Commit
•
1d0af1c
1
Parent(s):
71d8fad
Added files for image feature extraction
Browse files- configs/med_config.json +21 -0
- model_large_retrieval_coco.pth +3 -0
- blip.py → models/.ipynb_checkpoints/blip_decoder-checkpoint.py +4 -66
- models/.ipynb_checkpoints/blip_feature_extractor-checkpoint.py +135 -0
- models/__pycache__/blip_decoder.cpython-37.pyc +0 -0
- models/__pycache__/blip_feature_extractor.cpython-37.pyc +0 -0
- models/__pycache__/med.cpython-37.pyc +0 -0
- models/__pycache__/vit.cpython-37.pyc +0 -0
- models/blip_decoder.py +175 -0
- models/blip_feature_extractor.py +135 -0
- models/med.py +953 -0
- models/vit.py +305 -0
- pipeline.py +55 -0
configs/med_config.json
ADDED
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{
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"architectures": [
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"BertModel"
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],
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"attention_probs_dropout_prob": 0.1,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "bert",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"pad_token_id": 0,
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"type_vocab_size": 2,
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"vocab_size": 30524,
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"encoder_width": 768,
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"add_cross_attention": true
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}
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model_large_retrieval_coco.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:18e10e32c7a152f087afbb72ee6ddc817ca18e10641607b9fb72e20f9b4a5f63
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size 3687945348
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blip.py → models/.ipynb_checkpoints/blip_decoder-checkpoint.py
RENAMED
@@ -8,8 +8,8 @@
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import warnings
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warnings.filterwarnings("ignore")
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from vit import VisionTransformer, interpolate_pos_embed
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from med import BertConfig, BertModel, BertLMHeadModel
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from transformers import BertTokenizer
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import torch
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from urllib.parse import urlparse
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from timm.models.hub import download_cached_file
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class BLIP_Base(nn.Module):
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def __init__(self,
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med_config = 'configs/med_config.json',
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image_size = 224,
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vit = 'base',
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vit_grad_ckpt = False,
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vit_ckpt_layer = 0,
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):
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"""
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Args:
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med_config (str): path for the mixture of encoder-decoder model's configuration file
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image_size (int): input image size
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vit (str): model size of vision transformer
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"""
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super().__init__()
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self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer)
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self.tokenizer = init_tokenizer()
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med_config = BertConfig.from_json_file(med_config)
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med_config.encoder_width = vision_width
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self.text_encoder = BertModel(config=med_config, add_pooling_layer=False)
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def forward(self, image, caption, mode):
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assert mode in ['image', 'text', 'multimodal'], "mode parameter must be image, text, or multimodal"
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text = self.tokenizer(caption, return_tensors="pt").to(image.device)
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if mode=='image':
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# return image features
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image_embeds = self.visual_encoder(image)
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return image_embeds
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elif mode=='text':
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# return text features
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text_output = self.text_encoder(text.input_ids, attention_mask = text.attention_mask,
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return_dict = True, mode = 'text')
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return text_output.last_hidden_state
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elif mode=='multimodal':
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# return multimodel features
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image_embeds = self.visual_encoder(image)
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image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device)
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text.input_ids[:,0] = self.tokenizer.enc_token_id
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output = self.text_encoder(text.input_ids,
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attention_mask = text.attention_mask,
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encoder_hidden_states = image_embeds,
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encoder_attention_mask = image_atts,
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return_dict = True,
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)
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return output.last_hidden_state
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class BLIP_Decoder(nn.Module):
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def __init__(self,
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med_config = 'configs/med_config.json',
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caption = self.tokenizer.decode(output, skip_special_tokens=True)
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captions.append(caption[len(self.prompt):])
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return captions
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def blip_decoder(pretrained='',**kwargs):
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model = BLIP_Decoder(**kwargs)
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model,msg = load_checkpoint(model,pretrained)
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assert(len(msg.missing_keys)==0)
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return model
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def blip_feature_extractor(pretrained='',**kwargs):
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model = BLIP_Base(**kwargs)
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if pretrained:
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model,msg = load_checkpoint(model,pretrained)
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assert(len(msg.missing_keys)==0)
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return model
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def init_tokenizer():
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tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
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msg = model.load_state_dict(state_dict,strict=False)
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print('load checkpoint from %s'%url_or_filename)
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return model,msg
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import warnings
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warnings.filterwarnings("ignore")
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from models.vit import VisionTransformer, interpolate_pos_embed
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from models.med import BertConfig, BertModel, BertLMHeadModel
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from transformers import BertTokenizer
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import torch
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from urllib.parse import urlparse
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from timm.models.hub import download_cached_file
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class BLIP_Decoder(nn.Module):
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def __init__(self,
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med_config = 'configs/med_config.json',
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caption = self.tokenizer.decode(output, skip_special_tokens=True)
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captions.append(caption[len(self.prompt):])
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return captions
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def blip_decoder(pretrained='',**kwargs):
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model = BLIP_Decoder(**kwargs)
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model,msg = load_checkpoint(model,pretrained)
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assert(len(msg.missing_keys)==0)
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return model
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def init_tokenizer():
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tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
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msg = model.load_state_dict(state_dict,strict=False)
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print('load checkpoint from %s'%url_or_filename)
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return model,msg
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models/.ipynb_checkpoints/blip_feature_extractor-checkpoint.py
ADDED
@@ -0,0 +1,135 @@
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'''
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* Copyright (c) 2022, salesforce.com, inc.
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* All rights reserved.
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* SPDX-License-Identifier: BSD-3-Clause
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* For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
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* By Junnan Li
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'''
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import warnings
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warnings.filterwarnings("ignore")
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from models.vit import VisionTransformer, interpolate_pos_embed
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from models.med import BertConfig, BertModel, BertLMHeadModel
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from transformers import BertTokenizer
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import torch
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from torch import nn
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import torch.nn.functional as F
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import os
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from urllib.parse import urlparse
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from timm.models.hub import download_cached_file
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class BLIP_Base(nn.Module):
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def __init__(self,
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med_config = 'configs/med_config.json',
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image_size = 224,
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vit = 'base',
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vit_grad_ckpt = False,
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vit_ckpt_layer = 0,
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):
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"""
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Args:
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med_config (str): path for the mixture of encoder-decoder model's configuration file
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image_size (int): input image size
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vit (str): model size of vision transformer
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"""
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super().__init__()
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self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer)
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self.tokenizer = init_tokenizer()
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med_config = BertConfig.from_json_file(med_config)
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med_config.encoder_width = vision_width
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self.text_encoder = BertModel(config=med_config, add_pooling_layer=False)
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def forward(self, image, caption, mode):
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assert mode in ['image', 'text', 'multimodal'], "mode parameter must be image, text, or multimodal"
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text = self.tokenizer(caption, return_tensors="pt").to(image.device)
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if mode=='image':
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# return image features
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image_embeds = self.visual_encoder(image)
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return image_embeds
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elif mode=='text':
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# return text features
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text_output = self.text_encoder(text.input_ids, attention_mask = text.attention_mask,
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return_dict = True, mode = 'text')
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return text_output.last_hidden_state
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elif mode=='multimodal':
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# return multimodel features
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image_embeds = self.visual_encoder(image)
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image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device)
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text.input_ids[:,0] = self.tokenizer.enc_token_id
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output = self.text_encoder(text.input_ids,
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attention_mask = text.attention_mask,
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encoder_hidden_states = image_embeds,
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encoder_attention_mask = image_atts,
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return_dict = True,
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)
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return output.last_hidden_state
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def blip_feature_extractor(pretrained='',**kwargs):
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model = BLIP_Base(**kwargs)
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if pretrained:
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model,msg = load_checkpoint(model,pretrained)
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assert(len(msg.missing_keys)==0)
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return model
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def init_tokenizer():
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tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
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tokenizer.add_special_tokens({'bos_token':'[DEC]'})
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tokenizer.add_special_tokens({'additional_special_tokens':['[ENC]']})
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tokenizer.enc_token_id = tokenizer.additional_special_tokens_ids[0]
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return tokenizer
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def create_vit(vit, image_size, use_grad_checkpointing=False, ckpt_layer=0, drop_path_rate=0):
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assert vit in ['base', 'large'], "vit parameter must be base or large"
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if vit=='base':
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vision_width = 768
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visual_encoder = VisionTransformer(img_size=image_size, patch_size=16, embed_dim=vision_width, depth=12,
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num_heads=12, use_grad_checkpointing=use_grad_checkpointing, ckpt_layer=ckpt_layer,
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drop_path_rate=0 or drop_path_rate
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)
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elif vit=='large':
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vision_width = 1024
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visual_encoder = VisionTransformer(img_size=image_size, patch_size=16, embed_dim=vision_width, depth=24,
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num_heads=16, use_grad_checkpointing=use_grad_checkpointing, ckpt_layer=ckpt_layer,
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drop_path_rate=0.1 or drop_path_rate
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)
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return visual_encoder, vision_width
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def is_url(url_or_filename):
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parsed = urlparse(url_or_filename)
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return parsed.scheme in ("http", "https")
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def load_checkpoint(model,url_or_filename):
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if is_url(url_or_filename):
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cached_file = download_cached_file(url_or_filename, check_hash=False, progress=True)
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checkpoint = torch.load(cached_file, map_location='cpu')
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elif os.path.isfile(url_or_filename):
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checkpoint = torch.load(url_or_filename, map_location='cpu')
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else:
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raise RuntimeError('checkpoint url or path is invalid')
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state_dict = checkpoint['model']
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state_dict['visual_encoder.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder.pos_embed'],model.visual_encoder)
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if 'visual_encoder_m.pos_embed' in model.state_dict().keys():
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state_dict['visual_encoder_m.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder_m.pos_embed'],
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model.visual_encoder_m)
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for key in model.state_dict().keys():
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if key in state_dict.keys():
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if state_dict[key].shape!=model.state_dict()[key].shape:
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del state_dict[key]
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msg = model.load_state_dict(state_dict,strict=False)
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print('load checkpoint from %s'%url_or_filename)
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return model,msg
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models/__pycache__/blip_decoder.cpython-37.pyc
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Binary file (5.54 kB). View file
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models/__pycache__/blip_feature_extractor.cpython-37.pyc
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Binary file (4.51 kB). View file
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models/__pycache__/med.cpython-37.pyc
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Binary file (28.2 kB). View file
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models/__pycache__/vit.cpython-37.pyc
ADDED
Binary file (12.3 kB). View file
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models/blip_decoder.py
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|
|
1 |
+
'''
|
2 |
+
* Copyright (c) 2022, salesforce.com, inc.
|
3 |
+
* All rights reserved.
