233 lines
8.5 KiB
Python
233 lines
8.5 KiB
Python
# copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# Code was heavily based on https://github.com/facebookresearch/ConvNeXt
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import paddle
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import paddle.nn as nn
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from paddle.nn.initializer import TruncatedNormal, Constant
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from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
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MODEL_URLS = {
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"ConvNeXt_tiny": "", # TODO
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}
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__all__ = list(MODEL_URLS.keys())
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trunc_normal_ = TruncatedNormal(std=.02)
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zeros_ = Constant(value=0.)
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ones_ = Constant(value=1.)
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def drop_path(x, drop_prob=0., training=False):
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"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
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the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
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See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ...
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"""
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if drop_prob == 0. or not training:
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return x
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keep_prob = paddle.to_tensor(1 - drop_prob)
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shape = (paddle.shape(x)[0], ) + (1, ) * (x.ndim - 1)
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random_tensor = keep_prob + paddle.rand(shape, dtype=x.dtype)
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random_tensor = paddle.floor(random_tensor) # binarize
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output = x.divide(keep_prob) * random_tensor
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return output
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class DropPath(nn.Layer):
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"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
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"""
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def __init__(self, drop_prob=None):
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super(DropPath, self).__init__()
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self.drop_prob = drop_prob
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def forward(self, x):
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return drop_path(x, self.drop_prob, self.training)
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class ChannelsFirstLayerNorm(nn.Layer):
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r""" LayerNorm that supports two data formats: channels_last (default) or channels_first.
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The ordering of the dimensions in the inputs. channels_last corresponds to inputs with
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shape (batch_size, height, width, channels) while channels_first corresponds to inputs
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with shape (batch_size, channels, height, width).
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"""
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def __init__(self, normalized_shape, epsilon=1e-5):
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super().__init__()
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self.weight = self.create_parameter(
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shape=[normalized_shape], default_initializer=ones_)
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self.bias = self.create_parameter(
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shape=[normalized_shape], default_initializer=zeros_)
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self.epsilon = epsilon
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self.normalized_shape = [normalized_shape]
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def forward(self, x):
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u = x.mean(1, keepdim=True)
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s = (x - u).pow(2).mean(1, keepdim=True)
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x = (x - u) / paddle.sqrt(s + self.epsilon)
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x = self.weight[:, None, None] * x + self.bias[:, None, None]
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return x
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class Block(nn.Layer):
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r""" ConvNeXt Block. There are two equivalent implementations:
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(1) DwConv -> LayerNorm (channels_first) -> 1x1 Conv -> GELU -> 1x1 Conv; all in (N, C, H, W)
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(2) DwConv -> Permute to (N, H, W, C); LayerNorm (channels_last) -> Linear -> GELU -> Linear; Permute back
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We use (2) as we find it slightly faster in PyTorch
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Args:
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dim (int): Number of input channels.
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drop_path (float): Stochastic depth rate. Default: 0.0
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layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.
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"""
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def __init__(self, dim, drop_path=0., layer_scale_init_value=1e-6):
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super().__init__()
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self.dwconv = nn.Conv2D(
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dim, dim, 7, padding=3, groups=dim) # depthwise conv
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self.norm = nn.LayerNorm(dim, epsilon=1e-6)
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# pointwise/1x1 convs, implemented with linear layers
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self.pwconv1 = nn.Linear(dim, 4 * dim)
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self.act = nn.GELU()
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self.pwconv2 = nn.Linear(4 * dim, dim)
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if layer_scale_init_value > 0:
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self.gamma = self.create_parameter(
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shape=[dim],
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default_initializer=Constant(value=layer_scale_init_value))
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else:
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self.gamma = None
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self.drop_path = DropPath(
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drop_path) if drop_path > 0. else nn.Identity()
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def forward(self, x):
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input = x
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x = self.dwconv(x)
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x = x.transpose([0, 2, 3, 1]) # (N, C, H, W) -> (N, H, W, C)
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x = self.norm(x)
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x = self.pwconv1(x)
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x = self.act(x)
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x = self.pwconv2(x)
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if self.gamma is not None:
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x = self.gamma * x
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x = x.transpose([0, 3, 1, 2]) # (N, H, W, C) -> (N, C, H, W)
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x = input + self.drop_path(x)
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return x
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class ConvNeXt(nn.Layer):
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r""" ConvNeXt
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A PaddlePaddle impl of : `A ConvNet for the 2020s` -
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https://arxiv.org/pdf/2201.03545.pdf
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Args:
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in_chans (int): Number of input image channels. Default: 3
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class_num (int): Number of classes for classification head. Default: 1000
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depths (tuple(int)): Number of blocks at each stage. Default: [3, 3, 9, 3]
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dims (int): Feature dimension at each stage. Default: [96, 192, 384, 768]
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drop_path_rate (float): Stochastic depth rate. Default: 0.
