1. grouping for inter layer 0 (befe9e85) · Commits · Elphel / python3-imagej-tiff

nn_ds_neibs1_tmp.py

+115 −34

Original line number	Original line	Diff line number	Diff line
	@@ -48,8 +48,8 @@ RUN_TOT_AVG = 100 # last batches to average. Epoch is 307 training batche
	#BATCH_SIZE = 1080//9 # == 120 Each batch of tiles has balanced D/S tiles, shuffled batches but not inside batches		#BATCH_SIZE = 1080//9 # == 120 Each batch of tiles has balanced D/S tiles, shuffled batches but not inside batches
	BATCH_SIZE = 2*1080//9 # == 120 Each batch of tiles has balanced D/S tiles, shuffled batches but not inside batches		BATCH_SIZE = 2*1080//9 # == 120 Each batch of tiles has balanced D/S tiles, shuffled batches but not inside batches
	SHUFFLE_EPOCH = True		SHUFFLE_EPOCH = True
	NET_ARCH1 = 6 #0 # 4 # 3 # overwrite with argv?		NET_ARCH1 = 0 #0 # 4 # 3 # overwrite with argv?
	NET_ARCH2 = 6 # 0 # 3 # overwrite with argv?		NET_ARCH2 = 0 # 0 # 3 # overwrite with argv?
	ONLY_TILE = None # 4 # None # 0 # 4# None # (remove all but center tile data), put None here for normal operation)		ONLY_TILE = 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

	@@ -249,6 +249,11 @@ files_train_hvar = ["/home/oleg/GIT/python3-imagej-tiff/data_sets/tf_data_rand2/
	"/home/oleg/GIT/python3-imagej-tiff/data_sets/tf_data_rand2/train006_R1_GT_1.5.tfrecords",		"/home/oleg/GIT/python3-imagej-tiff/data_sets/tf_data_rand2/train006_R1_GT_1.5.tfrecords",
	"/home/oleg/GIT/python3-imagej-tiff/data_sets/tf_data_rand2/train007_R1_GT_1.5.tfrecords",		"/home/oleg/GIT/python3-imagej-tiff/data_sets/tf_data_rand2/train007_R1_GT_1.5.tfrecords",
	]		]

			files_train_lvar = ["/home/oleg/GIT/python3-imagej-tiff/data_sets/tf_data_rand2/train000_R1_LE_1.5.tfrecords",
			]
			files_train_hvar = ["/home/oleg/GIT/python3-imagej-tiff/data_sets/tf_data_rand2/train000_R1_GT_1.5.tfrecords",
			]
	#files_train_hvar = []		#files_train_hvar = []
	#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_3x3a/train000_R1_LE_1.5.tfrecords" # "/home/eyesis/x3d_data/data_sets/train-000_R1_LE_1.5.tfrecords"
	file_test_lvar= "/home/oleg/GIT/python3-imagej-tiff/data_sets/tf_data_rand2/testTEST_R1_LE_1.5.tfrecords"		file_test_lvar= "/home/oleg/GIT/python3-imagej-tiff/data_sets/tf_data_rand2/testTEST_R1_LE_1.5.tfrecords"
	@@ -438,6 +443,7 @@ def network_summary_w_b(scope, in_shape, out_shape, layout, index, network_scope
	# lowest index		# lowest index
	l1 = layout.index(next(filter(lambda x: x!=0, layout)))		l1 = layout.index(next(filter(lambda x: x!=0, layout)))

			global test_op

	with tf.variable_scope(scope,reuse=tf.AUTO_REUSE):		with tf.variable_scope(scope,reuse=tf.AUTO_REUSE):
	# histograms		# histograms
	@@ -446,6 +452,7 @@ def network_summary_w_b(scope, in_shape, out_shape, layout, index, network_scope
	tf.summary.histogram("weights",w)		tf.summary.histogram("weights",w)
	tf.summary.histogram("biases",b)		tf.summary.histogram("biases",b)
	# weights 2D pics		# weights 2D pics
			tmpvar = tf.get_variable('tmp_tile',shape=(TILE_SIDE,TILE_SIDE))

