From cd31275061b93810adee4f0ca2b989b09aedc1b9 Mon Sep 17 00:00:00 2001 From: Xinghao Chen Date: Thu, 25 Mar 2021 22:56:08 +0800 Subject: [PATCH] add HourNAS model zoo --- model_zoo/research/cv/HourNAS/README.md | 104 +++ model_zoo/research/cv/HourNAS/eval.py | 100 +++ .../research/cv/HourNAS/mindpsore_hub_conf.py | 22 + .../research/cv/HourNAS/src/architectures.py | 55 ++ model_zoo/research/cv/HourNAS/src/dataset.py | 200 +++++ .../research/cv/HourNAS/src/hournasnet.py | 766 ++++++++++++++++++ model_zoo/research/cv/HourNAS/src/utils.py | 89 ++ 7 files changed, 1336 insertions(+) create mode 100644 model_zoo/research/cv/HourNAS/README.md create mode 100644 model_zoo/research/cv/HourNAS/eval.py create mode 100644 model_zoo/research/cv/HourNAS/mindpsore_hub_conf.py create mode 100644 model_zoo/research/cv/HourNAS/src/architectures.py create mode 100644 model_zoo/research/cv/HourNAS/src/dataset.py create mode 100644 model_zoo/research/cv/HourNAS/src/hournasnet.py create mode 100644 model_zoo/research/cv/HourNAS/src/utils.py diff --git a/model_zoo/research/cv/HourNAS/README.md b/model_zoo/research/cv/HourNAS/README.md new file mode 100644 index 0000000000..067a1ceaf4 --- /dev/null +++ b/model_zoo/research/cv/HourNAS/README.md @@ -0,0 +1,104 @@ +# Contents + +- [HourNAS Description](#tinynet-description) +- [Model Architecture](#model-architecture) +- [Dataset](#dataset) +- [Environment Requirements](#environment-requirements) +- [Script Description](#script-description) + - [Script and Sample Code](#script-and-sample-code) + - [Training Process](#training-process) + - [Evaluation Process](#evaluation-process) +- [Model Description](#model-description) + - [Performance](#performance) + - [Evaluation Performance](#evaluation-performance) +- [Description of Random Situation](#description-of-random-situation) +- [ModelZoo Homepage](#modelzoo-homepage) + +# [HourNAS Description](#contents) + +HourNAS is an efficient neural architecture search method. Only using 3 hours (0.1 days) with one GPU, HourNAS can search an architecture that achieves a 77.0% Top-1 accuracy, which outperforms the state-of-the-art methods. + +[Paper](https://arxiv.org/abs/2005.14446): Zhaohui Yang, Yunhe Wang, Xinghao Chen, Jianyuan Guo, Wei Zhang, Chao Xu, Chunjing Xu, Dacheng Tao, Chang Xu. HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass Lens. In CVPR 2021. + +# [Model architecture](#contents) + +The overall network architecture of HourNAS is show below: + +[Link](https://arxiv.org/abs/2005.14446) + +# [Dataset](#contents) + +Dataset used: [CIFAR-10](http://www.cs.toronto.edu/~kriz/cifar.html) + +- Dataset size：175M，60,000 32*32 colorful images in 10 classes + - Train：146M，50,000 images + - Test：29M，10,000 images +- Data format：binary files + - Note：Data will be processed in src/dataset.py + +# [Environment Requirements](#contents) + +- Hardware (GPU) +- Framework + - [MindSpore](https://www.mindspore.cn/install/en) +- For more information, please check the resources below: + - [MindSpore Tutorials](https://www.mindspore.cn/tutorial/training/en/master/index.html) + - [MindSpore Python API](https://www.mindspore.cn/doc/api_python/en/master/index.html) + +# [Script Description](#contents) + +## [Script and Sample Code](#contents) + +```markdown +.HourNAS +├── README.md # descriptions about HourNAS +├── src +│ ├── architectures.py # definition of HourNAS-F model +│ ├── dataset.py # data preprocessing +│ ├── hournasnet.py # HourNAS general architecture +│ └── utils.py # utility functions +├── eval.py # evaluation interface +``` + +### [Training process](#contents) + +To Be Done + +### [Evaluation Process](#contents) + +#### Launch + +```bash +# infer example + +python eval.py --model hournas_f_c10 --dataset_path [DATA_PATH] --GPU --ckpt [CHECKPOINT_PATH] +``` + +### Result + +```bash +result: {'Top1-Acc': 0.9618389423076923} ckpt= ./hournas_f_cifar10.ckpt +``` + +# [Model Description](#contents) + +## [Performance](#contents) + +### Evaluation Performance + +| Model | FLOPs (M) | Params (M) | ImageNet Top-1 | +| --------------- | --------- | ---------- | -------------- | +| MnasNet-A1 | 312 | 3.9 | 75.2% | +| HourNAS-E | 313 | 3.8 | 75.7% | +| EfficientNet-B0 | 390 | 5.3 | 76.8% | +| HourNAS-F | 383 | 5.3 | 77.0% | + +More details in [Paper](https://arxiv.org/abs/2005.14446). + +# [Description of Random Situation](#contents) + +We set the seed inside dataset.py. + +# [ModelZoo Homepage](#contents) + +Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo). diff --git a/model_zoo/research/cv/HourNAS/eval.py b/model_zoo/research/cv/HourNAS/eval.py new file mode 100644 index 0000000000..61ea58f706 --- /dev/null +++ b/model_zoo/research/cv/HourNAS/eval.py @@ -0,0 +1,100 @@ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# 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. +# ============================================================================ +"""Inference Interface""" +import sys +import argparse + +from mindspore.train.model import Model +from mindspore.train.serialization import load_checkpoint, load_param_into_net +from mindspore.nn import Loss, Top1CategoricalAccuracy, Top5CategoricalAccuracy +from mindspore import context +from mindspore import nn + +from src.dataset import create_dataset_cifar10 +from src.utils import count_params +from src.hournasnet import hournasnet + +from easydict import EasyDict as edict + +parser = argparse.ArgumentParser(description='Evaluation') +parser.add_argument('--data_path', type=str, default='/home/workspace/mindspore_dataset/', + metavar='DIR', help='path to dataset') +parser.add_argument('--model', default='hournas_f_c10', type=str, metavar='MODEL', + help='Name