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Commit 86663aa5 authored by Khalique's avatar Khalique
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add gen files

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test/onnx/gen_onnx.py 0 → 100644
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import numpy as np
import onnx
from onnx import helper
from onnx import numpy_helper
from onnx import AttributeProto, TensorProto, GraphProto
def acos_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])
node = onnx.helper.make_node(
'Acos',
inputs=['x'],
outputs=['y'],
)
graph_def = helper.make_graph(
[node],
'test_acos',
[x],
[y],
)
model_def = helper.make_model(graph_def, producer_name='acos-example')
onnx.save(model_def, 'onnx_acos.onnx')
def add_bcast_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 4])
z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [2, 3, 4,5])
node = onnx.helper.make_node(
'Add',
inputs=['0', '1'],
broadcast=1,
axis=1,
outputs=['2']
)
graph_def = helper.make_graph(
[node],
'test-add_bcast',
[x,y],
[z]
)
model_def = helper.make_model(graph_def, producer_name='add_bcast-example')
onnx.save(model_def, 'add_bcast_test.onnx')
def add_fp16_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT16, [1])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT16, [1])
z = helper.make_tensor_value_info('2', TensorProto.FLOAT16, [1])
node = onnx.helper.make_node(
'Add',
inputs=['0', '1'],
outputs=['2'],
)
graph_def = helper.make_graph(
[node],
'test-add-fp16',
[x,y],
[z],
# '0' -> 1.5, '1' -> 2.5
initializer=[onnx.helper.make_tensor('0', TensorProto.FLOAT16, [1], [15872]),
onnx.helper.make_tensor('1', TensorProto.FLOAT16, [1], [16640])]
)
model_def = helper.make_model(graph_def, producer_name=('add-fp16-example'))
onnx.save(model_def, 'add_fp16_test.onnx')
def add_scalar_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [])
z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [2, 3, 4,5])
node = onnx.helper.make_node(
'Add',
inputs=['0', '1'],
outputs=['2']
)
graph_def = helper.make_graph(
[node],
'test-add-scalar',
[x,y],
[z],
initializer=[helper.make_tensor('1', TensorProto.FLOAT, [], [1])]
)
model_def = helper.make_model(graph_def, producer_name='add_scalar-example')
onnx.save(model_def, 'add_scalar_test.onnx')
def argmax_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 6])
node = onnx.helper.make_node(
'ArgMax',
inputs=['x'],
outputs=['y'],
axis=2,
keepdims = 0
)
graph_def = helper.make_graph(
[node],
'test_argmax',
[x],
[y],
)
model_def = helper.make_model(graph_def, producer_name='argmax-example')
onnx.save(model_def, 'argmax_test.onnx')
def argmin_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 5])
node = onnx.helper.make_node(
'ArgMin',
inputs=['x'],
outputs=['y'],
axis=3,
keepdims = 0
)
graph_def = helper.make_graph(
[node],
'test_argmin',
[x],
[y],
)
model_def = helper.make_model(graph_def, producer_name='argmin-example')
onnx.save(model_def, 'argmin_test.onnx')
def asin_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])
node = onnx.helper.make_node(
'Asin',
inputs=['x'],
outputs=['y'],
)
graph_def = helper.make_graph(
[node],
'test_asin',
[x],
[y],
)
model_def = helper.make_model(graph_def, producer_name='asin-example')
onnx.save(model_def, 'asin_test.onnx')
def atan_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])
node = onnx.helper.make_node(
'Atan',
inputs=['x'],
outputs=['y'],
)
graph_def = helper.make_graph(
[node],
'test_atan',
[x],
[y],
)
model_def = helper.make_model(graph_def, producer_name='atan-example')
onnx.save(model_def, 'atan_test.onnx')
def cast_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT16, [10])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])
node = onnx.helper.make_node(
'Cast',
inputs=['x'],
outputs=['y'],
to = 1
)
graph_def = helper.make_graph(
[node],
'test_cast',
[x],
[y],
)
model_def = helper.make_model(graph_def, producer_name='cast-example')
onnx.save(model_def, 'cast_test.onnx')