|
4 |
+
* SPDX-License-Identifier: BSD-3-Clause
|
5 |
+
* For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
|
6 |
+
* By Junnan Li
|
7 |
+
'''
|
8 |
+
import warnings
|
9 |
+
warnings.filterwarnings("ignore")
|
10 |
+
|
11 |
+
from models.vit import VisionTransformer, interpolate_pos_embed
|
12 |
+
from models.med import BertConfig, BertModel, BertLMHeadModel
|
13 |
+
from transformers import BertTokenizer
|
14 |
+
|
15 |
+
import torch
|
16 |
+
from torch import nn
|
17 |
+
import torch.nn.functional as F
|
18 |
+
|
19 |
+
import os
|
20 |
+
from urllib.parse import urlparse
|
21 |
+
from timm.models.hub import download_cached_file
|
22 |
+
|
23 |
+
class BLIP_Decoder(nn.Module):
|
24 |
+
def __init__(self,
|
25 |
+
med_config = 'configs/med_config.json',
|
26 |
+
image_size = 384,
|
27 |
+
vit = 'base',
|
28 |
+
vit_grad_ckpt = False,
|
29 |
+
vit_ckpt_layer = 0,
|
30 |
+
prompt = 'a picture of ',
|
31 |
+
):
|
32 |
+
"""
|
33 |
+
Args:
|
34 |
+
med_config (str): path for the mixture of encoder-decoder model's configuration file
|
35 |
+
image_size (int): input image size
|
36 |
+
vit (str): model size of vision transformer
|
37 |
+
"""
|
38 |
+
super().__init__()
|
39 |
+
|
40 |
+
self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer)
|
41 |
+
self.tokenizer = init_tokenizer()
|
42 |
+
med_config = BertConfig.from_json_file(med_config)
|
43 |
+
med_config.encoder_width = vision_width
|
44 |
+
self.text_decoder = BertLMHeadModel(config=med_config)
|
45 |
+
|
46 |
+
self.prompt = prompt
|
47 |
+
self.prompt_length = len(self.tokenizer(self.prompt).input_ids)-1
|
48 |
+
|
49 |
+
|
50 |
+
def forward(self, image, caption):
|
51 |
+
|
52 |
+
image_embeds = self.visual_encoder(image)
|
53 |
+
image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device)
|
54 |
+
|
55 |
+
text = self.tokenizer(caption, padding='longest', truncation=True, max_length=40, return_tensors="pt").to(image.device)
|
56 |
+
|
57 |
+
text.input_ids[:,0] = self.tokenizer.bos_token_id
|
58 |
+
|
59 |
+
decoder_targets = text.input_ids.masked_fill(text.input_ids == self.tokenizer.pad_token_id, -100)
|
60 |
+
decoder_targets[:,:self.prompt_length] = -100
|
61 |
+
|
62 |
+
decoder_output = self.text_decoder(text.input_ids,
|
63 |
+
attention_mask = text.attention_mask,
|
64 |
+
encoder_hidden_states = image_embeds,
|
65 |
+
encoder_attention_mask = image_atts,
|
66 |
+
labels = decoder_targets,
|
67 |
+
return_dict = True,
|
68 |
+
)
|
69 |
+
loss_lm = decoder_output.loss
|
70 |
+
|
71 |
+
return loss_lm
|
72 |
+
|
73 |
+
def generate(self, image, sample=False, num_beams=3, max_length=30, min_length=10, top_p=0.9, repetition_penalty=1.0):
|
74 |
+
image_embeds = self.visual_encoder(image)
|
75 |
+
|
76 |
+
if not sample:
|
77 |
+
image_embeds = image_embeds.repeat_interleave(num_beams,dim=0)
|
78 |
+
|
79 |
+
image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device)
|
80 |
+
model_kwargs = {"encoder_hidden_states": image_embeds, "encoder_attention_mask":image_atts}
|
81 |
+
|
82 |
+
prompt = [self.prompt] * image.size(0)
|
83 |
+
input_ids = self.tokenizer(prompt, return_tensors="pt").input_ids.to(image.device)
|
84 |
+
input_ids[:,0] = self.tokenizer.bos_token_id
|
85 |
+
input_ids = input_ids[:, :-1]
|
86 |
+
|
87 |
+
if sample:
|
88 |
+
#nucleus sampling
|
89 |
+
outputs = self.text_decoder.generate(input_ids=input_ids,
|
90 |
+
max_length=max_length,
|
91 |
+
min_length=min_length,
|
92 |
+
do_sample=True,
|
93 |
+
top_p=top_p,
|
94 |
+
num_return_sequences=1,
|
95 |
+
eos_token_id=self.tokenizer.sep_token_id,
|
96 |
+
pad_token_id=self.tokenizer.pad_token_id,
|
97 |
+
repetition_penalty=1.1,
|
98 |
+
**model_kwargs)
|
99 |
+
else:
|
100 |
+
#beam search
|
101 |
+
outputs = self.text_decoder.generate(input_ids=input_ids,
|
102 |
+
max_length=max_length,
|
103 |
+
min_length=min_length,
|
104 |
+
num_beams=num_beams,
|
105 |
+
eos_token_id=self.tokenizer.sep_token_id,
|
106 |
+
pad_token_id=self.tokenizer.pad_token_id,
|
107 |
+
repetition_penalty=repetition_penalty,
|
108 |
+
**model_kwargs)
|
109 |
+
|
110 |
+
captions = []
|
111 |
+
for output in outputs:
|
112 |
+
caption = self.tokenizer.decode(output, skip_special_tokens=True)
|
113 |
+
captions.append(caption[len(self.prompt):])
|
114 |
+
return captions
|
115 |
+
|
116 |
+
|
117 |
+
def blip_decoder(pretrained='',**kwargs):
|
118 |
+
model = BLIP_Decoder(**kwargs)
|
119 |
+
if pretrained:
|
120 |
+
model,msg = load_checkpoint(model,pretrained)
|
121 |
+
assert(len(msg.missing_keys)==0)
|
122 |
+
return model
|
123 |
+
|
124 |
+
def init_tokenizer():
|
125 |
+
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
|
126 |
+
tokenizer.add_special_tokens({'bos_token':'[DEC]'})
|
127 |
+
tokenizer.add_special_tokens({'additional_special_tokens':['[ENC]']})
|
128 |
+
tokenizer.enc_token_id = tokenizer.additional_special_tokens_ids[0]
|
129 |
+
return tokenizer
|
130 |
+
|
131 |
+
|
132 |
+
def create_vit(vit, image_size, use_grad_checkpointing=False, ckpt_layer=0, drop_path_rate=0):
|
133 |
+
|
134 |
+
assert vit in ['base', 'large'], "vit parameter must be base or large"
|
135 |
+
if vit=='base':
|
136 |
+
vision_width = 768
|
137 |
+
visual_encoder = VisionTransformer(img_size=image_size, patch_size=16, embed_dim=vision_width, depth=12,
|
138 |
+
num_heads=12, use_grad_checkpointing=use_grad_checkpointing, ckpt_layer=ckpt_layer,
|
139 |
+
drop_path_rate=0 or drop_path_rate
|
140 |
+
)
|
141 |
+
elif vit=='large':
|
142 |
+
vision_width = 1024
|
143 |
+
visual_encoder = VisionTransformer(img_size=image_size, patch_size=16, embed_dim=vision_width, depth=24,
|
144 |
+
num_heads=16, use_grad_checkpointing=use_grad_checkpointing, ckpt_layer=ckpt_layer,
|
145 |
+
drop_path_rate=0.1 or drop_path_rate
|
146 |
+
)
|
147 |
+
return visual_encoder, vision_width
|
148 |
+
|
149 |
+
def is_url(url_or_filename):
|
150 |
+
parsed = urlparse(url_or_filename)
|
151 |
+
return parsed.scheme in ("http", "https")
|
152 |
+
|
153 |
+
def load_checkpoint(model,url_or_filename):
|
154 |
+
if is_url(url_or_filename):
|
155 |
+
cached_file = download_cached_file(url_or_filename, check_hash=False, progress=True)
|
156 |
+
checkpoint = torch.load(cached_file, map_location='cpu')
|
157 |
+
elif os.path.isfile(url_or_filename):
|
158 |
+
checkpoint = torch.load(url_or_filename, map_location='cpu')
|
159 |
+
else:
|
160 |
+
raise RuntimeError('checkpoint url or path is invalid')
|
161 |
+
|
162 |
+
state_dict = checkpoint['model']
|
163 |
+
|
164 |
+
state_dict['visual_encoder.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder.pos_embed'],model.visual_encoder)
|
165 |
+
if 'visual_encoder_m.pos_embed' in model.state_dict().keys():
|
166 |
+
state_dict['visual_encoder_m.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder_m.pos_embed'],
|
167 |
+
model.visual_encoder_m)
|
168 |
+
for key in model.state_dict().keys():
|
169 |
+
if key in state_dict.keys():
|
170 |
+
if state_dict[key].shape!=model.state_dict()[key].shape:
|
171 |
+
del state_dict[key]
|
172 |
+
|
173 |
+
msg = model.load_state_dict(state_dict,strict=False)
|
174 |
+
print('load checkpoint from %s'%url_or_filename)
|
175 |
+
return model,msg
|
models/blip_feature_extractor.py
ADDED
@@ -0,0 +1,135 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
'''
|
2 |
+
* Copyright (c) 2022, salesforce.com, inc.
|
3 |
+
* All rights reserved.