of model to train (default: "tinynet_c"') +parser.add_argument('--num-classes', type=int, default=10, metavar='N', + help='number of label classes (default: 10)') +parser.add_argument('-b', '--batch-size', type=int, default=256, metavar='N', + help='input batch size for training (default: 256)') +parser.add_argument('-j', '--workers', type=int, default=4, metavar='N', + help='how many training processes to use (default: 4)') +parser.add_argument('--ckpt', type=str, default='./ms_hournas_f_c10.ckpt', + help='model checkpoint to load') +parser.add_argument('--GPU', action='store_true', default=True, + help='Use GPU for training (default: True)') +parser.add_argument('--dataset_sink', action='store_true', default=True) +parser.add_argument('--image-size', type=int, default=32, metavar='N', + help='input image size (default: 32)') + +def main(): + """Main entrance for training""" + args = parser.parse_args() + print(sys.argv) + + #context.set_context(mode=context.GRAPH_MODE) + context.set_context(mode=context.PYNATIVE_MODE) + + if args.GPU: + context.set_context(device_target='GPU') + + # parse model argument + assert args.model.startswith( + "hournas"), "Only Tinynet models are supported." + #_, sub_name = args.model.split("_") + net = hournasnet(args.model, + num_classes=args.num_classes, + drop_rate=0.0, + drop_connect_rate=0.0, + global_pool="avg", + bn_tf=False, + bn_momentum=None, + bn_eps=None) + print(net) + print("Total number of parameters:", count_params(net)) + cfg = edict({'image_height': args.image_size, 'image_width': args.image_size,}) + cfg.batch_size = args.batch_size + print(cfg) + + #input_size = net.default_cfg['input_size'][1] + val_data_url = args.data_path #os.path.join(args.data_path, 'val') + val_dataset = create_dataset_cifar10(val_data_url, repeat_num=1, training=False, cifar_cfg=cfg) + + loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean') + + eval_metrics = {'Validation-Loss': Loss(), + 'Top1-Acc': Top1CategoricalAccuracy(), + 'Top5-Acc': Top5CategoricalAccuracy()} + + ckpt = load_checkpoint(args.ckpt) + load_param_into_net(net, ckpt) + net.set_train(False) + + model = Model(net, loss, metrics=eval_metrics) + + metrics = model.eval(val_dataset, dataset_sink_mode=False) + print(metrics) + + +if __name__ == '__main__': + main() diff --git a/model_zoo/research/cv/HourNAS/mindpsore_hub_conf.py b/model_zoo/research/cv/HourNAS/mindpsore_hub_conf.py new file mode 100644 index 0000000000..4fe26bfe0f --- /dev/null +++ b/model_zoo/research/cv/HourNAS/mindpsore_hub_conf.py @@ -0,0 +1,22 @@ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# 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. +# ============================================================================ +"""hub config.""" +from src.hournasnet import hournasnet + + +def create_network(name, *args, **kwargs): + if name == 'HourNAS': + return hournasnet(*args, **kwargs) + raise NotImplementedError(f"{name} is not implemented in the repo") diff --git a/model_zoo/research/cv/HourNAS/src/architectures.py b/model_zoo/research/cv/HourNAS/src/architectures.py new file mode 100644 index 0000000000..d1ea1611c1 --- /dev/null +++ b/model_zoo/research/cv/HourNAS/src/architectures.py @@ -0,0 +1,55 @@ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# 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. +# ============================================================================ +"""Architecture of HourNAS""" +predefine_archs = { + 'hournas_f_c10': { + 'genotypes': [ + #'conv3bnrelu', + 'ir_k3_e1_se', + 'ir_k5_e6_se', 'ir_k5_e1_se', 'ir_k5_e1_se', 'ir_k3_e1_se', + 'ir_k5_e6_se', 'ir_k5_e1_se', 'ir_k3_e1_se', 'ir_k5_e1_se', + 'ir_k5_e6_se', 'ir_k3_e6_se', 'ir_k3_e6_se', 'ir_k3_e6_se', + 'ir_k5_e6_se', 'ir_k5_e3_se', 'ir_k5_e3_se', 'ir_k5_e3_se', + 'ir_k5_e6_se', 'ir_k5_e6_se', 'ir_k3_e6_se', 'ir_k5_e6_se', + 'ir_k5_e6_se', + #'conv1', 'adaavgpool' + ], + 'strides': [ + #1, + 1, + 1, 1, 1, 1, + 1, 1, 1, 1, + 2, 1, 1, 1, + 1, 1, 1, 1, + 2, 1, 1, 1, + 1, + #1, 1 + ], + 'out_channels': [ + #32, + 16, + 24, 24, 24, 24, + 40, 40, 40, 40, + 80, 80, 80, 80, + 112, 112, 112, 112, + 192, 192, 192, 192, + 320, + #1280, 1280, + ], + 'dropout_ratio': 0.2, + 'default_init': 'True', + 'se_ratio': '0.05' + }, +} diff --git a/model_zoo/research/cv/HourNAS/src/dataset.py b/model_zoo/research/cv/HourNAS/src/dataset.py new file mode 100644 index 0000000000..0ac34747d8 --- /dev/null +++ b/model_zoo/research/cv/HourNAS/src/dataset.py @@ -0,0 +1,200 @@ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# 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. +# ============================================================================ +"""Data operations, will be used in train.py and eval.py""" +import math +import os + +import numpy as np +import mindspore.dataset.vision.py_transforms as py_vision +import mindspore.dataset.transforms.py_transforms as py_transforms +import mindspore.dataset.transforms.c_transforms as c_transforms +import mindspore.common.dtype as mstype +import mindspore.dataset as ds +from mindspore.communication.management import get_rank, get_group_size +from mindspore.dataset.vision import Inter +import mindspore.dataset.vision.c_transforms as vision + +# values that should remain constant +DEFAULT_CROP_PCT = 0.875 +IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406) +IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225) + +# data preprocess configs +SCALE = (0.08, 1.0) +RATIO = (3./4., 4./3.) + +ds.config.set_seed(1) + + +def split_imgs_and_labels(imgs, labels, batchInfo): + """split data into labels