DirectML/Python/samples/mnist.py at master · microsoft/DirectML · GitHub

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#
# DirectML MNIST sample
# Based on the following model: https://github.com/onnx/models/blob/master/vision/classification/mnist/model/mnist-8.onnx
#

importpydirectmlasdml
importnumpyasnp
fromPILimportImage, ImageOps
importsys
importos

argument_count=len(sys.argv)

image_file_path="mnist_image1.png"
tensor_data_path="mnist-8_tensor_data"

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()

input_bindings= []

defappend_input_tensor(builder: dml.GraphBuilder, input_bindings: list, input_tensor: dml.TensorDesc, file_name: str):
tensor=dml.input_tensor(builder, len(input_bindings), input_tensor)
iffile_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)))
returntensor

# 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]))
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)

# pooling66
pooling66=dml.max_pooling(relu32, strides= [2, 2], window_sizes= [2, 2])

# 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])

# Compute the result
output_data=device.compute(op, input_bindings, [softmax])
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))