content: # Copyright 2014 Google Inc. All rights reserved.
  # 
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  # 
  #    http://www.apache.org/licenses/LICENSE-2.0
  # 
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  import os
  import sys
  from tarfile import TarFile, TarInfo
  from matplotlib import pylab as pl
  import numpy as n
  import getopt as opt
  from python_util.util import *
  from math import sqrt, ceil, floor
  from python_util.gpumodel import IGPUModel
  import random as r
  import numpy.random as nr
  from convnet import ConvNet
  from python_util.options import *
  from PIL import Image
  from time import sleep
  
  class ShowNetError(Exception):
      pass
  
  class ShowConvNet(ConvNet):
      def __init__(self, op, load_dic):
          ConvNet.__init__(self, op, load_dic)
  
      def init_data_providers(self):
          self.need_gpu = self.op.get_value('show_preds') 
          class Dummy:
              def advance_batch(self):
                  pass
          if self.need_gpu:
              ConvNet.init_data_providers(self)
          else:
              self.train_data_provider = self.test_data_provider = Dummy()
      
      def import_model(self):
          if self.need_gpu:
              ConvNet.import_model(self)
              
      def init_model_state(self):
          if self.op.get_value('show_preds'):
              self.softmax_name = self.op.get_value('show_preds')
              
      def init_model_lib(self):
          if self.need_gpu:
              ConvNet.init_model_lib(self)
  
      def plot_cost(self):
          if self.show_cost not in self.train_outputs[0][0]:
              raise ShowNetError("Cost function with name '%s' not defined by given convnet." % self.show_cost)
  #        print self.test_outputs
          train_errors = [eval(self.layers[self.show_cost]['outputFilter'])(o[0][self.show_cost], o[1])[self.cost_idx] for o in self.train_outputs]
          test_errors = [eval(self.layers[self.show_cost]['outputFilter'])(o[0][self.show_cost], o[1])[self.cost_idx] for o in self.test_outputs]
          if self.smooth_test_errors:
              test_errors = [sum(test_errors[max(0,i-len(self.test_batch_range)):i])/(i-max(0,i-len(self.test_batch_range))) for i in xrange(1,len(test_errors)+1)]
          numbatches = len(self.train_batch_range)
          test_errors = n.row_stack(test_errors)
          test_errors = n.tile(test_errors, (1, self.testing_freq))
          test_errors = list(test_errors.flatten())
          test_errors += [test_errors[-1]] * max(0,len(train_errors) - len(test_errors))
          test_errors = test_errors[:len(train_errors)]
  
          numepochs = len(train_errors) / float(numbatches)
          pl.figure(1)
          x = range(0, len(train_errors))
          pl.plot(x, train_errors, 'k-', label='Training set')
          pl.plot(x, test_errors, 'r-', label='Test set')
          pl.legend()
          ticklocs = range(numbatches, len(train_errors) - len(train_errors) % numbatches + 1, numbatches)
          epoch_label_gran = int(ceil(numepochs / 20.)) 
          epoch_label_gran = int(ceil(float(epoch_label_gran) / 10) * 10) if numepochs >= 10 else epoch_label_gran 
          ticklabels = map(lambda x: str((x[1] / numbatches)) if x[0] % epoch_label_gran == epoch_label_gran-1 else '', enumerate(ticklocs))
  
          pl.xticks(ticklocs, ticklabels)
          pl.xlabel('Epoch')
  #        pl.ylabel(self.show_cost)
          pl.title('%s[%d]' % (self.show_cost, self.cost_idx))
  #        print "plotted cost"
          
      def make_filter_fig(self, filters, filter_start, fignum, _title, num_filters, combine_chans, FILTERS_PER_ROW=16):
          MAX_ROWS = 24
          MAX_FILTERS = FILTERS_PER_ROW * MAX_ROWS
          num_colors = filters.shape[0]
          f_per_row = int(ceil(FILTERS_PER_ROW / float(1 if combine_chans else num_colors)))
          filter_end = min(filter_start+MAX_FILTERS, num_filters)
          filter_rows = int(ceil(float(filter_end - filter_start) / f_per_row))
      
          filter_pixels = filters.shape[1]
          filter_size = int(sqrt(filters.shape[1]))
          fig = pl.figure(fignum)
          fig.text(.5, .95, '%s %dx%d filters %d-%d' % (_title, filter_size, filter_size, filter_start, filter_end-1), horizontalalignment='center') 
          num_filters = filter_end - filter_start
          if not combine_chans:
              bigpic = n.zeros((filter_size * filter_rows + filter_rows + 1, filter_size*num_colors * f_per_row + f_per_row + 1), dtype=n.single)
          else:
              bigpic = n.zeros((3, filter_size * filter_rows + filter_rows + 1, filter_size * f_per_row + f_per_row + 1), dtype=n.single)
      