and images""" + ret_imgs = [] + ret_labels = [] + + for i, image in enumerate(imgs): + ret_imgs.append(image) + ret_labels.append(labels[i]) + return np.array(ret_imgs), np.array(ret_labels) + + +def create_dataset(batch_size, train_data_url='', workers=8, distributed=False, + input_size=224, color_jitter=0.4): + """Create ImageNet training dataset""" + if not os.path.exists(train_data_url): + raise ValueError('Path not exists') + decode_op = py_vision.Decode() + type_cast_op = c_transforms.TypeCast(mstype.int32) + + random_resize_crop_bicubic = py_vision.RandomResizedCrop(size=(input_size, input_size), + scale=SCALE, ratio=RATIO, + interpolation=Inter.BICUBIC) + random_horizontal_flip_op = py_vision.RandomHorizontalFlip(0.5) + adjust_range = (max(0, 1 - color_jitter), 1 + color_jitter) + random_color_jitter_op = py_vision.RandomColorAdjust(brightness=adjust_range, + contrast=adjust_range, + saturation=adjust_range) + to_tensor = py_vision.ToTensor() + normalize_op = py_vision.Normalize( + IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD) + + # assemble all the transforms + image_ops = py_transforms.Compose([decode_op, random_resize_crop_bicubic, + random_horizontal_flip_op, random_color_jitter_op, to_tensor, normalize_op]) + + rank_id = get_rank() if distributed else 0 + rank_size = get_group_size() if distributed else 1 + + dataset_train = ds.ImageFolderDataset(train_data_url, + num_parallel_workers=workers, + shuffle=True, + num_shards=rank_size, + shard_id=rank_id) + + dataset_train = dataset_train.map(input_columns=["image"], + operations=image_ops, + num_parallel_workers=workers) + + dataset_train = dataset_train.map(input_columns=["label"], + operations=type_cast_op, + num_parallel_workers=workers) + + # batch dealing + ds_train = dataset_train.batch(batch_size, + per_batch_map=split_imgs_and_labels, + input_columns=["image", "label"], + num_parallel_workers=2, + drop_remainder=True) + + ds_train = ds_train.repeat(1) + return ds_train + + +def create_dataset_val(batch_size=128, val_data_url='', workers=8, distributed=False, + input_size=224): + """Create ImageNet validation dataset""" + if not os.path.exists(val_data_url): + raise ValueError('Path not exists') + rank_id = get_rank() if distributed else 0 + rank_size = get_group_size() if distributed else 1 + dataset = ds.ImageFolderDataset(val_data_url, num_parallel_workers=workers, + num_shards=rank_size, shard_id=rank_id) + scale_size = None + + if isinstance(input_size, tuple): + assert len(input_size) == 2 + if input_size[-1] == input_size[-2]: + scale_size = int(math.floor(input_size[0] / DEFAULT_CROP_PCT)) + else: + scale_size = tuple([int(x / DEFAULT_CROP_PCT) for x in input_size]) + else: + scale_size = int(math.floor(input_size / DEFAULT_CROP_PCT)) + + type_cast_op = c_transforms.TypeCast(mstype.int32) + decode_op = py_vision.Decode() + resize_op = py_vision.Resize(size=scale_size, interpolation=Inter.BICUBIC) + center_crop = py_vision.CenterCrop(size=input_size) + to_tensor = py_vision.ToTensor() + normalize_op = py_vision.Normalize( + IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD) + + image_ops = py_transforms.Compose([decode_op, resize_op, center_crop, + to_tensor, normalize_op]) + + dataset = dataset.map(input_columns=["label"], operations=type_cast_op, + num_parallel_workers=workers) + dataset = dataset.map(input_columns=["image"], operations=image_ops, + num_parallel_workers=workers) + dataset = dataset.batch(batch_size, per_batch_map=split_imgs_and_labels, + input_columns=["image", "label"], + num_parallel_workers=2, + drop_remainder=True) + dataset = dataset.repeat(1) + return dataset + +def _get_rank_info(): + """ + get rank size and rank id + """ + rank_size = int(os.environ.get("RANK_SIZE", 1)) + + if rank_size > 1: + rank_size = get_group_size() + rank_id = get_rank() + else: + rank_size = rank_id = None + + return rank_size, rank_id + +def create_dataset_cifar10(data_home, repeat_num=1, training=True, cifar_cfg=None): + """Data operations.""" + data_dir = os.path.join(data_home, "cifar-10-batches-bin") + if not training: + data_dir = os.path.join(data_home, "cifar-10-verify-bin") + + rank_size, rank_id = _get_rank_info() + if training: + data_set = ds.Cifar10Dataset(data_dir, num_shards=rank_size, shard_id=rank_id, shuffle=True) + else: + data_set = ds.Cifar10Dataset(data_dir, num_shards=rank_size, shard_id=rank_id, shuffle=False) + + resize_height = cifar_cfg.image_height + resize_width = cifar_cfg.image_width + + # define map operations + random_crop_op = vision.RandomCrop((32, 32), (4, 4, 4, 4)) # padding_mode default CONSTANT + random_horizontal_op = vision.RandomHorizontalFlip() + resize_op = vision.Resize((resize_height, resize_width)) # interpolation default BILINEAR + rescale_op = vision.Rescale(1.0 / 255.0, 0.0) + #normalize_op = vision.