使用JAX实现完整的Vision Transformer

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：