x, y = int(x), int(y)
img = cv2.imread(img_name)
label = np.array([x, y], dtype=np.float32)
return img[np.newaxis, :, :, :], label.reshape((1, label.shape[0]))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-g', '--gpu', default=None, type=int)
args = parser.parse_args()
if args is not None:
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu)
net = JumpModel()
dataset = JumpData()
img = tf.placeholder(tf.float32, [None, 640, 720, 3], name='img')
label = tf.placeholder(tf.float32, [None, 2], name='label')
is_training = tf.placeholder(np.bool, name='is_training')
keep_prob = tf.placeholder(np.float32, name='keep_prob')
lr = tf.placeholder(np.float32, name='lr')
pred = net.forward(img, is_training, keep_prob, 'coarse')
loss = tf.reduce_mean(tf.sqrt(tf.pow(pred - label, 2) + 1e-12))
tf.summary.scalar('loss', loss)
optimizer = tf.train.AdamOptimizer(lr)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = optimizer.minimize(loss)
saver = tf.train.Saver()
x, y = name[name.index('_h_') + 3: name.index('_h_') + 6], name[name.index('_w_') + 3: name.index('_w_') + 6]
x, y = int(x), int(y)
img = cv2.imread(img_name)
label = np.array([x, y], dtype=np.float32)
return img[np.newaxis, :, :, :], label.reshape((1, label.shape[0]))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-g', '--gpu', default=None, type=int)
args = parser.parse_args()
if args is not None:
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu)
net = JumpModel()
dataset = JumpData()
img = tf.placeholder(tf.float32, [None, 640, 720, 3], name='img')
label = tf.placeholder(tf.float32, [None, 2], name='label')
is_training = tf.placeholder(np.bool, name='is_training')
keep_prob = tf.placeholder(np.float32, name='keep_prob')
lr = tf.placeholder(np.float32, name='lr')
pred = net.forward(img, is_training, keep_prob, 'coarse')
loss = tf.reduce_mean(tf.sqrt(tf.pow(pred - label, 2) + 1e-12))
tf.summary.scalar('loss', loss)
optimizer = tf.train.AdamOptimizer(lr)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = optimizer.minimize(loss)
saver = tf.train.Saver()
lr = tf.placeholder(np.float32, name='lr')
pred = net.forward(img, is_training, keep_prob)
saver = tf.train.Saver()
sess = tf.Session()
sess.run(tf.global_variables_initializer())
ckpt = tf.train.get_checkpoint_state('./train_logs')
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
print('==== successfully restored ====')
val_img = get_a_img(path)
feed_dict = {
img: val_img,
is_training: False,
keep_prob: 1.0,
}
pred_out = sess.run(pred, feed_dict=feed_dict)
return pred_out
if __name__ == '__main__':
dataset = JumpData()
name = dataset.val_name_list[0]
posi = name.index('_res')
img_name = name[:posi] + '.png'
a = time.time()
pred = inference(img_name)
print(pred, time.time() - a)