diff --git a/example/auto_compression/pytorch_yolo_series/README.md b/example/auto_compression/pytorch_yolo_series/README.md index 4dc9c9cba1729344ab2d76899e3db94bd425ea96..e7d4af31575fa20119d2db8ba988b6697a6fc79d 100644 --- a/example/auto_compression/pytorch_yolo_series/README.md +++ b/example/auto_compression/pytorch_yolo_series/README.md @@ -62,25 +62,47 @@ pip install paddleslim==2.3.3 #### 3.2 准备数据集 -本示例默认以COCO数据进行自动压缩实验，可以从[MS COCO官网](https://cocodataset.org)下载[Train](http://images.cocodataset.org/zips/train2017.zip)、[Val](http://images.cocodataset.org/zips/val2017.zip)、[annotation](http://images.cocodataset.org/annotations/annotations_trainval2017.zip)。 +**选择(1)或(2)中一种方法准备数据即可。** -目录格式如下： -``` -dataset/coco/ -├── annotations -│ ├── instances_train2017.json -│ ├── instances_val2017.json -│ | ... -├── train2017 -│ ├── 000000000009.jpg -│ ├── 000000580008.jpg -│ | ... -├── val2017 -│ ├── 000000000139.jpg -│ ├── 000000000285.jpg -``` +- （1）支持无标注图片，直接传入图片文件夹，但不支持评估模型mAP + + 修改[config](./configs)中`image_path`路径为真实预测场景下的图片文件夹，图片数量依据数据集大小来定，尽量覆盖所有部署场景。 + ```yaml + Global: + image_path: dataset/coco/val2017 + ``` + +- （2）支持加载COCO格式数据集，**可支持实时评估模型mAP** + + 可以从[MS COCO官网](https://cocodataset.org)下载[Train](http://images.cocodataset.org/zips/train2017.zip)、[Val](http://images.cocodataset.org/zips/val2017.zip)、[annotation](http://images.cocodataset.org/annotations/annotations_trainval2017.zip)。 -如果是自定义数据集，请按照如上COCO数据格式准备数据。 + 目录格式如下： + ``` + dataset/coco/ + ├── annotations + │ ├── instances_train2017.json + │ ├── instances_val2017.json + │ | ... + ├── train2017 + │ ├── 000000000009.jpg + │ ├── 000000580008.jpg + │ | ... + ├── val2017 + │ ├── 000000000139.jpg + │ ├── 000000000285.jpg + ``` + + 如果是自定义数据集，请按照如上COCO数据格式准备数据。 + + 准备好数据集后，修改[config](./configs)中`coco_dataset_dir`路径。 + ```yaml + Global: + coco_dataset_dir: dataset/coco/ + coco_train_image_dir: train2017 + coco_train_anno_path: annotations/instances_train2017.json + coco_val_image_dir: val2017 + coco_val_anno_path: annotations/instances_val2017.json + ``` #### 3.3 准备预测模型 diff --git a/example/auto_compression/pytorch_yolo_series/configs/yolov5s_qat_dis.yaml b/example/auto_compression/pytorch_yolo_series/configs/yolov5s_qat_dis.yaml index d5c853bedeffa09771d60806d4d45845251c1a30..c2b230d89bd5fe70063f6e4cb698bd6deca488ee 100644 --- a/example/auto_compression/pytorch_yolo_series/configs/yolov5s_qat_dis.yaml +++ b/example/auto_compression/pytorch_yolo_series/configs/yolov5s_qat_dis.yaml @@ -1,11 +1,11 @@ Global: model_dir: ./yolov5s.onnx - dataset_dir: dataset/coco/ - train_image_dir: train2017 - val_image_dir: val2017 - train_anno_path: annotations/instances_train2017.json - val_anno_path: annotations/instances_val2017.json - Evaluation: True + image_path: None # If image_path is set, it will be trained directly based on unlabeled images, no need to set the COCO dataset path. + coco_dataset_dir: dataset/coco/ + coco_train_image_dir: train2017 + coco_train_anno_path: annotations/instances_train2017.json + coco_val_image_dir: val2017 + coco_val_anno_path: annotations/instances_val2017.json arch: YOLOv5 Distillation: diff --git a/example/auto_compression/pytorch_yolo_series/configs/yolov6s_qat_dis.yaml b/example/auto_compression/pytorch_yolo_series/configs/yolov6s_qat_dis.yaml index e14a6b6512fb76b846393d2bb84265fb58cd6390..9a3f7af3b6abd408319a66b43de751ce0865e853 100644 --- a/example/auto_compression/pytorch_yolo_series/configs/yolov6s_qat_dis.yaml +++ b/example/auto_compression/pytorch_yolo_series/configs/yolov6s_qat_dis.yaml @@ -1,11 +1,11 @@ Global: model_dir: ./yolov6s.onnx - dataset_dir: dataset/coco/ - train_image_dir: train2017 - val_image_dir: val2017 - train_anno_path: annotations/instances_train2017.json - val_anno_path: annotations/instances_val2017.json - Evaluation: True + image_path: None # If image_path is set, it will be trained directly based on unlabeled images, no need to set the COCO dataset path. + coco_dataset_dir: dataset/coco/ + coco_train_image_dir: train2017 + coco_train_anno_path: annotations/instances_train2017.json + coco_val_image_dir: val2017 + coco_val_anno_path: annotations/instances_val2017.json arch: YOLOv6 Distillation: diff --git a/example/auto_compression/pytorch_yolo_series/configs/yolov7_qat_dis.yaml b/example/auto_compression/pytorch_yolo_series/configs/yolov7_qat_dis.yaml index 437ceea90fd0ce835e528910cadf607d3c5e78bf..b7dcce83bc7f4968ad6bf450370ca8da7bd7a614 100644 --- a/example/auto_compression/pytorch_yolo_series/configs/yolov7_qat_dis.yaml +++ b/example/auto_compression/pytorch_yolo_series/configs/yolov7_qat_dis.yaml @@ -1,11 +1,11 @@ Global: model_dir: ./yolov7.onnx - dataset_dir: dataset/coco/ - train_image_dir: train2017 - val_image_dir: val2017 - train_anno_path: annotations/instances_train2017.json - val_anno_path: annotations/instances_val2017.json - Evaluation: True + image_path: None # If image_path is set, it will be trained directly based on unlabeled images, no need to set the COCO dataset path. + coco_dataset_dir: dataset/coco/ + coco_train_image_dir: train2017 + coco_train_anno_path: annotations/instances_train2017.json + coco_val_image_dir: val2017 + coco_val_anno_path: annotations/instances_val2017.json arch: YOLOv7 Distillation: diff --git a/example/auto_compression/pytorch_yolo_series/configs/yolov7_tiny_qat_dis.yaml b/example/auto_compression/pytorch_yolo_series/configs/yolov7_tiny_qat_dis.yaml index 958182f6c9ef48d9f10d191a7cce6e8c5ef65f2e..7359e0ee6bd35c093e482607117f30c7c7be23c8 100644 --- a/example/auto_compression/pytorch_yolo_series/configs/yolov7_tiny_qat_dis.yaml +++ b/example/auto_compression/pytorch_yolo_series/configs/yolov7_tiny_qat_dis.yaml @@ -1,11 +1,11 @@ Global: model_dir: ./yolov7-tiny.onnx - dataset_dir: dataset/coco/ - train_image_dir: train2017 - val_image_dir: val2017 - train_anno_path: annotations/instances_train2017.json - val_anno_path: annotations/instances_val2017.json - Evaluation: True + image_path: None # If image_path is set, it will be trained directly based on unlabeled images, no need to set the COCO dataset path. + coco_dataset_dir: dataset/coco/ + coco_train_image_dir: train2017 + coco_train_anno_path: annotations/instances_train2017.json + coco_val_image_dir: val2017 + coco_val_anno_path: annotations/instances_val2017.json arch: YOLOv7 Distillation: diff --git a/example/auto_compression/pytorch_yolo_series/dataset.py b/example/auto_compression/pytorch_yolo_series/dataset.py index 0250b936ffeef4497c03354333b515c6fc3551bf..f6c11ea3c876db0b6d90cae3be573941c52f7e0a 100644 --- a/example/auto_compression/pytorch_yolo_series/dataset.py +++ b/example/auto_compression/pytorch_yolo_series/dataset.py @@ -1,4 +1,17 @@ -from pycocotools.coco import COCO +# Copyright (c) 2022 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 cv2 import os import numpy as np @@ -12,6 +25,7 @@ class COCOValDataset(paddle.io.Dataset): anno_path=None, img_size=[640, 640], input_name='x2paddle_images'): + from pycocotools.coco import COCO self.dataset_dir = dataset_dir self.image_dir = image_dir self.img_size = img_size @@ -113,3 +127,39 @@ class COCOTrainDataset(COCOValDataset): img = self._get_img_data_from_img_id(img_id) img, scale_factor = self.image_preprocess(img, self.img_size) return {self.input_name: img} + + +def _generate_scale(im, target_shape): + origin_shape = im.shape[:2] + im_size_min = np.min(origin_shape) + im_size_max = np.max(origin_shape) + target_size_min = np.min(target_shape) + target_size_max = np.max(target_shape) + im_scale = float(target_size_min) / float(im_size_min) + if np.round(im_scale * im_size_max) > target_size_max: + im_scale = float(target_size_max) / float(im_size_max) + im_scale_x = im_scale + im_scale_y = im_scale + return im_scale_y, im_scale_x + + +def yolo_image_preprocess(img, target_shape=[640, 640]): + # Resize image + im_scale_y, im_scale_x = _generate_scale(img, target_shape) + img = cv2.resize( + img, + None, + None, + fx=im_scale_x, + fy=im_scale_y, + interpolation=cv2.INTER_LINEAR) + # Pad + im_h, im_w = img.shape[:2] + h, w = target_shape[:] + if h != im_h or w != im_w: + canvas = np.ones((h, w, 3), dtype=np.float32) + canvas *= np.array([114.0, 114.0, 114.0], dtype=np.float32) + canvas[0:im_h, 0:im_w, :] = img.astype(np.float32) + img = canvas + img = np.transpose(img / 255, [2, 0, 1]) + return img.astype(np.float32) diff --git