Receiving the probability map

[adigol]

Is it possible to receive the probability map of each pixel in the image(or in any bounding box) like in the attached images?

[glenn-jocher]

@adigol this information is contained in the Detect() forward method feature maps here. You'd have to index these as appropriate to extract the values you are interested in:

yolov5/models/yolo.py

Lines 36 to 74 in 15e8c4c

	class Detect(nn.Module):
	stride = None # strides computed during build
	onnx_dynamic = False # ONNX export parameter
	def __init__(self, nc=80, anchors=(), ch=(), inplace=True): # detection layer
	super().__init__()
	self.nc = nc # number of classes
	self.no = nc + 5 # number of outputs per anchor
	self.nl = len(anchors) # number of detection layers
	self.na = len(anchors[0]) // 2 # number of anchors
	self.grid = [torch.zeros(1)] * self.nl # init grid
	self.anchor_grid = [torch.zeros(1)] * self.nl # init anchor grid
	self.register_buffer('anchors', torch.tensor(anchors).float().view(self.nl, -1, 2)) # shape(nl,na,2)
	self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv
	self.inplace = inplace # use in-place ops (e.g. slice assignment)
	def forward(self, x):
	z = [] # inference output
	for i in range(self.nl):
	x[i] = self.m[i](x[i]) # conv
	bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85)
	x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
	if not self.training: # inference
	if self.grid[i].shape[2:4] != x[i].shape[2:4] or self.onnx_dynamic:
	self.grid[i], self.anchor_grid[i] = self._make_grid(nx, ny, i)
	y = x[i].sigmoid()
	if self.inplace:
	y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i] # xy
	y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh
	else: # for YOLOv5 on AWS Inferentia https://github.com/ultralytics/yolov5/pull/2953
	xy = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i] # xy
	wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh
	y = torch.cat((xy, wh, y[..., 4:]), -1)
	z.append(y.view(bs, -1, self.no))
	return x if self.training else (torch.cat(z, 1), x)

[DoSquared]

@adigol this information is contained in the Detect() forward method feature maps here. You'd have to index these as appropriate to extract the values you are interested in:

yolov5/models/yolo.py

Lines 36 to 74 in 15e8c4c

	class Detect(nn.Module):
	stride = None # strides computed during build
	onnx_dynamic = False # ONNX export parameter
	def __init__(self, nc=80, anchors=(), ch=(), inplace=True): # detection layer
	super().__init__()
	self.nc = nc # number of classes
	self.no = nc + 5 # number of outputs per anchor
	self.nl = len(anchors) # number of detection layers
	self.na = len(anchors[0]) // 2 # number of anchors
	self.grid = [torch.zeros(1)] * self.nl # init grid
	self.anchor_grid = [torch.zeros(1)] * self.nl # init anchor grid
	self.register_buffer('anchors', torch.tensor(anchors).float().view(self.nl, -1, 2)) # shape(nl,na,2)
	self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv
	self.inplace = inplace # use in-place ops (e.g. slice assignment)
	def forward(self, x):
	z = [] # inference output
	for i in range(self.nl):
	x[i] = self.m[i](x[i]) # conv
	bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85)
	x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
	if not self.training: # inference
	if self.grid[i].shape[2:4] != x[i].shape[2:4] or self.onnx_dynamic:
	self.grid[i], self.anchor_grid[i] = self._make_grid(nx, ny, i)
	y = x[i].sigmoid()
	if self.inplace:
	y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i] # xy
	y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh
	else: # for YOLOv5 on AWS Inferentia https://github.com/ultralytics/yolov5/pull/2953
	xy = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i] # xy
	wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh
	y = torch.cat((xy, wh, y[..., 4:]), -1)
	z.append(y.view(bs, -1, self.no))
	return x if self.training else (torch.cat(z, 1), x)

Can you please elaborate more?

[DoSquared]

I have the same question

[amartyacodes]

Hi, do you have the information regarding how to obtain it? I would be grateful if you can share