def init_platform():
config_file = cfg_from_file('config.yml')
default_file = cfg_from_file('default.yml')
logger.info(pprint.pformat(default_file))
logger.info(pprint.pformat(config_file))
merge_a_into_b(config_file, config)
merge_a_into_b(default_file, default)
default.best_model_path = ''
if default.gpu == '':
default.gpu = None
if default.gpu is not None:
os.environ["CUDA_VISIBLE_DEVICES"] = default.gpu
default.distributed = default.world_size > 1
if default.distributed:
dist.init_process_group(backend=default.dist_backend, init_method=default.dist_url,
world_size=default.world_size)
default.lr_epoch = [int(ep) for ep in default.lr_step.split(',')]
if default.seed is not None:
seed = default.seed
np.random.seed(seed)
random.seed(seed)
torch.manual_seed(seed)
cudnn.deterministic = True
def init_platform():
config_file = cfg_from_file('config.yml')
default_file = cfg_from_file('default.yml')
logger.info(pprint.pformat(default_file))
logger.info(pprint.pformat(config_file))
merge_a_into_b(config_file, config)
merge_a_into_b(default_file, default)
default.best_model_path = ''
if default.gpu == '':
default.gpu = None
if default.gpu is not None:
os.environ["CUDA_VISIBLE_DEVICES"] = default.gpu
default.distributed = default.world_size > 1
if default.distributed:
dist.init_process_group(backend=default.dist_backend,
init_method=default.dist_url,
world_size=default.world_size)
default.lr_epoch = [int(ep) for ep in default.lr_step.split(',')]
if default.seed is not None:
seed = default.seed
np.random.seed(seed)
random.seed(seed)
torch.manual_seed(seed)
cudnn.deterministic = True
def main(_):
parser = argparse.ArgumentParser(
description='Classification model training')
parser.add_argument('--config_file', type=str, default=None,
help='Optional config file for params')
parser.add_argument('opts', help='see config.py for all options',
default=None, nargs=argparse.REMAINDER)
args = parser.parse_args()
if args.config_file is not None:
cfg_from_file(args.config_file)
if args.opts is not None:
cfg_from_list(args.opts)
assert_and_infer_cfg()
print_cfg()
os.environ["CUDA_VISIBLE_DEVICES"] = str(cfg.GPU_ID)
logger = utils.setup_custom_logger('root')
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
tf_config = tf.ConfigProto(device_count=dict(
GPU=1), gpu_options=tf.GPUOptions(allow_growth=True))
tf.enable_resource_variables()
train(tf_config, logger)
test(tf_config, logger)
def demo_ori():
model_file = "../test_res18.prototxt"
weights_file = "../weights_resize_res18.caffemodel"
data_file = "../test_pad_30_resize_112.txt"
img_root = "/home_1/data/caffe/DTY_Side"
cfg_file = "../test_yxx_resize.yml"
pkl_dir = "../pkl_feature"
batch_size = 512
cfg_from_file(cfg_file)
net = load_net_(model_file,
weights_file=weights_file,
GPU_index=3,
batch_size=batch_size,
forward_type="test")
img_file_list = open(data_file, "r").readlines()
img_list = img_file_list[0:batch_size]
input_data, input_label, input_data_name = load_data_batch_with_label(
net, img_root, img_list)
feature_v = get_feature_information(net, model_file, input_data)
# print feature_v["prob"]
if not os.path.exists(pkl_dir):
os.makedirs(pkl_dir)
save_name = os.path.join(pkl_dir, "feature_res_{}.pkl".format(batch_size))
pickle.dump(feature_v, open(save_name, 'wb'))
def test_net_cls(args):
if args.cfg_file is not None:
cfg_from_file(args.cfg_file)
net = load_net_(args.deploy_file,
args.weights_file,
args.gpu_id,
batch_size=-1,
forward_type="test")
img_list = open(args.img_list_file, "r").readlines()
input_layer_name = net._layer_names[net._inputs[0]]
batch_size, c, h, w = net.blobs[input_layer_name].shape
epoch_num = int(len(img_list) / batch_size)
predict_res = []
for epoch_i in range(epoch_num):
if epoch_i % 10 == 0:
print "TODO: {}% ".format(1.0 * epoch_i / epoch_num * 100)
batch_img_list = img_list[epoch_i * batch_size:(epoch_i + 1) *
batch_size]
input_data, input_label, input_data_name = load_data_batch_with_label(
net, args.img_root_path, batch_img_list)
single_batch_res = predict_with_label(net, input_data, input_label,
input_data_name)
predict_res.extend(single_batch_res)
pkl_dir = os.path.join(args.save_root_path, "res_pkl")
if not os.path.exists(pkl_dir):
os.makedirs(pkl_dir)
save_name = os.path.join(
pkl_dir, "{}_res.pkl".format(
os.path.splitext(os.path.basename(args.weights_file))[0]))
pickle.dump(predict_res, open(save_name, 'wb'))
def main():
global args
args = parser.parse_args()
if args.cfg_file is not None:
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
files = os.listdir(args.model_dir)
cp_files = [f for f in files if f.endswith('.pth.tar')]
if args.file_ptn is not None:
ptn = re.compile(args.file_ptn)
cp_files = [f for f in files if ptn.search(f)]
model_info = []
for cp_file in cp_files:
file_name = cp_file.rsplit('.', 2)[0]
model_group_name, model_name, _ = file_name.split('-')
cp_path = os.path.join(args.model_dir, cp_file)
model_info.append((model_group_name, model_name, cp_path))
att_query = AttQuery(model_info, args.split, args.model_dir)
visualizer = Visualizer()
while True:
att_query.print_query()
command = input('input command: ')
if ' ' in command:
opt, arg = command.split(' ', 1)
else:
opt, arg = command, None
if opt in ('q', 'question'):
att_query.set_question(arg)
elif opt in ('a', 'answer'):
att_query.set_answer(arg)
elif opt in ('c', 'condition'):
att_query.set_condition(arg)
elif opt in ('x', 'clean'):
att_query.set_question(None)
att_query.set_answer(None)
att_query.set_condition(None)
elif opt in ('s', 'save'):
if arg is None:
inds = range(att_query.get_res_cnt())
else:
inds = map(int, arg.split(','))
att_query.save(inds)
elif opt in ('r', 'run'):
if arg is None:
arg = 1
result = att_query.run(int(arg))
visualizer.visualize(result, att_query.model_info)
elif opt in ('e', 'exit'):
break
else:
print('wrong command !')
def main():
global args
args = parser.parse_args()
args_str = json.dumps(vars(args), indent=2)
print('[Info] called with: ' + args_str)
if args.cfg_file is not None:
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
# checkpoint directory
cfg.LOG_DIR = os.path.join(cfg.LOG_DIR, args.checkpoint)
# select device
torch.cuda.set_device(args.gpu_id)
print('[Info] use gpu: {}'.format(torch.cuda.current_device()))
# data
print('[Info] init dataset')
model_group_name, model_name = args.model.split('/')
val_set = VQADataset('test', model_group_name)
val_loader = torch.utils.data.DataLoader(val_set,
batch_size=args.bs,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
print('sample count: {}'.format(len(val_set)))
# model
print('[Info] construct model')
model_group = import_module('models.' + model_group_name)
model = getattr(model_group, model_name)(num_words=val_set.num_words,
num_ans=val_set.num_ans,
emb_size=get_emb_size())
model.cuda()
cudnn.benchmark = True
print('[Info] model name: ' + args.model)
# predict
fnames = [(i, 'checkpoint-{:03}.pth.tar'.format(i))
for i in range(args.start_epoch, args.end_epoch, args.epoch_freq)
]
cp_files = [(i, os.path.join(cfg.LOG_DIR, fname)) for i, fname in fnames]
for epoch, cp_file in cp_files:
if os.path.isfile(cp_file):
print("[Info] loading checkpoint '{}'".format(cp_file))
checkpoint = torch.load(cp_file)
model.load_state_dict(checkpoint['state_dict'])
else:
print("[Info] no checkpoint found at '{}'".format(cp_file))
continue
results = predict(val_loader, model)
result_file = os.path.join(cfg.LOG_DIR,
'result-{:03}.json'.format(epoch))
json.dump(results, open(result_file, 'w'))
def init_config():
parser = ArgumentParser(description='configs of DLA')
parser.add_argument('--cfg', type=str, default='./cfg.yml')
parser.add_argument("--random-train", action="store_true",
help="not fixing random seed.")
parser.add_argument("--visaulDL", action="store_true",
help="visualize training loss with visualDL.")
args = parser.parse_args()
print('Called with args:')
print(args)
assert args.cfg is not None, 'Missing cfg file'
cfg_from_file(args.cfg)
return cfg
def main():
start_time = time.time()
args = parse_args()
if args.toy:
paths = [
'/mnt/synology/breast/projects/lisa/toy_data/',
'/input'
]
else:
paths = [
'/mnt/synology/breast/archives/screenpoint3/processed_dataset/',
'/input'
]
if args.no_rsync:
rsyncing = False
print('Working with symbolic links', flush=True)
data_cmd = ['ln', '-s']
else:
rsyncing = True
print('Rsyncing data', flush=True)
data_cmd = ['/usr/bin/rsync', '-am', '--stats']
subprocess.call(data_cmd + paths)
print('Preparing dataset took {:.2f} seconds.'.format(time.time() - start_time), flush=True)
# HERE
qstar = True
# Benchmark best convolution algorithm
from torch.backends import cudnn
cudnn.benchmark = True
if args.cfg_file:
print('Loading config {}.'.format(args.cfg_file))
cfg_from_file(args.cfg_file)
else:
print('No config given, using standard settings.')
