# MIT License # # Copyright (c) Meta Platforms, Inc. and affiliates. # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. # # Code was heavily based on https://github.com/facebookresearch/ConvNeXt import paddle import paddle.nn as nn from paddle.nn.initializer import TruncatedNormal, Constant from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url MODEL_URLS = { "ConvNext_tiny": "", # TODO } __all__ = list(MODEL_URLS.keys()) trunc_normal_ = TruncatedNormal(std=.02) zeros_ = Constant(value=0.) ones_ = Constant(value=1.) 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): """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). """ 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 ChannelsFirstLayerNorm(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, epsilon=1e-5): super().__init__() self.weight = self.create_parameter( shape=[normalized_shape], default_initializer=ones_) self.bias = self.create_parameter( shape=[normalized_shape], default_initializer=zeros_) self.epsilon = epsilon self.normalized_shape = [normalized_shape] def forward(self, x): u = x.mean(1, keepdim=True) s = (x - u).pow(2).mean(1, keepdim=True) x = (x - u) / paddle.sqrt(s + self.epsilon) x = self.weight[:, None, None] * x + self.bias[:, None, None] return x 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 PyTorch 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, 7, padding=3, groups=dim) # depthwise conv self.norm = nn.LayerNorm(dim, epsilon=1e-6) # pointwise/1x1 convs, implemented with linear layers self.pwconv1 = nn.Linear(dim, 4 * dim) self.act = nn.GELU() self.pwconv2 = nn.Linear(4 * dim, dim) if layer_scale_init_value > 0: self.gamma = self.create_parameter( shape=[dim], default_initializer=Constant(value=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]) # (N, C, H, W) -> (N, H, W, C) 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]) # (N, H, W, C) -> (N, C, H, W) x = input + self.drop_path(x) return x class ConvNeXt(nn.Layer): r""" ConvNeXt A PyTorch 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 class_num (int): Number of classes for classification head. Default: 1000 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, class_num=1000, depths=[3, 3, 9, 3], dims=[96, 192, 384, 768], drop_path_rate=0., layer_scale_init_value=1e-6, head_init_scale=1.): super().__init__() # stem and 3 intermediate downsampling conv layers self.downsample_layers = nn.LayerList() stem = nn.Sequential( nn.Conv2D( in_chans, dims[0], 4, stride=4), ChannelsFirstLayerNorm( dims[0], epsilon=1e-6)) self.downsample_layers.append(stem) for i in range(3): downsample_layer = nn.Sequential( ChannelsFirstLayerNorm( dims[i], epsilon=1e-6), nn.Conv2D( dims[i], dims[i + 1], 2, stride=2), ) self.downsample_layers.append(downsample_layer) # 4 feature resolution stages, each consisting of multiple residual blocks self.stages = nn.LayerList() dp_rates = [ x.item() for x in paddle.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.norm = nn.LayerNorm(dims[-1], epsilon=1e-6) # final norm layer self.head = nn.Linear(dims[-1], class_num) self.apply(self._init_weights) self.head.weight.set_value(self.head.weight * head_init_scale) self.head.bias.set_value(self.head.bias * head_init_scale) def _init_weights(self, m): if isinstance(m, (nn.Conv2D, nn.Linear)): trunc_normal_(m.weight) if m.bias is not None: zeros_(m.bias) def forward_features(self, x): for i in range(4): x = self.downsample_layers[i](x) x = self.stages[i](x) # global average pooling, (N, C, H, W) -> (N, C) return self.norm(x.mean([-2, -1])) def forward(self, x): x = self.forward_features(x) x = self.head(x) 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 ConvNext_tiny(pretrained=False, use_ssld=False, **kwargs): model = ConvNeXt(depths=[3, 3, 9, 3], dims=[96, 192, 384, 768], **kwargs) _load_pretrained( pretrained, model, MODEL_URLS["ConvNext_tiny"], use_ssld=use_ssld) return model