本文复现了PVT v2模型，其基于v1改进，亮点是Linear SRA。代码包含导入包、基础模块定义、模型组网等部分，还提供了不同缩放结构及预训练权重。通过在Cifar10数据集上训练5轮验证性能，模型表现良好。PVT v2引入卷积等操作提升性能，参数量和计算量较小，下游任务表现佳。

前言

Hi guy，我们怎么又见面了，(俗套的开场白)，哈哈哈哈，那么这次来复现一个PVT v2，它是基于v1进行改动

免费影视、动漫、音乐、游戏、小说资源长期稳定更新！ 👉 点此立即查看 👈

完整代码

导入所需要的包

In [ ]

import paddleimport paddle.nn as nnimport paddle.nn.functional as Ffrom functools import partialimport mathtrunc_normal_ = nn.initializer.TruncatedNormal(std=.02)zeros_ = nn.initializer.Constant(value=0.)ones_ = nn.initializer.Constant(value=1.)kaiming_normal_ = nn.initializer.KaimingNormal()

登录后复制

/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/utils.py:26: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations  def convert_to_list(value, n, name, dtype=np.int):

登录后复制

基础模块定义

In [ ]

def to_2tuple(x):    return tuple([x] * 2)def swapdim(x, dim1, dim2):    a = list(range(len(x.shape)))    a[dim1], a[dim2] = a[dim2], a[dim1]    return x.transpose(a)def drop_path(x, drop_prob = 0., training = False):    if drop_prob == 0. or not training:        return x    keep_prob = 1 - drop_prob    shape = (x.shape[0],) + (1,) * (x.ndim - 1)      random_tensor = paddle.to_tensor(keep_prob) + paddle.rand(shape)    random_tensor = paddle.floor(random_tensor)     output = x.divide(keep_prob) * random_tensor    return outputclass DropPath(nn.Layer):    def __init__(self, drop_prob=None):        super(DropPath, self).__init__()        self.drop_prob = drop_prob    def forward(self, x):        return drop_path(x, self.drop_prob, self.training)class Identity(nn.Layer):                          def __init__(self, *args, **kwargs):        super(Identity, self).__init__()     def forward(self, input):        return input

登录后复制

模型组网

网络大概结构如下图所示

其中亮点是 PVT v2 的 Linear SRA

In [ ]

