just changing dataset file lists (6363bf5b) · Commits · Elphel / python3-imagej-tiff

nn_ds_neibs.py

+80 −91

Original line number	Diff line number	Diff line
		@@ -168,24 +168,28 @@ except IndexError:
		#FILES_PER_SCENE
		train_filenameTFR1 = "/mnt/dde6f983-d149-435e-b4a2-88749245cc6c/home/eyesis/x3d_data/data_sets/tf_data/train_01.tfrecords"

		files_train_lvar = ["/home/eyesis/x3d_data/data_sets/tf_data_3x3a/train000_R1_LE_1.5.tfrecords"]
		"""
		"/home/eyesis/x3d_data/data_sets/tf_data_3x3/train-000_R1_LE_1.5.tfrecords",
		"/home/eyesis/x3d_data/data_sets/tf_data_3x3/train-001_R1_LE_1.5.tfrecords",
		"/home/eyesis/x3d_data/data_sets/tf_data_3x3/train-002_R1_LE_1.5.tfrecords",
		"/home/eyesis/x3d_data/data_sets/tf_data_3x3/train-003_R1_LE_1.5.tfrecords",
		"/home/eyesis/x3d_data/data_sets/tf_data_3x3/train-004_R1_LE_1.5.tfrecords",
		"/home/eyesis/x3d_data/data_sets/tf_data_3x3/train-005_R1_LE_1.5.tfrecords",
		"/home/eyesis/x3d_data/data_sets/tf_data_3x3/train-006_R1_LE_1.5.tfrecords",
		"/home/eyesis/x3d_data/data_sets/tf_data_3x3/train-007_R1_LE_1.5.tfrecords"]
		"""
		#files_train_hvar = ["/home/eyesis/x3d_data/data_sets/tf_data_3x3/train-000_R1_LE_1.5.tfrecords",
		# "/home/eyesis/x3d_data/data_sets/tf_data_3x3/train-001_R1_LE_1.5.tfrecords"]
		files_train_hvar = []

		#file_test_lvar= "/home/eyesis/x3d_data/data_sets/tf_data_3x3/test-TEST_R1_LE_1.5.tfrecords" # "/home/eyesis/x3d_data/data_sets/train-000_R1_LE_1.5.tfrecords"
		file_test_lvar= "/home/eyesis/x3d_data/data_sets/tf_data_3x3a/train000_R1_LE_1.5.tfrecords" # "/home/eyesis/x3d_data/data_sets/train-000_R1_LE_1.5.tfrecords"
		file_test_hvar= None # "/home/eyesis/x3d_data/data_sets/tf_data_3x3/train-002_R1_LE_1.5.tfrecords" # "/home/eyesis/x3d_data/data_sets/train-000_R1_LE_1.5.tfrecords"
		files_train_lvar = ["/home/eyesis/x3d_data/data_sets/tf_data_3x3b/train000_R1_LE_1.5.tfrecords",
		"/home/eyesis/x3d_data/data_sets/tf_data_3x3b/train001_R1_LE_1.5.tfrecords",
		"/home/eyesis/x3d_data/data_sets/tf_data_3x3b/train002_R1_LE_1.5.tfrecords",
		"/home/eyesis/x3d_data/data_sets/tf_data_3x3b/train003_R1_LE_1.5.tfrecords",
		"/home/eyesis/x3d_data/data_sets/tf_data_3x3b/train004_R1_LE_1.5.tfrecords",
		"/home/eyesis/x3d_data/data_sets/tf_data_3x3b/train005_R1_LE_1.5.tfrecords",
		"/home/eyesis/x3d_data/data_sets/tf_data_3x3b/train006_R1_LE_1.5.tfrecords",
		"/home/eyesis/x3d_data/data_sets/tf_data_3x3b/train007_R1_LE_1.5.tfrecords",
		]
		files_train_hvar = ["/home/eyesis/x3d_data/data_sets/tf_data_3x3b/train000_R1_GT_1.5.tfrecords",
		"/home/eyesis/x3d_data/data_sets/tf_data_3x3b/train001_R1_GT_1.5.tfrecords",
		"/home/eyesis/x3d_data/data_sets/tf_data_3x3b/train002_R1_GT_1.5.tfrecords",
		"/home/eyesis/x3d_data/data_sets/tf_data_3x3b/train003_R1_GT_1.5.tfrecords",
		"/home/eyesis/x3d_data/data_sets/tf_data_3x3b/train004_R1_GT_1.5.tfrecords",
		"/home/eyesis/x3d_data/data_sets/tf_data_3x3b/train005_R1_GT_1.5.tfrecords",
		"/home/eyesis/x3d_data/data_sets/tf_data_3x3b/train006_R1_GT_1.5.tfrecords",
		"/home/eyesis/x3d_data/data_sets/tf_data_3x3b/train007_R1_GT_1.5.tfrecords",
		]

