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PaddleDetection
未验证 提交 63e7cfa4 编写于 作者: W Wenyu 提交者： GitHub
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add vit, adamw_ld (#6059) 

* add vit, adamw_ld

* update
上级 ff62e6ff
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Showing with 947 addition and 51 deletion
ppdet/modeling/backbones/swin_transformer.py
浏览文件 @ 63e7cfa4
......@@ -25,57 +25,9 @@ from ppdet.modeling.shape_spec import ShapeSpec
from ppdet.core.workspace import register, serializable
import numpy as np
# Common initializations
ones_ = Constant(value=1.)
zeros_ = Constant(value=0.)
trunc_normal_ = TruncatedNormal(std=.02)
# Common Functions
def to_2tuple(x):
return tuple([x] * 2)
def add_parameter(layer, datas, name=None):
parameter = layer.create_parameter(
shape=(datas.shape), default_initializer=Assign(datas))
if name:
layer.add_parameter(name, parameter)
return parameter
# Common Layers
def drop_path(x, drop_prob=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. 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 output
class DropPath(nn.Layer):
def __init__(self, drop_prob=None):
super(DropPath, self).__init__()
self.drop_prob = drop_prob
def forward(self, x):
return drop_path(x, self.drop_prob, self.training)
class Identity(nn.Layer):
def __init__(self):
super(Identity, self).__init__()
def forward(self, input):
return input
from .transformer_utils import DropPath, Identity
from .transformer_utils import add_parameter, to_2tuple
from .transformer_utils import ones_, zeros_, trunc_normal_
class Mlp(nn.Layer):
......
ppdet/modeling/backbones/transformer_utils.py 0 → 100644
浏览文件 @ 63e7cfa4
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
import paddle
import paddle.nn as nn
from paddle.nn.initializer import TruncatedNormal, Constant, Assign
# Common initializations
ones_ = Constant(value=1.)
zeros_ = Constant(value=0.)
trunc_normal_ = TruncatedNormal(std=.02)
# Common Layers
def drop_path(x, drop_prob=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. 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 output
class DropPath(nn.Layer):
def __init__(self, drop_prob=None):
super(DropPath, self).__init__()
self.drop_prob = drop_prob
def forward(self, x):
return drop_path(x, self.drop_prob, self.training)
class Identity(nn.Layer):
def __init__(self):
super(Identity, self).__init__()
def forward(self, input):
return input
# common funcs
def to_2tuple(x):
if isinstance(x, (list, tuple)):
return x
return tuple([x] * 2)
def add_parameter(layer, datas, name=None):
parameter = layer.create_parameter(
shape=(datas.shape), default_initializer=Assign(datas))
if name:
layer.add_parameter(name, parameter)
