From abb30ef20d85ffc36bf5ac0d9f47be782d37acdb Mon Sep 17 00:00:00 2001 From: Bai Yifan Date: Wed, 8 Apr 2020 13:34:51 +0800 Subject: [PATCH] Add DARTS series methods (#213) --- demo/darts/README.md | 89 ++++++++ demo/darts/genotypes.py | 108 +++++++++ demo/darts/images/networks.gif | Bin 0 -> 2350332 bytes demo/darts/model.py | 311 +++++++++++++++++++++++++ demo/darts/model_search.py | 325 ++++++++++++++++++++++++++ demo/darts/operations.py | 267 ++++++++++++++++++++++ demo/darts/reader.py | 328 +++++++++++++++++++++++++++ demo/darts/search.py | 98 ++++++++ demo/darts/train.py | 222 ++++++++++++++++++ demo/darts/train_imagenet.py | 238 +++++++++++++++++++ demo/darts/visualize.py | 97 ++++++++ paddleslim/common/__init__.py | 3 +- paddleslim/common/meter.py | 30 +++ paddleslim/nas/__init__.py | 3 + paddleslim/nas/darts/__init__.py | 20 ++ paddleslim/nas/darts/architect.py | 137 +++++++++++ paddleslim/nas/darts/train_search.py | 220 ++++++++++++++++++ 17 files changed, 2495 insertions(+), 1 deletion(-) create mode 100644 demo/darts/README.md create mode 100644 demo/darts/genotypes.py create mode 100755 demo/darts/images/networks.gif create mode 100644 demo/darts/model.py create mode 100644 demo/darts/model_search.py create mode 100644 demo/darts/operations.py create mode 100644 demo/darts/reader.py create mode 100644 demo/darts/search.py create mode 100644 demo/darts/train.py create mode 100644 demo/darts/train_imagenet.py create mode 100644 demo/darts/visualize.py create mode 100644 paddleslim/common/meter.py create mode 100644 paddleslim/nas/darts/__init__.py create mode 100644 paddleslim/nas/darts/architect.py create mode 100644 paddleslim/nas/darts/train_search.py diff --git a/demo/darts/README.md b/demo/darts/README.md new file mode 100644 index 00000000..6787177a --- /dev/null +++ b/demo/darts/README.md @@ -0,0 +1,89 @@ +# 可微分架构搜索DARTS(Differentiable Architecture Search)方法使用示例 + +本示例介绍如何使用PaddlePaddle进行可微分架构搜索,可以直接使用[DARTS](https://arxiv.org/abs/1806.09055)和[PC-DARTS](https://arxiv.org/abs/1907.05737)两种方法,也支持自定义修改后使用其他可微分架构搜索算法。 + +## 依赖项 + +> PaddlePaddle >= 1.7.0, graphviz >= 0.11.1 + +## 数据集 + +本示例使用`CIFAR10`数据集进行架构搜索,可选择在`CIFAR10`或`ImageNet`数据集上做架构评估。 +`CIFAR10`数据集可以在进行架构搜索或评估的过程中自动下载,`ImageNet`数据集需要自行下载,可参照此[教程](https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/image_classification#%E6%95%B0%E6%8D%AE%E5%87%86%E5%A4%87) + + +## 网络结构搜索 + +搜索方法支持DARTS的一阶、二阶近似搜索方法和PC-DARTS的搜索方法: +``` bash +python search.py # DARTS一阶近似搜索方法 +python search.py --unrolled=True # DARTS的二阶近似搜索方法 +python search.py --method='PC-DARTS' # PC-DARTS搜索方法 +``` + +模型结构随搜索轮数的变化如图1所示。需要注意的是,图中准确率Acc并不代表该结构最终准确率,为了获得当前结构的最佳准确率,请对得到的genotype做网络结构评估训练。 + +![networks](images/networks.gif) + +

+图1: 在CIFAR10数据集上进行搜索的模型结构变化,上半部分为reduction cell,下半部分为normal cell +

+ +使用三种搜索方法得到的结构Genotype已添加到了genotypes.py文件中,`DARTS_V1`、`DARTS_V2`和`PC-DARTS`分别代表使用DARTS一阶、二阶近似方法和PC-DARTS搜索方法得到的网络结构。 + +## 网络结构评估训练 + +在得到搜索结构Genotype之后,可以对其进行评估训练,从而获得它在特定数据集上的真实性能 + +```bash +python train.py --arch='PC-DARTS' # 在CIFAR10数据集上对搜索到的结构评估训练 +python train_imagenet.py --arch='PC-DARTS' # 在ImageNet数据集上对搜索得到的结构评估训练 +``` + +对搜索到的`DARTS_V1`、`DARTS_V2`和`PC-DARTS`做评估训练的结果如下: + +| 模型结构 | 数据集 | 准确率 | +| --------------------------- | -------- | --------------- | +| DARTS_V1 | CIFAR10 | 97.01% | +| DARTS(一阶搜索,论文数据) | CIFAR10 | 97.00$\pm$0.14% | +| DARTS_V2 | CIFAR10 | 97.26% | +| DARTS (二阶搜索,论文数据) | CIFAR10 | 97.24$\pm$0.09% | +| DARTS_V2 | ImageNet | 74.12% | +| DARTS (二阶搜索,论文数据) | ImageNet | 73.30% | +| PC-DARTS | CIFAR10 | 97.41% | +| PC-DARTS (论文数据) | CIFAR10 | 97.43$\pm$0.07% | + +## 自定义数据集与搜索空间 + +### 修改数据集 + +本示例默认使用CIFAR10数据集进行搜索,如果需要替换为其他自定义数据集只需要对reader.py进行少量代码修改: + +```python +def train_search(batch_size, train_portion, is_shuffle, args): + datasets = cifar10_reader( #对此进行替换 + paddle.dataset.common.download(CIFAR10_URL, 'cifar', CIFAR10_MD5), + 'data_batch', is_shuffle, args) +``` + +将默认使用的`cifar10_reader`替换为特定数据集的reader即可 + +### 修改搜索空间 + +本示例提供了DARTS和PC-DARTS两种方法,定义在model_search.py中 + +可以直接修改model_search.py中定义的`class Network`对搜索空间进行自定义,使用paddleslim.nas.DARTSearch对该结构进行搜索 + +搜索结束后对model.py做相应的修改进行评估训练。 + + + +## 搜索结构可视化 + +使用以下命令对搜索得到的Genotype结构进行可视化观察 + +```python +python visualize.py PC-DARTS +``` + +`PC-DARTS`代表某个Genotype结构,需要预先添加到genotype.py中 \ No newline at end of file diff --git a/demo/darts/genotypes.py b/demo/darts/genotypes.py new file mode 100644 index 00000000..a90998da --- /dev/null +++ b/demo/darts/genotypes.py @@ -0,0 +1,108 @@ +# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. +# +# 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. + +from collections import namedtuple + +Genotype = namedtuple('Genotype', 'normal normal_concat reduce reduce_concat') + +PRIMITIVES = [ + 'none', 'max_pool_3x3', 'avg_pool_3x3', 'skip_connect', 'sep_conv_3x3', + 'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5' +] + +NASNet = Genotype( + normal=[ + ('sep_conv_5x5', 1), + ('sep_conv_3x3', 0), + ('sep_conv_5x5', 0), + ('sep_conv_3x3', 0), + ('avg_pool_3x3', 1), + ('skip_connect', 0), + ('avg_pool_3x3', 0), + ('avg_pool_3x3', 0), + ('sep_conv_3x3', 1), + ('skip_connect', 1), + ], + normal_concat=[2, 3, 4, 5, 6], + reduce=[ + ('sep_conv_5x5', 1), + ('sep_conv_7x7', 0), + ('max_pool_3x3', 1), + ('sep_conv_7x7', 0), + ('avg_pool_3x3', 1), + ('sep_conv_5x5', 0), + ('skip_connect', 3), + ('avg_pool_3x3', 2), + ('sep_conv_3x3', 2), + ('max_pool_3x3', 1), + ], + reduce_concat=[4, 5, 6], ) + +AmoebaNet = Genotype( + normal=[ + ('avg_pool_3x3', 0), + ('max_pool_3x3', 1), + ('sep_conv_3x3', 0), + ('sep_conv_5x5', 2), + ('sep_conv_3x3', 0), + ('avg_pool_3x3', 3), + ('sep_conv_3x3', 1), + ('skip_connect', 1), + ('skip_connect', 0), + ('avg_pool_3x3', 1), + ], + normal_concat=[4, 5, 6], + reduce=[ + ('avg_pool_3x3', 0), + ('sep_conv_3x3', 1), + ('max_pool_3x3', 0), + ('sep_conv_7x7', 2), + ('sep_conv_7x7', 0), + ('avg_pool_3x3', 1), + ('max_pool_3x3', 0), + ('max_pool_3x3', 1), + ('conv_7x1_1x7', 0), + ('sep_conv_3x3', 5), + ], + reduce_concat=[3, 4, 6]) + +DARTS_V1 = Genotype( + normal=[('sep_conv_5x5', 0), ('dil_conv_3x3', 1), ('sep_conv_3x3', 2), + ('sep_conv_5x5', 0), ('sep_conv_5x5', 0), ('dil_conv_3x3', 3), + ('sep_conv_3x3', 0), ('max_pool_3x3', 1)], + normal_concat=range(2, 6), + reduce=[('max_pool_3x3', 1), ('max_pool_3x3', 0), ('dil_conv_3x3', 2), + ('sep_conv_5x5', 0), ('max_pool_3x3', 0), ('dil_conv_3x3', 3), + ('avg_pool_3x3', 3), ('avg_pool_3x3', 4)], + reduce_concat=range(2, 6)) + +DARTS_V2 = Genotype( + normal=[('dil_conv_3x3', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 0), + ('sep_conv_3x3', 1), ('sep_conv_3x3', 1), ('sep_conv_3x3', 0), + ('skip_connect', 0), ('sep_conv_3x3', 1)], + normal_concat=range(2, 6), + reduce=[('skip_connect', 1), ('max_pool_3x3', 0), ('max_pool_3x3', 1), + ('skip_connect', 2), ('skip_connect', 2), ('dil_conv_5x5', 3), + ('skip_connect', 2), ('max_pool_3x3', 1)], + reduce_concat=range(2, 6)) + +PC_DARTS = Genotype( + normal=[('sep_conv_3x3', 1), ('skip_connect', 0), ('sep_conv_5x5', 0), + ('dil_conv_5x5', 2), ('sep_conv_5x5', 0), ('sep_conv_3x3', 2), + ('sep_conv_3x3', 0), ('dil_conv_3x3', 1)], + normal_concat=range(2, 6), + reduce=[('avg_pool_3x3', 0), ('sep_conv_3x3', 1), ('skip_connect', 2), + ('avg_pool_3x3', 0), ('dil_conv_5x5', 3), ('skip_connect', 2), + ('skip_connect', 2), ('avg_pool_3x3', 0)], + reduce_concat=range(2, 6)) diff --git a/demo/darts/images/networks.gif b/demo/darts/images/networks.gif new file mode 100755 index 0000000000000000000000000000000000000000..41a1145c6c185312d519bbf93578e75c98fcab0a GIT binary patch literal 2350332 zcmdp7=U3BBu>Pe70+`Udp@$-dUd2$QOD|Fk9T5>wiaiMdLJxv;Fd!YofOJp;(m_Q5 zMMVfrs)!1T<;Ckg_rJLFWzX49GxO}s?lUuc%F04l&yOG03;qEB00@91;T#+Y1d;=V zLhO>8yFbiRi&{< zg*41G^zHO@taMH|p0GNl>FRvK@|32Uk%^|Ux!x&TBReNEGc#LTTVpq4b1!qtvzFdx zOw8T9Or1SlTwJ`py=?<+PoF#O6z=46&By(cdsv9gl~}i^2nQRV0DHesr{Hks(2E`s zF|OyMeJ)=+6B!p26cipFPP|2oiHW&!;|4MAmfu-(!b#P?0Pq)u`YR~-S5EHlv15OA zbSO7{{#si8_4NFE?%dzFxIk)PSbA6pEhOT0L{x56Y)R~msvC*qvnfgbc{%6vZl5nN 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HcmV?d00001 diff --git a/demo/darts/model.py b/demo/darts/model.py new file mode 100644 index 00000000..111f3abd --- /dev/null +++ b/demo/darts/model.py @@ -0,0 +1,311 @@ +# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved +# +# 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. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import paddle.fluid as fluid +from paddle.fluid.param_attr import ParamAttr +from paddle.fluid.initializer import ConstantInitializer, MSRAInitializer +from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear +from paddle.fluid.dygraph.base import to_variable +from genotypes import PRIMITIVES +from genotypes import Genotype +from operations import * + + +class ConvBN(fluid.dygraph.Layer): + def __init__(self, c_curr, c_out, kernel_size, padding, stride, name=None): + super(ConvBN, self).__init__() + self.conv = Conv2D( + num_channels=c_curr, + num_filters=c_out, + filter_size=kernel_size, + stride=stride, + padding=padding, + param_attr=fluid.ParamAttr( + name=name + "_conv" if name is not None else None, + initializer=MSRAInitializer()), + bias_attr=False) + self.bn = BatchNorm( + num_channels=c_out, + param_attr=fluid.ParamAttr( + name=name + "_bn_scale" if name is not None else None, + initializer=ConstantInitializer(value=1)), + bias_attr=fluid.ParamAttr( + name=name + "_bn_offset" if name is not None else None, + initializer=ConstantInitializer(value=0)), + moving_mean_name=name + "_bn_mean" if name is not None else None, + moving_variance_name=name + "_bn_variance" + if name is not None else None) + + def forward(self, x): + conv = self.conv(x) + bn = self.bn(conv) + return bn + + +class Classifier(fluid.dygraph.Layer): + def __init__(self, input_dim, num_classes, name=None): + super(Classifier, self).