def test_sum(self):
input_shape = [2, 3]
input_x1 = np.random.random(input_shape)
input_x2 = np.random.random(input_shape)
# Create one output (ValueInfoProto)
X = onnx.helper.make_tensor_value_info('X', onnx.TensorProto.FLOAT,
[4, 3])
Y = onnx.helper.make_tensor_value_info('Y', onnx.TensorProto.FLOAT,
[1, 3])
sum_node = onnx.helper.make_node(op_type='Sum',
inputs=['X'],
outputs=['Y'])
# Create the graph (GraphProto)
onnx_graph = onnx.helper.make_graph(
nodes=[sum_node],
name='test-sum',
inputs=[X],
outputs=[Y],
)
# Create the model (ModelProto)
onnx_model = onnx.helper.make_model(onnx_graph, producer_name='ONNX')
onnx.checker.check_model(onnx_model)
loaded_model = load_model_proto(onnx_model)
bigdl_model = CAddTable()
expected_out = bigdl_model.forward([input_x1, input_x2])
loaded_out = loaded_model.forward([input_x1, input_x2])
assert (np.array_equal(expected_out, loaded_out))
def test_conv(self):
kernel_width, kernel_height = (3, 3)
stride_width, stride_height = (1, 1)
pad_width, pad_height = (0, 0)
input_shape = [1, 3, 224, 224]
output_shape = [1, 8, 222, 222]
weight_shape = [8, 3, 3, 3]
input_x = np.random.random(input_shape)
weight_values = np.random.random(weight_shape)
# Create input (ValueInfoProto)
X = onnx.helper.make_tensor_value_info('X', onnx.TensorProto.FLOAT,
input_shape)
W = onnx.helper.make_tensor_value_info('W', onnx.TensorProto.FLOAT,
weight_shape)
# Create one output (ValueInfoProto)
Y = onnx.helper.make_tensor_value_info('Y', onnx.TensorProto.FLOAT,
output_shape)
init_weight = onnx.helper.make_tensor(
name='W',
data_type=onnx.TensorProto.FLOAT,
dims=weight_shape,
vals=weight_values.flatten().astype(float),
)
conv_node = onnx.helper.make_node(
op_type='Conv',
inputs=['X', 'W'],
outputs=['Y'],
kernel_shape=(kernel_width, kernel_height),
)
# Create the graph (GraphProto)
onnx_graph = onnx.helper.make_graph(nodes=[conv_node],
name='test-conv',
inputs=[X],
outputs=[Y],
initializer=[init_weight])
# Create the model (ModelProto)
onnx_model = onnx.helper.make_model(onnx_graph, producer_name='ONNX')
onnx.checker.check_model(onnx_model)
loaded_model = load_model_proto(onnx_model)
bigdl_model = SpatialConvolution(n_input_plane=3,
n_output_plane=8,
kernel_w=kernel_width,
kernel_h=kernel_height,
stride_w=stride_width,
stride_h=stride_height,
pad_w=pad_width,
pad_h=pad_height,
init_weight=weight_values,
with_bias=False)
loaded_out = loaded_model.forward(input_x)
expected_out = bigdl_model.forward(input_x)
assert (np.array_equal(loaded_out, expected_out))
def test_softmax(self):
input_shape = [1, 3, 224, 224]
output_shape = [1, 3, 224, 224]
input_x = np.random.random(input_shape)
# Create one output (ValueInfoProto)
X = onnx.helper.make_tensor_value_info('X', onnx.TensorProto.FLOAT,
input_shape)
Y = onnx.helper.make_tensor_value_info('Y', onnx.TensorProto.FLOAT,
output_shape)
softmax_node = onnx.helper.make_node(op_type='Softmax',
inputs=['X'],
outputs=['Y'])
# Create the graph (GraphProto)
onnx_graph = onnx.helper.make_graph(
nodes=[softmax_node],
name='test-softmax',
inputs=[X],
outputs=[Y],
)
# Create the model (ModelProto)
onnx_model = onnx.helper.make_model(onnx_graph, producer_name='ONNX')
onnx.checker.check_model(onnx_model)
loaded_model = load_model_proto(onnx_model)
bigdl_model = SoftMax()
