def test_table_label(self):
"""
Test for table as label in Sample.
For test purpose only.
"""
def gen_rand_sample():
features1 = np.random.uniform(0, 1, 3)
features2 = np.random.uniform(0, 1, 3)
label = np.array((2 * (features1 + features2)).sum() + 0.4)
return Sample.from_ndarray([features1, features2], [label, label])
training_data = self.sc.parallelize(range(0, 50)).map(
lambda i: gen_rand_sample())
model_test = Sequential()
branches = ParallelTable()
branch1 = Sequential().add(Linear(3, 1)).add(Tanh())
branch2 = Sequential().add(Linear(3, 1)).add(ReLU())
branches.add(branch1).add(branch2)
model_test.add(branches)
optimizer = Optimizer.create(
model=model_test,
training_set=training_data,
criterion=MarginRankingCriterion(),
optim_method=SGD(),
end_trigger=MaxEpoch(5),
batch_size=32)
optimizer.optimize()
def test_predict(self):
np.random.seed(100)
total_length = 6
features = np.random.uniform(0, 1, (total_length, 2))
label = (features).sum() + 0.4
predict_data = self.sc.parallelize(range(0, total_length)).map(
lambda i: Sample.from_ndarray(features[i], label))
model = Linear(2, 1).set_init_method(Xavier(), Zeros()) \
.set_name("linear1").set_seed(1234).reset()
predict_result = model.predict(predict_data)
p = predict_result.take(6)
ground_label = np.array([[-0.47596836], [-0.37598032], [-0.00492062],
[-0.5906958], [-0.12307882], [-0.77907401]], dtype="float32")
for i in range(0, total_length):
assert_allclose(p[i], ground_label[i], atol=1e-6, rtol=0)
predict_result_with_batch = model.predict(features=predict_data,
batch_size=4)
p_with_batch = predict_result_with_batch.take(6)
for i in range(0, total_length):
assert_allclose(p_with_batch[i], ground_label[i], atol=1e-6, rtol=0)
predict_class = model.predict_class(predict_data)
predict_labels = predict_class.take(6)
for i in range(0, total_length):
assert predict_labels[i] == 1
def test_predict(self):
np.random.seed(100)
total_length = 6
features = np.random.uniform(0, 1, (total_length, 2))
label = (features).sum() + 0.4
predict_data = self.sc.parallelize(range(0, total_length)).map(
lambda i: Sample.from_ndarray(features[i], label))
model = Linear(2, 1).set_init_method(Xavier(), Zeros()) \
.set_name("linear1").set_seed(1234).reset()
predict_result = model.predict(predict_data)
p = predict_result.take(6)
ground_label = np.array([[-0.47596836], [-0.37598032], [-0.00492062],
[-0.5906958], [-0.12307882], [-0.77907401]],
dtype="float32")
for i in range(0, total_length):
assert_allclose(p[i], ground_label[i], atol=1e-6, rtol=0)
predict_result_with_batch = model.predict(features=predict_data,
batch_size=4)
p_with_batch = predict_result_with_batch.take(6)
for i in range(0, total_length):
assert_allclose(p_with_batch[i],
ground_label[i],
atol=1e-6,
rtol=0)
predict_class = model.predict_class(predict_data)
predict_labels = predict_class.take(6)
for i in range(0, total_length):
assert predict_labels[i] == 1
def test_local_predict_multiple_input(self):
l1 = Linear(3, 2)()
l2 = Linear(3, 3)()
joinTable = JoinTable(dimension=1, n_input_dims=1)([l1, l2])
model = Model(inputs=[l1, l2], outputs=joinTable)
result = model.predict_local([np.ones([4, 3]), np.ones([4, 3])])
