#!/usr/bin/env python
# coding: utf-8

# # OpenVINO™ Runtime API Tutorial

# This notebook explains the basics of the OpenVINO Runtime API. It covers:
# 
# - [Loading OpenVINO Runtime and Showing Info](#Loading-OpenVINO-Runtime-and-Showing-Info)
# - [Loading a Model](#Loading-a-Model)
#   - [OpenVINO IR Model](#OpenVINO-IR-Model)
#   - [ONNX Model](#ONNX-Model)
# - [Getting Information about a Model](#Getting-Information-about-a-Model)
#   - [Model Inputs](#Model-Inputs)
#   - [Model Outputs](#Model-Outputs)
# - [Doing Inference on a Model](#Doing-Inference-on-a-Model)
# - [Reshaping and Resizing](#Reshaping-and-Resizing)
#   - [Change Image Size](#Change-Image-Size)
#   - [Change Batch Size](#Change-Batch-Size)
#  - [Caching a Model](#Caching-a-Model)
#     
# The notebook is divided into sections with headers. Each section is standalone and does not depend on previous sections. A segmentation and classification OpenVINO IR model and a segmentation ONNX model are provided as examples. These model files can be replaced with your own models. The exact outputs will be different, but the process is the same. 

# ## Loading OpenVINO Runtime and Showing Info
# 
# Initialize OpenVINO Runtime with Core()

# In[ ]:


from openvino.runtime import Core

ie = Core()


# OpenVINO Runtime can load a network on a device. A device in this context means a CPU, an Intel GPU, a Neural Compute Stick 2, etc. The `available_devices` property shows the available devices in your system. The "FULL_DEVICE_NAME" option to `ie.get_property()` shows the name of the device.
# 
# In this notebook, the CPU device is used. To use an integrated GPU, use `device_name="GPU"` instead. Be aware that loading a network on GPU will be slower than loading a network on CPU, but inference will likely be faster.

# In[ ]:


devices = ie.available_devices

for device in devices:
    device_name = ie.get_property(device, "FULL_DEVICE_NAME")
    print(f"{device}: {device_name}")


# ## Loading a Model
# 
# After initializing OpenVINO Runtime, first read the model file with `read_model()`, then compile it to the specified device with the `compile_model()` method. 
# 
# ### OpenVINO IR Model
# 
# An OpenVINO IR (Intermediate Representation) model consists of an `.xml` file, containing information about network topology, and a `.bin` file, containing the weights and biases binary data. The `read_model()` function expects the `.bin` weights file to have the same filename and be located in the same directory as the `.xml` file: `model_weights_file == Path(model_xml).with_suffix(".bin")`. If this is the case, specifying the weights file is optional. If the weights file has a different filename, it can be specified with the `weights` parameter to `read_model()`.
# 
# For information on how to convert your existing TensorFlow, PyTorch or ONNX model to OpenVINO IR format with Model Optimizer, refer to the [tensorflow-to-openvino](../101-tensorflow-to-openvino/101-tensorflow-to-openvino.ipynb) and [pytorch-onnx-to-openvino](../102-pytorch-onnx-to-openvino/102-pytorch-onnx-to-openvino.ipynb) notebooks. For exporting ONNX models to OpenVINO IR with default settings, the `.serialize()` method can also be used.

# In[ ]:


from openvino.runtime import Core

ie = Core()
classification_model_xml = "model/classification.xml"

model = ie.read_model(model=classification_model_xml)
compiled_model = ie.compile_model(model=model, device_name="CPU")


# ### ONNX Model
# 
# Reading and loading an ONNX model, which is a single file, works the same way as with an OpenVINO IR model. The `model` argument points to the filename of an ONNX model.

# In[ ]:


from openvino.runtime import Core

ie = Core()
onnx_model_path = "model/segmentation.onnx"

model_onnx = ie.read_model(model=onnx_model_path)
compiled_model_onnx = ie.compile_model(model=model_onnx, device_name="CPU")


# The ONNX model can be exported to OpenVINO IR with `serialize():`

# In[ ]:


from openvino.runtime import serialize

serialize(model, xml_path="model/exported_onnx_model.xml")


# ## Getting Information about a Model
# 
# The OpenVINO IENetwork instance stores information about the model. Information about the inputs and outputs of the model are in `model.inputs` and `model.outputs`. These are also properties of the ExecutableNetwork instance. While using `model.inputs` and `model.outputs` in the cells below, you can also use `compiled_model.inputs` and `compiled_model.outputs`.

# ### Model Inputs

# In[ ]:


from openvino.runtime import Core

ie = Core()
classification_model_xml = "model/classification.xml"
model = ie.read_model(model=classification_model_xml)
model.input(0).any_name


# The cell above shows that the model loaded expects one input, with the name _input_. If you loaded a different model, you may see a different input layer name, and you may see more inputs.
# 
# It is often useful to have a reference to the name of the first input layer. For a model with one input, `model.input(0)` gets this name.

