in evaluation/tiny_benchmark/maskrcnn_benchmark/modeling/rpn/locnet/loss.py [0:0]
def __call__(self, labels, box_cls):
if (self.counter // self.area_ths + 1) % self.show_iter != 0:
self.counter += 1
return
self.counter += 1
labels = labels.copy()
for i, (label, cls) in enumerate(zip(labels, box_cls)):
# labels[i] = (label > 0).float().reshape((2, 1, cls.shape[-2], cls.shape[-1]))
if self.show_classes is not None:
label = label[:, self.show_classes]
if self.merge_method == 'sum':
labels[i] = label.sum(dim=1)
elif self.merge_method == 'max':
labels[i] = label.max(dim=1)[0]
labels[i] = labels[i].reshape((cls.shape[0], 1, cls.shape[-2], cls.shape[-1]))
if self.merge_levels:
label_map = 0
else:
label_maps = []
shape, pos_count = None, []
for i in range(0, len(labels)):
label_sum = labels[i].sum()
if shape is None:
if label_sum > 0:
shape = labels[i].shape
label = labels[i]
if not self.merge_levels:
label_maps.append(label)
else:
label_map = label
elif label_sum > 0:
if self.merge_levels:
label = F.upsample(labels[i], shape[2:], mode='bilinear')
if self.merge_method == 'max':
label_map = torch.max(torch.stack([label_map, label]), dim=0)[0]
elif self.merge_method == 'sum':
label_map += label
else:
label_maps.append(labels[i])
pos_count.append(int(label_sum.cpu().numpy()))
# print(label_map.shape)
import matplotlib.pyplot as plt
import numpy as np
if self.merge_levels:
label_maps = [label_map]
else:
# ms = max([max(label_map.shape) for label_map in label_maps])
plt.figure(figsize=(5*len(label_maps), 5*1))
for i, label_map in enumerate(label_maps):
label_map = F.upsample(label_map, (140, 100), mode='bilinear')
label_map = label_map[0].permute((1, 2, 0)).cpu().numpy()[:, :, 0].astype(np.float32) ** 2
max_l = label_map.max()
if max_l > 0:
label_map /= max_l
if len(label_maps) > 1:
plt.subplot(1, len(label_maps), i + 1)
plt.imshow(label_map)
plt.title("pos_count:{} ".format(pos_count))
plt.show()