Photos to Anime with PaddleGAN and OpenVINO

This tutorial is also available as a Jupyter notebook that can be cloned directly from GitHub. See the installation guide for instructions to run this tutorial locally on Windows, Linux or macOS.

This tutorial demonstrates converting a PaddlePaddle/PaddleGAN AnimeGAN model to OpenVINO IR format, and shows inference results on the PaddleGAN and OpenVINO IR models.

For more information about the model, see PaddleGAN’s AnimeGAN documentation

anime

Imports

!pip install -q "paddlepaddle==2.5.0rc0" "paddle2onnx>=0.6"

!pip install -q "imageio==2.9.0" "imageio-ffmpeg" "numba>=0.53.1" "easydict" "munch" "natsort"
!pip install -q "git+https://github.com/PaddlePaddle/PaddleGAN.git" --no-deps

ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
paddleclas 2.5.1 requires faiss-cpu==1.7.1.post2, but you have faiss-cpu 1.7.4 which is incompatible.
paddleclas 2.5.1 requires gast==0.3.3, but you have gast 0.4.0 which is incompatible.

import sys
import time
import os
from pathlib import Path

import cv2
import matplotlib.pyplot as plt
import numpy as np
from IPython.display import HTML, display
from openvino.runtime import Core

# PaddlePaddle requires a C++ compiler. If importing the paddle packages fails,
# install C++.
try:
    import paddle
    from paddle.static import InputSpec
    from ppgan.apps import AnimeGANPredictor
except NameError:
    if sys.platform == "win32":
        install_message = (
            "To use this notebook, please install the free Microsoft "
            "Visual C++ redistributable from <a href='https://aka.ms/vs/16/release/vc_redist.x64.exe'>"
            "https://aka.ms/vs/16/release/vc_redist.x64.exe</a>"
        )
    else:
        install_message = (
            "To use this notebook, please install a C++ compiler. On macOS, "
            "`xcode-select --install` installs many developer tools, including C++. On Linux, "
            "install gcc with your distribution's package manager."
        )
    display(
        HTML(
            f"""<div class="alert alert-danger" ><i>
    <b>Error: </b>PaddlePaddle requires installation of C++. {install_message}"""
        )
    )
    raise

Settings

MODEL_DIR = "model"
MODEL_NAME = "paddlegan_anime"

os.makedirs(MODEL_DIR, exist_ok=True)

# Create filenames of the models that will be converted in this notebook.
model_path = Path(f"{MODEL_DIR}/{MODEL_NAME}")
ir_path = model_path.with_suffix(".xml")
onnx_path = model_path.with_suffix(".onnx")

Functions

def resize_to_max_width(image, max_width):
    """
    Resize `image` to `max_width`, preserving the aspect ratio of the image.
    """
    if image.shape[1] > max_width:
        hw_ratio = image.shape[0] / image.shape[1]
        new_height = int(max_width * hw_ratio)
        image = cv2.resize(image, (max_width, new_height))
    return image

Inference on PaddleGAN Model

The PaddleGAN documentation explains how to run the model with .run() method. Find out what that function does with Jupyter’s ?? shortcut to show the docstring and source of the function.

# This cell will initialize the AnimeGANPredictor() and download the weights from PaddlePaddle.
# This may take a while. The weights are stored in a cache and are downloaded once.
predictor = AnimeGANPredictor()

[05/17 23:18:25] ppgan INFO: Found /opt/home/k8sworker/.cache/ppgan/animeganv2_hayao.pdparams

# In a Jupyter Notebook, ?? shows the source and docstring
predictor.run??

The AnimeGANPredictor.run() method works as follow:

1. Loads an image with OpenCV and converts it to RGB.

2. Transforms the image.

3. Propagates the transformed image through the generator model and postprocesses the results to return an array with a [0,255] range.

4. Transposes the result from (C,H,W) to (H,W,C) shape.

5. Resizes the result image to the original image size.

6. (optional) Adjusts the brightness of the result image.

