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LOHJC
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77a9008
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Parent(s):
e96dcf8
added initial files
Browse files- animalImageGAN_full.onnx +3 -0
- app.py +60 -0
- flagged/log.csv +2 -0
- requirements.txt +4 -0
animalImageGAN_full.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:494ad8f4fc71738510d6accdb1fe1b47a50f4d18b5fccfdc58c107b927ee6e4d
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size 12669054
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app.py
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import onnx
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import onnxruntime as ort
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import numpy as np
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MODEL_PATH = r"./"
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model_name = "animalImageGAN_full.onnx"
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ONNX_MODEL_PATH = MODEL_PATH+model_name
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onnx_model = onnx.load(ONNX_MODEL_PATH)
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onnx.checker.check_model(onnx_model)
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rng = np.random.default_rng()
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desired_mean = 0
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desired_variance = 1
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generator_input_size = 50
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latent_space_samples = np.random.rand(generator_input_size,1,1).astype(np.float32)
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ort_sess = ort.InferenceSession(ONNX_MODEL_PATH)
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import gradio as gr
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def generateImage():
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random_input = rng.random((generator_input_size, 1, 1),dtype=np.float32)
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current_mean = np.mean(random_input)
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current_variance = np.var(random_input)
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scaled_values = (random_input - current_mean) / np.sqrt(current_variance)
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random_input = scaled_values * np.sqrt(desired_variance) + desired_mean
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outputs = ort_sess.run(None, {'input': random_input})
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output = outputs[0]
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denorm_output = np.clip((output * 0.5) + 0.5,0,1)
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#print("i: {}, min:{},max:{}".format(i,denorm_output.min(),denorm_output.max()))
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return denorm_output.transpose(1,2,0)
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DESCRIPTION = "<div style='text-align:center'><h1 style='justify-content: center'>Animal Portrait Generator</h1>"
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DESCRIPTION += "<p>This is a model trained by using DCGAN</p>"
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DESCRIPTION += "<p>More details:</p>"
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DESCRIPTION += "<ul><li><a href='https://medium.com/@jiachiewloh/dcgan-animal-image-generator-85e466fb6254'>Article</a></li>"
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DESCRIPTION += "<li><a href='https://www.kaggle.com/code/jclohjc/animal-image-generator-dcgan'>Code</a></li></ul>"
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DESCRIPTION += "</div>"
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with gr.Blocks(css="#img_window {text-align:center; justify-content: center;}\
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.image-container {margin: auto; height: 250px; width: 250px; !important}") as demo:
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# with gr.Row():
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# gr.Markdown(DESCRIPTION)
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# with gr.Column():
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# img_window = gr.Image(interactive=False,height=250,width=250)
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# with gr.Row():
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# gr.Button("Generate").click(fn=generateImage,outputs=img_window)
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# gr.ClearButton().add(img_window)
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gr.Markdown(DESCRIPTION)
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img_window = gr.Image(interactive=False,elem_id="img_window")
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with gr.Row():
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gr.Button("Generate").click(fn=generateImage,outputs=img_window)
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gr.ClearButton().add(img_window)
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demo.launch()
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flagged/log.csv
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output,flag,username,timestamp
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,,,2024-02-25 01:05:52.118984
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requirements.txt
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onnx
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onnxruntime
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numpy
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matplotlib
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