def build_network(self):
"""
Define the FastEstimator network flow.
Args:
None
Returns:
network: KerasNetwork object
"""
epsilon = 0.04
network = fe.Network(ops=[
Watch(inputs="x"),
ModelOp(model=self.model, inputs="x", outputs="y_pred"),
CrossEntropy(inputs=("y_pred", "y"), outputs="base_ce"),
FGSM(
data="x", loss="base_ce", outputs="x_adverse",
epsilon=epsilon),
ModelOp(model=self.model, inputs="x_adverse",
outputs="y_pred_adv"),
CrossEntropy(inputs=("y_pred_adv", "y"), outputs="adv_ce"),
Average(inputs=("base_ce", "adv_ce"), outputs="avg_ce"),
UpdateOp(model=self.model, loss_name="avg_ce")
])
return network
def get_estimator(epochs=10, batch_size=50, epsilon=0.04, save_dir=tempfile.mkdtemp()):
train_data, eval_data = cifar10.load_data()
test_data = eval_data.split(0.5)
pipeline = fe.Pipeline(
train_data=train_data,
eval_data=eval_data,
test_data=test_data,
batch_size=batch_size,
ops=[Normalize(inputs="x", outputs="x", mean=(0.4914, 0.4822, 0.4465), std=(0.2471, 0.2435, 0.2616))])
model = fe.build(model_fn=lambda: LeNet(input_shape=(32, 32, 3)), optimizer_fn="adam", model_name="adv_model")
network = fe.Network(ops=[
Watch(inputs="x"),
ModelOp(model=model, inputs="x", outputs="y_pred"),
CrossEntropy(inputs=("y_pred", "y"), outputs="base_ce"),
FGSM(data="x", loss="base_ce", outputs="x_adverse", epsilon=epsilon),
ModelOp(model=model, inputs="x_adverse", outputs="y_pred_adv"),
CrossEntropy(inputs=("y_pred_adv", "y"), outputs="adv_ce"),
Average(inputs=("base_ce", "adv_ce"), outputs="avg_ce"),
UpdateOp(model=model, loss_name="avg_ce")
])
traces = [
Accuracy(true_key="y", pred_key="y_pred", output_name="clean_accuracy"),
Accuracy(true_key="y", pred_key="y_pred_adv", output_name="adversarial_accuracy"),
BestModelSaver(model=model, save_dir=save_dir, metric="base_ce", save_best_mode="min"),
]
estimator = fe.Estimator(pipeline=pipeline,
network=network,
epochs=epochs,
traces=traces,
monitor_names=["base_ce", "adv_ce"],
log_steps=1000)
return estimator
def get_estimator(epsilon=0.04,
epochs=20,
batch_size=32,
code_length=16,
train_steps_per_epoch=None,
eval_steps_per_epoch=None,
save_dir=tempfile.mkdtemp()):
# step 1
train_data, eval_data = cifair10.load_data()
test_data = eval_data.split(0.5)
pipeline = fe.Pipeline(
train_data=train_data,
eval_data=eval_data,
test_data=test_data,
batch_size=batch_size,
ops=[Normalize(inputs="x", outputs="x", mean=(0.4914, 0.4822, 0.4465), std=(0.2471, 0.2435, 0.2616))])
# step 2
model = fe.build(model_fn=lambda: ecc_lenet(code_length=code_length), optimizer_fn="adam")
network = fe.Network(ops=[
Watch(inputs="x", mode=('eval', 'test')),
ModelOp(model=model, inputs="x", outputs="y_pred"),
CrossEntropy(inputs=("y_pred", "y"), outputs="base_ce"),
UpdateOp(model=model, loss_name="base_ce"),
FGSM(data="x", loss="base_ce", outputs="x_adverse", epsilon=epsilon, mode=('eval', 'test')),
ModelOp(model=model, inputs="x_adverse", outputs="y_pred_adv", mode=('eval', 'test')),
CrossEntropy(inputs=("y_pred_adv", "y"), outputs="adv_ce", mode=('eval', 'test')),
Average(inputs=("base_ce", "adv_ce"), outputs="avg_ce", mode='eval')
])
# step 3
traces = [
