205 lines
7.0 KiB
Python
205 lines
7.0 KiB
Python
# copyright (c) 2021 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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# Code was based on https://github.com/d-li14/involution
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# reference: https://arxiv.org/abs/2103.06255
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import paddle
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import paddle.nn as nn
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from paddle.vision.models import resnet
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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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"RedNet26":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RedNet26_pretrained.pdparams",
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"RedNet38":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RedNet38_pretrained.pdparams",
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"RedNet50":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RedNet50_pretrained.pdparams",
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"RedNet101":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RedNet101_pretrained.pdparams",
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"RedNet152":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RedNet152_pretrained.pdparams"
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}
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__all__ = MODEL_URLS.keys()
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class Involution(nn.Layer):
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def __init__(self, channels, kernel_size, stride):
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super(Involution, self).__init__()
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self.kernel_size = kernel_size
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self.stride = stride
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self.channels = channels
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reduction_ratio = 4
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self.group_channels = 16
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self.groups = self.channels // self.group_channels
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self.conv1 = nn.Sequential(
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('conv', nn.Conv2D(
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in_channels=channels,
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out_channels=channels // reduction_ratio,
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kernel_size=1,
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bias_attr=False)),
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('bn', nn.BatchNorm2D(channels // reduction_ratio)),
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('activate', nn.ReLU()))
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self.conv2 = nn.Sequential(('conv', nn.Conv2D(
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in_channels=channels // reduction_ratio,
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out_channels=kernel_size**2 * self.groups,
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kernel_size=1,
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stride=1)))
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if stride > 1:
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self.avgpool = nn.AvgPool2D(stride, stride)
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def forward(self, x):
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weight = self.conv2(
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self.conv1(x if self.stride == 1 else self.avgpool(x)))
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b, c, h, w = weight.shape
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weight = weight.reshape(
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(b, self.groups, self.kernel_size**2, h, w)).unsqueeze(2)
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out = nn.functional.unfold(x, self.kernel_size, self.stride,
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(self.kernel_size - 1) // 2, 1)
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out = out.reshape(
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(b, self.groups, self.group_channels, self.kernel_size**2, h, w))
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out = (weight * out).sum(axis=3).reshape((b, self.channels, h, w))
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return out
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class BottleneckBlock(resnet.BottleneckBlock):
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def __init__(self,
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inplanes,
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planes,
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stride=1,
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downsample=None,
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groups=1,
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base_width=64,
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dilation=1,
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norm_layer=None):
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super(BottleneckBlock, self).__init__(inplanes, planes, stride,
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downsample, groups, base_width,
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dilation, norm_layer)
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width = int(planes * (base_width / 64.)) * groups
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self.conv2 = Involution(width, 7, stride)
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class RedNet(resnet.ResNet):
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def __init__(self, block, depth, class_num=1000, with_pool=True):
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super(RedNet, self).__init__(
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block=block, depth=50, num_classes=class_num, with_pool=with_pool)
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layer_cfg = {
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26: [1, 2, 4, 1],
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38: [2, 3, 5, 2],
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50: [3, 4, 6, 3],
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101: [3, 4, 23, 3],
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152: [3, 8, 36, 3]
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}
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layers = layer_cfg[depth]
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self.conv1 = None
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self.bn1 = None
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self.relu = None
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self.inplanes = 64
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self.class_num = class_num
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self.stem = nn.Sequential(
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nn.Sequential(
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('conv', nn.Conv2D(
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in_channels=3,
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out_channels=self.inplanes // 2,
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kernel_size=3,
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stride=2,
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padding=1,
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bias_attr=False)),
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('bn', nn.BatchNorm2D(self.inplanes // 2)),
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('activate', nn.ReLU())),
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Involution(self.inplanes // 2, 3, 1),
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nn.BatchNorm2D(self.inplanes // 2),
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nn.ReLU(),
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nn.Sequential(
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('conv', nn.Conv2D(
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in_channels=self.inplanes // 2,
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out_channels=self.inplanes,
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kernel_size=3,
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stride=1,
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padding=1,
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bias_attr=False)), ('bn', nn.BatchNorm2D(self.inplanes)),
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('activate', nn.ReLU())))
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self.layer1 = self._make_layer(block, 64, layers[0])
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self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
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self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
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self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
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def forward(self, x):
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x = self.stem(x)
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x = self.maxpool(x)
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x = self.layer1(x)
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x = self.layer2(x)
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x = self.layer3(x)
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x = self.layer4(x)
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if self.with_pool:
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x = self.avgpool(x)
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if self.class_num > 0:
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x = paddle.flatten(x, 1)
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x = self.fc(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 RedNet26(pretrained=False, **kwargs):
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model = RedNet(BottleneckBlock, 26, **kwargs)
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_load_pretrained(pretrained, model, MODEL_URLS["RedNet26"])
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return model
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def RedNet38(pretrained=False, **kwargs):
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model = RedNet(BottleneckBlock, 38, **kwargs)
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_load_pretrained(pretrained, model, MODEL_URLS["RedNet38"])
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return model
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def RedNet50(pretrained=False, **kwargs):
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model = RedNet(BottleneckBlock, 50, **kwargs)
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_load_pretrained(pretrained, model, MODEL_URLS["RedNet50"])
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return model
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def RedNet101(pretrained=False, **kwargs):
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model = RedNet(BottleneckBlock, 101, **kwargs)
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_load_pretrained(pretrained, model, MODEL_URLS["RedNet101"])
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return model
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def RedNet152(pretrained=False, **kwargs):
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model = RedNet(BottleneckBlock, 152, **kwargs)
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_load_pretrained(pretrained, model, MODEL_URLS["RedNet152"])
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return model
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