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# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Code was based on https://github.com/microsoft/Swin-Transformer
# reference: https://arxiv.org/abs/2111.09883
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddle.nn.initializer import TruncatedNormal, Constant, Normal
import numpy as np
import math
from ..model_zoo.vision_transformer import trunc_normal_, zeros_, ones_, to_2tuple, DropPath, Identity
from ..base.theseus_layer import TheseusLayer
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"SwinTransformerV2_tiny_patch4_window8_256":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformerV2_tiny_patch4_window8_256_pretrained.pdparams",
"SwinTransformerV2_tiny_patch4_window16_256":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformerV2_tiny_patch4_window16_256_pretrained.pdparams",
"SwinTransformerV2_small_patch4_window8_256":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformerV2_small_patch4_window8_256_pretrained.pdparams",
"SwinTransformerV2_small_patch4_window16_256":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformerV2_small_patch4_window16_256_pretrained.pdparams",
"SwinTransformerV2_base_patch4_window8_256":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformerV2_base_patch4_window8_256_pretrained.pdparams",
"SwinTransformerV2_base_patch4_window16_256":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformerV2_base_patch4_window16_256_pretrained.pdparams",
"SwinTransformerV2_base_patch4_window12to16_256":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformerV2_base_patch4_window12to16_256_22kto1k_pretrained.pdparams",
"SwinTransformerV2_base_patch4_window24_384":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformerV2_base_patch4_window24_384_22kto1k_pretrained.pdparams",
"SwinTransformerV2_large_patch4_window16_256":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformerV2_large_patch4_window16_256_22kto1k_pretrained.pdparams",
"SwinTransformerV2_large_patch4_window24_384":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformerV2_large_patch4_window24_384_22kto1k_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
class RollWithIndexSelect(paddle.autograd.PyLayer):
@staticmethod
def forward(ctx, input1, index_fp, index_bp):
N, H, W, C = input1.shape
ctx.input1 = input1
ctx.index_bp = index_bp
result = input1.reshape([N, H * W, C]).index_select(
index_fp, 1).reshape([N, H, W, C])
return result
@staticmethod
def backward(ctx, grad):
input1 = ctx.input1
N, H, W, C = input1.shape
index_bp = ctx.index_bp
grad_input = grad.reshape([N, H * W, C]).index_select(
index_bp, 1).reshape([N, H, W, C])
return grad_input, None, None
def get_roll_index(H, W, shifts, place):
index = np.arange(0, H * W, dtype=np.int64).reshape([H, W])
index_fp = np.roll(index, shift=shifts, axis=(0, 1)).reshape([-1])
index_bp = {i: idx for idx, i in enumerate(index_fp.tolist())}
index_bp = [index_bp[i] for i in range(H * W)]
index_fp = paddle.to_tensor(index_fp, place=place)
index_bp = paddle.to_tensor(index_fp, dtype='int64', place=place)
return [index_fp, index_bp]
class NpuRollWithIndexSelect():
def __init__(self):
self.index_dict = {}
self.roll_with_index_select = RollWithIndexSelect.apply
def __call__(self, x, shifts, axis):
assert x.dim() == 4
assert len(shifts) == 2
assert len(axis) == 2
N, H, W, C = x.shape
key = (H, W, shifts, axis)
if key not in self.index_dict:
self.index_dict[key] = get_roll_index(H, W, shifts, x.place)
index_fp, index_bp = self.index_dict[key]
return self.roll_with_index_select(x, index_fp, index_bp)
class RollWrapper(object):
_roll = None
@staticmethod
def roll(x, shifts, axis):
if RollWrapper._roll is None:
RollWrapper._roll = NpuRollWithIndexSelect(
) if 'npu' in paddle.device.get_all_custom_device_type(
) else paddle.roll
return RollWrapper._roll(x, shifts, axis)
def masked_fill(x, mask, value):
y = paddle.full(x.shape, value, x.dtype)
return paddle.where(mask, y, x)
class Mlp(nn.Layer):
def __init__(self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.GELU,
drop=0.):
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.act = act_layer()
self.fc2 = nn.Linear(hidden_features, out_features)
self.drop = nn.Dropout(drop)
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.drop(x)
x = self.fc2(x)
x = self.drop(x)
return x
def window_partition(x, window_size):
"""
Args:
x: (B, H, W, C)
window_size (int): window size
Returns:
windows: (num_windows*B, window_size, window_size, C)
"""
B, H, W, C = x.shape
x = x.reshape(
[B, H // window_size, window_size, W // window_size, window_size, C])
windows = x.transpose(perm=[0, 1, 3, 2, 4, 5]).reshape(
[-1, window_size, window_size, C])
return windows
def window_reverse(windows, window_size, H, W):
"""
Args:
windows: (num_windows*B, window_size, window_size, C)
window_size (int): Window size
H (int): Height of image
W (int): Width of image
Returns:
x: (B, H, W, C)
"""
C = windows.shape[-1]
B = int(windows.shape[0] / (H * W / window_size / window_size))
x = windows.reshape(
[B, H // window_size, W // window_size, window_size, window_size, C])
x = x.transpose(perm=[0, 1, 3, 2, 4, 5]).reshape([B, H, W, C])
return x
class WindowAttention(nn.Layer):
r""" Window based multi-head self attention (W-MSA) module with relative position bias.
It supports both of shifted and non-shifted window.
Args:
dim (int): Number of input channels.
window_size (tuple[int]): The height and width of the window.
num_heads (int): Number of attention heads.
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
proj_drop (float, optional): Dropout ratio of output. Default: 0.0
pretrained_window_size (tuple[int]): The height and width of the window in pre-training.
"""
def __init__(self,
dim,
window_size,
num_heads,
qkv_bias=True,
attn_drop=0.,
proj_drop=0.,
pretrained_window_size=[0, 0]):
super().__init__()
self.dim = dim
self.window_size = window_size # Wh, Ww
self.pretrained_window_size = pretrained_window_size
self.num_heads = num_heads
self.logit_scale = self.create_parameter(
[num_heads, 1, 1],
dtype='float32',
default_initializer=Constant(math.log(10.)))
