input_shape就是指输入张量的shape。例如,input_dim=784,dim是指dimension(维度),说明输入是一个784维的向量,784维的向量怎么表示呢?[[...[1],[2],[3]]...],左边有784个左括号,这相当于一个一阶的张量,它的shape就是(784,)。因此,input_shape=(784,)。 参考链接:https://blog.csdn.net/x_ym/ar...
$$\begin{aligned} {\mathcal {L}} _{MIoU-C}= & {} 1-IoU+\frac{\rho ^2\left( b,b^{gt} \right) }{c^2}+\alpha \nu \nonumber \\{} & {} +\frac{\delta _x\left( b,b^{gt} \right) }{c_w}+\frac{\delta _y\left( b,b^{gt} \right) }{c_h} \end{aligned}$$ ...
Module): """ An Unflatten module receives an input of shape (N, C*H*W) and reshapes it to produce an output of shape (N, C, H, W). """ def __init__(self, N=-1, C=128, H=7, W=7): super(Unflatten, self).__init__() self.N = N self.C = C self.H = H self...
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deflinear(input:Tensor,weight:Tensor,bias:Optional[Tensor]=None)->Tensor:r"""Applies a linear transformation to the incoming data: :math:`y = xA^T + b`.This operator supports :ref:`TensorFloat32<tf32_on_ampere>`.Shape:- Input: :math:`(N, *, in\_features)` N is the batch size...
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大佬的分析指点,存在两个bug,第一个bug似乎是ncnn的GlobalAveragePool算子做的pack优化有问题(?),out tensor的shape不对,第二个bug是ncnn的进行Conv时,如果输入是dims=1,并且卷积核大小是1x1时,float32和int8执行的forward不一样,int8会直接执行forward_int8x86,而float32执行时会进行判断并将其优化为点积(因为...
Inputs: - X: A numpy array of shape (N, D) giving training data. - y: A numpy array f shape (N,) giving training labels; y[i] = c means that X[i] has label c, where 0 <= c < C. - X_val: A numpy array of shape (N_val, D) giving validation data. - y_val: A...
2 with the new input, which is shown in Fig. 7. Both the shape of the K¯N→K¯N and πΣ→πΣ amplitude module squared are same. The strength of the K¯N→K¯N amplitude is not much affected, but the one of πΣ→πΣ is reduced by about a factor of two if the ...