def create_mb_source(data_set, img_height, img_width, n_classes, n_rois, data_path, randomize): # set paths map_file = join(data_path, data_set + '.txt') roi_file = join(data_path, data_set + '.rois.txt') label_file = join(data_path, data_set + '.roilabels.txt') if not os.path.exists(map_file) or not os.path.exists(roi_file) or not os.path.exists(label_file): raise RuntimeError("File '%s', '%s' or '%s' does not exist. " % (map_file, roi_file, label_file)) # read images nrImages = len(readTable(map_file)) transforms = [scale(width=img_width, height=img_height, channels=3, scale_mode="pad", pad_value=114, interpolations='linear')] image_source = ImageDeserializer(map_file, StreamDefs(features = StreamDef(field='image', transforms=transforms))) # read rois and labels rois_dim = 4 * n_rois label_dim = n_classes * n_rois roi_source = CTFDeserializer(roi_file, StreamDefs( rois = StreamDef(field='rois', shape=rois_dim, is_sparse=False))) label_source = CTFDeserializer(label_file, StreamDefs( roiLabels = StreamDef(field='roiLabels', shape=label_dim, is_sparse=False))) # define a composite reader mb = MinibatchSource([image_source, roi_source, label_source], epoch_size=sys.maxsize, randomize=randomize) return (mb, nrImages)
def test_create_two_image_deserializers(tmpdir): mbdata = r'''filename 0 filename2 0 ''' map_file = str(tmpdir / 'mbdata.txt') with open(map_file, 'w') as f: f.write(mbdata) image_width = 100 image_height = 200 num_channels = 3 transforms = [ xforms.crop(crop_type='randomside', side_ratio=0.5, jitter_type='uniratio'), xforms.scale(width=image_width, height=image_height, channels=num_channels, interpolations='linear') ] image1 = ImageDeserializer( map_file, StreamDefs(f1=StreamDef(field='image', transforms=transforms))) image2 = ImageDeserializer( map_file, StreamDefs(f2=StreamDef(field='image', transforms=transforms))) mb_source = MinibatchSource([image1, image2]) assert isinstance(mb_source, MinibatchSource)
def create_mb_source(img_height, img_width, img_channels, n_classes, n_rois, data_path, data_set): rois_dim = 4 * n_rois label_dim = n_classes * n_rois path = os.path.normpath(os.path.join(abs_path, data_path)) if data_set == 'test': map_file = os.path.join(path, test_map_filename) else: map_file = os.path.join(path, train_map_filename) roi_file = os.path.join(path, data_set + rois_filename_postfix) label_file = os.path.join(path, data_set + roilabels_filename_postfix) if not os.path.exists(map_file) or not os.path.exists(roi_file) or not os.path.exists(label_file): raise RuntimeError("File '%s', '%s' or '%s' does not exist. " "Please run install_fastrcnn.py from Examples/Image/Detection/FastRCNN to fetch them" % (map_file, roi_file, label_file)) # read images transforms = [scale(width=img_width, height=img_height, channels=img_channels, scale_mode="pad", pad_value=114, interpolations='linear')] image_source = ImageDeserializer(map_file, StreamDefs( features = StreamDef(field='image', transforms=transforms))) # read rois and labels roi_source = CTFDeserializer(roi_file, StreamDefs( rois = StreamDef(field=roi_stream_name, shape=rois_dim, is_sparse=False))) label_source = CTFDeserializer(label_file, StreamDefs( roiLabels = StreamDef(field=label_stream_name, shape=label_dim, is_sparse=False))) # define a composite reader return MinibatchSource([image_source, roi_source, label_source], epoch_size=sys.maxsize, randomize=data_set == "train")
