RegionViT提出从区域到局部的视觉Transformer结构，以金字塔结构引入区域到局部注意替代全局自注意。先生成不同贴片大小的区域和局部令牌，经区域自注意提取全局信息，再通过局部自注意传递给局部令牌，结合相对位置编码。在多视觉任务上表现优异，实现高效且兼具全局感受野与局部性。
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近年来，视觉Transformer(VIT)在图像分类方面显示出了与卷积神经网络(CNNs)相当的强大能力。 然而，原始ViT只是直接从自然语言处理中继承了相同的体系结构，而自然语言处理通常没有针对视觉应用进行优化。 基于此，本文提出了一种新的视觉Transformer结构，该结构采用金字塔结构，在视觉Transformer中引入了新的区域到局部的注意而不是全局的自注意。 更具体地说，我们的模型首先从具有不同贴片大小的图像中生成区域令牌和局部令牌，其中每个区域令牌与基于空间位置的一组局部令牌相关联。 区域到局部注意包括两个步骤：首先，区域自注意在所有区域令牌之间提取全局信息，然后局部自注意通过自注意在一个区域令牌和相关的局部令牌之间交换信息。 因此，即使局部自我注意的范围局限于局部区域，但它仍然可以接收到全局信息。 在图像分类、目标和关键点检测、语义分割和动作识别等四个视觉任务上的大量实验表明，我们的方法优于或与包括许多并行工作在内的现有ViT变体相当。
由于全局自注意力计算太贵，很多工作提出使用局部自注意力，即在一个小区域内进行全局自注意力，但是局部自注意力又会带来另外一个问题，即感受野过小。为此，本文提出了一种新的从粗到细的Transformer——RegionViT。通过区域令牌进行全局交互，并将区域令牌包含的全局信息通过局部自注意力传递给对应的局部Token。本文方法的整体架构如图2所示：
本文的核心模块是区域到局部的Transformer编码器，主要思想就是通过区域令牌进行全局交互，并将区域令牌包含的全局信息通过局部自注意力传递给对应的局部Token，具体操作如下公式所示：
yrd=xrd−1+RSA(LN(xrd−1)),yi,jd=[yri,jd∥{xli,j,m,nd−1}m,n∈M]zi,jd=yi,jd+LSA(LN(yi,jd)),xi,jd=zi,jd+FFN(LN(zi,jd))
局部性是理解视觉内容的重要线索。因此，本文提出使用相对位置编码，值得注意的是，该位置编码只添加到局部Token中，不添加区域Token到局部Token的位置编码。具体公式如下：
a(xm,ym),(xn,yn)=softmax(q(xm,ym)k(xn,yn)T+b(xm−xn,ym−yn)),
%matplotlib inlineimport paddleimport numpy as npimport matplotlib.pyplot as pltfrom paddle.vision.datasets import Cifar10from paddle.vision.transforms import Transposefrom paddle.io import Dataset, DataLoaderfrom paddle import nnimport paddle.nn.functional as Fimport paddle.vision.transforms as transformsimport osimport matplotlib.pyplot as pltfrom matplotlib.pyplot import figureimport itertoolsfrom functools import partial
train_tfm = transforms.Compose([
transforms.RandomResizedCrop(224, scale=(0.6, 1.0)),
transforms.ColorJitter(brightness=0.2,contrast=0.2, saturation=0.2),
transforms.RandomHorizontalFlip(0.5),
transforms.RandomRotation(20),
transforms.ToTensor(),
transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
])
test_tfm = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
])paddle.vision.set_image_backend('cv2')# 使用Cifar10数据集train_dataset = Cifar10(data_file='data/data152754/cifar-10-python.tar.gz', mode='train', transform = train_tfm, )
val_dataset = Cifar10(data_file='data/data152754/cifar-10-python.tar.gz', mode='test',transform = test_tfm)print("train_dataset: %d" % len(train_dataset))print("val_dataset: %d" % len(val_dataset))train_dataset: 50000 val_dataset: 10000
batch_size=64
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, drop_last=True, num_workers=4) val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, drop_last=False, num_workers=4)
class LabelSmoothingCrossEntropy(nn.Layer):
def __init__(self, smoothing=0.1):
super().__init__()
self.smoothing = smoothing def forward(self, pred, target):
confidence = 1. - self.smoothing
log_probs = F.log_softmax(pred, axis=-1)
idx = paddle.stack([paddle.arange(log_probs.shape[0]), target], axis=1)
nll_loss = paddle.gather_nd(-log_probs, index=idx)
smooth_loss = paddle.mean(-log_probs, axis=-1)
loss = confidence * nll_loss + self.smoothing * smooth_loss return loss.mean()def drop_path(x, drop_prob=0.0, training=False):
"""
Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ...