assert result.shape == (4, 5)
result2 = model.predict_class([np.ones([4, 3]), np.ones([4, 3])])
assert result2.shape == (4, )
result3 = model.predict_local([
JTensor.from_ndarray(np.ones([4, 3])),
JTensor.from_ndarray(np.ones([4, 3]))
])
assert result3.shape == (4, 5)
result4 = model.predict_class([
JTensor.from_ndarray(np.ones([4, 3])),
JTensor.from_ndarray(np.ones([4, 3]))
])
assert result4.shape == (4, )
result5 = model.predict_local([
JTensor.from_ndarray(np.ones([4, 3])),
JTensor.from_ndarray(np.ones([4, 3]))
],
batch_size=2)
assert result5.shape == (4, 5)
def test_init_method(self):
initializers = [
Zeros(),
Ones(),
ConstInitMethod(5),
RandomUniform(-1, 1),
RandomNormal(0, 1),
None
]
special_initializers = [
MsraFiller(False),
Xavier(),
RandomUniform(),
]
layers = [
SpatialConvolution(6, 12, 5, 5),
SpatialShareConvolution(1, 1, 1, 1),
LookupTable(1, 1, 1e-5, 1e-5, 1e-5, True),
Bilinear(1, 1, 1, True),
Cosine(2, 3),
SpatialFullConvolution(1, 1, 1, 1),
Add(1),
Linear(100, 10),
CMul([1, 2]),
Mul(),
PReLU(1),
Euclidean(1, 1, True),
SpatialDilatedConvolution(1, 1, 1, 1),
SpatialBatchNormalization(1),
BatchNormalization(1, 1e-5, 1e-5, True),
]
special_layers = [
SpatialConvolution(6, 12, 5, 5),
SpatialShareConvolution(1, 1, 1, 1),
Cosine(2, 3),
SpatialFullConvolution(1, 1, 1, 1),
Add(1),
Linear(100, 10),
CMul([1, 2]),
Mul(),
PReLU(1),
Euclidean(1, 1, True),
SpatialDilatedConvolution(1, 1, 1, 1),
SpatialBatchNormalization(1),
BatchNormalization(1, 1e-5, 1e-5, True),
]
for layer in layers:
for init1 in initializers:
for init2 in initializers:
layer.set_init_method(init1, init2)
for layer in special_layers:
for init1 in special_initializers:
for init2 in special_initializers:
layer.set_init_method(init1, init2)
SpatialFullConvolution(1, 1, 1, 1).set_init_method(BilinearFiller(), Zeros())
def test_save_graph_topology(self):
fc1 = Linear(4, 2)()
fc2 = Linear(4, 2)()
cadd = CAddTable()([fc1, fc2])
output1 = ReLU()(cadd)
output2 = Threshold(10.0)(cadd)
model = Model([fc1, fc2], [output1, output2])
model.save_graph_topology(tempfile.mkdtemp())
def test_get_node(self):
fc1 = Linear(4, 2)()
fc2 = Linear(4, 2)()
fc1.element().set_name("fc1")
cadd = CAddTable()([fc1, fc2])
output1 = ReLU()(cadd)
model = Model([fc1, fc2], [output1])
res = model.node("fc1")
assert res.element().name() == "fc1"
def test_get_node(self):
fc1 = Linear(4, 2)()
fc2 = Linear(4, 2)()
fc1.element().set_name("fc1")
cadd = CAddTable()([fc1, fc2])
output1 = ReLU()(cadd)
model = Model([fc1, fc2], [output1])
res = model.node("fc1")
assert res.element().name() == "fc1"
def test_set_seed(self):
w_init = Xavier()
b_init = Zeros()
l1 = Linear(10, 20).set_init_method(w_init, b_init).set_name("linear1").set_seed(
1234).reset() # noqa
l2 = Linear(10, 20).set_init_method(w_init, b_init).set_name("linear2").set_seed(
1234).reset() # noqa
p1 = l1.parameters()
p2 = l2.parameters()
assert (p1["linear1"]["weight"] == p2["linear2"]["weight"]).all() # noqa
def test_model_broadcast(self):
init_executor_gateway(self.sc)
model = Linear(3, 2)
broadcasted = broadcast_model(self.sc, model)
input_data = np.random.rand(3)