# mlp to generate continuous relative position bias
self.cpb_mlp = nn.Sequential(
nn.Linear(
2, 512, bias_attr=True),
nn.ReLU(),
nn.Linear(
512, num_heads, bias_attr=False))
# get relative_coords_table
relative_coords_h = paddle.arange(
-(self.window_size[0] - 1), self.window_size[0], dtype='float32')
relative_coords_w = paddle.arange(
-(self.window_size[1] - 1), self.window_size[1], dtype='float32')
relative_coords_table = paddle.stack(
paddle.meshgrid([relative_coords_h, relative_coords_w])).transpose(
perm=[1, 2, 0]).unsqueeze(0) # 1, 2*Wh-1, 2*Ww-1, 2
if pretrained_window_size[0] > 0:
relative_coords_table[:, :, :, 0] /= (
pretrained_window_size[0] - 1)
relative_coords_table[:, :, :, 1] /= (
pretrained_window_size[1] - 1)
else:
relative_coords_table[:, :, :, 0] /= (self.window_size[0] - 1)
relative_coords_table[:, :, :, 1] /= (self.window_size[1] - 1)
relative_coords_table *= 8 # normalize to -8, 8
relative_coords_table = paddle.sign(
relative_coords_table) * paddle.log2(
paddle.abs(relative_coords_table) + 1.0) / np.log2(8)
self.register_buffer("relative_coords_table", relative_coords_table)
# get pair-wise relative position index for each token inside the window
coords_h = paddle.arange(self.window_size[0])
coords_w = paddle.arange(self.window_size[1])
coords = paddle.stack(paddle.meshgrid(
[coords_h, coords_w])) # 2, Wh, Ww
coords_flatten = paddle.flatten(coords, 1) # 2, Wh*Ww
relative_coords = coords_flatten[:, :,
None] - coords_flatten[:,
None, :] # 2, Wh*Ww, Wh*Ww
relative_coords = relative_coords.transpose(
perm=[1, 2, 0]) # Wh*Ww, Wh*Ww, 2
relative_coords[:, :, 0] += self.window_size[
0] - 1 # shift to start from 0
relative_coords[:, :, 1] += self.window_size[1] - 1
relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
self.register_buffer("relative_position_index",
relative_position_index)
self.qkv = nn.Linear(dim, dim * 3, bias_attr=False)
if qkv_bias:
self.q_bias = self.create_parameter(
[dim], dtype='float32', default_initializer=zeros_)
self.v_bias = self.create_parameter(
[dim], dtype='float32', default_initializer=zeros_)
else:
self.q_bias = None
self.v_bias = None
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
self.softmax = nn.Softmax(axis=-1)
def forward(self, x, mask=None):
"""
Args:
x: input features with shape of (num_windows*B, N, C)
mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
"""
B_, N, C = x.shape
qkv_bias = None
if self.q_bias is not None:
qkv_bias = paddle.concat(
x=[self.q_bias, paddle.zeros_like(self.v_bias), self.v_bias])
qkv = F.linear(x=x, weight=self.qkv.weight, bias=qkv_bias)
qkv = qkv.reshape(shape=[
B_, N, 3, self.num_heads, qkv.shape[-1] // (3 * self.num_heads)
]).transpose(perm=[2, 0, 3, 1, 4])
q, k, v = qkv[0], qkv[1], qkv[
2] # make paddlescript happy (cannot use tensor as tuple)
# cosine attention
attn = (F.normalize(
q, axis=-1) @F.normalize(
k, axis=-1).transpose(perm=[0, 1, 3, 2]))
logit_scale = paddle.clip(
self.logit_scale, max=math.log(1. / 0.01)).exp()
attn = attn * logit_scale
relative_position_bias_table = self.cpb_mlp(
self.relative_coords_table).reshape([-1, self.num_heads])
relative_position_bias = relative_position_bias_table[
self.relative_position_index.reshape([-1])].reshape([
self.window_size[0] * self.window_size[1],
self.window_size[0] * self.window_size[1], -1
]) # Wh*Ww,Wh*Ww,nH
relative_position_bias = relative_position_bias.transpose(
perm=[2, 0, 1]) # nH, Wh*Ww, Wh*Ww
relative_position_bias = 16 * F.sigmoid(relative_position_bias)
attn = attn + relative_position_bias.unsqueeze(0)
if mask is not None:
nW = mask.shape[0]
attn = attn.reshape([B_ // nW, nW, self.num_heads, N, N
]) + mask.unsqueeze(1).unsqueeze(0)
attn = attn.reshape([-1, self.num_heads, N, N])
attn = self.softmax(attn)
else:
attn = self.softmax(attn)
attn = self.attn_drop(attn)
x = (attn @v).transpose(perm=[0, 2, 1, 3]).reshape(shape=[B_, N, C])
x = self.proj(x)
x = self.proj_drop(x)
return x
def extra_repr(self):
return f'dim={self.dim}, window_size={self.window_size}, ' \
f'pretrained_window_size={self.pretrained_window_size}, num_heads={self.num_heads}'
def flops(self, N):
# calculate flops for 1 window with token length of N
flops = 0
# qkv = self.qkv(x)
flops += N * self.dim * 3 * self.dim
# attn = (q @ k.transpose(-2, -1))
flops += self.num_heads * N * (self.dim // self.num_heads) * N
# x = (attn @ v)
flops += self.num_heads * N * N * (self.dim // self.num_heads)
# x = self.proj(x)
flops += N * self.dim * self.dim
return flops
class SwinTransformerBlock(nn.Layer):
r""" Swin Transformer Block.
Args:
dim (int): Number of input channels.
input_resolution (tuple[int]): Input resulotion.
num_heads (int): Number of attention heads.
window_size (int): Window size.
shift_size (int): Shift size for SW-MSA.
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
drop (float, optional): Dropout rate. Default: 0.0
attn_drop (float, optional): Attention dropout rate. Default: 0.0
drop_path (float, optional): Stochastic depth rate. Default: 0.0
act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
pretrained_window_size (int): Window size in pre-training.
"""
def __init__(self,
dim,
input_resolution,
num_heads,
window_size=8,
shift_size=0,
mlp_ratio=4.,
qkv_bias=True,
drop=0.,
attn_drop=0.,
drop_path=0.,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm,
pretrained_window_size=0):
super().__init__()
self.dim = dim
self.input_resolution = input_resolution
self.num_heads = num_heads
self.window_size = window_size
self.shift_size = shift_size
self.mlp_ratio = mlp_ratio
if min(self.input_resolution) <= self.window_size:
# if window size is larger than input resolution, we don't partition windows
self.shift_size = 0
self.window_size = min(self.input_resolution)
assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
self.norm1 = norm_layer(dim)
self.attn = WindowAttention(
dim,
window_size=to_2tuple(self.window_size),
num_heads=num_heads,
qkv_bias=qkv_bias,
attn_drop=attn_drop,
proj_drop=drop,
pretrained_window_size=to_2tuple(pretrained_window_size))
self.drop_path = DropPath(
drop_path) if drop_path > 0. else nn.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)
if self.shift_size > 0:
# calculate attention mask for SW-MSA
H, W = self.input_resolution
img_mask = paddle.zeros([1, H, W, 1]) # 1 H W 1
h_slices = (slice(0, -self.window_size),
slice(-self.window_size, -self.shift_size),
slice(-self.shift_size, None))
w_slices = (slice(0, -self.window_size),
slice(-self.window_size, -self.shift_size),
slice(-self.shift_size, None))
cnt = 0
for h in h_slices:
for w in w_slices:
img_mask[:, h, w, :] = cnt
cnt += 1
mask_windows = window_partition(
img_mask, self.window_size) # nW, window_size, window_size, 1
mask_windows = mask_windows.reshape(
shape=[-1, self.window_size * self.window_size])
attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
attn_mask = masked_fill(attn_mask, attn_mask != 0, float(-100.0))
attn_mask = masked_fill(attn_mask, attn_mask == 0, float(0.0))
else:
attn_mask = None
self.register_buffer("attn_mask", attn_mask)
def forward(self, x):
H, W = self.input_resolution
B, L, C = x.shape
assert L == H * W, "input feature has wrong size"
shortcut = x
x = x.reshape(shape=[B, H, W, C])
# cyclic shift
if self.shift_size > 0:
shifted_x = RollWrapper.roll(
x, shifts=(-self.shift_size, -self.shift_size), axis=(1, 2))
else:
shifted_x = x
# partition windows
x_windows = window_partition(
shifted_x, self.window_size) # nW*B, window_size, window_size, C
x_windows = x_windows.reshape(
[-1, self.window_size * self.window_size,
C]) # nW*B, window_size*window_size, C
# W-MSA/SW-MSA
attn_windows = self.attn(
x_windows, mask=self.attn_mask) # nW*B, window_size*window_size, C
# merge windows
attn_windows = attn_windows.reshape(
[-1, self.window_size, self.window_size, C])
shifted_x = window_reverse(attn_windows, self.window_size, H,
W) # B H' W' C
# reverse cyclic shift
if self.shift_size > 0:
x = RollWrapper.roll(
shifted_x,
shifts=(self.shift_size, self.shift_size),
axis=(1, 2))
else:
x = shifted_x
x = x.reshape([B, H * W, C])
x = shortcut + self.drop_path(self.norm1(x))
# FFN
x = x + self.drop_path(self.norm2(self.mlp(x)))
return x
def extra_repr(self):
return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
def flops(self):
flops = 0
H, W = self.input_resolution
# norm1
flops += self.dim * H * W
# W-MSA/SW-MSA
nW = H * W / self.window_size / self.window_size
flops += nW * self.attn.flops(self.window_size * self.window_size)
# mlp
flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
# norm2
flops += self.dim * H * W
return flops
class PatchMerging(nn.Layer):
r""" Patch Merging Layer.