def clip_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3])
node = onnx.helper.make_node(
'Clip',
inputs=['0'],
outputs=['1'],
max=6.0,
min=0.0
)
graph_def = helper.make_graph(
[node],
'test-model',
[x],
[y]
)
model_def = helper.make_model(graph_def, producer_name='clip-example')
onnx.save(model_def, 'clip_test.onnx')
def concat_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 4, 3])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [7, 4, 3])
z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [9, 4, 3])
node = onnx.helper.make_node(
'Concat',
inputs=['0', '1'],
axis=0,
outputs=['2'],
)
graph_def = helper.make_graph(
[node],
'test-concat',
[x,y],
[z]
)
model_def = helper.make_model(graph_def, producer_name='concat-example')
onnx.save(model_def, 'concat_test.onnx')
def constant_test():
x = np.array([0, 1, 2])
y = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3])
node = onnx.helper.make_node(
'Constant',
inputs=[],
outputs=['0'],
value=onnx.helper.make_tensor(
name='const_tensor',
data_type=TensorProto.FLOAT,
dims=x.shape,
vals=x.flatten().astype(float),
),
)
graph_def = helper.make_graph(
[node],
'test-constant',
[],
[y]
)
model_def = helper.make_model(graph_def, producer_name=('constant-example'))
onnx.save(model_def, 'constant_test.onnx')
def constant_fill_test():
np_shape = np.array([2, 3])
ts_shape = helper.make_tensor(
name = 'shape_tensor',
data_type = TensorProto.INT32,
dims = np_shape.shape,
vals = np_shape.flatten().astype(int)
)
const_shape_node = onnx.helper.make_node(
'Constant',
inputs=[],
outputs=['shape'],
value=ts_shape,
)
shape = helper.make_tensor_value_info('shape', TensorProto.INT32, [2])
value = helper.make_tensor_value_info('value', TensorProto.FLOAT, [2, 3])
node = onnx.helper.make_node(
'ConstantFill',
inputs=['shape'],
outputs=['value'],
dtype = 1,
value = 1.0,
input_as_shape = 1,
)
graph_def = helper.make_graph(
[const_shape_node, node],
'constant_fill',
[],
[value],
)
model_def = helper.make_model(graph_def, producer_name='constant-fill-example')
onnx.save(model_def, 'const_fill1_test.onnx')
node = onnx.helper.make_node(
'ConstantFill',
inputs=[],
outputs=['value'],
dtype = 1,
value = 1.0,
shape = [2, 3],
input_as_shape = 0,
)
graph_def = helper.make_graph(
[node],
'constant_fill',
[],
[value],
)
model_def = helper.make_model(graph_def, producer_name='constant-fill-example')
onnx.save(model_def, 'const_fill2_test.onnx')
def constant_scalar_test():
x = np.array([1])
y = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1])
node = onnx.helper.make_node(
'Constant',
inputs=[],
outputs=['0'],
value=onnx.helper.make_tensor(
name='const_tensor',
data_type=TensorProto.INT32,
dims=x.shape,
vals=x.flatten().astype(int),
),
)
graph_def = helper.make_graph(
[node],
'test-constant',
[],
[y]
)
model_def = helper.make_model(graph_def, producer_name=('constant-scalar-example'))
onnx.save(model_def, 'constant_scalar_test.onnx')
def const_of_shape_empty_input_test():
tensor_val = onnx.helper.make_tensor(
'value',
onnx.TensorProto.INT64, [1],[10]
)
shape_val = np.array([2, 3, 4]).astype(np.int64)
empty_val = np.array([]).astype(np.int64)
empty_ts = helper.make_tensor(
name='empty_tensor',
data_type = TensorProto.INT32,
dims=empty_val.shape,
vals=empty_val.flatten().astype(int)
)
shape_const = helper.make_node(
'Constant',
inputs=[],
outputs=['shape'],
value=empty_ts,
)
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 3, 4])
node = onnx.helper.make_node(
'ConstantOfShape',
inputs=['shape'],
outputs=['y'],
value = tensor_val,
)
graph_def = helper.make_graph(
[shape_const, node],
'constant_of_shape',
[],
[y],
)
model_def = helper.make_model(graph_def, producer_name='constant-of-shape')
onnx.save(model_def, 'const_of_shape_empty_input_test.onnx')
def const_of_shape_float_test():
tensor_val = onnx.helper.make_tensor(
'value',
onnx.TensorProto.FLOAT, [1],[10])
shape_val = np.array([2, 3, 4]).astype(np.int64)
shape_ts = helper.make_tensor(
