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train.py
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train.py
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from __future__ import absolute_import, division, print_function
import os
import sys
import gc
import json
import pickle
import random
import shutil
import operator
import argparse
import numpy as np
import torch
from tools import get_embedding, get_split, get_config, logWritter, MeaninglessError, scores_gzsl, Step_Scheduler, Const_Scheduler, construct_gt_st
from libs.datasets import get_dataset
from trainer import Trainer
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--config', default='***.yaml', help='configuration file for train/val')
parser.add_argument('--experimentid', default='0', help='model name/save dir')
parser.add_argument('--resume_from', type=int, default=0, help='continue train(>0) or train from scratch/val(<=0)')
parser.add_argument('--schedule', default='step1', help='[step1/mixed/st/st_mixed] schedule method for training (omitted in val)')
parser.add_argument('--init_model', default='none', help='overwrite <init_model> in the config file if not none')
parser.add_argument('--val', action='store_true', default=False, help='only do validation if set True')
parser.add_argument('--multigpus', default=False, action='store_true', help='use multiple GPUs or single GPU')
parser.add_argument('--ngpu', type=int, default=0, help='number of GPUs to be used if multigpus is Ture, GPU id otherwise')
return parser.parse_args()
def main():
"""
Acquire args and config
"""
args = parse_args()
assert (os.path.exists(args.config))
assert (args.schedule in ['step1', 'mixed', 'st', 'st_mixed'])
assert ((args.multigpus == False and args.ngpu >= 0) or (args.multigpus == True and args.ngpu > 1))
assert (not (args.val and args.resume_from > 0))
config = get_config(args.config)
assert (not (args.val and config['init_model'] == 'none' and args.init_model == 'none'))
if args.init_model != 'none':
assert (os.path.exists(args.init_model))
config['init_model'] = args.init_model
"""
Path to save results.
"""
dataset_path = os.path.join(config['save_path'], config['dataset'])
if not os.path.exists(dataset_path):
os.makedirs(dataset_path)
save_path = os.path.join(dataset_path, args.experimentid)
if not os.path.exists(save_path) and not args.val:
os.makedirs(save_path)
if args.schedule == 'step1':
model_path = os.path.join(save_path, 'models')
elif args.schedule == 'mixed':
model_path = os.path.join(save_path, 'models_transfer')
elif args.schedule == 'st':
model_path = os.path.join(save_path, 'models_st')
else:
model_path = os.path.join(save_path, 'models_st_transfer')
if args.resume_from > 0:
assert (os.path.exists(model_path))
if not os.path.exists(model_path) and not args.val:
os.makedirs(model_path)
if args.schedule == 'step1':
log_file = os.path.join(save_path, 'logs.txt')
elif args.schedule == 'mixed':
log_file = os.path.join(save_path, 'logs_transfer.txt')
elif args.schedule == 'st':
log_file = os.path.join(save_path, 'logs_st.txt')
else:
log_file = os.path.join(save_path, 'logs_st_transfer.txt')
if args.val:
log_file = os.path.join(dataset_path, 'logs_test.txt')
logger = logWritter(log_file)
if args.schedule == 'step1':
config_path = os.path.join(save_path, 'configs.yaml')
elif args.schedule == 'mixed':
config_path = os.path.join(save_path, 'configs_transfer.yaml')
elif args.schedule == 'st':
config_path = os.path.join(save_path, 'configs_st.yaml')
else:
config_path = os.path.join(save_path, 'configs_st_transfer.yaml')
"""
Start
"""
if args.val:
print("\n***Testing of model {0}***\n".format(config['init_model']))
logger.write("\n***Testing of model {0}***\n".format(config['init_model']))
else:
print("\n***Training of model {0}***\n".format(args.experimentid))
logger.write("\n***Training of model {0}***\n".format(args.experimentid))
"""
Continue train or train from scratch
"""
if args.resume_from >= 1:
assert (args.val == False)
if not os.path.exists(config_path):
assert 0, "Old config not found."
