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| import onnx | |
| import onnxruntime as ort | |
| import numpy as np | |
| MODEL_PATH = r"./" | |
| model_name = "animalImageGAN_full.onnx" | |
| ONNX_MODEL_PATH = MODEL_PATH+model_name | |
| onnx_model = onnx.load(ONNX_MODEL_PATH) | |
| onnx.checker.check_model(onnx_model) | |
| rng = np.random.default_rng() | |
| desired_mean = 0 | |
| desired_variance = 1 | |
| generator_input_size = 50 | |
| latent_space_samples = np.random.rand(generator_input_size,1,1).astype(np.float32) | |
| ort_sess = ort.InferenceSession(ONNX_MODEL_PATH) | |
| import gradio as gr | |
| def generateImage(): | |
| random_input = rng.random((generator_input_size, 1, 1),dtype=np.float32) | |
| current_mean = np.mean(random_input) | |
| current_variance = np.var(random_input) | |
| scaled_values = (random_input - current_mean) / np.sqrt(current_variance) | |
| random_input = scaled_values * np.sqrt(desired_variance) + desired_mean | |
| outputs = ort_sess.run(None, {'input': random_input}) | |
| output = outputs[0] | |
| denorm_output = np.clip((output * 0.5) + 0.5,0,1) | |
| #print("i: {}, min:{},max:{}".format(i,denorm_output.min(),denorm_output.max())) | |
| return denorm_output.transpose(1,2,0) | |
| DESCRIPTION = "<div style='text-align:center'><h1 style='justify-content: center'>Animal Portrait Generator</h1>" | |
| DESCRIPTION += "<p>This is a model trained by using DCGAN</p>" | |
| DESCRIPTION += "<p>More details:</p>" | |
| DESCRIPTION += "<ul><li><a href='https://medium.com/@jiachiewloh/dcgan-animal-image-generator-85e466fb6254'>Article</a></li>" | |
| DESCRIPTION += "<li><a href='https://www.kaggle.com/code/jclohjc/animal-image-generator-dcgan'>Code</a></li></ul>" | |
| DESCRIPTION += "</div>" | |
| with gr.Blocks(css="#img_window {text-align:center; justify-content: center;}\ | |
| .image-container {margin: auto; height: 250px; width: 250px; !important}") as demo: | |
| # with gr.Row(): | |
| # gr.Markdown(DESCRIPTION) | |
| # with gr.Column(): | |
| # img_window = gr.Image(interactive=False,height=250,width=250) | |
| # with gr.Row(): | |
| # gr.Button("Generate").click(fn=generateImage,outputs=img_window) | |
| # gr.ClearButton().add(img_window) | |
| gr.Markdown(DESCRIPTION) | |
| img_window = gr.Image(interactive=False,elem_id="img_window") | |
| with gr.Row(): | |
| gr.Button("Generate").click(fn=generateImage,outputs=img_window) | |
| gr.ClearButton().add(img_window) | |
| demo.launch() |