def test_weight_decay(self):
from caffe2.python import brew
from caffe2.python.model_helper import ModelHelper
model = ModelHelper(name="test", arg_scope={'order': 'NCHW'})
cnv = brew.conv(model, 'data', 'cnv', 32, 32, 4)
a = brew.fc(model, cnv, 'a', 100, 200)
pred = brew.fc(model, a, 'b', 200, 5)
(softmax, loss) = model.SoftmaxWithLoss(
[pred, 'label'],
['softmax', 'loss'],
)
model.AddGradientOperators([loss])
add_weight_decay(model, weight_decay=1e-4)
build_sgd(model, 0.11)
expected_weight_grad = {'b_w_grad', 'a_w_grad', 'cnv_w_grad'}
# Check the proto that all weights are decayed and not non-weights
# are decayed.
for op in model.net.Proto().op:
if op.type == 'WeightedSum' and 'wd_0_0' in op.input:
if op.output[0] not in expected_weight_grad:
print("Unexpected param for weight_decay: {}".format(
op.output[0]))
self.assertTrue(op.output[0] in expected_weight_grad)
expected_weight_grad.remove(op.output[0])
self.assertEqual(
expected_weight_grad, set(),
"Not all weights were decayed: {}".format(expected_weight_grad))
def test_caffe2_simple_model(self):
model = ModelHelper(name="mnist")
# how come those inputs don't break the forward pass =.=a
workspace.FeedBlob("data", np.random.randn(1, 3, 64, 64).astype(np.float32))
workspace.FeedBlob("label", np.random.randn(1, 1000).astype(np.int))
with core.NameScope("conv1"):
conv1 = brew.conv(model, "data", 'conv1', dim_in=1, dim_out=20, kernel=5)
# Image size: 24 x 24 -> 12 x 12
pool1 = brew.max_pool(model, conv1, 'pool1', kernel=2, stride=2)
# Image size: 12 x 12 -> 8 x 8
conv2 = brew.conv(model, pool1, 'conv2', dim_in=20, dim_out=100, kernel=5)
# Image size: 8 x 8 -> 4 x 4
pool2 = brew.max_pool(model, conv2, 'pool2', kernel=2, stride=2)
with core.NameScope("classifier"):
# 50 * 4 * 4 stands for dim_out from previous layer multiplied by the image size
fc3 = brew.fc(model, pool2, 'fc3', dim_in=100 * 4 * 4, dim_out=500)
relu = brew.relu(model, fc3, fc3)
pred = brew.fc(model, relu, 'pred', 500, 10)
softmax = brew.softmax(model, pred, 'softmax')
xent = model.LabelCrossEntropy([softmax, "label"], 'xent')
# compute the expected loss
loss = model.AveragedLoss(xent, "loss")
model.net.RunAllOnMKL()
model.param_init_net.RunAllOnMKL()
model.AddGradientOperators([loss], skip=1)
blob_name_tracker = {}
graph = c2_graph.model_to_graph_def(
model,
blob_name_tracker=blob_name_tracker,
shapes={},
show_simplified=False,
)
compare_proto(graph, self)
def _createDense(self, dtype=core.DataType.FLOAT):
perfect_model = np.array([2, 6, 5, 0, 1]).astype(np.float32)
np.random.seed(123) # make test deterministic
numpy_dtype = np.float32 if dtype == core.DataType.FLOAT else np.float16
initializer = Initializer if dtype == core.DataType.FLOAT else pFP16Initializer
data = np.random.randint(2,
size=(20,
perfect_model.size)).astype(numpy_dtype)
label = np.dot(data, perfect_model)[:, np.newaxis]
model = ModelHelper(name="test", arg_scope={'order': 'NCHW'})
out = brew.fc(model,
'data',
'fc',
perfect_model.size,
1, ('ConstantFill', {}), ('ConstantFill', {}),
axis=0,
WeightInitializer=initializer,
BiasInitializer=initializer)
if dtype == core.DataType.FLOAT16:
out = model.HalfToFloat(out, out + "_fp32")
sq = model.SquaredL2Distance([out, 'label'])
loss = model.AveragedLoss(sq, "avg_loss")
grad_map = model.AddGradientOperators([loss])
self.assertIsInstance(grad_map['fc_w'], core.BlobReference)
return (model, perfect_model, data, label)
def test_optimizer_context(self):
from caffe2.python import brew, optimizer
from caffe2.python.model_helper import ModelHelper
model = ModelHelper(name="test", arg_scope={'order': 'NCHW'})
count = optimizer._optimizer_instance_count['SgdOptimizer']
cnv_optim = SgdOptimizer(0.15)
weight_optim = SgdOptimizer(0.2)
bias_optim = SgdOptimizer(0.1)
with UseOptimizer(cnv_optim):
cnv = brew.conv(model, 'data', 'cnv', 32, 32, 4)
