From 20d1e9bfbf214c7b63281134216f7d4cb836d051 Mon Sep 17 00:00:00 2001 From: hong <43953930+phlrain@users.noreply.github.com> Date: Tue, 9 Jun 2020 19:29:56 +0800 Subject: [PATCH] update new reader for resnet, mobilenet; test=develop (#4685) --- dygraph/mobilenet/README.md | 8 +- dygraph/mobilenet/imagenet_dataset.py | 57 +++++ dygraph/mobilenet/train.py | 32 ++- dygraph/resnet/reader.py | 290 ++++++++++++++++++++++++++ dygraph/resnet/train.py | 129 ++++++++++-- 5 files changed, 493 insertions(+), 23 deletions(-) create mode 100644 dygraph/mobilenet/imagenet_dataset.py create mode 100644 dygraph/resnet/reader.py diff --git a/dygraph/mobilenet/README.md b/dygraph/mobilenet/README.md index c2e0477e..e57498bf 100644 --- a/dygraph/mobilenet/README.md +++ b/dygraph/mobilenet/README.md @@ -2,6 +2,10 @@ 图像分类是计算机视觉的重要领域，它的目标是将图像分类到预定义的标签。CNN模型在图像分类领域取得了突破的成果，同时模型复杂度也在不断增加。MobileNet是一种小巧而高效CNN模型，本文介绍如何使PaddlePaddle的动态图MobileNet进行图像分类。 +**版本要求** +该模型在develop版本必须使用develop版本或者paddle 2.0alpha的版本才能够运行 +如果期望在paddle 1.8 上执行，请切换到 release/1.8 分支 + **代码结构** ├── run_mul_v1.sh # 多卡训练启动脚本_v1 @@ -55,9 +59,9 @@ **模型性能** Model Top-1(单卡/4卡) Top-5(单卡/4卡) 收敛时间(单卡/4卡) - + MobileNetV1 0.707/0.711 0.897/0.899 116小时/30.9小时 - + MobileNetV2 0.708/0.724 0.899/0.906 227.8小时/60.8小时 **参考论文** diff --git a/dygraph/mobilenet/imagenet_dataset.py b/dygraph/mobilenet/imagenet_dataset.py new file mode 100644 index 00000000..27a90091 --- /dev/null +++ b/dygraph/mobilenet/imagenet_dataset.py @@ -0,0 +1,57 @@ +# 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 os +import cv2 +import math +import random +import numpy as np + +from paddle.incubate.hapi.datasets import DatasetFolder +from paddle.incubate.hapi.vision.transforms import transforms +from paddle import fluid + + +class ImageNetDataset(DatasetFolder): + def __init__(self, + path, + mode='train', + image_size=224, + resize_short_size=256): + super(ImageNetDataset, self).__init__(path) + self.mode = mode + + normalize = transforms.Normalize( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.120, 57.375]) + if self.mode == 'train': + self.transform = transforms.Compose([ + transforms.RandomResizedCrop(image_size), + transforms.RandomHorizontalFlip(), + transforms.Permute(mode='CHW'), normalize + ]) + else: + self.transform = transforms.Compose([ + transforms.Resize(resize_short_size), + transforms.CenterCrop(image_size), + transforms.Permute(mode='CHW'), normalize + ]) + + def __getitem__(self, idx): + img_path, label = self.samples[idx] + img = cv2.imread(img_path).astype(np.float32) + label = np.array([label]).astype(np.int64) + return self.transform(img), label + + def __len__(self): + return len(self.samples) diff --git a/dygraph/mobilenet/train.py b/dygraph/mobilenet/train.py index 547e9d45..0650f04e 100644 --- a/dygraph/mobilenet/train.py +++ b/dygraph/mobilenet/train.py @@ -31,6 +31,9 @@ from utils import * from mobilenet_v1 import * from mobilenet_v2 import * +from imagenet_dataset import ImageNetDataset +from paddle.io import DataLoader + args = parse_args() if int(os.getenv("PADDLE_TRAINER_ID", 0)) == 0: print_arguments(args) @@ -116,14 +119,31 @@ def train_mobilenet(): optimizer.set_dict(opti_dict) # 3. reader - train_data_loader = utility.create_data_loader(is_train=True, args=args) test_data_loader = utility.create_data_loader(is_train=False, args=args) num_trainers = int(os.environ.get('PADDLE_TRAINERS_NUM', 1)) imagenet_reader = reader.ImageNetReader(seed=0, place_num=place_num) - train_reader = imagenet_reader.train(settings=args) - test_reader = imagenet_reader.val(settings=args) - train_data_loader.set_sample_list_generator(train_reader, place) - test_data_loader.set_sample_list_generator(test_reader, place) + + train_dataset = ImageNetDataset( + os.path.join(args.data_dir, "train"), mode='train') + + train_data_loader = DataLoader( + train_dataset, + batch_size=args.batch_size, + places=place, + shuffle=True, + drop_last=True, + num_workers=10) + + test_dataset = ImageNetDataset( + os.path.join(args.data_dir, "val"), mode='val') + + test_data_loader = DataLoader( + test_dataset, + batch_size=args.batch_size, + places=place, + shuffle=True, + drop_last=True, + num_workers=1) # 4. train loop total_batch_num = 0 #this is for benchmark @@ -184,7 +204,7 @@ def train_mobilenet(): total_sample += 1 batch_id += 1 t_last = time.time() - + # NOTE: used for benchmark total_batch_num = total_batch_num + 1 diff --git a/dygraph/resnet/reader.py b/dygraph/resnet/reader.py new file mode 100644 index 00000000..92db91b8 --- /dev/null +++ b/dygraph/resnet/reader.py @@ -0,0 +1,290 @@ +#copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve. +# +#Licensed under the Apache License, Version 2.0 (the "License"); +#you may not use this file except in compliance with the License. +#You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +#Unless required by applicable law or agreed to in writing, software +#distributed under the License is distributed on an "AS IS" BASIS, +#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +#See the License for the specific language governing permissions and +#limitations under the License. + +import sys +import os +import math +import random +import functools +import numpy as np +import cv2 + +import paddle +from paddle import fluid +from PIL import Image + +policy = None + +random.seed(0) +np.random.seed(0) + + +def random_crop(img, size, interpolation=Image.BILINEAR): + """random crop image + + Args: + img: image data + size: crop size + + Returns: + random cropped image data + """ + lower_scale = 0.08 + lower_ratio = 3. / 4. + upper_ratio = 4. / 3. + scale = [lower_scale, 1.0] + ratio = [lower_ratio, upper_ratio] + + aspect_ratio = math.sqrt(np.random.uniform(*ratio)) + w = 1. * aspect_ratio + h = 1. / aspect_ratio + + bound = min((float(img.shape[0]) / img.shape[1]) / (h**2), + (float(img.shape[1]) / img.shape[0]) / (w**2)) + + scale_max = min(scale[1], bound) + scale_min = min(scale[0], bound) + + target_area = img.shape[0] * img.shape[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.shape[0] - h + 1) + j = np.random.randint(0, img.shape[1] - w + 1) + img = img[i:i + h, j:j + w, :] + + if interpolation: + resized = cv2.resize(img, (size, size), interpolation=interpolation) + else: + resized = cv2.resize(img, (size, size)) + return resized + + +def resize_short(img, target_size, interpolation=None): + """resize image + + Args: + img: image data + target_size: resize short target size + interpolation: interpolation mode + + Returns: + resized image data + """ + percent = float(target_size) / min(img.shape[0], img.shape[1]) + resized_width = int(round(img.shape[1] * percent)) + resized_height = int(round(img.shape[0] * percent)) + if interpolation: + resized = cv2.resize( + img, (resized_width, resized_height), interpolation=interpolation) + else: + resized = cv2.resize(img, (resized_width, resized_height)) + return resized + + +def crop_image(img, target_size, center): + """crop image + + Args: + img: images data + target_size: crop target size + center: crop mode + + Returns: + img: cropped image data + """ + height, width = img.shape[:2] + 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[h_start:h_end, w_start:w_end, :] + return img + + +def process_image(sample, settings, mode): + """ process_image """ + + mean = [0.485, 0.456, 0.406] + std = [0.229, 0.224, 0.225] + crop_size = 224 + + img_path = sample[0] + img = cv2.imread(img_path) + + if mode == 'train': + img = random_crop(img, crop_size) + if np.random.randint(0, 2) == 1: + img = img[:, ::-1, :] + else: + if crop_size > 0: + target_size = settings.resize_short_size + img = resize_short( + img, target_size, interpolation=settings.interpolation) + img = crop_image(img, target_size=crop_size, center=True) + + img = img[:, :, ::-1] + + img = img.astype('float32').transpose((2, 0, 1)) / 255 + img_mean = np.array(mean).reshape((3, 1, 1)) + img_std = np.array(std).reshape((3, 1, 1)) + img -= img_mean + img /= img_std + + if mode == 'train' or mode == 'val': + return (img, np.array([sample[1]])) + elif mode == 'test': + return (img, ) + + +def process_batch_data(input_data, settings, mode): + batch_data = [] + for sample in input_data: + if os.path.isfile(sample[0]): + batch_data.append(process_image(sample, settings, mode)) + else: + print("File not exist : %s" % sample[0]) + return batch_data + + +class ImageNetReader: + def __init__(self, seed=None): + self.shuffle_seed = seed + + def set_shuffle_seed(self, seed): + assert isinstance(seed, int), "shuffle seed must be int" + self.shuffle_seed = seed + + def _reader_creator(self, + settings, + file_list, + mode, + shuffle=False, + data_dir=None): + if mode == 'test': + batch_size = 1 + else: + batch_size = settings.batch_size + + def reader(): + def read_file_list(): + with open(file_list) as flist: + full_lines = [line.strip() for line in flist] + if mode != "test" and len(full_lines) < settings.batch_size: + print( + "Warning: The number of the whole data ({}) is smaller than the batch_size ({}), and drop_last is turnning on, so nothing will feed in program, Terminated now. Please reset batch_size to a smaller number or feed more data!" + .format(len(full_lines), settings.batch_size)) + os._exit(1) + if shuffle: + assert self.shuffle_seed is not None, "multiprocess train, shuffle seed must be set!" + np.random.RandomState(self.shuffle_seed).shuffle( + full_lines) + + batch_data = [] + for line in full_lines: + img_path, label = line.split() + img_path = os.path.join(data_dir, img_path) + batch_data.append([img_path, int(label)]) + if len(batch_data) == batch_size: + if mode == 'train' or mode == 'val' or mde == 'test': + yield batch_data + batch_data = [] + + return read_file_list + + data_reader = reader() + mapper = functools.partial( + process_batch_data, settings=settings, mode=mode) + + ret = fluid.io.xmap_readers( + mapper, + data_reader, + settings.reader_thread, + settings.reader_buf_size, + order=False) + + return ret + + def train(self, settings): + """Create a reader for trainning + + Args: + settings: arguments + + Returns: + train reader + """ + file_list = os.path.join(settings.data_dir, 'train_list.txt') + assert os.path.isfile( + file_list), "{} doesn't exist, please check data list path".format( + file_list) + + reader = self._reader_creator( + settings, + file_list, + 'train', + shuffle=True, + data_dir=settings.data_dir) + + return reader + + def val(self, settings): + """Create a reader for eval + + Args: + settings: arguments + + Returns: + eval reader + """ + file_list = os.path.join(settings.data_dir, 'val_list.txt') + + assert os.path.isfile( + file_list), "{} doesn't exist, please check data list path".format( + file_list) + + return self._reader_creator( + settings, + file_list, + 'val', + shuffle=False, + data_dir=settings.data_dir) + + def test(self, settings): + """Create a reader for testing + + Args: + settings: arguments + + Returns: + test reader + """ + file_list = os.path.join(settings.data_dir, 'val_list.txt') + + assert os.path.isfile( + file_list), "{} doesn't exist, please check data list path".format( + file_list) + return self._reader_creator( + settings, + file_list, + 'test', + shuffle=False, + data_dir=settings.data_dir) diff --git a/dygraph/resnet/train.py b/dygraph/resnet/train.py index 5339cadc..a6780fee 100644 --- a/dygraph/resnet/train.py +++ b/dygraph/resnet/train.py @@ -26,6 +26,7 @@ from paddle.fluid import framework import math import sys import time +import reader IMAGENET1000 = 1281167 base_lr = 0.1 @@ -46,9 +47,90 @@ def parse_args(): parser.add_argument( "-b", "--batch_size", default=32, type=int, help="set epoch") parser.add_argument("--ce", action="store_true", help="run ce") - + # NOTE:used in benchmark - parser.add_argument("--max_iter", default=0, type=int, help="the max iters to train, used in benchmark") + parser.add_argument( + "--max_iter", + default=0, + type=int, + help="the max iters to train, used in benchmark") + parser.add_argument( + "--class_dim", + default=102, + type=int, + help="the class number of flowers dataset") + parser.add_argument( + "--use_imagenet_data", + action="store_true", + help="Use