segmentTestSet.py
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	import cv2
	import IPython.display
	import importlib
	import skimage.io as imgio
	import numpy as np
	import torch
	import torch.nn.functional as F
	from torch.autograd import Variable
	from net_v1 import UNet3d
	import os
	from networkTraining.forwardOnBigImages import processChunk

	imgdir="/cvlabdata2/home/kozinski/experimentsTorch/bbp_neurons/data_npy/img/test/"
	exec(open("testFiles.txt").read())

	log_dir="log_v1"
	net = UNet3d().cuda()
	saved_net=torch.load(os.path.join(log_dir,"net_last.pth"))
	net.load_state_dict(saved_net['state_dict'])
	net.eval();

	out_dir="test_last"

	def process_output(o):
	e=np.exp(o[0,1,:,:,:])
	prob=e/(e+1)
	return prob

	outdir=os.path.join(log_dir,out_dir)
	os.makedirs(outdir)

	for f in testFiles:
	img=np.load(os.path.join(imgdir,f[0])).astype(np.float32)
	inp=img.reshape(1,1,img.shape[-3],img.shape[-2],img.shape[-1])
	oup=processChunk(inp,(104,104,104),(22,22,22),2,net,outChannels=2)
	prob=process_output(oup)
	np.save(os.path.join(outdir,os.path.basename(f[0])),prob)