def test_multiple_mlf_files(): os.chdir(data_path) feature_dim = 33 num_classes = 132 context = 2 test_mlf_path = "../../../../Tests/EndToEndTests/Speech/Data/glob_00001.mlf" features_file = "glob_0000.scp" label_files = ["glob_0000.mlf", test_mlf_path] label_mapping_file = "state.list" fd = HTKFeatureDeserializer( StreamDefs(amazing_features=StreamDef( shape=feature_dim, context=(context, context), scp=features_file))) ld = HTKMLFDeserializer( label_mapping_file, StreamDefs( awesome_labels=StreamDef(shape=num_classes, mlf=label_files))) # Make sure we can read at least one minibatch. mbsource = MinibatchSource([fd, ld]) mbsource.next_minibatch(1) os.chdir(abs_path)
def test_base64_image_deserializer(tmpdir): import io, base64, uuid from PIL import Image images, b64_images = [], [] np.random.seed(1) for i in range(10): data = np.random.randint(0, 2**8, (5, 7, 3)) image = Image.fromarray(data.astype('uint8'), "RGB") buf = io.BytesIO() image.save(buf, format='PNG') assert image.width == 7 and image.height == 5 b64_images.append(base64.b64encode(buf.getvalue())) images.append(np.array(image)) image_data = str(tmpdir / 'mbdata1.txt') seq_ids = [] uid = uuid.uuid1().int >> 64 with open(image_data, 'wb') as f: for i, data in enumerate(b64_images): seq_id = uid ^ i seq_id = str(seq_id).encode('ascii') seq_ids.append(seq_id) line = seq_id + b'\t' label = str(i).encode('ascii') line += label + b'\t' + data + b'\n' f.write(line) ctf_data = str(tmpdir / 'mbdata2.txt') with open(ctf_data, 'wb') as f: for i, sid in enumerate(seq_ids): line = sid + b'\t' + b'|index ' + str(i).encode('ascii') + b'\n' f.write(line) transforms = [xforms.scale(width=7, height=5, channels=3)] b64_deserializer = Base64ImageDeserializer( image_data, StreamDefs(images=StreamDef(field='image', transforms=transforms), labels=StreamDef(field='label', shape=10))) ctf_deserializer = CTFDeserializer( ctf_data, StreamDefs(index=StreamDef(field='index', shape=1))) mb_source = MinibatchSource([ctf_deserializer, b64_deserializer]) assert isinstance(mb_source, MinibatchSource) for j in range(100): mb = mb_source.next_minibatch(10) index_stream = mb_source.streams['index'] index = mb[index_stream].asarray().flatten() image_stream = mb_source.streams['images'] results = mb[image_stream].asarray() for i in range(10): # original images are RBG, openCV produces BGR images, # reverse the last dimension of the original images bgrImage = images[int(index[i])][:, :, ::-1] assert (bgrImage == results[i][0]).all()
def mb_source(tmpdir, fileprefix, max_samples=FULL_DATA_SWEEP, ctf=ctf_data, streams=['S0', 'S1'], max_sweeps=None): ctf_file = str(tmpdir / (fileprefix + '2seqtest.txt')) with open(ctf_file, 'w') as f: f.write(ctf) if max_sweeps is None: mbs = MinibatchSource(CTFDeserializer( ctf_file, StreamDefs(features=StreamDef(field=streams[0], shape=input_dim, is_sparse=True), labels=StreamDef(field=streams[1], shape=input_dim, is_sparse=True))), randomize=False, max_samples=max_samples) else: mbs = MinibatchSource(CTFDeserializer( ctf_file, StreamDefs(features=StreamDef(field=streams[0], shape=input_dim, is_sparse=True), labels=StreamDef(field=streams[1], shape=input_dim, is_sparse=True))), randomize=False, max_sweeps=max_sweeps) return mbs