class Mlp(nn.Layer):    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0., linear=False):        super().__init__()        out_features = out_features or in_features        hidden_features = hidden_features or in_features        self.fc1 = nn.Linear(in_features, hidden_features)        self.dwconv = DWConv(hidden_features)        self.act = act_layer()        self.fc2 = nn.Linear(hidden_features, out_features)        self.drop = nn.Dropout(drop)        self.linear = linear        if self.linear:            self.relu = nn.ReLU()    def forward(self, x, H, W):        x = self.fc1(x)        if self.linear:            x = self.relu(x)        x = self.dwconv(x, H, W)        x = self.act(x)        x = self.drop(x)        x = self.fc2(x)        x = self.drop(x)        return xclass Attention(nn.Layer):    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., sr_ratio=1, linear=False):        super().__init__()        assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}."        self.dim = dim        self.num_heads = num_heads        head_dim = dim // num_heads        self.scale = qk_scale or head_dim ** -0.5        self.q = nn.Linear(dim, dim, bias_attr=qkv_bias)        self.kv = nn.Linear(dim, dim * 2, bias_attr=qkv_bias)        self.attn_drop = nn.Dropout(attn_drop)        self.proj = nn.Linear(dim, dim)        self.proj_drop = nn.Dropout(proj_drop)        self.linear = linear        self.sr_ratio = sr_ratio        if not linear:            if sr_ratio > 1:                self.sr = nn.Conv2D(dim, dim, kernel_size=sr_ratio, stride=sr_ratio)                self.norm = nn.LayerNorm(dim)        else:            self.pool = nn.AdaptiveAvgPool2D(7)            self.sr = nn.Conv2D(dim, dim, kernel_size=1, stride=1)            self.norm = nn.LayerNorm(dim)            self.act = nn.GELU()    def forward(self, x, H, W):        B, N, C = x.shape        q = self.q(x).reshape([B, N, self.num_heads, C // self.num_heads]).transpose([0, 2, 1, 3])        if not self.linear:            if self.sr_ratio > 1:                x_ = x.transpose([0, 2, 1]).reshape([B, C, H, W])                x_ = self.sr(x_).reshape([B, C, -1]).transpose([0, 2, 1])                x_ = self.norm(x_)                kv = self.kv(x_).reshape([B, -1, 2, self.num_heads, C // self.num_heads]).transpose([2, 0, 3, 1, 4])            else:                kv = self.kv(x).reshape([B, -1, 2, self.num_heads, C // self.num_heads]).transpose([2, 0, 3, 1, 4])        else:            x_ = x.transpose([0, 2, 1]).reshape([B, C, H, W])            x_ = self.sr(self.pool(x_)).reshape([B, C, -1]).transpose([0, 2, 1])            x_ = self.norm(x_)            x_ = self.act(x_)            kv = self.kv(x_).reshape([B, -1, 2, self.num_heads, C // self.num_heads]).transpose([2, 0, 3, 1, 4])        k, v = kv[0], kv[1]        attn = (q @ swapdim(k, -2, -1)) * self.scale        attn = F.softmax(attn, axis=-1)        attn = self.attn_drop(attn)        x = swapdim((attn @ v), 1, 2).reshape([B, N, C])        x = self.proj(x)        x = self.proj_drop(x)        return xclass Block(nn.Layer):    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, sr_ratio=1, linear=False):        super().__init__()        self.norm1 = norm_layer(dim)        self.attn = Attention(            dim,            num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,            attn_drop=attn_drop, proj_drop=drop, sr_ratio=sr_ratio, linear=linear)        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here        self.drop_path = DropPath(drop_path) if drop_path > 0. else Identity()        self.norm2 = norm_layer(dim)        mlp_hidden_dim = int(dim * mlp_ratio)        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop, linear=linear)    def forward(self, x, H, W):        x = x + self.drop_path(self.attn(self.norm1(x), H, W))        x = x + self.drop_path(self.mlp(self.norm2(x), H, W))        return xclass OverlapPatchEmbed(nn.Layer):    """ Image to Patch Embedding    """    def __init__(self, img_size=224, patch_size=7, stride=4, in_chans=3, embed_dim=768):        super().__init__()        img_size = to_2tuple(img_size)        patch_size = to_2tuple(patch_size)        self.img_size = img_size        self.patch_size = patch_size        self.H, self.W = img_size[0] // patch_size[0], img_size[1] // patch_size[1]        self.num_patches = self.H * self.W        self.proj = nn.Conv2D(in_chans, embed_dim, kernel_size=patch_size, stride=stride,                              padding=(patch_size[0] // 2, patch_size[1] // 2))        self.norm = nn.LayerNorm(embed_dim)    def forward(self, x):        x = self.proj(x)        _, _, H, W = x.shape        x = x.flatten(2)        x = swapdim(x, 1, 2)        x = self.norm(x)        return x, H, Wclass PyramidVisionTransformerV2(nn.Layer):    def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dims=[64, 128, 256, 512],                 num_heads=[1, 2, 4, 8], mlp_ratios=[4, 4, 4, 4], qkv_bias=False, qk_scale=None, drop_rate=0.,                 attn_drop_rate=0., drop_path_rate=0., norm_layer=nn.LayerNorm,                 depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1], num_stages=4, linear=False):        super().__init__()        self.num_classes = num_classes        self.depths = depths        self.num_stages = num_stages        dpr = [x for x in paddle.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule        cur = 0        for i in range(num_stages):            patch_embed = OverlapPatchEmbed(img_size=img_size if i == 0 else img_size // (2 ** (i + 1)),                                            patch_size=7 if i == 0 else 3,                                            stride=4 if i == 0 else 2,                                            in_chans=in_chans if i == 0 else embed_dims[i - 1],                                            embed_dim=embed_dims[i])            block = nn.LayerList([Block(                dim=embed_dims[i], num_heads=num_heads[i], mlp_ratio=mlp_ratios[i], qkv_bias=qkv_bias, qk_scale=qk_scale,                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + j], norm_layer=norm_layer,                sr_ratio=sr_ratios[i], linear=linear)                for j in range(depths[i])])            norm = norm_layer(embed_dims[i])            cur += depths[i]            setattr(self, f"patch_embed{i + 1}", patch_embed)            setattr(self, f"block{i + 1}", block)            setattr(self, f"norm{i + 1}", norm)        # classification head        self.head = nn.Linear(embed_dims[3], num_classes) if num_classes > 0 else Identity()    def freeze_patch_emb(self):        self.patch_embed1.requires_grad = False    def reset_classifier(self, num_classes, global_pool=''):        self.num_classes = num_classes        self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else Identity()    def forward_features(self, x):        B = x.shape[0]        for i in range(self.num_stages):            patch_embed = getattr(self, f"patch_embed{i + 1}")            block = getattr(self, f"block{i + 1}")            norm = getattr(self, f"norm{i + 1}")            x, H, W = patch_embed(x)            for blk in block:                x = blk(x, H, W)            x = norm(x)            if i != self.num_stages - 1:                x = x.reshape([B, H, W, -1]).transpose([0, 3, 1, 2])        return x.mean(axis=1)    def forward(self, x):        x = self.forward_features(x)        x = self.head(x)        return xclass DWConv(nn.Layer):    def __init__(self, dim=768):        super(DWConv, self).__init__()        self.dwconv = nn.Conv2D(dim, dim, 3, 1, 1, bias_attr=True, groups=dim)    def forward(self, x, H, W):        B, N, C = x.shape        x = swapdim(x, 1, 2)        x = x.reshape([B, C, H, W])        x = self.dwconv(x)        x = x.flatten(2)        x = swapdim(x, 1, 2)        return x