output = self.sc.parallelize([input_data], 1)\
.map(lambda x: broadcasted.value.forward(x)).first()
expected = model.forward(input_data)
assert_allclose(output, expected)
def test_model_broadcast(self):
init_executor_gateway(self.sc)
model = Linear(3, 2)
broadcasted = broadcast_model(self.sc, model)
input_data = np.random.rand(3)
output = self.sc.parallelize([input_data], 1)\
.map(lambda x: broadcasted.value.forward(x)).first()
expected = model.forward(input_data)
assert_allclose(output, expected)
def test_graph_preprocessor(self):
fc1 = Linear(4, 2)()
fc2 = Linear(4, 2)()
cadd = CAddTable()([fc1, fc2])
preprocessor = Model([fc1, fc2], [cadd])
relu = ReLU()()
fc3 = Linear(2, 1)(relu)
trainable = Model([relu], [fc3])
model = Model(preprocessor, trainable)
model.forward([np.array([0.1, 0.2, -0.3, -0.4]), np.array([0.5, 0.4, -0.2, -0.1])])
model.backward([np.array([0.1, 0.2, -0.3, -0.4]), np.array([0.5, 0.4, -0.2, -0.1])],
np.array([1.0]))
def _create_cnn_model():
model = Sequential()
model.add(SpatialConvolution(3, 1, 5, 5))
model.add(View([1 * 220 * 220]))
model.add(Linear(1 * 220 * 220, 20))
model.add(LogSoftMax())
return model
def test_local_optimizer_predict(self):
feature_num = 2
data_len = 1000
batch_size = 32
epoch_num = 500
X_ = np.random.uniform(0, 1, (data_len, feature_num))
y_ = (2 * X_).sum(1) + 0.4
model = Sequential()
l1 = Linear(feature_num, 1)
model.add(l1)
localOptimizer = Optimizer.create(
model=model,
training_set=(X_, y_),
criterion=MSECriterion(),
optim_method=SGD(learningrate=1e-2),
end_trigger=MaxEpoch(epoch_num),
batch_size=batch_size)
trained_model = localOptimizer.optimize()
trained_model = model
w = trained_model.get_weights()
assert_allclose(w[0], np.array([2, 2]).reshape([1, 2]), rtol=1e-1)
assert_allclose(w[1], np.array([0.4]), rtol=1e-1)
predict_result = trained_model.predict_local(X_)
assert_allclose(y_, predict_result.reshape((data_len,)), rtol=1e-1)
def test_load_optim_method(self):
FEATURES_DIM = 2
data_len = 100
batch_size = 32
epoch_num = 5
def gen_rand_sample():
features = np.random.uniform(0, 1, (FEATURES_DIM))
label = (2 * features).sum() + 0.4
return Sample.from_ndarray(features, label)
trainingData = self.sc.parallelize(range(0, data_len)).map(lambda i: gen_rand_sample())
model = Sequential()
l1 = Linear(FEATURES_DIM, 1).set_init_method(Xavier(), Zeros()).set_name("linear1")
model.add(l1)
sgd = SGD(learningrate=0.01, learningrate_decay=0.0002, weightdecay=0.0,
momentum=0.0, dampening=0.0, nesterov=False,
leaningrate_schedule=Poly(0.5, int((data_len / batch_size) * epoch_num)))
tmp_path = tempfile.mktemp()
sgd.save(tmp_path, True)
optim_method = OptimMethod.load(tmp_path)
assert optim_method.learningRate() == sgd.value.learningRate()
assert optim_method.momentum() == sgd.value.momentum()
assert optim_method.nesterov() == sgd.value.nesterov()
optimizer = Optimizer(
model=model,
training_rdd=trainingData,
criterion=MSECriterion(),
optim_method=optim_method,
end_trigger=MaxEpoch(epoch_num),
batch_size=batch_size)
optimizer.optimize()
def test_multiple_input(self):
"""
Test training data of samples with several tensors as feature
using a sequential model with multiple inputs.