return parameter
ppdet/modeling/backbones/vision_transformer.py 0 → 100644
浏览文件 @ 63e7cfa4
# copyright (c) 2022 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.
import math
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
import numpy as np
from paddle.nn.initializer import Constant
from ppdet.modeling.shape_spec import ShapeSpec
from ppdet.core.workspace import register, serializable
from .transformer_utils import zeros_, DropPath, Identity
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
class Attention(nn.Layer):
def __init__(self,
dim,
num_heads=8,
qkv_bias=False,
qk_scale=None,
attn_drop=0.,
proj_drop=0.,
window_size=None):
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=False)
if qkv_bias:
self.q_bias = self.create_parameter(
shape=([dim]), default_initializer=zeros_)
self.v_bias = self.create_parameter(
shape=([dim]), default_initializer=zeros_)
else:
self.q_bias = None
self.v_bias = None
if window_size:
self.window_size = window_size
self.num_relative_distance = (2 * window_size[0] - 1) * (
2 * window_size[1] - 1) + 3
self.relative_position_bias_table = self.create_parameter(
shape=(self.num_relative_distance, num_heads),
default_initializer=zeros_) # 2*Wh-1 * 2*Ww-1, nH
# cls to token & token 2 cls & cls to cls
# get pair-wise relative position index for each token inside the window
coords_h = paddle.arange(window_size[0])
coords_w = paddle.arange(window_size[1])
coords = paddle.stack(paddle.meshgrid(
[coords_h, coords_w])) # 2, Wh, Ww
coords_flatten = paddle.flatten(coords, 1) # 2, Wh*Ww
coords_flatten_1 = paddle.unsqueeze(coords_flatten, 2)
coords_flatten_2 = paddle.unsqueeze(coords_flatten, 1)
relative_coords = coords_flatten_1.clone() - coords_flatten_2.clone(
)
#relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Wh
relative_coords = relative_coords.transpose(
(1, 2, 0)) #.contiguous() # Wh*Ww, Wh*Ww, 2
relative_coords[:, :, 0] += window_size[
0] - 1 # shift to start from 0
relative_coords[:, :, 1] += window_size[1] - 1
relative_coords[:, :, 0] *= 2 * window_size[1] - 1
relative_position_index = \
paddle.zeros(shape=(window_size[0] * window_size[1] + 1, ) * 2, dtype=relative_coords.dtype)
relative_position_index[1:, 1:] = relative_coords.sum(
-1) # Wh*Ww, Wh*Ww
relative_position_index[0, 0:] = self.num_relative_distance - 3
relative_position_index[0:, 0] = self.num_relative_distance - 2
relative_position_index[0, 0] = self.num_relative_distance - 1
self.register_buffer("relative_position_index",
relative_position_index)
# trunc_normal_(self.relative_position_bias_table, std=.0)
else:
self.window_size = None
self.relative_position_bias_table = None
self.relative_position_index = None
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x, rel_pos_bias=None):
x_shape = paddle.shape(x)
N, C = x_shape[1], x_shape[2]
qkv_bias = None
if self.q_bias is not None:
qkv_bias = paddle.concat(
(self.q_bias, paddle.zeros_like(self.v_bias), self.v_bias))
qkv = F.linear(x, weight=self.qkv.weight, bias=qkv_bias)
qkv = qkv.reshape((-1, 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.matmul(k.transpose((0, 1, 3, 2)))) * self.scale
if self.relative_position_bias_table is not None:
relative_position_bias = self.relative_position_bias_table[
self.relative_position_index.reshape([-1])].reshape([
self.window_size[0] * self.window_size[1] + 1,
self.window_size[0] * self.window_size[1] + 1, -1
]) # Wh*Ww,Wh*Ww,nH
relative_position_bias = relative_position_bias.transpose(
(2, 0, 1)) #.contiguous() # nH, Wh*Ww, Wh*Ww
attn = attn + relative_position_bias.unsqueeze(0)
if rel_pos_bias is not None:
attn = attn + rel_pos_bias
attn = nn.functional.softmax(attn, axis=-1)
attn = self.attn_drop(attn)
x = (attn.matmul(v)).transpose((0, 2, 1, 3)).reshape((-1, 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.,
window_size=None,
init_values=None,
act_layer=nn.GELU,
norm_layer='nn.LayerNorm',
epsilon=1e-5):
super().__init__()
