Spaces:
Sleeping
Sleeping
File size: 2,036 Bytes
b6f1234 |
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 |
import torch
import numpy as np
import pandas as pd
import time
from torch_geometric.data import DataLoader
from model.model_concatenation import PLANet
from utils.args import ArgsInit
from utils.model import get_dataset_inference, test_gcn
def main(args):
if args.use_gpu:
device = torch.device("cuda:" + str(args.device)) if torch.cuda.is_available() else torch.device("cpu")
else:
device = torch.device('cpu')
#Numpy and torch seeds
torch.manual_seed(args.seed)
np.random.seed(args.seed)
if device.type == 'cuda':
torch.cuda.manual_seed(args.seed)
print('%s' % args)
data_inference = pd.read_csv(
args.input_file_smiles,
names=["Smiles"],
header=0
)
print("Data Inference: ", data_inference)
data_target = pd.read_csv(
args.target_list, names=["Fasta", "Target", "Label"]
)
data_target = data_target[data_target.Target == args.target]
print("Data Target: ", data_target)
test = get_dataset_inference(
data_inference,
use_prot=args.use_prot,
target=data_target,
args=args,
advs=False,
saliency=False,
)
test_loader = DataLoader(test, batch_size=args.batch_size, shuffle=False,
num_workers=args.num_workers)
model = PLANet(args).to(device)
print('Model inference in: {}'.format(args.inference_path))
start_time = time.time()
#Load pre-trained molecule model
print('Evaluating...')
test_gcn(model, device, test_loader, args)
end_time = time.time()
total_time = end_time - start_time
print('Total time: {}'.format(time.strftime('%H:%M:%S', time.gmtime(total_time))))
if __name__ == "__main__":
args = ArgsInit().args
# Default args for inference
args.nclasses = 2
args.batch_size = 10
args.use_prot = True
args.freeze_molecule = True
args.conv_encode_edge = True
args.learn_t = True
args.binary = True
main(args)
|