7. Saves the image.

You can execute these steps manually and confirm that the result looks correct. To speed up inference time, resize large images before propagating them through the network. The inference step in the next cell will still take some time to execute. If you want to skip this step, set PADDLEGAN_INFERENCE = False in the first line of the next cell.

PADDLEGAN_INFERENCE = True
OUTPUT_DIR = "output"

os.makedirs(OUTPUT_DIR, exist_ok=True)
# Step 1. Load the image and convert to RGB.
image_path = Path("../data/image/coco_bricks.png")

image = cv2.cvtColor(cv2.imread(str(image_path), flags=cv2.IMREAD_COLOR), cv2.COLOR_BGR2RGB)

## Inference takes a long time on large images. Resize to a max width of 600.
image = resize_to_max_width(image, 600)

# Step 2. Transform the image.
transformed_image = predictor.transform(image)
input_tensor = paddle.to_tensor(transformed_image[None, ::])

if PADDLEGAN_INFERENCE:
    # Step 3. Do inference.
    predictor.generator.eval()
    with paddle.no_grad():
        result = predictor.generator(input_tensor)

    # Step 4. Convert the inference result to an image, following the same steps as
    # PaddleGAN's predictor.run() function.
    result_image_pg = (result * 0.5 + 0.5)[0].numpy() * 255
    result_image_pg = result_image_pg.transpose((1, 2, 0))

    # Step 5. Resize the result image.
    result_image_pg = cv2.resize(result_image_pg, image.shape[:2][::-1])

    # Step 6. Adjust the brightness.
    result_image_pg = predictor.adjust_brightness(result_image_pg, image)

    # Step 7. Save the result image.
    anime_image_path_pg = Path(f"{OUTPUT_DIR}/{image_path.stem}_anime_pg").with_suffix(".jpg")
    if cv2.imwrite(str(anime_image_path_pg), result_image_pg[:, :, (2, 1, 0)]):
        print(f"The anime image was saved to {anime_image_path_pg}")

The anime image was saved to output/coco_bricks_anime_pg.jpg

Show Inference Results on PaddleGAN model

if PADDLEGAN_INFERENCE:
    fig, ax = plt.subplots(1, 2, figsize=(25, 15))
    ax[0].imshow(image)
    ax[1].imshow(result_image_pg)
else:
    print("PADDLEGAN_INFERENCE is not enabled. Set PADDLEGAN_INFERENCE = True in the previous cell and run that cell to show inference results.")

Model Conversion to ONNX and OpenVINO IR

Convert the PaddleGAN model to OpenVINO IR by first converting PaddleGAN to ONNX with paddle2onnx and then converting the ONNX model to OpenVINO IR with Model Optimizer.

Convert to ONNX

Exporting to ONNX requires specifying an input shape with PaddlePaddle InputSpec and calling paddle.onnx.export. Then, check the input shape of the transformed image and use that as the input shape for the ONNX model. Exporting to ONNX should not take long. If the export succeeds, the output of the next cell will include ONNX model saved in paddlegan_anime.onnx.

target_height, target_width = transformed_image.shape[1:]
target_height, target_width

(448, 576)

predictor.generator.eval()
x_spec = InputSpec([None, 3, target_height, target_width], "float32", "x")
paddle.onnx.export(predictor.generator, str(model_path), input_spec=[x_spec], opset_version=11)

2023-05-17 23:18:33 [INFO]  Static PaddlePaddle model saved in model/paddle_model_static_onnx_temp_dir.
[Paddle2ONNX] Start to parse PaddlePaddle model...
[Paddle2ONNX] Model file path: model/paddle_model_static_onnx_temp_dir/model.pdmodel
[Paddle2ONNX] Paramters file path: model/paddle_model_static_onnx_temp_dir/model.pdiparams
[Paddle2ONNX] Start to parsing Paddle model...
[Paddle2ONNX] Use opset_version = 11 for ONNX export.
[Paddle2ONNX] PaddlePaddle model is exported as ONNX format now.
2023-05-17 23:18:33 [INFO]  ONNX model saved in model/paddlegan_anime.onnx.

I0517 23:18:33.405802 1523471 interpretercore.cc:267] New Executor is Running.

