Commit 45a473f0 authored by Oleg Dzhimiev's avatar Oleg Dzhimiev
Browse files

testing

parent 9c8f781e

test_nn_feed.py

+39 −134
Original line number Diff line number Diff line
@@ -65,30 +65,12 @@ import tensorflow.contrib.slim as slim 
print("TensorCrawl imported")

print_time()



IS_TEST = False



# BEGIN IF IS_TEST

if not IS_TEST:



tlist = glob.glob(src+"/*.tiff")



  print("\n".join(tlist))

print("Found "+str(len(tlist))+" preprocessed tiff files:")

print("\n".join(tlist))

print_time()



  pass



  ''' WARNING, assuming:

        - timestamps and part of names match

        - layer order and names are identical

  '''



  # open the first one to get dimensions and other info

tiff = ijt.imagej_tiff(tlist[0])

  #del tlist[0]



  # shape as tiles? make a copy or make writeable

  # (242, 324, 9, 9, 5)



# get labels

labels = tiff.labels.copy()
@@ -98,85 +80,28 @@ if not IS_TEST: 
print("Layers of interest: "+str(LAYERS_OF_INTEREST))

print("Values layer: "+str([VALUES_LAYER_NAME]))



  # create copies

  tiles  = np.copy(tiff.getstack(labels,shape_as_tiles=True))

  values = np.copy(tiff.getvalues(label=VALUES_LAYER_NAME))



  #gt = values[:,:,1:3]



  print("Mixed tiled input data shape: "+str(tiles.shape))

  #print_time()



  # now generate a layer of indices to get other tiles

  indices = np.random.random_integers(0,len(tlist)-1,size=(tiles.shape[0],tiles.shape[1]))



  #print(indices.shape)



  # counts tiles from a certain tiff

  shuffle_counter = np.zeros(len(tlist),np.int32)

  shuffle_counter[0] = tiles.shape[0]*tiles.shape[1]



  for i in range(1,len(tlist)):

    #print(tlist[i])

    tmp_tiff  = ijt.imagej_tiff(tlist[i])

    tmp_tiles = tmp_tiff.getstack(labels,shape_as_tiles=True)

    tmp_vals  = tmp_tiff.getvalues(label=VALUES_LAYER_NAME)

    #tmp_tiles =

    #tiles[indices==i] = tmp_tiff[indices==i]



    # straight and clear

    # can do quicker?

    for y,x in itertools.product(range(indices.shape[0]),range(indices.shape[1])):

      if indices[y,x]==i:

        tiles[y,x]  = tmp_tiles[y,x]

        values[y,x] = tmp_vals[y,x]

        shuffle_counter[i] +=1



  # check shuffle counter

  for i in range(1,len(shuffle_counter)):

    shuffle_counter[0] -= shuffle_counter[i]



  print("Tiff files parts count in the mixed input = "+str(shuffle_counter))

  print_time()



  # test later



  # might not need it because going to loop through anyway

  packed_tiles = pile.pack(tiles)

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



  print("Packed (81x4 -> 1x(25*4+1)) tiled input shape: "+str(packed_tiles.shape))

  print("Values shape "+str(values.shape))

  print_time()



# END IF IS_TEST





#print("CHECKPOINTE")



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

#  for j in range(tiles.shape[1]):

#    nn_input = pile.get_tile_with_neighbors(tiles,i,j,RADIUS)

#    print("tile: "+str(i)+", "+str(j)+": shape = "+str(nn_input.shape))

#print_time()



result_dir = './result/'

checkpoint_dir = './result/'

save_freq = 500



def lrelu(x):

    return tf.maximum(x*0.2,x)

    #return tf.maximum(x*0.2,x)

    return tf.nn.relu(x)



def network(input):



  fc1  = slim.fully_connected(input,2048,activation_fn=lrelu,scope='g_fc1')

  fc2  = slim.fully_connected(fc1,  1024,activation_fn=lrelu,scope='g_fc2')

  fc3  = slim.fully_connected(fc2,   512,activation_fn=lrelu,scope='g_fc3')

  fc4  = slim.fully_connected(fc3,     8,activation_fn=lrelu,scope='g_fc4')

  fc5  = slim.fully_connected(fc4,     4,activation_fn=lrelu,scope='g_fc5')

  fc6  = slim.fully_connected(fc5,     2,activation_fn=lrelu,scope='g_fc6')

  fc1  = slim.fully_connected(input,512,activation_fn=lrelu,scope='g_fc1')

  fc2  = slim.fully_connected(fc1,     2,activation_fn=lrelu,scope='g_fc2')

  return fc2



  #fc2  = slim.fully_connected(fc1,  1024,activation_fn=lrelu,scope='g_fc2')

  #fc3  = slim.fully_connected(fc2,   512,activation_fn=lrelu,scope='g_fc3')

  #fc4  = slim.fully_connected(fc3,     8,activation_fn=lrelu,scope='g_fc4')

  #fc5  = slim.fully_connected(fc4,     4,activation_fn=lrelu,scope='g_fc5')

  #fc6  = slim.fully_connected(fc5,     2,activation_fn=lrelu,scope='g_fc6')



  return fc6

  #return fc6





sess = tf.Session()
@@ -241,9 +166,6 @@ lastepoch = 0 
for folder in allfolders:

  lastepoch = np.maximum(lastepoch, int(folder[-4:]))



g_loss = np.zeros((packed_tiles.shape[0]*packed_tiles.shape[1],1))





recorded_loss = []

recorded_mean_loss = []
@@ -253,7 +175,7 @@ recorded_gt_c = [] 
recorded_pr_d = []

recorded_pr_c = []