name = 'shape_tensor',
data_type = TensorProto.INT32,
dims = shape_val.shape,
vals = shape_val.flatten().astype(int)
)
shape_const = helper.make_node(
'Constant',
inputs=[],
outputs=['shape'],
value=shape_ts,
)
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 3, 4])
node = onnx.helper.make_node(
'ConstantOfShape',
inputs=['shape'],
outputs=['y'],
value = tensor_val
)
graph_def = helper.make_graph(
[shape_const, node],
'constant_of_shape',
[],
[y],
)
model_def = helper.make_model(graph_def, producer_name='constant-of-shape')
onnx.save(model_def, 'const_of_shape_float_test.onnx')
def const_of_shape_int64_test():
tensor_val = onnx.helper.make_tensor(
'value',
onnx.TensorProto.INT64, [1],[10]
)
shape_val = np.array([2, 3, 4]).astype(np.int64)
shape_ts = helper.make_tensor(
name = 'shape_tensor',
data_type = TensorProto.INT32,
dims = shape_val.shape,
vals = shape_val.flatten().astype(int)
)
shape_const = helper.make_node(
'Constant',
inputs=[],
outputs=['shape'],
value=shape_ts,
)
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 3, 4])
node = onnx.helper.make_node(
'ConstantOfShape',
inputs=['shape'],
outputs=['y'],
value = tensor_val
)
graph_def = helper.make_graph(
[shape_const, node],
'constant_of_shape',
[],
[y],
)
model_def = helper.make_model(graph_def, producer_name='constant-of-shape')
onnx.save(model_def, 'const_of_shape_int64_test.onnx')
def const_of_shape_no_value_attr_test():
tensor_val = onnx.helper.make_tensor(
'value',
onnx.TensorProto.INT64, [1],[10]
)
shape_val = np.array([2, 3, 4]).astype(np.int64)
shape_ts = helper.make_tensor(
name = 'shape_tensor',
data_type = TensorProto.INT32,
dims = shape_val.shape,
vals = shape_val.flatten().astype(int)
)
shape_const = helper.make_node(
'Constant',
inputs=[],
outputs=['shape'],
value=shape_ts,
)
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 3, 4])
node = onnx.helper.make_node(
'ConstantOfShape',
inputs=['shape'],
outputs=['y'],
)
graph_def = helper.make_graph(
[shape_const, node],
'constant_of_shape',
[],
[y],
)
model_def = helper.make_model(graph_def, producer_name='constant-of-shape')
onnx.save(model_def, 'const_of_shape_no_value_attr_test.onnx')
def cos_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])
node = onnx.helper.make_node(
'Cos',
inputs=['x'],
outputs=['y'],
)
graph_def = helper.make_graph(
[node],
'test_cos',
[x],
[y],
)
model_def = helper.make_model(graph_def, producer_name='cos-example')
onnx.save(model_def, 'cos_test.onnx')
def cosh_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1])
node = onnx.helper.make_node(
'Cosh',
inputs=['x'],
outputs=['y'],
)
graph_def = helper.make_graph(
[node],
'test_cosh',
[x],
[y],
)
model_def = helper.make_model(graph_def, producer_name='cosh-example')
onnx.save(model_def, 'cosh_test.onnx')
def dropout_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 2, 2])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 2, 2])
node = onnx.helper.make_node(
'Dropout',
inputs=['0'],
outputs=['1'],
)
graph_def = helper.make_graph(
[node],
'test-dropout',
[x],
[y]
)
model_def = helper.make_model(graph_def, producer_name='dropout-example')
onnx.save(model_def, 'dropout_test.onnx')
def elu_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3])
node = onnx.helper.make_node(
'Elu',
inputs=['0'],
outputs=['1'],
alpha=0.01
)
graph_def = helper.make_graph(
[node],
'test-model',
[x],
[y]
)
model_def = helper.make_model(graph_def, producer_name='elu-example')
onnx.save(model_def, 'elu_test.onnx')
def erf_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10, 15])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10, 15])
node = onnx.helper.make_node(
'Erf',
inputs=['x'],
outputs=['y'],
)
graph_def = helper.make_graph(
[node],
'test_erf',
[x],
[y],
)
model_def = helper.make_model(graph_def, producer_name='erf-example')
onnx.save(model_def, 'erf_test.onnx')
def exp_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])
node = onnx.helper.make_node(
'Exp',
inputs=['x'],
