在用你们的代码测试时，遇到以下问题：
（测试命令：python -W ignore inference.py -i example.png -o 1.bin -m_dir ./ckpts -m 3 --encode）
Traceback (most recent call last):
File "inference.py", line 363, in <module>
encode(args.input, args.output, args.model_dir, args.model, args.block_width, args.block_height)
File "/home/dyf/anaconda3/envs/c2f/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 28, in decorate_context
return func(*args, **kwargs)
File "inference.py", line 89, in encode
xp3, params_prob = context(y_main_q, hyper_dec)
File "/home/dyf/anaconda3/envs/c2f/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/mnt/d/dev/NIC/code/Model/context_model.py", line 112, in forward
p3 = self.gaussin_entropy_func(torch.squeeze(x, dim=1), output)
File "/home/dyf/anaconda3/envs/c2f/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/mnt/d/dev/NIC/code/Model/gaussian_entropy_model.py", line 98, in forward
m0 = torch.distributions.normal.Normal(mean0, scale0)
File "/home/dyf/anaconda3/envs/c2f/lib/python3.8/site-packages/torch/distributions/normal.py", line 50, in __init__
super(Normal, self).__init__(batch_shape, validate_args=validate_args)
File "/home/dyf/anaconda3/envs/c2f/lib/python3.8/site-packages/torch/distributions/distribution.py", line 56, in __init__
raise ValueError(
ValueError: Expected parameter scale (Tensor of shape (1, 192, 32, 48)) of distribution Normal(loc: torch.Size([1, 192, 32, 48]), scale: torch.Size([1, 192, 32, 48])) to satisfy the constraint GreaterThan(lower_bound=0.0), but found invalid values:
分析报错，发现gaussian_entropy_model中：
class Distribution_for_entropy2(nn.Module):
def __init__(self):
super(Distribution_for_entropy2, self).__init__()
def forward(self, x, p_dec):
# you can use use 3 gaussian
prob0, mean0, scale0, prob1, mean1, scale1, prob2, mean2, scale2 = [
torch.chunk(p_dec, 9, dim=1)[i].squeeze(1) for i in range(9)]
# keep the weight summation of prob == 1
probs = torch.stack([prob0, prob1, prob2], dim=-1)
probs = f.softmax(probs, dim=-1)
# process the scale value to non-zero
scale0[scale0 == 0] = 1e-6
scale1[scale1 == 0] = 1e-6
scale2[scale2 == 0] = 1e-6
# 3 gaussian distribution
m0 = torch.distributions.normal.Normal(mean0, scale0)
m1 = torch.distributions.normal.Normal(mean1, scale1)
m2 = torch.distributions.normal.Normal(mean2, scale2)
likelihood0 = torch.abs(m0.cdf(x + 0.5)-m0.cdf(x-0.5))
likelihood1 = torch.abs(m1.cdf(x + 0.5)-m1.cdf(x-0.5))
likelihood2 = torch.abs(m2.cdf(x + 0.5)-m2.cdf(x-0.5))
likelihoods = Low_bound.apply(
probs[:, :, :, :, 0]*likelihood0+probs[:, :, :, :, 1]*likelihood1+probs[:, :, :, :, 2]*likelihood2)
return likelihoods
其中的scale0, scale1和scale2部分出现了小于0的值，众所周知，方差是不会小于0的，查找输入的p_dec，发现对应context_model.py中的output：
class Weighted_Gaussian(nn.Module):
def __init__(self, M):
super(Weighted_Gaussian, self).__init__()
self.conv1 = MaskConv3d('A', 1, 24, 11, 1, 5)
self.conv2 = nn.Sequential(nn.Conv3d(25, 48, 1, 1, 0), nn.ReLU(), nn.Conv3d(48, 96, 1, 1, 0), nn.ReLU(),
nn.Conv3d(96, 9, 1, 1, 0))
self.conv3 = nn.Conv2d(M*2, M, 3, 1, 1)
self.gaussin_entropy_func = Distribution_for_entropy2()
def forward(self, x, hyper):
x = torch.unsqueeze(x, dim=1)
hyper = torch.unsqueeze(self.conv3(hyper), dim=1)
x1 = self.conv1(x)
output = self.conv2(torch.cat((x1, hyper), dim=1))
p3 = self.gaussin_entropy_func(torch.squeeze(x, dim=1), output)
return p3, output
请问我该如何让测试跑起来？
在用你们的代码测试时，遇到以下问题：
（测试命令：
python -W ignore inference.py -i example.png -o 1.bin -m_dir ./ckpts -m 3 --encode）分析报错，发现gaussian_entropy_model中：
其中的
scale0,scale1和scale2部分出现了小于0的值，众所周知，方差是不会小于0的，查找输入的p_dec，发现对应context_model.py中的output：请问我该如何让测试跑起来？