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```markdown |
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This folder contains the pre-trained weights for the **PenCL** model (Stage 1 of BioM3). The PenCL model aligns protein sequences and text descriptions to compute joint latent embeddings. |
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--- |
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## **Downloading Pre-trained Weights** |
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To download the **PenCL epoch 20 pre-trained weights** as a `.bin` file from Google Drive, use the following command: |
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```bash |
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pip install gdown |
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gdown --id 1Lup7Xqwa1NjJpoM2uvvBAdghoM-fecEj -O BioM3_PenCL_epoch20.bin |
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``` |
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--- |
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## **Usage** |
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Once available, the pre-trained weights can be loaded as follows: |
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```python |
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import json |
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import torch |
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from argparse import Namespace |
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import Stage1_source.model as mod |
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# Step 1: Load JSON Configuration |
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def load_json_config(json_path): |
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""" |
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Load a JSON configuration file and return it as a dictionary. |
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""" |
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with open(json_path, "r") as f: |
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config = json.load(f) |
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return config |
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# Step 2: Convert JSON Dictionary to Namespace |
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def convert_to_namespace(config_dict): |
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""" |
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Recursively convert a dictionary to an argparse Namespace. |
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""" |
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for key, value in config_dict.items(): |
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if isinstance(value, dict): |
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config_dict[key] = convert_to_namespace(value) |
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return Namespace(**config_dict) |
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if __name__ == '__main__': |
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# Path to configuration and weights |
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config_path = "stage1_config.json" |
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model_weights_path = "weights/PenCL/BioM3_PenCL_epoch20.bin" |
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# Load Configuration |
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print("Loading configuration...") |
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config_dict = load_json_config(config_path) |
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config_args = convert_to_namespace(config_dict) |
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# Load Model |
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print("Loading pre-trained model weights...") |
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model = mod.pfam_PEN_CL(args=config_args) # Initialize the model with arguments |
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model.load_state_dict(torch.load(model_weights_path, map_location="cpu")) |
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model.eval() |
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print("Model loaded successfully with weights!") |
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``` |
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