"""
if drop_prob == 0.0 or not training: return x
keep_prob = paddle.to_tensor(1 - drop_prob)
shape = (paddle.shape(x)[0],) + (1,) * (x.ndim - 1)
random_tensor = keep_prob + paddle.rand(shape, dtype=x.dtype)
random_tensor = paddle.floor(random_tensor) # binarize
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 LayerNorm2D(nn.Layer):
def __init__(self, channels, eps=1e-5, elementwise_affine=True):
super().__init__()
self.channels = channels
self.eps = paddle.to_tensor(eps)
self.elementwise_affine = elementwise_affine if self.elementwise_affine:
self.weight = self.create_parameter(shape=(1, channels, 1, 1), default_initializer=nn.initializer.Constant(1.0))
self.bias = self.create_parameter(shape=(1, channels, 1, 1), default_initializer=nn.initializer.Constant(0.0)) else:
self.register_buffer('weight', None)
self.register_buffer('bias', None) def forward(self, input):
mean = input.mean(1, keepdim=True)
std = paddle.sqrt(input.var(1, unbiased=False, keepdim=True) + self.eps)
out = (input - mean) / std if self.elementwise_affine:
out = out * self.weight + self.bias return outclass AttentionWithRelPos(nn.Layer):
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.,
attn_map_dim=None, num_cls_tokens=1):
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = qk_scale or head_dim ** -0.5
self.qkv = nn.Linear(dim, dim * 3, 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.num_cls_tokens = num_cls_tokens if attn_map_dim is not None:
one_dim = attn_map_dim[0]
rel_pos_dim = (2 * one_dim - 1)
self.rel_pos = self.create_parameter(shape=(num_heads, rel_pos_dim ** 2), default_initializer=nn.initializer.Constant(0.0))
tmp = paddle.arange(rel_pos_dim ** 2).reshape((rel_pos_dim, rel_pos_dim))
out = []
offset_x = offset_y = one_dim // 2
for y in range(one_dim): for x in range(one_dim): for dy in range(one_dim): for dx in range(one_dim):
out.append(tmp[dy - y + offset_y, dx - x + offset_x])
self.rel_pos_index = paddle.to_tensor(out, dtype=paddle.int32)
tn = nn.initializer.TruncatedNormal(std=.02)
tn(self.rel_pos) else:
self.rel_pos = None
def forward(self, x, patch_attn=False, mask=None):
B, N, C = x.shape
qkv = self.qkv(x).reshape((B, N, 3, self.num_heads, C // self.num_heads)).transpose([2, 0, 3, 1, 4])
q, k, v = qkv[0], qkv[1], qkv[2]
attn = (q @ k.transpose([0, 1, 3, 2])) * self.scale if self.rel_pos is not None and patch_attn: # use for the indicating patch + cls:
rel_pos = self.rel_pos[:, self.rel_pos_index].reshape((self.num_heads, N - self.num_cls_tokens, N - self.num_cls_tokens))
attn[:, :, self.num_cls_tokens:, self.num_cls_tokens:] = attn[:, :, self.num_cls_tokens:, self.num_cls_tokens:] + rel_pos if mask is not None: ## mask is only (BH_sW_s)(ksks)(ksks), need to expand it
mask = mask.unsqueeze(1).expand((-1, self.num_heads, -1, -1))
attn = attn.masked_fill(mask == 0, paddle.finfo(attn.dtype).min)
attn = F.softmax(attn, axis=-1)
attn = self.attn_drop(attn)
x = (attn @ v).transpose([0, 2, 1, 3]).reshape((B, N, C))
x = self.proj(x)
x = self.proj_drop(x) return xdef to_2tuple(x):
return (x, x)class PatchEmbed(nn.Layer):
""" Image to Patch Embedding
"""
def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, patch_conv_type='linear'):
super().__init__()
img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size)
num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
self.img_size = img_size
self.patch_size = patch_size
self.num_patches = num_patches if patch_conv_type == '3conv': if patch_size[0] == 4:
tmp = [
nn.Conv2D(in_chans, embed_dim // 4, kernel_size=3, stride=2, padding=1),
LayerNorm2D(embed_dim // 4),
nn.GELU(),
nn.Conv2D(embed_dim // 4, embed_dim // 2, kernel_size=3, stride=2, padding=1),
LayerNorm2D(embed_dim // 2),
nn.GELU(),
nn.Conv2D(embed_dim // 2, embed_dim, kernel_size=3, stride=1, padding=1),
] else: raise ValueError(f"Unknown patch size {patch_size[0]}")
self.proj = nn.Sequential(*tmp) else: if patch_conv_type == '1conv':
kernel_size = (2 * patch_size[0], 2 * patch_size[1])
stride = (patch_size[0], patch_size[1])
padding = (patch_size[0] - 1, patch_size[1] - 1) else:
kernel_size = patch_size
stride = patch_size
padding = 0
