Suit avgpoolgrad operator with latest Atlas package

mindspore/nn/loss/loss.py

@@ -208,7 +208,7 @@ class SoftmaxCrossEntropyWithLogits(_Loss):
 
     .. math::
         \ell(x_i, t_i) = - \log\left(\frac{\exp(x_{t_i})}{\sum_j \exp(x_j)}\right)
-        =  -x_{t_i} + \log\left(\sum_j \exp(x_i)\right),
+        =  -x_{t_i} + \log\left(\sum_j \exp(x_j)\right),
     where :math:`x_i` is a 1D score Tensor, :math:`t_i` is a scalar.
 
     Note:

mindspore/ops/_grad/grad_nn_ops.py

@@ -269,8 +269,6 @@ def _get_mean_matrix(x_shape, ksize, stride, padding, x_dtype):
 
     n_input, c_input, h_input, w_input = x_shape
     h_ksize, w_ksize = ksize[2], ksize[3]
-    if h_ksize == h_input and w_ksize == w_input and padding == "VALID":
-        return None
     h_stride, w_stride = stride[2], stride[3]
     n_output = n_input
     c_output = c_input
@@ -308,10 +306,6 @@ def _get_mean_matrix(x_shape, ksize, stride, padding, x_dtype):
 
 @constexpr
 def _get_kernel_matrix(x_shape_nchw, kernel_matrix_shape, padding, x_dtype):
-    if x_shape_nchw[2] == kernel_matrix_shape[2] \
-        and x_shape_nchw[3] == kernel_matrix_shape[3] \
-        and padding == 'VALID':
-        return None
     kernel_matrix = np.ones(kernel_matrix_shape)
     return Tensor(kernel_matrix, x_dtype)