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使用JAX实现完整的Vision Transformer

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2023-03-04 09:17

来源：DeepHub IMBA
本文约3200字，建议阅读10+分钟

本文将展示如何使用JAX/Flax实现Vision Transformer (ViT)，以及如何使用JAX/Flax训练ViT。


Vision Transformer

在实现Vision Transformer时，首先要记住这张图。

以下是论文描述的ViT执行过程。

  • 从输入图像中提取补丁图像，并将其转换为平面向量。
  • 投影到 Transformer Encoder 来处理的维度。
  • 预先添加一个可学习的嵌入([class]标记)，并添加一个位置嵌入。
  • 由 Transformer Encoder 进行编码处理。

  • 使用[class]令牌作为输出，输入到MLP进行分类。


细节实现

下面，我们将使用JAX/Flax创建每个模块。

1. 图像到展平的图像补丁

下面的代码从输入图像中提取图像补丁。这个过程通过卷积来实现，内核大小为patch_size * patch_size, stride为patch_size * patch_size，以避免重复。

 class Patches(nn.Module):   patch_size: int   embed_dim: int
   def setup(self):     self.conv = nn.Conv(         features=self.embed_dim,         kernel_size=(self.patch_size, self.patch_size),         strides=(self.patch_size, self.patch_size),         padding='VALID'    )
   def __call__(self, images):     patches = self.conv(images)     b, h, w, c = patches.shape     patches = jnp.reshape(patches, (b, h*w, c))     return patches

2和3. 对展平补丁块的线性投影/添加[CLS]标记/位置嵌入

Transformer Encoder 对所有层使用相同的尺寸大小hidden_dim。上面创建的补丁块向量被投影到hidden_dim维度向量上。与BERT一样，有一个CLS令牌被添加到序列的开头，还增加了一个可学习的位置嵌入来保存位置信息。

 class PatchEncoder(nn.Module):   hidden_dim: int
   @nn.compact   def __call__(self, x):     assert x.ndim == 3     n, seq_len, _ = x.shape     # Hidden dim     x = nn.Dense(self.hidden_dim)(x)     # Add cls token     cls = self.param('cls_token', nn.initializers.zeros, (1, 1, self.hidden_dim))     cls = jnp.tile(cls, (n, 1, 1))     x = jnp.concatenate([cls, x], axis=1)     # Add position embedding     pos_embed = self.param(         'position_embedding',         nn.initializers.normal(stddev=0.02), # From BERT        (1, seq_len + 1, self.hidden_dim)    )     return x + pos_embed

4. Transformer encoder

如上图所示，编码器由多头自注意(MSA)和MLP交替层组成。Norm层 (LN)在MSA和MLP块之前，残差连接在块之后。

 class TransformerEncoder(nn.Module):   embed_dim: int   hidden_dim: int   n_heads: int   drop_p: float   mlp_dim: int
   def setup(self):     self.mha = MultiHeadSelfAttention(self.hidden_dim, self.n_heads, self.drop_p)     self.mlp = MLP(self.mlp_dim, self.drop_p)     self.layer_norm = nn.LayerNorm(epsilon=1e-6)
   def __call__(self, inputs, train=True):     # Attention Block     x = self.layer_norm(inputs)     x = self.mha(x, train)     x = inputs + x     # MLP block     y = self.layer_norm(x)     y = self.mlp(y, train)
     return x + y

MLP是一个两层网络。激活函数是GELU。本文将Dropout应用于Dense层之后。

 class MLP(nn.Module):   mlp_dim: int   drop_p: float   out_dim: Optional[int] = None
   @nn.compact   def __call__(self, inputs, train=True):     actual_out_dim = inputs.shape[-1] if self.out_dim is None else self.out_dim     x = nn.Dense(features=self.mlp_dim)(inputs)     x = nn.gelu(x)     x = nn.Dropout(rate=self.drop_p, deterministic=not train)(x)     x = nn.Dense(features=actual_out_dim)(x)     x = nn.Dropout(rate=self.drop_p, deterministic=not train)(x)     return x

