PyArmNN example scripts
Change-Id: I2a5c3d291d19982c536c6b7341c01bb7c289871a
Signed-off-by: Pavel Macenauer <pavel.macenauer@nxp.com>
diff --git a/python/pyarmnn/examples/onnx_mobilenetv2.py b/python/pyarmnn/examples/onnx_mobilenetv2.py
new file mode 100644
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+++ b/python/pyarmnn/examples/onnx_mobilenetv2.py
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+# Copyright 2020 NXP
+# SPDX-License-Identifier: MIT
+
+import pyarmnn as ann
+import numpy as np
+from PIL import Image
+import example_utils as eu
+
+
+def preprocess_onnx(img: Image, width: int, height: int, data_type, scale: float, mean: list,
+ stddev: list):
+ """Preprocessing function for ONNX imagenet models based on:
+ https://github.com/onnx/models/blob/master/vision/classification/imagenet_inference.ipynb
+
+ Args:
+ img (PIL.Image): Loaded PIL.Image
+ width (int): Target image width
+ height (int): Target image height
+ data_type: Image datatype (np.uint8 or np.float32)
+ scale (float): Scaling factor
+ mean: RGB mean values
+ stddev: RGB standard deviation
+
+ Returns:
+ np.array: Preprocess image as Numpy array
+ """
+ img = img.resize((256, 256), Image.BILINEAR)
+ # first rescale to 256,256 and then center crop
+ left = (256 - width) / 2
+ top = (256 - height) / 2
+ right = (256 + width) / 2
+ bottom = (256 + height) / 2
+ img = img.crop((left, top, right, bottom))
+ img = img.convert('RGB')
+ img = np.array(img)
+ img = np.reshape(img, (-1, 3)) # reshape to [RGB][RGB]...
+ img = ((img / scale) - mean) / stddev
+ # NHWC to NCHW conversion, by default NHWC is expected
+ # image is loaded as [RGB][RGB][RGB]... transposing it makes it [RRR...][GGG...][BBB...]
+ img = np.transpose(img)
+ img = img.flatten().astype(data_type) # flatten into a 1D tensor and convert to float32
+ return img
+
+
+if __name__ == "__main__":
+ # Download resources
+ kitten_filename = eu.download_file('https://s3.amazonaws.com/model-server/inputs/kitten.jpg')
+ labels_filename = eu.download_file('https://s3.amazonaws.com/onnx-model-zoo/synset.txt')
+ model_filename = eu.download_file(
+ 'https://s3.amazonaws.com/onnx-model-zoo/mobilenet/mobilenetv2-1.0/mobilenetv2-1.0.onnx')
+
+ # Create a network from a model file
+ net_id, parser, runtime = eu.create_onnx_network(model_filename)
+
+ # Load input information from the model and create input tensors
+ input_binding_info = parser.GetNetworkInputBindingInfo("data")
+
+ # Load output information from the model and create output tensors
+ output_binding_info = parser.GetNetworkOutputBindingInfo("mobilenetv20_output_flatten0_reshape0")
+ output_tensors = ann.make_output_tensors([output_binding_info])
+
+ # Load labels
+ labels = eu.load_labels(labels_filename)
+
+ # Load images and resize to expected size
+ image_names = [kitten_filename]
+ images = eu.load_images(image_names,
+ 224, 224,
+ np.float32,
+ 255.0,
+ [0.485, 0.456, 0.406],
+ [0.229, 0.224, 0.225],
+ preprocess_onnx)
+
+ for idx, im in enumerate(images):
+ # Create input tensors
+ input_tensors = ann.make_input_tensors([input_binding_info], [im])
+
+ # Run inference
+ print("Running inference on '{0}' ...".format(image_names[idx]))
+ runtime.EnqueueWorkload(net_id, input_tensors, output_tensors)
+
+ # Process output
+ out_tensor = ann.workload_tensors_to_ndarray(output_tensors)[0][0]
+ results = np.argsort(out_tensor)[::-1]
+ eu.print_top_n(5, results, labels, out_tensor)