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import os
import numpy as np
from mmseg.apis import init_model, inference_model, show_result_pyplot#, inference_segmentor
import torch
from PIL import Image
from sklearn.metrics import confusion_matrix
from torchmetrics import JaccardIndex
def main():
config_file = 'mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_4xb4-320k_coco-stuff164k-512x512.py'
checkpoint_file = 'evaluations/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k_20210709_155402-3cbca14d.pth'
# build the model from a config file and a checkpoint file
model = init_model(config_file, checkpoint_file, device='cuda:1')
# Image and segmentation labels directories
img_dir = 'sample/cocostuff/visualization'
ann_dir = 'sample/cocostuff/annotations'
# List all image files
img_fns = [f for f in sorted(os.listdir(img_dir)) if f.endswith(".png")]
total_mIoU = 0
from tqdm import tqdm
i = 0
num_classes = 171
jaccard_index = JaccardIndex(task="multiclass", num_classes=num_classes)
conf_matrix = np.zeros((num_classes+1, num_classes+1), dtype=np.int64)
for img_fn in tqdm(img_fns):
ann_fn = img_fn
i += 1
# if i == 4891:
# continue
try:
img_path = os.path.join(img_dir, img_fn)
ann_path = os.path.join(ann_dir, img_fn)
result = inference_model(model, img_path)
except Exception as e:
continue
# Read ground truth segmentation map
gt_semantic_seg = np.array(Image.open(ann_path))
ignore_label = 255
gt = gt_semantic_seg.copy()
# import pdb;pdb.set_trace()
# print(np.unique(gt), np.unique(result.pred_sem_seg.data[0].cpu().numpy()))
pred = result.pred_sem_seg.data[0].cpu().numpy().copy()#+1
gt[gt == ignore_label] = num_classes
conf_matrix += np.bincount(
(num_classes+1) * pred.reshape(-1) + gt.reshape(-1),
minlength=conf_matrix.size,
).reshape(conf_matrix.shape)
# calculate miou
acc = np.full(num_classes, np.nan, dtype=np.float64)
iou = np.full(num_classes, np.nan, dtype=np.float64)
tp = conf_matrix.diagonal()[:-1].astype(np.float64)
pos_gt = np.sum(conf_matrix[:-1, :-1], axis=0).astype(np.float64)
pos_pred = np.sum(conf_matrix[:-1, :-1], axis=1).astype(np.float64)
acc_valid = pos_gt > 0
acc[acc_valid] = tp[acc_valid] / pos_gt[acc_valid]
iou_valid = (pos_gt + pos_pred) > 0
union = pos_gt + pos_pred - tp
iou[acc_valid] = tp[acc_valid] / union[acc_valid]
miou = np.sum(iou[acc_valid]) / np.sum(iou_valid)
print(f"mIoU: {miou}")
if __name__ == '__main__':
main() |