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load_backward_model.py
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162 lines (141 loc) · 5.93 KB
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import os
import torch
from Mol2Spec_Unified.model import BERT, BERTConfig
from molecule_pretrain.model import GPT as MolGPT, GPTConfig as MolConfig
from spectra_prediction_token.model import GPT as HNMRSpectraGPT, GPTConfig as HNMRSpectraConfig
from spectra_prediction_token.model_cnmr import GPT as CNMRSpectraGPT, GPTConfig as CNMRSpectraConfig
from spectra_prediction_token.model_hsqc import GPT as HSQCSpectraGPT, GPTConfig as HSQCSpectraConfig
# 加载模型的函数（复用训练脚本中的函数）
def load_HNMRSpectraGPT(config: dict, relative_dir: str):
# 与训练脚本中的 load_HNMRSpectraGPT 一致
if relative_dir == '':
out_dir = config['out_dir']
else:
out_dir = os.path.join(relative_dir, config['out_dir'])
os.makedirs(out_dir, exist_ok=True)
ckpt_path = os.path.join(out_dir, 'ckpt.pt')
device = config['device']
n_layer = config['n_layer']
n_head = config['n_head']
n_embd = config['n_embd']
dropout = config['dropout']
bias = config['bias']
block_size = config['block_size']
prompt_pe = config.get('prompt_pe', True)
start_generate_token = config.get('start_generate_token', False)
gpt_config = HNMRSpectraConfig(
n_layer=n_layer, n_head=n_head, n_embd=n_embd,
dropout=dropout, bias=bias, prompt_pe=prompt_pe,
block_size=block_size, start_generate_token=start_generate_token
)
model = HNMRSpectraGPT(gpt_config)
checkpoint = torch.load(ckpt_path, map_location='cpu')
model.load_state_dict(checkpoint['model'], strict=False)
model_args = checkpoint['model_args']
return model, model_args
def load_CNMRSpectraGPT(config: dict, relative_dir: str):
# 与训练脚本中的 load_CNMRSpectraGPT 一致
if relative_dir == '':
out_dir = config['out_dir']
else:
out_dir = os.path.join(relative_dir, config['out_dir'])
os.makedirs(out_dir, exist_ok=True)
ckpt_path = os.path.join(out_dir, 'ckpt.pt')
device = config['device']
n_layer = config['n_layer']
n_head = config['n_head']
n_embd = config['n_embd']
dropout = config['dropout']
bias = config['bias']
block_size = config['block_size']
prompt_pe = config.get('prompt_pe',False)
start_generate_token = config.get('start_generate_token', True)
use_intensity = config.get('use_intensity', False)
print('if use_intensity', use_intensity)
gpt_config = CNMRSpectraConfig(
n_layer=n_layer, n_head=n_head, n_embd=n_embd,
dropout=dropout, bias=bias, prompt_pe=prompt_pe,
block_size=block_size, start_generate_token=start_generate_token,
use_intensity=use_intensity
)
model = CNMRSpectraGPT(gpt_config)
checkpoint = torch.load(ckpt_path, map_location='cpu')
model.load_state_dict(checkpoint['model'], strict=False)
model_args = checkpoint['model_args']
return model, model_args
def load_HSQCSpectraGPT(config: dict, relative_dir: str):
# 与训练脚本中的 load_HSQCSpectraGPT 一致
if relative_dir == '':
out_dir = config['out_dir']
else:
out_dir = os.path.join(relative_dir, config['out_dir'])
os.makedirs(out_dir, exist_ok=True)
ckpt_path = os.path.join(out_dir, 'ckpt.pt')
device = config['device']
n_layer = config['n_layer']
n_head = config['n_head']
n_embd = config['n_embd']
dropout = config['dropout']
bias = config['bias']
block_size = config['block_size']
prompt_pe = config.get('prompt_pe', True)
start_generate_token = config.get('start_generate_token', False)
gpt_config = HSQCSpectraConfig(
n_layer=n_layer, n_head=n_head, n_embd=n_embd,
dropout=dropout, bias=bias, prompt_pe=prompt_pe,
block_size=block_size, start_generate_token=start_generate_token
)
model = HSQCSpectraGPT(gpt_config)
checkpoint = torch.load(ckpt_path, map_location='cpu')
model.load_state_dict(checkpoint['model'], strict=False)
model_args = checkpoint['model_args']
return model, model_args
def load_MolGPT(config: dict, relative_dir: str):
# 与训练脚本中的 load_MolGPT 一致
if relative_dir == '':
out_dir = config['out_dir']
else:
out_dir = os.path.join(relative_dir, config['out_dir'])
os.makedirs(out_dir, exist_ok=True)
ckpt_path = os.path.join(out_dir, 'ckpt.pt')
device = config['device']
n_layer = config['n_layer']
n_head = config['n_head']
n_embd = config['n_embd']
dropout = config['dropout']
bias = config['bias']
block_size = config['block_size']
vocab_size = config['vocab_size']
gpt_config = MolConfig(
n_layer=n_layer, n_head=n_head, n_embd=n_embd,
vocab_size=vocab_size, block_size=block_size,
dropout=dropout, bias=bias
)
model = MolGPT(gpt_config)
checkpoint = torch.load(ckpt_path, map_location='cpu')
model.load_state_dict(checkpoint['model'], strict=False)
model_args = checkpoint['model_args']
return model, model_args
def load_BERT(config: dict):
# 与训练脚本中的 load_BERT 一致
out_dir = config['out_dir']
os.makedirs(out_dir, exist_ok=True)
ckpt_path = os.path.join(out_dir, 'ckpt.pt')
device = config['device']
n_layer = config['n_layer']
n_head = config['n_head']
n_embd = config['n_embd']
block_size = config['block_size']
n_learnable_tokens = config['n_learnable_tokens']
dropout = config['dropout']
bias = config['bias']
gpt_config = BERTConfig(
n_layer=n_layer, n_head=n_head, n_embd=n_embd,
block_size=block_size, n_learnable_tokens=n_learnable_tokens,
dropout=dropout, bias=bias
)
model = BERT(gpt_config)
checkpoint = torch.load(ckpt_path, map_location='cpu')
model.load_state_dict(checkpoint['model'], strict=False)
model_args = checkpoint['model_args']
return model, model_args