Args:
input_resolution (tuple[int]): Resolution of input feature.
dim (int): Number of input channels.
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
"""
def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
super().__init__()
self.input_resolution = input_resolution
self.dim = dim
self.reduction = nn.Linear(4 * dim, 2 * dim, bias_attr=False)
self.norm = norm_layer(2 * dim)
def forward(self, x):
"""
x: B, H*W, C
"""
H, W = self.input_resolution
B, L, C = x.shape
assert L == H * W, "input feature has wrong size"
assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
x = x.reshape([B, H // 2, 2, W // 2, 2, C])
x = x.transpose((0, 1, 3, 4, 2, 5))
x = x.reshape([B, H * W // 4, 4 * C]) # B H/2*W/2 4*C
x = self.reduction(x)
x = self.norm(x)
return x
def extra_repr(self):
return f"input_resolution={self.input_resolution}, dim={self.dim}"
def flops(self):
H, W = self.input_resolution
flops = (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
flops += H * W * self.dim // 2
return flops
class BasicLayer(nn.Layer):
""" A basic Swin Transformer layer for one stage.
Args:
dim (int): Number of input channels.
input_resolution (tuple[int]): Input resolution.
depth (int): Number of blocks.
num_heads (int): Number of attention heads.
window_size (int): Local window size.
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
drop (float, optional): Dropout rate. Default: 0.0
attn_drop (float, optional): Attention dropout rate. Default: 0.0
drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
pretrained_window_size (int): Local window size in pre-training.
"""
def __init__(self,
dim,
input_resolution,
depth,
num_heads,
window_size,
mlp_ratio=4.,
qkv_bias=True,
drop=0.,
attn_drop=0.,
drop_path=0.,
norm_layer=nn.LayerNorm,
downsample=None,
pretrained_window_size=0):
super().__init__()
self.dim = dim
self.input_resolution = input_resolution
self.depth = depth
# build blocks
self.blocks = nn.LayerList([
SwinTransformerBlock(
dim=dim,
input_resolution=input_resolution,
num_heads=num_heads,
window_size=window_size,
shift_size=0 if (i % 2 == 0) else window_size // 2,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
drop=drop,
attn_drop=attn_drop,
drop_path=drop_path[i]
if isinstance(drop_path, list) else drop_path,
norm_layer=norm_layer,
pretrained_window_size=pretrained_window_size)
for i in range(depth)
])
# patch merging layer
if downsample is not None:
self.downsample = downsample(
input_resolution, dim=dim, norm_layer=norm_layer)
else:
self.downsample = None
def forward(self, x):
for blk in self.blocks:
x = blk(x)
if self.downsample is not None:
x = self.downsample(x)
return x
def extra_repr(self):
return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
def flops(self):
flops = 0
for blk in self.blocks:
flops += blk.flops()
if self.downsample is not None:
flops += self.downsample.flops()
return flops
class PatchEmbed(nn.Layer):
r""" Image to Patch Embedding
Args:
img_size (int): Image size. Default: 256.
patch_size (int): Patch token size. Default: 4.
in_chans (int): Number of input image channels. Default: 3.
embed_dim (int): Number of linear projection output channels. Default: 96.
norm_layer (nn.Module, optional): Normalization layer. Default: None
"""
def __init__(self,
img_size=256,
patch_size=4,
in_chans=3,
embed_dim=96,
norm_layer=None):
super().__init__()
img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size)
patches_resolution = [
img_size[0] // patch_size[0], img_size[1] // patch_size[1]
]
self.img_size = img_size
self.patch_size = patch_size
self.patches_resolution = patches_resolution
self.num_patches = patches_resolution[0] * patches_resolution[1]
self.in_chans = in_chans
self.embed_dim = embed_dim
self.proj = nn.Conv2D(
in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
if norm_layer is not None:
self.norm = norm_layer(embed_dim)
else:
self.norm = None
def forward(self, x):
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]})."
x = self.proj(x).flatten(2).transpose([0, 2, 1]) # B Ph*Pw C
if self.norm is not None:
x = self.norm(x)
return x
def flops(self):
Ho, Wo = self.patches_resolution
flops = Ho * Wo * self.embed_dim * self.in_chans * (
self.patch_size[0] * self.patch_size[1])
if self.norm is not None:
flops += Ho * Wo * self.embed_dim
return flops
class SwinTransformerV2(nn.Layer):
r""" Swin TransformerV2
A PaddlePaddle impl of : `Swin Transformer V2: Scaling Up Capacity and Resolution` -
https://arxiv.org/abs/2111.09883
Args:
img_size (int | tuple(int)): Input image size. Default 256
patch_size (int | tuple(int)): Patch size. Default: 4
in_chans (int): Number of input image channels. Default: 3
class_num (int): Number of classes for classification head. Default: 1000
embed_dim (int): Patch embedding dimension. Default: 96
depths (tuple(int)): Depth of each Swin Transformer layer.
num_heads (tuple(int)): Number of attention heads in different layers.
window_size (int): Window size. Default: 7
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
drop_rate (float): Dropout rate. Default: 0
attn_drop_rate (float): Attention dropout rate. Default: 0
drop_path_rate (float): Stochastic depth rate. Default: 0.1
norm_layer (nn.Layer): Normalization layer. Default: nn.LayerNorm.
ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
patch_norm (bool): If True, add normalization after patch embedding. Default: True
pretrained_window_sizes (tuple(int)): Pretrained window sizes of each layer.