多头自注意(MSA)

qkv的形式应为[B, N, T, D]，如Single Head中计算权重和注意力后，应输出回原维度[B, T, C=N*D]。

 class MultiHeadSelfAttention(nn.Module):   hidden_dim: int   n_heads: int   drop_p: float
   def setup(self):     self.q_net = nn.Dense(self.hidden_dim)     self.k_net = nn.Dense(self.hidden_dim)     self.v_net = nn.Dense(self.hidden_dim)
     self.proj_net = nn.Dense(self.hidden_dim)
     self.att_drop = nn.Dropout(self.drop_p)     self.proj_drop = nn.Dropout(self.drop_p)
   def __call__(self, x, train=True):     B, T, C = x.shape # batch_size, seq_length, hidden_dim     N, D = self.n_heads, C // self.n_heads # num_heads, head_dim     q = self.q_net(x).reshape(B, T, N, D).transpose(0, 2, 1, 3) # (B, N, T, D)     k = self.k_net(x).reshape(B, T, N, D).transpose(0, 2, 1, 3)     v = self.v_net(x).reshape(B, T, N, D).transpose(0, 2, 1, 3)
     # weights (B, N, T, T)     weights = jnp.matmul(q, jnp.swapaxes(k, -2, -1)) / math.sqrt(D)     normalized_weights = nn.softmax(weights, axis=-1)
     # attention (B, N, T, D)     attention = jnp.matmul(normalized_weights, v)     attention = self.att_drop(attention, deterministic=not train)
     # gather heads     attention = attention.transpose(0, 2, 1, 3).reshape(B, T, N*D)
     # project     out = self.proj_drop(self.proj_net(attention), deterministic=not train)
     return out

5. 使用CLS嵌入进行分类

最后MLP头(分类头)。

 class ViT(nn.Module):   patch_size: int   embed_dim: int   hidden_dim: int   n_heads: int   drop_p: float   num_layers: int   mlp_dim: int   num_classes: int
   def setup(self):     self.patch_extracter = Patches(self.patch_size, self.embed_dim)     self.patch_encoder = PatchEncoder(self.hidden_dim)     self.dropout = nn.Dropout(self.drop_p)     self.transformer_encoder = TransformerEncoder(self.embed_dim, self.hidden_dim, self.n_heads, self.drop_p, self.mlp_dim)     self.cls_head = nn.Dense(features=self.num_classes)
   def __call__(self, x, train=True):     x = self.patch_extracter(x)     x = self.patch_encoder(x)     x = self.dropout(x, deterministic=not train)     for i in range(self.num_layers):       x = self.transformer_encoder(x, train)     # MLP head     x = x[:, 0] # [CLS] token     x = self.cls_head(x)     return x

使用JAX/Flax训练

现在已经创建了模型，下面就是使用JAX/Flax来训练。

数据集

这里我们直接使用 torchvision的CIFAR10。

首先是一些工具函数：

 def image_to_numpy(img):   img = np.array(img, dtype=np.float32)   img = (img / 255. - DATA_MEANS) / DATA_STD   return img  def numpy_collate(batch):   if isinstance(batch[0], np.ndarray):     return np.stack(batch)   elif isinstance(batch[0], (tuple, list)):     transposed = zip(*batch)     return [numpy_collate(samples) for samples in transposed]   else:     return np.array(batch)

然后是训练和测试的dataloader：

 test_transform = image_to_numpy train_transform = transforms.Compose([     transforms.RandomHorizontalFlip(),     transforms.RandomResizedCrop((IMAGE_SIZE, IMAGE_SIZE), scale=CROP_SCALES, ratio=CROP_RATIO),     image_to_numpy ])  # Validation set should not use the augmentation. train_dataset = CIFAR10('data', train=True, transform=train_transform, download=True) val_dataset = CIFAR10('data', train=True, transform=test_transform, download=True) train_set, _ = torch.utils.data.random_split(train_dataset, [45000, 5000], generator=torch.Generator().manual_seed(SEED)) _, val_set = torch.utils.data.random_split(val_dataset, [45000, 5000], generator=torch.Generator().manual_seed(SEED)) test_set = CIFAR10('data', train=False, transform=test_transform, download=True)  train_loader = torch.utils.data.DataLoader(     train_set, batch_size=BATCH_SIZE, shuffle=True, drop_last=True, num_workers=2, persistent_workers=True, collate_fn=numpy_collate, ) val_loader = torch.utils.data.DataLoader(     val_set, batch_size=BATCH_SIZE, shuffle=False, drop_last=False, num_workers=2, persistent_workers=True, collate_fn=numpy_collate, ) test_loader = torch.utils.data.DataLoader(     test_set, batch_size=BATCH_SIZE, shuffle=False, drop_last=False, num_workers=2, persistent_workers=True, collate_fn=numpy_collate, )