outputs=['y'],
)
graph_def = helper.make_graph(
[node],
'test_exp',
[x],
[y],
)
model_def = helper.make_model(graph_def, producer_name='exp-example')
onnx.save(model_def, 'exp_test.onnx')
def expand_test():
shape_val = np.array([2, 3, 4, 5]).astype(np.int64)
shape_ts = helper.make_tensor(
name = 'shape_tensor',
data_type = TensorProto.INT32,
dims = shape_val.shape,
vals = shape_val.flatten().astype(int)
)
shape_const = helper.make_node(
'Constant',
inputs=[],
outputs=['shape'],
value=shape_ts,
)
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 1, 1])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 3, 4, 5])
node = onnx.helper.make_node(
'Expand',
inputs=['x', 'shape'],
outputs=['y']
)
graph_def = helper.make_graph(
[shape_const, node],
'expand',
[x],
[y],
)
model_def = helper.make_model(graph_def, producer_name='expand')
onnx.save(model_def, 'expand_test.onnx')
def flatten_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])
y = helper.make_tensor_value_info('2', TensorProto.FLOAT, [6, 20])
y2 = helper.make_tensor_value_info('3', TensorProto.FLOAT, [2, 60])
node = onnx.helper.make_node(
'Flatten',
inputs=['0'],
axis=2,
outputs=['2']
)
node2 = onnx.helper.make_node(
'Flatten',
inputs=['0'],
outputs=['3']
)
graph_def = helper.make_graph(
[node,node2],
'test-flatten',
[x],
[y,y2]
)
model_def = helper.make_model(graph_def, producer_name=('flatten-example'))
onnx.save(model_def, 'flatten_test.onnx')
def gather_test():
x = helper.make_tensor_value_info('data', TensorProto.FLOAT, [3, 4, 5, 6])
i = helper.make_tensor_value_info('indices', TensorProto.INT32, [2, 3, 4, 5])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 3, 4, 5])
node = onnx.helper.make_node(
'Gather',
inputs=['data', 'indices'],
outputs=['y'],
axis=1,
)
graph_def = helper.make_graph(
[node],
'test_gather',
[x, i],
[y],
)
model_def = helper.make_model(graph_def, producer_name='gather-example')
onnx.save(model_def, 'gather_test.onnx')
def gemm_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [5, 7])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [11, 5])
z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [])
a = helper.make_tensor_value_info('3', TensorProto.FLOAT, [7, 11])
node = onnx.helper.make_node(
'Gemm',
inputs=['0', '1', '2'],
outputs=['3'],
alpha=2.0,
beta=2.0,
transA=1,
transB=1
)
graph_def = helper.make_graph(
[node],
'test-gemm',
[x, y, z],
[a]
)
model_def = helper.make_model(graph_def, producer_name=('gemm-example'))
onnx.save(model_def, 'gemm_test.onnx')
def gemm_ex_test():
m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 1, 5, 6])
m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [1, 1, 5, 7])
m3 = helper.make_tensor_value_info('3', TensorProto.FLOAT, [1, 1, 6, 7])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 1, 6, 7])
node = onnx.helper.make_node(
'Gemm',
inputs=['1', '2', '3'],
outputs=['y'],
alpha = 0.5,
beta = 0.8,
transA = 1
)
graph_def = helper.make_graph(
[node],
'test_gemm_ex',
[m1, m2, m3],
[y],
)
model_def = helper.make_model(graph_def, producer_name='gemm-example')
onnx.save(model_def, 'gemm_ex_test.onnx')
def gemm_ex_brcst_test():
m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 1, 5, 6])
m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [1, 1, 5, 7])
m3 = helper.make_tensor_value_info('3', TensorProto.FLOAT, [1, 1, 6, 1])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 1, 6, 7])
node = onnx.helper.make_node(
'Gemm',
inputs=['1', '2', '3'],
outputs=['y'],
alpha = 0.5,
beta = 0.8,
transA = 1
)
graph_def = helper.make_graph(
[node],
'test_gemm_ex',
[m1, m2, m3],
[y],
)
model_def = helper.make_model(graph_def, producer_name='gemm-example')
onnx.save(model_def, 'gemm_ex_brcst_test.onnx')
def globalavgpool_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1,3,16,16])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1,3,1,1])
node = onnx.helper.make_node(
'GlobalAveragePool',
inputs=['0'],
outputs=['1'],
)
graph_def = helper.make_graph(