|
4 |
+
* SPDX-License-Identifier: BSD-3-Clause
|
5 |
+
* For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
|
6 |
+
* By Junnan Li
|
7 |
+
'''
|
8 |
+
import warnings
|
9 |
+
warnings.filterwarnings("ignore")
|
10 |
+
|
11 |
+
from models.vit import VisionTransformer, interpolate_pos_embed
|
12 |
+
from models.med import BertConfig, BertModel, BertLMHeadModel
|
13 |
+
from transformers import BertTokenizer
|
14 |
+
|
15 |
+
import torch
|
16 |
+
from torch import nn
|
17 |
+
import torch.nn.functional as F
|
18 |
+
|
19 |
+
import os
|
20 |
+
from urllib.parse import urlparse
|
21 |
+
from timm.models.hub import download_cached_file
|
22 |
+
|
23 |
+
class BLIP_Base(nn.Module):
|
24 |
+
def __init__(self,
|
25 |
+
med_config = 'configs/med_config.json',
|
26 |
+
image_size = 224,
|
27 |
+
vit = 'base',
|
28 |
+
vit_grad_ckpt = False,
|
29 |
+
vit_ckpt_layer = 0,
|
30 |
+
):
|
31 |
+
"""
|
32 |
+
Args:
|
33 |
+
med_config (str): path for the mixture of encoder-decoder model's configuration file
|
34 |
+
image_size (int): input image size
|
35 |
+
vit (str): model size of vision transformer
|
36 |
+
"""
|
37 |
+
super().__init__()
|
38 |
+
|
39 |
+
self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer)
|
40 |
+
self.tokenizer = init_tokenizer()
|
41 |
+
med_config = BertConfig.from_json_file(med_config)
|
42 |
+
med_config.encoder_width = vision_width
|
43 |
+
self.text_encoder = BertModel(config=med_config, add_pooling_layer=False)
|
44 |
+
|
45 |
+
|
46 |
+
def forward(self, image, caption, mode):
|
47 |
+
|
48 |
+
assert mode in ['image', 'text', 'multimodal'], "mode parameter must be image, text, or multimodal"
|
49 |
+
text = self.tokenizer(caption, return_tensors="pt").to(image.device)
|
50 |
+
|
51 |
+
if mode=='image':
|
52 |
+
# return image features
|
53 |
+
image_embeds = self.visual_encoder(image)
|
54 |
+
return image_embeds
|
55 |
+
|
56 |
+
elif mode=='text':
|
57 |
+
# return text features
|
58 |
+
text_output = self.text_encoder(text.input_ids, attention_mask = text.attention_mask,
|
59 |
+
return_dict = True, mode = 'text')
|
60 |
+
return text_output.last_hidden_state
|
61 |
+
|
62 |
+
elif mode=='multimodal':
|
63 |
+
# return multimodel features
|
64 |
+
image_embeds = self.visual_encoder(image)
|
65 |
+
image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device)
|
66 |
+
|
67 |
+
text.input_ids[:,0] = self.tokenizer.enc_token_id
|
68 |
+
output = self.text_encoder(text.input_ids,
|
69 |
+
attention_mask = text.attention_mask,
|
70 |
+
encoder_hidden_states = image_embeds,
|
71 |
+
encoder_attention_mask = image_atts,
|
72 |
+
return_dict = True,
|
73 |
+
)
|
74 |
+
return output.last_hidden_state
|
75 |
+
|
76 |
+
|
77 |
+
def blip_feature_extractor(pretrained='',**kwargs):
|
78 |
+
model = BLIP_Base(**kwargs)
|
79 |
+
if pretrained:
|
80 |
+
model,msg = load_checkpoint(model,pretrained)
|
81 |
+
assert(len(msg.missing_keys)==0)
|
82 |
+
return model
|
83 |
+
|
84 |
+
def init_tokenizer():
|
85 |
+
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
|
86 |
+
tokenizer.add_special_tokens({'bos_token':'[DEC]'})
|
87 |
+
tokenizer.add_special_tokens({'additional_special_tokens':['[ENC]']})
|
88 |
+
tokenizer.enc_token_id = tokenizer.additional_special_tokens_ids[0]
|
89 |
+
return tokenizer
|
90 |
+
|
91 |
+
|
92 |
+
def create_vit(vit, image_size, use_grad_checkpointing=False, ckpt_layer=0, drop_path_rate=0):
|
93 |
+
|
94 |
+
assert vit in ['base', 'large'], "vit parameter must be base or large"
|
95 |
+
if vit=='base':
|
96 |
+
vision_width = 768
|
97 |
+
visual_encoder = VisionTransformer(img_size=image_size, patch_size=16, embed_dim=vision_width, depth=12,
|
98 |
+
num_heads=12, use_grad_checkpointing=use_grad_checkpointing, ckpt_layer=ckpt_layer,
|
99 |
+
drop_path_rate=0 or drop_path_rate
|
100 |
+
)
|
101 |
+
elif vit=='large':
|
102 |
+
vision_width = 1024
|
103 |
+
visual_encoder = VisionTransformer(img_size=image_size, patch_size=16, embed_dim=vision_width, depth=24,
|
104 |
+
num_heads=16, use_grad_checkpointing=use_grad_checkpointing, ckpt_layer=ckpt_layer,
|
105 |
+
drop_path_rate=0.1 or drop_path_rate
|
106 |
+
)
|
107 |
+
return visual_encoder, vision_width
|
108 |
+
|
109 |
+
def is_url(url_or_filename):
|
110 |
+
parsed = urlparse(url_or_filename)
|
111 |
+
return parsed.scheme in ("http", "https")
|
112 |
+
|
113 |
+
def load_checkpoint(model,url_or_filename):
|
114 |
+
if is_url(url_or_filename):
|
115 |
+
cached_file = download_cached_file(url_or_filename, check_hash=False, progress=True)
|
116 |
+
checkpoint = torch.load(cached_file, map_location='cpu')
|
117 |
+
elif os.path.isfile(url_or_filename):
|
118 |
+
checkpoint = torch.load(url_or_filename, map_location='cpu')
|
119 |
+
else:
|
120 |
+
raise RuntimeError('checkpoint url or path is invalid')
|
121 |
+
|
122 |
+
state_dict = checkpoint['model']
|
123 |
+
|
124 |
+
state_dict['visual_encoder.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder.pos_embed'],model.visual_encoder)
|
125 |
+
if 'visual_encoder_m.pos_embed' in model.state_dict().keys():
|
126 |
+
state_dict['visual_encoder_m.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder_m.pos_embed'],
|
127 |
+
model.visual_encoder_m)
|
128 |
+
for key in model.state_dict().keys():
|
129 |
+
if key in state_dict.keys():
|
130 |
+
if state_dict[key].shape!=model.state_dict()[key].shape:
|
131 |
+
del state_dict[key]
|
132 |
+
|
133 |
+
msg = model.load_state_dict(state_dict,strict=False)
|
134 |
+
print('load checkpoint from %s'%url_or_filename)
|
135 |
+
return model,msg
|
models/med.py
ADDED
@@ -0,0 +1,953 @@
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|
|
1 |
+
'''
|
2 |
+
* Copyright (c) 2022, salesforce.com, inc.
|
3 |
+
* All rights reserved.
|
4 |
+
* SPDX-License-Identifier: BSD-3-Clause
|
5 |
+
* For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
|
6 |
+
* By Junnan Li
|
7 |
+
* Based on huggingface code base
|
8 |
+
* https://github.com/huggingface/transformers/blob/v4.15.0/src/transformers/models/bert
|
9 |
+
'''
|
10 |
+
|
11 |
+
import math
|
12 |
+
import os
|
13 |
+
import warnings
|
14 |
+
from dataclasses import dataclass
|
15 |
+
from typing import Optional, Tuple
|
16 |
+
|
17 |
+
import torch
|
18 |
+
from torch import Tensor, device, dtype, nn
|
19 |
+
import torch.utils.checkpoint
|
20 |
+
from torch import nn
|
21 |
+
from torch.nn import CrossEntropyLoss
|
22 |
+
import torch.nn.functional as F
|
23 |
+
|
24 |
+
from transformers.activations import ACT2FN
|
25 |
+
from transformers.file_utils import (
|
26 |
+
ModelOutput,
|
27 |
+
)
|
28 |
+
from transformers.modeling_outputs import (
|
29 |
+
BaseModelOutputWithPastAndCrossAttentions,
|
30 |
+
BaseModelOutputWithPoolingAndCrossAttentions,
|
31 |
+
CausalLMOutputWithCrossAttentions,
|
32 |
+
MaskedLMOutput,
|
33 |
+
MultipleChoiceModelOutput,
|
34 |
+
NextSentencePredictorOutput,
|
35 |
+
QuestionAnsweringModelOutput,
|
36 |
+
SequenceClassifierOutput,
|
37 |
+
TokenClassifierOutput,
|
38 |
+
)
|
39 |
+
from transformers.modeling_utils import (
|
40 |
+
PreTrainedModel,
|
41 |
+
apply_chunking_to_forward,
|
42 |
+
find_pruneable_heads_and_indices,
|
43 |
+
prune_linear_layer,
|
44 |
+
)
|
45 |
+
from transformers.utils import logging
|
46 |
+
from transformers.models.bert.configuration_bert import BertConfig
|
47 |
+
|
48 |
+
|
49 |
+
logger = logging.get_logger(__name__)
|
50 |
+
|
51 |
+
|
52 |
+
class BertEmbeddings(nn.Module):
|
53 |
+
"""Construct the embeddings from word and position embeddings."""
|
54 |
+
|
55 |
+
def __init__(self, config):
|
56 |
+
super().__init__()
|
57 |
+
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
|
58 |
+
self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
|
59 |
+
|
60 |
+
# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
|
61 |
+
# any TensorFlow checkpoint file
|
62 |
+
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
63 |
+
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
64 |
+
|
65 |
+
# position_ids (1, len position emb) is contiguous in memory and exported when serialized
|
66 |
+
self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
|
67 |
+
self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
|
68 |
+
|
69 |
+
self.config = config
|
70 |
+
|
71 |
+
def forward(
|
72 |
+
self, input_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0
|
73 |
+
):
|
74 |
+
if input_ids is not None:
|
75 |
+
input_shape = input_ids.size()
|
76 |
+
else:
|
77 |
+
input_shape = inputs_embeds.size()[:-1]
|
78 |
+
|
79 |
+
seq_length = input_shape[1]
|
80 |
+
|
81 |
+
if position_ids is None:
|
82 |
+
position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length]
|
83 |
+
|
84 |
+
if inputs_embeds is None:
|
85 |
+
inputs_embeds = self.word_embeddings(input_ids)
|
86 |
+
|
87 |
+
embeddings = inputs_embeds
|
88 |
+
|
89 |
+
if self.position_embedding_type == "absolute":
|
90 |
+
position_embeddings = self.position_embeddings(position_ids)
|
91 |
+
embeddings += position_embeddings
|
92 |
+
embeddings = self.LayerNorm(embeddings)
|
93 |
+
embeddings = self.dropout(embeddings)
|
94 |
+
return embeddings
|
95 |
+
|
96 |
+
|
97 |
+
class BertSelfAttention(nn.Module):
|
98 |
+
def __init__(self, config, is_cross_attention):
|
99 |
+
super().__init__()
|
100 |
+
self.config = config
|
101 |
+
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
|
102 |
+
raise ValueError(
|
103 |
+
"The hidden size (%d) is not a multiple of the number of attention "
|
104 |
+
"heads (%d)" % (config.hidden_size, config.num_attention_heads)
|
105 |
+
)
|
106 |
+
|
107 |
+
self.num_attention_heads = config.num_attention_heads
|
108 |
+
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
|
109 |
+
self.all_head_size = self.num_attention_heads * self.attention_head_size
|
110 |
+
|
111 |
+
self.query = nn.Linear(config.hidden_size, self.all_head_size)
|
112 |
+
if is_cross_attention:
|
113 |
+
self.key = nn.Linear(config.encoder_width, self.all_head_size)
|
114 |
+
self.value = nn.Linear(config.encoder_width, self.all_head_size)
|
115 |
+
else:
|
116 |
+
self.key = nn.Linear(config.hidden_size, self.all_head_size)
|
117 |
+
self.value = nn.Linear(config.hidden_size, self.all_head_size)
|
118 |
+
|
119 |
+
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
|
120 |
+
self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
|
121 |
+
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
|
122 |
+
self.max_position_embeddings = config.max_position_embeddings
|
123 |
+
self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
|
124 |
+
self.save_attention = False
|
125 |
+
|
126 |
+
def save_attn_gradients(self, attn_gradients):
|
127 |
+
self.attn_gradients = attn_gradients
|
128 |
+
|
129 |
+
def get_attn_gradients(self):
|
130 |
+
return self.attn_gradients
|
131 |
+
|
132 |
+
def save_attention_map(self, attention_map):
|
133 |
+
self.attention_map = attention_map
|
134 |
+
|
135 |
+
def get_attention_map(self):
|
136 |
+
return self.attention_map
|
137 |
+
|
138 |
+
def transpose_for_scores(self, x):
|
139 |
+
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
|
140 |
+
x = x.view(*new_x_shape)
|
141 |
+
return x.permute(0, 2, 1, 3)
|
142 |
+
|
143 |
+
def forward(
|
144 |
+
self,
|
145 |
+
hidden_states,
|
146 |
+
attention_mask=None,
|
147 |
+
head_mask=None,
|
148 |
+
encoder_hidden_states=None,
|
149 |
+
encoder_attention_mask=None,
|
150 |
+
past_key_value=None,
|
151 |
+
output_attentions=False,
|
152 |
+
):
|
153 |
+
mixed_query_layer = self.query(hidden_states)