self.proj = nn.Conv2D(in_chans, embed_dim, kernel_size=kernel_size,
stride=stride, padding=padding) def forward(self, x, extra_padding=False):
B, C, H, W = x.shape # FIXME look at relaxing size constraints
# assert H == self.img_size[0] and W == self.img_size[1], \
# f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
if extra_padding and (H % self.patch_size[0] != 0 or W % self.patch_size[1] != 0):
p_l = (self.patch_size[1] - W % self.patch_size[1]) // 2
p_r = (self.patch_size[1] - W % self.patch_size[1]) - p_l
p_t = (self.patch_size[0] - H % self.patch_size[0]) // 2
p_b = (self.patch_size[0] - H % self.patch_size[0]) - p_t
x = F.pad(x, (p_l, p_r, p_t, p_b))
x = self.proj(x) return xclass Mlp(nn.Layer):
""" MLP as used in Vision Transformer, MLP-Mixer and related networks
"""
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, bias=True, drop=0.):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
bias = to_2tuple(bias)
drop_probs = to_2tuple(drop)
self.fc1 = nn.Linear(in_features, hidden_features, bias_attr=bias[0])
self.act = act_layer()
self.drop1 = nn.Dropout(drop_probs[0])
self.fc2 = nn.Linear(hidden_features, out_features, bias_attr=bias[1])
self.drop2 = nn.Dropout(drop_probs[1]) def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.drop1(x)
x = self.fc2(x)
x = self.drop2(x) return xclass R2LAttentionPlusFFN(nn.Layer):
def __init__(self, input_channels, output_channels, kernel_size, num_heads, mlp_ratio=1., qkv_bias=False, qk_scale=None,
act_layer=nn.GELU, norm_layer=nn.LayerNorm, drop_path=0., attn_drop=0., drop=0.,
cls_attn=True):
super().__init__() if not isinstance(kernel_size, (tuple, list)):
kernel_size = [(kernel_size, kernel_size), (kernel_size, kernel_size), 0]
self.kernel_size = kernel_size if cls_attn:
self.norm0 = norm_layer(input_channels) else:
self.norm0 = None
self.norm1 = norm_layer(input_channels)
self.attn = AttentionWithRelPos(
input_channels, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop,
attn_map_dim=(kernel_size[0][0], kernel_size[0][1]), num_cls_tokens=1) # 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 nn.Identity()
self.norm2 = norm_layer(input_channels)
self.mlp = Mlp(in_features=input_channels, hidden_features=int(output_channels * mlp_ratio), out_features=output_channels, act_layer=act_layer, drop=drop)
self.expand = nn.Sequential(
norm_layer(input_channels),
act_layer(),
nn.Linear(input_channels, output_channels)
) if input_channels != output_channels else None
self.output_channels = output_channels
self.input_channels = input_channels def forward(self, xs):
out, B, H, W, mask = xs
cls_tokens = out[:, 0:1, ...]
C = cls_tokens.shape[-1]
cls_tokens = cls_tokens.reshape((B, -1, C)) # (N)x(H/sxW/s)xC
if self.norm0 is not None:
cls_tokens = cls_tokens + self.drop_path(self.attn(self.norm0(cls_tokens))) # (N)x(H/sxK/s)xC
# ks, stride, padding = self.kernel_size
cls_tokens = cls_tokens.reshape((-1, 1, C)) # (NxH/sxK/s)x1xC
out = paddle.concat((cls_tokens, out[:, 1:, ...]), axis=1)
tmp = out
tmp = tmp + self.drop_path(self.attn(self.norm1(tmp), patch_attn=True, mask=mask))
identity = self.expand(tmp) if self.expand is not None else tmp
tmp = identity + self.drop_path(self.mlp(self.norm2(tmp))) return tmpclass Projection(nn.Layer):
def __init__(self, input_channels, output_channels, act_layer, mode='sc'):
super().__init__()
tmp = [] if 'c' in mode:
ks = 2 if 's' in mode else 1
if ks == 2:
stride = ks
ks = ks + 1
padding = ks // 2
else:
stride = ks
padding = 0
if input_channels == output_channels and ks == 1:
tmp.append(nn.Identity()) else:
tmp.extend([
LayerNorm2D(input_channels),
act_layer(),
])
tmp.append(nn.Conv2D(in_channels=input_channels, out_channels=output_channels, kernel_size=ks, stride=stride, padding=padding, groups=input_channels))
self.proj = nn.Sequential(*tmp)
self.proj_cls = self.proj def forward(self, xs):
cls_tokens, patch_tokens = xs # x: BxCxHxW
cls_tokens = self.proj_cls(cls_tokens)
patch_tokens = self.proj(patch_tokens) return cls_tokens, patch_tokensdef convert_to_flatten_layout(cls_tokens, patch_tokens, ws):
"""
Convert the token layer in a flatten form, it will speed up the model.
