leandron commented on a change in pull request #7640: URL: https://github.com/apache/tvm/pull/7640#discussion_r595362741
########## File path: tutorials/get_started/tvmc_command_line_driver.py ########## @@ -97,114 +97,134 @@ ###################################################################### -# Compiling the model -# ------------------- +# Compiling an ONNX Model to the TVM Runtime +# ------------------------------------------ # -# The next step once we've downloaded ResNet-50, is to compile it, -# To accomplish that, we are going to use ``tvmc compile``. The -# output we get from the compilation process is a TAR package, -# that can be used to run our model on the target device. +# Once we've downloaded the ResNet-50 model, the next step is to compile it. To accomplish that, we are +# going to use ``tvmc compile``. The output we get from the compilation process is a TAR package of the model +# compiled to a dynamic library for our target platform. We can run that model on our target device using the +# TVM runtime. # # .. code-block:: bash # # tvmc compile \ -# --target "llvm" \ -# --output compiled_module.tar \ -# resnet50-v2-7.onnx +# --target "llvm" \ +# --output resnet50-v2-7-tvm.tar \ +# resnet50-v2-7.onnx +# +# Let's take a look at the files that ``tvmc compile`` creates: +# +# .. code-block:: bash # -# Once compilation finishes, the output ``compiled_module.tar`` will be created. This -# can be directly loaded by your application and run via the TVM runtime APIs. +# mkdir model +# tar -xvf resnet50-v2-7-tvm.tar -C model +# ls model +# +# You will see three files listed. +# +# * ``mod.so`` is the model, represented as a C++ library, that can be loaded by the TVM runtime. +# * ``mod.json`` is a text representation of the TVM Relay computation graph. +# * ``mod.params`` is a file containing the parameters for the pre-trained model. +# +# This model can be directly loaded by your application and run via the TVM runtime APIs. # ###################################################################### -# .. note:: Defining the correct target +# .. note:: Defining the Correct Target # # Specifying the correct target (option ``--target``) can have a huge # impact on the performance of the compiled module, as it can take # advantage of hardware features available on the target. For more # information, please refer to `Auto-tuning a convolutional network # for x86 CPU <https://tvm.apache.org/docs/tutorials/autotvm/tune_relay_x86.html#define-network>`_. +# We recommend identifying which CPU you are running, along with optional features, +# and set the target appropriately. # - ###################################################################### +# Running the TVM IR Model with TVMC +# ---------------------------------- +# +# Now that we've compiled the model, we can use the TVM runtime to make predictions with it. +# TVMC has the TVM runtime built in to it, allowing you to run compiled TVM models. To use TVMC to run the +# model and make predictions, we need two things: +# +# - The compiled model, which we just produced. +# - Valid input to the model to make predictions on. # -# In the next step, we are going to use the compiled module, providing it -# with some inputs, to generate some predictions. +# Each model is particular when it comes to expected tensor shapes, formats and data types. For this reason, +# most models require some pre and post-processing, to ensure the input is valid and to interpret the output. +# TVMC has adopted NumPy's ``.npz`` format for both input and output data. This is a well-supported NumPy +# format to serialize multiple arrays into a file # +# As input for this tutorial, we will use the image of a cat, but you can feel free to substitute image for +# any of your choosing. +# +# .. image:: https://s3.amazonaws.com/model-server/inputs/kitten.jpg +# :height: 224px +# :width: 224px +# :align: center ###################################################################### # Input pre-processing -# -------------------- +# ~~~~~~~~~~~~~~~~~~~~ # -# In order to generate predictions, we will need two things: +# For our ResNet 50 V2 model, the input is expected to be in ImageNet format. +# Here is an example of a script to pre-process an image for ResNet 50 V2. # -# - the compiled module, which we just produced; -# - a valid input to the model +# .. code-block:: python +# :caption: preprocess.py +# :name: preprocess.py # -# Each model is particular when it comes to expected tensor shapes, formats and data -# types. For this reason, most models require some pre and -# post processing, to ensure the input(s) is valid and to interpret the output(s). +# #!python ./preprocess.py +# from tvm.contrib.download import download_testdata +# from PIL import Image +# import numpy as np # -# In TVMC, we adopted NumPy's ``.npz`` format for both input and output data. -# This is a well-supported NumPy format to serialize multiple arrays into a file. +# img_url = "https://s3.amazonaws.com/model-server/inputs/kitten.jpg"; +# img_path = download_testdata(img_url, "imagenet_cat.png", module="data") # -# We will use the usual cat image, similar to other TVM tutorials: +# # Resize it to 224x224 +# resized_image = Image.open(img_path).resize((224, 224)) +# img_data = np.asarray(resized_image).astype("float32") # -# .. image:: https://s3.amazonaws.com/model-server/inputs/kitten.jpg -# :height: 224px -# :width: 224px -# :align: center +# # ONNX expects NCHW input, so convert the array +# img_data = np.transpose(img_data, (2, 0, 1)) # -# For our ResNet 50 V2 model, the input is expected to be in ImageNet format. -# Here is an example of a script to pre-process an image for ResNet 50 V2. +# # Normalize according to ImageNet +# imagenet_mean = np.array([0.485, 0.456, 0.406]) +# imagenet_stddev = np.array([0.229, 0.224, 0.225]) +# norm_img_data = np.zeros(img_data.shape).astype("float32") +# for i in range(img_data.shape[0]): +# norm_img_data[i, :, :] = (img_data[i, :, :] / 255 - imagenet_mean[i]) / imagenet_stddev[i] +# +# # Add batch dimension +# img_data = np.expand_dims(norm_img_data, axis=0) +# +# # Save to .npz (outputs imagenet_cat.npz) +# np.savez("imagenet_cat", data=img_data) # -from tvm.contrib.download import download_testdata -from PIL import Image -import numpy as np - -img_url = "https://s3.amazonaws.com/model-server/inputs/kitten.jpg"; -img_path = download_testdata(img_url, "imagenet_cat.png", module="data") - -# Resize it to 224x224 -resized_image = Image.open(img_path).resize((224, 224)) -img_data = np.asarray(resized_image).astype("float32") - -# ONNX expects NCHW input, so convert the array -img_data = np.transpose(img_data, (2, 0, 1)) - -# Normalize according to ImageNet -imagenet_mean = np.array([0.485, 0.456, 0.406]) -imagenet_stddev = np.array([0.229, 0.224, 0.225]) -norm_img_data = np.zeros(img_data.shape).astype("float32") -for i in range(img_data.shape[0]): - norm_img_data[i, :, :] = (img_data[i, :, :] / 255 - imagenet_mean[i]) / imagenet_stddev[i] - -# Add batch dimension -img_data = np.expand_dims(norm_img_data, axis=0) - -# Save to .npz (outputs imagenet_cat.npz) -np.savez("imagenet_cat", data=img_data) ###################################################################### -# Running the compiled module -# --------------------------- +# Running the Compiled Module Review comment: I think this way it fine now. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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![https://s3.amazonaws.com/model-server/inputs/kitten.jpg"](https://s3.amazonaws.com/model-server/inputs/kitten.jpg"){kind=link}