LR = 1e-5

LR = 1e-3



print(bcolors.HEADER+"Last Epoch = "+str(lastepoch)+bcolors.ENDC)
@@ -274,12 +196,9 @@ for epoch in range(lastepoch,lastepoch+len(tlist)): 


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



#for epoch in range(lastepoch,4001):

  if os.path.isdir("result/%04d"%epoch):

    continue



  cnt=0



  tlist_index = epoch - lastepoch



  print(bcolors.OKGREEN+"Processing "+tlist[tlist_index]+bcolors.ENDC)
@@ -295,49 +214,35 @@ for epoch in range(lastepoch,lastepoch+len(tlist)): 
  #if epoch > 2000:

  #  LR = 1e-5



  vsteps = packed_tiles.shape[0]//5

  hsteps = packed_tiles.shape[1]//5

  # so, here get the image, remove nans and run for 100x times

  packed_tiles[np.isnan(packed_tiles)] = 0.0

  tmp_vals[np.isnan(tmp_vals)] = 0.0



  for ind in range(hsteps*vsteps):

  #for ind in np.random.permutation(packed_tiles.shape[0]*packed_tiles.shape[1]):

  #packed_tiles = packed_tiles[::,::]

  values       = tmp_vals



    #print("Iteration "+str(cnt))



    st=time.time()

    cnt+=1

  input_patch = np.reshape(packed_tiles,(-1,101))

  gt_patch    = np.reshape(values[:,:,1:3],(-1,2))



    #i = int(ind/packed_tiles.shape[1])

    #j = ind%packed_tiles.shape[1]

  g_loss = np.zeros(input_patch.shape[0])



    i = 2 + 5*(ind//hsteps)

    j = 2 + 5*(ind%hsteps)



    #input_patch = tiles[i,j]

    input_patch = np.empty((vsteps*hsteps,packed_tiles.shape[2]))

    input_patch = np.reshape(packed_tiles[i-2:i+3,j-2:j+3],(-1,101))

  for i in range(100):



    gt_patch = np.empty((vsteps*hsteps,2))

    gt_patch = np.reshape(values[i-2:i+3,j-2:j+3,1:3],(-1,2))

    print(bcolors.OKBLUE+"Iteration "+str(i)+bcolors.ENDC)



    #print(input_patch)

    #print(gt_patch)



    #gt_patch[gt_patch==-256] = np.nan

    #gt_patch[np.isnan(gt_patch)] = 0



    input_patch[np.isnan(input_patch)] = 0

    st=time.time()



    skip_iteration = False



    # if nan skip run!

    if np.isnan(np.sum(gt_patch)):

      print("GT has NaNs")

      skip_iteration = True

      #skip_iteration = True



    if np.isnan(np.sum(input_patch)):

      print("Patch has NaNs")

      skip_iteration = True



      #skip_iteration = True



    if skip_iteration:

      #print(bcolors.WARNING+"Found NaN, skipping iteration for tile "+str(i)+","+str(j)+bcolors.ENDC)
@@ -350,7 +255,7 @@ for epoch in range(lastepoch,lastepoch+len(tlist)): 
      _,G_current,output,summary = sess.run([G_opt,G_loss,out,merged],feed_dict={in_tile:input_patch,gt:gt_patch,lr:LR},options=run_options,run_metadata=run_metadata)

      #_,G_current,output = sess.run([G_opt,G_loss,out],feed_dict={in_tile:input_patch,gt:gt_patch,lr:LR})



      g_loss[ind]=G_current

      g_loss[i]=G_current

      mean_loss = np.mean(g_loss[np.where(g_loss)])



      if DEBUG_PLT_LOSS:
@@ -394,7 +299,7 @@ for epoch in range(lastepoch,lastepoch+len(tlist)): 
        plt.pause(0.001)



      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,i,mean_loss,G_current,time.time()-st))

        #train_writer.add_run_metadata(run_metadata, 'step%d' % cnt)

        #test_writer.add_summary(summary,cnt)

        #train_writer.add_summary(summary, cnt)

test_nn_infer.py

+11 −8
Original line number Diff line number Diff line
@@ -68,14 +68,17 @@ def lrelu(x): 


def network(input):



  fc1  = slim.fully_connected(input,2048,activation_fn=lrelu,scope='g_fc1')

  fc2  = slim.fully_connected(fc1,  1024,activation_fn=lrelu,scope='g_fc2')

  fc3  = slim.fully_connected(fc2,   512,activation_fn=lrelu,scope='g_fc3')

  fc4  = slim.fully_connected(fc3,     8,activation_fn=lrelu,scope='g_fc4')

  fc5  = slim.fully_connected(fc4,     4,activation_fn=lrelu,scope='g_fc5')

  fc6  = slim.fully_connected(fc5,     2,activation_fn=lrelu,scope='g_fc6')



  return fc6

  fc1  = slim.fully_connected(input,512,activation_fn=lrelu,scope='g_fc1')

  fc2  = slim.fully_connected(fc1,    2,activation_fn=lrelu,scope='g_fc2')

  return fc2



  #fc2  = slim.fully_connected(fc1,  1024,activation_fn=lrelu,scope='g_fc2')

  #fc3  = slim.fully_connected(fc2,   512,activation_fn=lrelu,scope='g_fc3')

  #fc4  = slim.fully_connected(fc3,     8,activation_fn=lrelu,scope='g_fc4')

  #fc5  = slim.fully_connected(fc4,     4,activation_fn=lrelu,scope='g_fc5')

  #fc6  = slim.fully_connected(fc5,     2,activation_fn=lrelu,scope='g_fc6')



  #return fc6





sess = tf.Session()