|
154 |
+
|
155 |
+
# If this is instantiated as a cross-attention module, the keys
|
156 |
+
# and values come from an encoder; the attention mask needs to be
|
157 |
+
# such that the encoder's padding tokens are not attended to.
|
158 |
+
is_cross_attention = encoder_hidden_states is not None
|
159 |
+
|
160 |
+
if is_cross_attention:
|
161 |
+
key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
|
162 |
+
value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
|
163 |
+
attention_mask = encoder_attention_mask
|
164 |
+
elif past_key_value is not None:
|
165 |
+
key_layer = self.transpose_for_scores(self.key(hidden_states))
|
166 |
+
value_layer = self.transpose_for_scores(self.value(hidden_states))
|
167 |
+
key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
|
168 |
+
value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
|
169 |
+
else:
|
170 |
+
key_layer = self.transpose_for_scores(self.key(hidden_states))
|
171 |
+
value_layer = self.transpose_for_scores(self.value(hidden_states))
|
172 |
+
|
173 |
+
query_layer = self.transpose_for_scores(mixed_query_layer)
|
174 |
+
|
175 |
+
past_key_value = (key_layer, value_layer)
|
176 |
+
|
177 |
+
# Take the dot product between "query" and "key" to get the raw attention scores.
|
178 |
+
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
|
179 |
+
|
180 |
+
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
|
181 |
+
seq_length = hidden_states.size()[1]
|
182 |
+
position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
|
183 |
+
position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
|
184 |
+
distance = position_ids_l - position_ids_r
|
185 |
+
positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
|
186 |
+
positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility
|
187 |
+
|
188 |
+
if self.position_embedding_type == "relative_key":
|
189 |
+
relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
|
190 |
+
attention_scores = attention_scores + relative_position_scores
|
191 |
+
elif self.position_embedding_type == "relative_key_query":
|
192 |
+
relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
|
193 |
+
relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
|
194 |
+
attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
|
195 |
+
|
196 |
+
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
|
197 |
+
if attention_mask is not None:
|
198 |
+
# Apply the attention mask is (precomputed for all layers in BertModel forward() function)
|
199 |
+
attention_scores = attention_scores + attention_mask
|
200 |
+
|
201 |
+
# Normalize the attention scores to probabilities.
|
202 |
+
attention_probs = nn.Softmax(dim=-1)(attention_scores)
|
203 |
+
|
204 |
+
if is_cross_attention and self.save_attention:
|
205 |
+
self.save_attention_map(attention_probs)
|
206 |
+
attention_probs.register_hook(self.save_attn_gradients)
|
207 |
+
|
208 |
+
# This is actually dropping out entire tokens to attend to, which might
|
209 |
+
# seem a bit unusual, but is taken from the original Transformer paper.
|
210 |
+
attention_probs_dropped = self.dropout(attention_probs)
|
211 |
+
|
212 |
+
# Mask heads if we want to
|
213 |
+
if head_mask is not None:
|
214 |
+
attention_probs_dropped = attention_probs_dropped * head_mask
|
215 |
+
|
216 |
+
context_layer = torch.matmul(attention_probs_dropped, value_layer)
|
217 |
+
|
218 |
+
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
|
219 |
+
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
|
220 |
+
context_layer = context_layer.view(*new_context_layer_shape)
|
221 |
+
|
222 |
+
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
|
223 |
+
|
224 |
+
outputs = outputs + (past_key_value,)
|
225 |
+
return outputs
|
226 |
+
|
227 |
+
|
228 |
+
class BertSelfOutput(nn.Module):
|
229 |
+
def __init__(self, config):
|
230 |
+
super().__init__()
|
231 |
+
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
232 |
+
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
233 |
+
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
234 |
+
|
235 |
+
def forward(self, hidden_states, input_tensor):
|
236 |
+
hidden_states = self.dense(hidden_states)
|
237 |
+
hidden_states = self.dropout(hidden_states)
|
238 |
+
hidden_states = self.LayerNorm(hidden_states + input_tensor)
|
239 |
+
return hidden_states
|
240 |
+
|
241 |
+
|
242 |
+
class BertAttention(nn.Module):
|
243 |
+
def __init__(self, config, is_cross_attention=False):
|
244 |
+
super().__init__()
|
245 |
+
self.self = BertSelfAttention(config, is_cross_attention)
|
246 |
+
self.output = BertSelfOutput(config)
|
247 |
+
self.pruned_heads = set()
|
248 |
+
|
249 |
+
def prune_heads(self, heads):
|
250 |
+
if len(heads) == 0:
|
251 |
+
return
|
252 |
+
heads, index = find_pruneable_heads_and_indices(
|
253 |
+
heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
|
254 |
+
)
|
255 |
+
|
256 |
+
# Prune linear layers
|
257 |
+
self.self.query = prune_linear_layer(self.self.query, index)
|
258 |
+
self.self.key = prune_linear_layer(self.self.key, index)
|
259 |
+
self.self.value = prune_linear_layer(self.self.value, index)
|
260 |
+
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
|
261 |
+
|
262 |
+
# Update hyper params and store pruned heads
|
263 |
+
self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
|
264 |
+
self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
|
265 |
+
self.pruned_heads = self.pruned_heads.union(heads)
|
266 |
+
|
267 |
+
def forward(
|
268 |
+
self,
|
269 |
+
hidden_states,
|
270 |
+
attention_mask=None,
|
271 |
+
head_mask=None,
|
272 |
+
encoder_hidden_states=None,
|
273 |
+
encoder_attention_mask=None,
|
274 |
+
past_key_value=None,
|
275 |
+
output_attentions=False,
|
276 |
+
):
|
277 |
+
self_outputs = self.self(
|
278 |
+
hidden_states,
|
279 |
+
attention_mask,
|
280 |
+
head_mask,
|
281 |
+
encoder_hidden_states,
|
282 |
+
encoder_attention_mask,
|
283 |
+
past_key_value,
|
284 |
+
output_attentions,
|
285 |
+
)
|
286 |
+
attention_output = self.output(self_outputs[0], hidden_states)
|
287 |
+
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
|
288 |
+
return outputs
|
289 |
+
|
290 |
+
|
291 |
+
class BertIntermediate(nn.Module):
|
292 |
+
def __init__(self, config):
|
293 |
+
super().__init__()
|
294 |
+
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
|
295 |
+
if isinstance(config.hidden_act, str):
|
296 |
+
self.intermediate_act_fn = ACT2FN[config.hidden_act]
|
297 |
+
else:
|
298 |
+
self.intermediate_act_fn = config.hidden_act
|
299 |
+
|
300 |
+
def forward(self, hidden_states):
|
301 |
+
hidden_states = self.dense(hidden_states)
|
302 |
+
hidden_states = self.intermediate_act_fn(hidden_states)
|
303 |
+
return hidden_states
|
304 |
+
|
305 |
+
|
306 |
+
class BertOutput(nn.Module):
|
307 |
+
def __init__(self, config):
|
308 |
+
super().__init__()
|
309 |
+
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
|
310 |
+
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
311 |
+
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
312 |
+
|
313 |
+
def forward(self, hidden_states, input_tensor):
|
314 |
+
hidden_states = self.dense(hidden_states)
|
315 |
+
hidden_states = self.dropout(hidden_states)
|
316 |
+
hidden_states = self.LayerNorm(hidden_states + input_tensor)
|
317 |
+
return hidden_states
|
318 |
+
|
319 |
+
|
320 |
+
class BertLayer(nn.Module):
|
321 |
+
def __init__(self, config, layer_num):
|
322 |
+
super().__init__()
|
323 |
+
self.config = config
|
324 |
+
self.chunk_size_feed_forward = config.chunk_size_feed_forward
|
325 |
+
self.seq_len_dim = 1
|
326 |
+
self.attention = BertAttention(config)
|
327 |
+
self.layer_num = layer_num
|
328 |
+
if self.config.add_cross_attention:
|
329 |
+
self.crossattention = BertAttention(config, is_cross_attention=self.config.add_cross_attention)
|
330 |
+
self.intermediate = BertIntermediate(config)
|
331 |
+
self.output = BertOutput(config)
|
332 |
+
|
333 |
+
def forward(
|
334 |
+
self,
|
335 |
+
hidden_states,
|
336 |
+
attention_mask=None,
|
337 |
+
head_mask=None,
|
338 |
+
encoder_hidden_states=None,
|
339 |
+
encoder_attention_mask=None,
|
340 |
+
past_key_value=None,
|
341 |
+
output_attentions=False,
|
342 |
+
mode=None,
|
343 |
+
):
|
344 |
+
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
|
345 |
+
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
|
346 |
+
self_attention_outputs = self.attention(
|
347 |
+
hidden_states,
|
348 |
+
attention_mask,
|
349 |
+
head_mask,
|
350 |
+
output_attentions=output_attentions,
|
351 |
+
past_key_value=self_attn_past_key_value,
|
352 |
+
)
|
353 |
+
attention_output = self_attention_outputs[0]
|
354 |
+
|
355 |
+
outputs = self_attention_outputs[1:-1]
|
356 |
+
present_key_value = self_attention_outputs[-1]
|
357 |
+
|
358 |
+
if mode=='multimodal':
|
359 |
+
assert encoder_hidden_states is not None, "encoder_hidden_states must be given for cross-attention layers"
|
360 |
+
|
361 |
+
cross_attention_outputs = self.crossattention(
|
362 |
+
attention_output,
|
363 |
+
attention_mask,
|
364 |
+
head_mask,
|
365 |
+
encoder_hidden_states,
|
366 |
+
encoder_attention_mask,
|
367 |
+
output_attentions=output_attentions,
|
368 |
+
)
|
369 |
+
attention_output = cross_attention_outputs[0]
|
370 |
+
outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights
|
371 |
+
layer_output = apply_chunking_to_forward(
|
372 |
