Add resnet50 example used with onnxruntime inferencing

Useful for benchmarking if we can perform a resnet50 through MIGraphX Execution
Provider thats run through onnxruntime.

Run preq_steps.sh to build and install onnxruntime and pytorch for rocm.

examples/onnxruntime/resnet50/prereq_steps.sh

+#Install most recent stable version of pytorch
+pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm5.4.2
+
+# Download ImageNet labels
+curl -o imagenet_classes.txt https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt
+
+../../../tools/build_and_test_onnxrt.sh
+
+pip3 install /onnxruntime/build/Release/Linux/dist/*.whl

examples/onnxruntime/resnet50/resnet50.py

+# Inference with ONNX Runtime
+import onnxruntime
+from onnx import numpy_helper
+import time
+from torchvision import models, datasets, transforms as T
+import torch
+from PIL import Image
+import numpy as np
+
+resnet50 = models.resnet50(pretrained=True)
+
+# Download ImageNet labels
+#!curl -o imagenet_classes.txt https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt
+
+# Read the categories
+with open("imagenet_classes.txt", "r") as f:
+    categories = [s.strip() for s in f.readlines()]
+
+# Export the model to ONNX
+image_height = 224
+image_width = 224
+x = torch.randn(1, 3, image_height, image_width, requires_grad=True)
+torch_out = resnet50(x)
+torch.onnx.export(
+    resnet50,  # model being run
+    x,  # model input (or a tuple for multiple inputs)
+    "resnet50.onnx",  # where to save the model (can be a file or file-like object)
+    export_params=
+    True,  # store the trained parameter weights inside the model file
+    opset_version=12,  # the ONNX version to export the model to
+    do_constant_folding=
+    True,  # whether to execute constant folding for optimization
+    input_names=['input'],  # the model's input names
+    output_names=['output'])  # the model's output names
+
+# Pre-processing for ResNet-50 Inferencing, from https://pytorch.org/hub/pytorch_vision_resnet/
+resnet50.eval()
+filename = 'cat.jpg'  # change to your filename
+
+input_image = Image.open(filename)
+preprocess = T.Compose([
+    T.Resize(256),
+    T.CenterCrop(224),
+    T.ToTensor(),
+    T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+])
+input_tensor = preprocess(input_image)
+input_batch = input_tensor.unsqueeze(
+    0)  # create a mini-batch as expected by the model
+
+# move the input and model to GPU for speed if available
+print("GPU Availability: ", torch.cuda.is_available())
+if torch.cuda.is_available():
+    input_batch = input_batch.to('cuda')
+    resnet50.to('cuda')
+
+session_fp32 = onnxruntime.InferenceSession(
+    "resnet50.onnx", providers=['MIGraphXExecutionProvider'])
+
+
+def softmax(x):
+    """Compute softmax values for each sets of scores in x."""
+    e_x = np.exp(x - np.max(x))
+    return e_x / e_x.sum()
+
+
+latency = []
+
+
+def run_sample(session, image_file, categories, inputs):
+    start = time.time()
+    input_arr = inputs.cpu().detach().numpy()
+    ort_outputs = session.run([], {'input': input_arr})[0]
+    latency.append(time.time() - start)
+    output = ort_outputs.flatten()
+    output = softmax(output)  # this is optional
+    top5_catid = np.argsort(-output)[:5]
+    for catid in top5_catid:
+        print(categories[catid], output[catid])
+    return ort_outputs
+
+
+ort_output = run_sample(session_fp32, 'cat.jpg', categories, input_batch)
+print("resnet50, time = {} ms".format(
+    format(sum(latency) * 1000 / len(latency), '.2f')))

tools/build_and_test_onnxrt.sh

@@ -31,7 +31,7 @@ pip3 install -r requirements-dev.txt
 # Add newer cmake to the path
 export PATH="/opt/cmake/bin:$PATH"
 export CXXFLAGS="-D__HIP_PLATFORM_AMD__=1 -w"
-./build.sh --config Release  --cmake_extra_defines CMAKE_HIP_COMPILER=/opt/rocm/llvm/bin/clang++ --update --build --parallel --cmake_extra_defines ONNXRUNTIME_VERSION=$(cat ./VERSION_NUMBER) --skip_tests --rocm_home /opt/rocm --use_migraphx --migraphx_home /opt/rocm --rocm_version=`cat /opt/rocm/.info/version-dev` --allow_running_as_root
+./build.sh --config Release  --cmake_extra_defines CMAKE_HIP_COMPILER=/opt/rocm/llvm/bin/clang++ --update --build --build_wheel --parallel --cmake_extra_defines ONNXRUNTIME_VERSION=$(cat ./VERSION_NUMBER) --skip_tests --rocm_home /opt/rocm --use_migraphx --migraphx_home /opt/rocm --rocm_version=`cat /opt/rocm/.info/version-dev` --allow_running_as_root
 
 cd build/Linux/Release
 #Add test launcher for onnxrt tests