def fastestimator_run():
pos_real, _ = mnist.load_data(image_key="x1", label_key="y1")
neg_real, _ = mnist.load_data(image_key="x2", label_key="y2")
neg_sim, _ = mnist.load_data()
neg_sim = NegativeImageSimulatedTube(neg_sim)
batch_ds = BatchDataset(datasets=(pos_real, neg_real, neg_sim), num_samples=(10, 10, 2))
pipeline = fe.Pipeline(train_data=batch_ds)
data = pipeline.get_results()
for key, value in data.items():
print(key)
print(value.shape)
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 test_mode_ds_id_interaction(self):
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=32,
ops=[
ExpandDims(inputs="x", outputs="x"),
Minmax(inputs="x", outputs="x")
])
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", ds_id="ds_1")
])
pipeline_data = pipeline.transform(data=train_data[0], mode="train")
data1 = network.transform(data=pipeline_data,
mode="infer",
ds_id="ds_1")
assert "ce" not in data1
data2 = network.transform(data=pipeline_data,
mode="infer",
ds_id="ds_2")
assert "ce" not in data2
def create_pipeline():
train_data, eval_data = mnist.load_data()
pipeline = fe.Pipeline(train_data=train_data,
eval_data=eval_data,
batch_size=32,
ops=[ExpandDims(inputs="x", outputs="x1"), Minmax(inputs="x1", outputs="x")])
return pipeline
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))
]
estimator = fe.Estimator(pipeline=pipeline,
network=network,
epochs=epochs,
traces=traces)
return estimator
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 get_estimator(epochs=2, batch_size=32):
# step 1
train_data, eval_data = mnist.load_data()
pipeline = fe.Pipeline(train_data=train_data,
eval_data=eval_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", "feature_vector"],
intermediate_layers='dense'),
CrossEntropy(inputs=("y_pred", "y"), outputs="ce"),
CustomLoss(inputs=("feature_vector", "feature_selected"),
outputs="feature_loss"),
LambdaOp(fn=lambda x, y: x + y,
inputs=("ce", "feature_loss"),
outputs="total_loss"),
UpdateOp(model=model, loss_name="total_loss")
])
# step 3
traces = [
MemoryBank(inputs=("feature_vector", "y"), outputs="feature_selected")
]
estimator = fe.Estimator(pipeline=pipeline,
network=network,
epochs=epochs,
traces=traces)
return estimator
def get_estimator():
pos_real, _ = mnist.load_data()
neg_real, _ = mnist.load_data()
neg_sim, _ = mnist.load_data()
neg_sim = NegativeImageSimulatedTube(neg_sim)
batch_ds = BatchDataset(datasets=(pos_real, neg_real, neg_sim), num_samples=(2, 2, 1))
pipeline = fe.Pipeline(train_data=batch_ds,
ops=[ExpandDims(inputs="x", outputs="x"), Minmax(inputs="x", outputs="x")])
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")
])
estimator = fe.Estimator(pipeline=pipeline, network=network, epochs=2)
return estimator
def get_estimator(epochs=2,
batch_size=32,
max_train_steps_per_epoch=None,
max_eval_steps_per_epoch=None,
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")
],
num_process=0)
# step 2
model = fe.build(model_fn=LeNet, optimizer_fn="adam")
print([f"{idx}: {x.name}" for idx, x in enumerate(model.submodules)])
network = fe.Network(ops=[
Watch(inputs="x"),
ModelOp(model=model,
inputs="x",
outputs=["y_pred", "embedding"],
intermediate_layers='dense'),
CrossEntropy(inputs=("y_pred", "y"), outputs="ce"),
GradientOp(finals="embedding", inputs="x", outputs="grads"),
UpdateOp(model=model, loss_name="ce")
])
# step 3
traces = [
Accuracy(true_key="y", pred_key="y_pred"),
Inspector(),
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)),
TensorBoard(log_dir="tf_logs",
write_embeddings="embedding",
embedding_labels="y")
]
estimator = fe.Estimator(
pipeline=pipeline,
network=network,
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 test_pytorch_weight_decay_vs_l2(self):
# Get Data
train_data, _ = mnist.load_data()
t_d = train_data.split(128)
# Initializing models
pytorch_wd = fe.build(model_fn=MyNet_torch,
optimizer_fn=lambda x: torch.optim.SGD(params=x, lr=0.01, weight_decay=self.beta))
pytorch_l2 = fe.build(model_fn=MyNet_torch, optimizer_fn=lambda x: torch.optim.SGD(params=x, lr=0.01))
# Initialize pipeline
pipeline = fe.Pipeline(train_data=t_d,
batch_size=128,
ops=[ExpandDims(inputs="x", outputs="x", axis=0), Minmax(inputs="x", outputs="x")])
# Define the two pytorch networks
network_weight_decay = fe.Network(ops=[
ModelOp(model=pytorch_wd, inputs="x", outputs="y_pred"),
CrossEntropy(inputs=("y_pred", "y"), outputs="ce"),
UpdateOp(model=pytorch_wd, loss_name="ce")
])
network_l2 = fe.Network(ops=[
ModelOp(model=pytorch_l2, inputs="x", outputs="y_pred"),
CrossEntropy(inputs=("y_pred", "y"), outputs="ce"),
L2Regularizaton(inputs="ce", outputs="l2", model=pytorch_l2, beta=self.beta),
UpdateOp(model=pytorch_l2, loss_name="l2")
])
# defining traces
traces = [Accuracy(true_key="y", pred_key="y_pred")]
# Setting up estimators
estimator_wd = fe.Estimator(pipeline=pipeline,
network=network_weight_decay,
epochs=1,
traces=traces,
train_steps_per_epoch=1)
estimator_l2 = fe.Estimator(pipeline=pipeline,
network=network_l2,
epochs=1,
traces=traces,
train_steps_per_epoch=1)
