Merge commit '6822bb5e0a694a9a23c749b0d629c65484e6219a' into wilrich/miscAlpha2
This commit is contained in:
Mark Hillebrand
Коммит
27684e284d
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@ -19,16 +19,17 @@ from examples.common.nn import conv_bn_relu_layer, conv_bn_layer, resnet_node2,
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TRAIN_MAP_FILENAME = 'train_map.txt'
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MEAN_FILENAME = 'CIFAR-10_mean.xml'
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TEST_MAP_FILENAME = 'test_map.txt'
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# Instantiates the CNTK built-in minibatch source for reading images to be used for training the residual net
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# The minibatch source is configured using a hierarchical dictionary of
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# key:value pairs
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# The minibatch source is configured using a hierarchical dictionary of key:value pairs
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def create_mb_source(features_stream_name, labels_stream_name, image_height,
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image_width, num_channels, num_classes, cifar_data_path):
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map_file = os.path.join(cifar_data_path, TRAIN_MAP_FILENAME)
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mean_file = os.path.join(cifar_data_path, MEAN_FILENAME)
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path = os.path.normpath(os.path.join(abs_path, cifar_data_path))
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map_file = os.path.join(path, TRAIN_MAP_FILENAME)
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mean_file = os.path.join(path, MEAN_FILENAME)
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if not os.path.exists(map_file) or not os.path.exists(mean_file):
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cifar_py3 = "" if sys.version_info.major < 3 else "_py3"
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@ -36,7 +37,6 @@ def create_mb_source(features_stream_name, labels_stream_name, image_height,
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(map_file, mean_file, cifar_py3, cifar_py3))
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image = ImageDeserializer(map_file)
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<<<<<<< 391432ca77060ad88807339d773f288de6557c4a
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image.map_features(features_stream_name,
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[ImageDeserializer.crop(crop_type='Random', ratio=0.8,
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jitter_type='uniRatio'),
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@ -47,23 +47,32 @@ def create_mb_source(features_stream_name, labels_stream_name, image_height,
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rc = ReaderConfig(image, epoch_size=sys.maxsize)
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return rc.minibatch_source()
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=======
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image.map_features(feature_name,
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def create_test_mb_source(features_stream_name, labels_stream_name, image_height,
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image_width, num_channels, num_classes, cifar_data_path):
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path = os.path.normpath(os.path.join(abs_path, cifar_data_path))
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map_file = os.path.join(path, TEST_MAP_FILENAME)
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mean_file = os.path.join(path, MEAN_FILENAME)
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if not os.path.exists(map_file) or not os.path.exists(mean_file):
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cifar_py3 = "" if sys.version_info.major < 3 else "_py3"
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raise RuntimeError("File '%s' or '%s' do not exist. Please run CifarDownload%s.py and CifarConverter%s.py from CIFAR-10 to fetch them" %
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(map_file, mean_file, cifar_py3, cifar_py3))
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image = ImageDeserializer(map_file)
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image.map_features(features_stream_name,
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[ImageDeserializer.crop(crop_type='Random', ratio=0.8,
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jitter_type='uniRatio'),
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ImageDeserializer.scale(width=image_width, height=image_height,
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channels=num_channels, interpolations='linear'),
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ImageDeserializer.mean(mean_file)])
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image.map_labels(label_name, num_classes)
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image.map_labels(labels_stream_name, num_classes)
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rc = ReaderConfig(image, epoch_size=sys.maxsize)
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input_streams_config = {
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features_stream_name: features_stream_config, labels_stream_name: labels_stream_config}
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deserializer_config = {"type": "ImageDeserializer",
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"file": map_file, "input": input_streams_config}
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return rc.minibatch_source()
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>>>>>>> Address comments in CR
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def get_projection_map(out_dim, in_dim):
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if in_dim > out_dim:
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raise ValueError(
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@ -125,40 +134,23 @@ def resnet_classifer(input, num_classes):
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poolh_stride = 1
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poolv_stride = 1
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<<<<<<< 391432ca77060ad88807339d773f288de6557c4a
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pool = pooling(rn3_3, AVG_POOLING, (1, poolh, poolw), (1, poolv_stride, poolh_stride))
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out_times_params = parameter(shape=(c_map3, 1, 1, num_classes), initializer=glorot_uniform())
