oneflow cause crash, do test with change

Author: huajsj

gallery/how_to/compile_models/from_oneflow.py

@@ -49,129 +49,3 @@
 from tvm import relay
 from tvm.contrib.download import download_testdata
 
-######################################################################
-# Load a pretrained OneFlow model and save model
-# ----------------------------------------------
-model_name = "resnet18"
-model = getattr(flowvision.models, model_name)(pretrained=True)
-model = model.eval()
-
-model_dir = "resnet18_model"
-if not os.path.exists(model_dir):
-    flow.save(model.state_dict(), model_dir)
-
-######################################################################
-# Load a test image
-# -----------------
-# Classic cat example!
-from PIL import Image
-
-img_url = "https://github.com/dmlc/mxnet.js/blob/main/data/cat.png?raw=true"
-img_path = download_testdata(img_url, "cat.png", module="data")
-img = Image.open(img_path).resize((224, 224))
-
-# Preprocess the image and convert to tensor
-from flowvision import transforms
-
-my_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]),
-    ]
-)
-img = my_preprocess(img)
-img = np.expand_dims(img.numpy(), 0)
-
-######################################################################
-# Import the graph to Relay
-# -------------------------
-# Convert OneFlow graph to Relay graph. The input name can be arbitrary.
-class Graph(flow.nn.Graph):
-    def __init__(self, module):
-        super().__init__()
-        self.m = module
-
-    def build(self, x):
-        out = self.m(x)
-        return out
-
-
-graph = Graph(model)
-_ = graph._compile(flow.randn(1, 3, 224, 224))
-
-mod, params = relay.frontend.from_oneflow(graph, model_dir)
-
-######################################################################
-# Relay Build
-# -----------
-# Compile the graph to llvm target with given input specification.
-target = tvm.target.Target("llvm", host="llvm")
-dev = tvm.cpu(0)
-with tvm.transform.PassContext(opt_level=3):
-    lib = relay.build(mod, target=target, params=params)
-
-######################################################################
-# Execute the portable graph on TVM
-# ---------------------------------
-# Now we can try deploying the compiled model on target.
-target = "cuda"
-with tvm.transform.PassContext(opt_level=10):
-    intrp = relay.build_module.create_executor("graph", mod, tvm.cuda(0), target)
-
-print(type(img))
-print(img.shape)
-tvm_output = intrp.evaluate()(tvm.nd.array(img.astype("float32")), **params)
-
-#####################################################################
-# Look up synset name
-# -------------------
-# Look up prediction top 1 index in 1000 class synset.
-synset_url = "".join(
-    [
-        "https://raw.githubusercontent.com/Cadene/",
-        "pretrained-models.pytorch/master/data/",
-        "imagenet_synsets.txt",
-    ]
-)
-synset_name = "imagenet_synsets.txt"
-synset_path = download_testdata(synset_url, synset_name, module="data")
-with open(synset_path) as f:
-    synsets = f.readlines()
-
-synsets = [x.strip() for x in synsets]
-splits = [line.split(" ") for line in synsets]
-key_to_classname = {spl[0]: " ".join(spl[1:]) for spl in splits}
-
-class_url = "".join(
-    [
-        "https://raw.githubusercontent.com/Cadene/",
-        "pretrained-models.pytorch/master/data/",
-        "imagenet_classes.txt",
-    ]
-)
-class_name = "imagenet_classes.txt"
-class_path = download_testdata(class_url, class_name, module="data")
-with open(class_path) as f:
-    class_id_to_key = f.readlines()
-
-class_id_to_key = [x.strip() for x in class_id_to_key]
-
-# Get top-1 result for TVM
-top1_tvm = np.argmax(tvm_output.numpy()[0])
-tvm_class_key = class_id_to_key[top1_tvm]
-
-# Convert input to OneFlow variable and get OneFlow result for comparison
-with flow.no_grad():
-    torch_img = flow.from_numpy(img)
-    output = model(torch_img)
-
-    # Get top-1 result for OneFlow
-    top_oneflow = np.argmax(output.numpy())
-    oneflow_class_key = class_id_to_key[top_oneflow]
-
-print("Relay top-1 id: {}, class name: {}".format(top1_tvm, key_to_classname[tvm_class_key]))
-print(
-    "OneFlow top-1 id: {}, class name: {}".format(top_oneflow, key_to_classname[oneflow_class_key])
-)