Furthermore, it also handle the case that if the size between regional tokens and local tokens are not consistent.
"""
# padding if needed, and all paddings are happened at bottom and right.
B, C, H, W = patch_tokens.shape
_, _, H_ks, W_ks = cls_tokens.shape
need_mask = False
p_l, p_r, p_t, p_b = 0, 0, 0, 0
if H % (H_ks * ws) != 0 or W % (W_ks * ws) != 0:
p_l, p_r = 0, W_ks * ws - W
p_t, p_b = 0, H_ks * ws - H
patch_tokens = F.pad(patch_tokens, (p_l, p_r, p_t, p_b))
need_mask = True
B, C, H, W = patch_tokens.shape
kernel_size = [H // H_ks, W // W_ks]
tmp = F.unfold(patch_tokens, kernel_sizes=kernel_size, strides=kernel_size, paddings=[0, 0]) # Nx(Cxksxks)x(H/sxK/s)
patch_tokens = tmp.transpose([0, 2, 1]).reshape((-1, C, kernel_size[0] * kernel_size[1])).transpose([0, 2, 1]) # (NxH/sxK/s)x(ksxks)xC
if need_mask:
BH_sK_s, ksks, C = patch_tokens.shape
H_s, W_s = H // ws, W // ws
mask = paddle.ones(BH_sK_s // B, 1 + ksks, 1 + ksks, dtype='float32')
right = paddle.zeros(1 + ksks, 1 + ksks, dtype='float32')
tmp = paddle.zeros(ws, ws, dtype='float32')
tmp[0:(ws - p_r), 0:(ws - p_r)] = 1.
tmp = tmp.repeat(ws, ws)
right[1:, 1:] = tmp
right[0, 0] = 1
right[0, 1:] = paddle.to_tensor([1.] * (ws - p_r) + [0.] * p_r).repeat(ws)
right[1:, 0] = paddle.to_tensor([1.] * (ws - p_r) + [0.] * p_r).repeat(ws)
bottom = paddle.zeros_like(right)
bottom[0:ws * (ws - p_b) + 1, 0:ws * (ws - p_b) + 1] = 1.
bottom_right = copy.deepcopy(right)
bottom_right[0:ws * (ws - p_b) + 1, 0:ws * (ws - p_b) + 1] = 1.
mask[W_s - 1:(H_s - 1) * W_s:W_s, ...] = right
mask[(H_s - 1) * W_s:, ...] = bottom
mask[-1, ...] = bottom_right
mask = mask.repeat(B, 1, 1) else:
mask = None
cls_tokens = cls_tokens.flatten(2).transpose([0, 2, 1]) # (N)x(H/sxK/s)xC
cls_tokens = cls_tokens.reshape((-1, 1, cls_tokens.shape[-1])) # (NxH/sxK/s)x1xC
out = paddle.concat((cls_tokens, patch_tokens), axis=1) return out, mask, p_l, p_r, p_t, p_b, B, C, H, Wdef convert_to_spatial_layout(out, output_channels, B, H, W, kernel_size, mask, p_l, p_r, p_t, p_b):
"""
Convert the token layer from flatten into 2-D, will be used to downsample the spatial dimension.
"""
cls_tokens = out[:, 0:1, ...]