Accuracy(true_key="y", pred_key="y_pred", output_name="base_accuracy"),
Accuracy(true_key="y", pred_key="y_pred_adv", output_name="adversarial_accuracy"),
BestModelSaver(model=model, save_dir=save_dir, metric="avg_ce", save_best_mode="min", load_best_final=True)
]
estimator = fe.Estimator(pipeline=pipeline,
network=network,
epochs=epochs,
traces=traces,
train_steps_per_epoch=train_steps_per_epoch,
eval_steps_per_epoch=eval_steps_per_epoch,
monitor_names=["adv_ce", "avg_ce"])
return estimator
def get_estimator(data_dir=None,
model_dir=tempfile.mkdtemp(),
epochs=200,
batch_size_per_gpu=32,
train_steps_per_epoch=None,
eval_steps_per_epoch=None):
num_device = get_num_devices()
train_ds, val_ds = mscoco.load_data(root_dir=data_dir)
train_ds = PreMosaicDataset(mscoco_ds=train_ds)
batch_size = num_device * batch_size_per_gpu
pipeline = fe.Pipeline(
train_data=train_ds,
eval_data=val_ds,
ops=[
ReadImage(inputs=("image1", "image2", "image3", "image4"),
outputs=("image1", "image2", "image3", "image4"),
mode="train"),
ReadImage(inputs="image", outputs="image", mode="eval"),
LongestMaxSize(max_size=640,
image_in="image1",
bbox_in="bbox1",
bbox_params=BboxParams("coco", min_area=1.0),
mode="train"),
LongestMaxSize(max_size=640,
image_in="image2",
bbox_in="bbox2",
bbox_params=BboxParams("coco", min_area=1.0),
mode="train"),
LongestMaxSize(max_size=640,
image_in="image3",
bbox_in="bbox3",
bbox_params=BboxParams("coco", min_area=1.0),
mode="train"),
LongestMaxSize(max_size=640,
image_in="image4",
bbox_in="bbox4",
bbox_params=BboxParams("coco", min_area=1.0),
mode="train"),
LongestMaxSize(max_size=640,
image_in="image",
bbox_in="bbox",
bbox_params=BboxParams("coco", min_area=1.0),
mode="eval"),
PadIfNeeded(min_height=640,
min_width=640,
image_in="image",
bbox_in="bbox",
bbox_params=BboxParams("coco", min_area=1.0),
mode="eval",
border_mode=cv2.BORDER_CONSTANT,
value=(114, 114, 114)),
CombineMosaic(inputs=("image1", "image2", "image3", "image4",
"bbox1", "bbox2", "bbox3", "bbox4"),
outputs=("image", "bbox"),
mode="train"),
CenterCrop(height=640,
width=640,
image_in="image",
bbox_in="bbox",
bbox_params=BboxParams("coco", min_area=1.0),
mode="train"),
Sometimes(
HorizontalFlip(image_in="image",
bbox_in="bbox",
bbox_params=BboxParams("coco", min_area=1.0),
mode="train")),
HSVAugment(inputs="image", outputs="image", mode="train"),
ToArray(inputs="bbox", outputs="bbox", dtype="float32"),
CategoryID2ClassID(inputs="bbox", outputs="bbox"),
GTBox(inputs="bbox",
outputs=("gt_sbbox", "gt_mbbox", "gt_lbbox"),
image_size=640),
Delete(keys=("image1", "image2", "image3", "image4", "bbox1",
"bbox2", "bbox3", "bbox4", "bbox"),
mode="train"),
Delete(keys="image_id", mode="eval"),
Batch(batch_size=batch_size, pad_value=0)
])
init_lr = 1e-2 / 64 * batch_size
model = fe.build(
lambda: YoloV5(w=640, h=640, c=3),
optimizer_fn=lambda x: torch.optim.SGD(
x, lr=init_lr, momentum=0.937, weight_decay=0.0005, nesterov=True),
mixed_precision=True)
network = fe.Network(ops=[
RescaleTranspose(inputs="image", outputs="image"),
ModelOp(model=model,
inputs="image",
outputs=("pred_s", "pred_m", "pred_l")),
