def save_checkpoint(state,
is_best,
save_best_only=False,
file_directory=None,
epoch=0,
ind='#'):
if ind == '#':
filename = 'checkpoint_{:04d}.pth.tar'
else:
filename = 'checkpoint_{:04d}_{:01d}.pth.tar'
if file_directory is None:
file_directory = ''
else:
file_directory = get_dir(file_directory)
if ind == '':
file_path = os.path.join(file_directory, filename.format(epoch))
else:
file_path = os.path.join(file_directory, filename.format(epoch, ind))
torch.save(state, file_path)
if is_best:
if ind == '#':
model_best_path = os.path.join(file_directory,
'model_best.pth.tar')
else:
model_best_path = os.path.join(
file_directory, 'model_best_' + str(ind) + '.pth.tar')
if save_best_only:
shutil.move(file_path, model_best_path)
else:
shutil.copyfile(file_path, model_best_path)
def save_checkpoint(state, is_best, file_directory=None, epoch=0):
filename = 'checkpoint_{:04d}.pth.tar'
if file_directory is None:
file_directory = ''
else:
file_directory = get_dir(file_directory)
file_path = os.path.join(file_directory, filename.format(epoch))
torch.save(state, file_path)
if is_best:
model_best_path = os.path.join(file_directory, 'model_best.pth.tar')
shutil.copyfile(file_path, model_best_path)
def main():
import argparse
parser = argparse.ArgumentParser(description="Pytorch Image CNN training from Configure Files")
parser.add_argument('--config_file', required=True, help="This scripts only accepts parameters from Json files")
input_args = parser.parse_args()
config_file = input_args.config_file
args = parse_config(config_file)
if args.name is None:
args.name = get_stem(config_file)
torch.set_default_tensor_type('torch.FloatTensor')
best_prec1 = 0
args.script_name = get_stem(__file__)
current_time_str = get_date_str()
# if args.resume is None:
if args.save_directory is None:
save_directory = get_dir(os.path.join(project_root, 'ckpts2', '{:s}'.format(args.name), '{:s}-{:s}'.format(args.ID, current_time_str)))
else:
save_directory = get_dir(os.path.join(project_root, 'ckpts2', args.save_directory))
# else:
# save_directory = os.path.dirname(args.resume)
print("Save to {}".format(save_directory))
log_file = os.path.join(save_directory, 'log-{0}.txt'.format(current_time_str))
logger = log_utils.get_logger(log_file)
log_utils.print_config(vars(args), logger)
print_func = logger.info
print_func('ConfigFile: {}'.format(config_file))
args.log_file = log_file
if args.device:
os.environ["CUDA_VISIBLE_DEVICES"]=args.device
if args.seed is not None:
random.seed(args.seed)
torch.manual_seed(args.seed)
cudnn.deterministic = True
warnings.warn('You have chosen to seed training. '
'This will turn on the CUDNN deterministic setting, '
'which can slow down your training considerably! '
'You may see unexpected behavior when restarting '
'from checkpoints.')
if args.gpu is not None:
warnings.warn('You have chosen a specific GPU. This will completely '
'disable data parallelism.')
args.distributed = args.world_size > 1
if args.distributed:
dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
world_size=args.world_size)
if args.pretrained:
print_func("=> using pre-trained model '{}'".format(args.arch))
visual_model = models.__dict__[args.arch](pretrained=True, num_classes=args.num_classes)
else:
print_func("=> creating model '{}'".format(args.arch))
visual_model = models.__dict__[args.arch](pretrained=False, num_classes=args.num_classes)
if args.freeze:
visual_model = CNN_utils.freeze_all_except_fc(visual_model)
if os.path.isfile(args.text_ckpt):
print_func("=> loading checkpoint '{}'".format(args.text_ckpt))
text_data = torch.load(args.text_ckpt, map_location=lambda storage, loc:storage)
text_model = TextCNN(text_data['args_model'])
# load_state_dict(text_model, text_data['state_dict'])
text_model.load_state_dict(text_data['state_dict'], strict=True)
text_model.eval()
print_func("=> loaded checkpoint '{}' for text classification"
.format(args.text_ckpt))
args.vocab_size = text_data['args_model'].vocab_size
else:
print_func("=> no checkpoint found at '{}'".format(args.text_ckpt))
return
args.tag2clsidx = text_data['args_data'].tag2idx
args.vocab_size = len(args.tag2clsidx)
args.text_embed = loadpickle(args.text_embed)
args.idx2tag = loadpickle(args.idx2tag)['idx2tag']
if args.gpu is not None:
visual_model = visual_model.cuda(args.gpu)
text_model = text_model.cuda((args.gpu))
elif args.distributed:
visual_model.cuda()
visual_model = torch.nn.parallel.DistributedDataParallel(visual_model)
else:
if args.arch.startswith('alexnet') or args.arch.startswith('vgg'):
visual_model.features = torch.nn.DataParallel(visual_model.features)
visual_model.cuda()
else:
visual_model = torch.nn.DataParallel(visual_model).cuda()
text_model = torch.nn.DataParallel(text_model).cuda()
criterion = nn.CrossEntropyLoss(ignore_index=-1).cuda(args.gpu)
