Trying next versions with partial second stage - 5x5+3x3+1x1 (758c8a5d) · Commits · Elphel / python3-imagej-tiff

explore_data2.py

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Original line number	Original line	Diff line number	Diff line
	@@ -1030,7 +1030,7 @@ if __name__ == "__main__":

	ml_list_train=ex_data.getMLList(ml_subdir, ex_data.files_train)		ml_list_train=ex_data.getMLList(ml_subdir, ex_data.files_train)
	ml_list_test= ex_data.getMLList(ml_subdir, ex_data.files_test)		ml_list_test= ex_data.getMLList(ml_subdir, ex_data.files_test)
	num_test_scenes = [ex_data.files_test, ex_data.files_train][TEST_SAME_LENGTH_AS_TRAIN]		num_test_scenes = len([ex_data.files_test, ex_data.files_train][TEST_SAME_LENGTH_AS_TRAIN])


	if RADIUS == 0 :		if RADIUS == 0 :

nn_ds_neibs10.py

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nn_ds_neibs11.py

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nn_ds_neibs9.py

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Original line number	Original line	Diff line number	Diff line
	@@ -58,13 +58,13 @@ SHUFFLE_EPOCH = True
	NET_ARCH1 = 0 # 4 # #4 # 8 # 4 # 0 #3 # 0 # 0 # 0 # 0 # 8 # 0 # 7 # 2 #0 # 6 #0 # 4 # 3 # overwrite with argv?		NET_ARCH1 = 0 # 4 # #4 # 8 # 4 # 0 #3 # 0 # 0 # 0 # 0 # 8 # 0 # 7 # 2 #0 # 6 #0 # 4 # 3 # overwrite with argv?
	NET_ARCH2 = 0 # 4 # 0 # 0 # 4 # 0 # 0 # 3 # 0 # 3 # 0 # 3 # 0 # 2 #0 # 6 # 0 # 3 # overwrite with argv?		NET_ARCH2 = 0 # 4 # 0 # 0 # 4 # 0 # 0 # 3 # 0 # 3 # 0 # 3 # 0 # 2 #0 # 6 # 0 # 3 # overwrite with argv?
	SYM8_SUB = False # True #False # True # False # True # False # True # False # enforce inputs from 2d correlation have symmetrical ones (groups of 8)		SYM8_SUB = False # True #False # True # False # True # False # True # False # enforce inputs from 2d correlation have symmetrical ones (groups of 8)
	ONLY_TILE = None # 4 # None # 0 # 4# None # (remove all but center tile data), put None here for normal operation)		ONLY_TILE = 12 # None # 4 # None # 0 # 4# None # (remove all but center tile data), put None here for normal operation)
	ZIP_LHVAR = True # combine _lvar and _hvar as odd/even elements		ZIP_LHVAR = True # combine _lvar and _hvar as odd/even elements
	#DEBUG_PACK_TILES = True		#DEBUG_PACK_TILES = True
	# CLUSTER_RADIUS should match input data		# CLUSTER_RADIUS should match input data
	CLUSTER_RADIUS = 2 # 1 # 1 - 3x3, 2 - 5x5 tiles		CLUSTER_RADIUS = 2 # 1 # 1 - 3x3, 2 - 5x5 tiles
	SHUFFLE_FILES = True		SHUFFLE_FILES = True
	WLOSS_LAMBDA = 0.0 # 50.0 # 5.0 # 1.0 # fraction of the W_loss (input layers weight non-uniformity) added to G_loss		WLOSS_LAMBDA = 2.0 #3.0 # 1.0 # 0.3 # 0.0 # 50.0 # 5.0 # 1.0 # fraction of the W_loss (input layers weight non-uniformity) added to G_loss
	WBORDERS_ZERO = True # Border conditions for first layer weights: False - free, True - tied to 0		WBORDERS_ZERO = True # Border conditions for first layer weights: False - free, True - tied to 0
	MAX_FILES_PER_GROUP = 6 # just to try, normally should be 8		MAX_FILES_PER_GROUP = 6 # just to try, normally should be 8
	FILE_UPDATE_EPOCHS = 2 # update train files each this many epochs. 0 - do not update		FILE_UPDATE_EPOCHS = 2 # update train files each this many epochs. 0 - do not update
	@@ -439,7 +439,8 @@ dir_train_hvar = data_dir # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main"
	dir_train_lvar1 = data_dir1 #"/home/eyesis/x3d_data/data_sets/tf_data_5x5_main_1" # data_dir # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main"		dir_train_lvar1 = data_dir1 #"/home/eyesis/x3d_data/data_sets/tf_data_5x5_main_1" # data_dir # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main"
	dir_train_hvar1 = data_dir1 # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main"		dir_train_hvar1 = data_dir1 # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main"
	dir_test_lvar = data_dir # data_dir #"/home/eyesis/x3d_data/data_sets/tf_data_5x5_center/" # data_dir # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main"		dir_test_lvar = data_dir # data_dir #"/home/eyesis/x3d_data/data_sets/tf_data_5x5_center/" # data_dir # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main"
	dir_test_hvar = "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main_3" # data_dir #"/home/eyesis/x3d_data/data_sets/tf_data_5x5_center" # data_dir # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main"		#dir_test_hvar = "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main_3" # data_dir #"/home/eyesis/x3d_data/data_sets/tf_data_5x5_center" # data_dir # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main"
			dir_test_hvar = "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main_5" # data_dir #"/home/eyesis/x3d_data/data_sets/tf_data_5x5_center" # data_dir # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main"
	dir_img = os.path.join(img_dir,"img") # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main/img"		dir_img = os.path.join(img_dir,"img") # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main/img"
	dir_result = os.path.join(data_dir,"result") # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main/result"		dir_result = os.path.join(data_dir,"result") # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main/result"