DecodePred(inputs=("pred_s", "pred_m", "pred_l"),
outputs=("pred_s", "pred_m", "pred_l")),
ComputeLoss(inputs=("pred_s", "gt_sbbox"),
outputs=("sbbox_loss", "sconf_loss", "scls_loss")),
ComputeLoss(inputs=("pred_m", "gt_mbbox"),
outputs=("mbbox_loss", "mconf_loss", "mcls_loss")),
ComputeLoss(inputs=("pred_l", "gt_lbbox"),
outputs=("lbbox_loss", "lconf_loss", "lcls_loss")),
Average(inputs=("sbbox_loss", "mbbox_loss", "lbbox_loss"),
outputs="bbox_loss"),
Average(inputs=("sconf_loss", "mconf_loss", "lconf_loss"),
outputs="conf_loss"),
Average(inputs=("scls_loss", "mcls_loss", "lcls_loss"),
outputs="cls_loss"),
Average(inputs=("bbox_loss", "conf_loss", "cls_loss"),
outputs="total_loss"),
PredictBox(width=640,
height=640,
inputs=("pred_s", "pred_m", "pred_l"),
outputs="box_pred",
mode="eval"),
UpdateOp(model=model, loss_name="total_loss")
])
traces = [
MeanAveragePrecision(num_classes=80,
true_key='bbox',
pred_key='box_pred',
mode="eval"),
BestModelSaver(model=model,
save_dir=model_dir,
metric='mAP',
save_best_mode="max")
]
lr_schedule = {
1:
LRScheduler(model=model,
lr_fn=lambda step: lr_schedule_warmup(
step,
train_steps_epoch=np.ceil(len(train_ds) / batch_size),
init_lr=init_lr)),
4:
LRScheduler(model=model,
lr_fn=lambda epoch: cosine_decay(epoch,
cycle_length=epochs - 3,
init_lr=init_lr,
min_lr=init_lr / 100,
start=4))
}
traces.append(EpochScheduler(lr_schedule))
estimator = fe.Estimator(
pipeline=pipeline,
network=network,
epochs=epochs,
traces=traces,
monitor_names=["bbox_loss", "conf_loss", "cls_loss"],
train_steps_per_epoch=train_steps_per_epoch,
eval_steps_per_epoch=eval_steps_per_epoch)
return estimator
def get_estimator(epochs=10,
batch_size=32,
epsilon=0.01,
warmup=0,
model_dir=tempfile.mkdtemp()):
(x_train, y_train), (x_eval,
y_eval) = tf.keras.datasets.cifar10.load_data()
data = {
"train": {
"x": x_train,
"y": y_train
},
"eval": {
"x": x_eval,
"y": y_eval
}
}
num_classes = 10
pipeline = Pipeline(batch_size=batch_size,
data=data,
ops=Minmax(inputs="x", outputs="x"))
model = FEModel(model_def=lambda: LeNet(input_shape=x_train.shape[1:],
classes=num_classes),
model_name="LeNet",
optimizer="adam")
adv_img = {
warmup:
AdversarialSample(inputs=("loss", "x"),
outputs="x_adverse",
epsilon=epsilon,
mode="train")
}
adv_eval = {
warmup:
ModelOp(inputs="x_adverse",
model=model,
outputs="y_pred_adverse",
mode="train")
}
adv_loss = {
warmup:
SparseCategoricalCrossentropy(y_true="y",
y_pred="y_pred_adverse",
outputs="adverse_loss",
mode="train")
}
adv_avg = {
warmup:
Average(inputs=("loss", "adverse_loss"), outputs="loss", mode="train")
}
network = Network(ops=[
ModelOp(inputs="x", model=model, outputs="y_pred", track_input=True),
SparseCategoricalCrossentropy(
y_true="y", y_pred="y_pred", outputs="loss"),
Scheduler(adv_img),
Scheduler(adv_eval),
Scheduler(adv_loss),
Scheduler(adv_avg)
])
traces = [
Accuracy(true_key="y", pred_key="y_pred"),
ConfusionMatrix(true_key="y",
pred_key="y_pred",
num_classes=num_classes),
ModelSaver(model_name="LeNet", save_dir=model_dir, save_freq=2)
]
estimator = Estimator(network=network,
pipeline=pipeline,
epochs=epochs,
traces=traces)
return estimator