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train.py
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train.py
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from __future__ import division
from __future__ import print_function
import os
import glob
import time
import random
import argparse
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import logging
from utils import setup_logger
from torch.autograd import Variable
from utils import load_data, accuracy
from models import GAT, SpGAT, Encoder, Encoder_New
from dataset import RS_Dataset, RS_Dataset_New, RS_Dataset_New1
from torch.utils.data.dataloader import DataLoader
# Training settings
parser = argparse.ArgumentParser()
parser.add_argument('--no-cuda', action='store_true', default=False, help='Disables CUDA training.')
parser.add_argument('--fastmode', action='store_true', default=False, help='Validate during training pass.')
parser.add_argument('--sparse', action='store_true', default=False, help='GAT with sparse version or not.')
parser.add_argument('--seed', type=int, default=72, help='Random seed.')
parser.add_argument('--train_path', type=str, default=r'D:\gat_dataset\train', help='train dataset path.')
parser.add_argument('--val_path', type=str, default=r'D:D:\gat_dataset\val', help='val dataset path.')
parser.add_argument('--output_path', type=str, default=r'D:\gat_dataset\output/gat_baseline', help='output path.')
parser.add_argument('--epochs', type=int, default=10000, help='Number of epochs to train.')
parser.add_argument('--batch_size', type=int, default=1, help='Number of epochs to train.')
parser.add_argument('--lr', type=float, default=0.01, help='Initial learning rate.')
parser.add_argument('--weight_decay', type=float, default=5e-4, help='Weight decay (L2 loss on parameters).')
parser.add_argument('--hidden', type=int, default=128, help='Number of hidden units.')
parser.add_argument('--nb_heads', type=int, default=8, help='Number of head attentions.')
parser.add_argument('--dropout', type=float, default=0.5, help='Dropout rate (1 - keep probability).')
parser.add_argument('--alpha', type=float, default=0.2, help='Alpha for the leaky_relu.')
parser.add_argument('--patience', type=int, default=10000, help='Patience')
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
logger = setup_logger('baseline', args.output_path, 0)
if args.output_path and not os.path.exists(args.output_path):
os.makedirs(args.output_path)
# Load data
# adj, features, labels, idx_train, idx_val, idx_test = load_data()
# train_feature_node_path = os.path.join(args.train_path, 'node_features')
train_img_path = os.path.join(args.train_path, 'imgs')
train_edge_path = os.path.join(args.train_path, 'edge_adjs')
train_label_path = os.path.join(args.train_path, 'labels')
train_roi_path = os.path.join(args.train_path, 'roi')
# train_obj_path = os.path.join(args.train_path, 'mask_objs')
# val_feature_node_path = os.path.join(args.val_path, 'node_features')
val_img_path = os.path.join(args.val_path, 'imgs')
val_edge_path = os.path.join(args.val_path, 'edge_adjs')
val_label_path = os.path.join(args.val_path, 'labels')
val_roi_path = os.path.join(args.val_path, 'roi')
# val_obj_path = os.path.join(args.val_path, 'mask_objs')
train_dataset = RS_Dataset_New(train_img_path, train_edge_path, train_label_path, train_roi_path)
val_dataset = RS_Dataset_New(val_img_path, val_edge_path, val_label_path, val_roi_path)
train_loader = DataLoader(
train_dataset, batch_size=args.batch_size, shuffle=True,
num_workers=1
)
val_loader = DataLoader(
val_dataset, batch_size=args.batch_size, shuffle=False,
num_workers=1
)
# Model and optimizer
# if args.sparse:
# model = SpGAT(nfeat=3,
# nhid=args.hidden,
# nclass=12,
# dropout=args.dropout,
# nheads=args.nb_heads,
# alpha=args.alpha)
# else:
# model = GAT(nfeat=3,
# nhid=args.hidden,
# nclass=12,
# dropout=args.dropout,
# nheads=args.nb_heads,
# alpha=args.alpha)
model = Encoder(output_size=(7, 7), spatial_scale=1.0, hidden=args.hidden, nclass=12,
