def pretrain(batch_size,
epochs,
model_dir=tempfile.mkdtemp(),
train_steps_per_epoch=None,
eval_steps_per_epoch=None):
train_data, eval_data = cifair100.load_data()
pipeline = fe.Pipeline(
train_data=train_data,
eval_data=eval_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)),
PadIfNeeded(min_height=40, min_width=40, image_in="x", image_out="x", mode="train"),
RandomCrop(32, 32, image_in="x", image_out="x", mode="train"),
Sometimes(HorizontalFlip(image_in="x", image_out="x", mode="train")),
CoarseDropout(inputs="x", outputs="x", mode="train", max_holes=1)
])
backbone, vit = fe.build(
model_fn=lambda: vision_transformer(
num_class=100, image_size=(32, 32, 3), patch_size=4, num_layers=6, em_dim=256, num_heads=8, dff=512),
optimizer_fn=[None, lambda: tf.optimizers.SGD(0.01, momentum=0.9)])
network = fe.Network(ops=[
ModelOp(model=vit, inputs="x", outputs="y_pred"),
CrossEntropy(inputs=("y_pred", "y"), outputs="ce", from_logits=True),
UpdateOp(model=vit, loss_name="ce")
])
traces = [
Accuracy(true_key="y", pred_key="y_pred"),
BestModelSaver(model=backbone, save_dir=model_dir, metric="accuracy", save_best_mode="max")
]
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)
estimator.fit(warmup=False)
return traces[1].model_path # return the weights path
def get_estimator(epochs=50,
batch_size=256,
max_train_steps_per_epoch=None,
save_dir=tempfile.mkdtemp()):
train_data, _ = mnist.load_data()
pipeline = fe.Pipeline(
train_data=train_data,
batch_size=batch_size,
ops=[
ExpandDims(inputs="x", outputs="x"),
Normalize(inputs="x",
outputs="x",
mean=1.0,
std=1.0,
max_pixel_value=127.5),
LambdaOp(fn=lambda: np.random.normal(size=[100]).astype('float32'),
outputs="z")
])
gen_model = fe.build(model_fn=generator,
optimizer_fn=lambda: tf.optimizers.Adam(1e-4))
disc_model = fe.build(model_fn=discriminator,
optimizer_fn=lambda: tf.optimizers.Adam(1e-4))
network = fe.Network(ops=[
ModelOp(model=gen_model, inputs="z", outputs="x_fake"),
ModelOp(model=disc_model, inputs="x_fake", outputs="fake_score"),
GLoss(inputs="fake_score", outputs="gloss"),
UpdateOp(model=gen_model, loss_name="gloss"),
ModelOp(inputs="x", model=disc_model, outputs="true_score"),
DLoss(inputs=("true_score", "fake_score"), outputs="dloss"),
UpdateOp(model=disc_model, loss_name="dloss")
])
estimator = fe.Estimator(
pipeline=pipeline,
network=network,
epochs=epochs,
traces=ModelSaver(model=gen_model, save_dir=save_dir, frequency=5),
max_train_steps_per_epoch=max_train_steps_per_epoch)
return estimator
def instantiate_system():
system = sample_system_object()
x_train = np.ones((2, 28, 28, 3))
y_train = np.ones((2, ))
data = {
0: {
'x': x_train[0],
'y': y_train[0]
},
1: {
'x': x_train[1],
'y': y_train[1]
}
}
train_data = RepeatScheduler([
TestNonTraceableDataset(data=data, var=3),
TestDataset(data={
'x': x_train,
'y': y_train
}, var=7), None
])
system.pipeline = fe.Pipeline(train_data=train_data, batch_size=1)
return system
def get_estimator(epochs=24, batch_size=512, max_train_steps_per_epoch=None, save_dir=tempfile.mkdtemp()):
# step 1: prepare dataset
train_data, test_data = load_data()
pipeline = fe.Pipeline(
train_data=train_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)),
PadIfNeeded(min_height=40, min_width=40, image_in="x", image_out="x", mode="train"),
RandomCrop(32, 32, image_in="x", image_out="x", mode="train"),
Sometimes(HorizontalFlip(image_in="x", image_out="x", mode="train")),
CoarseDropout(inputs="x", outputs="x", mode="train", max_holes=1),
ChannelTranspose(inputs="x", outputs="x"),
Onehot(inputs="y", outputs="y", mode="train", num_classes=10, label_smoothing=0.2)
])
# step 2: prepare network
model = fe.build(model_fn=FastCifar, optimizer_fn="adam")
network = fe.Network(ops=[
ModelOp(model=model, inputs="x", outputs="y_pred"),
CrossEntropy(inputs=("y_pred", "y"), outputs="ce"),
UpdateOp(model=model, loss_name="ce")
])
# step 3 prepare estimator
traces = [
Accuracy(true_key="y", pred_key="y_pred"),
BestModelSaver(model=model, save_dir=save_dir, metric="accuracy", save_best_mode="max"),
LRScheduler(model=model, lr_fn=lr_schedule)
]
estimator = fe.Estimator(pipeline=pipeline,
network=network,
epochs=epochs,
traces=traces,
max_train_steps_per_epoch=max_train_steps_per_epoch)
return estimator
def get_estimator(epochs=2,
batch_size=32,
steps_per_epoch=None,
validation_steps=None,
model_dir=tempfile.mkdtemp()):
# step 1. prepare data
(x_train, y_train), (x_eval, y_eval) = tf.keras.datasets.mnist.load_data()
train_data = {"x": np.expand_dims(x_train, -1), "y": y_train}
eval_data = {"x": np.expand_dims(x_eval, -1), "y": y_eval}
data = {"train": train_data, "eval": eval_data}
pipeline = fe.Pipeline(batch_size=batch_size,
data=data,
ops=Minmax(inputs="x", outputs="x"))
# step 2. prepare model
model = fe.build(model_def=LeNet,