登录后复制

模型缩放

模型各种缩放结构最新性能如下所示

In [ ]

def pvt_v2_b0(**kwargs):    model = PyramidVisionTransformerV2(        patch_size=4, embed_dims=[32, 64, 160, 256], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4], qkv_bias=True,        norm_layer=partial(nn.LayerNorm, epsilon=1e-6), depths=[2, 2, 2, 2], sr_ratios=[8, 4, 2, 1],        **kwargs)    return modeldef pvt_v2_b1(**kwargs):    model = PyramidVisionTransformerV2(        patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4], qkv_bias=True,        norm_layer=partial(nn.LayerNorm, epsilon=1e-6), depths=[2, 2, 2, 2], sr_ratios=[8, 4, 2, 1],        **kwargs)    return modeldef pvt_v2_b2(**kwargs):    model = PyramidVisionTransformerV2(        patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4], qkv_bias=True,        norm_layer=partial(nn.LayerNorm, epsilon=1e-6), depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1], **kwargs)    return modeldef pvt_v2_b3(**kwargs):    model = PyramidVisionTransformerV2(        patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4], qkv_bias=True,        norm_layer=partial(nn.LayerNorm, epsilon=1e-6), depths=[3, 4, 18, 3], sr_ratios=[8, 4, 2, 1],        **kwargs)    return modeldef pvt_v2_b4(**kwargs):    model = PyramidVisionTransformerV2(        patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4], qkv_bias=True,        norm_layer=partial(nn.LayerNorm, epsilon=1e-6), depths=[3, 8, 27, 3], sr_ratios=[8, 4, 2, 1],        **kwargs)    return modeldef pvt_v2_b5(**kwargs):    model = PyramidVisionTransformerV2(        patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4], qkv_bias=True,        norm_layer=partial(nn.LayerNorm, epsilon=1e-6), depths=[3, 6, 40, 3], sr_ratios=[8, 4, 2, 1],        **kwargs)    return modeldef pvt_v2_b2_li(**kwargs):    model = PyramidVisionTransformerV2(        patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4], qkv_bias=True,        norm_layer=partial(nn.LayerNorm, epsilon=1e-6), depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1], linear=True,         **kwargs)    return model

登录后复制

查看模型

In [ ]

# 模型各层查看m = pvt_v2_b0()print(m)# 前向计算x = paddle.randn([2, 3, 224, 224])out = m(x)loss = out.sum()loss.backward()print('Single iteration completed successfully')

登录后复制

预训练权重加载

Results on ImageNet-1K

In [ ]