	if network_scope=='sub':		if network_scope=='sub':
	# draw for the 1st layer		# draw for the 1st layer
	@@ -455,43 +462,67 @@ def network_summary_w_b(scope, in_shape, out_shape, layout, index, network_scope
	# red - the values will be automapped to 0-255 range		# red - the values will be automapped to 0-255 range
	# grid = tf.stack([tf.reduce_max(w),tf.reduce_min(w),tf.reduce_min(w)])		# grid = tf.stack([tf.reduce_max(w),tf.reduce_min(w),tf.reduce_min(w)])
	# yellow - the values will be automapped to 0-255 range		# yellow - the values will be automapped to 0-255 range
	grid = tf.stack([tf.reduce_max(w),tf.reduce_max(w),tf.reduce_max(w)/2])		grid_y = tf.stack([tf.reduce_max(w),tf.reduce_max(w),tf.reduce_max(w)/2])
			grid_r = tf.stack([tf.reduce_max(w),tf.reduce_min(w),tf.reduce_min(w)])

	wt = tf.transpose(w,[1,0])		wt = tf.transpose(w,[1,0])
	wt = wt[:,:-1]		wt = wt[:,:-1]
	tmp1 = []		tmp1 = []
	for i in range(layout[index]):		for i in range(layout[index]):

			# reset when even
			if i%2==0:
	tmp2 = []		tmp2 = []

	for j in range(TILE_LAYERS):		for j in range(TILE_LAYERS):
	si = (j+0)*TILE_SIZE		si = (j+0)*TILE_SIZE
	ei = (j+1)*TILE_SIZE		ei = (j+1)*TILE_SIZE

	tile = tf.reshape(wt[i,si:ei],shape=(TILE_SIDE,TILE_SIDE))		tile = tf.reshape(wt[i,si:ei],shape=(TILE_SIDE,TILE_SIDE))
	# color here?		zers = tf.zeros(shape=(TILE_SIDE,TILE_SIDE))
	#tile = tf.cond()
	# stack to RGB		test_op = tmpvar.assign(tile)
			#tile = tmpvar

	tiles = tf.stack([tile]*3,axis=2)		tiles = tf.stack([tile]*3,axis=2)
	tiles = tf.concat([tiles, tf.expand_dims((TILE_SIDE+0)*[grid],0)],axis=0)
	tiles = tf.concat([tiles, tf.expand_dims((TILE_SIDE+1)*[grid],1)],axis=1)		# vertical border
			if (j==TILE_LAYERS-1):
			tiles = tf.concat([tiles, tf.expand_dims((TILE_SIDE+0)*[grid_r],1)],axis=1)
			else:
			tiles = tf.concat([tiles, tf.expand_dims((TILE_SIDE+0)*[grid_y],1)],axis=1)

			# horizontal border
			tiles = tf.concat([tiles, tf.expand_dims((TILE_SIDE+1)*[grid_r],0)],axis=0)

	tmp2.append(tiles)		tmp2.append(tiles)

			# concat when odd
			if i%2==1:
	ts = tf.concat(tmp2,axis=1)		ts = tf.concat(tmp2,axis=1)
	tmp1.append(ts)		tmp1.append(ts)

	imsum1 = tf.concat(tmp1,axis=0)		imsum1 = tf.concat(tmp1,axis=0)
	tf.summary.image("sub_w8s",tf.reshape(imsum1,[1,layout[index](TILE_SIDE+1),TILE_LAYERS(TILE_SIDE+1),3]))		imsum1_1 = tf.reshape(imsum1,[1,layout[index](TILE_SIDE+1)//2,2TILE_LAYERS*(TILE_SIDE+1),3])

			tf.summary.image("sub_w8s",imsum1_1)

	# tests		# tests
	#tf.summary.image("s_weights_test",tf.reshape(w,[1,w.shape[0],w.shape[1],1]))		#tf.summary.image("s_weights_test",tf.reshape(w,[1,w.shape[0],w.shape[1],1]))
	#tf.summary.image("s_weights_test_transposed",tf.reshape(wt,[1,wt.shape[0],wt.shape[1],1]))		#tf.summary.image("s_weights_test_transposed",tf.reshape(wt,[1,wt.shape[0],wt.shape[1],1]))

	if network_scope=='inter':		if network_scope=='inter':

	blocks_number = int(math.pow(2*CLUSTER_RADIUS+1,2))		cluster_side = 2*CLUSTER_RADIUS+1
			blocks_number = int(math.pow(cluster_side,2))

	if index==l1:		if index==l1:

	# red - the values will be automapped to 0-255 range		# red - the values will be automapped to 0-255 range
	# grid = tf.stack([tf.reduce_max(w),tf.reduce_min(w),tf.reduce_min(w)])		# grid = tf.stack([tf.reduce_max(w),tf.reduce_min(w),tf.reduce_min(w)])
	# yellow - the values will be automapped to 0-255 range		# yellow - the values will be automapped to 0-255 range
	grid = tf.stack([tf.reduce_max(w),tf.reduce_max(w),tf.reduce_max(w)/2])		grid_y = tf.stack([tf.reduce_max(w),tf.reduce_max(w),tf.reduce_max(w)/2])
			grid_r = tf.stack([tf.reduce_max(w),tf.reduce_min(w),tf.reduce_min(w)])

	wt = tf.transpose(w,[1,0])		wt = tf.transpose(w,[1,0])

	@@ -505,28 +536,61 @@ def network_summary_w_b(scope, in_shape, out_shape, layout, index, network_scope

	tmp1 = []		tmp1 = []
	for i in range(layout[index]):		for i in range(layout[index]):

			# reset when even
			if i%4==0:
	tmp2 = []		tmp2 = []
	for j in range(blocks_number):
	si = (j+0)*block_size
	ei = (j+1)*block_size

	# wtm is expanded... only tested for 0		tmp4 = []
	if missing_in_block!=0:		# need to group these
	wtm = tf.concat(wt[i,si:ei],missing_in_block*[tf.reduce_min(w)])		for j1 in range(cluster_side):
	else:
			tmp3 = []

			for j2 in range(cluster_side):
			si = (cluster_sidej1+j2+0)block_size
			ei = (cluster_sidej1+j2+1)block_size

	wtm = wt[i,si:ei]		wtm = wt[i,si:ei]

	tile = tf.reshape(wtm,shape=(block_side,block_side))		tile = tf.reshape(wtm,shape=(block_side,block_side))
	# stack to RGB		# stack to RGB
	tiles = tf.stack([tile]*3,axis=2)		tiles = tf.stack([tile]*3,axis=2)
	tiles = tf.concat([tiles, tf.expand_dims((block_side+0)*[grid],0)],axis=0)
	tiles = tf.concat([tiles, tf.expand_dims((block_side+1)*[grid],1)],axis=1)		# yellow first
	tmp2.append(tiles)		if j2==cluster_side-1:

			if j1==cluster_side-1:
			tiles = tf.concat([tiles, tf.expand_dims((block_side+0)*[grid_r],0)],axis=0)
			else:
			tiles = tf.concat([tiles, tf.expand_dims((block_side+0)*[grid_y],0)],axis=0)

			tiles = tf.concat([tiles, tf.expand_dims((block_side+1)*[grid_r],1)],axis=1)

			else:

			tiles = tf.concat([tiles, tf.expand_dims((block_side+0)*[grid_y],1)],axis=1)

			if j1==cluster_side-1:
			tiles = tf.concat([tiles, tf.expand_dims((block_side+1)*[grid_r],0)],axis=0)
			else:
			tiles = tf.concat([tiles, tf.expand_dims((block_side+1)*[grid_y],0)],axis=0)

			tmp3.append(tiles)

			# hor
			tmp4.append(tf.concat(tmp3,axis=1))

			tmp2.append(tf.concat(tmp4,axis=0))

			if i%4==3:
	ts = tf.concat(tmp2,axis=1)		ts = tf.concat(tmp2,axis=1)
	tmp1.append(ts)		tmp1.append(ts)

	imsum2 = tf.concat(tmp1,axis=0)		imsum2 = tf.concat(tmp1,axis=0)
	tf.summary.image("inter_w8s",tf.reshape(imsum2,[1,layout[index](block_side+1),blocks_number(block_side+1),3]))		print("imsum2 shape: ")
			print(imsum2.shape)
			tf.summary.image("inter_w8s",tf.reshape(imsum2,[1,layout[index]cluster_side(block_side+1)//4,4cluster_side(block_side+1),3]))