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out_bias_params = parameter(shape=(num_classes), value=0)
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=======
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pool = pooling(rn3_3, AVG_POOLING, (1, poolh, poolw),
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(1, poolv_stride, poolh_stride))
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out_times_params = parameter(shape=(c_map3, 1, 1, num_classes))
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out_bias_params = parameter(shape=(num_classes))
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>>>>>>> Address comments in CR
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t = times(pool, out_times_params)
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return t + out_bias_params
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# Trains a residual network model on the Cifar image dataset
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<<<<<<< 391432ca77060ad88807339d773f288de6557c4a
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def cifar_resnet(base_path):
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=======
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pool = pooling(rn3_3, AVG_POOLING, (1, poolh, poolw), (1, poolv_stride, poolh_stride))
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out_times_params = parameter(shape=(c_map3, 1, 1, num_classes), initializer=glorot_uniform_initializer())
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out_bias_params = parameter(shape=(num_classes), value=0)
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def cifar_resnet(base_path, debug_output=False):
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image_height = 32
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image_width = 32
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num_channels = 3
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num_classes = 10
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def cifar_resnet(base_path):
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feats_stream_name = 'features'
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labels_stream_name = 'labels'
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<<<<<<< 391432ca77060ad88807339d773f288de6557c4a
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minibatch_source = create_mb_source(feats_stream_name, labels_stream_name,
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image_height, image_width, num_channels, num_classes, base_path)
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=======
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minibatch_source = create_mb_source(feats_stream_name, labels_stream_name,
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image_height, image_width, num_channels, num_classes)
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>>>>>>> Address comments in CR
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features_si = minibatch_source.stream_info(feats_stream_name)
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labels_si = minibatch_source.stream_info(labels_stream_name)
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@ -181,6 +173,7 @@ def cifar_resnet(base_path):
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mb_size = 32
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training_progress_output_freq = 20
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num_mbs = 1000
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for i in range(0, num_mbs):
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mb = minibatch_source.get_next_minibatch(mb_size)
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@ -190,8 +183,30 @@ def cifar_resnet(base_path):
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features_si].m_data, label_var: mb[labels_si].m_data}
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trainer.train_minibatch(arguments)
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if debug_output:
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print_training_progress(trainer, i, training_progress_output_freq)
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test_minibatch_source = create_test_mb_source(feats_stream_name, labels_stream_name,
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image_height, image_width, num_channels, num_classes, base_path)
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features_si = test_minibatch_source.stream_info(feats_stream_name)
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labels_si = test_minibatch_source.stream_info(labels_stream_name)
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mb_size = 64
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num_mbs = 300
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total_error = 0.0
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for i in range(0, num_mbs):
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mb = test_minibatch_source.get_next_minibatch(mb_size)
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# Specify the mapping of input variables in the model to actual
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# minibatch data to be trained with
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arguments = {image_input: mb[
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features_si].m_data, label_var: mb[labels_si].m_data}
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error = trainer.test_minibatch(arguments)
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total_error += error
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return total_error / num_mbs
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# Place holder for real test
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def test_TODO_remove_me(device_id):
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#FIXME: need a backdoor to work around the limitation of changing the default device not possible
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@ -215,4 +230,6 @@ if __name__ == '__main__':
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base_path = os.path.normpath(os.path.join(
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*"../../../../Examples/Image/Miscellaneous/CIFAR-10/cifar-10-batches-py".split("/")))
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os.chdir(os.path.join(base_path, '..'))
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cifar_resnet(base_path)
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@ -15,8 +15,6 @@ abs_path = os.path.dirname(os.path.abspath(__file__))
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sys.path.append(os.path.join(abs_path, "..", ".."))
