mindspore/tests/st/probability/test_gpu_svi_vae.py

# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
import os

import mindspore.common.dtype as mstype
import mindspore.dataset as ds
import mindspore.dataset.transforms.vision.c_transforms as CV
import mindspore.nn as nn
from mindspore import context, Tensor
from mindspore.ops import operations as P
from mindspore.nn.probability.dpn import VAE
from mindspore.nn.probability.infer import ELBO, SVI

context.set_context(mode=context.GRAPH_MODE, save_graphs=False, device_target="GPU")
IMAGE_SHAPE = (-1, 1, 32, 32)
image_path = os.path.join('/home/workspace/mindspore_dataset/mnist', "train")


class Encoder(nn.Cell):
    def __init__(self):
        super(Encoder, self).__init__()
        self.fc1 = nn.Dense(1024, 800)
        self.fc2 = nn.Dense(800, 400)
        self.relu = nn.ReLU()
        self.flatten = nn.Flatten()

    def construct(self, x):
        x = self.flatten(x)
        x = self.fc1(x)
        x = self.relu(x)
        x = self.fc2(x)
        x = self.relu(x)
        return x


class Decoder(nn.Cell):
    def __init__(self):
        super(Decoder, self).__init__()
        self.fc1 = nn.Dense(400, 1024)
        self.sigmoid = nn.Sigmoid()
        self.reshape = P.Reshape()

    def construct(self, z):
        z = self.fc1(z)
        z = self.reshape(z, IMAGE_SHAPE)
        z = self.sigmoid(z)
        return z


def create_dataset(data_path, batch_size=32, repeat_size=1,
                   num_parallel_workers=1):
    """
    create dataset for train or test
    """
    # define dataset
    mnist_ds = ds.MnistDataset(data_path)

    resize_height, resize_width = 32, 32
    rescale = 1.0 / 255.0
    shift = 0.0

    # define map operations
    resize_op = CV.Resize((resize_height, resize_width))  # Bilinear mode
    rescale_op = CV.Rescale(rescale, shift)
    hwc2chw_op = CV.HWC2CHW()

    # apply map operations on images
    mnist_ds = mnist_ds.map(input_columns="image", operations=resize_op, num_parallel_workers=num_parallel_workers)
    mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_op, num_parallel_workers=num_parallel_workers)
    mnist_ds = mnist_ds.map(input_columns="image", operations=hwc2chw_op, num_parallel_workers=num_parallel_workers)

    # apply DatasetOps
    mnist_ds = mnist_ds.batch(batch_size)
    mnist_ds = mnist_ds.repeat(repeat_size)

    return mnist_ds


def test_svi_vae():
    # define the encoder and decoder
    encoder = Encoder()
    decoder = Decoder()
    # define the vae model
    vae = VAE(encoder, decoder, hidden_size=400, latent_size=20)
    # define the loss function
    net_loss = ELBO(latent_prior='Normal', output_prior='Normal')
    # define the optimizer
    optimizer = nn.Adam(params=vae.trainable_params(), learning_rate=0.001)
    # define the training dataset
    ds_train = create_dataset(image_path, 128, 1)
    net_with_loss = nn.WithLossCell(vae, net_loss)
    # define the variational inference
    vi = SVI(net_with_loss=net_with_loss, optimizer=optimizer)
    # run the vi to return the trained network.
    vae = vi.run(train_dataset=ds_train, epochs=5)
    # get the trained loss
    trained_loss = vi.get_train_loss()
    # test function: generate_sample
    generated_sample = vae.generate_sample(64, IMAGE_SHAPE)
    # test function: reconstruct_sample
    for sample in ds_train.create_dict_iterator():
        sample_x = Tensor(sample['image'], dtype=mstype.float32)
        reconstructed_sample = vae.reconstruct_sample(sample_x)
    print('The loss of the trained network is ', trained_loss)
    print('The hape of the generated sample is ', generated_sample.shape)
    print('The shape of the reconstructed sample is ', reconstructed_sample.shape)