From 99bee73a283156ee575934a3c59410f310123c5a Mon Sep 17 00:00:00 2001 From: lixingjian <1762778193@qq.com> Date: Tue, 7 Jan 2020 15:44:29 +0800 Subject: [PATCH] Add AutoDL Models (#4165) --- PaddleCV/image_classification/README.md | 9 + .../image_classification/models/__init__.py | 1 + .../image_classification/models/autodl.py | 562 ++++++++++++++++++ 3 files changed, 572 insertions(+) create mode 100644 PaddleCV/image_classification/models/autodl.py diff --git a/PaddleCV/image_classification/README.md b/PaddleCV/image_classification/README.md index 9398c29d..9c5e5a09 100644 --- a/PaddleCV/image_classification/README.md +++ b/PaddleCV/image_classification/README.md @@ -602,6 +602,13 @@ python -m paddle.distributed.launch train.py \ |[ShuffleNetV2_x2_0](https://paddle-imagenet-models-name.bj.bcebos.com/ShuffleNetV2_x2_0_pretrained.tar) | 73.15% | 91.20% | 6.430 | 3.954 | |[ShuffleNetV2_swish](https://paddle-imagenet-models-name.bj.bcebos.com/ShuffleNetV2_swish_pretrained.tar) | 70.03% | 89.17% | 6.078 | 4.976 | +### AutoDL Series +|Model | Top-1 | Top-5 | Paddle Fluid inference time(ms) | Paddle TensorRT inference time(ms) | +|- |:-: |:-: |:-: |:-: | +|[DARTS_4M](https://paddle-imagenet-models-name.bj.bcebos.com/DARTS_GS_4M_pretrained.tar) | 75.23% | 92.15% | 13.572 | 6.335 | +|[DARTS_6M](https://paddle-imagenet-models-name.bj.bcebos.com/DARTS_GS_6M_pretrained.tar) | 76.03% | 92.79% | 16.406 | 6.864 | +- AutoDL基于可微结构搜索思路DARTS改进，引入Local Rademacher Complexity控制过拟合，并通过Resource Constraining灵活调整模型大小。 + ### ResNet Series |Model | Top-1 | Top-5 | Paddle Fluid inference time(ms) | Paddle TensorRT inference time(ms) | |- |:-: |:-: |:-: |:-: | @@ -767,6 +774,7 @@ python -m paddle.distributed.launch train.py \ - EfficientNet: [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/abs/1905.11946), Mingxing Tan, Quoc V. Le - Res2Net: [Res2Net: A New Multi-scale Backbone Architecture](https://arxiv.org/abs/1904.01169), Shang-Hua Gao, Ming-Ming Cheng, Kai Zhao, Xin-Yu Zhang, Ming-Hsuan Yang, Philip Torr - HRNet: [Deep High-Resolution Representation Learning for Visual Recognition](https://arxiv.org/abs/1908.07919), Jingdong Wang, Ke Sun, Tianheng Cheng, Borui Jiang, Chaorui Deng, Yang Zhao, Dong Liu, Yadong Mu, Mingkui Tan, Xinggang Wang, Wenyu Liu, Bin Xiao +- DARTS: [DARTS: Differentiable Architecture Search](https://arxiv.org/pdf/1806.09055.pdf), Hanxiao Liu, Karen Simonyan, Yiming Yang ## 版本更新 - 2018/12/03 **Stage1**: 更新AlexNet，ResNet50，ResNet101，MobileNetV1 @@ -781,6 +789,7 @@ python -m paddle.distributed.launch train.py \ - 2019/09/11 **Stage8**: 更新ResNet18_vd，ResNet34_vd，MobileNetV1_x0_25，MobileNetV1_x0_5，MobileNetV1_x0_75，MobileNetV2_x0_75，MobilenNetV3_small_x1_0，DPN68，DPN92，DPN98，DPN107，DPN131，ResNeXt101_vd_32x4d，ResNeXt152_vd_64x4d，Xception65，Xception71，Xception41_deeplab，Xception65_deeplab，SE_ResNet50_vd - 2019/09/20 更新EfficientNet - 2019/11/28 **Stage9**: 更新SE_ResNet18_vd，SE_ResNet34_vd，SE_ResNeXt50_vd_32x4d，ResNeXt152_vd_32x4d，Res2Net50_26w_4s，Res2Net50_14w_8s，Res2Net50_vd_26w_4s，HRNet_W18_C，HRNet_W30_C，HRNet_W32_C，HRNet_W40_C，HRNet_W44_C，HRNet_W48_C，HRNet_W64_C +- 2020/1/7 **Stage10**: 添加AutoDL Series ## 如何贡献代码 diff --git a/PaddleCV/image_classification/models/__init__.py b/PaddleCV/image_classification/models/__init__.py index 6cf52fb5..bb12a824 100644 --- a/PaddleCV/image_classification/models/__init__.py +++ b/PaddleCV/image_classification/models/__init__.py @@ -41,3 +41,4 @@ from .efficientnet import EfficientNet, EfficientNetB0, EfficientNetB1, Efficien from .res2net