          for m in xrange(filter_start,filter_end ):
              filter = filters[:,:,m]
              y, x = (m - filter_start) / f_per_row, (m - filter_start) % f_per_row
              if not combine_chans:
                  for c in xrange(num_colors):
                      filter_pic = filter[c,:].reshape((filter_size,filter_size))
                      bigpic[1 + (1 + filter_size) * y:1 + (1 + filter_size) * y + filter_size,
                             1 + (1 + filter_size*num_colors) * x + filter_size*c:1 + (1 + filter_size*num_colors) * x + filter_size*(c+1)] = filter_pic
              else:
                  filter_pic = filter.reshape((3, filter_size,filter_size))
                  bigpic[:,
                         1 + (1 + filter_size) * y:1 + (1 + filter_size) * y + filter_size,
                         1 + (1 + filter_size) * x:1 + (1 + filter_size) * x + filter_size] = filter_pic
                  
          pl.xticks([])
          pl.yticks([])
          if not combine_chans:
              pl.imshow(bigpic, cmap=pl.cm.gray, interpolation='nearest')
          else:
              bigpic = bigpic.swapaxes(0,2).swapaxes(0,1)
              pl.imshow(bigpic, interpolation='nearest')        
          
      def plot_filters(self):
          FILTERS_PER_ROW = 16
          filter_start = 0 # First filter to show
          if self.show_filters not in self.layers:
              raise ShowNetError("Layer with name '%s' not defined by given convnet." % self.show_filters)
          layer = self.layers[self.show_filters]
          filters = layer['weights'][self.input_idx]
  #        filters = filters - filters.min()
  #        filters = filters / filters.max()
          if layer['type'] == 'fc': # Fully-connected layer
              num_filters = layer['outputs']
              channels = self.channels
              filters = filters.reshape(channels, filters.shape[0]/channels, filters.shape[1])
          elif layer['type'] in ('conv', 'local'): # Conv layer
              num_filters = layer['filters']
              channels = layer['filterChannels'][self.input_idx]
              if layer['type'] == 'local':
                  filters = filters.reshape((layer['modules'], channels, layer['filterPixels'][self.input_idx], num_filters))
                  filters = filters[:, :, :, self.local_plane] # first map for now (modules, channels, pixels)
                  filters = filters.swapaxes(0,2).swapaxes(0,1)
                  num_filters = layer['modules']
  #                filters = filters.swapaxes(0,1).reshape(channels * layer['filterPixels'][self.input_idx], num_filters * layer['modules'])
  #                num_filters *= layer['modules']
                  FILTERS_PER_ROW = layer['modulesX']
              else:
                  filters = filters.reshape(channels, filters.shape[0]/channels, filters.shape[1])
          
          
          # Convert YUV filters to RGB
          if self.yuv_to_rgb and channels == 3:
              R = filters[0,:,:] + 1.28033 * filters[2,:,:]
              G = filters[0,:,:] + -0.21482 * filters[1,:,:] + -0.38059 * filters[2,:,:]
              B = filters[0,:,:] + 2.12798 * filters[1,:,:]
              filters[0,:,:], filters[1,:,:], filters[2,:,:] = R, G, B
          combine_chans = not self.no_rgb and channels == 3
          
          # Make sure you don't modify the backing array itself here -- so no -= or /=
          if self.norm_filters:
              #print filters.shape
              filters = filters - n.tile(filters.reshape((filters.shape[0] * filters.shape[1], filters.shape[2])).mean(axis=0).reshape(1, 1, filters.shape[2]), (filters.shape[0], filters.shape[1], 1))
              filters = filters / n.sqrt(n.tile(filters.reshape((filters.shape[0] * filters.shape[1], filters.shape[2])).var(axis=0).reshape(1, 1, filters.shape[2]), (filters.shape[0], filters.shape[1], 1)))
              #filters = filters - n.tile(filters.min(axis=0).min(axis=0), (3, filters.shape[1], 1))
              #filters = filters / n.tile(filters.max(axis=0).max(axis=0), (3, filters.shape[1], 1))
          #else:
          filters = filters - filters.min()
          filters = filters / filters.max()
  
          self.make_filter_fig(filters, filter_start, 2, 'Layer %s' % self.show_filters, num_filters, combine_chans, FILTERS_PER_ROW=FILTERS_PER_ROW)
      