"""
FEATURES_DIM = 2
data_len = 100
batch_size = 32
epoch_num = 5
def gen_rand_sample():
features1 = np.random.uniform(0, 1, (FEATURES_DIM))
features2 = np.random.uniform(0, 1, (FEATURES_DIM))
label = np.array((2 * (features1 + features2)).sum() + 0.4)
return Sample.from_ndarray([features1, features2], label)
trainingData = self.sc.parallelize(range(
0, data_len)).map(lambda i: gen_rand_sample())
model_test = Sequential()
branches = ParallelTable()
branch1 = Sequential().add(Linear(FEATURES_DIM, 1)).add(ReLU())
branch2 = Sequential().add(Linear(FEATURES_DIM, 1)).add(ReLU())
branches.add(branch1).add(branch2)
model_test.add(branches).add(CAddTable())
optim_method = SGD(learningrate=0.01,
learningrate_decay=0.0002,
weightdecay=0.0,
momentum=0.0,
dampening=0.0,
nesterov=False,
leaningrate_schedule=Poly(
0.5, int((data_len / batch_size) * epoch_num)))
optimizer = Optimizer.create(model=model_test,
training_set=trainingData,
criterion=MSECriterion(),
optim_method=optim_method,
end_trigger=MaxEpoch(epoch_num),
batch_size=batch_size)
optimizer.set_validation(batch_size=batch_size,
val_rdd=trainingData,
trigger=EveryEpoch(),
val_method=[Top1Accuracy()])
optimizer.optimize()
def test_set_input_output_format(self):
input1 = Input()
lstm1 = Recurrent().add(LSTM(128, 128))(input1)
fc1 = Linear(128, 10)
t1 = TimeDistributed(fc1)(lstm1)
model = Model(inputs=input1, outputs=t1)
model.set_input_formats([4])
model.set_output_formats([27])
def test_predict(self):
np.random.seed(100)
total_length = 6
features = np.random.uniform(0, 1, (total_length, 2))
label = (features).sum() + 0.4
predict_data = self.sc.parallelize(range(0, total_length)).map(
lambda i: Sample.from_ndarray(features[i], label))
model = Linear(2, 1,
"Xavier").set_name("linear1").set_seed(1234).reset()
predict_result = model.predict(predict_data)
p = predict_result.take(6)
ground_label = np.array([[-0.47596836], [-0.37598032], [-0.00492062],
[-0.5906958], [-0.12307882], [-0.77907401]],
dtype="float32")
for i in range(0, total_length):
self.assertTrue(
np.allclose(p[i], ground_label[i], atol=1e-6, rtol=0))
def test_load_model_proto(self):
fc1 = Linear(4, 2)
fc1.set_weights([np.ones((4, 2)), np.ones((2, ))])
tmp_path = tempfile.mktemp()
fc1.saveModel(tmp_path, None, True)
fc1_loaded = Model.loadModel(tmp_path)
assert_allclose(fc1_loaded.get_weights()[0], fc1.get_weights()[0])
def test_load_sequential_of(self):
fc1 = Linear(4, 2)
model = Sequential()
model.add(fc1)
tmp_path = tempfile.mktemp()
model.save(tmp_path, True)
model_loaded = Model.load(tmp_path)
assert "Sequential" in str(type(model_loaded))
assert len(model_loaded.layers) == 1
def test_load_model_of(self):
input = Input()
fc1 = Linear(4, 2)(input)
model = Model(input, fc1)
tmp_path = tempfile.mktemp()
model.save(tmp_path, True)
model_loaded = Model.load(tmp_path)
assert "Model" in str(type(model_loaded))
assert len(model_loaded.layers) == 2
def test_load_model(self):
fc1 = Linear(4, 2)
fc1.set_weights([np.ones((4, 2)), np.ones((2, ))])
tmp_path = tempfile.mktemp()
fc1.save(tmp_path, True)
fc1_loaded = Model.load(tmp_path)
self.assertTrue(np.allclose(fc1_loaded.get_weights()[0],
fc1.get_weights()[0]))
def test_forward_multiple(self):
from bigdl.nn.layer import Linear
rng = RNG()
rng.set_seed(100)
input = [rng.uniform(0.0, 0.1, [2]),
rng.uniform(0.0, 0.1, [2]) + 0.2]
grad_output = [rng.uniform(0.0, 0.1, [3]),
