Note
This tutorial can be used interactively with Google Colab! You can also click here to run the Jupyter notebook locally.
Compile Keras Models¶
Author: Yuwei Hu
This article is an introductory tutorial to deploy Keras models with Relay.
For us to begin with, keras should be installed. Tensorflow is also required since it’s used as the default backend of keras.
A quick solution is to install via pip
pip install keras tensorflow
or please refer to official site https://keras.io/#installation
import tvm
from tvm import te
import tvm.relay as relay
from tvm.contrib.download import download_testdata
import keras
import tensorflow as tf
import numpy as np
Load pretrained keras model¶
We load a pretrained resnet-50 classification model provided by keras.
if tuple(keras.__version__.split(".")) < ("2", "4", "0"):
weights_url = "".join(
[
"https://github.com/fchollet/deep-learning-models/releases/",
"download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels.h5",
]
)
weights_file = "resnet50_keras_old.h5"
else:
weights_url = "".join(
[
" https://storage.googleapis.com/tensorflow/keras-applications/",
"resnet/resnet50_weights_tf_dim_ordering_tf_kernels.h5",
]
)
weights_file = "resnet50_keras_new.h5"
weights_path = download_testdata(weights_url, weights_file, module="keras")
keras_resnet50 = tf.keras.applications.resnet50.ResNet50(
include_top=True, weights=None, input_shape=(224, 224, 3), classes=1000
)
keras_resnet50.load_weights(weights_path)
Load a test image¶
A single cat dominates the examples!
from PIL import Image
from matplotlib import pyplot as plt
from tensorflow.keras.applications.resnet50 import preprocess_input
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))
plt.imshow(img)
plt.show()
# input preprocess
data = np.array(img)[np.newaxis, :].astype("float32")
data = preprocess_input(data).transpose([0, 3, 1, 2])
print("input_1", data.shape)
![from keras](../../_images/sphx_glr_from_keras_001.png)
input_1 (1, 3, 224, 224)
Compile the model with Relay¶
convert the keras model(NHWC layout) to Relay format(NCHW layout).
shape_dict = {"input_1": data.shape}
mod, params = relay.frontend.from_keras(keras_resnet50, shape_dict)
# compile the model
target = "cuda"
dev = tvm.cuda(0)
# TODO(mbs): opt_level=3 causes nn.contrib_conv2d_winograd_weight_transform
# to end up in the module which fails memory validation on cuda most likely
# due to a latent bug. Note that the pass context only has an effect within
# evaluate() and is not captured by create_executor().
with tvm.transform.PassContext(opt_level=0):
model = relay.build_module.create_executor("graph", mod, dev, target, params).evaluate()
Execute on TVM¶
dtype = "float32"
tvm_out = model(tvm.nd.array(data.astype(dtype)))
top1_tvm = np.argmax(tvm_out.numpy()[0])
Look up synset name¶
Look up prediction top 1 index in 1000 class synset.
synset_url = "".join(
[
"https://gist.githubusercontent.com/zhreshold/",
"4d0b62f3d01426887599d4f7ede23ee5/raw/",
"596b27d23537e5a1b5751d2b0481ef172f58b539/",
"imagenet1000_clsid_to_human.txt",
]
)
synset_name = "imagenet1000_clsid_to_human.txt"
synset_path = download_testdata(synset_url, synset_name, module="data")
with open(synset_path) as f:
synset = eval(f.read())
print("Relay top-1 id: {}, class name: {}".format(top1_tvm, synset[top1_tvm]))
# confirm correctness with keras output
keras_out = keras_resnet50.predict(data.transpose([0, 2, 3, 1]))
top1_keras = np.argmax(keras_out)
print("Keras top-1 id: {}, class name: {}".format(top1_keras, synset[top1_keras]))
Relay top-1 id: 285, class name: Egyptian cat
1/1 [==============================] - ETA: 0s
1/1 [==============================] - 1s 1s/step
Keras top-1 id: 285, class name: Egyptian cat