import Res2Net50_48w_2s, Res2Net50_26w_4s, Res2Net50_14w_8s, Res2Net50_26w_6s, Res2Net50_26w_8s, Res2Net101_26w_4s, Res2Net152_26w_4s from .res2net_vd import Res2Net50_vd_48w_2s, Res2Net50_vd_26w_4s, Res2Net50_vd_14w_8s, Res2Net50_vd_26w_6s, Res2Net50_vd_26w_8s, Res2Net101_vd_26w_4s, Res2Net152_vd_26w_4s, Res2Net200_vd_26w_4s from .hrnet import HRNet_W18_C, HRNet_W30_C, HRNet_W32_C, HRNet_W40_C, HRNet_W44_C, HRNet_W48_C, HRNet_W60_C, HRNet_W64_C, SE_HRNet_W18_C, SE_HRNet_W30_C, SE_HRNet_W32_C, SE_HRNet_W40_C, SE_HRNet_W44_C, SE_HRNet_W48_C, SE_HRNet_W60_C, SE_HRNet_W64_C +from .autodl import DARTS_6M, DARTS_4M diff --git a/PaddleCV/image_classification/models/autodl.py b/PaddleCV/image_classification/models/autodl.py new file mode 100644 index 00000000..915bc163 --- /dev/null +++ b/PaddleCV/image_classification/models/autodl.py @@ -0,0 +1,562 @@ +# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. +# +#Licensed under the Apache License, Version 2.0 (the "License"); +#you may not use this file except in compliance with the License. +#You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +#Unless required by applicable law or agreed to in writing, software +#distributed under the License is distributed on an "AS IS" BASIS, +#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +#See the License for the specific language governing permissions and +#limitations under the License. +# +# Based on: +# -------------------------------------------------------- +# DARTS +# Copyright (c) 2018, Hanxiao Liu. +# Licensed under the Apache License, Version 2.0; +# -------------------------------------------------------- + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +import os +import sys +import numpy as np +import time +import functools +import paddle +import paddle.fluid as fluid +from paddle.fluid.param_attr import ParamAttr +from paddle.fluid.initializer import Xavier +from paddle.fluid.initializer import Normal +from paddle.fluid.initializer import Constant + + +from collections import namedtuple +Genotype = namedtuple('Genotype', 'normal normal_concat reduce reduce_concat') + +arch_dict = { + 'DARTS_6M': Genotype(normal=[('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('sep_conv_5x5', 1), ('sep_conv_5x5', 0), ('sep_conv_3x3', 2), ('sep_conv_3x3', 1), ('skip_connect', 4), ('sep_conv_3x3', 3)], normal_concat=range(2, 6), reduce=[('sep_conv_5x5', 0), ('max_pool_3x3', 1), ('dil_conv_5x5', 2), ('sep_conv_5x5', 0), ('sep_conv_3x3', 1), ('dil_conv_5x5', 3), ('dil_conv_3x3', 1), ('sep_conv_3x3', 2)], reduce_concat=range(2, 6)), + 'DARTS_4M': Genotype(normal=[('sep_conv_3x3', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 1), ('skip_connect', 0), ('skip_connect', 0), ('dil_conv_3x3', 1)], normal_concat=range(2, 6), reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('max_pool_3x3', 0), ('avg_pool_3x3', 1), ('skip_connect', 3), ('skip_connect', 2), ('sep_conv_3x3', 0), ('sep_conv_5x5', 2)], reduce_concat=range(2, 6)), +} + +__all__ = list(arch_dict.keys()) + +train_parameters = { + "input_size": [3, 224, 224], + "input_mean": [0.485, 0.456, 0.406], + "input_std": [0.229, 0.224, 0.225], + "learning_strategy": { + "name": "piecewise_decay", + "batch_size": 256, + "epochs": [30, 60, 90], + "steps": [0.1, 0.01, 0.001, 0.0001] + } +} + +OPS = { + 'none' : lambda input, C, stride, name, affine: Zero(input, stride, name), + 'avg_pool_3x3' : lambda input, C, stride, name, affine: fluid.layers.pool2d(input, 3, 'avg', pool_stride=stride, pool_padding=1, name=name), + 