[Doc] TFLite frontend tutorial (#2508)

* TFLite frontend tutorial

* Modify as suggestion

tutorials/frontend/from_tflite.py

+"""
+Compile TFLite Models
+===================
+**Author**: `Zhao Wu <https://github.com/FrozenGene>`_
+
+This article is an introductory tutorial to deploy TFLite models with Relay.
+
+To get started, Flatbuffers and TFLite package needs to be installed as prerequisites.
+
+A quick solution is to install Flatbuffers via pip
+
+.. code-block:: bash
+
+    pip install flatbuffers --user
+
+To install TFlite packages, you could use our prebuilt wheel:
+
+.. code-block:: bash
+
+    # For python3:
+    wget https://github.com/dmlc/web-data/tree/master/tensorflow/tflite/whl/tflite-0.0.1-py3-none-any.whl
+    pip install tflite-0.0.1-py3-none-any.whl --user
+
+    # For python2:
+    wget https://github.com/dmlc/web-data/tree/master/tensorflow/tflite/whl/tflite-0.0.1-py2-none-any.whl
+    pip install tflite-0.0.1-py2-none-any.whl --user
+
+
+or you could generate TFLite package by yourself. The steps are as following:
+
+.. code-block:: bash
+
+    # Get the flatc compiler.
+    # Please refer to https://github.com/google/flatbuffers for details
+    # and make sure it is properly installed.
+    flatc --version
+
+    # Get the TFLite schema.
+    wget https://raw.githubusercontent.com/tensorflow/tensorflow/r1.12/tensorflow/contrib/lite/schema/schema.fbs
+
+    # Generate TFLite package.
+    flatc --python schema.fbs
+
+    # Add it to PYTHONPATH.
+    export PYTHONPATH=/path/to/tflite
+
+
+Now please check if TFLite package is installed successfully, ``python -c "import tflite"``
+
+Below you can find an example for how to compile TFLite model using TVM.
+"""
+######################################################################
+# Utils for downloading and extracting zip files
+# ---------------------------------------------
+
+def download(url, path, overwrite=False):
+    import os
+    if os.path.isfile(path) and not overwrite:
+        print('File {} existed, skip.'.format(path))
+        return
+    print('Downloading from url {} to {}'.format(url, path))
+    try:
+        import urllib.request
+        urllib.request.urlretrieve(url, path)
+    except:
+        import urllib
+        urllib.urlretrieve(url, path)
+
+def extract(path):
+    import tarfile
+    if path.endswith("tgz") or path.endswith("gz"):
+        tar = tarfile.open(path)
+        tar.extractall()
+        tar.close()
+    else:
+        raise RuntimeError('Could not decompress the file: ' + path)
+
+
+######################################################################
+# Load pretrained TFLite model
+# ---------------------------------------------
+# we load mobilenet V1 TFLite model provided by Google
+model_url = "http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224.tgz"
+
+# we download model tar file and extract, finally get mobilenet_v1_1.0_224.tflite
+download(model_url, "mobilenet_v1_1.0_224.tgz", False)
+extract("mobilenet_v1_1.0_224.tgz")
+
+# now we have mobilenet_v1_1.0_224.tflite on disk and open it
+tflite_model_file = "mobilenet_v1_1.0_224.tflite"
+tflite_model_buf = open(tflite_model_file, "rb").read()
+
+# get TFLite model from buffer
+import tflite.Model
+tflite_model = tflite.Model.Model.GetRootAsModel(tflite_model_buf, 0)
+
+######################################################################
+# Load a test image
+# ---------------------------------------------
+# A single cat dominates the examples!
+from PIL import Image
+from matplotlib import pyplot as plt
+import numpy as np
+
+image_url = 'https://github.com/dmlc/mxnet.js/blob/master/data/cat.png?raw=true'
+download(image_url, 'cat.png')
+resized_image = Image.open('cat.png').resize((224, 224))
+plt.imshow(resized_image)
+plt.show()
+image_data = np.asarray(resized_image).astype("float32")
+
+# convert HWC to CHW
+image_data = image_data.transpose((2, 0, 1))
+
+# after expand_dims, we have format NCHW
+image_data = np.expand_dims(image_data, axis=0)
+
+# preprocess image as described here:
+# https://github.com/tensorflow/models/blob/edb6ed22a801665946c63d650ab9a0b23d98e1b1/research/slim/preprocessing/inception_preprocessing.py#L243
+image_data[:, 0, :, :] = 2.0 / 255.0 * image_data[:, 0, :, :] - 1
+image_data[:, 1, :, :] = 2.0 / 255.0 * image_data[:, 1, :, :] - 1
+image_data[:, 2, :, :] = 2.0 / 255.0 * image_data[:, 2, :, :] - 1
+print('input', image_data.shape)
+
+####################################################################
+#
+# .. note:: Input layout:
+#
+#   Currently, TVM TFLite frontend accepts ``NCHW`` as input layout.
+
+######################################################################
+# Compile the model with relay
+# ---------------------------------------------
+
+# TFLite input tensor name, shape and type
+input_tensor = "input"
+input_shape = (1, 3, 224, 224)
+input_dtype = "float32"
+
+# parse TFLite model and convert into Relay computation graph
+from tvm import relay
+func, params = relay.frontend.from_tflite(tflite_model,
+                                          shape_dict={input_tensor: input_shape},
+                                          dtype_dict={input_tensor: input_dtype})
+
+# targt x86 cpu
+target = "llvm"
+with relay.build_module.build_config(opt_level=3):
+    graph, lib, params = relay.build(func, target, params=params)
+
+######################################################################
+# Execute on TVM
+# ---------------------------------------------
+import tvm
+from tvm.contrib import graph_runtime as runtime
+
+# create a runtime executor module
+module = runtime.create(graph, lib, tvm.cpu())
+
+# feed input data
+module.set_input(input_tensor, tvm.nd.array(image_data))
+
+# feed related params
+module.set_input(**params)
+
+# run
+module.run()
+
+# get output
+tvm_output = module.get_output(0).asnumpy()
+
+######################################################################
+# Display results
+# ---------------------------------------------
+
+# load label file
+label_file_url = ''.join(['https://raw.githubusercontent.com/',
+                          'tensorflow/tensorflow/master/tensorflow/lite/java/demo/',
+                          'app/src/main/assets/',
+                          'labels_mobilenet_quant_v1_224.txt'])
+label_file = "labels_mobilenet_quant_v1_224.txt"
+download(label_file_url, label_file)
+
+# map id to 1001 classes
+labels = dict()
+with open(label_file) as f:
+    for id, line in enumerate(f):
+        labels[id] = line
+
+# convert result to 1D data
+predictions = np.squeeze(tvm_output)
+
+# get top 1 prediction
+prediction = np.argmax(predictions)
+
+# convert id to class name and show the result
+print("The image prediction result is: id " + str(prediction) + " name: " + labels[prediction])