config_old = get_config(config_path)
if config['save_path'] != config_old['save_path'] or config['dataset'] != config_old['dataset']:
assert 0, "New config does not coordinate with old config."
config = config_old
start_iter = args.resume_from
print("Continue training from Iter - [{0:0>6d}] ...".format(start_iter + 1))
logger.write("Continue training from Iter - [{0:0>6d}] ...".format(start_iter + 1))
else:
start_iter = 0
if not args.val:
shutil.copy(args.config, config_path)
print("Train from scratch ...")
logger.write("Train from scratch ...")
"""
Modify config
"""
if args.schedule == 'step1':
config['back_scheduler']['init_lr'] = config['back_opt']['lr']
elif args.schedule == 'mixed':
config['back_scheduler']['init_lr_transfer'] = config['back_opt']['lr_transfer']
elif args.schedule == 'st':
config['back_scheduler']['init_lr_st'] = config['back_opt']['lr_st']
else:
config['back_scheduler']['init_lr_st_transfer'] = config['back_opt']['lr_st_transfer']
if args.schedule == 'step1':
config['back_scheduler']['max_iter'] = config['ITER_MAX']
elif args.schedule == 'mixed':
config['back_scheduler']['max_iter_transfer'] = config['ITER_MAX_TRANSFER']
elif args.schedule == 'st':
config['back_scheduler']['max_iter_st'] = config['ITER_MAX_ST']
else:
config['back_scheduler']['max_iter_st_transfer'] = config['ITER_MAX_ST_TRANSFER']
"""
Schedule method
"""
s = "Schedule method: {0}".format(args.schedule)
if args.schedule == 'mixed' or args.schedule == 'st_mixed':
s += ", interval_step1={0}, interval_step2={1}".format(config['interval_step1'], config['interval_step2'])
s += '\n'
print(s)
logger.write(s)
"""
Use GPU
"""
device = torch.device("cuda")
if not args.multigpus:
torch.cuda.set_device(args.ngpu)
torch.backends.cudnn.benchmark = True
"""
Get dataLoader
"""
vals_cls, valu_cls, all_labels, visible_classes, visible_classes_test, train, val, sampler, visibility_mask, cls_map, cls_map_test = get_split(config)
assert (visible_classes_test.shape[0] == config['dis']['out_dim_cls'] - 1)
dataset = get_dataset(config['DATAMODE'])(
train=train,
test=None,
root=config['ROOT'],
split=config['SPLIT']['TRAIN'],
base_size=513,
crop_size=config['IMAGE']['SIZE']['TRAIN'],
mean=(config['IMAGE']['MEAN']['B'], config['IMAGE']['MEAN']['G'], config['IMAGE']['MEAN']['R']),
warp=config['WARP_IMAGE'],
scale=(0.5, 1.5),
flip=True,
visibility_mask=visibility_mask
)
loader = torch.utils.data.DataLoader(
dataset=dataset,
batch_size=config['BATCH_SIZE']['TRAIN'],
num_workers=config['NUM_WORKERS'],
sampler=sampler
)
dataset_test = get_dataset(config['DATAMODE'])(
train=None,
test=val,
root=config['ROOT'],
split=config['SPLIT']['TEST'],
base_size=513,
crop_size=config['IMAGE']['SIZE']['TEST'],
mean=(config['IMAGE']['MEAN']['B'], config['IMAGE']['MEAN']['G'], config['IMAGE']['MEAN']['R']),
warp=config['WARP_IMAGE'],
scale=None,
flip=False
)
loader_test = torch.utils.data.DataLoader(
dataset=dataset_test,
batch_size=config['BATCH_SIZE']['TEST'],
num_workers=config['NUM_WORKERS'],
shuffle=False
)
"""
Load Class embedding
"""
class_emb = get_embedding(config)