      def plot_predictions(self):
          epoch, batch, data = self.get_next_batch(train=False) # get a test batch
          num_classes = self.test_data_provider.get_num_classes()
          NUM_ROWS = 2
          NUM_COLS = 4
          NUM_IMGS = NUM_ROWS * NUM_COLS if not self.save_preds else data[0].shape[1]
          NUM_TOP_CLASSES = min(num_classes, 5) # show this many top labels
          NUM_OUTPUTS = self.model_state['layers'][self.softmax_name]['outputs']
          PRED_IDX = 1
          
          label_names = [lab.split(',')[0] for lab in self.test_data_provider.batch_meta['label_names']]
          if self.only_errors:
              preds = n.zeros((data[0].shape[1], NUM_OUTPUTS), dtype=n.single)
          else:
              preds = n.zeros((NUM_IMGS, NUM_OUTPUTS), dtype=n.single)
              #rand_idx = nr.permutation(n.r_[n.arange(1), n.where(data[1] == 552)[1], n.where(data[1] == 795)[1], n.where(data[1] == 449)[1], n.where(data[1] == 274)[1]])[:NUM_IMGS]
              rand_idx = nr.randint(0, data[0].shape[1], NUM_IMGS)
              if NUM_IMGS < data[0].shape[1]:
                  data = [n.require(d[:,rand_idx], requirements='C') for d in data]
  #        data += [preds]
          # Run the model
          print  [d.shape for d in data], preds.shape
          self.libmodel.startFeatureWriter(data, [preds], [self.softmax_name])
          IGPUModel.finish_batch(self)
          print preds
          data[0] = self.test_data_provider.get_plottable_data(data[0])
  
          if self.save_preds:
              if not gfile.Exists(self.save_preds):
                  gfile.MakeDirs(self.save_preds)
              preds_thresh = preds > 0.5 # Binarize predictions
              data[0] = data[0] * 255.0
              data[0][data[0]<0] = 0
              data[0][data[0]>255] = 255
              data[0] = n.require(data[0], dtype=n.uint8)
              dir_name = '%s_predictions_batch_%d' % (os.path.basename(self.save_file), batch)
              tar_name = os.path.join(self.save_preds, '%s.tar' % dir_name)
              tfo = gfile.GFile(tar_name, "w")
              tf = TarFile(fileobj=tfo, mode='w')
              for img_idx in xrange(NUM_IMGS):
                  img = data[0][img_idx,:,:,:]
                  imsave = Image.fromarray(img)
                  prefix = "CORRECT" if data[1][0,img_idx] == preds_thresh[img_idx,PRED_IDX] else "FALSE_POS" if preds_thresh[img_idx,PRED_IDX] == 1 else "FALSE_NEG"
                  file_name = "%s_%.2f_%d_%05d_%d.png" % (prefix, preds[img_idx,PRED_IDX], batch, img_idx, data[1][0,img_idx])
  #                gf = gfile.GFile(file_name, "w")
                  file_string = StringIO()
                  imsave.save(file_string, "PNG")
                  tarinf = TarInfo(os.path.join(dir_name, file_name))
                  tarinf.size = file_string.tell()
                  file_string.seek(0)
                  tf.addfile(tarinf, file_string)
              tf.close()
              tfo.close()
  #                gf.close()
              print "Wrote %d prediction PNGs to %s" % (preds.shape[0], tar_name)
          else:
              fig = pl.figure(3, figsize=(12,9))
              fig.text(.4, .95, '%s test samples' % ('Mistaken' if self.only_errors else 'Random'))
              if self.only_errors:
                  # what the net got wrong
                  if NUM_OUTPUTS > 1:
                      err_idx = [i for i,p in enumerate(preds.argmax(axis=1)) if p not in n.where(data[2][:,i] > 0)[0]]
                  else:
                      err_idx = n.where(data[1][0,:] != preds[:,0].T)[0]
                      print err_idx
                  err_idx = r.sample(err_idx, min(len(err_idx), NUM_IMGS))
                  data[0], data[1], preds = data[0][:,err_idx], data[1][:,err_idx], preds[err_idx,:]
                  
