thr = None
thr_result = None
trains_to_update = [
train_next[n_train]['more_files'] for n_train in range(len(train_next))
]
for epoch in range(EPOCHS_TO_RUN):
"""
update files after each epoch, all 4.
Convert to threads after testing
"""
if (FILE_UPDATE_EPOCHS > 0) and (epoch % FILE_UPDATE_EPOCHS == 0):
if not thr is None:
if thr.is_alive():
qsf.print_time("***WAITING*** until tfrecord gets loaded",
end=" ")
else:
qsf.print_time("tfrecord is ***ALREADY LOADED*** ",
end=" ")
thr.join()
qsf.print_time("Done")
qsf.print_time("Inserting new data", end=" ")
for n_train in range(len(trains_to_update)):
if trains_to_update[n_train]:
qsf.add_file_to_dataset(
dataset=dataset_train,
new_dataset=thr_result[n_train],
train_next=train_next[n_train])
qsf.print_time("Done")
thr_result = []
dataset_img = qsf.readImageData(
image_data = image_data,
files = files,
indx = nimg,
cluster_radius = 0, # CLUSTER_RADIUS,
tile_layers = TILE_LAYERS,
tile_side = TILE_SIDE,
width = IMG_WIDTH,
replace_nans = True,
infer = True,
keep_gt = True) # to generate same output files
img_corr2d = dataset_img['corr2d'] # (?,324)
img_target = dataset_img['target_disparity'] # (?,1)
img_ntile = dataset_img['ntile'].reshape([-1]) # (?) - 0...78k int32
#run first stage network
qsf.print_time("Running inferred model, stage1", end=" ")
_ = sess.run([stage1done],
feed_dict={ph_corr2d: img_corr2d,
ph_target_disparity: img_target,
ph_ntile: img_ntile })
qsf.print_time("Done.")
qsf.print_time("Running inferred model, stage2", end=" ")
disp_out, = sess.run([stage2_out_sparse],
feed_dict={ph_ntile_out: img_ntile })
qsf.print_time("Done.")
result_file = files['result'][nimg].replace('.npy','-infer.npy') #not to overwrite training result files that are more complete
try:
os.makedirs(os.path.dirname(result_file))
except:
pass
rslt = np.concatenate(