def test_minibatch_defined_by_labels(tmpdir): input_dim = 1000 num_output_classes = 5 def assert_data(mb_source): features_si = mb_source.stream_info('features') labels_si = mb_source.stream_info('labels') mb = mb_source.next_minibatch(2) features = mb[features_si] # 2 samples, max seq len 4, 1000 dim assert features.shape == (2, 4, input_dim) assert features.end_of_sweep assert features.num_sequences == 2 assert features.num_samples == 7 assert features.is_sparse labels = mb[labels_si] # 2 samples, max seq len 1, 5 dim assert labels.shape == (2, 1, num_output_classes) assert labels.end_of_sweep assert labels.num_sequences == 2 assert labels.num_samples == 2 assert not labels.is_sparse label_data = labels.asarray() assert np.allclose(label_data, np.asarray([ [[1., 0., 0., 0., 0.]], [[0., 1., 0., 0., 0.]] ])) mb = mb_source.next_minibatch(3) features = mb[features_si] labels = mb[labels_si] assert features.num_samples == 10 assert labels.num_samples == 3 tmpfile = _write_data(tmpdir, MBDATA_SPARSE) mb_source = MinibatchSource(CTFDeserializer(tmpfile, StreamDefs( features=StreamDef(field='x', shape=input_dim, is_sparse=True), labels=StreamDef(field='y', shape=num_output_classes, is_sparse=False, defines_mb_size=True) )), randomize=False) assert_data(mb_source) tmpfile1 = _write_data(tmpdir, MBDATA_SPARSE1, '1') tmpfile2 = _write_data(tmpdir, MBDATA_SPARSE2, '2') combined_mb_source = MinibatchSource([ CTFDeserializer(tmpfile1, StreamDefs( features=StreamDef(field='x', shape=input_dim, is_sparse=True))), CTFDeserializer(tmpfile2, StreamDefs( labels=StreamDef(field='y', shape=num_output_classes, is_sparse=False, defines_mb_size=True) ))], randomize=False) assert_data(combined_mb_source)
def test_htk_deserializers(): mbsize = 640 epoch_size = 1000 * mbsize lr = [0.001] feature_dim = 33 num_classes = 132 context = 2 os.chdir(data_path) features_file = "glob_0000.scp" labels_file = "glob_0000.mlf" label_mapping_file = "state.list" fd = HTKFeatureDeserializer( StreamDefs(amazing_features=StreamDef( shape=feature_dim, context=(context, context), scp=features_file))) ld = HTKMLFDeserializer( label_mapping_file, StreamDefs( awesome_labels=StreamDef(shape=num_classes, mlf=labels_file))) reader = MinibatchSource([fd, ld]) features = C.input_variable(((2 * context + 1) * feature_dim)) labels = C.input_variable((num_classes)) model = Sequential( [For(range(3), lambda: Recurrence(LSTM(256))), Dense(num_classes)]) z = model(features) ce = C.cross_entropy_with_softmax(z, labels) errs = C.classification_error(z, labels) learner = C.adam_sgd(z.parameters, lr=C.learning_rate_schedule(lr, C.UnitType.sample, epoch_size), momentum=C.momentum_as_time_constant_schedule(1000), low_memory=True, gradient_clipping_threshold_per_sample=15, gradient_clipping_with_truncation=True) trainer = C.Trainer(z, (ce, errs), learner) input_map = { features: reader.streams.amazing_features, labels: reader.streams.awesome_labels } pp = C.ProgressPrinter(freq=0) # just run and verify it doesn't crash for i in range(3): mb_data = reader.next_minibatch(mbsize, input_map=input_map) trainer.train_minibatch(mb_data) pp.update_with_trainer(trainer, with_metric=True) assert True os.chdir(abs_path)
def create_mb_source(features_file, labels_file, label_mapping_filem, total_number_of_samples): for file_name in [features_file, labels_file, label_mapping_file]: if not os.path.exists(file_name): raise RuntimeError("File '%s' does not exist. Please check that datadir argument is set correctly." % (file_name)) fd = HTKFeatureDeserializer(StreamDefs( amazing_features = StreamDef(shape=feature_dim, context=(context,context), scp=features_file))) ld = HTKMLFDeserializer(label_mapping_file, StreamDefs( awesome_labels = StreamDef(shape=num_classes, mlf=labels_file))) # Enabling BPTT with truncated_length > 0 return MinibatchSource([fd,ld], truncation_length=250, epoch_size=total_number_of_samples)