self.norm1 = nn.LayerNorm(dim, epsilon=1e-6)
self.attn = Attention(
dim,
num_heads=num_heads,
qkv_bias=qkv_bias,
qk_scale=qk_scale,
attn_drop=attn_drop,
proj_drop=drop,
window_size=window_size)
# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
self.drop_path = DropPath(drop_path) if drop_path > 0. else Identity()
self.norm2 = eval(norm_layer)(dim, epsilon=epsilon)
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 init_values is not None:
self.gamma_1 = self.create_parameter(
shape=([dim]), default_initializer=Constant(value=init_values))
self.gamma_2 = self.create_parameter(
shape=([dim]), default_initializer=Constant(value=init_values))
else:
self.gamma_1, self.gamma_2 = None, None
def forward(self, x, rel_pos_bias=None):
if self.gamma_1 is None:
x = x + self.drop_path(
self.attn(
self.norm1(x), rel_pos_bias=rel_pos_bias))
x = x + self.drop_path(self.mlp(self.norm2(x)))
else:
x = x + self.drop_path(self.gamma_1 * self.attn(
self.norm1(x), rel_pos_bias=rel_pos_bias))
x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x)))
return x
class PatchEmbed(nn.Layer):
""" Image to Patch Embedding
"""
def __init__(self,
img_size=[224, 224],
patch_size=16,
in_chans=3,
embed_dim=768):
super().__init__()
self.num_patches_w = img_size[0] // patch_size
self.num_patches_h = img_size[1] // patch_size
num_patches = self.num_patches_w * self.num_patches_h
self.patch_shape = (img_size[0] // patch_size,
img_size[1] // patch_size)
self.img_size = img_size
self.patch_size = patch_size
self.num_patches = num_patches
self.proj = nn.Conv2D(
in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
@property
def num_patches_in_h(self):
return self.img_size[1] // self.patch_size
@property
def num_patches_in_w(self):
return self.img_size[0] // self.patch_size
def forward(self, x, mask=None):
B, C, H, W = x.shape
return self.proj(x)
class RelativePositionBias(nn.Layer):
def __init__(self, window_size, num_heads):
super().__init__()
self.window_size = window_size
self.num_relative_distance = (2 * window_size[0] - 1) * (
2 * window_size[1] - 1) + 3
self.relative_position_bias_table = self.create_parameter(
shape=(self.num_relative_distance, num_heads),
default_initialize=zeros_)
# cls to token & token 2 cls & cls to cls
# get pair-wise relative position index for each token inside the window
coords_h = paddle.arange(window_size[0])
coords_w = paddle.arange(window_size[1])
coords = paddle.stack(paddle.meshgrid(
[coords_h, coords_w])) # 2, Wh, Ww
coords_flatten = coords.flatten(1) # 2, Wh*Ww
relative_coords = coords_flatten[:, :,
None] - coords_flatten[:,
None, :] # 2, Wh*Ww, Wh*Ww
relative_coords = relative_coords.transpos(
(1, 2, 0)) # Wh*Ww, Wh*Ww, 2
relative_coords[:, :, 0] += window_size[0] - 1 # shift to start from 0
relative_coords[:, :, 1] += window_size[1] - 1
relative_coords[:, :, 0] *= 2 * window_size[1] - 1
relative_position_index = \
paddle.zeros(size=(window_size[0] * window_size[1] + 1,) * 2, dtype=relative_coords.dtype)
relative_position_index[1:, 1:] = relative_coords.sum(
-1) # Wh*Ww, Wh*Ww
relative_position_index[0, 0:] = self.num_relative_distance - 3
relative_position_index[0:, 0] = self.num_relative_distance - 2
relative_position_index[0, 0] = self.num_relative_distance - 1
self.register_buffer("relative_position_index", relative_position_index)
def forward(self):
relative_position_bias = \
self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
self.window_size[0] * self.window_size[1] + 1,
self.window_size[0] * self.window_size[1] + 1, -1) # Wh*Ww,Wh*Ww,nH
return relative_position_bias.transpose((2, 0, 1)) # nH, Wh*Ww, Wh*Ww
def get_sinusoid_encoding_table(n_position, d_hid, token=False):
''' Sinusoid position encoding table '''
def get_position_angle_vec(position):
return [
position / np.power(10000, 2 * (hid_j // 2) / d_hid)
for hid_j in range(d_hid)
]
sinusoid_table = np.array(
[get_position_angle_vec(pos_i) for pos_i in range(n_position)])
sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) # dim 2i
sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # dim 2i+1
if token:
sinusoid_table = np.concatenate(
[sinusoid_table, np.zeros([1, d_hid])], dim=0)
return paddle.to_tensor(sinusoid_table, dtype=paddle.float32).unsqueeze(0)
@register
@serializable
class VisionTransformer(nn.Layer):
""" Vision Transformer with support for patch input
"""
def __init__(self,
img_size=[672, 1092],
patch_size=16,
in_chans=3,
embed_dim=768,
depth=12,
num_heads=12,
mlp_ratio=4,
qkv_bias=False,
qk_scale=None,
drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.,
norm_layer='nn.LayerNorm',
init_values=None,
use_rel_pos_bias=False,
use_shared_rel_pos_bias=False,
epsilon=1e-5,
final_norm=False,
pretrained=None,
out_indices=[3, 5, 7, 11],
use_abs_pos_emb=False,
use_sincos_pos_emb=True,
with_fpn=True,
**args):
super().__init__()
self.img_size = img_size
self.embed_dim = embed_dim
self.with_fpn = with_fpn
self.patch_embed = PatchEmbed(
img_size=img_size,
patch_size=patch_size,
in_chans=in_chans,
embed_dim=embed_dim)
self.pos_w = self.patch_embed.num_patches_in_w
self.pos_h = self.patch_embed.num_patches_in_h
self.cls_token = self.create_parameter(
shape=(1, 1, embed_dim),
default_initializer=paddle.nn.initializer.Constant(value=0.))
if use_abs_pos_emb:
self.pos_embed = self.create_parameter(
shape=(1, self.pos_w * self.pos_h + 1, embed_dim),
default_initializer=paddle.nn.initializer.TruncatedNormal(
std=.02))
elif use_sincos_pos_emb:
pos_embed = self.build_2d_sincos_position_embedding(embed_dim)
self.pos_embed = pos_embed
self.pos_embed = self.create_parameter(shape=pos_embed.shape)
self.pos_embed.set_value(pos_embed.numpy())
self.pos_embed.stop_gradient = True
else:
self.pos_embed = None
self.pos_drop = nn.Dropout(p=drop_rate)
if use_shared_rel_pos_bias:
self.rel_pos_bias = RelativePositionBias(
window_size=self.patch_embed.patch_shape, num_heads=num_heads)
elif self.use_sincos_pos_emb:
self.pos_embed = self.build_2d_sincos_position_embedding(embed_dim)
else:
self.rel_pos_bias = None
self.use_rel_pos_bias = use_rel_pos_bias
dpr = np.linspace(0, drop_path_rate, depth)
self.blocks = nn.LayerList([
Block(
dim=embed_dim,
num_heads=num_heads,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
qk_scale=qk_scale,
drop=drop_rate,
attn_drop=attn_drop_rate,
drop_path=dpr[i],
norm_layer=norm_layer,
init_values=init_values,
window_size=self.patch_embed.patch_shape
if use_rel_pos_bias else None,
epsilon=epsilon) for i in range(depth)
])
self.final_norm = final_norm
######### del by xy
#if self.final_norm:
# self.norm = eval(norm_layer)(embed_dim, epsilon=epsilon)
self.pretrained = pretrained
self.init_weight()
assert len(out_indices) <= 4, ''
self.out_indices = out_indices
self.out_channels = [embed_dim for _ in range(len(out_indices))]
self.out_strides = [4, 8, 16, 32][-len(out_indices):]
self.norm = Identity()
if self.with_fpn:
self.init_fpn(
embed_dim=embed_dim,
patch_size=patch_size, )
def init_weight(self):
pretrained = self.pretrained
if pretrained:
if 'http' in pretrained: #URL
path = paddle.utils.download.get_weights_path_from_url(
pretrained)
else: #model in local path
path = pretrained
load_state_dict = paddle.load(path)
model_state_dict = self.state_dict()
pos_embed_name = "pos_embed"
if pos_embed_name in load_state_dict.keys():
load_pos_embed = paddle.to_tensor(
load_state_dict[pos_embed_name], dtype="float32")
if self.pos_embed.shape != load_pos_embed.shape:
pos_size = int(math.sqrt(load_pos_embed.shape[1] - 1))
model_state_dict[pos_embed_name] = self.resize_pos_embed(
load_pos_embed, (pos_size, pos_size),
(self.pos_h, self.pos_w))
# self.set_state_dict(model_state_dict)
load_state_dict[pos_embed_name] = model_state_dict[
pos_embed_name]
print("Load pos_embed and resize it from {} to {} .".format(
load_pos_embed.shape, self.pos_embed.shape))
self.set_state_dict(load_state_dict)
print("Load load_state_dict....")