		#file_test_lvar= "/home/eyesis/x3d_data/data_sets/tf_data_3x3a/train000_R1_LE_1.5.tfrecords" # "/home/eyesis/x3d_data/data_sets/train-000_R1_LE_1.5.tfrecords"
		file_test_lvar= "/home/eyesis/x3d_data/data_sets/tf_data_3x3b/testTEST_R1_LE_1.5.tfrecords"
		file_test_hvar= "/home/eyesis/x3d_data/data_sets/tf_data_3x3b/testTEST_R1_GT_1.5.tfrecords" # None # "/home/eyesis/x3d_data/data_sets/tf_data_3x3/train-002_R1_LE_1.5.tfrecords" # "/home/eyesis/x3d_data/data_sets/train-000_R1_LE_1.5.tfrecords"
		weight_hvar = 0.13
		weight_lvar = 1.0 - weight_hvar

		@@ -254,16 +258,27 @@ if (file_test_hvar):
		print_time(" Done")
		pass

		#Alternate lvar/hvar
		datasets_train = []
		datasets_weights_train = []
		for indx in range(max(len(datasets_train_lvar),len(datasets_train_hvar))):
		if (indx < len(datasets_train_lvar)):
		datasets_train.append(datasets_train_lvar[indx])
		datasets_weights_train.append(weight_lvar)
		if (indx < len(datasets_train_hvar)):
		datasets_train.append(datasets_train_hvar[indx])
		datasets_weights_train.append(weight_hvar)

		datasets_test = []
		datasets_weights_test = []
		if (file_test_lvar):
		datasets_test.append(dataset_test_lvar)
		datasets_weights_test.append(weight_lvar)
		if (file_test_hvar):
		datasets_test.append(dataset_test_hvar)
		datasets_weights_test.append(weight_hvar)

		"""
		corr2d_trains = [corr2d_train, corr2d_train1]
		target_disparity_trains = [target_disparity_train, target_disparity_train1]
		gt_ds_trains = [gt_ds_train, gt_ds_train1]

		corr2d_train_placeholder = tf.placeholder(corr2d_train.dtype, (None,FEATURES_PER_TILE)) # corr2d_train.shape)
		target_disparity_train_placeholder = tf.placeholder(target_disparity_train.dtype, (None,1)) #target_disparity_train.shape)
		gt_ds_train_placeholder = tf.placeholder(gt_ds_train.dtype, (None,2)) #gt_ds_train.shape)
		"""
		corr2d_train_placeholder = tf.placeholder(datasets_train_lvar[0]['corr2d'].dtype, (None,FEATURES_PER_TILE * cluster_size)) # corr2d_train.shape)
		target_disparity_train_placeholder = tf.placeholder(datasets_train_lvar[0]['target_disparity'].dtype, (None,1 * cluster_size)) #target_disparity_train.shape)
		gt_ds_train_placeholder = tf.placeholder(datasets_train_lvar[0]['gt_ds'].dtype, (None,2 * cluster_size)) #gt_ds_train.shape)
		@@ -461,24 +476,13 @@ def batchLoss(out_batch, # [batch_size,(1..2)] tf_result
		iw_sum = tf.divide(tf_1f, w_sum, name = "iw_sum")
		w_norm = tf.multiply (w_all, iw_sum, name = "w_norm")

		# disp_slice = tf.slice(out_batch,[0,0],[-1,1], name = "disp_slice")
		# d_gt_slice = tf.slice(gt_ds_batch,[0,0],[-1,1], name = "d_gt_slice")
		disp_slice = tf.reshape(out_batch[:,0],[-1], name = "disp_slice")
		d_gt_slice = tf.reshape(gt_ds_batch[:,0],[-1], name = "d_gt_slice")

