263 lines
9.0 KiB
Python
263 lines
9.0 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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# Code was heavily based on https://github.com/zhoudaquan/rethinking_bottleneck_design
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# reference: https://arxiv.org/abs/2007.02269
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import math
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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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MODEL_URLS = {
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"MobileNeXt_x0_35": "", # TODO
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"MobileNeXt_x0_5": "", # TODO
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"MobileNeXt_x0_75": "", # TODO
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"MobileNeXt_x1_0":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNeXt_x1_0_pretrained.pdparams",
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"MobileNeXt_x1_4": "", # TODO
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}
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__all__ = list(MODEL_URLS.keys())
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def _make_divisible(v, divisor, min_value=None):
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if min_value is None:
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min_value = divisor
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new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
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# Make sure that round down does not go down by more than 10%.
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if new_v < 0.9 * v:
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new_v += divisor
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return new_v
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def conv_3x3_bn(inp, oup, stride):
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return nn.Sequential(
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nn.Conv2D(
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inp, oup, 3, stride, 1, bias_attr=False),
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nn.BatchNorm2D(oup),
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nn.ReLU6())
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class SGBlock(nn.Layer):
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def __init__(self, inp, oup, stride, expand_ratio, keep_3x3=False):
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super(SGBlock, self).__init__()
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assert stride in [1, 2]
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hidden_dim = inp // expand_ratio
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if hidden_dim < oup / 6.:
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hidden_dim = math.ceil(oup / 6.)
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hidden_dim = _make_divisible(hidden_dim, 16) # + 16
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self.identity = False
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self.identity_div = 1
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self.expand_ratio = expand_ratio
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if expand_ratio == 2:
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self.conv = nn.Sequential(
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# dw
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nn.Conv2D(
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inp, inp, 3, 1, 1, groups=inp, bias_attr=False),
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nn.BatchNorm2D(inp),
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nn.ReLU6(),
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# pw-linear
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nn.Conv2D(
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inp, hidden_dim, 1, 1, 0, bias_attr=False),
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nn.BatchNorm2D(hidden_dim),
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# pw-linear
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nn.Conv2D(
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hidden_dim, oup, 1, 1, 0, bias_attr=False),
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nn.BatchNorm2D(oup),
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nn.ReLU6(),
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# dw
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nn.Conv2D(
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oup, oup, 3, stride, 1, groups=oup, bias_attr=False),
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nn.BatchNorm2D(oup))
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elif inp != oup and stride == 1 and keep_3x3 == False:
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self.conv = nn.Sequential(
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# pw-linear
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nn.Conv2D(
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inp, hidden_dim, 1, 1, 0, bias_attr=False),
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nn.BatchNorm2D(hidden_dim),
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# pw-linear
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nn.Conv2D(
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hidden_dim, oup, 1, 1, 0, bias_attr=False),
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nn.BatchNorm2D(oup),
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nn.ReLU6())
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elif inp != oup and stride == 2 and keep_3x3 == False:
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self.conv = nn.Sequential(
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# pw-linear
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nn.Conv2D(
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inp, hidden_dim, 1, 1, 0, bias_attr=False),
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nn.BatchNorm2D(hidden_dim),
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# pw-linear
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nn.Conv2D(
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hidden_dim, oup, 1, 1, 0, bias_attr=False),
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nn.BatchNorm2D(oup),
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nn.ReLU6(),
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# dw
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nn.Conv2D(
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oup, oup, 3, stride, 1, groups=oup, bias_attr=False),
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nn.BatchNorm2D(oup))
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else:
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if keep_3x3 == False:
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self.identity = True
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self.conv = nn.Sequential(
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# dw
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nn.Conv2D(
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inp, inp, 3, 1, 1, groups=inp, bias_attr=False),
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nn.BatchNorm2D(inp),
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nn.ReLU6(),
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# pw
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nn.Conv2D(
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inp, hidden_dim, 1, 1, 0, bias_attr=False),
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nn.BatchNorm2D(hidden_dim),
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#nn.ReLU6(),
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# pw
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nn.Conv2D(
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hidden_dim, oup, 1, 1, 0, bias_attr=False),
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nn.BatchNorm2D(oup),
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nn.ReLU6(),
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# dw
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nn.Conv2D(