loaded_out = loaded_model.forward(input_x)
expected_out = bigdl_model.forward(input_x)
assert (np.array_equal(expected_out, loaded_out))
def test_average_pool(self):
ceil_mode = 0
kernel_width, kernel_height = 3, 3
pad_width, pad_height = 0, 0
stride_width, stride_height = 1, 1
input_shape = [1, 3, 224, 224]
output_shape = [1, 3, 222, 222]
input_x = np.random.random(input_shape)
# Create one input (ValueInfoProto)
X = onnx.helper.make_tensor_value_info('X', onnx.TensorProto.FLOAT,
input_shape)
# Create one output (ValueInfoProto)
Y = onnx.helper.make_tensor_value_info('Y', onnx.TensorProto.FLOAT,
output_shape)
# Create a node (NodeProto)
avgpool_node = onnx.helper.make_node(
op_type='AveragePool', # node name
inputs=['X'], # inputs
outputs=['Y'], # outputs
auto_pad='NOTSET',
ceil_mode=ceil_mode,
kernel_shape=(kernel_width, kernel_height),
pads=(pad_width, pad_height),
strides=(stride_width, stride_height))
# Create the graph (GraphProto)
onnx_graph = onnx.helper.make_graph(
nodes=[avgpool_node],
name='test-averagePool',
inputs=[X],
outputs=[Y],
)
# Create the model (ModelProto)
onnx_model = onnx.helper.make_model(onnx_graph, producer_name='ONNX')
onnx.checker.check_model(onnx_model)
loaded_model = load_model_proto(onnx_model)
bigdl_model = SpatialAveragePooling(
kw=kernel_width,
kh=kernel_height,
dw=stride_width,
dh=stride_height,
pad_w=pad_width,
pad_h=pad_height,
ceil_mode=False if ceil_mode == 0 else True)
loaded_out = loaded_model.forward(input_x)
expected_out = bigdl_model.forward(input_x)
assert (np.array_equal(expected_out, loaded_out))
def test_gather(self):
axis = 0
input_x = np.array([
[1.0, 1.2],
[2.3, 3.4],
[4.5, 5.7],
], dtype=float)
indices_val = np.array([[0, 1], [1, 2]], dtype=float)
expected_out = np.array(
[[[1, 1.2], [2.3, 3.4]], [[2.3, 3.4], [4.5, 5.7]]], dtype=float)
input_shape = input_x.shape
indices_shape = indices_val.shape
output_shape = [2, 2, 2]
# Create one output (ValueInfoProto)
data = onnx.helper.make_tensor_value_info('data',
onnx.TensorProto.FLOAT,
input_shape)
indices = onnx.helper.make_tensor_value_info('indices',
onnx.TensorProto.FLOAT,
indices_shape)
Y = onnx.helper.make_tensor_value_info('Y', onnx.TensorProto.FLOAT,
output_shape)
init_indices = onnx.helper.make_tensor(
name='indices',
data_type=onnx.TensorProto.FLOAT,
dims=indices_shape,
vals=indices_val.flatten().tolist(),
)
gather_node = onnx.helper.make_node(op_type='Gather',
inputs=['data', 'indices'],
outputs=['Y'],
axis=axis)
# Create the graph (GraphProto)
onnx_graph = onnx.helper.make_graph(nodes=[gather_node],
name='test-gather',
inputs=[data, indices],
outputs=[Y],
initializer=[init_indices])
# Create the model (ModelProto)
onnx_model = onnx.helper.make_model(onnx_graph, producer_name='ONNX')
onnx.checker.check_model(onnx_model)
loaded_model = load_model_proto(onnx_model)
loaded_out = loaded_model.forward([input_x, indices_val])
assert (np.allclose(loaded_out, expected_out))
def test_max_poll(self):
kernel_width, kernel_height = 2, 2
stride_width, stride_height = 1, 1
pad_width, pad_height = 0, 0
ceil_mode = 0
input_shape = [1, 3, 224, 224]
output_shape = [1, 3, 223, 223]
input_x = np.random.random(input_shape)
# Create one output (ValueInfoProto)
X = onnx.helper.make_tensor_value_info('X', onnx.TensorProto.FLOAT,
