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由于官方不支持grid_sample，我使用的是别人实现的bilinear_grid_sample
def bilinear_grid_sample(im, grid, align_corners=False):
"""Given an input and a flow-field grid, computes the output using input
values and pixel locations from grid. Supported only bilinear interpolation
method to sample the input pixels.
Args:
im (torch.Tensor): Input feature map, shape (N, C, H, W)
grid (torch.Tensor): Point coordinates, shape (N, Hg, Wg, 2)
align_corners {bool}: If set to True, the extrema (-1 and 1) are
considered as referring to the center points of the input’s
corner pixels. If set to False, they are instead considered as
referring to the corner points of the input’s corner pixels,
making the sampling more resolution agnostic.
Returns:
torch.Tensor: A tensor with sampled points, shape (N, C, Hg, Wg)
"""
n, c, h, w = im.shape
gn, gh, gw, _ = grid.shape
assert n == gn
x = grid[:, :, :, 0]
y = grid[:, :, :, 1]
if align_corners:
x = ((x + 1) / 2) * (w - 1)
y = ((y + 1) / 2) * (h - 1)
else:
x = ((x + 1) * w - 1) / 2
y = ((y + 1) * h - 1) / 2
x = x.view(n, -1)
y = y.view(n, -1)
x0 = torch.floor(x).long()
y0 = torch.floor(y).long()
x1 = x0 + 1
y1 = y0 + 1
wa = ((x1 - x) * (y1 - y)).unsqueeze(1)
wb = ((x1 - x) * (y - y0)).unsqueeze(1)
wc = ((x - x0) * (y1 - y)).unsqueeze(1)
wd = ((x - x0) * (y - y0)).unsqueeze(1)
# Apply default for grid_sample function zero padding
im_padded = torch.nn.functional.pad(im, pad=[1, 1, 1, 1], mode='constant', value=0)
padded_h = h + 2
padded_w = w + 2
# save points positions after padding
x0, x1, y0, y1 = x0 + 1, x1 + 1, y0 + 1, y1 + 1
x0 = torch.where(x0 < 0, torch.tensor(0, device=im.device), x0)
x0 = torch.where(x0 > padded_w - 1, torch.tensor(padded_w - 1, device=im.device), x0)
x1 = torch.where(x1 < 0, torch.tensor(0, device=im.device), x1)
x1 = torch.where(x1 > padded_w - 1, torch.tensor(padded_w - 1, device=im.device), x1)
y0 = torch.where(y0 < 0, torch.tensor(0, device=im.device), y0)
y0 = torch.where(y0 > padded_h - 1, torch.tensor(padded_h - 1, device=im.device), y0)
y1 = torch.where(y1 < 0, torch.tensor(0, device=im.device), y1)
y1 = torch.where(y1 > padded_h - 1, torch.tensor(padded_h - 1, device=im.device), y1)
im_padded = im_padded.view(n, c, -1)
x0_y0 = (x0 + y0 * padded_w).unsqueeze(1).expand(-1, c, -1)
x0_y1 = (x0 + y1 * padded_w).unsqueeze(1).expand(-1, c, -1)
x1_y0 = (x1 + y0 * padded_w).unsqueeze(1).expand(-1, c, -1)
x1_y1 = (x1 + y1 * padded_w).unsqueeze(1).expand(-1, c, -1)
Ia = torch.gather(im_padded, 2, x0_y0)
Ib = torch.gather(im_padded, 2, x0_y1)
Ic = torch.gather(im_padded, 2, x1_y0)
Id = torch.gather(im_padded, 2, x1_y1)
return (Ia * wa + Ib * wb + Ic * wc + Id * wd).reshape(n, c, gh, gw)
但是在最后Ia * wa + Ib * wb + Ic * wc + Id * wd 会报错：
Conv clip_truncate < 0, plz make sure bias_scale > weight_scale * input_scale, Output TensorName : /GatherElements_1_output_0-rs_exSecondary
E RKNN: [08:25:32.715] REGTASK: The bit width of field value exceeds the limit, target: f3, offset: 0x3024, shift = 0, limit: 0x1f, value: 0x80000000
E RKNN: [08:25:32.715] REGTASK: The bit width of field value exceeds the limit, target: f3, offset: 0x40b4, shift = 0, limit: 0xfff, value: 0x80000000
貌似是量化的问题，为了解决这个问题，我想在乘法时不使用量化：
set_op_attr={
'/Mul_65_output_0-rs': { # 替换为实际层名
'quantize': False # 禁用该层的量化，保留float32
}}
但是会报错Op [Mul] not support set attr，那么我该怎么解决这个问题
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