	@@ -792,6 +856,17 @@ with tf.Session() as sess:
	sess.run(tf.local_variables_initializer())		sess.run(tf.local_variables_initializer())

	merged = tf.summary.merge_all()		merged = tf.summary.merge_all()

			l1 = NN_LAYOUT1.index(next(filter(lambda x: x!=0, NN_LAYOUT1)))
			l2 = NN_LAYOUT2.index(next(filter(lambda x: x!=0, NN_LAYOUT2)))
			with tf.variable_scope('g_fc_sub'+str(l1),reuse=tf.AUTO_REUSE):
			w = tf.get_variable('weights',shape=[325,32])
			wd = w[...,tf.newaxis]
			wds = tf.stack([wd]*3,axis=0)
			#print(wd.shape)
			#some_image = tf.summary.image("tfsi_test",wds.eval(),max_outputs=1)
			some_image = tf.summary.image("tfsi_test",wds,max_outputs=1)

	train_writer = tf.summary.FileWriter(TRAIN_PATH, sess.graph)		train_writer = tf.summary.FileWriter(TRAIN_PATH, sess.graph)
	test_writer = tf.summary.FileWriter(TEST_PATH, sess.graph)		test_writer = tf.summary.FileWriter(TEST_PATH, sess.graph)
	test_writer1 = tf.summary.FileWriter(TEST_PATH1, sess.graph)		test_writer1 = tf.summary.FileWriter(TEST_PATH1, sess.graph)
	@@ -823,8 +898,8 @@ with tf.Session() as sess:
	for i in range(dataset_train_size):		for i in range(dataset_train_size):
	try:		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(		# 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(
	train_summary,_, G_loss_trained, output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, gt_variance = sess.run(		_, train_summary,_, G_loss_trained, output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, gt_variance = sess.run(
	[ merged,		[ test_op, merged,
	G_opt,		G_opt,
	G_loss,		G_loss,
	out,		out,
	@@ -846,6 +921,7 @@ with tf.Session() as sess:
	loss_train_hist[i] = G_loss_trained		loss_train_hist[i] = G_loss_trained
	loss2_train_hist[i] = out_cost1		loss2_train_hist[i] = out_cost1
	gtvar_train_hist[i] = gt_variance		gtvar_train_hist[i] = gt_variance

	except tf.errors.OutOfRangeError:		except tf.errors.OutOfRangeError:
	print("train done at step %d"%(i))		print("train done at step %d"%(i))
	break		break
	@@ -879,6 +955,9 @@ with tf.Session() as sess:
	loss_test_hist[i] = G_loss_tested		loss_test_hist[i] = G_loss_tested
	loss2_test_hist[i] = out_cost1		loss2_test_hist[i] = out_cost1
	gtvar_test_hist[i] = gt_variance		gtvar_test_hist[i] = gt_variance

			# #print(str(wed.shape)+" "+str(wed[0,0]))

	except tf.errors.OutOfRangeError:		except tf.errors.OutOfRangeError:
	print("test done at step %d"%(i))		print("test done at step %d"%(i))
	break		break
	@@ -889,6 +968,8 @@ with tf.Session() as sess:

	# _,_=sess.run([tf_ph_G_loss,tf_ph_sq_diff],feed_dict={tf_ph_G_loss:test_avg, tf_ph_sq_diff:test2_avg})		# _,_=sess.run([tf_ph_G_loss,tf_ph_sq_diff],feed_dict={tf_ph_G_loss:test_avg, tf_ph_sq_diff:test2_avg})

			train_writer.add_summary(some_image.eval(), epoch)

	train_writer.add_summary(train_summary, epoch)		train_writer.add_summary(train_summary, epoch)
	test_writer.add_summary(test_summaries[0], epoch)		test_writer.add_summary(test_summaries[0], epoch)
	test_writer1.add_summary(test_summaries[1], epoch)		test_writer1.add_summary(test_summaries[1], epoch)

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