Provide a test case to reproduce conv2d issue on DirectML 1.7.0/1.8.0…

… version

Python/samples/mnist.py

@@ -5,112 +5,50 @@
 
 import pydirectml as dml
 import numpy as np
-from PIL import Image, ImageOps
 import sys
 import os
 
-argument_count = len(sys.argv)
+input_data = [10, 10, 10, 10, 21, 22, 23, 24, 10, 20, 30, 40, 0, 0, 0, 0]
+input_data_array = np.array(input_data, np.float32)
 
-image_file_path = "mnist_image1.png"
-tensor_data_path = "mnist-8_tensor_data"
+weight_data = [0.25, 0.25, 0.25, 0.25, 0.0, 1.0, 0.0, 1.0, 10.0, 20.0, 30.0, 40.0, 50.0,
+               50.0, 50.0, 50.0]
+weight_data_array = np.array(weight_data, np.float32)
 
-if (argument_count >= 2):
-    image_file_path = sys.argv[1]
-
-if (argument_count >= 3):
-    tensor_data_path = sys.argv[2]
-
-if (os.path.exists(image_file_path) == False):
-    print("File not found at: " + str(image_file_path))
-    sys.exit(1)
-
-# Opens image, converts to grayscale, resizes, and crops to the input size.
-image = ImageOps.fit(ImageOps.grayscale(Image.open(image_file_path)), (28, 28), method = 0, bleed = 0, centering = (0.5, 0.5))
-
-# Check top left pixel's color, and inverts image color if pixel is bright.
-x, y = 0, 0
-coordinate = x, y
-if (image.getpixel(coordinate) >= 128):
-    image = ImageOps.invert(image)
-
-# Expand dimensions to 4d tensor format, and rescale values in range of 0.0 and 1.0.
-img_array = np.array(image, np.float32)
-ndarray_image = np.expand_dims(img_array, axis = (0, 1))
-rescaled_image = ndarray_image / ndarray_image.max()
+bias_data = [6000, 7000, 8000, 9000]
+bias_data_array = np.array(bias_data, np.float32)
 
 input_bindings = []
 
-def append_input_tensor(builder: dml.GraphBuilder, input_bindings: list, input_tensor: dml.TensorDesc, file_name: str):
+
+def append_input_tensor(builder: dml.GraphBuilder, input_bindings: list, input_tensor: dml.TensorDesc, tensor_data_array):
     tensor = dml.input_tensor(builder, len(input_bindings), input_tensor)
-    if file_name == "":
-        input_bindings.append(dml.Binding(tensor, np.zeros(tensor.get_output_desc().sizes)))
-    else:
-        input_bindings.append(dml.Binding(tensor, np.load(tensor_data_path + "/" + file_name)))
+    input_bindings.append(dml.Binding(tensor, tensor_data_array))
     return tensor
 
-# Create a GPU device, and build a model graph.
+
 device = dml.Device(True, True)
 builder = dml.GraphBuilder(device)
 
 data_type = dml.TensorDataType.FLOAT32
-input = dml.input_tensor(builder, 0, dml.TensorDesc(data_type, [1, 1, 28, 28]))
+input = dml.input_tensor(builder, 0, dml.TensorDesc(data_type, [1, 4, 2, 2]))
 flags = dml.TensorFlags.OWNED_BY_DML
 
-input_bindings.append(dml.Binding(input, rescaled_image))
-
-# convolution28
-convolution28_weight = append_input_tensor(builder, input_bindings, dml.TensorDesc(data_type, flags, [8, 1, 5, 5]), "Parameter5.npy")
-convolution28_bias = append_input_tensor(builder, input_bindings, dml.TensorDesc(data_type, flags, [1, 8, 1, 1]), "")
-convolution28 = dml.convolution(input, convolution28_weight, convolution28_bias, strides = [1, 1], start_padding = [2, 2], end_padding = [2, 2])
-
-# plus30
-plus30_param6 = append_input_tensor(builder, input_bindings, dml.TensorDesc(data_type, [1, 8, 28, 28], [8, 1, 0, 0]), "Parameter6.npy")
-plus30 = dml.add(convolution28, plus30_param6)
-
-# relu32
-relu32 = dml.activation_relu(plus30)
+input_bindings.append(dml.Binding(input, input_data_array))
 