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)) + normalize_op = vision.Normalize((0.4914, 0.4822, 0.4465), (0.24703233, 0.24348505, 0.26158768)) + changeswap_op = vision.HWC2CHW() + type_cast_op = c_transforms.TypeCast(mstype.int32) + + c_trans = [] + if training: + c_trans = [random_crop_op, random_horizontal_op] + c_trans += [resize_op, rescale_op, normalize_op, changeswap_op] + + # apply map operations on images + data_set = data_set.map(operations=type_cast_op, input_columns="label") + data_set = data_set.map(operations=c_trans, input_columns="image") + + # apply batch operations + data_set = data_set.batch(batch_size=cifar_cfg.batch_size, drop_remainder=True) + + # apply repeat operations + data_set = data_set.repeat(repeat_num) + + return data_set diff --git a/model_zoo/research/cv/HourNAS/src/hournasnet.py b/model_zoo/research/cv/HourNAS/src/hournasnet.py new file mode 100644 index 0000000000..c534bc8cdd --- /dev/null +++ b/model_zoo/research/cv/HourNAS/src/hournasnet.py @@ -0,0 +1,766 @@ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# 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. +# ============================================================================ +"""Tinynet model definition""" +import math +import re +from copy import deepcopy + +import mindspore.nn as nn +import mindspore.common.dtype as mstype +from mindspore.ops import operations as P +from mindspore.common.initializer import Normal, Zero, One, Uniform +from mindspore import ms_function +from mindspore import Tensor +from src.architectures import predefine_archs + +# Imagenet constant values +IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406) +IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225) + +relu = P.ReLU() +sigmoid = P.Sigmoid() + +_DEBUG = False + +# Default args for PyTorch BN impl +_BN_MOMENTUM_PT_DEFAULT = 0.1 +_BN_EPS_PT_DEFAULT = 1e-5 +_BN_ARGS_PT = dict(momentum=_BN_MOMENTUM_PT_DEFAULT, eps=_BN_EPS_PT_DEFAULT) + +# Defaults used for Google/Tensorflow training of mobile networks /w +# RMSprop as per papers and TF reference implementations. PT momentum +# equiv for TF decay is (1 - TF decay) +# NOTE: momentum varies btw .99 and .9997 depending on source +# .99 in official TF TPU impl +# .9997 (/w .999 in search space) for paper +_BN_MOMENTUM_TF_DEFAULT = 1 - 0.99 +_BN_EPS_TF_DEFAULT = 1e-3 +_BN_ARGS_TF = dict(momentum=_BN_MOMENTUM_TF_DEFAULT, eps=_BN_EPS_TF_DEFAULT) + + +def _initialize_weight_goog(shape=None, layer_type='conv', bias=False): + """Google style weight initialization""" + if layer_type not in ('conv', 'bn', 'fc'): + raise ValueError( + 'The layer type is not known, the supported are conv, bn and fc') + if bias: + return Zero() + if layer_type == 'conv': + assert isinstance(shape, (tuple, list)) and len( + shape) == 3, 'The shape must be 3 scalars, and are in_chs, ks, out_chs respectively' + n = shape[1] * shape[1] * shape[2] + return Normal(math.sqrt(2.0 / n)) + if layer_type == 'bn': + return One() + + assert isinstance(shape, (tuple, list)) and len( + shape) == 2, 'The shape must be 2 scalars, and are in_chs, out_chs respectively' + n = shape[1] + init_range = 1.0 / math.sqrt(n) + return Uniform(init_range) + + +def _conv(in_channels, out_channels, kernel_size=3, stride=1, padding=0, + pad_mode='same', bias=False): + """convolution wrapper""" + weight_init_value = _initialize_weight_goog( + shape=(in_channels, kernel_size, out_channels)) + bias_init_value = _initialize_weight_goog(bias=True) if bias else None + if bias: + return nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, + padding=padding, pad_mode=pad_mode, weight_init=weight_init_value, + has_bias=bias, bias_init=bias_init_value) + + return nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, + padding=padding, pad_mode=pad_mode, weight_init=weight_init_value, + has_bias=bias) + + +def _conv1x1(in_channels, out_channels, stride=1, padding=0, pad_mode='same', bias=False): + """1x1 convolution wrapper""" + weight_init_value = _initialize_weight_goog( + shape=(in_channels, 1, out_channels)) + bias_init_value = _initialize_weight_goog(bias=True) if bias else None + if bias: + return nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, + padding=padding, pad_mode=pad_mode, weight_init=weight_init_value, + has_bias=bias, bias_init=bias_init_value) + + return nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, + padding=padding, pad_mode=pad_mode, weight_init=weight_init_value, + has_bias=bias) + + +def _conv_group(in_channels, out_channels, group, kernel_size=3, stride=1, padding=0, + pad_mode='same', bias=False): + """group convolution wrapper""" + weight_init_value = _initialize_weight_goog( + shape=(in_channels, kernel_size, out_channels)) + bias_init_value = _initialize_weight_goog(bias=True) if bias else None + if bias: + return nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, + padding=padding, pad_mode=pad_mode, weight_init=weight_init_value, + group=group, has_bias=bias, bias_init=bias_init_value) + + return nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, + padding=padding, pad_mode=pad_mode, weight_init=weight_init_value, + group=group, has_bias=bias) + + +def _fused_bn(channels, momentum=0.1, eps=1e-4, gamma_init=1, beta_init=0): + return nn.BatchNorm2d(channels, eps=eps, momentum=1-momentum, gamma_init=gamma_init, + beta_init=beta_init) + + +def _dense(in_channels, output_channels, bias=True, activation=None): + weight_init_value = _initialize_weight_goog(shape=(in_channels, output_channels), + layer_type='fc') + bias_init_value = _initialize_weight_goog(bias=True) if bias else None + if bias: + return nn.Dense(in_channels, output_channels, weight_init=weight_init_value, + bias_init=bias_init_value, has_bias=bias, activation=activation) + + return nn.Dense(in_channels, output_channels, weight_init=weight_init_value, + has_bias=bias, activation=activation) + + +def _resolve_bn_args(kwargs): + bn_args = _BN_ARGS_TF.copy() if kwargs.pop( + 'bn_tf', False) else _BN_ARGS_PT.copy() + bn_momentum = kwargs.pop('bn_momentum', None) + if bn_momentum is not None: + bn_args['momentum'] = bn_momentum + bn_eps = kwargs.pop('bn_eps', None) + if bn_eps is not None: + bn_args['eps'] = bn_eps + return bn_args + + +def _round_channels(channels, multiplier=1.0, divisor=8, channel_min=None): + """Round number of filters based on depth multiplier.""" + if not multiplier: + return channels + channels *= multiplier + channel_min = channel_min or divisor + new_channels = max( + int(channels + divisor / 2) // divisor * divisor, + channel_min) + # Make sure that round down does not go down by more than 10%. + if new_channels < 0.9 * channels: + new_channels += divisor + return new_channels + + +def _parse_ksize(ss): + if ss.isdigit(): + return int(ss) + return [int(k) for k in ss.split('.')] + + +def _decode_block_str(block_str, depth_multiplier=1.0): + """ Decode block definition string + + Gets a list of block arg (dicts) through a string notation of arguments. + E.g. ir_r2_k3_s2_e1_i32_o16_se0.25_noskip + + All args can exist in any order with the exception of the leading string which + is assumed to indicate the block type. + + leading string - block type ( + ir = InvertedResidual, ds = DepthwiseSep, dsa = DeptwhiseSep with pw act, cn = ConvBnAct) + r - number of repeat blocks, + k - kernel size, + s - strides (1-9), + e - expansion ratio, + c - output channels, + se - squeeze/excitation ratio + n - activation fn ('re', 'r6', 'hs', or 'sw') + Args: + block_str: a string representation of block arguments. + Returns: + A list of block args (dicts) + Raises: + ValueError: if the string def not properly specified (TODO) + """ + assert isinstance(block_str, str) + ops = block_str.split('_') + block_type = ops[0] # take the block type off the front + ops = ops[1:] + options = {} + noskip = False + for op in ops: + if op == 'noskip': + noskip = True + elif op.startswith('n'): + # activation fn + key = op[0] + v = op[1:] + if v in ('re', 'r6', 'hs', 'sw'): + print('not support') + else: + continue + options[key] = value + else: + # all numeric options + splits = re.split(r'(\d.*)', op) + if len(splits) >= 2: + key, value = splits[:2] + options[key] = value + + act_fn = options['n'] if 'n' in options else None + exp_kernel_size = _parse_ksize(options['a']) if 'a' in options else 1 + pw_kernel_size = _parse_ksize(options['p']) if 'p' in options else 1 + fake_in_chs = int(options['fc']) if 'fc' in options else 0 + + num_repeat = int(options['r']) + # each type of block has different valid arguments, fill accordingly + if block_type == 'ir': + block_args = dict( + block_type=block_type, + dw_kernel_size=_parse_ksize(options['k']), + exp_kernel_size=exp_kernel_size, + pw_kernel_size=pw_kernel_size, + out_chs=int(options['c']), + exp_ratio=float(options['e']), + se_ratio=float(options['se']) if 'se' in options else None, + stride=int(options['s']), + act_fn=act_fn, + noskip=noskip, + ) + elif block_type in ('ds', 'dsa'): + block_args = dict( + block_type=block_type, + dw_kernel_size=_parse_ksize(options['k']), + pw_kernel_size=pw_kernel_size, + out_chs=int(options['c']), + se_ratio=float(options['se']) if 'se' in options else None, + stride=int(options['s']), + act_fn=act_fn, + pw_act=block_type == 'dsa', + noskip=block_type == 'dsa' or noskip, + ) + elif block_type == 'er': + block_args = dict( + block_type=block_type, + exp_kernel_size=_parse_ksize(options['k']), + pw_kernel_size=pw_kernel_size, + out_chs=int(options['c']), + exp_ratio=float(options['e']), + fake_in_chs=fake_in_chs, + se_ratio=float(options['se']) if 'se' in options else None, + stride=int(options['s']), + act_fn=act_fn, + noskip=noskip, + ) + elif block_type == 'cn': + block_args = dict( + block_type=block_type, + kernel_size=int(options['k']), + out_chs=int(options['c']), + stride=int(options['s']), + act_fn=act_fn, + ) + else: + assert False, 'Unknown block type (%s)' % block_type + + return block_args, num_repeat + + +def _scale_stage_depth(stack_args, repeats, depth_multiplier=1.0, depth_trunc='ceil'): + """ Per-stage depth scaling + Scales the block repeats in each stage. This depth scaling impl maintains + compatibility with the EfficientNet scaling method, while allowing sensible + scaling for other models that may have multiple block arg definitions in each stage. + """ + + # We scale the total repeat count for each stage, there may be multiple + # block arg defs per stage so we need to sum. + num_repeat = sum(repeats) + if depth_trunc == 'round': + # Truncating to int by rounding allows stages with few repeats to remain + # proportionally smaller for longer. This is a good choice when stage definitions + # include single repeat stages that we'd prefer to keep that way as long as possible + num_repeat_scaled = max(1, round(num_repeat * depth_multiplier)) + else: + # The default for EfficientNet truncates repeats to int via 'ceil'. + # Any multiplier > 1.0 will result in an increased depth for every stage. + num_repeat_scaled = int(math.ceil(num_repeat * depth_multiplier)) + # Proportionally distribute repeat count scaling to each block definition in the stage. + # Allocation is done in reverse as it results in the first block being less likely to be scaled. + # The first block makes less sense to repeat in most of the arch definitions. + repeats_scaled = [] + for r in repeats[::-1]: + rs = max(1, round((r / num_repeat * num_repeat_scaled))) + repeats_scaled.append(rs) + num_repeat -= r + num_repeat_scaled -= rs + repeats_scaled = repeats_scaled[::-1] + # Apply the calculated scaling to each block arg in the stage + sa_scaled = [] + for ba, rep in zip(stack_args, repeats_scaled): + sa_scaled.extend([deepcopy(ba) for _ in range(rep)]) + return sa_scaled + + +def _decode_arch_def(arch_def, depth_multiplier=1.0, depth_trunc='ceil'): + """further decode the architecture definition into model-ready format""" + arch_args = [] + for _, block_strings in enumerate(arch_def): + assert isinstance(block_strings, list) + stack_args = [] + repeats = [] + for block_str in block_strings: + assert isinstance(block_str, str) + ba, rep = _decode_block_str(block_str) + stack_args.append(ba) + repeats.append(rep) + arch_args.append(_scale_stage_depth( + stack_args, repeats, depth_multiplier, depth_trunc)) + return arch_args + + +class Swish(nn.Cell): + """swish activation function""" + + def __init__(self): + super(Swish, self).