!4955 Fixes for Dynamic Augmentation Ops

Merge pull request !4955 from MahdiRahmaniHanzaki/dynamic-ops-fix

mindspore/ccsrc/minddata/dataset/api/transforms.cc

@@ -382,7 +382,7 @@ CutMixBatchOperation::CutMixBatchOperation(ImageBatchFormat image_batch_format,
     : image_batch_format_(image_batch_format), alpha_(alpha), prob_(prob) {}
 
 bool CutMixBatchOperation::ValidateParams() {
-  if (alpha_ < 0) {
+  if (alpha_ <= 0) {
     MS_LOG(ERROR) << "CutMixBatch: alpha cannot be negative.";
     return false;
   }
@@ -434,7 +434,7 @@ std::shared_ptr<TensorOp> HwcToChwOperation::Build() { return std::make_shared<H
 MixUpBatchOperation::MixUpBatchOperation(float alpha) : alpha_(alpha) {}
 
 bool MixUpBatchOperation::ValidateParams() {
-  if (alpha_ < 0) {
+  if (alpha_ <= 0) {
     MS_LOG(ERROR) << "MixUpBatch: alpha must be a positive floating value however it is: " << alpha_;
     return false;
   }

mindspore/ccsrc/minddata/dataset/kernels/image/cutmix_batch_op.cc

@@ -59,7 +59,7 @@ Status CutMixBatchOp::Compute(const TensorRow &input, TensorRow *output) {
 
   // Check inputs
   if (image_shape.size() != 4 || image_shape[0] != label_shape[0]) {
-    RETURN_STATUS_UNEXPECTED("You must batch before calling CutMixBatch.");
+    RETURN_STATUS_UNEXPECTED("You must make sure images are HWC or CHW and batch before calling CutMixBatch.");
   }
   if (label_shape.size() != 2) {
     RETURN_STATUS_UNEXPECTED("CutMixBatch: Label's must be in one-hot format and in a batch");
@@ -139,10 +139,17 @@ Status CutMixBatchOp::Compute(const TensorRow &input, TensorRow *output) {
 
       // Compute labels
       for (int j = 0; j < label_shape[1]; j++) {
-        uint64_t first_value, second_value;
+        if (input.at(1)->type().IsSignedInt()) {
-        RETURN_IF_NOT_OK(input.at(1)->GetItemAt(&first_value, {i, j}));
+          int64_t first_value, second_value;
-        RETURN_IF_NOT_OK(input.at(1)->GetItemAt(&second_value, {rand_indx[i] % label_shape[0], j}));
+          RETURN_IF_NOT_OK(input.at(1)->GetItemAt(&first_value, {i, j}));
-        RETURN_IF_NOT_OK(out_labels->SetItemAt({i, j}, label_lam * first_value + (1 - label_lam) * second_value));
+          RETURN_IF_NOT_OK(input.at(1)->GetItemAt(&second_value, {rand_indx[i] % label_shape[0], j}));
+          RETURN_IF_NOT_OK(out_labels->SetItemAt({i, j}, label_lam * first_value + (1 - label_lam) * second_value));
+        } else {
+          uint64_t first_value, second_value;
+          RETURN_IF_NOT_OK(input.at(1)->GetItemAt(&first_value, {i, j}));
+          RETURN_IF_NOT_OK(input.at(1)->GetItemAt(&second_value, {rand_indx[i] % label_shape[0], j}));
+          RETURN_IF_NOT_OK(out_labels->SetItemAt({i, j}, label_lam * first_value + (1 - label_lam) * second_value));
+        }
       }
     }
   }

mindspore/ccsrc/minddata/dataset/kernels/image/mixup_batch_op.cc

@@ -38,7 +38,7 @@ Status MixUpBatchOp::Compute(const TensorRow &input, TensorRow *output) {
 