# Training
print('********************************Pytorch weight decay training************************************')
estimator_wd.fit()
print()
print('********************************Pytorch L2 Regularization training************************************')
estimator_l2.fit()
# testing weights
count = 0
for wt, l2 in zip(pytorch_wd.parameters(), pytorch_l2.parameters()):
if ((wt - l2).abs()).sum() < torch.tensor(10**-6):
count += 1
self.assertTrue(count == 6)
def setUpClass(cls):
train_data, eval_data = mnist.load_data()
cls.pipeline = fe.Pipeline(train_data=train_data)
model = fe.build(model_fn=LeNetTf, optimizer_fn="adam")
cls.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")
])
def test_tf_model_end_to_end_gradient(self):
train_data, _ = mnist.load_data()
pipeline = fe.Pipeline(train_data=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="adam")
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="gradients"),
UpdateOp(model=model, gradients="gradients", loss_name="ce")
])
estimator = fe.Estimator(pipeline=pipeline, network=network, epochs=2, max_train_steps_per_epoch=10)
estimator.fit()
def get_estimator():
ds, _ = mnist.load_data()
ds = NegativeImageSimulatedTube(ds)
pipeline = fe.Pipeline(train_data=ds,
ops=[
ExpandDims(inputs="x", outputs="x"),
Minmax(inputs="x", outputs="x")
])
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")
])
estimator = fe.Estimator(pipeline=pipeline, network=network, epochs=2)
return estimator
def get_estimator(epochs=2, batch_size=32):
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")])
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"),
DebugOp(inputs="ce", outputs="ce", mode="train"),
UpdateOp(model=model, loss_name="ce")
])
estimator = fe.Estimator(pipeline=pipeline, network=network, epochs=epochs)
return estimator
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 get_estimator():
# step 1
train_data, eval_data = mnist.load_data()
pipeline = fe.Pipeline(train_data=train_data,
eval_data=eval_data,
batch_size=32,
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", merge_grad=4)
])
# step 3
estimator = fe.Estimator(pipeline=pipeline, network=network, epochs=2)
return estimator
def create_estimator_for_arc(self, model, use_eval, axis):
train_data, eval_data = mnist.load_data()
pipeline = fe.Pipeline(train_data=train_data,
eval_data=eval_data if use_eval else None,
batch_size=8,
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")
])
estimator = fe.Estimator(pipeline=pipeline,
network=network,
epochs=2,
traces=LRScheduler(model=model, lr_fn=ARC(1)),
max_train_steps_per_epoch=10)
return estimator
def setUpClass(cls):
cls.train_data, _ = mnist.load_data()
def test_pytorch_l2_vs_tensorflow_l2(self):
# Get Data
train_data, eval_data = mnist.load_data()
t_d = train_data.split(128)
# Initializing Pytorch model
pytorch_l2 = fe.build(model_fn=MyNet_torch, optimizer_fn=lambda x: torch.optim.SGD(params=x, lr=0.01))
# Initialize Pytorch pipeline
pipeline = fe.Pipeline(train_data=t_d,
eval_data=eval_data,
batch_size=128,
ops=[ExpandDims(inputs="x", outputs="x", axis=0),
Minmax(inputs="x", outputs="x")])
# Initialize Pytorch Network
network_l2 = fe.Network(ops=[
ModelOp(model=pytorch_l2, inputs="x", outputs="y_pred"),
CrossEntropy(inputs=("y_pred", "y"), outputs="ce"),
L2Regularizaton(inputs="ce",outputs="l2",model=pytorch_l2,beta = self.beta),
UpdateOp(model=pytorch_l2, loss_name="l2")
])
# step 3
traces = [
Accuracy(true_key="y", pred_key="y_pred")
]
# Initialize Pytorch estimator
estimator_l2 = fe.Estimator(pipeline=pipeline,
network=network_l2,
epochs=1,
traces=traces,
train_steps_per_epoch=1,
monitor_names=["ce","l2"])
print('********************************Pytorch L2 Regularization training************************************')
estimator_l2.fit()
# Converting Pytorch weights to numpy
torch_wt = []
for _, param in pytorch_l2.named_parameters():
if param.requires_grad:
torch_wt.append(param.detach().numpy())
# step 1
pipeline = fe.Pipeline(train_data=t_d,
eval_data=eval_data,
batch_size=128,
ops=[ExpandDims(inputs="x", outputs="x"), Minmax(inputs="x", outputs="x")])
# step 2
model_tf = fe.build(model_fn=MyNet_tf, optimizer_fn=lambda: tf.optimizers.SGD(learning_rate=0.01))
network = fe.Network(ops=[
ModelOp(model=model_tf, inputs="x", outputs="y_pred"),
CrossEntropy(inputs=("y_pred", "y"), outputs="ce"),
L2Regularizaton(inputs="ce",outputs="l2",model=model_tf,beta = self.beta),
UpdateOp(model=model_tf, loss_name="l2")
])
# step 3
traces = [
Accuracy(true_key="y", pred_key="y_pred")
]
estimator = fe.Estimator(pipeline=pipeline,
network=network,
epochs=1,
traces=traces,
train_steps_per_epoch=1,
monitor_names=["ce","l2"])
print('*******************************Tensorflow L2 Regularization training***********************************')
estimator.fit()
# Converting TF weights to numpy
tf_wt = []
for layer in model_tf.layers:
for w in layer.trainable_variables:
tf_wt.append(w.numpy())
# testing weights
count = 0
for tf_t,tr in zip(tf_wt,torch_wt):
if np.sum(np.abs(tf_t-np.transpose(tr))) < (10**-5):
count += 1
self.assertTrue(count == 6)
