[Docs] Translate CN 'train_a_gan' to EN (#860)

* Doc： update

* update gan

* Doc: update

docs/en/examples/train_a_gan.md

@@ -1,3 +1,302 @@
 # Train a GAN
 
-Coming soon. Please refer to [chinese documentation](https://mmengine.readthedocs.io/zh_CN/latest/examples/train_a_gan.html).
+Generative Adversarial Network (GAN) can be used to generate data such as images and videos. This tutorial will show you how to train a GAN with MMEngine step by step!
+
+It will be divided into the following steps:
+
+> - [Train Generative Adversarial Network](#train-a-gan)
+>   - [Build a DataLoader](#building-a-dataloader)
+>     - [Build a Dataset](#building-a-dataset)
+> - [Build a Generator Network and a Discriminator Network](#build-a-generator-network-and-a-discriminator-network)
+> - [Build a Generative Adversarial Network Model](#build-a-generative-adversarial-network-model)
+> - [Build an Optimizer](#building-an-optimizer)
+> - [Train with Runner](#training-with-runner)
+
+## Building a DataLoader
+
+### Building a Dataset
+
+First, we will build a dataset class `MNISTDataset` for the MNIST dataset, inheriting from the base dataset class [BaseDataset](mmengine.dataset.BaseDataset), and overwrite the `load_data_list` function of the base dataset class to ensure that the return value is a `list[dict]`, where each `dict` represents a data sample.
+More details about using datasets in MMEngine, refer to [the Dataset tutorial](../tutorials/basedataset.md).
+
+```python
+import numpy as np
+from mmcv.transforms import to_tensor
+from torch.utils.data import random_split
+from torchvision.datasets import MNIST
+
+from mmengine.dataset import BaseDataset
+
+
+class MNISTDataset(BaseDataset):
+
+    def __init__(self, data_root, pipeline, test_mode=False):
+        # Download MNIST Dataset
+        if test_mode:
+            mnist_full = MNIST(data_root, train=True, download=True)
+            self.mnist_dataset, _ = random_split(mnist_full, [55000, 5000])
+        else:
+            self.mnist_dataset = MNIST(data_root, train=False, download=True)
+
+        super().__init__(
+            data_root=data_root, pipeline=pipeline, test_mode=test_mode)
+
+    @staticmethod
+    def totensor(img):
+        if len(img.shape) < 3:
+            img = np.expand_dims(img, -1)
+        img = np.ascontiguousarray(img.transpose(2, 0, 1))
+        return to_tensor(img)
+
+    def load_data_list(self):
+        return [
+            dict(inputs=self.totensor(np.array(x[0]))) for x in self.mnist_dataset
+        ]
+
+
+dataset = MNISTDataset("./data", [])
+
+```
+
+Use the function `build_dataloader` in Runner to build the dataloader.
+
+```python
+import os
+import torch
+from mmengine.runner import Runner
+
+NUM_WORKERS = int(os.cpu_count() / 2)
+BATCH_SIZE = 256 if torch.cuda.is_available() else 64
+
+train_dataloader = dict(
+    batch_size=BATCH_SIZE,
+    num_workers=NUM_WORKERS,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    dataset=dataset)
+train_dataloader = Runner.build_dataloader(train_dataloader)
+```
+
+## Build a Generator Network and a Discriminator Network
+
+The following code builds and instantiates a Generator and a Discriminator.
+
+```python
+import torch.nn as nn
+
+class Generator(nn.Module):
+    def __init__(self, noise_size, img_shape):
+        super().__init__()
+        self.img_shape = img_shape
+        self.noise_size = noise_size
+
+        def block(in_feat, out_feat, normalize=True):
+            layers = [nn.Linear(in_feat, out_feat)]
+            if normalize:
+                layers.append(nn.BatchNorm1d(out_feat, 0.8))
+            layers.append(nn.LeakyReLU(0.2, inplace=True))
+            return layers
+
+        self.model = nn.Sequential(
+            *block(noise_size, 128, normalize=False),
