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train_kth.py
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__author__ = 'liuyu' ### check
import os.path
import time
import numpy as np
import tensorflow as tf
import cv2
import sys
import random
from nets import models_factory
from data_provider import datasets_factory
from utils import preprocess
from utils import metrics
from skimage.measure import compare_ssim
os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
os.environ['CUDA_VISIBLE_DEVICES'] = '3'
# -----------------------------------------------------------------------------
FLAGS = tf.app.flags.FLAGS
# data I/O
tf.app.flags.DEFINE_string('dataset_name', 'action',
'The name of dataset.')
tf.app.flags.DEFINE_string('train_data_paths',
'data/kth_action',
'train data paths.')
tf.app.flags.DEFINE_string('valid_data_paths',
'data/kth_action',
'validation data paths.')
tf.app.flags.DEFINE_string('save_dir', 'checkpoints/kth_stdlstm',
'dir to store trained net.')
tf.app.flags.DEFINE_string('gen_frm_dir', 'results/kth_stdlstm',
'dir to store result.')
# model
tf.app.flags.DEFINE_string('model_name', 'stdlstm',
'The name of the architecture.')
tf.app.flags.DEFINE_string('pretrained_model', '',
'file of a pretrained model to initialize from.')
tf.app.flags.DEFINE_integer('input_length', 10,
'encoder hidden states.')
tf.app.flags.DEFINE_integer('seq_length', 20,
'total input and output length.')
tf.app.flags.DEFINE_integer('img_width', 128,
'input image width.')
tf.app.flags.DEFINE_integer('img_channel', 1,
'number of image channel.')
tf.app.flags.DEFINE_integer('stride', 1,
'stride of a convlstm layer.')
tf.app.flags.DEFINE_integer('filter_size', 5,
'filter of a convlstm layer.')
tf.app.flags.DEFINE_string('num_hidden', '128,128,128,128',
'COMMA separated number of units in a convlstm layer.')
tf.app.flags.DEFINE_integer('patch_size', 8,
'patch size on one dimension.')
tf.app.flags.DEFINE_boolean('layer_norm', True,
'whether to apply tensor layer norm.')
# optimization
tf.app.flags.DEFINE_float('lr', 0.001,
'base learning rate.')
tf.app.flags.DEFINE_boolean('reverse_input', True, ### change 1.20
'whether to reverse the input frames while training.')
tf.app.flags.DEFINE_integer('batch_size', 8,
'batch size for training.')
# tf.app.flags.DEFINE_integer('batch_size', 128,
# 'batch size for training.')
tf.app.flags.DEFINE_integer('max_iterations', 80000,
'max num of steps.')
# tf.app.flags.DEFINE_integer('max_iterations', 20,
# 'max num of steps.')
tf.app.flags.DEFINE_integer('display_interval', 1,
'number of iters showing training loss.')
tf.app.flags.DEFINE_integer('test_interval', 2000,
'number of iters for test.')
# tf.app.flags.DEFINE_integer('test_interval', 2,
# 'number of iters for test.')
tf.app.flags.DEFINE_integer('snapshot_interval', 10000,
'number of iters saving models.')
# tf.app.flags.DEFINE_integer('snapshot_interval', 5,
# 'number of iters saving models.')
class Model(object):
def __init__(self):
# inputs
self.x = tf.placeholder(tf.float32,
[FLAGS.batch_size,
FLAGS.seq_length,
FLAGS.img_width / FLAGS.patch_size,
FLAGS.img_width / FLAGS.patch_size,
FLAGS.patch_size * FLAGS.patch_size * FLAGS.img_channel])
self.mask_true = tf.placeholder(tf.float32,
[FLAGS.batch_size,
FLAGS.seq_length - FLAGS.input_length - 1,
FLAGS.img_width / FLAGS.patch_size,
FLAGS.img_width / FLAGS.patch_size,
FLAGS.patch_size * FLAGS.patch_size * FLAGS.img_channel])
grads = []
loss_train = []
self.pred_seq = []
self.tf_lr = tf.placeholder(tf.float32, shape=[])
num_hidden = [int(x) for x in FLAGS.num_hidden.split(',')]
print(num_hidden)
num_layers = len(num_hidden)
with tf.variable_scope(tf.get_variable_scope()):
# define a model
output_list = models_factory.construct_model(
FLAGS.model_name, self.x,
self.mask_true,
num_layers, num_hidden,
FLAGS.filter_size, FLAGS.stride,
FLAGS.seq_length, FLAGS.input_length,
FLAGS.layer_norm)