"""
def __init__(self,
img_size=256,
patch_size=4,
in_chans=3,
class_num=1000,
embed_dim=96,
depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 24],
window_size=7,
mlp_ratio=4.,
qkv_bias=True,
drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.1,
norm_layer=nn.LayerNorm,
ape=False,
patch_norm=True,
pretrained_window_sizes=[0, 0, 0, 0],
**kwargs):
super().__init__()
self.class_num = class_num
self.num_layers = len(depths)
self.embed_dim = embed_dim
self.ape = ape
self.patch_norm = patch_norm
self.num_features = int(embed_dim * 2**(self.num_layers - 1))
self.mlp_ratio = mlp_ratio
# split image into non-overlapping patches
self.patch_embed = PatchEmbed(
img_size=img_size,
patch_size=patch_size,
in_chans=in_chans,
embed_dim=embed_dim,
norm_layer=norm_layer if self.patch_norm else None)
num_patches = self.patch_embed.num_patches
patches_resolution = self.patch_embed.patches_resolution
self.patches_resolution = patches_resolution
# absolute position embedding
if self.ape:
self.absolute_pos_embed = self.create_parameter(
shape=(1, num_patches, embed_dim), default_initializer=zeros_)
trunc_normal_(self.absolute_pos_embed)
self.pos_drop = nn.Dropout(p=drop_rate)
# stochastic depth
dpr = [
x.item() for x in paddle.linspace(0, drop_path_rate, sum(depths))
] # stochastic depth decay rule
# build layers
self.layers = nn.LayerList()
for i_layer in range(self.num_layers):
layer = BasicLayer(
dim=int(embed_dim * 2**i_layer),
input_resolution=(patches_resolution[0] // (2**i_layer),
patches_resolution[1] // (2**i_layer)),
depth=depths[i_layer],
num_heads=num_heads[i_layer],
window_size=window_size,
mlp_ratio=self.mlp_ratio,
qkv_bias=qkv_bias,
drop=drop_rate,
attn_drop=attn_drop_rate,
drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
norm_layer=norm_layer,
downsample=PatchMerging
if (i_layer < self.num_layers - 1) else None,
pretrained_window_size=pretrained_window_sizes[i_layer])
self.layers.append(layer)
self.norm = norm_layer(self.num_features)
self.avgpool = nn.AdaptiveAvgPool1D(1)
self.head = nn.Linear(self.num_features,
class_num) if class_num > 0 else nn.Identity()
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(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):
x = self.patch_embed(x)
if self.ape:
x = x + self.absolute_pos_embed
x = self.pos_drop(x)
for layer in self.layers:
x = layer(x)
x = self.norm(x) # B L C
x = self.avgpool(x.transpose([0, 2, 1])) # B C 1
x = paddle.flatten(x, 1)
return x
def forward(self, x):
x = self.forward_features(x)
x = self.head(x)
return x
def flops(self):
flops = 0
flops += self.patch_embed.flops()
for i, layer in enumerate(self.layers):
flops += layer.flops()
flops += self.num_features * self.patches_resolution[
0] * self.patches_resolution[1] // (2**self.num_layers)
flops += self.num_features * self.class_num
return flops
def _load_pretrained(pretrained, model, model_url, use_ssld=False):
if pretrained is False:
pass
elif pretrained is True:
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
elif isinstance(pretrained, str):
load_dygraph_pretrain(model, pretrained)
else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def SwinTransformerV2_tiny_patch4_window8_256(pretrained=False,
use_ssld=False,
**kwargs):
model = SwinTransformerV2(
img_size=256,
embed_dim=96,
depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 24],
window_size=8,
drop_path_rate=0.2,
**kwargs)
_load_pretrained(
pretrained,
model,
MODEL_URLS["SwinTransformerV2_tiny_patch4_window8_256"],
use_ssld=use_ssld)
return model
def SwinTransformerV2_tiny_patch4_window16_256(pretrained=False,
use_ssld=False,
**kwargs):
model = SwinTransformerV2(
img_size=256,
embed_dim=96,
depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 24],
window_size=16,
drop_path_rate=0.2,
**kwargs)
_load_pretrained(
pretrained,
model,
MODEL_URLS["SwinTransformerV2_tiny_patch4_window16_256"],
use_ssld=use_ssld)
return model
def SwinTransformerV2_small_patch4_window8_256(pretrained=False,
use_ssld=False,
**kwargs):
model = SwinTransformerV2(
img_size=256,
embed_dim=96,
depths=[2, 2, 18, 2],
num_heads=[3, 6, 12, 24],
window_size=8,
drop_path_rate=0.3,
**kwargs)
_load_pretrained(
pretrained,
model,
MODEL_URLS["SwinTransformerV2_small_patch4_window8_256"],
use_ssld=use_ssld)
return model
def SwinTransformerV2_small_patch4_window16_256(pretrained=False,
use_ssld=False,
**kwargs):
model = SwinTransformerV2(
img_size=256,
embed_dim=96,
depths=[2, 2, 18, 2],
num_heads=[3, 6, 12, 24],
window_size=16,
drop_path_rate=0.3,
**kwargs)
_load_pretrained(
pretrained,
model,
MODEL_URLS["SwinTransformerV2_small_patch4_window16_256"],
use_ssld=use_ssld)
return model
def SwinTransformerV2_base_patch4_window8_256(pretrained=False,
use_ssld=False,
**kwargs):
model = SwinTransformerV2(
img_size=256,
embed_dim=128,
depths=[2, 2, 18, 2],
num_heads=[4, 8, 16, 32],
window_size=8,
drop_path_rate=0.5,
**kwargs)
_load_pretrained(
pretrained,
model,
MODEL_URLS["SwinTransformerV2_base_patch4_window8_256"],
use_ssld=use_ssld)
return model
def SwinTransformerV2_base_patch4_window16_256(pretrained=False,
use_ssld=False,
**kwargs):
model = SwinTransformerV2(
img_size=256,
embed_dim=128,
depths=[2, 2, 18, 2],
num_heads=[4, 8, 16, 32],
window_size=16,
drop_path_rate=0.5,
**kwargs)
_load_pretrained(
pretrained,
model,
MODEL_URLS["SwinTransformerV2_base_patch4_window16_256"],
use_ssld=use_ssld)
return model
def SwinTransformerV2_base_patch4_window12to16_256(pretrained=False,
use_ssld=False,
**kwargs):
model = SwinTransformerV2(
img_size=256,
embed_dim=128,
depths=[2, 2, 18, 2],
num_heads=[4, 8, 16, 32],
window_size=16,
drop_path_rate=0.2,
**kwargs)
_load_pretrained(
pretrained,
model,
MODEL_URLS["SwinTransformerV2_base_patch4_window12to16_256"],
use_ssld=use_ssld)
return model
def SwinTransformerV2_base_patch4_window24_384(pretrained=False,
use_ssld=False,
**kwargs):
model = SwinTransformerV2(
img_size=384,
embed_dim=128,
depths=[2, 2, 18, 2],
num_heads=[4, 8, 16, 32],
window_size=24,
drop_path_rate=0.2,
**kwargs)
_load_pretrained(
pretrained,
model,
MODEL_URLS["SwinTransformerV2_base_patch4_window24_384"],
use_ssld=use_ssld)
return model
def SwinTransformerV2_large_patch4_window16_256(pretrained=False,
use_ssld=False,
**kwargs):
model = SwinTransformerV2(
img_size=256,
embed_dim=192,
depths=[2, 2, 18, 2],
num_heads=[6, 12, 24, 48],
window_size=16,
drop_path_rate=0.2,
**kwargs)
_load_pretrained(
pretrained,
model,
MODEL_URLS["SwinTransformerV2_large_patch4_window16_256"],
use_ssld=use_ssld)
return model
