use torchvision's mobilenet_v2 instead of mxnet

apps/ios_rpc/tests/ios_rpc_mobilenet.py

@@ -23,7 +23,6 @@
 import coremltools
 import numpy as np
 import tvm
-from mxnet import gluon
 from PIL import Image
 from tvm import relay, rpc
 from tvm.contrib import coreml_runtime, graph_executor, utils, xcode
@@ -51,6 +50,8 @@ def compile_metal(src, target):
 
 
 def prepare_input():
+    from torchvision import transforms
+
     img_url = "https://github.com/dmlc/mxnet.js/blob/main/data/cat.png?raw=true"
     img_name = "cat.png"
     synset_url = "".join(
@@ -62,22 +63,36 @@ def prepare_input():
         ]
     )
     synset_name = "imagenet1000_clsid_to_human.txt"
-    img_path = download_testdata(img_url, "cat.png", module="data")
+    img_path = download_testdata(img_url, img_name, module="data")
     synset_path = download_testdata(synset_url, synset_name, module="data")
     with open(synset_path) as f:
         synset = eval(f.read())
-        image = Image.open(img_path).resize((224, 224))
+        input_image = Image.open(img_path)
 
-    image = np.array(image) - np.array([123.0, 117.0, 104.0])
-    image /= np.array([58.395, 57.12, 57.375])
-    image = image.transpose((2, 0, 1))
-    image = image[np.newaxis, :]
-    return image.astype("float32"), synset
+    preprocess = transforms.Compose(
+        [
+            transforms.Resize(256),
+            transforms.CenterCrop(224),
+            transforms.ToTensor(),
+            transforms.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)
+    return input_batch.detach().cpu().numpy(), synset
 
 
 def get_model(model_name, data_shape):
-    gluon_model = gluon.model_zoo.vision.get_model(model_name, pretrained=True)
-    mod, params = relay.frontend.from_mxnet(gluon_model, {"data": data_shape})
+    import torch
+    import torchvision
+
+    torch_model = getattr(torchvision.models, model_name)(weights="IMAGENET1K_V1").eval()
+    input_data = torch.randn(data_shape)
+    scripted_model = torch.jit.trace(torch_model, input_data)
+
+    input_infos = [("data", input_data.shape)]
+    mod, params = relay.frontend.from_pytorch(scripted_model, input_infos)
+
     # we want a probability so add a softmax operator
     func = mod["main"]
     func = relay.Function(
@@ -90,7 +105,7 @@ def get_model(model_name, data_shape):
 def test_mobilenet(host, port, key, mode):
     temp = utils.tempdir()
     image, synset = prepare_input()
-    model, params = get_model("mobilenetv2_1.0", image.shape)
+    model, params = get_model("mobilenet_v2", image.shape)
 
     def run(mod, target):
         with relay.build_config(opt_level=3):