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[update visactmap] change channel order

pull/218/head
KaiyangZhou 2019-08-03 23:01:21 +01:00
parent d021fafff1
commit 84249ad412
1 changed files with 5 additions and 5 deletions
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10
torchreid/engine/engine.py
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@ -50,7 +50,7 @@ class Engine(object):
def run(self, save_dir='log', max_epoch=0, start_epoch=0, fixbase_epoch=0, open_layers=None,
start_eval=0, eval_freq=-1, test_only=False, print_freq=10,
dist_metric='euclidean', normalize_feature=False, visrank=False, visrank_topk=20,
dist_metric='euclidean', normalize_feature=False, visrank=False, visrank_topk=10,
use_metric_cuhk03=False, ranks=[1, 5, 10, 20], rerank=False, visactmap=False):
r"""A unified pipeline for training and evaluating a model.
@ -75,7 +75,7 @@ class Engine(object):
visrank (bool, optional): visualizes ranked results. Default is False. It is recommended to
enable ``visrank`` when ``test_only`` is True. The ranked images will be saved to
"save_dir/visrank_dataset", e.g. "save_dir/visrank_market1501".
visrank_topk (int, optional): top-k ranked images to be visualized. Default is 20.
visrank_topk (int, optional): top-k ranked images to be visualized. Default is 10.
use_metric_cuhk03 (bool, optional): use single-gallery-shot setting for cuhk03.
Default is False. This should be enabled when using cuhk03 classic split.
ranks (list, optional): cmc ranks to be computed. Default is [1, 5, 10, 20].
@ -163,7 +163,7 @@ class Engine(object):
raise NotImplementedError
def test(self, epoch, testloader, dist_metric='euclidean', normalize_feature=False,
visrank=False, visrank_topk=20, save_dir='', use_metric_cuhk03=False,
visrank=False, visrank_topk=10, save_dir='', use_metric_cuhk03=False,
ranks=[1, 5, 10, 20], rerank=False):
r"""Tests model on target datasets.
@ -205,7 +205,7 @@ class Engine(object):
@torch.no_grad()
def _evaluate(self, epoch, dataset_name='', queryloader=None, galleryloader=None,
dist_metric='euclidean', normalize_feature=False, visrank=False,
visrank_topk=20, save_dir='', use_metric_cuhk03=False, ranks=[1, 5, 10, 20],
visrank_topk=10, save_dir='', use_metric_cuhk03=False, ranks=[1, 5, 10, 20],
rerank=False):
batch_time = AverageMeter()
@ -374,7 +374,7 @@ class Engine(object):
# save images in a single figure (add white spacing between images)
# from left to right: original image, activation map, overlapped image
grid_img = 255 * np.ones((height, 3*width+2*GRID_SPACING, 3), dtype=np.uint8)
grid_img[:, :width, :] = img_np
grid_img[:, :width, :] = img_np[:, :, ::-1]
grid_img[:, width+GRID_SPACING: 2*width+GRID_SPACING, :] = am
grid_img[:, 2*width+2*GRID_SPACING:, :] = overlapped
cv2.imwrite(osp.join(actmap_dir, imname+'.jpg'), grid_img)