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train_mdc.py
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train_mdc.py
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import argparse
import os
import time as t
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from data.coco_train_dataset import TrainCOCO
from data.coco_eval_dataset import EvalCOCO
from utils import *
from commons import *
from modules import fpn
def parse_arguments():
parser = argparse.ArgumentParser()
parser.add_argument('--data_root', type=str, required=True)
parser.add_argument('--save_root', type=str, required=True)
parser.add_argument('--restart_path', type=str)
parser.add_argument('--comment', type=str, default='')
parser.add_argument('--seed', type=int, default=2021, help='Random seed for reproducability.')
parser.add_argument('--num_workers', type=int, default=4, help='Number of workers.')
parser.add_argument('--restart', action='store_true', default=False)
parser.add_argument('--num_epoch', type=int, default=10)
parser.add_argument('--repeats', type=int, default=10)
# Train.
parser.add_argument('--arch', type=str, default='resnet18')
parser.add_argument('--pretrain', action='store_true', default=False)
parser.add_argument('--res', type=int, default=320, help='Input size.')
parser.add_argument('--res1', type=int, default=320, help='Input size scale from.')
parser.add_argument('--res2', type=int, default=320, help='Input size scale to.')
parser.add_argument('--batch_size_cluster', type=int, default=128)
parser.add_argument('--batch_size_train', type=int, default=128)
parser.add_argument('--batch_size_test', type=int, default=128)
parser.add_argument('--lr', type=float, default=1e-3)
parser.add_argument('--weight_decay', type=float, default=5e-4)
parser.add_argument('--momentum', type=float, default=0.9)
parser.add_argument('--optim_type', type=str, default='Adam')
parser.add_argument('--num_init_batches', type=int, default=30)
parser.add_argument('--num_batches', type=int, default=30)
parser.add_argument('--kmeans_n_iter', type=int, default=30)
parser.add_argument('--in_dim', type=int, default=128)
parser.add_argument('--X', type=int, default=80)
parser.add_argument('--nonparametric', action='store_true', default=False)
# Loss.
parser.add_argument('--metric_train', type=str, default='cosine')
parser.add_argument('--metric_test', type=str, default='cosine')
parser.add_argument('--K_train', type=int, default=1000) # COCO Stuff-15
parser.add_argument('--K_test', type=int, default=27) # COCO Stuff-15 / COCO Thing-12 / COCO All-27
# Dataset.
parser.add_argument('--augment', action='store_true', default=False)
parser.add_argument('--equiv', action='store_true', default=False)
parser.add_argument('--min_scale', type=float, default=0.5)
parser.add_argument('--stuff', action='store_true', default=False)
parser.add_argument('--thing', action='store_true', default=False)
parser.add_argument('--jitter', action='store_true', default=False)
parser.add_argument('--grey', action='store_true', default=False)
parser.add_argument('--blur', action='store_true', default=False)
parser.add_argument('--h_flip', action='store_true', default=False)
parser.add_argument('--v_flip', action='store_true', default=False)
parser.add_argument('--random_crop', action='store_true', default=False)
parser.add_argument('--val_type', type=str, default='val')
parser.add_argument('--version', type=int, default=7)
parser.add_argument('--fullcoco', action='store_true', default=False)
# Eval-only
parser.add_argument('--eval_only', action='store_true', default=False)
parser.add_argument('--eval_path', type=str)
# Cityscapes-specific.
parser.add_argument('--cityscapes', action='store_true', default=False)
parser.add_argument('--label_mode', type=str, default='gtFine')
parser.add_argument('--long_image', action='store_true', default=False)
return parser.parse_args()
def train(args, logger, dataloader, model, classifier, criterion, optimizer, optimizer_loop):
losses = AverageMeter()
# switch to train mode
model.train()
classifier.train(not args.nonparametric)
for i, (indice, image, label) in enumerate(dataloader):
image = eqv_transform_if_needed(args, dataloader, indice, image.cuda(non_blocking=True))
label = label.cuda(non_blocking=True)
feats = model(image)
B, C, _ = feats.shape[:3]
if i == 0:
logger.info('Batch input size : {}'.format(list(image.shape)))
logger.info('Batch label size : {}'.format(list(label.shape)))
logger.info('Batch feature size : {}\n'.format(list(feats.shape)))
if args.metric_train == 'cosine':
feats = F.normalize(feats, dim=1, p=2)