   // Check inputs
   if (image_shape.size() != 4 || image_shape[0] != label_shape[0]) {
-    RETURN_STATUS_UNEXPECTED("You must batch before calling MixUpBatch");
+    RETURN_STATUS_UNEXPECTED("You must make sure images are HWC or CHW and batch before calling MixUpBatch");
   }
   if (label_shape.size() != 2) {
     RETURN_STATUS_UNEXPECTED("MixUpBatch: Label's must be in one-hot format and in a batch");
@@ -68,10 +68,17 @@ Status MixUpBatchOp::Compute(const TensorRow &input, TensorRow *output) {
   RETURN_IF_NOT_OK(TypeCast(std::move(input.at(1)), &out_labels, DataType("float32")));
   for (int64_t i = 0; i < label_shape[0]; i++) {
     for (int64_t j = 0; j < label_shape[1]; j++) {
-      uint64_t first_value, second_value;
+      if (input.at(1)->type().IsSignedInt()) {
-      RETURN_IF_NOT_OK(input.at(1)->GetItemAt(&first_value, {i, j}));
+        int64_t first_value, second_value;
-      RETURN_IF_NOT_OK(input.at(1)->GetItemAt(&second_value, {rand_indx[i], j}));
+        RETURN_IF_NOT_OK(input.at(1)->GetItemAt(&first_value, {i, j}));
-      RETURN_IF_NOT_OK(out_labels->SetItemAt({i, j}, lam * first_value + (1 - lam) * second_value));
+        RETURN_IF_NOT_OK(input.at(1)->GetItemAt(&second_value, {rand_indx[i], j}));
+        RETURN_IF_NOT_OK(out_labels->SetItemAt({i, j}, lam * first_value + (1 - lam) * second_value));
+      } else {
+        uint64_t first_value, second_value;
+        RETURN_IF_NOT_OK(input.at(1)->GetItemAt(&first_value, {i, j}));
+        RETURN_IF_NOT_OK(input.at(1)->GetItemAt(&second_value, {rand_indx[i], j}));
+        RETURN_IF_NOT_OK(out_labels->SetItemAt({i, j}, lam * first_value + (1 - lam) * second_value));
+      }
     }
   }
 

mindspore/dataset/transforms/vision/c_transforms.py

@@ -231,7 +231,7 @@ class Normalize(cde.NormalizeOp):
 
 class RandomAffine(cde.RandomAffineOp):
     """
-    Apply Random affine transformation to the input PIL image.
+    Apply Random affine transformation to the input image.
 
     Args:
         degrees (int or float or sequence): Range of the rotation degrees.
@@ -681,12 +681,12 @@ class CenterCrop(cde.CenterCropOp):
 class RandomColor(cde.RandomColorOp):
     """
     Adjust the color of the input image by a fixed or random degree.
+    This operation works only with 3-channel color images.
 
     Args:
          degrees (sequence): Range of random color adjustment degrees.
             It should be in (min, max) format. If min=max, then it is a
             single fixed magnitude operation (default=(0.1,1.9)).
-            Works with 3-channel color images.
     """
 
     @check_positive_degrees

mindspore/dataset/transforms/vision/py_transforms.py

@@ -1169,39 +1169,12 @@ class RandomAffine:
         Returns:
             img (PIL Image), Randomly affine transformed image.
         """
-        # rotation
-        angle = random.uniform(self.degrees[0], self.degrees[1])
-
-        # translation
-        if self.translate is not None:
-            max_dx = self.translate[0] * img.size[0]
-            max_dy = self.translate[1] * img.size[1]
-            translations = (np.round(random.uniform(-max_dx, max_dx)),
-                            np.round(random.uniform(-max_dy, max_dy)))
-        else:
-            translations = (0, 0)
-
-        # scale
-        if self.scale_ranges is not None:
-            scale = random.uniform(self.scale_ranges[0], self.scale_ranges[1])
-        else:
-            scale = 1.0
-
-        # shear
-        if self.shear is not None:
-            if len(self.shear) == 2:
-                shear = [random.uniform(self.shear[0], self.shear[1]), 0.]
-            elif len(self.shear) == 4:
-                shear = [random.uniform(self.shear[0], self.shear[1]),
-                         random.uniform(self.shear[2], self.shear[3])]
-        else:
-            shear = 0.0
 
