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PaddleDetection

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未验证 提交 10e7fe23 编写于 作者: S shangliang Xu 提交者： GitHub
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[deploy] alter save coco format json in deploy/python/infer.py (#6705)

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deploy/python/README.md
浏览文件 @ 10e7fe23
......@@ -75,23 +75,24 @@ python deploy/python/mot_keypoint_unite_infer.py --mot_model_dir=output_inferenc
参数说明如下:
| 参数 | 是否必须|含义 |
|-------|-------|----------|
| --model_dir | Yes| 上述导出的模型路径 |
| --image_file | Option | 需要预测的图片 |
| --image_dir | Option | 要预测的图片文件夹路径 |
| --video_file | Option | 需要预测的视频 |
| --camera_id | Option | 用来预测的摄像头ID，默认为-1(表示不使用摄像头预测，可设置为：0 - (摄像头数目-1) )，预测过程中在可视化界面按`q`退出输出预测结果到：output/output.mp4|
| --device | Option | 运行时的设备，可选择`CPU/GPU/XPU`，默认为`CPU`|
| --run_mode | Option |使用GPU时，默认为paddle, 可选（paddle/trt_fp32/trt_fp16/trt_int8）|
| --batch_size | Option |预测时的batch size，在指定`image_dir`时有效，默认为1 |
| --threshold | Option|预测得分的阈值，默认为0.5|
| --output_dir | Option|可视化结果保存的根目录，默认为output/|
| --run_benchmark | Option| 是否运行benchmark，同时需指定`--image_file`或`--image_dir`，默认为False |
| --enable_mkldnn | Option | CPU预测中是否开启MKLDNN加速，默认为False |
| --cpu_threads | Option| 设置cpu线程数，默认为1 |
| --trt_calib_mode | Option| TensorRT是否使用校准功能，默认为False。使用TensorRT的int8功能时，需设置为True，使用PaddleSlim量化后的模型时需要设置为False |
| --save_results | Option| 是否在文件夹下将图片的预测结果以JSON的形式保存 |
| 参数 | 是否必须| 含义 |
|-------|-------|---------------------------------------------------------------------------------------------|
| --model_dir | Yes| 上述导出的模型路径 |
| --image_file | Option | 需要预测的图片 |
| --image_dir | Option | 要预测的图片文件夹路径 |
| --video_file | Option | 需要预测的视频 |
| --camera_id | Option | 用来预测的摄像头ID，默认为-1(表示不使用摄像头预测，可设置为：0 - (摄像头数目-1) )，预测过程中在可视化界面按`q`退出输出预测结果到：output/output.mp4 |
| --device | Option | 运行时的设备，可选择`CPU/GPU/XPU`，默认为`CPU` |
| --run_mode | Option | 使用GPU时，默认为paddle, 可选（paddle/trt_fp32/trt_fp16/trt_int8） |
| --batch_size | Option | 预测时的batch size，在指定`image_dir`时有效，默认为1 |
| --threshold | Option| 预测得分的阈值，默认为0.5 |
| --output_dir | Option| 可视化结果保存的根目录，默认为output/ |
| --run_benchmark | Option| 是否运行benchmark，同时需指定`--image_file`或`--image_dir`，默认为False |
| --enable_mkldnn | Option | CPU预测中是否开启MKLDNN加速，默认为False |
| --cpu_threads | Option| 设置cpu线程数，默认为1 |
| --trt_calib_mode | Option| TensorRT是否使用校准功能，默认为False。使用TensorRT的int8功能时，需设置为True，使用PaddleSlim量化后的模型时需要设置为False |
| --save_images | Option| 是否保存可视化结果 |
| --save_results | Option| 是否在文件夹下将图片的预测结果以JSON的形式保存 |
说明：
......@@ -100,3 +101,4 @@ python deploy/python/mot_keypoint_unite_infer.py --mot_model_dir=output_inferenc
- run_mode：paddle代表使用AnalysisPredictor，精度float32来推理，其他参数指用AnalysisPredictor，TensorRT不同精度来推理。
- 如果安装的PaddlePaddle不支持基于TensorRT进行预测，需要自行编译，详细可参考[预测库编译教程](https://paddleinference.paddlepaddle.org.cn/user_guides/source_compile.html)。
- --run_benchmark如果设置为True，则需要安装依赖`pip install pynvml psutil GPUtil`。
- 如果需要使用导出模型在coco数据集上进行评估，请在推理时添加`--save_results`和`--use_coco_category`参数用以保存coco评估所需要的json文件
deploy/python/infer.py
浏览文件 @ 10e7fe23
......@@ -36,7 +36,7 @@ from picodet_postprocess import PicoDetPostProcess
from preprocess import preprocess, Resize, NormalizeImage, Permute, PadStride, LetterBoxResize, WarpAffine, Pad, decode_image
from keypoint_preprocess import EvalAffine, TopDownEvalAffine, expand_crop