+            *block(128, 256),
+            *block(256, 512),
+            *block(512, 1024),
+            nn.Linear(1024, int(np.prod(img_shape))),
+            nn.Tanh(),
+        )
+
+    def forward(self, z):
+        img = self.model(z)
+        img = img.view(img.size(0), *self.img_shape)
+        return img
+```
+
+```python
+class Discriminator(nn.Module):
+    def __init__(self, img_shape):
+        super().__init__()
+
+        self.model = nn.Sequential(
+            nn.Linear(int(np.prod(img_shape)), 512),
+            nn.LeakyReLU(0.2, inplace=True),
+            nn.Linear(512, 256),
+            nn.LeakyReLU(0.2, inplace=True),
+            nn.Linear(256, 1),
+            nn.Sigmoid(),
+        )
+
+    def forward(self, img):
+        img_flat = img.view(img.size(0), -1)
+        validity = self.model(img_flat)
+
+        return validity
+```
+
+```python
+generator = Generator(100, (1, 28, 28))
+discriminator = Discriminator((1, 28, 28))
+```
+
+## Build a Generative Adversarial Network Model
+
+In MMEngine, we use [ImgDataPreprocessor](mmengine.model.ImgDataPreprocessor) to normalize the data and convert the color channels.
+
+```python
+from mmengine.model import ImgDataPreprocessor
+
+data_preprocessor = ImgDataPreprocessor(mean=([127.5]), std=([127.5]))
+```
+
+The following code implements the basic algorithm of GAN. To implement the algorithm using MMEngine, you need to inherit from the [BaseModel](mmengine.model.BaseModel) and implement the training process in the train_step.  GAN requires alternating training of the generator and discriminator, which are implemented by train_discriminator and train_generator and implement disc_loss and gen_loss to calculate the discriminator loss function and generator loss function.
+More details about BaseModel, refer to [Model tutorial](../tutorials/model.md).
+
+```python
+import torch.nn.functional as F
+from mmengine.model import BaseModel
+
+class GAN(BaseModel):
+
+    def __init__(self, generator, discriminator, noise_size,
+                 data_preprocessor):
+        super().__init__(data_preprocessor=data_preprocessor)
+        assert generator.noise_size == noise_size
+        self.generator = generator
+        self.discriminator = discriminator
+        self.noise_size = noise_size
+
+    def train_step(self, data, optim_wrapper):
+        # Acquiring and preprocessing data
+        inputs_dict = self.data_preprocessor(data, True)
+        # Training the discriminator
+        disc_optimizer_wrapper = optim_wrapper['discriminator']
+        with disc_optimizer_wrapper.optim_context(self.discriminator):
+            log_vars = self.train_discriminator(inputs_dict,
+                                                disc_optimizer_wrapper)
+
+        # Training the generator
+        set_requires_grad(self.discriminator, False)
+        gen_optimizer_wrapper = optim_wrapper['generator']
+        with gen_optimizer_wrapper.optim_context(self.generator):
+            log_vars_gen = self.train_generator(inputs_dict,
+                                                gen_optimizer_wrapper)
+
+        set_requires_grad(self.discriminator, True)
+        log_vars.update(log_vars_gen)
+
+        return log_vars
+
+    def forward(self, batch_inputs, data_samples=None, mode=None):
+        return self.generator(batch_inputs)
+
+    def disc_loss(self, disc_pred_fake, disc_pred_real):
+        losses_dict = dict()
+        losses_dict['loss_disc_fake'] = F.binary_cross_entropy(
+            disc_pred_fake, 0. * torch.ones_like(disc_pred_fake))
+        losses_dict['loss_disc_real'] = F.binary_cross_entropy(
+            disc_pred_real, 1. * torch.ones_like(disc_pred_real))
+
+        loss, log_var = self.parse_losses(losses_dict)