         return util.random_affine(img,
-                                  angle,
+                                  self.degrees,
-                                  translations,
+                                  self.translate,
-                                  scale,
+                                  self.scale_ranges,
-                                  shear,
+                                  self.shear,
                                   self.resample,
                                   self.fill_value)
 

mindspore/dataset/transforms/vision/py_transforms_util.py

@@ -1153,6 +1153,34 @@ def random_affine(img, angle, translations, scale, shear, resample, fill_value=0
     if not is_pil(img):
         raise ValueError("Input image should be a Pillow image.")
 
+    # rotation
+    angle = random.uniform(angle[0], angle[1])
+
+    # translation
+    if translations is not None:
+        max_dx = translations[0] * img.size[0]
+        max_dy = translations[1] * img.size[1]
+        translations = (np.round(random.uniform(-max_dx, max_dx)),
+                        np.round(random.uniform(-max_dy, max_dy)))
+    else:
+        translations = (0, 0)
+
+    # scale
+    if scale is not None:
+        scale = random.uniform(scale[0], scale[1])
+    else:
+        scale = 1.0
+
+    # shear
+    if shear is not None:
+        if len(shear) == 2:
+            shear = [random.uniform(shear[0], shear[1]), 0.]
+        elif len(shear) == 4:
+            shear = [random.uniform(shear[0], shear[1]),
+                     random.uniform(shear[2], shear[3])]
+    else:
+        shear = 0.0
+
     output_size = img.size
     center = (img.size[0] * 0.5 + 0.5, img.size[1] * 0.5 + 0.5)
 
@@ -1416,7 +1444,6 @@ def hsv_to_rgbs(np_hsv_imgs, is_hwc):
 
 
 def random_color(img, degrees):
-
     """
     Adjust the color of the input PIL image by a random degree.
 
@@ -1437,7 +1464,6 @@ def random_color(img, degrees):
 
 
 def random_sharpness(img, degrees):
-
     """
     Adjust the sharpness of the input PIL image by a random degree.
 
@@ -1458,7 +1484,6 @@ def random_sharpness(img, degrees):
 
 
 def auto_contrast(img, cutoff, ignore):
-
     """
     Automatically maximize the contrast of the input PIL image.
 
@@ -1479,7 +1504,6 @@ def auto_contrast(img, cutoff, ignore):
 
 
 def invert_color(img):
-
     """
     Invert colors of input PIL image.
 
@@ -1498,7 +1522,6 @@ def invert_color(img):
 
 
 def equalize(img):
-
     """
     Equalize the histogram of input PIL image.
 
@@ -1517,7 +1540,6 @@ def equalize(img):
 
 
 def uniform_augment(img, transforms, num_ops):
-
     """
     Uniformly select and apply a number of transforms sequentially from
     a list of transforms. Randomly assigns a probability to each transform for

mindspore/dataset/transforms/vision/validators.py

@@ -45,6 +45,7 @@ def check_cut_mix_batch_c(method):
         [image_batch_format, alpha, prob], _ = parse_user_args(method, *args, **kwargs)
         type_check(image_batch_format, (ImageBatchFormat,), "image_batch_format")
         check_pos_float32(alpha)
+        check_positive(alpha, "alpha")
         check_value(prob, [0, 1], "prob")
         return method(self, *args, **kwargs)
 
@@ -68,6 +69,7 @@ def check_mix_up_batch_c(method):
     @wraps(method)
     def new_method(self, *args, **kwargs):
         [alpha], _ = parse_user_args(method, *args, **kwargs)
+        check_positive(alpha, "alpha")
         check_pos_float32(alpha)
 
         return method(self, *args, **kwargs)

tests/ut/cpp/dataset/c_api_transforms_test.cc

@@ -191,11 +191,37 @@ TEST_F(MindDataTestPipeline, TestCutMixBatchFail2) {
   ds = ds->Map({one_hot_op},{"label"});
   EXPECT_NE(ds, nullptr);
 