'max_pool_3x3' : lambda input, C, stride, name, affine: fluid.layers.pool2d(input, 3, 'max', pool_stride=stride, pool_padding=1, name=name), + 'skip_connect' : lambda input,C, stride, name, affine: Identity(input, name) if stride == 1 else FactorizedReduce(input, C, name=name, affine=affine), + 'sep_conv_3x3' : lambda input,C, stride, name, affine: SepConv(input, C, C, 3, stride, 1, name=name, affine=affine), + 'sep_conv_5x5' : lambda input,C, stride, name, affine: SepConv(input, C, C, 5, stride, 2, name=name, affine=affine), + 'sep_conv_7x7' : lambda input,C, stride, name, affine: SepConv(input, C, C, 7, stride, 3, name=name, affine=affine), + 'dil_conv_3x3' : lambda input,C, stride, name, affine: DilConv(input, C, C, 3, stride, 2, 2, name=name, affine=affine), + 'dil_conv_5x5' : lambda input,C, stride, name, affine: DilConv(input, C, C, 5, stride, 4, 2, name=name, affine=affine), + 'conv_7x1_1x7' : lambda input,C, stride, name, affine: SevenConv(input, C, name=name, affine=affine) +} + +def ReLUConvBN(input, C_out, kernel_size, stride, padding, name='', + affine=True): + relu_a = fluid.layers.relu(input) + conv2d_a = fluid.layers.conv2d( + relu_a, + C_out, + kernel_size, + stride, + padding, + bias_attr=False) + if affine: + reluconvbn_out = fluid.layers.batch_norm( + conv2d_a, + param_attr=ParamAttr( + initializer=Constant(1.), name=name + 'op.2.weight'), + bias_attr=ParamAttr( + initializer=Constant(0.), name=name + 'op.2.bias'), + moving_mean_name=name + 'op.2.running_mean', + moving_variance_name=name + 'op.2.running_var') + else: + reluconvbn_out = fluid.layers.batch_norm( + conv2d_a, + param_attr=ParamAttr( + initializer=Constant(1.), + learning_rate=0., + name=name + 'op.2.weight'), + bias_attr=ParamAttr( + initializer=Constant(0.), + learning_rate=0., + name=name + 'op.2.bias'), + moving_mean_name=name + 'op.2.running_mean', + moving_variance_name=name + 'op.2.running_var') + return reluconvbn_out + +def DilConv(input, + C_in, + C_out, + kernel_size, + stride, + padding, + dilation, + name='', + affine=True): + relu_a = fluid.layers.relu(input) + conv2d_a = fluid.layers.conv2d( + relu_a, + C_in, + kernel_size, + stride, + padding, + dilation, + groups=C_in, + bias_attr=False, + use_cudnn=False) + conv2d_b = fluid.layers.conv2d( + conv2d_a, + C_out, + 1, + bias_attr=False) + if affine: + dilconv_out = fluid.layers.batch_norm( + conv2d_b, + param_attr=ParamAttr( + initializer=Constant(1.), name=name + 'op.3.weight'), + bias_attr=ParamAttr( + initializer=Constant(0.), name=name + 'op.3.bias'), + moving_mean_name=name + 'op.3.running_mean', + moving_variance_name=name + 'op.3.running_var') + else: + dilconv_out = fluid.layers.batch_norm( + conv2d_b, + param_attr=ParamAttr( + initializer=Constant(1.), + learning_rate=0., + name=name + 'op.3.weight'), + bias_attr=ParamAttr( + initializer=Constant(0.), + learning_rate=0., + name=name + 'op.3.bias'), + moving_mean_name=name + 'op.3.running_mean', + moving_variance_name=name + 'op.3.running_var') + return dilconv_out + +def SepConv(input, + C_in, + C_out, + kernel_size, + stride, + padding, + name='', + affine=True): + relu_a = fluid.layers.relu(input) + conv2d_a = fluid.layers.conv2d( + relu_a, + C_in, + kernel_size, + stride, + padding, + groups=C_in, + bias_attr=False, + use_cudnn=False) + conv2d_b = fluid.layers.conv2d( + conv2d_a, + C_in, + 1, + bias_attr=False) + if affine: + bn_a = fluid.layers.batch_norm( + conv2d_b, + param_attr=ParamAttr( + initializer=Constant(1.), name=name + 