[node],
'test-globalavgpool',
[x],
[y]
)
model_def = helper.make_model(graph_def, producer_name='globalavgpool-example')
onnx.save(model_def, 'globalavgpool_test.onnx')
def globalmaxpool_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1,3,16,16])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1,3,1,1])
node = onnx.helper.make_node(
'GlobalMaxPool',
inputs=['0'],
outputs=['1'],
)
graph_def = helper.make_graph(
[node],
'test-globalmaxpool',
[x],
[y]
)
model_def = helper.make_model(graph_def, producer_name='globalmaxpool-example')
onnx.save(model_def, 'globalmaxpool_test.onnx')
def group_conv_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 4, 16, 16])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [4, 1, 3, 3])
z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [1, 4, 14, 14])
node = onnx.helper.make_node(
'Conv',
inputs=['0', '1'],
group=4,
outputs=['2'],
)
graph_def = helper.make_graph(
[node],
'test-group_conv',
[x,y],
[z]
)
model_def = helper.make_model(graph_def, producer_name='group_conv-example')
onnx.save(model_def, 'group_conv_test.onnx')
def imagescaler_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1,3,16,16])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1,3,16,16])
node = onnx.helper.make_node(
'ImageScaler',
inputs=['0'],
outputs=['1'],
bias=[0.01,0.02,0.03],
scale=0.5
)
graph_def = helper.make_graph(
[node],
'test-imagescaler',
[x],
[y]
)
model_def = helper.make_model(graph_def, producer_name='imagescaler-example')
onnx.save(model_def, 'imagescaler_test.onnx')
def implicit_add_bcast_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 4, 1])
z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [2, 3, 4, 5])
node = onnx.helper.make_node(
'Add',
inputs=['0', '1'],
outputs=['2'],
)
graph_def = helper.make_graph(
[node],
'test-multi_bcast',
[x,y],
[z]
)
model_def = helper.make_model(graph_def, producer_name='implicit_bcast-example')
onnx.save(model_def, 'implicit_add_bcast_test.onnx')
def implicit_pow_bcast_test():
arg0 = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])
arg1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 4, 1])
arg_out = helper.make_tensor_value_info('out', TensorProto.FLOAT, [2, 3, 4, 5])
node = onnx.helper.make_node(
'Pow',
inputs=['0', '1'],
outputs=['out'],
)
graph_def = helper.make_graph(
[node],
'pow_test',
[arg0, arg1],
[arg_out],
)
model_def = helper.make_model(graph_def, producer_name='pow2')
onnx.save(model_def, 'implicit_pow_bcast_test.onnx')
def implicit_sub_bcast_test():
arg0 = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])
arg1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [4, 5])
arg_out = helper.make_tensor_value_info('out', TensorProto.FLOAT, [2, 3, 4, 5])
node = onnx.helper.make_node(
'Sub',
inputs=['0', '1'],
outputs=['out'],
)
graph_def = helper.make_graph(
[node],
'subtraction2',
[arg0, arg1],
[arg_out],
)
model_def = helper.make_model(graph_def, producer_name='add2')
onnx.save(model_def, 'implicit_sub_bcast_test.onnx')
def leaky_relu_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3])
node = onnx.helper.make_node(
'LeakyRelu',
inputs=['0'],
outputs=['1'],
alpha=0.01
)
graph_def = helper.make_graph(
[node],
'test-model',
[x],
[y]
)
model_def = helper.make_model(graph_def, producer_name='leaky_relu-example')
onnx.save(model_def, 'leaky_relu_test.onnx')
def log_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])
node = onnx.helper.make_node(
'Log',
inputs=['x'],
outputs=['y'],
)
graph_def = helper.make_graph(
[node],
'test_log',
[x],
[y],
)
model_def = helper.make_model(graph_def, producer_name='log-example')
onnx.save(model_def, 'log_test.onnx')
def logsoftmax_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 5, 6])
node = onnx.helper.make_node(
'LogSoftmax',
inputs=['x'],
outputs=['y'],
axis = 1
)
graph_def = helper.make_graph(
[node],
'test_logsoftmax',
[x],
[y]
)
model_def = helper.make_model(graph_def, producer_name='logsoftmax-example')