              
              import matplotlib.gridspec as gridspec
              import matplotlib.colors as colors
              cconv = colors.ColorConverter()
              gs = gridspec.GridSpec(NUM_ROWS*2, NUM_COLS,
                                     width_ratios=[1]*NUM_COLS, height_ratios=[2,1]*NUM_ROWS )
              #print data[1]
              for row in xrange(NUM_ROWS):
                  for col in xrange(NUM_COLS):
                      img_idx = row * NUM_COLS + col
                      if data[0].shape[0] <= img_idx:
                          break
                      pl.subplot(gs[(row * 2) * NUM_COLS + col])
                      #pl.subplot(NUM_ROWS*2, NUM_COLS, row * 2 * NUM_COLS + col + 1)
                      pl.xticks([])
                      pl.yticks([])
                      img = data[0][img_idx,:,:,:]
                      pl.imshow(img, interpolation='lanczos')
                      show_title = data[1].shape[0] == 1
                      true_label = [int(data[1][0,img_idx])] if show_title else n.where(data[1][:,img_idx]==1)[0]
                      #print true_label
                      #print preds[img_idx,:].shape
                      #print preds[img_idx,:].max()
                      true_label_names = [label_names[i] for i in true_label]
                      img_labels = sorted(zip(preds[img_idx,:], label_names), key=lambda x: x[0])[-NUM_TOP_CLASSES:]
                      #print img_labels
                      axes = pl.subplot(gs[(row * 2 + 1) * NUM_COLS + col])
                      height = 0.5
                      ylocs = n.array(range(NUM_TOP_CLASSES))*height
                      pl.barh(ylocs, [l[0] for l in img_labels], height=height, \
                              color=['#ffaaaa' if l[1] in true_label_names else '#aaaaff' for l in img_labels])
                      #pl.title(", ".join(true_labels))
                      if show_title:
                          pl.title(", ".join(true_label_names), fontsize=15, fontweight='bold')
                      else:
                          print true_label_names
                      pl.yticks(ylocs + height/2, [l[1] for l in img_labels], x=1, backgroundcolor=cconv.to_rgba('0.65', alpha=0.5), weight='bold')
                      for line in enumerate(axes.get_yticklines()): 
                          line[1].set_visible(False) 
                      #pl.xticks([width], [''])
                      #pl.yticks([])
                      pl.xticks([])
                      pl.ylim(0, ylocs[-1] + height)
                      pl.xlim(0, 1)
  
      def start(self):
          self.op.print_values()
  #        print self.show_cost
          if self.show_cost:
              self.plot_cost()
          if self.show_filters:
              self.plot_filters()
          if self.show_preds:
              self.plot_predictions()
  
          if pl:
              pl.show()
          sys.exit(0)
              
      @classmethod
      def get_options_parser(cls):
          op = ConvNet.get_options_parser()
          for option in list(op.options):
              if option not in ('gpu', 'load_file', 'inner_size', 'train_batch_range', 'test_batch_range', 'multiview_test', 'data_path', 'pca_noise', 'scalar_mean'):
                  op.delete_option(option)
          op.add_option("show-cost", "show_cost", StringOptionParser, "Show specified objective function", default="")
          op.add_option("show-filters", "show_filters", StringOptionParser, "Show learned filters in specified layer", default="")
          op.add_option("norm-filters", "norm_filters", BooleanOptionParser, "Individually normalize filters shown with --show-filters", default=0)
          op.add_option("input-idx", "input_idx", IntegerOptionParser, "Input index for layer given to --show-filters", default=0)
          op.add_option("cost-idx", "cost_idx", IntegerOptionParser, "Cost function return value index for --show-cost", default=0)
          op.add_option("no-rgb", "no_rgb", BooleanOptionParser, "Don't combine filter channels into RGB in layer given to --show-filters", default=False)
          op.add_option("yuv-to-rgb", "yuv_to_rgb", BooleanOptionParser, "Convert RGB filters to YUV in layer given to --show-filters", default=False)
          op.add_option("channels", "channels", IntegerOptionParser, "Number of channels in layer given to --show-filters (fully-connected layers only)", default=0)
          op.add_option("show-preds", "show_preds", StringOptionParser, "Show predictions made by given softmax on test set", default="")
          op.add_option("save-preds", "save_preds", StringOptionParser, "Save predictions to given path instead of showing them", default="")
          op.add_option("only-errors", "only_errors", BooleanOptionParser, "Show only mistaken predictions (to be used with --show-preds)", default=False, requires=['show_preds'])
          op.add_option("local-plane", "local_plane", IntegerOptionParser, "Local plane to show", default=0)
          op.add_option("smooth-test-errors", "smooth_test_errors", BooleanOptionParser, "Use running average for test error plot?", default=1)
  
          op.options['load_file'].default = None
          return op
      
  if __name__ == "__main__":
      #nr.seed(6)
      try:
          op = ShowConvNet.get_options_parser()
          op, load_dic = IGPUModel.parse_options(op)
          model = ShowConvNet(op, load_dic)
          model.start()
      except (UnpickleError, ShowNetError, opt.GetoptError), e:
          print "----------------"
          print "Error:"
          print e 
  