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import scipy.io as sio

from torch.utils.data import TensorDataset, DataLoader

import numpy as np

import torch

import torch.nn as nn

from torch.autograd import Variable

import torch.nn.functional as F

import matplotlib.pyplot as plt

import math

import pandas as pd

from model.res_net_use_this import ResNet, Bottleneck

#

batch_size = 100

# prepare data, nSample x nChannel x width x height

data = sio.loadmat('data/test.mat')

test_data = data['test_data']

test_label = data['test_label']

test_label[:, 0] = test_label[:, 0] - 1

num_test_instances = len(test_data)

test_data = torch.from_numpy(test_data).type(torch.FloatTensor).view(num_test_instances, 30, 1, 1)

test_label = torch.from_numpy(test_label).type(torch.FloatTensor)

test_dataset = TensorDataset(test_data, test_label)

test_data_loader = DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=False)

resnet = torch.load('weights/res18_Train100.0Test93.001.pkl')

resnet = resnet.cuda()

resnet.eval()

correct = 0

for i, (samples, labels) in enumerate(test_data_loader):

with torch.no_grad():

samplesV = Variable(samples.cuda())

labelsV = Variable(labels.cuda())

predict_label = resnet(samplesV)

pred = predict_label[0][:, 0:30].data.max(1)[1]

correct += pred.eq(labelsV[:, 0].data.long()).sum()

if i == 0:

tempC = predict_label[0].data

tempR = predict_label[1].data

temp = np.concatenate((tempC, tempR), axis=1)

human_prediction = temp

elif i > 0:

tempC = predict_label[0].data

tempR = predict_label[1].data

temp = np.concatenate((tempC, tempR), axis=1)

human_prediction = np.concatenate((human_prediction, temp), axis=0)

print('Accuracy:', 100 * correct / len(test_data_loader.dataset))

sio.savemat('results/test_93.mat', {'human_prediction': human_prediction})