still testing (fc63a9dd) · Commits · Elphel / python3-imagej-tiff

test_nn_feed.py

+33 −8

Original line number	Original line	Diff line number	Diff line
	@@ -47,7 +47,7 @@ VALUES_LAYER_NAME = 'other'
	LAYERS_OF_INTEREST = ['diagm-pair', 'diago-pair', 'hor-pairs', 'vert-pairs']		LAYERS_OF_INTEREST = ['diagm-pair', 'diago-pair', 'hor-pairs', 'vert-pairs']
	RADIUS = 1		RADIUS = 1

	DEBUG_PLT_LOSS = False		DEBUG_PLT_LOSS = True
	# If false - will not pack or rescal		# If false - will not pack or rescal
	DEBUG_PACK_TILES = True		DEBUG_PACK_TILES = True

	@@ -75,6 +75,9 @@ if not IS_TEST:
	print("\n".join(tlist))		print("\n".join(tlist))
	print("Found "+str(len(tlist))+" preprocessed tiff files:")		print("Found "+str(len(tlist))+" preprocessed tiff files:")
	print_time()		print_time()

			pass

	''' WARNING, assuming:		''' WARNING, assuming:
	- timestamps and part of names match		- timestamps and part of names match
	- layer order and names are identical		- layer order and names are identical
	@@ -148,7 +151,6 @@ if not IS_TEST:

	# might not need it because going to loop through anyway		# might not need it because going to loop through anyway
	packed_tiles = np.array([[pile.pack_tile(tiles[i,j],ptab) for j in range(tiles.shape[1])] for i in range(tiles.shape[0])])		packed_tiles = np.array([[pile.pack_tile(tiles[i,j],ptab) for j in range(tiles.shape[1])] for i in range(tiles.shape[0])])

	packed_tiles = np.dstack((packed_tiles,values[:,:,0]))		packed_tiles = np.dstack((packed_tiles,values[:,:,0]))

	print("Packed (81x4 -> 1x(25*4+1)) tiled input shape: "+str(packed_tiles.shape))		print("Packed (81x4 -> 1x(25*4+1)) tiled input shape: "+str(packed_tiles.shape))
	@@ -260,8 +262,9 @@ cf_w_norm = tf.nn.softmax(cf_w)
	#out_cf = out[:,1]		#out_cf = out[:,1]

	#G_loss = tf.reduce_mean(tf.abs(tf.nn.softmax(out[:,1])out[:,0]-cf_w_normgt[:,0]))		#G_loss = tf.reduce_mean(tf.abs(tf.nn.softmax(out[:,1])out[:,0]-cf_w_normgt[:,0]))
	G_loss = tf.reduce_mean(tf.squared_difference(out[:,0], gt[:,0]))		#G_loss = tf.reduce_mean(tf.squared_difference(out[:,0], gt[:,0]))
	#G_loss = tf.reduce_mean(tf.abs(out[:,0]-gt[:,0]))		#G_loss = tf.reduce_mean(tf.abs(out[:,0]-gt[:,0]))
			G_loss = tf.losses.mean_squared_error(gt[:,0],out[:,0],cf_w)

	tf.summary.scalar('loss', G_loss)		tf.summary.scalar('loss', G_loss)
	tf.summary.scalar('prediction', out[0,0])		tf.summary.scalar('prediction', out[0,0])
	@@ -303,7 +306,7 @@ recorded_gt_c = []
	recorded_pr_d = []		recorded_pr_d = []
	recorded_pr_c = []		recorded_pr_c = []

	LR = 1e-4		LR = 1e-5

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

	@@ -316,14 +319,37 @@ if DEBUG_PLT_LOSS:


	# RUN		# RUN
			# epoch is one image



			for epoch in range(lastepoch,lastepoch+len(tlist)):

			print(bcolors.HEADER+"Epoch #"+str(epoch)+bcolors.ENDC)

	for epoch in range(lastepoch,1):
	#for epoch in range(lastepoch,4001):		#for epoch in range(lastepoch,4001):
	if os.path.isdir("result/%04d"%epoch):		if os.path.isdir("result/%04d"%epoch):
	continue		continue

	cnt=0		cnt=0

			tlist_index = epoch - lastepoch

			print(bcolors.OKGREEN+"Processing "+tlist[tlist_index]+bcolors.ENDC)

			tmp_tiff = ijt.imagej_tiff(tlist[tlist_index])
			tmp_tiles = tmp_tiff.getstack(labels,shape_as_tiles=True)
			tmp_vals = tmp_tiff.getvalues(label=VALUES_LAYER_NAME)

			# Parse packing table
			# packing table name
			ptab_name = "tile_packing_table.xml"
			ptab = pile.PackingTable(ptab_name,LAYERS_OF_INTEREST).lut

			# might not need it because going to loop through anyway
			packed_tiles = np.array([[pile.pack_tile(tmp_tiles[i,j],ptab) for j in range(tmp_tiles.shape[1])] for i in range(tmp_tiles.shape[0])])
			packed_tiles = np.dstack((packed_tiles,tmp_vals[:,:,0]))

