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acbef3a | 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 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 | import argparse
from dataset import PeptidePairDataset, PeptidePairPicDataset
from network import DMutaPeptide, DMutaPeptideCNN
from train import move_to_device
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
import numpy as np
from utils import set_seed
import pandas as pd
from torchmetrics import MeanAbsoluteError, RelativeSquaredError, PearsonCorrCoef, KendallRankCorrCoef, F1Score, Accuracy, AveragePrecision, AUROC
parser = argparse.ArgumentParser(description='resnet26')
# model setting
parser.add_argument('--model', type=str, default='resnet34',
help='resnet34 resnet50 densenet')
parser.add_argument('--q-encoder', dest='q_encoder', type=str, default='cnn',
help='lstm mamba mla')
parser.add_argument('--channels', type=int, default=16)
parser.add_argument("--side-enc", dest='side_enc', type=str, default=None,
help="use side features")
parser.add_argument('--fusion', type=str, default='diff',
help='mlp att')
parser.add_argument('--glob-feat', dest='glob_feat', action='store_true', default=False,
help="use global features")
parser.add_argument('--non-siamese', dest='non_siamese', action='store_true', default=False,
help="use non-siamese architecture")
# task & dataset setting
parser.add_argument('--task', type=str, default='cls',
help='reg or cls')
parser.add_argument('--pdb-src', type=str, dest='pdb_src', default='af',
help='af or hf')
parser.add_argument('--data-ver', type=str, dest='data_ver', default='250228',
help='data version')
parser.add_argument('--one-way', action='store_true', dest='one_way', default=False,
help='use one-way constructed dataset')
parser.add_argument('--max-length', dest='max_length', type=int, default=30,
help='Max length for sequence filtering')
parser.add_argument('--resize', type=int, default=[768], nargs='+',
help='resize the image')
parser.add_argument('--split', type=int, default=5,
help="Split k fold in cross validation (default: 5)")
parser.add_argument('--seed', type=int, default=1,
help="Seed (default: 1)")
parser.add_argument('--pcs', action='store_true', default=False,
help='Consider protease cut site')
parser.add_argument('--mix-pcs', dest='mix_pcs', action='store_true', default=False,
help='Consider protease cut site')
# training setting
parser.add_argument('--gpu', type=int, default=0,
help='GPU index to use, -1 for CPU (default: 0)')
parser.add_argument('--batch-size', type=int, dest='batch_size', default=32,
help='input batch size for training (default: 128)')
parser.add_argument('--epochs', type=int, default=50,
help='number of epochs to train (default: 100)')
parser.add_argument('--lr', type=float, default=0.001,
help='learning rate (default: 0.001)')
parser.add_argument('--decay', type=float, default=0.0005,
help='weight decay (default: 0.0005)')
parser.add_argument('--warm-steps', type=int, dest='warm_steps', default=0,
help='number of warm start steps for learning rate (default: 10)')
parser.add_argument('--patience', type=int, default=10,
help='patience for early stopping (default: 10)')
parser.add_argument('--pretrain', type=str, dest='pretrain', default='',
help='path of the pretrain model') # /home/duadua/Desktop/fetal/3dpretrain/runs/e50.pth
parser.add_argument('--metric-avg', type=str, dest='metric_avg', default='macro',
help='metric average type')
parser.add_argument('--loss', type=str, default='ce',
help='loss function')
parser.add_argument('--dir', action='store_true', default=False,
help='use DIR')
parser.add_argument('--simple', dest='simple', action='store_true', default=False)
parser.add_argument('--llm-data', dest='llm_data', action='store_true', default=False)
parser.add_argument('--uda', type=str, default=None)
args = parser.parse_args()
if args.llm_data:
args.simple = True
if args.simple:
args.one_way = True
if args.mix_pcs:
args.pcs = 'mix'
if args.q_encoder in ['cnn', 'rn18']:
weight_dir = f'./run-{args.task}/{f"non-siamese-" if args.non_siamese else ""}{args.q_encoder}-{args.fusion}-{args.channels}{f"-{args.side_enc}" if args.side_enc else ""}{"-mixpcs" if args.mix_pcs else ""}{"-pcs" if args.pcs==True else ""}{"-simple" if args.simple else ""}{"-llm" if args.llm_data else ""}{"-" + "x".join(str(n) for n in args.resize) if args.resize else ""}{"-gf" if args.glob_feat else ""}{"-oneway" if args.one_way else ""}-{args.loss + "-dir" if args.dir else args.loss}-{str(args.batch_size)}-{str(args.lr)}-{str(args.epochs)}'
else:
weight_dir = f'./run-{args.task}/{f"non-siamese-" if args.non_siamese else ""}{args.q_encoder}-{args.fusion}-{args.channels}{"-simple" if args.simple else ""}{"-llm" if args.llm_data else ""}{"-gf" if args.glob_feat else ""}{"-oneway" if args.one_way else ""}-{args.loss + "-dir" if args.dir else args.loss}-{str(args.batch_size)}-{str(args.lr)}-{str(args.epochs)}'
if args.uda:
weight_dir += f'/uda_{args.uda}'
print(weight_dir)
def metrics(preds, gt, task):
avg = 'marco'
device = preds.device
if task == 'cls':
metric_1 = AveragePrecision(average=avg, task='binary').to(device)
metric_2 = AUROC(average=avg, task='binary').to(device)
metric_3 = F1Score(average=avg, task='binary').to(device)
metric_4 = Accuracy(average=avg, task='binary').to(device)
all_metrics = [metric_1(preds, gt).item(),
metric_2(preds, gt).item(),
metric_3(preds, gt).item(),
metric_4(preds, gt).item()]
elif task == 'reg':
metric_1 = MeanAbsoluteError().to(device)
metric_2 = RelativeSquaredError(num_outputs=1).to(device)
metric_3 = PearsonCorrCoef(num_outputs=1).to(device)
metric_4 = KendallRankCorrCoef(num_outputs=1).to(device)
all_metrics = [metric_1(preds, gt).item(),
metric_2(preds, gt).item(),
metric_3(preds.squeeze(), gt.squeeze()).mean().item(),
metric_4(preds.squeeze(), gt.squeeze()).mean().item()]
return [f'{i * 100:.2f}' for i in all_metrics]
def main(dataset):
set_seed(args.seed)
if args.task == 'reg':
args.classes = 1
elif args.task == 'cls':
args.classes = 2
else:
raise NotImplementedError("unimplemented task")
device = torch.device("cpu" if args.gpu == -1 or not torch.cuda.is_available() else f"cuda:{args.gpu}")
if args.q_encoder in ['cnn', 'rn18']:
model = DMutaPeptideCNN(q_encoder=args.q_encoder, classes=args.classes, channels=args.channels, dir=args.dir, gf=args.glob_feat, side_enc=args.side_enc, fusion=args.fusion, non_siamese=args.non_siamese).to(device).eval()
test_set = PeptidePairPicDataset(mode=dataset, pad_length=args.max_length, task=args.task, gf=args.glob_feat, side_enc=args.side_enc, pcs=args.pcs, resize=args.resize)
else:
model = DMutaPeptide(q_encoder=args.q_encoder, classes=args.classes, channels=args.channels, dir=args.dir, gf=args.glob_feat, fusion=args.fusion, non_siamese=args.non_siamese).to(device).eval()
test_set = PeptidePairDataset(mode=dataset, pad_length=args.max_length, task=args.task, gf=args.glob_feat)
test_loader = DataLoader(test_set, batch_size=args.batch_size, shuffle=False)
raw_preds = []
ckpt_names = ['model_uda_teacher'] if args.uda else [f'model_{i}_test' for i in range(5)]
for i in ckpt_names:
model.load_state_dict(torch.load(f'{weight_dir}/{i}.pth', map_location=device))
preds = []
gt_list_valid = []
with torch.no_grad():
for data in test_loader:
x, gt = data
gt_list_valid.append(gt.to(device))
out = model(move_to_device(x, device))
if args.dir:
out, _ = out
preds.append(out)
r_pred = torch.cat(preds, dim=0)
if args.task == 'reg':
preds = r_pred.cpu().numpy()
elif args.task == 'cls':
preds = torch.softmax(r_pred, dim=-1)[:, 1].cpu().numpy()
gt_tensor = torch.cat(gt_list_valid, dim=0)
gt_list_valid = gt_tensor.cpu().numpy()
raw_preds.append(r_pred)
if args.task == 'cls':
preds_tensor = torch.softmax(torch.stack(raw_preds, 0), dim=-1)[:, :, 1]
elif args.task == 'reg':
preds_tensor = torch.stack(raw_preds, 0)
return [metrics(preds_tensor[i], gt_tensor, args.task) for i in range(len(ckpt_names))]
if __name__ == '__main__':
if args.task == 'cls':
# df = pd.DataFrame(columns=['dataset', 'AUPRC', 'AUROC', 'F1', 'ACC'])
print(','.join(['AUPRC', 'AUROC', 'F1', 'ACC']))
elif args.task == 'reg':
# df = pd.DataFrame(columns=['dataset', 'MAE', 'RSE', 'PCC', 'KCC'])
print(','.join(['MAE', 'RSE', 'PCC', 'KCC']))
results = main('r2_case')
for result in results:
print(','.join(result))
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