plot_fct(cfg, rsyncing, qstar=qstar, toy=args.toy)
def main():
args = parse_args()
#print('Called with args:')
#print(args)
# Set main gpu
theano.sandbox.cuda.use(args.gpu_id)
if args.cfg_files is not None:
for cfg_file in args.cfg_files:
cfg_from_file(cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
if not args.randomize:
np.random.seed(cfg.CONST.RNG_SEED)
if args.batch_size is not None:
cfg_from_list(['CONST.BATCH_SIZE', args.batch_size])
if args.iter is not None:
cfg_from_list(['TRAIN.NUM_ITERATION', args.iter])
if args.net_name is not None:
cfg_from_list(['NET_NAME', args.net_name])
if args.model_name is not None:
cfg_from_list(['CONST.NETWORK_CLASS', args.model_name])
if args.dataset is not None:
cfg_from_list(['DATASET', args.dataset])
if args.exp is not None:
cfg_from_list(['TEST.EXP_NAME', args.exp])
if args.out_path is not None:
cfg_from_list(['DIR.OUT_PATH', args.out_path])
if args.weights is not None:
cfg_from_list(['CONST.WEIGHTS', args.weights, 'TRAIN.RESUME_TRAIN', True,
'TRAIN.INITIAL_ITERATION', int(args.init_iter)])
print('Using config:')
pprint.pprint(cfg)
if not args.test:
train()
def main():
parser = argparse.ArgumentParser()
# parser.add_argument('--num_epochs', type=int, default=30, help='training epochs')
# parser.add_argument('--display_step', type=int, default=20, help='display step')
# parser.add_argument('--checkpoint_step', type=int, default=5, help='checkpoint step')
# parser.add_argument('--task', type=str, default="autoencoding", help='autoencoding|classification')
# parser.add_argument('--batch_size', type=int, default=64, help='batch size')
# parser.add_argument('--z_dim', type=int, default=10, help='latent variable dimensionality')
# parser.add_argument('--learning_rate', type=float, default=0.0002, help='learning rate')
# parser.add_argument('--dataset', type=str, default='svhn', help='mnist|svhn|cifar10')
# parser.add_argument('--restore', type=int, default=0, help='restore')
# parser.add_argument('--transfer', type=bool, default=True, help="")
# parser.add_argument('--source_task', type=str, default="autoencoding")
# parser.add_argument('--target_task', type=str, default="classification")
# parser.add_argument('--mode', type=str, default="train", help="train|test|mi") # test or train or edge-detection
# parser.add_argument('--remove_dims', type=str, default='')
# parser.add_argument('--gpu', type=str, default='s0')
parser.add_argument("--cfg",
dest='cfg_file',
default='./config/transfer-learning.yml',
type=str,
help="An optional config file"
" to be loaded")
args = parser.parse_args()
if args.cfg_file is not None:
cfg_from_file(args.cfg_file)
# set gpu
os.environ['CUDA_VISIBLE_DEVICES'] = cfg.gpu
if (cfg.mode == "train"):
return train(cfg)
elif (cfg.mode == "test"):
return test(cfg)
elif (cfg.mode == "mi"):
return MI(cfg)
def test_main( iGpuId, iGpuIdCaller, strPrototxtPath, strCaffeModel ):
args = parse_args()
if iGpuId==iGpuIdCaller:
print"[ERROR] [callee]callee GpuId = %d, caller GpuId = %d, they are the same!!!" %(iGpuId, iGpuIdCaller);
sys.exit(1);
args.gpu_id = iGpuId;
args.prototxt = strPrototxtPath;
args.caffemodel = strCaffeModel;
print('Called with args:')
print(args)
if args.cfg_file is not None:
cfg_from_file(args.cfg_file)
print('Using config:')
pprint.pprint(cfg)
print "[callee]callee is called!"
def restore(self):
if not cfg_from_file():
return -1
if not osp.exists(cfg.SRC_DIR) or not osp.exists(cfg.DST_DIR):
return -2
image_names = self.getImageNamesFromDisk(cfg.SRC_DIR)
if len(image_names) == 0:
return -3
self.image_names = image_names
if cfg.IMAGE_NAME == '':
self.cur_image_no = 0
self.cur_image_name = self.image_names[self.cur_image_no]
else:
try:
self.cur_image_no = self.image_names.index(cfg.IMAGE_NAME) + 1
except ValueError:
return -4
if self.cur_image_no >= self.getTotalImageNum():
return -5
self.cur_image_name = self.image_names[self.cur_image_no]
return 0
def main():
global args
args = parser.parse_args()
args_str = json.dumps(vars(args), indent=2)
if args.cfg_file is not None:
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
# use timestamp as log subdirectory
timestamp = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
cfg.LOG_DIR = os.path.join(cfg.LOG_DIR, timestamp)
os.mkdir(cfg.LOG_DIR)
json.dump(cfg, open(cfg.LOG_DIR + '/config.json', 'w'), indent=2)
model_group_name, model_name = args.model.split('/')
shutil.copy('models/' + model_group_name + '.py', cfg.LOG_DIR)
# init ploter
ploter = Ploter(timestamp)
# setting log handlers
fh = logging.FileHandler(os.path.join(cfg.LOG_DIR, 'log'))
fh.setLevel(logging.DEBUG)
fhc = logging.FileHandler('current.log')
fhc.setLevel(logging.DEBUG)
sh = logging.StreamHandler(sys.stdout)
sh.setLevel(logging.DEBUG)
fmt = '[%(asctime)-15s] %(message)s'
datefmt = '%Y-%m-%d %H:%M:%S'
formatter = logging.Formatter(fmt, datefmt)
fh.setFormatter(formatter)
fhc.setFormatter(formatter)
logger.addHandler(fh)
logger.addHandler(fhc)
logger.addHandler(sh)
logger.debug('[Info] called with: ' + args_str)
logger.debug('[Info] timestamp: ' + timestamp)
logger.debug('[Info] CPU random seed: {}'.format(torch.initial_seed()))
logger.debug('[Info] GPU random seed: {}'.format(
torch.cuda.initial_seed()))
# select device
torch.cuda.set_device(args.gpu_id)
logger.debug('[Info] use gpu: {}'.format(torch.cuda.current_device()))
# data
logger.debug('[Info] init dataset')
do_test = (len(cfg.TEST.SPLITS) == 1
and cfg.TEST.SPLITS[0] in ('train2014', 'val2014'))
trn_set = VQADataset('train', model_group_name)
train_loader = torch.utils.data.DataLoader(trn_set,
batch_size=args.bs,
shuffle=True,
num_workers=args.workers,
pin_memory=True)
if do_test:
val_set = VQADataset('test', model_group_name)
val_loader = torch.utils.data.DataLoader(val_set,
batch_size=args.bs,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
# model
emb_size = 300
if cfg.WORD_EMBEDDINGS:
word_vec = merge_embeddings(cfg.WORD_EMBEDDINGS)
aword = next(iter(word_vec))
emb_size = len(word_vec[aword])
logger.debug('[Info] embedding size: {}'.format(emb_size))
logger.debug('[Info] construct model, criterion and optimizer')
model_group = import_module('models.' + model_group_name)
model = getattr(model_group, model_name)(num_words=trn_set.num_words,
num_ans=trn_set.num_ans,
emb_size=emb_size)
logger.debug('[Info] model name: ' + args.model)
total_param = 0
for param in model.parameters():
total_param += param.nelement()
logger.debug('[Info] total parameters: {}M'.format(
math.ceil(total_param / 2**20)))
# initialize word embedding with pretrained
if cfg.WORD_EMBEDDINGS:
emb = model.we.weight.data.numpy()
words = trn_set.codebook['itow']
assert '<PAD>' not in word_vec
fill_cnt = 0
for i, w in enumerate(words):
if w in word_vec:
emb[i] = word_vec[w]
fill_cnt += 1
logger.debug('[debug] word embedding filling count: {}/{}'.format(
fill_cnt, len(words)))
model.we.weight = nn.Parameter(torch.from_numpy(emb))
if model_group_name in ('onehot_label', 'prob_label'):
# initialize object embedding with pretrained
obj_emb = model.obj_net[0].weight.data.numpy()
if model_group_name == 'prob_label':
obj_emb = obj_emb.T
fill_cnt = 0
for i, line in enumerate(trn_set.objects_vocab):
avail, vec = get_class_embedding(line, word_vec, emb_size)
if avail:
obj_emb[i] = vec
fill_cnt += 1
logger.debug('[debug] class embedding filling count: {}/{}'.format(
fill_cnt, len(trn_set.objects_vocab)))
if model_group_name == 'prob_label':
obj_emb = obj_emb.T
model.obj_net[0].weight = nn.Parameter(torch.from_numpy(obj_emb))
model.cuda()
if cfg.SOFT_LOSS:
criterion = nn.BCEWithLogitsLoss().cuda()
else:
criterion = nn.CrossEntropyLoss().cuda()
logger.debug('[Info] criterion name: ' + criterion.__class__.__name__)
optimizer = torch.optim.Adam(model.parameters(),
args.lr,
weight_decay=args.wd)
cudnn.benchmark = True
# train
logger.debug('[Info] start training...')
