From 36fffb7706035dce6f9fcd84c30b961b6c012d47 Mon Sep 17 00:00:00 2001
From: eric <eric.zhang1@huawei.com>
Date: Mon, 4 May 2020 11:50:53 -0400
Subject: [PATCH] Added example md5 generation

Comparison example

Added md5 and comparison example for py_transforms

Added md5 check for images
---
 mindspore/dataset/core/configuration.py       |  11 ++-
 .../golden/test_center_crop_01_result.npz     | Bin 0 -> 644 bytes
 tests/ut/python/dataset/test_center_crop.py   |  78 ++++++++++++++----
 .../dataset/test_random_color_adjust.py       |   3 -
 tests/ut/python/dataset/util.py               |  59 ++++++++++++-
 5 files changed, 126 insertions(+), 25 deletions(-)
 create mode 100644 tests/ut/data/dataset/golden/test_center_crop_01_result.npz

diff --git a/mindspore/dataset/core/configuration.py b/mindspore/dataset/core/configuration.py
index c08f47526e..38b25368b3 100644
--- a/mindspore/dataset/core/configuration.py
+++ b/mindspore/dataset/core/configuration.py
@@ -16,6 +16,7 @@
 The configuration manager.
 """
 import random
+import numpy
 import mindspore._c_dataengine as cde
 
 INT32_MAX = 2147483647
@@ -33,10 +34,10 @@ class ConfigurationManager:
         Set the seed to be used in any random generator. This is used to produce deterministic results.
 
         Note:
-            This set_seed function sets the seed in the python random library function for deterministic
-            python augmentations using randomness. This set_seed function should be called with every
-            iterator created to reset the random seed. In our pipeline this does not guarantee
-            deterministic results with num_parallel_workers > 1.
+            This set_seed function sets the seed in the python random library and numpy.random library
+            for deterministic python augmentations using randomness. This set_seed function should
+            be called with every iterator created to reset the random seed. In our pipeline this
+            does not guarantee deterministic results with num_parallel_workers > 1.
 
         Args:
             seed(int): seed to be set
@@ -54,6 +55,8 @@ class ConfigurationManager:
             raise ValueError("Seed given is not within the required range")
         self.config.set_seed(seed)
         random.seed(seed)
+        # numpy.random isn't thread safe
+        numpy.random.seed(seed)
 
     def get_seed(self):
         """
diff --git a/tests/ut/data/dataset/golden/test_center_crop_01_result.npz b/tests/ut/data/dataset/golden/test_center_crop_01_result.npz
new file mode 100644
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GIT binary patch
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diff --git a/tests/ut/python/dataset/test_center_crop.py b/tests/ut/python/dataset/test_center_crop.py
index 596c1e1c72..62d4ebb042 100644
--- a/tests/ut/python/dataset/test_center_crop.py
+++ b/tests/ut/python/dataset/test_center_crop.py
@@ -13,53 +13,96 @@
 # limitations under the License.
 # ==============================================================================
 import mindspore.dataset.transforms.vision.c_transforms as vision
+import mindspore.dataset.transforms.vision.py_transforms as py_vision
 import numpy as np
 import matplotlib.pyplot as plt
 import mindspore.dataset as ds
 from mindspore import log as logger
+from util import diff_mse, visualize, save_and_check_md5
+
+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"
 
 
-def visualize(image_original, image_cropped):
+def test_center_crop_op(height=375, width=375, plot=False):
     """
-    visualizes the image using DE op and Numpy op
+    Test random_vertical
     """
-    num = len(image_cropped)
-    for i in range(num):
-        plt.subplot(2, num, i + 1)
-        plt.imshow(image_original[i])
-        plt.title("Original image")
+    logger.info("Test CenterCrop")
+
+    # First dataset
+    data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"])
+    decode_op = vision.Decode()
+    # 3 images [375, 500] [600, 500] [512, 512]
+    center_crop_op = vision.CenterCrop([height, width])
+    data1 = data1.map(input_columns=["image"], operations=decode_op)
+    data1 = data1.map(input_columns=["image"], operations=center_crop_op)
 
-        plt.subplot(2, num, i + num + 1)
-        plt.imshow(image_cropped[i])
-        plt.title("DE center_crop image")
+    # Second dataset
+    data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"])
+    data2 = data2.map(input_columns=["image"], operations=decode_op)
 
-    plt.show()
+    image_cropped = []
+    image = []
+    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+        image_cropped.append(item1["image"].copy())
+        image.append(item2["image"].copy())
+    if plot:
+        visualize(image, image_cropped)
 
 
-def test_center_crop_op(height=375, width=375, plot=False):
+def test_center_crop_md5(height=375, width=375):
     """
     Test random_vertical
     """
     logger.info("Test CenterCrop")
 
     # First dataset
-    data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"])
+    data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle =False)
     decode_op = vision.Decode()
     # 3 images [375, 500] [600, 500] [512, 512]
-    center_crop_op = vision.CenterCrop(height, width)
+    center_crop_op = vision.CenterCrop([height, width])
+    data1 = data1.map(input_columns=["image"], operations=decode_op)
+    data1 = data1.map(input_columns=["image"], operations=center_crop_op)
+    # expected md5 from images 
+
+    filename = "test_center_crop_01_result.npz"
+    parameters = {"params": {}}
+    save_and_check_md5(data1, parameters, filename, generate_golden=GENERATE_GOLDEN)
+
+
+def test_center_crop_comp(height=375, width=375, plot=False):
+    """
+    Test random_vertical between python and c image augmentation
+    """
+    logger.info("Test CenterCrop")
+
+    # First dataset
+    data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
+    decode_op = vision.Decode()
+    center_crop_op = vision.CenterCrop([height, width])
     data1 = data1.map(input_columns=["image"], operations=decode_op)
     data1 = data1.map(input_columns=["image"], operations=center_crop_op)
 