input_shape)
Y = onnx.helper.make_tensor_value_info('Y', onnx.TensorProto.FLOAT,
output_shape)
maxpool_node = onnx.helper.make_node(
op_type='MaxPool',
inputs=['X'],
outputs=['Y'],
kernel_shape=(kernel_width, kernel_height),
)
# Create the graph (GraphProto)
onnx_graph = onnx.helper.make_graph(
nodes=[maxpool_node],
name='test-maxpool',
inputs=[X],
outputs=[Y],
)
# Create the model (ModelProto)
onnx_model = onnx.helper.make_model(onnx_graph, producer_name='ONNX')
onnx.checker.check_model(onnx_model)
loaded_model = load_model_proto(onnx_model)
bigdl_model = SpatialMaxPooling(
kw=kernel_width,
kh=kernel_height,
dw=stride_width,
dh=stride_height,
pad_w=pad_width,
pad_h=pad_height,
to_ceil=False if ceil_mode == 0 else True)
loaded_out = loaded_model.forward(input_x)
expected_out = bigdl_model.forward(input_x)
assert (np.array_equal(expected_out, loaded_out))
def test_reshape(self):
input_x = np.random.random([1, 3, 4, 4])
# Create one output (ValueInfoProto)
X = onnx.helper.make_tensor_value_info('X', onnx.TensorProto.FLOAT,
[1, 3, 4, 4])
Y = onnx.helper.make_tensor_value_info('Y', onnx.TensorProto.FLOAT,
[2, 3, 8])
shape = onnx.helper.make_tensor_value_info('shape',
onnx.TensorProto.FLOAT,
[1, 3])
init_shape = onnx.helper.make_tensor(
name='shape', # type: Text
data_type=onnx.TensorProto.FLOAT, # type: int
dims=[1, 3], # type: Sequence[int]
vals=[2, 3, 8], # type: Any
)
reshape_node = onnx.helper.make_node(op_type='Reshape',
inputs=['X', 'shape'],
outputs=['Y'])
# Create the graph (GraphProto)
onnx_graph = onnx.helper.make_graph(nodes=[reshape_node],
name='test-reshape',
inputs=[X, shape],
outputs=[Y],
initializer=[init_shape])
# Create the model (ModelProto)
onnx_model = onnx.helper.make_model(onnx_graph, producer_name='ONNX')
onnx.checker.check_model(onnx_model)
loaded_model = load_model_proto(onnx_model)
bigdl_model = Reshape([2, 3, 8])
loaded_out = loaded_model.forward(input_x)
expected_out = bigdl_model.forward(input_x)
assert (np.array_equal(expected_out, loaded_out))
def test_constant(self):
shape = [5, 5]
values = np.float32(np.round(np.random.random(shape), 6))
dummy_input = np.random.random([1])
# Create one output (ValueInfoProto)
Y = onnx.helper.make_tensor_value_info('Y', onnx.TensorProto.FLOAT,
values.shape)
constant_node = onnx.helper.make_node(
op_type='Constant',
inputs=[],
outputs=['Y'],
value=onnx.helper.make_tensor(
name='const_tensor',
data_type=onnx.TensorProto.FLOAT,
dims=values.shape,
vals=values.flatten().tolist(),
),
)
# Create the graph (GraphProto)
onnx_graph = onnx.helper.make_graph(
nodes=[constant_node],
name='test-constant',
inputs=[],
outputs=[Y],
)
# Create the model (ModelProto)
onnx_model = onnx.helper.make_model(onnx_graph, producer_name='ONNX')
onnx.checker.check_model(onnx_model)
loaded_model = load_model_proto(onnx_model)
loaded_out = loaded_model.forward(dummy_input)
expected_out = values
assert (np.array_equal(loaded_out, expected_out))
def test_unsqueeze(self):
axis = 0
input_shape = [3, 4, 5]
output_shape = [1, 3, 4, 5]
input_x = np.random.random([3, 4, 5])
# Create one output (ValueInfoProto)
X = onnx.helper.make_tensor_value_info('X', onnx.TensorProto.FLOAT,
input_shape)
Y = onnx.helper.make_tensor_value_info('Y', onnx.TensorProto.FLOAT,
output_shape)