optimizer = torch.optim.SGD(filter(lambda p: p.requires_grad, visual_model.parameters()), lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
if args.lr_schedule:
print_func("Using scheduled learning rate")
scheduler = lr_scheduler.MultiStepLR(
optimizer, [int(i) for i in args.lr_schedule.split(',')], gamma=0.1)
else:
scheduler = lr_scheduler.ReduceLROnPlateau(
optimizer, 'min', patience=args.lr_patience)
# optimizer = torch.optim.SGD(model.parameters(), args.lr,
# momentum=args.momentum,
# weight_decay=args.weight_decay)
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print_func("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
import collections
if isinstance(checkpoint, collections.OrderedDict):
load_state_dict(visual_model, checkpoint)
else:
load_state_dict(visual_model, checkpoint['state_dict'])
print_func("=> loaded checkpoint '{}' (epoch {})"
.format(args.resume, checkpoint['epoch']))
else:
print_func("=> no checkpoint found at '{}'".format(args.resume))
cudnn.benchmark = True
model_total_params = sum(p.numel() for p in visual_model.parameters())
model_grad_params = sum(p.numel() for p in visual_model.parameters() if p.requires_grad)
print_func("Total Parameters: {0}\t Gradient Parameters: {1}".format(model_total_params, model_grad_params))
# Data loading code
val_dataset = get_instance(custom_datasets, '{0}'.format(args.valloader), args)
if val_dataset is None:
val_loader = None
else:
val_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True, collate_fn=none_collate)
if args.evaluate:
print_func('Validation Only')
validate(val_loader, visual_model, criterion, args, print_func)
return
else:
train_dataset = get_instance(custom_datasets, '{0}'.format(args.trainloader), args)
if args.distributed:
train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
else:
train_sampler = None
train_loader = torch.utils.data.DataLoader(
train_dataset, batch_size=args.batch_size, shuffle=(train_sampler is None),
num_workers=args.workers, pin_memory=True, sampler=train_sampler, collate_fn=none_collate)
for epoch in range(args.start_epoch, args.epochs):
if args.distributed:
train_sampler.set_epoch(epoch)
if args.lr_schedule:
# CNN_utils.adjust_learning_rate(optimizer, epoch, args.lr)
scheduler.step()
current_lr = optimizer.param_groups[0]['lr']
print_func("Epoch: [{}], learning rate: {}".format(epoch, current_lr))
# train for one epoch
train(train_loader, visual_model, text_model, criterion, optimizer, epoch, args, print_func)
# evaluate on validation set
if val_loader:
prec1, val_loss = validate(val_loader, visual_model, criterion, args, print_func)
else:
prec1 = 0
val_loss = 0
# remember best prec@1 and save checkpoint
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
CNN_utils.save_checkpoint({
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': visual_model.state_dict(),
'best_prec1': best_prec1,
'optimizer' : optimizer.state_dict(),
}, is_best, file_directory=save_directory, epoch=epoch)
if not args.lr_schedule:
scheduler.step(val_loss)
import logging
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s',
level=logging.INFO)
import tqdm
from PyUtils.file_utils import get_dir
from PyUtils.pickle_utils import loadpickle
from gensim import models
import random
random.seed(0)
user_root = os.path.expanduser('~')
training_data = loadpickle(
os.path.join(user_root,
'Dev/AttributeNet3/LanguageData/raw_tag_sentences.pkl'))
save_directory = get_dir(
os.path.join(user_root, 'Dev/AttributeNet3/LanguageData/word2vec_models'))
training_sentences = training_data['data']
max_len = training_data['max_len']
embedding_dim = 300
window_size = max_len
texts = []
shuffle_times = 10
def random_drop(s_text, drop_rate=0.1):
updated_text = []
for s_tag in s_text:
p = random.uniform(0, 1)
def main():
best_prec1 = 0
args = parser.parser.parse_args()
if args.config is not None:
args = parse_config(args.config)
script_name_stem = get_stem(__file__)
current_time_str = get_date_str()
if args.save_directory is None:
raise FileNotFoundError(
"Saving directory should be specified for feature extraction tasks"
)
save_directory = get_dir(args.save_directory)
print("Save to {}".format(save_directory))
log_file = os.path.join(save_directory,
'log-{0}.txt'.format(current_time_str))
logger = log_utils.get_logger(log_file)
log_utils.print_config(vars(args), logger)
print_func = logger.info
args.log_file = log_file
if args.device:
os.environ["CUDA_VISIBLE_DEVICES"] = args.device
if args.seed is not None:
random.seed(args.seed)
torch.manual_seed(args.seed)
cudnn.deterministic = True
warnings.warn('You have chosen to seed training. '
'This will turn on the CUDNN deterministic setting, '
'which can slow down your training considerably! '
'You may see unexpected behavior when restarting '
'from checkpoints.')