is_best = False
best_acc = 0
best_epoch = -1
for epoch in range(args.start_epoch, args.epochs):
lr = adjust_learning_rate(optimizer, epoch)
ploter.append(epoch, lr, 'lr')
loss = train(train_loader, model, criterion, optimizer, epoch)
ploter.append(epoch, loss, 'train-loss')
if do_test:
acc = validate(val_loader, model, criterion, epoch)
ploter.append(epoch, acc, 'val-acc')
if acc > best_acc:
is_best = True
best_acc = acc
best_epoch = epoch
logger.debug('Evaluate Result:\t'
'Acc {0}\t'
'Best {1} ({2})'.format(acc, best_acc, best_epoch))
# save checkpoint
cp_fname = 'checkpoint-{:03}.pth.tar'.format(epoch)
cp_path = os.path.join(cfg.LOG_DIR, cp_fname)
state = {
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'best_acc': best_acc,
'optimizer': optimizer.state_dict()
}
if epoch % args.save_freq == 0:
torch.save(state, cp_path)
if is_best:
best_path = os.path.join(cfg.LOG_DIR, 'model-best.pth.tar')
torch.save(state, best_path)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--cfg',
dest='cfg_file',
help='optional config file',
default='tgif_qa_action.yml',
type=str)
args = parser.parse_args()
if args.cfg_file is not None:
cfg_from_file(args.cfg_file)
assert cfg.dataset.name in ['tgif-qa', 'msrvtt-qa', 'msvd-qa', 'svqad-qa']
assert cfg.dataset.question_type in [
'frameqa', 'count', 'transition', 'action', 'none'
]
# check if the data folder exists
assert os.path.exists(cfg.dataset.data_dir)
# check if k_max is set correctly
assert cfg.train.k_max_frame_level <= 16
assert cfg.train.k_max_clip_level <= 8
if not cfg.multi_gpus:
torch.cuda.set_device(cfg.gpu_id)
# make logging.info display into both shell and file
cfg.dataset.save_dir = os.path.join(cfg.dataset.save_dir, cfg.exp_name)
if not os.path.exists(cfg.dataset.save_dir):
os.makedirs(cfg.dataset.save_dir)
else:
assert os.path.isdir(cfg.dataset.save_dir)
log_file = os.path.join(cfg.dataset.save_dir, "log")
if not cfg.train.restore and not os.path.exists(log_file):
os.mkdir(log_file)
else:
assert os.path.isdir(log_file)
fileHandler = logging.FileHandler(os.path.join(log_file, 'stdout.log'),
'w+')
fileHandler.setFormatter(logFormatter)
rootLogger.addHandler(fileHandler)
# args display
for k, v in vars(cfg).items():
logging.info(k + ':' + str(v))
# concat absolute path of input files
if cfg.dataset.name == 'tgif-qa':
cfg.dataset.train_question_pt = os.path.join(
cfg.dataset.data_dir,
cfg.dataset.train_question_pt.format(cfg.dataset.name,
cfg.dataset.question_type))
cfg.dataset.val_question_pt = os.path.join(
cfg.dataset.data_dir,
cfg.dataset.val_question_pt.format(cfg.dataset.name,
cfg.dataset.question_type))
cfg.dataset.vocab_json = os.path.join(
cfg.dataset.data_dir,
cfg.dataset.vocab_json.format(cfg.dataset.name,
cfg.dataset.question_type))
cfg.dataset.appearance_feat = os.path.join(
cfg.dataset.data_dir,
cfg.dataset.appearance_feat.format(cfg.dataset.name,
cfg.dataset.question_type))
cfg.dataset.motion_feat = os.path.join(
cfg.dataset.data_dir,
cfg.dataset.motion_feat.format(cfg.dataset.name,
cfg.dataset.question_type))
else:
cfg.dataset.question_type = 'none'
cfg.dataset.appearance_feat = '{}_appearance_feat.h5'
cfg.dataset.motion_feat = '{}_motion_feat.h5'
cfg.dataset.vocab_json = '{}_vocab.json'
cfg.dataset.train_question_pt = '{}_train_questions.pt'
cfg.dataset.val_question_pt = '{}_val_questions.pt'
cfg.dataset.train_question_pt = os.path.join(
cfg.dataset.data_dir,
cfg.dataset.train_question_pt.format(cfg.dataset.name))
cfg.dataset.val_question_pt = os.path.join(
cfg.dataset.data_dir,
cfg.dataset.val_question_pt.format(cfg.dataset.name))
cfg.dataset.vocab_json = os.path.join(
cfg.dataset.data_dir,
cfg.dataset.vocab_json.format(cfg.dataset.name))
cfg.dataset.appearance_feat = os.path.join(
cfg.dataset.data_dir,
cfg.dataset.appearance_feat.format(cfg.dataset.name))
cfg.dataset.motion_feat = os.path.join(
cfg.dataset.data_dir,
cfg.dataset.motion_feat.format(cfg.dataset.name))
# set random seed
torch.manual_seed(cfg.seed)
np.random.seed(cfg.seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(cfg.seed)
train(cfg)
def main():
mode_list = ['train', 'test', 'train_pos', 'stats']
models_list = ['vgg', 'qnet', 'resnet', 'fcresnet', 'simple', 'auto']
start_time = time.time()
parser = argparse.ArgumentParser()
parser.add_argument('--cfg', help='configuration file to use.')
parser.add_argument('--mode', help='mode (train or test')
parser.add_argument('--model', help='model to train')
parser.add_argument('--no-rsync',
dest='no_rsync',
help='use symbolic links instead',
action='store_true')
parser.add_argument('--toy',
dest='toy',
help='use toy data set',
action='store_true')
args = parser.parse_args()
if args.cfg:
print('Loading config {}.'.format(args.cfg))
cfg_from_file(args.cfg)
else:
print('No config given, using standard settings.')
pprint(cfg)
if args.model not in models_list:
print('Model does not exist. Choose from {}. Exiting.'.format(
models_list))
elif args.mode not in mode_list:
print(
'Mode does not exist. Choose from {}. Exiting.'.format(mode_list))
else:
rsyncing = False if args.no_rsync else True
if rsyncing:
print('Rsynced data! (run_script)', flush=True)
else:
print('Using symbolic links! (run_script)', flush=True)
if args.mode == 'train':
if args.model == 'vgg':
train_feat(model_string='vgg', rsyncing=rsyncing, toy=args.toy)
elif args.model == 'qnet':
exp_name = get_f_save_check_tensorB_expName(cfg)[2]
train_qnet(feat_model_string=exp_name,
rsyncing=rsyncing,
toy=args.toy)
elif args.model == 'simple':
train_feat(model_string='simple',
rsyncing=rsyncing,
toy=args.toy)
elif args.model == 'resnet':
train_feat(model_string='resnet',
rsyncing=rsyncing,
toy=args.toy)
elif args.model == 'resnet_less':
train_feat(model_string='resnet_less',
rsyncing=rsyncing,
toy=args.toy)
elif args.model == 'fcresnet':
train_feat(model_string='fcresnet',
rsyncing=rsyncing,
toy=args.toy)
elif args.model == 'auto':
train_feat('auto', rsyncing=rsyncing, toy=args.toy)
elif args.mode == 'train_pos':
if args.model == 'vgg':
train_feat_pos(model_string='vgg',
rsyncing=rsyncing,
toy=args.toy)
elif args.model == 'simple':
train_feat(model_string='simple',
rsyncing=rsyncing,
toy=args.toy)
elif args.model == 'resnet':
train_feat_pos(model_string='resnet',
rsyncing=rsyncing,
toy=args.toy)
else:
print('Use pos only for feature net training')
elif args.mode == 'test':
if args.model == 'vgg':
test_vgg()
# Not yet implemented
elif args.model == 'qnet':
exp_name = get_f_save_check_tensorB_expName(cfg)[2]
test_qnet(feat_model_string=exp_name,
rsyncing=rsyncing,
toy=args.toy)
elif args.model == 'resnet' or args.model == 'simple':
exp_name = get_f_save_check_tensorB_expName(cfg)[2]
test_feat(feat_model_string=exp_name,
rsyncing=rsyncing,
toy=args.toy)
# Not yet implemented
elif args.mode == 'stats':
exp_name = get_f_save_check_tensorB_expName(cfg)[2]
get_stats(feat_model_string=exp_name,
rsyncing=rsyncing,
toy=args.toy)
total_time = time.time() - start_time
print('Main program executed in {:.0f} seconds.'.format(total_time))
def main():
args = parser.parse_args()
if args.cfg_file is not None:
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
# select device
torch.cuda.set_device(args.gpu_id)
print('[Info] use gpu: {}'.format(torch.cuda.current_device()))
# get parameters
sys.path.insert(0, args.model_dir)
from params import params
assert len(params) > 1
last_cfg = params[0][-1]
last_cfg()
get_data.main()
dataset = VQADataset('test', params[0][1])
itoa = dataset.codebook['itoa']
vote_buff = [{} for i in range(len(dataset))]
conf_buff = np.zeros((len(dataset), len(itoa)))
sm_conf_buff = np.zeros((len(dataset), len(itoa)))
l2_conf_buff = np.zeros((len(dataset), len(itoa)))
que_ids = dataset.que_id
for fpath, mgrp, mname, acc, cfg_func, in params:
# data
if cfg_func != last_cfg:
cfg_func()
get_data.main()
last_cfg = cfg_func