onnx.save(model_def, 'logsoftmax_test.onnx')
def lrn_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 28, 24, 24])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 28, 24, 24])
node = onnx.helper.make_node(
'LRN',
inputs=['0'],
size=5,
alpha=0.0001,
beta=0.75,
bias=1.0,
outputs=['1']
)
graph_def = helper.make_graph(
[node],
'test-lrn',
[x],
[y]
)
model_def = helper.make_model(graph_def, producer_name=('lrn-example'))
onnx.save(model_def, 'lrn_test.onnx')
def matmul_bmbm_test():
m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 6, 7])
m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [5, 2, 1, 7, 8])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [5, 2, 3, 6, 8])
node = onnx.helper.make_node(
'MatMul',
inputs=['1', '2'],
outputs=['y'],
)
graph_def = helper.make_graph(
[node],
'test_matmul',
[m1, m2],
[y],
)
model_def = helper.make_model(graph_def, producer_name='matmul-example')
onnx.save(model_def, 'matmul_bmbm_test.onnx')
def matmul_bmv_test():
m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 6, 7])
m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [7])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 6])
node = onnx.helper.make_node(
'MatMul',
inputs=['1', '2'],
outputs=['y'],
)
graph_def = helper.make_graph(
[node],
'test_matmul',
[m1, m2],
[y],
)
model_def = helper.make_model(graph_def, producer_name='matmul-example')
onnx.save(model_def, 'matmul_bmv_test.onnx')
def matmul_mv_test():
m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [6, 7])
m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [7])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [6])
node = onnx.helper.make_node(
'MatMul',
inputs=['1', '2'],
outputs=['y'],
)
graph_def = helper.make_graph(
[node],
'test_matmul',
[m1, m2],
[y],
)
model_def = helper.make_model(graph_def, producer_name='matmul-example')
onnx.save(model_def, 'matmul_mv_test.onnx')
def matmul_vbm_test():
m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [7])
m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [5, 7, 8])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [5, 8])
node = onnx.helper.make_node(
'MatMul',
inputs=['1', '2'],
outputs=['y'],
)
graph_def = helper.make_graph(
[node],
'test_matmul',
[m1, m2],
[y],
)
model_def = helper.make_model(graph_def, producer_name='matmul-example')
onnx.save(model_def, 'matmul_vbm_test.onnx')
def matmul_vm_test():
m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [7])
m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [7, 8])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [8])
node = onnx.helper.make_node(
'MatMul',
inputs=['1', '2'],
outputs=['y'],
)
graph_def = helper.make_graph(
[node],
'test_matmul',
[m1, m2],
[y],
)
model_def = helper.make_model(graph_def, producer_name='matmul-example')
onnx.save(model_def, 'matmul_vm_test.onnx')
def matmul_vv_test():
m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [7])
m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [7])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1])
node = onnx.helper.make_node(
'MatMul',
inputs=['1', '2'],
outputs=['y'],
)
graph_def = helper.make_graph(
[node],
'test_matmul',
[m1, m2],
[y],
)
model_def = helper.make_model(graph_def, producer_name='matmul-example')
onnx.save(model_def, 'matmul_vv_test.onnx')
def max_test():
a = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3])
b = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3])
c = helper.make_tensor_value_info('2', TensorProto.FLOAT, [3])
y = helper.make_tensor_value_info('2', TensorProto.FLOAT, [3])
node = onnx.helper.make_node(
'Max',
inputs=['0', '1', '2'],
outputs=['3'],
)
graph_def = helper.make_graph(
[node],
'test-dropout',
[a, b, c],
[y]
)
model_def = helper.make_model(graph_def, producer_name='max-example')
onnx.save(model_def, 'max_test.onnx')
def min_test():
a = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3])
b = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3])
c = helper.make_tensor_value_info('2', TensorProto.FLOAT, [3])