from visualize import visualize_box_mask
from utils import argsparser, Timer, get_current_memory_mb, multiclass_nms
from utils import argsparser, Timer, get_current_memory_mb, multiclass_nms, coco_clsid2catid
# Global dictionary
SUPPORT_MODELS = {
......@@ -226,7 +226,7 @@ class Detector(object):
match_threshold=0.6,
match_metric='iou',
visual=True,
save_file=None):
save_results=False):
# slice infer only support bs=1
results = []
try:
......@@ -295,14 +295,13 @@ class Detector(object):
threshold=self.threshold)
results.append(merged_results)
if visual:
print('Test iter {}'.format(i))
if save_file is not None:
Path(self.output_dir).mkdir(exist_ok=True)
self.format_coco_results(image_list, results, save_file=save_file)
print('Test iter {}'.format(i))
results = self.merge_batch_result(results)
if save_results:
Path(self.output_dir).mkdir(exist_ok=True)
self.save_coco_results(
img_list, results, use_coco_category=FLAGS.use_coco_category)
return results
def predict_image(self,
......@@ -310,7 +309,7 @@ class Detector(object):
run_benchmark=False,
repeats=1,
visual=True,
save_file=None):
save_results=False):
batch_loop_cnt = math.ceil(float(len(image_list)) / self.batch_size)
results = []
for i in range(batch_loop_cnt):
......@@ -367,14 +366,13 @@ class Detector(object):
threshold=self.threshold)
results.append(result)
if visual:
print('Test iter {}'.format(i))
if save_file is not None:
Path(self.output_dir).mkdir(exist_ok=True)
self.format_coco_results(image_list, results, save_file=save_file)
print('Test iter {}'.format(i))
results = self.merge_batch_result(results)
if save_results:
Path(self.output_dir).mkdir(exist_ok=True)
self.save_coco_results(
image_list, results, use_coco_category=FLAGS.use_coco_category)
return results
def predict_video(self, video_file, camera_id):
......@@ -394,7 +392,7 @@ class Detector(object):
if not os.path.exists(self.output_dir):
os.makedirs(self.output_dir)
out_path = os.path.join(self.output_dir, video_out_name)
fourcc = cv2.VideoWriter_fourcc(* 'mp4v')
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
writer = cv2.VideoWriter(out_path, fourcc, fps, (width, height))
index = 1
while (1):
......@@ -418,67 +416,62 @@ class Detector(object):
break
writer.release()
@staticmethod
def format_coco_results(image_list, results, save_file=None):
coco_results = []
image_id = 0
for result in results:
start_idx = 0
for box_num in result['boxes_num']:
idx_slice = slice(start_idx, start_idx + box_num)
start_idx += box_num
image_file = image_list[image_id]
image_id += 1
if 'boxes' in result:
boxes = result['boxes'][idx_slice, :]
per_result = [
{
'image_file': image_file,
'bbox':
[box[2], box[3], box[4] - box[2],
box[5] - box[3]], # xyxy -> xywh
'score': box[1],
'category_id': int(box[0]),
} for k, box in enumerate(boxes.tolist())
]
elif 'segm' in result:
import pycocotools.mask as mask_util
scores = result['score'][idx_slice].tolist()
category_ids = result['label'][idx_slice].tolist()
segms = result['segm'][idx_slice, :]
rles = [
mask_util.encode(
np.array(
mask[:, :, np.newaxis],
dtype=np.uint8,
order='F'))[0] for mask in segms
]
for rle in rles:
rle['counts'] = rle['counts'].decode('utf-8')
per_result = [{
'image_file': image_file,
def save_coco_results(self, image_list, results, use_coco_category=False):
bbox_results = []
mask_results = []
idx = 0
print("Start saving coco json files...")
