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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 file in the repo root or https://opensource.org/licenses/BSD-3-Clause
"""
import warnings
import torch
import torch.nn.functional as F
from lavis.common.registry import registry
from lavis.common.utils import get_abs_path
from lavis.models.albef_models import AlbefBase
from lavis.models.albef_models.albef_outputs import AlbefOutputFeatures
from lavis.models.med import BertForMaskedLM
from lavis.models.vit import VisionTransformerEncoder
from torch import nn
from transformers import BertConfig
@registry.register_model("albef_feature_extractor")
class AlbefFeatureExtractor(AlbefBase):
PRETRAINED_MODEL_CONFIG_DICT = {
"base": "configs/models/albef_feature_extractor.yaml",
}
def __init__(self, image_encoder, text_encoder, embed_dim=256, max_txt_len=30):
super().__init__()
self.tokenizer = self.init_tokenizer()
self.visual_encoder = image_encoder
self.text_encoder = text_encoder
text_width = text_encoder.config.hidden_size
vision_width = image_encoder.vision_width
self.embed_dim = embed_dim
self.vision_proj = nn.Linear(vision_width, embed_dim)
self.text_proj = nn.Linear(text_width, embed_dim)
self.max_txt_len = max_txt_len
self.temp = nn.Parameter(0.07 * torch.ones([]))
@torch.no_grad()
def extract_features(self, samples, mode="multimodal"):
"""
Extract features for multimodal or unimodal samples.
Args:
samples (dict): A dictionary of samples, containing the following keys:
- image (torch.Tensor): A tensor of shape (B, C, H, W) containing the image.
Raw images should be preprocessed before being passed to feature extractor.
- text_input (list): A list of strings containing the text, length B.
mode (str): The mode of feature extraction. Can be either "multimodal", "text" or "image".
If "multimodal", return image features and multimodal features;
if "text", return text features;
if "image", return image features.
Default: "multimodal".
Returns:
An AlbefOutputFeatures object, see lavis/models/albef_models/albef_outputs.py for details.
Examples:
```python
>>> from PIL import Image
>>> from lavis.models import load_model_and_preprocess
>>> raw_image = Image.open("docs/data/merlion.png").convert("RGB")
>>> caption = "a large fountain spewing water into the air"
>>> model, vis_processors, txt_processors = load_model_and_preprocess("albef_feature_extractor", is_eval=True)
>>> image = vis_processors["eval"](raw_image).unsqueeze(0)
>>> text_input = txt_processors["eval"](caption)
>>> sample = {"image": image, "text_input": [text_input]}
>>> features_multimodal = model.extract_features(sample)
>>> features_multimodal.keys()
odict_keys(['image_embeds', 'multimodal_embeds'])
>>> features_multimodal.image_embeds.shape
torch.Size([1, 197, 768])
>>> features_multimodal.multimodal_embeds.shape
torch.Size([1, 12, 768])
>>> features_text = model.extract_features(sample, mode="text")
>>> features_text.keys()
odict_keys(['text_embeds', 'text_features'])
>>> features_text.text_embeds.shape
torch.Size([1, 12, 768])
>>> features_text.text_features.shape
torch.Size([1, 12, 256])
>>> features_image = model.extract_features(sample, mode="image")
>>> features_image.keys()
odict_keys(['image_embeds', 'image_features'])
>>> features_image.image_embeds.shape
torch.Size([1, 197, 768])
>>> features_image.image_features.shape
torch.Size([1, 197, 256])
```
"""
image = samples["image"]
caption = samples["text_input"]
if isinstance(mode, str):
mode = [mode]
for m in mode:
assert m in [
"multimodal",
"image",
"text",
], "mode must be one of [multimodal, image, text], but got {}".format(m)
# initalize output
image_embeds, text_embeds, multimodal_embeds = None, None, None
image_features, text_features = None, None
if "image" in mode or "multimodal" in mode:
assert (
image is not None
), "image must be provided if mode is 'image' or 'multimodal'"
image_embeds = self.visual_encoder.forward_features(image)
image_features = F.normalize(self.vision_proj(image_embeds), dim=-1)
if "text" in mode or "multimodal" in mode:
assert (
caption is not None
), "text must be provided if mode is 'text' or 'multimodal'"
text = self.tokenizer(
caption,
padding=True,
return_tensors="pt",
).to(self.device)
text_output = self.text_encoder.bert(
text.input_ids,
attention_mask=text.attention_mask,
return_dict=True,
mode="text",
)
text_embeds = text_output.last_hidden_state
text_features = F.normalize(self.text_proj(text_embeds), dim=-1)
if "multimodal" in mode:
image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(
self.device
)
# forward the positve image-text pair
output = self.text_encoder.bert(
encoder_embeds=text_embeds,
attention_mask=text.attention_mask,
encoder_hidden_states=image_embeds,
encoder_attention_mask=image_atts,
return_dict=True,
mode="fusion",
)
multimodal_embeds = output.last_hidden_state
return AlbefOutputFeatures(
image_embeds=image_embeds,
image_embeds_proj=image_features,
text_embeds=text_embeds,
text_embeds_proj=text_features,
multimodal_embeds=multimodal_embeds,
)
@classmethod
def from_config(cls, cfg=None):
image_encoder = VisionTransformerEncoder.from_config(cfg, from_pretrained=True)
config_text_encoder = BertConfig.from_json_file(
get_abs_path(cfg["med_config_path"])
)
config_text_encoder.fusion_layer = 6
text_encoder = BertForMaskedLM.from_pretrained(
"bert-base-uncased", config=config_text_encoder
)
embed_dim = cfg.get("embed_dim", 256)
max_txt_len = cfg.get("max_txt_len", 30)
model = cls(
image_encoder=image_encoder,
text_encoder=text_encoder,
embed_dim=embed_dim,
max_txt_len=max_txt_len,
)
# load pre-trained weights
pretrain_path = cfg.get("pretrained", None)
if pretrain_path is not None:
msg = model.load_from_pretrained(
url_or_filename=pretrain_path, rename_text_keys=False
)
else:
warnings.warn("No pretrained weights are loaded.")
return model
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