	#if epoch > 2000:		#if epoch > 2000:
	# LR = 1e-5		# LR = 1e-5

	@@ -429,10 +455,9 @@ for epoch in range(lastepoch,1):

	else:		else:
	print("%d %d Loss=%.3f CurrentLoss=%.3f Time=%.3f"%(epoch,cnt,mean_loss,G_current,time.time()-st))		print("%d %d Loss=%.3f CurrentLoss=%.3f Time=%.3f"%(epoch,cnt,mean_loss,G_current,time.time()-st))
	train_writer.add_run_metadata(run_metadata, 'step%d' % cnt)		#train_writer.add_run_metadata(run_metadata, 'step%d' % cnt)

	#test_writer.add_summary(summary,cnt)		#test_writer.add_summary(summary,cnt)
	train_writer.add_summary(summary, cnt)		#train_writer.add_summary(summary, cnt)

	if epoch%save_freq==0:		if epoch%save_freq==0:
	if not os.path.isdir(result_dir + '%04d'%epoch):		if not os.path.isdir(result_dir + '%04d'%epoch):

test_nn_infer.py

+36 −29

Original line number	Original line	Diff line number	Diff line
	@@ -151,24 +151,49 @@ for item in tlist:
	print(packed_tiles.shape)		print(packed_tiles.shape)
	print("ENDDD!")		print("ENDDD!")

			# NO
	# flatten		# flatten
	packed_tiles_flat = packed_tiles.reshape(-1, packed_tiles.shape[-1])		#packed_tiles_flat = packed_tiles.reshape(-1, packed_tiles.shape[-1])
	values_flat = values.reshape(-1, values.shape[-1])		#values_flat = values.reshape(-1, values.shape[-1])

	print("Packed (81x4 -> 1x(25*4+1)) tiled input shape: "+str(packed_tiles_flat.shape))		#print("Packed (81x4 -> 1x(25*4+1)) tiled input shape: "+str(packed_tiles_flat.shape))
	print("Values shape "+str(values_flat.shape))		#print("Values shape "+str(values_flat.shape))
	print_time()		#print_time()

	# do line by line?!		# do line by line?!
			output_image = np.empty((packed_tiles.shape[0],packed_tiles.shape[1],2))
			print("Output shape = "+str(output_image.shape))

			for i in range(packed_tiles.shape[0]):

	# now run prediction		# now run prediction
			packed_tiles_flat = packed_tiles[i]
			values_flat = values[i]

	output = sess.run(out,feed_dict={in_tile:packed_tiles_flat})		output = sess.run(out,feed_dict={in_tile:packed_tiles_flat})
			output_image[i] = output

			# so, let's print
			for j in range(output.shape[0]):
			p = output[j,0]
			pc = output[j,1]
			fv = values_flat[j,0]
			gt = values_flat[j,1]
			cf = values_flat[j,2]

	print("Output shape: "+str(output.shape))		vstring = "["+"{0:.2f}".format(fv)+", "+"{0:.2f}".format(gt)+", "+"{0:.2f}".format(cf)+"]"
			pstring = "["+"{0:.2f}".format(p)+", "+"{0:.2f}".format(pc)+"]"

	output_image = np.reshape(output,(tiles.shape[0],tiles.shape[1],-1))		if not np.isnan(p):
	print(output_image.shape)		outstring = "i,j: "+str(i)+" "+str(j)+" Values: "+vstring+" Prediction: "+pstring
			if abs(cf)<0.5:
			print(outstring)
			#pass
			else:
			print(bcolors.WARNING+outstring+bcolors.ENDC)


			sess.close()
	import imagej_tiffwriter		import imagej_tiffwriter

	# 1 prediction		# 1 prediction
	@@ -183,25 +208,7 @@ for item in tlist:

	imagej_tiffwriter.save('prediction_results.tiff',tif)		imagej_tiffwriter.save('prediction_results.tiff',tif)

	sys.exit(0)		#sys.exit(0)

	# so, let's print
	for i in range(output.shape[0]):
	p = output[i,0]
	pc = output[i,1]
	fv = values_flat[i,0]
	gt = values_flat[i,1]
	cf = values_flat[i,2]

	vstring = "["+"{0:.2f}".format(fv)+", "+"{0:.2f}".format(gt)+", "+"{0:.2f}".format(cf)+"]"
	pstring = "["+"{0:.2f}".format(p)+", "+"{0:.2f}".format(pc)+"]"

	if not np.isnan(p):
	outstring = "i: "+str(i)+" Values: "+vstring+" Prediction: "+pstring
	if cf<0.5:
	print(outstring)
	else:
	print(bcolors.WARNING+outstring+bcolors.ENDC)

	#else:		#else:
	# print("i: "+str(i)+" NaNs")		# print("i: "+str(i)+" NaNs")

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