-  std::shared_ptr<TensorOperation> cutmix_batch_op = vision::CutMixBatch(mindspore::dataset::ImageBatchFormat::kNHWC, 1, -0.5);
+  std::shared_ptr<TensorOperation> cutmix_batch_op = vision::CutMixBatch(mindspore::dataset::ImageBatchFormat::kNHWC,
+                                                                         1, -0.5);
   EXPECT_EQ(cutmix_batch_op, nullptr);
 
 }
 
+TEST_F(MindDataTestPipeline, TestCutMixBatchFail3) {
+  // Must fail because alpha can't be zero
+  // Create a Cifar10 Dataset
+  std::string folder_path = datasets_root_path_ + "/testCifar10Data/";
+  std::shared_ptr<Dataset> ds = Cifar10(folder_path, RandomSampler(false, 10));
+  EXPECT_NE(ds, nullptr);
+
+  // Create a Batch operation on ds
+  int32_t batch_size = 5;
+  ds = ds->Batch(batch_size);
+  EXPECT_NE(ds, nullptr);
+
+  // Create objects for the tensor ops
+  std::shared_ptr<TensorOperation> one_hot_op = vision::OneHot(10);
+  EXPECT_NE(one_hot_op, nullptr);
+
+  // Create a Map operation on ds
+  ds = ds->Map({one_hot_op},{"label"});
+  EXPECT_NE(ds, nullptr);
+
+  std::shared_ptr<TensorOperation> cutmix_batch_op = vision::CutMixBatch(mindspore::dataset::ImageBatchFormat::kNHWC,
+                                                                         0.0, 0.5);
+  EXPECT_EQ(cutmix_batch_op, nullptr);
+}
+
 TEST_F(MindDataTestPipeline, TestCutOut) {
   // Create an ImageFolder Dataset
   std::string folder_path = datasets_root_path_ + "/testPK/data/";
@@ -365,6 +391,30 @@ TEST_F(MindDataTestPipeline, TestMixUpBatchFail1) {
   EXPECT_EQ(mixup_batch_op, nullptr);
 }
 
+TEST_F(MindDataTestPipeline, TestMixUpBatchFail2) {
+  // This should fail because alpha can't be zero
+  // Create a Cifar10 Dataset
+  std::string folder_path = datasets_root_path_ + "/testCifar10Data/";
+  std::shared_ptr<Dataset> ds = Cifar10(folder_path, RandomSampler(false, 10));
+  EXPECT_NE(ds, nullptr);
+
+  // Create a Batch operation on ds
+  int32_t batch_size = 5;
+  ds = ds->Batch(batch_size);
+  EXPECT_NE(ds, nullptr);
+
+  // Create objects for the tensor ops
+  std::shared_ptr<TensorOperation> one_hot_op = vision::OneHot(10);
+  EXPECT_NE(one_hot_op, nullptr);
+
+  // Create a Map operation on ds
+  ds = ds->Map({one_hot_op}, {"label"});
+  EXPECT_NE(ds, nullptr);
+
+  std::shared_ptr<TensorOperation> mixup_batch_op = vision::MixUpBatch(0.0);
+  EXPECT_EQ(mixup_batch_op, nullptr);
+}
+
 TEST_F(MindDataTestPipeline, TestMixUpBatchSuccess1) {
   // Create a Cifar10 Dataset
   std::string folder_path = datasets_root_path_ + "/testCifar10Data/";
@@ -384,7 +434,7 @@ TEST_F(MindDataTestPipeline, TestMixUpBatchSuccess1) {
   ds = ds->Map({one_hot_op}, {"label"});
   EXPECT_NE(ds, nullptr);
 
-  std::shared_ptr<TensorOperation> mixup_batch_op = vision::MixUpBatch(0.5);
+  std::shared_ptr<TensorOperation> mixup_batch_op = vision::MixUpBatch(2.0);
   EXPECT_NE(mixup_batch_op, nullptr);
 
