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PaddleClas/ppcls/arch/backbone/model_zoo/pvt_v2.py

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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 heavily based on https://github.com/whai362/PVT
# reference: https://arxiv.org/abs/2106.13797
from functools import partial
import math
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddle.nn.initializer import TruncatedNormal, Constant
from .vision_transformer import trunc_normal_, zeros_, ones_, to_2tuple, DropPath, Identity, drop_path
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"PVT_V2_B0":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/PVT_V2_B0_pretrained.pdparams",
"PVT_V2_B1":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/PVT_V2_B1_pretrained.pdparams",
"PVT_V2_B2":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/PVT_V2_B2_pretrained.pdparams",
"PVT_V2_B2_Linear":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/PVT_V2_B2_Linear_pretrained.pdparams",
"PVT_V2_B3":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/PVT_V2_B3_pretrained.pdparams",
"PVT_V2_B4":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/PVT_V2_B4_pretrained.pdparams",
"PVT_V2_B5":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/PVT_V2_B5_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
@paddle.jit.not_to_static
def swapdim(x, dim1, dim2):
a = list(range(len(x.shape)))
a[dim1], a[dim2] = a[dim2], a[dim1]
return x.transpose(a)
class Mlp(nn.Layer):
def __init__(self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.GELU,
drop=0.,
linear=False):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = nn.Linear(in_features, hidden_features)
self.dwconv = DWConv(hidden_features)
self.act = act_layer()
self.fc2 = nn.Linear(hidden_features, out_features)
self.drop = nn.Dropout(drop)
self.linear = linear
if self.linear:
self.relu = nn.ReLU()
def forward(self, x, H, W):
x = self.fc1(x)
if self.linear:
x = self.relu(x)
x = self.dwconv(x, H, W)
x = self.act(x)
x = self.drop(x)
x = self.fc2(x)
x = self.drop(x)
return x
class Attention(nn.Layer):
def __init__(self,
dim,
num_heads=8,
qkv_bias=False,
qk_scale=None,
attn_drop=0.,
proj_drop=0.,
sr_ratio=1,
linear=False):
super().__init__()
assert dim % num_heads == 0
self.dim = dim
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = qk_scale or head_dim**-0.5
self.q = nn.Linear(dim, dim, bias_attr=qkv_bias)
self.kv = nn.Linear(dim, dim * 2, bias_attr=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
self.linear = linear
self.sr_ratio = sr_ratio
if not linear:
if sr_ratio > 1:
self.sr = nn.Conv2D(
dim, dim, kernel_size=sr_ratio, stride=sr_ratio)
self.norm = nn.LayerNorm(dim)
else:
self.pool = nn.AdaptiveAvgPool2D(7)
self.sr = nn.Conv2D(dim, dim, kernel_size=1, stride=1)
self.norm = nn.LayerNorm(dim)
self.act = nn.GELU()
def forward(self, x, H, W):
B, N, C = x.shape
q = self.q(x).reshape(
[B, N, self.num_heads, C // self.num_heads]).transpose(
[0, 2, 1, 3])
if not self.linear:
if self.sr_ratio > 1:
x_ = x.transpose([0, 2, 1]).reshape([B, C, H, W])
x_ = self.sr(x_)
h_, w_ = x_.shape[-2:]
x_ = x_.reshape([B, C, h_ * w_]).transpose([0, 2, 1])
x_ = self.norm(x_)
kv = self.kv(x_)
kv = kv.reshape([
B, kv.shape[2] * kv.shape[1] // 2 // C, 2, self.num_heads,
C // self.num_heads
]).transpose([2, 0, 3, 1, 4])
else:
kv = self.kv(x)
kv = kv.reshape([
B, kv.shape[2] * kv.shape[1] // 2 // C, 2, self.num_heads,
C // self.num_heads
]).transpose([2, 0, 3, 1, 4])
else:
x_ = x.transpose([0, 2, 1]).reshape([B, C, H, W])
x_ = self.sr(self.pool(x_))
