forked from mindspore-Ecosystem/mindspore
119 lines
4.3 KiB
Python
119 lines
4.3 KiB
Python
# Copyright 2020-2022 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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import os
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from mindspore import dtype as mstype
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import mindspore.dataset as ds
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import mindspore.dataset.vision as CV
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import mindspore.nn as nn
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from mindspore import context, Tensor
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import mindspore.ops as ops
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from mindspore.nn.probability.dpn import VAE
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from mindspore.nn.probability.infer import ELBO, SVI
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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IMAGE_SHAPE = (-1, 1, 32, 32)
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image_path = os.path.join('/home/workspace/mindspore_dataset/mnist', "train")
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class Encoder(nn.Cell):
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def __init__(self):
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super(Encoder, self).__init__()
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self.fc1 = nn.Dense(1024, 800)
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self.fc2 = nn.Dense(800, 400)
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self.relu = nn.ReLU()
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self.flatten = nn.Flatten()
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def construct(self, 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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return x
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class Decoder(nn.Cell):
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def __init__(self):
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super(Decoder, self).__init__()
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self.fc1 = nn.Dense(400, 1024)
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self.sigmoid = nn.Sigmoid()
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self.reshape = ops.Reshape()
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def construct(self, z):
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z = self.fc1(z)
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z = self.reshape(z, IMAGE_SHAPE)
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z = self.sigmoid(z)
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return z
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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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# define map operations
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resize_op = CV.Resize((resize_height, resize_width)) # Bilinear mode
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rescale_op = CV.Rescale(rescale, shift)
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hwc2chw_op = CV.HWC2CHW()
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# apply map operations on images
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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=hwc2chw_op, input_columns="image", num_parallel_workers=num_parallel_workers)
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# apply DatasetOps
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mnist_ds = mnist_ds.batch(batch_size)
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mnist_ds = mnist_ds.repeat(repeat_size)
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return mnist_ds
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def test_svi_vae():
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# define the encoder and decoder
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encoder = Encoder()
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decoder = Decoder()
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# define the vae model
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vae = VAE(encoder, decoder, hidden_size=400, latent_size=20)
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# define the loss function
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net_loss = ELBO(latent_prior='Normal', output_prior='Normal')
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# define the optimizer
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optimizer = nn.Adam(params=vae.trainable_params(), learning_rate=0.001)
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# define the training dataset
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ds_train = create_dataset(image_path, 128, 1)
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net_with_loss = nn.WithLossCell(vae, net_loss)
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# define the variational inference
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vi = SVI(net_with_loss=net_with_loss, optimizer=optimizer)
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# run the vi to return the trained network.
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vae = vi.run(train_dataset=ds_train, epochs=5)
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# get the trained loss
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trained_loss = vi.get_train_loss()
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# test function: generate_sample
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generated_sample = vae.generate_sample(64, IMAGE_SHAPE)
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# test function: reconstruct_sample
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for sample in ds_train.create_dict_iterator(output_numpy=True, num_epochs=1):
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sample_x = Tensor(sample['image'], dtype=mstype.float32)
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reconstructed_sample = vae.reconstruct_sample(sample_x)
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print('The loss of the trained network is ', trained_loss)
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print('The hape of the generated sample is ', generated_sample.shape)
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print('The shape of the reconstructed sample is ', reconstructed_sample.shape)
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