When transforming a tf,keras model into TVM IRModule using statement irmod, params = relay.frontend.from_keras(predict_model, shape_dict), TVM threw an exception:
import keras
import os
import tvm
from tvm import te
import tvm.relay as relay
import tvm.relay.transform as transform
import numpy as np
from PIL import Image
import tvm.runtime as runtime
from tvm.contrib import graph_runtime
input_tensor = 'input_4'
def image_resize(x, shape):
x_return = []
for x_test in x:
tmp = np.copy(x_test)
img = Image.fromarray(tmp.astype('uint8')).convert('RGB')
img = img.resize(shape, Image.ANTIALIAS)
x_return.append(np.array(img))
return np.array(x_return)
input_precessor = keras.applications.vgg16.preprocess_input
input_shape = (224,224) #excption (299,299)
dataset_dir = "/tensorflow/data/tvm/dataset"
data_path = os.path.join(dataset_dir,"imagenet-val-1500.npz")
data = np.load(data_path)
x, y = data['x_test'], data['y_test']
x_resize = image_resize(np.copy(x),input_shape)
x_test = input_precessor(x_resize)
y_test = keras.utils.to_categorical(y, num_classes=1000)
model_path = 'your-path/vgg16-cifar10.h5'
predict_model = keras.models.load_model(model_path)
shape_dict = {input_tensor: (1,3,224,224)}
irmod, params = relay.frontend.from_keras(predict_model, shape_dict)
target = 'llvm'
ctx = tvm.cpu(0)
irmod, params = relay.optimize(irmod,target=target,params=params)
with transform.PassContext(opt_level=3):
graph, lib, params = relay.build_module.build(irmod, target, params=params)
The involving model vgg16-cifar10.h5 can be installed through google drive: https://drive.google.com/file/d/1CQOtLADOOjNwm34OfHc7IiPKBikS3NT7/view?usp=sharing
After searching online about similar problems, I found this github issue and it seems that TVM still use layer.inbound_nodes, which has been deprecated by Tensorflow2.0. However, I am not an expert in it and I can tell whether or not it is the root cause.
Thank you in advance for your petential answer.