-# pooling66
-pooling66 = dml.max_pooling(relu32, strides = [2, 2], window_sizes = [2, 2])
+convolution_weight = append_input_tensor(builder, input_bindings, dml.TensorDesc(
+    data_type, flags, [4, 1, 2, 2]), weight_data_array)
+convolution_bias = append_input_tensor(builder, input_bindings, dml.TensorDesc(
+    data_type, flags, [1, 4, 1, 1]), bias_data_array)
+convolution = dml.convolution(input, convolution_weight, convolution_bias, strides=[
+                              1, 1], start_padding=[0, 0], end_padding=[0, 0], group_count=4)
 
-# convolution110
-convolution110_weight = append_input_tensor(builder, input_bindings, dml.TensorDesc(data_type, flags, [16, 8, 5, 5]), "Parameter87.npy")
-convolution110_bias = append_input_tensor(builder, input_bindings, dml.TensorDesc(data_type, flags, [1, 16, 1, 1]), "")
-convolution110 = dml.convolution(pooling66.values, convolution110_weight, convolution110_bias, strides = [1, 1], start_padding = [2, 2], end_padding = [2, 2])
-
-# plus112
-plus112_param88 = append_input_tensor(builder, input_bindings, dml.TensorDesc(data_type, [1, 16, 14, 14], [16, 1, 0, 0]), "Parameter88.npy")
-plus112 = dml.add(convolution110, plus112_param88)
-
-# relu114
-relu114 = dml.activation_relu(plus112)
-
-# pooling160
-pooling160 = dml.max_pooling(relu114, strides = [3, 3], window_sizes = [3, 3])
-
-# times212_reshape0
-times212_reshape0 = dml.reinterpret(pooling160.values, dml.TensorDataType.FLOAT32, [1, 1, 1, 256], [256, 256, 256, 1])
-
-# times212_reshape1
-times212_reshape1_param193 = append_input_tensor(builder, input_bindings, dml.TensorDesc(data_type, flags, [16, 4, 4, 10]), "Parameter193.npy")
-identity = dml.activation_identity(times212_reshape1_param193)
-times212_reshape1 = dml.reinterpret(identity, dml.TensorDataType.FLOAT32, [1, 1, 256, 10], [2560, 2560, 10, 1])
-
-# times212
-times212 = dml.gemm(times212_reshape0, times212_reshape1)
-
-# plus214
-plus214_param194 = append_input_tensor(builder, input_bindings, dml.TensorDesc(data_type, flags, [1, 1, 1, 10]), "Parameter194.npy")
-plus214 = dml.add(times212, plus214_param194)
-
-softmax = dml.activation_soft_max(plus214)
-# Compile the expression graph into a compiled operator
-op = builder.build(dml.ExecutionFlags.NONE, [softmax])
+op = builder.build(dml.ExecutionFlags.NONE, [convolution])
 
 # Compute the result
-output_data = device.compute(op, input_bindings, [softmax])
+output_data = device.compute(op, input_bindings, [convolution])
 output_tensor = np.array(output_data[0], np.float32)
-
-number = np.argmax(output_tensor)
-print("\nNumber is: {}".format(number, end=''))
-print("Confidence: {:2.2f}%".format(np.amax(output_tensor) * 100))
+print(output_tensor)
+# The correct result should be [6010, 7046, 11000, 9000],
+# microsoft.ai.directml.1.5.1 and microsoft.ai.directml.1.6.0 got the same correct result,
+# but microsoft.ai.directml.1.7.0 and microsoft.ai.directml.1.8.0 actually got wrong result as [6010, 7000, 8000, 9000];