def main():
arg_parser = train_app_MTMC.create_default_argument_parser("DukeMTMC-reID")
arg_parser.add_argument("--dataset_dir",
help="Path to DukeMTMC dataset directory.",
default="./DukeMTMC-reID/DukeMTMC-reID")
#print("######################arg_parser",arg_parser)
arg_parser.add_argument(
"--sdk_dir",
help="Path to DukeMTMC baseline evaluation software.",
default="resources/Market-1501-v15.09.15-baseline")
args = arg_parser.parse_args()
dataset = DukeMTMC(args.dataset_dir, num_validation_y=0.1, seed=1234)
if args.mode == "train":
train_x, train_y, _ = dataset.read_train()
print("Train set size: %d images, %d identities" %
(len(train_x), len(np.unique(train_y))))
print("##############################", train_x[0])
network_factory = net.create_network_factory(
is_training=True,
num_classes=MTMC.MAX_LABEL + 1,
add_logits=args.loss_mode == "cosine-softmax")
train_kwargs = train_app_MTMC.to_train_kwargs(args)
train_app_MTMC.train_loop(net.preprocess,
network_factory,
train_x,
train_y,
num_images_per_id=4,
image_shape=MTMC.IMAGE_SHAPE,
**train_kwargs)
elif args.mode == "eval":
valid_x, valid_y, camera_indices = dataset.read_validation()
print("Validation set size: %d images, %d identities" %
(len(valid_x), len(np.unique(valid_y))))
network_factory = net.create_network_factory(
is_training=False,
num_classes=MTMC.MAX_LABEL + 1,
add_logits=args.loss_mode == "cosine-softmax")
eval_kwargs = train_app_MTMC.to_eval_kwargs(args)
train_app_MTMC.eval_loop(net.preprocess,
network_factory,
valid_x,
valid_y,
camera_indices,
image_shape=MTMC.IMAGE_SHAPE,
**eval_kwargs)
elif args.mode == "export":
# Export one specific model.
gallery_filenames, _, query_filenames, _, _ = dataset.read_test()
network_factory = net.create_network_factory(
is_training=False,
num_classes=MTMC.MAX_LABEL + 1,
add_logits=False,
reuse=None)
gallery_features = train_app_MTMC.encode(net.preprocess,
network_factory,
args.restore_path,
gallery_filenames,
image_shape=MTMC.IMAGE_SHAPE)
sio.savemat(os.path.join(args.sdk_dir, "feat_test.mat"),
{"features": gallery_features})
network_factory = net.create_network_factory(
is_training=False,
num_classes=MTMC.MAX_LABEL + 1,
add_logits=False,
reuse=True)
query_features = train_app_MTMC.encode(net.preprocess,
network_factory,
args.restore_path,
query_filenames,
image_shape=MTMC.IMAGE_SHAPE)
sio.savemat(os.path.join(args.sdk_dir, "feat_query.mat"),
{"features": query_features})
elif args.mode == "finalize":
network_factory = net.create_network_factory(
is_training=False,
num_classes=MTMC.MAX_LABEL + 1,
add_logits=False,
reuse=None)
train_app_MTMC.finalize(functools.partial(net.preprocess,
input_is_bgr=True),
network_factory,
args.restore_path,
image_shape=MTMC.IMAGE_SHAPE,
output_filename="./MTMC.ckpt")
elif args.mode == "freeze":
network_factory = net.create_network_factory(
is_training=False,
num_classes=MTMC.MAX_LABEL + 1,
add_logits=False,
reuse=None)
train_app_MTMC.freeze(functools.partial(net.preprocess,
input_is_bgr=True),
network_factory,
args.restore_path,
image_shape=MTMC.IMAGE_SHAPE,
output_filename="./MTMC.pb")
else:
raise ValueError("Invalid mode argument.")