gen_ims = output_list[0]
loss = output_list[1]
pred_ims = gen_ims[:, FLAGS.input_length - 1:]
self.loss_train = loss / FLAGS.batch_size
# gradients
all_params = tf.trainable_variables()
grads.append(tf.gradients(loss, all_params))
self.pred_seq.append(pred_ims)
self.train_op = tf.train.AdamOptimizer(FLAGS.lr).minimize(loss)
# session
variables = tf.global_variables()
self.saver = tf.train.Saver(variables)
init = tf.global_variables_initializer()
configProt = tf.ConfigProto()
configProt.gpu_options.allow_growth = True
configProt.allow_soft_placement = True
self.sess = tf.Session(config=configProt)
self.sess.run(init)
if FLAGS.pretrained_model:
self.saver.restore(self.sess, FLAGS.pretrained_model)
def train(self, inputs, lr, mask_true):
feed_dict = {self.x: inputs}
feed_dict.update({self.tf_lr: lr})
feed_dict.update({self.mask_true: mask_true})
loss, _ = self.sess.run((self.loss_train, self.train_op), feed_dict)
return loss
def test(self, inputs, mask_true):
feed_dict = {self.x: inputs}
feed_dict.update({self.mask_true: mask_true})
gen_ims = self.sess.run(self.pred_seq, feed_dict)
return gen_ims
def save(self, itr):
checkpoint_path = os.path.join(FLAGS.save_dir, 'model.ckpt')
self.saver.save(self.sess, checkpoint_path, global_step=itr)
print('saved to ' + FLAGS.save_dir)
def main(argv=None):
if tf.gfile.Exists(FLAGS.save_dir):
tf.gfile.DeleteRecursively(FLAGS.save_dir) ## if file is not none, clean all recursively - note by liuyu
tf.gfile.MakeDirs(FLAGS.save_dir)
if tf.gfile.Exists(FLAGS.gen_frm_dir):
tf.gfile.DeleteRecursively(FLAGS.gen_frm_dir)
tf.gfile.MakeDirs(FLAGS.gen_frm_dir)
# load data
train_input_handle, test_input_handle = datasets_factory.data_provider(
FLAGS.dataset_name, FLAGS.train_data_paths, FLAGS.valid_data_paths,
FLAGS.batch_size, FLAGS.img_width, FLAGS.seq_length)
f = open(FLAGS.save_dir + '/summary.txt', 'a') #### add
print("Initializing models")
model = Model()
### total parameters
total_parameters = 0
for variable in tf.trainable_variables():
variable_parameters = 1
for dim in variable.get_shape():
variable_parameters *= dim.value
total_parameters += variable_parameters
print("Total number of trainable parameters: %d" % total_parameters)
f.write('\nTotal number of trainable parameters: %d' % total_parameters)
lr = FLAGS.lr
delta = 0.00002
base = 0.99998
eta = 1
for itr in xrange(1, FLAGS.max_iterations + 1):
if train_input_handle.no_batch_left():
train_input_handle.begin(do_shuffle=True)
ims = train_input_handle.get_batch()
ims = preprocess.reshape_patch(ims, FLAGS.patch_size)
if itr < 50000: ### change
eta -= delta
else:
eta = 0.0
random_flip = np.random.random_sample(
(FLAGS.batch_size, FLAGS.seq_length - FLAGS.input_length - 1))
true_token = (random_flip < eta)
# true_token = (random_flip < pow(base,itr))
ones = np.ones((FLAGS.img_width / FLAGS.patch_size,
FLAGS.img_width / FLAGS.patch_size,
FLAGS.patch_size ** 2 * FLAGS.img_channel))
zeros = np.zeros((FLAGS.img_width / FLAGS.patch_size,
FLAGS.img_width / FLAGS.patch_size,
FLAGS.patch_size ** 2 * FLAGS.img_channel))
mask_true = []
for i in xrange(FLAGS.batch_size):
for j in xrange(FLAGS.seq_length - FLAGS.input_length - 1):
if true_token[i, j]:
mask_true.append(ones)
else:
mask_true.append(zeros)
mask_true = np.array(mask_true)
mask_true = np.reshape(mask_true, (FLAGS.batch_size,
FLAGS.seq_length - FLAGS.input_length - 1,
FLAGS.img_width / FLAGS.patch_size,
FLAGS.img_width / FLAGS.patch_size,
FLAGS.patch_size ** 2 * FLAGS.img_channel))
cost = model.train(ims, lr, mask_true)
if FLAGS.reverse_input:
ims_rev = ims[:, ::-1]
cost += model.train(ims_rev, lr, mask_true)
cost = cost / 2
if itr % FLAGS.display_interval == 0:
print('itr: ' + str(itr))
print('training loss: ' + str(cost))
#### add
if itr % FLAGS.test_interval == 0:
f.write('\nitr: %d' % itr)
f.write('\ntraining loss: %f' % cost)
if itr % FLAGS.test_interval == 0:
print('test...')
