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evaluate.py
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evaluate.py
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import argparse
import os
import numpy as np
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torchvision.transforms as transforms
from dataloader.image_dataloader import ImageDataset, load_filenames_and_labels_multitask, get_datasets
from model.cnn_model_utils import load_model, evaluate_on_multitask
from model.train_utils import load_smiles
from utils.public_utils import cal_torch_model_params, setup_device
from utils.splitter import scaffold_split_train_val_test
def parse_args():
parser = argparse.ArgumentParser(description='PyTorch Implementation of ImageMol')
# basic
parser.add_argument('--dataset', type=str, default="BBBP", help='dataset name, e.g. BBBP, tox21, ...')
parser.add_argument('--dataroot', type=str, default="./data_process/data/", help='data root')
parser.add_argument('--gpu', default='0', type=str, help='index of GPU to use')
parser.add_argument('--workers', default=2, type=int, help='number of data loading workers (default: 2)')
# evaluation
parser.add_argument('--batch', default=128, type=int, help='mini-batch size (default: 128)')
parser.add_argument('--resume', default='None', type=str, metavar='PATH', help='path to checkpoint (default: None)')
parser.add_argument('--imageSize', type=int, default=224, help='the height / width of the input image to network')
parser.add_argument('--image_model', type=str, default="ResNet18", help='e.g. ResNet18, ResNet34')
parser.add_argument('--task_type', type=str, default="classification", choices=["classification", "regression"],
help='task type')
return parser.parse_args()
def main(args):
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
args.image_folder, args.txt_file = get_datasets(args.dataset, args.dataroot, data_type="processed")
args.verbose = True
device, device_ids = setup_device(1)
# architecture name
if args.verbose:
print('Architecture: {}'.format(args.image_model))
##################################### initialize some parameters #####################################
if args.task_type == "classification":
eval_metric = "rocauc"
elif args.task_type == "regression":
if args.dataset == "qm7" or args.dataset == "qm8" or args.dataset == "qm9":
eval_metric = "mae"
else:
eval_metric = "rmse"
else:
raise Exception("{} is not supported".format(args.task_type))
print("eval_metric: {}".format(eval_metric))
##################################### load data #####################################
img_transformer_test = [transforms.CenterCrop(args.imageSize), transforms.ToTensor()]
names, labels = load_filenames_and_labels_multitask(args.image_folder, args.txt_file, task_type=args.task_type)
names, labels = np.array(names), np.array(labels)
num_tasks = labels.shape[1]
smiles = load_smiles(args.txt_file)
train_idx, val_idx, test_idx = scaffold_split_train_val_test(list(range(0, len(names))), smiles, frac_train=0.8,
frac_valid=0.1, frac_test=0.1)
name_train, name_val, name_test, labels_train, labels_val, labels_test = names[train_idx], names[val_idx], names[
test_idx], labels[train_idx], labels[val_idx], labels[test_idx]
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
test_dataset = ImageDataset(name_test, labels_test, img_transformer=transforms.Compose(img_transformer_test),
normalize=normalize, args=args)
test_dataloader = torch.utils.data.DataLoader(test_dataset,
batch_size=args.batch,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
##################################### load model #####################################
model = load_model(args.image_model, imageSize=args.imageSize, num_classes=num_tasks)
if args.resume:
if os.path.isfile(args.resume): # only support ResNet18 when loading resume
print("=> loading checkpoint '{}'".format(args.resume))
try:
checkpoint = torch.load(args.resume)
model.load_state_dict(checkpoint)
except:
checkpoint = torch.load(args.resume)["model_state_dict"]
model.load_state_dict(checkpoint)
print("=> loading completed")
else:
print("=> no checkpoint found at '{}'".format(args.resume))
print("params: {}".format(cal_torch_model_params(model)))
model = model.cuda()
if len(device_ids) > 1:
model = torch.nn.DataParallel(model, device_ids=device_ids)
if args.task_type == "classification":
criterion = nn.BCEWithLogitsLoss(reduction="none")
elif args.task_type == "regression":
criterion = nn.MSELoss()
else:
raise Exception("param {} is not supported.".format(args.task_type))
##################################### evaluation #####################################
test_loss, test_results, test_data_dict = evaluate_on_multitask(model=model, data_loader=test_dataloader,
criterion=criterion, device=device, epoch=-1,
task_type=args.task_type, return_data_dict=True)
test_result = test_results[eval_metric.upper()]
print("[test] {}: {:.1f}%".format(eval_metric, test_result * 100))
if __name__ == "__main__":
args = parse_args()
main(args)