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import abc
from enum import Enum
import os
import tensorflow as tf
from .flowlib import flow_to_image, write_flow
import numpy as np
# from scipy.misc import imread, imsave, imresize
import cv2
import uuid
from .training_schedules import LONG_SCHEDULE
slim = tf.contrib.slim
os.environ['CUDA_DEVICES_ORDER'] = "PCI_BUS_ID"
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
class Mode(Enum):
TRAIN = 1
TEST = 2
class Net(object):
__metaclass__ = abc.ABCMeta
def __init__(self, mode=Mode.TRAIN, debug=False):
self.global_step = slim.get_or_create_global_step()
self.mode = mode
self.debug = debug
@abc.abstractmethod
def model(self, inputs, training_schedule, trainable=True):
"""
Defines the model and returns a tuple of Tensors needed for calculating the loss.
"""
return
@abc.abstractmethod
def loss(self, **kwargs):
"""
Accepts prediction Tensors from the output of `model`.
Returns a single Tensor representing the total loss of the model.
"""
return
"""
python -m src.flownet_sd.test --input_a /home/liuwen/ssd/videogan/Save_2017_05_31/Images/ped1_adv/Evaluate/model.ckpt-100000/01/gen_6.png \
--input_b /home/liuwen/ssd/videogan/Save_2017_05_31/Images/ped1_adv/Evaluate/model.ckpt-100000/01/gen_7.png \
--out ./
python -m src.flownet_sd.test --input_a 006.png --input_b 007.png --out ./
python -m src.flownet_sd.test --input_a /home/liuwen/ssd/videogan/ped1/frames/testing/01/006.jpg \
--input_b /home/liuwen/ssd/videogan/ped1/frames/testing/01/007.jpg \
--out ./
"""
def test(self, checkpoint, input_a_path, input_b_path, out_path, save_image=True, save_flo=False):
input_a = cv2.imread(input_a_path)
input_b = cv2.imread(input_b_path)
input_a = cv2.resize(input_a, (512, 384))
input_b = cv2.resize(input_b, (512, 384))
print(input_a.shape, input_b.shape)
# Convert from RGB -> BGR
# input_a = input_a[..., [2, 1, 0]]
# input_b = input_b[..., [2, 1, 0]]
# Scale from [0, 255] -> [0.0, 1.0] if needed
if input_a.max() > 1.0:
input_a = input_a / 255.0
if input_b.max() > 1.0:
input_b = input_b / 255.0
# TODO: This is a hack, we should get rid of this
training_schedule = LONG_SCHEDULE
inputs = {
'input_a': tf.expand_dims(tf.constant(input_a, dtype=tf.float32), 0),
'input_b': tf.expand_dims(tf.constant(input_b, dtype=tf.float32), 0),
}
predictions = self.model(inputs, training_schedule)
pred_flow = predictions['flow']
saver = tf.train.Saver()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
saver.restore(sess, checkpoint)
pred_flow = sess.run(pred_flow)[0, :, :, :]
np.save('temporal_ped1', pred_flow)
unique_name = 'flow-' + str(uuid.uuid4())
if save_image:
flow_img = flow_to_image(pred_flow)
full_out_path = os.path.join(out_path, unique_name + '.png')
cv2.imwrite(full_out_path, flow_img)
if save_flo:
full_out_path = os.path.join(out_path, unique_name + '.flo')
write_flow(pred_flow, full_out_path)
def train(self, log_dir, training_schedule, input_a, input_b, flow, checkpoints=None):
tf.summary.image("image_a", input_a, max_outputs=2)
tf.summary.image("image_b", input_b, max_outputs=2)
self.learning_rate = tf.train.piecewise_constant(
self.global_step,
[tf.cast(v, tf.int64) for v in training_schedule['step_values']],
training_schedule['learning_rates'])
optimizer = tf.train.AdamOptimizer(
self.learning_rate,
training_schedule['momentum'],
training_schedule['momentum2'])
inputs = {
'input_a': input_a,
'input_b': input_b,
}
predictions = self.model(inputs, training_schedule)
total_loss = self.loss(flow, predictions)
tf.summary.scalar('loss', total_loss)
if checkpoints:
for (checkpoint_path, (scope, new_scope)) in checkpoints.iteritems():
variables_to_restore = slim.get_variables(scope=scope)
renamed_variables = {
var.op.name.split(new_scope + '/')[1]: var
for var in variables_to_restore
}
restorer = tf.train.Saver(renamed_variables)
with tf.Session() as sess:
restorer.restore(sess, checkpoint_path)
# Show the generated flow in TensorBoard
if 'flow' in predictions:
pred_flow_0 = predictions['flow'][0, :, :, :]
pred_flow_0 = tf.py_func(flow_to_image, [pred_flow_0], tf.uint8)
pred_flow_1 = predictions['flow'][1, :, :, :]
pred_flow_1 = tf.py_func(flow_to_image, [pred_flow_1], tf.uint8)
pred_flow_img = tf.stack([pred_flow_0, pred_flow_1], 0)
tf.summary.image('pred_flow', pred_flow_img, max_outputs=2)
true_flow_0 = flow[0, :, :, :]
true_flow_0 = tf.py_func(flow_to_image, [true_flow_0], tf.uint8)
true_flow_1 = flow[1, :, :, :]
true_flow_1 = tf.py_func(flow_to_image, [true_flow_1], tf.uint8)
true_flow_img = tf.stack([true_flow_0, true_flow_1], 0)
tf.summary.image('true_flow', true_flow_img, max_outputs=2)
train_op = slim.learning.create_train_op(
total_loss,
optimizer,
summarize_gradients=True)
if self.debug:
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
tf.train.start_queue_runners(sess)
slim.learning.train_step(
sess,
train_op,
self.global_step,
{
'should_trace': tf.constant(1),
'should_log': tf.constant(1),
'logdir': log_dir + '/debug',
}
)
else:
slim.learning.train(
train_op,
log_dir,
# session_config=tf.ConfigProto(allow_soft_placement=True),
global_step=self.global_step,
save_summaries_secs=60,
number_of_steps=training_schedule['max_iter']
)
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