def SwinTransformerV2_large_patch4_window24_384(pretrained=False,
use_ssld=False,
**kwargs):
model = SwinTransformerV2(
img_size=384,
embed_dim=192,
depths=[2, 2, 18, 2],
num_heads=[6, 12, 24, 48],
window_size=24,
drop_path_rate=0.2,
**kwargs)
_load_pretrained(
pretrained,
model,
MODEL_URLS["SwinTransformerV2_large_patch4_window24_384"],
use_ssld=use_ssld)
return model
ppcls/arch/backbone/model_zoo/swin_transformerv2.py 已删除 100644 → 0
浏览文件 @ 272e7536
# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Code was based on https://github.com/microsoft/Swin-Transformer
# reference: https://arxiv.org/abs/2111.09883
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddle.nn.initializer import TruncatedNormal, Constant, Normal
import numpy as np
import math
from .vision_transformer import trunc_normal_, zeros_, ones_, to_2tuple, DropPath, Identity
from ..base.theseus_layer import TheseusLayer
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"SwinTransformerV2_tiny_patch4_window8_256":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformerV2_tiny_patch4_window8_256_pretrained.pdparams",
"SwinTransformerV2_tiny_patch4_window16_256":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformerV2_tiny_patch4_window16_256_pretrained.pdparams",
"SwinTransformerV2_small_patch4_window8_256":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformerV2_small_patch4_window8_256_pretrained.pdparams",
"SwinTransformerV2_small_patch4_window16_256":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformerV2_small_patch4_window16_256_pretrained.pdparams",
"SwinTransformerV2_base_patch4_window8_256":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformerV2_base_patch4_window8_256_pretrained.pdparams",
"SwinTransformerV2_base_patch4_window16_256":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformerV2_base_patch4_window16_256_pretrained.pdparams",
"SwinTransformerV2_base_patch4_window12to16_256":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformerV2_base_patch4_window12to16_256_22kto1k_pretrained.pdparams",
"SwinTransformerV2_base_patch4_window24_384":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformerV2_base_patch4_window24_384_22kto1k_pretrained.pdparams",
"SwinTransformerV2_large_patch4_window16_256":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformerV2_large_patch4_window16_256_22kto1k_pretrained.pdparams",
"SwinTransformerV2_large_patch4_window24_384":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformerV2_large_patch4_window24_384_22kto1k_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
class RollWithIndexSelect(paddle.autograd.PyLayer):
@staticmethod
def forward(ctx, input1, index_fp, index_bp):
N, H, W, C = input1.shape
ctx.input1 = input1
ctx.index_bp = index_bp
result = input1.reshape([N, H * W, C]).index_select(
index_fp, 1).reshape([N, H, W, C])
return result
@staticmethod
def backward(ctx, grad):
input1 = ctx.input1
N, H, W, C = input1.shape
index_bp = ctx.index_bp
grad_input = grad.reshape([N, H * W, C]).index_select(
index_bp, 1).reshape([N, H, W, C])
return grad_input, None, None
def get_roll_index(H, W, shifts, place):
index = np.arange(0, H * W, dtype=np.int64).reshape([H, W])
index_fp = np.roll(index, shift=shifts, axis=(0, 1)).reshape([-1])
index_bp = {i: idx for idx, i in enumerate(index_fp.tolist())}
index_bp = [index_bp[i] for i in range(H * W)]
index_fp = paddle.to_tensor(index_fp, place=place)
index_bp = paddle.to_tensor(index_fp, dtype='int64', place=place)
return [index_fp, index_bp]
class NpuRollWithIndexSelect():
def __init__(self):
self.index_dict = {}
self.roll_with_index_select = RollWithIndexSelect.apply
def __call__(self, x, shifts, axis):
assert x.dim() == 4
assert len(shifts) == 2
assert len(axis) == 2
N, H, W, C = x.shape
key = (H, W, shifts, axis)
if key not in self.index_dict:
self.index_dict[key] = get_roll_index(H, W, shifts, x.place)
index_fp, index_bp = self.index_dict[key]
return self.roll_with_index_select(x, index_fp, index_bp)
class RollWrapper(object):
_roll = None
@staticmethod
def roll(x, shifts, axis):
if RollWrapper._roll is None:
RollWrapper._roll = NpuRollWithIndexSelect(
) if 'npu' in paddle.device.get_all_custom_device_type(
) else paddle.roll
return RollWrapper._roll(x, shifts, axis)
def masked_fill(x, mask, value):
y = paddle.full(x.shape, value, x.dtype)
return paddle.where(mask, y, x)
class Mlp(nn.Layer):
def __init__(self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.GELU,
drop=0.):
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.act = act_layer()
self.fc2 = nn.Linear(hidden_features, out_features)
self.drop = nn.Dropout(drop)
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.drop(x)
x = self.fc2(x)
x = self.drop(x)
return x
def window_partition(x, window_size):
"""
Args:
x: (B, H, W, C)
window_size (int): window size
Returns:
windows: (num_windows*B, window_size, window_size, C)
"""
B, H, W, C = x.shape
x = x.reshape(
[B, H // window_size, window_size, W // window_size, window_size, C])
windows = x.transpose(perm=[0, 1, 3, 2, 4, 5]).reshape(
[-1, window_size, window_size, C])
return windows
def window_reverse(windows, window_size, H, W):
"""
Args:
windows: (num_windows*B, window_size, window_size, C)
window_size (int): Window size
H (int): Height of image
W (int): Width of image
Returns:
x: (B, H, W, C)
"""
C = windows.shape[-1]
B = int(windows.shape[0] / (H * W / window_size / window_size))
x = windows.reshape(
[-1, H // window_size, W // window_size, window_size, window_size, C])
x = x.transpose(perm=[0, 1, 3, 2, 4, 5]).reshape([B, H, W, C])
return x
class WindowAttention(nn.Layer):
r""" Window based multi-head self attention (W-MSA) module with relative position bias.
It supports both of shifted and non-shifted window.
Args:
dim (int): Number of input channels.
window_size (tuple[int]): The height and width of the window.
num_heads (int): Number of attention heads.
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
proj_drop (float, optional): Dropout ratio of output. Default: 0.0
pretrained_window_size (tuple[int]): The height and width of the window in pre-training.
"""
def __init__(self,
dim,
window_size,
num_heads,
qkv_bias=True,
attn_drop=0.,
proj_drop=0.,
pretrained_window_size=[0, 0]):
super().__init__()
self.dim = dim
self.window_size = window_size # Wh, Ww
self.pretrained_window_size = pretrained_window_size
self.num_heads = num_heads
self.logit_scale = self.create_parameter(
[num_heads, 1, 1],
dtype='float32',
default_initializer=Constant(math.log(10.)))