class_emb_vis = class_emb[visible_classes]
class_emb_vis_ = torch.zeros((config['ignore_index'] + 1 - class_emb_vis.shape[0], class_emb_vis.shape[1]), dtype = torch.float32)
class_emb_vis_aug = torch.cat((class_emb_vis, class_emb_vis_), dim=0)
class_emb_all = class_emb[visible_classes_test]
"""
Get trainer
"""
trainer = Trainer(
cfg=config,
class_emb_vis=class_emb_vis_aug,
class_emb_all=class_emb_all,
schedule=args.schedule,
checkpoint_dir=model_path, # for model loading in continued train
resume_from=start_iter # for model loading in continued train
).to(device)
if args.multigpus:
trainer.model = torch.nn.DataParallel(trainer.model, device_ids=range(args.ngpu))
"""
Train/Val
"""
if args.val:
"""
Only do validation
"""
loader_iter_test = iter(loader_test)
targets, outputs = [], []
while True:
try:
data_test, gt_test, image_id = next(loader_iter_test) # gt_test: torch.LongTensor with shape (N,H,W). elements: 0-19,255 in voc12
except:
break # finish test
data_test = torch.Tensor(data_test).to(device)
with torch.no_grad():
try:
test_res = trainer.test(data_test, gt_test, multigpus=args.multigpus)
except MeaninglessError:
continue # skip meaningless batch
pred_cls_test = test_res['pred_cls_real'].cpu() # torch.LongTensor with shape (N,H',W'). elements: 0-20 in voc12
resized_gt_test = test_res['resized_gt'].cpu() # torch.LongTensor with shape (N,H',W'). elements: 0-19,255 in voc12
##### gt mapping to target #####
resized_target = cls_map_test[resized_gt_test]
for o, t in zip(pred_cls_test.numpy(), resized_target):
outputs.append(o)
targets.append(t)
score, class_iou = scores_gzsl(targets, outputs, n_class=len(visible_classes_test), seen_cls=cls_map_test[vals_cls], unseen_cls=cls_map_test[valu_cls])
print("Test results:")
logger.write("Test results:")
for k, v in score.items():
print(k + ': ' + json.dumps(v))
logger.write(k + ': ' + json.dumps(v))
score["Class IoU"] = {}
for i in range(len(visible_classes_test)):
score["Class IoU"][all_labels[visible_classes_test[i]]] = class_iou[i]
print("Class IoU: " + json.dumps(score["Class IoU"]))
logger.write("Class IoU: " + json.dumps(score["Class IoU"]))
print("Test finished.\n\n")
logger.write("Test finished.\n\n")
else:
"""
Training loop
"""
if args.schedule == 'step1':
ITER_MAX = config['ITER_MAX']
elif args.schedule == 'mixed':
ITER_MAX = config['ITER_MAX_TRANSFER']
elif args.schedule == 'st':
ITER_MAX = config['ITER_MAX_ST']
else:
ITER_MAX = config['ITER_MAX_ST_TRANSFER']
assert (start_iter < ITER_MAX)
# dealing with 'st_mixed' is the same as dealing with 'mixed'
if args.schedule == 'st_mixed':
args.schedule = 'mixed'
assert (args.schedule in ['step1', 'mixed', 'st'])
if args.schedule == 'step1':
step_scheduler = Const_Scheduler(step_n='step1')
elif args.schedule == 'mixed':
step_scheduler = Step_Scheduler(config['interval_step1'], config['interval_step2'], config['first'])
else:
step_scheduler = Const_Scheduler(step_n='self_training')
iteration = start_iter
loader_iter = iter(loader)
while True:
if iteration == start_iter or iteration % 1000 == 0:
now_lr = trainer.get_lr()
