def evaluate(self, ins: EvaluateIns) -> EvaluateRes:
# Set the set so we are sure to generate the same batches
# accross all clients.
np.random.seed(123)
print(f"Client {self.cid}: evaluate")
config = ins[1]
#batch_size = int(config["batch_size"])
weights = fl.common.parameters_to_weights(ins[0])
# Use provided weights to update the local model
#self.model.set_weights(weights)
set_weights(self.model, weights)
# get IID test dataset
testset = dataset_afterpartition(
train=False,
client_id=int(self.cid),
num_partitions=self.nb_clients,
xy_train_partitions=self.xy_train_partitions,
xy_test_partitions=self.xy_test_partitions)
# Evaluate the updated model on the local dataset
testloader = torch.utils.data.DataLoader(testset,
batch_size=32,
shuffle=False)
loss, accuracy = cifar.test(self.model, testloader, device=DEVICE)
# Return the number of evaluation examples and the evaluation result (loss)
return len(testset), float(loss), float(accuracy)
def evaluate(
self, parameters: List[np.ndarray], config: Dict[str, str]
) -> Tuple[int, float, float]:
# Set model parameters, evaluate model on local test dataset, return result
self.set_parameters(parameters)
loss, accuracy = cifar.test(self.model, self.testloader, device=DEVICE)
return float(loss), len(self.testloader), {"accuracy": float(accuracy)}
def evaluate(weights: fl.common.Weights) -> Optional[Tuple[float, float]]:
"""Use the entire CIFAR-10 test set for evaluation."""
#model = cifar.load_model()
#model.set_weights(weights)
model = models.resnet18()
set_weights(model, weights)
model.to(DEVICE)
model.eval()
testloader = torch.utils.data.DataLoader(testset, batch_size=32, shuffle=False)
return cifar.test(model, testloader, device=DEVICE)
def evaluate(self, ins: EvaluateIns) -> EvaluateRes:
print(f"Client {self.cid}: evaluate")
weights = fl.common.parameters_to_weights(ins[0])
# Use provided weights to update the local model
#self.model.set_weights(weights)
set_weights(self.model, weights)
# Evaluate the updated model on the local dataset
testloader = torch.utils.data.DataLoader(self.testset,
batch_size=32,
shuffle=False)
loss, accuracy = cifar.test(self.model, testloader, device=DEVICE)
# Return the number of evaluation examples and the evaluation result (loss)
return len(self.testset), float(loss), float(accuracy)