mindspore/tests/st/probability/dpn/test_gpu_svi_vae.py

# Copyright 2020-2022 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

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

context.set_context(mode=context.GRAPH_MODE, 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 = ops.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(operations=resize_op, input_columns="image", num_parallel_workers=num_parallel_workers)
    mnist_ds = mnist_ds.map(operations=rescale_op, input_columns="image", num_parallel_workers=num_parallel_workers)
    mnist_ds = mnist_ds.map(operations=hwc2chw_op, input_columns="image", 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(output_numpy=True, num_epochs=1):
        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)