def _form_nndata(images, attributes, load_from_cache=False, save_to_cache=False): print "Forming NN-data ..." print "===================" if load_from_cache: with open(_cached_nndata, 'rb') as f: nndata = cPickle.load(f) else: nndata = Datasets(images, attributes) nndata.split(0.8, 0.1) if save_to_cache: with open(_cached_nndata, 'wb') as f: cPickle.dump(nndata, f, protocol=cPickle.HIGHEST_PROTOCOL) return nndata
def _prepare_data(load_from_cache=False, save_to_cache=False): if load_from_cache: with open(_cached_datasets, 'rb') as f: datasets = cPickle.load(f) else: # Setup data files input_data = DataLoader( '../data/parse/cuhk_large_labeled_subsampled.mat', verbose=True) target_data = DataLoader( '../data/parse/cuhk_large_labeled_subsampled_parse.mat', verbose=True) input_images = input_data.get_all_images() target_images = target_data.get_all_images() # Pre-processing print "Pre-processing ..." inputs = [_input_preproc(image) for image in input_images] inputs = imageproc.images2mat(inputs) targets = [_target_preproc(image) for image in target_images] targets = imageproc.images2mat(targets) # Prepare the datasets print "Prepare the datasets ..." datasets = Datasets(inputs, targets) datasets.split(train_ratio=0.5, valid_ratio=0.3) if save_to_cache: with open(_cached_datasets, 'wb') as f: cPickle.dump(datasets, f, protocol=cPickle.HIGHEST_PROTOCOL) return datasets
def _prepare_data(load_from_cache=False, save_to_cache=False): if load_from_cache: with open(_cached_datasets, 'rb') as f: views_data, datasets = cPickle.load(f) else: image_data = DataLoader('../data/cuhk_small_masked.mat', verbose=True) # Prepare the view-first order data representation print "Preparing the view-first order data ..." n_pedes, n_views = [], [] for gid in xrange(image_data.get_n_groups()): m, v = image_data.get_n_pedes_views(gid) n_pedes.append(m) n_views.append(v) assert min(n_views) == max(n_views), \ "The number of views in each group should be equal" v = n_views[0] views_data = [[] for __ in xrange(v)] for gid in xrange(image_data.get_n_groups()): bias = sum(n_pedes[0:gid]) group_data = data_manager.view_repr(image_data.get_pedes(gid)) for vid in xrange(v): view_data = group_data[vid] view_data = [(pid+bias, image) for pid, image in view_data] views_data[vid].extend(view_data) # Prepare the datasets print "Prepare the datasets ..." X, Y = [], [] for gid in xrange(image_data.get_n_groups()): m, v = image_data.get_n_pedes_views(gid) for pid in xrange(m): n_images = image_data.get_n_images(gid, pid) for vi in xrange(v): for vj in xrange(vi+1, v): for i in xrange(n_images[vi]): for j in xrange(n_images[vj]): X.append(_preproc( image_data.get_image(gid, pid, vi, i))) Y.append(_preproc( image_data.get_image(gid, pid, vj, j))) X = imageproc.images2mat(X).astype(theano.config.floatX) Y = imageproc.images2mat(Y).astype(theano.config.floatX) datasets = Datasets(X, Y) datasets.split(train_ratio=0.8, valid_ratio=0.1) if save_to_cache: with open(_cached_datasets, 'wb') as f: cPickle.dump((views_data, datasets), f, protocol=cPickle.HIGHEST_PROTOCOL) return (views_data, datasets)
def _prepare_data(load_from_cache=False, save_to_cache=False): if load_from_cache: with open(_cached_datasets, 'rb') as f: views_data, datasets = cPickle.load(f) else: image_data = DataLoader('../data/cuhk_small_masked.mat', verbose=True) # Prepare the view-first order data representation print "Preparing the view-first order data ..." n_pedes, n_views = [], [] for gid in xrange(image_data.get_n_groups()): m, v = image_data.get_n_pedes_views(gid) n_pedes.append(m) n_views.append(v) assert min(n_views) == max(n_views), \ "The number of views in each group should be equal" v = n_views[0] views_data = [[] for __ in xrange(v)] for gid in xrange(image_data.get_n_groups()): bias = sum(n_pedes[0:gid]) group_data = data_manager.view_repr(image_data.get_pedes(gid)) for vid in xrange(v): view_data = group_data[vid] view_data = [(pid + bias, image) for pid, image in view_data] views_data[vid].extend(view_data) # Prepare the datasets print "Prepare the datasets ..." X, Y = [], [] for gid in xrange(image_data.get_n_groups()): m, v = image_data.get_n_pedes_views(gid) for pid in xrange(m): n_images = image_data.get_n_images(gid, pid) for vi in xrange(v): for vj in xrange(vi + 1, v): for i in xrange(n_images[vi]): for j in xrange(n_images[vj]): X.append( _preproc( image_data.get_image(gid, pid, vi, i))) Y.append( _preproc( image_data.get_image(gid, pid, vj, j))) X = imageproc.images2mat(X).astype(theano.config.floatX) Y = imageproc.images2mat(Y).astype(theano.config.floatX) datasets = Datasets(X, Y) datasets.split(train_ratio=0.8, valid_ratio=0.1) if save_to_cache: with open(_cached_datasets, 'wb') as f: cPickle.dump((views_data, datasets), f, protocol=cPickle.HIGHEST_PROTOCOL) return (views_data, datasets)