with UseOptimizer({'WEIGHT': weight_optim, 'BIAS': bias_optim}):
a = brew.fc(model, cnv, 'a', 100, 200)
pred = brew.fc(model, a, 'b', 200, 5)
(softmax, loss) = model.SoftmaxWithLoss(
[pred, 'label'],
['softmax', 'loss'],
)
model.AddGradientOperators([loss])
add_weight_decay(model, weight_decay=1e-4)
# use the following optimizer if none specified in param_info
build_sgd(model, 0.11)
expected_weight_grad = {'b_w_grad', 'a_w_grad', 'cnv_w_grad'}
expected_learning_rate = {
"SgdOptimizer_{}_lr_cpu".format(count): -0.15,
"SgdOptimizer_{}_lr_cpu".format(count + 1): -0.2,
"SgdOptimizer_{}_lr_cpu".format(count + 2): -0.1,
"SgdOptimizer_{}_lr_cpu".format(count + 3): -0.11
}
for op in model.net.Proto().op:
# Check the proto that all weights are decayed and not non-weights
# are decayed.
if op.type == 'WeightedSum' and 'wd_0_0' in op.input:
if op.output[0] not in expected_weight_grad:
print(
"Unexpected param for weight_decay: {}".
format(op.output[0])
)
self.assertTrue(op.output[0] in expected_weight_grad)
expected_weight_grad.remove(op.output[0])
# Check the learning rate for each parameter
if op.type == 'LearningRate':
val = 0
for arg in op.arg:
if arg.name == 'base_lr':
val = arg.f
self.assertAlmostEqual(
val,
expected_learning_rate[op.output[0]]
)
self.assertEqual(
expected_weight_grad,
set(),
"Not all weights were decayed: {}".format(expected_weight_grad)
)
def createTrainModel(lmdb_path):
"""Create and return a training model, complete with training ops."""
model = ModelHelper(name='train', arg_scope={'order': 'NCHW'})
reader = model.CreateDB('train_reader', db=lmdb_path, db_type='lmdb')
AddInputOps(model, reader, BATCH_SIZE)
losses = AddForwardPassOps(model)
model.AddGradientOperators(losses)
AddOptimizerOps(model)
workspace.RunNetOnce(model.param_init_net)
workspace.CreateNet(model.net)
return model
def main(opt_name):
workspace.FeedBlob('input', np.random.randn(2, 16).astype(np.float32))
workspace.FeedBlob('label', np.array([0, 1]).astype(np.float32))
helper = ModelHelper("sample_model")
fc = brew.fc(helper, "input", "fc", dim_in=16, dim_out=8)
relu = helper.Relu(fc, 'relu')
fc2 = brew.fc(helper, relu, "fc2", dim_in=8, dim_out=1)
label_ex = helper.ExpandDims("label", "label_ex", dims=[1])
xent = helper.SigmoidCrossEntropyWithLogits([fc2, label_ex], 'xent')
loss = helper.AveragedLoss(xent, 'loss')
helper.AddGradientOperators([loss])
if opt_name == "manual":
ONE = helper.param_init_net.ConstantFill([],
"ONE",
shape=[1],
value=1.0)
LR = helper.param_init_net.ConstantFill([],
"LR",
shape=[1],
value=-0.03)
for param in helper.params:
param_grad = helper.param_to_grad[param]
helper.WeightedSum([param, ONE, param_grad, LR], param)
elif opt_name == "sgd":
optimizer.build_sgd(helper, 0.03)
elif opt_name == "adagrad":
optimizer.build_adagrad(helper, 0.03)
# caffe2 does not support rowwise adagrad for dense parameters
# caffe2 seems not have lamb support yet
elif opt_name == "adam":
optimizer.build_adam(helper, 0.03)
else:
assert False, f"Unsupported optimizer {opt_name}"
workspace.RunNetOnce(helper.param_init_net)
workspace.RunNetOnce(helper.net)
import pdb
pdb.set_trace()
def test_multiple_optimizers(self):
from caffe2.python import brew, core, optimizer
from caffe2.python.model_helper import ModelHelper
model = ModelHelper(name="test")
fc1 = brew.fc(model, 'data', 'fc1', 100, 50)
fc2 = brew.fc(model, fc1, 'fc2', 50, 25)
pred = brew.fc(model, fc2, 'fc3', 25, 10)
(softmax, loss) = model.SoftmaxWithLoss(
[pred, 'label'],
['softmax', 'loss'],
)
model.AddGradientOperators([loss])
param_to_device = optimizer._get_param_to_device(model)
def infer_blob_device(blob_name):
return optimizer.get_param_device(blob_name,
"{}_grad".format(blob_name),
param_to_device)
sgd_1 = optimizer.SgdOptimizer(base_learning_rate=0.1)
sgd_2 = optimizer.SgdOptimizer(base_learning_rate=0.2)
adagrad = optimizer.AdagradOptimizer()
