# 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_ __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. head_init_scale (float): Init scaling value for classifier weights and biases. Default: 1. """ def __init__( self, in_chans=3, depths=[3, 3, 9, 3], dims=[96, 192, 384, 768], drop_path_rate=0., layer_scale_init_value=1e-6, head_init_scale=1., return_idx=[1, 2, 3], norm_output=True, pretrained=None, ): super().__init__() 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) # self.head.weight.set_value(self.head.weight.numpy() * head_init_scale) # self.head.bias.set_value(self.head.weight.numpy() * head_init_scale) 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, std=.02) nn.init.constant_(m.bias, 0) 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]