testing train&test (09000d5a) · Commits · Elphel / python3-imagej-tiff

nn_ds_inmem_tmp.py

+11 −179

Original line number	Original line	Diff line number	Diff line
	@@ -297,7 +297,6 @@ t_vars=tf.trainable_variables()
	lr=tf.placeholder(tf.float32)		lr=tf.placeholder(tf.float32)
	G_opt=tf.train.AdamOptimizer(learning_rate=lr).minimize(G_loss)		G_opt=tf.train.AdamOptimizer(learning_rate=lr).minimize(G_loss)


	saver=tf.train.Saver()		saver=tf.train.Saver()

	ROOT_PATH = './attic/nn_ds_inmem_graph1/'		ROOT_PATH = './attic/nn_ds_inmem_graph1/'
	@@ -327,15 +326,15 @@ with tf.Session() as sess:
	while True:		while True:

	# overall are 307, start 'testing' testing from START_TEST		# overall are 307, start 'testing' testing from START_TEST
	START_TEST = 300		START_TEST = 200

	# Train run		# Train run
	if i<START_TEST:		if i<START_TEST:

	try:		try:
	# _, G_current, output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, corr2d325_out, target_disparity_out, gt_ds_out = sess.run(		# _, G_current, output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, corr2d325_out, target_disparity_out, gt_ds_out = sess.run(
	_, G_current, output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, corr2d325_out = sess.run(		train_summary,_, G_loss_trained, output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, corr2d325_out = sess.run(
	[		[ merged,
	G_opt,		G_opt,
	G_loss,		G_loss,
	out,		out,
	@@ -354,6 +353,7 @@ with tf.Session() as sess:

	# save all for now as a test		# save all for now as a test
	#train_writer.add_summary(summary, i)		#train_writer.add_summary(summary, i)
			#train_writer.add_summary(train_summary, i)

	except tf.errors.OutOfRangeError:		except tf.errors.OutOfRangeError:
	break		break
	@@ -362,7 +362,7 @@ with tf.Session() as sess:
	else:		else:

	try:		try:
	summary, G_current, output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, corr2d325_out = sess.run(		test_summary, G_loss_tested, output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, corr2d325_out = sess.run(
	[merged,		[merged,
	G_loss,		G_loss,
	out,		out,
	@@ -377,13 +377,18 @@ with tf.Session() as sess:
	],		],
	feed_dict={lr:LR})		feed_dict={lr:LR})

			#test_writer.add_summary(test_summary, i)

	except tf.errors.OutOfRangeError:		except tf.errors.OutOfRangeError:
	break		break

	i+=1		i+=1

	# print_time("%d:%d -> %f"%(epoch,i,G_current))		# print_time("%d:%d -> %f"%(epoch,i,G_current))
	print_time("%d:%d -> %f"%(epoch,i,G_current))		train_writer.add_summary(train_summary, epoch)
			test_writer.add_summary(test_summary, epoch)

			print_time("%d:%d -> %f"%(epoch,i,G_loss_trained))

	# Close writers		# Close writers
	train_writer.close()		train_writer.close()
	@@ -392,176 +397,3 @@ with tf.Session() as sess:

	print("All done")		print("All done")
	exit (0)		exit (0)





	filename_queue = tf.train.string_input_producer(
	[train_filenameTFR], num_epochs = EPOCHS_TO_RUN) #0)

	# Even when reading in multiple threads, share the filename
	# queue.
	corr2d325, target_disparity, gt_ds = read_and_decode(filename_queue)

	# The op for initializing the variables.
	init_op = tf.group(tf.global_variables_initializer(),
	tf.local_variables_initializer())

	#sess = tf.Session()

	out = network(corr2d325)

	#Try standard loss functions first
	G_loss, _disp_slice, _d_gt_slice, _out_diff, _out_diff2, _w_norm, _out_wdiff2, _cost1 = batchLoss(out_batch = out, # [batch_size,(1..2)] tf_result
	target_disparity_batch= target_disparity, ### target_d, # [batch_size] tf placeholder
	gt_ds_batch = gt_ds, ### gt, # [batch_size,2] tf placeholder
	absolute_disparity = ABSOLUTE_DISPARITY,
	use_confidence = USE_CONFIDENCE, # True,
	lambda_conf_avg = 0.01,
	lambda_conf_pwr = 0.1,
	conf_pwr = 2.0,
	gt_conf_offset = 0.08,
	gt_conf_pwr = 1.0)

	t_vars=tf.trainable_variables()
	lr=tf.placeholder(tf.float32)
	G_opt=tf.train.AdamOptimizer(learning_rate=lr).minimize(G_loss)

	saver=tf.train.Saver()
	# ?!!!!!
	#merged = tf.summary.merge_all()
	#train_writer = tf.summary.FileWriter(result_dir + '/train', sess.graph)
	#test_writer = tf.summary.FileWriter(result_dir + '/test')