__init__() + self.pool2d = Pool2D(pool_type='avg', global_pooling=True) + self.fc = Linear( + input_dim=input_dim, + output_dim=num_classes, + param_attr=fluid.ParamAttr( + name=name + "_fc_weights" if name is not None else None, + initializer=MSRAInitializer()), + bias_attr=fluid.ParamAttr( + name=name + "_fc_bias" if name is not None else None, + initializer=MSRAInitializer())) + + def forward(self, x): + x = self.pool2d(x) + x = fluid.layers.squeeze(x, axes=[2, 3]) + out = self.fc(x) + return out + + +def drop_path(x, drop_prob): + if drop_prob > 0: + keep_prob = 1. - drop_prob + mask = 1 - np.random.binomial( + 1, drop_prob, size=[x.shape[0]]).astype(np.float32) + mask = to_variable(mask) + x = fluid.layers.elementwise_mul(x / keep_prob, mask, axis=0) + return x + + +class Cell(fluid.dygraph.Layer): + def __init__(self, genotype, c_prev_prev, c_prev, c_curr, reduction, + reduction_prev): + super(Cell, self).__init__() + print(c_prev_prev, c_prev, c_curr) + + if reduction_prev: + self.preprocess0 = FactorizedReduce(c_prev_prev, c_curr) + else: + self.preprocess0 = ReLUConvBN(c_prev_prev, c_curr, 1, 1, 0) + self.preprocess1 = ReLUConvBN(c_prev, c_curr, 1, 1, 0) + + if reduction: + op_names, indices = zip(*genotype.reduce) + concat = genotype.reduce_concat + else: + op_names, indices = zip(*genotype.normal) + concat = genotype.normal_concat + + multiplier = len(concat) + self._multiplier = multiplier + self._compile(c_curr, op_names, indices, multiplier, reduction) + + def _compile(self, c_curr, op_names, indices, multiplier, reduction): + assert len(op_names) == len(indices) + self._steps = len(op_names) // 2 + ops = [] + edge_index = 0 + for op_name, index in zip(op_names, indices): + stride = 2 if reduction and index < 2 else 1 + op = OPS[op_name](c_curr, stride, True) + ops += [op] + edge_index += 1 + self._ops = fluid.dygraph.LayerList(ops) + self._indices = indices + + def forward(self, s0, s1, drop_prob, training): + s0 = self.preprocess0(s0) + s1 = self.preprocess1(s1) + + states = [s0, s1] + for i in range(self._steps): + h1 = states[self._indices[2 * i]] + h2 = states[self._indices[2 * i + 1]] + op1 = self._ops[2 * i] + op2 = self._ops[2 * i + 1] + h1 = op1(h1) + h2 = op2(h2) + if training and drop_prob > 0.: + if not isinstance(op1, Identity): + h1 = drop_path(h1, drop_prob) + if not isinstance(op2, Identity): + h2 = drop_path(h2, drop_prob) + states += [h1 + h2] + out = fluid.layers.concat(input=states[-self._multiplier:], axis=1) + return out + + +class AuxiliaryHeadCIFAR(fluid.dygraph.Layer): + def __init__(self, C, num_classes): + super(AuxiliaryHeadCIFAR, self).__init__() + self.avgpool = Pool2D( + pool_size=5, pool_stride=3, pool_padding=0, pool_type='avg') + self.conv_bn1 = ConvBN( + c_curr=C, + c_out=128, + kernel_size=1, + padding=0, + stride=1, + name='aux_conv_bn1') + self.conv_bn2 = ConvBN( + c_curr=128, + c_out=768, + kernel_size=2, + padding=0, + stride=1, + name='aux_conv_bn2') + self.classifier = Classifier(768, num_classes, 'aux') + + def forward(self, x): + x = fluid.layers.relu(x) + x = self.avgpool(x) + conv1 = self.conv_bn1(x) + conv1 = fluid.layers.relu(conv1) + conv2 = self.conv_bn2(conv1) + conv2 = fluid.layers.relu(conv2) + out = self.classifier(conv2) + return out + + +class NetworkCIFAR(fluid.dygraph.Layer): + def __init__(self, C, num_classes, layers, auxiliary, genotype): + super(NetworkCIFAR, self).__init__() + self._layers = layers + self._auxiliary = auxiliary + + stem_multiplier = 3 + c_curr = stem_multiplier * C + self.stem = ConvBN( + c_curr=3, c_out=c_curr, kernel_size=3, padding=1, stride=1) + + c_prev_prev, c_prev, c_curr = c_curr, c_curr, C + cells = [] + reduction_prev = False + 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 + cells += [cell] + c_prev_prev, c_prev = c_prev, cell._multiplier * c_curr + if i == 2 * layers // 3: + c_to_auxiliary = c_prev + self.cells = fluid.dygraph.LayerList(cells) + + if auxiliary: + self.auxiliary_head = AuxiliaryHeadCIFAR(c_to_auxiliary, + num_classes) + self.classifier = Classifier(c_prev, num_classes) + + def forward(self, input, drop_path_prob, training): + logits_aux = None + s0 = s1 = self.stem(input) + for i, cell in enumerate(self.cells): + s0, s1 = s1, cell(s0, s1, drop_path_prob, training) + if i == 2 * self._layers // 3: + if self._auxiliary and training: + logits_aux = self.auxiliary_head(s1) + logits = self.classifier(s1) + return logits, logits_aux + + +class AuxiliaryHeadImageNet(fluid.dygraph.Layer): + def __init__(self, C, num_classes): + super(AuxiliaryHeadImageNet, self).__init__() + self.avgpool = Pool2D( + pool_size=5, pool_stride=2, pool_padding=0, pool_type='avg') + self.conv_bn1 = ConvBN( + c_curr=C, + c_out=128, + kernel_size=1, + padding=0, + stride=1, + name='aux_conv_bn1') + self.conv_bn2 = ConvBN( + c_curr=128, + c_out=768, + kernel_size=2, + padding=0, + stride=1, + name='aux_conv_bn2') + self.classifier = Classifier(768, num_classes, 'aux') + + def forward(self, x): + x = fluid.layers.relu(x) + x = self.avgpool(x) + conv1 = self.conv_bn1(x) + conv1 = fluid.layers.relu(conv1) + conv2 = self.conv_bn2(conv1) + conv2 = fluid.layers.relu(conv2) + out = self.classifier(conv2) + return out + + +class NetworkImageNet(fluid.dygraph.Layer): + def __init__(self, C, num_classes, layers, auxiliary, genotype): + super(NetworkImageNet, self).__init__() + self._layers = layers + self._auxiliary = auxiliary + + self.stem_a0 = ConvBN( + c_curr=3, c_out=C // 2, kernel_size=3, padding=1, stride=2) + + self.stem_a1 = ConvBN( + c_curr=C // 2, c_out=C, kernel_size=3, padding=1, stride=2) + + self.stem_b = ConvBN( + c_curr=C, c_out=C, kernel_size=3, padding=1, stride=2) + + c_prev_prev, c_prev, c_curr = C, C, C + 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 + cells += [cell] + c_prev_prev, c_prev = c_prev, cell._multiplier * c_curr + if i == 2 * layers // 3: + c_to_auxiliary = c_prev + self.cells = fluid.dygraph.LayerList(cells) + + if auxiliary: + self.auxiliary_head = AuxiliaryHeadImageNet(c_to_auxiliary, + num_classes) + self.classifier = Classifier(c_prev, num_classes) + + def forward(self, input, training): + logits_aux = None + s0 = self.stem_a0(input) + s0 = fluid.layers.relu(s0) + s0 = self.stem_a1(s0) + s1 = fluid.layers.relu(s0) + s1 = self.stem_b(s1) + + for i, cell in enumerate(self.cells): + s0, s1 = s1, cell(s0, s1, 0, training) + if i == 2 * self._layers // 3: + if self._auxiliary and training: + logits_aux = self.auxiliary_head(s1) + logits = self.classifier(s1) + return logits, logits_aux diff --git a/demo/darts/model_search.py b/demo/darts/model_search.py new file mode 100644 index 00000000..a5173f29 --- /dev/null +++ b/demo/darts/model_search.py @@ -0,0 +1,325 @@ +# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved +# +# 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. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import paddle.fluid as fluid +from paddle.fluid.param_attr import ParamAttr +from paddle.fluid.initializer import NormalInitializer, MSRAInitializer, ConstantInitializer +from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear +from paddle.fluid.dygraph.base import to_variable +from genotypes import PRIMITIVES +from genotypes import Genotype +from operations import * + + +def channel_shuffle(x, groups): + batchsize, num_channels, height, width = x.shape + channels_per_group = num_channels // groups + + # reshape + x = fluid.layers.reshape( + x, [batchsize, groups, channels_per_group, height, width]) + x = fluid.layers.transpose(x, [0, 2, 1, 3, 4]) + + # flatten + x = fluid.layers.reshape(x, [batchsize, num_channels, height, width]) + return x + + +class MixedOp(fluid.dygraph.Layer): + def __init__(self, c_cur, stride, method): + super(MixedOp, self).__init__() + self._method = method + self._k = 4 if self._method == "PC-DARTS" else 1 + self.mp = Pool2D( + pool_size=2, + pool_stride=2, + pool_type='max', ) + ops = [] + for primitive in PRIMITIVES: + op = OPS[primitive](c_cur // self._k, stride, False) + if 'pool' in primitive: + gama = ParamAttr( + initializer=fluid.initializer.Constant(value=1), + trainable=False) + beta = ParamAttr( + initializer=fluid.initializer.Constant(value=0), + trainable=False) + BN = BatchNorm( + c_cur // self._k, param_attr=gama, bias_attr=beta) + op = fluid.dygraph.Sequential(op, BN) + ops.append(op) + self._ops = fluid.dygraph.LayerList(ops) + + def forward(self, x, weights): + if self._method == "PC-DARTS": + dim_2 = x.shape[1] + xtemp = x[:, :dim_2 // self._k, :, :] + xtemp2 = x[:, dim_2 // self._k:, :, :] + + temp1 = fluid.layers.sums( + [weights[i] * op(xtemp) for i, op in enumerate(self._ops)]) + + if temp1.shape[2] == x.shape[2]: + out = fluid.layers.concat([temp1, xtemp2], axis=1) + else: + out = fluid.layers.concat([temp1, self.mp(xtemp2)], axis=1) + out = channel_shuffle(out, self._k) + else: + out = fluid.layers.sums( + [weights[i] * op(x) for i, op in enumerate(self._ops)]) + return out + + +class Cell(fluid.dygraph.Layer): + def __init__(self, steps, multiplier, c_prev_prev, c_prev, c_cur, + reduction, reduction_prev, method): + super(Cell, self).