model_name="lenet",
optimizer="adam",
loss_name="loss")
network = fe.Network(ops=[
ModelOp(inputs="x", model=model, outputs="y_pred"),
SparseCategoricalCrossentropy(inputs=("y", "y_pred"), outputs="loss")
])
# step 3.prepare estimator
traces = [
Accuracy(true_key="y", pred_key="y_pred", output_name='acc'),
ModelSaver(model_name="lenet", save_dir=model_dir, save_best=True)
]
estimator = fe.Estimator(network=network,
pipeline=pipeline,
epochs=epochs,
traces=traces,
steps_per_epoch=steps_per_epoch,
validation_steps=validation_steps)
return estimator
def get_estimator(epochs=2, batch_size=32, save_dir=tempfile.mkdtemp()):
# step 1
train_data, eval_data = mnist.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=[
ExpandDims(inputs="x", outputs="x"),
Minmax(inputs="x", outputs="x")
])
# step 2
model = fe.build(model_fn=LeNet, optimizer_fn="adam")
network = fe.Network(ops=[
ModelOp(model=model, inputs="x", outputs="y_pred"),
CrossEntropy(inputs=("y_pred", "y"), outputs="ce"),
UpdateOp(model=model, loss_name="ce")
])
# step 3
traces = [
Accuracy(true_key="y", pred_key="y_pred"),
BestModelSaver(model=model,
save_dir=save_dir,
metric="accuracy",
save_best_mode="max"),
LRScheduler(model=model,
lr_fn=lambda step: cosine_decay(
step, cycle_length=3750, init_lr=1e-3)),
Traceability(save_path=save_dir)
]
estimator = fe.Estimator(pipeline=pipeline,
network=network,
epochs=epochs,
traces=traces)
return estimator
def get_estimator(max_words=10000,
max_len=500,
epochs=10,
batch_size=64,
max_train_steps_per_epoch=None,
max_eval_steps_per_epoch=None,
save_dir=tempfile.mkdtemp()):
# step 1. prepare data
train_data, eval_data = imdb_review.load_data(max_len, max_words)
pipeline = fe.Pipeline(train_data=train_data,
eval_data=eval_data,
batch_size=batch_size,
ops=Reshape(1, inputs="y", outputs="y"))
# step 2. prepare model
model = fe.build(model_fn=lambda: create_lstm(max_len, max_words),
optimizer_fn="adam")
network = fe.Network(ops=[
ModelOp(model=model, inputs="x", outputs="y_pred"),
CrossEntropy(inputs=("y_pred", "y"), outputs="loss"),
UpdateOp(model=model, loss_name="loss")
])
traces = [
Accuracy(true_key="y", pred_key="y_pred"),
BestModelSaver(model=model, save_dir=save_dir)
]
# step 3.prepare estimator
estimator = fe.Estimator(
network=network,
pipeline=pipeline,
epochs=epochs,
traces=traces,
max_train_steps_per_epoch=max_train_steps_per_epoch,
max_eval_steps_per_epoch=max_eval_steps_per_epoch)
return estimator
def get_estimator(batch_size=100, epochs=100, model_dir=tempfile.mkdtemp()):
# prepare data
(x_train, _), (x_eval, _) = tf.keras.datasets.mnist.load_data()
x_train = x_train.reshape(x_train.shape[0], 28, 28, 1).astype('float32')
x_eval = x_eval.reshape(x_eval.shape[0], 28, 28, 1).astype('float32')
data = {"train": {"x": x_train}, "eval": {"x": x_eval}}
pipeline = fe.Pipeline(batch_size=batch_size,
data=data,
ops=[
Myrescale(inputs="x", outputs="x"),
Mybinarize(inputs="x", outputs="x")
])
# prepare model
infer_model = fe.FEModel(model_def=inference_net,
model_name="encoder",
loss_name="loss",
optimizer=tf.optimizers.Adam(1e-4))
gen_model = fe.FEModel(model_def=generative_net,
model_name="decoder",
loss_name="loss",
optimizer=tf.optimizers.Adam(1e-4))
network = fe.Network(ops=[
ModelOp(inputs="x", model=infer_model, outputs="meanlogvar",
mode=None),
SplitOp(inputs="meanlogvar", outputs=("mean", "logvar"), mode=None),
ReparameterizepOp(inputs=("mean", "logvar"), outputs="z", mode=None),
ModelOp(inputs="z", model=gen_model, outputs="x_logit"),
CVAELoss(inputs=("x", "mean", "logvar", "z", "x_logit"), mode=None)
])
estimator = fe.Estimator(network=network,
pipeline=pipeline,
epochs=epochs,
traces=ModelSaver(model_name="decoder",
save_dir=model_dir,
save_best=True))
return estimator
def run_test(mixed_precision, merge_grad, gradient):
lr = 0.1
lr2 = 0.01
lr3 = 0.001
pipeline = fe.Pipeline(train_data=self.train_data,
batch_size=4,
ops=[ExpandDims(inputs="x", outputs="x", axis=0), Minmax(inputs="x", outputs="x")])
optimizer_fn = EpochScheduler({
1: lambda x: torch.optim.SGD(params=x, lr=lr),
2: lambda x: torch.optim.SGD(params=x, lr=lr2),
3: lambda x: torch.optim.SGD(params=x, lr=lr3)
})
model = fe.build(model_fn=LeNet_torch, optimizer_fn=optimizer_fn, mixed_precision=mixed_precision)
network = fe.Network(ops=[
ModelOp(model=model, inputs="x", outputs="y_pred"),
CrossEntropy(inputs=("y_pred", "y"), outputs="ce"),
GradientOp(model=model, finals="ce", outputs="grad"),
UpdateOp(model=model, loss_name="ce", gradients=gradient, merge_grad=merge_grad),
])
traces = [
CheckNetworkWeight(model=model,
grad_key="grad",
merge_grad=merge_grad,
test_self=self,
framework="torch",
lrs=[lr, lr2, lr3],
work_intervals=[[1, 2], [2, 3], [3, 4]])
]
estimator = fe.Estimator(pipeline=pipeline,
network=network,
epochs=3,
traces=traces,