def test_base64_is_equal_image(tmpdir): import io, base64 from PIL import Image np.random.seed(1) file_mapping_path = str(tmpdir / 'file_mapping.txt') base64_mapping_path = str(tmpdir / 'base64_mapping.txt') with open(file_mapping_path, 'w') as file_mapping: with open(base64_mapping_path, 'w') as base64_mapping: for i in range(10): data = np.random.randint(0, 2**8, (5, 7, 3)) image = Image.fromarray(data.astype('uint8'), "RGB") buf = io.BytesIO() image.save(buf, format='PNG') assert image.width == 7 and image.height == 5 label = str(i) # save to base 64 mapping file encoded = base64.b64encode(buf.getvalue()).decode('ascii') base64_mapping.write('%s\t%s\n' % (label, encoded)) # save to mapping + png file file_name = label + '.png' with open(str(tmpdir / file_name), 'wb') as f: f.write(buf.getvalue()) file_mapping.write('.../%s\t%s\n' % (file_name, label)) transforms = [xforms.scale(width=7, height=5, channels=3)] b64_deserializer = Base64ImageDeserializer( base64_mapping_path, StreamDefs(images1=StreamDef(field='image', transforms=transforms), labels1=StreamDef(field='label', shape=10))) file_image_deserializer = ImageDeserializer( file_mapping_path, StreamDefs(images2=StreamDef(field='image', transforms=transforms), labels2=StreamDef(field='label', shape=10))) mb_source = MinibatchSource([b64_deserializer, file_image_deserializer]) for j in range(20): mb = mb_source.next_minibatch(1) images1_stream = mb_source.streams['images1'] images1 = mb[images1_stream].asarray() images2_stream = mb_source.streams['images2'] images2 = mb[images2_stream].asarray() assert (images1 == images2).all()
def test_crop_dimensionality(tmpdir): import io; from PIL import Image np.random.seed(1) file_mapping_path = str(tmpdir / 'file_mapping.txt') with open(file_mapping_path, 'w') as file_mapping: for i in range(5): data = np.random.randint(0, 2**8, (20, 40, 3)) image = Image.fromarray(data.astype('uint8'), "RGB") buf = io.BytesIO() image.save(buf, format='PNG') assert image.width == 40 and image.height == 20 label = str(i) # save to mapping + png file file_name = label + '.png' with open(str(tmpdir/file_name), 'wb') as f: f.write(buf.getvalue()) file_mapping.write('.../%s\t%s\n' % (file_name, label)) transforms1 = [ xforms.scale(width=40, height=20, channels=3), xforms.crop(crop_type='randomside', crop_size=(20, 10), side_ratio=(0.2, 0.5), jitter_type='uniratio')] transforms2 = [ xforms.crop(crop_type='randomside', crop_size=(20, 10), side_ratio=(0.2, 0.5), jitter_type='uniratio')] d1 = ImageDeserializer(file_mapping_path, StreamDefs( images1=StreamDef(field='image', transforms=transforms1), labels1=StreamDef(field='label', shape=10))) d2 = ImageDeserializer(file_mapping_path, StreamDefs( images2=StreamDef(field='image', transforms=transforms2), labels2=StreamDef(field='label', shape=10))) mbs = MinibatchSource([d1, d2]) for j in range(5): mb = mbs.next_minibatch(1) images1 = mb[mbs.streams.images1].asarray() images2 = mb[mbs.streams.images2].asarray() assert images1.shape == (1, 1, 3, 10, 20) assert (images1 == images2).all()
def test_distributed_mb_source_again(tmpdir): import random from cntk.io import MinibatchSource, CTFDeserializer, StreamDef, StreamDefs ctf_data = '''0 |S0 1 |S1 1 0 |S0 2 |S1 2 0 |S0 3 1 |S0 4 1 |S0 5 |S1 3 1 |S0 6 |S1 4 ''' ctf_file = str(tmpdir/'2seqtest.txt') with open(ctf_file, 'w') as f: f.write(ctf_data) ctf = CTFDeserializer(ctf_file, StreamDefs( features = StreamDef(field='S0', shape=1), labels = StreamDef(field='S1', shape=1) )) random.seed(1234) mb_sources = [] for randomize in [True, False]: mb_sources.append(MinibatchSource(ctf, randomize=randomize)) mb_sources.append(MinibatchSource(ctf, randomize=randomize, max_sweeps=random.randint(1, 10))) mb_sources.append(MinibatchSource(ctf, randomize=randomize, max_samples=random.randint(1, 30))) for i in range(20): for source in mb_sources: data = source.next_minibatch(minibatch_size_in_samples=5, num_data_partitions=2, partition_index=i % 2) features = source.streams['features'] assert(len(data) == 0 or data[features].num_samples == 3)