dataset = VQADataset('test', mgrp)
itoa = dataset.codebook['itoa']
dataset.reload_obj(mgrp)
dataloader = torch.utils.data.DataLoader(
dataset, batch_size=args.bs, shuffle=False,
num_workers=2, pin_memory=True)
# model
model_group = import_module('models.' + mgrp)
model = getattr(model_group, mname)
num_words=dataset.num_words,
num_ans=dataset.num_ans,
emb_size=get_emb_size())
cp_file = os.path.join(args.model_dir, fpath)
checkpoint = torch.load(cp_file, map_location=lambda s, l: s.cuda(0))
model.load_state_dict(checkpoint['state_dict'])
model.cuda()
model.eval()
# predict
bar = progressbar.ProgressBar()
start = 0
# sample: (que_id, img, que, [obj])
for sample in bar(dataloader):
sample_var = [Variable(d).cuda() for d in list(sample)[1:]]
score = model(*sample_var)
sm_score = torch.nn.functional.softmax(score)
l2_score = torch.nn.functional.normalize(score)
bs = score.size(0)
conf_buff[start:start+bs] += score.data.cpu().numpy()
sm_conf_buff[start:start+bs] += sm_score.data.cpu().numpy()
l2_conf_buff[start:start+bs] += l2_score.data.cpu().numpy()
_, ans_ids = torch.max(score.data, dim=1)
for i, ans_id in enumerate(ans_ids):
ans = itoa[ans_id]
ans_score = acc + vote_buff[start + i].get(ans, 0)
vote_buff[start + i][ans] = ans_score
start += bs
def main():
#-------------解析参数-------------#
args = _parse_args()
if args.cfg_file is not None:
cfg_from_file(args.cfg_file) #读取args.cfg_file文件内容并融合到cfg中
pprint.pprint(cfg)
#-------------任务相关配置-------------#
#os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
#os.environ['CUDA_VISBLE_DEVICES'] = cfg.GPUS
#os.environ['CUDA_VISBLE_DEVICES'] = '0'
tf.logging.set_verbosity(tf.logging.INFO) #设置日志级别
#-------------搭建计算图-------------#
with tf.device('/cpu:0'):
# 操作密集型放在CPU上进行
query = tf.placeholder(dtype=tf.float32, shape=[None, 2])
num_gpus = len(cfg.GPUS.split(','))
# 建立dataset，获取iterator
ite_val = reader.get_dataset_iter_valid(cfg)
mem_val, mem_adj_val, gt_val = ite_val.get_next()
# 在GPU上运行预测
with tf.variable_scope(tf.get_variable_scope(
)) as vscope: # 见https://github.com/tensorflow/tensorflow/issues/6220
for i in range(num_gpus):
with tf.device('/gpu:%d' % i), tf.name_scope('GPU_%d' %
i) as scope:
# 获取网络，并完成前传
graph_mem_net = GraphMemNet(cfg)
logits = graph_mem_net.inference(mem_val, mem_adj_val, query)
tf.get_variable_scope().reuse_variables()
# saver
model_variables_map_save = {}
for variable in tf.trainable_variables():
model_variables_map_save[variable.name.replace(':0', '')] = variable
print '#####################################################'
for save_item in model_variables_map_save.keys():
print save_item
print '#####################################################'
saver_save = tf.train.Saver(var_list=model_variables_map_save,
max_to_keep=cfg.TRAIN.MAX_MODELS_TO_KEEP)
#-------------启动Session-------------#
# (预测验证集，求取精度)
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.8)
config = tf.ConfigProto(gpu_options=gpu_options, allow_soft_placement=True)
with tf.Session(config=config) as sess:
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
#加载pretrained models
tf.global_variables_initializer().run()
saver_save.restore(
sess, '/data/yinzhiyu/results/Graph-Memory-Networks/models-1')
sess.graph.finalize()
start_time = time.time()
query_ = np.array([[1.0, 0.0]] * cfg.TRAIN.BATCH_SIZE, dtype=np.float)
num_pred = 0
T = 0
P = 0
TP = 0
for i in range(cfg.TRAIN.MAX_ITE):
try:
print 'predicting %dth mol' % i
output, gt = sess.run([logits, gt_val],
feed_dict={query: query_},
options=run_options)
ind_out = np.argmax(output, axis=1)[0]
ind_gt = np.argmax(gt, axis=1)[0]
num_pred += 1
if ind_gt == 0:
P += 1
if ind_out == ind_gt:
T += 1
TP += 1
else:
if ind_out == ind_gt:
T += 1
except:
pre = float(T) / float(num_pred)
recall = float(TP) / float(P)
print 'finished!!!!'
print 'F1 score is %.2f ' % (100.0 * 2.0 * (pre * recall /
(pre + recall)))
break
def main():
args = _parse_args()
if args.cfg_file is not None:
cfg_from_file(args.cfg_file) #读取args.cfg_file文件内容并融合到cfg中
pprint.pprint(cfg)
# 读取索引文件
with open(osp.join(cfg.INPUT.DATA_DIR, cfg.INPUT.INDEX), 'r') as f:
index = f.readlines()
# 原子数最多的分子共含146个原子,可认为最大为150个，故mem_size=150，167数据集共86个原子，故one-hot设为90维...单个分子，最大bond数为162
suppl = Chem.SDMolSupplier(osp.join(cfg.INPUT.DATA_DIR, cfg.INPUT.SDF))
for i in range(len(suppl)):
m = suppl[i]
if m is None:
continue
# atom的one-hot编码,degree,H attached
bond_feature = [np.array([], dtype=np.float)]
for bond in m.GetBonds():
if bond.GetBondType() == rdkit.Chem.rdchem.BondType.SINGLE:
bond_feature.append(
np.array([1.0, 0.0, 0.0, 0.0], dtype=np.float))
elif bond.GetBondType() == rdkit.Chem.rdchem.BondType.DOUBLE:
bond_feature.append(
np.array([0.0, 1.0, 0.0, 0.0], dtype=np.float))
elif bond.GetBondType() == rdkit.Chem.rdchem.BondType.TRIPLE:
bond_feature.append(
np.array([0.0, 0.0, 1.0, 0.0], dtype=np.float))
else:
bond_feature.append(
np.array([0.0, 0.0, 0.0, 1.0], dtype=np.float))
# is bond in ring
if bond.IsInRing():
bond_feature.append(np.array([1.0, 0.0], dtype=float))
else:
bond_feature.append(np.array([0.0, 1.0], dtype=float))
bond_feature = np.concatenate(bond_feature)
feature_mol = []
for atom in m.GetAtoms():
feature_atom = []
one_hot = np.zeros(cfg.NETWORK.ONE_HOT_DIM, dtype=np.float)
one_hot[atom.GetAtomicNum()] = 1.0
feature_atom.append(one_hot)
degree_onehot = np.zeros(cfg.NETWORK.MAX_NUM_DEGREE,
dtype=np.float)
degree = len([x.GetAtomicNum() for x in atom.GetNeighbors()])
degree_onehot[degree - 1] = 1.0
feature_atom.append(degree_onehot)
feature_atom.append(bond_feature)
# 整理feature_atom，并加入mem,
feature_atom = np.concatenate(feature_atom)
feature_mol.append(feature_atom) #此处未padding!!!!!!!!!!!
feature_mol = np.array(feature_mol)
# 构建邻接矩阵
adj = np.zeros((cfg.NETWORK.NUM_BOND_TYPE, cfg.NETWORK.MEM_SIZE,
cfg.NETWORK.MEM_SIZE),
dtype=np.float)
for ii in range(len(m.GetAtoms())):
for jj in range(len(m.GetAtoms())):
if m.GetBondBetweenAtoms(ii, jj) == None:
continue
else:
if m.GetBondBetweenAtoms(ii, jj).GetBondType(
) == rdkit.Chem.rdchem.BondType.SINGLE:
adj[0][ii][jj] = 1.0
elif m.GetBondBetweenAtoms(ii, jj).GetBondType(
) == rdkit.Chem.rdchem.BondType.DOUBLE:
adj[1][ii][jj] = 1.0
elif m.GetBondBetweenAtoms(ii, jj).GetBondType(
) == rdkit.Chem.rdchem.BondType.TRIPLE:
adj[2][ii][jj] = 1.0
else:
adj[3][ii][jj] = 1.0
# 保存
file_name = index[i].split()[0]
mol_fea_adj = np.array([
feature_mol, adj,
np.array([1.0, 0.0], dtype=np.float) if index[i].split()[1]
== 'Active' else np.array([0.0, 1.0], dtype=np.float)
])
np.save(
osp.join(cfg.INPUT.DATA_DIR, cfg.INPUT.FEATURE,
file_name + '.npy'), mol_fea_adj)
print '%d precessed\n' % i
"""
import os
import pymysql
from config import cfg_from_file
from config import cfg
# Whether to import external configuration
if os.path.exists("resource/config.yaml"):
cfg_from_file("resource/config.yaml")
# MySQL Configuration
host = cfg.database.mysql.host
user = cfg.database.mysql.user
password = cfg.database.mysql.passwd
charset = cfg.database.mysql.charset
db = cfg.database.mysql.db # Database name used
"""
database:数据库连接层
"""
def get_conn():
# 建立连接
conn = pymysql.connect(host=host,
user=user,
password=password,
db=db,
charset=charset)
# c创建游标
cursor = conn.cursor()
return conn, cursor
def parse_args():
"""
Parse input arguments
"""
parser = argparse.ArgumentParser(description='Train a keypoint regressor.')
parser.add_argument('--cfg',
dest='cfg_file',
help='optional config file',
default=None,
type=str)
parser.add_argument('--gpu',
dest='gpu',
help='GPU to use for running this.',
default='0',
type=str)
parser.add_argument(
'--save',
dest='save',
action='store_const',
const=True,
default=False,
help='Set to save the features. Works only in mAP mode. '
'(Set in cfg).')
parser.add_argument(
'--outfpath',
default=None,
help='(Optional) Give a custom path to save the features. '
'By def. picks a path in ckpt directory.')