__init__() + self.reduction = reduction + + if reduction_prev: + self.preprocess0 = FactorizedReduce(c_prev_prev, c_cur, False) + else: + self.preprocess0 = ReLUConvBN(c_prev_prev, c_cur, 1, 1, 0, False) + self.preprocess1 = ReLUConvBN(c_prev, c_cur, 1, 1, 0, affine=False) + self._steps = steps + self._multiplier = multiplier + self._method = method + + ops = [] + for i in range(self._steps): + for j in range(2 + i): + stride = 2 if reduction and j < 2 else 1 + op = MixedOp(c_cur, stride, method) + ops.append(op) + self._ops = fluid.dygraph.LayerList(ops) + + def forward(self, s0, s1, weights, weights2=None): + s0 = self.preprocess0(s0) + s1 = self.preprocess1(s1) + + states = [s0, s1] + offset = 0 + for i in range(self._steps): + if self._method == "PC-DARTS": + s = fluid.layers.sums([ + weights2[offset + j] * + self._ops[offset + j](h, weights[offset + j]) + for j, h in enumerate(states) + ]) + else: + s = fluid.layers.sums([ + self._ops[offset + j](h, weights[offset + j]) + for j, h in enumerate(states) + ]) + offset += len(states) + states.append(s) + out = fluid.layers.concat(input=states[-self._multiplier:], axis=1) + return out + + +class Network(fluid.dygraph.Layer): + def __init__(self, + c_in, + num_classes, + layers, + method, + steps=4, + multiplier=4, + stem_multiplier=3): + super(Network, self).__init__() + self._c_in = c_in + self._num_classes = num_classes + self._layers = layers + self._steps = steps + self._multiplier = multiplier + self._method = method + + c_cur = stem_multiplier * c_in + self.stem = fluid.dygraph.Sequential( + Conv2D( + num_channels=3, + num_filters=c_cur, + filter_size=3, + padding=1, + param_attr=fluid.ParamAttr(initializer=MSRAInitializer()), + bias_attr=False), + BatchNorm( + num_channels=c_cur, + param_attr=fluid.ParamAttr( + initializer=ConstantInitializer(value=1)), + bias_attr=fluid.ParamAttr( + initializer=ConstantInitializer(value=0)))) + + c_prev_prev, c_prev, c_cur = c_cur, c_cur, c_in + cells = [] + reduction_prev = False + for i in range(layers): + if i in [layers // 3, 2 * layers // 3]: + c_cur *= 2 + reduction = True + else: + reduction = False + cell = Cell(steps, multiplier, c_prev_prev, c_prev, c_cur, + reduction, reduction_prev, method) + reduction_prev = reduction + cells.append(cell) + c_prev_prev, c_prev = c_prev, multiplier * c_cur + self.cells = fluid.dygraph.LayerList(cells) + self.global_pooling = Pool2D(pool_type='avg', global_pooling=True) + self.classifier = Linear( + input_dim=c_prev, + output_dim=num_classes, + param_attr=ParamAttr(initializer=MSRAInitializer()), + bias_attr=ParamAttr(initializer=MSRAInitializer())) + + self._initialize_alphas() + + def forward(self, input): + s0 = s1 = self.stem(input) + weights2 = None + for i, cell in enumerate(self.cells): + if cell.reduction: + weights = fluid.layers.softmax(self.alphas_reduce) + if self._method == "PC-DARTS": + n = 3 + start = 2 + weights2 = fluid.layers.softmax(self.betas_reduce[0:2]) + for i in range(self._steps - 1): + end = start + n + tw2 = fluid.layers.softmax(self.betas_reduce[start: + end]) + start = end + n += 1 + weights2 = fluid.layers.concat([weights2, tw2]) + else: + weights = fluid.layers.softmax(self.alphas_normal) + if self._method == "PC-DARTS": + n = 3 + start = 2 + weights2 = fluid.layers.softmax(self.betas_normal[0:2]) + for i in range(self._steps - 1): + end = start + n + tw2 = fluid.layers.softmax(self.betas_normal[start: + end]) + start = end + n += 1 + weights2 = fluid.layers.concat([weights2, tw2]) + s0, s1 = s1, cell(s0, s1, weights, weights2) + out = self.global_pooling(s1) + out = fluid.layers.squeeze(out, axes=[2, 3]) + logits = self.classifier(out) + return logits + + def _loss(self, input, target): + logits = self(input) + loss = fluid.layers.reduce_mean( + fluid.layers.softmax_with_cross_entropy(logits, target)) + return loss + + def new(self): + model_new = Network(self._c_in, self._num_classes, self._layers, + self._method) + return model_new + + def _initialize_alphas(self): + k = sum(1 for i in range(self._steps) for n in range(2 + i)) + num_ops = len(PRIMITIVES) + self.alphas_normal = fluid.layers.create_parameter( + shape=[k, num_ops], + dtype="float32", + default_initializer=NormalInitializer( + loc=0.0, scale=1e-3)) + self.alphas_reduce = fluid.layers.create_parameter( + shape=[k, num_ops], + dtype="float32", + default_initializer=NormalInitializer( + loc=0.0, scale=1e-3)) + self._arch_parameters = [ + self.alphas_normal, + self.alphas_reduce, + ] + if self._method == "PC-DARTS": + self.betas_normal = fluid.layers.create_parameter( + shape=[k], + dtype="float32", + default_initializer=NormalInitializer( + loc=0.0, scale=1e-3)) + self.betas_reduce = fluid.layers.create_parameter( + shape=[k], + dtype="float32", + default_initializer=NormalInitializer( + loc=0.0, scale=1e-3)) + self._arch_parameters += [self.betas_normal, self.betas_reduce] + + def arch_parameters(self): + return self._arch_parameters + + def genotype(self): + def _parse(weights, weights2=None): + gene = [] + n = 2 + start = 0 + for i in range(self._steps): + end = start + n + W = weights[start:end].copy() + if self._method == "PC-DARTS": + W2 = weights2[start:end].copy() + for j in range(n): + W[j, :] = W[j, :] * W2[j] + edges = sorted(range(i + 2), key=lambda x: -max(W[x][k] for k in range(len(W[x])) if k != PRIMITIVES.index('none')))[:2] + for j in edges: + k_best = None + for k in range(len(W[j])): + if k != PRIMITIVES.index('none'): + if k_best is None or W[j][k] > W[j][k_best]: + k_best = k + gene.append((PRIMITIVES[k_best], j)) + start = end + n += 1 + return gene + + weightsr2 = None + weightsn2 = None + if self._method == "PC-DARTS": + n = 3 + start = 2 + weightsr2 = fluid.layers.softmax(self.betas_reduce[0:2]) + weightsn2 = fluid.layers.softmax(self.betas_normal[0:2]) + for i in range(self._steps - 1): + end = start + n + tw2 = fluid.layers.softmax(self.betas_reduce[start:end]) + tn2 = fluid.layers.softmax(self.betas_normal[start:end]) + start = end + n += 1 + weightsr2 = fluid.layers.concat([weightsr2, tw2]) + weightsn2 = fluid.layers.concat([weightsn2, tn2]) + weightsr2 = weightsr2.numpy() + weightsn2 = weightsn2.numpy() + + gene_normal = _parse( + fluid.layers.softmax(self.alphas_normal).numpy(), weightsn2) + gene_reduce = _parse( + fluid.layers.softmax(self.alphas_reduce).numpy(), weightsr2) + + concat = range(2 + self._steps - self._multiplier, self._steps + 2) + genotype = Genotype( + normal=gene_normal, + normal_concat=concat, + reduce=gene_reduce, + reduce_concat=concat) + return genotype diff --git a/demo/darts/operations.py b/demo/darts/operations.py new file mode 100644 index 00000000..cf362c1e --- /dev/null +++ b/demo/darts/operations.py @@ -0,0 +1,267 @@ +# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved +# +# 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. + +import paddle.fluid as fluid +from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm +from paddle.fluid.param_attr import ParamAttr +from paddle.fluid.initializer import ConstantInitializer, MSRAInitializer + + +OPS = { + 'none': + lambda C, stride, affine: Zero(stride), + 'avg_pool_3x3': + lambda C, stride, affine: Pool2D( + pool_size=3, + pool_type="avg", + pool_stride=stride, + pool_padding=1), + 'max_pool_3x3': + lambda C, stride, affine: Pool2D( + pool_size=3, + pool_type="max", + pool_stride=stride, + pool_padding=1), + 'skip_connect': + lambda C, stride, affine: Identity() + if stride == 1 else FactorizedReduce(C, C, affine), + 'sep_conv_3x3': + lambda C, stride, affine: SepConv(C, C, 3, stride, 1, + affine), + 'sep_conv_5x5': + lambda C, stride, affine: SepConv(C, C, 5, stride, 2, + affine), + 'sep_conv_7x7': + lambda C, stride, affine: SepConv(C, C, 7, stride, 3, + affine), + 'dil_conv_3x3': + lambda C, stride, affine: DilConv(C, C, 3, stride, 2, + 2, affine), + 'dil_conv_5x5': + lambda C, stride, affine: DilConv(C, C, 5, stride, 4, + 2, affine), + 'conv_7x1_1x7': + lambda C, stride, affine: Conv_7x1_1x7( + C, C, stride, affine), +} + + +def bn_param_config(affine=False): + gama = ParamAttr( + initializer=ConstantInitializer(value=1), trainable=affine) + beta = ParamAttr( + initializer=ConstantInitializer(value=0), trainable=affine) + return gama, beta + + +class Zero(fluid.dygraph.Layer): + def __init__(self, stride): + super(Zero, self).__init__() + self.stride = stride + self.pool = Pool2D(pool_size=1, pool_stride=2) + + def forward(self, x): + pooled = self.pool(x) + x = fluid.layers.zeros_like( + x) if self.stride == 1 else fluid.layers.zeros_like(pooled) + return x + + +class Identity(fluid.dygraph.Layer): + def __init__(self): + super(Identity, self).__init__() + + def forward(self, x): + return x + + +class FactorizedReduce(fluid.dygraph.Layer): + def __init__(self, c_in, c_out, affine=True): + super(FactorizedReduce, self).__init__() + assert c_out % 2 == 0 + self.conv1 = Conv2D( + num_channels=c_in, + num_filters=c_out // 2, + filter_size=1, + stride=2, + padding=0, + param_attr=fluid.ParamAttr(initializer=MSRAInitializer()), + bias_attr=False) + self.conv2 = Conv2D( + num_channels=c_in, + num_filters=c_out // 2, + filter_size=1, + stride=2, + padding=0, + param_attr=fluid.ParamAttr(initializer=MSRAInitializer()), + bias_attr=False) + gama, beta = bn_param_config(affine) + self.bn = BatchNorm( + num_channels=c_out, param_attr=gama, bias_attr=beta) + + def forward(self, x): + x = fluid.layers.relu(x) + out = fluid.layers.concat( + input=[self.conv1(x), self.conv2(x[:, :, 1:, 1:])], axis=1) + out = self.bn(out) + return out + + +class SepConv(fluid.dygraph.Layer): + def __init__(self, c_in, c_out, kernel_size, stride, padding, affine=True): + super(SepConv, self).