testing

test_nn_feed.py

@@ -65,118 +65,43 @@ 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_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()
-  labels.remove(VALUES_LAYER_NAME)
-
-  print("Image data layers:  "+str(labels))
-  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()
+tlist = glob.glob(src+"/*.tiff")
+print("Found "+str(len(tlist))+" preprocessed tiff files:")
+print("\n".join(tlist))
+print_time()
 
-# END IF IS_TEST
+tiff = ijt.imagej_tiff(tlist[0])
 
+# get labels
+labels = tiff.labels.copy()
+labels.remove(VALUES_LAYER_NAME)
 
-#print("CHECKPOINTE")
+print("Image data layers:  "+str(labels))
+print("Layers of interest: "+str(LAYERS_OF_INTEREST))
+print("Values layer: "+str([VALUES_LAYER_NAME]))
 
-#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
 
-  return fc6
+  #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()
@@ -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
-
-  for ind in range(hsteps*vsteps):
-  #for ind in np.random.permutation(packed_tiles.shape[0]*packed_tiles.shape[1]):
-
-    #print("Iteration "+str(cnt))
+  # 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
 
-    st=time.time()
-    cnt+=1
-
-    #i = int(ind/packed_tiles.shape[1])
-    #j = ind%packed_tiles.shape[1]
+  #packed_tiles = packed_tiles[::,::]
+  values       = tmp_vals
 
-    i = 2 + 5*(ind//hsteps)
-    j = 2 + 5*(ind%hsteps)
+  input_patch = np.reshape(packed_tiles,(-1,101))
+  gt_patch    = np.reshape(values[:,:,1:3],(-1,2))
 
-    #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))
+  g_loss = np.zeros(input_patch.shape[0])
 
-    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(input_patch)
-    #print(gt_patch)
+  for i in range(100):
 
-    #gt_patch[gt_patch==-256] = np.nan
-    #gt_patch[np.isnan(gt_patch)] = 0
+    print(bcolors.OKBLUE+"Iteration "+str(i)+bcolors.ENDC)
 
-    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

@@ -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()