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train.py
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train.py
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import tensorflow as tf
import numpy as np
# import sys; sys.path.append("../")
from get_batch import Batch
from u_net import create_conv_net as unet
import argparse
import os
import cv2
from vat import virtual_adversarial_loss
def train(input_t, output_map, alpha, max_it, root, batch_size, is_training, id, use_vat, use_pseudo_labels,
use_mean_teacher, dataset):
"""
:param input_t: input tensor
:param output_map: output layer of the network
:param alpha: placeholder for leaky relu
:param max_it: maximum training iterations
:param root: base directory that contains the images
:param batch_size: batch size
:param is_training: toggle training
:param id: GPU id
:param use_vat: Enable VAT
:param use_pseudo_labels: Use pseudo labels
:param use_mean_teacher: Use mean teacher
:param dataset: Choose dataset
:return:
"""
h = 256 if dataset == "ENDOVIS" else 288
w = 320 if dataset == "ENDOVIS" else 384
num_parts = 5 if dataset == "ENDOVIS" else 4
num_connections = 4 if dataset == "ENDOVIS" else 0
# GPU Config
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=.95)
# Set up placeholders
y = tf.placeholder(tf.float32, shape=[None, h, w, num_parts + num_connections])
lr = tf.placeholder(tf.float32)
loss_mask = tf.placeholder(tf.float32, shape=[batch_size])
# Loss
if not use_mean_teacher:
avr_loss = tf.losses.mean_squared_error(y, output_map,
weights=tf.reshape(loss_mask,
[batch_size, 1, 1, 1]))
if use_mean_teacher:
ema = tf.train.ExponentialMovingAverage(decay=.95)
def ema_getter(getter, name, *args, **kwargs):
var = getter(name, *args, **kwargs)
ema_var = ema.average(var)
return ema_var if ema_var else var
tf.get_variable_scope().set_custom_getter(ema_getter)
model_vars = tf.trainable_variables()
output_student = output_map
tf.add_to_collection(tf.GraphKeys.UPDATE_OPS, ema.apply(model_vars))
output_teacher, _ = unet(input_t, .9 if dataset == "RMIT" else .7, 3,
num_parts + num_connections,
is_training=is_training,
features_root=64,
alpha=alpha)
output_teacher = tf.stop_gradient(output_teacher)
avr_loss = batch_size / tf.reduce_sum(loss_mask) * \
tf.losses.mean_squared_error(y, output_student,
weights=tf.reshape(loss_mask,
[batch_size, 1, 1, 1]))
m = tf.placeholder(tf.float32, shape=[])
avr_loss = avr_loss + m * .1 * tf.losses.mean_squared_error(output_teacher, output_student)
if use_vat:
avr_loss = batch_size / tf.reduce_sum(loss_mask) * avr_loss + \
virtual_adversarial_loss(input_t, y, is_training=is_training, alpha=alpha)
# Adam solver
with tf.variable_scope("Adam", reuse=tf.AUTO_REUSE):
opt = tf.train.AdamOptimizer(lr).minimize(avr_loss)
# Start session and initialize weights
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options,
allow_soft_placement=True,
log_device_placement=True))
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(max_to_keep=10000)
b_train = Batch(root, batch_size, dataset="ENDOVIS",
include_unlabelled=use_vat or use_mean_teacher or use_tvm,
pseudo_label=use_pseudo_labels)
b_test = Batch(root, batch_size, dataset="ENDOVIS", include_unlabelled=False, testing=True, augment=False,
train_postprocessing=False)
current_lr = 1e-3
print("Chosen lr:", current_lr)
# if model_dir is not None:
# restore_op, restore_dict = tf.contrib.framework.assign_from_checkpoint(
# model_dir + "/model.ckpt",
# tf.contrib.slim.get_variables_to_restore(),
# ignore_missing_vars=True
# )
# sess.run(restore_op, feed_dict=restore_dict)
# print("Restored session")
# save graph
writer = tf.summary.FileWriter(logdir='logdir', graph=sess.graph)
writer.flush()
if use_vat:
test_interval = 250
else:
test_interval = 200
def sigmoid_schedule(global_step, warm_up_steps=20000):
if global_step > warm_up_steps:
return 1.
return np.exp(-5. * (1. - (global_step / warm_up_steps)) ** 2)
for i in range(max_it):
imgs, targets, _, mask = b_train.get_batch()
current_loss, net_out, _ = sess.run(
[avr_loss, output_map, opt],
feed_dict={input_t: imgs,
y: targets,
lr: current_lr,
is_training: True,
alpha: 1 / np.random.uniform(low=3, high=8),
loss_mask: mask,
m: sigmoid_schedule(i)
}
)
if i % 100 == 0:
print("Current regression loss:", current_loss.sum())
loc_pred = []
loc_true = []
for ch in range(num_parts):
if b_train.batch_instrument_count[0] == 1:
_, _, _, m_loc1 = cv2.minMaxLoc(net_out[0, :, :, ch])
loc_pred.append(m_loc1)
_, _, _, m_loc2 = cv2.minMaxLoc(targets[0][:, :, ch])
loc_true.append(m_loc2)
else:
pass
print("For the first sample-> Predicted: {} Ground Truth: {}\n".format(loc_pred, loc_true))
# save model for evaluation
if i % test_interval == 0 and i != 0:
print("Testing at iteration", i, "...")
dir2save = os.path.join("tmp" + str(i), "model.ckpt")
save_path = saver.save(sess, dir2save)
print("Saved model to", save_path)
sess.close()
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--root", help="Directory that contains the data", type=str, required=True)
parser.add_argument("--batch_size", type=int, default=1, help="Batch size for training")
parser.add_argument("--gpu_id", type=str, default="1", help="Select a gpu")
parser.add_argument("--use_vat", type=int, default=0, help="Enables VAT")
parser.add_argument("--use_pseudo_labels", type=int, default=0, help="Enables pseudo-label usage")
parser.add_argument("--use_mean_teacher", type=int, default=0, help="Enables mean teacher")
parser.add_argument("--dataset", type=str, default="ENDOVIS", help="Choose RMIT or Endovis to train on.")
args = parser.parse_args()
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_id
dataset = args.dataset.upper()
# init network
ch = 3
h = 256 if dataset == "ENDOVIS" else 288
w = 320 if dataset == "ENDOVIS" else 384
x = tf.placeholder(tf.float32, shape=[args.batch_size, h, w, ch])
is_training = tf.placeholder(tf.bool)
alpha = tf.placeholder_with_default(1 / 5.5, [], name="alpha_lrelu")
num_parts = 5 if dataset == "ENDOVIS" else 4
num_connections = 4 if dataset == "ENDOVIS" else 0
keep_prob = .9 if dataset == "RMIT" else .7
output_map, _ = unet(x, keep_prob, ch,
num_parts + num_connections,
is_training=is_training,
features_root=64,
alpha=alpha)
train(x, output_map, alpha, 50000, args.root, args.batch_size,
is_training, args.gpu_id, args.use_vat,
args.use_pseudo_labels, args.use_mean_teacher, args.dataset)
if __name__ == "__main__":
main()