add py test

python/paddle/v2/framework/tests/test_block_expand_op.py

+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+def get_output_shape(attrs, X):
+    img_height = X.shape[2]
+    img_width = X.shpe[3]
+    padding_height = attrs['padding_height']
+    padding_width = attrs['padding_width']
+    block_height = attrs['block_height']
+    block_width = attrs['block_width']
+    stride_height = attrs['stride_height']
+    stride_width = attrs['stride_width']
+    output_height = \
+      1 +  \
+      (img_height + 2 * padding_height - block_height + stride_height - 1) / \
+          stride_height
+
+    output_width = \
+      1 + \
+      (img_width + 2 * padding_width - block_width + stride_width - 1) / \
+          stride_width
+
+    return output_height, output_width
+
+
+"""
+img: {CHW}
+col:
+    {output_height, output_width, inputChannels, filterHeight, filterWidth}
+"""
+
+
+def img2col(attrs, im, col):
+    input_channels = im.shape.dims[0]
+    input_height = im.shape.dims[1]
+    input_width = im.shape.dims[2]
+    filter_height = col.shape.dims[3]
+    filter_width = col.shape.dims[4]
+    output_height = col.shape.dims[0]
+    output_width = col.shape.dims[1]
+
+    for col_row_idx in range(0, output_height):
+        for col_col_idx in range(0, output_width):
+            for channel in range(0, input_channels):
+                for filter_row_idx in range(0, filter_height):
+                    for filter_col_idx in range(0, filter_width):
+                        im_row_offset = col_row_idx * stride_height \
+                            + filter_row_idx - padding_height
+                        im_col_offset = col_col_idx * stride_width \
+                            + filter_col_idx - padding_width
+                        if (im_row_offset < 0 or
+                                im_row_offset >= input_height or
+                                im_col_offset < 0 or
+                                im_col_offset >= input_width):
+                            col[col_row_idx][col_col_idx][channel][
+                                filter_row_idx][filter_col_idx] = 0.0
+                        else:
+                            im_offset = (channel * input_height + im_row_offset
+                                         ) * input_width + im_col_offset
+                            col[col_row_idx][col_col_idx][channel][
+                                filter_row_idx][filter_col_idx] = im[channel][
+                                    im_row_offset][im_col_offset]
+
+
+"""
+img: {CHW}
+col:
+    {output_height, output_width, inputChannels, filterHeight, filterWidth}
+"""
+
+
+def col2img(attrs, col, img):
+    input_channels = im.shape.dims[0]
+    input_height = im.shape.dims[1]
+    input_width = im.shape.dims[2]
+    filter_height = col.shape.dims[3]
+    filter_width = col.shape.dims[4]
+    output_height = col.shape.dims[0]
+    output_width = col.shape.dims[1]
+
+    for col_row_idx in range(0, output_height):
+        for col_col_idx in range(0, output_width):
+            for channel in range(0, input_channels):
+                for filter_row_idx in range(0, filter_height):
+                    for filter_col_idx in range(0, filter_width):
+                        im_row_offset = \
+                            col_row_idx * stride_height + filter_row_idx - padding_height
+                        im_col_offset = \
+                            col_col_idx * stride_width + filter_col_idx - padding_width
+                        if (im_row_offset >= 0 and
+                                im_row_offset < input_height and
+                                im_col_offset >= 0 and
+                                im_col_offset < input_width):
+                            im[channel][im_row_offset][im_col_offset] = \
+                                col[col_row_idx][col_col_idx][channel][filter_row_idx][filter_col_idx]
+
+
+class TestBlockExpandMulOp(OpTest):
+    def setUp(self):
+        self.op_type = "block_expand"
+        self.inputs = {
+            'X': np.random.uniform(0.1, 1, [2, 3, 9, 9]).astype("float64"),
+        }
+        self.attrs = {
+            'block_height': 3,
+            'block_width': 3,
+            'stride_height': 2,
+            'stride_width': 2,
+            'padding_height': 3,
+            'padding_width': 3,
+        }
+
+        self.outputs = {'Out': np.multiply(self.inputs['X'], self.inputs['Y'])}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad_normal(self):
+        self.check_grad(['X'], 'Out')