if args.gpu is not None:
warnings.warn(
'You have chosen a specific GPU. This will completely disable data parallelism.'
)
args.distributed = args.world_size > 1
if args.distributed:
dist.init_process_group(backend=args.dist_backend,
init_method=args.dist_url,
world_size=args.world_size)
if args.arch == 'resnet50_feature_extractor':
print_func("=> using pre-trained model '{}' to LOAD FEATURES".format(
args.arch))
model = models.__dict__[args.arch](pretrained=True,
num_classes=args.num_classes,
param_name=args.paramname)
else:
print_func(
"This is only for feature extractors!, Please double check the parameters!"
)
return
# if args.freeze:
# model = CNN_utils.freeze_all_except_fc(model)
if args.gpu is not None:
model = model.cuda(args.gpu)
else:
print_func(
'Please only specify one GPU since we are working in batch size 1 model'
)
return
cudnn.benchmark = True
model_total_params = sum(p.numel() for p in model.parameters())
model_grad_params = sum(p.numel() for p in model.parameters()
if p.requires_grad)
print_func("Total Parameters: {0}\t Gradient Parameters: {1}".format(
model_total_params, model_grad_params))
# Data loading code
val_dataset = get_instance(custom_datasets,
'{0}'.format(args.dataset.name), args,
**args.dataset.args)
import tqdm
import numpy as np
if args.individual_feat:
feature_save_directory = get_dir(
os.path.join(save_directory, 'individual-features'))
created_paths = set()
else:
data_dict = {}
feature_save_directory = os.path.join(save_directory, 'feature.pkl')
model.eval()
for s_data in tqdm.tqdm(val_dataset, desc="Extracting Features"):
if s_data is None:
continue
s_image_name = s_data[1]
s_image_data = s_data[0]
if args.gpu is not None:
s_image_data = s_image_data.cuda(args.gpu, non_blocking=True)
output = model(s_image_data.unsqueeze_(0))
output = output.cpu().data.numpy()
image_rel_path = os.path.join(
*(s_image_name.split(os.sep)[-args.rel_path_depth:]))
if args.individual_feat:
image_directory = os.path.dirname(image_rel_path)
if image_directory in created_paths:
np.save(
os.path.join(feature_save_directory,
'{}.npy'.format(image_rel_path)), output)
else:
get_dir(os.path.join(feature_save_directory, image_directory))
np.save(
os.path.join(feature_save_directory,
'{}.npy'.format(image_rel_path)), output)
created_paths.add(image_directory)
else:
data_dict[image_rel_path] = output
# image_name = os.path.basename(s_image_name)
#
# if args.individual_feat:
# # image_name = os.path.basename(s_image_name)
#
# np.save(os.path.join(feature_save_directory, '{}.npy'.format(image_name)), output)
# # created_paths.add(image_directory)
# else:
# data_dict[get_stem(image_name)] = output
if args.individual_feat:
print_func("Done")
else:
from PyUtils.pickle_utils import save2pickle
print_func("Saving to a single big file!")