   // Create a Map operation on ds

tests/ut/python/dataset/test_cutmix_batch_op.py

@@ -26,6 +26,7 @@ from util import save_and_check_md5, diff_mse, visualize_list, config_get_set_se
     config_get_set_num_parallel_workers
 
 DATA_DIR = "../data/dataset/testCifar10Data"
+DATA_DIR2 = "../data/dataset/testImageNetData2/train/"
 
 GENERATE_GOLDEN = False
 
@@ -114,6 +115,53 @@ def test_cutmix_batch_success2(plot=False):
     logger.info("MSE= {}".format(str(np.mean(mse))))
 
 
+def test_cutmix_batch_success3(plot=False):
+    """
+    Test CutMixBatch op with default values for alpha and prob on a batch of HWC images on ImageFolderDatasetV2
+    """
+    logger.info("test_cutmix_batch_success3")
+
+    ds_original = ds.ImageFolderDatasetV2(dataset_dir=DATA_DIR2, shuffle=False)
+    decode_op = vision.Decode()
+    ds_original = ds_original.map(input_columns=["image"], operations=[decode_op])
+    ds_original = ds_original.batch(4, pad_info={}, drop_remainder=True)
+
+    images_original = None
+    for idx, (image, _) in enumerate(ds_original):
+        if idx == 0:
+            images_original = image
+        else:
+            images_original = np.append(images_original, image, axis=0)
+
+    # CutMix Images
+    data1 = ds.ImageFolderDatasetV2(dataset_dir=DATA_DIR2, shuffle=False)
+
+    decode_op = vision.Decode()
+    data1 = data1.map(input_columns=["image"], operations=[decode_op])
+
+    one_hot_op = data_trans.OneHot(num_classes=10)
+    data1 = data1.map(input_columns=["label"], operations=one_hot_op)
+
+    cutmix_batch_op = vision.CutMixBatch(mode.ImageBatchFormat.NHWC)
+    data1 = data1.batch(4, pad_info={}, drop_remainder=True)
+    data1 = data1.map(input_columns=["image", "label"], operations=cutmix_batch_op)
+
+    images_cutmix = None
+    for idx, (image, _) in enumerate(data1):
+        if idx == 0:
+            images_cutmix = image
+        else:
+            images_cutmix = np.append(images_cutmix, image, axis=0)
+    if plot:
+        visualize_list(images_original, images_cutmix)
+
+    num_samples = images_original.shape[0]
+    mse = np.zeros(num_samples)
+    for i in range(num_samples):
+        mse[i] = diff_mse(images_cutmix[i], images_original[i])
+    logger.info("MSE= {}".format(str(np.mean(mse))))
+
+
 def test_cutmix_batch_nhwc_md5():
     """
     Test CutMixBatch on a batch of HWC images with MD5:
@@ -185,7 +233,7 @@ def test_cutmix_batch_fail1():
                 images_cutmix = image
             else:
                 images_cutmix = np.append(images_cutmix, image, axis=0)
-        error_message = "You must batch before calling CutMixBatch"
+        error_message = "You must make sure images are HWC or CHW and batch "
         assert error_message in str(error.value)
 
 
@@ -322,9 +370,28 @@ def test_cutmix_batch_fail7():
     assert error_message in str(error.value)
 