parser.add_argument('--preprocs',
default=[],
nargs='*',
help='Set additional preprocs to do when testing. Eg. '
'can put \'flips\'. This will flip images before '
'pushing through the network. Can be useful for '
'late fusion of multiple features.')
parser.add_argument('--ept',
dest='ept',
nargs='+',
type=str,
default=[],
help='Optional end point to store. '
'By def store the softmax logits.')
parser.add_argument('--split_name',
default=None,
type=str,
help='Set to change the dataset split to run on. '
'Eg, \'train\' or \'test\'.')
parser.add_argument('--frames_per_video',
default=None,
type=int,
help='Set to change the '
'cfg.TRAIN.VIDEO_FRAMES_PER_VIDEO.')
parser.add_argument('--dataset_list_dir',
default=None,
type=str,
help='Set to change the train_test_lists dir.')
args = parser.parse_args()
if args.cfg_file is not None:
cfg_from_file(args.cfg_file)
# Change config for some options
if args.split_name is not None:
cfg.TEST.DATASET_SPLIT_NAME = args.split_name
if args.frames_per_video is not None:
cfg.TEST.VIDEO_FRAMES_PER_VIDEO = args.frames_per_video
if args.outfpath is not None:
args.save = True
return args, cfg
def test(para, iter, mode="Test"):
model = AVGRunner(para, mode)
model.run_benchmark(iter, mode=mode)
if __name__ == '__main__':
FLAGS = tf.flags.FLAGS
device = FLAGS.device
config = FLAGS.config
paras = FLAGS.restore
mode = FLAGS.mode
step = FLAGS.iter
os.environ['CUDA_VISIBLE_DEVICES'] = device
cfg_from_file(config)
print(c.SAVE_PATH)
print(device, config, paras)
runner = AVGRunner(paras, mode)
try:
if mode == 'train':
runner.train()
else:
runner.run_benchmark(step, mode=mode)
except Exception as e:
runner.notifier.send("Something wrong\n" + str(e))
runner.logger.error(str(e))
else:
runner.notifier.send("Done")
volume_index = [x.strip() for x in f.readlines()]
img_list = []
for v in volume_index:
fd_path = os.path.join(data_dir, v)
if os.path.isfile(fd_path) and fd_path.endswith(IMG_SUFFIX):
img_list += [v]
else:
img_list1 = [f for f in os.listdir(fd_path) if f.endswith(IMG_SUFFIX)]
img_list1.sort(key=lambda x: int(x[:-4]))
img_list1 = [os.path.join(v,f) for f in img_list1]
img_list += img_list1
return img_list
if __name__ == '__main__':
cfg_from_file(default_cfg)
caffe.set_device(GPU_ID)
caffe.set_mode_gpu()
net = caffe.Net(default_prototxt, default_model, caffe.TEST)
print 'Processing img ...'
image_index = get_image_index(image_set_file)
im_num = len(image_index)
vals = np.empty((im_num,))
for j in xrange(im_num):
fn = os.path.join(data_dir, image_index[j])
print fn, '\t',
im = load_img(fn)
im -= cfg.PIXEL_MEANS
data = np.zeros((1, 3, im.shape[0], im.shape[1]), dtype=np.float32)
def get_args():
"""Get arguments from stdin."""
parser = argparse.ArgumentParser(description='Pytorch acoustic model.')
parser.add_argument('--encoder', type=str, default='gru',
help='encoder type {default: gru}')
parser.add_argument('--num-anchor', type=int, default=10, metavar='HF',
help='Num anchors per frame {default: 10.0}')
parser.add_argument('--lambda-factor', type=float, default=5.0, metavar='HF',
help='Balance factor between classification and regression loss (default: 5.0).')
parser.add_argument('--input-dim', type=int, default=40, metavar='N',
help='Input feature dimension without context (default: 40).')
parser.add_argument('--kernel-size', type=int, default=3, metavar='N',
help='Kernel size of Wavenet or CNN (default:3).')
parser.add_argument('--hidden-dim', type=int, default=128, metavar='N',
help='Hidden dimension of feature extractor (default: 128).')
parser.add_argument('--num-layers', type=int, default=2, metavar='N',
help='Numbers of hidden layers of feature extractor (default: 2).')
parser.add_argument('--output-dim', type=int, default=2000, metavar='N',
help='Output dimension, number of classes (default: 2000).')
parser.add_argument('--dropout', type=float, default=0.0001, metavar='DR',
help='dropout of feature extractor (default: 0.0001).')
parser.add_argument('--left-context', type=int, default=5, metavar='N',
help='Left context length for splicing feature (default: 5).')
parser.add_argument('--right-context', type=int, default=5, metavar='N',
help='Right context length for splicing feature (default: 5).')
parser.add_argument('--max-epochs', type=int, default=20, metavar='N',
help='Maximum epochs to train (default: 20).')
parser.add_argument('--min-epochs', type=int, default=0, metavar='N',
help='Minimum epochs to train (default: 0).')
parser.add_argument('--batch-size', type=int, default=8, metavar='N',
help='Batch size for training (default: 8).')
parser.add_argument('--learning-rate', type=float, default=0.001, metavar='LR',
help='Initial learning rate (default: 0.001).')
parser.add_argument('--halving-factor', type=float, default=0.5, metavar='HF',
help='Half factor for learning rate (default: 0.5).')
parser.add_argument('--start-halving-impr', type=float, default=0.01, metavar='S',
help='Improvement threshold to half the learning rate (default: 0.01).')
parser.add_argument('--end-halving-impr', type=float, default=0.001, metavar='E',
help='Improvement threshold to stop half learning rate (default: 0.001).')
parser.add_argument('--init-weight-decay', type=float, default=1e-5, metavar='E',
help='Weight decay of L2 normalization (default: 1e-5).')
parser.add_argument('--seed', type=int, default=1234, metavar='S',
help='Random seed (default: 1234).')
parser.add_argument('--use-cuda', type=int, default=1, metavar='C',
help='Use cuda (1) or cpu(0).')
parser.add_argument('--multi-gpu', type=int, default=0, metavar='G',
help='Use multi gpu (1) or not (0).')
parser.add_argument('--train', type=int, default=1,
help='Executing mode, train (1) or test (0).')
parser.add_argument('--train-scp', type=str, default='',
help='Training data file.')
parser.add_argument('--dev-scp', type=str, default='',
help='Development data file.')
parser.add_argument('--save-dir', type=str, default='',
help='Directory to output the model.')
parser.add_argument('--load-model', type=str, default='',
help='Previous model to load.')
parser.add_argument('--test', type=int, default=0,
help='Executing mode, 1 for test, 0 no test')
parser.add_argument('--test-scp', type=str, default='',
help='Test data file.')
parser.add_argument('--output-file', type=str, default='',
help='Test output file')
parser.add_argument('--region-output-file', type=str, default='',
help='Region output file')
parser.add_argument('--log-interval', type=int, default=1000, metavar='N',
help='How many batches to wait before logging training status.')
parser.add_argument('--num-workers', type=int, default=1, metavar='N',
help='How many workers used to load data')
parser.add_argument('--config-file', type=str, default='',
help='config file in yaml format')
args = parser.parse_args()
if args.config_file != '':
cfg_from_file(args.config_file)
return args
for v in volume_index:
fd_path = os.path.join(data_dir, v)
if os.path.isfile(fd_path) and fd_path.endswith(IMG_SUFFIX):
img_list += [v]
else:
img_list1 = [
f for f in os.listdir(fd_path) if f.endswith(IMG_SUFFIX)
]
img_list1.sort(key=lambda x: int(x[:-4]))
img_list1 = [os.path.join(v, f) for f in img_list1]
img_list += img_list1
return img_list
if __name__ == '__main__':
cfg_from_file(default_cfg)
caffe.set_device(GPU_ID)
caffe.set_mode_gpu()
net = caffe.Net(default_prototxt, default_model, caffe.TEST)
print 'Processing img ...'
image_index = get_image_index(image_set_file)
im_num = len(image_index)
vals = np.empty((im_num, ))
for j in xrange(im_num):
fn = os.path.join(data_dir, image_index[j])
print fn, '\t',
im = load_img(fn)
im -= cfg.PIXEL_MEANS
data = np.zeros((1, 3, im.shape[0], im.shape[1]), dtype=np.float32)
os.chdir(main_wd)
im.save(fullpath)
os.chdir(model_wd)
for i in range(dataloader.batch_size):
R_count += 1
if R_count >= 2000:
cont = False
break
ii += 1
if __name__ == "__main__":
main_wd = os.getcwd()
cfg_from_file('experiments_bird.yml')
print('Using config:')
pprint.pprint(cfg)
now = datetime.datetime.now(dateutil.tz.tzlocal())
timestamp = now.strftime('%Y_%m_%d_%H_%M_%S')
output_dir = '%s/%s_%s_%s_%s' % \
(cfg.OUTPUT_PATH, cfg.DATASET_NAME, cfg.CONFIG_NAME, cfg.RUN, timestamp)
output_global_dir = '%s/global' % cfg.OUTPUT_PATH
mkdir_p(output_dir)
mkdir_p(output_global_dir)
os.chdir(main_wd)
model_info = cfg.MODELS[cfg.RUN]
def main():
args=parse_args()
# 将args.cfg_file融合到cfg中
from config import cfg, cfg_from_file
if args.cfg_file is not None:
cfg_from_file(args.cfg_file)
tf.logging.info('Using Config:')
pprint.pprint(cfg)