x_ = x_.reshape([B, C, x_.shape[2] * x_.shape[3]]).transpose(
[0, 2, 1])
x_ = self.norm(x_)
x_ = self.act(x_)
kv = self.kv(x_)
kv = kv.reshape([
B, kv.shape[2] * kv.shape[1] // 2 // C, 2, self.num_heads,
C // self.num_heads
]).transpose([2, 0, 3, 1, 4])
k, v = kv[0], kv[1]
attn = (q @swapdim(k, -2, -1)) * self.scale
attn = F.softmax(attn, axis=-1)
attn = self.attn_drop(attn)
x = swapdim((attn @v), 1, 2).reshape([B, N, C])
x = self.proj(x)
x = self.proj_drop(x)
return x
class Block(nn.Layer):
def __init__(self,
dim,
num_heads,
mlp_ratio=4.,
qkv_bias=False,
qk_scale=None,
drop=0.,
attn_drop=0.,
drop_path=0.,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm,
sr_ratio=1,
linear=False):
super().__init__()
self.norm1 = norm_layer(dim)
self.attn = Attention(
dim,
num_heads=num_heads,
qkv_bias=qkv_bias,
qk_scale=qk_scale,
attn_drop=attn_drop,
proj_drop=drop,
sr_ratio=sr_ratio,
linear=linear)
self.drop_path = DropPath(drop_path) if drop_path > 0. else Identity()
self.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = Mlp(in_features=dim,
hidden_features=mlp_hidden_dim,
act_layer=act_layer,
drop=drop,
linear=linear)
def forward(self, x, H, W):
x = x + self.drop_path(self.attn(self.norm1(x), H, W))
x = x + self.drop_path(self.mlp(self.norm2(x), H, W))
return x
class OverlapPatchEmbed(nn.Layer):
""" Image to Patch Embedding
"""
def __init__(self,
img_size=224,
patch_size=7,
stride=4,
in_chans=3,
embed_dim=768):
super().__init__()
img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size)
self.img_size = img_size
self.patch_size = patch_size
self.H, self.W = img_size[0] // patch_size[0], img_size[
1] // patch_size[1]
self.num_patches = self.H * self.W
self.proj = nn.Conv2D(
in_chans,
embed_dim,
kernel_size=patch_size,
stride=stride,
padding=(patch_size[0] // 2, patch_size[1] // 2))
self.norm = nn.LayerNorm(embed_dim)
def forward(self, x):
x = self.proj(x)
_, _, H, W = x.shape
x = x.flatten(2)
x = swapdim(x, 1, 2)
x = self.norm(x)
return x, H, W
class PyramidVisionTransformerV2(nn.Layer):
def __init__(self,
img_size=224,
patch_size=16,
in_chans=3,
class_num=1000,
embed_dims=[64, 128, 256, 512],
num_heads=[1, 2, 4, 8],
mlp_ratios=[4, 4, 4, 4],
qkv_bias=False,
qk_scale=None,
drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.,
norm_layer=nn.LayerNorm,
depths=[3, 4, 6, 3],
sr_ratios=[8, 4, 2, 1],
num_stages=4,
linear=False):
super().__init__()
self.class_num = class_num
self.depths = depths
self.num_stages = num_stages
dpr = [x for x in paddle.linspace(0, drop_path_rate, sum(depths))
] # stochastic depth decay rule
cur = 0
for i in range(num_stages):
patch_embed = OverlapPatchEmbed(
img_size=img_size if i == 0 else img_size // (2**(i + 1)),
patch_size=7 if i == 0 else 3,
stride=4 if i == 0 else 2,
in_chans=in_chans if i == 0 else embed_dims[i - 1],
embed_dim=embed_dims[i])
block = nn.LayerList([
Block(
dim=embed_dims[i],
num_heads=num_heads[i],
mlp_ratio=mlp_ratios[i],
qkv_bias=qkv_bias,
qk_scale=qk_scale,
drop=drop_rate,
attn_drop=attn_drop_rate,
drop_path=dpr[cur + j],
norm_layer=norm_layer,
sr_ratio=sr_ratios[i],
linear=linear) for j in range(depths[i])
])
norm = norm_layer(embed_dims[i])
cur += depths[i]
setattr(self, f"patch_embed{i + 1}", patch_embed)
setattr(self, f"block{i + 1}", block)
setattr(self, f"norm{i + 1}", norm)
# classification head
self.head = nn.Linear(embed_dims[3],
class_num) if class_num > 0 else 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):
B = x.shape[0]
for i in range(self.num_stages):
patch_embed = getattr(self, f"patch_embed{i + 1}")
block = getattr(self, f"block{i + 1}")