def init_fpn(self, embed_dim=768, patch_size=16, out_with_norm=False):
if patch_size == 16:
self.fpn1 = nn.Sequential(
nn.Conv2DTranspose(
embed_dim, embed_dim, kernel_size=2, stride=2),
nn.BatchNorm2D(embed_dim),
nn.GELU(),
nn.Conv2DTranspose(
embed_dim, embed_dim, kernel_size=2, stride=2), )
self.fpn2 = nn.Sequential(
nn.Conv2DTranspose(
embed_dim, embed_dim, kernel_size=2, stride=2), )
self.fpn3 = Identity()
self.fpn4 = nn.MaxPool2D(kernel_size=2, stride=2)
elif patch_size == 8:
self.fpn1 = nn.Sequential(
nn.Conv2DTranspose(
embed_dim, embed_dim, kernel_size=2, stride=2), )
self.fpn2 = Identity()
self.fpn3 = nn.Sequential(nn.MaxPool2D(kernel_size=2, stride=2), )
self.fpn4 = nn.Sequential(nn.MaxPool2D(kernel_size=4, stride=4), )
if not out_with_norm:
self.norm = Identity()
else:
self.norm = nn.LayerNorm(embed_dim, epsilon=1e-6)
def interpolate_pos_encoding(self, x, w, h):
npatch = x.shape[1] - 1
N = self.pos_embed.shape[1] - 1
w0 = w // self.patch_embed.patch_size
h0 = h // self.patch_embed.patch_size
if npatch == N and w0 == self.patch_embed.num_patches_w and h0 == self.patch_embed.num_patches_h:
return self.pos_embed
class_pos_embed = self.pos_embed[:, 0]
patch_pos_embed = self.pos_embed[:, 1:]
dim = x.shape[-1]
# we add a small number to avoid floating point error in the interpolation
# see discussion at https://github.com/facebookresearch/dino/issues/8
w0, h0 = w0 + 0.1, h0 + 0.1
patch_pos_embed = nn.functional.interpolate(
patch_pos_embed.reshape([
1, self.patch_embed.num_patches_w,
self.patch_embed.num_patches_h, dim
]).transpose((0, 3, 1, 2)),
scale_factor=(w0 / self.patch_embed.num_patches_w,
h0 / self.patch_embed.num_patches_h),
mode='bicubic', )
assert int(w0) == patch_pos_embed.shape[-2] and int(
h0) == patch_pos_embed.shape[-1]
patch_pos_embed = patch_pos_embed.transpose(
(0, 2, 3, 1)).reshape([1, -1, dim])
return paddle.concat(
(class_pos_embed.unsqueeze(0), patch_pos_embed), axis=1)
def resize_pos_embed(self, pos_embed, old_hw, new_hw):
"""
Resize pos_embed weight.