+
self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
|
373 |
+
)
|
374 |
+
outputs = (layer_output,) + outputs
|
375 |
+
|
376 |
+
outputs = outputs + (present_key_value,)
|
377 |
+
|
378 |
+
return outputs
|
379 |
+
|
380 |
+
def feed_forward_chunk(self, attention_output):
|
381 |
+
intermediate_output = self.intermediate(attention_output)
|
382 |
+
layer_output = self.output(intermediate_output, attention_output)
|
383 |
+
return layer_output
|
384 |
+
|
385 |
+
|
386 |
+
class BertEncoder(nn.Module):
|
387 |
+
def __init__(self, config):
|
388 |
+
super().__init__()
|
389 |
+
self.config = config
|
390 |
+
self.layer = nn.ModuleList([BertLayer(config,i) for i in range(config.num_hidden_layers)])
|
391 |
+
self.gradient_checkpointing = False
|
392 |
+
|
393 |
+
def forward(
|
394 |
+
self,
|
395 |
+
hidden_states,
|
396 |
+
attention_mask=None,
|
397 |
+
head_mask=None,
|
398 |
+
encoder_hidden_states=None,
|
399 |
+
encoder_attention_mask=None,
|
400 |
+
past_key_values=None,
|
401 |
+
use_cache=None,
|
402 |
+
output_attentions=False,
|
403 |
+
output_hidden_states=False,
|
404 |
+
return_dict=True,
|
405 |
+
mode='multimodal',
|
406 |
+
):
|
407 |
+
all_hidden_states = () if output_hidden_states else None
|
408 |
+
all_self_attentions = () if output_attentions else None
|
409 |
+
all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
|
410 |
+
|
411 |
+
next_decoder_cache = () if use_cache else None
|
412 |
+
|
413 |
+
for i in range(self.config.num_hidden_layers):
|
414 |
+
layer_module = self.layer[i]
|
415 |
+
if output_hidden_states:
|
416 |
+
all_hidden_states = all_hidden_states + (hidden_states,)
|
417 |
+
|
418 |
+
layer_head_mask = head_mask[i] if head_mask is not None else None
|
419 |
+
past_key_value = past_key_values[i] if past_key_values is not None else None
|
420 |
+
|
421 |
+
if self.gradient_checkpointing and self.training:
|
422 |
+
|
423 |
+
if use_cache:
|
424 |
+
logger.warn(
|
425 |
+
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
426 |
+
)
|
427 |
+
use_cache = False
|
428 |
+
|
429 |
+
def create_custom_forward(module):
|
430 |
+
def custom_forward(*inputs):
|
431 |
+
return module(*inputs, past_key_value, output_attentions)
|
432 |
+
|
433 |
+
return custom_forward
|
434 |
+
|
435 |
+
layer_outputs = torch.utils.checkpoint.checkpoint(
|
436 |
+
create_custom_forward(layer_module),
|
437 |
+
hidden_states,
|
438 |
+
attention_mask,
|
439 |
+
layer_head_mask,
|
440 |
+
encoder_hidden_states,
|
441 |
+
encoder_attention_mask,
|
442 |
+
mode=mode,
|
443 |
+
)
|
444 |
+
else:
|
445 |
+
layer_outputs = layer_module(
|
446 |
+
hidden_states,
|
447 |
+
attention_mask,
|
448 |
+
layer_head_mask,
|
449 |
+
encoder_hidden_states,
|
450 |
+
encoder_attention_mask,
|
451 |
+
past_key_value,
|
452 |
+
output_attentions,
|
453 |
+
mode=mode,
|
454 |
+
)
|
455 |
+
|
456 |
+
hidden_states = layer_outputs[0]
|
457 |
+
if use_cache:
|
458 |
+
next_decoder_cache += (layer_outputs[-1],)
|
459 |
+
if output_attentions:
|
460 |
+
all_self_attentions = all_self_attentions + (layer_outputs[1],)
|
461 |
+
|
462 |
+
if output_hidden_states:
|
463 |
+
all_hidden_states = all_hidden_states + (hidden_states,)
|
464 |
+
|
465 |
+
if not return_dict:
|
466 |
+
return tuple(
|
467 |
+
v
|
468 |
+
for v in [
|
469 |
+
hidden_states,
|
470 |
+
next_decoder_cache,
|
471 |
+
all_hidden_states,
|
472 |
+
all_self_attentions,
|
473 |
+
all_cross_attentions,
|
474 |
+
]
|
475 |
+
if v is not None
|
476 |
+
)
|
477 |
+
return BaseModelOutputWithPastAndCrossAttentions(
|
478 |
+
last_hidden_state=hidden_states,
|
479 |
+
past_key_values=next_decoder_cache,
|
480 |
+
hidden_states=all_hidden_states,
|
481 |
+
attentions=all_self_attentions,
|
482 |
+
cross_attentions=all_cross_attentions,
|
483 |
+
)
|
484 |
+
|
485 |
+
|
486 |
+
class BertPooler(nn.Module):
|
487 |
+
def __init__(self, config):
|
488 |
+
super().__init__()
|
489 |
+
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
490 |
+
self.activation = nn.Tanh()
|
491 |
+
|
492 |
+
def forward(self, hidden_states):
|
493 |
+
# We "pool" the model by simply taking the hidden state corresponding
|
494 |
+
# to the first token.
|
495 |
+
first_token_tensor = hidden_states[:, 0]
|
496 |
+
pooled_output = self.dense(first_token_tensor)
|
497 |
+
pooled_output = self.activation(pooled_output)
|
498 |
+
return pooled_output
|
499 |
+
|
500 |
+
|
501 |
+
class BertPredictionHeadTransform(nn.Module):
|
502 |
+
def __init__(self, config):
|
503 |
+
super().__init__()
|
504 |
+
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
505 |
+
if isinstance(config.hidden_act, str):
|
506 |
+
self.transform_act_fn = ACT2FN[config.hidden_act]
|
507 |
+
else:
|
508 |
+
self.transform_act_fn = config.hidden_act
|
509 |
+
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
510 |
+
|
511 |
+
def forward(self, hidden_states):
|
512 |
+
hidden_states = self.dense(hidden_states)
|
513 |
+
hidden_states = self.transform_act_fn(hidden_states)
|
514 |
+
hidden_states = self.LayerNorm(hidden_states)
|
515 |
+
return hidden_states
|
516 |
+
|
517 |
+
|
518 |
+
class BertLMPredictionHead(nn.Module):
|
519 |
+
def __init__(self, config):
|
520 |
+
super().__init__()
|
521 |
+
self.transform = BertPredictionHeadTransform(config)
|
522 |
+
|
523 |
+
# The output weights are the same as the input embeddings, but there is
|
524 |
+
# an output-only bias for each token.
|
525 |
+
self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
526 |
+
|
527 |
+
self.bias = nn.Parameter(torch.zeros(config.vocab_size))
|
528 |
+
|
529 |
+
# Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
|
530 |
+
self.decoder.bias = self.bias
|
531 |
+
|
532 |
+
def forward(self, hidden_states):
|
533 |
+
hidden_states = self.transform(hidden_states)
|
534 |
+
hidden_states = self.decoder(hidden_states)
|
535 |
+
return hidden_states
|
536 |
+
|
537 |
+
|
538 |
+
class BertOnlyMLMHead(nn.Module):
|
539 |
+
def __init__(self, config):
|
540 |
+
super().__init__()
|
541 |
+
self.predictions = BertLMPredictionHead(config)
|
542 |
+
|
543 |
+
def forward(self, sequence_output):
|
544 |
+
prediction_scores = self.predictions(sequence_output)
|
545 |
+
return prediction_scores
|
546 |
+
|
547 |
+
|
548 |
+
class BertPreTrainedModel(PreTrainedModel):
|
549 |
+
"""
|
550 |
+
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
|
551 |
+
models.
|
552 |
+
"""
|
553 |
+
|
554 |
+
config_class = BertConfig
|
555 |
+
base_model_prefix = "bert"
|
556 |
+
_keys_to_ignore_on_load_missing = [r"position_ids"]
|
557 |
+
|
558 |
+
def _init_weights(self, module):
|
559 |
+
""" Initialize the weights """
|
560 |
+
if isinstance(module, (nn.Linear, nn.Embedding)):
|
561 |
+
# Slightly different from the TF version which uses truncated_normal for initialization
|
562 |
+
# cf https://github.com/pytorch/pytorch/pull/5617
|
563 |
+
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
564 |
+
elif isinstance(module, nn.LayerNorm):
|
565 |
+
module.bias.data.zero_()
|
566 |
+
module.weight.data.fill_(1.0)
|
567 |
+
if isinstance(module, nn.Linear) and module.bias is not None:
|
568 |
+
module.bias.data.zero_()
|
569 |
+
|
570 |
+
|
571 |
+
class BertModel(BertPreTrainedModel):
|
572 |
+
"""
|
573 |
+
The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
|
574 |
+
cross-attention is added between the self-attention layers, following the architecture described in `Attention is
|
575 |
+
all you need <https://arxiv.org/abs/1706.03762>`__ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
|
576 |
+
Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
|
577 |
+
argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an
|
578 |
+
input to the forward pass.
|
579 |
+
"""
|
580 |
+
|
581 |
+
def __init__(self, config, add_pooling_layer=True):
|
582 |
+
super().__init__(config)
|
583 |
+
self.config = config
|
584 |
+
|
585 |
+
self.embeddings = BertEmbeddings(config)
|
586 |
+
|
587 |
+
self.encoder = BertEncoder(config)
|
588 |
+
|
589 |
+
self.pooler = BertPooler(config) if add_pooling_layer else None
|
590 |
+
|
591 |
+
self.init_weights()
|
592 |
+
|
593 |
+
|
594 |
+
def get_input_embeddings(self):
|
595 |
+
return self.embeddings.word_embeddings
|
596 |
+
|
597 |
+
def set_input_embeddings(self, value):
|
598 |
+
self.embeddings.word_embeddings = value
|
599 |
+
|
600 |
+
def _prune_heads(self, heads_to_prune):
|
601 |
+
"""
|
602 |
+
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
|
603 |
+
class PreTrainedModel
|
604 |
+
"""
|
605 |
+
for layer, heads in heads_to_prune.items():
|
606 |
+
self.encoder.layer[layer].attention.prune_heads(heads)
|
607 |
+
|
608 |
+
|
609 |
+
def get_extended_attention_mask(self, attention_mask: Tensor, input_shape: Tuple[int], device: device, is_decoder: bool) -> Tensor:
|
610 |
+
"""
|
611 |
+
Makes broadcastable attention and causal masks so that future and masked tokens are ignored.
|
612 |
+
Arguments:
|
613 |
+
attention_mask (:obj:`torch.Tensor`):
|
614 |
+
Mask with ones indicating tokens to attend to, zeros for tokens to ignore.
|
615 |
+
input_shape (:obj:`Tuple[int]`):
|
616 |
+
The shape of the input to the model.
|
617 |
+
device: (:obj:`torch.device`):
|
618 |
+
The device of the input to the model.
|
619 |
+
Returns:
|
620 |
+
:obj:`torch.Tensor` The extended attention mask, with a the same dtype as :obj:`attention_mask.dtype`.