	#http://rtfcode.com/xref/tensorflow-1.4.1/tensorflow/docs_src/api_guides/python/reading_data.md
	with tf.Session() as sess:
	sess.run(tf.global_variables_initializer())
	sess.run(tf.local_variables_initializer())
	# sess.run(init_op) # Was reporting beta1 not initialized in Adam

	coord = tf.train.Coordinator()
	threads = tf.train.start_queue_runners(coord=coord)

	writer = tf.summary.FileWriter('./attic/nn_ds_inmem_graph1', sess.graph)
	writer.close()

	# for i in range(1000):
	loss_hist = np.zeros(RUN_TOT_AVG, dtype=np.float32)
	i = 0
	try:
	while not coord.should_stop():
	print_time("%d: Run "%(i), end = "")
	_,G_current,output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, corr2d325_out, target_disparity_out, gt_ds_out = sess.run(
	[G_opt,G_loss,out,_disp_slice, _d_gt_slice, _out_diff, _out_diff2, _w_norm, _out_wdiff2, _cost1, corr2d325, target_disparity, gt_ds],
	feed_dict={lr: LR})
	# print_time("loss=%f, running average=%f"%(G_current,mean_loss))
	loss_hist[i % RUN_TOT_AVG] = G_current
	if (i < RUN_TOT_AVG):
	loss_avg = np.average(loss_hist[:i])
	else:
	loss_avg = np.average(loss_hist)
	print_time("loss=%f, running average=%f"%(G_current,loss_avg))
	# print ("%d: corr2d_out.shape="%(i),corr2d325_out.shape)
	## print ("target_disparity_out.shape=",target_disparity_out.shape)
	## print ("gt_ds_out.shape=",gt_ds_out.shape)
	i += 1
	except tf.errors.OutOfRangeError:
	print('Done training -- epoch limit reached')
	finally:
	# When done, ask the threads to stop.
	coord.request_stop()
	coord.join(threads)
	#sess.close() ('whith' does that)










	'''

	ckpt=tf.train.get_checkpoint_state(checkpoint_dir)

	if ckpt:
	print('loaded '+ckpt.model_checkpoint_path)
	saver.restore(sess,ckpt.model_checkpoint_path)


	allfolders = glob.glob('./result/*0')
	lastepoch = 0
	for folder in allfolders:
	lastepoch = np.maximum(lastepoch, int(folder[-4:]))

	recorded_loss = []
	recorded_mean_loss = []

	recorded_gt_d = []
	recorded_gt_c = []

	recorded_pr_d = []
	recorded_pr_c = []

	LR = 1e-3

	print(bcolors.HEADER+"Last Epoch = "+str(lastepoch)+bcolors.ENDC)

	if DEBUG_PLT_LOSS:
	plt.ion() # something about plotting
	plt.figure(1, figsize=(4,12))
	pass


	training_tiles = np.array([])
	training_values = np.array([])



	graph_saved = False
	for epoch in range(20): #MAX_EPOCH):
	print_time("epoch="+str(epoch))
	train_seed_list = np.arange(len(ex_data.files_train))
	np.random.shuffle(train_seed_list)
	g_loss = np.zeros(len(train_seed_list))
	for nscene, seed_index in enumerate(train_seed_list):
	corr2d_batch, target_disparity_batch, gt_ds_batch = ex_data.prepareBatchData(seed_index)
	num_tiles = corr2d_batch.shape[0] # 1000
	num_tile_slices = corr2d_batch.shape[1] # 4
	num_cell_in_slice = corr2d_batch.shape[2] # 81
	in_data = np.empty((num_tiles, num_tile_slices*num_cell_in_slice + 1), dtype = np.float32)
	in_data[...,0:num_tile_slicesnum_cell_in_slice] = corr2d_batch.reshape((corr2d_batch.shape[0],corr2d_batch.shape[1]corr2d_batch.shape[2]))
	in_data[...,num_tile_slices*num_cell_in_slice] = target_disparity_batch
	st=time.time()

	#run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
	#run_metadata = tf.RunMetadata()
	#_,G_current,output = sess.run([G_opt,G_loss,out],feed_dict={in_tile:input_patch,gt:gt_patch,lr:LR},options=run_options,run_metadata=run_metadata)

	print_time("%d:%d Run "%(epoch, nscene), end = "")
	_,G_current,output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm = sess.run([G_opt,G_loss,out,_disp_slice, _d_gt_slice, _out_diff, _out_diff2, _w_norm],
	feed_dict={in_tile: in_data,
	gt: gt_ds_batch,
	target_d: target_disparity_batch,
	lr: LR})
	if not graph_saved:
	writer = tf.summary.FileWriter('./attic/nn_ds_single_graph1', sess.graph)
	writer.close()
	graph_saved = True
	# exit(0)

	g_loss[nscene]=G_current
	mean_loss = np.mean(g_loss[np.where(g_loss)])
	print_time("loss=%f, running average=%f"%(G_current,mean_loss))
	pass
	'''




	#if wait_and_show: # wait and show images
	# plt.show()
	print_time("All done, exiting...")
	No newline at end of file

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