Args:
pos_embed (Tensor): the pos_embed weight
old_hw (list[int]): the height and width of old pos_embed
new_hw (list[int]): the height and width of new pos_embed
Returns:
Tensor: the resized pos_embed weight
"""
cls_pos_embed = pos_embed[:, :1, :]
pos_embed = pos_embed[:, 1:, :]
pos_embed = pos_embed.transpose([0, 2, 1])
pos_embed = pos_embed.reshape([1, -1, old_hw[0], old_hw[1]])
pos_embed = F.interpolate(
pos_embed, new_hw, mode='bicubic', align_corners=False)
pos_embed = pos_embed.flatten(2).transpose([0, 2, 1])
pos_embed = paddle.concat([cls_pos_embed, pos_embed], axis=1)
return pos_embed
def build_2d_sincos_position_embedding(
self,
embed_dim=768,
temperature=10000., ):
h, w = self.patch_embed.patch_shape
grid_w = paddle.arange(w, dtype=paddle.float32)
grid_h = paddle.arange(h, dtype=paddle.float32)
grid_w, grid_h = paddle.meshgrid(grid_w, grid_h)
assert embed_dim % 4 == 0, 'Embed dimension must be divisible by 4 for 2D sin-cos position embedding'
pos_dim = embed_dim // 4
omega = paddle.arange(pos_dim, dtype=paddle.float32) / pos_dim
omega = 1. / (temperature**omega)
out_w = grid_w.flatten()[..., None] @omega[None]
out_h = grid_h.flatten()[..., None] @omega[None]
pos_emb = paddle.concat(
[
paddle.sin(out_w), paddle.cos(out_w), paddle.sin(out_h),
paddle.cos(out_h)
],
axis=1)[None, :, :]
pe_token = paddle.zeros([1, 1, embed_dim], dtype=paddle.float32)
pos_embed = paddle.concat([pe_token, pos_emb], axis=1)
# pos_embed.stop_gradient = True
return pos_embed
def forward(self, x):
x = x['image'] if isinstance(x, dict) else x
_, _, w, h = x.shape
x = self.patch_embed(x)
x_shape = paddle.shape(x) # b * c * h * w
cls_tokens = self.cls_token.expand((x_shape[0], -1, -1))
x = x.flatten(2).transpose([0, 2, 1]) # b * hw * c
x = paddle.concat([cls_tokens, x], axis=1)
if self.pos_embed is not None:
x = x + self.interpolate_pos_encoding(x, w, h)
x = self.pos_drop(x)
rel_pos_bias = self.rel_pos_bias(
) if self.rel_pos_bias is not None else None
B, _, Hp, Wp = x_shape
feats = []
for idx, blk in enumerate(self.blocks):
x = blk(x, rel_pos_bias)
if idx in self.out_indices:
xp = paddle.reshape(
paddle.transpose(
self.norm(x[:, 1:, :]), perm=[0, 2, 1]),
shape=[B, -1, Hp, Wp])
feats.append(xp)
if self.with_fpn:
fpns = [self.fpn1, self.fpn2, self.fpn3, self.fpn4]
for i in range(len(feats)):
feats[i] = fpns[i](feats[i])
return feats
@property
def num_layers(self):
return len(self.blocks)
@property
def no_weight_decay(self):
return {'pos_embed', 'cls_token'}
@property
def out_shape(self):
return [
ShapeSpec(
channels=c, stride=s)
for c, s in zip(self.out_channels, self.out_strides)
]
ppdet/optimizer/adamw.py 0 → 100644
浏览文件 @ 63e7cfa4
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from paddle.optimizer import AdamW
from functools import partial
def layerwise_lr_decay(decay_rate, name_dict, n_layers, param):
"""
Args:
decay_rate (float):
The layer-wise decay ratio.
name_dict (dict):
The keys of name_dict is dynamic name of model while the value
of name_dict is static name.
Use model.named_parameters() to get name_dict.
n_layers (int):
Total number of layers in the transformer encoder.
"""
ratio = 1.0
static_name = name_dict[param.name]
if "blocks" in static_name:
idx = static_name.find("blocks.")
layer = int(static_name[idx:].split(".")[1])
ratio = decay_rate**(n_layers - layer)
elif "cls_token" in static_name or 'patch_embed' in static_name:
ratio = decay_rate**(n_layers + 1)
param.optimize_attr["learning_rate"] *= ratio
class AdamWDL(AdamW):
r"""
The AdamWDL optimizer is implemented based on the AdamW Optimization with dynamic lr setting.