y = helper.make_tensor_value_info('2', TensorProto.FLOAT, [3])
node = onnx.helper.make_node(
'Min',
inputs=['0', '1', '2'],
outputs=['3'],
)
graph_def = helper.make_graph(
[node],
'test-dropout',
[a, b, c],
[y]
)
model_def = helper.make_model(graph_def, producer_name='min-example')
onnx.save(model_def, 'min_test.onnx')
def no_pad_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 2])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [2, 2])
node = onnx.helper.make_node(
'Pad',
inputs=['0'],
pads=[0,0,0,0],
outputs=['1']
)
graph_def = helper.make_graph(
[node],
'test-no-pad',
[x],
[y]
)
model_def = helper.make_model(graph_def, producer_name='no-pad-example')
onnx.save(model_def, 'no_pad_test.onnx')
def pad_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 2])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [4, 4])
node = onnx.helper.make_node(
'Pad',
inputs=['0'],
pads=[1,1,1,1],
outputs=['1']
)
graph_def = helper.make_graph(
[node],
'test-pad',
[x],
[y]
)
model_def = helper.make_model(graph_def, producer_name='pad-example')
onnx.save(model_def, 'pad_test.onnx')
def pow_test():
arg0 = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])
arg1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [2, 3, 4, 5])
arg_out = helper.make_tensor_value_info('out', TensorProto.FLOAT, [2, 3, 4, 5])
node = onnx.helper.make_node(
'Pow',
inputs=['0', '1'],
outputs=['out'],
)
graph_def = helper.make_graph(
[node],
'pow_test',
[arg0, arg1],
[arg_out],
)
model_def = helper.make_model(graph_def, producer_name='pow2')
onnx.save(model_def, 'pow_test.onnx')
def reducemean_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4])
axes=[2, 3]
node = onnx.helper.make_node(
'ReduceMean',
inputs=['x'],
outputs=['y'],
axes=axes,
keepdims = 0
)
graph_def = helper.make_graph(
[node],
'test_reducemean',
[x],
[y],
)
model_def = helper.make_model(graph_def, producer_name='reducemean-example')
onnx.save(model_def, 'reducemean_test.onnx')
def reducemean_keepdims_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 1, 6])
axes=[2]
node = onnx.helper.make_node(
'ReduceMean',
inputs=['x'],
outputs=['y'],
axes=axes,
keepdims = 1
)
graph_def = helper.make_graph(
[node],
'test_reducemean',
[x],
[y],
)
model_def = helper.make_model(graph_def, producer_name='reducemean-example')
print('The mode is:{}'.format(model_def))
onnx.checker.check_model(model_def)
onnx.save(model_def, 'reducemean_keepdims_test.onnx')
def reducesum_keepdims_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 1, 1])
axes=[2, 3]
node = onnx.helper.make_node(
'ReduceSum',
inputs=['x'],
outputs=['y'],
axes=axes,
keepdims = 1
)
graph_def = helper.make_graph(
[node],
'test_reducesum',
[x],
[y],
)
model_def = helper.make_model(graph_def, producer_name='reducesum-example')
print('The mode is:{}'.format(model_def))
onnx.checker.check_model(model_def)
onnx.save(model_def, 'reducesum_keepdims_test.onnx')
def reshape_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [4, 2, 3])
x_shape = helper.make_tensor_value_info('1', TensorProto.INT64, [2])
x_shape_list = [3,8]
y = helper.make_tensor_value_info('2', TensorProto.FLOAT, [3, 8])
y2 = helper.make_tensor_value_info('3', TensorProto.FLOAT, [3, 8])
node = onnx.helper.make_node(
'Reshape',
inputs=['0', '1'],
outputs=['2']
)
node2 = onnx.helper.make_node(
'Reshape',
inputs=['0'],
shape=x_shape_list,
outputs=['3']
)
graph_def = helper.make_graph(
[node,node2],
'test-reshape',
[x, x_shape],
[y,y2],
initializer=[helper.make_tensor('1', TensorProto.INT64, [2], [3, 8])]
)
model_def = helper.make_model(graph_def, producer_name=('reshape-example'))
onnx.save(model_def, 'reshape_test.onnx')
def slice_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3, 2])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 2])
node = onnx.helper.make_node(
'Slice',
inputs=['0'],
axes=[0, 1],
starts=[1,0],
ends=[2, 2],
outputs=['1']
)
graph_def = helper.make_graph(
[node],
'test-slice',
[x],
[y]