def main():
# output to file
this_time = time.strftime('%Y-%m-%dT%H%M%S')
sys.stdout = open(this_time + 'sdd.txt', 'w')
sys.stderr = open(this_time + 'sdd.log', 'w')
arg_parser = train_app.create_default_argument_parser("sdd")
arg_parser.add_argument("--dataset_dir",
help="Path to Stanford drone dataset directory.",
default="../StanfordDroneDataset")
arg_parser.add_argument("--sdk_dir",
help="Path to sdd baseline evaluation software.",
default="resources/StanfordDroneDataset-baseline")
arg_parser.add_argument(
"--annotation_file_name",
help="Path to Stanford drone dataset annotation file.",
default="/annotations/nexus/video0/annotations.txt")
arg_parser.add_argument("--video_file_name",
help="Path to Stanford drone dataset video file.",
default="/videos/nexus/video0/video.mov")
args = arg_parser.parse_args()
dataset = sdd_dataset(args.dataset_dir,
args.annotation_file_name,
args.video_file_name,
num_validation_y=0.1,
seed=1234)
if args.mode == "train":
train_x, train_y, _ = dataset.read_train()
print("Train set size: %d images, %d identities" %
(len(train_x), len(np.unique(train_y))))
network_factory = net.create_network_factory(
is_training=True,
num_classes=sdd.calculate_max_label(dataset._dataset_dir +
dataset._annotation_file_name)
+ 1,
add_logits=args.loss_mode == "cosine-softmax")
train_kwargs = train_app.to_train_kwargs(args)
train_app.train_loop(net.preprocess,
network_factory,
train_x,
train_y,
num_images_per_id=4,
image_shape=sdd.IMAGE_SHAPE,
**train_kwargs)
elif args.mode == "eval":
valid_x, valid_y, camera_indices = dataset.read_validation()
print("Validation set size: %d images, %d identities" %
(len(valid_x), len(np.unique(valid_y))))
network_factory = net.create_network_factory(
is_training=False,
num_classes=sdd.calculate_max_label(self._dataset_dir +
self._annotation_file_name) +
1,
add_logits=args.loss_mode == "cosine-softmax")
eval_kwargs = train_app.to_eval_kwargs(args)
train_app.eval_loop(net.preprocess,
network_factory,
valid_x,
valid_y,
camera_indices,
image_shape=sdd.IMAGE_SHAPE,
**eval_kwargs)
elif args.mode == "export":
# Export one specific model.
gallery_filenames, _, query_filenames, _, _ = dataset.read_test()
network_factory = net.create_network_factory(
is_training=False,
num_classes=sdd.calculate_max_label(self._dataset_dir +
self._annotation_file_name) +
1,
add_logits=False,
reuse=None)
gallery_features = train_app.encode(net.preprocess,
network_factory,
args.restore_path,
gallery_filenames,
image_shape=sdd.IMAGE_SHAPE)
sio.savemat(os.path.join(args.sdk_dir, "feat_test.mat"),
{"features": gallery_features})
network_factory = net.create_network_factory(
is_training=False,
num_classes=sdd.calculate_max_label(self._dataset_dir +
self._annotation_file_name) +
1,
add_logits=False,
reuse=True)
query_features = train_app.encode(net.preprocess,
network_factory,
args.restore_path,
query_filenames,
image_shape=sdd.IMAGE_SHAPE)
sio.savemat(os.path.join(args.sdk_dir, "feat_query.mat"),
{"features": query_features})
elif args.mode == "finalize":
network_factory = net.create_network_factory(
is_training=False,
num_classes=sdd.calculate_max_label(self._dataset_dir +
self._annotation_file_name) +
1,
add_logits=False,
reuse=None)
train_app.finalize(functools.partial(net.preprocess,
input_is_bgr=True),
network_factory,
args.restore_path,
image_shape=sdd.IMAGE_SHAPE,
output_filename="./sdd.ckpt")
elif args.mode == "freeze":
network_factory = net.create_network_factory(
is_training=False,
num_classes=sdd.calculate_max_label(self._dataset_dir +
self._annotation_file_name) +
1,
add_logits=False,
reuse=None)
train_app.freeze(functools.partial(net.preprocess, input_is_bgr=True),
network_factory,
args.restore_path,
image_shape=sdd.IMAGE_SHAPE,
output_filename="./sdd.pb")
else:
raise ValueError("Invalid mode argument.")
