[Apache TVM Discuss] [Questions] Check failed: (reporter->AssertEQ(data->shape[data->shape.size() - 1], weight->shape[1])) is false: DenseRel: input dimension doesn't match, data shape=[1, 512], weight shape=[512, 1000]

[JJZOU via Apache TVM Discuss]

I'm sorry that I didn't reply you in time because there are too many courses 
recently
Here's the code:
import tvm

from tvm import relay

import numpy as np

from tvm.contrib.download import download_testdata

# PyTorch imports

import torch

import torchvision

######################################################################

# Load a pretrained PyTorch model

# -------------------------------

model_name = "resnet18"

model = getattr(torchvision.models, model_name)(pretrained=True)

model = model.eval()

# We grab the TorchScripted model via tracing

input_shape = [1, 3, 224, 224]

input_data = torch.randn(input_shape)

scripted_model = torch.jit.trace(model, input_data).eval()

######################################################################

# Load a test image

# -----------------

# Classic cat example!

from PIL import Image

import PIL

img_url = "https://github.com/dmlc/mxnet.js/blob/main/data/cat.png?raw=true";

img_path = "C:\\Users\\Batman\\Desktop\\Ⅲ\\CV\\cat.png" 
#download_testdata(img_url, "cat.png", module="data")

img = Image.open(img_path).resize((224, 224))

# Preprocess the image and convert to tensor

from torchvision 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 = np.array(img)

print(np.array(img[:,:,0:3]).shape)

img = Image.fromarray(img[:,:,0:3])

img = my_preprocess(img)

img = np.expand_dims(img, 0)

######################################################################

# Import the graph to Relay

# -------------------------

# Convert PyTorch graph to Relay graph. The input name can be arbitrary.

input_name = "input0"

shape_list = [(input_name, img.shape)]

mod, params = relay.frontend.from_pytorch(scripted_model, shape_list)

######################################################################

# 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.

from tvm.contrib import graph_executor

dtype = "float32"

m = graph_executor.GraphModule(lib["default"](dev))

# Set inputs

m.set_input(input_name, tvm.nd.array(img.astype(dtype)))

# Execute

m.run()

# Get outputs

tvm_output = m.get_output(0)

print(tvm_output.shape)

#####################################################################

# 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(np.array(tvm_output))

tvm_class_key = class_id_to_key[top1_tvm]

# Convert input to PyTorch variable and get PyTorch result for comparison

with torch.no_grad():

    torch_img = torch.from_numpy(img)

    output = model(torch_img)

    # Get top-1 result for PyTorch

    top1_torch = np.argmax(output.numpy())

    torch_class_key = class_id_to_key[top1_torch]

print("Relay top-1 id: {}, class name: {}".format(top1_tvm, 
key_to_classname[tvm_class_key]))

print("Torch top-1 id: {}, class name: {}".format(top1_torch, 
key_to_classname[torch_class_key]))





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