, also the mask token.
"""
def __init__(self, config: FlavaImageConfig, use_mask_token: bool = False) -> None:
super().__init__() | 3,105 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
use_mask_token = use_mask_token or config.mask_token
self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
self.mask_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size)) if use_mask_token else None
self.patch_embeddings = PatchEmbeddings(
image_size=config.image... | 3,105 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
# Copied from transformers.models.vit.modeling_vit.ViTEmbeddings.interpolate_pos_encoding
def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
"""
This method allows to interpolate the pre-trained position encodings, to be able to use the model on hi... | 3,105 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
# always interpolate when tracing to ensure the exported model works for dynamic input shapes
if not torch.jit.is_tracing() and num_patches == num_positions and height == width:
return self.position_embeddings
class_pos_embed = self.position_embeddings[:, :1]
patch_pos_embed = self.... | 3,105 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
return torch.cat((class_pos_embed, patch_pos_embed), dim=1)
def forward(
self,
pixel_values: torch.Tensor,
bool_masked_pos: Optional[torch.BoolTensor] = None,
interpolate_pos_encoding: bool = False,
) -> torch.Tensor:
batch_size, num_channels, height, width = pixel_value... | 3,105 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
# add the [CLS] token to the embedded patch tokens
cls_tokens = self.cls_token.expand(batch_size, -1, -1)
embeddings = torch.cat((cls_tokens, embeddings), dim=1)
# add positional encoding to each token
if interpolate_pos_encoding:
embeddings = embeddings + self.interpolate_p... | 3,105 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
class PatchEmbeddings(nn.Module):
"""
Image to Patch Embedding.
"""
def __init__(
self,
image_size: int = 224,
patch_size: Union[int, Tuple[int, int]] = 16,
num_channels: int = 3,
embed_dim: int = 768,
):
super().__init__()
if not isinstance(i... | 3,106 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
def forward(self, pixel_values: torch.Tensor, interpolate_pos_encoding: bool = False) -> torch.Tensor:
batch_size, num_channels, height, width = pixel_values.shape
if not interpolate_pos_encoding:
if height != self.image_size[0] or width != self.image_size[1]:
raise ValueErro... | 3,106 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
class FlavaTextEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.position_em... | 3,107 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
# any TensorFlow checkpoint file
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
# position_ids (1, len ... | 3,107 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
):
input_shape = input_ids.size()
seq_length = input_shape[1]
if position_ids is None:
positio... | 3,107 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
# Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs
# when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves
# issue #5664
if token_type_ids is None:
if hasattr(... | 3,107 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
embeddings = inputs_embeds + token_type_embeddings
if self.position_embedding_type == "absolute":
position_embeddings = self.position_embeddings(position_ids)
embeddings += position_embeddings
embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddings)
... | 3,107 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
class FlavaSelfAttention(nn.Module):
def __init__(self, config: FlavaPossibleConfigs) -> None:
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size {config.hidden_size,} i... | 3,108 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(*new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: O... | 3,108 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in BertModel forward() function)
attention_scores = attention_scores + attention_mask
# Normalize the attention scor... | 3,108 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(*new_context_layer_shape)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
... | 3,108 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
class FlavaSelfOutput(nn.Module):
"""
The residual connection is defined in FlavaLayer (same as ViTLayer) instead of here (as is the case with other
models), due to the layernorm applied before each block.
"""
def __init__(self, config: FlavaPossibleConfigs) -> None:
super().__init__()
... | 3,109 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
class FlavaAttention(nn.Module):
def __init__(self, config: FlavaPossibleConfigs) -> None:
super().__init__()
self.attention = FlavaSelfAttention(config)
self.output = FlavaSelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads: Set[int]) -> None:
if le... | 3,110 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
# Update hyper params and store pruned heads
self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads)
self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads)
def for... | 3,110 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
class FlavaIntermediate(nn.Module):
def __init__(self, config: FlavaPossibleConfigs) -> None:
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
... | 3,111 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
class FlavaOutput(nn.Module):
def __init__(self, config: FlavaPossibleConfigs) -> None:
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
# Copied from transformers.models.vit.modeling_vit.ViTOut... | 3,112 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
class FlavaLayer(nn.Module):
"""This corresponds to the Block class in the timm implementation."""