def main():
arg_parser = train_app.create_default_argument_parser("vric")
arg_parser.add_argument("--dataset_dir",
help="Path to MARS dataset directory.",
default="resources/MARS-evaluation-master")
args = arg_parser.parse_args()
dataset = VRIC(args.dataset_dir, num_validation_y=0.1, seed=1234)
if args.mode == "train":
train_x, train_y, _ = dataset.read_train()
print("Train set size: %d images, %d identities" %
(len(train_x), len(np.unique(train_y))))
network_factory = net.create_network_factory(
is_training=True,
num_classes=vric.MAX_LABEL + 1,
add_logits=args.loss_mode == "cosine-softmax")
train_kwargs = train_app.to_train_kwargs(args)
train_app.train_loop(net.preprocess,
network_factory,
train_x,
train_y,
num_images_per_id=4,
image_shape=IMAGE_SHAPE,
**train_kwargs)
elif args.mode == "eval":
valid_x, valid_y, camera_indices = dataset.read_validation()
print("Validation set size: %d images, %d identities" %
(len(valid_x), len(np.unique(valid_y))))
network_factory = net.create_network_factory(
is_training=False,
num_classes=vric.MAX_LABEL + 1,
add_logits=args.loss_mode == "cosine-softmax")
eval_kwargs = train_app.to_eval_kwargs(args)
train_app.eval_loop(net.preprocess,
network_factory,
valid_x,
valid_y,
camera_indices,
image_shape=IMAGE_SHAPE,
num_galleries=20,
**eval_kwargs)
elif args.mode == "export":
filenames = dataset.read_test_filenames()
network_factory = net.create_network_factory(
is_training=False,
num_classes=vric.MAX_LABEL + 1,
add_logits=False,
reuse=None)
features = train_app.encode(net.preprocess,
network_factory,
args.restore_path,
filenames,
image_shape=IMAGE_SHAPE)
sio.savemat(os.path.join(args.dataset_dir, "feat_test.mat"),
{"features": features})
elif args.mode == "finalize":
network_factory = net.create_network_factory(
is_training=False,
num_classes=vric.MAX_LABEL + 1,
add_logits=False,
reuse=None)
train_app.finalize(functools.partial(net.preprocess,
input_is_bgr=True),
network_factory,
args.restore_path,
image_shape=IMAGE_SHAPE,
output_filename="./vric.ckpt")
elif args.mode == "freeze":
network_factory = net.create_network_factory(
is_training=False,
num_classes=vric.MAX_LABEL + 1,
add_logits=False,
reuse=None)
train_app.freeze(functools.partial(net.preprocess, input_is_bgr=True),
network_factory,
args.restore_path,
image_shape=IMAGE_SHAPE,
output_filename="./vric.pb")
else:
raise ValueError("Invalid mode argument.")
def main():
arg_parser = train_app.create_default_argument_parser("veri")
arg_parser.add_argument("--dataset_dir",
help="Path to the VeRi data",
default="./VeRi")
# arg_parser.add_argument(
# "--sdk_dir", help="Path to Market1501 baseline evaluation software.",
# default="resources/Market-1501-v15.09.15-baseline")
args = arg_parser.parse_args()
dataset = Veri(args.dataset_dir, num_validation_y=0.1, seed=1234)
if args.mode == "train":
train_x, train_y, _ = dataset.read_train()
print("Train set size: %d images, %d identities" %
(len(train_x), len(np.unique(train_y))))
network_factory = net.create_network_factory(
is_training=True,
num_classes=veri.MAX_LABEL + 1,
add_logits=args.loss_mode == "cosine-softmax")
train_kwargs = train_app.to_train_kwargs(args)