# 2. Get scores.
output = feature_flatten(classifier(feats))
label = label.flatten()
loss = criterion(output, label)
# record loss
losses.update(loss.item(), B)
# compute gradient and do step
optimizer.zero_grad()
if not args.nonparametric:
optimizer_loop.zero_grad()
loss.backward()
optimizer.step()
if not args.nonparametric:
optimizer_loop.step()
if (i % 200) == 0:
logger.info('{0} / {1}\t'.format(i, len(dataloader)))
return losses.avg
def adjust_learning_rate(optimizer, epoch, args):
"""Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
lr = args.lr * (0.1 ** (epoch // 20))
for param_group in optimizer.param_groups:
param_group['lr'] = lr
def main(args, logger):
logger.info(args)
# Use random seed.
fix_seed_for_reproducability(args.seed)
# Start time.
t_start = t.time()
# Get model and optimizer.
model, optimizer, classifier = get_model_and_optimizer(args, logger)
# New trainset inside for-loop.
inv_list, eqv_list = get_transform_params(args)
trainset = get_dataset(args, mode='train', inv_list=inv_list, eqv_list=eqv_list)
trainloader = torch.utils.data.DataLoader(trainset,
batch_size=args.batch_size_cluster,
shuffle=True,
num_workers=args.num_workers,
pin_memory=True,
collate_fn=collate_train_baseline,
worker_init_fn=worker_init_fn(args.seed))
testset = get_dataset(args, mode='train_val')
testloader = torch.utils.data.DataLoader(testset,
batch_size=args.batch_size_test,
shuffle=False,
num_workers=args.num_workers,
pin_memory=True,
collate_fn=collate_eval,
worker_init_fn=worker_init_fn(args.seed))
# Before train.
_, _ = evaluate(args, logger, testloader, classifier, model)
if not args.eval_only:
# Train start.
for epoch in range(args.start_epoch, args.num_epoch):
# Assign probs.
trainloader.dataset.mode = 'compute'
trainloader.dataset.reshuffle()
# Adjust lr if needed.
adjust_learning_rate(optimizer, epoch, args)
logger.info('\n============================= [Epoch {}] =============================\n'.format(epoch))
logger.info('Start computing centroids.')
t1 = t.time()
centroids, kmloss = run_mini_batch_kmeans(args, logger, trainloader, model, view=1)
logger.info('-Centroids ready. [{}]\n'.format(get_datetime(int(t.time())-int(t1))))
# Compute cluster assignment.
t2 = t.time()
weight = compute_labels(args, logger, trainloader, model, centroids, view=1)
logger.info('-Cluster labels ready. [{}]\n'.format(get_datetime(int(t.time())-int(t2))))
# Criterion.
criterion = torch.nn.CrossEntropyLoss(weight=weight).cuda()
# Set nonparametric classifier.
classifier = initialize_classifier(args)
if args.nonparametric:
classifier.module.weight.data = centroids.unsqueeze(-1).unsqueeze(-1)
freeze_all(classifier)
if args.nonparametric:
optimizer_loop = None
else:
if args.optim_type == 'SGD':
optimizer_loop = torch.optim.SGD(filter(lambda x: x.requires_grad, classifier.module.parameters()), lr=args.lr, \
momentum=args.momentum, weight_decay=args.weight_decay)
elif args.optim_type == 'Adam':
optimizer_loop = torch.optim.Adam(filter(lambda x: x.requires_grad, classifier.module.parameters()), lr=args.lr)
# Set-up train loader.
trainset.mode = 'baseline_train'
trainset.labeldir = args.save_model_path
trainloader_loop = torch.utils.data.DataLoader(trainset,
batch_size=args.batch_size_train,
shuffle=True,
num_workers=args.num_workers,
pin_memory=True,
collate_fn=collate_train_baseline,
worker_init_fn=worker_init_fn(args.seed)
)
logger.info('Start training ...')