__init__() + self.conv1 = Conv2D( + num_channels=c_in, + num_filters=c_in, + filter_size=kernel_size, + stride=stride, + padding=padding, + groups=c_in, + use_cudnn=False, + param_attr=fluid.ParamAttr(initializer=MSRAInitializer()), + bias_attr=False) + self.conv2 = Conv2D( + num_channels=c_in, + num_filters=c_in, + filter_size=1, + stride=1, + padding=0, + param_attr=fluid.ParamAttr(initializer=MSRAInitializer()), + bias_attr=False) + gama, beta = bn_param_config(affine) + self.bn1 = BatchNorm( + num_channels=c_in, param_attr=gama, bias_attr=beta) + self.conv3 = Conv2D( + num_channels=c_in, + num_filters=c_in, + filter_size=kernel_size, + stride=1, + padding=padding, + groups=c_in, + use_cudnn=False, + param_attr=fluid.ParamAttr(initializer=MSRAInitializer()), + bias_attr=False) + self.conv4 = Conv2D( + num_channels=c_in, + num_filters=c_out, + filter_size=1, + stride=1, + padding=0, + param_attr=fluid.ParamAttr(initializer=MSRAInitializer()), + bias_attr=False) + gama, beta = bn_param_config(affine) + self.bn2 = BatchNorm( + num_channels=c_out, param_attr=gama, bias_attr=beta) + + def forward(self, x): + x = fluid.layers.relu(x) + x = self.conv1(x) + x = self.conv2(x) + bn1 = self.bn1(x) + x = fluid.layers.relu(bn1) + x = self.conv3(x) + x = self.conv4(x) + bn2 = self.bn2(x) + return bn2 + + +class DilConv(fluid.dygraph.Layer): + def __init__(self, + c_in, + c_out, + kernel_size, + stride, + padding, + dilation, + affine=True): + super(DilConv, self).__init__() + self.conv1 = Conv2D( + num_channels=c_in, + num_filters=c_in, + filter_size=kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + groups=c_in, + use_cudnn=False, + param_attr=fluid.ParamAttr(initializer=MSRAInitializer()), + bias_attr=False) + self.conv2 = Conv2D( + num_channels=c_in, + num_filters=c_out, + filter_size=1, + padding=0, + param_attr=fluid.ParamAttr(initializer=MSRAInitializer()), + bias_attr=False) + gama, beta = bn_param_config(affine) + self.bn1 = BatchNorm( + num_channels=c_out, param_attr=gama, bias_attr=beta) + + def forward(self, x): + x = fluid.layers.relu(x) + x = self.conv1(x) + x = self.conv2(x) + out = self.bn1(x) + return out + + +class Conv_7x1_1x7(fluid.dygraph.Layer): + def __init__(self, c_in, c_out, stride, affine=True): + super(Conv_7x1_1x7, self).__init__() + self.conv1 = Conv2D( + num_channels=c_in, + num_filters=c_out, + filter_size=(1, 7), + padding=(0, 3), + param_attr=fluid.ParamAttr(initializer=MSRAInitializer()), + bias_attr=False) + self.conv2 = Conv2D( + num_channels=c_in, + num_filters=c_out, + filter_size=(7, 1), + padding=(3, 0), + param_attr=fluid.ParamAttr(initializer=MSRAInitializer()), + bias_attr=False) + gama, beta = bn_param_config(affine) + self.bn1 = BatchNorm( + num_channels=c_out, param_attr=gama, bias_attr=beta) + + def forward(self, x): + x = fluid.layers.relu(x) + x = self.conv1(x) + x = self.conv2(x) + out = self.bn1(x) + return out + + +class ReLUConvBN(fluid.dygraph.Layer): + def __init__(self, c_in, c_out, kernel_size, stride, padding, affine=True): + super(ReLUConvBN, self).__init__() + self.conv = Conv2D( + num_channels=c_in, + num_filters=c_out, + filter_size=kernel_size, + stride=stride, + padding=padding, + param_attr=fluid.ParamAttr(initializer=MSRAInitializer()), + bias_attr=False) + gama, beta = bn_param_config(affine) + self.bn = BatchNorm( + num_channels=c_out, param_attr=gama, bias_attr=beta) + + def forward(self, x): + x = fluid.layers.relu(x) + x = self.conv(x) + out = self.bn(x) + return out diff --git a/demo/darts/reader.py b/demo/darts/reader.py new file mode 100644 index 00000000..9a3718bc --- /dev/null +++ b/demo/darts/reader.py @@ -0,0 +1,328 @@ +# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved +# +# 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. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from PIL import Image +from PIL import ImageOps +import os +import math +import random +import tarfile +import functools +import numpy as np +from PIL import Image, ImageEnhance +import paddle +# for python2/python3 compatiablity +try: + import cPickle +except: + import _pickle as cPickle + +IMAGE_SIZE = 32 +IMAGE_DEPTH = 3 +CIFAR_MEAN = [0.49139968, 0.48215827, 0.44653124] +CIFAR_STD = [0.24703233, 0.24348505, 0.26158768] + +URL_PREFIX = 'https://www.cs.toronto.edu/~kriz/' +CIFAR10_URL = URL_PREFIX + 'cifar-10-python.tar.gz' +CIFAR10_MD5 = 'c58f30108f718f92721af3b95e74349a' + +paddle.dataset.common.DATA_HOME = "dataset/" + +THREAD = 16 +BUF_SIZE = 10240 + +IMAGENET_MEAN = np.array([0.485, 0.456, 0.406]).reshape((3, 1, 1)) +IMAGENET_STD = np.array([0.229, 0.224, 0.225]).reshape((3, 1, 1)) +IMAGENET_DIM = 224 + + +def preprocess(sample, is_training, args): + image_array = sample.reshape(IMAGE_DEPTH, IMAGE_SIZE, IMAGE_SIZE) + rgb_array = np.transpose(image_array, (1, 2, 0)) + img = Image.fromarray(rgb_array, 'RGB') + + if is_training: + # pad, ramdom crop, random_flip_left_right + img = ImageOps.expand(img, (4, 4, 4, 4), fill=0) + left_top = np.random.randint(8, size=2) + img = img.crop((left_top[1], left_top[0], left_top[1] + IMAGE_SIZE, + left_top[0] + IMAGE_SIZE)) + if np.random.randint(2): + img = img.transpose(Image.FLIP_LEFT_RIGHT) + img = np.array(img).astype(np.float32) + + img_float = img / 255.0 + img = (img_float - CIFAR_MEAN) / CIFAR_STD + + if is_training and args.cutout: + center = np.random.randint(IMAGE_SIZE, size=2) + offset_width = max(0, center[0] - args.cutout_length // 2) + offset_height = max(0, center[1] - args.cutout_length // 2) + target_width = min(center[0] + args.cutout_length // 2, IMAGE_SIZE) + target_height = min(center[1] + args.cutout_length // 2, IMAGE_SIZE) + + for i in range(offset_height, target_height): + for j in range(offset_width, target_width): + img[i][j][:] = 0.0 + + img = np.transpose(img, (2, 0, 1)) + return img + + +def reader_generator(datasets, batch_size, is_training, is_shuffle, args): + def read_batch(datasets, args): + if is_shuffle: + random.shuffle(datasets) + for im, label in datasets: + im = preprocess(im, is_training, args) + yield im, [int(label)] + + def reader(): + batch_data = [] + batch_label = [] + for data in read_batch(datasets, args): + batch_data.append(data[0]) + batch_label.append(data[1]) + if len(batch_data) == batch_size: + batch_data = np.array(batch_data, dtype='float32') + batch_label = np.array(batch_label, dtype='int64') + batch_out = [batch_data, batch_label] + yield batch_out + batch_data = [] + batch_label = [] + + return reader + + +def cifar10_reader(file_name, data_name, is_shuffle, args): + with tarfile.open(file_name, mode='r') as f: + names = [ + each_item.name for each_item in f if data_name in each_item.name + ] + names.sort() + datasets = [] + for name in names: + print("Reading file " + name) + try: + batch = cPickle.load( + f.extractfile(name), encoding='iso-8859-1') + except: + batch = cPickle.load(f.extractfile(name)) + data = batch['data'] + labels = batch.get('labels', batch.get('fine_labels', None)) + assert labels is not None + dataset = zip(data, labels) + datasets.extend(dataset) + if is_shuffle: + random.shuffle(datasets) + return datasets + + +def train_search(batch_size, train_portion, is_shuffle, args): + datasets = cifar10_reader( + paddle.dataset.common.download(CIFAR10_URL, 'cifar', CIFAR10_MD5), + 'data_batch', is_shuffle, args) + split_point = int(np.floor(train_portion * len(datasets))) + train_datasets = datasets[:split_point] + val_datasets = datasets[split_point:] + train_readers = [] + val_readers = [] + n = int(math.ceil(len(train_datasets) // args.num_workers) + ) if args.use_multiprocess else len(train_datasets) + train_datasets_lists = [ + train_datasets[i:i + n] for i in range(0, len(train_datasets), n) + ] + val_datasets_lists = [ + val_datasets[i:i + n] for i in range(0, len(val_datasets), n) + ] + + for pid in range(len(train_datasets_lists)): + train_readers.append( + reader_generator(train_datasets_lists[pid], batch_size, True, True, + args)) + val_readers.append( + reader_generator(val_datasets_lists[pid], batch_size, True, True, + args)) + if args.use_multiprocess: + reader = [ + paddle.reader.multiprocess_reader(train_readers, False), + paddle.reader.multiprocess_reader(val_readers, False) + ] + else: + reader = [train_readers[0], val_readers[0]] + + return reader + + +def train_valid(batch_size, is_train, is_shuffle, args): + name = 'data_batch' if is_train else 'test_batch' + datasets = cifar10_reader( + paddle.dataset.common.download(CIFAR10_URL, 'cifar', CIFAR10_MD5), + name, is_shuffle, args) + n = int(math.ceil(len(datasets) // args. + num_workers)) if args.use_multiprocess else len(datasets) + datasets_lists = [datasets[i:i + n] for i in range(0, len(datasets), n)] + multi_readers = [] + for pid in range(len(datasets_lists)): + multi_readers.append( + reader_generator(datasets_lists[pid], batch_size, is_train, + is_shuffle, args)) + if args.use_multiprocess: + reader = paddle.reader.multiprocess_reader(multi_readers, False) + else: + reader = multi_readers[0] + return reader + + +def crop_image(img, target_size, center): + width, height = img.size + size = target_size + if center == True: + w_start = (width - size) / 2 + h_start = (height - size) / 2 + else: + w_start = np.random.randint(0, width - size + 1) + h_start = np.random.randint(0, height - size + 1) + w_end = w_start + size + h_end = h_start + size + img = img.crop((w_start, h_start, w_end, h_end)) + return img + + +def resize_short(img, target_size): + percent = float(target_size) / min(img.size[0], img.size[1]) + resized_width = int(round(img.size[0] * percent)) + resized_height = int(round(img.size[1] * percent)) + img = img.resize((resized_width, resized_height), Image.LANCZOS) + return img + + +def random_crop(img, size, scale=[0.08, 1.0], ratio=[3. / 4., 4. / 3.]): + aspect_ratio = math.sqrt(np.random.uniform(*ratio)) + w = 1. * aspect_ratio + h = 1. / aspect_ratio + + bound = min((float(img.size[0]) / img.size[1]) / (w**2), + (float(img.size[1]) / img.size[0]) / (h**2)) + scale_max = min(scale[1], bound) + scale_min = min(scale[0], bound) + + target_area = img.size[0] * img.size[1] * np.random.uniform(scale_min, + scale_max) + target_size = math.sqrt(target_area) + w = int(target_size * w) + h = int(target_size * h) + + i = np.random.randint(0, img.size[0] - w + 1) + j = np.random.randint(0, img.size[1] - h + 1) + + img = img.crop((i, j, i + w, j + h)) + img = img.resize((size, size), Image.BILINEAR) + return img + + +def distort_color(img): + def random_brightness(img, lower=0.5, upper=1.5): + e = np.random.uniform(lower, upper) + return ImageEnhance.Brightness(img).enhance(e) + + def random_contrast(img, lower=0.5, upper=1.5): + e = np.random.uniform(lower, upper) + return ImageEnhance.Contrast(img).enhance(e) + + def random_color(img, lower=0.5, upper=1.5): + e = np.random.uniform(lower, upper) + return ImageEnhance.Color(img).enhance(e) + + ops = [random_brightness, random_contrast, random_color] + np.random.shuffle(ops) + + img = ops[0](img) + img = ops[1](img) + img = ops[2](img) + + return img + + +def process_image(sample, mode, color_jitter, rotate): + img_path = sample[0] + + img = Image.open(img_path) + if mode == 'train': + img = random_crop(img, IMAGENET_DIM) + if np.random.randint(0, 2) == 1: + img = img.transpose(Image.FLIP_LEFT_RIGHT) + if color_jitter: + img = distort_color(img) + + else: + img = resize_short(img, target_size=256) + img = crop_image(img, target_size=IMAGENET_DIM, center=True) + + if img.mode != 'RGB': + img = img.convert('RGB') + + img = np.array(img).astype('float32').transpose((2, 0, 1)) / 255 + img -= IMAGENET_MEAN + img /= IMAGENET_STD + + if mode == 'train' or mode == 'val': + return img, np.array([sample[1]], dtype='int64') + elif mode == 'test': + return [img] + + +def _reader_creator(file_list, + mode, + shuffle=False, + color_jitter=False, + rotate=False, + data_dir=None): + def reader(): + try: + with open(file_list) as flist: + full_lines = [line.strip() for line in flist] + if shuffle: + np.random.shuffle(full_lines) + lines = full_lines + for line in lines: + if mode == 'train' or mode == 'val': + img_path, label = line.split() + img_path = os.path.join(data_dir, img_path) + yield img_path, int(label) + elif mode == 'test': + img_path = os.path.join(data_dir, line) + yield [img_path] + except Exception as e: + print("Reader failed!\n{}".format(str(e))) + os._exit(1) + + mapper = functools.partial( + process_image, mode=mode, color_jitter=color_jitter, rotate=rotate) + return paddle.reader.xmap_readers(mapper, reader, THREAD, BUF_SIZE) + + +def imagenet_reader(data_dir, mode): + if mode is 'train': + shuffle = True + suffix = 'train_list.txt' + elif mode is 'val': + shuffle = False + suffix = 'val_list.txt' + file_list = os.path.join(data_dir, suffix) + return _reader_creator(file_list, mode, shuffle=shuffle, data_dir=data_dir) diff --git a/demo/darts/search.py b/demo/darts/search.py new file mode 100644 index 00000000..8c0e92be --- /dev/null +++ b/demo/darts/search.py @@ -0,0 +1,98 @@ +# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved +# +# 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. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import sys +import ast +import argparse +import functools + +import paddle.fluid as fluid +from paddle.fluid.dygraph.base import to_variable +import reader +from model_search import Network +from paddleslim.nas.darts import DARTSearch +sys.path[0] = os.path.join(os.path.dirname("__file__"), os.path.pardir) +from utility import add_arguments, print_arguments + +parser = argparse.ArgumentParser(description=__doc__) +add_arg = functools.partial(add_arguments, argparser=parser) + +# yapf: disable +add_arg('log_freq', int, 50, "Log frequency.") +add_arg('use_multiprocess', bool, True, "Whether use multiprocess reader.") +add_arg('num_workers', int, 4, "The multiprocess reader number.") +add_arg('data', str, 'dataset/cifar10',"The dir of dataset.") +add_arg('batch_size', int, 64, "Minibatch size.") +add_arg('learning_rate', float, 0.025, "The start learning rate.") +add_arg('momentum', float, 0.9, "Momentum.") +add_arg('use_gpu', bool, True, "Whether use GPU.") +add_arg('epochs', int, 50, "Epoch number.") +add_arg('init_channels', int, 16, "Init channel number.") +add_arg('layers', int, 8, "Total number of layers.") +add_arg('class_num', int, 10, "Class number of dataset.") +add_arg('trainset_num', int, 50000, "images number of trainset.") +add_arg('model_save_dir', str, 'search_cifar', "The path to save model.") +add_arg('grad_clip', float, 5, "Gradient clipping.") +add_arg('arch_learning_rate',float, 3e-4, "Learning rate for arch encoding.") +add_arg('method', str, 'DARTS', "The search method you would like to use") +add_arg('cutout_length', int, 16, "Cutout length.") +add_arg('cutout', ast.literal_eval, False, "Whether use cutout.") +add_arg('unrolled', ast.literal_eval, False, "Use one-step unrolled validation loss") +add_arg('use_data_parallel', ast.literal_eval, False, "The flag indicating whether to use data parallel mode to train the model.") +# yapf: enable + + +def main(args): + if not args.use_gpu: + place = fluid.CPUPlace() + elif not args.use_data_parallel: + place = fluid.CUDAPlace(0) + else: + place = fluid.CUDAPlace(fluid.dygraph.parallel.Env().dev_id) + + train_reader, valid_reader = reader.train_search( + batch_size=args.batch_size, + train_portion=0.5, + is_shuffle=True, + args=args) + + with fluid.dygraph.guard(place): + model = Network(args.init_channels, args.class_num, args.layers, + args.method) + searcher = DARTSearch( + model, + train_reader, + valid_reader, + learning_rate=args.learning_rate, + batchsize=args.batch_size, + num_imgs=args.trainset_num, + arch_learning_rate=args.arch_learning_rate, + unrolled=args.unrolled, + method=args.method, + num_epochs=args.epochs, + use_data_parallel=args.use_data_parallel, + log_freq=args.log_freq) + searcher.train() + + +if __name__ == '__main__': + args = parser.parse_args() + print_arguments(args) + + main(args) diff --git a/demo/darts/train.py b/demo/darts/train.py new file mode 100644 index 00000000..b665c1d0 --- /dev/null +++ b/demo/darts/train.py @@ -0,0 +1,222 @@ +# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved +# +# 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. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import sys +import ast +import argparse +import functools + +import logging +FORMAT = '%(asctime)s-%(levelname)s: %(message)s' +logging.basicConfig(level=logging.INFO, format=FORMAT) +logger = logging.getLogger(__name__) + +import paddle.fluid as fluid +from paddle.fluid.dygraph.base import to_variable +from model import NetworkCIFAR as Network +from paddleslim.common import AvgrageMeter +import genotypes +import reader +sys.path[0] = os.path.join(os.path.dirname("__file__"), os.path.pardir) +from utility import add_arguments, print_arguments + +parser = argparse.ArgumentParser(description=__doc__) +add_arg = functools.partial(add_arguments, argparser=parser) + +# yapf: disable +add_arg('use_multiprocess', bool, True, "Whether use multiprocess reader.") +add_arg('num_workers', int, 4, "The multiprocess reader number.") +add_arg('data', str, 'dataset/cifar10',"The dir of dataset.") +add_arg('batch_size', int, 96, "Minibatch size.") +add_arg('learning_rate', float, 0.025, "The start learning rate.") +add_arg('momentum', float, 0.9, "Momentum.") +add_arg('weight_decay', float, 3e-4, "Weight_decay.") +add_arg('use_gpu', bool, True, "Whether use GPU.") +add_arg('epochs', int, 600, "Epoch number.") +add_arg('init_channels', int, 36, "Init channel number.") +add_arg('layers', int, 20, "Total number of layers.") +add_arg('class_num', int, 10, "Class number of dataset.") +add_arg('trainset_num', int, 50000, "images number of trainset.") +add_arg('model_save_dir', str, 'eval_cifar', "The path to save model.") +add_arg('cutout', bool, True, 'Whether use cutout.') +add_arg('cutout_length', int, 16, "Cutout length.") +add_arg('auxiliary', bool, True, 'Use auxiliary tower.') +add_arg('auxiliary_weight', float, 0.4, "Weight for auxiliary loss.") +add_arg('drop_path_prob', float, 0.2, "Drop path probability.") +add_arg('grad_clip', float, 5, "Gradient clipping.") +add_arg('arch', str, 'DARTS_V2', "Which architecture to use") +add_arg('report_freq', int, 50, 'Report frequency') +add_arg('use_data_parallel', ast.literal_eval, False, "The