save2pickle(feature_save_directory, data_dict)
print_func("Done")
target_dir = '/home/zwei/datasets/tarPublicEmotion/AffectNet/images-256'
src_dir = '/home/zwei/datasets/tarPublicEmotion/AffectNet/Manually_Annotated_Images_256'
selected_files = []
selected_copies = []
subdirs = set()
for s_file in files:
src_path = os.path.join(src_dir, s_file[0])
if os.path.exists(src_path):
dst_path = os.path.join(target_dir, s_file[0])
selected_files.append(s_file)
subdirs.add(os.path.dirname(dst_path))
selected_copies.append([src_path, dst_path])
for s_dir in subdirs:
get_dir(s_dir)
import tqdm
from multiprocessing import Pool
num_jobs = 100
pool = Pool(processes=num_jobs)
for s_status in tqdm.tqdm(pool.imap_unordered(mThreadCopy, selected_copies),
total=len(selected_copies)):
if not s_status[0]:
print(s_status[1])
save2pickle(
'/home/zwei/Dev/AttributeNet3/PublicEmotionDatasets/AffectNet/validation_exist.pkl',
selected_files)
def main():
import argparse
parser = argparse.ArgumentParser(
description="Pytorch Image CNN training from Configure Files")
parser.add_argument(
'--config_file',
required=True,
help="This scripts only accepts parameters from Json files")
input_args = parser.parse_args()
config_file = input_args.config_file
args = parse_config(config_file)
if args.name is None:
args.name = get_stem(config_file)
torch.set_default_tensor_type('torch.FloatTensor')
best_prec1 = 0
args.script_name = get_stem(__file__)
current_time_str = get_date_str()
if args.save_directory is None:
save_directory = get_dir(
os.path.join(project_root, args.ckpts_dir,
'{:s}'.format(args.name),
'{:s}-{:s}'.format(args.ID, current_time_str)))
else:
save_directory = get_dir(
os.path.join(project_root, args.ckpts_dir, args.save_directory))
print("Save to {}".format(save_directory))
log_file = os.path.join(save_directory,
'log-{0}.txt'.format(current_time_str))
logger = log_utils.get_logger(log_file)
log_utils.print_config(vars(args), logger)
print_func = logger.info
print_func('ConfigFile: {}'.format(config_file))
args.log_file = log_file
if args.device:
os.environ["CUDA_VISIBLE_DEVICES"] = args.device
if args.seed is not None:
random.seed(args.seed)
torch.manual_seed(args.seed)
cudnn.deterministic = True
warnings.warn('You have chosen to seed training. '
'This will turn on the CUDNN deterministic setting, '
'which can slow down your training considerably! '
'You may see unexpected behavior when restarting '
'from checkpoints.')
if args.gpu is not None:
warnings.warn('You have chosen a specific GPU. This will completely '
'disable data parallelism.')
args.distributed = args.world_size > 1
if args.distributed:
dist.init_process_group(backend=args.dist_backend,
init_method=args.dist_url,
world_size=args.world_size)
if args.pretrained:
print_func("=> using pre-trained model '{}'".format(args.arch))
model = models.__dict__[args.arch](pretrained=True,
num_classes=args.num_classes)
else:
print_func("=> creating model '{}'".format(args.arch))
model = models.__dict__[args.arch](pretrained=False,
num_classes=args.num_classes)
if args.freeze:
model = CNN_utils.freeze_all_except_fc(model)
if args.gpu is not None:
model = model.cuda(args.gpu)
elif args.distributed:
model.cuda()
model = torch.nn.parallel.DistributedDataParallel(model)
else:
print_func(
'Please only specify one GPU since we are working in batch size 1 model'
)
return
if args.resume:
if os.path.isfile(args.resume):
print_func("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
import collections
if not args.evaluate:
if isinstance(checkpoint, collections.OrderedDict):
load_state_dict(model,
checkpoint,
exclude_layers=['fc.weight', 'fc.bias'])
else:
load_state_dict(
model,
checkpoint['state_dict'],
exclude_layers=['module.fc.weight', 'module.fc.bias'])
print_func("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
if isinstance(checkpoint, collections.OrderedDict):
load_state_dict(model, checkpoint, strict=True)
else:
load_state_dict(model,
checkpoint['state_dict'],
strict=True)
print_func("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
print_func("=> no checkpoint found at '{}'".format(args.resume))
return
else:
print_func(
"=> This script is for fine-tuning only, please double check '{}'".
format(args.resume))
print_func("Now using randomly initialized parameters!")
cudnn.benchmark = True
model_total_params = sum(p.numel() for p in model.parameters())
model_grad_params = sum(p.numel() for p in model.parameters()
if p.requires_grad)
print_func("Total Parameters: {0}\t Gradient Parameters: {1}".format(
model_total_params, model_grad_params))
# Data loading code
# val_dataset = get_instance(custom_datasets, '{0}'.format(args.valloader), args)
from PyUtils.pickle_utils import loadpickle
from torchvision.datasets.folder import default_loader
val_dataset = loadpickle(args.val_file)
image_directory = args.data_dir
from CNNs.datasets.multilabel import get_val_simple_transform
val_transform = get_val_simple_transform()
import tqdm
import numpy as np
if args.individual_feat:
feature_save_directory = get_dir(
os.path.join(save_directory, 'individual-features'))
created_paths = set()
else:
data_dict = {}
feature_save_directory = os.path.join(save_directory, 'feature.pkl')
model.eval()
for s_data in tqdm.tqdm(val_dataset, desc="Extracting Features"):
if s_data is None:
continue
image_path = os.path.join(image_directory, s_data[0])
try:
input_image = default_loader(image_path)
except:
print("WARN: {} Problematic!, Skip!".format(image_path))
continue
input_image = val_transform(input_image)
if args.gpu is not None:
input_image = input_image.cuda(args.gpu, non_blocking=True)
output = model(input_image.unsqueeze_(0))
output = output.cpu().data.numpy()
# image_rel_path = os.path.join(*(s_image_name.split(os.sep)[-int(args.rel_path_depth):]))
if args.individual_feat:
if image_directory in created_paths:
np.save(
os.path.join(feature_save_directory,
'{}.npy'.format(s_data[0])), output)
else:
get_dir(os.path.join(feature_save_directory, image_directory))
np.save(
os.path.join(feature_save_directory,
'{}.npy'.format(s_data[0])), output)
created_paths.add(image_directory)
else:
data_dict[s_data[0]] = output
# image_name = os.path.basename(s_image_name)
#
# if args.individual_feat:
# # image_name = os.path.basename(s_image_name)
#
# np.save(os.path.join(feature_save_directory, '{}.npy'.format(image_name)), output)
# # created_paths.add(image_directory)
# else:
# data_dict[get_stem(image_name)] = output
if args.individual_feat:
print_func("Done")
else:
from PyUtils.pickle_utils import save2pickle
print_func("Saving to a single big file!")