Convert to OpenVINO IR

The OpenVINO IR format enables storing the preprocessing normalization in the model file. It is then no longer necessary to normalize input images manually. See the transforms that the .run() method used:

predictor.__init__??

t = predictor.transform.transforms[0]
t.params

{'taget_size': (448, 576)}

## Uncomment the line below to see the documentation and code of the ResizeToScale transformation
# t??

There are three transformations: resize, transpose, and normalize, where normalize uses a mean and scale of [127.5, 127.5, 127.5].

The ResizeToScale class is called with (256,256) as the argument for size. Further analysis shows that this is the minimum size to resize to. The ResizeToScale class transform resizes images to the size specified in the ResizeToScale params, with width and height as multiples of 32.

Once the mean and standard deviation values, and the shape of the model inputs are known, you can use Model Optimizer and convert the model to OpenVINO IR with these values. Use FP16 precision and set log level to CRITICAL to ignore warnings that are irrelevant for this demo. For information about setting the parameters, see the Model Optimizer Documentation .

Convert ONNX Model to OpenVINO IR withModel Optimizer Python API

from openvino.tools import mo
from openvino.runtime import serialize

print("Exporting ONNX model to OpenVINO IR... This may take a few minutes.")

model = mo.convert_model(
    onnx_path,
    input_shape=[1, 3, target_height, target_width],
    mean_values=[127.5,127.5,127.5],
    scale_values=[127.5,127.5,127.5],
    compress_to_fp16=True
)

# Serialize model in IR format
serialize(model, str(ir_path))

Exporting ONNX model to OpenVINO IR... This may take a few minutes.

Show Inference Results on OpenVINO IR and PaddleGAN Models

If the output of Model Optimizer in the cell above showed SUCCESS, the model conversion succeeded and the OpenVINO IR model has been generated.

Now, use the model for inference with the adjust_brightness() method from the PaddleGAN model. However, in order to use the OpenVINO IR model without installing PaddleGAN, it is useful to check what these functions do and extract them.

Create Postprocessing Functions

predictor.adjust_brightness??

predictor.calc_avg_brightness??

The average brightness is computed by a standard formula. To adjust the brightness, the difference in brightness between the source and destination (anime) image is computed and the brightness of the destination image is adjusted based on that. Then, the image is converted to an 8-bit image.

Copy these functions to the next cell, use them for inference on the OpenVINO IR model

# Copyright (c) 2020 PaddlePaddle Authors. Licensed under the Apache License, Version 2.0


def calc_avg_brightness(img):
    R = img[..., 0].mean()
    G = img[..., 1].mean()
    B = img[..., 2].mean()

    brightness = 0.299 * R + 0.587 * G + 0.114 * B
    return brightness, B, G, R


def adjust_brightness(dst, src):
    brightness1, B1, G1, R1 = AnimeGANPredictor.calc_avg_brightness(src)
    brightness2, B2, G2, R2 = AnimeGANPredictor.calc_avg_brightness(dst)
    brightness_difference = brightness1 / brightness2
    dstf = dst * brightness_difference
    dstf = np.clip(dstf, 0, 255)
    dstf = np.uint8(dstf)
    return dstf

Do Inference on OpenVINO IR Model

Load the OpenVINO IR model and do inference, following the same steps as for the PaddleGAN model. For more information about inference on OpenVINO IR models, see the OpenVINO Runtime API notebook.

The OpenVINO IR model is generated with an input shape that is computed based on the input image. If you do inference on images with different input shapes, results may differ from the PaddleGAN results.

