This is an automated email from the ASF dual-hosted git repository. tqchen pushed a commit to branch revert-5595-tutorial_tflite in repository https://gitbox.apache.org/repos/asf/incubator-tvm.git
commit e7b24ca3c983f198fa566ed4dd4e894029db0d3c Author: Tianqi Chen <[email protected]> AuthorDate: Thu May 21 07:50:49 2020 -0700 Revert "[TUTORIAL]TFLite QNN Tutorial (#5595)" This reverts commit 019da5dae15d2bd13536673ab689203c799629f0. --- tutorials/frontend/deploy_prequantized_tflite.py | 251 ----------------------- 1 file changed, 251 deletions(-) diff --git a/tutorials/frontend/deploy_prequantized_tflite.py b/tutorials/frontend/deploy_prequantized_tflite.py deleted file mode 100644 index f6c4544..0000000 --- a/tutorials/frontend/deploy_prequantized_tflite.py +++ /dev/null @@ -1,251 +0,0 @@ -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. -""" -Deploy a Framework-prequantized Model with TVM - Part 3 (TFLite) -================================================================ -**Author**: `Siju Samuel <https://github.com/siju-samuel>`_ -Welcome to part 3 of the Deploy Framework-Prequantized Model with TVM tutorial. -In this part, we will start with a Quantized TFLite graph and then compile and execute it via TVM. - - -For more details on quantizing the model using TFLite, readers are encouraged to -go through `Converting Quantized Models -<https://www.tensorflow.org/lite/convert/quantization>`_. - -The TFLite models can be downloaded from this `link -<https://www.tensorflow.org/lite/guide/hosted_models>`_. - -To get started, Tensorflow and TFLite package needs to be installed as prerequisite. - -.. code-block:: bash - - # install tensorflow and tflite - pip install tensorflow==2.1.0 - pip install tflite==2.1.0 - -Now please check if TFLite package is installed successfully, ``python -c "import tflite"`` - -""" - -############################################################################### -# Necessary imports -# ----------------- -import os - -import numpy as np -import tflite - -import tvm -from tvm import relay - - -###################################################################### -# Download pretrained Quantized TFLite model -# ------------------------------------------ - -# Download mobilenet V2 TFLite model provided by Google -from tvm.contrib.download import download_testdata - -model_url = "https://storage.googleapis.com/download.tensorflow.org/models/"; \ - "tflite_11_05_08/mobilenet_v2_1.0_224_quant.tgz" - -# Download model tar file and extract it to get mobilenet_v2_1.0_224.tflite -model_path = download_testdata(model_url, "mobilenet_v2_1.0_224_quant.tgz", - module=['tf', 'official']) -model_dir = os.path.dirname(model_path) - - -###################################################################### -# Utils for downloading and extracting zip files -# ---------------------------------------------- -def extract(path): - import tarfile - if path.endswith("tgz") or path.endswith("gz"): - dir_path = os.path.dirname(path) - tar = tarfile.open(path) - tar.extractall(path=dir_path) - tar.close() - else: - raise RuntimeError('Could not decompress the file: ' + path) - -extract(model_path) - - -###################################################################### -# Load a test image -# ----------------- - -####################################################################### -# Get a real image for e2e testing -# -------------------------------- -def get_real_image(im_height, im_width): - from PIL import Image - repo_base = 'https://github.com/dmlc/web-data/raw/master/tensorflow/models/InceptionV1/' - img_name = 'elephant-299.jpg' - image_url = os.path.join(repo_base, img_name) - img_path = download_testdata(image_url, img_name, module='data') - image = Image.open(img_path).resize((im_height, im_width)) - x = np.array(image).astype('uint8') - data = np.reshape(x, (1, im_height, im_width, 3)) - return data - -data = get_real_image(224, 224) - -###################################################################### -# Load a tflite model -# ------------------- - -###################################################################### -# Now we can open mobilenet_v2_1.0_224.tflite -tflite_model_file = os.path.join(model_dir, "mobilenet_v2_1.0_224_quant.tflite") -tflite_model_buf = open(tflite_model_file, "rb").read() - -tflite_model = tflite.Model.GetRootAsModel(tflite_model_buf, 0) - - -############################################################################### -# Lets run TFLite pre-quantized model inference and get the TFLite prediction. -def run_tflite_model(tflite_model_buf, input_data): - """ Generic function to execute TFLite """ - try: - from tensorflow import lite as interpreter_wrapper - except ImportError: - from tensorflow.contrib import lite as interpreter_wrapper - - input_data = input_data if isinstance(input_data, list) else [input_data] - - interpreter = interpreter_wrapper.Interpreter(model_content=tflite_model_buf) - interpreter.allocate_tensors() - - input_details = interpreter.get_input_details() - output_details = interpreter.get_output_details() - - # set input - assert len(input_data) == len(input_details) - for i in