unsqueeze_node = onnx.helper.make_node(
op_type='Unsqueeze',
inputs=['X'],
outputs=['Y'],
axes=[axis],
)
# Create the graph (GraphProto)
onnx_graph = onnx.helper.make_graph(
nodes=[unsqueeze_node],
name='test-unsqueeze',
inputs=[X],
outputs=[Y],
)
# Create the model (ModelProto)
onnx_model = onnx.helper.make_model(onnx_graph, producer_name='ONNX')
onnx.checker.check_model(onnx_model)
bigdl_model = Unsqueeze(pos=axis, num_input_dims=len(input_shape))
expected_out = bigdl_model.forward(input_x)
loaded_model = load_model_proto(onnx_model)
loaded_out = loaded_model.forward(input_x)
assert (np.array_equal(expected_out, loaded_out))
def test_batch_normalization(self):
input_shape = [1, 3, 224, 224]
output_shape = [1, 3, 224, 224]
# Create inputs (ValueInfoProto)
X = onnx.helper.make_tensor_value_info('X', onnx.TensorProto.FLOAT,
input_shape)
scale = onnx.helper.make_tensor_value_info('scale',
onnx.TensorProto.FLOAT,
input_shape[:2])
bias = onnx.helper.make_tensor_value_info('bias',
onnx.TensorProto.FLOAT,
input_shape[:2])
mean = onnx.helper.make_tensor_value_info('mean',
onnx.TensorProto.FLOAT,
input_shape[:2])
var = onnx.helper.make_tensor_value_info('var', onnx.TensorProto.FLOAT,
input_shape[:2])
# Create one output (ValueInfoProto)
Y = onnx.helper.make_tensor_value_info('Y', onnx.TensorProto.FLOAT,
output_shape)
scale_vals = np.random.random(input_shape[1]) * 10
bias_vals = np.random.random(input_shape[1]) * 10
mean_vals = np.random.random(input_shape[1]) * 10
var_vals = np.random.random(input_shape[1]) * 10
input_x = np.random.random(input_shape) * 10
epsilon = float(1e-05)
momentum = float(0.9)
init_scale = onnx.helper.make_tensor(
name='scale',
data_type=onnx.TensorProto.FLOAT,
dims=input_shape[:2],
vals=scale_vals.tolist(),
)
init_bias = onnx.helper.make_tensor(
name='bias',
data_type=onnx.TensorProto.FLOAT,
dims=input_shape[:2],
vals=bias_vals.tolist(),
)
init_mean = onnx.helper.make_tensor(
name='mean',
data_type=onnx.TensorProto.FLOAT,
dims=input_shape[:2],
vals=mean_vals.tolist(),
)
init_var = onnx.helper.make_tensor(
name='var',
data_type=onnx.TensorProto.FLOAT,
dims=input_shape[:2],
vals=var_vals.tolist(),
)
# Create a node (NodeProto)
batch_norm_node = onnx.helper.make_node(
op_type='BatchNormalization', # node name
inputs=['X', 'scale', 'bias', 'mean', 'var'], # inputs
outputs=['Y'], # outputs
epsilon=epsilon,
momentum=momentum)
# Create the graph (GraphProto)
onnx_graph = onnx.helper.make_graph(
nodes=[batch_norm_node],
name='test-batch_norm',
inputs=[X],
outputs=[Y],
initializer=[init_scale, init_bias, init_mean, init_var])
# Create the model (ModelProto)
onnx_model = onnx.helper.make_model(onnx_graph, producer_name='ONNX')
onnx.checker.check_model(onnx_model)
loaded_model = load_model_proto(onnx_model)
bigdl_model = SpatialBatchNormalization(
n_output=input_shape[1],
eps=epsilon,
momentum=momentum,
init_weight=scale_vals,
init_bias=bias_vals,
init_grad_weight=None,
init_grad_bias=None,
)
bigdl_model.set_running_mean(mean_vals)
bigdl_model.set_running_std(var_vals)
loaded_out = loaded_model.forward(input_x)
expected_out = bigdl_model.forward(input_x)
assert (np.array_equal(loaded_out, expected_out))
def test_gemm(self):
mata_shape = [2, 7]
matb_shape = [7, 4]
matc_shape = [2, 4]