# mlp to generate continuous relative position bias
self.cpb_mlp = nn.Sequential(
nn.Linear(
2, 512, bias_attr=True),
nn.ReLU(),
nn.Linear(
512, num_heads, bias_attr=False))
# get relative_coords_table
relative_coords_h = paddle.arange(
-(self.window_size[0] - 1), self.window_size[0], dtype='float32')
relative_coords_w = paddle.arange(
-(self.window_size[1] - 1), self.window_size[1], dtype='float32')
relative_coords_table = paddle.stack(
paddle.meshgrid([relative_coords_h, relative_coords_w])).transpose(
perm=[1, 2, 0]).unsqueeze(0) # 1, 2*Wh-1, 2*Ww-1, 2
if pretrained_window_size[0] > 0:
relative_coords_table[:, :, :, 0] /= (
pretrained_window_size[0] - 1)
relative_coords_table[:, :, :, 1] /= (
pretrained_window_size[1] - 1)
else:
relative_coords_table[:, :, :, 0] /= (self.window_size[0] - 1)
relative_coords_table[:, :, :, 1] /= (self.window_size[1] - 1)
relative_coords_table *= 8 # normalize to -8, 8
relative_coords_table = paddle.sign(
relative_coords_table) * paddle.log2(
paddle.abs(relative_coords_table) + 1.0) / np.log2(8)
self.register_buffer("relative_coords_table", relative_coords_table)
# get pair-wise relative position index for each token inside the window
coords_h = paddle.arange(self.window_size[0])
coords_w = paddle.arange(self.window_size[1])
coords = paddle.stack(paddle.meshgrid(
[coords_h, coords_w])) # 2, Wh, Ww
coords_flatten = paddle.flatten(coords, 1) # 2, Wh*Ww
relative_coords = coords_flatten[:, :,
None] - coords_flatten[:,
None, :] # 2, Wh*Ww, Wh*Ww
relative_coords = relative_coords.transpose(
perm=[1, 2, 0]) # Wh*Ww, Wh*Ww, 2
relative_coords[:, :, 0] += self.window_size[
0] - 1 # shift to start from 0
relative_coords[:, :, 1] += self.window_size[1] - 1
relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
self.register_buffer("relative_position_index",
relative_position_index)
self.qkv = nn.Linear(dim, dim * 3, bias_attr=False)
if qkv_bias:
self.q_bias = self.create_parameter(
[dim], dtype='float32', default_initializer=zeros_)
self.v_bias = self.create_parameter(
[dim], dtype='float32', default_initializer=zeros_)
else:
self.q_bias = None
self.v_bias = None
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
self.softmax = nn.Softmax(axis=-1)
def forward(self, x, mask=None):
"""
Args:
x: input features with shape of (num_windows*B, N, C)
mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
"""
B_, N, C = x.shape
qkv_bias = None
if self.q_bias is not None:
qkv_bias = paddle.concat(
x=[self.q_bias, paddle.zeros_like(self.v_bias), self.v_bias])
qkv = F.linear(x=x, weight=self.qkv.weight, bias=qkv_bias)
qkv = qkv.reshape(shape=[
B_, N, 3, self.num_heads, qkv.shape[-1] // (3 * self.num_heads)
]).transpose(perm=[2, 0, 3, 1, 4])
q, k, v = qkv[0], qkv[1], qkv[
2] # make paddlescript happy (cannot use tensor as tuple)
# cosine attention
attn = (F.normalize(
q, axis=-1) @F.normalize(
k, axis=-1).transpose(perm=[0, 1, 3, 2]))
logit_scale = paddle.clip(
self.logit_scale, max=math.log(1. / 0.01)).exp()
attn = attn * logit_scale
relative_position_bias_table = self.cpb_mlp(
self.relative_coords_table).reshape([-1, self.num_heads])
relative_position_bias = relative_position_bias_table[
self.relative_position_index.reshape([-1])].reshape([
self.window_size[0] * self.window_size[1],
self.window_size[0] * self.window_size[1], -1
]) # Wh*Ww,Wh*Ww,nH
relative_position_bias = relative_position_bias.transpose(
perm=[2, 0, 1]) # nH, Wh*Ww, Wh*Ww
relative_position_bias = 16 * F.sigmoid(relative_position_bias)
attn = attn + relative_position_bias.unsqueeze(0)
if mask is not None:
nW = mask.shape[0]
attn = attn.reshape([B_ // nW, nW, self.num_heads, N, N
]) + mask.unsqueeze(1).unsqueeze(0)
attn = attn.reshape([-1, self.num_heads, N, N])
attn = self.softmax(attn)
else:
attn = self.softmax(attn)
attn = self.attn_drop(attn)
x = (attn @v).transpose(perm=[0, 2, 1, 3]).reshape(shape=[B_, N, C])
x = self.proj(x)
x = self.proj_drop(x)
return x
def extra_repr(self):
return f'dim={self.dim}, window_size={self.window_size}, ' \
f'pretrained_window_size={self.pretrained_window_size}, num_heads={self.num_heads}'
def flops(self, N):
# calculate flops for 1 window with token length of N
flops = 0
# qkv = self.qkv(x)
flops += N * self.dim * 3 * self.dim
# attn = (q @ k.transpose(-2, -1))
flops += self.num_heads * N * (self.dim // self.num_heads) * N
# x = (attn @ v)
flops += self.num_heads * N * N * (self.dim // self.num_heads)
# x = self.proj(x)
flops += N * self.dim * self.dim
return flops
class SwinTransformerBlock(nn.Layer):
r""" Swin Transformer Block.
Args:
dim (int): Number of input channels.
input_resolution (tuple[int]): Input resulotion.
num_heads (int): Number of attention heads.
window_size (int): Window size.
shift_size (int): Shift size for SW-MSA.
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
drop (float, optional): Dropout rate. Default: 0.0
attn_drop (float, optional): Attention dropout rate. Default: 0.0
drop_path (float, optional): Stochastic depth rate. Default: 0.0
act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
pretrained_window_size (int): Window size in pre-training.
"""
def __init__(self,
dim,
input_resolution,
num_heads,
window_size=8,
shift_size=0,
mlp_ratio=4.,
qkv_bias=True,
drop=0.,
attn_drop=0.,
drop_path=0.,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm,
pretrained_window_size=0):
super().__init__()
self.dim = dim
self.input_resolution = input_resolution
self.num_heads = num_heads
self.window_size = window_size
self.shift_size = shift_size
self.mlp_ratio = mlp_ratio
if min(self.input_resolution) <= self.window_size:
# if window size is larger than input resolution, we don't partition windows
self.shift_size = 0
self.window_size = min(self.input_resolution)
assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
self.norm1 = norm_layer(dim)
self.attn = WindowAttention(
dim,
window_size=to_2tuple(self.window_size),
num_heads=num_heads,
qkv_bias=qkv_bias,
attn_drop=attn_drop,
proj_drop=drop,
pretrained_window_size=to_2tuple(pretrained_window_size))
self.drop_path = DropPath(
drop_path) if drop_path > 0. else nn.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)
if self.shift_size > 0:
# calculate attention mask for SW-MSA
H, W = self.input_resolution
img_mask = paddle.zeros([1, H, W, 1]) # 1 H W 1
h_slices = (slice(0, -self.window_size),
slice(-self.window_size, -self.shift_size),
slice(-self.shift_size, None))
w_slices = (slice(0, -self.window_size),
slice(-self.window_size, -self.shift_size),
slice(-self.shift_size, None))
cnt = 0
for h in h_slices:
for w in w_slices:
img_mask[:, h, w, :] = cnt
cnt += 1
mask_windows = window_partition(
img_mask, self.window_size) # nW, window_size, window_size, 1
mask_windows = mask_windows.reshape(
shape=[-1, self.window_size * self.window_size])
attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
attn_mask = masked_fill(attn_mask, attn_mask != 0, float(-100.0))
attn_mask = masked_fill(attn_mask, attn_mask == 0, float(0.0))
else:
attn_mask = None
self.register_buffer("attn_mask", attn_mask)
def forward(self, x):
H, W = self.input_resolution
B, L, C = x.shape
assert L == H * W, "input feature has wrong size"
shortcut = x
x = x.reshape(shape=[B, H, W, C])
# cyclic shift
if self.shift_size > 0:
shifted_x = RollWrapper.roll(
x, shifts=(-self.shift_size, -self.shift_size), axis=(1, 2))
else:
shifted_x = x
# partition windows
x_windows = window_partition(
shifted_x, self.window_size) # nW*B, window_size, window_size, C
x_windows = x_windows.reshape(
[-1, self.window_size * self.window_size,
C]) # nW*B, window_size*window_size, C
# W-MSA/SW-MSA
attn_windows = self.attn(
x_windows, mask=self.attn_mask) # nW*B, window_size*window_size, C
# merge windows
attn_windows = attn_windows.reshape(
[-1, self.window_size, self.window_size, C])
shifted_x = window_reverse(attn_windows, self.window_size, H,
W) # B H' W' C
# reverse cyclic shift
if self.shift_size > 0:
x = RollWrapper.roll(
shifted_x,
shifts=(self.shift_size, self.shift_size),
axis=(1, 2))
else:
x = shifted_x
x = x.reshape([B, H * W, C])
x = shortcut + self.drop_path(self.norm1(x))
# FFN
x = x + self.drop_path(self.norm2(self.mlp(x)))
return x
def extra_repr(self):
return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
def flops(self):
flops = 0
H, W = self.input_resolution
# norm1
flops += self.dim * H * W
# W-MSA/SW-MSA
nW = H * W / self.window_size / self.window_size
flops += nW * self.attn.flops(self.window_size * self.window_size)
# mlp
flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
# norm2
flops += self.dim * H * W
return flops
class PatchMerging(nn.Layer):
r""" Patch Merging Layer.