patch_tokens = out[:, 1:, ...] # cls_tokens: (BxH/sxW/s)x(1)xC, patch_tokens: (BxH/sxW/s)x(ksxks)xC
C = output_channels
kernel_size = kernel_size[0]
H_ks = H // kernel_size[0]
W_ks = W // kernel_size[1] # reorganize data, need to convert back to cls_tokens: BxCxH/sxW/s, patch_tokens: BxCxHxW
cls_tokens = cls_tokens.reshape((B, -1, C)).transpose([0, 2, 1]).reshape((B, C, H_ks, W_ks))
patch_tokens = patch_tokens.transpose([0, 2, 1]).reshape((B, -1, kernel_size[0] * kernel_size[1] * C)).transpose([0, 2, 1])
patch_tokens = F.fold(patch_tokens, [H, W], kernel_sizes=kernel_size, strides=kernel_size, paddings=[0, 0]) if mask is not None: if p_b > 0:
patch_tokens = patch_tokens[:, :, :-p_b, :] if p_r > 0:
patch_tokens = patch_tokens[:, :, :, :-p_r] return cls_tokens, patch_tokensclass ConvAttBlock(nn.Layer):
def __init__(self, input_channels, output_channels, kernel_size, num_blocks, num_heads, mlp_ratio=1., qkv_bias=False, qk_scale=None, pool='sc',
act_layer=nn.GELU, norm_layer=nn.LayerNorm, drop_path_rate=(0.,), attn_drop_rate=0., drop_rate=0.,
cls_attn=True, peg=False):
super().__init__()
tmp = [] if pool:
tmp.append(Projection(input_channels, output_channels, act_layer=act_layer, mode=pool)) for i in range(num_blocks):
kernel_size_ = kernel_size
tmp.append(R2LAttentionPlusFFN(output_channels, output_channels, kernel_size_, num_heads, mlp_ratio, qkv_bias, qk_scale,
act_layer=act_layer, norm_layer=norm_layer, drop_path=drop_path_rate[i], attn_drop=attn_drop_rate, drop=drop_rate,
cls_attn=cls_attn))
self.block = nn.LayerList(tmp)
self.output_channels = output_channels
self.ws = kernel_size if not isinstance(kernel_size, (tuple, list)):
kernel_size = [[kernel_size, kernel_size], [kernel_size, kernel_size], 0]
self.kernel_size = kernel_size
self.peg = nn.Conv2D(output_channels, output_channels, kernel_size=3, padding=1, groups=output_channels, bias=False) if peg else None
def forward(self, xs):
cls_tokens, patch_tokens = xs
cls_tokens, patch_tokens = self.block[0]((cls_tokens, patch_tokens))
out, mask, p_l, p_r, p_t, p_b, B, C, H, W = convert_to_flatten_layout(cls_tokens, patch_tokens, self.ws) for i in range(1, len(self.block)):
blk = self.block[i]
out = blk((out, B, H, W, mask)) if self.peg is not None and i == 1:
cls_tokens, patch_tokens = convert_to_spatial_layout(out, self.output_channels, B, H, W, self.kernel_size, mask, p_l, p_r, p_t, p_b)
cls_tokens = cls_tokens + self.peg(cls_tokens)
patch_tokens = patch_tokens + self.peg(patch_tokens)
out, mask, p_l, p_r, p_t, p_b, B, C, H, W = convert_to_flatten_layout(cls_tokens, patch_tokens, self.ws)
cls_tokens, patch_tokens = convert_to_spatial_layout(out, self.output_channels, B, H, W, self.kernel_size, mask, p_l, p_r, p_t, p_b) return cls_tokens, patch_tokensclass RegionViT(nn.Layer):
"""
Note:
The variable naming mapping between codes and papers:
- cls_tokens -> regional tokens
- patch_tokens -> local tokens
"""
def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=(768,), depth=(12,),
num_heads=(12,), mlp_ratio=4., qkv_bias=True, qk_scale=None, drop_rate=0., attn_drop_rate=0.,
drop_path_rate=0., norm_layer=partial(nn.LayerNorm, epsilon=1e-6), # regionvit parameters
kernel_sizes=None, downsampling=None,
patch_conv_type='3conv',
computed_cls_token=True, peg=False,
det_norm=False):
super().__init__()
self.num_classes = num_classes
self.kernel_sizes = kernel_sizes
self.num_features = embed_dim[-1] # num_features for consistency with other models
self.embed_dim = embed_dim
self.patch_size = patch_size
self.img_size = img_size
self.patch_embed = PatchEmbed(
img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim[0],
patch_conv_type=patch_conv_type) if not isinstance(mlp_ratio, (list, tuple)):
mlp_ratio = [mlp_ratio] * len(depth)