初始化模型

初始化ViT模型：

 def initialize_model(     seed=42,     patch_size=16, embed_dim=192, hidden_dim=192,     n_heads=3, drop_p=0.1, num_layers=12, mlp_dim=768, num_classes=10 ):   main_rng = jax.random.PRNGKey(seed)   x = jnp.ones(shape=(5, 32, 32, 3))   # ViT   model = ViT(       patch_size=patch_size,       embed_dim=embed_dim,       hidden_dim=hidden_dim,       n_heads=n_heads,       drop_p=drop_p,       num_layers=num_layers,       mlp_dim=mlp_dim,       num_classes=num_classes  )   main_rng, init_rng, drop_rng = random.split(main_rng, 3)   params = model.init({'params': init_rng, 'dropout': drop_rng}, x, train=True)['params']   return model, params, main_rng  vit_model, vit_params, vit_rng = initialize_model()

创建TrainState

在Flax中常见的模式是创建管理训练的状态的类，包括轮次、优化器状态和模型参数等等。还可以通过在apply_fn中指定apply_fn来减少学习循环中的函数参数列表，apply_fn对应于模型的前向传播。

 def create_train_state(     model, params, learning_rate ):   optimizer = optax.adam(learning_rate)   return train_state.TrainState.create(       apply_fn=model.apply,       tx=optimizer,       params=params  )      state = create_train_state(vit_model, vit_params, 3e-4)

循环训练

 def train_model(train_loader, val_loader, state, rng, num_epochs=100):   best_eval = 0.0   for epoch_idx in tqdm(range(1, num_epochs + 1)):     state, rng = train_epoch(train_loader, epoch_idx, state, rng)     if epoch_idx % 1 == 0:       eval_acc = eval_model(val_loader, state, rng)       logger.add_scalar('val/acc', eval_acc, global_step=epoch_idx)       if eval_acc >= best_eval:         best_eval = eval_acc         save_model(state, step=epoch_idx)       logger.flush()   # Evaluate after training   test_acc = eval_model(test_loader, state, rng)   print(f'test_acc: {test_acc}')    def train_epoch(train_loader, epoch_idx, state, rng):   metrics = defaultdict(list)   for batch in tqdm(train_loader, desc='Training', leave=False):     state, rng, loss, acc = train_step(state, rng, batch)     metrics['loss'].append(loss)     metrics['acc'].append(acc)   for key in metrics.keys():     arg_val = np.stack(jax.device_get(metrics[key])).mean()     logger.add_scalar('train/' + key, arg_val, global_step=epoch_idx)     print(f'[epoch {epoch_idx}] {key}: {arg_val}')   return state, rng

验证

 def eval_model(data_loader, state, rng):   # Test model on all images of a data loader and return avg loss   correct_class, count = 0, 0   for batch in data_loader:     rng, acc = eval_step(state, rng, batch)     correct_class += acc * batch[0].shape[0]     count += batch[0].shape[0]   eval_acc = (correct_class / count).item()   return eval_acc

训练步骤

在train_step中定义损失函数，计算模型参数的梯度，并根据梯度更新参数;在value_and_gradients方法中，计算状态的梯度。在apply_gradients中，更新TrainState。交叉熵损失是通过apply_fn(与model.apply相同)计算logits来计算的，apply_fn是在创建TrainState时指定的。

 @jax.jit def train_step(state, rng, batch):   loss_fn = lambda params: calculate_loss(params, state, rng, batch, train=True)   # Get loss, gradients for loss, and other outputs of loss function   (loss, (acc, rng)), grads = jax.value_and_grad(loss_fn, has_aux=True)(state.params)   # Update parameters and batch statistics   state = state.apply_gradients(grads=grads)   return state, rng, loss, acc

计算损失

 def calculate_loss(params, state, rng, batch, train):   imgs, labels = batch   rng, drop_rng = random.split(rng)   logits = state.apply_fn({'params': params}, imgs, train=train, rngs={'dropout': drop_rng})   loss = optax.softmax_cross_entropy_with_integer_labels(logits=logits, labels=labels).mean()   acc = (logits.argmax(axis=-1) == labels).mean()   return loss, (acc, rng)

结果

训练结果如下所示。在Colab pro的标准GPU上，训练时间约为1.5小时。

 test_acc: 0.7704000473022461

如果你对JAX感兴趣，请看这里是本文的完整代码：

https://github.com/satojkovic/vit-jax-flax

作者：satojkovic


编辑：黄继彦


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