flag indicating whether to use data parallel mode to train the model.") +# yapf: enable + + +def train(model, train_reader, optimizer, epoch, drop_path_prob, args): + objs = AvgrageMeter() + top1 = AvgrageMeter() + top5 = AvgrageMeter() + model.train() + + for step_id, data in enumerate(train_reader()): + image_np, label_np = data + image = to_variable(image_np) + label = to_variable(label_np) + label.stop_gradient = True + logits, logits_aux = model(image, drop_path_prob, True) + + prec1 = fluid.layers.accuracy(input=logits, label=label, k=1) + prec5 = fluid.layers.accuracy(input=logits, label=label, k=5) + loss = fluid.layers.reduce_mean( + fluid.layers.softmax_with_cross_entropy(logits, label)) + if args.auxiliary: + loss_aux = fluid.layers.reduce_mean( + fluid.layers.softmax_with_cross_entropy(logits_aux, label)) + loss = loss + args.auxiliary_weight * loss_aux + + if args.use_data_parallel: + loss = model.scale_loss(loss) + loss.backward() + model.apply_collective_grads() + else: + loss.backward() + + grad_clip = fluid.dygraph_grad_clip.GradClipByGlobalNorm( + args.grad_clip) + optimizer.minimize(loss, grad_clip=grad_clip) + model.clear_gradients() + + n = image.shape[0] + objs.update(loss.numpy(), n) + top1.update(prec1.numpy(), n) + top5.update(prec5.numpy(), n) + + if step_id % args.report_freq == 0: + logger.info( + "Train Epoch {}, Step {}, loss {:.6f}, acc_1 {:.6f}, acc_5 {:.6f}". + format(epoch, step_id, objs.avg[0], top1.avg[0], top5.avg[0])) + return top1.avg[0] + + +def valid(model, valid_reader, epoch, args): + objs = AvgrageMeter() + top1 = AvgrageMeter() + top5 = AvgrageMeter() + model.eval() + + for step_id, data in enumerate(valid_reader()): + image_np, label_np = data + image = to_variable(image_np) + label = to_variable(label_np) + logits, _ = model(image, 0, False) + prec1 = fluid.layers.accuracy(input=logits, label=label, k=1) + prec5 = fluid.layers.accuracy(input=logits, label=label, k=5) + loss = fluid.layers.reduce_mean( + fluid.layers.softmax_with_cross_entropy(logits, label)) + + n = image.shape[0] + objs.update(loss.numpy(), n) + top1.update(prec1.numpy(), n) + top5.update(prec5.numpy(), n) + if step_id % args.report_freq == 0: + logger.info( + "Valid Epoch {}, Step {}, loss {:.6f}, acc_1 {:.6f}, acc_5 {:.6f}". + format(epoch, step_id, objs.avg[0], top1.avg[0], top5.avg[0])) + return top1.avg[0] + + +def main(args): + place = fluid.CUDAPlace(fluid.dygraph.parallel.Env().dev_id) \ + if args.use_data_parallel else fluid.CUDAPlace(0) + + with fluid.dygraph.guard(place): + if args.use_data_parallel: + strategy = fluid.dygraph.parallel.prepare_context() + + genotype = eval("genotypes.%s" % args.arch) + model = Network( + C=args.init_channels, + num_classes=args.class_num, + layers=args.layers, + auxiliary=args.auxiliary, + genotype=genotype) + + step_per_epoch = int(args.trainset_num / args.batch_size) + learning_rate = fluid.dygraph.CosineDecay(args.learning_rate, + step_per_epoch, args.epochs) + optimizer = fluid.optimizer.MomentumOptimizer( + learning_rate, + momentum=args.momentum, + regularization=fluid.regularizer.L2Decay(args.weight_decay), + parameter_list=model.parameters()) + + if args.use_data_parallel: + model = fluid.dygraph.parallel.DataParallel(model, strategy) + + train_loader = fluid.io.DataLoader.from_generator( + capacity=64, + use_double_buffer=True, + iterable=True, + return_list=True) + valid_loader = fluid.io.DataLoader.from_generator( + capacity=64, + use_double_buffer=True, + iterable=True, + return_list=True) + + train_reader = reader.train_valid( + batch_size=args.batch_size, + is_train=True, + is_shuffle=True, + args=args) + valid_reader = reader.train_valid( + batch_size=args.batch_size, + is_train=False, + is_shuffle=False, + args=args) + train_loader.set_batch_generator(train_reader, places=place) + valid_loader.set_batch_generator(valid_reader, places=place) + + if args.use_data_parallel: + train_reader = fluid.contrib.reader.distributed_batch_reader( + train_reader) + + save_parameters = (not args.use_data_parallel) or ( + args.use_data_parallel and + fluid.dygraph.parallel.Env().local_rank == 0) + best_acc = 0 + for epoch in range(args.epochs): + drop_path_prob = args.drop_path_prob * epoch / args.epochs + logger.info('Epoch {}, lr {:.6f}'.format( + epoch, optimizer.current_step_lr())) + train_top1 = train(model, train_loader, optimizer, epoch, + drop_path_prob, args) + logger.info("Epoch {}, train_acc {:.6f}".format(epoch, train_top1)) + valid_top1 = valid(model, valid_loader, epoch, args) + if valid_top1 > best_acc: + best_acc = valid_top1 + if save_parameters: + fluid.save_dygraph(model.state_dict(), + args.model_save_dir + "/best_model") + logger.info("Epoch {}, valid_acc {:.6f}, best_valid_acc {:.6f}". + format(epoch, valid_top1, best_acc)) + + +if __name__ == '__main__': + args = parser.parse_args() + print_arguments(args) + main(args) diff --git a/demo/darts/train_imagenet.py b/demo/darts/train_imagenet.py new file mode 100644 index 00000000..09349900 --- /dev/null +++ b/demo/darts/train_imagenet.py @@ -0,0 +1,238 @@ +# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved +# +# 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. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import sys +import ast +import argparse +import functools + +import logging +FORMAT = '%(asctime)s-%(levelname)s: %(message)s' +logging.basicConfig(level=logging.INFO, format=FORMAT) +logger = logging.getLogger(__name__) + +import paddle.fluid as fluid +from paddle.fluid.dygraph.base import to_variable +from model import NetworkImageNet as Network +from paddleslim.common import AvgrageMeter +import genotypes +import reader +sys.path[0] = os.path.join(os.path.dirname("__file__"), os.path.pardir) +from utility import add_arguments, print_arguments + +parser = argparse.ArgumentParser(description=__doc__) +add_arg = functools.partial(add_arguments, argparser=parser) + +# yapf: disable +add_arg('use_multiprocess', bool, True, "Whether use multiprocess reader.") +add_arg('num_workers', int, 4, "The multiprocess reader number.") +add_arg('data_dir', str, 'dataset/ILSVRC2012',"The dir of dataset.") +add_arg('batch_size', int, 128, "Minibatch size.") +add_arg('learning_rate', float, 0.1, "The start learning rate.") +add_arg('decay_rate', float, 0.97, "The lr decay rate.") +add_arg('momentum', float, 0.9, "Momentum.") +add_arg('weight_decay', float, 3e-5, "Weight_decay.") +add_arg('use_gpu', bool, True, "Whether use GPU.") +add_arg('epochs', int, 250, "Epoch number.") +add_arg('init_channels', int, 48, "Init channel number.") +add_arg('layers', int, 14, "Total number of layers.") +add_arg('class_num', int, 1000, "Class number of dataset.") +add_arg('trainset_num', int, 1281167, "Images number of trainset.") +add_arg('model_save_dir', str, 'eval_imagenet', "The path to save model.") +add_arg('auxiliary', bool, True, 'Use auxiliary tower.') +add_arg('auxiliary_weight', float, 0.4, "Weight for auxiliary loss.") +add_arg('drop_path_prob', float, 0.0, "Drop path probability.") +add_arg('dropout', float, 0.0, "Dropout probability.") +add_arg('grad_clip', float, 5, "Gradient clipping.") +add_arg('label_smooth', float, 0.1, "Label smoothing.") +add_arg('arch', str, 'DARTS_V2', "Which architecture to use") +add_arg('report_freq', int, 100, 'Report frequency') +add_arg('use_data_parallel', ast.literal_eval, False, "The flag indicating whether to use data parallel mode to train the model.") +# yapf: enable + + +def cross_entropy_label_smooth(preds, targets, epsilon): + preds = fluid.layers.softmax(preds) + targets_one_hot = fluid.layers.one_hot(input=targets, depth=args.class_num) + targets_smooth = fluid.layers.label_smooth( + targets_one_hot, epsilon=epsilon, dtype="float32") + loss = fluid.layers.cross_entropy( + input=preds, label=targets_smooth, soft_label=True) + return loss + + +def train(model, train_reader, optimizer, epoch, args): + objs = AvgrageMeter() + top1 = AvgrageMeter() + top5 = AvgrageMeter() + model.train() + + for step_id, data in enumerate(train_reader()): + image_np, label_np = data + image = to_variable(image_np) + label = to_variable(label_np) + label.stop_gradient = True + logits, logits_aux = model(image, True) + + prec1 = fluid.layers.accuracy(input=logits, label=label, k=1) + prec5 = fluid.layers.accuracy(input=logits, label=label, k=5) + loss = fluid.layers.reduce_mean( + cross_entropy_label_smooth(logits, label, args.label_smooth)) + + if args.auxiliary: + loss_aux = fluid.layers.reduce_mean( + cross_entropy_label_smooth(logits_aux, label, + args.label_smooth)) + loss = loss + args.auxiliary_weight * loss_aux + + if args.use_data_parallel: + loss = model.scale_loss(loss) + loss.backward() + model.apply_collective_grads() + else: + loss.backward() + + grad_clip = fluid.dygraph_grad_clip.GradClipByGlobalNorm( + args.grad_clip) + optimizer.minimize(loss, grad_clip=grad_clip) + model.clear_gradients() + + n = image.shape[0] + objs.update(loss.numpy(), n) + top1.update(prec1.numpy(), n) + top5.update(prec5.numpy(), n) + + if step_id % args.report_freq == 0: + logger.info( + "Train Epoch {}, Step {}, loss {:.6f}, acc_1 {:.6f}, acc_5 {:.6f}". + format(epoch, step_id, objs.avg[0], top1.avg[0], top5.avg[0])) + return top1.avg[0], top5.avg[0] + + +def valid(model, valid_reader, epoch, args): + objs = AvgrageMeter() + top1 = AvgrageMeter() + top5 = AvgrageMeter() + model.eval() + + for step_id, data in enumerate(valid_reader()): + image_np, label_np = data + image = to_variable(image_np) + label = to_variable(label_np) + logits, _ = model(image, False) + prec1 = fluid.layers.accuracy(input=logits, label=label, k=1) + prec5 = fluid.layers.accuracy(input=logits, label=label, k=5) + loss = fluid.layers.reduce_mean( + cross_entropy_label_smooth(logits, label, args.label_smooth)) + + n = image.shape[0] + objs.update(loss.numpy(), n) + top1.update(prec1.numpy(), n) + top5.update(prec5.numpy(), n) + if step_id % args.report_freq == 0: + logger.info( + "Valid Epoch {}, Step {}, loss {:.6f}, acc_1 {:.6f}, acc_5 {:.6f}". + format(epoch, step_id, objs.avg[0], top1.avg[0], top5.avg[0])) + return top1.avg[0], top5.avg[0] + + +def main(args): + place = fluid.CUDAPlace(fluid.dygraph.parallel.Env().dev_id) \ + if args.use_data_parallel else fluid.CUDAPlace(0) + + with fluid.dygraph.guard(place): + if args.use_data_parallel: + strategy = fluid.dygraph.parallel.prepare_context() + + genotype = eval("genotypes.