def evaluate(
model: TEDD1104,
data_loader: DataLoader,
device: torch.device,
fp16: bool,
) -> float:
"""
Given a set of input examples and the golds for these examples evaluates the model accuracy
Input:
- model: TEDD1104 model to evaluate
- data_loader: torch.utils.data.DataLoader with the examples to evaluate
- device: string, use cuda or cpu
-batch_size: integer batch size
Output:
- Accuracy: float
"""
model.eval()
correct = 0
total = 0
for batch in tqdm(data_loader, desc="Evaluating model"):
x = torch.flatten(
torch.stack(
(
batch["image1"],
batch["image2"],
batch["image3"],
batch["image4"],
batch["image5"],
),
dim=1,
),
start_dim=0,
end_dim=1,
).to(device)
y = batch["y"]
if fp16:
with autocast():
predictions: np.ndarray = model.predict(x).cpu()
else:
predictions: np.ndarray = model.predict(x).cpu()
correct += (predictions == y).sum().numpy()
total += len(predictions)
return correct / total
def evaluate(
model: TEDD1104,
X: torch.tensor,
golds: torch.tensor,
device: torch.device,
batch_size: int,
) -> float:
"""
Given a set of input examples and the golds for these examples evaluates the model accuracy
Input:
- model: TEDD1104 model to evaluate
- X: input examples [num_examples, sequence_size, 3, H, W]
- golds: golds for the input examples [num_examples]
- device: string, use cuda or cpu
-batch_size: integer batch size
Output:
- Accuracy: float
"""
model.eval()
correct = 0
for X_batch, y_batch in nn_batchs(X, golds, batch_size):
predictions: np.ndarray = model.predict(X_batch.to(device)).cpu().numpy()
correct += np.sum(predictions == y_batch)
return correct / len(golds)