self.computed_cls_token = computed_cls_token
self.cls_token = PatchEmbed(
img_size=img_size, patch_size=patch_size * kernel_sizes[0], in_chans=in_chans, embed_dim=embed_dim[0],
patch_conv_type='linear'
)
self.pos_drop = nn.Dropout(p=drop_rate)
total_depth = sum(depth)
dpr = [x.item() for x in paddle.linspace(0, drop_path_rate, total_depth)] # stochastic depth decay rule
dpr_ptr = 0
self.layers = nn.LayerList() for i in range(len(embed_dim) - 1):
curr_depth = depth[i]
dpr_ = dpr[dpr_ptr: dpr_ptr + curr_depth]
self.layers.append(
ConvAttBlock(embed_dim[i], embed_dim[i + 1], kernel_size=kernel_sizes[i], num_blocks=depth[i], drop_path_rate=dpr_,
num_heads=num_heads[i], mlp_ratio=mlp_ratio[i], qkv_bias=qkv_bias, qk_scale=qk_scale,
pool=downsampling[i], norm_layer=norm_layer, attn_drop_rate=attn_drop_rate, drop_rate=drop_rate,
cls_attn=True, peg=peg)
)
dpr_ptr += curr_depth
self.norm = norm_layer(embed_dim[-1]) # Classifier head
self.head = nn.Linear(embed_dim[-1], num_classes) if num_classes > 0 else nn.Identity() if not computed_cls_token:
tn = nn.initializer.TruncatedNormal(std=.02)
tn(self.cls_token)
self.det_norm = det_norm if self.det_norm: # add a norm layer for the outputs at each stage, for detection
for i in range(4):
layer = LayerNorm2D(embed_dim[1 + i])
layer_name = f'norm{i}'
self.add_module(layer_name, layer)
self.apply(self._init_weights) def _init_weights(self, m):
tn = nn.initializer.TruncatedNormal(std=.02)
ones = nn.initializer.Constant(1.0)
zeros = nn.initializer.Constant(0.0) if isinstance(m, nn.Linear):
tn(m.weight) if isinstance(m, nn.Linear) and m.bias is not None:
zeros(m.bias) elif isinstance(m, nn.LayerNorm):
zeros(m.bias)
ones(m.weight) def forward_features(self, x, detection=False):
o_x = x
x = self.patch_embed(x) # B x branches x classes
cls_tokens = self.cls_token(o_x, extra_padding=True)
x = self.pos_drop(x) # N C H W
tmp_out = [] for idx, layer in enumerate(self.layers):
cls_tokens, x = layer((cls_tokens, x)) if self.det_norm:
norm_layer = getattr(self, f'norm{idx}')
x = norm_layer(x)
tmp_out.append(x) if detection: return tmp_out
N, C, H, W = cls_tokens.shape
cls_tokens = cls_tokens.reshape((N, C, -1)).transpose([0, 2, 1])
cls_tokens = self.norm(cls_tokens)
out = paddle.mean(cls_tokens, axis=1) return out def forward(self, x):
x = self.forward_features(x)
x = self.head(x) return x_model_cfg = { 'tiny': { 'img_size': 224, 'patch_conv_type': '3conv', 'patch_size': 4, 'embed_dim': [64, 64, 128, 256, 512], 'num_heads': [2, 4, 8, 16], 'mlp_ratio': 4., 'depth': [2, 2, 8, 2], 'kernel_sizes': [7, 7, 7, 7], # 8x8, 4x4, 2x2, 1x1,
'downsampling': ['c', 'sc', 'sc', 'sc'],
}, 'small': { 'img_size': 224, 'patch_conv_type': '3conv', 'patch_size': 4, 'embed_dim': [96, 96, 192, 384, 768], 'num_heads': [3, 6, 12, 24], 'mlp_ratio': 4., 'depth': [2, 2, 8, 2], 'kernel_sizes': [7, 7, 7, 7], # 8x8, 4x4, 2x2, 1x1,
'downsampling': ['c', 'sc', 'sc', 'sc'],
}, 'medium': { 'img_size': 224, 'patch_conv_type': '1conv', 'patch_size': 4, 'embed_dim': [96] + [96 * (2 ** i) for i in range(4)], 'num_heads': [3, 6, 12, 24], 'mlp_ratio': 4., 'depth': [2, 2, 14, 2], 'kernel_sizes': [7, 7, 7, 7], # 8x8, 4x4, 2x2, 1x1,
'downsampling': ['c', 'sc', 'sc', 'sc'],
}, 'base': { 'img_size': 224, 'patch_conv_type': '1conv', 'patch_size': 4, 'embed_dim': [128, 128, 256, 512, 1024], 'num_heads': [4, 8, 16, 32], 'mlp_ratio': 4., 'depth': [2, 2, 14, 2], 'kernel_sizes': [7, 7, 7, 7], # 8x8, 4x4, 2x2, 1x1,
'downsampling': ['c', 'sc', 'sc', 'sc'],
}, 'small_w14': { 'img_size': 224, 'patch_conv_type': '3conv', 'patch_size': 4, 'embed_dim': [96, 96, 192, 384, 768], 'num_heads': [3, 6, 12, 24], 'mlp_ratio': 4., 'depth': [2, 2, 8, 2], 'kernel_sizes': [14, 14, 14, 14], # 8x8, 4x4, 2x2, 1x1,
'downsampling': ['c', 'sc', 'sc', 'sc'],