save2pickle(feature_save_directory, data_dict)
print_func("Done")
dst_image.save(target_file)
return True, 'Success'
except:
return False, 'Fail'
dataset_directory = '/home/zwei/datasets/PublicEmotion/EMOTIC'
original_image_directory = os.path.join(dataset_directory, 'images')
target_image_directory = get_dir(os.path.join(dataset_directory, 'images_person_crop'))
data_split = 'test'
annotation_file = os.path.join(dataset_directory, 'new_z_data', '{}_person_crop.pkl'.format(data_split))
raw_annotation_list = loadpickle(annotation_file)
from PyUtils.file_utils import get_stem, get_extension
for s_annotation in tqdm.tqdm(raw_annotation_list, desc="Processing data"):
s_target_file = s_annotation[0]
s_bbox = s_annotation[-1]
s_file_ext = get_extension(s_target_file)
s_original_file_path = os.path.join(original_image_directory, '{}.{}'.format(s_target_file[:-8],s_file_ext))
s_target_file_path = os.path.join(target_image_directory, s_target_file)
def main():
import argparse
parser = argparse.ArgumentParser(
description="Pytorch Image CNN training from Configure Files")
parser.add_argument(
'--config_file',
required=True,
help="This scripts only accepts parameters from Json files")
input_args = parser.parse_args()
config_file = input_args.config_file
args = parse_config(config_file)
if args.name is None:
args.name = get_stem(config_file)
torch.set_default_tensor_type('torch.FloatTensor')
args.script_name = get_stem(__file__)
current_time_str = get_date_str()
if args.resume is None:
if args.save_directory is None:
save_directory = get_dir(
os.path.join(project_root, 'ckpts', '{:s}'.format(args.name),
'{:s}-{:s}'.format(args.ID, current_time_str)))
else:
save_directory = get_dir(
os.path.join(project_root, 'ckpts', args.save_directory))
else:
if args.save_directory is None:
save_directory = os.path.dirname(args.resume)
else:
current_time_str = get_date_str()
save_directory = get_dir(
os.path.join(args.save_directory, '{:s}'.format(args.name),
'{:s}-{:s}'.format(args.ID, current_time_str)))
print("Save to {}".format(save_directory))
log_file = os.path.join(save_directory,
'log-{0}.txt'.format(current_time_str))
logger = log_utils.get_logger(log_file)
log_utils.print_config(vars(args), logger)
print_func = logger.info
print_func('ConfigFile: {}'.format(config_file))
args.log_file = log_file
if args.device:
os.environ["CUDA_VISIBLE_DEVICES"] = args.device
if args.seed is not None:
random.seed(args.seed)
torch.manual_seed(args.seed)
cudnn.deterministic = True
warnings.warn('You have chosen to seed training. '
'This will turn on the CUDNN deterministic setting, '
'which can slow down your training considerably! '
'You may see unexpected behavior when restarting '
'from checkpoints.')
if args.gpu is not None:
warnings.warn('You have chosen a specific GPU. This will completely '
'disable data parallelism.')
