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[Other]Refactor PaddleSeg with preprocessor && postprocessor && support batch (#639)

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* Refactor PaddleSeg with preprocessor && postprocessor

* Fix bugs

* Delete redundancy code

* Modify by comments

* Refactor according to comments

* Add batch evaluation

* Add single test script

* Add ppliteseg single test script && fix eval(raise) error

* fix bug

* Fix evaluation segmentation.py batch predict

* Fix segmentation evaluation bug

* Fix evaluation segmentation bugs

Co-authored-by: Jason <jiangjiajun@baidu.com>
This commit is contained in:
huangjianhui
2022-11-28 15:50:12 +08:00
committed by GitHub
parent d0307192f9
commit 312e1b097d
26 changed files with 1173 additions and 449 deletions
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38
python/fastdeploy/vision/evaluation/segmentation.py
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@@ -20,7 +20,7 @@ import math
import time
def eval_segmentation(model, data_dir):
def eval_segmentation(model, data_dir, batch_size=1):
import cv2
from .utils import Cityscapes
from .utils import f1_score, calculate_area, mean_iou, accuracy, kappa
@@ -39,6 +39,8 @@ def eval_segmentation(model, data_dir):
start_time = 0
end_time = 0
average_inference_time = 0
im_list = []
label_list = []
for image_label_path, i in zip(file_list,
trange(
image_num, desc="Inference Progress")):
@@ -46,19 +48,31 @@ def eval_segmentation(model, data_dir):
start_time = time.time()
im = cv2.imread(image_label_path[0])
label = cv2.imread(image_label_path[1], cv2.IMREAD_GRAYSCALE)
result = model.predict(im)
label_list.append(label)
if batch_size == 1:
result = model.predict(im)
results = [result]
else:
im_list.append(im)
# If the batch_size is not satisfied, the remaining pictures are formed into a batch
if (i + 1) % batch_size != 0 and i != image_num - 1:
continue
results = model.batch_predict(im_list)
if i == image_num - 1:
end_time = time.time()
average_inference_time = round(
(end_time - start_time) / (image_num - twenty_percent_image_num),
4)
pred = np.array(result.label_map).reshape(result.shape[0],
result.shape[1])
intersect_area, pred_area, label_area = calculate_area(pred, label,
num_classes)
intersect_area_all = intersect_area_all + intersect_area
pred_area_all = pred_area_all + pred_area
label_area_all = label_area_all + label_area
average_inference_time = round(
(end_time - start_time) /
(image_num - twenty_percent_image_num), 4)
for result, label in zip(results, label_list):
pred = np.array(result.label_map).reshape(result.shape[0],
result.shape[1])
intersect_area, pred_area, label_area = calculate_area(pred, label,
num_classes)
intersect_area_all = intersect_area_all + intersect_area
pred_area_all = pred_area_all + pred_area
label_area_all = label_area_all + label_area
im_list.clear()
label_list.clear()
class_iou, miou = mean_iou(intersect_area_all, pred_area_all,
label_area_all)