train_app.train_loop(net.preprocess,
network_factory,
train_x,
train_y,
num_images_per_id=16,
image_shape=veri.IMAGE_SHAPE,
**train_kwargs) #,
# elif args.mode == "eval":
# valid_x, valid_y, camera_indices = dataset.read_validation()
# print("Validation set size: %d images, %d identities" % (
# len(valid_x), len(np.unique(valid_y))))
#
# network_factory = net.create_network_factory(
# is_training=False, num_classes=market1501.MAX_LABEL + 1,
# add_logits=args.loss_mode == "cosine-softmax")
# eval_kwargs = train_app.to_eval_kwargs(args)
# train_app.eval_loop(
# net.preprocess, network_factory, valid_x, valid_y, camera_indices,
# image_shape=market1501.IMAGE_SHAPE, **eval_kwargs)
# elif args.mode == "export":
# # Export one specific model.
# gallery_filenames, _, query_filenames, _, _ = dataset.read_test()
#
# network_factory = net.create_network_factory(
# is_training=False, num_classes=market1501.MAX_LABEL + 1,
# add_logits=False, reuse=None)
# gallery_features = train_app.encode(
# net.preprocess, network_factory, args.restore_path,
# gallery_filenames, image_shape=market1501.IMAGE_SHAPE)
# sio.savemat(
# os.path.join(args.sdk_dir, "feat_test.mat"),
# {"features": gallery_features})
#
# network_factory = net.create_network_factory(
# is_training=False, num_classes=market1501.MAX_LABEL + 1,
# add_logits=False, reuse=True)
# query_features = train_app.encode(
# net.preprocess, network_factory, args.restore_path,
# query_filenames, image_shape=market1501.IMAGE_SHAPE)
# sio.savemat(
# os.path.join(args.sdk_dir, "feat_query.mat"),
# {"features": query_features})
# elif args.mode == "finalize":
# network_factory = net.create_network_factory(
# is_training=False, num_classes=market1501.MAX_LABEL + 1,
# add_logits=False, reuse=None)
# train_app.finalize(
# functools.partial(net.preprocess, input_is_bgr=True),
# network_factory, args.restore_path,
# image_shape=market1501.IMAGE_SHAPE,
# output_filename="./market1501.ckpt")
elif args.mode == "freeze":
network_factory = net.create_network_factory(
is_training=False,
num_classes=veri.MAX_LABEL + 1,
add_logits=False,
reuse=None)
train_app.freeze(functools.partial(net.preprocess, input_is_bgr=True),
network_factory,
args.restore_path,
image_shape=veri.IMAGE_SHAPE,
output_filename="./veri.pb")
else:
raise ValueError("Invalid mode argument.")
def main():
arg_parser = train_app.create_default_argument_parser("bdd")
arg_parser.add_argument(
"--dataset_dir", help="Path to BDD tracking dataset directory.",
default="data/bdd-tracking-reid")
arg_parser.add_argument(
"--sdk_dir", help="Path to BDD baseline evaluation software.",
default="/path/to/bdd/sdk")
args = arg_parser.parse_args()
dataset = BddTracking(args.dataset_dir, num_validation_y=0.1, seed=1234)
if args.mode == "train":
train_x, train_y = dataset.read_train()
print("Train set size: %d images, %d identities" % (
len(train_x), len(np.unique(train_y))))
network_factory = net.create_network_factory(
is_training=True, num_classes=bdd.MAX_LABEL + 1,
add_logits=args.loss_mode == "cosine-softmax")
train_kwargs = train_app.to_train_kwargs(args)
train_app.train_loop(
net.preprocess, network_factory, train_x, train_y,
num_images_per_id=4, image_shape=bdd.IMAGE_SHAPE,
**train_kwargs)
elif args.mode == "eval":
valid_x, valid_y = dataset.read_validation()
print("Validation set size: %d images, %d identities" % (
len(valid_x), len(np.unique(valid_y))))
network_factory = net.create_network_factory(
is_training=False, num_classes=bdd.MAX_LABEL + 1,
add_logits=args.loss_mode == "cosine-softmax")
eval_kwargs = train_app.to_eval_kwargs(args)
train_app.eval_loop(
net.preprocess, network_factory, valid_x, valid_y, None,
image_shape=bdd.IMAGE_SHAPE, **eval_kwargs)
elif args.mode == "export":