a/example/auto_compression/pytorch_yolo_series/eval.py b/example/auto_compression/pytorch_yolo_series/eval.py index de11989e4da7455d92cba55bcca30cbea67c9628..4654f823e0823d9b7199a4e1ab1181ebcd3ef29c 100644 --- a/example/auto_compression/pytorch_yolo_series/eval.py +++ b/example/auto_compression/pytorch_yolo_series/eval.py @@ -80,9 +80,9 @@ def main(): global val_loader dataset = COCOValDataset( - dataset_dir=global_config['dataset_dir'], - image_dir=global_config['val_image_dir'], - anno_path=global_config['val_anno_path']) + dataset_dir=global_config['coco_dataset_dir'], + image_dir=global_config['coco_val_image_dir'], + anno_path=global_config['coco_val_anno_path']) global anno_file anno_file = dataset.ann_file val_loader = paddle.io.DataLoader( diff --git a/example/auto_compression/pytorch_yolo_series/run.py b/example/auto_compression/pytorch_yolo_series/run.py index 1a22d822044fd2cbc8f445359ba398ed7738a96e..89c0d946cdd18446f351682f7446f4301bb997dd 100644 --- a/example/auto_compression/pytorch_yolo_series/run.py +++ b/example/auto_compression/pytorch_yolo_series/run.py @@ -20,7 +20,7 @@ from tqdm import tqdm import paddle from paddleslim.common import load_config from paddleslim.auto_compression import AutoCompression -from dataset import COCOValDataset, COCOTrainDataset +from dataset import COCOValDataset, COCOTrainDataset, yolo_image_preprocess from post_process import YOLOPostProcess, coco_metric @@ -42,12 +42,18 @@ def argsparser(): type=str, default='gpu', help="which device used to compress.") - parser.add_argument( - '--eval', type=bool, default=False, help="whether to run evaluation.") return parser +def reader_wrapper(reader, input_name='x2paddle_images'): + def gen(): + for data in reader: + yield {input_name: data[0]} + + return gen + + def eval_function(exe, compiled_test_program, test_feed_names, test_fetch_list): bboxes_list, bbox_nums_list, image_id_list = [], [], [] with tqdm( @@ -79,32 +85,45 @@ def main(): global_config = all_config["Global"] input_name = 'x2paddle_image_arrays' if global_config[ 'arch'] == 'YOLOv6' else 'x2paddle_images' - dataset = COCOTrainDataset( - dataset_dir=global_config['dataset_dir'], - image_dir=global_config['train_image_dir'], - anno_path=global_config['train_anno_path'], - input_name=input_name) - train_loader = paddle.io.DataLoader( - dataset, batch_size=1, shuffle=True, drop_last=True, num_workers=0) - - if 'Evaluation' in global_config.keys() and global_config[ - 'Evaluation'] and paddle.distributed.get_rank() == 0: - eval_func = eval_function - global val_loader - dataset = COCOValDataset( - dataset_dir=global_config['dataset_dir'], - image_dir=global_config['val_image_dir'], - anno_path=global_config['val_anno_path']) - global anno_file - anno_file = dataset.ann_file - val_loader = paddle.io.DataLoader( - dataset, + + if global_config['image_path'] != 'None': + assert os.path.exists(global_config['image_path']) + paddle.vision.image.set_image_backend('cv2') + train_dataset = paddle.vision.datasets.ImageFolder( + global_config['image_path'], transform=yolo_image_preprocess) + train_loader = paddle.io.DataLoader( + train_dataset, batch_size=1, - shuffle=False, - drop_last=False, + shuffle=True, + drop_last=True, num_workers=0) - else: + train_loader = reader_wrapper(train_loader, input_name=input_name) eval_func = None + else: + dataset = COCOTrainDataset( + dataset_dir=global_config['coco_dataset_dir'], + image_dir=global_config['coco_train_image_dir'], + anno_path=global_config['coco_train_anno_path'], + input_name=input_name) + train_loader = paddle.io.DataLoader( + dataset, batch_size=1, shuffle=True, drop_last=True, num_workers=0) + if paddle.distributed.get_rank() == 0: + eval_func = eval_function + global val_loader + dataset = COCOValDataset( + dataset_dir=global_config['coco_dataset_dir'], + image_dir=global_config['coco_val_image_dir'], + anno_path=global_config['coco_val_anno_path']) + global anno_file + anno_file = dataset.ann_file + val_loader = paddle.io.DataLoader( + dataset, + batch_size=1, + shuffle=False, + drop_last=False, + num_workers=0) + else: + eval_func = None ac = AutoCompression( model_dir=global_config["model_dir"],