Generally it's used for transformer model.
We use "layerwise_lr_decay" as default dynamic lr setting method of AdamWDL.
“Layer-wise decay” means exponentially decaying the learning rates of individual
layers in a top-down manner. For example, suppose the 24-th layer uses a learning
rate l, and the Layer-wise decay rate is α, then the learning rate of layer m
is lα^(24-m). See more details on: https://arxiv.org/abs/1906.08237.
.. math::
& t = t + 1
& moment\_1\_out = {\beta}_1 * moment\_1 + (1 - {\beta}_1) * grad
& moment\_2\_out = {\beta}_2 * moment\_2 + (1 - {\beta}_2) * grad * grad
& learning\_rate = learning\_rate * \frac{\sqrt{1 - {\beta}_2^t}}{1 - {\beta}_1^t}
& param\_out = param - learning\_rate * (\frac{moment\_1}{\sqrt{moment\_2} + \epsilon} + \lambda * param)
Args:
learning_rate (float|LRScheduler, optional): The learning rate used to update ``Parameter``.
It can be a float value or a LRScheduler. The default value is 0.001.
beta1 (float, optional): The exponential decay rate for the 1st moment estimates.
It should be a float number or a Tensor with shape [1] and data type as float32.
The default value is 0.9.
beta2 (float, optional): The exponential decay rate for the 2nd moment estimates.
It should be a float number or a Tensor with shape [1] and data type as float32.
The default value is 0.999.
epsilon (float, optional): A small float value for numerical stability.
It should be a float number or a Tensor with shape [1] and data type as float32.
The default value is 1e-08.
parameters (list|tuple, optional): List/Tuple of ``Tensor`` to update to minimize ``loss``. \
This parameter is required in dygraph mode. \
The default value is None in static mode, at this time all parameters will be updated.
weight_decay (float, optional): The weight decay coefficient, it can be float or Tensor. The default value is 0.01.
apply_decay_param_fun (function|None, optional): If it is not None,
only tensors that makes apply_decay_param_fun(Tensor.name)==True
will be updated. It only works when we want to specify tensors.
Default: None.
grad_clip (GradientClipBase, optional): Gradient cliping strategy, it's an instance of
some derived class of ``GradientClipBase`` . There are three cliping strategies
( :ref:`api_fluid_clip_GradientClipByGlobalNorm` , :ref:`api_fluid_clip_GradientClipByNorm` ,
:ref:`api_fluid_clip_GradientClipByValue` ). Default None, meaning there is no gradient clipping.
lazy_mode (bool, optional): The official Adam algorithm has two moving-average accumulators.
The accumulators are updated at every step. Every element of the two moving-average
is updated in both dense mode and sparse mode. If the size of parameter is very large,
then the update may be very slow. The lazy mode only update the element that has
gradient in current mini-batch, so it will be much more faster. But this mode has
different semantics with the original Adam algorithm and may lead to different result.
The default value is False.
multi_precision (bool, optional): Whether to use multi-precision during weight updating. Default is false.
layerwise_decay (float, optional): The layer-wise decay ratio. Defaults to 1.0.
n_layers (int, optional): The total number of encoder layers. Defaults to 12.
set_param_lr_fun (function|None, optional): If it's not None, set_param_lr_fun() will set the the parameter
learning rate before it executes Adam Operator. Defaults to :ref:`layerwise_lr_decay`.
name_dict (dict, optional): The keys of name_dict is dynamic name of model while the value
of name_dict is static name. Use model.named_parameters() to get name_dict.
name (str, optional): Normally there is no need for user to set this property.
For more information, please refer to :ref:`api_guide_Name`.
The default value is None.