		"""
		if absolute_disparity:
		out_diff = tf.subtract(disp_slice, d_gt_slice, name = "out_diff")
		else:
		td_flat = tf.reshape(target_disparity_batch,[-1], name = "td_flat")
		residual_disp = tf.subtract(d_gt_slice, td_flat, name = "residual_disp")
		out_diff = tf.subtract(disp_slice, residual_disp, name = "out_diff")
		"""
		td_flat = tf.reshape(target_disparity_batch,[-1], name = "td_flat")
		if absolute_disparity:
		adisp = disp_slice
		else:
		# td_flat = tf.reshape(target_disparity_batch,[-1], name = "td_flat")
		adisp = tf.add(disp_slice, td_flat, name = "adisp")
		out_diff = tf.subtract(adisp, d_gt_slice, name = "out_diff")

		@@ -610,21 +614,16 @@ with tf.Session() as sess:
		gtvar_train_avg = 0.0
		gtvar_test_avg = 0.0

		num_train_variants = len(files_train_lvar)
		# num_train_variants = len(files_train_lvar)
		num_train_variants = len(datasets_train)
		for epoch in range (EPOCHS_TO_RUN):
		# file_index = (epoch // 20) % 2
		file_index = epoch % num_train_variants
		learning_rate = [LR,LR100][epoch >=100]
		# if SHUFFLE_EPOCH:
		# dataset_tt = dataset_tt.shuffle(buffer_size=10000)
		"""
		sess.run(iterator_tt.initializer, feed_dict={corr2d_train_placeholder: corr2d_trains[file_index],
		target_disparity_train_placeholder: target_disparity_trains[file_index],
		gt_ds_train_placeholder: gt_ds_trains[file_index]})
		"""
		sess.run(iterator_tt.initializer, feed_dict={corr2d_train_placeholder: datasets_train_lvar[file_index]['corr2d'],
		target_disparity_train_placeholder: datasets_train_lvar[file_index]['target_disparity'],
		gt_ds_train_placeholder: datasets_train_lvar[file_index]['gt_ds']})

		sess.run(iterator_tt.initializer, feed_dict={corr2d_train_placeholder: datasets_train[file_index]['corr2d'],
		target_disparity_train_placeholder: datasets_train[file_index]['target_disparity'],
		gt_ds_train_placeholder: datasets_train[file_index]['gt_ds']})
		for i in range(dataset_train_size):
		try:
		# 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(
		@@ -658,21 +657,13 @@ with tf.Session() as sess:
		train2_avg = np.average(loss2_train_hist).astype(np.float32)
		gtvar_train_avg = np.average(gtvar_train_hist).astype(np.float32)

		# _,_=sess.run([tf_ph_G_loss,tf_ph_sq_diff],feed_dict={tf_ph_G_loss:train_avg, tf_ph_sq_diff:train2_avg})
		#tf_ph_G_loss = tf.placeholder(tf.float32,shape=None,name='G_loss_avg')
		#tf_ph_sq_diff = tf.placeholder(tf.float32,shape=None,name='sq_diff_avg')
		"""
		sess.run(iterator_tt.initializer, feed_dict={corr2d_train_placeholder: corr2d_test,
		target_disparity_train_placeholder: target_disparity_test,
		gt_ds_train_placeholder: gt_ds_test})
		"""
		sess.run(iterator_tt.initializer, feed_dict={corr2d_train_placeholder: dataset_test_lvar['corr2d'],
		target_disparity_train_placeholder: dataset_test_lvar['target_disparity'],
		gt_ds_train_placeholder: dataset_test_lvar['gt_ds']})
		for dataset_test in datasets_test:
		sess.run(iterator_tt.initializer, feed_dict={corr2d_train_placeholder: dataset_test['corr2d'],
		target_disparity_train_placeholder: dataset_test['target_disparity'],
		gt_ds_train_placeholder: dataset_test['gt_ds']})

		for i in range(dataset_test_size):
		try:
		# 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(
		test_summary, G_loss_tested, output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, gt_variance = sess.run(
		[merged,
		G_loss,
		@@ -685,7 +676,6 @@ with tf.Session() as sess:
		_out_wdiff2,
		_cost1,
		GT_variance
		# corr2d325,
		],
		feed_dict={lr:learning_rate,tf_ph_G_loss:test_avg, tf_ph_sq_diff:test2_avg, tf_gtvar_diff:gtvar_test_avg}) # previous value of *_avg
		loss_test_hist[i] = G_loss_tested
		@@ -695,7 +685,6 @@ with tf.Session() as sess:
		print("test done at step %d"%(i))
		break

		# print_time("%d:%d -> %f"%(epoch,i,G_current))
		test_avg = np.average(loss_test_hist).astype(np.float32)
		test2_avg = np.average(loss2_test_hist).astype(np.float32)
		gtvar_test_avg = np.average(gtvar_test_hist).astype(np.float32)