def create_reader(map_file, mean_file, train, distributed_communicator=None): if not os.path.exists(map_file) or not os.path.exists(mean_file): cifar_py3 = "" if sys.version_info.major < 3 else "_py3" raise RuntimeError( "File '%s' or '%s' does not exist. Please run CifarDownload%s.py and CifarConverter%s.py from CIFAR-10 to fetch them" % (map_file, mean_file, cifar_py3, cifar_py3)) # transformation pipeline for the features has jitter/crop only when training transforms = [] if train: transforms += [ ImageDeserializer.crop(crop_type='Random', ratio=0.8, jitter_type='uniRatio') # train uses jitter ] transforms += [ ImageDeserializer.scale(width=image_width, height=image_height, channels=num_channels, interpolations='linear'), ImageDeserializer.mean(mean_file) ] # deserializer return MinibatchSource( ImageDeserializer( map_file, StreamDefs( features=StreamDef( field='image', transforms=transforms ), # first column in map file is referred to as 'image' labels=StreamDef(field='label', shape=num_classes))), # and second as 'label' distributed_communicator=distributed_communicator)
def create_reader(map_file, mean_file, train): # transformation pipeline for the features has jitter/crop only when training trs = [] # if train: # transforms += [ # ImageDeserializer.crop(crop_type='Random', ratio=0.8, jitter_type='uniRatio') # train uses jitter # ] trs += [ transforms.scale(width=image_width, height=image_height, channels=num_channels, interpolations='linear'), transforms.mean(mean_file) ] # deserializer return MinibatchSource( ImageDeserializer( map_file, StreamDefs( features=StreamDef( field='image', transforms=trs ), # first column in map file is referred to as 'image' labels=StreamDef(field='label', shape=num_classes) # and second as 'label' )))
def create_video_mb_source(map_files, num_channels, image_height, image_width, num_classes): transforms = [ xforms.crop(crop_type='center', crop_size=224), xforms.scale(width=image_width, height=image_height, channels=num_channels, interpolations='linear') ] map_files = sorted(map_files, key=lambda x: int(x.split('Map_')[1].split('.')[0])) print(map_files) # Create multiple image sources sources = [] for i, map_file in enumerate(map_files): streams = { "feature" + str(i): StreamDef(field='image', transforms=transforms), "label" + str(i): StreamDef(field='label', shape=num_classes) } sources.append(ImageDeserializer(map_file, StreamDefs(**streams))) return MinibatchSource(sources, max_sweeps=1, randomize=False)
def create_mask_deserializer(path): return CTFDeserializer( path, StreamDefs( mask = StreamDef(field = 'mask', shape = numLabels) ) )
def create_reader(map_file, mean_file, train, image_height=64, image_width=64, num_channels=3, num_classes=32): # transformation pipeline for the features has jitter/crop only when training # https://docs.microsoft.com/en-us/python/api/cntk.io.transforms?view=cntk-py-2.2 trs = [] if train: trs += [ transforms.crop(crop_type='randomside', side_ratio=0, jitter_type='none') # Horizontal flip enabled ] trs += [ transforms.scale(width=image_width, height=image_height, channels=num_channels, interpolations='linear'), transforms.mean(mean_file) ] # deserializer image_source = ImageDeserializer( map_file, StreamDefs( features=StreamDef( field='image', transforms=trs ), # first column in map file is referred to as 'image' labels=StreamDef(field='label', shape=num_classes) # and second as 'label' )) return MinibatchSource(image_source)
