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53 lines
2.0 KiB
53 lines
2.0 KiB
1 year ago
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import torch
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import numpy as np
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import scipy.io as scio
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from config import *
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def load_data():
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train_data = np.empty((train_amount*2,row_size,col_size),dtype="double")
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train_ans = np.empty((train_amount*2,),dtype="double")
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test_data = np.empty((test_amount*2,row_size,col_size),dtype="double")
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test_ans = np.empty((test_amount*2,),dtype="double")
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print("Load training data")
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for i in range(1,400):
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dir = CurvedMatPath + str(i) + ".mat"
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data = scio.loadmat(dir)
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train_data[i] = np.pad(data['mat'],((0,6),(0,20)),'constant',constant_values=(0,0))
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dir = CurvedAnsPath + str(i) + ".mat"
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data = scio.loadmat(dir)
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train_ans[i] = data['res']
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# negtive area
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dir = CurvedMatPath + str(i) + "_neg.mat"
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data = scio.loadmat(dir)
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train_data[i + train_amount] = np.pad(data['mat'],((0,6),(0,20)),'constant',constant_values=(0,0))
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dir = CurvedAnsPath + str(i) + "_neg.mat"
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data = scio.loadmat(dir)
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train_ans[i + train_amount] = data['res']
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print("Load test data")
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for i in range(401,500):
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dir = CurvedMatPath + str(i) + ".mat"
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data = scio.loadmat(dir)
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test_data[i-400] = np.pad(data['mat'],((0,6),(0,20)),'constant',constant_values=(0,0))
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dir = CurvedAnsPath + str(i) + ".mat"
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data = scio.loadmat(dir)
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test_ans[i-400] = data['res']
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# negtive area
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dir = CurvedMatPath + str(i) + "_neg.mat"
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data = scio.loadmat(dir)
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test_data[i-400+test_amount] = np.pad(data['mat'],((0,6),(0,20)),'constant',constant_values=(0,0))
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dir = CurvedAnsPath + str(i) + "_neg.mat"
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data = scio.loadmat(dir)
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test_ans[i-400+test_amount] = data['res']
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if np.any(np.isnan(train_data)) or np.any(np.isnan(train_ans)) or np.any(np.isnan(test_data)) or np.any(np.isnan(test_ans)):
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print("EXIST NAN")
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return train_data, train_ans, test_data, test_ans
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