Revert "speedup sparse block (#1022)" (#1058)

paddleslim/prune/unstructured_pruner_utils.py

@@ -8,25 +8,11 @@ BLOCK_SPARSE_ACCURATE_THRESHOLD = 0.05
 
 def cal_mxn_avg_matrix(mat, m=1, n=1):
     if m == 1 and n == 1: return copy.deepcopy(mat)
-
-    ori_row, ori_col = mat.shape[0], mat.shape[1]
-    if len(mat.shape) == 4:
-        assert mat.shape[2:] == (1, 1), "Only support for (n, n, 1, 1) for now."
-        mat = mat.reshape(ori_row, ori_col)
-
-    res_col = n - len(mat[0]) % n
-    res_row = m - len(mat) % m
-
-    mat = np.pad(mat, ((0, res_col), (0, res_col)), 'reflect')
     avg_mat = np.zeros_like(mat)
-    new_shape = [len(mat) // m, len(mat[0]) // n, m, n]
-    strides = mat.itemsize * np.array([len(mat) * m, n, len(mat), 1])
-    mat = np.lib.stride_tricks.as_strided(mat, shape=new_shape, strides=strides)
-    mat = mat.mean((2, 3), keepdims=True)
-    mat = np.tile(mat, (1, 1, m, n))
-    for i in range(len(mat)):
-        avg_mat[i * m:i * m + m] = np.concatenate(list(mat[i]), axis=1)
-    avg_mat = avg_mat[:ori_row, :ori_col]
-    if len(mat.shape) == 4:
-        avg_mat = avg_mat.reshape(ori_row, ori_col, 1, 1)
+    rows = len(mat) // m + 1
+    cols = len(mat[0]) // n + 1
+    for row in range(rows):
+        for col in range(cols):
+            avg_mat[m * row:m * row + m, n * col:n * col + n] = np.mean(mat[
+                m * row:m * row + m, n * col:n * col + n])
     return avg_mat