print("Now lr of dis: {0:.10f}".format(now_lr['dis_lr']))
print("Now lr of gen: {0:.10f}".format(now_lr['gen_lr']))
print("Now lr of back: {0:.10f}".format(now_lr['back_lr']))
logger.write("Now lr of dis: {0:.10f}".format(now_lr['dis_lr']))
logger.write("Now lr of gen: {0:.10f}".format(now_lr['gen_lr']))
logger.write("Now lr of back: {0:.10f}".format(now_lr['back_lr']))
sum_loss_train = np.zeros(config['loss_count'], dtype=np.float64)
sum_acc_real_train, sum_acc_fake_train = 0, 0
temp_iter = 0
sum_loss_train_transfer = 0
sum_acc_fake_train_transfer = 0
temp_iter_transfer = 0
# mode should be constant 'step1' in non-zero-shot-learning
# mode should be switched between 'step1' and 'step2' in zero-shot-learning
mode = step_scheduler.now()
assert (mode in ['step1', 'step2', 'self_training'])
if mode == 'step1' or mode == 'self_training':
try:
data, gt = next(loader_iter)
except:
loader_iter = iter(loader)
data, gt = next(loader_iter)
data = torch.Tensor(data).to(device)
if mode == 'step1' or mode == 'step2':
try:
loss = trainer.train(data, gt, mode=mode, multigpus=args.multigpus)
except MeaninglessError:
print("Skipping meaningless batch...")
continue
else: # self training mode
try:
with torch.no_grad():
test_res = trainer.test(data, gt, multigpus=args.multigpus)
resized_gt_for_st = test_res['resized_gt'].cpu() # torch.LongTensor with shape (N,H',W'). elements: 0-14,255 in voc12
sorted_indices = test_res['sorted_indices'].cpu() # torch.LongTensor with shape (N,H',W',C)
gt_new = construct_gt_st(resized_gt_for_st, sorted_indices, config)
loss = trainer.train(data, gt_new, mode='step1', multigpus=args.multigpus)
except MeaninglessError:
print("Skipping meaningless batch...")
continue
if mode == 'step1' or mode == 'self_training':
loss_G_GAN = loss['loss_G_GAN']
loss_G_Content = loss['loss_G_Content']
loss_B_KLD = loss['loss_B_KLD']
loss_D_real = loss['loss_D_real']
loss_D_fake = loss['loss_D_fake']
loss_D_gp = loss['loss_D_gp']
loss_cls_real = loss['loss_cls_real']
loss_cls_fake = loss['loss_cls_fake']
acc_cls_real = loss['acc_cls_real']
acc_cls_fake = loss['acc_cls_fake']
sum_loss_train += np.array([loss_G_GAN, loss_G_Content, loss_B_KLD, loss_D_real, loss_D_fake, loss_D_gp, loss_cls_real, loss_cls_fake]).astype(np.float64)
sum_acc_real_train += acc_cls_real
sum_acc_fake_train += acc_cls_fake
temp_iter += 1
tal = sum_loss_train / temp_iter
tsar = sum_acc_real_train / temp_iter
tsaf = sum_acc_fake_train / temp_iter
# display accumulated average loss and accuracy in step1
if (iteration + 1) % config['display_interval'] == 0:
print("Iter - [{0:0>6d}] AAL: G_G-[{1:.4f}] G_C-[{2:.4f}] B_K-[{3:.4f}] D_r-[{4:.4f}] D_f-[{5:.4f}] D_gp-[{6:.4f}] cls_r-[{7:.4f}] cls_f-[{8:.4f}] Acc: cls_r-[{9:.4f}] cls_f-[{10:.4f}]".format(\
iteration + 1, tal[0], tal[1], tal[2], tal[3], tal[4], tal[5], tal[6], tal[7], tsar, tsaf))
if (iteration + 1) % config['log_interval'] == 0:
logger.write("Iter - [{0:0>6d}] AAL: G_G-[{1:.4f}] G_C-[{2:.4f}] B_K-[{3:.4f}] D_r-[{4:.4f}] D_f-[{5:.4f}] D_gp-[{6:.4f}] cls_r-[{7:.4f}] cls_f-[{8:.4f}] Acc: cls_r-[{9:.4f}] cls_f-[{10:.4f}]".format(\