# In[ ]:


input_layer = model.input(0)


# Information for this input layer is stored in `inputs`. The next cell prints the input layout, precision and a shape.

# In[ ]:


print(f"input precision: {input_layer.element_type}")
print(f"input shape: {input_layer.shape}")


# This cell shows that the model expects inputs with a shape of [1,3,224,224], and that this is in the `NCHW` layout. This means that the model expects input data with the batch size of 1 (`N`), 3 channels (`C`) , and images with a height (`H`) and width (`W`) equal to 224. The input data is expected to be of `FP32` (floating point) precision.

# ### Model Outputs

# In[ ]:


from openvino.runtime import Core

ie = Core()
classification_model_xml = "model/classification.xml"
model = ie.read_model(model=classification_model_xml)
model.output(0).any_name


# Model output info is stored in `model.outputs`. The cell above shows that the model returns one output, with the `MobilenetV3/Predictions/Softmax` name. Loading a different model will result in different output layer name, and more outputs might be returned.
# 
# Since this model has one output, follow the same method as for the input layer to get its name.

# In[ ]:


output_layer = model.output(0)
output_layer


# Getting the output precision and shape is similar to getting the input precision and shape.

# In[ ]:


print(f"output precision: {output_layer.element_type}")
print(f"output shape: {output_layer.shape}")


# This cell shows that the model returns outputs with a shape of [1, 1001], where 1 is the batch size (`N`) and 1001 is the number of classes (`C`). The output is returned as 32-bit floating point.

# ## Doing Inference on a Model
# 
# To do inference on a model, first create an inference request by calling the `create_infer_request()` method of `ExecutableNetwork`, `exec_net` that was loaded with `compile_model()`. Then, call the `infer()` method of `InferRequest`. It expects one argument: `inputs`. This is a dictionary that maps input layer names to input data.

# **Load the network**

# In[ ]:


from openvino.runtime import Core

ie = Core()
classification_model_xml = "model/classification.xml"
model = ie.read_model(model=classification_model_xml)
compiled_model = ie.compile_model(model=model, device_name="CPU")
input_layer = compiled_model.input(0)
output_layer = compiled_model.output(0)


# **Load an image and convert to the input shape**
# 
# To propagate an image through the network, it needs to be loaded into an array, resized to the shape that the network expects, and converted to the input layout of the network.

# In[ ]:


import cv2

image_filename = "data/coco_hollywood.jpg"
image = cv2.imread(image_filename)
image.shape


# The image has a shape of (663,994,3). It is 663 pixels in height, 994 pixels in width, and has 3 color channels. A reference to the height and width expected by the network is obtained and the image is resized to these dimensions.

# In[ ]:


# N,C,H,W = batch size, number of channels, height, width.
N, C, H, W = input_layer.shape
# OpenCV resize expects the destination size as (width, height).
resized_image = cv2.resize(src=image, dsize=(W, H))
resized_image.shape


# Now, the image has the width and height that the network expects. This is still in `HWC` format and must be changed to `NCHW` format. First, call the `np.transpose()` method to change to `CHW` and then add the `N` dimension (where `N`= 1) by calling the `np.expand_dims()` method. Next, convert the data to `FP32` with `np.astype()` method.

# In[ ]:


import numpy as np

input_data = np.expand_dims(np.transpose(resized_image, (2, 0, 1)), 0).astype(np.float32)
input_data.shape


# **Do inference**
# 
# Now that the input data is in the right shape, do the inference.

# In[ ]:


result = compiled_model([input_data])[output_layer]


# You can also create `InferRequest` and run `infer` method on request.

# In[ ]:


request = compiled_model.create_infer_request()
request.infer(inputs={input_layer.any_name: input_data})
result = request.get_output_tensor(output_layer.index).data


# The `.infer()` function sets output tensor, that can be reached, using `get_output_tensor()`. Since this network returns one output, and the reference to the output layer is in the `output_layer.index` parameter, you can get the data with `request.get_output_tensor(output_layer.index)`. To get a numpy array from the output, use the `.data` parameter.

# In[ ]:


result.shape


# The output shape is (1,1001), which is the expected output shape. This shape indicates that the network returns probabilities for 1001 classes. To learn more about this notion, refer to the [hello world notebook](../001-hello-world/001-hello-world.ipynb).

# ## Reshaping and Resizing
# 
# ### Change Image Size

# Instead of reshaping the image to fit the model, it is also possible to reshape the model to fit the image. Be aware that not all models support reshaping, and models that do, may not support all input shapes. The model accuracy may also suffer if you reshape the model input shape.
# 
# First check the input shape of the model, then reshape it to the new input shape.