# pvt v2 b0m = pvt_v2_b0()m.set_state_dict(paddle.load('/home/aistudio/data/data97429/pvt_v2_b0.pdparams'))# pvt v2 b1m = pvt_v2_b1()m.set_state_dict(paddle.load('/home/aistudio/data/data97429/pvt_v2_b1.pdparams'))# pvt v2 b2m = pvt_v2_b2()m.set_state_dict(paddle.load('/home/aistudio/data/data97429/pvt_v2_b2.pdparams'))# pvt v2 b2 linearm = pvt_v2_b2_li()m.set_state_dict(paddle.load('/home/aistudio/data/data97429/pvt_v2_b2_li.pdparams'))# pvt v2 b3m = pvt_v2_b3()m.set_state_dict(paddle.load('/home/aistudio/data/data97429/pvt_v2_b3.pdparams'))# pvt v2 b4m = pvt_v2_b4()m.set_state_dict(paddle.load('/home/aistudio/data/data97429/pvt_v2_b4.pdparams'))# pvt v2 b5m = pvt_v2_b5()m.set_state_dict(paddle.load('/home/aistudio/data/data97429/pvt_v2_b5.pdparams'))

登录后复制

Cifar10 验证性能

数据准备

In [15]

import paddle.vision.transforms as Tfrom paddle.vision.datasets import Cifar10paddle.set_device('gpu')#数据准备transform = T.Compose([    T.Resize(size=(224,224)),    T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225],data_format='HWC'),    T.ToTensor()])train_dataset = Cifar10(mode='train', transform=transform)val_dataset = Cifar10(mode='test',  transform=transform)

登录后复制

Cache file /home/aistudio/.cache/paddle/dataset/cifar/cifar-10-python.tar.gz not found, downloading https://dataset.bj.bcebos.com/cifar/cifar-10-python.tar.gz Begin to downloadDownload finished

登录后复制

模型准备

In [16]

m = pvt_v2_b0()m.set_state_dict(paddle.load('/home/aistudio/data/data97429/pvt_v2_b0.pdparams'))model = paddle.Model(m)

登录后复制

开始训练

由于时间篇幅只训练5轮，感兴趣的同学可以继续训练

In [17]

model.prepare(optimizer=paddle.optimizer.AdamW(learning_rate=0.0001, parameters=model.parameters()),              loss=paddle.nn.CrossEntropyLoss(),              metrics=paddle.metric.Accuracy())visualdl=paddle.callbacks.VisualDL(log_dir='visual_log') # 开启训练可视化model.fit(    train_data=train_dataset,     eval_data=val_dataset,     batch_size=16,     epochs=5,    verbose=1,    callbacks=[visualdl] )

登录后复制

The loss value printed in the log is the current step, and the metric is the average value of previous step.Epoch 1/5

登录后复制

/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/utils.py:77: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working  return (isinstance(seq, collections.Sequence) and

登录后复制

step 3125/3125 [==============================] - loss: 0.2681 - acc: 0.8151 - 124ms/step        Eval begin...The loss value printed in the log is the current batch, and the metric is the average value of previous step.step 625/625 [==============================] - loss: 0.2054 - acc: 0.9154 - 57ms/step         Eval samples: 10000Epoch 2/5step 3125/3125 [==============================] - loss: 0.3357 - acc: 0.9383 - 126ms/step         Eval begin...The loss value printed in the log is the current batch, and the metric is the average value of previous step.step 625/625 [==============================] - loss: 0.1274 - acc: 0.9245 - 58ms/step        Eval samples: 10000Epoch 3/5step 3125/3125 [==============================] - loss: 0.0413 - acc: 0.9594 - 126ms/step         Eval begin...The loss value printed in the log is the current batch, and the metric is the average value of previous step.step 625/625 [==============================] - loss: 0.0439 - acc: 0.9367 - 57ms/step         Eval samples: 10000Epoch 4/5step 3125/3125 [==============================] - loss: 0.0458 - acc: 0.9696 - 126ms/step        Eval begin...The loss value printed in the log is the current batch, and the metric is the average value of previous step.step 625/625 [==============================] - loss: 0.2247 - acc: 0.9351 - 56ms/step         Eval samples: 10000Epoch 5/5step 3125/3125 [==============================] - loss: 0.0024 - acc: 0.9760 - 128ms/step         Eval begin...The loss value printed in the log is the current batch, and the metric is the average value of previous step.step 625/625 [==============================] - loss: 0.0181 - acc: 0.9374 - 57ms/step         Eval samples: 10000

登录后复制

训练可视化

总结

PVT v2 引入了卷积操作、zero-padding、avgpool的注意力层，从三个方面提升了性能相比同时期的ViT模型，具有更小的参数量和计算量在下游任务，PVT v2 展现了良好的性能

相关攻略

热门推荐