iteration + 1, tal[0], tal[1], tal[2], tal[3], tal[4], tal[5], tal[6], tal[7], tsar, tsaf))
elif mode == 'step2':
loss_cls_fake_transfer = loss['loss_cls_fake']
acc_cls_fake_transfer = loss['acc_cls_fake']
sum_loss_train_transfer += loss_cls_fake_transfer
sum_acc_fake_train_transfer += acc_cls_fake_transfer
temp_iter_transfer += 1
talt = sum_loss_train_transfer / temp_iter_transfer
tsaft = sum_acc_fake_train_transfer / temp_iter_transfer
# display accumulated average loss and accuracy in step2 (transfer learning)
if (iteration + 1) % config['display_interval'] == 0:
print("Iter - [{0:0>6d}] Transfer Learning: aal_cls_f-[{1:.4f}] acc_cls_f-[{2:.4f}]".format(\
iteration + 1, talt, tsaft))
if (iteration + 1) % config['log_interval'] == 0:
logger.write("Iter - [{0:0>6d}] Transfer Learning: aal_cls_f-[{1:.4f}] acc_cls_f-[{2:.4f}]".format(\
iteration + 1, talt, tsaft))
else:
raise NotImplementedError('Mode {} not implemented' % mode)
# Save the temporary model
if (iteration + 1) % config['snapshot'] == 0:
trainer.save(model_path, iteration, args.multigpus)
print("Temporary model of Iter - [{0:0>6d}] successfully stored.\n".format(iteration + 1))
logger.write("Temporary model of Iter - [{0:0>6d}] successfully stored.\n".format(iteration + 1))
# Test the saved model
if (iteration + 1) % config['snapshot'] == 0:
print("Testing model of Iter - [{0:0>6d}] ...".format(iteration + 1))
logger.write("Testing model of Iter - [{0:0>6d}] ...".format(iteration + 1))
loader_iter_test = iter(loader_test)
targets, outputs = [], []
while True:
try:
data_test, gt_test,image_id = next(loader_iter_test) # gt_test: torch.LongTensor with shape (N,H,W). elements: 0-19,255 in voc12
except:
break # finish test
data_test = torch.Tensor(data_test).to(device)
with torch.no_grad():
try:
test_res = trainer.test(data_test, gt_test, multigpus=args.multigpus)
except MeaninglessError:
continue # skip meaningless batch
pred_cls_test = test_res['pred_cls_real'].cpu() # torch.LongTensor with shape (N,H',W'). elements: 0-20 in voc12
resized_gt_test = test_res['resized_gt'].cpu() # torch.LongTensor with shape (N,H',W'). elements: 0-19,255 in voc12
##### gt mapping to target #####
resized_target = cls_map_test[resized_gt_test]
for o, t in zip(pred_cls_test.numpy(), resized_target):
outputs.append(o)
targets.append(t)
score, class_iou = scores_gzsl(targets, outputs, n_class=len(visible_classes_test), seen_cls=cls_map_test[vals_cls], unseen_cls=cls_map_test[valu_cls])
print("Test results:")
logger.write("Test results:")
for k, v in score.items():
print(k + ': ' + json.dumps(v))
logger.write(k + ': ' + json.dumps(v))
score["Class IoU"] = {}
for i in range(len(visible_classes_test)):
score["Class IoU"][all_labels[visible_classes_test[i]]] = class_iou[i]
print("Class IoU: " + json.dumps(score["Class IoU"]))
logger.write("Class IoU: " + json.dumps(score["Class IoU"]))
print("Test finished.\n")
logger.write("Test finished.\n")
step_scheduler.step()
iteration += 1
if iteration == ITER_MAX:
break
print("Train finished.\n\n")
logger.write("Train finished.\n\n")
if __name__ == '__main__':
main()