# Check same optimizer share the same learning rate.
with core.DeviceScope(infer_blob_device("fc1_w")):
sgd_1(model.net, model.param_init_net, "fc1_w", "fc1_w_grad")
with core.DeviceScope(infer_blob_device("fc1_b")):
sgd_1(model.net, model.param_init_net, "fc1_b", "fc1_b_grad")
fc1_lr_blobs = []
for op in model.net.Proto().op:
if op.type == 'WeightedSum' and op.input[0] == 'fc1_w' or \
op.input[0] == 'fc1_b':
fc1_lr_blobs.append(op.input[3])
self.assertEqual(fc1_lr_blobs[0], fc1_lr_blobs[1])
# Check different instance of the same optimizer has a different lr.
with core.DeviceScope(infer_blob_device("fc2_w")):
sgd_2(model.net, model.param_init_net, "fc2_w", "fc2_w_grad")
with core.DeviceScope(infer_blob_device("fc2_b")):
sgd_2(model.net, model.param_init_net, "fc2_b", "fc2_b_grad")
fc2_lr_blobs = []
for op in model.net.Proto().op:
if op.type == 'WeightedSum' and op.input[0] == 'fc2_w' or \
op.input[0] == 'fc2_b':
self.assertTrue(op.input[3] not in fc1_lr_blobs)
fc2_lr_blobs.append(op.input[3])
self.assertEqual(fc2_lr_blobs[0], fc2_lr_blobs[1])
# Check different optimizer type case
with core.DeviceScope(infer_blob_device("fc3_w")):
adagrad(model.net, model.param_init_net, "fc3_w", "fc3_w_grad")
with core.DeviceScope(infer_blob_device("fc3_b")):
adagrad(model.net, model.param_init_net, "fc3_b", "fc3_b_grad")
fc3_lr_blobs = []
for op in model.net.Proto().op:
if op.type == 'Adagrad' and op.input[0] == 'fc3_w' or \
op.input[0] == 'fc3_b':
self.assertTrue(op.input[3] not in fc2_lr_blobs)
self.assertTrue(op.input[3] not in fc1_lr_blobs)
fc3_lr_blobs.append(op.input[3])
self.assertEqual(fc3_lr_blobs[0], fc3_lr_blobs[1])
def test_convolution_sync(self, net_type, num_workers, engine, gc, dc):
m = ModelHelper(name="test_model")
n = 1
d = 2
depth = 3
iters = 5
h = 5
w = 5
workspace.ResetWorkspace()
use_cudnn = (engine == 'CUDNN')
np.random.seed(1701)