# 指定或创建文件夹?????
from config import get_output_dir
train_dir = get_output_dir('default' if args.cfg_file is None else args.cfg_file)
# 指定使用哪块GPU
os.environ['CUDA_VISIBLE_DEVICES']=cfg.GPUS
num_clones=len(cfg.GPUS.split(','))
# 设置日志显示级别
tf.logging.set_verbosity(tf.logging.INFO)
# 创建计算图，并设置为默认图
with tf.Graph().as_default():
tf.set_random_seed(cfg.RNG_SEED)#？？？？？
# 关于之后要单间的模型的部署设置
deploy_config=model_deploy.DeploymentConfig(num_clones=num_clones,clone_on_cpu=False,replica_id=0,num_replica=1,num_ps.task=0)
# 创建global_step
with tf.device(deploy_config.variables_device()):
global_step=slim.creat_global_step()
# ------------------------------数据集------------------------------#
kwargs={}# 保存关于如何使用视频的超参数视频
if cfg.TRAIN.VIDEO_FRAMES_PER_VIDEO>1:
kwargs['num_samples']=cfg.TRAIN.VIDEO_FRAMES_PER_VIDEO
kwargs['randomFromSegmentStyle']=cfg.TRAIN.READ_SEGMENT_STYLE
kwargs['modality'] = cfg.INPUT.VIDEO.MODALITY #输入模态：默认为rgb
kwargs['split_id'] = cfg.INPUT.SPLIT_ID
# 还有俩不知啥意思？？？？？？
# 选择预处理函数(也是作者重新修改过的！！！！！！！！！！！！！！！)
from preprocessing import preprocessing_factory
image_preprocessing_fn=preprocessing_factory.get_preprocessing(preprocessing_name,is_training=True)
# 读取数据——获取Dataset对象
from datasets import dataset_factory #注意此datasets在本目录下，是作者自己编写的,其中的get_dataset函数没有发生变化,只是其调用的函数选项发生变化，是作者自定义的，返回Dataset对象和一个整数
dataset,num_pose_keypoints=dataset_factory.get_dataset(cfg.DATASET_NAME,cfg.DATASET_SPLIT_NAME,cfg.DATASET_DIR,**kwargs)
# 读取数据——创建provider，读取+预处理，打包成batch，建立预取队列！！！！！
with tf.device(deploy_config.inputs_device()):
provider=slim.dataset_data_provider.DatasetDataProvider(dataset,
num_readers= cfg.NUM_READERS,
common_queue_capacity= 20*cfg.TRAIN.BATCH_SIZE,
common_queue_min= 10*cfg.TRAIN.BATCH_SIZE)
from preprocess_pipeline import train_preprocess_pipeline #该函数依据provider 和image_preprocessing_cn作为参数，从provider中读数据并且预处理
[image,pose_label_hmap,pose_label_valid,action_label]=train_preprocess_pipeline(provider,cfg, ,image_preprocessing_fn)# 真正读取数据？？？？？？？？？？？？？？
# 打包batch
images,pose_labels_hmap,pose_labels_valid,action_labels=tf.train.batch([image,pose_label_hmap,pose_label_valid,action_label],
batch_size=cfg.TRAIN.BATCH_SIZE,
num_thread=cfg.NUM_PREPROCESSING_THREADS,
capacity=5*cfg.TRAIN.BATCH_SIZE)
# 建立数据读取队列
batch_queue=slim.prefetch_queue.prefetch_queue([images,pose_labels_hmap,pose_labels_valid,action_labels],
capacity=5*deploy_config.clones.cfg.TRAIN.ITER_SIZE)
# ------------------------------选择网络?????------------------------------#
def clone_fn(batch_queue):
# 出队一个batch
images,labels_pose,labels_pose_valid,labels_action=batch_queue.dequeue()
labels_pose=tf.concat(tf.unstack(labels_pose),axis=0)
labels_pose_valid=tf.concat(tf.unstack(labels_pose_valid),axis=0)
# 前传(输入images) 注意:网络不仅会输出分类logits，还会输出姿态，但姿态输出记录在end_points
logits,end_points=network_fn(images)
pose_logits=end_points['PoseLogits']
# 指定loss function 并计算loss
# 该作者把一切都存进end_points里面了，
end_points['Images']= images # 存储信息只end_points中
end_points['PoseLabels']= labels_pose
end_points['ActionLabels']= labels_action
end_points['ActionLogits']=logits
gen_loss(labels_action,logits,cfg.TRAIN.LOSS_FN_ACTION,
dataset.num_calsses,cfg.TRAIN.LOSS_FN_ACTION_WT,
labels_pose,pose_logits,cfg.TRAIN.LOSS_FN_POSE,
labels_pose_valid,cfg.TRAIN.LOSS_FN_POSE_WT,
end_points,cfg)# 计算loss 该函数在loss模块中，loss.py就在当前路径下??????????????????
return end_points
# 收集summary
summaries=set(tf.get_collection(tf.GRAPH.SUMMARIES))
# clone是对输出和名称nz空间的封装
clones=model_deploy.creat_clones(deploy_config,clone_fn,[batch_queue])
first_clone_scope=deploy_config.clone_scope(0)
update_ops=tf.get_collection(tf.GraphKeys.UPDATE_OPS,first_clone_scope)
from nets import nets_factory
network_fn=net_factory.get_network_fn(cfg.MODEL_NAME,num_calsses=,num)#该函数作者又重新写过
# 为每一个end_point节点加入监控
for end_point in end_points:
x= end_points[end_point]
summaries.add(tf.summary.histogram('activations/'+ end_point, x))
# 加入图片summary
sum_img=tf.concat(tf.unstack(end_points['Image'])) # unstack作用是取消堆叠，也就是一帧一帧零散出来，用list包裹 concat感觉像是将所有图片按空间拼接起来，方便看每一帧
if sum_img.get_shape().as_list()[-1] not in [1, 3, 4]:
# 再做点处理 还不太懂？？？？？？？
# 加入summary
summaries.add(tf.summary.image('images',sum_img))
# 加入由于加入pose而导致模型中新增的endpoi
for epname in cfg.TRAIN.OTHER_IMG_SUMMARY_TO_ADD: # OTHER_IMG_SUMMARIES_TO_ADD = ['PosePrelogitsBasedAttention']
if epname in end_points:
summary.add(tf.summary.image('image_vis/'+ epname, end_points[epname]))
summaries=summaries.union() # 求summaries和参数的并集，还赋给summaries？？？？？？？
# 为loss增加summaries
for loss in tf.get_collection(tf.Graphkeys.LOSSES,first_clone_scope):
summaries.add(tf.summary.scalar(tensor=loss,name='losses/%s'% loss.op.name))
# 为变量增加summies
for variable in slim.get_model_variables():
summaries.add(tf.summary.histogram(variable.op.name, variable))
# 配置滑动平均 (moving average)
# 配置优化程序
with tf.device(deploy_config.optimizer_device()):
# 设置学习率
learning_rate= _configure_learning_rate(dataset.num_samples, num_clones, global_step)
#
optimizer=_configure_optimizer(learning_rate)
summaries.add(tf.summary.scalar(tensor=learning_rate,name='learning_rate'))
# 设置哪些变量需要参与训练
variables_to_train=_get_variables_to_train()
def set_global_vars(use_arg_parser=True):
global globalvars
global image_width
global image_height
global dims_input_const
global img_pad_value
global normalization_const
global map_file_path
global epoch_size
global num_test_images
global model_folder
global base_model_file
global feature_node_name
global last_conv_node_name
global start_train_conv_node_name
global pool_node_name
global last_hidden_node_name
global roi_dim
global prediction
global prediction_in
global prediction_out
if use_arg_parser:
parser = argparse.ArgumentParser()
parser.add_argument('-c',
'--config',
help='Configuration file in YAML format',
required=False,
default=None)
parser.add_argument('-t',
'--device_type',
type=str,
help="The type of the device (cpu|gpu)",
required=False,
default="cpu")
parser.add_argument(
'-d',
'--device',
type=int,
help="Force to run the script on a specified device",
required=False,
default=None)
parser.add_argument('-l',
'--list_devices',
action='store_true',
help="Lists the available devices and exits",
required=False,
default=False)
parser.add_argument('--prediction',
action='store_true',
help="Switches to prediction mode",
required=False,
default=False)
parser.add_argument(
'--prediction_in',
action='append',
type=str,
help=
"The input directory for images in prediction mode. Can be supplied mulitple times.",
required=False,
default=list())
parser.add_argument(
'--prediction_out',
action='append',
type=str,
help=
"The output directory for processed images and predicitons in prediction mode. Can be supplied mulitple times.",
required=False,
default=list())
parser.add_argument(
'--no_headers',
action='store_true',
help="Whether to suppress the header row in the ROI CSV files",
required=False,
default=False)
parser.add_argument(
'--output_width_height',
action='store_true',
help=
"Whether to output width/height instead of second x/y in the ROI CSV files",
required=False,
default=False)
parser.add_argument(
'--suppressed_labels',
type=str,
help=
"Comma-separated list of labels to suppress from being output in ROI CSV files.",
required=False,
default="")
args = vars(parser.parse_args())
# prediction mode?
prediction = args['prediction']
if prediction:
prediction_in = args['prediction_in']
if len(prediction_in) == 0:
raise RuntimeError("No prediction input directory provided!")
for p in prediction_in:
if not os.path.exists(p):
raise RuntimeError(
"Prediction input directory '%s' does not exist" % p)
prediction_out = args['prediction_out']
if len(prediction_out) == 0:
raise RuntimeError("No prediction output directory provided!")