# Export one specific model.
gallery_filenames, _, query_filenames, _, _ = dataset.read_test()
network_factory = net.create_network_factory(
is_training=False, num_classes=bdd.MAX_LABEL + 1,
add_logits=False, reuse=None)
gallery_features = train_app.encode(
net.preprocess, network_factory, args.restore_path,
gallery_filenames, image_shape=bdd.IMAGE_SHAPE)
sio.savemat(
os.path.join(args.sdk_dir, "feat_test.mat"),
{"features": gallery_features})
network_factory = net.create_network_factory(
is_training=False, num_classes=bdd.MAX_LABEL + 1,
add_logits=False, reuse=True)
query_features = train_app.encode(
net.preprocess, network_factory, args.restore_path,
query_filenames, image_shape=bdd.IMAGE_SHAPE)
sio.savemat(
os.path.join(args.sdk_dir, "feat_query.mat"),
{"features": query_features})
elif args.mode == "finalize":
network_factory = net.create_network_factory(
is_training=False, num_classes=bdd.MAX_LABEL + 1,
add_logits=False, reuse=None)
train_app.finalize(
functools.partial(net.preprocess, input_is_bgr=True),
network_factory, args.restore_path,
image_shape=bdd.IMAGE_SHAPE,
output_filename="./bdd.ckpt")
elif args.mode == "freeze":
network_factory = net.create_network_factory(
is_training=False, num_classes=bdd.MAX_LABEL + 1,
add_logits=False, reuse=None)
train_app.freeze(
functools.partial(net.preprocess, input_is_bgr=True),
network_factory, args.restore_path,
image_shape=bdd.IMAGE_SHAPE,
output_filename="/data1/haofeng/out/cml/cml-bdd-tracking-train/bdd-tracking-val.pb")
else:
raise ValueError("Invalid mode argument.")
def main():
arg_parser = train_app.create_default_argument_parser("veri")
arg_parser.add_argument("--dataset_dir",
help="Path to VeRi dataset directory.",
default="resources/VeRi")
args = arg_parser.parse_args()
dataset = VeRi(args.dataset_dir, num_validation_y=0.1, seed=1234)
if args.mode == "train":
train_x, train_y, _ = dataset.read_train()
print("Train set size: %d images, %d identites" %
(len(train_x), len(np.unique(train_y))))
network_factory = net.create_network_factory(
is_training=True,
num_classes=veri.MAX_LABEL + 1,
add_logits=args.loss_mode == "cosine-softmax")
train_kwargs = train_app.to_train_kwargs(args)
train_app.train_loop(net.preprocess,
network_factory,
train_x,
train_y,
num_images_per_id=4,
image_shape=veri.IMAGE_SHAPE,
**train_kwargs)
elif args.mode == "eval":
valid_x, valid_y, camera_indices = dataset.read_validation()
print("Validation set size: %d images, %d identites" %
(len(valid_x), len(np.unique(valid_y))))
network_factory = net.create_network_factory(
is_training=False,
num_classes=veri.MAX_LABEL + 1,
add_logits=args.loss_mode == "cosine-softmax")
eval_kwargs = train_app.to_eval_kwargs(args)
train_app.eval_loop(net.preprocess,
network_factory,
valid_x,
valid_y,
camera_indices,
image_shape=veri.IMAGE_SHAPE,
**eval_kwargs)
elif args.mode == "export":
raise NotImplementedError()
elif args.mode == "finalize":
network_factory = net.create_network_factory(
is_training=False,
num_classes=veri.MAX_LABEL + 1,
add_logits=False,
reuse=None)
train_app.finalize(functools.partial(net.preprocess,
input_is_bgr=True),
network_factory,
args.restore_path,
image_shape=veri.IMAGE_SHAPE,
output_filename="./veri.ckpt")
elif args.mode == "freeze":
network_factory = net.create_network_factory(
is_training=False,
num_classes=veri.MAX_LABEL + 1,
add_logits=False,
reuse=None)
train_app.freeze(functools.partial(net.preprocess, input_is_bgr=True),
network_factory,
args.restore_path,
image_shape=veri.IMAGE_SHAPE,
output_filename="./veri.pb")
else:
raise ValueError("Invalid mode argument.")