Args:
input_resolution (tuple[int]): Resolution of input feature.
dim (int): Number of input channels.
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
"""
def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
super().__init__()
self.input_resolution = input_resolution
self.dim = dim
self.reduction = nn.Linear(4 * dim, 2 * dim, bias_attr=False)
self.norm = norm_layer(2 * dim)
def forward(self, x):
"""
x: B, H*W, C
"""
H, W = self.input_resolution
B, L, C = x.shape
assert L == H * W, "input feature has wrong size"
assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
x = x.reshape([B, H // 2, 2, W // 2, 2, C])
x = x.transpose((0, 1, 3, 4, 2, 5))
x = x.reshape([B, H * W // 4, 4 * C]) # B H/2*W/2 4*C
x = self.reduction(x)
x = self.norm(x)
return x
def extra_repr(self):
return f"input_resolution={self.input_resolution}, dim={self.dim}"
def flops(self):
H, W = self.input_resolution
flops = (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
flops += H * W * self.dim // 2
return flops
class BasicLayer(nn.Layer):
""" A basic Swin Transformer layer for one stage.
Args:
dim (int): Number of input channels.
input_resolution (tuple[int]): Input resolution.
depth (int): Number of blocks.
num_heads (int): Number of attention heads.
window_size (int): Local window size.
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
drop (float, optional): Dropout rate. Default: 0.0
attn_drop (float, optional): Attention dropout rate. Default: 0.0
drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
pretrained_window_size (int): Local window size in pre-training.
"""
def __init__(self,
dim,
input_resolution,
depth,
num_heads,
window_size,
mlp_ratio=4.,
qkv_bias=True,
drop=0.,
attn_drop=0.,
drop_path=0.,
norm_layer=nn.LayerNorm,
downsample=None,
pretrained_window_size=0):
super().__init__()
self.dim = dim
self.input_resolution = input_resolution
self.depth = depth
# build blocks
self.blocks = nn.LayerList([
SwinTransformerBlock(
dim=dim,
input_resolution=input_resolution,
num_heads=num_heads,
window_size=window_size,
shift_size=0 if (i % 2 == 0) else window_size // 2,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
drop=drop,
attn_drop=attn_drop,
drop_path=drop_path[i]
if isinstance(drop_path, list) else drop_path,
norm_layer=norm_layer,
pretrained_window_size=pretrained_window_size)
for i in range(depth)
])
# patch merging layer
if downsample is not None:
self.downsample = downsample(
input_resolution, dim=dim, norm_layer=norm_layer)
else:
self.downsample = None
def forward(self, x):
for blk in self.blocks:
x = blk(x)
if self.downsample is not None:
x = self.downsample(x)
return x
def extra_repr(self):
return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
def flops(self):
flops = 0
for blk in self.blocks:
flops += blk.flops()
if self.downsample is not None:
flops += self.downsample.flops()
return flops
class PatchEmbed(nn.Layer):
r""" Image to Patch Embedding
Args:
img_size (int): Image size. Default: 256.
patch_size (int): Patch token size. Default: 4.
in_chans (int): Number of input image channels. Default: 3.
embed_dim (int): Number of linear projection output channels. Default: 96.
norm_layer (nn.Module, optional): Normalization layer. Default: None
"""
def __init__(self,
img_size=256,
patch_size=4,
in_chans=3,
embed_dim=96,
norm_layer=None):
super().__init__()
img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size)
patches_resolution = [
img_size[0] // patch_size[0], img_size[1] // patch_size[1]
]
self.img_size = img_size
self.patch_size = patch_size
self.patches_resolution = patches_resolution
self.num_patches = patches_resolution[0] * patches_resolution[1]
self.in_chans = in_chans
self.embed_dim = embed_dim
self.proj = nn.Conv2D(
in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
if norm_layer is not None:
self.norm = norm_layer(embed_dim)
else:
self.norm = None
def forward(self, x):
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]})."
x = self.proj(x).flatten(2).transpose([0, 2, 1]) # B Ph*Pw C
if self.norm is not None:
x = self.norm(x)
return x
def flops(self):
Ho, Wo = self.patches_resolution
flops = Ho * Wo * self.embed_dim * self.in_chans * (
self.patch_size[0] * self.patch_size[1])
if self.norm is not None:
flops += Ho * Wo * self.embed_dim
return flops
class SwinTransformerV2(nn.Layer):
r""" Swin TransformerV2
A PaddlePaddle impl of : `Swin Transformer V2: Scaling Up Capacity and Resolution` -
https://arxiv.org/abs/2111.09883
Args:
img_size (int | tuple(int)): Input image size. Default 256
patch_size (int | tuple(int)): Patch size. Default: 4
in_chans (int): Number of input image channels. Default: 3
class_num (int): Number of classes for classification head. Default: 1000
embed_dim (int): Patch embedding dimension. Default: 96
depths (tuple(int)): Depth of each Swin Transformer layer.
num_heads (tuple(int)): Number of attention heads in different layers.
window_size (int): Window size. Default: 7
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
drop_rate (float): Dropout rate. Default: 0
attn_drop_rate (float): Attention dropout rate. Default: 0
drop_path_rate (float): Stochastic depth rate. Default: 0.1
norm_layer (nn.Layer): Normalization layer. Default: nn.LayerNorm.
ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
patch_norm (bool): If True, add normalization after patch embedding. Default: True
pretrained_window_sizes (tuple(int)): Pretrained window sizes of each layer.