'op.3.weight'), + bias_attr=ParamAttr( + initializer=Constant(0.), name=name + 'op.3.bias'), + moving_mean_name=name + 'op.3.running_mean', + moving_variance_name=name + 'op.3.running_var') + else: + bn_a = fluid.layers.batch_norm( + conv2d_b, + param_attr=ParamAttr( + initializer=Constant(1.), + learning_rate=0., + name=name + 'op.3.weight'), + bias_attr=ParamAttr( + initializer=Constant(0.), + learning_rate=0., + name=name + 'op.3.bias'), + moving_mean_name=name + 'op.3.running_mean', + moving_variance_name=name + 'op.3.running_var') + + relu_b = fluid.layers.relu(bn_a) + conv2d_d = fluid.layers.conv2d( + relu_b, + C_in, + kernel_size, + 1, + padding, + groups=C_in, + bias_attr=False, + use_cudnn=False) + conv2d_e = fluid.layers.conv2d( + conv2d_d, + C_out, + 1, + bias_attr=False) + if affine: + sepconv_out = fluid.layers.batch_norm( + conv2d_e, + param_attr=ParamAttr( + initializer=Constant(1.), name=name + 'op.7.weight'), + bias_attr=ParamAttr( + initializer=Constant(0.), name=name + 'op.7.bias'), + moving_mean_name=name + 'op.7.running_mean', + moving_variance_name=name + 'op.7.running_var') + else: + sepconv_out = fluid.layers.batch_norm( + conv2d_e, + param_attr=ParamAttr( + initializer=Constant(1.), + learning_rate=0., + name=name + 'op.7.weight'), + bias_attr=ParamAttr( + initializer=Constant(0.), + learning_rate=0., + name=name + 'op.7.bias'), + moving_mean_name=name + 'op.7.running_mean', + moving_variance_name=name + 'op.7.running_var') + return sepconv_out + +def SevenConv(input, C_out, stride, name='', affine=True): + relu_a = fluid.layers.relu(input) + conv2d_a = fluid.layers.conv2d( + relu_a, + C_out, (1, 7), (1, stride), (0, 3), + param_attr=ParamAttr( + initializer=Xavier( + uniform=False, fan_in=0), + name=name + 'op.1.weight'), + bias_attr=False) + conv2d_b = fluid.layers.conv2d( + conv2d_a, + C_out, (7, 1), (stride, 1), (3, 0), + param_attr=ParamAttr( + initializer=Xavier( + uniform=False, fan_in=0), + name=name + 'op.2.weight'), + bias_attr=False) + if affine: + out = fluid.layers.batch_norm( + conv2d_b, + param_attr=ParamAttr( + initializer=Constant(1.), name=name + 'op.3.weight'), + bias_attr=ParamAttr( + initializer=Constant(0.), name=name + 'op.3.bias'), + moving_mean_name=name + 'op.3.running_mean', + moving_variance_name=name + 'op.3.running_var') + else: + out = fluid.layers.batch_norm( + conv2d_b, + param_attr=ParamAttr( + initializer=Constant(1.), + learning_rate=0., + name=name + 'op.3.weight'), + bias_attr=ParamAttr( + initializer=Constant(0.), + learning_rate=0., + name=name + 'op.3.bias'), + moving_mean_name=name + 'op.3.running_mean', + moving_variance_name=name + 'op.3.running_var') + +def Identity(input, name=''): + return input + +def Zero(input, stride, name=''): + ones = np.ones(input.shape[-2:]) + ones[::stride, ::stride] = 0 + ones = fluid.layers.assign(ones) + return input * ones + +def FactorizedReduce(input, C_out, name='', affine=True): + relu_a = fluid.layers.relu(input) + conv2d_a = fluid.layers.conv2d( + relu_a, + C_out // 2, + 1, + 2, + param_attr=ParamAttr( + initializer=Xavier( + uniform=False, fan_in=0), + name=name + 'conv_1.weight'), + bias_attr=False) + h_end = relu_a.shape[2] + w_end = relu_a.shape[3] + slice_a = fluid.layers.slice(relu_a, [2, 3], [1, 1], [h_end, w_end]) + conv2d_b = fluid.layers.conv2d( + slice_a, + C_out // 2, + 1, + 2, + param_attr=ParamAttr( + initializer=Xavier( + uniform=False, fan_in=0), + name=name + 'conv_2.weight'), + bias_attr=False) + out = fluid.layers.concat([conv2d_a, conv2d_b], axis=1) + if affine: + out = fluid.layers.batch_norm( + out, + param_attr=ParamAttr( + initializer=Constant(1.), name=name + 'bn.weight'), + bias_attr=ParamAttr( + initializer=Constant(0.), name=name + 'bn.bias'), + moving_mean_name=name + 'bn.running_mean', + moving_variance_name=name + 'bn.running_var') + else: + out = fluid.layers.batch_norm( + out, + param_attr=ParamAttr( + initializer=Constant(1.), + learning_rate=0., + name=name + 'bn.weight'), + bias_attr=ParamAttr( + initializer=Constant(0.), + learning_rate=0., + name=name + 'bn.bias'), + moving_mean_name=name + 'bn.running_mean', + moving_variance_name=name + 'bn.running_var') + return out + +class Cell(): + def __init__(self, genotype, C_prev_prev, C_prev, C, reduction, + reduction_prev): + + if reduction_prev: + self.preprocess0 = functools.partial(FactorizedReduce, C_out=C) + else: + self.preprocess0 = functools.partial( + ReLUConvBN, C_out=C, kernel_size=1, stride=1, padding=0) + self.preprocess1 = functools.partial( + ReLUConvBN, C_out=C, kernel_size=1, stride=1, padding=0) + if reduction: + op_names, indices = zip(*genotype.reduce) + concat = genotype.reduce_concat + else: + op_names, indices = zip(*genotype.normal) + concat = genotype.normal_concat + print(op_names, indices, concat, reduction) + self._compile(C, op_names, indices, concat, reduction) + + def _compile(self, C, op_names, indices, concat, reduction): + assert len(op_names) == len(indices) + self._steps = len(op_names) // 2 + self._concat = concat + self.multiplier = len(concat) + + self._ops = [] + for name, index in zip(op_names, indices): + stride = 2 if reduction and index < 2 else 1 + op = functools.partial(OPS[name], C=C, stride=stride, affine=True) + self._ops += [op] + self._indices = indices + + def forward(self, s0, s1, drop_prob, is_train, name): + self.training = is_train + preprocess0_name = name + 'preprocess0.' + preprocess1_name = name + 'preprocess1.' + s0 = self.preprocess0(s0, name=preprocess0_name) + s1 = self.preprocess1(s1, name=preprocess1_name) + out = [s0, s1] + for i in range(self._steps): + h1 = out[self._indices[2 * i]] + h2 = out[self._indices[2 * i + 1]] + op1 = self._ops[2 * i] + op2 = self._ops[2 * i + 1] + h3 = op1(h1, name=name + '_ops.' + str(2 * i) + '.') + h4 = op2(h2, name=name + '_ops.' + str(2 * i + 1) + '.') + if self.training and drop_prob > 0.: + if h3 != h1: + h3 = fluid.layers.dropout( + h3, + drop_prob, + dropout_implementation='upscale_in_train') + if h4 != h2: + h4 = fluid.layers.dropout( + h4, + drop_prob, + dropout_implementation='upscale_in_train') + s = h3 + h4 + out += [s] + return fluid.layers.concat([out[i] for i in self._concat], axis=1) + +def AuxiliaryHeadImageNet(input, num_classes, aux_name='auxiliary_head'): + relu_a = fluid.layers.relu(input) + pool_a = fluid.layers.pool2d(relu_a, 5, 'avg', 2) + conv2d_a = fluid.layers.conv2d( + pool_a, + 128, + 1, + name=aux_name + '.features.2', + bias_attr=False) + bn_a_name = aux_name + '.features.3' + bn_a = fluid.layers.batch_norm( + conv2d_a, + act='relu', + name=bn_a_name, + param_attr=ParamAttr( + initializer=Constant(1.), name=bn_a_name + '.weight'), + bias_attr=ParamAttr( + initializer=Constant(0.), name=bn_a_name + '.bias'), + moving_mean_name=bn_a_name + '.running_mean', + moving_variance_name=bn_a_name + '.running_var') + conv2d_b = fluid.layers.conv2d( + bn_a, + 768, + 2, + name=aux_name + '.features.5', + bias_attr=False) + bn_b_name = aux_name + '.features.6' + bn_b = fluid.layers.batch_norm( + conv2d_b, + act='relu', + name=bn_b_name, + param_attr=ParamAttr( + initializer=Constant(1.), name=bn_b_name + '.weight'), + bias_attr=ParamAttr( + initializer=Constant(0.), name=bn_b_name + '.bias'), + moving_mean_name=bn_b_name + '.running_mean', + moving_variance_name=bn_b_name + '.running_var') + pool_b = fluid.layers.adaptive_pool2d(bn_b, (1, 1), "avg") + fc_name = aux_name + '.classifier' + fc = fluid.layers.fc(pool_b, + num_classes, + name=fc_name, + param_attr=ParamAttr( + initializer=Normal(scale=1e-3), + name=fc_name + '.weight'), + bias_attr=ParamAttr( + initializer=Constant(0.), name=fc_name + '.bias')) + return fc + + +def StemConv0(input, C_out): + conv_a = fluid.layers.conv2d( + input, + C_out // 2, + 3, + stride=2, + padding=1, + bias_attr=False) + bn_a = fluid.layers.batch_norm( + conv_a, + act='relu', + param_attr=ParamAttr( + initializer=Constant(1.), name='stem0.1.weight'), + bias_attr=ParamAttr( + initializer=Constant(0.), name='stem0.1.bias'), + moving_mean_name='stem0.1.running_mean', + moving_variance_name='stem0.1.running_var') + + conv_b = fluid.layers.conv2d( + bn_a, + C_out, + 3, + stride=2, + padding=1, + bias_attr=False) + bn_b = fluid.layers.batch_norm( + conv_b, + param_attr=ParamAttr( + initializer=Constant(1.), name='stem0.3.weight'), + bias_attr=ParamAttr( + initializer=Constant(0.), name='stem0.3.bias'), + moving_mean_name='stem0.3.running_mean', + moving_variance_name='stem0.3.running_var') + return bn_b + +def StemConv1(input, C_out): + relu_a = fluid.layers.relu(input) + conv_a = fluid.layers.conv2d( + relu_a, + C_out, + 3, + stride=2, + padding=1, + bias_attr=False) + bn_a = fluid.layers.batch_norm( + conv_a, + param_attr=ParamAttr( + initializer=Constant(1.), name='stem1.1.weight'), + bias_attr=ParamAttr( + initializer=Constant(0.), name='stem1.1.bias'), + moving_mean_name='stem1.1.running_mean', + moving_variance_name='stem1.1.running_var') + return bn_a + +class NetworkImageNet(object): + def __init__(self, arch='DARTS_6M'): + self.params = train_parameters + self.class_num = 1000 + self.init_channel = 48 + self._layers = 14 + self._auxiliary = False + self.drop_path_prob = 0 + genotype = arch_dict[arch] + + C = self.init_channel + layers = self._layers + C_prev_prev, C_prev, C_curr = C, C, C + self.cells = [] + reduction_prev = True + for i in range(layers): + if i in [layers // 3, 2 * layers // 3]: + C_curr *= 2 + reduction = True + else: + reduction = False + cell = Cell(genotype, C_prev_prev, C_prev, C_curr, reduction, + reduction_prev) + reduction_prev = reduction + self.cells += [cell] + C_prev_prev, C_prev = C_prev, cell.multiplier * C_curr + if i == 2 * layers // 3: + C_to_auxiliary = C_prev + + def net(self, input, class_dim=1000, is_train=True): + self.logits_aux = None + num_channel = self.init_channel + s0 = StemConv0(input, num_channel) + s1 = StemConv1(s0, num_channel) + for i, cell in enumerate(self.cells): + name = 'cells.' + str(i) + '.' + s0, s1 = s1, cell.forward(s0, s1, self.drop_path_prob, is_train, + name) + if i == int(2 * self._layers // 3): + if self._auxiliary and is_train: + self.logits_aux = AuxiliaryHeadImageNet(s1, self.class_num) + out = fluid.layers.adaptive_pool2d(s1, (1, 1), "avg") + self.logits = fluid.layers.fc(out, + size=self.class_num, + param_attr=ParamAttr( + initializer=Normal(scale=1e-4), + name='classifier.weight'), + bias_attr=ParamAttr( + initializer=Constant(0.), + name='classifier.bias')) + return self.logits + +def DARTS_6M(): + return NetworkImageNet(arch = 'DARTS_6M') +def DARTS_4M(): + return NetworkImageNet(arch = 'DARTS_4M') -- GitLab