norm = getattr(self, f"norm{i + 1}")
x, H, W = patch_embed(x)
for blk in block:
x = blk(x, H, W)
x = norm(x)
if i != self.num_stages - 1:
x = x.reshape([B, H, W, x.shape[2]]).transpose([0, 3, 1, 2])
return x.mean(axis=1)
def forward(self, x):
x = self.forward_features(x)
x = self.head(x)
return x
class DWConv(nn.Layer):
def __init__(self, dim=768):
super().__init__()
self.dwconv = nn.Conv2D(dim, dim, 3, 1, 1, bias_attr=True, groups=dim)
def forward(self, x, H, W):
B, N, C = x.shape
x = swapdim(x, 1, 2)
x = x.reshape([B, C, H, W])
x = self.dwconv(x)
x = x.flatten(2)
x = swapdim(x, 1, 2)
return x
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 PVT_V2_B0(pretrained=False, use_ssld=False, **kwargs):
model = PyramidVisionTransformerV2(
patch_size=4,
embed_dims=[32, 64, 160, 256],
num_heads=[1, 2, 5, 8],
mlp_ratios=[8, 8, 4, 4],
qkv_bias=True,
norm_layer=partial(
nn.LayerNorm, epsilon=1e-6),
depths=[2, 2, 2, 2],
sr_ratios=[8, 4, 2, 1],
**kwargs)
_load_pretrained(
pretrained, model, MODEL_URLS["PVT_V2_B0"], use_ssld=use_ssld)
return model
def PVT_V2_B1(pretrained=False, use_ssld=False, **kwargs):
model = PyramidVisionTransformerV2(
patch_size=4,
embed_dims=[64, 128, 320, 512],
num_heads=[1, 2, 5, 8],
mlp_ratios=[8, 8, 4, 4],
qkv_bias=True,
norm_layer=partial(
nn.LayerNorm, epsilon=1e-6),
depths=[2, 2, 2, 2],
sr_ratios=[8, 4, 2, 1],
**kwargs)
_load_pretrained(
pretrained, model, MODEL_URLS["PVT_V2_B1"], use_ssld=use_ssld)
return model
def PVT_V2_B2(pretrained=False, use_ssld=False, **kwargs):
model = PyramidVisionTransformerV2(
patch_size=4,
embed_dims=[64, 128, 320, 512],
num_heads=[1, 2, 5, 8],
mlp_ratios=[8, 8, 4, 4],
qkv_bias=True,
norm_layer=partial(
nn.LayerNorm, epsilon=1e-6),
depths=[3, 4, 6, 3],
sr_ratios=[8, 4, 2, 1],
**kwargs)
_load_pretrained(
pretrained, model, MODEL_URLS["PVT_V2_B2"], use_ssld=use_ssld)
return model
def PVT_V2_B3(pretrained=False, use_ssld=False, **kwargs):
model = PyramidVisionTransformerV2(
patch_size=4,
embed_dims=[64, 128, 320, 512],
num_heads=[1, 2, 5, 8],
mlp_ratios=[8, 8, 4, 4],
qkv_bias=True,
norm_layer=partial(
nn.LayerNorm, epsilon=1e-6),
depths=[3, 4, 18, 3],
sr_ratios=[8, 4, 2, 1],
**kwargs)
_load_pretrained(
pretrained, model, MODEL_URLS["PVT_V2_B3"], use_ssld=use_ssld)
return model
def PVT_V2_B4(pretrained=False, use_ssld=False, **kwargs):
model = PyramidVisionTransformerV2(
patch_size=4,
embed_dims=[64, 128, 320, 512],
num_heads=[1, 2, 5, 8],
mlp_ratios=[8, 8, 4, 4],
qkv_bias=True,
norm_layer=partial(
nn.LayerNorm, epsilon=1e-6),
depths=[3, 8, 27, 3],
sr_ratios=[8, 4, 2, 1],
**kwargs)
_load_pretrained(
pretrained, model, MODEL_URLS["PVT_V2_B4"], use_ssld=use_ssld)
return model
def PVT_V2_B5(pretrained=False, use_ssld=False, **kwargs):
model = PyramidVisionTransformerV2(
patch_size=4,
embed_dims=[64, 128, 320, 512],
num_heads=[1, 2, 5, 8],
mlp_ratios=[4, 4, 4, 4],
qkv_bias=True,
norm_layer=partial(
nn.LayerNorm, epsilon=1e-6),
depths=[3, 6, 40, 3],
sr_ratios=[8, 4, 2, 1],
**kwargs)
_load_pretrained(
pretrained, model, MODEL_URLS["PVT_V2_B5"], use_ssld=use_ssld)
return model
def PVT_V2_B2_Linear(pretrained=False, use_ssld=False, **kwargs):
model = PyramidVisionTransformerV2(
patch_size=4,
embed_dims=[64, 128, 320, 512],
num_heads=[1, 2, 5, 8],
mlp_ratios=[8, 8, 4, 4],
qkv_bias=True,
norm_layer=partial(
nn.LayerNorm, epsilon=1e-6),
depths=[3, 4, 6, 3],
sr_ratios=[8, 4, 2, 1],
linear=True,
**kwargs)
_load_pretrained(
pretrained, model, MODEL_URLS["PVT_V2_B2_Linear"], use_ssld=use_ssld)
return model
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