"""
def __init__(self,
img_size=256,
patch_size=4,
in_chans=3,
class_num=1000,
embed_dim=96,
depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 24],
window_size=7,
mlp_ratio=4.,
qkv_bias=True,
drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.1,
norm_layer=nn.LayerNorm,
ape=False,
patch_norm=True,
pretrained_window_sizes=[0, 0, 0, 0],
**kwargs):
super().__init__()
self.class_num = class_num
self.num_layers = len(depths)
self.embed_dim = embed_dim
self.ape = ape
self.patch_norm = patch_norm
self.num_features = int(embed_dim * 2**(self.num_layers - 1))
self.mlp_ratio = mlp_ratio
# split image into non-overlapping patches
self.patch_embed = PatchEmbed(
img_size=img_size,
patch_size=patch_size,
in_chans=in_chans,
embed_dim=embed_dim,
norm_layer=norm_layer if self.patch_norm else None)
num_patches = self.patch_embed.num_patches
patches_resolution = self.patch_embed.patches_resolution
self.patches_resolution = patches_resolution
# absolute position embedding
if self.ape:
self.absolute_pos_embed = self.create_parameter(
shape=(1, num_patches, embed_dim), default_initializer=zeros_)
trunc_normal_(self.absolute_pos_embed)
self.pos_drop = nn.Dropout(p=drop_rate)
# stochastic depth
dpr = [
x.item() for x in paddle.linspace(0, drop_path_rate, sum(depths))
] # stochastic depth decay rule
# build layers
self.layers = nn.LayerList()
for i_layer in range(self.num_layers):
layer = BasicLayer(
dim=int(embed_dim * 2**i_layer),
input_resolution=(patches_resolution[0] // (2**i_layer),
patches_resolution[1] // (2**i_layer)),
depth=depths[i_layer],
num_heads=num_heads[i_layer],
window_size=window_size,
mlp_ratio=self.mlp_ratio,
qkv_bias=qkv_bias,
drop=drop_rate,
attn_drop=attn_drop_rate,
drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
norm_layer=norm_layer,
downsample=PatchMerging
if (i_layer < self.num_layers - 1) else None,
pretrained_window_size=pretrained_window_sizes[i_layer])
self.layers.append(layer)
self.norm = norm_layer(self.num_features)
self.avgpool = nn.AdaptiveAvgPool1D(1)
self.head = nn.Linear(self.num_features,
class_num) if class_num > 0 else nn.Identity()
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(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):
x = self.patch_embed(x)
if self.ape:
x = x + self.absolute_pos_embed
x = self.pos_drop(x)
for layer in self.layers:
x = layer(x)
x = self.norm(x) # B L C
x = self.avgpool(x.transpose([0, 2, 1])) # B C 1
x = paddle.flatten(x, 1)
return x
def forward(self, x):
x = self.forward_features(x)
x = self.head(x)
return x
def flops(self):
flops = 0
flops += self.patch_embed.flops()
for i, layer in enumerate(self.layers):
flops += layer.flops()
flops += self.num_features * self.patches_resolution[
0] * self.patches_resolution[1] // (2**self.num_layers)
flops += self.num_features * self.class_num
return flops
def _load_pretrained(pretrained, model, model_url, use_ssld=False):
if pretrained is False:
pass
elif pretrained is True:
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
elif isinstance(pretrained, str):
load_dygraph_pretrain(model, pretrained)
else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def SwinTransformerV2_tiny_patch4_window8_256(pretrained=False,
use_ssld=False,
**kwargs):
model = SwinTransformerV2(
img_size=256,
embed_dim=96,
depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 24],
window_size=8,
drop_path_rate=0.2,
**kwargs)
_load_pretrained(
pretrained,
model,
MODEL_URLS["SwinTransformerV2_tiny_patch4_window8_256"],
use_ssld=use_ssld)
return model
def SwinTransformerV2_tiny_patch4_window16_256(pretrained=False,
use_ssld=False,
**kwargs):
model = SwinTransformerV2(
img_size=256,
embed_dim=96,
depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 24],
window_size=16,
drop_path_rate=0.2,
**kwargs)
_load_pretrained(
pretrained,
model,
MODEL_URLS["SwinTransformerV2_tiny_patch4_window16_256"],
use_ssld=use_ssld)
return model
def SwinTransformerV2_small_patch4_window8_256(pretrained=False,
use_ssld=False,
**kwargs):
model = SwinTransformerV2(
img_size=256,
embed_dim=96,
depths=[2, 2, 18, 2],
num_heads=[3, 6, 12, 24],
window_size=8,
drop_path_rate=0.3,
**kwargs)
_load_pretrained(
pretrained,
model,
MODEL_URLS["SwinTransformerV2_small_patch4_window8_256"],
use_ssld=use_ssld)
return model
def SwinTransformerV2_small_patch4_window16_256(pretrained=False,
use_ssld=False,
**kwargs):
model = SwinTransformerV2(
img_size=256,
embed_dim=96,
depths=[2, 2, 18, 2],
num_heads=[3, 6, 12, 24],
window_size=16,
drop_path_rate=0.3,
**kwargs)
_load_pretrained(
pretrained,
model,
MODEL_URLS["SwinTransformerV2_small_patch4_window16_256"],
use_ssld=use_ssld)
return model
def SwinTransformerV2_base_patch4_window8_256(pretrained=False,
use_ssld=False,
**kwargs):
model = SwinTransformerV2(
img_size=256,
embed_dim=128,
depths=[2, 2, 18, 2],
num_heads=[4, 8, 16, 32],
window_size=8,
drop_path_rate=0.5,
**kwargs)
_load_pretrained(
pretrained,
model,
MODEL_URLS["SwinTransformerV2_base_patch4_window8_256"],
use_ssld=use_ssld)
return model
def SwinTransformerV2_base_patch4_window16_256(pretrained=False,
use_ssld=False,
**kwargs):
model = SwinTransformerV2(
img_size=256,
embed_dim=128,
depths=[2, 2, 18, 2],
num_heads=[4, 8, 16, 32],
window_size=16,
drop_path_rate=0.5,
**kwargs)
_load_pretrained(
pretrained,
model,
MODEL_URLS["SwinTransformerV2_base_patch4_window16_256"],
use_ssld=use_ssld)
return model
def SwinTransformerV2_base_patch4_window12to16_256(pretrained=False,
use_ssld=False,
**kwargs):
model = SwinTransformerV2(
img_size=256,
embed_dim=128,
depths=[2, 2, 18, 2],
num_heads=[4, 8, 16, 32],
window_size=16,
drop_path_rate=0.2,
**kwargs)
_load_pretrained(
pretrained,
model,
MODEL_URLS["SwinTransformerV2_base_patch4_window12to16_256"],
use_ssld=use_ssld)
return model
def SwinTransformerV2_base_patch4_window24_384(pretrained=False,
use_ssld=False,
**kwargs):
model = SwinTransformerV2(
img_size=384,
embed_dim=128,
depths=[2, 2, 18, 2],
num_heads=[4, 8, 16, 32],
window_size=24,
drop_path_rate=0.2,
**kwargs)
_load_pretrained(
pretrained,
model,
MODEL_URLS["SwinTransformerV2_base_patch4_window24_384"],
use_ssld=use_ssld)
return model
def SwinTransformerV2_large_patch4_window16_256(pretrained=False,
use_ssld=False,
**kwargs):
model = SwinTransformerV2(
img_size=256,
embed_dim=192,
depths=[2, 2, 18, 2],
num_heads=[6, 12, 24, 48],
window_size=16,
drop_path_rate=0.2,
**kwargs)
_load_pretrained(
pretrained,
model,
MODEL_URLS["SwinTransformerV2_large_patch4_window16_256"],
use_ssld=use_ssld)
return model
def SwinTransformerV2_large_patch4_window24_384(pretrained=False,
use_ssld=False,
**kwargs):
model = SwinTransformerV2(
img_size=384,
embed_dim=192,
depths=[2, 2, 18, 2],
num_heads=[6, 12, 24, 48],
window_size=24,
drop_path_rate=0.2,
**kwargs)
_load_pretrained(
pretrained,
model,
MODEL_URLS["SwinTransformerV2_large_patch4_window24_384"],
use_ssld=use_ssld)
return model
\ No newline at end of file
ppcls/utils/save_load.py
浏览文件 @ a5a3b0f5
......@@ -64,10 +64,20 @@ def load_dygraph_pretrain(model, path=None):
return
def load_dygraph_pretrain_from_url(model, pretrained_url, use_ssld=False):
def load_dygraph_pretrain_from_url(model,
pretrained_url,
use_ssld=False,
use_imagenet22k_pretrained=False,
use_imagenet22kto1k_pretrained=False):
if use_ssld:
pretrained_url = pretrained_url.replace("_pretrained",
"_ssld_pretrained")
if use_imagenet22k_pretrained:
pretrained_url = pretrained_url.replace("_pretrained",
"_22k_pretrained")
if use_imagenet22kto1k_pretrained:
pretrained_url = pretrained_url.replace("_pretrained",
"_22kto1k_pretrained")
local_weight_path = get_weights_path_from_url(pretrained_url).replace(
".pdparams", "")
load_dygraph_pretrain(model, path=local_weight_path)
......
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