198 lines
6.9 KiB
Python
198 lines
6.9 KiB
Python
# copyright (c) 2024 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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# reference: https://arxiv.org/abs/2403.19967
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import paddle
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import paddle.nn as nn
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from ....utils.save_load import load_dygraph_pretrain
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from ..model_zoo.vision_transformer import DropPath
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MODEL_URLS = {
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"StarNet_S1":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/StarNet_S1_pretrained.pdparams",
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"StarNet_S2":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/StarNet_S2_pretrained.pdparams",
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"StarNet_S3":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/StarNet_S3_pretrained.pdparams",
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"StarNet_S4":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/StarNet_S4_pretrained.pdparams",
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}
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__all__ = MODEL_URLS.keys()
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NET_CONFIG = {
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"StarNet_S1": [24, [2, 2, 8, 3]],
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"StarNet_S2": [32, [1, 2, 6, 2]],
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"StarNet_S3": [32, [2, 2, 8, 4]],
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"StarNet_S4": [32, [3, 3, 12, 5]],
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}
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class ConvBN(nn.Sequential):
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def __init__(self,
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in_planes,
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out_planes,
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kernel_size=1,
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stride=1,
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padding=0,
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dilation=1,
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groups=1,
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with_bn=True):
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super().__init__()
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self.add_sublayer(
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name='conv',
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sublayer=nn.Conv2D(
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in_channels=in_planes,
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out_channels=out_planes,
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kernel_size=kernel_size,
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stride=stride,
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padding=padding,
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dilation=dilation,
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groups=groups))
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if with_bn:
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self.add_sublayer(
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name='bn', sublayer=nn.BatchNorm2D(num_features=out_planes))
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init_Constant = nn.initializer.Constant(value=1)
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init_Constant(self.bn.weight)
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init_Constant = nn.initializer.Constant(value=0)
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init_Constant(self.bn.bias)
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class Block(nn.Layer):
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def __init__(self, dim, mlp_ratio=3, drop_path=0.0):
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super().__init__()
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self.dwconv = ConvBN(
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dim, dim, 7, 1, (7 - 1) // 2, groups=dim, with_bn=True)
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self.f1 = ConvBN(dim, mlp_ratio * dim, 1, with_bn=False)
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self.f2 = ConvBN(dim, mlp_ratio * dim, 1, with_bn=False)
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self.g = ConvBN(mlp_ratio * dim, dim, 1, with_bn=True)
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self.dwconv2 = ConvBN(
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dim, dim, 7, 1, (7 - 1) // 2, groups=dim, with_bn=False)
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self.act = nn.ReLU6()
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self.drop_path = (DropPath(drop_path)
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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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x1, x2 = self.f1(x), self.f2(x)
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x = self.act(x1) * x2
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x = self.dwconv2(self.g(x))
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x = input + self.drop_path(x)
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return x
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def _load_pretrained(pretrained, model, model_url, use_ssld):
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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(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("pretrained type is not available. ")
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class StarNet(nn.Layer):
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"""
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StarNet: StarNet for Image Classification
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Args:
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base_dim: int, base dimension of the model, default 32.
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depths: list, number of blocks in each stage, default [3, 3, 12, 5].
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mlp_ratio: int, ratio of hidden dim to mlp_dim, default 4.
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drop_path_rate: float, default 0.0, stochastic depth rate.
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class_num: int, default 1000, number of classes.
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"""
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def __init__(self,
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base_dim=32,
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depths=[3, 3, 12, 5],
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mlp_ratio=4,
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drop_path_rate=0.0,
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class_num=1000,
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**kwargs):
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super().__init__()
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self.class_num = class_num
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self.in_channel = 32
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self.stem = nn.Sequential(
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ConvBN(
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3, self.in_channel, kernel_size=3, stride=2, padding=1),
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nn.ReLU6())
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dpr = [
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x.item()
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for x in paddle.linspace(
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start=0, stop=drop_path_rate, num=sum(depths))
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]
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self.stages = nn.LayerList()
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cur = 0
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for i_layer in range(len(depths)):
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embed_dim = base_dim * 2**i_layer
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down_sampler = ConvBN(self.in_channel, embed_dim, 3, 2, 1)
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self.in_channel = embed_dim
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blocks = [
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Block(self.in_channel, mlp_ratio, dpr[cur + i])
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for i in range(depths[i_layer])
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]
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cur += depths[i_layer]
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self.stages.append(nn.Sequential(down_sampler, *blocks))
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self.norm = nn.BatchNorm2D(num_features=self.in_channel)
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self.avgpool = nn.AdaptiveAvgPool2D(output_size=1)
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self.head = nn.Linear(
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in_features=self.in_channel, out_features=class_num)
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self.apply(self._init_weights)
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def _init_weights(self, m):
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if isinstance(m, nn.Linear or nn.Conv2D):
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pass
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if isinstance(m, nn.Linear) and m.bias is not None:
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init_Constant = nn.initializer.Constant(value=0)
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init_Constant(m.bias)
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elif isinstance(m, nn.LayerNorm or nn.BatchNorm2D):
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init_Constant = nn.initializer.Constant(value=0)
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init_Constant(m.bias)
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init_Constant = nn.initializer.Constant(value=1.0)
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init_Constant(m.weight)
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def forward(self, x):
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x = self.stem(x)
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for stage in self.stages:
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x = stage(x)
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x = paddle.flatten(x=self.avgpool(self.norm(x)), start_axis=1)
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return self.head(x)
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def StarNet_S1(pretrained=False, use_ssld=False, **kwargs):
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model = StarNet(*NET_CONFIG["StarNet_S1"], **kwargs)
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_load_pretrained(pretrained, model, MODEL_URLS["StarNet_S1"], use_ssld)
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return model
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def StarNet_S2(pretrained=False, use_ssld=False, **kwargs):
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model = StarNet(*NET_CONFIG["StarNet_S2"], **kwargs)
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_load_pretrained(pretrained, model, MODEL_URLS["StarNet_S2"], use_ssld)
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return model
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def StarNet_S3(pretrained=False, use_ssld=False, **kwargs):
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model = StarNet(*NET_CONFIG["StarNet_S3"], **kwargs)
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_load_pretrained(pretrained, model, MODEL_URLS["StarNet_S3"], use_ssld)
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return model
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def StarNet_S4(pretrained=False, use_ssld=False, **kwargs):
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model = StarNet(*NET_CONFIG["StarNet_S4"], **kwargs)
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_load_pretrained(pretrained, model, MODEL_URLS["StarNet_S4"], use_ssld)
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return model
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