train_steps_per_epoch=2)
estimator.fit(warmup=False)
def get_estimator(batch_size=256,
epochs=50,
steps_per_epoch=None,
validation_steps=None,
model_dir=tempfile.mkdtemp()):
# prepare data
(x_train, _), (_, _) = tf.keras.datasets.mnist.load_data()
data = {"train": {"x": np.expand_dims(x_train, -1)}}
pipeline = fe.Pipeline(batch_size=batch_size,
data=data,
ops=Myrescale(inputs="x", outputs="x"))
# prepare model
g_femodel = fe.build(model_def=make_generator_model,
model_name="gen",
loss_name="gloss",
optimizer=tf.optimizers.Adam(1e-4))
d_femodel = fe.build(model_def=make_discriminator_model,
model_name="disc",
loss_name="dloss",
optimizer=tf.optimizers.Adam(1e-4))
network = fe.Network(ops=[
ModelOp(inputs=lambda: tf.random.normal([batch_size, 100]),
model=g_femodel),
ModelOp(model=d_femodel, outputs="pred_fake"),
ModelOp(inputs="x", model=d_femodel, outputs="pred_true"),
GLoss(inputs=("pred_fake"), outputs="gloss"),
DLoss(inputs=("pred_true", "pred_fake"), outputs="dloss")
])
# prepare estimator
traces = [ModelSaver(model_name='gen', save_dir=model_dir, save_freq=5)]
estimator = fe.Estimator(network=network,
pipeline=pipeline,
epochs=epochs,
traces=traces,
steps_per_epoch=steps_per_epoch,
validation_steps=validation_steps)
return estimator
def get_estimator(epochs=50,
batch_size=32,
steps_per_epoch=None,
validation_steps=None,
model_dir=tempfile.mkdtemp()):
(X, y) = load_breast_cancer(True)
x_train, x_eval, y_train, y_eval = train_test_split(X, y, test_size=0.2)
# step 1. prepare data
scaler = StandardScaler()
x_train = scaler.fit_transform(x_train)
x_eval = scaler.transform(x_eval)
train_data = {"x": x_train, "y": np.expand_dims(y_train, -1)}
eval_data = {"x": x_eval, "y": np.expand_dims(y_eval, -1)}
data = {"train": train_data, "eval": eval_data}
pipeline = fe.Pipeline(batch_size=batch_size, data=data)
# step 2. prepare model
model = fe.build(model_def=create_dnn,
model_name="dnn",
optimizer="adam",
loss_name="loss")
network = fe.Network(ops=[
ModelOp(inputs="x", model=model, outputs="y_pred"),
MeanSquaredError(inputs=("y", "y_pred"), outputs="loss")
])
# step 3.prepare estimator
traces = [ModelSaver(model_name="dnn", save_dir=model_dir, save_best=True)]
estimator = fe.Estimator(network=network,
pipeline=pipeline,
epochs=epochs,
steps_per_epoch=steps_per_epoch,
validation_steps=validation_steps,
log_steps=10,
traces=traces)
return estimator
def test_estimator_configure_loader_tf_data_loader_torch_model(self):
loader = get_sample_tf_dataset()
pipeline = fe.Pipeline(train_data=loader)
model = fe.build(model_fn=LeNetTorch, optimizer_fn="adam")
network = fe.Network(ops=[
ModelOp(model=model, inputs="x_out", outputs="y_pred"),
CrossEntropy(inputs=("y_pred", "y"), outputs="ce"),
UpdateOp(model=model, loss_name="ce")
])
est = fe.Estimator(pipeline=pipeline,
network=network,
max_train_steps_per_epoch=3,
epochs=1)
est.system.mode = "train"
new_loader = est._configure_loader(loader)
with self.subTest(
"check loader type"): # it didn't change the data type
strategy = tf.distribute.get_strategy()
if isinstance(strategy,
tf.distribute.MirroredStrategy) and isinstance(
network, TFNetwork):
self.assertIsInstance(new_loader,
tf.distribute.DistributedDataset)
else:
self.assertIsInstance(new_loader, tf.data.Dataset)
with self.subTest("max_train_steps_per_epoch=3"):
iterator = iter(new_loader)
for i in range(3):
batch = next(iterator)
with self.assertRaises(StopIteration):
batch = next(iterator)
def test_pipeline_get_result_dict_batch_size_train_eval(self):
pipeline = fe.Pipeline(train_data=self.sample_torch_dataset,
eval_data=self.sample_torch_dataset,
ops=NumpyOpAdd1(inputs="x", outputs="y"),
batch_size={
"train": 2,
"eval": 1
})
data_train = pipeline.get_results(mode="train", epoch=1)
data_eval = pipeline.get_results(mode="eval", epoch=1)
data_train["x"] = data_train["x"].numpy()
data_train["y"] = data_train["y"].numpy()
data_eval["x"] = data_eval["x"].numpy()
data_eval["y"] = data_eval["y"].numpy()
ans_train = {
"x": np.array([[0], [1]], dtype=np.float32),
"y": np.array([[1], [2]], dtype=np.float32)
}
ans_eval = {
"x": np.array([[0]], dtype=np.float32),
"y": np.array([[1]], dtype=np.float32)
}
self.assertTrue(is_equal(data_train, ans_train))
self.assertTrue(is_equal(data_eval, ans_eval))
def _build_estimator(model: Union[tf.keras.Model, torch.nn.Module],
trace: Traceability,
axis: int = -1):
train_data, eval_data = mnist.load_data()
test_data = eval_data.split(0.5)
batch_size = 32
pipeline = fe.Pipeline(train_data=train_data,
eval_data=eval_data,
test_data=test_data,
batch_size=batch_size,
ops=[
ExpandDims(inputs="x", outputs="x", axis=axis),
Minmax(inputs="x", outputs="x")
])
network = fe.Network(ops=[
ModelOp(model=model, inputs="x", outputs="y_pred"),
CrossEntropy(inputs=("y_pred", "y"), outputs="ce"),
