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''' |
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* Adapted from BLIP (https://github.com/salesforce/BLIP) |
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''' |
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import transformers |
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transformers.logging.set_verbosity_error() |
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from torch import nn |
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import os |
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from .med import BertConfig, BertModel |
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from .blip import create_vit, init_tokenizer |
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class BLIP_Pretrain(nn.Module): |
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def __init__(self, |
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med_config = "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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embed_dim = 256, |
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queue_size = 57600, |
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momentum = 0.995, |
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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, 0) |
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self.tokenizer = init_tokenizer() |
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encoder_config = BertConfig.from_json_file(med_config) |
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encoder_config.encoder_width = vision_width |
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self.text_encoder = BertModel(config=encoder_config, add_pooling_layer=False) |
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text_width = self.text_encoder.config.hidden_size |
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self.vision_proj = nn.Linear(vision_width, embed_dim) |
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self.text_proj = nn.Linear(text_width, embed_dim) |
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