|
621 |
+
"""
|
622 |
+
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
|
623 |
+
# ourselves in which case we just need to make it broadcastable to all heads.
|
624 |
+
if attention_mask.dim() == 3:
|
625 |
+
extended_attention_mask = attention_mask[:, None, :, :]
|
626 |
+
elif attention_mask.dim() == 2:
|
627 |
+
# Provided a padding mask of dimensions [batch_size, seq_length]
|
628 |
+
# - if the model is a decoder, apply a causal mask in addition to the padding mask
|
629 |
+
# - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]
|
630 |
+
if is_decoder:
|
631 |
+
batch_size, seq_length = input_shape
|
632 |
+
|
633 |
+
seq_ids = torch.arange(seq_length, device=device)
|
634 |
+
causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None]
|
635 |
+
# in case past_key_values are used we need to add a prefix ones mask to the causal mask
|
636 |
+
# causal and attention masks must have same type with pytorch version < 1.3
|
637 |
+
causal_mask = causal_mask.to(attention_mask.dtype)
|
638 |
+
|
639 |
+
if causal_mask.shape[1] < attention_mask.shape[1]:
|
640 |
+
prefix_seq_len = attention_mask.shape[1] - causal_mask.shape[1]
|
641 |
+
causal_mask = torch.cat(
|
642 |
+
[
|
643 |
+
torch.ones((batch_size, seq_length, prefix_seq_len), device=device, dtype=causal_mask.dtype),
|
644 |
+
causal_mask,
|
645 |
+
],
|
646 |
+
axis=-1,
|
647 |
+
)
|
648 |
+
|
649 |
+
extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :]
|
650 |
+
else:
|
651 |
+
extended_attention_mask = attention_mask[:, None, None, :]
|
652 |
+
else:
|
653 |
+
raise ValueError(
|
654 |
+
"Wrong shape for input_ids (shape {}) or attention_mask (shape {})".format(
|
655 |
+
input_shape, attention_mask.shape
|
656 |
+
)
|
657 |
+
)
|
658 |
+
|
659 |
+
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
|
660 |
+
# masked positions, this operation will create a tensor which is 0.0 for
|
661 |
+
# positions we want to attend and -10000.0 for masked positions.
|
662 |
+
# Since we are adding it to the raw scores before the softmax, this is
|
663 |
+
# effectively the same as removing these entirely.
|
664 |
+
extended_attention_mask = extended_attention_mask.to(dtype=self.dtype) # fp16 compatibility
|
665 |
+
extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
|
666 |
+
return extended_attention_mask
|
667 |
+
|
668 |
+
def forward(
|
669 |
+
self,
|
670 |
+
input_ids=None,
|
671 |
+
attention_mask=None,
|
672 |
+
position_ids=None,
|
673 |
+
head_mask=None,
|
674 |
+
inputs_embeds=None,
|
675 |
+
encoder_embeds=None,
|
676 |
+
encoder_hidden_states=None,
|
677 |
+
encoder_attention_mask=None,
|
678 |
+
past_key_values=None,
|
679 |
+
use_cache=None,
|
680 |
+
output_attentions=None,
|
681 |
+
output_hidden_states=None,
|
682 |
+
return_dict=None,
|
683 |
+
is_decoder=False,
|
684 |
+
mode='multimodal',
|
685 |
+
):
|
686 |
+
r"""
|
687 |
+
encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
|
688 |
+
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
|
689 |
+
the model is configured as a decoder.
|
690 |
+
encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
|
691 |
+
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
|
692 |
+
the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
|
693 |
+
- 1 for tokens that are **not masked**,
|
694 |
+
- 0 for tokens that are **masked**.
|
695 |
+
past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
|
696 |
+
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
|
697 |
+
If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
|
698 |
+
(those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
|
699 |
+
instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
|
700 |
+
use_cache (:obj:`bool`, `optional`):
|
701 |
+
If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
|
702 |
+
decoding (see :obj:`past_key_values`).
|
703 |
+
"""
|
704 |
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
705 |
+
output_hidden_states = (
|
706 |
+
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
707 |
+
)
|
708 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
709 |
+
|
710 |
+
if is_decoder:
|
711 |
+
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
712 |
+
else:
|
713 |
+
use_cache = False
|
714 |
+
|
715 |
+
if input_ids is not None and inputs_embeds is not None:
|
716 |
+
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
|
717 |
+
elif input_ids is not None:
|
718 |
+
input_shape = input_ids.size()
|
719 |
+
batch_size, seq_length = input_shape
|
720 |
+
device = input_ids.device
|
721 |
+
elif inputs_embeds is not None:
|
722 |
+
input_shape = inputs_embeds.size()[:-1]
|
723 |
+
batch_size, seq_length = input_shape
|
724 |
+
device = inputs_embeds.device
|
725 |
+
elif encoder_embeds is not None:
|
726 |
+
input_shape = encoder_embeds.size()[:-1]
|
727 |
+
batch_size, seq_length = input_shape
|
728 |
+
device = encoder_embeds.device
|
729 |
+
else:
|
730 |
+
raise ValueError("You have to specify either input_ids or inputs_embeds or encoder_embeds")
|
731 |
+
|
732 |
+
# past_key_values_length
|
733 |
+
past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
|
734 |
+
|
735 |
+
if attention_mask is None:
|
736 |
+
attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)
|
737 |
+
|
738 |
+
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
|
739 |
+
# ourselves in which case we just need to make it broadcastable to all heads.
|
740 |
+
extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape,
|
741 |
+
device, is_decoder)
|
742 |
+
|
743 |
+
# If a 2D or 3D attention mask is provided for the cross-attention
|
744 |
+
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
|
745 |
+
if encoder_hidden_states is not None:
|
746 |
+
if type(encoder_hidden_states) == list:
|
747 |
+
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[0].size()
|
748 |
+
else:
|
749 |
+
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
|
750 |
+
encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
|
751 |
+
|
752 |
+
if type(encoder_attention_mask) == list:
|
753 |
+
encoder_extended_attention_mask = [self.invert_attention_mask(mask) for mask in encoder_attention_mask]
|
754 |
+
elif encoder_attention_mask is None:
|
755 |
+
encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
|
756 |
+
encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
|
757 |
+
else:
|
758 |
+
encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
|
759 |
+
else:
|
760 |
+
encoder_extended_attention_mask = None
|
761 |
+
|
762 |
+
# Prepare head mask if needed
|
763 |
+
# 1.0 in head_mask indicate we keep the head
|
764 |
+
# attention_probs has shape bsz x n_heads x N x N
|
765 |
+
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
|
766 |
+
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
|
767 |
+
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
|
768 |
+
|
769 |
+
if encoder_embeds is None:
|
770 |
+
embedding_output = self.embeddings(
|
771 |
+
input_ids=input_ids,
|
772 |
+
position_ids=position_ids,
|
773 |
+
inputs_embeds=inputs_embeds,
|
774 |
+
past_key_values_length=past_key_values_length,
|
775 |
+
)
|
776 |
+
else:
|
777 |
+
embedding_output = encoder_embeds
|
778 |
+
|
779 |
+
encoder_outputs = self.encoder(
|
780 |
+
embedding_output,
|
781 |
+
attention_mask=extended_attention_mask,
|
782 |
+
head_mask=head_mask,
|
783 |
+
encoder_hidden_states=encoder_hidden_states,
|
784 |
+
encoder_attention_mask=encoder_extended_attention_mask,
|
785 |
+
past_key_values=past_key_values,
|
786 |
+
use_cache=use_cache,
|
787 |
+
output_attentions=output_attentions,
|
788 |
+
output_hidden_states=output_hidden_states,
|
789 |
+
return_dict=return_dict,
|
790 |
+
mode=mode,
|
791 |
+
)
|
792 |
+
sequence_output = encoder_outputs[0]
|
793 |
+
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
|
794 |
+
|
795 |
+
if not return_dict:
|
796 |
+
return (sequence_output, pooled_output) + encoder_outputs[1:]
|
797 |
+
|
798 |
+
return BaseModelOutputWithPoolingAndCrossAttentions(
|
799 |
+
last_hidden_state=sequence_output,
|
800 |
+
pooler_output=pooled_output,
|
801 |
+
past_key_values=encoder_outputs.past_key_values,
|
802 |
+
hidden_states=encoder_outputs.hidden_states,
|
803 |
+
attentions=encoder_outputs.attentions,
|
804 |
+
cross_attentions=encoder_outputs.cross_attentions,
|
805 |
+
)
|
806 |
+
|
807 |
+
|
808 |
+
|
809 |
+
class BertLMHeadModel(BertPreTrainedModel):
|
810 |
+
|
811 |
+
_keys_to_ignore_on_load_unexpected = [r"pooler"]
|
812 |
+
_keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
|
813 |
+
|
814 |
+
def __init__(self, config):
|
815 |
+
super().__init__(config)
|
816 |
+
|
817 |
+
self.bert = BertModel(config, add_pooling_layer=False)
|
818 |
+
self.cls = BertOnlyMLMHead(config)
|
819 |
+
|
820 |
+
self.init_weights()
|
821 |
+
|
822 |
+
def get_output_embeddings(self):
|
823 |
+
return self.cls.predictions.decoder
|
824 |
+
|
825 |
+
def set_output_embeddings(self, new_embeddings):
|
826 |
+
self.cls.predictions.decoder = new_embeddings
|
827 |
+
|
828 |
+
def forward(
|
829 |
+
self,
|
830 |
+
input_ids=None,
|
831 |
+
attention_mask=None,
|
832 |
+
position_ids=None,
|
833 |
+
head_mask=None,
|
834 |
+
inputs_embeds=None,
|
835 |
+
encoder_hidden_states=None,
|
836 |
+
encoder_attention_mask=None,
|
837 |
+
labels=None,
|
838 |
+
past_key_values=None,
|
839 |
+
use_cache=None,
|
840 |
+
output_attentions=None,
|
841 |
+
output_hidden_states=None,
|
842 |
+
return_dict=None,
|
843 |
+
return_logits=False,
|
844 |
+
is_decoder=True,
|
845 |
+
reduction='mean',
|
846 |
+
mode='multimodal',
|
847 |
+
):
|
848 |
+
r"""
|
849 |
+
encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
|
850 |
+
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
|
851 |
+
the model is configured as a decoder.
|
852 |
+
encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
|
853 |
+
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
|
854 |
+
the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
|
855 |
+
- 1 for tokens that are **not masked**,
|
856 |
+
- 0 for tokens that are **masked**.
|
857 |
+
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
|
858 |
+
Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
|
859 |
+
``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are
|
860 |
+
ignored (masked), the loss is only computed for the tokens with labels n ``[0, ..., config.vocab_size]``
|
861 |
+
past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
|
862 |
+
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
|
863 |
+
If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
|
864 |
+
(those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
|
865 |
+
instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
|
866 |
+
use_cache (:obj:`bool`, `optional`):
|
867 |
+
If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
|
868 |
+
decoding (see :obj:`past_key_values`).