rng.uniform(0.0, 0.1, [3]) + 0.2]
linear1 = Linear(2, 3)
linear2 = Linear(2, 3)
module = ParallelTable()
module.add(linear1)
module.add(linear2)
module.forward(input)
module.backward(input, grad_output)
def test_create_node(self):
import numpy as np
fc1 = Linear(4, 2)()
fc2 = Linear(4, 2)()
cadd = CAddTable()([fc1, fc2])
output1 = ReLU()(cadd)
model = Model([fc1, fc2], [output1])
fc1.element().set_weights([np.ones((4, 2)), np.ones((2,))])
fc2.element().set_weights([np.ones((4, 2)), np.ones((2,))])
output = model.forward([np.array([0.1, 0.2, -0.3, -0.4]),
np.array([0.5, 0.4, -0.2, -0.1])])
assert_allclose(output,
np.array([2.2, 2.2]))
def test_local_predict_class(self):
feature_num = 2
data_len = 3
X_ = np.random.uniform(-1, 1, (data_len, feature_num))
model = Sequential()
l1 = Linear(feature_num, 1)
model.add(l1)
model.add(Sigmoid())
model.set_seed(1234).reset()
predict_result = model.predict_class(X_)
assert_array_equal(predict_result, np.ones([3]))
def test_forward_backward(self):
from bigdl.nn.layer import Linear
rng = RNG()
rng.set_seed(100)
linear = Linear(4, 5)
input = rng.uniform(0.0, 1.0, [4])
output = linear.forward(input)
assert_allclose(output,
np.array([0.41366524,
0.009532653,
-0.677581,
0.07945433,
-0.5742568]),
atol=1e-6, rtol=0)
mse = MSECriterion()
target = rng.uniform(0.0, 1.0, [5])
loss = mse.forward(output, target)
print("loss: " + str(loss))
grad_output = mse.backward(output, rng.uniform(0.0, 1.0, [5]))
l_grad_output = linear.backward(input, grad_output)
def test_forward_backward(self):
from bigdl.nn.layer import Linear
rng = RNG()
rng.set_seed(100)
linear = Linear(4, 5)
input = rng.uniform(0.0, 1.0, [4])
output = linear.forward(input)
assert_allclose(output,
np.array([0.41366524,
0.009532653,
-0.677581,
0.07945433,
-0.5742568]),
atol=1e-6, rtol=0)
mse = MSECriterion()
target = rng.uniform(0.0, 1.0, [5])
loss = mse.forward(output, target)
print("loss: " + str(loss))
grad_output = mse.backward(output, rng.uniform(0.0, 1.0, [5]))
l_grad_output = linear.backward(input, grad_output)
def test_load_model_proto(self):
fc1 = Linear(4, 2)
fc1.set_weights([np.ones((4, 2)), np.ones((2,))])
tmp_path = tempfile.mktemp()
fc1.saveModel(tmp_path, None, True)
fc1_loaded = Model.loadModel(tmp_path)
assert_allclose(fc1_loaded.get_weights()[0],
fc1.get_weights()[0])
def test_set_seed(self):
w_init = Xavier()
b_init = Zeros()
l1 = Linear(10, 20).set_init_method(w_init, b_init).set_name("linear1").set_seed(1234).reset() # noqa
l2 = Linear(10, 20).set_init_method(w_init, b_init).set_name("linear2").set_seed(1234).reset() # noqa
p1 = l1.parameters()
p2 = l2.parameters()
self.assertTrue((p1["linear1"]["weight"] == p2["linear2"]["weight"]).all()) # noqa
def test_tf_load(self):
linear = Linear(10, 2)()
sigmoid = Sigmoid()(linear)
softmax = SoftMax().set_name("output")(sigmoid)
model = BModel(linear, softmax)
input = np.random.random((4, 10))
tmp_path = create_tmp_path() + "/model.pb"
model.save_tensorflow([("input", [4, 10])], tmp_path)
model_reloaded = Net.load_tf(tmp_path, ["input"], ["output"])
expected_output = model.forward(input)
output = model_reloaded.forward(input)
self.assert_allclose(output, expected_output)
def test_set_seed(self):
l1 = Linear(
10, 20,
"Xavier").set_name("linear1").set_seed(1234).reset() # noqa
l2 = Linear(
10, 20,
"Xavier").set_name("linear2").set_seed(1234).reset() # noqa
p1 = l1.parameters()
p2 = l2.parameters()