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layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.
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head_init_scale (float): Init scaling value for classifier weights and biases. Default: 1.
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"""
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def __init__(self,
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in_chans=3,
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class_num=1000,
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depths=[3, 3, 9, 3],
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dims=[96, 192, 384, 768],
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drop_path_rate=0.,
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layer_scale_init_value=1e-6,
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head_init_scale=1.):
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super().__init__()
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# stem and 3 intermediate downsampling conv layers
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self.downsample_layers = nn.LayerList()
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stem = nn.Sequential(
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nn.Conv2D(
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in_chans, dims[0], 4, stride=4),
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ChannelsFirstLayerNorm(
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dims[0], epsilon=1e-6))
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self.downsample_layers.append(stem)
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for i in range(3):
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downsample_layer = nn.Sequential(
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ChannelsFirstLayerNorm(
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dims[i], epsilon=1e-6),
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nn.Conv2D(
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dims[i], dims[i + 1], 2, stride=2), )
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self.downsample_layers.append(downsample_layer)
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# 4 feature resolution stages, each consisting of multiple residual blocks
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self.stages = nn.LayerList()
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dp_rates = [
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x.item() for x in paddle.linspace(0, drop_path_rate, sum(depths))
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]
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cur = 0
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for i in range(4):
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stage = nn.Sequential(* [
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Block(
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dim=dims[i],
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drop_path=dp_rates[cur + j],
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layer_scale_init_value=layer_scale_init_value)
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for j in range(depths[i])
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])
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self.stages.append(stage)
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cur += depths[i]
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self.norm = nn.LayerNorm(dims[-1], epsilon=1e-6) # final norm layer
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self.head = nn.Linear(dims[-1], class_num)
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self.apply(self._init_weights)
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self.head.weight.set_value(self.head.weight * head_init_scale)
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self.head.bias.set_value(self.head.bias * head_init_scale)
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def _init_weights(self, m):
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if isinstance(m, (nn.Conv2D, nn.Linear)):
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trunc_normal_(m.weight)
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if m.bias is not None:
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zeros_(m.bias)
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def forward_features(self, x):
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for i in range(4):
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x = self.downsample_layers[i](x)
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x = self.stages[i](x)
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# global average pooling, (N, C, H, W) -> (N, C)
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return self.norm(x.mean([-2, -1]))
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def forward(self, x):
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x = self.forward_features(x)
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x = self.head(x)
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return x
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def _load_pretrained(pretrained, model, model_url, use_ssld=False):
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if pretrained is False:
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pass
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elif pretrained is True:
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load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
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elif isinstance(pretrained, str):
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load_dygraph_pretrain(model, pretrained)
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else:
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raise RuntimeError(
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"pretrained type is not available. Please use `string` or `boolean` type."
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)
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def ConvNeXt_tiny(pretrained=False, use_ssld=False, **kwargs):
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model = ConvNeXt(depths=[3, 3, 9, 3], dims=[96, 192, 384, 768], **kwargs)
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_load_pretrained(
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pretrained, model, MODEL_URLS["ConvNeXt_tiny"], use_ssld=use_ssld)
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return model
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