add unit test for backward

apache · Apr 2, 2019 · 3f6583d · 3f6583d
1 parent 40b2546
commit 3f6583d
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Showing 2 changed files with 34 additions and 10 deletions.
diff --git a/src/operator/image/crop-inl.h b/src/operator/image/crop-inl.h
@@ -145,12 +145,12 @@ inline void CropBackwardImpl(int x,
       begin[1] = y;
       begin[2] = x;
     }
-    MSHADOW_TYPE_SWITCH(input_grad.type_flag_, DType, {
+    MSHADOW_TYPE_SWITCH(output_grad.type_flag_, DType, {
       MXNET_ASSIGN_REQ_SWITCH(req[0], Req, {
         size_t num_threads = output_grad.shape_.FlatTo2D()[0];
         mxnet_op::Kernel<slice_assign<ndim, Req, cpu>, cpu>::Launch(s, num_threads,
           input_grad.dptr<DType>(), output_grad.dptr<DType>(),
-          output_grad.shape_.get<ndim>(), input_grad.shape_.get<ndim>(), begin, step);
+          input_grad.shape_.get<ndim>(), output_grad.shape_.get<ndim>(), begin, step);
       })
     })
   })

diff --git a/tests/python/unittest/test_gluon_data_vision.py b/tests/python/unittest/test_gluon_data_vision.py
@@ -15,15 +15,16 @@
 # specific language governing permissions and limitations
 # under the License.
 from __future__ import print_function
+from collections import namedtuple
+
 import mxnet as mx
 import mxnet.ndarray as nd
 from mxnet.base import MXNetError
 from mxnet import gluon
 from mxnet.base import MXNetError
 from mxnet.gluon.data.vision import transforms
 from mxnet import image
-from mxnet.test_utils import assert_almost_equal
-from mxnet.test_utils import almost_equal
+from mxnet.test_utils import *
 from common import assertRaises, setup_module, with_seed, teardown
 
 import numpy as np
@@ -158,12 +159,35 @@ def _test_crop_resize_with_diff_type(dtype):
 
     for dtype in ['uint8', 'float32', 'float64']:
         _test_crop_resize_with_diff_type(dtype)  
-    # test for gradient
-    data = mx.sym.Variable('data')
-    slice_sym = mx.sym.slice(data, begin=begin, end=end, step=step)
-    expected_in_grad = np.zeros_like(a_np)
-    expected_in_grad[index] = b_np
-    check_symbolic_backward(slice_sym, [a_np], [b_np], [expected_in_grad])
+
+    # test nd.image.crop backward
+    def test_crop_backward(test_nd_arr, TestCase):
+        a_np = test_nd_arr.asnumpy()
+        b_np = a_np[(slice(TestCase.y, TestCase.y + TestCase.height), slice(TestCase.x, TestCase.x + TestCase.width), slice(0, 3))]
+
+        data = mx.sym.Variable('data')
+        crop_sym = mx.sym.image.crop(data, TestCase.x, TestCase.y, TestCase.width, TestCase.height)
+
+        expected_in_grad = np.zeros_like(a_np)
+        expected_in_grad[(slice(TestCase.y, TestCase.y + TestCase.height), slice(TestCase.x, TestCase.x + TestCase.width), slice(0, 3))] = b_np
+        check_symbolic_backward(crop_sym, [a_np], [b_np], [expected_in_grad])
+
+    TestCase = namedtuple('TestCase', ['x', 'y', 'width', 'height'])
+    test_list = [TestCase(0, 0, 3, 3), TestCase(2, 1, 1, 2), TestCase(0, 1, 3, 2)]
+
+    for dtype in ['uint8', 'float32', 'float64']:
+        data_in = nd.arange(60).reshape((5, 4, 3)).astype(dtype)
+        for test_case in test_list:
+            test_crop_backward(data_in, test_case)
+
+
+
+    # check numeric gradient of nd.image.crop
+    # in_data = np.arange(36).reshape(3, 4, 3)
+    # data = mx.sym.Variable('data')
+    # image_crop_sym = mx.sym.image.crop(data, 0, 0, 2, 2)
+    # check_numeric_gradient(image_crop_sym, [in_data])
+
 
 @with_seed()
 def test_flip_left_right():