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