import numpy import sys import getopt as opt from util import * from math import sqrt, ceil, floor import os from gpumodel import IGPUModel import random as r import numpy.random as nr from convnet import ConvNet from options import * #import pygame as pg import Image #from pygame.locals import * from time import sleep #from collections import Counter import gc #import cv #this is important for capturing/displaying images #from opencv import highgui as hg try: import pylab as pl except: print "This script requires the matplotlib python library (Ubuntu/Fedora package name python-matplotlib). Please install it." sys.exit(1) class ShowNetError(Exception): pass class ShowConvNet(ConvNet): def __init__(self, op, load_dic): ConvNet.__init__(self, op, load_dic) def get_gpus(self): self.need_gpu = self.op.get_value('show_preds') or self.op.get_value('write_features') \ or self.op.get_value('show_data_grad') or self.op.get_value('webcam') or self.op.get_value('top5') \ or self.op.get_value('show_maps') if self.need_gpu: ConvNet.get_gpus(self) def init_data_providers(self): 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): #ConvNet.init_model_state(self) if self.op.get_value('show_maps'): self.map_layer_idx = self.get_layer_idx(self.op.get_value('show_maps')) if self.op.get_value('show_preds') or self.op.get_value('webcam'): self.softmax_name = self.op.get_value('show_preds') or self.op.get_value('webcam') if self.op.get_value('write_features'): self.ftr_layer_name = self.op.get_value('write_features') if self.op.get_value('top5'): self.ftr_layer_idx = self.get_layer_idx(self.op.get_value('top5')) if self.op.get_value('show_data_grad'): self.data_layer_idx = self.get_layer_idx('data') self.softmax_idx = self.get_layer_idx('probs') for l in self.model_state['layers']: if l['name'] != 'labels': l['actsGradTarget'] = -1 l['gradConsumer'] = True if l['name'] != 'data': l['conserveMem'] = True 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) train_errors = [o[0][self.show_cost][self.cost_idx] for o in self.train_outputs] test_errors = [o[0][self.show_cost][self.cost_idx] for o in self.test_outputs] numbatches = len(self.train_batch_range) test_errors = numpy.row_stack(test_errors) test_errors = numpy.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.)) # aim for about 20 labels epoch_label_gran = int(ceil(float(epoch_label_gran) / 10) * 10) # but round to nearest 10 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)) 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]), (3, 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]), (3, 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): data = self.get_next_batch(train=False)[2] # 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 NUM_TOP_CLASSES = min(num_classes, 5) # show this many top labels 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_classes), dtype=n.single) else: preds = n.zeros((NUM_IMGS, num_classes), 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) data[0] = n.require(data[0][:,rand_idx], requirements='C') data[1] = n.require(data[1][:,rand_idx], requirements='C') # data += [preds] # Run the model self.libmodel.startFeatureWriter(data, [preds], [self.softmax_name]) self.finish_batch() 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 err_idx = [i for i,p in enumerate(preds.argmax(axis=1)) if p not in n.where(data[1][:,i] > 0)[0]] 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,:] data[0] = self.test_data_provider.get_plottable_data(data[0]) 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 do_write_features(self): if not os.path.exists(self.feature_path): os.makedirs(self.feature_path) next_data = self.get_next_batch(train=False) b1 = next_data[1] num_ftrs = self.layers[self.ftr_layer_name]['outputs'] # def showimg(img): # pixels = img.shape[0] / 3 # size = int(sqrt(pixels)) # img = img.reshape((3,size,size)).swapaxes(0,2).swapaxes(0,1) # pl.imshow(img, interpolation='nearest') # pl.show() while True: batch = next_data[1] data = next_data[2] ftrs = n.zeros((data[0].shape[1], num_ftrs), dtype=n.single) self.libmodel.startFeatureWriter(data, [ftrs], [self.ftr_layer_name]) # load the next batch while the current one is computing next_data = self.get_next_batch(train=False) self.finish_batch() path_out = os.path.join(self.feature_path, 'data_batch_%d' % batch) # print ftrs # ftrs += self.train_data_provider.batch_meta['data_mean'].mean() # ftrs /= 255 # showimg(ftrs[1,:]); sys.exit(0) pickle(path_out, {'data': ftrs, 'labels': data[1]}) print "Wrote feature file %s" % path_out if next_data[1] == b1: break pickle(os.path.join(self.feature_path, 'batches.meta'), {'source_model':self.load_file, 'num_vis':num_ftrs}) def do_top5(self): num_classes = self.test_data_provider.get_num_classes() nv = self.train_data_provider.num_views next_data = self.get_next_batch(train=False) batch = next_data[1] data = next_data[2] print data[0].shape num_cases = data[0].shape[1] / nv print "num cases: %d" % num_cases ftrs = [n.zeros((num_cases, num_classes), dtype=n.single) for i in xrange(2)] for v in xrange(self.train_data_provider.num_views): vdata = [d[:,v*num_cases:(v+1)*num_cases] for d in data] + [ftrs[1]] print [d.shape for d in vdata] self.libmodel.startFeatureWriter(vdata, self.ftr_layer_idx) self.finish_batch() ftrs[0] += ftrs[1] ftrs = ftrs[0] print ftrs.max() print "Batch %d top5 error: i dunno" % batch print ftrs labels = data[1][:,:num_cases].astype(n.int32) print labels, labels.shape v = 0 for m in xrange(5): maxlocs = ftrs.argmax(axis=1) v += (maxlocs == labels).sum() ftrs[n.arange(ftrs.shape[0]),maxlocs] = 0 print v # NOTE: THIS ROUTINE APPLIES RELU NONLINAERITY TO MAPS # Change this if you're not actually using relu units def do_showmaps(self): NUM_MAPS = 16 NUM_IMGS = 12 nr.seed(87213) data = self.get_next_batch(train=False)[2] rand_idx = nr.randint(0, data[0].shape[1], NUM_IMGS) data[0] = n.require(data[0][:,rand_idx], requirements='C') data[1] = n.require(data[1][:,rand_idx], requirements='C') cases = data[0].shape[1] ldic = dict([(l['name'], l) for l in self.layers]) print ldic.keys() num_ftrs = self.layers[self.map_layer_idx]['outputs'] map_size = self.layers[self.map_layer_idx]['modulesX'] if 'modulesX' in self.layers[self.map_layer_idx] else self.layers[self.map_layer_idx]['outputsX'] num_maps = num_ftrs / map_size**2 ftrs = n.zeros((data[0].shape[1], num_ftrs), dtype=n.single) self.libmodel.startFeatureWriter(data + [ftrs], self.map_layer_idx) self.finish_batch() fig = pl.figure(5) fig.text(.4, .95, 'Layer %s feature maps' % self.show_maps) data[0] = self.test_data_provider.get_plottable_data(data[0]) # This map will have size (cases, num_maps, map_size, map_size) print ftrs.shape ftrs = ftrs.reshape(cases, num_maps, map_size, map_size) print ftrs.min(), ftrs.max() print ftrs.shape ftrs[ftrs<0] = 0 ftrs -= ftrs.min() ftrs /= ftrs.max() rand_idx = nr.permutation(range(NUM_MAPS))[:ftrs.shape[1]] ftrs = ftrs[:,rand_idx,:,:] # ftrs = self.test_data_provider.get_plottable_data(ftrs.T, add_mean=False) for i in xrange(NUM_IMGS): pl.subplot(NUM_IMGS, NUM_MAPS + 1, i * (NUM_MAPS + 1) + 1) pl.xticks([]) pl.yticks([]) img = data[0][i,:,:,:] pl.imshow(img, interpolation='lanczos') # return for m in xrange(NUM_MAPS): pl.subplot(NUM_IMGS, NUM_MAPS + 1, i * (NUM_MAPS + 1) + m + 2) pl.xticks([]) pl.yticks([]) img = ftrs[i,m, :,:] pl.imshow(img, cmap=pl.cm.gray, interpolation='nearest') def do_show_data_grad(self): NUM_ROWS = 2 NUM_COLS = 4 NUM_IMGS = NUM_ROWS * NUM_COLS data = self.get_next_batch(train=False)[2] rand_idx = nr.randint(0, data[0].shape[1], NUM_IMGS) data[0] = n.require(data[0][:,rand_idx], requirements='C') data[1] = n.require(data[1][:,rand_idx], requirements='C') label_names = [lab.split(',')[0] for lab in self.test_data_provider.batch_meta['label_names']] data_dim = self.layers[self.data_layer_idx]['outputs'] grads = n.zeros((data[0].shape[1], data_dim), dtype=n.single) self.libmodel.startDataGrad(data + [grads], self.data_layer_idx, self.softmax_idx) self.finish_batch() fig = pl.figure(4) fig.text(.4, .95, 'Data gradients') print grads.shape, data[0].shape grads = self.test_data_provider.get_plottable_data(grads.T, add_mean=False) # grads -= grads.min() # grads /= grads.max() # grads[grads<0] = 0; # grads[grads>0] = 0; grads = -grads; data[0] = self.test_data_provider.get_plottable_data(data[0]) 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(NUM_ROWS*2, NUM_COLS, row * 2 * NUM_COLS + col + 1) pl.xticks([]) pl.yticks([]) img = data[0][img_idx,:,:,:] pl.imshow(img, interpolation='nearest') true_label = int(data[1][0,img_idx]) #true_labels = set(label_names[l] for l in list(n.where(data[1][:,img_idx] > 0)[0])) pl.subplot(NUM_ROWS*2, NUM_COLS, (row * 2 + 1) * NUM_COLS + col + 1) #pl.title(", ".join(true_labels)) pl.title(label_names[true_label]) img = grads[img_idx,:] # Suppress small grads img -= img.mean() s = n.sqrt(img.var()) img[n.abs(img)<3*s] = 0 img -= img.min() img /= img.max() pl.imshow(img, interpolation='nearest') def do_webcam(self): num_classes = self.test_data_provider.get_num_classes() label_names = [lab.split(',')[0] for lab in self.test_data_provider.batch_meta['label_names']] camera = hg.cvCreateCameraCapture(1) #highgui.cvSetCaptureProperty(camera, highgui.CV_CAP_PROP_FRAME_WIDTH, 320 ); #highgui.cvSetCaptureProperty(camera, highgui.CV_CAP_PROP_FRAME_HEIGHT, 240 ); def get_image(): im = hg.cvQueryFrame(camera) # Add the line below if you need it (Ubuntu 8.04+) # im = cv.cvGetMat(im) #convert Ipl image to PIL image return cv.adaptors.Ipl2NumPy(im) # fps = 30.0 frames_per_run = 4 frames = 0 pg.init() pg.display.set_mode((224,224)) pg.display.set_caption("WebCam Demo") screen = pg.display.get_surface() images = n.zeros((self.test_data_provider.get_data_dims(), 32), dtype=n.single) labels = n.zeros((1, 32), dtype=n.single) # dummy preds = [n.zeros((32, num_classes), dtype=n.single) for i in xrange(2)] preds_idx = 0 while True: im = get_image() images[:,0:28] = images[:,4:] cropped = im[128:352,208:432,:] cropped_swapped = cropped.swapaxes(0,2).swapaxes(1,2) images[:,28] = cropped_swapped.reshape((self.test_data_provider.get_data_dims(),)) images[:,29] = cropped_swapped[:,:,::-1].reshape((self.test_data_provider.get_data_dims(),)) cropped = im[16:464,96:544,:] im = cv.adaptors.NumPy2PIL(cropped) cropped = cv.adaptors.PIL2NumPy(im.resize((224,224))) cropped_swapped = cropped.swapaxes(0,2).swapaxes(1,2) images[:,30] = cropped_swapped.reshape((self.test_data_provider.get_data_dims(),)) images[:,31] = cropped_swapped[:,:,::-1].reshape((self.test_data_provider.get_data_dims(),)) im = cv.adaptors.NumPy2PIL(cropped) pg_img = pg.image.frombuffer(im.tostring(), im.size, im.mode) screen.blit(pg_img, (0,0)) pg.display.flip() images[:,28:] -= self.test_data_provider.data_mean_crop if frames % frames_per_run == 0 and frames >= 32: # Run convnet if frames - frames_per_run >= 32: # Wait for last batch to finish, if it hasn't yet self.finish_batch() p = preds[1 - preds_idx].mean(axis=0) m = p.argmax() # m = Counter(preds[1 - preds_idx].argmax(axis=1)).most_common(1)[0][0] print "Label: %s (%.2f)" % (label_names[m] if p[m] > 0.0 else "<>", p[m]) # ent = -(n.log(p) * p).sum(axis=0) # print "Label: %s (entropy: %.2f)" % (label_names[m], ent) # print "Label: %s " % (label_names[m]) # Run the model self.libmodel.startFeatureWriter([images, labels, preds[preds_idx]], self.softmax_idx) preds_idx = 1 - preds_idx frames += 1 # sleep(1.0 / fps) def start(self): self.op.print_values() if self.show_cost: self.plot_cost() if self.show_filters: self.plot_filters() if self.show_preds: self.plot_predictions() if self.write_features: self.do_write_features() if self.show_data_grad: self.do_show_data_grad() if self.webcam: self.do_webcam() if self.top5: self.do_top5() if self.show_maps: self.do_showmaps() 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', 'train_batch_range', 'test_batch_range', 'multiview_test', 'data_path', 'logreg_name', '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("only-errors", "only_errors", BooleanOptionParser, "Show only mistaken predictions (to be used with --show-preds)", default=False, requires=['show_preds']) op.add_option("write-features", "write_features", StringOptionParser, "Write test data features from given layer", default="", requires=['feature-path']) op.add_option("feature-path", "feature_path", StringOptionParser, "Write test data features to this path (to be used with --write-features)", default="") op.add_option("show-data-grad", "show_data_grad", BooleanOptionParser, "Show data gradient in given data layer", default=False) op.add_option("webcam", "webcam", StringOptionParser, "Show webcam demo with given softmax layer's predictions", default="") op.add_option("local-plane", "local_plane", IntegerOptionParser, "Local plane to show", default=0) op.add_option("top5", "top5", StringOptionParser, "Compute top5 test error from given layer", default=False) op.add_option("show-maps", "show_maps", StringOptionParser, "Show feature maps in given layer", default="") 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