def main_train():
"""Fine tune a model from step2 and continue training.
Train and evaluate model."""
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # only show error and warning
#os.environ['CUDA_VISIBLE_DEVICES'] = GPU
os.environ['CUDA_VISIBLE_DEVICES'] = "2"
### Defind a session
config = tf.ConfigProto(
allow_soft_placement=True
) # if GPU is not usable, then turn to CPU automatically
sess = tf.Session(config=config)
### Set placeholder
x1 = tf.placeholder(tf.float32,
[BATCH_SIZE, HEIGHT, WIDTH, CHANNEL]) # pre
x2 = tf.placeholder(tf.float32,
[BATCH_SIZE, HEIGHT, WIDTH, CHANNEL]) # cmp
x3 = tf.placeholder(tf.float32,
[BATCH_SIZE, HEIGHT, WIDTH, CHANNEL]) # sub
x5 = tf.placeholder(tf.float32,
[BATCH_SIZE, HEIGHT, WIDTH, CHANNEL]) # raw
if int(QP) in net1_list:
is_training = tf.placeholder_with_default(
False, shape=()) # for BN training/testing. default testing.
PSNR_0 = cal_PSNR(x2, x5) # PSNR before enhancement (cmp and raw)
### Motion compensation
#x1to2 = net_MFCNN.warp_img(tf.shape(x2)[0], x2, x1, False)
#x3to2 = net_MFCNN.warp_img(tf.shape(x2)[0], x2, x3, True)
x1to2 = net_MFCNN.warp_img(tf.shape(x2)[0], x2, x1, False)
x3to2 = net_MFCNN.warp_img(tf.shape(x2)[0], x2, x3, True)
### Flow loss
FlowLoss_1 = cal_MSE(x1to2, x2)
FlowLoss_2 = cal_MSE(x3to2, x2)
flow_loss = FlowLoss_1 + FlowLoss_2
### Enhance cmp frames
if int(QP) in net1_list:
x2_enhanced = net_MFCNN.network(x1to2, x2, x3to2, is_training)
else:
x2_enhanced = net_MFCNN.network2(x1to2, x2, x3to2)
MSE = cal_MSE(x2_enhanced, x5)
PSNR = cal_PSNR(x2_enhanced,
x5) # PSNR after enhancement (enhanced and raw)
delta_PSNR = PSNR - PSNR_0
### 2 kinds of loss for 2-step training
OptimizeLoss_1 = flow_loss + ratio_small * MSE # step1: the key is MC-subnet.
OptimizeLoss_2 = ratio_small * flow_loss + MSE # step2: the key is QE-subnet.
### Defind optimizer
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
Training_step1 = tf.train.AdamOptimizer(lr_ori).minimize(
OptimizeLoss_1)
Training_step2 = tf.train.AdamOptimizer(lr_ori).minimize(
OptimizeLoss_2)
### TensorBoard
tf.summary.scalar('PSNR improvement', delta_PSNR)
#tf.summary.scalar('PSNR before enhancement', PSNR_0)
#tf.summary.scalar('PSNR after enhancement', PSNR)
tf.summary.scalar('MSE loss of motion compensation', flow_loss)
#tf.summary.scalar('MSE loss of final quality enhancement', MSE)
tf.summary.scalar('MSE loss for training step1 (mainly MC-subnet)',
OptimizeLoss_1)
tf.summary.scalar('MSE loss for training step2 (mainly QE-subnet)',
OptimizeLoss_2)
tf.summary.image('cmp', x2)
tf.summary.image('enhanced', x2_enhanced)
tf.summary.image('raw', x5)
tf.summary.image('x1to2', x1to2)
tf.summary.image('x3to2', x3to2)
summary_writer = tf.summary.FileWriter(dir_model, sess.graph)
summary_op = tf.summary.merge_all()
saver = tf.train.Saver(max_to_keep=None) # define a saver
sess.run(tf.global_variables_initializer()) # initialize network variables
### Calculate the num of parameters
num_params = 0
for variable in tf.trainable_variables():
shape = variable.get_shape()
num_params += reduce(mul, [dim.value for dim in shape], 1)
print("# num of parameters: %d #" % num_params)
file_object.write("# num of parameters: %d #\n" % num_params)
file_object.flush()
### Find all stacks then cal their number
stack_name = os.path.join(dir_stack, "stack_tra_pre_*")