     # Second dataset
-    data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"])
-    data2 = data2.map(input_columns=["image"], operations=decode_op)
+    data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
+    transforms = [
+        py_vision.Decode(),
+        py_vision.CenterCrop([height, width]), 
+        py_vision.ToTensor()
+    ]
+    transform = py_vision.ComposeOp(transforms)
+    data2 = data2.map(input_columns=["image"], operations=transform())
 
     image_cropped = []
     image = []
     for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+        c_image = item1["image"]
+        py_image = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
+        # the images aren't exactly the same due to rouding error 
+        assert (diff_mse(py_image, c_image) < 0.001)
         image_cropped.append(item1["image"].copy())
         image.append(item2["image"].copy())
     if plot:
@@ -67,7 +110,8 @@ def test_center_crop_op(height=375, width=375, plot=False):
 
 
 if __name__ == "__main__":
-    test_center_crop_op()
     test_center_crop_op(600, 600)
     test_center_crop_op(300, 600)
     test_center_crop_op(600, 300)
+    test_center_crop_md5(600, 600)
+    test_center_crop_comp()
\ No newline at end of file
diff --git a/tests/ut/python/dataset/test_random_color_adjust.py b/tests/ut/python/dataset/test_random_color_adjust.py
index c3e7bd3d7c..8a650e2315 100644
--- a/tests/ut/python/dataset/test_random_color_adjust.py
+++ b/tests/ut/python/dataset/test_random_color_adjust.py
@@ -256,9 +256,6 @@ def test_random_color_adjust_op_hue():
         mse = diff_mse(c_image, py_image)
         logger.info("mse is {}".format(mse))
         assert mse < 0.01
-        # logger.info("random_rotation_op_{}, mse: {}".format(num_iter + 1, mse))
-        # if mse != 0:
-        #     logger.info("mse is: {}".format(mse))
         # Uncomment below line if you want to visualize images
         # visualize(c_image, mse, py_image)
 
diff --git a/tests/ut/python/dataset/util.py b/tests/ut/python/dataset/util.py
index d2f35cee63..753528d3f8 100644
--- a/tests/ut/python/dataset/util.py
+++ b/tests/ut/python/dataset/util.py
@@ -16,6 +16,9 @@
 import json
 import os
 import numpy as np
+import matplotlib.pyplot as plt
+import hashlib
+
 #import jsbeautifier
 from mindspore import log as logger
 
@@ -41,6 +44,15 @@ def save_golden_dict(cur_dir, golden_ref_dir, result_dict):
     np.savez(golden_ref_dir, np.array(list(result_dict.items())))
 
 
+def save_golden_md5(cur_dir, golden_ref_dir, result_dict):
+    """
+    Save the dictionary (both keys and values) as the golden result in .npz file
+    """
+    logger.info("cur_dir is {}".format(cur_dir))
+    logger.info("golden_ref_dir is {}".format(golden_ref_dir))
+    np.savez(golden_ref_dir, np.array(list(result_dict.items())))
+
+
 def compare_to_golden(golden_ref_dir, result_dict):
     """
     Compare as numpy arrays the test result to the golden result
@@ -55,7 +67,8 @@ def compare_to_golden_dict(golden_ref_dir, result_dict):
     Compare as dictionaries the test result to the golden result
     """
     golden_array = np.load(golden_ref_dir, allow_pickle=True)['arr_0']
-    assert result_dict == dict(golden_array)
+    np.testing.assert_equal (result_dict, dict(golden_array))
+    # assert result_dict == dict(golden_array)
 
 
 def save_json(filename, parameters, result_dict):
@@ -131,6 +144,33 @@ def save_and_check_dict(data, parameters, filename, generate_golden=False):
     # save_json(filename, parameters, result_dict)
 
 
+def save_and_check_md5(data, parameters, filename, generate_golden=False):
+    """
+    Save the dataset dictionary and compare (as dictionary) with golden file (md5).
+    Use create_dict_iterator to access the dataset.
+    """
+    num_iter = 0
+    result_dict = {}
+
+    for item in data.create_dict_iterator():  # each data is a dictionary
+        for data_key in list(item.keys()):
+            if data_key not in result_dict:
+                result_dict[data_key] = []
+            # save the md5 as numpy array
+            result_dict[data_key].append(np.frombuffer(hashlib.md5(item[data_key]).digest(), dtype='<f4'))
+        num_iter += 1
+
+    logger.info("Number of data in ds1: {}".format(num_iter))
+
+    cur_dir = os.path.dirname(os.path.realpath(__file__))
+    golden_ref_dir = os.path.join(cur_dir, "../../data/dataset", 'golden', filename)
+    if generate_golden:
+        # Save as the golden result
+        save_golden_dict(cur_dir, golden_ref_dir, result_dict)
+
+    compare_to_golden_dict(golden_ref_dir, result_dict)
+
+
 def ordered_save_and_check(data, parameters, filename, generate_golden=False):
     """
     Save the dataset dictionary and compare (as numpy array) with golden file.
@@ -175,3 +215,20 @@ def diff_ssim(in1, in2):
     from skimage.measure import compare_ssim as ssim
     val = ssim(in1, in2, multichannel=True)
     return (1 - val) * 100
+
+
+def visualize(image_original, image_transformed):
+    """
+    visualizes the image using DE op and Numpy op
+    """
+    num = len(image_cropped)
+    for i in range(num):
+        plt.subplot(2, num, i + 1)
+        plt.imshow(image_original[i])
+        plt.title("Original image")
+
+        plt.subplot(2, num, i + num + 1)
+        plt.imshow(image_cropped[i])
+        plt.title("Transformed image")
+
+    plt.show()