#args.distributed = args.world_size > 1
args.distributed = False
if args.distributed:
dist.init_process_group(backend=args.dist_backend,
init_method=args.dist_url,
world_size=args.world_size)
num_datasets = args.num_datasets
# model_list = [None for x in range(num_datasets)]
# for j in range(num_datasets):
if args.pretrained:
print_func("=> using pre-trained model '{}'".format(args.arch))
model = models.__dict__[args.arch](pretrained=True,
num_classes=args.class_len)
else:
print_func("=> creating model '{}'".format(args.arch))
model = models.__dict__[args.arch](pretrained=False,
num_classes=args.class_len)
if args.freeze:
model = CNN_utils.freeze_all_except_fc(model)
if args.gpu is not None:
model = model.cuda(args.gpu)
elif args.distributed:
model.cuda()
model = torch.nn.parallel.DistributedDataParallel(model)
else:
if args.arch.startswith('alexnet') or args.arch.startswith('vgg'):
model.features = torch.nn.DataParallel(model.features)
model.cuda()
else:
model = torch.nn.DataParallel(model).cuda()
# # define loss function (criterion) and optimizer
# # # Update: here
# # config = {'loss': {'type': 'simpleCrossEntropyLoss', 'args': {'param': None}}}
# # criterion = get_instance(loss_funcs, 'loss', config)
# # criterion = criterion.cuda(args.gpu)
#
criterion = nn.CrossEntropyLoss(ignore_index=-1).cuda(args.gpu)
# criterion = MclassCrossEntropyLoss().cuda(args.gpu)
# params = list()
# for j in range(num_datasets):
# params += list(model_list[j].parameters())
optimizer = torch.optim.SGD(filter(lambda p: p.requires_grad,
model.parameters()),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
if args.lr_schedule:
print_func("Using scheduled learning rate")
scheduler = lr_scheduler.MultiStepLR(
optimizer, [int(i) for i in args.lr_schedule.split(',')],
gamma=0.1)
else:
scheduler = lr_scheduler.ReduceLROnPlateau(optimizer,
'min',
patience=args.lr_patience)
'''
if args.resume:
if os.path.isfile(args.resume):
print_func("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
import collections
if not args.evaluate:
if isinstance(checkpoint, collections.OrderedDict):
load_state_dict(model, checkpoint, exclude_layers=['fc.weight', 'fc.bias'])
else:
load_state_dict(model, checkpoint['state_dict'], exclude_layers=['module.fc.weight', 'module.fc.bias'])
print_func("=> loaded checkpoint '{}' (epoch {})"
.format(args.resume, checkpoint['epoch']))
else:
if isinstance(checkpoint, collections.OrderedDict):
load_state_dict(model, checkpoint, strict=True)
else:
load_state_dict(model, checkpoint['state_dict'], strict=True)
print_func("=> loaded checkpoint '{}' (epoch {})"
.format(args.resume, checkpoint['epoch']))
else:
print_func("=> no checkpoint found at '{}'".format(args.resume))
return
'''
cudnn.benchmark = True
model_total_params = sum(p.numel() for p in model.parameters())
model_grad_params = sum(p.numel() for p in model.parameters()
if p.requires_grad)
print_func("Total Parameters: {0}\t Gradient Parameters: {1}".format(
model_total_params, model_grad_params))
# Data loading code
val_loaders = [None for x in range(num_datasets)]
test_loaders = [None for x in range(num_datasets)]
train_loaders = [None for x in range(num_datasets)]
num_iter = 0
for k in range(num_datasets):
args.ind = k
val_dataset = get_instance(custom_datasets, args.val_loader, args)
if val_dataset is None or k == num_datasets - 1:
val_loaders[args.ind] = None
else:
val_loaders[args.ind] = torch.utils.data.DataLoader(
val_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True,
collate_fn=none_collate)
if hasattr(args, 'test_files') and hasattr(args, 'test_loader'):
test_dataset = get_instance(custom_datasets, args.test_loader,
args)
test_loaders[args.ind] = torch.utils.data.DataLoader(
test_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True,
collate_fn=none_collate)
else:
# test_dataset = None
test_loaders[args.ind] = None
#if args.evaluate:
# validate(test_loaders[args.ind], model_list[k], criterion, args, print_func)
# return
# if not args.evaluate: #else:
# train_samplers = [None for x in range(num_datasets)]
# train_dataset = get_instance(custom_datasets, args.train_loader, args)
#
# if args.distributed:
# train_samplers[args.ind] = torch.utils.data.distributed.DistributedSampler(train_dataset)
# else:
# train_samplers[args.ind] = None
#
# train_loaders[args.ind] = torch.utils.data.DataLoader(
# train_dataset, batch_size=args.batch_size, shuffle=(train_samplers[args.ind] is None),
# num_workers=args.workers, pin_memory=True, sampler=train_samplers[args.ind], collate_fn=none_collate)
if not args.evaluate: #else:
