def main(args):
dataset_splitBy = args.dataset + '_' + args.splitBy
if not osp.isdir(osp.join('cache/feats/', dataset_splitBy)):
os.makedirs(osp.join('cache/feats/', dataset_splitBy))
# Image Directory
if 'coco' in dataset_splitBy or 'combined' in dataset_splitBy:
IMAGE_DIR = 'data/images/mscoco/images/train2014'
elif 'clef' in dataset_splitBy:
IMAGE_DIR = 'data/images/saiapr_tc-12'
elif 'sunspot' in dataset_splitBy:
IMAGE_DIR = 'data/images/SUNRGBD'
else:
print('No image directory prepared for ', args.dataset)
sys.exit(0)
# load dataset
data_json = osp.join('cache/prepro', dataset_splitBy, 'data.json')
data_h5 = osp.join('cache/prepro', dataset_splitBy, 'data.h5')
dets_json = osp.join(
'cache/detections', dataset_splitBy,
'%s_%s_%s_dets.json' % (args.net_name, args.imdb_name, args.tag))
loader = DetsLoader(data_json, data_h5, dets_json)
images = loader.images
dets = loader.dets
num_dets = len(dets)
assert sum([len(image['det_ids']) for image in images]) == num_dets
# load mrcn model
mrcn = inference_no_imdb.Inference(args)
# feats_h5
file_name = '%s_%s_%s_det_feats.h5' % (args.net_name, args.imdb_name,
args.tag)
feats_h5 = osp.join('cache/feats', dataset_splitBy, 'mrcn', file_name)
f = h5py.File(feats_h5, 'w')
fc7_set = f.create_dataset('fc7', (num_dets, 2048), dtype=np.float32)
pool5_set = f.create_dataset('pool5', (num_dets, 1024), dtype=np.float32)
# extract
feats_dir = '%s_%s_%s' % (args.net_name, args.imdb_name, args.tag)
head_feats_dir = osp.join('cache/feats/', dataset_splitBy, 'mrcn',
feats_dir)
for i, image in enumerate(images):
image_id = image['image_id']
net_conv, im_info = image_to_head(head_feats_dir, image_id)
det_ids = image['det_ids']
for det_id in det_ids:
det = loader.Dets[det_id]
det_pool5, det_fc7 = det_to_pool5_fc7(mrcn, det, net_conv, im_info)
det_h5_id = det['h5_id']
fc7_set[det_h5_id] = det_fc7.data.cpu().numpy()
pool5_set[det_h5_id] = det_pool5.data.cpu().numpy()
if i % 20 == 0:
print('%s/%s done.' % (i + 1, len(images)))
f.close()
print('%s written.' % feats_h5)
def main(args):
dataset_splitBy = args.dataset + '_' + args.splitBy
if not osp.isdir(osp.join('cache/feats/', dataset_splitBy)):
os.makedirs(osp.join('cache/feats/', dataset_splitBy))
# Image Directory
if 'coco' or 'combined' in dataset_splitBy:
IMAGE_DIR = 'data/images/mscoco/images/train2014'
elif 'clef' in dataset_splitBy:
IMAGE_DIR = 'data/images/saiapr_tc-12'
else:
print('No image directory prepared for ', args.dataset)
sys.exit(0)
# load dataset
data_json = osp.join('cache/prepro', dataset_splitBy, 'data.json')
data_h5 = osp.join('cache/prepro', dataset_splitBy, 'data.h5')
loader = Loader(data_json, data_h5)
images = loader.images
anns = loader.anns
num_anns = len(anns)
assert sum([len(image['ann_ids']) for image in images]) == num_anns
# load mrcn model
mrcn = inference_no_imdb.Inference(args)
# feats_h5
# feats_h5 = osp.join('cache/feats', dataset_splitBy, args.file_name)
file_name = '%s_%s_%s_ann_feats.h5' % (args.net_name, args.imdb_name,
args.tag)
feats_h5 = osp.join('cache/feats', dataset_splitBy, 'mrcn', file_name)
f = h5py.File(feats_h5, 'w')
pool5_set = f.create_dataset('pool5', (num_anns, 1024), dtype=np.float32)
fc7_set = f.create_dataset('fc7', (num_anns, 2048), dtype=np.float32)
# extract
feats_dir = '%s_%s_%s' % (args.net_name, args.imdb_name, args.tag)
head_feats_dir = osp.join('cache/feats/', dataset_splitBy, 'mrcn',
feats_dir)
for i, image in enumerate(images):
image_id = image['image_id']
net_conv, im_info = image_to_head(head_feats_dir, image_id)
ann_ids = image['ann_ids']
for ann_id in ann_ids:
ann = loader.Anns[ann_id]
ann_pool5, ann_fc7 = ann_to_pool5_fc7(mrcn, ann, net_conv, im_info)
ann_h5_id = ann['h5_id']
pool5_set[ann_h5_id] = ann_pool5.data.cpu().numpy()
fc7_set[ann_h5_id] = ann_fc7.data.cpu().numpy()
if i % 20 == 0:
print('%s/%s done.' % (i + 1, len(images)))
f.close()
print('%s written.' % feats_h5)
def prepare_mrcn(self, head_feats_dir, args):
"""
Arguments:
head_feats_dir: cache/feats/dataset_splitBy/net_imdb_tag, containing all image conv_net feats
args: imdb_name, net_name, iters, tag
"""
self.head_feats_dir = head_feats_dir
self.mrcn = inference_no_imdb.Inference(args)
assert args.net_name == 'res101'
self.pool5_dim = 1024
self.fc7_dim = 2048