self.assertTrue(
(p1["linear1"]["weight"] == p2["linear2"]["weight"]).all()) # noqa
def test_train_DataSet(self):
batch_size = 8
epoch_num = 5
images = []
labels = []
for i in range(0, 8):
features = np.random.uniform(0, 1, (200, 200, 3))
label = np.array([2])
images.append(features)
labels.append(label)
image_frame = DistributedImageFrame(self.sc.parallelize(images),
self.sc.parallelize(labels))
transformer = Pipeline([
BytesToMat(),
Resize(256, 256),
CenterCrop(224, 224),
ChannelNormalize(0.485, 0.456, 0.406, 0.229, 0.224, 0.225),
MatToTensor(),
ImageFrameToSample(target_keys=['label'])
])
data_set = DataSet.image_frame(image_frame).transform(transformer)
model = Sequential()
model.add(SpatialConvolution(3, 1, 5, 5))
model.add(View([1 * 220 * 220]))
model.add(Linear(1 * 220 * 220, 20))
model.add(LogSoftMax())
optim_method = SGD(learningrate=0.01)
optimizer = Optimizer.create(model=model,
training_set=data_set,
criterion=ClassNLLCriterion(),
optim_method=optim_method,
end_trigger=MaxEpoch(epoch_num),
batch_size=batch_size)
optimizer.set_validation(batch_size=batch_size,
val_rdd=data_set,
trigger=EveryEpoch(),
val_method=[Top1Accuracy()])
trained_model = optimizer.optimize()
predict_result = trained_model.predict_image(
image_frame.transform(transformer))
assert_allclose(predict_result.get_predict().count(), 8)
def test_graph_backward(self):
fc1 = Linear(4, 2)()
fc2 = Linear(4, 2)()
cadd = CAddTable()([fc1, fc2])
output1 = ReLU()(cadd)
output2 = Threshold(10.0)(cadd)
model = Model([fc1, fc2], [output1, output2])
fc1.element().set_weights([np.ones((4, 2)), np.ones((2,))])
fc2.element().set_weights([np.ones((4, 2)) * 2, np.ones((2,)) * 2])
output = model.forward([np.array([0.1, 0.2, -0.3, -0.4]),
np.array([0.5, 0.4, -0.2, -0.1])])
gradInput = model.backward([np.array([0.1, 0.2, -0.3, -0.4]),
np.array([0.5, 0.4, -0.2, -0.1])],
[np.array([1.0, 2.0]),
np.array([3.0, 4.0])])
assert_allclose(gradInput[0],
np.array([3.0, 3.0, 3.0, 3.0]))
assert_allclose(gradInput[1],
np.array([6.0, 6.0, 6.0, 6.0]))
def test_create_node(self):
import numpy as np
fc1 = Linear(4, 2)()
fc2 = Linear(4, 2)()
cadd = CAddTable()([fc1, fc2])
output1 = ReLU()(cadd)
model = Model([fc1, fc2], [output1])
fc1.element().set_weights([np.ones((4, 2)), np.ones((2,))])
fc2.element().set_weights([np.ones((4, 2)), np.ones((2,))])
output = model.forward([np.array([0.1, 0.2, -0.3, -0.4]),
np.array([0.5, 0.4, -0.2, -0.1])])
assert_allclose(output,
np.array([2.2, 2.2]))
def test_graph_backward(self):
fc1 = Linear(4, 2)()
fc2 = Linear(4, 2)()
cadd = CAddTable()([fc1, fc2])
output1 = ReLU()(cadd)
output2 = Threshold(10.0)(cadd)
model = Model([fc1, fc2], [output1, output2])
fc1.element().set_weights([np.ones((4, 2)), np.ones((2,))])
fc2.element().set_weights([np.ones((4, 2)) * 2, np.ones((2,)) * 2])
output = model.forward([np.array([0.1, 0.2, -0.3, -0.4]),
np.array([0.5, 0.4, -0.2, -0.1])])
gradInput = model.backward([np.array([0.1, 0.2, -0.3, -0.4]),
np.array([0.5, 0.4, -0.2, -0.1])],
[np.array([1.0, 2.0]),
np.array([3.0, 4.0])])
assert_allclose(gradInput[0],
np.array([3.0, 3.0, 3.0, 3.0]))
assert_allclose(gradInput[1],
np.array([6.0, 6.0, 6.0, 6.0]))
# load pretrained caffe model
preTrained_model = Net.load_caffe(model_def_path, model_weight_path)
# create a new model by remove layers after pool5/drop_7x7_s1
part_model = preTrained_model.new_graph(["pool5/drop_7x7_s1"])