UpdateOp(model=model, loss_name="ce")
])
traces = [
Accuracy(true_key="y", pred_key="y_pred"),
LRScheduler(model=model,
lr_fn=lambda step: cosine_decay(
step, cycle_length=3750, init_lr=1e-3)), trace
]
estimator = fe.Estimator(pipeline=pipeline,
network=network,
epochs=1,
traces=traces,
max_train_steps_per_epoch=1,
max_eval_steps_per_epoch=None)
fake_data = tf.ones(shape=(batch_size, 28, 28,
1)) if axis == -1 else torch.ones(
size=(batch_size, 1, 28, 28))
model.fe_input_spec = FeInputSpec(fake_data, model)
return estimator
def run_test(mixed_precision, merge_grad, gradient):
lr = 0.1
pipeline = fe.Pipeline(train_data=self.train_data,
batch_size=4,
ops=[
ExpandDims(inputs="x", outputs="x"),
Minmax(inputs="x", outputs="x")
])
model = fe.build(model_fn=LeNet_tf,
optimizer_fn=lambda: tf.optimizers.SGD(lr),
mixed_precision=mixed_precision)
network = fe.Network(ops=[
ModelOp(model=model, inputs="x", outputs="y_pred"),
CrossEntropy(inputs=("y_pred", "y"), outputs="ce"),
GradientOp(model=model, finals="ce", outputs="grad"),
UpdateOp(model=model,
loss_name="ce",
gradients=gradient,
merge_grad=merge_grad),
])
traces = [
CheckNetworkWeight(model=model,
grad_key="grad",
merge_grad=merge_grad,
test_self=self,
lrs=lr,
framework="tf")
]
estimator = fe.Estimator(pipeline=pipeline,
network=network,
epochs=2,
traces=traces,
train_steps_per_epoch=2)
estimator.fit(warmup=False)
def get_estimator(batch_size=100, epochs=20, max_train_steps_per_epoch=None, save_dir=tempfile.mkdtemp()):
train_data, _ = load_data()
pipeline = fe.Pipeline(
train_data=train_data,
batch_size=batch_size,
ops=[
ExpandDims(inputs="x", outputs="x", axis=0),
Minmax(inputs="x", outputs="x"),
Binarize(inputs="x", outputs="x", threshold=0.5),
])
encode_model = fe.build(model_fn=EncoderNet, optimizer_fn="adam", model_name="encoder")
decode_model = fe.build(model_fn=DecoderNet, optimizer_fn="adam", model_name="decoder")
network = fe.Network(ops=[
ModelOp(model=encode_model, inputs="x", outputs="meanlogvar"),
SplitOp(inputs="meanlogvar", outputs=("mean", "logvar")),
ReparameterizepOp(inputs=("mean", "logvar"), outputs="z"),
ModelOp(model=decode_model, inputs="z", outputs="x_logit"),
CrossEntropy(inputs=("x_logit", "x"), outputs="cross_entropy"),
CVAELoss(inputs=("cross_entropy", "mean", "logvar", "z"), outputs="loss"),
UpdateOp(model=encode_model, loss_name="loss"),
UpdateOp(model=decode_model, loss_name="loss"),
])
traces = [
BestModelSaver(model=encode_model, save_dir=save_dir), BestModelSaver(model=decode_model, save_dir=save_dir)
]
estimator = fe.Estimator(pipeline=pipeline,
network=network,
epochs=epochs,
traces=traces,
max_train_steps_per_epoch=max_train_steps_per_epoch)
return estimator
def sample_system_object_torch():
x_train = np.random.rand(3, 28, 28, 3)
y_train = np.random.randint(10, size=(3, ))
x_eval = np.random.rand(2, 28, 28, 3)
y_eval = np.random.randint(10, size=(2, ))
train_data = NumpyDataset({'x': x_train, 'y': y_train})
eval_data = NumpyDataset({'x': x_eval, 'y': y_eval})
test_data = eval_data.split(0.5)
model = fe.build(model_fn=fe.architecture.pytorch.LeNet,
optimizer_fn='adam',
model_name='torch')
pipeline = fe.Pipeline(train_data=train_data,
eval_data=eval_data,
test_data=test_data,
batch_size=1)
network = fe.Network(
ops=[ModelOp(model=model, inputs="x_out", outputs="y_pred")])
system = System(network=network,
pipeline=pipeline,
traces=[],
total_epochs=10,
mode='train')
return system
def get_estimator(data_dir=None,
model_dir=tempfile.mkdtemp(),
epochs=20,
em_dim=128,
batch_size=32,
train_steps_per_epoch=None,
eval_steps_per_epoch=None):
train_ds, eval_ds, test_ds = tednmt.load_data(data_dir,
translate_option="pt_to_en")
pt_tokenizer = BertTokenizer.from_pretrained(
"neuralmind/bert-base-portuguese-cased")
en_tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
pipeline = fe.Pipeline(train_data=train_ds,
eval_data=eval_ds,
test_data=test_ds,
ops=[
Encode(inputs="source",
outputs="source",
tokenizer=pt_tokenizer),
Encode(inputs="target",
outputs="target",
tokenizer=en_tokenizer),
Batch(batch_size=batch_size, pad_value=0)
])
model = fe.build(
model_fn=lambda: Transformer(num_layers=4,
em_dim=em_dim,
num_heads=8,
ff_dim=512,
input_vocab=pt_tokenizer.vocab_size,
target_vocab=en_tokenizer.vocab_size,
max_pos_enc=1000,
max_pos_dec=1000),
optimizer_fn="adam")
network = fe.Network(ops=[
ShiftData(inputs="target", outputs=("target_inp", "target_real")),
CreateMasks(inputs=("source", "target_inp"),
outputs=("encode_pad_mask", "decode_pad_mask",
"dec_look_ahead_mask")),
ModelOp(model=model,
inputs=("source", "target_inp", "encode_pad_mask",
"decode_pad_mask", "dec_look_ahead_mask"),
outputs="pred"),
MaskedCrossEntropy(inputs=("pred", "target_real"), outputs="ce"),
UpdateOp(model=model, loss_name="ce")
])
traces = [
MaskedAccuracy(inputs=("pred", "target_real"),