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from examples.common.nn import fully_connected_classifier_net, print_training_progress
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TOLERANCE_ABSOLUTE = 1E-03
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# make sure we get always the same "randomness"
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np.random.seed(0)
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@ -35,7 +33,7 @@ def generate_random_data(sample_size, feature_dim, num_classes):
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# Creates and trains a feedforward classification model
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def ffnet(debug_output=True):
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def ffnet(debug_output=False):
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input_dim = 2
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num_output_classes = 2
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num_hidden_layers = 2
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@ -77,7 +75,7 @@ def ffnet(debug_output=True):
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{input: test_features, label: test_labels})
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return avg_error
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def test_error(device_id):
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def test_error_TODO(device_id):
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#FIXME: need a backdoor to work around the limitation of changing the default device not possible
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#from cntk.utils import cntk_device
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#DeviceDescriptor.set_default_device(cntk_device(device_id))
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@ -1,4 +1,4 @@
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# Copyright (c) Microsoft. All rights reserved.
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# Copyright (c) Microsoft. All rights reserved.
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# Licensed under the MIT license. See LICENSE.md file in the project root
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# for full license information.
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@ -19,8 +19,7 @@ from examples.common.nn import LSTMP_component_with_self_stabilization, stabiliz
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# Creates and trains a sequence to sequence translation model
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def train_sequence_to_sequence_translator():
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def sequence_to_sequence_translator(debug_output=False):
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input_vocab_dim = 69
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label_vocab_dim = 69
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@ -94,6 +93,16 @@ def train_sequence_to_sequence_translator():
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ce = cross_entropy_with_softmax(z, label_sequence)
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errs = classification_error(z, label_sequence)
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# Instantiate the trainer object to drive the model training
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lr = 0.007
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momentum_time_constant = 1100
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momentum_per_sample = momentums_per_sample(
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math.exp(-1.0 / momentum_time_constant))
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clipping_threshold_per_sample = 2.3
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gradient_clipping_with_truncation = True
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trainer = Trainer(z, ce, errs, [momentum_sgd(z.parameters(), lr, momentum_per_sample, clipping_threshold_per_sample, gradient_clipping_with_truncation)])
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rel_path = r"../../../../Examples/SequenceToSequence/CMUDict/Data/cmudict-0.7b.train-dev-20-21.ctf"
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path = os.path.join(os.path.dirname(os.path.abspath(__file__)), rel_path)
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feature_stream_name = 'features'
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@ -105,16 +114,6 @@ def train_sequence_to_sequence_translator():
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features_si = mb_source.stream_info(feature_stream_name)
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labels_si = mb_source.stream_info(labels_stream_name)
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# Instantiate the trainer object to drive the model training
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lr = 0.007
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momentum_time_constant = 1100
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momentum_per_sample = momentums_per_sample(
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math.exp(-1.0 / momentum_time_constant))
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clipping_threshold_per_sample = 2.3
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gradient_clipping_with_truncation = True
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trainer = Trainer(z, ce, errs, [momentum_sgd(z.parameters(), lr, momentum_per_sample, clipping_threshold_per_sample, gradient_clipping_with_truncation)])
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# Get minibatches of sequences to train with and perform model training
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minibatch_size = 72
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training_progress_output_freq = 10
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@ -129,13 +128,51 @@ def train_sequence_to_sequence_translator():
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raw_labels: mb[labels_si].m_data}
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trainer.train_minibatch(arguments)
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if debug_output:
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print_training_progress(trainer, i, training_progress_output_freq)
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i += 1
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rel_path = r"../../../../Examples/SequenceToSequence/CMUDict/Data/cmudict-0.7b.test.ctf"
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path = os.path.join(os.path.dirname(os.path.abspath(__file__)), rel_path)
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test_mb_source = text_format_minibatch_source(path, [
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StreamConfiguration(feature_stream_name, input_vocab_dim, True, 'S0'),
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StreamConfiguration(labels_stream_name, label_vocab_dim, True, 'S1')], 10000)
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features_si = test_mb_source.stream_info(feature_stream_name)
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labels_si = test_mb_source.stream_info(labels_stream_name)
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# choose this to be big enough for the longest sentence
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train_minibatch_size = 1024
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# Get minibatches of sequences to test and perform testing
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i = 0
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total_error = 0.0
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while True:
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mb = test_mb_source.get_next_minibatch(train_minibatch_size)
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if len(mb) == 0:
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break
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# Specify the mapping of input variables in the model to actual
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# minibatch data to be tested with
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arguments = {raw_input: mb[features_si].m_data,
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raw_labels: mb[labels_si].m_data}
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mb_error = trainer.test_minibatch(arguments)
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total_error += mb_error
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if debug_output:
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print("Minibatch {}, Error {} ".format(i, mb_error))
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i += 1
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# Average of evaluation errors of all test minibatches
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return total_error / i
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if __name__ == '__main__':
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# Specify the target device to be used for computing
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target_device = DeviceDescriptor.cpu_device()
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DeviceDescriptor.set_default_device(target_device)
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train_sequence_to_sequence_translator()
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error = sequence_to_sequence_translator()
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print("test: %f" % error)
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@ -0,0 +1,29 @@
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# Copyright (c) Microsoft. All rights reserved.