def create_image_mb_source(map_file, is_training, total_number_of_samples): if not os.path.exists(map_file): raise RuntimeError("File '%s' does not exist." %map_file) # transformation pipeline for the features has jitter/crop only when training transforms = [] if is_training: transforms += [ xforms.crop(crop_type='randomside', side_ratio=0.88671875, jitter_type='uniratio') # train uses jitter ] else: transforms += [ xforms.crop(crop_type='center', side_ratio=0.88671875) # test has no jitter ] transforms += [ xforms.scale(width=image_width, height=image_height, channels=num_channels, interpolations='linear'), ] # deserializer return MinibatchSource( ImageDeserializer(map_file, StreamDefs( features = StreamDef(field='image', transforms=transforms), # first column in map file is referred to as 'image' labels = StreamDef(field='label', shape=num_classes))), # and second as 'label' randomize = is_training, epoch_size=total_number_of_samples, multithreaded_deserializer = True)
def create_reader(map_file, train, dimensions, classes, total_number_of_samples): print( f"Reading map file: {map_file} with number of samples {total_number_of_samples}" ) # transformation pipeline for the features has jitter/crop only when training transforms = [] # finalize_network uses data augmentation (translation only) if train: transforms += [ xforms.crop(crop_type='randomside', area_ratio=(0.08, 1.0), aspect_ratio=(0.75, 1.3333), jitter_type='uniratio'), xforms.color(brightness_radius=0.4, contrast_radius=0.4, saturation_radius=0.4) ] transforms += [ xforms.scale(width=dimensions['width'], height=dimensions['height'], channels=dimensions['depth'], interpolations='linear') ] source = MinibatchSource(ImageDeserializer( map_file, StreamDefs(features=StreamDef(field='image', transforms=transforms), labels=StreamDef(field='label', shape=len(classes)))), randomize=train, max_samples=total_number_of_samples, multithreaded_deserializer=True) return source
def create_reader(path, vocab_dim, entity_dim, randomize, rand_size=DEFAULT_RANDOMIZATION_WINDOW, size=INFINITELY_REPEAT): """ Create data reader for the model Args: path: The data path vocab_dim: The dimention of the vocabulary entity_dim: The dimention of entities randomize: Where to shuffle the data before feed into the trainer """ return MinibatchSource(CTFDeserializer( path, StreamDefs(context=StreamDef(field='C', shape=vocab_dim, is_sparse=True), query=StreamDef(field='Q', shape=vocab_dim, is_sparse=True), entities=StreamDef(field='E', shape=1, is_sparse=False), label=StreamDef(field='L', shape=1, is_sparse=False), entity_ids=StreamDef(field='EID', shape=entity_dim, is_sparse=True))), randomize=randomize)
def create_image_mb_source(map_file, mean_file, is_training, total_number_of_samples): if not os.path.exists(map_file) or not os.path.exists(mean_file): raise RuntimeError("File '%s' or '%s' does not exist." % (map_file, mean_file)) # transformation pipeline for the features has jitter/crop only when training transforms = [] if is_training: transforms += [ xforms.crop(crop_type='randomside', side_ratio=0.8, jitter_type='uniratio') # train uses jitter ] else: transforms += [ xforms.crop(crop_type='center', crop_size=IMAGE_WIDTH) ] transforms += [ xforms.scale(width=IMAGE_WIDTH, height=IMAGE_HEIGHT, channels=NUM_CHANNELS, interpolations='linear'), xforms.mean(mean_file) ] # deserializer return MinibatchSource( ImageDeserializer(map_file, StreamDefs( features=StreamDef(field='image', transforms=transforms), # first column in map file is referred to as 'image' labels=StreamDef(field='label', shape=NUM_CLASSES))), # and second as 'label' randomize=is_training, max_samples=total_number_of_samples, multithreaded_deserializer = True)
