File size: 3,597 Bytes
3ac1d94 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 | import argparse
from tqdm.auto import tqdm
import torch
import torch.utils.tensorboard
from torch_geometric.transforms import Compose
from onescience.datapipes.targetdiff import get_dataset
import onescience.utils.targetdiff.transforms_prop as utils_trans
import onescience.utils.targetdiff.misc as utils_misc
import numpy as np
from onescience.datapipes.targetdiff.protein_ligand import KMAP
from scripts.property_prediction.local_misc_prop import get_model, get_dataloader, get_eval_scores
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--ckpt_path', type=str)
parser.add_argument('--device', type=str, default='cuda')
parser.add_argument('--seed', type=int, default=2021)
args = parser.parse_args()
utils_misc.seed_all(args.seed)
# Logging
logger = utils_misc.get_logger('eval')
logger.info(args)
# Load config
logger.info(f'Loading model from {args.ckpt_path}')
ckpt_restore = torch.load(args.ckpt_path, map_location=torch.device('cpu'))
config = ckpt_restore['config']
logger.info(f'ckpt_config: {config}')
# Transforms
protein_featurizer = utils_trans.FeaturizeProteinAtom()
ligand_featurizer = utils_trans.FeaturizeLigandAtom()
transform = Compose([
protein_featurizer,
ligand_featurizer,
])
# Load model
model = get_model(config, protein_featurizer.feature_dim, ligand_featurizer.feature_dim)
model.load_state_dict(ckpt_restore['model'])
model = model.to(args.device)
# print(model)
# logger.info(f'# trainable parameters: {utils_misc.count_parameters(model) / 1e6:.4f} M')
model.eval()
# Datasets and loaders
# config.dataset.path = './data/pdbbind_v2020/pocket_10_refined'
# config.dataset.split = './data/pdbbind_v2020/pocket_10_refined/split.pt'
logger.info('Loading dataset...')
dataset, subsets = get_dataset(
config=config.dataset,
transform=transform,
heavy_only=config.dataset.get('heavy_only', False)
)
train_set, val_set, test_set = subsets['train'], subsets['val'], subsets['test']
logger.info(f'Train set: {len(train_set)} Val set: {len(val_set)} Test set: {len(test_set)}')
train_loader, val_loader, test_loader = get_dataloader(train_set, val_set, test_set, config)
def validate(epoch, data_loader, prefix='Test'):
sum_loss, sum_n = 0, 0
ytrue_arr, ypred_arr = [], []
y_kind = []
with torch.no_grad():
model.eval()
for batch in tqdm(data_loader, desc=prefix):
batch = batch.to(args.device)
loss, pred = model.get_loss(batch, pos_noise_std=0., return_pred=True)
sum_loss += loss.item() * len(batch.y)
sum_n += len(batch.y)
ypred_arr.append(pred.view(-1))
ytrue_arr.append(batch.y)
y_kind.append(batch.kind)
avg_loss = sum_loss / sum_n
logger.info('[%s] Epoch %03d | Loss %.6f' % (
prefix, epoch, avg_loss,
))
ypred_arr = torch.cat(ypred_arr).cpu().numpy().astype(np.float64)
ytrue_arr = torch.cat(ytrue_arr).cpu().numpy().astype(np.float64)
y_kind = torch.cat(y_kind).cpu().numpy()
rmse = get_eval_scores(ypred_arr, ytrue_arr, logger)
for k, v in KMAP.items():
get_eval_scores(ypred_arr[y_kind == v], ytrue_arr[y_kind == v], logger, prefix=k)
return avg_loss
test_loss = validate(ckpt_restore['epoch'], test_loader)
print('Test loss: ', test_loss)
if __name__ == '__main__':
main()
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