# Build a binary tree of conv layers, summing at each node.
for i in reversed(range(depth)):
for j in range(2**i):
bottom_1 = "{}_{}".format(i + 1, 2 * j)
bottom_2 = "{}_{}".format(i + 1, 2 * j + 1)
mid_1 = "{}_{}_m".format(i + 1, 2 * j)
mid_2 = "{}_{}_m".format(i + 1, 2 * j + 1)
top = "{}_{}".format(i, j)
w1, b1, w2, b2 = np.random.randn(4).tolist()
brew.conv(m,
bottom_1,
mid_1,
dim_in=d,
dim_out=d,
kernel=3,
weight_init=('ConstantFill', dict(value=w1)),
bias_init=('ConstantFill', dict(value=b1)),
cudnn_state=np.random.randint(0, 3),
stride=1,
pad=1,
deterministic=1,
use_cudnn=use_cudnn,
engine=engine)
brew.conv(m,
bottom_2,
mid_2,
dim_in=d,
dim_out=d,
kernel=3,
stride=1,
pad=1,
weight_init=('ConstantFill', dict(value=w2)),
bias_init=('ConstantFill', dict(value=b2)),
deterministic=1,
cudnn_state=np.random.randint(0, 3),
use_cudnn=use_cudnn,
engine=engine)
m.net.Sum([mid_1, mid_2], top)
m.net.Flatten(["0_0"], ["0_0_flat"])
m.net.SquaredL2Distance(["0_0_flat", "label"], "xent")
m.net.AveragedLoss("xent", "loss")
input_to_grad = m.AddGradientOperators(["loss"])
m.Proto().device_option.CopyFrom(gc)
m.param_init_net.Proto().device_option.CopyFrom(gc)
m.Proto().type = net_type
m.Proto().num_workers = num_workers
self.ws.run(m.param_init_net)
def run():
import numpy as np
np.random.seed(1701)
input_blobs = ["{}_{}".format(depth, j) for j in range(2**depth)]
for input_blob in input_blobs:
self.ws.create_blob(input_blob).feed(np.random.randn(
n, d, h, w).astype(np.float32),
device_option=gc)
self.ws.create_blob("label").feed(np.random.randn(
n, d * h * w).astype(np.float32),
device_option=gc)
self.ws.run(m.net)
gradients = [
self.ws.blobs[str(input_to_grad[input_blob])].fetch()
for input_blob in input_blobs
]
return gradients
outputs = [run() for _ in range(iters)]
for output in outputs[1:]:
np.testing.assert_array_equal(outputs[0], output)
np.testing.assert_allclose(np.sum(np.square(output)),
1763719461732352.0,
rtol=1e-5)
class MLTrainer:
""" This is meant to be a generic neural net trainer. It uses minibatch and
ADAM for momentum/smoothing.
"""
def __init__(
self,
name: str,
parameters: TrainingParameters,
) -> None:
"""
:param name: A unique name for this trainer used to create the data on the
caffe2 workspace
:param parameters: The set of training parameters
"""
self.model_id = name
self.optimizer = parameters.optimizer
self.layers = parameters.layers
self.activations = parameters.activations
self.learning_rate = parameters.learning_rate
self.gamma = parameters.gamma
self.lr_policy = parameters.lr_policy
self.dropout_ratio = parameters.dropout_ratio
self._validate_inputs()
self._setup_initial_blobs()
self._build_fwd_pass_score_model()
self._build_fwd_pass_train_model()
self._generate_train_model_loss()
self._update_train_model()
workspace.RunNetOnce(self.score_model.param_init_net)
workspace.CreateNet(self.score_model.net)
workspace.RunNetOnce(self.train_model.param_init_net)
workspace.CreateNet(self.train_model.net)
def _validate_inputs(self):
num_layers = len(self.layers)
num_activations = len(self.activations)
if num_activations != num_layers - 1:
raise Exception(
"Incompatible input `layers` and `activations` sizes.")
if not all(x > 0 and int(x) == x for x in self.layers):
raise Exception(
"All values in `layers` should be positive integers.")