num_TrainingStack = len(glob.glob(stack_name))
stack_name = os.path.join(dir_stack, "stack_val_pre_*")
num_ValidationStack = len(glob.glob(stack_name))
### Restore ###注释掉了finetune训练，调用训练好的模型的代码
# saver_res = tf.train.Saver()
# saver_res.restore(sess, model_res_path)
# print("successfully restore model %d!" % (int(res_index) + 1))
# file_object.write("successfully restore model %d!\n" % (int(res_index) + 1))
# file_object.flush()
print("##### Start running! #####")
num_TrainingBatch_count = 0
### Step 1: converge MC-subnet; Step 2: converge QE-subnet
for ite_step in [1, 2]:
if ite_step == 1:
num_epoch = epoch_step1
else:
num_epoch = epoch_step2
### Epoch by Epoch
for ite_epoch in range(num_epoch):
### Train stack by stack
for ite_stack in range(num_TrainingStack):
#pre_list, cmp_list, sub_list, raw_list = [], [], [], []
#gc.collect()
if ite_step == 1 and ite_epoch == 0 and ite_stack == 0:
pre_list, cmp_list, sub_list, raw_list = load_stack(
"tra", ite_stack)
gc.collect()
num_batch = int(len(pre_list) / BATCH_SIZE)
### Batch by batch
for ite_batch in range(num_batch):
print("\rstep %1d - epoch %2d/%2d - training stack %2d/%2d - batch %3d/%3d" % \
(ite_step, ite_epoch+1, num_epoch, ite_stack+1, num_TrainingStack, ite_batch+1, num_batch), end="")
start_index = ite_batch * BATCH_SIZE
next_start_index = (ite_batch + 1) * BATCH_SIZE
if ite_step == 1:
if int(QP) in net1_list:
Training_step1.run(
session=sess,
feed_dict={
x1: pre_list[start_index:next_start_index],
x2: cmp_list[start_index:next_start_index],
x3: sub_list[start_index:next_start_index],
x5: raw_list[start_index:next_start_index],
is_training: True
}) # train
else:
Training_step1.run(
session=sess,
feed_dict={
x1: pre_list[start_index:next_start_index],
x2: cmp_list[start_index:next_start_index],
x3: sub_list[start_index:next_start_index],
x5: raw_list[start_index:next_start_index]
}) # train
else:
if int(QP) in net1_list:
Training_step2.run(
session=sess,
feed_dict={
x1: pre_list[start_index:next_start_index],
x2: cmp_list[start_index:next_start_index],
x3: sub_list[start_index:next_start_index],
x5: raw_list[start_index:next_start_index],
is_training: True
})
else:
Training_step2.run(
session=sess,
feed_dict={
x1: pre_list[start_index:next_start_index],
x2: cmp_list[start_index:next_start_index],
x3: sub_list[start_index:next_start_index],
x5: raw_list[start_index:next_start_index]
})
# Update TensorBoard and print result
num_TrainingBatch_count += 1
if ((ite_batch + 1) == int(
num_batch / 2)) or ((ite_batch + 1) == num_batch):
if int(QP) in net1_list:
summary, delta_PSNR_batch, PSNR_0_batch, FlowLoss_batch, MSE_batch = sess.run(
[
summary_op, delta_PSNR, PSNR_0, flow_loss,
MSE
],
feed_dict={
x1: pre_list[start_index:next_start_index],
x2: cmp_list[start_index:next_start_index],
x3: sub_list[start_index:next_start_index],
x5: raw_list[start_index:next_start_index],
is_training: False
})
else:
summary, delta_PSNR_batch, PSNR_0_batch, FlowLoss_batch, MSE_batch = sess.run(
[
summary_op, delta_PSNR, PSNR_0, flow_loss,
MSE
],
feed_dict={
x1: pre_list[start_index:next_start_index],
x2: cmp_list[start_index:next_start_index],
x3: sub_list[start_index:next_start_index],
x5: raw_list[start_index:next_start_index]
})