__init__() + self.sigmoid = P.Sigmoid() + + def construct(self, x): + return x * self.sigmoid(x) + + +@ms_function +def swish(x): + return x * nn.Sigmoid()(x) + + +class BlockBuilder(nn.Cell): + """build efficient-net convolution blocks""" + + def __init__(self, builder_in_channels, builder_block_args, channel_multiplier=1.0, + channel_divisor=8, channel_min=None, pad_type='', act_fn=relu, + se_gate_fn=sigmoid, se_reduce_mid=False, bn_args=None, + drop_connect_rate=0., verbose=False): + super(BlockBuilder, self).__init__() + + self.channel_multiplier = channel_multiplier + self.channel_divisor = channel_divisor + self.channel_min = channel_min + self.pad_type = pad_type + self.act_fn = act_fn #Swish() + self.se_gate_fn = se_gate_fn + self.se_reduce_mid = se_reduce_mid + self.bn_args = bn_args + self.drop_connect_rate = drop_connect_rate + self.verbose = verbose + + # updated during build + self.in_chs = None + self.block_idx = 0 + self.block_count = 0 + self.layer = self._make_layer(builder_in_channels, builder_block_args) + + def _round_channels(self, chs): + return _round_channels(chs, self.channel_multiplier, self.channel_divisor, self.channel_min) + + def _make_block(self, ba): + """make the current block based on the block argument""" + bt = ba.pop('block_type') + ba['in_chs'] = self.in_chs + ba['out_chs'] = self._round_channels(ba['out_chs']) + if 'fake_in_chs' in ba and ba['fake_in_chs']: + # this is a hack to work around mismatch in origin impl input filters + ba['fake_in_chs'] = self._round_channels(ba['fake_in_chs']) + ba['bn_args'] = self.bn_args + ba['pad_type'] = self.pad_type + # block act fn overrides the model default + ba['act_fn'] = ba['act_fn'] if ba['act_fn'] is not None else self.act_fn + assert ba['act_fn'] is not None + if bt == 'ir': + ba['drop_connect_rate'] = self.drop_connect_rate * \ + self.block_idx / self.block_count + ba['se_gate_fn'] = self.se_gate_fn + ba['se_reduce_mid'] = self.se_reduce_mid + block = InvertedResidual(**ba) + elif bt in ('ds', 'dsa'): + ba['drop_connect_rate'] = self.drop_connect_rate * \ + self.block_idx / self.block_count + block = DepthwiseSeparableConv(**ba) + else: + assert False, 'Uknkown block type (%s) while building model.' % bt + self.in_chs = ba['out_chs'] + + return block + + def _make_stack(self, stack_args): + """make a stack of blocks""" + blocks = [] + # each stack (stage) contains a list of block arguments + for i, ba in enumerate(stack_args): + if i >= 1: + # only the first block in any stack can have a stride > 1 + ba['stride'] = 1 + block = self._make_block(ba) + blocks.append(block) + self.block_idx += 1 # incr global idx (across all stacks) + return nn.SequentialCell(blocks) + + def _make_layer(self, in_chs, block_args): + """ Build the entire layer + Args: + in_chs: Number of input-channels passed to first block + block_args: A list of lists, outer list defines stages, inner + list contains strings defining block configuration(s) + Return: + List of block stacks (each stack wrapped in nn.Sequential) + """ + self.in_chs = in_chs + self.block_count = sum([len(x) for x in block_args]) + self.block_idx = 0 + blocks = [] + # outer list of block_args defines the stacks ('stages' by some conventions) + for _, stack in enumerate(block_args): + assert isinstance(stack, list) + stack = self._make_stack(stack) + blocks.append(stack) + return nn.SequentialCell(blocks) + + def construct(self, x): + return self.layer(x) + + +class DropConnect(nn.Cell): + """drop connect implementation""" + + def __init__(self, drop_connect_rate=0., seed0=0, seed1=0): + super(DropConnect, self).__init__() + self.shape = P.Shape() + self.dtype = P.DType() + self.keep_prob = 1 - drop_connect_rate + self.dropout = P.Dropout(keep_prob=self.keep_prob) + self.keep_prob_tensor = Tensor(self.keep_prob, dtype=mstype.float32) + + def construct(self, x): + shape = self.shape(x) + dtype = self.dtype(x) + ones_tensor = P.Fill()(dtype, (shape[0], 1, 1, 1), 1) + _, mask = self.dropout(ones_tensor) + x = x * mask + x = x / self.keep_prob_tensor + return x + + +def drop_connect(inputs, training=False, drop_connect_rate=0.): + if not training: + return inputs + return DropConnect(drop_connect_rate)(inputs) + + +class SqueezeExcite(nn.Cell): + """squeeze-excite implementation""" + + def __init__(self, in_chs, reduce_chs=None, act_fn=relu, gate_fn=sigmoid): + super(SqueezeExcite, self).__init__() + self.act_fn = act_fn #Swish() + self.gate_fn = gate_fn + reduce_chs = reduce_chs or in_chs + self.conv_reduce = nn.Conv2d(in_channels=in_chs, out_channels=reduce_chs, + kernel_size=1, has_bias=False, pad_mode='pad') + self.conv_expand = nn.Conv2d(in_channels=reduce_chs, out_channels=in_chs, + kernel_size=1, has_bias=False, pad_mode='pad') + self.avg_global_pool = P.ReduceMean(keep_dims=True) + + def construct(self, x): + x_se = self.avg_global_pool(x, (2, 3)) + x_se = self.conv_reduce(x_se) + x_se = self.act_fn(x_se) + x_se = self.conv_expand(x_se) + x_se = self.gate_fn(x_se) + x = x * x_se + return x + + +class DepthwiseSeparableConv(nn.Cell): + """depth-wise convolution -> (squeeze-excite) -> point-wise convolution""" + + def __init__(self, in_chs, out_chs, dw_kernel_size=3, + stride=1, pad_type='', act_fn=relu, noskip=False, + pw_kernel_size=1, pw_act=False, se_ratio=0., se_gate_fn=sigmoid, + bn_args=None, drop_connect_rate=0.): + super(DepthwiseSeparableConv, self).