+        return loss, log_var
+
+    def gen_loss(self, disc_pred_fake):
+        losses_dict = dict()
+        losses_dict['loss_gen'] = F.binary_cross_entropy(
+            disc_pred_fake, 1. * torch.ones_like(disc_pred_fake))
+        loss, log_var = self.parse_losses(losses_dict)
+        return loss, log_var
+
+    def train_discriminator(self, inputs, optimizer_wrapper):
+        real_imgs = inputs['inputs']
+        z = torch.randn(
+            (real_imgs.shape[0], self.noise_size)).type_as(real_imgs)
+        with torch.no_grad():
+            fake_imgs = self.generator(z)
+
+        disc_pred_fake = self.discriminator(fake_imgs)
+        disc_pred_real = self.discriminator(real_imgs)
+
+        parsed_losses, log_vars = self.disc_loss(disc_pred_fake,
+                                                 disc_pred_real)
+        optimizer_wrapper.update_params(parsed_losses)
+        return log_vars
+
+    def train_generator(self, inputs, optimizer_wrapper):
+        real_imgs = inputs['inputs']
+        z = torch.randn(real_imgs.shape[0], self.noise_size).type_as(real_imgs)
+
+        fake_imgs = self.generator(z)
+
+        disc_pred_fake = self.discriminator(fake_imgs)
+        parsed_loss, log_vars = self.gen_loss(disc_pred_fake)
+
+        optimizer_wrapper.update_params(parsed_loss)
+        return log_vars
+```
+
+The function, set_requires_grad, is used to lock the weights of the discriminator when training the generator.
+
+```python
+def set_requires_grad(nets, requires_grad=False):
+    """Set requires_grad for all the networks.
+
+    Args:
+        nets (nn.Module | list[nn.Module]): A list of networks or a single
+            network.
+        requires_grad (bool): Whether the networks require gradients or not.
+    """
+    if not isinstance(nets, list):
+        nets = [nets]
+    for net in nets:
+        if net is not None:
+            for param in net.parameters():
+                param.requires_grad = requires_grad
+```
+
+```python
+
+model = GAN(generator, discriminator, 100, data_preprocessor)
+
+```
+
+## Building an Optimizer
+
+MMEngine uses [OptimWrapper](mmengine.optim.OptimWrapper) to wrap optimizers. For multiple optimizers, we use [OptimWrapperDict](mmengine.optim.OptimWrapperDict) to further wrap OptimWrapper.
+More details about optimizers, refer to the [Optimizer tutorial](../tutorials/optimizer.md).
+
+```python
+from mmengine.optim import OptimWrapper, OptimWrapperDict
+
+opt_g = torch.optim.Adam(generator.parameters(), lr=0.0001, betas=(0.5, 0.999))
+opt_g_wrapper = OptimWrapper(opt_g)
+
+opt_d = torch.optim.Adam(
+    discriminator.parameters(), lr=0.0001, betas=(0.5, 0.999))
+opt_d_wrapper = OptimWrapper(opt_d)
+
+opt_wrapper_dict = OptimWrapperDict(
+    generator=opt_g_wrapper, discriminator=opt_d_wrapper)
+
+```
+
+## Training with Runner
+
+The following code demonstrates how to use Runner for model training.
+More details about Runner, please refer to the [Runner tutorial](../tutorials/runner.md).
+
+```python
+train_cfg = dict(by_epoch=True, max_epochs=220)
+runner = Runner(
+    model,
+    work_dir='runs/gan/',
+    train_dataloader=train_dataloader,
+    train_cfg=train_cfg,
+    optim_wrapper=opt_wrapper_dict)
+runner.train()
+```
+
+Till now, we have completed an example of training a GAN. The following code can be used to view the results generated by the GAN we just trained.
+
+![GAN generate an image](https://user-images.githubusercontent.com/22982797/186811532-1517a0f7-5452-4a39-b6d0-6c685e4545e2.png)
+
+If you want to learn more about using MMEngine to implement GAN and generative models, we highly recommend you try the generative framework [MMGeneration](https://github.com/open-mmlab/mmgeneration/tree/dev-1.x) based on MMEngine.