def __init__(self, config: FlavaPossibleConfigs) -> None:
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = Fla... | 3,113 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
self_attention_outputs = self.... | 3,113 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
# second residual connection is done here
layer_output = self.output(layer_output, hidden_states)
outputs = (layer_output,) + outputs
return outputs | 3,113 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
class FlavaEncoder(nn.Module):
def __init__(self, config: FlavaConfig) -> None:
super().__init__()
self.config = config
self.layer = nn.ModuleList([FlavaLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
... | 3,114 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
layer_module.__call__,
hidden_states,
attention_mask,
layer_head_mask,
output_attentions,
... | 3,114 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
class FlavaPooler(nn.Module):
def __init__(self, config: FlavaPossibleConfigs):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hidden_states: torch.Tensor):
# We "pool" the model by simply taking th... | 3,115 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
class FlavaPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = FlavaConfig
base_model_prefix = "flava"
supports_gradient_checkpointing = True | 3,116 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None:
"""Initialize the weights"""
if isinstance(module, (nn.Linear, nn.Conv2d)):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/... | 3,116 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
class FlavaImageModel(FlavaPreTrainedModel):
config_class = FlavaImageConfig
# This override allows us to load FlavaImageModel from FlavaModel/FlavaForPreTraining checkpoints.
base_model_prefix = "flava.image_model"
main_input_name = "pixel_values"
def __init__(self, config: FlavaImageConfig, add_p... | 3,117 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None:
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.encoder.l... | 3,117 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
@add_start_docstrings_to_model_forward(FLAVA_IMAGE_INPUTS_DOCSTRING.format("batch_size, image_num_patches"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPooling,
config_class=_CONFIG_CLASS_FOR_IMAGE_MODEL_DOC,
modality="vision",
... | 3,117 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 3,117 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
if pixel_values is None:
raise ValueError("You have to specify pixel_values")
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_head... | 3,117 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
encoder_outputs = self.encoder(
embedding_output,
attention_mask=attention_mask,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = encod... | 3,117 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
class FlavaTextModel(FlavaPreTrainedModel):
config_class = FlavaTextConfig
# This override allows us to load FlavaTextModel from FlavaModel/FlavaForPreTraining checkpoints.
base_model_prefix = "flava.text_model"
def __init__(self, config: FlavaTextConfig, add_pooling_layer: bool = True):
super(... | 3,118 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None:
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.encoder.l... | 3,118 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
@add_start_docstrings_to_model_forward(FLAVA_TEXT_INPUTS_DOCSTRING.format("batch_size, text_seq_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPooling,
config_class=_CONFIG_CLASS_FOR_TEXT_MODEL_DOC,
)
def forward(
self,
... | 3,118 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 3,118 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
if input_ids is None:
raise ValueError("You have to specify input_ids")
input_shape = input_ids.size()
if attention_mask is None:
attention_mask = torch.ones(input_shape, device=input_ids.device)
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep... | 3,118 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
encoder_outputs = self.encoder(
embedding_output,
attention_mask=extended_attention_mask,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_outpu... | 3,118 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
class FlavaMultimodalModel(FlavaPreTrainedModel):
config_class = FlavaMultimodalConfig
# This override allows us to load FlavaMultimodalModel from FlavaModel/FlavaForPreTraining checkpoints.
base_model_prefix = "flava.multimodal_model"
main_input_name = "hidden_states"
def __init__(self, config: Fl... | 3,119 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None:
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.encoder.l... | 3,119 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
@add_start_docstrings_to_model_forward(
FLAVA_MULTIMODAL_INPUTS_DOCSTRING.format("batch_size, image_num_patches + text_seq_len")
)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPooling,
config_class=_CONFIG_CLASS_FOR_MULTIMODAL_MODEL_... | 3,119 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 3,119 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
batch_size, seq_length, _ = hidden_states.size()
if self.use_cls_token:
cls_tokens = self.cls_token.expand(batch_size, -1, -1)
hidden_states = torch.cat((cls_tokens, hidden_states), dim=1)
seq_length += 1
if attention_mask is None:
attention_mask = torch... | 3,119 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
encoder_outputs = self.encoder(
hidden_states,
attention_mask=extended_attention_mask,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output =... | 3,119 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
class FlavaModel(FlavaPreTrainedModel):
config_class = FlavaConfig
def __init__(self, config: FlavaConfig):
super().__init__(config)