|
869 |
+
Returns:
|
870 |
+
Example::
|
871 |
+
>>> from transformers import BertTokenizer, BertLMHeadModel, BertConfig
|
872 |
+
>>> import torch
|
873 |
+
>>> tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
|
874 |
+
>>> config = BertConfig.from_pretrained("bert-base-cased")
|
875 |
+
>>> model = BertLMHeadModel.from_pretrained('bert-base-cased', config=config)
|
876 |
+
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
|
877 |
+
>>> outputs = model(**inputs)
|
878 |
+
>>> prediction_logits = outputs.logits
|
879 |
+
"""
|
880 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
881 |
+
if labels is not None:
|
882 |
+
use_cache = False
|
883 |
+
|
884 |
+
outputs = self.bert(
|
885 |
+
input_ids,
|
886 |
+
attention_mask=attention_mask,
|
887 |
+
position_ids=position_ids,
|
888 |
+
head_mask=head_mask,
|
889 |
+
inputs_embeds=inputs_embeds,
|
890 |
+
encoder_hidden_states=encoder_hidden_states,
|
891 |
+
encoder_attention_mask=encoder_attention_mask,
|
892 |
+
past_key_values=past_key_values,
|
893 |
+
use_cache=use_cache,
|
894 |
+
output_attentions=output_attentions,
|
895 |
+
output_hidden_states=output_hidden_states,
|
896 |
+
return_dict=return_dict,
|
897 |
+
is_decoder=is_decoder,
|
898 |
+
mode=mode,
|
899 |
+
)
|
900 |
+
|
901 |
+
sequence_output = outputs[0]
|
902 |
+
prediction_scores = self.cls(sequence_output)
|
903 |
+
|
904 |
+
if return_logits:
|
905 |
+
return prediction_scores[:, :-1, :].contiguous()
|
906 |
+
|
907 |
+
lm_loss = None
|
908 |
+
if labels is not None:
|
909 |
+
# we are doing next-token prediction; shift prediction scores and input ids by one
|
910 |
+
shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous()
|
911 |
+
labels = labels[:, 1:].contiguous()
|
912 |
+
loss_fct = CrossEntropyLoss(reduction=reduction, label_smoothing=0.1)
|
913 |
+
lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
|
914 |
+
if reduction=='none':
|
915 |
+
lm_loss = lm_loss.view(prediction_scores.size(0),-1).sum(1)
|
916 |
+
|
917 |
+
if not return_dict:
|
918 |
+
output = (prediction_scores,) + outputs[2:]
|
919 |
+
return ((lm_loss,) + output) if lm_loss is not None else output
|
920 |
+
|
921 |
+
return CausalLMOutputWithCrossAttentions(
|
922 |
+
loss=lm_loss,
|
923 |
+
logits=prediction_scores,
|
924 |
+
past_key_values=outputs.past_key_values,
|
925 |
+
hidden_states=outputs.hidden_states,
|
926 |
+
attentions=outputs.attentions,
|
927 |
+
cross_attentions=outputs.cross_attentions,
|
928 |
+
)
|
929 |
+
|
930 |
+
def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, **model_kwargs):
|
931 |
+
input_shape = input_ids.shape
|
932 |
+
# if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
|
933 |
+
if attention_mask is None:
|
934 |
+
attention_mask = input_ids.new_ones(input_shape)
|
935 |
+
|
936 |
+
# cut decoder_input_ids if past is used
|
937 |
+
if past is not None:
|
938 |
+
input_ids = input_ids[:, -1:]
|
939 |
+
|
940 |
+
return {
|
941 |
+
"input_ids": input_ids,
|
942 |
+
"attention_mask": attention_mask,
|
943 |
+
"past_key_values": past,
|
944 |
+
"encoder_hidden_states": model_kwargs.get("encoder_hidden_states", None),
|
945 |
+
"encoder_attention_mask": model_kwargs.get("encoder_attention_mask", None),
|
946 |
+
"is_decoder": True,
|
947 |
+
}
|
948 |
+
|
949 |
+
def _reorder_cache(self, past, beam_idx):
|
950 |
+
reordered_past = ()
|
951 |
+
for layer_past in past:
|
952 |
+
reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
|
953 |
+
return reordered_past
|
models/vit.py
ADDED
@@ -0,0 +1,305 @@
|
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|
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|
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|
|
|
|
|
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|
|
|
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|
|
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|
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|
|
|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
'''
|
2 |
+
* Copyright (c) 2022, salesforce.com, inc.
|
3 |
+
* All rights reserved.
|
4 |
+
* SPDX-License-Identifier: BSD-3-Clause
|
5 |
+
* For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
|
6 |
+
* By Junnan Li
|
7 |
+
* Based on timm code base
|
8 |
+
* https://github.com/rwightman/pytorch-image-models/tree/master/timm
|
9 |
+
'''
|
10 |
+
|
11 |
+
import torch
|
12 |
+
import torch.nn as nn
|
13 |
+
import torch.nn.functional as F
|
14 |
+
from functools import partial
|
15 |
+
|
16 |
+
from timm.models.vision_transformer import _cfg, PatchEmbed
|
17 |
+
from timm.models.registry import register_model
|
18 |
+
from timm.models.layers import trunc_normal_, DropPath
|
19 |
+
from timm.models.helpers import named_apply, adapt_input_conv
|
20 |
+
|
21 |
+
from fairscale.nn.checkpoint.checkpoint_activations import checkpoint_wrapper
|
22 |
+
|
23 |
+
class Mlp(nn.Module):
|
24 |
+
""" MLP as used in Vision Transformer, MLP-Mixer and related networks
|
25 |
+
"""
|
26 |
+
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
|
27 |
+
super().__init__()
|
28 |
+
out_features = out_features or in_features
|
29 |
+
hidden_features = hidden_features or in_features
|
30 |
+
self.fc1 = nn.Linear(in_features, hidden_features)
|
31 |
+
self.act = act_layer()
|
32 |
+
self.fc2 = nn.Linear(hidden_features, out_features)
|
33 |
+
self.drop = nn.Dropout(drop)
|
34 |
+
|
35 |
+
def forward(self, x):
|
36 |
+
x = self.fc1(x)
|
37 |
+
x = self.act(x)
|
38 |
+
x = self.drop(x)
|
39 |
+
x = self.fc2(x)
|
40 |
+
x = self.drop(x)
|
41 |
+
return x
|
42 |
+
|
43 |
+
|
44 |
+
class Attention(nn.Module):
|
45 |
+
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
|
46 |
+
super().__init__()
|
47 |
+
self.num_heads = num_heads
|
48 |
+
head_dim = dim // num_heads
|
49 |
+
# NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
|
50 |
+
self.scale = qk_scale or head_dim ** -0.5
|
51 |
+
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
|
52 |
+
self.attn_drop = nn.Dropout(attn_drop)
|
53 |
+
self.proj = nn.Linear(dim, dim)
|
54 |
+
self.proj_drop = nn.Dropout(proj_drop)
|
55 |
+
self.attn_gradients = None
|
56 |
+
self.attention_map = None
|
57 |
+
|
58 |
+
def save_attn_gradients(self, attn_gradients):
|
59 |
+
self.attn_gradients = attn_gradients
|
60 |
+
|
61 |
+
def get_attn_gradients(self):
|
62 |
+
return self.attn_gradients
|
63 |
+
|
64 |
+
def save_attention_map(self, attention_map):
|
65 |
+
self.attention_map = attention_map
|
66 |
+
|
67 |
+
def get_attention_map(self):
|
68 |
+
return self.attention_map
|
69 |
+
|
70 |
+
def forward(self, x, register_hook=False):
|
71 |
+
B, N, C = x.shape
|
72 |
+
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
|
73 |
+
q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
|
74 |
+
|
75 |
+
attn = (q @ k.transpose(-2, -1)) * self.scale
|
76 |
+
attn = attn.softmax(dim=-1)
|
77 |
+
attn = self.attn_drop(attn)
|
78 |
+
|
79 |
+
if register_hook:
|
80 |
+
self.save_attention_map(attn)
|
81 |
+
attn.register_hook(self.save_attn_gradients)
|
82 |
+
|
83 |
+
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
|
84 |
+
x = self.proj(x)
|
85 |
+
x = self.proj_drop(x)
|
86 |
+
return x
|
87 |
+
|
88 |
+
|
89 |
+
class Block(nn.Module):
|
90 |
+
|
91 |
+
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
|
92 |
+
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, use_grad_checkpointing=False):
|
93 |
+
super().__init__()
|
94 |
+
self.norm1 = norm_layer(dim)
|
95 |
+
self.attn = Attention(
|
96 |
+
dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
|
97 |
+
# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
|
98 |
+
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
|
99 |
+
self.norm2 = norm_layer(dim)
|
100 |
+
mlp_hidden_dim = int(dim * mlp_ratio)
|
101 |
+
self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
|
102 |
+
|
103 |
+
if use_grad_checkpointing:
|
104 |
+
self.attn = checkpoint_wrapper(self.attn)
|
105 |
+
self.mlp = checkpoint_wrapper(self.mlp)
|
106 |
+
|
107 |
+
def forward(self, x, register_hook=False):
|
108 |
+
x = x + self.drop_path(self.attn(self.norm1(x), register_hook=register_hook))
|
109 |
+
x = x + self.drop_path(self.mlp(self.norm2(x)))
|
110 |
+
return x
|
111 |
+
|
112 |
+
|
113 |
+
class VisionTransformer(nn.Module):
|
114 |
+
""" Vision Transformer
|
115 |
+
A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale` -
|
116 |
+
https://arxiv.org/abs/2010.11929
|
117 |
+
"""
|
118 |
+
def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,
|
119 |
+
num_heads=12, mlp_ratio=4., qkv_bias=True, qk_scale=None, representation_size=None,
|
120 |
+
drop_rate=0., attn_drop_rate=0., drop_path_rate=0., norm_layer=None,
|
121 |
+
use_grad_checkpointing=False, ckpt_layer=0):
|
122 |
+
"""
|
123 |
+
Args:
|
124 |
+
img_size (int, tuple): input image size
|
125 |
+
patch_size (int, tuple): patch size
|
126 |
+
in_chans (int): number of input channels
|
127 |
+
num_classes (int): number of classes for classification head
|
128 |
+
embed_dim (int): embedding dimension
|
129 |
+
depth (int): depth of transformer
|
130 |
+
num_heads (int): number of attention heads
|
131 |
+
mlp_ratio (int): ratio of mlp hidden dim to embedding dim
|
132 |
+
qkv_bias (bool): enable bias for qkv if True
|
133 |
+
qk_scale (float): override default qk scale of head_dim ** -0.5 if set
|
134 |
+
representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set
|
135 |
+
drop_rate (float): dropout rate
|
136 |
+
attn_drop_rate (float): attention dropout rate
|
137 |
+
drop_path_rate (float): stochastic depth rate
|
138 |
+
norm_layer: (nn.Module): normalization layer
|
139 |
+
"""
|
140 |
+
super().__init__()
|
141 |
+
self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models
|
142 |
+
norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
|
143 |
+
|
144 |
+
self.patch_embed = PatchEmbed(
|
145 |
+
img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
|
146 |
+
|
147 |
+
num_patches = self.patch_embed.num_patches
|
148 |
+
|
149 |
+
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
|
150 |
+
self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
|
151 |
+
self.pos_drop = nn.Dropout(p=drop_rate)
|
152 |
+
|
153 |
+
