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import datetime
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from matplotlib import pyplot as plt
from data_loader_interface import load_imu
from sklearn.metrics import accuracy_score, f1_score, confusion_matrix, classification_report, average_precision_score
from torch.utils.data import Dataset, DataLoader, TensorDataset, ConcatDataset
from einops import rearrange
from torch.optim.lr_scheduler import StepLR
def test(model, test_loader, output_file):
running_loss = 0.0
predlist = torch.zeros(0, dtype=torch.long, device='cpu')
lbllist = torch.zeros(0, dtype=torch.long, device='cpu')
number_of_nan_loss = 0
with torch.no_grad(), open(output_file, 'w') as f:
for batch_idx, (data) in enumerate(test_loader):
data = data.float().to('cpu')
outputs = model(data)
_, predicted = torch.max(outputs.data, 1)
predlist = torch.cat([predlist, predicted.view(-1).cpu()])
# lbllist = torch.cat([lbllist, labels.view(-1).cpu()])
for i, prediction in enumerate(predicted):
f.write(f"{prediction.item()}\n")
return running_loss / len(test_loader), predlist
class MyDataset_labeled(Dataset):
def __init__(self, x):
self.x_data = torch.from_numpy(np.array(x)).to(torch.float)
self.len = x.shape[0]
def __getitem__(self, idx):
return self.x_data[idx]
def __len__(self):
return self.len
def model_defining(test_data_array, name_model, output_file):
test_losses = []
accuracies = []
f1_scores = []
cm_list = []
cr_list = []
model = torch.load(str(name_model) + ".pth", map_location=torch.device('cpu'))
model.eval()
test_data = load_imu(test_data_array)
test_data = rearrange(test_data, 'n t c -> n c t')
test_dataset = MyDataset_labeled(test_data)
test_dataloaders = DataLoader(test_dataset, batch_size=256, shuffle=False, drop_last=False)
print("Data loaded ...")
test_loss, predlist = test(model, test_dataloaders, output_file)
test_losses.append(test_loss)
def main():
name_model = "./models_chadwick/training_3S_model_validation/S3_101_102_103_validation_epoch_10"
train_subjects = []
test_subjects = ['104']
output_file = "S3_101_102_103_validation_epoch_10_tested_104_predicted.txt"
label_file = "S3_101_102_103_validation_epoch_10_tested_104_label.txt"
classification_file = "S3_101_102_103_validation_epoch_10_tested_104_classification.txt"
model_defining(train_subjects, test_subjects, name_model, output_file, label_file, classification_file)
if __name__ == "__main__":
main() |