initial commit

models/backbones/blip2_outputs.py

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+"""
+ Copyright (c) 2022, salesforce.com, inc.
+ All rights reserved.
+ SPDX-License-Identifier: BSD-3-Clause
+ For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+"""
+
+from dataclasses import dataclass
+from typing import Optional
+
+import torch
+from transformers.modeling_outputs import (
+    ModelOutput,
+    BaseModelOutputWithPoolingAndCrossAttentions,
+    CausalLMOutputWithCrossAttentions,
+)
+
+
+@dataclass
+class BlipSimilarity(ModelOutput):
+    sim_i2t: torch.FloatTensor = None
+    sim_t2i: torch.FloatTensor = None
+
+    sim_i2t_m: Optional[torch.FloatTensor] = None
+    sim_t2i_m: Optional[torch.FloatTensor] = None
+
+    sim_i2t_targets: Optional[torch.FloatTensor] = None
+    sim_t2i_targets: Optional[torch.FloatTensor] = None
+
+
+@dataclass
+class BlipIntermediateOutput(ModelOutput):
+    """
+    Data class for intermediate outputs of BLIP models.
+
+    image_embeds (torch.FloatTensor): Image embeddings, shape (batch_size, num_patches, embed_dim).
+    text_embeds (torch.FloatTensor): Text embeddings, shape (batch_size, seq_len, embed_dim).
+
+    image_embeds_m (torch.FloatTensor): Image embeddings from momentum visual encoder, shape (batch_size, num_patches, embed_dim).
+    text_embeds_m (torch.FloatTensor): Text embeddings from momentum text encoder, shape (batch_size, seq_len, embed_dim).
+
+    encoder_output (BaseModelOutputWithPoolingAndCrossAttentions): output from the image-grounded text encoder.
+    encoder_output_neg (BaseModelOutputWithPoolingAndCrossAttentions): output from the image-grounded text encoder for negative pairs.
+
+    decoder_output (CausalLMOutputWithCrossAttentions): output from the image-grounded text decoder.
+    decoder_labels (torch.LongTensor): labels for the captioning loss.
+
+    itm_logits (torch.FloatTensor): logits for the image-text matching loss, shape (batch_size * 3, 2).
+    itm_labels (torch.LongTensor): labels for the image-text matching loss, shape (batch_size * 3,)
+
+    """
+
+    # uni-modal features
+    image_embeds: torch.FloatTensor = None
+    text_embeds: Optional[torch.FloatTensor] = None
+
+    image_embeds_m: Optional[torch.FloatTensor] = None
+    text_embeds_m: Optional[torch.FloatTensor] = None
+
+    # intermediate outputs of multimodal encoder
+    encoder_output: Optional[BaseModelOutputWithPoolingAndCrossAttentions] = None
+    encoder_output_neg: Optional[BaseModelOutputWithPoolingAndCrossAttentions] = None
+
+    itm_logits: Optional[torch.FloatTensor] = None
+    itm_labels: Optional[torch.LongTensor] = None
+
+    # intermediate outputs of multimodal decoder
+    decoder_output: Optional[CausalLMOutputWithCrossAttentions] = None
+    decoder_labels: Optional[torch.LongTensor] = None
+
+
+@dataclass
+class BlipOutput(ModelOutput):
+    # some finetuned models (e.g. BlipVQA) do not compute similarity, thus optional.
+    sims: Optional[BlipSimilarity] = None
+
+    intermediate_output: BlipIntermediateOutput = None
+
+    loss: Optional[torch.FloatTensor] = None
+
+    loss_itc: Optional[torch.FloatTensor] = None
+
+    loss_itm: Optional[torch.FloatTensor] = None
+
+    loss_lm: Optional[torch.FloatTensor] = None
+
+
+@dataclass
+class BlipOutputFeatures(ModelOutput):
+    """
+    Data class of features from BlipFeatureExtractor.
+
+    Args:
+        image_embeds: (torch.FloatTensor) of shape (batch_size, num_patches+1, embed_dim), optional
+        image_features: (torch.FloatTensor) of shape (batch_size, num_patches+1, feature_dim), optional
+        text_embeds: (torch.FloatTensor) of shape (batch_size, sequence_length+1, embed_dim), optional
+        text_features: (torch.FloatTensor) of shape (batch_size, sequence_length+1, feature_dim), optional
+
+        The first embedding or feature is for the [CLS] token.
+
+        Features are obtained by projecting the corresponding embedding into a normalized low-dimensional space.
+    """
+
+    image_embeds: Optional[torch.FloatTensor] = None
+    image_embeds_proj: Optional[torch.FloatTensor] = None
+
+    text_embeds: Optional[torch.FloatTensor] = None
+    text_embeds_proj: Optional[torch.FloatTensor] = None
+
+    multimodal_embeds: Optional[torch.FloatTensor] = None