summary_writer.add_summary(summary,
num_TrainingBatch_count)
print("\rstep %1d - epoch %2d - imp PSNR: %.3f - ori PSNR: %.3f " % \
(ite_step, ite_epoch + 1, delta_PSNR_batch, PSNR_0_batch))
file_object.write("step %1d - epoch %2d - imp PSNR: %.3f - ori PSNR: %.3f\n" % \
(ite_step, ite_epoch + 1, delta_PSNR_batch, PSNR_0_batch))
file_object.flush()
### Store the model of this epoch
if ite_step == 1:
CheckPoint_path = os.path.join(dir_model, "model_step1.ckpt")
else:
CheckPoint_path = os.path.join(dir_model, "model_step2.ckpt")
saver.save(sess, CheckPoint_path, global_step=ite_epoch)
sum_improved_PSNR = 0
num_patch_count = 0
### Eval stack by stack, and report together for this epoch
for ite_stack in range(num_ValidationStack):
pre_list, cmp_list, sub_list, raw_list = [], [], [], []
gc.collect()
pre_list, cmp_list, sub_list, raw_list = load_stack(
"val", ite_stack)
gc.collect()
num_batch = int(len(pre_list) / BATCH_SIZE)
### Batch by batch
for ite_batch in range(num_batch):
print("\rstep %1d - epoch %2d/%2d - validation stack %2d/%2d " % \
(ite_step, ite_epoch+1, num_epoch, ite_stack+1, num_ValidationStack), end="")
start_index = ite_batch * BATCH_SIZE
next_start_index = (ite_batch + 1) * BATCH_SIZE
if int(QP) in net1_list:
delta_PSNR_batch = sess.run(
delta_PSNR,
feed_dict={
x1: pre_list[start_index:next_start_index],
x2: cmp_list[start_index:next_start_index],
x3: sub_list[start_index:next_start_index],
x5: raw_list[start_index:next_start_index],
is_training: False
})
else:
delta_PSNR_batch = sess.run(
delta_PSNR,
feed_dict={
x1: pre_list[start_index:next_start_index],
x2: cmp_list[start_index:next_start_index],
x3: sub_list[start_index:next_start_index],
x5: raw_list[start_index:next_start_index]
})
sum_improved_PSNR += delta_PSNR_batch * BATCH_SIZE
num_patch_count += BATCH_SIZE
if num_patch_count != 0:
print("\n### imp PSNR by model after step %1d - epoch %2d/%2d: %.3f ###\n" % \
(ite_step, ite_epoch+1, num_epoch, sum_improved_PSNR/num_patch_count))
file_object.write("### imp PSNR by model after step %1d - epoch %2d/%2d: %.3f ###\n" % \
(ite_step, ite_epoch+1, num_epoch, sum_improved_PSNR/num_patch_count))
file_object.flush()
def func_enhance(dir_model_pre, QP, PreIndex_list, CmpIndex_list,
SubIndex_list):
"""Enhance PQFs or non-PQFs, record dpsnr, dssim and enhanced frames."""
global enhanced_list, sum_dpsnr, sum_dssim
tf.reset_default_graph()
### Defind enhancement process
x1 = tf.placeholder(tf.float32,
[BATCH_SIZE, height, width, CHANNEL]) # previous
x2 = tf.placeholder(tf.float32,
[BATCH_SIZE, height, width, CHANNEL]) # current
x3 = tf.placeholder(tf.float32,
[BATCH_SIZE, height, width, CHANNEL]) # subsequent
if QP in net1_list:
is_training = tf.placeholder_with_default(False, shape=())
x1to2 = net_MFCNN.warp_img(BATCH_SIZE, x2, x1, False)
x3to2 = net_MFCNN.warp_img(BATCH_SIZE, x2, x3, True)
if QP in net1_list:
x2_enhanced = net_MFCNN.network(x1to2, x2, x3to2, is_training)
else:
x2_enhanced = net_MFCNN.network2(x1to2, x2, x3to2)
saver = tf.train.Saver()
with tf.Session(config=config) as sess:
# Restore model
model_path = os.path.join(dir_model_pre, "model_step2.ckpt-" + str(QP))
saver.restore(sess, model_path)
nfs = len(CmpIndex_list)
sum_dpsnr_part = 0.0
sum_dssim_part = 0.0
for ite_frame in range(nfs):
