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nn_test.py
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nn_test.py
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from nn import IndoorNetwork
from dataset import IndoorSceneFeatureDataset
from torch.utils.data import Dataset, DataLoader
from utils import results
import torch
from torch.optim import RMSprop
from nn_train import evaluate
indoorscene_traindataset = IndoorSceneFeatureDataset(
text_file='Dataset/TrainImages.txt',
feature_file='Dataset/features.h5',
train=True)
train_loader = DataLoader(indoorscene_traindataset, batch_size=16, shuffle=True, num_workers=1)
indoorscene_testdataset = IndoorSceneFeatureDataset(
text_file='Dataset/TestImages.txt',
feature_file='Dataset/features.h5',
train=False)
val_loader = DataLoader(indoorscene_testdataset, batch_size=16, shuffle=True, num_workers=1)
def main():
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f'Running on : {device}')
network = IndoorNetwork()
network.to(device)
print(network)
optimizer = RMSprop(network.parameters(), lr=1e-5)
# Params
resume_exp_name = 'network-1e5'
resume_epoch = 900
# Load the model
checkpoint = f'checkpoints/{resume_exp_name}-{resume_epoch}'
state = torch.load(checkpoint, map_location=torch.device('cpu'))
network.load_state_dict(state['model_state_dict'])
optimizer.load_state_dict(state['optimizer_state_dict'])
print(f"Resuming from checkpoint : {checkpoint}")
evaluate(network, val_loader, device)
if __name__ == '__main__':
main()