Examples:
.. code-block:: python
import paddle
from paddlenlp.ops.optimizer import AdamWDL
def simple_lr_setting(decay_rate, name_dict, n_layers, param):
ratio = 1.0
static_name = name_dict[param.name]
if "weight" in static_name:
ratio = decay_rate**0.5
param.optimize_attr["learning_rate"] *= ratio
linear = paddle.nn.Linear(10, 10)
name_dict = dict()
for n, p in linear.named_parameters():
name_dict[p.name] = n
inp = paddle.rand([10,10], dtype="float32")
out = linear(inp)
loss = paddle.mean(out)
adamwdl = AdamWDL(
learning_rate=1e-4,
parameters=linear.parameters(),
set_param_lr_fun=simple_lr_setting,
layerwise_decay=0.8,
name_dict=name_dict)
loss.backward()
adamwdl.step()
adamwdl.clear_grad()
"""
def __init__(self,
learning_rate=0.001,
beta1=0.9,
beta2=0.999,
epsilon=1e-8,
parameters=None,
weight_decay=0.01,
apply_decay_param_fun=None,
grad_clip=None,
lazy_mode=False,
multi_precision=False,
layerwise_decay=1.0,
n_layers=12,
set_param_lr_fun=None,
name_dict=None,
name=None):
if not isinstance(layerwise_decay, float):
raise TypeError("coeff should be float or Tensor.")
self.layerwise_decay = layerwise_decay
self.n_layers = n_layers
self.set_param_lr_fun = partial(
set_param_lr_fun, layerwise_decay, name_dict,
n_layers) if set_param_lr_fun is not None else set_param_lr_fun
super(AdamWDL, self).__init__(
learning_rate=learning_rate,
parameters=parameters,
beta1=beta1,
beta2=beta2,
epsilon=epsilon,
grad_clip=grad_clip,
name=name,
apply_decay_param_fun=apply_decay_param_fun,
weight_decay=weight_decay,
lazy_mode=lazy_mode,
multi_precision=multi_precision)
def _append_optimize_op(self, block, param_and_grad):
if self.set_param_lr_fun is None:
return super(AdamWDL, self)._append_optimize_op(block,
param_and_grad)
self._append_decoupled_weight_decay(block, param_and_grad)
prev_lr = param_and_grad[0].optimize_attr["learning_rate"]
self.set_param_lr_fun(param_and_grad[0])
# excute Adam op
res = super(AdamW, self)._append_optimize_op(block, param_and_grad)
param_and_grad[0].optimize_attr["learning_rate"] = prev_lr
return res
def build_adamw(model,
lr=1e-4,
weight_decay=0.05,
betas=(0.9, 0.999),
layer_decay=0.65,
num_layers=None,
filter_bias_and_bn=True,
skip_decay_names=None,
set_param_lr_fun=None):
if skip_decay_names and filter_bias_and_bn:
decay_dict = {
param.name: not (len(param.shape) == 1 or name.endswith(".bias") or
any([_n in name for _n in skip_decay_names]))
for name, param in model.named_parameters()
}
parameters = [p for p in model.parameters()]
else:
parameters = model.parameters()
opt_args = dict(
parameters=parameters, learning_rate=lr, weight_decay=weight_decay)
if decay_dict is not None:
opt_args['apply_decay_param_fun'] = lambda n: decay_dict[n]
if isinstance(set_param_lr_fun, str):
set_param_lr_fun = eval(set_param_lr_fun)
opt_args['set_param_lr_fun'] = set_param_lr_fun
opt_args['beta1'] = betas[0]
opt_args['beta2'] = betas[1]
opt_args['layerwise_decay'] = layer_decay
name_dict = dict()
for n, p in model.named_parameters():
name_dict[p.name] = n
opt_args['name_dict'] = name_dict
opt_args['n_layers'] = num_layers
optimizer = AdamWDL(**opt_args)
return optimizer
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