def train(model, dataloader, optimizer, loss_fn, metric, params):
model.train()
loss_avg = utils.RunningAverage()
output = []
y = []
with tqdm(total=len(dataloader)) as t:
for X_batch, y_batch in dataloader:
X_batch = X_batch.to(params.device)
y_batch = y_batch.to(params.device)
output_batch = model(X_batch)
loss = loss_fn(output_batch, y_batch)
optimizer.zero_grad()
loss.backward()
optimizer.step()
loss_avg.update(loss.item())
y.append(y_batch.data.cpu().numpy())
output.append(output_batch.data.cpu().numpy())
t.set_postfix(loss='{:05.3f}'.format(loss_avg()))
t.update()
output = np.concatenate(output, axis=0)
y = np.concatenate(y, axis=0)
metric_score = metric(output, y)
avg_loss = loss_avg()
return avg_loss, metric_score
def eval_metrics(output, target):
metrics = [
mse, abs_rel_diff, scale_invariant_error, median_error, mean_error,
rms_linear
]
acc_metrics = np.zeros(len(metrics))
output = output[None, :][None, :]
target = target[None, :][None, :]
for i, metric in enumerate(metrics):
acc_metrics[i] += metric(output, target)
return acc_metrics
def test(config, model, test_data_loader):
metrics = [eval(metric) for metric in config['metrics']]
metrics_results = np.zeros([len(metrics)])
for q, p, y in test_data_loader:
output = model(q, p, test_data_loader.get_pretrained_embeddings())
output = output.cpu().data.numpy()
print(output)
metrics_results += np.array([metric(output, y) for metric in metrics])
def evaluate(model, dataloader, loss_fn, metric, params, test_mode=False):
model.eval()
loss_avg = utils.RunningAverage()
output = []
y = []
with torch.no_grad():
for X_batch, y_batch in dataloader:
X_batch = X_batch.to(params.device)
y_batch = y_batch.to(params.device)
output_batch = model(X_batch)
loss = loss_fn(output_batch, y_batch)
loss_avg.update(loss.item())
y.append(y_batch.data.cpu().numpy())
output.append(output_batch.data.cpu().numpy())
avg_loss = loss_avg()
output = np.concatenate(output, axis=0)
y = np.concatenate(y, axis=0)
metric_score = metric(output, y, test_mode)
return avg_loss, metric_score