if not isinstance(config.text_config, FlavaTextConfig):
raise TypeError(
"config.text_config is expected to be of type FlavaTextConfi... | 3,120 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
text_config = config.text_config
image_config = config.image_config
multimodal_config = config.multimodal_config
self.projection_dim = config.projection_dim
self.text_hidden_size = text_config.hidden_size
self.image_hidden_size = image_config.hidden_size
self.mm_hidden_s... | 3,120 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
self.image_to_mm_projection = nn.Linear(self.image_hidden_size, self.mm_hidden_size)
self.text_to_mm_projection = nn.Linear(self.text_hidden_size, self.mm_hidden_size)
# Initialize weights and apply final processing
self.post_init() | 3,120 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
@add_start_docstrings_to_model_forward(FLAVA_TEXT_INPUTS_DOCSTRING.format("batch_size, text_seq_length"))
def get_text_features(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
posit... | 3,120 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
>>> inputs = processor(
... text=["a photo of a cat", "a photo of a dog"], max_length=77, padding="max_length", return_tensors="pt"
... )
>>> text_features = model.get_text_features(**inputs)
```""".format(_CHECKPOINT_FOR_DOC)
text_outputs = self.text_model(
input... | 3,120 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
@add_start_docstrings_to_model_forward(FLAVA_IMAGE_INPUTS_DOCSTRING.format("batch_size, image_num_patches"))
def get_image_features(
self,
pixel_values: Optional[torch.Tensor] = None,
bool_masked_pos: Optional[torch.BoolTensor] = None,
interpolate_pos_encoding: Optional[bool] = None,... | 3,120 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
>>> model = FlavaModel.from_pretrained("{0}")
>>> processor = AutoProcessor.from_pretrained("{0}")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> inputs = processor(images=image, return_tensors="pt")
... | 3,120 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
@add_start_docstrings_to_model_forward(
FLAVA_MODEL_INPUTS_DOCSTRING.format("batch_size, image_num_patches + text_seq_len")
)
@replace_return_docstrings(output_type=FlavaModelOutput, config_class=FlavaConfig)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
pi... | 3,120 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, FlavaModel
>>> model = FlavaModel.from_pretrained("facebook/flava-full")
>>> processor = AutoProcessor.from_pretrained("facebook/flava-full")
>>> url = "http://images.coc... | 3,120 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
return_dict = return_dict if return_dict is not None else self.config.return_dict
if not output_hidden_states:
raise ValueError("FLAVA model requires hidden states to work. Please set `output_hidden_states=True`")
image_embeddings = None
image_states = None
image_mm_projectio... | 3,120 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
text_embeddings = None
text_states = None
text_mm_projection = None
text_output = None
if input_ids is not None:
text_output = self.text_model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
... | 3,120 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
multimodal_embeddings = None
multimodal_output = None
if image_mm_projection is not None and text_mm_projection is not None and not skip_multimodal_encoder:
if attention_mask is not None:
batch_size, seq_len, _ = image_mm_projection.shape
if self.multimodal_mo... | 3,120 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
if not return_dict:
return (
image_embeddings,
image_output,
text_embeddings,
text_output,
multimodal_embeddings,
multimodal_output,
)
return FlavaModelOutput(
image_embeddings=im... | 3,120 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
class FlavaImageCodebookResPath(nn.Module):
def __init__(self, in_size: int, out_size: int, **kwargs):
super().__init__()
hid_size = out_size // 4
path = OrderedDict()
path["relu_1"] = nn.ReLU()
path["conv_1"] = nn.Conv2d(in_size, hid_size, kernel_size=3, padding=1)
... | 3,121 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
class FlavaImageCodebookBlock(nn.Module):
def __init__(self, in_size: int, out_size: int, num_layers: int, **kwargs):
super().__init__()
self.post_gain = 1 / (num_layers**2)
if in_size != out_size:
self.id_path = nn.Conv2d(in_size, out_size, kernel_size=1, padding=0)
el... | 3,122 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
class FlavaImageCodebookLayerGroup(nn.Module):
def __init__(self, num_blocks: int, num_layers: int, in_size: int, out_size: int, use_pool: bool = True):
super().__init__()
blocks = OrderedDict()
for i in range(num_blocks):
if i == 0:
blocks[f"block_{i+1}"] = Flava... | 3,123 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
class FlavaImageCodebook(FlavaPreTrainedModel):
base_model_prefix = ""
config_class = FlavaImageCodebookConfig
main_input_name = "pixel_values"
supports_gradient_checkpointing = False
def __init__(
self,
config: FlavaImageCodebookConfig,
**kwargs: Any,
):
super()... | 3,124 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
blocks = OrderedDict()
blocks["input"] = nn.Conv2d(self.input_channels, 1 * self.hidden_size, kernel_size=7, padding=3)
blocks["group_1"] = FlavaImageCodebookLayerGroup(
self.num_blocks_per_group, num_layers, 1 * self.hidden_size, 1 * self.hidden_size
)
blocks["group_2"] = Fl... | 3,124 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
if self.config.freeze:
for param in self.parameters():
param.requires_grad = False
def get_codebook_indices(self, pixel_values: torch.Tensor) -> torch.Tensor:
"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
... | 3,124 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py |
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