outputs="masked_acc",
mode="!train"),
BestModelSaver(model=model,
save_dir=model_dir,
metric="masked_acc",
save_best_mode="max"),
LRScheduler(model=model, lr_fn=lambda step: lr_fn(step, em_dim))
]
estimator = fe.Estimator(pipeline=pipeline,
network=network,
traces=traces,
epochs=epochs,
train_steps_per_epoch=train_steps_per_epoch,
eval_steps_per_epoch=eval_steps_per_epoch)
return estimator
def test_pipeline_get_loader_torch_dataset_with_batch_size(self):
with self.subTest(shuffle=False):
pipeline = fe.Pipeline(train_data=self.sample_torch_dataset,
batch_size=2)
loader = pipeline.get_loader(mode="train", shuffle=False)
results = []
for idx, batch in enumerate(loader, start=1):
results.append(batch)
if idx == 2:
break
ans = [{
"x": torch.tensor([[0], [1]], dtype=torch.float32),
"y": torch.tensor([[-99], [-98]], dtype=torch.float32)
}, {
"x": torch.tensor([[2], [3]], dtype=torch.float32),
"y": torch.tensor([[-97], [-96]], dtype=torch.float32)
}]
self.assertTrue(is_equal(results, ans))
with self.subTest(shuffle=True):
pipeline = fe.Pipeline(train_data=self.sample_torch_dataset,
batch_size=2)
loader = pipeline.get_loader(mode="train", shuffle=True)
results = []
for idx, batch in enumerate(loader, start=1):
results.append(batch)
if idx == 2:
break
wrong_ans = [{
"x": torch.tensor([[0], [1]], dtype=torch.float32),
"y": torch.tensor([[-99], [-98]], dtype=torch.float32)
}, {
"x": torch.tensor([[2], [3]], dtype=torch.float32),
"y": torch.tensor([[-97], [-96]], dtype=torch.float32)
}]
self.assertFalse(is_equal(results, wrong_ans))
with self.subTest(shuffle=None):
pipeline = fe.Pipeline(train_data=self.sample_torch_dataset,
batch_size=2)
loader = pipeline.get_loader(mode="train", shuffle=None)
results = []
for idx, batch in enumerate(loader, start=1):
results.append(batch)
if idx == 2:
break
wrong_ans = [{
"x": torch.tensor([[0], [1]], dtype=torch.float32),
"y": torch.tensor([[-99], [-98]], dtype=torch.float32)
}, {
"x": torch.tensor([[2], [3]], dtype=torch.float32),
"y": torch.tensor([[-97], [-96]], dtype=torch.float32)
}]
self.assertFalse(
is_equal(results, wrong_ans)
) # if shuffle is None and has specify batch_size, it will shuffle
def test_pipeline_get_loader_torch_dataloader(self):
pipeline = fe.Pipeline(train_data=self.sample_torch_dataloader)
loader = pipeline.get_loader(mode="train")
self.assertEqual(loader, self.sample_torch_dataloader)
def test_pipeline_transform_no_ops(self):
pipeline = fe.Pipeline()
data = pipeline.transform(data=self.sample_data, mode="train")
ans = {"x": np.array([[1, 2, 3]], dtype=np.float32)}
self.assertTrue(is_equal(data, ans))
def get_estimator(target_size=128,
epochs=55,
save_dir=tempfile.mkdtemp(),
max_train_steps_per_epoch=None,
data_dir=None):
# assert growth parameters
num_grow = np.log2(target_size) - 2
assert num_grow >= 1 and num_grow % 1 == 0, "need exponential of 2 and greater than 8 as target size"
num_phases = int(2 * num_grow + 1)
assert epochs % num_phases == 0, "epoch must be multiple of {} for size {}".format(
num_phases, target_size)
num_grow, phase_length = int(num_grow), int(epochs / num_phases)
event_epoch = [1, 1 + phase_length] + [
phase_length * (2 * i + 1) + 1 for i in range(1, num_grow)
]
event_size = [4] + [2**(i + 3) for i in range(num_grow)]
# set up data schedules
dataset = nih_chestxray.load_data(root_dir=data_dir)
resize_map = {
epoch: Resize(image_in="x", image_out="x", height=size, width=size)
for (epoch, size) in zip(event_epoch, event_size)
}
resize_low_res_map1 = {
epoch: Resize(image_in="x",
image_out="x_low_res",
height=size // 2,
width=size // 2)
for (epoch, size) in zip(event_epoch, event_size)
}
resize_low_res_map2 = {
epoch: Resize(image_in="x_low_res",
image_out="x_low_res",
height=size,
width=size)
for (epoch, size) in zip(event_epoch, event_size)
}
batch_size_map = {
epoch: 512 // size * get_num_devices() if size <= 128 else 4 *
get_num_devices()
for (epoch, size) in zip(event_epoch, event_size)
}
batch_scheduler = EpochScheduler(epoch_dict=batch_size_map)
pipeline = fe.Pipeline(
batch_size=batch_scheduler,
train_data=dataset,
drop_last=True,
ops=[
ReadImage(inputs="x", outputs="x", color_flag='gray'),
EpochScheduler(epoch_dict=resize_map),
EpochScheduler(epoch_dict=resize_low_res_map1),
EpochScheduler(epoch_dict=resize_low_res_map2),
Normalize(inputs=["x", "x_low_res"],
outputs=["x", "x_low_res"],
mean=1.0,
std=1.0,
max_pixel_value=127.5),
LambdaOp(fn=lambda: np.random.normal(size=[512]).astype('float32'),
outputs="z")
])
# now model schedule
fade_in_alpha = tf.Variable(initial_value=1.0,
dtype='float32',
trainable=False)
d_models = fe.build(
model_fn=lambda: build_D(fade_in_alpha,
target_resolution=int(np.log2(target_size)),
num_channels=1),
optimizer_fn=[
lambda: Adam(0.001, beta_1=0.0, beta_2=0.99, epsilon=1e-8)