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
|
154 |
+
self.blocks = nn.ModuleList([
|
155 |
+
Block(
|
156 |
+
dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
|
157 |
+
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer,
|
158 |
+
use_grad_checkpointing=(use_grad_checkpointing and i>=depth-ckpt_layer)
|
159 |
+
)
|
160 |
+
for i in range(depth)])
|
161 |
+
self.norm = norm_layer(embed_dim)
|
162 |
+
|
163 |
+
trunc_normal_(self.pos_embed, std=.02)
|
164 |
+
trunc_normal_(self.cls_token, std=.02)
|
165 |
+
self.apply(self._init_weights)
|
166 |
+
|
167 |
+
def _init_weights(self, m):
|
168 |
+
if isinstance(m, nn.Linear):
|
169 |
+
trunc_normal_(m.weight, std=.02)
|
170 |
+
if isinstance(m, nn.Linear) and m.bias is not None:
|
171 |
+
nn.init.constant_(m.bias, 0)
|
172 |
+
elif isinstance(m, nn.LayerNorm):
|
173 |
+
nn.init.constant_(m.bias, 0)
|
174 |
+
nn.init.constant_(m.weight, 1.0)
|
175 |
+
|
176 |
+
@torch.jit.ignore
|
177 |
+
def no_weight_decay(self):
|
178 |
+
return {'pos_embed', 'cls_token'}
|
179 |
+
|
180 |
+
def forward(self, x, register_blk=-1):
|
181 |
+
B = x.shape[0]
|
182 |
+
x = self.patch_embed(x)
|
183 |
+
|
184 |
+
cls_tokens = self.cls_token.expand(B, -1, -1) # stole cls_tokens impl from Phil Wang, thanks
|
185 |
+
x = torch.cat((cls_tokens, x), dim=1)
|
186 |
+
|
187 |
+
x = x + self.pos_embed[:,:x.size(1),:]
|
188 |
+
x = self.pos_drop(x)
|
189 |
+
|
190 |
+
for i,blk in enumerate(self.blocks):
|
191 |
+
x = blk(x, register_blk==i)
|
192 |
+
x = self.norm(x)
|
193 |
+
|
194 |
+
return x
|
195 |
+
|
196 |
+
@torch.jit.ignore()
|
197 |
+
def load_pretrained(self, checkpoint_path, prefix=''):
|
198 |
+
_load_weights(self, checkpoint_path, prefix)
|
199 |
+
|
200 |
+
|
201 |
+
@torch.no_grad()
|
202 |
+
def _load_weights(model: VisionTransformer, checkpoint_path: str, prefix: str = ''):
|
203 |
+
""" Load weights from .npz checkpoints for official Google Brain Flax implementation
|
204 |
+
"""
|
205 |
+
import numpy as np
|
206 |
+
|
207 |
+
def _n2p(w, t=True):
|
208 |
+
if w.ndim == 4 and w.shape[0] == w.shape[1] == w.shape[2] == 1:
|
209 |
+
w = w.flatten()
|
210 |
+
if t:
|
211 |
+
if w.ndim == 4:
|
212 |
+
w = w.transpose([3, 2, 0, 1])
|
213 |
+
elif w.ndim == 3:
|
214 |
+
w = w.transpose([2, 0, 1])
|
215 |
+
elif w.ndim == 2:
|
216 |
+
w = w.transpose([1, 0])
|
217 |
+
return torch.from_numpy(w)
|
218 |
+
|
219 |
+
w = np.load(checkpoint_path)
|
220 |
+
if not prefix and 'opt/target/embedding/kernel' in w:
|
221 |
+
prefix = 'opt/target/'
|
222 |
+
|
223 |
+
if hasattr(model.patch_embed, 'backbone'):
|
224 |
+
# hybrid
|
225 |
+
backbone = model.patch_embed.backbone
|
226 |
+
stem_only = not hasattr(backbone, 'stem')
|
227 |
+
stem = backbone if stem_only else backbone.stem
|
228 |
+
stem.conv.weight.copy_(adapt_input_conv(stem.conv.weight.shape[1], _n2p(w[f'{prefix}conv_root/kernel'])))
|
229 |
+
stem.norm.weight.copy_(_n2p(w[f'{prefix}gn_root/scale']))
|
230 |
+
stem.norm.bias.copy_(_n2p(w[f'{prefix}gn_root/bias']))
|
231 |
+
if not stem_only:
|
232 |
+
for i, stage in enumerate(backbone.stages):
|
233 |
+
for j, block in enumerate(stage.blocks):
|
234 |
+
bp = f'{prefix}block{i + 1}/unit{j + 1}/'
|
235 |
+
for r in range(3):
|
236 |
+
getattr(block, f'conv{r + 1}').weight.copy_(_n2p(w[f'{bp}conv{r + 1}/kernel']))
|
237 |
+
getattr(block, f'norm{r + 1}').weight.copy_(_n2p(w[f'{bp}gn{r + 1}/scale']))
|
238 |
+
getattr(block, f'norm{r + 1}').bias.copy_(_n2p(w[f'{bp}gn{r + 1}/bias']))
|
239 |
+
if block.downsample is not None:
|
240 |
+
block.downsample.conv.weight.copy_(_n2p(w[f'{bp}conv_proj/kernel']))
|
241 |
+
block.downsample.norm.weight.copy_(_n2p(w[f'{bp}gn_proj/scale']))
|
242 |
+
block.downsample.norm.bias.copy_(_n2p(w[f'{bp}gn_proj/bias']))
|
243 |
+
embed_conv_w = _n2p(w[f'{prefix}embedding/kernel'])
|
244 |
+
else:
|
245 |
+
embed_conv_w = adapt_input_conv(
|
246 |
+
model.patch_embed.proj.weight.shape[1], _n2p(w[f'{prefix}embedding/kernel']))
|
247 |
+
model.patch_embed.proj.weight.copy_(embed_conv_w)
|
248 |
+
model.patch_embed.proj.bias.copy_(_n2p(w[f'{prefix}embedding/bias']))
|
249 |
+
model.cls_token.copy_(_n2p(w[f'{prefix}cls'], t=False))
|
250 |
+
pos_embed_w = _n2p(w[f'{prefix}Transformer/posembed_input/pos_embedding'], t=False)
|
251 |
+
if pos_embed_w.shape != model.pos_embed.shape:
|
252 |
+
pos_embed_w = resize_pos_embed( # resize pos embedding when different size from pretrained weights
|
253 |
+
pos_embed_w, model.pos_embed, getattr(model, 'num_tokens', 1), model.patch_embed.grid_size)
|
254 |
+
model.pos_embed.copy_(pos_embed_w)
|
255 |
+
model.norm.weight.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/scale']))
|
256 |
+
model.norm.bias.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/bias']))
|
257 |
+
# if isinstance(model.head, nn.Linear) and model.head.bias.shape[0] == w[f'{prefix}head/bias'].shape[-1]:
|
258 |
+
# model.head.weight.copy_(_n2p(w[f'{prefix}head/kernel']))
|
259 |
+
# model.head.bias.copy_(_n2p(w[f'{prefix}head/bias']))
|
260 |
+
# if isinstance(getattr(model.pre_logits, 'fc', None), nn.Linear) and f'{prefix}pre_logits/bias' in w:
|
261 |
+
# model.pre_logits.fc.weight.copy_(_n2p(w[f'{prefix}pre_logits/kernel']))
|
262 |
+
# model.pre_logits.fc.bias.copy_(_n2p(w[f'{prefix}pre_logits/bias']))
|
263 |
+
for i, block in enumerate(model.blocks.children()):
|
264 |
+
block_prefix = f'{prefix}Transformer/encoderblock_{i}/'
|
265 |
+
mha_prefix = block_prefix + 'MultiHeadDotProductAttention_1/'
|
266 |
+
block.norm1.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/scale']))
|
267 |
+
block.norm1.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/bias']))
|
268 |
+
block.attn.qkv.weight.copy_(torch.cat([
|
269 |
+
_n2p(w[f'{mha_prefix}{n}/kernel'], t=False).flatten(1).T for n in ('query', 'key', 'value')]))
|
270 |
+
block.attn.qkv.bias.copy_(torch.cat([
|
271 |
+
_n2p(w[f'{mha_prefix}{n}/bias'], t=False).reshape(-1) for n in ('query', 'key', 'value')]))
|
272 |
+
block.attn.proj.weight.copy_(_n2p(w[f'{mha_prefix}out/kernel']).flatten(1))
|
273 |
+
block.attn.proj.bias.copy_(_n2p(w[f'{mha_prefix}out/bias']))
|
274 |
+
for r in range(2):
|
275 |
+
getattr(block.mlp, f'fc{r + 1}').weight.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/kernel']))
|
276 |
+
getattr(block.mlp, f'fc{r + 1}').bias.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/bias']))
|
277 |
+
block.norm2.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/scale']))
|
278 |
+
block.norm2.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/bias']))
|
279 |
+
|
280 |
+
|
281 |
+
def interpolate_pos_embed(pos_embed_checkpoint, visual_encoder):
|
282 |
+
# interpolate position embedding
|
283 |
+
embedding_size = pos_embed_checkpoint.shape[-1]
|
284 |
+
num_patches = visual_encoder.patch_embed.num_patches
|
285 |
+
num_extra_tokens = visual_encoder.pos_embed.shape[-2] - num_patches
|
286 |
+
# height (== width) for the checkpoint position embedding
|
287 |
+
orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5)
|
288 |
+
# height (== width) for the new position embedding
|
289 |
+
new_size = int(num_patches ** 0.5)
|
290 |
+
|
291 |
+
if orig_size!=new_size:
|
292 |
+
# class_token and dist_token are kept unchanged
|
293 |
+
extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]
|
294 |
+
# only the position tokens are interpolated
|
295 |
+
pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]
|
296 |
+
pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2)
|
297 |
+
pos_tokens = torch.nn.functional.interpolate(
|
298 |
+
pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False)
|
299 |
+
pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)
|
300 |
+
new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
|
301 |
+
print('reshape position embedding from %d to %d'%(orig_size ** 2,new_size ** 2))
|
302 |
+
|
303 |
+
return new_pos_embed
|
304 |
+
else:
|
305 |
+
return pos_embed_checkpoint
|
pipeline.py
ADDED
@@ -0,0 +1,55 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from typing import Dict, List, Any
|
2 |
+
from PIL import Image
|
3 |
+
import requests
|
4 |
+
import torch
|
5 |
+
import base64
|
6 |
+
import os
|
7 |
+
from io import BytesIO
|
8 |
+
from models.blip_feature_extractor import blip_feature_extractor
|
9 |
+
from torchvision import transforms
|
10 |
+
from torchvision.transforms.functional import InterpolationMode
|
11 |
+
|
12 |
+
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
|
13 |
+
|
14 |
+
class PreTrainedPipeline():
|
15 |
+
def __init__(self, path=""):
|
16 |
+
# load the optimized model
|
17 |
+
self.model_path = os.path.join(path,'model_large_retrieval_coco.pth')
|
18 |
+
self.model = blip_feature_extractor(
|
19 |
+
pretrained=self.model_path,
|
20 |
+
image_size=384,
|
21 |
+
vit='large',
|
22 |
+
med_config=os.path.join(path, 'configs/med_config.json')
|
23 |
+
)
|
24 |
+
self.model.eval()
|
25 |
+
self.model = self.model.to(device)
|
26 |
+
|
27 |
+
image_size = 384
|
28 |
+
self.transform = transforms.Compose([
|
29 |
+
transforms.Resize((image_size,image_size),interpolation=InterpolationMode.BICUBIC),
|
30 |
+
transforms.ToTensor(),
|
31 |
+
transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
|
32 |
+
])
|
33 |
+
|
34 |
+
|
35 |
+
|
36 |
+
def __call__(self, data: Any) -> Dict[str, List[float]]:
|
37 |
+
"""
|
38 |
+
Args:
|
39 |
+
data (:obj:):
|
40 |
+
includes the input data and the parameters for the inference.
|
41 |
+
Return:
|
42 |
+
A :obj:`dict`:. The object returned should be a dict like {"feature_vector": [0.6331314444541931,0.8802216053009033,...,-0.7866355180740356,]} containing :
|
43 |
+
- "feature_vector": A list of floats corresponding to the image embedding.
|
44 |
+
"""
|
45 |
+
inputs = data.pop("inputs", data)
|
46 |
+
parameters = data.pop("parameters", {"mode": "image"})
|
47 |
+
|
48 |
+
# decode base64 image to PIL
|
49 |
+
image = Image.open(BytesIO(base64.b64decode(inputs['image'])))
|
50 |
+
image = self.transform(image).unsqueeze(0).to(device)
|
51 |
+
text=""
|
52 |
+
with torch.no_grad():
|
53 |
+
feature_vector = self.model(image, text, mode=parameters["mode"])[0,0].tolist()
|
54 |
+
# postprocess the prediction
|
55 |
+
return {"feature_vector": feature_vector}
|