range(len(input_details)): - interpreter.set_tensor(input_details[i]['index'], input_data[i]) - - # Run - interpreter.invoke() - - # get output - tflite_output = list() - for i in range(len(output_details)): - tflite_output.append(interpreter.get_tensor(output_details[i]['index'])) - - return tflite_output - -############################################################################### -# Lets run TVM compiled pre-quantized model inference and get the TVM prediction. -def run_tvm(graph, lib, params): - from tvm.contrib import graph_runtime - rt_mod = graph_runtime.create(graph, lib, ctx=tvm.cpu(0)) - rt_mod.set_input(**params) - rt_mod.set_input('input', data) - rt_mod.run() - tvm_res = rt_mod.get_output(0).asnumpy() - tvm_pred = np.squeeze(tvm_res).argsort()[-5:][::-1] - return tvm_pred, rt_mod - - -############################################################################### -# TFLite inference -# ---------------- - -############################################################################### -# Run TFLite inference on the quantized model. -tflite_res = run_tflite_model(tflite_model_buf, data) -tflite_pred = np.squeeze(tflite_res).argsort()[-5:][::-1] - -############################################################################### -# TVM compilation and inference -# ----------------------------- - -############################################################################### -# We use the TFLite-Relay parser to convert the TFLite pre-quantized graph into Relay IR. Note that -# frontend parser call for a pre-quantized model is exactly same as frontend parser call for a FP32 -# model. We encourage you to remove the comment from print(mod) and inspect the Relay module. You -# will see many QNN operators, like, Requantize, Quantize and QNN Conv2D. -dtype_dict = {'input': data.dtype.name} -shape_dict = {'input': data.shape} - -mod, params = relay.frontend.from_tflite(tflite_model, - shape_dict=shape_dict, - dtype_dict=dtype_dict) -# print(mod) - -############################################################################### -# Lets now the compile the Relay module. We use the "llvm" target here. Please replace it with the -# target platform that you are interested in. -target = 'llvm' -with relay.build_config(opt_level=3): - graph, lib, params = relay.build_module.build(mod, target=target, - params=params) - -############################################################################### -# Finally, lets call inference on the TVM compiled module. -tvm_pred, rt_mod = run_tvm(graph, lib, params) - -############################################################################### -# Accuracy comparison -# ------------------- - -############################################################################### -# Print the top-5 labels for MXNet and TVM inference. -# Checking the labels because the requantize implementation is different between -# TFLite and Relay. This cause final output numbers to mismatch. So, testing accuracy via labels. - -print("TVM Top-5 labels:", tvm_pred) -print("TFLite Top-5 labels:", tflite_pred) - - -########################################################################## -# Measure performance -# ------------------- -# Here we give an example of how to measure performance of TVM compiled models. -n_repeat = 100 # should be bigger to make the measurement more accurate -ctx = tvm.cpu(0) -ftimer = rt_mod.module.time_evaluator("run", ctx, number=1, repeat=n_repeat) -prof_res = np.array(ftimer().results) * 1e3 -print("Elapsed average ms:", np.mean(prof_res)) - -###################################################################### -# .. note:: -# -# Unless the hardware has special support for fast 8 bit instructions, quantized models are -# not expected to be any faster than FP32 models. Without fast 8 bit instructions, TVM does -# quantized convolution in 16 bit, even if the model itself is 8 bit. -# -# For x86, the best performance can be achieved on CPUs with AVX512 instructions set. -# In this case, TVM utilizes the fastest available 8 bit instructions for the given target. -# This includes support for the VNNI 8 bit dot product instruction (CascadeLake or newer). -# For EC2 C5.12x large instance, TVM latency for this tutorial is ~2 ms. -# -# Intel conv2d NCHWc schedule on ARM gives better end-to-end latency compared to ARM NCHW -# conv2d spatial pack schedule for many TFLite networks. ARM winograd performance is higher but -# it has a high memory footprint. -# -# Moreover, the following general tips for CPU performance equally applies: -# -# * Set the environment variable TVM_NUM_THREADS to the number of physical cores -# * Choose the best target for your hardware, such as "llvm -mcpu=skylake-avx512" or -# "llvm -mcpu=cascadelake" (more CPUs with AVX512 would come in the future) -# * Perform autotuning - `Auto-tuning a convolution network for x86 CPU -# <https://tvm.apache.org/docs/tutorials/autotvm/tune_relay_x86.html>`_. -# * To get best inference performance on ARM CPU, change target argument according to your -# device and follow `Auto-tuning a convolution network for ARM CPU -# <https://tvm.apache.org/docs/tutorials/autotvm/tune_relay_arm.html>`_.