for p in prediction_out:
if not os.path.exists(p):
raise RuntimeError(
"Prediction output directory '%s' does not exist" % p)
if len(prediction_in) != len(prediction_out):
raise RuntimeError(
"Number of input and output directories don't match: %i != %i"
% (len(prediction_in), len(prediction_out)))
for i in range(len(prediction_in)):
if prediction_in[i] == prediction_out[i]:
raise RuntimeError(
"Input and output directories #%i for prediction are the same: %s"
% ((i + 1), prediction_in[i]))
if args['list_devices']:
print("Available devices (Type - ID - description)")
for d in cntk.device.all_devices():
if d.type() == 0:
type = "cpu"
elif d.type() == 1:
type = "gpu"
else:
type = "<unknown:" + str(d.type()) + ">"
print(type + " - " + str(d.id()) + " - " + str(d))
sys.exit(0)
if args['config'] is not None:
cfg_from_file(args['config'])
if args['device'] is not None:
if args['device_type'] == 'gpu':
cntk.device.try_set_default_device(
cntk.device.gpu(args['device']))
else:
cntk.device.try_set_default_device(cntk.device.cpu())
image_width = cfg["CNTK"].IMAGE_WIDTH
image_height = cfg["CNTK"].IMAGE_HEIGHT
# dims_input -- (pad_width, pad_height, scaled_image_width, scaled_image_height, orig_img_width, orig_img_height)
dims_input_const = MinibatchData(
Value(batch=np.asarray([
image_width, image_height, image_width, image_height, image_width,
image_height
],
dtype=np.float32)), 1, 1, False)
# Color used for padding and normalization (Caffe model uses [102.98010, 115.94650, 122.77170])
img_pad_value = [103, 116, 123] if cfg["CNTK"].BASE_MODEL == "VGG16" else [
114, 114, 114
]
normalization_const = Constant([[[103]], [[116]], [[
123
]]]) if cfg["CNTK"].BASE_MODEL == "VGG16" else Constant([[[114]], [[114]],
[[114]]])
# dataset specific parameters
map_file_path = os.path.join(abs_path, cfg["CNTK"].MAP_FILE_PATH)
globalvars['class_map_file'] = cfg["CNTK"].CLASS_MAP_FILE
globalvars['train_map_file'] = cfg["CNTK"].TRAIN_MAP_FILE
globalvars['test_map_file'] = cfg["CNTK"].TEST_MAP_FILE
globalvars['train_roi_file'] = cfg["CNTK"].TRAIN_ROI_FILE
globalvars['test_roi_file'] = cfg["CNTK"].TEST_ROI_FILE
globalvars['output_path'] = cfg["CNTK"].OUTPUT_PATH
epoch_size = cfg["CNTK"].NUM_TRAIN_IMAGES
num_test_images = cfg["CNTK"].NUM_TEST_IMAGES
# model specific parameters
if cfg["CNTK"].PRETRAINED_MODELS.startswith(".."):
model_folder = os.path.join(abs_path, cfg["CNTK"].PRETRAINED_MODELS)
else:
model_folder = cfg["CNTK"].PRETRAINED_MODELS
base_model_file = os.path.join(model_folder, cfg["CNTK"].BASE_MODEL_FILE)
feature_node_name = cfg["CNTK"].FEATURE_NODE_NAME
last_conv_node_name = cfg["CNTK"].LAST_CONV_NODE_NAME
start_train_conv_node_name = cfg["CNTK"].START_TRAIN_CONV_NODE_NAME
pool_node_name = cfg["CNTK"].POOL_NODE_NAME
last_hidden_node_name = cfg["CNTK"].LAST_HIDDEN_NODE_NAME
roi_dim = cfg["CNTK"].ROI_DIM
data_path = map_file_path
# set and overwrite learning parameters
globalvars['rpn_lr_factor'] = cfg["CNTK"].RPN_LR_FACTOR
globalvars['frcn_lr_factor'] = cfg["CNTK"].FRCN_LR_FACTOR
globalvars['e2e_lr_factor'] = cfg["CNTK"].E2E_LR_FACTOR
globalvars['momentum_per_mb'] = cfg["CNTK"].MOMENTUM_PER_MB
globalvars['e2e_epochs'] = 1 if cfg["CNTK"].FAST_MODE else cfg[
"CNTK"].E2E_MAX_EPOCHS
globalvars[
'rpn_epochs'] = 1 if cfg["CNTK"].FAST_MODE else cfg["CNTK"].RPN_EPOCHS
globalvars['frcn_epochs'] = 1 if cfg["CNTK"].FAST_MODE else cfg[
"CNTK"].FRCN_EPOCHS
globalvars['rnd_seed'] = cfg.RNG_SEED
globalvars['train_conv'] = cfg["CNTK"].TRAIN_CONV_LAYERS
globalvars['train_e2e'] = cfg["CNTK"].TRAIN_E2E
if not os.path.isdir(data_path):
raise RuntimeError("Directory %s does not exist" % data_path)
globalvars['class_map_file'] = os.path.join(data_path,
globalvars['class_map_file'])
globalvars['train_map_file'] = os.path.join(data_path,
globalvars['train_map_file'])
globalvars['test_map_file'] = os.path.join(data_path,
globalvars['test_map_file'])
globalvars['train_roi_file'] = os.path.join(data_path,
globalvars['train_roi_file'])
globalvars['test_roi_file'] = os.path.join(data_path,
globalvars['test_roi_file'])
globalvars['headers'] = not args['no_headers']
globalvars['output_width_height'] = args['output_width_height']
suppressed_labels = []
if len(args['suppressed_labels']) > 0:
suppressed_labels = args['suppressed_labels'].split(",")
globalvars['suppressed_labels'] = suppressed_labels
if cfg["CNTK"].FORCE_DETERMINISTIC:
force_deterministic_algorithms()
np.random.seed(seed=globalvars['rnd_seed'])
globalvars['classes'] = parse_class_map_file(globalvars['class_map_file'])
globalvars['num_classes'] = len(globalvars['classes'])
if cfg["CNTK"].DEBUG_OUTPUT:
# report args
print("Using the following parameters:")
print("Flip image : {}".format(cfg["TRAIN"].USE_FLIPPED))
print("Train conv layers: {}".format(globalvars['train_conv']))
print("Random seed : {}".format(globalvars['rnd_seed']))
print("Momentum per MB : {}".format(globalvars['momentum_per_mb']))
if globalvars['train_e2e']:
print("E2E epochs : {}".format(globalvars['e2e_epochs']))
else:
print("RPN lr factor : {}".format(globalvars['rpn_lr_factor']))
print("RPN epochs : {}".format(globalvars['rpn_epochs']))
print("FRCN lr factor : {}".format(globalvars['frcn_lr_factor']))
print("FRCN epochs : {}".format(globalvars['frcn_epochs']))
def main():
global args
args = parser.parse_args()
args_str = json.dumps(vars(args), indent=2)
if args.cfg_file is not None:
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
# use timestamp as log subdirectory
timestamp = args.timestamp
cfg.LOG_DIR = os.path.join(cfg.LOG_DIR, timestamp)
model_group_name, model_name = args.model.split('/')
# setting log handlers
sh = logging.StreamHandler(sys.stdout)
sh.setLevel(logging.DEBUG)
logger.addHandler(sh)
logger.debug('[Info] called with: ' + args_str)
logger.debug('[Info] timestamp: ' + timestamp)
# select device
torch.cuda.set_device(args.gpu_id)
logger.debug('[Info] use gpu: {}'.format(torch.cuda.current_device()))
# data
assert (len(cfg.TEST.SPLITS) == 1 and cfg.TEST.SPLITS[0] in ('val2014'))
logger.debug('[Info] init dataset')
val_set = VQADataset('test', model_group_name)
RES_DIR = '/home/lyt/code/bert-as-service-test/result'
queIds, queFea, _ = load_data(split_name='val2014', RES_DIR=RES_DIR)
assert queIds.tolist() == val_set.que_id.tolist()
logger.debug('[Info] Clustering using {}, {} clusters'.format(
args.cluster_alg, args.n_clusters))
clusfilename = '{}/{}/{}_{}_n{}.pkl'.format(RES_DIR, 'v2', 'train2014',
args.cluster_alg,
args.n_clusters)
logger.debug('[Info] cluster file: {}'.format(clusfilename))
val_qTypeLabels = clustering(queFea,
clu_num=args.n_clusters,
clu_alg=args.cluster_alg,
savefname=clusfilename)
# model
logger.debug('[Info] construct model')
model_group = import_module('models.' + model_group_name)
model = getattr(model_group, model_name)(num_words=val_set.num_words,
num_ans=val_set.num_ans,
emb_size=get_emb_size())
logger.debug('[Info] model name: ' + args.model)
total_param = 0
for param in model.parameters():
total_param += param.nelement()
logger.debug('[Info] total parameters: {}M'.format(
math.ceil(total_param / 2**20)))
model.cuda()
cudnn.benchmark = True
# load best model, predict
logger.debug('[Info] load model ...')
best_path = os.path.join(cfg.LOG_DIR, 'model-best.pth.tar')
#if os.path.isfile(best_path):
assert os.path.isfile(best_path)
logger.debug("[Info] loading checkpoint '{}'".format(best_path))
cp_state = torch.load(best_path)
best_acc = cp_state['best_acc']
logger.debug('[Info] best model with best acc {}'.format(best_acc))
model.load_state_dict(cp_state['state_dict'])
#else:
# logger.debug("[Info] no checkpoint found at '{}'".format(best_path))
for i in range(args.n_clusters):
logger.debug('[Info] choose cluster ID: {}'.format(i))
#sel = val_qTypeLabels == args.clus_id
sel = val_qTypeLabels == i
val_quesIds = queIds[sel].tolist()
logger.debug(
'[Info] #Val set before/after clustering and choosing {}/{}'.