imagenet dataset instead of the flowers dataset(small dataset)" + ) + parser.add_argument( + '--data_dir', + default="./data/ILSVRC2012", + type=str, + help="The ImageNet dataset root directory.") + parser.add_argument( + '--lower_scale', + default=0.08, + type=float, + help="The value of lower_scale in ramdom_crop") + parser.add_argument( + '--lower_ratio', + default=3. / 4., + type=float, + help="The value of lower_ratio in ramdom_crop") + parser.add_argument( + '--upper_ratio', + default=4. / 3., + type=float, + help="The value of upper_ratio in ramdom_crop") + parser.add_argument( + '--resize_short_size', + default=256, + type=int, + help="The value of resize_short_size") + parser.add_argument( + '--crop_size', default=224, type=int, help="The value of crop size") + parser.add_argument( + '--use_mixup', default=False, type=bool, help="Whether to use mixup") + parser.add_argument( + '--mixup_alpha', + default=0.2, + type=float, + help="The value of mixup_alpha") + parser.add_argument( + '--reader_thread', + default=8, + type=int, + help="The number of multi thread reader") + parser.add_argument( + '--reader_buf_size', + default=16, + type=int, + help="The buf size of multi thread reader") + parser.add_argument( + '--interpolation', + default=None, + type=int, + help="The interpolation mode") + parser.add_argument( + '--use_aa', + default=False, + type=bool, + help="Whether to use auto augment") + parser.add_argument( + '--image_mean', + nargs='+', + type=float, + default=[0.485, 0.456, 0.406], + help="The mean of input image data") + parser.add_argument( + '--image_std', + nargs='+', + type=float, + default=[0.229, 0.224, 0.225], + help="The std of input image data") + args = parser.parse_args() return args @@ -288,30 +370,47 @@ def train_resnet(): if args.use_data_parallel: strategy = fluid.dygraph.parallel.prepare_context() - resnet = ResNet() + resnet = ResNet(class_dim=args.class_dim) optimizer = optimizer_setting(parameter_list=resnet.parameters()) if args.use_data_parallel: resnet = fluid.dygraph.parallel.DataParallel(resnet, strategy) - train_reader = paddle.batch( - reader_decorator(paddle.dataset.flowers.train(use_xmap=True)), - batch_size=batch_size, - drop_last=True) + if args.use_imagenet_data: + imagenet_reader = reader.ImageNetReader(0) + train_reader = imagenet_reader.train(settings=args) + else: + train_reader = paddle.batch( + reader_decorator(paddle.dataset.flowers.train(use_xmap=True)), + batch_size=batch_size, + drop_last=True) if args.use_data_parallel: train_reader = fluid.contrib.reader.distributed_batch_reader( train_reader) - test_reader = paddle.batch( - reader_decorator(paddle.dataset.flowers.test(use_xmap=True)), - batch_size=batch_size, - drop_last=True) - - train_loader = fluid.io.DataLoader.from_generator(capacity=10) + if args.use_imagenet_data: + test_reader = imagenet_reader.val(settings=args) + else: + test_reader = paddle.batch( + reader_decorator(paddle.dataset.flowers.test(use_xmap=True)), + batch_size=batch_size, + drop_last=True) + + train_loader = fluid.io.DataLoader.from_generator( + capacity=32, + use_double_buffer=True, + iterable=True, + return_list=True, + use_multiprocess=True) train_loader.set_sample_list_generator(train_reader, places=place) - test_loader = fluid.io.DataLoader.from_generator(capacity=10) + test_loader = fluid.io.DataLoader.from_generator( + capacity=64, + use_double_buffer=True, + iterable=True, + return_list=True, + use_multiprocess=True) test_loader.set_sample_list_generator(test_reader, places=place) #file_name = './model/epoch_0.npz' @@ -368,7 +467,7 @@ def train_resnet(): total_acc1 += acc_top1.numpy() total_acc5 += acc_top5.numpy() total_sample += 1 - total_batch_num = total_batch_num + 1 #this is for benchmark + total_batch_num = total_batch_num + 1 #this is for benchmark #print("epoch id: %d, batch step: %d, loss: %f" % (eop, batch_id, dy_out)) if batch_id % 10 == 0: print( "epoch %d | batch step %d, loss %0.3f acc1 %0.3f acc5 %0.3f, batch cost: %.5f" % \ -- GitLab