[BENCH] Add Benchmark for Rasp (#68)

* [BENCH] Add Benchmark for Rasp

* [BENCH] Add arg opt-level

* [BENCH] Add model choices

* [BENCH] Improve

examples/benchmark/rasp_imagenet_bench.py

@@ -0,0 +1,78 @@
+""" Benchmark script for performance on Raspberry Pi. For example, run the file with:
+`python rasp_imagenet_bench.py --model='modbilenet' --host='rasp0' --port=9090`. For
+more details about how to set up the inference environment on Raspberry Pi, Please
+refer to NNVM Tutorial: Deploy the Pretrained Model on Raspberry Pi """
+import time
+import argparse
+import numpy as np
+import tvm
+import nnvm.compiler
+import nnvm.testing
+from tvm.contrib import util, rpc
+from tvm.contrib import graph_runtime as runtime
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--model', type=str, required=True, choices=['resnet', 'mobilenet'],
+        help="The model type.")
+    parser.add_argument('--host', type=str, required=True, help="The host address of your Raspberry Pi.")
+    parser.add_argument('--port', type=int, required=True, help="The port number of your Raspberry Pi.")
+    parser.add_argument('--opt-level', type=int, default=1, help="Level of optimization.")
+    parser.add_argument('--num-iter', type=int, default=50, help="Number of iteration during benchmark.")
+    args = parser.parse_args()
+
+    opt_level = args.opt_level
+    target = "llvm -target=armv7l-none-linux-anueabihf -mcpu=cortex-a53 -mattr=+neon"
+
+    num_iter = args.num_iter
+    batch_size = 1
+    num_classes = 1000
+    image_shape = (3, 224, 224)
+
+    data_shape = (batch_size,) + image_shape
+    out_shape = (batch_size, num_classes)
+    if args.model == 'resnet':
+        net, params = nnvm.testing.resnet.get_workload(
+            batch_size=1, image_shape=image_shape)
+    elif args.model == 'mobilenet':
+        net, params = nnvm.testing.mobilenet.get_workload(
+            batch_size=1, image_shape=image_shape)
+    else:
+        raise ValueError('no benchmark prepared for {}.'.format(args.model))
+
+
+    with nnvm.compiler.build_config(opt_level=opt_level):
+        with tvm.target.rasp():
+            graph, lib, params = nnvm.compiler.build(
+                net, target, shape={"data": data_shape}, params=params)
+
+    tmp = util.tempdir()
+    lib_fname = tmp.relpath('net.o')
+    lib.save(lib_fname)
+
+    remote = rpc.connect(args.host, args.port)
+    remote.upload(lib_fname)
+
+    ctx = remote.cpu(0)
+    rlib = remote.load_module('net.o')
+    rparams = {k: tvm.nd.array(v, ctx) for k, v in params.items()}
+
+    module = runtime.create(graph, rlib, ctx)
+    module.set_input('data', tvm.nd.array(np.random.uniform(size=(data_shape)).astype("float32")))
+    module.set_input(**rparams)
+    module.run()
+    out = module.get_output(0, tvm.nd.empty(out_shape, ctx=ctx))
+    out.asnumpy()
+
+    print('benchmark args: {}'.format(args))
+    ftimer = module.module.time_evaluator("run", ctx, num_iter)
+    for i in range(3):
+        prof_res = ftimer()
+        print(prof_res)
+        # sleep for avoiding cpu overheat
+        time.sleep(45)
+
+
+if __name__ == '__main__':
+    main()