Add cuda imagenet inference benchmark script (#73)

* resnet example merged to imagenet

* resnet example merged to imagenet

* merge with master

* merge with master

* cuda imagenet benchmakr added

* benchmark syntax fixed

nnvm/examples/benchmark/cuda_imagenet_bench.py

+""" Benchmark script for performance on GPUs. For example, run the file with: `python cuda_imagenet_bench.py --model='mobilenet'`. For more details about how to set up the inference environment on GPUs, please refer to NNVM Tutorial: ImageNet Inference on the GPU """
+import time
+import argparse
+import numpy as np
+import tvm
+import nnvm.compiler
+import nnvm.testing
+from tvm.contrib import util, nvcc
+from tvm.contrib import graph_runtime as runtime
+
+@tvm.register_func
+def tvm_callback_cuda_compile(code):
+    ptx = nvcc.compile_cuda(code, target="ptx")
+    return ptx
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--model', type=str, required=True, choices=['resnet', 'mobilenet'],
+        help="The model type.")
+    parser.add_argument('--opt-level', type=int, default=1, help="Level of optimization.")
+    parser.add_argument('--num-iter', type=int, default=1000, help="Number of iteration during benchmark.")
+    args = parser.parse_args()
+    opt_level = args.opt_level
+    num_iter = args.num_iter
+    target = "cuda"
+    ctx = tvm.gpu(0)
+    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.build_config(auto_unroll_max_step=32,
+                              auto_unroll_min_depth=0,
+                              unroll_explicit=False):
+            graph, lib, params = nnvm.compiler.build(
+                net, target, shape={"data": data_shape}, params=params)
+
+    data = np.random.uniform(-1, 1, size=data_shape).astype("float32")
+    module = runtime.create(graph, lib, ctx)
+    module.set_input(**params)
+    module.set_input("data", data)
+    print('benchmark args: {}'.format(args))
+    # warm-up run
+    module.run()
+    ctx.sync()
+
+    tic = time.time()
+    for _ in range(num_iter):
+        module.run()
+    ctx.sync()
+    print("average time cost of %d runs = %g ms" % (num_iter, ((time.time() - tic)*1000/num_iter)))
+    out = module.get_output(0, tvm.nd.empty(out_shape))
+    out.asnumpy()
+
+
+if __name__ == '__main__':
+    main()
+