}, 'small_w14_peg': { 'img_size': 224, 'patch_conv_type': '3conv', 'patch_size': 4, 'embed_dim': [96, 96, 192, 384, 768], 'num_heads': [3, 6, 12, 24], 'mlp_ratio': 4., 'depth': [2, 2, 8, 2], 'kernel_sizes': [14, 14, 14, 14], # 8x8, 4x4, 2x2, 1x1,
'downsampling': ['c', 'sc', 'sc', 'sc'], 'peg': True
}, 'base_w14': { 'img_size': 224, 'patch_conv_type': '1conv', 'patch_size': 4, 'embed_dim': [128, 128, 256, 512, 1024], 'num_heads': [4, 8, 16, 32], 'mlp_ratio': 4., 'depth': [2, 2, 14, 2], 'kernel_sizes': [14, 14, 14, 14], # 8x8, 4x4, 2x2, 1x1,
'downsampling': ['c', 'sc', 'sc', 'sc'],
}, 'base_w14_peg': { 'img_size': 224, 'patch_conv_type': '1conv', 'patch_size': 4, 'embed_dim': [128, 128, 256, 512, 1024], 'num_heads': [4, 8, 16, 32], 'mlp_ratio': 4., 'depth': [2, 2, 14, 2], 'kernel_sizes': [14, 14, 14, 14], # 8x8, 4x4, 2x2, 1x1,
'downsampling': ['c', 'sc', 'sc', 'sc'], 'peg': True
},
}num_classes = 10def regionvit_tiny_224():
model_cfg = _model_cfg['tiny']
model = RegionViT(**model_cfg, num_classes=num_classes) return modeldef regionvit_small_224():
model_cfg = _model_cfg['small']
model = RegionViT(**model_cfg, num_classes=num_classes) return modeldef regionvit_small_w14_224():
model_cfg = _model_cfg['small_w14']
model = RegionViT(**model_cfg, num_classes=num_classes) return modeldef regionvit_small_w14_peg_224():
model_cfg = _model_cfg['small_w14_peg']
model = RegionViT(**model_cfg, num_classes=num_classes) return modeldef regionvit_medium_224():
model_cfg = _model_cfg['medium']
model = RegionViT(**model_cfg, num_classes=num_classes) return modeldef regionvit_base_224():
model_cfg = _model_cfg['base']
model = RegionViT(**model_cfg, num_classes=num_classes) return modeldef regionvit_base_w14_224():
model_cfg = _model_cfg['base_w14']
model = RegionViT(**model_cfg, num_classes=num_classes) return modeldef regionvit_base_w14_peg_224():
model_cfg = _model_cfg['base_w14_peg']
model = RegionViT(**model_cfg, num_classes=num_classes) return modelmodel = regionvit_tiny_224() paddle.summary(model, (1, 3, 224, 224))
learning_rate = 0.0001n_epochs = 100paddle.seed(42) np.random.seed(42)
work_path = 'work/model'# RegionViT-Tinymodel = regionvit_tiny_224()
criterion = LabelSmoothingCrossEntropy()
scheduler = paddle.optimizer.lr.CosineAnnealingDecay(learning_rate=learning_rate, T_max=50000 // batch_size * n_epochs, verbose=False)
optimizer = paddle.optimizer.Adam(parameters=model.parameters(), learning_rate=scheduler, weight_decay=1e-5)
gate = 0.0threshold = 0.0best_acc = 0.0val_acc = 0.0loss_record = {'train': {'loss': [], 'iter': []}, 'val': {'loss': [], 'iter': []}} # for recording lossacc_record = {'train': {'acc': [], 'iter': []}, 'val': {'acc': [], 'iter': []}} # for recording accuracyloss_iter = 0acc_iter = 0for epoch in range(n_epochs): # ---------- Training ----------
model.train()
train_num = 0.0
train_loss = 0.0
val_num = 0.0
val_loss = 0.0
accuracy_manager = paddle.metric.Accuracy()
val_accuracy_manager = paddle.metric.Accuracy() print("#===epoch: {}, lr={:.10f}===#".format(epoch, optimizer.get_lr())) for batch_id, data in enumerate(train_loader):
x_data, y_data = data
labels = paddle.unsqueeze(y_data, axis=1)
logits = model(x_data)
loss = criterion(logits, y_data)
acc = accuracy_manager.compute(logits, labels)
accuracy_manager.update(acc) if batch_id % 10 == 0:
loss_record['train']['loss'].append(loss.numpy())
loss_record['train']['iter'].append(loss_iter)
loss_iter += 1
loss.backward()
optimizer.step()
scheduler.step()
optimizer.clear_grad()
train_loss += loss
train_num += len(y_data)
total_train_loss = (train_loss / train_num) * batch_size
train_acc = accuracy_manager.accumulate()
acc_record['train']['acc'].append(train_acc)
acc_record['train']['iter'].append(acc_iter)