# In[ ]:


from openvino.runtime import Core, PartialShape

ie = Core()
segmentation_model_xml = "model/segmentation.xml"
segmentation_model = ie.read_model(model=segmentation_model_xml)
segmentation_input_layer = segmentation_model.input(0)
segmentation_output_layer = segmentation_model.output(0)

print("~~~~ ORIGINAL MODEL ~~~~")
print(f"input shape: {segmentation_input_layer.shape}")
print(f"output shape: {segmentation_output_layer.shape}")

new_shape = PartialShape([1, 3, 544, 544])
segmentation_model.reshape({segmentation_input_layer.any_name: new_shape})
segmentation_compiled_model = ie.compile_model(model=segmentation_model, device_name="CPU")
# help(segmentation_compiled_model)
print("~~~~ RESHAPED MODEL ~~~~")
print(f"model input shape: {segmentation_input_layer.shape}")
print(
    f"compiled_model input shape: "
    f"{segmentation_compiled_model.input(index=0).shape}"
)
print(f"compiled_model output shape: {segmentation_output_layer.shape}")


# The input shape for the segmentation network is [1,3,512,512], with the `NCHW` layout: the network expects 3-channel images with a width and height of 512 and a batch size of 1. Reshape the network with the `.reshape()` method of `IENetwork` to make it accept input images with a width and height of 544. This segmentation network always returns arrays with the input width and height of equal value. Therefore, setting the input dimensions to 544x544 also modifies the output dimensions. After reshaping, compile the network once again.

# ### Change Batch Size

# Use the `.reshape()` method to set the batch size, by increasing the first element of `new_shape`. For example, to set a batch size of two, set `new_shape = (2,3,544,544)` in the cell above. 

# In[ ]:


from openvino.runtime import Core, PartialShape

ie = Core()
segmentation_model_xml = "model/segmentation.xml"
segmentation_model = ie.read_model(model=segmentation_model_xml)
segmentation_input_layer = segmentation_model.input(0)
segmentation_output_layer = segmentation_model.output(0)
new_shape = PartialShape([2, 3, 544, 544])
segmentation_model.reshape({segmentation_input_layer.any_name: new_shape})
segmentation_compiled_model = ie.compile_model(model=segmentation_model, device_name="CPU")

print(f"input shape: {segmentation_input_layer.shape}")
print(f"output shape: {segmentation_output_layer.shape}")


# The output shows that by setting the batch size to 2, the first element (`N`) of the input and output shape has a value of 2. Propagate the input image through the network to see the result:

# In[ ]:


import numpy as np
from openvino.runtime import Core, PartialShape

ie = Core()
segmentation_model_xml = "model/segmentation.xml"
segmentation_model = ie.read_model(model=segmentation_model_xml)
segmentation_input_layer = segmentation_model.input(0)
segmentation_output_layer = segmentation_model.output(0)
new_shape = PartialShape([2, 3, 544, 544])
segmentation_model.reshape({segmentation_input_layer.any_name: new_shape})
segmentation_compiled_model = ie.compile_model(model=segmentation_model, device_name="CPU")
input_data = np.random.rand(2, 3, 544, 544)

output = segmentation_compiled_model([input_data])

print(f"input data shape: {input_data.shape}")
print(f"result data data shape: {segmentation_output_layer.shape}")


# ## Caching a Model
# 
# For some devices, like GPU, loading a model can take some time. Model Caching solves this issue by caching the model in a cache directory. If `ie.compile_model(model=net, device_name=device_name, config=config_dict)` is set, caching will be used. This option checks if a model exists in the cache. If so, it loads it from the cache. If not, it loads the model regularly, and stores it in the cache, so that the next time the model is loaded when this option is set, the model will be loaded from the cache.
# 
# In the cell below, we create a *model_cache* directory as a subdirectory of *model*, where the model will be cached for the specified device. The model will be loaded to the GPU. After running this cell once, the model will be cached, so subsequent runs of this cell will load the model from the cache.
# 
# *Note: Model Caching is also available on CPU devices*

# In[ ]:


import time
from pathlib import Path

from openvino.runtime import Core

ie = Core()

device_name = "GPU" 

if device_name in ie.available_devices:
    cache_path = Path("model/model_cache")
    cache_path.mkdir(exist_ok=True)
    # Enable caching for OpenVINO Runtime. To disable caching set enable_caching = False
    enable_caching = True
    config_dict = {"CACHE_DIR": str(cache_path)} if enable_caching else {}

    classification_model_xml = "model/classification.xml"
    model = ie.read_model(model=classification_model_xml)

    start_time = time.perf_counter()
    compiled_model = ie.compile_model(model=model, device_name=device_name, config=config_dict)
    end_time = time.perf_counter()
    print(f"Loading the network to the {device_name} device took {end_time-start_time:.2f} seconds.")


# After running the previous cell, we know the model exists in the cache directory. We delete the compiled model and load it again. We measure the time it takes now.

# In[ ]:


if device_name in ie.available_devices:
    del compiled_model
    start_time = time.perf_counter()
    compiled_model = ie.compile_model(model=model, device_name=device_name, config=config_dict)
    end_time = time.perf_counter()
    print(f"Loading the network to the {device_name} device took {end_time-start_time:.2f} seconds.")