] * len(event_size),
model_name=["d_{}".format(size) for size in event_size])
g_models = fe.build(
model_fn=lambda: build_G(fade_in_alpha,
target_resolution=int(np.log2(target_size)),
num_channels=1),
optimizer_fn=[
lambda: Adam(0.001, beta_1=0.0, beta_2=0.99, epsilon=1e-8)
] * len(event_size) + [None],
model_name=["g_{}".format(size) for size in event_size] + ["G"])
fake_img_map = {
epoch: ModelOp(inputs="z", outputs="x_fake", model=model)
for (epoch, model) in zip(event_epoch, g_models[:-1])
}
fake_score_map = {
epoch: ModelOp(inputs="x_fake", outputs="fake_score", model=model)
for (epoch, model) in zip(event_epoch, d_models)
}
real_score_map = {
epoch: ModelOp(inputs="x_blend", outputs="real_score", model=model)
for (epoch, model) in zip(event_epoch, d_models)
}
interp_score_map = {
epoch: ModelOp(inputs="x_interp", outputs="interp_score", model=model)
for (epoch, model) in zip(event_epoch, d_models)
}
g_update_map = {
epoch: UpdateOp(loss_name="gloss", model=model)
for (epoch, model) in zip(event_epoch, g_models[:-1])
}
d_update_map = {
epoch: UpdateOp(loss_name="dloss", model=model)
for (epoch, model) in zip(event_epoch, d_models)
}
network = fe.Network(ops=[
EpochScheduler(fake_img_map),
EpochScheduler(fake_score_map),
ImageBlender(
alpha=fade_in_alpha, inputs=("x", "x_low_res"), outputs="x_blend"),
EpochScheduler(real_score_map),
Interpolate(inputs=("x_fake", "x"), outputs="x_interp"),
EpochScheduler(interp_score_map),
GradientPenalty(inputs=("x_interp", "interp_score"), outputs="gp"),
GLoss(inputs="fake_score", outputs="gloss"),
DLoss(inputs=("real_score", "fake_score", "gp"), outputs="dloss"),
EpochScheduler(g_update_map),
EpochScheduler(d_update_map)
])
traces = [
AlphaController(alpha=fade_in_alpha,
fade_start_epochs=event_epoch[1:],
duration=phase_length,
batch_scheduler=batch_scheduler,
num_examples=len(dataset)),
ModelSaver(model=g_models[-1],
save_dir=save_dir,
frequency=phase_length),
ImageSaving(epoch_model_map={
epoch - 1: model
for (epoch,
model) in zip(event_epoch[1:] + [epochs + 1], g_models[:-1])
},
save_dir=save_dir)
]
estimator = fe.Estimator(
pipeline=pipeline,
network=network,
epochs=epochs,
traces=traces,
max_train_steps_per_epoch=max_train_steps_per_epoch)
return estimator
def get_estimator(batch_size=4,
epochs=200,
steps_per_epoch=1000,
validation_steps=None,
model_dir=tempfile.mkdtemp(),
imagenet_path=None,
srresnet_model_path=None):
"""Args:
imagenet_path: folder path of ImageNet dataset, containing train and val subdirs .
srresnet_model_path: srresnet model weights, srgan generator gets initialized with the weights.
"""
assert imagenet_path is not None, 'Pass valid folder path of Imagenet dataset'
assert srresnet_model_path is not None, 'srresnet model is needed to initialize srgan generator model'
# Ensure ImageNet dataset is downloaded. Pass the folder contianing train and val subdirectories.
# currently the script doesn't download the ImageNet data.
train_csv, val_csv, path = srgan.load_data(path_imgnet=imagenet_path)
writer = fe.RecordWriter(save_dir=os.path.join(path, "sr_tfrecords"),
train_data=train_csv,
validation_data=val_csv,
ops=[
ImageReader(inputs="lowres",
outputs="lowres"),
ImageReader(inputs="highres",
outputs="highres")
],
compression="GZIP",
write_feature=['lowres', 'highres'])
pipeline = fe.Pipeline(max_shuffle_buffer_mb=3000,
batch_size=batch_size,
data=writer,
ops=[
LowresRescale(inputs='lowres',
outputs='lowres'),
Rescale(inputs='highres', outputs='highres'),
])
# prepare model
model_gen = fe.build(model_def=srresnet_model_path,
model_name="srgan_gen",
optimizer=tf.optimizers.Adam(learning_rate=0.0001),
loss_name="mse_adv_loss",
custom_objects={'SubPixelConv2D': SubPixelConv2D})
model_desc = fe.build(
model_def=lambda: get_discriminator(input_shape=(96, 96, 3)),
model_name="srgan_desc",
optimizer=tf.optimizers.Adam(learning_rate=0.0001),
loss_name="desc_loss")
network = fe.Network(ops=[
ModelOp(inputs='lowres', model=model_gen, outputs='superres'),
ModelOp(inputs='superres', model=model_desc, outputs='pred_fake'),
ModelOp(inputs='highres', model=model_desc, outputs='pred_true'),
DLoss(inputs=("pred_true", "pred_fake"),
outputs=("desc_loss", "real_loss", "fake_loss")),
GLoss(inputs=('superres', 'highres', 'pred_fake'),
outputs=("mse_adv_loss", "mse_loss", "adv_loss"),
vgg_content=True)
])
model_dir = os.path.join(path)
estimator = fe.Estimator(
network=network,
pipeline=pipeline,
steps_per_epoch=steps_per_epoch,
epochs=epochs,
traces=[
ModelSaver(model_name="srgan_gen",
save_dir=model_dir,
save_best=True),
ModelSaver(model_name="srgan_desc",
save_dir=model_dir,
save_best=True),
LRController(model_name="srgan_gen",
lr_schedule=MyLRSchedule(schedule_mode='step')),
LRController(model_name="srgan_desc",
lr_schedule=MyLRSchedule(schedule_mode='step'))