%s" % args.arch) + model = Network( + C=args.init_channels, + num_classes=args.class_num, + layers=args.layers, + auxiliary=args.auxiliary, + genotype=genotype) + + step_per_epoch = int(args.trainset_num / args.batch_size) + learning_rate = fluid.dygraph.ExponentialDecay( + args.learning_rate, + step_per_epoch, + args.decay_rate, + staircase=True) + optimizer = fluid.optimizer.MomentumOptimizer( + learning_rate, + momentum=args.momentum, + regularization=fluid.regularizer.L2Decay(args.weight_decay), + parameter_list=model.parameters()) + + if args.use_data_parallel: + model = fluid.dygraph.parallel.DataParallel(model, strategy) + + train_loader = fluid.io.DataLoader.from_generator( + capacity=64, + use_double_buffer=True, + iterable=True, + return_list=True) + valid_loader = fluid.io.DataLoader.from_generator( + capacity=64, + use_double_buffer=True, + iterable=True, + return_list=True) + + train_reader = fluid.io.batch( + reader.imagenet_reader(args.data_dir, 'train'), + batch_size=args.batch_size, + drop_last=True) + valid_reader = fluid.io.batch( + reader.imagenet_reader(args.data_dir, 'val'), + batch_size=args.batch_size) + + train_loader.set_sample_list_generator(train_reader, places=place) + valid_loader.set_sample_list_generator(valid_reader, places=place) + + if args.use_data_parallel: + train_reader = fluid.contrib.reader.distributed_batch_reader( + train_reader) + + save_parameters = (not args.use_data_parallel) or ( + args.use_data_parallel and + fluid.dygraph.parallel.Env().local_rank == 0) + best_top1 = 0 + for epoch in range(args.epochs): + logging.info('Epoch {}, lr {:.6f}'.format( + epoch, optimizer.current_step_lr())) + train_top1, train_top5 = train(model, train_loader, optimizer, + epoch, args) + logger.info("Epoch {}, train_top1 {:.6f}, train_top5 {:.6f}". + format(epoch, train_top1, train_top5)) + valid_top1, valid_top5 = valid(model, valid_loader, epoch, args) + if valid_top1 > best_top1: + best_top1 = valid_top1 + if save_parameters: + fluid.save_dygraph(model.state_dict(), + args.model_save_dir + "/best_model") + logger.info( + "Epoch {}, valid_top1 {:.6f}, valid_top5 {:.6f}, best_valid_top1 {:6f}". + format(epoch, valid_top1, valid_top5, best_top1)) + + +if __name__ == '__main__': + args = parser.parse_args() + print_arguments(args) + + main(args) diff --git a/demo/darts/visualize.py b/demo/darts/visualize.py new file mode 100644 index 00000000..3c12a0d2 --- /dev/null +++ b/demo/darts/visualize.py @@ -0,0 +1,97 @@ +# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved +# +# 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. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import sys +import genotypes +from graphviz import Digraph + + +def plot(genotype_normal, genotype_reduce, filename): + g = Digraph( + format='png', + edge_attr=dict(fontname="times"), + node_attr=dict( + style='filled', + shape='ellipse', + align='center', + height='0.5', + width='0.5', + penwidth='2', + fontname="times"), + engine='dot') + + g.body.extend(['rankdir=LR']) + + g.node("reduce_c_{k-2}", fillcolor='darkseagreen2') + g.node("reduce_c_{k-1}", fillcolor='darkseagreen2') + g.node("normal_c_{k-2}", fillcolor='darkseagreen2') + g.node("normal_c_{k-1}", fillcolor='darkseagreen2') + assert len(genotype_normal) % 2 == 0 + steps = len(genotype_normal) // 2 + + for i in range(steps): + g.node('n_' + str(i), fillcolor='lightblue') + for i in range(steps): + g.node('r_' + str(i), fillcolor='lightblue') + + for i in range(steps): + for k in [2 * i, 2 * i + 1]: + op, j = genotype_normal[k] + if j == 0: + u = "normal_c_{k-2}" + elif j == 1: + u = "normal_c_{k-1}" + else: + u = 'n_' + str(j - 2) + v = 'n_' + str(i) + g.edge(u, v, label=op, fillcolor="gray") + + for i in range(steps): + for k in [2 * i, 2 * i + 1]: + op, j = genotype_reduce[k] + if j == 0: + u = "reduce_c_{k-2}" + elif j == 1: + u = "reduce_c_{k-1}" + else: + u = 'r_' + str(j - 2) + v = 'r_' + str(i) + g.edge(u, v, label=op, fillcolor="gray") + + g.node("r_c_{k}", fillcolor='palegoldenrod') + for i in range(steps): + g.edge('r_' + str(i), "r_c_{k}", fillcolor="gray") + g.node("n_c_{k}", fillcolor='palegoldenrod') + for i in range(steps): + g.edge('n_' + str(i), "n_c_{k}", fillcolor="gray") + g.render(filename, view=False) + + +if __name__ == '__main__': + if len(sys.argv) != 2: + print("usage:\n python {} ARCH_NAME".format(sys.argv[0])) + sys.exit(1) + genotype_name = sys.argv[1] + + try: + genotype = eval('genotypes.{}'.format(genotype_name)) + except AttributeError: + print("{} is not specified in genotypes.py".format(genotype_name)) + sys.exit(1) + + plot(genotype.normal, genotype.reduce, genotype_name) diff --git a/paddleslim/common/__init__.py b/paddleslim/common/__init__.py index e146e004..e8649cbe 100644 --- a/paddleslim/common/__init__.py +++ b/paddleslim/common/__init__.py @@ -18,8 +18,9 @@ from .controller_server import ControllerServer from .controller_client import ControllerClient from .lock import lock, unlock from .cached_reader import cached_reader +from .meter import AvgrageMeter __all__ = [ 'EvolutionaryController', 'SAController', 'get_logger', 'ControllerServer', - 'ControllerClient', 'lock', 'unlock', 'cached_reader' + 'ControllerClient', 'lock', 'unlock', 'cached_reader', 'AvgrageMeter' ] diff --git a/paddleslim/common/meter.py b/paddleslim/common/meter.py new file mode 100644 index 00000000..67702579 --- /dev/null +++ b/paddleslim/common/meter.py @@ -0,0 +1,30 @@ +# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. +# +# 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. + +__all__ = ['AvgrageMeter'] + + +class AvgrageMeter(object): + def __init__(self): + self.reset() + + def reset(self): + self.avg = 0 + self.sum = 0 + self.cnt = 0 + + def update(self, val, n=1): + self.sum += val * n + self.cnt += n + self.avg = self.sum / self.cnt diff --git a/paddleslim/nas/__init__.py b/paddleslim/nas/__init__.py index cf8c75ee..03ec7551 100644 --- a/paddleslim/nas/__init__.py +++ b/paddleslim/nas/__init__.py @@ -16,7 +16,10 @@ from ..nas import search_space from .search_space import * from ..nas import sa_nas from .sa_nas import * +from ..nas import darts +from .darts import * __all__ = [] __all__ += sa_nas.__all__ __all__ += search_space.__all__ +__all__ += darts.__all__ diff --git a/paddleslim/nas/darts/__init__.py b/paddleslim/nas/darts/__init__.py new file mode 100644 index 00000000..57d164f6 --- /dev/null +++ b/paddleslim/nas/darts/__init__.py @@ -0,0 +1,20 @@ +# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. +# +# 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. + +from __future__ import absolute_import +from ..darts import train_search +from .train_search import * + +__all__ = [] +__all__ += train_search.__all__ diff --git a/paddleslim/nas/darts/architect.py b/paddleslim/nas/darts/architect.py new file mode 100644 index 00000000..e2e90a06 --- /dev/null +++ b/paddleslim/nas/darts/architect.py @@ -0,0 +1,137 @@ +# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved +# +# 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. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import paddle.fluid as fluid +from paddle.fluid.dygraph.base import to_variable + + +class Architect(object): + def __init__(self, model, eta, arch_learning_rate, place, unrolled): + self.network_momentum = 0.9 + self.network_weight_decay = 3e-4 + self.eta = eta + self.model = model + self.optimizer = fluid.optimizer.Adam( + arch_learning_rate, + 0.5, + 0.999, + regularization=fluid.regularizer.L2Decay(1e-3), + parameter_list=self.model.arch_parameters()) + self.place = place + self.unrolled = unrolled + if self.unrolled: + self.unrolled_model = self.model.new() + self.unrolled_model_params = [ + p for p in self.unrolled_model.parameters() + if p.name not in [ + a.name for a in self.unrolled_model.arch_parameters() + ] and p.trainable + ] + self.unrolled_optimizer = fluid.optimizer.MomentumOptimizer( + self.eta, + self.network_momentum, + regularization=fluid.regularizer.L2DecayRegularizer( + self.network_weight_decay), + parameter_list=self.unrolled_model_params) + + def step(self, input_train, target_train, input_valid, target_valid): + if self.unrolled: + params_grads = self._backward_step_unrolled( + input_train, target_train, input_valid, target_valid) + self.optimizer.apply_gradients(params_grads) + else: + loss = self._backward_step(input_valid, target_valid) + self.optimizer.minimize(loss) + self.optimizer.clear_gradients() + + def _backward_step(self, input_valid, target_valid): + loss = self.model._loss(input_valid, target_valid) + loss.backward() + return loss + + def _backward_step_unrolled(self, input_train, target_train, input_valid, + target_valid): + self._compute_unrolled_model(input_train, target_train) + unrolled_loss = self.unrolled_model._loss(input_valid, target_valid) + + unrolled_loss.backward() + vector = [ + to_variable(param._grad_ivar().numpy()) + for param in self.unrolled_model_params + ] + arch_params_grads = [ + (alpha, to_variable(ualpha._grad_ivar().numpy())) + for alpha, ualpha in zip(self.model.arch_parameters(), + self.unrolled_model.arch_parameters()) + ] + self.unrolled_model.clear_gradients() + + implicit_grads = self._hessian_vector_product(vector, input_train, + target_train) + for (p, g), ig in zip(arch_params_grads, implicit_grads): + new_g = g - (ig * self.unrolled_optimizer.current_step_lr()) + g.value().get_tensor().set(new_g.numpy(), self.place) + return arch_params_grads + + def _compute_unrolled_model(self, input, target): + for x, y in zip(self.unrolled_model.parameters(), + self.model.parameters()): + x.value().get_tensor().set(y.numpy(), self.place) + loss = self.unrolled_model._loss(input, target) + loss.backward() + self.unrolled_optimizer.minimize(loss) + self.unrolled_model.clear_gradients() + + def _hessian_vector_product(self, vector, input, target, r=1e-2): + R = r * fluid.layers.rsqrt( + fluid.layers.sum([ + fluid.layers.reduce_sum(fluid.layers.square(v)) for v in vector + ])) + + model_params = [ + p for p in self.model.parameters() + if p.name not in [a.name for a in self.model.arch_parameters()] and + p.trainable + ] + for param, grad in zip(model_params, vector): + param_p = param + grad * R + param.value().get_tensor().set(param_p.numpy(), self.place) + loss = self.model._loss(input, target) + loss.backward() + grads_p = [ + to_variable(param._grad_ivar().numpy()) + for param in self.model.arch_parameters() + ] + + for param, grad in zip(model_params, vector): + param_n = param - grad * R * 2 + param.value().get_tensor().set(param_n.numpy(), self.place) + self.model.clear_gradients() + + loss = self.model._loss(input, target) + loss.backward() + grads_n = [ + to_variable(param._grad_ivar().numpy()) + for param in self.model.arch_parameters() + ] + for param, grad in zip(model_params, vector): + param_o = param + grad * R + param.value().get_tensor().set(param_o.numpy(), self.place) + self.model.clear_gradients() + arch_grad = [(p - n) / (2 * R) for p, n in zip(grads_p, grads_n)] + return arch_grad diff --git a/paddleslim/nas/darts/train_search.py b/paddleslim/nas/darts/train_search.py new file mode 100644 index 00000000..3af508cc --- /dev/null +++ b/paddleslim/nas/darts/train_search.py @@ -0,0 +1,220 @@ +# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved +# +# 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. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +__all__ = ['DARTSearch'] + +import logging +import numpy as np +import paddle.fluid as fluid +from paddle.fluid.dygraph.base import to_variable +from ...common import AvgrageMeter, get_logger +from .architect import Architect +logger = get_logger(__name__, level=logging.INFO) + +SUPPORTED_METHODS = ["PC-DARTS", "DARTS"] + + +def count_parameters_in_MB(all_params): + parameters_number = 0 + for param in all_params: + if param.trainable and 'aux' not in param.name: + parameters_number += np.prod(param.shape) + return parameters_number / 1e6 + + +class DARTSearch(object): + def __init__(self, + model, + train_reader, + valid_reader, + learning_rate=0.025, + batchsize=64, + num_imgs=50000, + arch_learning_rate=3e-4, + unrolled='False', + method='DARTS', + num_epochs=50, + use_gpu=True, + use_data_parallel=False, + log_freq=50): + self.model = model + self.train_reader = train_reader + self.valid_reader = valid_reader + self.learning_rate = learning_rate + self.batchsize = batchsize + self.num_imgs = num_imgs + self.arch_learning_rate = arch_learning_rate + self.unrolled = unrolled + self.method = method + assert (self.method in SUPPORTED_METHODS + ), "Currently only support PC-DARTS, DARTS two methods" + self.num_epochs = num_epochs + self.use_gpu = use_gpu + self.use_data_parallel = use_data_parallel + if not self.use_gpu: + self.place = fluid.CPUPlace() + elif not self.use_data_parallel: + self.place = fluid.CUDAPlace(0) + else: + self.place = fluid.CUDAPlace(fluid.dygraph.parallel.Env().dev_id) + self.log_freq = log_freq + + def train_one_epoch(self, train_loader, valid_loader, architect, optimizer, + epoch): + objs = AvgrageMeter() + top1 = AvgrageMeter() + top5 = AvgrageMeter() + self.model.train() + + for step_id, ( + train_data, + valid_data) in enumerate(zip(train_loader(), valid_loader())): + train_image, train_label = train_data + valid_image, valid_label = valid_data + train_image = to_variable(train_image) + train_label = to_variable(train_label) + train_label.stop_gradient = True + valid_image = to_variable(valid_image) + valid_label = to_variable(valid_label) + valid_label.stop_gradient = True + n = train_image.shape[0] + + if not (self.method == "PC-DARTS" and epoch < 15): + architect.step(train_image, train_label, valid_image, + valid_label) + + logits = self.model(train_image) + prec1 = fluid.layers.accuracy(input=logits, label=train_label, k=1) + prec5 = fluid.layers.accuracy(input=logits, label=train_label, k=5) + loss = fluid.layers.reduce_mean( + fluid.layers.softmax_with_cross_entropy(logits, train_label)) + + if self.use_data_parallel: + loss = self.model.scale_loss(loss) + loss.backward() + self.model.apply_collective_grads() + else: + loss.backward() + + grad_clip = fluid.dygraph_grad_clip.GradClipByGlobalNorm(5) + optimizer.minimize(loss, grad_clip) + self.model.clear_gradients() + + objs.update(loss.numpy(), n) + top1.update(prec1.numpy(), n) + top5.update(prec5.numpy(), n) + + if step_id % self.log_freq == 0: + logger.info( + "Train Epoch {}, Step {}, loss {:.6f}, acc_1 {:.6f}, acc_5 {:.6f}". + format(epoch, step_id, objs.avg[0], top1.avg[0], top5.avg[ + 0])) + return top1.avg[0] + + def valid_one_epoch(self, valid_loader, epoch): + objs = AvgrageMeter() + top1 = AvgrageMeter() + top5 = AvgrageMeter() + self.model.eval() + + for step_id, (image, label) in enumerate(valid_loader): + image = to_variable(image) + label = to_variable(label) + n = image.shape[0] + logits = self.model(image) + prec1 = fluid.layers.accuracy(input=logits, label=label, k=1) + prec5 = fluid.layers.accuracy(input=logits, label=label, k=5) + loss = fluid.layers.reduce_mean( + fluid.layers.softmax_with_cross_entropy(logits, label)) + objs.update(loss.numpy(), n) + top1.update(prec1.numpy(), n) + top5.update(prec5.numpy(), n) + + if step_id % self.log_freq == 0: + logger.info( + "Valid Epoch {}, Step {}, loss {:.6f}, acc_1 {:.6f}, acc_5 {:.6f}". + format(epoch, step_id, objs.avg[0], top1.avg[0], top5.avg[ + 0])) + return top1.avg[0] + + def train(self): + if self.use_data_parallel: + strategy = fluid.dygraph.parallel.prepare_context() + model_parameters = [ + p for p in self.model.parameters() + if p.name not in [a.name for a in self.model.arch_parameters()] + ] + logger.info("param size = {:.6f}MB".format( + count_parameters_in_MB(model_parameters))) + step_per_epoch = int(self.num_imgs * 0.5 / self.batchsize) + if self.unrolled: + step_per_epoch *= 2 + learning_rate = fluid.dygraph.CosineDecay( + self.learning_rate, step_per_epoch, self.num_epochs) + optimizer = fluid.optimizer.MomentumOptimizer( + learning_rate, + 0.9, + regularization=fluid.regularizer.L2DecayRegularizer(3e-4), + parameter_list=model_parameters) + + if self.use_data_parallel: + self.model = fluid.dygraph.parallel.DataParallel(self.model, + strategy) + self.train_reader = fluid.contrib.reader.distributed_batch_reader( + self.train_reader) + self.valid_reader = fluid.contrib.reader.distributed_batch_reader( + self.valid_reader) + + train_loader = fluid.io.DataLoader.from_generator( + capacity=64, + use_double_buffer=True, + iterable=True, + return_list=True) + valid_loader = fluid.io.DataLoader.from_generator( + capacity=64, + use_double_buffer=True, + iterable=True, + return_list=True) + + train_loader.set_batch_generator(self.train_reader, places=self.place) + valid_loader.set_batch_generator(self.valid_reader, places=self.place) + + architect = Architect(self.model, learning_rate, + self.arch_learning_rate, self.place, + self.unrolled) + + save_parameters = (not self.use_data_parallel) or ( + self.use_data_parallel and + fluid.dygraph.parallel.Env().local_rank == 0) + + for epoch in range(self.num_epochs): + logger.info('Epoch {}, lr {:.6f}'.format( + epoch, optimizer.current_step_lr())) + genotype = self.model.genotype() + logger.info('genotype = %s', genotype) + + train_top1 = self.train_one_epoch(train_loader, valid_loader, + architect, optimizer, epoch) + logger.info("Epoch {}, train_acc {:.6f}".format(epoch, train_top1)) + + if epoch == self.num_epochs - 1: + valid_top1 = self.valid_one_epoch(valid_loader, epoch) + logger.info("Epoch {}, valid_acc {:.6f}".format(epoch, + valid_top1)) + if save_parameters: + fluid.save_dygraph(self.model.state_dict(), "./weights") -- GitLab

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