def process(self, im1w_score):
"""
nms(n), topk(t), gaussian kernel(g) operation
:param im1w_score: warped score map
:return: processed score map, topk mask, topk value
"""
im1w_score = filter_border(im1w_score)
# apply nms to im1w_score
nms_mask = nms(im1w_score,
thresh=self.NMS_THRESH,
ksize=self.NMS_KSIZE)
im1w_score = im1w_score * nms_mask
topk_value = im1w_score
# apply topk to im1w_score
topk_mask = topk_map(im1w_score, self.TOPK)
im1w_score = topk_mask.to(torch.float) * im1w_score
# apply gaussian kernel to im1w_score
psf = get_gauss_filter_weight(
self.GAUSSIAN_KSIZE,
self.GAUSSIAN_SIGMA)[None, None, :, :].to(im1w_score.device)
# psf = im1w_score.new_tensor(
# get_gauss_filter_weight(self.GAUSSIAN_KSIZE, self.GAUSSIAN_SIGMA)[
# None, None, :, :
# ]
# )
im1w_score = F.conv2d(
input=im1w_score.permute(0, 3, 1, 2),
weight=psf,
stride=1,
padding=self.GAUSSIAN_KSIZE // 2,
).permute(0, 2, 3, 1) # (B, H, W, 1)
"""
apply tf.clamp to make sure all value in im1w_score isn't greater than 1
but this won't happend in correct way
"""
im1w_score = im1w_score.clamp(min=0.0, max=1.0)
return im1w_score, topk_mask, topk_value
def process(self, im1w_score):
"""
nms(n), topk(t), gaussian kernel(g) operation
:param im1w_score: warped score map
:return: processed score map, topk mask, topk value
"""
im1w_score = filter_border(im1w_score)
# apply nms to im1w_score
#nms:non-maximum suppression非最大值抑制
nms_mask = nms(im1w_score,
thresh=self.NMS_THRESH,
ksize=self.NMS_KSIZE)
im1w_score = im1w_score * nms_mask
topk_value = im1w_score
# apply topk to im1w_score
topk_mask = topk_map(im1w_score, self.TOPK)
im1w_score = topk_mask.to(torch.float) * im1w_score
# apply gaussian kernel to im1w_score
psf = im1w_score.new_tensor(
get_gauss_filter_weight(self.GAUSSIAN_KSIZE,
self.GAUSSIAN_SIGMA)[None, None, :, :])
im1w_score = F.conv2d(
input=im1w_score.permute(0, 3, 1, 2),
weight=psf,
stride=1,
padding=self.GAUSSIAN_KSIZE // 2,
).permute(0, 2, 3, 1) # (B, H, W, 1)
#python pytorch.permute函数用于变换参数的维数，比如这里的F.conv2d的参数有4个，现在将里面参数的顺序调整为0 2 3 1
"""
apply tf.clamp to make sure all value in im1w_score isn't greater than 1
but this won't happend in correct way
"""
im1w_score = im1w_score.clamp(min=0.0, max=1.0)
#python torch clamp函数，用于将参数的取值范围限制在某个范围内，相当于把数值夹在某个区间上
#如果clamp函数里面参数小于等于最小值，那么这个参数设置为最小值；如果这个参数大于最大值，那么这个参数设置为最大值
#如果clamp函数里面参数介于最大值与最小值之间，那么就取这个参数 本身的值
return im1w_score, topk_mask, topk_value