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import random |
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import pickle |
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import scipy.io as sio |
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import numpy as np |
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import matplotlib.pyplot as plt |
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from app import App |
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from g2p.plot import plot_fp |
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model_path = '../Interface/model/model.pth' |
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device='cuda' |
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train_path='../Interface/static/Data/data_train_converted.pkl' |
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tf_path='../Interface/retrieval/tf_train.npy' |
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centroid_path='../Interface/retrieval/centroids_train.npy' |
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cluster_path='../Interface/retrieval/clusters_train.npy' |
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dataset_path = '../Interface/static/Data/data_test_converted.pkl' |
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app = App(model_path,device,train_path,tf_path,centroid_path,cluster_path) |
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dataset = pickle.load(open(dataset_path,'rb'))['data'] |
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data_boundary = dataset[0] |
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data_graph = app.retrieve(data_boundary)[0] |
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data = app.transfer(data_boundary,data_graph) |
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data = app.forward(data,network_data=False) |
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data = app.align(data) |
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data = app.decorate(data) |
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ax = plot_fp(data.boundary,data.newBox[data.order],data.rType[data.order],data.doors,data.windows) |
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fig = plt.gcf() |
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fig.canvas.draw() |
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fig.canvas.print_figure('test_interface_data.png') |