])
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(max_len=20,
epochs=10,
batch_size=64,
max_train_steps_per_epoch=None,
max_eval_steps_per_epoch=None,
save_dir=tempfile.mkdtemp(),
pretrained_model='bert-base-uncased',
data_dir=None):
# step 1 prepare data
train_data, eval_data, data_vocab, label_vocab = german_ner.load_data(
root_dir=data_dir)
tokenizer = BertTokenizer.from_pretrained(pretrained_model,
do_lower_case=True)
tag2idx = char2idx(label_vocab)
pipeline = fe.Pipeline(train_data=train_data,
eval_data=eval_data,
batch_size=batch_size,
ops=[
Tokenize(inputs="x",
outputs="x",
tokenize_fn=tokenizer.tokenize),
WordtoId(
inputs="x",
outputs="x",
mapping=tokenizer.convert_tokens_to_ids),
WordtoId(inputs="y",
outputs="y",
mapping=tag2idx),
PadSequence(max_len=max_len,
inputs="x",
outputs="x"),
PadSequence(max_len=max_len,
value=len(tag2idx),
inputs="y",
outputs="y"),
AttentionMask(inputs="x", outputs="x_masks")
])
# step 2. prepare model
model = fe.build(model_fn=lambda: ner_model(max_len, pretrained_model),
optimizer_fn=lambda: tf.optimizers.Adam(1e-5))
network = fe.Network(ops=[
ModelOp(model=model, inputs=["x", "x_masks"], outputs="y_pred"),
Reshape(inputs="y", outputs="y", shape=(-1, )),
Reshape(inputs="y_pred", outputs="y_pred", shape=(-1, 24)),
CrossEntropy(inputs=("y_pred", "y"), outputs="loss"),
UpdateOp(model=model, loss_name="loss")
])
traces = [
Accuracy(true_key="y", pred_key="y_pred"),
BestModelSaver(model=model, save_dir=save_dir)
]
# step 3 prepare estimator
estimator = fe.Estimator(
network=network,
pipeline=pipeline,
epochs=epochs,
traces=traces,
max_train_steps_per_epoch=max_train_steps_per_epoch,
max_eval_steps_per_epoch=max_eval_steps_per_epoch)
return estimator
def get_estimator(batch_size=8,
epochs=50,
train_steps_per_epoch=None,
eval_steps_per_epoch=None,
save_dir=tempfile.mkdtemp(),
data_dir=None):
# load CUB200 dataset.
train_data = cub200.load_data(root_dir=data_dir)
eval_data = train_data.split(0.3)
test_data = eval_data.split(0.5)
# step 1, pipeline
pipeline = fe.Pipeline(batch_size=batch_size,
train_data=train_data,
eval_data=eval_data,
test_data=test_data,
ops=[
ReadImage(inputs="image",
outputs="image",
parent_path=train_data.parent_path),
Normalize(inputs="image",
outputs="image",
mean=1.0,
std=1.0,
max_pixel_value=127.5),
ReadMat(file='annotation',
keys="seg",
parent_path=train_data.parent_path),
LongestMaxSize(max_size=512,
image_in="image",
image_out="image",
mask_in="seg",
mask_out="seg"),
PadIfNeeded(min_height=512,
min_width=512,
image_in="image",
image_out="image",
mask_in="seg",
mask_out="seg",
border_mode=cv2.BORDER_CONSTANT,
value=0,
mask_value=0),
ShiftScaleRotate(
image_in="image",
mask_in="seg",
image_out="image",
mask_out="seg",
mode="train",
shift_limit=0.2,
rotate_limit=15.0,
scale_limit=0.2,
border_mode=cv2.BORDER_CONSTANT,
value=0,
mask_value=0),
Sometimes(
HorizontalFlip(image_in="image",
mask_in="seg",
image_out="image",
mask_out="seg",
mode="train")),
Reshape(shape=(512, 512, 1),
inputs="seg",
outputs="seg")
])
# step 2, network
resunet50 = fe.build(model_fn=ResUnet50,
model_name="resunet50",
optimizer_fn=lambda: tf.optimizers.Adam(1e-4))
uncertainty = fe.build(model_fn=UncertaintyLossNet,
model_name="uncertainty",
optimizer_fn=lambda: tf.optimizers.Adam(2e-5))
network = fe.Network(ops=[
ModelOp(inputs='image',
model=resunet50,
outputs=["label_pred", "mask_pred"]),
CrossEntropy(inputs=["label_pred", "label"],
outputs="cls_loss",
form="sparse",
average_loss=False),
CrossEntropy(inputs=["mask_pred", "seg"],
outputs="seg_loss",
form="binary",
average_loss=False),
ModelOp(inputs=["cls_loss", "seg_loss"],
model=uncertainty,
outputs="total_loss"),
ReduceLoss(inputs="total_loss", outputs="total_loss"),
UpdateOp(model=resunet50, loss_name="total_loss"),
UpdateOp(model=uncertainty, loss_name="total_loss")
])
# step 3, estimator
traces = [
Accuracy(true_key="label", pred_key="label_pred"),
Dice(true_key="seg", pred_key='mask_pred'),
BestModelSaver(model=resunet50,
save_dir=save_dir,
metric="total_loss",
save_best_mode="min"),
LRScheduler(model=resunet50,
lr_fn=lambda step: cosine_decay(
step, cycle_length=26400, init_lr=1e-4))
]
estimator = fe.Estimator(network=network,
pipeline=pipeline,
traces=traces,
epochs=epochs,
train_steps_per_epoch=train_steps_per_epoch,
eval_steps_per_epoch=eval_steps_per_epoch,
log_steps=500)
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 test_estimator_check_network_op_trace_invoke_sequence_tf_backend(self):
epochs = 1
batches = 10 # dataset has 100 sample, and batch_size is 10
iostream = StringIO()
loader = get_sample_tf_dataset()
pipeline = fe.Pipeline(test_data=loader)
model = fe.build(model_fn=LeNetTf, optimizer_fn="adam")
ops = [
ModelOp(model=model, inputs="x", outputs="y_pred"),
ShoutNameOp(name="A", iostream=iostream),
