def setup_paper_model(model_chkpt_path, sess):
net = resnet_fusion(batch_size=1, num_layers=101) # Using code from lib/model/test.py
# Load Model (instead of .json)
net.create_architecture(sess, "TEST", 2, tag='default',
anchor_scales=cfg.ANCHOR_SCALES,
anchor_ratios=[8, 16, 32, 64])
saver = tf.train.Saver()
saver.restore(sess, model_chkpt_path) # Load weights
return net
def test_net_creation(file_name, model_chkpt_path):
#imdb = get_imdb(imdb_name)
tfconfig = tf.ConfigProto(allow_soft_placement=True)
tfconfig.gpu_options.allow_growth=True
with tf.Session(config=tfconfig) as sess:
net = resnet_fusion(batch_size=1, num_layers=101)
# Load Model (instead of .json)
net.create_architecture(sess, "TEST", 2, tag='default',
anchor_scales=cfg.ANCHOR_SCALES,
anchor_ratios=[8, 16, 32, 64])
saver = tf.train.Saver()
saver.restore(sess, model_chkpt_path) # Load weights
run_image(sess, net, file_name)
def create_dataset(image_dir, model_chkpt_path, img_processed_truth_paths):
tfconfig = tf.ConfigProto(allow_soft_placement=True)
tfconfig.gpu_options.allow_growth=True
with tf.Session(config=tfconfig) as sess:
net = resnet_fusion(batch_size=1, num_layers=101) # Using code from lib/model/test.py
# Load Model (instead of .json)
net.create_architecture(sess, "TEST", 2, tag='default',
anchor_scales=cfg.ANCHOR_SCALES,
anchor_ratios=[8, 16, 32, 64])
saver = tf.train.Saver()
saver.restore(sess, model_chkpt_path) # Load weights
all_image_files = os.listdir(image_dir)
with open(img_processed_truth_paths, 'r') as fi_file:
im_processed_truth = json.load(fi_file)
img_rois = []
for i, image_path in enumerate(all_image_files):
try:
print(i)
if image_path in im_processed_truth:
roi_features, rois = run_image(sess, net, os.path.join(image_dir, image_path))
reduecd_roi_indices = reduce_data(rois, im_processed_truth[image_path])
if reduecd_roi_indices not None:
for roi_i, idx in enumerate(list(map(int, reduecd_roi_indices['gt'] + reduecd_roi_indices['other']))):
# save data
filename = "roi_feature_map_{0}_{1}".format(i, roi_i)
#print(os.path.join(X_DATA_DIR, filename))
np.save(os.path.join(X_DATA_DIR, filename), roi_features[idx, :, :, :])
# save rois
img_rois.append(rois)
except Exception as e:
print(e)
#periodically save img_rois
if i % 500 == 0:
np.save(os.path.join(Y_DATA_DIR, LABELS_PATH), img_rois)
net = vgg16(batch_size=cfg.TRAIN.IMS_PER_BATCH)
elif args.net == 'vgg16_noise':
net = vgg16_noise(batch_size=cfg.TRAIN.IMS_PER_BATCH)
elif args.net == 'res50':
net = resnetv1(batch_size=cfg.TRAIN.IMS_PER_BATCH, num_layers=50)
elif args.net == 'res50_noise':
net = resnet_noise(batch_size=cfg.TRAIN.IMS_PER_BATCH, num_layers=50)
elif args.net == 'res101':
net = resnetv1(batch_size=cfg.TRAIN.IMS_PER_BATCH, num_layers=101)
elif args.net == 'res101_noise':
net = resnet_noise(batch_size=cfg.TRAIN.IMS_PER_BATCH, num_layers=101)
elif args.net == 'res101_noise_init':
net = resnet_noise_init(batch_size=cfg.TRAIN.IMS_PER_BATCH,
num_layers=101)
elif args.net == 'res101_fusion':
net = resnet_fusion(batch_size=cfg.TRAIN.IMS_PER_BATCH, num_layers=101)
elif args.net == 'res101_fusion_2rpn':
net = resnet_fusion_2rpn(batch_size=cfg.TRAIN.IMS_PER_BATCH,
num_layers=101)
elif args.net == 'res101_fusion_2rpn_sep':
net = resnet_fusion_2rpn_sep(batch_size=cfg.TRAIN.IMS_PER_BATCH,
num_layers=101)
elif args.net == 'res101_fusion_late_fusion':
net = resnet_fusion_late_fusion(batch_size=cfg.TRAIN.IMS_PER_BATCH,
num_layers=101)
elif args.net == 'res101_fusion_noise':
net = resnet_fusion_noise(batch_size=cfg.TRAIN.IMS_PER_BATCH,
num_layers=101)
elif args.net == 'res101_fusion_fix':
net = resnet_fusion_fix(batch_size=cfg.TRAIN.IMS_PER_BATCH,
num_layers=101)
# init session
sess = tf.Session(config=tfconfig)
# load network
if args.net == 'vgg16':
net = vgg16(batch_size=1)
elif args.net == 'res50':
net = resnetv1(batch_size=1, num_layers=50)
elif args.net == 'res101':
net = resnetv1(batch_size=1, num_layers=101)
elif args.net == 'res101_noise':
net = resnet_noise(batch_size=1, num_layers=101)
elif args.net == 'res101_noise_init':
net = resnet_noise_init(batch_size=1, num_layers=101)
elif args.net == 'res101_fusion':
net = resnet_fusion(batch_size=1, num_layers=101)
elif args.net == 'res101_fusion_noise':
net = resnet_fusion_noise(batch_size=1, num_layers=101)
elif args.net == 'res152':
net = resnetv1(batch_size=1, num_layers=152)
else:
raise NotImplementedError
# load model
net.create_architecture(sess,
"TEST",
imdb.num_classes,
tag='default',
anchor_scales=cfg.ANCHOR_SCALES,
anchor_ratios=cfg.ANCHOR_RATIOS)