for i, box_num in enumerate(results['boxes_num']):
file_name = os.path.split(image_list[i])[-1]
if use_coco_category:
img_id = int(os.path.splitext(file_name)[0])
else:
img_id = i
if 'boxes' in results:
boxes = results['boxes'][idx:idx + box_num].tolist()
bbox_results.extend([{
'image_id': img_id,
'category_id': coco_clsid2catid[int(box[0])] \
if use_coco_category else int(box[0]),
'file_name': file_name,
'bbox': [box[2], box[3], box[4] - box[2],
box[5] - box[3]], # xyxy -> xywh
'score': box[1]} for box in boxes])
if 'masks' in results:
import pycocotools.mask as mask_util
boxes = results['boxes'][idx:idx + box_num].tolist()
masks = results['masks'][i][:box_num].astype(np.uint8)
seg_res = []
for box, mask in zip(boxes, masks):
rle = mask_util.encode(
np.array(
mask[:, :, None], dtype=np.uint8, order="F"))[0]
if 'counts' in rle:
rle['counts'] = rle['counts'].decode("utf8")
seg_res.append({
'image_id': img_id,
'category_id': coco_clsid2catid[int(box[0])] \
if use_coco_category else int(box[0]),
'file_name': file_name,
'segmentation': rle,
'score': scores[k],
'category_id': category_ids[k],
} for k, rle in enumerate(rles)]
'score': box[1]})
mask_results.extend(seg_res)
else:
raise RuntimeError('')
idx += box_num
# per_result = [item for item in per_result if item['score'] > threshold]
coco_results.extend(per_result)
if save_file:
with open(os.path.join(save_file), 'w') as f:
json.dump(coco_results, f)
return coco_results
if bbox_results:
bbox_file = os.path.join(self.output_dir, "bbox.json")
with open(bbox_file, 'w') as f:
json.dump(bbox_results, f)
print(f"The bbox result is saved to {bbox_file}")
if mask_results:
mask_file = os.path.join(self.output_dir, "mask.json")
with open(mask_file, 'w') as f:
json.dump(mask_results, f)
print(f"The mask result is saved to {mask_file}")
class DetectorSOLOv2(Detector):
......@@ -956,8 +949,6 @@ def main():
if FLAGS.image_dir is None and FLAGS.image_file is not None:
assert FLAGS.batch_size == 1, "batch_size should be 1, when image_file is not None"
img_list = get_test_images(FLAGS.image_dir, FLAGS.image_file)
save_file = os.path.join(FLAGS.output_dir,
'results.json') if FLAGS.save_results else None
if FLAGS.slice_infer:
detector.predict_image_slice(
img_list,
......@@ -966,10 +957,15 @@ def main():
FLAGS.combine_method,
FLAGS.match_threshold,
FLAGS.match_metric,
save_file=save_file)
visual=FLAGS.save_images,
save_results=FLAGS.save_results)
else:
detector.predict_image(
img_list, FLAGS.run_benchmark, repeats=100, save_file=save_file)
img_list,
FLAGS.run_benchmark,
repeats=100,
visual=FLAGS.save_images,
save_results=FLAGS.save_results)
if not FLAGS.run_benchmark:
detector.det_times.info(average=True)
else:
......
deploy/python/utils.py
浏览文件 @ 10e7fe23
......@@ -109,6 +109,7 @@ def argsparser():
parser.add_argument(
'--save_images',
action='store_true',
default=False,
help='Save visualization image results.')
parser.add_argument(
'--save_mot_txts',
......@@ -159,9 +160,14 @@ def argsparser():
help="Whether do random padding for action recognition.")
parser.add_argument(
"--save_results",
type=bool,
action='store_true',
default=False,
help="Whether save detection result to file using coco format")
parser.add_argument(
'--use_coco_category',
action='store_true',
default=False,
help='Whether to use the coco format dictionary `clsid2catid`')
parser.add_argument(
"--slice_infer",
action='store_true',
......@@ -386,3 +392,87 @@ def nms(dets, match_threshold=0.6, match_metric='iou'):
keep = np.where(suppressed == 0)[0]
dets = dets[keep, :]
return dets
coco_clsid2catid = {
0: 1,
1: 2,
2: 3,
3: 4,
4: 5,
5: 6,
6: 7,
7: 8,
8: 9,
9: 10,
10: 11,
11: 13,
12: 14,
13: 15,
14: 16,
15: 17,
16: 18,
17: 19,
18: 20,
19: 21,
20: 22,
21: 23,
22: 24,
23: 25,
24: 27,
25: 28,
26: 31,
27: 32,
28: 33,
29: 34,
30: 35,
31: 36,
32: 37,
33: 38,
34: 39,
35: 40,
36: 41,
37: 42,
38: 43,
39: 44,
40: 46,
41: 47,
42: 48,
43: 49,
44: 50,
45: 51,
46: 52,
47: 53,
48: 54,
49: 55,
50: 56,
51: 57,
52: 58,
53: 59,
54: 60,
55: 61,
56: 62,
57: 63,
58: 64,
59: 65,
60: 67,
61: 70,
62: 72,
63: 73,
64: 74,
65: 75,
66: 76,
67: 77,
68: 78,
69: 79,
70: 80,
71: 81,
72: 82,
73: 84,
74: 85,
75: 86,
76: 87,
77: 88,
78: 89,
79: 90
}
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