# train_samplers = [None for x in range(num_datasets)]
train_dataset = get_instance(custom_datasets, args.train_loader,
args)
num_iter = max(num_iter, len(train_dataset.samples))
if args.distributed:
train_samplers = torch.utils.data.distributed.DistributedSampler(
train_dataset)
else:
train_samplers = None
train_loaders[args.ind] = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.batch_size,
shuffle=train_samplers is None,
num_workers=args.workers,
pin_memory=True,
sampler=train_samplers,
collate_fn=none_collate)
setattr(args, 'num_iter', num_iter)
# TRAINING
best_prec1 = [-1 for _ in range(num_datasets)]
is_best = [None for _ in range(num_datasets)]
setattr(args, 'lam', 0.5)
start_data_time = time.time()
train_loads_iters = [iter(train_loaders[x]) for x in range(num_datasets)]
print_func("Loaded data in {:.3f} s".format(time.time() - start_data_time))
for epoch in range(args.start_epoch, args.epochs):
if args.distributed:
for x in range(num_datasets):
train_samplers[x].set_epoch(epoch)
if args.lr_schedule:
# CNN_utils.adjust_learning_rate(optimizer, epoch, args.lr)
scheduler.step()
current_lr = optimizer.param_groups[0]['lr']
print_func("Epoch: [{}], learning rate: {}".format(epoch, current_lr))
# train for one epoch
train(train_loads_iters, train_loaders, model, criterion, optimizer,
epoch, args, print_func)
# evaluate and save
val_prec1 = [None for x in range(num_datasets)]
test_prec1 = [None for x in range(num_datasets)]
for j in range(num_datasets):
# if j != args.ind:
# load_state_dict(model_list[j], model_list[args.ind].state_dict())
# evaluate on validation set
if val_loaders[j]:
val_prec1[j], _ = validate(val_loaders[j], model, criterion,
args, print_func, j)
else:
val_prec1[j] = 0
# remember best prec@1 and save checkpoint
is_best[j] = val_prec1[j] > best_prec1[j]
best_prec1[j] = max(val_prec1[j], best_prec1[j])
if is_best[j]:
save_ind = j
else:
save_ind = "#"
CNN_utils.save_checkpoint(
{
'epoch': epoch,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_prec1': best_prec1[j],
'optimizer': optimizer.state_dict(),
},
is_best[j],
file_directory=save_directory,
epoch=epoch,
save_best_only=args.save_best_only,
ind=save_ind)
test_prec1[j], _ = validate(test_loaders[j],
model,
criterion,
args,
print_func,
j,
phase='Test')
print_func("Val precisions: {}".format(val_prec1))
print_func("Test precisions: {}".format(test_prec1))
def main():
best_prec1 = 0
args = parser.parser.parse_args()
config_file = None
if args.config is not None:
config_file = args.config
args = parse_config(args.config)
script_name_stem = get_stem(__file__)
current_time_str = get_date_str()
if args.resume is None:
if args.save_directory is None:
save_directory = get_dir(
os.path.join(
project_root, 'ckpts', '{:s}'.format(args.name),
'{:s}-{:s}-{:s}'.format(script_name_stem, args.ID,
current_time_str)))
else:
save_directory = get_dir(
os.path.join(project_root, 'ckpts', args.save_directory))
else:
save_directory = os.path.dirname(args.resume)
print("Save to {}".format(save_directory))
log_file = os.path.join(save_directory,
'log-{0}.txt'.format(current_time_str))
logger = log_utils.get_logger(log_file)
log_utils.print_config(vars(args), logger)
print_func = logger.info
if config_file is not None:
print_func('ConfigFile: {}'.format(config_file))
else:
print_func('ConfigFile: None, params from argparse')
args.log_file = log_file
if args.device:
os.environ["CUDA_VISIBLE_DEVICES"] = args.device
if args.seed is not None:
random.seed(args.seed)
torch.manual_seed(args.seed)
cudnn.deterministic = True
warnings.warn('You have chosen to seed training. '
'This will turn on the CUDNN deterministic setting, '
'which can slow down your training considerably! '
'You may see unexpected behavior when restarting '
'from checkpoints.')
if args.gpu is not None:
warnings.warn('You have chosen a specific GPU. This will completely '
'disable data parallelism.')