 
+def test_cutmix_batch_fail8():
+    """
+    Test CutMixBatch Fail 8
+    We expect this to fail because alpha is zero
+    """
+    logger.info("test_cutmix_batch_fail8")
+
+    # CutMixBatch Images
+    data1 = ds.Cifar10Dataset(DATA_DIR, num_samples=10, shuffle=False)
+
+    one_hot_op = data_trans.OneHot(num_classes=10)
+    data1 = data1.map(input_columns=["label"], operations=one_hot_op)
+    with pytest.raises(ValueError) as error:
+        vision.CutMixBatch(mode.ImageBatchFormat.NHWC, 0.0)
+        error_message = "Input is not within the required interval"
+        assert error_message in str(error.value)
+
+
 if __name__ == "__main__":
     test_cutmix_batch_success1(plot=True)
     test_cutmix_batch_success2(plot=True)
+    test_cutmix_batch_success3(plot=True)
     test_cutmix_batch_nchw_md5()
     test_cutmix_batch_nhwc_md5()
     test_cutmix_batch_fail1()
@@ -334,3 +401,4 @@ if __name__ == "__main__":
     test_cutmix_batch_fail5()
     test_cutmix_batch_fail6()
     test_cutmix_batch_fail7()
+    test_cutmix_batch_fail8()

tests/ut/python/dataset/test_mixup_op.py

@@ -25,6 +25,7 @@ from util import save_and_check_md5, diff_mse, visualize_list, config_get_set_se
     config_get_set_num_parallel_workers
 
 DATA_DIR = "../data/dataset/testCifar10Data"
+DATA_DIR2 = "../data/dataset/testImageNetData2/train/"
 
 GENERATE_GOLDEN = False
 
@@ -71,11 +72,59 @@ def test_mixup_batch_success1(plot=False):
 
 
 def test_mixup_batch_success2(plot=False):
+    """
+    Test MixUpBatch op with specified alpha parameter on ImageFolderDatasetV2
+    """
+    logger.info("test_mixup_batch_success2")
+
+    # Original Images
+    ds_original = ds.ImageFolderDatasetV2(dataset_dir=DATA_DIR2, shuffle=False)
+    decode_op = vision.Decode()
+    ds_original = ds_original.map(input_columns=["image"], operations=[decode_op])
+    ds_original = ds_original.batch(4, pad_info={}, drop_remainder=True)
+
+    images_original = None
+    for idx, (image, _) in enumerate(ds_original):
+        if idx == 0:
+            images_original = image
+        else:
+            images_original = np.append(images_original, image, axis=0)
+
+    # MixUp Images
+    data1 = ds.ImageFolderDatasetV2(dataset_dir=DATA_DIR2, shuffle=False)
+
+    decode_op = vision.Decode()
+    data1 = data1.map(input_columns=["image"], operations=[decode_op])
+
+    one_hot_op = data_trans.OneHot(num_classes=10)
+    data1 = data1.map(input_columns=["label"], operations=one_hot_op)
+
+    mixup_batch_op = vision.MixUpBatch(2.0)
+    data1 = data1.batch(4, pad_info={}, drop_remainder=True)
+    data1 = data1.map(input_columns=["image", "label"], operations=mixup_batch_op)
+
+    images_mixup = None
+    for idx, (image, _) in enumerate(data1):
+        if idx == 0:
+            images_mixup = image
+        else:
+            images_mixup = np.append(images_mixup, image, axis=0)
+    if plot:
+        visualize_list(images_original, images_mixup)
+
+    num_samples = images_original.shape[0]
+    mse = np.zeros(num_samples)
+    for i in range(num_samples):
+        mse[i] = diff_mse(images_mixup[i], images_original[i])
+    logger.info("MSE= {}".format(str(np.mean(mse))))
+
+
+def test_mixup_batch_success3(plot=False):
     """
     Test MixUpBatch op without specified alpha parameter.
     Alpha parameter will be selected by default in this case
     """
-    logger.info("test_mixup_batch_success2")
+    logger.info("test_mixup_batch_success3")
 
     # Original Images
     ds_original = ds.Cifar10Dataset(DATA_DIR, num_samples=10, shuffle=False)
@@ -169,7 +218,7 @@ def test_mixup_batch_fail1():
                 images_mixup = image
             else:
                 images_mixup = np.append(images_mixup, image, axis=0)
-        error_message = "You must batch before calling MixUp"
+        error_message = "You must make sure images are HWC or CHW and batch"
         assert error_message in str(error.value)
 
