def main():
parser = argparse.ArgumentParser(
description='Patch-NetVLAD-Feature-Extract')
parser.add_argument(
'--config_path',
type=str,
default=join(PATCHNETVLAD_ROOT_DIR, 'configs/performance.ini'),
help=
'File name (with extension) to an ini file that stores most of the configuration data for patch-netvlad'
)
parser.add_argument(
'--dataset_file_path',
type=str,
required=True,
help=
'Full path (with extension) to a text file that stores the save location and name of all images in the dataset folder'
)
parser.add_argument(
'--dataset_root_dir',
type=str,
default='',
help=
'If the files in dataset_file_path are relative, use dataset_root_dir as prefix.'
)
parser.add_argument('--output_features_dir',
type=str,
default=join(PATCHNETVLAD_ROOT_DIR, 'output_features'),
help='Path to store all patch-netvlad features')
parser.add_argument('--nocuda',
action='store_true',
help='If true, use CPU only. Else use GPU.')
opt = parser.parse_args()
print(opt)
configfile = opt.config_path
assert os.path.isfile(configfile)
config = configparser.ConfigParser()
config.read(configfile)
cuda = not opt.nocuda
if cuda and not torch.cuda.is_available():
raise Exception("No GPU found, please run with --nocuda")
device = torch.device("cuda" if cuda else "cpu")
encoder_dim, encoder = get_backend()
if not os.path.isfile(opt.dataset_file_path):
opt.dataset_file_path = join(PATCHNETVLAD_ROOT_DIR,
'dataset_imagenames',
opt.dataset_file_path)
dataset = PlaceDataset(None, opt.dataset_file_path, opt.dataset_root_dir,
None, config['feature_extract'])
# must resume to do extraction
resume_ckpt = config['global_params']['resumePath'] + config[
'global_params']['num_pcs'] + '.pth.tar'
# backup: try whether resume_ckpt is relative to PATCHNETVLAD_ROOT_DIR
if not isfile(resume_ckpt):
resume_ckpt = join(PATCHNETVLAD_ROOT_DIR, resume_ckpt)
if not isfile(resume_ckpt):
from download_models import download_all_models
download_all_models(ask_for_permission=True)
if isfile(resume_ckpt):
print("=> loading checkpoint '{}'".format(resume_ckpt))
checkpoint = torch.load(resume_ckpt,
map_location=lambda storage, loc: storage)
assert checkpoint['state_dict']['WPCA.0.bias'].shape[0] == int(
config['global_params']['num_pcs'])
config['global_params']['num_clusters'] = str(
checkpoint['state_dict']['pool.centroids'].shape[0])
model = get_model(encoder,
encoder_dim,
opt,
config['global_params'],
append_pca_layer=True)
if int(config['global_params']
['nGPU']) > 1 and torch.cuda.device_count() > 1:
model.encoder = nn.DataParallel(model.encoder)
# if opt.mode.lower() != 'cluster':
model.pool = nn.DataParallel(model.pool)
model.load_state_dict(checkpoint['state_dict'])
model = model.to(device)
print("=> loaded checkpoint '{}'".format(resume_ckpt, ))
else:
raise FileNotFoundError(
"=> no checkpoint found at '{}'".format(resume_ckpt))
feature_extract(dataset, model, device, opt, config)
torch.cuda.empty_cache(
) # garbage clean GPU memory, a bug can occur when Pytorch doesn't automatically clear the
# memory after runs
print('\n\nDone. Finished extracting and saving features')
def main():
parser = argparse.ArgumentParser(description='Patch-NetVLAD-Match-Two')
parser.add_argument(
'--config_path',
type=str,
default=join(PATCHNETVLAD_ROOT_DIR, 'configs/performance.ini'),
help=
'File name (with extension) to an ini file that stores most of the configuration data for patch-netvlad'
)
parser.add_argument('--first_im_path',
type=str,
default=join(PATCHNETVLAD_ROOT_DIR,
'example_images/tokyo_db.png'),
help='Full path (with extension) to an image file')
parser.add_argument(
'--second_im_path',
type=str,
default=join(PATCHNETVLAD_ROOT_DIR, 'example_images/tokyo_query.jpg'),
help='Full path (with extension) to another image file')
parser.add_argument(
'--plot_save_path',
type=str,
default=join(PATCHNETVLAD_ROOT_DIR, 'results'),
help=
'Path plus optional prefix pointing to a location to save the output matching plot'
)
parser.add_argument('--nocuda',
action='store_true',
help='If true, use CPU only. Else use GPU.')
