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# 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.
'''
Modified from https://github.com/facebookresearch/ConvNeXt
Copyright (c) Meta Platforms, Inc. and affiliates.
All rights reserved.
This source code is licensed under the license found in the
LICENSE file in the root directory of this source tree.
'''
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddle import ParamAttr
from paddle.nn.initializer import Constant
import numpy as np
from ppdet.core.workspace import register, serializable
from ..shape_spec import ShapeSpec
from .transformer_utils import DropPath, trunc_normal_, zeros_
__all__ = ['ConvNeXt']
class Block(nn.Layer):
r""" ConvNeXt Block. There are two equivalent implementations:
(1) DwConv -> LayerNorm (channels_first) -> 1x1 Conv -> GELU -> 1x1 Conv; all in (N, C, H, W)
(2) DwConv -> Permute to (N, H, W, C); LayerNorm (channels_last) -> Linear -> GELU -> Linear; Permute back
We use (2) as we find it slightly faster in Pypaddle
Args:
dim (int): Number of input channels.
drop_path (float): Stochastic depth rate. Default: 0.0
layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.
"""
def __init__(self, dim, drop_path=0., layer_scale_init_value=1e-6):
super().__init__()
self.dwconv = nn.Conv2D(
dim, dim, kernel_size=7, padding=3, groups=dim) # depthwise conv
self.norm = LayerNorm(dim, eps=1e-6)
self.pwconv1 = nn.Linear(
dim, 4 * dim) # pointwise/1x1 convs, implemented with linear layers
self.act = nn.GELU()
self.pwconv2 = nn.Linear(4 * dim, dim)
if layer_scale_init_value > 0:
self.gamma = self.create_parameter(
shape=(dim, ),
attr=ParamAttr(initializer=Constant(layer_scale_init_value)))
else:
self.gamma = None
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity(
)
def forward(self, x):
input = x
x = self.dwconv(x)
x = x.transpose([0, 2, 3, 1])
x = self.norm(x)
x = self.pwconv1(x)
x = self.act(x)
x = self.pwconv2(x)
if self.gamma is not None:
x = self.gamma * x
x = x.transpose([0, 3, 1, 2])
x = input + self.drop_path(x)
return x
class LayerNorm(nn.Layer):
r""" LayerNorm that supports two data formats: channels_last (default) or channels_first.
The ordering of the dimensions in the inputs. channels_last corresponds to inputs with
shape (batch_size, height, width, channels) while channels_first corresponds to inputs
with shape (batch_size, channels, height, width).
"""
def __init__(self, normalized_shape, eps=1e-6, data_format="channels_last"):
super().__init__()
self.weight = self.create_parameter(
shape=(normalized_shape, ),
attr=ParamAttr(initializer=Constant(1.)))
self.bias = self.create_parameter(
shape=(normalized_shape, ),
attr=ParamAttr(initializer=Constant(0.)))
self.eps = eps
self.data_format = data_format
if self.data_format not in ["channels_last", "channels_first"]:
raise NotImplementedError
self.normalized_shape = (normalized_shape, )
def forward(self, x):
if self.data_format == "channels_last":
return F.layer_norm(x, self.normalized_shape, self.weight,
self.bias, self.eps)
elif self.data_format == "channels_first":
u = x.mean(1, keepdim=True)
s = (x - u).pow(2).mean(1, keepdim=True)
x = (x - u) / paddle.sqrt(s + self.eps)
x = self.weight[:, None, None] * x + self.bias[:, None, None]
return x
@register
@serializable
class ConvNeXt(nn.Layer):
r""" ConvNeXt
A Pypaddle impl of : `A ConvNet for the 2020s` -
https://arxiv.org/pdf/2201.03545.pdf
Args:
in_chans (int): Number of input image channels. Default: 3
depths (tuple(int)): Number of blocks at each stage. Default: [3, 3, 9, 3]
dims (int): Feature dimension at each stage. Default: [96, 192, 384, 768]
drop_path_rate (float): Stochastic depth rate. Default: 0.
layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.
"""
arch_settings = {
'tiny': {
'depths': [3, 3, 9, 3],
'dims': [96, 192, 384, 768]
},
'small': {
'depths': [3, 3, 27, 3],
'dims': [96, 192, 384, 768]
},
'base': {
'depths': [3, 3, 27, 3],
'dims': [128, 256, 512, 1024]
},
'large': {
'depths': [3, 3, 27, 3],
'dims': [192, 384, 768, 1536]
},
'xlarge': {
'depths': [3, 3, 27, 3],
'dims': [256, 512, 1024, 2048]
},
}
def __init__(
self,
arch='tiny',
in_chans=3,
drop_path_rate=0.,
layer_scale_init_value=1e-6,
return_idx=[1, 2, 3],
norm_output=True,
pretrained=None, ):
super().__init__()
depths = self.arch_settings[arch]['depths']
dims = self.arch_settings[arch]['dims']
self.downsample_layers = nn.LayerList(
) # stem and 3 intermediate downsampling conv layers
stem = nn.Sequential(
nn.Conv2D(
in_chans, dims[0], kernel_size=4, stride=4),
LayerNorm(
dims[0], eps=1e-6, data_format="channels_first"))
self.downsample_layers.append(stem)
for i in range(3):
downsample_layer = nn.Sequential(
LayerNorm(
dims[i], eps=1e-6, data_format="channels_first"),
nn.Conv2D(
dims[i], dims[i + 1], kernel_size=2, stride=2), )
self.downsample_layers.append(downsample_layer)
self.stages = nn.LayerList(
) # 4 feature resolution stages, each consisting of multiple residual blocks
dp_rates = [x for x in np.linspace(0, drop_path_rate, sum(depths))]
cur = 0
for i in range(4):
stage = nn.Sequential(* [
Block(
dim=dims[i],
drop_path=dp_rates[cur + j],
layer_scale_init_value=layer_scale_init_value)
for j in range(depths[i])
])
self.stages.append(stage)
cur += depths[i]
self.return_idx = return_idx
self.dims = [dims[i] for i in return_idx] # [::-1]
self.norm_output = norm_output
if norm_output:
self.norms = nn.LayerList([
LayerNorm(
c, eps=1e-6, data_format="channels_first")
for c in self.dims
])
self.apply(self._init_weights)
if pretrained is not None:
if 'http' in pretrained: #URL
path = paddle.utils.download.get_weights_path_from_url(
pretrained)
else: #model in local path
path = pretrained
self.set_state_dict(paddle.load(path))
def _init_weights(self, m):
if isinstance(m, (nn.Conv2D, nn.Linear)):
trunc_normal_(m.weight)
zeros_(m.bias)
def forward_features(self, x):
output = []
for i in range(4):
x = self.downsample_layers[i](x)
x = self.stages[i](x)
output.append(x)
outputs = [output[i] for i in self.return_idx]
if self.norm_output:
outputs = [self.norms[i](out) for i, out in enumerate(outputs)]
return outputs
def forward(self, x):
x = self.forward_features(x['image'])
return x
@property
def out_shape(self):
return [ShapeSpec(channels=c) for c in self.dims]
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