# use GPU if available
params.cuda = torch.cuda.is_available() # use GPU is available
# Set the random seed for reproducible experiments
torch.manual_seed(230)
if params.cuda: torch.cuda.manual_seed(230)
# Get the logger
utils.set_logger(os.path.join(dir_path, 'evaluate.log'))
# Create the input data pipeline
# logging.info("Creating the dataset...")
# fetch dataloaders
test_dl = data_loader.fetch_test_dataloader(dir.split('__')[3], args.data_dir, params)
# logging.info("- done.")
# Define the model and optimizer
models = {
'linear': LinearRegression().cuda() if params.cuda else LinearRegression(),
'mlp': MLP().cuda() if params.cuda else MLP(),
'cnn': CNN().cuda() if params.cuda else CNN()
}
model = models[dir.split('__')[1]]
# fetch loss function and metrics
losses = {
'crossentropy': CrossEntropyWithL1Loss(params.l1_reg),
'hinge': MultiMarginWithL1Loss(params.l1_reg),
# use GPU if available
params.cuda = torch.cuda.is_available() # use GPU is available
# Set the random seed for reproducible experiments
torch.manual_seed(230)
if params.cuda: torch.cuda.manual_seed(230)
# Get the logger
utils.set_logger(os.path.join(args.model_dir, 'evaluate.log'))
# Create the input data pipeline
logging.info("Creating the dataset...")
# fetch dataloaders
test_dl = data_loader.fetch_test_dataloader(args.dataset, args.data_dir,
params)
train_dl, val_dl = data_loader.fetch_train_dataloaders(
args.dataset, args.data_dir, params)
logging.info("- done.")
# Define the model and optimizer
models = {
'linear':
LinearRegression().cuda() if params.cuda else LinearRegression(),
'mlp': MLP().cuda() if params.cuda else MLP(),
'cnn': CNN().cuda() if params.cuda else CNN()
}
model = models[args.model]
# fetch loss function and metrics
model_dir=exp_dir,
data_dir='data')
print(cmd)
check_call(cmd, shell=True)
params = utils.Hyperparameters(os.path.join(exp_dir, 'params.json'))
params.cuda = torch.cuda.is_available()
torch.manual_seed(230)
if params.cuda:
torch.cuda.manual_seed(230)
utils.set_logger(os.path.join(exp_dir, 'lg.log'))
logging.info("Loading the datasets...")
# fetch dataloaders
test_dl = data_loader.fetch_test_dataloader(row['dataset'], 'data', params)
logging.info("- done.")
choices = {
'linear': LinearRegression().cuda() if params.cuda else LinearRegression(bottleneck=True),
'mlp': MLP(bottleneck=True).cuda() if params.cuda else MLP(bottleneck=True),
'cnn': CNN(bottleneck=True).cuda() if params.cuda else CNN(bottleneck=True)
}
model = choices[row['model']]
utils.load_checkpoint(os.path.join(exp_dir, 'best.pth.tar'), model)
test_embeddings, test_labels = extract_embeddings(test_dl, model)
if not os.path.isdir(figures_dir):
os.mkdir(figures_dir)