opt = parser.parse_args()
print(opt)
configfile = opt.config_path
assert os.path.isfile(configfile)
config = configparser.ConfigParser()
config.read(configfile)
cuda = opt.nocuda
if cuda and not torch.cuda.is_available():
raise Exception("No GPU found, please run with --nocuda")
device = torch.device("cuda" if cuda else "cpu")
encoder_dim, encoder = get_backend()
# must load from a resume to do extraction
resume_ckpt = config['global_params']['resumePath'] + config[
'global_params']['num_pcs'] + '.pth.tar'
model = get_model(encoder,
encoder_dim,
opt,
config['global_params'],
append_pca_layer=True)
if int(config['global_params']
['nGPU']) > 1 and torch.cuda.device_count() > 1:
model.encoder = nn.DataParallel(model.encoder)
# if opt.mode.lower() != 'cluster':
model.pool = nn.DataParallel(model.pool)
# backup: try whether resume_ckpt is relative to script path
if not isfile(resume_ckpt):
resume_ckpt = join(PATCHNETVLAD_ROOT_DIR, resume_ckpt)
if not isfile(resume_ckpt):
from download_models import download_all_models
download_all_models(ask_for_permission=True)
if isfile(resume_ckpt):
print("=> loading checkpoint '{}'".format(resume_ckpt))
checkpoint = torch.load(resume_ckpt,
map_location=lambda storage, loc: storage)
model.load_state_dict(checkpoint['state_dict'])
model = model.to(device)
print("=> loaded checkpoint '{}'".format(resume_ckpt, ))
else:
raise FileNotFoundError(
"=> no checkpoint found at '{}'".format(resume_ckpt))
match_two(model, device, opt, config)
torch.cuda.empty_cache(
) # garbage clean GPU memory, a bug can occur when Pytorch doesn't automatically clear the
# memory after runs
print('Done')
def main():
parser = argparse.ArgumentParser(description='Patch-NetVLAD-Match-Two')
parser.add_argument('--config_path', type=str, default=join(PATCHNETVLAD_ROOT_DIR, 'configs/performance.ini'),
help='File name (with extension) to an ini file that stores most of the configuration data for patch-netvlad')
parser.add_argument('--nocuda', action='store_true', help='If true, use CPU only. Else use GPU.')
opt = parser.parse_args()
print(opt)
configfile = opt.config_path
assert os.path.isfile(configfile)
config = configparser.ConfigParser()
config.read(configfile)
cuda = not opt.nocuda
if cuda and not torch.cuda.is_available():
raise Exception("No GPU found, please run with --nocuda")
device = torch.device("cuda" if cuda else "cpu")
encoder_dim, encoder = get_backend()
# must load from a resume to do extraction
resume_ckpt = config['global_params']['resumePath'] + config['global_params']['num_pcs'] + '.pth.tar'
# backup: try whether resume_ckpt is relative to script path
if not isfile(resume_ckpt):
resume_ckpt = join(PATCHNETVLAD_ROOT_DIR, resume_ckpt)
if not isfile(resume_ckpt):
from download_models import download_all_models
download_all_models(ask_for_permission=True)
if isfile(resume_ckpt):
print("=> loading checkpoint '{}'".format(resume_ckpt))
checkpoint = torch.load(resume_ckpt, map_location=lambda storage, loc: storage)
assert checkpoint['state_dict']['WPCA.0.bias'].shape[0] == int(config['global_params']['num_pcs'])
config['global_params']['num_clusters'] = str(checkpoint['state_dict']['pool.centroids'].shape[0])
model = get_model(encoder, encoder_dim, config['global_params'], append_pca_layer=True)
if int(config['global_params']['nGPU']) > 1 and torch.cuda.device_count() > 1:
model.encoder = nn.DataParallel(model.encoder)
model.pool = nn.DataParallel(model.pool)
model.load_state_dict(checkpoint['state_dict'])
model = model.to(device)
print("=> loaded checkpoint '{}'".format(resume_ckpt, ))
else:
raise FileNotFoundError("=> no checkpoint found at '{}'".format(resume_ckpt))
vid = cv2.VideoCapture(0)
_, last_frame = vid.read()
while(True):
_, frame = vid.read()
match_two(model, device, config, frame, last_frame, None)
key = cv2.waitKey(1)
if key & 0xFF == ord('q'):
break
elif key & 0xFF == ord('n'):
last_frame = frame
vid.release()
cv2.destroyAllWindows()
torch.cuda.empty_cache() # garbage clean GPU memory, a bug can occur when Pytorch doesn't automatically clear the
# memory after runs
print('Done')
print('===> Building model')
encoder_dim, encoder = get_backend()
if opt.resume_path: # must resume for PCA
if isfile(opt.resume_path):
print("=> loading checkpoint '{}'".format(opt.resume_path))
checkpoint = torch.load(opt.resume_path,
map_location=lambda storage, loc: storage)
config['global_params']['num_clusters'] = str(
checkpoint['state_dict']['pool.centroids'].shape[0])
model = get_model(encoder,
encoder_dim,
opt,
config['global_params'],
append_pca_layer=False)
model.load_state_dict(checkpoint['state_dict'])
opt.start_epoch = checkpoint['epoch']
print("=> loaded checkpoint '{}'".format(opt.resume_path, ))
else:
raise FileNotFoundError("=> no checkpoint found at '{}'".format(
opt.resume_path))
else:
raise ValueError("Need an existing checkpoint in order to run PCA")
isParallel = False
if int(config['global_params']