__init__() + assert stride in [1, 2], 'stride must be 1 or 2' + self.has_se = se_ratio is not None and se_ratio > 0. + self.has_residual = (stride == 1 and in_chs == out_chs) and not noskip + self.has_pw_act = pw_act + self.act_fn = act_fn #Swish() + self.drop_connect_rate = drop_connect_rate + self.conv_dw = nn.Conv2d(in_chs, in_chs, dw_kernel_size, stride, pad_mode="pad", + padding=int(dw_kernel_size/2), has_bias=False, group=in_chs, + weight_init=_initialize_weight_goog(shape=[1, dw_kernel_size, in_chs])) + self.bn1 = _fused_bn(in_chs, **bn_args) + + if self.has_se: + self.se = SqueezeExcite(in_chs, reduce_chs=max(1, int(in_chs * se_ratio)), + act_fn=act_fn, gate_fn=se_gate_fn) + self.conv_pw = _conv1x1(in_chs, out_chs) + self.bn2 = _fused_bn(out_chs, **bn_args) + + def construct(self, x): + """forward the depthwise separable conv""" + identity = x + + x = self.conv_dw(x) + x = self.bn1(x) + x = self.act_fn(x) + + if self.has_se: + x = self.se(x) + + x = self.conv_pw(x) + x = self.bn2(x) + + if self.has_pw_act: + x = self.act_fn(x) + + if self.has_residual: + if self.drop_connect_rate > 0.: + x = drop_connect(x, self.training, self.drop_connect_rate) + x = x + identity + + return x + + +class InvertedResidual(nn.Cell): + """inverted-residual block implementation""" + + def __init__(self, in_chs, out_chs, dw_kernel_size=3, stride=1, + pad_type='', act_fn=relu, pw_kernel_size=1, + noskip=False, exp_ratio=1., exp_kernel_size=1, se_ratio=0., + se_reduce_mid=False, se_gate_fn=sigmoid, shuffle_type=None, + bn_args=None, drop_connect_rate=0.): + super(InvertedResidual, self).__init__() + mid_chs = int(in_chs * exp_ratio) + self.has_se = se_ratio is not None and se_ratio > 0. + self.has_residual = (in_chs == out_chs and stride == 1) and not noskip + self.act_fn = act_fn #Swish() + self.drop_connect_rate = drop_connect_rate + + self.conv_pw = _conv(in_chs, mid_chs, exp_kernel_size) + self.bn1 = _fused_bn(mid_chs, **bn_args) + + self.shuffle_type = shuffle_type + if self.shuffle_type is not None and isinstance(exp_kernel_size, list): + self.shuffle = None + + self.conv_dw = nn.Conv2d(mid_chs, mid_chs, dw_kernel_size, stride, pad_mode="pad", + padding=int(dw_kernel_size/2), has_bias=False, group=mid_chs, + weight_init=_initialize_weight_goog(shape=[1, dw_kernel_size, mid_chs])) + self.bn2 = _fused_bn(mid_chs, **bn_args) + + if self.has_se: + se_base_chs = mid_chs if se_reduce_mid else in_chs + #print(se_base_chs) + self.se = SqueezeExcite( + mid_chs, reduce_chs=max(1, int(se_base_chs * se_ratio)), + act_fn=act_fn, gate_fn=se_gate_fn + ) + + self.conv_pwl = _conv(mid_chs, out_chs, pw_kernel_size) + self.bn3 = _fused_bn(out_chs, **bn_args) + + def construct(self, x): + """forward the inverted-residual block""" + identity = x + + x = self.conv_pw(x) + x = self.bn1(x) + x = self.act_fn(x) + + x = self.conv_dw(x) + x = self.bn2(x) + x = self.act_fn(x) + + if self.has_se: + x = self.se(x) + + x = self.conv_pwl(x) + x = self.bn3(x) + + if self.has_residual: + if self.drop_connect_rate > 0: + x = drop_connect(x, self.training, self.drop_connect_rate) + x = x + identity + return x + + +class GenEfficientNet(nn.Cell): + """Generate EfficientNet architecture""" + + def __init__(self, block_args, num_classes=1000, in_chans=3, stem_size=32, num_features=1280, + channel_multiplier=1.0, channel_divisor=8, channel_min=None, + pad_type='', act_fn=relu, drop_rate=0., drop_connect_rate=0., + se_gate_fn=sigmoid, se_reduce_mid=False, bn_args=None, + global_pool='avg', head_conv='default', weight_init='goog'): + + super(GenEfficientNet, self).__init__() + bn_args = _BN_ARGS_PT if bn_args is None else bn_args + self.num_classes = num_classes + self.drop_rate = drop_rate + self.num_features = num_features + + self.conv_stem = _conv(in_chans, stem_size, 3, + stride=1, padding=1, pad_mode='pad') + self.bn1 = _fused_bn(stem_size, **bn_args) + self.act_fn = relu #Swish() + in_chans = stem_size + self.blocks = BlockBuilder(in_chans, block_args, channel_multiplier, + channel_divisor, channel_min, + pad_type, act_fn, se_gate_fn, se_reduce_mid, + bn_args, drop_connect_rate, verbose=_DEBUG) + in_chs = self.blocks.in_chs + + if not head_conv or head_conv == 'none': + self.efficient_head = False + self.conv_head = None + assert in_chs == self.num_features + else: + self.efficient_head = head_conv == 'efficient' + self.conv_head = _conv1x1(in_chs, self.num_features) + self.bn2 = None if self.efficient_head else _fused_bn( + self.num_features, **bn_args) + + self.global_pool = P.ReduceMean(keep_dims=True) + self.classifier = _dense(self.num_features, self.num_classes) + self.reshape = P.Reshape() + self.shape = P.Shape() + self.drop_out = nn.Dropout(keep_prob=1-self.drop_rate) + + def construct(self, x): + """efficient net entry point""" + x = self.conv_stem(x) + #aux = x + x = self.bn1(x) + x = self.act_fn(x) + x = self.blocks(x) + if self.efficient_head: + x = self.global_pool(x, (2, 3)) + x = self.conv_head(x) + x = self.act_fn(x) + x = self.reshape(self.shape(x)[0], -1) + else: + if self.conv_head is not None: + x = self.conv_head(x) + x = self.bn2(x) + x = self.act_fn(x) + x = self.global_pool(x, (2, 3)) + x = self.reshape(x, (self.shape(x)[0], -1)) + + if self.training and self.drop_rate > 0.: + x = self.drop_out(x) + #print('forward') + #aux = x + return self.classifier(x) #, aux + +def _convert_arch_def(genotypes, strides, out_channels, se_ratio): + """Convert HourNAS architecture to EfficientNet arch_def. + + HourNAS style: + 'genotypes' : [ + #'conv3bnrelu', + 'ir_k3_e1_se', + 'ir_k5_e6_se', 'ir_k5_e1_se', 'ir_k5_e1_se', 'ir_k3_e1_se', + 