test_input_handle.begin(do_shuffle=False)
res_path = os.path.join(FLAGS.gen_frm_dir, str(itr))
os.mkdir(res_path)
avg_mse = 0
batch_id = 0
img_mse, ssim, psnr, fmae, sharp = [], [], [], [], []
for i in xrange(FLAGS.seq_length - FLAGS.input_length):
img_mse.append(0)
ssim.append(0)
psnr.append(0)
fmae.append(0)
sharp.append(0)
mask_true = np.zeros((FLAGS.batch_size,
FLAGS.seq_length - FLAGS.input_length - 1,
FLAGS.img_width / FLAGS.patch_size,
FLAGS.img_width / FLAGS.patch_size,
FLAGS.patch_size ** 2 * FLAGS.img_channel))
while (test_input_handle.no_batch_left() == False):
batch_id = batch_id + 1
test_ims = test_input_handle.get_batch()
test_dat = preprocess.reshape_patch(test_ims, FLAGS.patch_size)
img_gen = model.test(test_dat, mask_true)
# concat outputs of different gpus along batch
img_gen = np.concatenate(img_gen)
img_gen = preprocess.reshape_patch_back(img_gen, FLAGS.patch_size)
# MSE per frame
for i in xrange(FLAGS.seq_length - FLAGS.input_length):
x = test_ims[:, i + FLAGS.input_length, :, :, 0]
gx = img_gen[:, i, :, :, 0]
fmae[i] += metrics.batch_mae_frame_float(gx, x)
gx = np.maximum(gx, 0)
gx = np.minimum(gx, 1)
mse = np.square(x - gx).sum()
img_mse[i] += mse
avg_mse += mse
real_frm = np.uint8(x * 255)
pred_frm = np.uint8(gx * 255)
psnr[i] += metrics.batch_psnr(pred_frm, real_frm)
for b in xrange(FLAGS.batch_size):
sharp[i] += np.max(
cv2.convertScaleAbs(cv2.Laplacian(pred_frm[b], 3)))
score, _ = compare_ssim(pred_frm[b], real_frm[b], full=True)
ssim[i] += score
# save prediction examples
if batch_id <= 10:
path = os.path.join(res_path, str(batch_id))
os.mkdir(path)
for i in xrange(FLAGS.seq_length):
name = 'gt' + str(i + 1) + '.png'
file_name = os.path.join(path, name)
img_gt = np.uint8(test_ims[0, i, :, :, :] * 255)
cv2.imwrite(file_name, img_gt)
for i in xrange(FLAGS.seq_length - FLAGS.input_length):
name = 'pd' + str(i + 1 + FLAGS.input_length) + '.png'
file_name = os.path.join(path, name)
img_pd = img_gen[0, i, :, :, :]
img_pd = np.maximum(img_pd, 0)
img_pd = np.minimum(img_pd, 1)
img_pd = np.uint8(img_pd * 255)
cv2.imwrite(file_name, img_pd)
test_input_handle.next()
avg_mse = avg_mse / (batch_id * FLAGS.batch_size)
print('mse per seq: ' + str(avg_mse))
f.write('\nmse per seq: %f' % avg_mse) #### add
for i in xrange(FLAGS.seq_length - FLAGS.input_length):
print(img_mse[i] / (batch_id * FLAGS.batch_size))
f.write(' %f' % (img_mse[i] / (batch_id * FLAGS.batch_size))) #### add
psnr = np.asarray(psnr, dtype=np.float32) / batch_id
fmae = np.asarray(fmae, dtype=np.float32) / batch_id
ssim = np.asarray(ssim, dtype=np.float32) / (FLAGS.batch_size * batch_id)
sharp = np.asarray(sharp, dtype=np.float32) / (FLAGS.batch_size * batch_id)
print('psnr per frame: ' + str(np.mean(psnr)))
f.write('\npsnr per frame: %f' % np.mean(psnr))
for i in xrange(FLAGS.seq_length - FLAGS.input_length):
print(psnr[i])
f.write(' %f' % psnr[i])
print('fmae per frame: ' + str(np.mean(fmae)))
f.write('\nfmae per frame: %f' % np.mean(fmae))
for i in xrange(FLAGS.seq_length - FLAGS.input_length):
print(fmae[i])
f.write(' %f' % fmae[i])
print('ssim per frame: ' + str(np.mean(ssim)))
f.write('\nssim per frame: %f' % np.mean(ssim))
for i in xrange(FLAGS.seq_length - FLAGS.input_length):
print(ssim[i])
f.write(' %f' % ssim[i])
print('sharpness per frame: ' + str(np.mean(sharp)))
f.write('\nsharpness per frame: %f' % np.mean(sharp))
for i in xrange(FLAGS.seq_length - FLAGS.input_length):
print(sharp[i])
f.write(' %f' % sharp[i])
if itr % FLAGS.snapshot_interval == 0:
model.save(itr)
train_input_handle.next()
f.close()
if __name__ == '__main__':
tf.app.run()