output_shape = [2, 4]
alpha = np.round(np.random.rand(), 2)
beta = np.round(np.random.rand(), 2)
trans_a, trans_b = 0, 0
input_x = np.random.random(mata_shape)
b_val = np.random.random(matb_shape)
c_val = np.random.random(matc_shape)
# Create one output (ValueInfoProto)
a = onnx.helper.make_tensor_value_info('a', onnx.TensorProto.FLOAT,
mata_shape)
b = onnx.helper.make_tensor_value_info('b', onnx.TensorProto.FLOAT,
matb_shape)
c = onnx.helper.make_tensor_value_info('c', onnx.TensorProto.FLOAT,
matc_shape)
Y = onnx.helper.make_tensor_value_info('Y', onnx.TensorProto.FLOAT,
output_shape)
init_b = onnx.helper.make_tensor(
name='b',
data_type=onnx.TensorProto.FLOAT,
dims=matb_shape,
vals=b_val.flatten().tolist(),
)
init_c = onnx.helper.make_tensor(
name='c',
data_type=onnx.TensorProto.FLOAT,
dims=matc_shape,
vals=c_val.flatten().tolist(),
)
gemm_node = onnx.helper.make_node(op_type='Gemm',
inputs=['a', 'b', 'c'],
outputs=['Y'],
alpha=alpha,
beta=beta,
transA=trans_a,
transB=trans_b)
# Create the graph (GraphProto)
onnx_graph = onnx.helper.make_graph(nodes=[gemm_node],
name='test-gather',
inputs=[a, b, c],
outputs=[Y],
initializer=[init_b, init_c])
# Create the model (ModelProto)
onnx_model = onnx.helper.make_model(onnx_graph, producer_name='ONNX')
onnx.checker.check_model(onnx_model)
bigdl_model = Gemm(b_val,
c_val,
alpha=alpha,
beta=beta,
trans_a=trans_a,
trans_b=trans_b)
loaded_model = load_model_proto(onnx_model)
expected_out = bigdl_model.forward(input_x)
loaded_out = loaded_model.forward(input_x)
assert (np.array_equal(expected_out, loaded_out))
def test_concat(self):
axis = 0
input_shape = [2, 3]
output_shape = [4, 3]
x1_val = np.random.random(input_shape)
x2_val = np.random.random(input_shape)
# Create input (ValueInfoProto)
X = onnx.helper.make_tensor_value_info('X', onnx.TensorProto.FLOAT,
input_shape)
X1 = onnx.helper.make_tensor_value_info('X1', onnx.TensorProto.FLOAT,
input_shape)
X2 = onnx.helper.make_tensor_value_info('X2', onnx.TensorProto.FLOAT,
input_shape)
# Create one output (ValueInfoProto)
Y = onnx.helper.make_tensor_value_info('Y', onnx.TensorProto.FLOAT,
output_shape)
# Create a node (NodeProto)
const_X1 = onnx.helper.make_node(op_type='Constant',
inputs=[],
outputs=['X1'],
value=onnx.helper.make_tensor(
name='X1',
data_type=onnx.TensorProto.FLOAT,
dims=input_shape,
vals=x1_val.flatten().tolist(),
))
const_X2 = onnx.helper.make_node(op_type='Constant',
inputs=[],
outputs=['X2'],
value=onnx.helper.make_tensor(
name='X2',
data_type=onnx.TensorProto.FLOAT,
dims=input_shape,
vals=x2_val.flatten().tolist(),
))
concat_node = onnx.helper.make_node(
op_type='Concat', # node name
inputs=['X1', 'X2'], # inputs
outputs=['Y'], # outputs
axis=axis)
# Create the graph (GraphProto)
onnx_graph = onnx.helper.make_graph(
nodes=[const_X1, const_X2, concat_node],
name='test-concat',
inputs=[X],
outputs=[Y],
)
# Create the model (ModelProto)
onnx_model = onnx.helper.make_model(onnx_graph, producer_name='ONNX')
onnx.checker.check_model(onnx_model)
loaded_model = load_model_proto(onnx_model)
bigdl_model = JoinTable(dimension=axis + 1,
n_input_dims=len(input_shape))
loaded_out = loaded_model.forward([x1_val, x2_val])
expected_out = bigdl_model.forward([x1_val, x2_val])
assert (np.array_equal(loaded_out, expected_out))
