import pandas as pd
    import csv
    import torch
    from model import DFNet
    from Datasets_BFDF import get_dataloader
    import os
    import json
    from sklearn.metrics import mean_absolute_error
    from OtherTypeNet import *


    class LayerActivations:
        features = None

        def __init__(self, model, layer_num):
            self.hook = model.register_forward_hook(self.hook_fn)

        def hook_fn(self, module, input, output):
            self.features = output

        def remove(self):
            self.hook.remove()


    loader_train, loader_val, loader_test = get_dataloader(None)
    loaders = [loader_val, loader_test, loader_train]
    files = ['train', 'test', 'train']

    DEVICE = torch.device("cuda:1")
    df = 15
    model = DFNet(df=df, bf=0)
    # model = Densenet121(df)
    print(model)
    model.load_state_dict(torch.load('MODEL/model_epoch_50.ckpt',
                                    map_location=DEVICE)['state_dict'])

    model.to(DEVICE)
    model.eval()
    # print(model)

    BFPath = os.path.join('bodyfeature', 'BodyFeature_imagenet.json')
    with open(BFPath, 'r') as f:
        BodyFeatures = json.load(f)

    for loader, file in zip(loaders, files):
        cnt = 0
        with open(
                'ALL_feature/Image_{}.csv'.format(file),
                'a+', newline='') as fp:
            writer = csv.writer(fp)
            pred = []
            targ = []
            for (data, name, img_name, sex, age, height, weight), target in loader:
                cnt += 1
                print(cnt)
                values = []
                data, target = data.to(DEVICE), target.to(DEVICE)
                img_name = img_name[0]

                values.append(img_name)
                values.append(target.cpu().numpy()[0])
                values.append(sex.numpy()[0])

                if img_name not in BodyFeatures:
                    continue
                values.append(BodyFeatures[img_name]['WSR'])
                values.append(BodyFeatures[img_name]['WTR'])
                values.append(BodyFeatures[img_name]['WHpR'])
                values.append(BodyFeatures[img_name]['WHdR'])
                values.append(BodyFeatures[img_name]['HpHdR'])
                values.append(BodyFeatures[img_name]['Area'])
                values.append(BodyFeatures[img_name]['H2W'])

                conv_out = LayerActivations(model.fc1, None)
                out = model(data)
                pred.append(out.item())
                targ.append(target.item())
                conv_out.remove()
                xs = torch.squeeze(conv_out.features.cpu().detach()).numpy()

                for x in xs:
                    values.append(float(x))

                values.append(age.numpy()[0])
                values.append(height.numpy()[0])
                values.append(weight.numpy()[0])

                writer.writerow(values)
            MAE = mean_absolute_error(targ, pred)
            print(file, ' ', MAE)