def test_large_minibatch(tmpdir): tmpfile = _write_data(tmpdir, MBDATA_DENSE_2) mb_source = MinibatchSource(CTFDeserializer( tmpfile, StreamDefs(features=StreamDef(field='S0', shape=1), labels=StreamDef(field='S1', shape=1))), randomization_window_in_chunks=0) features_si = mb_source.stream_info('features') labels_si = mb_source.stream_info('labels') mb = mb_source.next_minibatch(1000) features = mb[features_si] labels = mb[labels_si] # Actually, the minibatch spans over multiple sweeps, # not sure if this is an artificial situation, but # maybe instead of a boolean flag we should indicate # the largest sweep index the data was taken from. assert features.end_of_sweep assert labels.end_of_sweep assert features.num_samples == 1000 - 1000 % 7 assert labels.num_samples == 5 * (1000 // 7) assert mb[features_si].num_sequences == (1000 // 7) assert mb[labels_si].num_sequences == (1000 // 7)
def create_mb_source(image_height, image_width, num_channels, map_file, mean_file, is_training): if not os.path.exists(map_file): raise RuntimeError("File '%s' does not exist." % (map_file)) # transformation pipeline for the features has jitter/crop only when training transforms = [] if is_training: transforms += [ xforms.crop(crop_type='randomside', side_ratio=0.875, jitter_type='uniratio') # train uses jitter ] else: transforms += [ xforms.crop(crop_type='center', side_ratio=0.875) # test has no jitter ] transforms += [ xforms.scale(width=image_width, height=image_height, channels=num_channels, interpolations='linear'), ] if mean_file != '': transforms += [ xforms.mean(mean_file), ] # deserializer return MinibatchSource( ImageDeserializer(map_file, StreamDefs( features = StreamDef(field='image', transforms=transforms) # first column in map file is referred to as 'image' )), randomize = is_training, multithreaded_deserializer = True, max_sweeps = 1)
def test_MinibatchData_and_Value_as_input(tmpdir): mbdata = r'''0 |S0 100''' tmpfile = str(tmpdir / 'mbtest.txt') with open(tmpfile, 'w') as f: f.write(mbdata) defs = StreamDefs(f1=StreamDef(field='S0', shape=1)) mb_source = MinibatchSource(CTFDeserializer(tmpfile, defs), randomize=False) f1_si = mb_source.stream_info('f1') mb = mb_source.next_minibatch(1) f1 = input(shape=(1, ), needs_gradient=True, name='f') res = f1 * 2 assert res.eval({f1: mb[f1_si]}) == [[200]] # Test MinibatchData assert res.eval(mb[f1_si]) == [[200]] # Test Value assert res.eval(mb[f1_si].data) == [[200]] # Test NumPy (converted back from MinibatchData) assert res.eval(mb[f1_si].asarray()) == [[200]] # Test Value assert res.eval(mb[f1_si].data) == [[200]]
def create_reader(map_file, mean_file, train): if not os.path.exists(map_file) or not os.path.exists(mean_file): raise RuntimeError( "File '%s' or '%s' does not exist. Please run install_cifar10.py from DataSets/CIFAR-10 to fetch them" % (map_file, mean_file)) # transformation pipeline for the features has jitter/crop only when training transforms = [] if train: transforms += [ xforms.crop(crop_type='randomside', side_ratio=0.8, jitter_type='uniratio') # train uses jitter ] transforms += [ xforms.scale(width=image_width, height=image_height, channels=num_channels, interpolations='linear'), xforms.mean(mean_file) ] # deserializer return MinibatchSource( ImageDeserializer( map_file, StreamDefs( features=StreamDef( field='image', transforms=transforms ), # first column in map file is referred to as 'image' labels=StreamDef(field='label', shape=num_classes)))) # and second as 'label'