'ir_k5_e6_se', 'ir_k5_e1_se', 'ir_k3_e1_se', 'ir_k5_e1_se', + 'ir_k5_e6_se', 'ir_k3_e6_se', 'ir_k3_e6_se', 'ir_k3_e6_se', + 'ir_k5_e6_se', 'ir_k5_e3_se', 'ir_k5_e3_se', 'ir_k5_e3_se', + 'ir_k5_e6_se', 'ir_k5_e6_se', 'ir_k3_e6_se', 'ir_k5_e6_se', + 'ir_k5_e6_se', + #'conv1', 'adaavgpool' + ], + + EfficientNet style: + arch_def = [ + ['ds_r1_k3_s1_e1_c16_se0.25'], + ['ir_r2_k3_s2_e6_c24_se0.25'], + ['ir_r2_k5_s2_e6_c40_se0.25'], + ['ir_r3_k3_s2_e6_c80_se0.25'], + ['ir_r3_k5_s1_e6_c112_se0.25'], + ['ir_r4_k5_s2_e6_c192_se0.25'], + ['ir_r1_k3_s1_e6_c320_se0.25'], + ] + """ + arch_def = [] + for genotype, stride, out_channel in zip(genotypes, strides, out_channels): + arch_str = genotype.replace('se', 'se'+se_ratio) + arch_str = arch_str + '_r1' + arch_str = arch_str + '_c{}'.format(out_channel) + arch_str = arch_str + '_s{}'.format(stride) + arch_def.append([arch_str]) + return arch_def + +def _gen_efficientnet(genotypes, strides, out_channels, se_ratio, num_classes=1000, **kwargs): + """Creates an EfficientNet model. + + Ref impl: https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/efficientnet_model.py + Paper: https://arxiv.org/abs/1905.11946 + + EfficientNet params + name: (channel_multiplier, depth_multiplier, resolution, dropout_rate) + 'efficientnet-b0': (1.0, 1.0, 224, 0.2), + 'efficientnet-b1': (1.0, 1.1, 240, 0.2), + 'efficientnet-b2': (1.1, 1.2, 260, 0.3), + 'efficientnet-b3': (1.2, 1.4, 300, 0.3), + 'efficientnet-b4': (1.4, 1.8, 380, 0.4), + 'efficientnet-b5': (1.6, 2.2, 456, 0.4), + 'efficientnet-b6': (1.8, 2.6, 528, 0.5), + 'efficientnet-b7': (2.0, 3.1, 600, 0.5), + + Args: + channel_multiplier (int): multiplier to number of channels per layer + depth_multiplier (int): multiplier to number of repeats per stage + + """ + arch_def = _convert_arch_def(genotypes, strides, out_channels, se_ratio) + print(arch_def) + channel_multiplier = 1.0 + depth_multiplier = 1.0 + num_features = max(1280, _round_channels( + 1280, channel_multiplier, 8, None)) + model = GenEfficientNet( + _decode_arch_def(arch_def, depth_multiplier, depth_trunc='round'), + num_classes=num_classes, + stem_size=32, + channel_multiplier=channel_multiplier, + num_features=num_features, + bn_args=_resolve_bn_args(kwargs), + act_fn=relu, + se_reduce_mid=True, + **kwargs) + return model + + +def hournasnet(arch="hournas_f_c10", num_classes=10, in_chans=3, **kwargs): + """ HourNAS Models """ + # choose a sub model + genotypes = predefine_archs[arch]['genotypes'] + strides = predefine_archs[arch]['strides'] + out_channels = predefine_archs[arch]['out_channels'] + se_ratio = predefine_archs[arch]['se_ratio'] + + model = _gen_efficientnet( + genotypes=genotypes, strides=strides, out_channels=out_channels, se_ratio=se_ratio, + num_classes=num_classes, in_chans=in_chans, **kwargs) + + return model diff --git a/model_zoo/research/cv/HourNAS/src/utils.py b/model_zoo/research/cv/HourNAS/src/utils.py new file mode 100644 index 0000000000..477f2905c1 --- /dev/null +++ b/model_zoo/research/cv/HourNAS/src/utils.py @@ -0,0 +1,89 @@ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# 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. +# ============================================================================ +"""model utils""" +import math +import argparse + +import numpy as np + + +def str2bool(value): + """Convert string arguments to bool type""" + if value.lower() in ('yes', 'true', 't', 'y', '1'): + return True + if value.lower() in ('no', 'false', 'f', 'n', '0'): + return False + raise argparse.ArgumentTypeError('Boolean value expected.') + + +def get_lr(base_lr, total_epochs, steps_per_epoch, decay_epochs=1, decay_rate=0.9, + warmup_epochs=0., warmup_lr_init=0., global_epoch=0): + """Get scheduled learning rate""" + lr_each_step = [] + total_steps = steps_per_epoch * total_epochs + global_steps = steps_per_epoch * global_epoch + self_warmup_delta = ((base_lr - warmup_lr_init) / \ + warmup_epochs) if warmup_epochs > 0 else 0 + self_decay_rate = decay_rate if decay_rate < 1 else 1/decay_rate + for i in range(total_steps): + epochs = math.floor(i/steps_per_epoch) + cond = 1 if (epochs < warmup_epochs) else 0 + warmup_lr = warmup_lr_init + epochs * self_warmup_delta + decay_nums = math.floor(epochs / decay_epochs) + decay_rate = math.pow(self_decay_rate, decay_nums) + decay_lr = base_lr * decay_rate + lr = cond * warmup_lr + (1 - cond) * decay_lr + lr_each_step.append(lr) + lr_each_step = lr_each_step[global_steps:] + lr_each_step = np.array(lr_each_step).astype(np.float32) + return lr_each_step + + +def add_weight_decay(net, weight_decay=1e-5, skip_list=None): + """Apply weight decay to only conv and dense layers (len(shape) > =2) + Args: + net (mindspore.nn.Cell): Mindspore network instance + weight_decay (float): weight decay tobe used. + skip_list (tuple): list of parameter names without weight decay + Returns: + A list of group of parameters, separated by different weight decay. + """ + decay = [] + no_decay = [] + if not skip_list: + skip_list = () + for param in net.trainable_params(): + if len(param.shape) == 1 or \ + param.name.endswith(".bias") or \ + param.name in skip_list: + no_decay.append(param) + else: + decay.append(param) + return [ + {'params': no_decay, 'weight_decay': 0.}, + {'params': decay, 'weight_decay': weight_decay}] + + +def count_params(net): + """Count number of parameters in the network + Args: + net (mindspore.nn.Cell): Mindspore network instance + Returns: + total_params (int): Total number of trainable params + """ + total_params = 0 + for param in net.trainable_params(): + total_params += np.prod(param.shape) + return total_params