def log_to_tensorboard(self, test_filename, psnr, save_meta_data=True):
if self.enable_log is False:
return
# todo
save_meta_data = False
org_image = util.set_image_alignment(util.load_image(test_filename, print_console=False), self.scale)
if len(org_image.shape) >= 3 and org_image.shape[2] == 3 and self.channels == 1:
org_image = util.convert_rgb_to_y(org_image)
input_image = util.resize_image_by_pil(org_image, 1.0 / self.scale, resampling_method=self.resampling_method)
bicubic_image = util.resize_image_by_pil(input_image, self.scale, resampling_method=self.resampling_method)
feed_dict = {self.x: input_image.reshape([1, input_image.shape[0], input_image.shape[1], input_image.shape[2]]),
self.x2: bicubic_image.reshape(
[1, bicubic_image.shape[0], bicubic_image.shape[1], bicubic_image.shape[2]]),
self.y: org_image.reshape([1, org_image.shape[0], org_image.shape[1], org_image.shape[2]]),
self.dropout: 1.0,
self.is_training: 0}
if save_meta_data:
# profiler = tf.profiler.Profile(self.sess.graph)
run_metadata = tf.RunMetadata()
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
summary_str, _ = self.sess.run([self.summary_op, self.loss], feed_dict=feed_dict, options=run_options,
run_metadata=run_metadata)
self.test_writer.add_run_metadata(run_metadata, "step%d" % self.epochs_completed)
filename = self.checkpoint_dir + "/" + self.name + "_metadata.txt"
with open(filename, "w") as out:
out.write(str(run_metadata))
# filename = self.checkpoint_dir + "/" + self.name + "_memory.txt"
# tf.profiler.write_op_log(
# tf.get_default_graph(),
# log_dir=self.checkpoint_dir,
# #op_log=op_log,
# run_meta=run_metadata)
tf.contrib.tfprof.model_analyzer.print_model_analysis(
tf.get_default_graph(), run_meta=run_metadata,
tfprof_options=tf.contrib.tfprof.model_analyzer.PRINT_ALL_TIMING_MEMORY)
else:
summary_str, _ = self.sess.run([self.summary_op, self.loss], feed_dict=feed_dict)
self.train_writer.add_summary(summary_str, self.epochs_completed)
if not self.use_l1_loss:
util.log_scalar_value(self.train_writer, 'PSNR', self.training_psnr_sum / self.training_step,
self.epochs_completed)
util.log_scalar_value(self.train_writer, 'LR', self.lr, self.epochs_completed)
self.train_writer.flush()
util.log_scalar_value(self.test_writer, 'PSNR', psnr, self.epochs_completed)
self.test_writer.flush()
def evaluate_test_batch(self,
save_meta_data=False,
trial=0,
log_profile=True):
save_meta_data = save_meta_data and self.save_meta_data and (trial
== 0)
feed_dict = {
self.x: self.test.input.images,
self.x2: self.test.input.hr_images,
self.y: self.test.true.hr_images,
self.dropout: 1.0,
self.is_training: 0
}
if log_profile and (self.save_loss or self.save_weights
or save_meta_data):
if save_meta_data:
# profiler = tf.profiler.Profile(self.sess.graph)
run_metadata = tf.RunMetadata()
run_options = tf.RunOptions(
trace_level=tf.RunOptions.FULL_TRACE)
summary_str, mse = self.sess.run([self.summary_op, self.mse],
feed_dict=feed_dict,
options=run_options,
run_metadata=run_metadata)
self.test_writer.add_run_metadata(
run_metadata, "step%d" % self.epochs_completed)
filename = self.checkpoint_dir + "/" + self.name + "_metadata.txt"
with open(filename, "w") as out:
out.write(str(run_metadata))
# filename = self.checkpoint_dir + "/" + self.name + "_memory.txt"
# tf.profiler.write_op_log(
# tf.get_default_graph(),
# log_dir=self.checkpoint_dir,
# #op_log=op_log,
# run_meta=run_metadata)
tf.contrib.tfprof.model_analyzer.print_model_analysis(
tf.get_default_graph(),
run_meta=run_metadata,
tfprof_options=tf.contrib.tfprof.model_analyzer.
PRINT_ALL_TIMING_MEMORY)
else:
summary_str, mse = self.sess.run([self.summary_op, self.mse],
feed_dict=feed_dict)
self.train_writer.add_summary(summary_str, self.epochs_completed)
util.log_scalar_value(self.train_writer, 'training_PSNR',
self.training_psnr_sum / self.training_step,
self.epochs_completed)
util.log_scalar_value(self.train_writer, 'LR', self.lr,
self.epochs_completed)
self.train_writer.flush()
util.log_scalar_value(self.test_writer, 'PSNR', util.get_psnr(mse),
self.epochs_completed)
self.test_writer.flush()
else:
mse = self.sess.run(self.mse, feed_dict=feed_dict)
return mse