args.distributed = args.world_size > 1
if args.distributed:
dist.init_process_group(backend=args.dist_backend,
init_method=args.dist_url,
world_size=args.world_size)
# create model
if args.arch == 'resnet50otherinits':
print_func("=> using pre-trained model '{}'".format(args.arch))
model = models.__dict__[args.arch](pretrained=True,
num_classes=args.num_classes,
param_name=args.paramname)
elif args.arch == 'resnet50_feature_extractor':
print_func("=> using pre-trained model '{}' to LOAD FEATURES".format(
args.arch))
model = models.__dict__[args.arch](pretrained=True,
num_classes=args.num_classes,
param_name=args.paramname)
else:
if args.pretrained:
print_func("=> using pre-trained model '{}'".format(args.arch))
model = models.__dict__[args.arch](pretrained=True,
num_classes=args.num_classes)
else:
print_func("=> creating model '{}'".format(args.arch))
model = models.__dict__[args.arch](pretrained=False,
num_classes=args.num_classes)
if args.freeze:
model = CNN_utils.freeze_all_except_fc(model)
if args.gpu is not None:
model = model.cuda(args.gpu)
elif args.distributed:
model.cuda()
model = torch.nn.parallel.DistributedDataParallel(model)
else:
if args.arch.startswith('alexnet') or args.arch.startswith('vgg'):
model.features = torch.nn.DataParallel(model.features)
model.cuda()
else:
model = torch.nn.DataParallel(model).cuda()
# define loss function (criterion) and optimizer
# # Update: here
# config = {'loss': {'type': 'simpleCrossEntropyLoss', 'args': {'param': None}}}
# criterion = get_instance(loss_funcs, 'loss', config)
# criterion = criterion.cuda(args.gpu)
criterion = nn.CrossEntropyLoss(ignore_index=-1).cuda(args.gpu)
optimizer = torch.optim.SGD(filter(lambda p: p.requires_grad,
model.parameters()),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
if args.lr_schedule:
print_func("Using scheduled learning rate")
scheduler = lr_scheduler.MultiStepLR(
optimizer, [int(i) for i in args.lr_schedule.split(',')],
gamma=0.1)
else:
scheduler = lr_scheduler.ReduceLROnPlateau(optimizer,
'min',
patience=args.lr_patience)
# optimizer = torch.optim.SGD(model.parameters(), args.lr,
# momentum=args.momentum,
# weight_decay=args.weight_decay)
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print_func("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print_func("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
print_func("=> no checkpoint found at '{}'".format(args.resume))
cudnn.benchmark = True
model_total_params = sum(p.numel() for p in model.parameters())
model_grad_params = sum(p.numel() for p in model.parameters()
if p.requires_grad)
print_func("Total Parameters: {0}\t Gradient Parameters: {1}".format(
model_total_params, model_grad_params))
# Data loading code
val_dataset = get_instance(custom_datasets,
'{0}_val'.format(args.dataset.name), args,
**args.dataset.args)
if val_dataset is None:
val_loader = None
else:
val_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True,
collate_fn=none_collate)
if args.evaluate:
validate(val_loader, model, criterion, args, print_func)
return
else:
train_dataset = get_instance(custom_datasets,
'{0}_train'.format(args.dataset.name),
args, **args.dataset.args)
if args.distributed:
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset)
else:
train_sampler = None
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.batch_size,
shuffle=(train_sampler is None),
num_workers=args.workers,
pin_memory=True,
sampler=train_sampler,
collate_fn=none_collate)
for epoch in range(args.start_epoch, args.epochs):
if args.distributed:
train_sampler.set_epoch(epoch)
if args.lr_schedule:
# CNN_utils.adjust_learning_rate(optimizer, epoch, args.lr)
scheduler.step()
current_lr = optimizer.param_groups[0]['lr']
print_func("Epoch: [{}], learning rate: {}".format(epoch, current_lr))
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch, args,
print_func)
# evaluate on validation set
if val_loader:
prec1, val_loss = validate(val_loader, model, criterion, args,
print_func)
else:
prec1 = 0
val_loss = 0
# remember best prec@1 and save checkpoint
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
CNN_utils.save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
'optimizer': optimizer.state_dict(),
},
is_best,
file_directory=save_directory,
epoch=epoch)
if not args.lr_schedule:
scheduler.step(val_loss)
# Copyright (c) 2019 Zijun Wei. # Licensed under the MIT License. # Author: Zijun Wei # Usage(TODO): create an image directory that only contains the images used in rameswar # Email: [email protected] # Created: 14/Mar/2019 16:42 import shutil import os from PyUtils.file_utils import get_dir, get_stem from PyUtils.pickle_utils import save2pickle src_image_directory = '/home/zwei/datasets/PublicEmotion/tars/Ads/advt_dataset/images' target_image_directory = get_dir( '/home/zwei/datasets/PublicEmotion/Advertisement/images') image_list_files = [ '/home/zwei/datasets/PublicEmotion/tars/Ads/advt_dataset/train_list.txt', '/home/zwei/datasets/PublicEmotion/tars/Ads/advt_dataset/test_list_2403.txt' ] create_directories = [] copy_image_list = [] for s_file in image_list_files: image_list = [] with open(s_file, 'r') as of_: lines = of_.readlines() for s_line in lines: s_line_parts = s_line.strip().split(' ') s_image_fullpath = s_line_parts[0]
