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from __future__ import print_function, division |
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import os |
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import sys |
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import time |
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import argparse |
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import warnings |
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import torch |
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import pickle |
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import torch.nn as nn |
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import torch.optim as optim |
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import pandas as pd |
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import numpy as np |
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import matplotlib.pyplot as plt |
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from torch.utils.data import Dataset, DataLoader, TensorDataset |
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from torchvision import transforms, utils |
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from models.modeling import PATHOLOGICAL_CLASSFIER, CONFIGS |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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def load_weights(model, weight_path): |
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print("Loading PATHOLOGICAL_CLASSFIER...",weight_path) |
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loadnet = torch.load(weight_path,map_location=device) |
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if "model_state_dict" in loadnet: |
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keyname = "model_state_dict" |
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else: |
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keyname = "model_state_dict" |
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model.load_state_dict(loadnet[keyname], strict=True) |
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return model |
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class MyDataset(Dataset): |
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def __init__(self, root_path): |
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m_data = [] |
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img_pkl_file_path = os.path.join(root_path, "img_feature") |
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txt_pkl_file_path = os.path.join(root_path, "txt_feature") |
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target_pkl_file_path = os.path.join(root_path, "target") |
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for file in os.listdir(img_pkl_file_path): |
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img_pkl_file = os.path.join(img_pkl_file_path, file) |
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txt_pkl_file = os.path.join(txt_pkl_file_path, file) |
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target_pkl_file = os.path.join(target_pkl_file_path, file) |
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with open(img_pkl_file, "rb") as img_f: |
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img_load_dict = pickle.load(img_f) |
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m_input_img = img_load_dict["img_feature"] |
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with open(txt_pkl_file, "rb") as txt_f: |
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txt_load_dict = pickle.load(txt_f) |
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m_input_txt = txt_load_dict["txt_feature"] |
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with open(target_pkl_file, "rb") as target_f: |
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target_load_dict = pickle.load(target_f) |
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m_output_os = target_load_dict["target_os"] |
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m_output_dfs = target_load_dict["target_dfs"] |
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m_data.append((m_input_img, m_input_txt, m_output_os, m_output_dfs,file)) |
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self.m_data = m_data |
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def __getitem__(self, idx): |
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inp_i, inp_txt, oup_os, oup_dfs,f_name = self.m_data[idx] |
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return inp_i, inp_txt, oup_os, oup_dfs,f_name |
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def __len__(self): |
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return len(self.m_data) |
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def valid(args): |
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torch.manual_seed(0) |
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num_classes = 2 |
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config = CONFIGS["PATHOLOGICAL_CLASSFIER"] |
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model = PATHOLOGICAL_CLASSFIER(config, num_classes=num_classes, vis=True, mm=True) |
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model_path = '/your/trained/model/path/' |
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p_c_model = load_weights(model, model_path) |
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p_c_model.to(device) |
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test_dataset = MyDataset("/your/dataset/path/" ) |
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test_loader = DataLoader(test_dataset, batch_size=1) |
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print("--------Start testing-------") |
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p_c_model.eval() |
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valid_1_acc = 0 |
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valid_1_total = 0 |
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valid_1_cnt = 0 |
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valid_2_acc = 0 |
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valid_2_total = 0 |
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valid_2_cnt = 0 |
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valid_total_cnt=0 |
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target_cnt_0=0 |
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target_cnt_1=0 |
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with torch.no_grad(): |
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for imgs, txt, target_1, target_2,file_name in test_loader: |
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output_1, output_2, = model(imgs.to(device), txt.to(device)) |
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out_1_list_prob = (torch.softmax(output_1.squeeze(1), axis=-1).cpu().numpy().tolist()) |
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out_1_list = (torch.argmax(output_1.squeeze(1), axis=-1).cpu().numpy().tolist()) |
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target_1_list = target_1.tolist() |
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out_2_list = (torch.argmax(output_2.squeeze(1), axis=-1).cpu().numpy().tolist()) |
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target_2_list = target_2.tolist() |
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valid_1_total += len(out_1_list) |
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valid_2_total += len(out_2_list) |
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for i in range(len(out_1_list)): |
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if out_1_list[i] == target_1_list[i]: |
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valid_1_cnt += 1 |
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if out_2_list[i] == target_2_list[i]: |
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valid_2_cnt += 1 |
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if out_1_list[i] == target_1_list[i] and out_2_list[i] == target_2_list[i]: |
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valid_total_cnt+=1 |
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valid_1_acc = valid_1_cnt / valid_1_total |
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valid_2_acc = valid_2_cnt / valid_2_total |
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valid_total_acc =valid_total_cnt/valid_1_total |
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print(valid_1_acc,valid_1_total, valid_2_acc,valid_2_total,valid_total_acc,valid_total_cnt) |
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print("="*100) |
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if __name__ == "__main__": |
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parser = argparse.ArgumentParser(description="") |
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args = parser.parse_args() |
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valid(args) |
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