%matplotlib inline

Compile Tensorflow Models#

This article is an introductory tutorial to deploy tensorflow models with TVM.

For us to begin with, tensorflow python module is required to be installed.

Please refer to https://www.tensorflow.org/install

# tvm, relay
import tvm
from tvm import te
from tvm import relay

# os and numpy
import numpy as np
import os.path

# Tensorflow imports
import tensorflow as tf


# Ask tensorflow to limit its GPU memory to what's actually needed
# instead of gobbling everything that's available.
# https://www.tensorflow.org/guide/gpu#limiting_gpu_memory_growth
# This way this tutorial is a little more friendly to sphinx-gallery.
gpus = tf.config.list_physical_devices("GPU")
if gpus:
    try:
        for gpu in gpus:
            tf.config.experimental.set_memory_growth(gpu, True)
        print("tensorflow will use experimental.set_memory_growth(True)")
    except RuntimeError as e:
        print("experimental.set_memory_growth option is not available: {}".format(e))


try:
    tf_compat_v1 = tf.compat.v1
except ImportError:
    tf_compat_v1 = tf

# Tensorflow utility functions
import tvm.relay.testing.tf as tf_testing

# Base location for model related files.
repo_base = "https://github.com/dmlc/web-data/raw/main/tensorflow/models/InceptionV1/"

# Test image
img_name = "elephant-299.jpg"
image_url = os.path.join(repo_base, img_name)

Tutorials#

Please refer docs/frontend/tensorflow.md for more details for various models from tensorflow.

model_name = "classify_image_graph_def-with_shapes.pb"
model_url = os.path.join(repo_base, model_name)

# Image label map
map_proto = "imagenet_2012_challenge_label_map_proto.pbtxt"
map_proto_url = os.path.join(repo_base, map_proto)

# Human readable text for labels
label_map = "imagenet_synset_to_human_label_map.txt"
label_map_url = os.path.join(repo_base, label_map)

# Target settings
# Use these commented settings to build for cuda.
# target = tvm.target.Target("cuda", host="llvm")
# layout = "NCHW"
# dev = tvm.cuda(0)
target = tvm.target.Target("llvm", host="llvm")
layout = None
dev = tvm.cpu(0)

Download required files#

Download files listed above.

from tvm.contrib.download import download_testdata

img_path = download_testdata(image_url, img_name, module="data")
model_path = download_testdata(model_url, model_name, module=["tf", "InceptionV1"])
map_proto_path = download_testdata(map_proto_url, map_proto, module="data")
label_path = download_testdata(label_map_url, label_map, module="data")

Import model#

Creates tensorflow graph definition from protobuf file.

with tf_compat_v1.gfile.GFile(model_path, "rb") as f:
    graph_def = tf_compat_v1.GraphDef()
    graph_def.ParseFromString(f.read())
    graph = tf.import_graph_def(graph_def, name="")
    # Call the utility to import the graph definition into default graph.
    graph_def = tf_testing.ProcessGraphDefParam(graph_def)
    # Add shapes to the graph.
    with tf_compat_v1.Session() as sess:
        graph_def = tf_testing.AddShapesToGraphDef(sess, "softmax")

Decode image#

Note

tensorflow frontend import doesn't support preprocessing ops like JpegDecode. JpegDecode is bypassed (just return source node). Hence we supply decoded frame to TVM instead.

from PIL import Image

image = Image.open(img_path).resize((299, 299))

x = np.array(image)

Import the graph to Relay#

Import tensorflow graph definition to relay frontend.

Results: sym: relay expr for given tensorflow protobuf. params: params converted from tensorflow params (tensor protobuf).

shape_dict = {"DecodeJpeg/contents": x.shape}
dtype_dict = {"DecodeJpeg/contents": "uint8"}
mod, params = relay.frontend.from_tensorflow(graph_def, layout=layout, shape=shape_dict)

print("Tensorflow protobuf imported to relay frontend.")

Relay Build#

Compile the graph to llvm target with given input specification.

Results: graph: Final graph after compilation. params: final params after compilation. lib: target library which can be deployed on target with TVM runtime.

with tvm.transform.PassContext(opt_level=3):
    lib = relay.build(mod, 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 = "uint8"
m = graph_executor.GraphModule(lib["default"](dev))
# set inputs
m.set_input("DecodeJpeg/contents", tvm.nd.array(x.astype(dtype)))
# execute
m.run()
# get outputs
tvm_output = m.get_output(0, tvm.nd.empty(((1, 1008)), "float32"))

Process the output#

Process the model output to human readable text for InceptionV1.

predictions = tvm_output.numpy()
predictions = np.squeeze(predictions)

# Creates node ID --> English string lookup.
node_lookup = tf_testing.NodeLookup(label_lookup_path=map_proto_path, uid_lookup_path=label_path)

# Print top 5 predictions from TVM output.
top_k = predictions.argsort()[-5:][::-1]
for node_id in top_k:
    human_string = node_lookup.id_to_string(node_id)
    score = predictions[node_id]
    print("%s (score = %.5f)" % (human_string, score))

Inference on tensorflow#

Run the corresponding model on tensorflow

def create_graph():
    """Creates a graph from saved GraphDef file and returns a saver."""
    # Creates graph from saved graph_def.pb.
    with tf_compat_v1.gfile.GFile(model_path, "rb") as f:
        graph_def = tf_compat_v1.GraphDef()
        graph_def.ParseFromString(f.read())
        graph = tf.import_graph_def(graph_def, name="")
        # Call the utility to import the graph definition into default graph.
        graph_def = tf_testing.ProcessGraphDefParam(graph_def)


def run_inference_on_image(image):
    """Runs inference on an image.

    Parameters
    ----------
    image: String
        Image file name.

    Returns
    -------
        Nothing
    """
    if not tf_compat_v1.gfile.Exists(image):
        tf.logging.fatal("File does not exist %s", image)
    image_data = tf_compat_v1.gfile.GFile(image, "rb").read()

    # Creates graph from saved GraphDef.
    create_graph()

    with tf_compat_v1.Session() as sess:
        softmax_tensor = sess.graph.get_tensor_by_name("softmax:0")
        predictions = sess.run(softmax_tensor, {"DecodeJpeg/contents:0": image_data})

        predictions = np.squeeze(predictions)

        # Creates node ID --> English string lookup.
        node_lookup = tf_testing.NodeLookup(
            label_lookup_path=map_proto_path, uid_lookup_path=label_path
        )

        # Print top 5 predictions from tensorflow.
        top_k = predictions.argsort()[-5:][::-1]
        print("===== TENSORFLOW RESULTS =======")
        for node_id in top_k:
            human_string = node_lookup.id_to_string(node_id)
            score = predictions[node_id]
            print("%s (score = %.5f)" % (human_string, score))


run_inference_on_image(img_path)

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Compile Tensorflow Models

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