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oup, oup, 3, 1, 1, groups=oup, bias_attr=False),
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nn.BatchNorm2D(oup))
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def forward(self, x):
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out = self.conv(x)
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if self.identity:
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if self.identity_div == 1:
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out = out + x
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else:
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shape = x.shape
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id_tensor = x[:, :shape[1] // self.identity_div, :, :]
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out[:, :shape[1] // self.identity_div, :, :] = \
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out[:, :shape[1] // self.identity_div, :, :] + id_tensor
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return out
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class MobileNeXt(nn.Layer):
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def __init__(self, class_num=1000, width_mult=1.00):
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super().__init__()
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# setting of inverted residual blocks
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self.cfgs = [
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# t, c, n, s
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[2, 96, 1, 2],
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[6, 144, 1, 1],
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[6, 192, 3, 2],
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[6, 288, 3, 2],
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[6, 384, 4, 1],
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[6, 576, 4, 2],
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[6, 960, 3, 1],
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[6, 1280, 1, 1],
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]
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# building first layer
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input_channel = _make_divisible(32 * width_mult, 4
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if width_mult == 0.1 else 8)
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layers = [conv_3x3_bn(3, input_channel, 2)]
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# building inverted residual blocks
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block = SGBlock
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for t, c, n, s in self.cfgs:
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output_channel = _make_divisible(c * width_mult, 4
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if width_mult == 0.1 else 8)
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if c == 1280 and width_mult < 1:
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output_channel = 1280
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layers.append(
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block(input_channel, output_channel, s, t, n == 1 and s == 1))
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input_channel = output_channel
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for _ in range(n - 1):
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layers.append(block(input_channel, output_channel, 1, t))
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input_channel = output_channel
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self.features = nn.Sequential(*layers)
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# building last several layers
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input_channel = output_channel
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output_channel = _make_divisible(input_channel, 4)
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self.avgpool = nn.AdaptiveAvgPool2D((1, 1))
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self.classifier = nn.Sequential(
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nn.Dropout(0.2), nn.Linear(output_channel, class_num))
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self.apply(self._initialize_weights)
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def _initialize_weights(self, m):
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if isinstance(m, nn.Conv2D):
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n = m._kernel_size[0] * m._kernel_size[1] * m._out_channels
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nn.initializer.Normal(std=math.sqrt(2. / n))(m.weight)
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if m.bias is not None:
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nn.initializer.Constant(0)(m.bias)
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elif isinstance(m, nn.BatchNorm2D):
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nn.initializer.Constant(1)(m.weight)
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nn.initializer.Constant(0)(m.bias)
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elif isinstance(m, nn.Linear):
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nn.initializer.Normal(std=0.01)(m.weight)
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nn.initializer.Constant(0)(m.bias)
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def forward(self, x):
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x = self.features(x)
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x = self.avgpool(x)
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x = x.flatten(1)
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x = self.classifier(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(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 MobileNeXt_x0_35(pretrained=False, use_ssld=False, **kwargs):
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model = MobileNeXt(width_mult=0.35, **kwargs)
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_load_pretrained(
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pretrained, model, MODEL_URLS["MobileNeXt_x0_35"], use_ssld=use_ssld)
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return model
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def MobileNeXt_x0_5(pretrained=False, use_ssld=False, **kwargs):
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model = MobileNeXt(width_mult=0.50, **kwargs)
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_load_pretrained(
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pretrained, model, MODEL_URLS["MobileNeXt_x0_5"], use_ssld=use_ssld)
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return model
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def MobileNeXt_x0_75(pretrained=False, use_ssld=False, **kwargs):
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model = MobileNeXt(width_mult=0.75, **kwargs)
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_load_pretrained(
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pretrained, model, MODEL_URLS["MobileNeXt_x0_75"], use_ssld=use_ssld)
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return model
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def MobileNeXt_x1_0(pretrained=False, use_ssld=False, **kwargs):
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model = MobileNeXt(width_mult=1.00, **kwargs)
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_load_pretrained(
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pretrained, model, MODEL_URLS["MobileNeXt_x1_0"], use_ssld=use_ssld)
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return model
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def MobileNeXt_x1_4(pretrained=False, use_ssld=False, **kwargs):
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model = MobileNeXt(width_mult=1.40, **kwargs)
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_load_pretrained(
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pretrained, model, MODEL_URLS["MobileNeXt_x1_4"], use_ssld=use_ssld)
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return model
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