def main(_):
"""Train FlowNet for a FLAGS.max_steps."""
with tf.Graph().as_default():
imgs_0, imgs_1, flows = flownet_tools.get_data(FLAGS.datadir, True)
# img summary after loading
flownet.image_summary(imgs_0, imgs_1, "A_input", flows)
# apply augmentation
imgs_0, imgs_1, flows = apply_augmentation(imgs_0, imgs_1, flows)
# model
calc_flows = architectures.flownet_dropout(imgs_0, imgs_1, flows)
# img summary of result
flownet.image_summary(None, None, "E_result", calc_flows)
global_step = slim.get_or_create_global_step()
train_op = flownet.create_train_op(global_step)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
saver = tf_saver.Saver(
max_to_keep=FLAGS.max_checkpoints,
keep_checkpoint_every_n_hours=FLAGS.keep_checkpoint_every_n_hours)
slim.learning.train(
train_op,
logdir=FLAGS.logdir + '/train',
save_summaries_secs=FLAGS.save_summaries_secs,
save_interval_secs=FLAGS.save_interval_secs,
summary_op=tf.summary.merge_all(),
log_every_n_steps=FLAGS.log_every_n_steps,
trace_every_n_steps=FLAGS.trace_every_n_steps,
session_config=config,
saver=saver,
number_of_steps=FLAGS.max_steps,
)
def main(_):
"""Evaluate FlowNet for FlyingChair test set"""
with tf.Graph().as_default():
# Generate tensors from numpy images and flows.
var_num = 1
img_0, img_1, flow = flownet_tools.get_data_flow_s(
FLAGS.datadir, False, var_num)
imgs_0 = tf.squeeze(tf.stack([img_0 for i in range(FLAGS.batchsize)]))
imgs_1 = tf.squeeze(tf.stack([img_1 for i in range(FLAGS.batchsize)]))
flows = tf.squeeze(tf.stack([flow for i in range(FLAGS.batchsize)]))
# img summary after loading
flownet.image_summary(imgs_0, imgs_1, "A_input", flows)
# Get flow tensor from flownet model
calc_flows = architectures.flownet_dropout(imgs_0, imgs_1, flows)
flow_mean, confidence, conf_img = var_mean(calc_flows)
# confidence = tf.image.convert_image_dtype(confidence, tf.uint16)
# calc EPE / AEE = ((x1-x2)^2 + (y1-y2)^2)^1/2
# https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3478865/
aee = aee_f(flow, flow_mean, var_num)
# bilateral solverc
img_0 = tf.squeeze(tf.stack(img_0))
flow_s = tf.squeeze(tf.stack(flow))
solved_flow = flownet.bil_solv_var(img_0, flow_mean, confidence,
flow_s)
aee_bs = aee_f(flow, solved_flow, var_num)
metrics_to_values, metrics_to_updates = slim.metrics.aggregate_metric_map(
{
"AEE": slim.metrics.streaming_mean(aee),
"AEE_BS": slim.metrics.streaming_mean(aee_bs),
# "AEE_BS_No_Confidence": slim.metrics.streaming_mean(aee_bs),
})
for name, value in metrics_to_values.iteritems():
tf.summary.scalar(name, value)
# Define the summaries to write:
flownet.image_summary(None, None, "FlowNetS_no_mean", calc_flows)
solved_flows = tf.squeeze(
tf.stack([solved_flow for i in range(FLAGS.batchsize)]))
flow_means = tf.squeeze(
tf.stack([flow_mean for i in range(FLAGS.batchsize)]))
conf_imgs = tf.squeeze(
tf.stack([conf_img for i in range(FLAGS.batchsize)]))
flownet.image_summary(None, None, "FlowNetS BS", solved_flows)
flownet.image_summary(None, None, "FlowNetS Mean", flow_means)
flownet.image_summary(conf_imgs, conf_imgs, "Confidence", None)
# Run the actual evaluation loop.
num_batches = math.ceil(FLAGS.testsize)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
slim.evaluation.evaluation_loop(
master=FLAGS.master,
checkpoint_dir=FLAGS.logdir + '/train',
logdir=FLAGS.logdir + '/eval_var_flownet_s',
num_evals=num_batches,
eval_op=metrics_to_updates.values(),
eval_interval_secs=FLAGS.eval_interval_secs,
summary_op=tf.summary.merge_all(),
session_config=config,
timeout=60 * 60)