anijain2305 commented on a change in pull request #5595: URL: https://github.com/apache/incubator-tvm/pull/5595#discussion_r425316098
########## File path: tutorials/frontend/deploy_prequantized_tflite.py ########## @@ -0,0 +1,244 @@ +# 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. +# +# 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 Review comment: Can you please add a comment about ARM tuning here? It is more likely that TVM user interested in this tutorial is looking to run on ARM device. ########## File path: tutorials/frontend/deploy_prequantized_tflite.py ########## @@ -0,0 +1,244 @@ +# 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']) Review comment: Please align. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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