| #!/usr/bin/env python3 |
| # 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) |