# Load frames
pre_frame = y_import(CmpVideo_path, height, width, 1,
PreIndex_list[ite_frame])[:, :, :,
np.newaxis] / 255.0
cmp_frame = y_import(CmpVideo_path, height, width, 1,
CmpIndex_list[ite_frame])[:, :, :,
np.newaxis] / 255.0
sub_frame = y_import(CmpVideo_path, height, width, 1,
SubIndex_list[ite_frame])[:, :, :,
np.newaxis] / 255.0
# if cmp frame is plane?
if isplane(cmp_frame):
continue
# if PQF frames are plane?
if isplane(pre_frame):
pre_frame = np.copy(cmp_frame)
if isplane(sub_frame):
sub_frame = np.copy(cmp_frame)
# Enhance
if QP in net1_list:
enhanced_frame = sess.run(x2_enhanced,
feed_dict={
x1: pre_frame,
x2: cmp_frame,
x3: sub_frame,
is_training: False
})
else:
enhanced_frame = sess.run(x2_enhanced,
feed_dict={
x1: pre_frame,
x2: cmp_frame,
x3: sub_frame
})
# Record for output video
enhanced_list[CmpIndex_list[ite_frame]] = np.squeeze(
enhanced_frame)
# Evaluate and accumulate dpsnr
raw_frame = np.squeeze(
y_import(RawVideo_path, height, width, 1,
CmpIndex_list[ite_frame])) / 255.0
cmp_frame = np.squeeze(cmp_frame)
enhanced_frame = np.squeeze(enhanced_frame)
raw_frame = np.float32(raw_frame)
cmp_frame = np.float32(cmp_frame)
psnr_ori = compare_psnr(cmp_frame, raw_frame, data_range=1.0)
psnr_aft = compare_psnr(enhanced_frame, raw_frame, data_range=1.0)
ssim_ori = compare_ssim(cmp_frame, raw_frame, data_range=1.0)
ssim_aft = compare_ssim(enhanced_frame, raw_frame, data_range=1.0)
sum_dpsnr_part += psnr_aft - psnr_ori
sum_dssim_part += ssim_aft - ssim_ori
print("%d | %d at QP = %d" % (ite_frame + 1, nfs, QP), end="\r")
print(" ", end="\r")
sum_dpsnr += sum_dpsnr_part
sum_dssim += sum_dssim_part
average_dpsnr = sum_dpsnr_part / nfs
average_dssim = sum_dssim_part / nfs
print("dPSNR: %.3f - dSSIM: %.3f - nfs: %4d" %
(average_dpsnr, average_dssim, nfs),
flush=True)
file_object.write("dPSNR: %.3f - dSSIM: %.3f - nfs: %4d\n" %
(average_dpsnr, average_dssim, nfs))
file_object.flush()
def func_enhance(dir_model_pre, QP, PreIndex_list, CmpIndex_list,
SubIndex_list):
"""Enhance PQFs or non-PQFs, record dpsnr, dssim and enhanced frames."""
#ywz
global ywz_times
global enhanced_list, sum_dpsnr, sum_dssim
tf.reset_default_graph()
### Defind enhancement process
x1 = tf.placeholder(tf.float32,
[BATCH_SIZE, height, width, CHANNEL]) # previous
x2 = tf.placeholder(tf.float32,
[BATCH_SIZE, height, width, CHANNEL]) # current
x3 = tf.placeholder(tf.float32,
[BATCH_SIZE, height, width, CHANNEL]) # subsequent
if QP in net1_list:
is_training = tf.placeholder_with_default(False, shape=())
x1to2 = net_MFCNN.warp_img(BATCH_SIZE, x2, x1, False)
x3to2 = net_MFCNN.warp_img(BATCH_SIZE, x2, x3, True)
if QP in net1_list:
x2_enhanced = net_MFCNN.network(x1to2, x2, x3to2, is_training)
else:
x2_enhanced = net_MFCNN.network2(x1to2, x2, x3to2)
saver = tf.train.Saver()
with tf.Session(config=config) as sess:
# ywz
if ywz_times == 0:
options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
#
# Restore model
model_path = os.path.join(dir_model_pre, "model_step2.ckpt-0")
# saver.restore(sess, model_path)
module_file = tf.train.latest_checkpoint(model_path)
# with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
if module_file is not None:
saver.restore(sess, module_file)
nfs = len(CmpIndex_list)
sum_dpsnr_part = 0.0
sum_dssim_part = 0.0
for ite_frame in range(nfs):