dropout=args.dropout, nb_heads=args.nb_heads, alpha=args.alpha)
optimizer = optim.SGD(model.parameters(),
lr=args.lr)
if args.cuda:
model.cuda()
# features = features.cuda()
# adj = adj.cuda()
# labels = labels.cuda()
# idx_train = idx_train.cuda()
# idx_val = idx_val.cuda()
# idx_test = idx_test.cuda()
# features, adj, labels = Variable(features), Variable(adj), Variable(labels)
def train(epoch, train_loader, val_loader, logger=None):
t = time.time()
# model.eval()
model.train()
all_output = list()
all_target = list()
for rs_imgs, adj, labels, rois in train_loader:
# rs_imgs = rs_imgs.squeeze(0)
rois = rois.squeeze(0)
labels = labels.squeeze(0)
# objs = objs.squeeze(0)
# print(features.shape, adj.shape, labels.shape)
if args.cuda:
rs_imgs = rs_imgs.cuda()
adj = adj.cuda()
labels = labels.cuda()
rois = rois.cuda()
# objs = objs.cuda()
optimizer.zero_grad()
output = model(rs_imgs, adj, rois)
# print(output, labels)
loss_train = F.nll_loss(output, labels)
# print(loss_train.item())
# acc_train = accuracy(output[idx_train], labels[idx_train])
loss_train.backward()
optimizer.step()
all_output.append(output)
all_target.append(labels)
all_output = torch.cat(all_output, 0)
all_target = torch.cat(all_target, 0)
loss = F.nll_loss(all_output, all_target)
acc = accuracy(all_output, all_target)
total_output = list()
total_target = list()
model.eval()
for rs_imgs, adj, labels, rois in val_loader:
# features = features.squeeze(0)
rois = rois.squeeze(0)
labels = labels.squeeze(0)
# objs = objs.squeeze(0)
if args.cuda:
rs_imgs = rs_imgs.cuda()
adj = adj.cuda()
labels = labels.cuda()
rois = rois.cuda()
# objs = objs.cuda()
with torch.no_grad():
output = model(rs_imgs, adj, rois)
total_output.append(output)
total_target.append(labels)
total_output = torch.cat(total_output, 0)
total_target = torch.cat(total_target, 0)
loss_val = F.nll_loss(total_output, total_target)
acc_val = accuracy(total_output, total_target)
print('Epoch: {:04d}'.format(epoch + 1),
'loss_train: {:.4f}'.format(loss.data.item()),
'acc_train: {:.4f}'.format(acc.data.item()),
'loss_val: {:.4f}'.format(loss_val.data.item()),
'acc_val: {:.4f}'.format(acc_val.data.item()),
'time: {:.4f}s'.format(time.time() - t))
logger.info('Epoch: {:04d}'.format(epoch + 1),
'loss_train: {:.4f}'.format(loss.data.item()),
'acc_train: {:.4f}'.format(acc.data.item()),
'loss_val: {:.4f}'.format(loss_val.data.item()),
'acc_val: {:.4f}'.format(acc_val.data.item()),
'time: {:.4f}s'.format(time.time() - t))
return loss_val.data.item()
# def compute_test():
# model.eval()
# output = model(features, adj)
# loss_test = F.nll_loss(output[idx_test], labels[idx_test])
# acc_test = accuracy(output[idx_test], labels[idx_test])
# print("Test set results:",
# "loss= {:.4f}".format(loss_test.data[0]),
# "accuracy= {:.4f}".format(acc_test.data[0]))
if __name__ == "__main__":
# Train model
t_total = time.time()
loss_values = []
bad_counter = 0
best = args.epochs + 1
best_epoch = 0
logger.info('Trainer Built')
for epoch in range(args.epochs):
loss_values.append(train(epoch, train_loader, val_loader, logging))
torch.save(model.state_dict(), os.path.join(args.output_path, '{}.pkl'.format(epoch)))
if loss_values[-1] < best:
best = loss_values[-1]
best_epoch = epoch
bad_counter = 0
else:
bad_counter += 1
if bad_counter == args.patience:
break
files = glob.glob(os.path.join(args.output_path, '*.pkl'))
for file in files:
epoch_nb = int(file.split('.')[0])
if epoch_nb < best_epoch:
os.remove(file)
files = glob.glob(os.path.join(args.output_path, '*.pkl'))
for file in files:
epoch_nb = int(file.split('.')[0])
if epoch_nb > best_epoch:
os.remove(file)
print("Optimization Finished!")
print("Total time elapsed: {:.4f}s".format(time.time() - t_total))
# Restore best model
# print('Loading {}th epoch'.format(best_epoch))
# model.load_state_dict(torch.load('{}.pkl'.format(best_epoch)))
# Testing
# compute_test()