# optionally freeze layers from input to pool4/3x3_s2 inclusive
# model.freeze_up_to(["pool4/3x3_s2"])
from bigdl.nn.layer import Sequential, Linear, View, LogSoftMax
from bigdl.nn.criterion import CrossEntropyCriterion
# add a new linear layer with numClass outputs, in our example, it's 6.
scene_network = Sequential().add(part_model).add(View([1024])).add(Linear(1024, 6)).add(LogSoftMax())
transformer = ChainedPreprocessing(
[RowToImageFeature(), ImageResize(256, 256), ImageCenterCrop(224, 224),
ImageChannelNormalize(123.0, 117.0, 104.0), ImageMatToTensor(), ImageFeatureToTensor()])
classifier = NNClassifier(scene_network, CrossEntropyCriterion(), transformer).setLearningRate(0.001).setLearningRateDecay(1e-3).setBatchSize(20).setMaxEpoch(2).setFeaturesCol("image").setCachingSample(False)
# train the model
scene_classification_model = classifier.fit(trainingDF)
print("Finished training")
# evaluate the model
print("Start evaluation:")
predictionDF = scene_classification_model.transform(validationDF).cache()
def test_simple_flow(self):
FEATURES_DIM = 2
data_len = 100
batch_size = 32
epoch_num = 5
def gen_rand_sample():
features = np.random.uniform(0, 1, (FEATURES_DIM))
label = np.array((2 * features).sum() + 0.4)
return Sample.from_ndarray(features, label)
trainingData = self.sc.parallelize(range(0, data_len)).map(
lambda i: gen_rand_sample())
model_test = Sequential()
l1_test = Linear(FEATURES_DIM, 1).set_init_method(Xavier(), Zeros()) \
.set_name("linear1_test")
assert "linear1_test" == l1_test.name()
model_test.add(l1_test)
model_test.add(Sigmoid())
model = Sequential()
l1 = Linear(FEATURES_DIM, 1).set_init_method(Xavier(), Zeros()).set_name("linear1")
assert "linear1" == l1.name()
model.add(l1)
optim_method = SGD(learningrate=0.01, learningrate_decay=0.0002, weightdecay=0.0,
momentum=0.0, dampening=0.0, nesterov=False,
leaningrate_schedule=Poly(0.5, int((data_len / batch_size) * epoch_num)))
optimizer = Optimizer.create(
model=model_test,
training_set=trainingData,
criterion=MSECriterion(),
optim_method=optim_method,
end_trigger=MaxEpoch(epoch_num),
batch_size=batch_size)
optimizer.set_validation(
batch_size=batch_size,
val_rdd=trainingData,
trigger=EveryEpoch(),
val_method=[Top1Accuracy()]
)
optimizer.optimize()
optimizer.set_model(model=model)
tmp_dir = tempfile.mkdtemp()
optimizer.set_checkpoint(SeveralIteration(1), tmp_dir)
train_summary = TrainSummary(log_dir=tmp_dir,
app_name="run1")
train_summary.set_summary_trigger("LearningRate", SeveralIteration(1))
val_summary = ValidationSummary(log_dir=tmp_dir,
app_name="run1")
optimizer.set_train_summary(train_summary)
optimizer.set_val_summary(val_summary)
optimizer.set_end_when(MaxEpoch(epoch_num * 2))
trained_model = optimizer.optimize()
lr_result = train_summary.read_scalar("LearningRate")
top1_result = val_summary.read_scalar("Top1Accuracy")
# TODO: add result validation
parameters = trained_model.parameters()
assert parameters["linear1"] is not None
print("parameters %s" % parameters["linear1"])
predict_result = trained_model.predict(trainingData)
p = predict_result.take(2)
print("predict predict: \n")
for i in p:
print(str(i) + "\n")
print(len(p))
test_results = trained_model.evaluate(trainingData, 32, [Top1Accuracy()])
for test_result in test_results:
print(test_result)
avg_pool = end_points['Mixed_3c']
export_tf(sess,
"file:///home/hduser/slim/tfnet/",
inputs=[images],