	@@ -574,7 +575,7 @@ Compare with other (not used) train sets (use 7 of each instead of 8, 8-th as te
	#just testing:		#just testing:
	#files_train_lvar = files_train_hvar		#files_train_lvar = files_train_hvar
	files_test_lvar = ["train004_R2_GT_1.5.tfrecords"]# ["train007_R2_LE_1.5.tfrecords"]# "testTEST_R2_LE_1.5.tfrecords"] # testTEST_R2_LE_1.5.tfrecords"]		files_test_lvar = ["train004_R2_GT_1.5.tfrecords"]# ["train007_R2_LE_1.5.tfrecords"]# "testTEST_R2_LE_1.5.tfrecords"] # testTEST_R2_LE_1.5.tfrecords"]
	files_test_hvar = ["train000_R2_GT_1.5.tfrecords"]#"testTEST_R2_GT_1.5.tfrecords"] # "testTEST_R2_GT_1.5.tfrecords"]		files_test_hvar = ["testTEST_R2_GT_1.5.tfrecords"] # Now same size as train! # ["train000_R2_GT_1.5.tfrecords"]#"testTEST_R2_GT_1.5.tfrecords"] # "testTEST_R2_GT_1.5.tfrecords"]
	#files_img = ['1527257933_150165-v04'] # overlook		#files_img = ['1527257933_150165-v04'] # overlook
	#files_img = ['1527256858_150165-v01'] # State Street		#files_img = ['1527256858_150165-v01'] # State Street
	#files_img = ['1527256816_150165-v02'] # State Street		#files_img = ['1527256816_150165-v02'] # State Street
	@@ -1067,7 +1068,7 @@ def weightsLoss(inp_weights): # [batch_size,(1..2)] tf_result
	tf_inp = tf.reshape(inp_weights[:TILE_LAYERS * TILE_SIZE,:], [TILE_LAYERS, FILE_TILE_SIDE, FILE_TILE_SIDE, inp_weights.shape[1]], name = "tf_inp")		tf_inp = tf.reshape(inp_weights[:TILE_LAYERS * TILE_SIZE,:], [TILE_LAYERS, FILE_TILE_SIDE, FILE_TILE_SIDE, inp_weights.shape[1]], name = "tf_inp")
	if WBORDERS_ZERO:		if WBORDERS_ZERO:
	tf_zero_col = tf.constant(0.0, dtype=tf.float32, shape=[tf_inp.shape[0], tf_inp.shape[1], 1, tf_inp.shape[3]], name = "tf_zero_col")		tf_zero_col = tf.constant(0.0, dtype=tf.float32, shape=[tf_inp.shape[0], tf_inp.shape[1], 1, tf_inp.shape[3]], name = "tf_zero_col")
	tf_zero_row = tf.constant(0.0, dtype=tf.float32, shape=[tf_inp.shape[0], 1 , tf_inp.shape[2], tf_inp.shape[3]], name = "tf_zero_row")		tf_zero_row = tf.constant(0.0, dtype=tf.float32, shape=[tf_inp.shape[0], 1 , tf_inp.shape[2] + 2, tf_inp.shape[3]], name = "tf_zero_row")
	tf_inp_ext_h = tf.concat([tf_zero_col, tf_inp, tf_zero_col ], axis = 2, name ="tf_inp_ext_h")		tf_inp_ext_h = tf.concat([tf_zero_col, tf_inp, tf_zero_col ], axis = 2, name ="tf_inp_ext_h")
	tf_inp_ext = tf.concat([tf_zero_row, tf_inp_ext_h, tf_zero_row ], axis = 1, name ="tf_inp_ext")		tf_inp_ext = tf.concat([tf_zero_row, tf_inp_ext_h, tf_zero_row ], axis = 1, name ="tf_inp_ext")
	else:		else:

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