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# Licensed under the MIT license. See LICENSE.md file in the project root
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# for full license information.
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# ==============================================================================
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import numpy as np
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import os
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from cntk import DeviceDescriptor
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from cntk.io import ReaderConfig, ImageDeserializer
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from examples.CifarResNet.CifarResNet import cifar_resnet
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TOLERANCE_ABSOLUTE = 1E-1
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def test_cifar_resnet_error(device_id):
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target_device = DeviceDescriptor.gpu_device(0)
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DeviceDescriptor.set_default_device(target_device)
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base_path = os.path.normpath(os.path.join(
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*"../../../../Examples/Image/Miscellaneous/CIFAR-10/cifar-10-batches-py".split("/")))
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os.chdir(os.path.join(base_path, '..'))
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test_error = cifar_resnet(base_path)
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expected_test_error = 0.7
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assert np.allclose(test_error, expected_test_error,
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atol=TOLERANCE_ABSOLUTE)
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@ -11,7 +11,7 @@ from examples.NumpyInterop.FeedForwardNet import ffnet
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TOLERANCE_ABSOLUTE = 1E-03
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def test_error(device_id):
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def test_ffnet_error(device_id):
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#from cntk.utils import cntk_device
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#DeviceDescriptor.set_default_device(cntk_device(device_id))
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|
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@ -11,7 +11,7 @@ from examples.SequenceClassification.SequenceClassification import train_sequenc
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TOLERANCE_ABSOLUTE = 1E-2
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def test_error(device_id):
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def test_seq_classification_error(device_id):
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#from cntk.utils import cntk_device
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#DeviceDescriptor.set_default_device(cntk_device(device_id))
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|
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@ -0,0 +1,21 @@
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# Copyright (c) Microsoft. All rights reserved.
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# Licensed under the MIT license. See LICENSE.md file in the project root
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# for full license information.
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# ==============================================================================
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import numpy as np
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from cntk import DeviceDescriptor
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from examples.Sequence2Sequence.Sequence2Sequence import sequence_to_sequence_translator
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TOLERANCE_ABSOLUTE = 1E-1
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def test_sequence_to_sequence(device_id):
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#from cntk.utils import cntk_device
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#DeviceDescriptor.set_default_device(cntk_device(device_id))
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error = sequence_to_sequence_translator()
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expected_error = 0.758458
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assert np.allclose(error, expected_error, atol=TOLERANCE_ABSOLUTE)
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@ -11,12 +11,12 @@ from examples.MNIST.SimpleMNIST import simple_mnist
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TOLERANCE_ABSOLUTE = 1E-1
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def test_error(device_id):
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def test_simple_mnist_error(device_id):
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#from cntk.utils import cntk_device
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#DeviceDescriptor.set_default_device(cntk_device(device_id))
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test_error = simple_mnist()
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expected_test_error = 0.7
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expected_test_error = 0.09
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assert np.allclose([test_error], [expected_test_error],
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assert np.allclose(test_error, expected_test_error,
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atol=TOLERANCE_ABSOLUTE)
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