outputs=[avg_pool])
from zoo.pipeline.api.net import TFNet
amodel = TFNet.from_export_folder("file:///home/hduser/slim/tfnet/")
from bigdl.nn.layer import Sequential, Transpose, Contiguous, Linear, ReLU, SoftMax, Reshape, View, MulConstant, SpatialAveragePooling
full_model = Sequential()
full_model.add(Transpose([(2, 4), (2, 3)]))
scalar = 1. / 255
full_model.add(MulConstant(scalar))
full_model.add(Contiguous())
full_model.add(amodel)
full_model.add(View([1024]))
full_model.add(Linear(1024, 5))
import re
from bigdl.nn.criterion import CrossEntropyCriterion
from pyspark import SparkConf
from pyspark.ml import Pipeline
from pyspark.sql import SQLContext
from pyspark.sql.functions import col, udf
from pyspark.sql.types import DoubleType, StringType
from zoo.common.nncontext import *
from zoo.feature.image import *
from zoo.pipeline.api.keras.layers import Dense, Input, Flatten
from zoo.pipeline.api.keras.models import *
from zoo.pipeline.api.net import *
from zoo.pipeline.nnframes import *
image_path = "hdfs:///project_data/pets/train_images/*"
csv_path = "hdfs:///project_data/pets/train/train.csv"
def test_simple_flow(self):
FEATURES_DIM = 2
data_len = 100
batch_size = 32
epoch_num = 5
def gen_rand_sample():
features = np.random.uniform(0, 1, (FEATURES_DIM))
label = np.array((2 * features).sum() + 0.4)
return Sample.from_ndarray(features, label)
trainingData = self.sc.parallelize(range(0, data_len)).map(
lambda i: gen_rand_sample())
model_test = Sequential()
l1_test = Linear(FEATURES_DIM, 1).set_init_method(Xavier(), Zeros()) \
.set_name("linear1_test")
assert "linear1_test" == l1_test.name()
model_test.add(l1_test)
model_test.add(Sigmoid())
model = Sequential()
l1 = Linear(FEATURES_DIM, 1).set_init_method(Xavier(), Zeros()).set_name("linear1")
assert "linear1" == l1.name()
model.add(l1)
optim_method = SGD(learningrate=0.01, learningrate_decay=0.0002, weightdecay=0.0,
momentum=0.0, dampening=0.0, nesterov=False,
leaningrate_schedule=Poly(0.5, int((data_len / batch_size) * epoch_num)))
optimizer = Optimizer.create(
model=model_test,
training_set=trainingData,
criterion=MSECriterion(),
optim_method=optim_method,
end_trigger=MaxEpoch(epoch_num),
batch_size=batch_size)
optimizer.set_validation(
batch_size=batch_size,
val_rdd=trainingData,
trigger=EveryEpoch(),
val_method=[Top1Accuracy()]
)
optimizer.optimize()
optimizer.set_model(model=model)
tmp_dir = tempfile.mkdtemp()
optimizer.set_checkpoint(SeveralIteration(1), tmp_dir)
train_summary = TrainSummary(log_dir=tmp_dir,
app_name="run1")
train_summary.set_summary_trigger("LearningRate", SeveralIteration(1))
val_summary = ValidationSummary(log_dir=tmp_dir,
app_name="run1")
optimizer.set_train_summary(train_summary)
optimizer.set_val_summary(val_summary)
optimizer.set_end_when(MaxEpoch(epoch_num * 2))
trained_model = optimizer.optimize()
lr_result = train_summary.read_scalar("LearningRate")
top1_result = val_summary.read_scalar("Top1Accuracy")
# TODO: add result validation
parameters = trained_model.parameters()
assert parameters["linear1"] is not None
print("parameters %s" % parameters["linear1"])
predict_result = trained_model.predict(trainingData)
p = predict_result.take(2)
print("predict predict: \n")
for i in p:
print(str(i) + "\n")
print(len(p))
test_results = trained_model.evaluate(trainingData, 32, [Top1Accuracy()])
for test_result in test_results:
print(test_result)