# Load frames
pre_frame = y_import(CmpVideo_path, height, width, 1,
PreIndex_list[ite_frame])[:, :, :,
np.newaxis] / 255.0
cmp_frame = y_import(CmpVideo_path, height, width, 1,
CmpIndex_list[ite_frame])[:, :, :,
np.newaxis] / 255.0
sub_frame = y_import(CmpVideo_path, height, width, 1,
SubIndex_list[ite_frame])[:, :, :,
np.newaxis] / 255.0
# if cmp frame is plane?
if isplane(cmp_frame):
continue
# if PQF frames are plane?
if isplane(pre_frame):
pre_frame = np.copy(cmp_frame)
if isplane(sub_frame):
sub_frame = np.copy(cmp_frame)
# ywz
if ywz_times == 0:
run_metadata = tf.RunMetadata()
# Enhance
if QP in net1_list:
enhanced_frame = sess.run(x2_enhanced,
options=options,
feed_dict={
x1: pre_frame,
x2: cmp_frame,
x3: sub_frame,
is_training: False
},
run_metadata=run_metadata)
else:
enhanced_frame = sess.run(x2_enhanced,
options=options,
feed_dict={
x1: pre_frame,
x2: cmp_frame,
x3: sub_frame
},
run_metadata=run_metadata)
else:
# Enhance
if QP in net1_list:
enhanced_frame = sess.run(x2_enhanced,
feed_dict={
x1: pre_frame,
x2: cmp_frame,
x3: sub_frame,
is_training: False
})
else:
enhanced_frame = sess.run(x2_enhanced,
feed_dict={
x1: pre_frame,
x2: cmp_frame,
x3: sub_frame
})
#
# ywz
# timeline record
# Create the Timeline object, and write it to a json file
if ywz_times == 0:
fetched_timeline = timeline.Timeline(run_metadata.step_stats)
chrome_trace = fetched_timeline.generate_chrome_trace_format()
with open("timeline/timeline_" + str(ite_frame) + ".json",
'w') as f:
f.write(chrome_trace)
#
# Record for output video
enhanced_list[CmpIndex_list[ite_frame]] = np.squeeze(
enhanced_frame)
# Evaluate and accumulate dpsnr
raw_frame = np.squeeze(
y_import(RawVideo_path, height, width, 1,
CmpIndex_list[ite_frame])) / 255.0
cmp_frame = np.squeeze(cmp_frame)
enhanced_frame = np.squeeze(enhanced_frame)
raw_frame = np.float32(raw_frame)
cmp_frame = np.float32(cmp_frame)
psnr_ori = compare_psnr(cmp_frame, raw_frame, data_range=1.0)
psnr_aft = compare_psnr(enhanced_frame, raw_frame, data_range=1.0)
ssim_ori = compare_ssim(cmp_frame, raw_frame, data_range=1.0)
ssim_aft = compare_ssim(enhanced_frame, raw_frame, data_range=1.0)
sum_dpsnr_part += psnr_aft - psnr_ori
sum_dssim_part += ssim_aft - ssim_ori
print("\r %d | %d at QP = %d" % (ite_frame + 1, nfs, QP), end="")
#ywz 只记一次
# ywz_times += 1
print(" ", end="\r")
sum_dpsnr += sum_dpsnr_part
sum_dssim += sum_dssim_part
average_dpsnr = sum_dpsnr_part / nfs
average_dssim = sum_dssim_part / nfs
print("dPSNR: %.3f - dSSIM: %.3f - nfs: %4d" %
(average_dpsnr, average_dssim, nfs),
flush=True)
file_object.write("dPSNR: %.3f - dSSIM: %.3f - nfs: %4d\n" %
(average_dpsnr, average_dssim, nfs))
file_object.flush()