def main():
arg_parser = train_app.create_default_argument_parser("youtube_faces")
arg_parser.add_argument("--dataset_dir",
help="path to youtube_faces dataset directory.",
default="/home/max/Desktop/yt_test_data")
args = arg_parser.parse_args()
dataset = Youtube_faces(args.dataset_dir, num_validation_y=0.1, seed=1234)
if args.mode == "train":
train_x, train_y, _ = dataset.read_train()
print("Train set size: %d images, %d persons" %
(len(train_x), len(np.unique(train_y))))
network_factory = net.create_network_factory(
is_training=True,
num_classes=youtube_faces.MAX_LABEL + 1,
add_logits=args.loss_mode == "cosine-softmax")
train_kwargs = train_app.to_train_kwargs(args)
train_app.train_loop(net.preprocess,
network_factory,
train_x,
train_y,
num_images_per_id=4,
image_shape=youtube_faces.IMAGE_SHAPE,
**train_kwargs)
elif args.mode == "eval":
valid_x, valid_y, camera_indices = dataset.read_validation()
print("Validation set size: %d images, %d persons" %
(len(valid_x), len(np.unique(valid_y))))
network_factory = net.create_network_factory(
is_training=False,
num_classes=youtube_faces.MAX_LABEL + 1,
add_logits=args.loss_mode == "cosine-softmax")
eval_kwargs = train_app.to_eval_kwargs(args)
train_app.eval_loop(net.preprocess,
network_factory,
valid_x,
valid_y,
camera_indices,
image_shape=youtube_faces.IMAGE_SHAPE,
**eval_kwargs)
elif args.mode == "export":
raise NotImplementedError()
# elif args.mode == "export":
# # Export one specific model.
# gallery_filenames, _, query_filenames, _, _ = dataset.read_test()
# network_factory = net.create_network_factory(
# is_training=False, num_classes=market1501.MAX_LABEL + 1,
# add_logits=False, reuse=None)
# gallery_features = train_app.encode(
# net.preprocess, network_factory, args.restore_path,
# gallery_filenames, image_shape=market1501.IMAGE_SHAPE)
# sio.savemat(
# os.path.join(args.sdk_dir, "feat_test.mat"),
# {"features": gallery_features})
# network_factory = net.create_network_factory(
# is_training=False, num_classes=market1501.MAX_LABEL + 1,
# add_logits=False, reuse=True)
# query_features = train_app.encode(
# net.preprocess, network_factory, args.restore_path,
# query_filenames, image_shape=market1501.IMAGE_SHAPE)
# sio.savemat(
# os.path.join(args.sdk_dir, "feat_query.mat"),
# {"features": query_features})
elif args.mode == "finalize":
network_factory = net.create_network_factory(
is_training=False,
num_classes=youtube_faces.MAX_LABEL + 1,
add_logits=False,
reuse=None)
train_app.finalize(functools.partial(net.preprocess,
input_is_bgr=True),
network_factory,
args.restore_path,
image_shape=IMAGE_SHAPE,
output_filename="./youtube_faces.ckpt")
elif args.mode == "freeze":
network_factory = net.create_network_factory(
is_training=False,
num_classes=youtube_faces.MAX_LABEL + 1,
add_logits=False,
reuse=None)
train_app.freeze(functools.partial(net.preprocess, input_is_bgr=True),
network_factory,
args.restore_path,
image_shape=youtube_faces.IMAGE_SHAPE,
output_filename="./youtube_faces.pb")
else:
raise ValueError("Invalid mode argument.")
