使用JAX实现完整的VisionTransformer

　　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dataloadertest_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
　　作者：satojkovic