def _generate_train_model_loss(self):
GenerateLossOps(self.train_model, self.model_id + "_train",
self.output_blob, self.labels_blob, self.loss_blob)
def _build_fwd_pass_train_model(self):
self.train_model.StopGradient(self.labels_blob, self.labels_blob)
MakeForwardPassOps(self.train_model, self.model_id + "_train",
self.input_blob, self.output_blob, self.weights,
self.biases, self.activations, self.layers,
self.dropout_ratio, False)
def _build_fwd_pass_score_model(self):
MakeForwardPassOps(self.score_model, self.model_id + "_score",
self.input_blob, self.output_blob, self.weights,
self.biases, self.activations, self.layers,
self.dropout_ratio, True)
def _setup_initial_blobs(self):
# Define models
self.score_model = ModelHelper(name="score_" + self.model_id)
self.train_model = ModelHelper(name="train_" + self.model_id)
# Create input, output, labels, and loss blobs
self.input_blob = "ModelInput_" + self.model_id
workspace.FeedBlob(self.input_blob, np.zeros(1, dtype=np.float32))
self.output_blob = "ModelOutput_" + self.model_id
workspace.FeedBlob(self.output_blob, np.zeros(1, dtype=np.float32))
self.labels_blob = "ModelLabels_" + self.model_id
workspace.FeedBlob(self.labels_blob, np.zeros(1, dtype=np.float32))
self.loss_blob = "loss" # "ModelLoss_" + self.model_id
workspace.FeedBlob(self.loss_blob, np.zeros(1, dtype=np.float32))
# Create blobs for model parameters
self.weights: List[str] = []
self.biases: List[str] = []
for x in six.moves.range(len(self.layers) - 1):
dim_in = self.layers[x]
dim_out = self.layers[x + 1]
weight_name = "Weights_" + str(x) + "_" + self.model_id
bias_name = "Biases_" + str(x) + "_" + self.model_id
self.weights.append(weight_name)
self.biases.append(bias_name)
bias = np.zeros(shape=[
dim_out,
], dtype=np.float32)
workspace.FeedBlob(bias_name, bias)
gain = np.sqrt(2) if self.activations[x] == 'relu' else 1
weights = scipy.stats.norm(0, gain * np.sqrt(1 / dim_in)).rvs(
size=[dim_out, dim_in]).astype(np.float32)
workspace.FeedBlob(weight_name, weights)
def _update_train_model(self):
self.train_model.AddGradientOperators([self.loss_blob])
for param in self.train_model.params:
param_grad = self.train_model.param_to_grad[param]
self.train_model.net.NanCheck([param_grad], [param_grad])
AddParameterUpdateOps(
self.train_model,
optimizer_input=self.optimizer,
base_learning_rate=self.learning_rate,
gamma=self.gamma,
policy=self.lr_policy,
)
def build_predictor(self, model, input_blob, output_blob) -> List[str]:
MakeForwardPassOps(model,
self.model_id + "_score",
input_blob,
output_blob,
self.weights,
self.biases,
self.activations,
self.layers,
self.dropout_ratio,
is_test=True)
return self.weights + self.biases
def score(self, inputs: np.ndarray) -> np.ndarray:
"""
Runs the net on a set of data and returns the outputs.
:param inputs: Numpy array containing examples to score.
"""
workspace.FeedBlob(self.input_blob, inputs)
workspace.RunNet(self.score_model.net)
return workspace.FetchBlob(self.output_blob)
def train_batch(self, inputs: np.ndarray, labels: np.ndarray) -> None:
"""
Trains net on inputs and labels. Please ensure that inputs are batched
to an appropriate size and are shuffled.
:param inputs: Numpy array containing training examples.
:param labels: Numpy array containing training labels.
"""
workspace.FeedBlob(self.input_blob, inputs)
workspace.FeedBlob(self.labels_blob, labels)
workspace.RunNet(self.train_model.net)
@property
def output(self) -> np.ndarray:
return workspace.FetchBlob(self.output_blob)
@property
def loss(self) -> np.ndarray:
return workspace.FetchBlob('loss')
print("\n************* Init Net *************")
print(regression_model.param_init_net.Proto())
# #### Add the training operators and prime the workspace
#
# In this **very important** step, we specify the loss function, setup the SGD training algorithm, prime and initialize the workspace, and initialize our model's weights and biases.
# In[5]:
# The loss function is computed by a squared L2 distance,
# and then averaged over all items.
dist = regression_model.SquaredL2Distance(['Y_gt', y_pred], "dist")
loss = regression_model.AveragedLoss(dist, "loss")
# Add the gradient operators and setup the SGD algorithm
regression_model.AddGradientOperators([loss])
optimizer.build_sgd(regression_model, base_learning_rate=learning_rate)
# Prime the workspace with some data
workspace.FeedBlob("Y_gt", Y_gt.astype(np.float32))
workspace.FeedBlob("X", X.astype(np.float32))
# Run the init net to prepare the workspace then create the net
workspace.RunNetOnce(regression_model.param_init_net)
workspace.CreateNet(regression_model.net)
# Inject our desired initial weights and bias
workspace.FeedBlob("y_pred_w", np.array([initial_weights]).astype(np.float32))
workspace.FeedBlob("y_pred_b", np.array([0.]).astype(np.float32))
# #### Run the training
