add convnext (#6184)

* add convnext

* remove invalid args

ppdet/modeling/backbones/__init__.py

@@ -31,6 +31,7 @@ from . import hardnet
 from . import esnet
 from . import cspresnet
 from . import csp_darknet
+from . import convnext
 
 from .vgg import *
 from .resnet import *
@@ -51,3 +52,4 @@ from .hardnet import *
 from .esnet import *
 from .cspresnet import *
 from .csp_darknet import *
+from .convnext import *
\ No newline at end of file

ppdet/modeling/backbones/convnext.py

+# 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],
+            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.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)
+
+        output = [output[i] for i in self.return_idx]
+
+        return output
+
+    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]