format(queIds.shape[0], len(val_quesIds)))
val_set_sub = select_subset(val_set, sel)
val_loader = torch.utils.data.DataLoader(val_set_sub,
batch_size=args.bs,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
logger.debug('sample count: {}'.format(len(val_set_sub)))
acc = validate(val_loader, model, None, None, quesIds=val_quesIds)
logger.debug('Evaluate Result:\tAcc {0}'.format(acc))
parser.print_help()
sys.exit(1)
args = parser.parse_args()
return args
if __name__ == '__main__':
args = parse_args()
print('Called with args:')
pprint.pprint(args)
# set the global cfg variable from the file
if args.cfg_file is not None:
for cfg_file in args.cfg_file:
cfg_from_file(cfg_file)
if args.batch_size is not None:
cfg_from_list(['BATCH_SIZE', args.batch_size])
if args.no_prefetch:
cfg_from_list(['USE_PREFETCH', False])
print('Using config:')
pprint.pprint(cfg)
if not args.randomize:
# fix the random seeds (numpy and caffe) for reproducibility
np.random.seed(cfg.RNG_SEED)
caffe.set_random_seed(cfg.RNG_SEED)
# set up caffe
def main():
#-------------解析参数-------------#
args = _parse_args()
if args.cfg_file is not None:
cfg_from_file(args.cfg_file) #读取args.cfg_file文件内容并融合到cfg中
pprint.pprint(cfg)
#-------------任务相关配置-------------#
#os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
#os.environ['CUDA_VISBLE_DEVICES'] = cfg.GPUS
tf.logging.set_verbosity(tf.logging.INFO) #设置日志级别
#-------------搭建计算图-------------#
with tf.device('/cpu:0'):
# 操作密集型放在CPU上进行
query = tf.placeholder(dtype=tf.float32, shape=[None, 2])
global_step = tf.get_variable('global_step', [],
dtype=None,
initializer=tf.constant_initializer(0),
trainable=False)
lr = tf.train.exponential_decay(cfg.TRAIN.LEARNING_RATE_BASE,
global_step,
cfg.TRAIN.DECAY_STEP,
cfg.TRAIN.DECAY_RATE,
staircase=True) # 学习率
tf.summary.scalar('learnrate', lr)
opt = tf.train.MomentumOptimizer(lr, cfg.TRAIN.MOMENTUM) # 优化函数
#opt = tf.train.GradientDescentOptimizer(lr) # 优化函数
num_gpus = len(cfg.GPUS.split(','))
# 建立dataset，获取iterator
ite_train = reader.get_dataset_iter(cfg)
mem, mem_adj, gt = ite_train.get_next()
# 在GPU上运行训练
#tower_grads = []
with tf.variable_scope(tf.get_variable_scope(
)) as vscope: # 见https://github.com/tensorflow/tensorflow/issues/6220
for i in range(num_gpus):
with tf.device('/gpu:%d' % i), tf.name_scope('GPU_%d' %
i) as scope:
#query = np.array([[1.0,0.0]]*cfg.TRAIN.BATCH_SIZE,dtype=np.float)
#query = tf.cast(tf.convert_to_tensor(query),tf.float32)
# 获取网络，并完成前传
#with tf.Graph().as_default():
graph_mem_net = GraphMemNet(cfg)
logits = graph_mem_net.inference(mem, mem_adj, query)
tf.get_variable_scope().reuse_variables()
# 做一个batch准确度的预测
prediction = tf.nn.softmax(logits)
acc_batch = tf.reduce_mean(
tf.cast(
tf.equal(tf.argmax(prediction, 1), tf.argmax(gt, 1)),
tf.float32))
tf.summary.scalar('acc_on_batch', acc_batch)
# 求loss
for variable in tf.global_variables():
if variable.name.find(
'weights'
) > 0: # 把参数w加入集合tf.GraphKeys.WEIGHTS，方便做正则化(此句必须放在正则化之前)
tf.add_to_collection(tf.GraphKeys.WEIGHTS, variable)
loss = loss_func(cfg, logits, gt, regularization=True)
tf.summary.scalar('loss', loss)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_step = opt.minimize(loss,
global_step=global_step,
var_list=tf.trainable_variables())
merged = tf.summary.merge_all()
# saver
model_variables_map_save = {}
for variable in tf.trainable_variables():
model_variables_map_save[variable.name.replace(':0', '')] = variable
print '#####################################################'
for save_item in model_variables_map_save.keys():
print save_item
print '#####################################################'
saver_save = tf.train.Saver(var_list=model_variables_map_save,
max_to_keep=cfg.TRAIN.MAX_MODELS_TO_KEEP)
#-------------启动Session-------------#
# (预测验证集，求取精度)
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.8)
config = tf.ConfigProto(gpu_options=gpu_options, allow_soft_placement=True)
with tf.Session(config=config) as sess:
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
joint_writer = tf.summary.FileWriter(cfg.SUMMARY_DIR, sess.graph)
summary_writer = tf.summary.FileWriter(cfg.SUMMARY_DIR, sess.graph)
#初始化变量(或加载pretrained models)
tf.global_variables_initializer().run()
#saver_save.restore(sess,'/data/yinzhiyu/results/Graph-Memory-Networks/models-4001')
sess.graph.finalize()
start_time = time.time()
query_ = np.array([[1.0, 0.0]] * cfg.TRAIN.BATCH_SIZE, dtype=np.float)
#query = tf.cast(tf.convert_to_tensor(query),tf.float32)
for i in range(cfg.TRAIN.MAX_ITE):
_, learnrate, loss_value, step, summary = sess.run(
[train_step, lr, loss, global_step, merged],
feed_dict={query: query_},
options=run_options,
run_metadata=run_metadata)
if i == 0:
start_time = time.time()
if i % 10 == 0:
if i >= 1:
end_time = time.time()
avg_time = (end_time - start_time) / float(i + 1)
print("Average time consumed per step is %0.2f secs." %
avg_time)
print(
"After %d training step(s), learning rate is %g, loss on training batch is %g."
% (step, learnrate, loss_value))
# 每个epoch验证一次，保存模型
if i % 2000 == 0:
print '#############################################'
print 'saving model...'
saver_save.save(sess,
cfg.TRAIN.SAVED_MODEL_PATTERN,
global_step=global_step)
print 'successfully saved !'
print '#############################################'
if i % 200 == 0:
joint_writer.add_run_metadata(run_metadata, 'step%03d' % i)
summary_writer.add_summary(summary, i)
end_time = time.time()
#print '%dth time step,consuming %f secs'%(i, start_time-end_time)
summary_writer.close()
choices=['TH14', 'AN'])
parser.add_argument('--expname', type=str, required=True)
parser.add_argument('--rsltname', type=str, default='rslt')
# This argument would only be used when testing.
parser.add_argument('--pretrained',
type=str,
default='default.caffemodel')
parser.add_argument('--num_workers', type=int, default=16)
args = parser.parse_args()
config_path = osp.join(cfg.EXP_DIR, args.dataset, args.expname,
'config.yml')
# Init
cfg_from_file(config_path)
args.phase = args.phase.upper()
setup(args.phase, args.dataset, args.expname, args.rsltname)
# Check whether specified <pretrained> when testing:
# a work-around for conditional required argument.
if args.phase == 'TEST':
assert args.pretrained != 'default.caffemodel', \
'Speicify pretrained model when testing.'
cfg.SNAPSHOT_PATH = osp.join(cfg.LOCAL_SNAPSHOT_PATH,
args.pretrained)
caffe.init_glog(
osp.join(cfg.LOG_PATH, '{}{}.'.format(args.phase, cfg.INFIX)))
pred_rslts = proc_prll(args.phase, args.num_workers)
action='store_true')
args = parser.parse_args()
return args
if __name__ == '__main__':
print('Doing training...')
args = parse_args_train()
print('Called with args:')
print(args)
if args.cfg_file is not None:
cfg_from_file(args.cfg_file)
cfg.GPU_ID = args.gpu_id
cfg.train_imdb = args.train_imdb
cfg.val_prototxt = args.test_prototxt
cfg.test_prototxt = args.test_prototxt
cfg.TRAIN.VALIDATION_ITERATION = eval(cfg.TRAIN.VALIDATION_ITERATION)
print('Using config:')
pprint.pprint(cfg)
# if not args.randomize:
# fix the random seeds (numpy and caffe) for reproducibility
np.random.seed(cfg.RNG_SEED)
caffe.set_random_seed(cfg.RNG_SEED)
action='store_true')
args = parser.parse_args()
return args
if __name__ == '__main__':
print('Doing training...')
args = parse_args_train()
print('Called with args:')
print(args)
if args.cfg_file is not None:
cfg_from_file(args.cfg_file)
cfg.GPU_ID = args.gpu_id
cfg.train_imdb = args.train_imdb
cfg.val_prototxt = args.test_prototxt
cfg.test_prototxt = args.test_prototxt
cfg.TRAIN.VALIDATION_ITERATION = eval(cfg.TRAIN.VALIDATION_ITERATION)
print('Using config:')
pprint.pprint(cfg)
# if not args.randomize:
# fix the random seeds (numpy and caffe) for reproducibility
np.random.seed(cfg.RNG_SEED)
caffe.set_random_seed(cfg.RNG_SEED)