# Load and prepare the IR model.
ie = Core()
model = ie.read_model(model=ir_path)
compiled_model = ie.compile_model(model=model, device_name="CPU")
input_key = compiled_model.input(0)
output_key = compiled_model.output(0)

# Step 1. Load an image and convert it to RGB.
image_path = Path("../data/image/coco_bricks.png")
image = cv2.cvtColor(cv2.imread(str(image_path), flags=cv2.IMREAD_COLOR), cv2.COLOR_BGR2RGB)

# Step 2. Transform the image (only resize and transpose are still required).
resized_image = cv2.resize(image, (target_width, target_height))
input_image = resized_image.transpose(2, 0, 1)[None, :, :, :]

# Step 3. Do inference.
result_ir = compiled_model([input_image])[output_key]

# Step 4. Convert the inference result to an image, following the same steps as
# PaddleGAN's predictor.run() function.
result_image_ir = (result_ir * 0.5 + 0.5)[0] * 255
result_image_ir = result_image_ir.transpose((1, 2, 0))

# Step 5. Resize the result image.
result_image_ir = cv2.resize(result_image_ir, image.shape[:2][::-1])

# Step 6. Adjust the brightness.
result_image_ir = adjust_brightness(result_image_ir, image)

# Step 7. Save the result image.
anime_fn_ir = Path(f"{OUTPUT_DIR}/{image_path.stem}_anime_ir").with_suffix(".jpg")
if cv2.imwrite(str(anime_fn_ir), result_image_ir[:, :, (2, 1, 0)]):
    print(f"The anime image was saved to {anime_fn_ir}")

The anime image was saved to output/coco_bricks_anime_ir.jpg

Show Inference Results

fig, ax = plt.subplots(1, 2, figsize=(25, 15))
ax[0].imshow(image)
ax[1].imshow(result_image_ir)
ax[0].set_title("Image")
ax[1].set_title("OpenVINO IR result");

Performance Comparison

Measure the time it takes to do inference on an image. This gives an indication of performance. It is not a perfect measure. Since the PaddleGAN model requires quite a bit of memory for inference, only measure inference on one image. For more accurate benchmarking, use Benchmark Tool.

NUM_IMAGES = 1
start = time.perf_counter()
for _ in range(NUM_IMAGES):
    compiled_model([input_image])
end = time.perf_counter()
time_ir = end - start
print(
    f"OpenVINO IR model in OpenVINO Runtime/CPU: {time_ir/NUM_IMAGES:.3f} "
    f"seconds per image, FPS: {NUM_IMAGES/time_ir:.2f}"
)

## Uncomment the lines below to measure inference time on an Intel iGPU.
## Note that it will take some time to load the model to GPU.

# If "GPU" in ie.available_devices:
#     # Loading the IR model on GPU takes some time.
#     compiled_model = ie.compile_model(model=model, device_name="GPU")
#     start = time.perf_counter()
#     for _ in range(NUM_IMAGES):
#         exec_net_multi([input_image])
#     end = time.perf_counter()
#     time_ir = end - start
#     print(
#         f"OpenVINO IR model in OpenVINO Runtime/GPU: {time_ir/NUM_IMAGES:.3f} "
#         f"seconds per image, FPS: {NUM_IMAGES/time_ir:.2f}"
#     )
# else:
#     print("A supported iGPU device is not available on this system.")

## `PADDLEGAN_INFERENCE` is defined in the "Inference on PaddleGAN model" section above.
## Uncomment the next line to enable a performance comparison with the PaddleGAN model
## if you disabled it earlier.

# PADDLEGAN_INFERENCE = True

if PADDLEGAN_INFERENCE:
    with paddle.no_grad():
        start = time.perf_counter()
        for _ in range(NUM_IMAGES):
            predictor.generator(input_tensor)
        end = time.perf_counter()
        time_paddle = end - start
    print(
        f"PaddleGAN model on CPU: {time_paddle/NUM_IMAGES:.3f} seconds per image, "
        f"FPS: {NUM_IMAGES/time_paddle:.2f}"
    )

OpenVINO IR model in OpenVINO Runtime/CPU: 0.466 seconds per image, FPS: 2.15
PaddleGAN model on CPU: 6.556 seconds per image, FPS: 0.15

References

• PaddleGAN

• Paddle2ONNX

• OpenVINO ONNX support

• OpenVINO Model Optimizer Documentation

The PaddleGAN code that is shown in this notebook is written by PaddlePaddle Authors and licensed under the Apache 2.0 license. The license for this code is displayed below.

#  Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
#Licensed 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.