train_loss = train(args, logger, trainloader_loop, model, classifier, criterion, optimizer, optimizer_loop)
acc, res = evaluate(args, logger, testloader, classifier, model)
logger.info('========== Epoch [{}] =========='.format(epoch))
logger.info(' Time total : [{}].'.format(get_datetime(int(t.time())-int(t1))))
logger.info(' K-Means loss : {:.5f}.'.format(kmloss))
logger.info(' Training loss : {:.5f}.'.format(train_loss))
logger.info(' ACC: {:.4f} | mIoU: {:.4f}'.format(acc, res['mean_iou']))
logger.info('================================\n')
torch.save({'epoch': epoch+1,
'args' : args,
'state_dict': model.state_dict(),
'classifier1_state_dict' : classifier.state_dict(),
'optimizer' : optimizer.state_dict(),
},
os.path.join(args.save_model_path, 'checkpoint_{}.pth.tar'.format(epoch)))
torch.save({'epoch': epoch+1,
'args' : args,
'state_dict': model.state_dict(),
'classifier1_state_dict' : classifier.state_dict(),
'optimizer' : optimizer.state_dict(),
},
os.path.join(args.save_model_path, 'checkpoint.pth.tar'))
# Evaluate.
trainset = get_dataset(args, mode='eval_val')
trainloader = torch.utils.data.DataLoader(trainset,
batch_size=args.batch_size_cluster,
shuffle=True,
num_workers=args.num_workers,
pin_memory=True,
collate_fn=collate_train_baseline,
worker_init_fn=worker_init_fn(args.seed)
)
testset = get_dataset(args, mode='eval_test')
testloader = torch.utils.data.DataLoader(testset,
batch_size=args.batch_size_test,
shuffle=False,
num_workers=args.num_workers,
pin_memory=True,
collate_fn=collate_eval,
worker_init_fn=worker_init_fn(args.seed))
# Evaluate with fresh clusters.
acc_list_new = []
res_list_new = []
logger.info('Start computing centroids.')
if args.repeats > 0:
for _ in range(args.repeats):
t1 = t.time()
centroids, kmloss = run_mini_batch_kmeans(args, logger, trainloader, model, view=-1)
logger.info('-Centroids ready. [Loss: {:.5f}/ Time: {}]\n'.format(kmloss, get_datetime(int(t.time())-int(t1))))
classifier = initialize_classifier(args)
classifier.module.weight.data = centroids.unsqueeze(-1).unsqueeze(-1)
freeze_all(classifier)
acc_new, res_new = evaluate(args, logger, testloader, classifier, model)
acc_list_new.append(acc_new)
res_list_new.append(res_new)
else:
acc_new, res_new = evaluate(args, logger, testloader, classifier, model)
acc_list_new.append(acc_new)
res_list_new.append(res_new)
logger.info('Average overall pixel accuracy [NEW] : {} +/- {}.'.format(round(np.mean(acc_list_new), 2), np.std(acc_list_new)))
logger.info('Average mIoU [NEW] : {:.3f} +/- {:.3f}. '.format(np.mean([res['mean_iou'] for res in res_list_new]),
np.std([res['mean_iou'] for res in res_list_new])))
logger.info('Experiment done. [{}]\n'.format(get_datetime(int(t.time())-int(t_start))))
if __name__=='__main__':
args = parse_arguments()
# Setup the path to save.
if not args.pretrain:
args.save_root += '/scratch'
if args.augment:
args.save_root += '/augmented/res1={}_res2={}/jitter={}_blur={}_grey={}'.format(args.res1, args.res2, args.jitter, args.blur, args.grey)
if args.equiv:
args.save_root += '/equiv/h_flip={}_v_flip={}_crop={}/min_scale\={}'.format(args.h_flip, args.v_flip, args.random_crop, args.min_scale)
if args.nonparametric:
args.save_root += '/nonparam'
args.save_model_path = os.path.join(args.save_root, args.comment, 'K_train={}_{}'.format(args.K_train, args.metric_train))
args.save_eval_path = os.path.join(args.save_model_path, 'K_test={}_{}'.format(args.K_test, args.metric_test))
if not os.path.exists(args.save_eval_path):
os.makedirs(args.save_eval_path)
# Setup logger.
logger = set_logger(os.path.join(args.save_eval_path, 'train.log'))
# Start.
main(args, logger)