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136 lines
5.4 KiB
136 lines
5.4 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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""" test uncertainty toolbox """
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import mindspore.dataset as ds
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import mindspore.dataset.transforms.c_transforms as C
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import mindspore.dataset.vision.c_transforms as CV
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import mindspore.nn as nn
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from mindspore import context, Tensor
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from mindspore.common import dtype as mstype
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from mindspore.common.initializer import TruncatedNormal
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from mindspore.dataset.vision import Inter
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from mindspore.nn.probability.toolbox.uncertainty_evaluation import UncertaintyEvaluation
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):
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"""weight initial for conv layer"""
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weight = weight_variable()
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return nn.Conv2d(in_channels, out_channels,
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kernel_size=kernel_size, stride=stride, padding=padding,
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weight_init=weight, has_bias=False, pad_mode="valid")
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def fc_with_initialize(input_channels, out_channels):
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"""weight initial for fc layer"""
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weight = weight_variable()
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bias = weight_variable()
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return nn.Dense(input_channels, out_channels, weight, bias)
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def weight_variable():
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"""weight initial"""
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return TruncatedNormal(0.02)
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class LeNet5(nn.Cell):
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def __init__(self, num_class=10, channel=1):
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super(LeNet5, self).__init__()
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self.num_class = num_class
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self.conv1 = conv(channel, 6, 5)
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self.conv2 = conv(6, 16, 5)
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self.fc1 = fc_with_initialize(16 * 5 * 5, 120)
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self.fc2 = fc_with_initialize(120, 84)
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self.fc3 = fc_with_initialize(84, self.num_class)
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self.relu = nn.ReLU()
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self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
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self.flatten = nn.Flatten()
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def construct(self, x):
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x = self.conv1(x)
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x = self.relu(x)
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x = self.max_pool2d(x)
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x = self.conv2(x)
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x = self.relu(x)
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x = self.max_pool2d(x)
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x = self.flatten(x)
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x = self.fc1(x)
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x = self.relu(x)
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x = self.fc2(x)
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x = self.relu(x)
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x = self.fc3(x)
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return x
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def create_dataset(data_path, batch_size=32, repeat_size=1,
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num_parallel_workers=1):
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"""
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create dataset for train or test
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"""
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# define dataset
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mnist_ds = ds.MnistDataset(data_path)
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resize_height, resize_width = 32, 32
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rescale = 1.0 / 255.0
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shift = 0.0
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rescale_nml = 1 / 0.3081
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shift_nml = -1 * 0.1307 / 0.3081
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# define map operations
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resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR) # Bilinear mode
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rescale_nml_op = CV.Rescale(rescale_nml, shift_nml)
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rescale_op = CV.Rescale(rescale, shift)
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hwc2chw_op = CV.HWC2CHW()
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type_cast_op = C.TypeCast(mstype.int32)
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# apply map operations on images
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mnist_ds = mnist_ds.map(operations=type_cast_op, input_columns="label", num_parallel_workers=num_parallel_workers)
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mnist_ds = mnist_ds.map(operations=resize_op, input_columns="image", num_parallel_workers=num_parallel_workers)
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mnist_ds = mnist_ds.map(operations=rescale_op, input_columns="image", num_parallel_workers=num_parallel_workers)
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mnist_ds = mnist_ds.map(operations=rescale_nml_op, input_columns="image", num_parallel_workers=num_parallel_workers)
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mnist_ds = mnist_ds.map(operations=hwc2chw_op, input_columns="image", num_parallel_workers=num_parallel_workers)
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# apply DatasetOps
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buffer_size = 10000
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mnist_ds = mnist_ds.shuffle(buffer_size=buffer_size) # 10000 as in LeNet train script
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mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True)
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mnist_ds = mnist_ds.repeat(repeat_size)
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return mnist_ds
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if __name__ == '__main__':
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# get trained model
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network = LeNet5()
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param_dict = load_checkpoint('checkpoint_lenet.ckpt')
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load_param_into_net(network, param_dict)
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# get train and eval dataset
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ds_train = create_dataset('workspace/mnist/train')
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ds_eval = create_dataset('workspace/mnist/test')
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evaluation = UncertaintyEvaluation(model=network,
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train_dataset=ds_train,
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task_type='classification',
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num_classes=10,
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epochs=1,
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epi_uncer_model_path=None,
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ale_uncer_model_path=None,
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save_model=False)
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for eval_data in ds_eval.create_dict_iterator(output_numpy=True, num_epochs=1):
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eval_data = Tensor(eval_data['image'], mstype.float32)
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epistemic_uncertainty = evaluation.eval_epistemic_uncertainty(eval_data)
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aleatoric_uncertainty = evaluation.eval_aleatoric_uncertainty(eval_data)
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