def test_eval_sparse_dense(tmpdir, device_id): from cntk import Axis from cntk.io import MinibatchSource, CTFDeserializer, StreamDef, StreamDefs from cntk.ops import input, times input_vocab_dim = label_vocab_dim = 69 ctf_data = '''\ 0 |S0 3:1 |# <s> |S1 3:1 |# <s> 0 |S0 4:1 |# A |S1 32:1 |# ~AH 0 |S0 5:1 |# B |S1 36:1 |# ~B 0 |S0 4:1 |# A |S1 31:1 |# ~AE 0 |S0 7:1 |# D |S1 38:1 |# ~D 0 |S0 12:1 |# I |S1 47:1 |# ~IY 0 |S0 1:1 |# </s> |S1 1:1 |# </s> 2 |S0 60:1 |# <s> |S1 3:1 |# <s> 2 |S0 61:1 |# A |S1 32:1 |# ~AH ''' ctf_file = str(tmpdir / '2seqtest.txt') with open(ctf_file, 'w') as f: f.write(ctf_data) mbs = MinibatchSource(CTFDeserializer( ctf_file, StreamDefs(features=StreamDef(field='S0', shape=input_vocab_dim, is_sparse=True), labels=StreamDef(field='S1', shape=label_vocab_dim, is_sparse=True))), randomize=False, epoch_size=2) raw_input = sequence.input(shape=input_vocab_dim, sequence_axis=Axis('inputAxis'), name='raw_input', is_sparse=True) mb_valid = mbs.next_minibatch(minibatch_size_in_samples=100, input_map={raw_input: mbs.streams.features}, device=cntk_device(device_id)) z = times(raw_input, np.eye(input_vocab_dim)) e_reader = z.eval(mb_valid, device=cntk_device(device_id)) # CSR with the raw_input encoding in ctf_data one_hot_data = [[3, 4, 5, 4, 7, 12, 1], [60, 61]] data = [ csr(np.eye(input_vocab_dim, dtype=np.float32)[d]) for d in one_hot_data ] e_csr = z.eval({raw_input: data}, device=cntk_device(device_id)) assert np.all([np.allclose(a, b) for a, b in zip(e_reader, e_csr)]) # One-hot with the raw_input encoding in ctf_data data = Value.one_hot(one_hot_data, num_classes=input_vocab_dim, device=cntk_device(device_id)) e_hot = z.eval({raw_input: data}, device=cntk_device(device_id)) assert np.all([np.allclose(a, b) for a, b in zip(e_reader, e_hot)])
def create_reader(path, is_training, input_dim, label_dim): return MinibatchSource( CTFDeserializer( path, StreamDefs(features=StreamDef(field='features', shape=input_dim), labels=StreamDef(field='labels', shape=label_dim))), randomize=is_training, epoch_size=INFINITELY_REPEAT if is_training else FULL_DATA_SWEEP)
def create_reader(path, randomize, input_vocab_dim, label_vocab_dim, size=INFINITELY_REPEAT): if not os.path.exists(path): raise RuntimeError("File '%s' does not exist." % (path)) return MinibatchSource(CTFDeserializer(path, StreamDefs( features = StreamDef(field='S0', shape=input_vocab_dim, is_sparse=True), labels = StreamDef(field='S1', shape=label_vocab_dim, is_sparse=True) )), randomize=randomize, max_samples = size)
def create_reader_raw(path, is_training, input_dim, num_label_classes): """ Reads in the unstardized values. """ return MinibatchSource(CTFDeserializer(path, StreamDefs( labels = StreamDef(field='rawlabels', shape=num_label_classes), features = StreamDef(field='rawfeatures', shape=input_dim) )), randomize = is_training, max_sweeps = INFINITELY_REPEAT if is_training else 1)
def create_reader(path, is_training, input_dim, num_label_classes): """ reads CNTK formatted file with 'labels' and 'features' """ return MinibatchSource(CTFDeserializer(path, StreamDefs( labels = StreamDef(field='labels', shape=num_label_classes), features = StreamDef(field='features', shape=input_dim) )), randomize = is_training, max_sweeps = INFINITELY_REPEAT if is_training else 1)