acc_iter += 1
# Print the information.
print("#===epoch: {}, train loss is: {}, train acc is: {:2.2f}%===#".format(epoch, total_train_loss.numpy(), train_acc*100)) # ---------- Validation ----------
model.eval() for batch_id, data in enumerate(val_loader):
x_data, y_data = data
labels = paddle.unsqueeze(y_data, axis=1) with paddle.no_grad():
logits = model(x_data)
loss = criterion(logits, y_data)
acc = val_accuracy_manager.compute(logits, labels)
val_accuracy_manager.update(acc)
val_loss += loss
val_num += len(y_data)
total_val_loss = (val_loss / val_num) * batch_size
loss_record['val']['loss'].append(total_val_loss.numpy())
loss_record['val']['iter'].append(loss_iter)
val_acc = val_accuracy_manager.accumulate()
acc_record['val']['acc'].append(val_acc)
acc_record['val']['iter'].append(acc_iter) print("#===epoch: {}, val loss is: {}, val acc is: {:2.2f}%===#".format(epoch, total_val_loss.numpy(), val_acc*100)) # ===================save====================
if val_acc > best_acc:
best_acc = val_acc
paddle.save(model.state_dict(), os.path.join(work_path, 'best_model.pdparams'))
paddle.save(optimizer.state_dict(), os.path.join(work_path, 'best_optimizer.pdopt'))print(best_acc)
paddle.save(model.state_dict(), os.path.join(work_path, 'final_model.pdparams'))
paddle.save(optimizer.state_dict(), os.path.join(work_path, 'final_optimizer.pdopt'))
def plot_learning_curve(record, title='loss', ylabel='CE Loss'):
''' Plot learning curve of your CNN '''
maxtrain = max(map(float, record['train'][title]))
maxval = max(map(float, record['val'][title]))
ymax = max(maxtrain, maxval) * 1.1
mintrain = min(map(float, record['train'][title]))
minval = min(map(float, record['val'][title]))
ymin = min(mintrain, minval) * 0.9
total_steps = len(record['train'][title])
x_1 = list(map(int, record['train']['iter']))
x_2 = list(map(int, record['val']['iter']))
figure(figsize=(10, 6))
plt.plot(x_1, record['train'][title], c='tab:red', label='train')
plt.plot(x_2, record['val'][title], c='tab:cyan', label='val')
plt.ylim(ymin, ymax)
plt.xlabel('Training steps')
plt.ylabel(ylabel)
plt.title('Learning curve of {}'.format(title))
plt.legend()
plt.show()plot_learning_curve(loss_record, title='loss', ylabel='CE Loss')
<Figure size 1000x600 with 1 Axes>
plot_learning_curve(acc_record, title='acc', ylabel='Accuracy')
<Figure size 1000x600 with 1 Axes>
import time
work_path = 'work/model'model = regionvit_tiny_224()
model_state_dict = paddle.load(os.path.join(work_path, 'best_model.pdparams'))
model.set_state_dict(model_state_dict)
model.eval()
aa = time.time()for batch_id, data in enumerate(val_loader):
x_data, y_data = data
labels = paddle.unsqueeze(y_data, axis=1) with paddle.no_grad():
logits = model(x_data)
bb = time.time()print("Throughout:{}".format(int(len(val_dataset)//(bb - aa))))Throughout:678
def get_cifar10_labels(labels):
"""返回CIFAR10数据集的文本标签。"""
text_labels = [ 'airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck'] return [text_labels[int(i)] for i in labels]def show_images(imgs, num_rows, num_cols, pred=None, gt=None, scale=1.5):
"""Plot a list of images."""
figsize = (num_cols * scale, num_rows * scale)
_, axes = plt.subplots(num_rows, num_cols, figsize=figsize)
axes = axes.flatten() for i, (ax, img) in enumerate(zip(axes, imgs)): if paddle.is_tensor(img):
ax.imshow(img.numpy()) else:
ax.imshow(img)
ax.axes.get_xaxis().set_visible(False)
ax.axes.get_yaxis().set_visible(False) if pred or gt:
ax.set_title("pt: " + pred[i] + "\ngt: " + gt[i]) return axeswork_path = 'work/model'X, y = next(iter(DataLoader(val_dataset, batch_size=18))) model = regionvit_tiny_224() model_state_dict = paddle.load(os.path.join(work_path, 'best_model.pdparams')) model.set_state_dict(model_state_dict) model.eval() logits = model(X) y_pred = paddle.argmax(logits, -1) X = paddle.transpose(X, [0, 2, 3, 1]) axes = show_images(X.reshape((18, 224, 224, 3)), 1, 18, pred=get_cifar10_labels(y_pred), gt=get_cifar10_labels(y)) plt.show()
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
<Figure size 2700x150 with 18 Axes>
本文提出了一种从区域到局部的一种从粗到细的Transformer，既具有全局的感受野，又具有局部性，实现简单高效。
以上就是【ICLR 2022】RegionViT：从区域到局部的ViT的详细内容，更多请关注php中文网其它相关文章！
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