From 0e0264fdfc82f88b00b68889324b5fe13495b343 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E8=91=9B=E5=B3=BB=E6=81=BA?= <202115006@mail.sdu.edu.cn>
Date: Tue, 2 Sep 2025 12:07:57 +0000
Subject: [PATCH] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=208=5Fpred?=
 =?UTF-8?q?iction.py?=
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---
 8_prediction.py | 62 -------------------------------------------------
 1 file changed, 62 deletions(-)
 delete mode 100644 8_prediction.py

diff --git a/8_prediction.py b/8_prediction.py
deleted file mode 100644
index 7340afd..0000000
--- a/8_prediction.py
+++ /dev/null
@@ -1,62 +0,0 @@
-import os
-import torch
-from torch.utils.data import DataLoader
-import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-from Network.MyDataset import *
-from utils.train_val_lr import train, validate, adjust_learning_rate
-from Network.Model import *
-from scipy.signal import butter, lfilter, sosfilt
-from config import Network_prediction_Config as cfg
-from config import Path_Config as pcfg
-from utils.plot import plot_permittivity_constant
-
-# Set parameters
-BATCH_SIZE = cfg.BATCH_SIZE
-TEST_FILE = pcfg.PROCESSED_TEST_FILE
-inversion_time_result_file=pcfg.inversion_time_result_file
-inversion_time_result_img= pcfg.inversion_time_result_img
-MODEL_PATH = pcfg.LATEST_MODEL_PATH
-impulse_field_file = pcfg.field_impulse
-impulse_sim_file = pcfg.sim_impulse
-smooth_window_size = cfg.smooth_window_size
-max_permittivity=cfg.max_permittvity
-initial_params = cfg.initial_params
-
-
-# Load the pre-trained model
-model = Model(inplanes=2, outplanes=1, layers=cfg.network_layers).cuda()
-model.load_state_dict(torch.load(MODEL_PATH))
-model.eval()
-
-# Load dataset
-dataset = MyDataset(TEST_FILE, TEST_FILE, impulse_field_file, impulse_sim_file, mode='apply', initial_params=initial_params)
-test_loader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=False, num_workers=cfg.num_workers, pin_memory=True)
-
-# Initialize results container
-results = []
-
-# Inference without gradient tracking
-with torch.no_grad():
-    for idx, (data_data, label) in enumerate(test_loader):
-        data_data = data_data.cuda()
-        
-        # Model inference
-        data_data_out = model(data_data.type(torch.cuda.FloatTensor))
-        
-        # Move output to CPU and squeeze dimensions
-        data_data_out = data_data_out.cpu().numpy().squeeze()
-        results.append(data_data_out)
-
-# Concatenate and save final results
-final_result = np.column_stack(results)
-
-# Apply smoothing filter (moving average)
-final_result = np.apply_along_axis(lambda x: np.convolve(x, np.ones(smooth_window_size)/smooth_window_size, mode='valid'), axis=1, arr=final_result)
-pd.DataFrame(final_result* max_permittivity).to_csv(inversion_time_result_file, index=False)
-# Plot results
-plot_permittivity_constant(final_result * max_permittivity, inversion_time_result_img, line_length=cfg.distance, time_length=cfg.time_window)
-
-
-