 
@@ -207,6 +256,7 @@ def test_mixup_batch_fail3():
     Test MixUpBatch op
     We expect this to fail because label column is not passed to mixup_batch
     """
+    logger.info("test_mixup_batch_fail3")
     # Original Images
     ds_original = ds.Cifar10Dataset(DATA_DIR, num_samples=10, shuffle=False)
     ds_original = ds_original.batch(5, drop_remainder=True)
@@ -237,11 +287,41 @@ def test_mixup_batch_fail3():
     error_message = "Both images and labels columns are required"
     assert error_message in str(error.value)
 
+def test_mixup_batch_fail4():
+    """
+    Test MixUpBatch Fail 2
+    We expect this to fail because alpha is zero
+    """
+    logger.info("test_mixup_batch_fail4")
+
+    # Original Images
+    ds_original = ds.Cifar10Dataset(DATA_DIR, num_samples=10, shuffle=False)
+    ds_original = ds_original.batch(5)
+
+    images_original = np.array([])
+    for idx, (image, _) in enumerate(ds_original):
+        if idx == 0:
+            images_original = image
+        else:
+            images_original = np.append(images_original, image, axis=0)
+
+    # MixUp Images
+    data1 = ds.Cifar10Dataset(DATA_DIR, num_samples=10, shuffle=False)
+
+    one_hot_op = data_trans.OneHot(num_classes=10)
+    data1 = data1.map(input_columns=["label"], operations=one_hot_op)
+    with pytest.raises(ValueError) as error:
+        vision.MixUpBatch(0.0)
+        error_message = "Input is not within the required interval"
+        assert error_message in str(error.value)
+
 
 if __name__ == "__main__":
     test_mixup_batch_success1(plot=True)
     test_mixup_batch_success2(plot=True)
+    test_mixup_batch_success3(plot=True)
     test_mixup_batch_md5()
     test_mixup_batch_fail1()
     test_mixup_batch_fail2()
     test_mixup_batch_fail3()
+    test_mixup_batch_fail4()

tests/ut/python/dataset/test_random_affine.py

@@ -27,6 +27,7 @@ GENERATE_GOLDEN = False
 
 DATA_DIR = ["../data/dataset/test_tf_file_3_images/train-0000-of-0001.data"]
 SCHEMA_DIR = "../data/dataset/test_tf_file_3_images/datasetSchema.json"
+MNIST_DATA_DIR = "../data/dataset/testMnistData"
 
 
 def test_random_affine_op(plot=False):
@@ -155,6 +156,24 @@ def test_random_affine_c_md5():
     ds.config.set_num_parallel_workers((original_num_parallel_workers))
 
 
+def test_random_affine_py_exception_non_pil_images():
+    """
+    Test RandomAffine: input img is ndarray and not PIL, expected to raise TypeError
+    """
+    logger.info("test_random_affine_exception_negative_degrees")
+    dataset = ds.MnistDataset(MNIST_DATA_DIR, num_parallel_workers=3)
+    try:
+        transform = py_vision.ComposeOp([py_vision.ToTensor(),
+                                         py_vision.RandomAffine(degrees=(15, 15))])
+        dataset = dataset.map(input_columns=["image"], operations=transform(), num_parallel_workers=3,
+                              python_multiprocessing=True)
+        for _ in dataset.create_dict_iterator():
+            break
+    except RuntimeError as e:
+        logger.info("Got an exception in DE: {}".format(str(e)))
+        assert "Pillow image" in str(e)
+
+
 def test_random_affine_exception_negative_degrees():
     """
     Test RandomAffine: input degrees in negative, expected to raise ValueError
@@ -289,6 +308,7 @@ if __name__ == "__main__":
     test_random_affine_op_c(plot=True)
     test_random_affine_md5()
     test_random_affine_c_md5()
+    test_random_affine_py_exception_non_pil_images()
     test_random_affine_exception_negative_degrees()
     test_random_affine_exception_translation_range()
     test_random_affine_exception_scale_value()