ShoutNameOp(name="B", iostream=iostream)
]
network = fe.Network(ops=ops)
traces = [
ShoutNameTrace(name="a", iostream=iostream),
ShoutNameTrace(name="b", iostream=iostream)
]
est = fe.Estimator(pipeline=pipeline,
network=network,
epochs=epochs,
traces=traces)
est.test()
# create the expected calling sequence in another iostream
iostream2 = StringIO()
ops2 = [
ShoutNameOp(name="A", iostream=iostream2),
ShoutNameOp(name="B", iostream=iostream2)
]
traces2 = [
ShoutNameTrace(name="a", iostream=iostream2),
ShoutNameTrace(name="b", iostream=iostream2)
]
# determine if running environment is multi-gpu (only needed in tf backend)
strategy = tf.distribute.get_strategy()
if isinstance(strategy, tf.distribute.MirroredStrategy):
device_count = len(
tf.config.list_physical_devices(device_type="GPU"))
else:
device_count = 1
for trace in traces2:
trace.on_begin(None)
for epoch in range(epochs):
for trace in traces2:
trace.on_epoch_begin(None)
for batch in range(batches):
for trace in traces2:
trace.on_batch_begin(None)
if batch == 0:
# ShoutOutTrace will only be invoked the number of times equal to device count while building static
# graph (for tf backend). ex: 4 GPU -> 4 times, CPU -> 1 time
for _ in range(device_count):
for op in ops2:
op.forward(None, None)
for trace in traces2:
trace.on_batch_end(None)
for trace in traces2:
trace.on_epoch_end(None)
for trace in traces2:
trace.on_end(None)
self.assertEqual(iostream.getvalue(), iostream2.getvalue())
def get_estimator(epochs=20,
batch_size=4,
train_steps_per_epoch=None,
eval_steps_per_epoch=None,
save_dir=tempfile.mkdtemp(),
log_steps=20,
data_dir=None):
# step 1
csv = montgomery.load_data(root_dir=data_dir)
pipeline = fe.Pipeline(
train_data=csv,
eval_data=csv.split(0.2),
batch_size=batch_size,
ops=[
ReadImage(inputs="image",
parent_path=csv.parent_path,
outputs="image",
color_flag='gray'),
ReadImage(inputs="mask_left",
parent_path=csv.parent_path,
outputs="mask_left",
color_flag='gray',
mode='!infer'),
ReadImage(inputs="mask_right",
parent_path=csv.parent_path,
outputs="mask_right",
color_flag='gray',
mode='!infer'),
CombineLeftRightMask(inputs=("mask_left", "mask_right"),
outputs="mask",
mode='!infer'),
Resize(image_in="image", width=512, height=512),
Resize(image_in="mask", width=512, height=512, mode='!infer'),
Sometimes(numpy_op=HorizontalFlip(
image_in="image", mask_in="mask", mode='train')),
Sometimes(numpy_op=Rotate(image_in="image",
mask_in="mask",
limit=(-10, 10),
border_mode=cv2.BORDER_CONSTANT,
mode='train')),
Minmax(inputs="image", outputs="image"),
Minmax(inputs="mask", outputs="mask", mode='!infer')
])
# step 2
model = fe.build(
model_fn=lambda: UNet(input_size=(512, 512, 1)),
optimizer_fn=lambda: tf.keras.optimizers.Adam(learning_rate=0.0001),
model_name="lung_segmentation")
network = fe.Network(ops=[
ModelOp(inputs="image", model=model, outputs="pred_segment"),
CrossEntropy(
inputs=("pred_segment", "mask"), outputs="loss", form="binary"),
UpdateOp(model=model, loss_name="loss")
])
# step 3
traces = [
Dice(true_key="mask", pred_key="pred_segment"),
BestModelSaver(model=model,
save_dir=save_dir,
metric='Dice',
save_best_mode='max')
]
estimator = fe.Estimator(network=network,
pipeline=pipeline,
epochs=epochs,
log_steps=log_steps,
traces=traces,
train_steps_per_epoch=train_steps_per_epoch,
eval_steps_per_epoch=eval_steps_per_epoch)
return estimator
def get_estimator(epochs=50,
batch_size=128,
train_steps_per_epoch=None,
eval_steps_per_epoch=None,
save_dir=tempfile.mkdtemp()):
# step 1
train_data, eval_data = cifair100.load_data()
# Add label noise to simulate real-world labeling problems
corrupt_dataset(train_data)
test_data = eval_data.split(range(len(eval_data) // 2))
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)),
PadIfNeeded(min_height=40,
min_width=40,
image_in="x",
image_out="x",
mode="train"),
RandomCrop(32,
32,
image_in="x",
image_out="x",
mode="train"),
Sometimes(
HorizontalFlip(image_in="x",
image_out="x",
mode="train")),
CoarseDropout(inputs="x",
outputs="x",
mode="train",
max_holes=1),
])
# step 2
model = fe.build(model_fn=big_lenet, optimizer_fn='adam')
network = fe.Network(ops=[
ModelOp(model=model, inputs="x", outputs="y_pred"),
SuperLoss(CrossEntropy(inputs=("y_pred", "y"), outputs="ce"),
output_confidence="confidence"),
UpdateOp(model=model, loss_name="ce")
])
# step 3
traces = [
MCC(true_key="y", pred_key="y_pred"),
BestModelSaver(model=model,
save_dir=save_dir,
metric="mcc",
save_best_mode="max",
load_best_final=True),
LabelTracker(metric="confidence",
label="data_labels",
label_mapping={
"Normal": 0,
"Corrupted": 1
},
mode="train",
outputs="label_confidence"),
ImageSaver(inputs="label_confidence", save_dir=save_dir, mode="train"),
]
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)
return estimator