)
model_def = helper.make_model(graph_def, producer_name=('slice-example'))
onnx.save(model_def, 'slice_test.onnx')
def softmax_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3])
node = onnx.helper.make_node(
'Softmax',
inputs=['0'],
outputs=['1']
)
graph_def = helper.make_graph(
[node],
'test-softmax',
[x],
[y]
)
model_def = helper.make_model(graph_def, producer_name=('softmax-example'))
onnx.save(model_def, 'softmax_test.onnx')
def squeeze_unsqueeze_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 1, 1, 2, 1])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 2])
z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [1, 1, 3, 1, 2, 1])
node = onnx.helper.make_node(
'Squeeze',
inputs=['0'],
axes=[0, 2, 3, 5],
outputs=['1']
)
node2 = onnx.helper.make_node(
'Unsqueeze',
inputs=['1'],
axes=[0, 1, 3, 5],
outputs=['2']
)
graph_def = helper.make_graph(
[node,node2],
'test-squeeze-unsqueeze',
[x],
[z]
)
model_def = helper.make_model(graph_def, producer_name=('squeeze-unsqueeze-example'))
onnx.save(model_def, 'squeeze_unsqueeze_test.onnx')
def sum_test():
a = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3])
b = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3])
c = helper.make_tensor_value_info('2', TensorProto.FLOAT, [3])
y = helper.make_tensor_value_info('3', TensorProto.FLOAT, [3])
node = onnx.helper.make_node(
'Sum',
inputs=['0', '1', '2'],
outputs=['3'],
)
graph_def = helper.make_graph(
[node],
'test-sum',
[a, b, c],
[y]
)
model_def = helper.make_model(graph_def, producer_name='sum-example')
onnx.save(model_def, 'sum_test.onnx')
def sum_test():
a = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3])
b = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3])
c = helper.make_tensor_value_info('2', TensorProto.FLOAT, [3])
y = helper.make_tensor_value_info('3', TensorProto.FLOAT, [3])
node = onnx.helper.make_node(
'Sum',
inputs=['0', '1', '2'],
outputs=['3'],
)
graph_def = helper.make_graph(
[node],
'test-sum',
[a, b, c],
[y]
)
model_def = helper.make_model(graph_def, producer_name='sum-example')
onnx.save(model_def, 'sum_test.onnx')
def transpose_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 2, 2, 3])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 2, 2])
node = onnx.helper.make_node(
'Transpose',
perm=[0, 3, 1, 2],
inputs=['0'],
outputs=['1'],
)
graph_def = helper.make_graph(
[node],
'test-transpose',
[x],
[y]
)
model_def = helper.make_model(graph_def, producer_name='transpose-example')
onnx.save(model_def, 'transpose_test.onnx')
def unknown_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 4])
z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [2, 3, 4, 5])
a = helper.make_tensor_value_info('3', TensorProto.FLOAT, [2, 3, 4, 5])
node = onnx.helper.make_node(
'Unknown',
inputs=['0', '1'],
outputs=['2']
)
node2 = onnx.helper.make_node(
'Unknown',
inputs=['2'],
outputs=['3']
)
graph_def = helper.make_graph(
[node,node2],
'test-unknown',
[x,y],
[a]
)
model_def = helper.make_model(graph_def, producer_name='unknown-example')
onnx.save(model_def, 'unknown_test.onnx')
\ No newline at end of file
test/tf/gen_tf_pb.py 0 → 100644
View file @ 86663aa5
import numpy as np
import tensorflow as tf
def add_test(g1=tf.Graph()):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,2,2,3), name = '0')
g2_input = tf.placeholder(tf.float32, shape=(1,2,2,3), name = '1')
tf.add(g1_input, g2_input, name = 'add1')
tf.train.write_graph(g1, '.', 'add_test.pb', as_text=False)
def add_bcast_test(g1=tf.Graph()):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(2,3), name = '0')
g2_input = tf.placeholder(tf.float32, shape=(2,1), name = '1')
tf.math.add(g1_input, g2_input, name = 'add_bcast1')
tf.train.write_graph(g1, '.', 'add_bcast_test.pb', as_text=False)
def assert_less_equal_test(g1=tf.Graph()):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(2,3), name = '0')
g2_input = tf.placeholder(tf.float32, shape=(2,3), name = '1')
with tf.control_dependencies([tf.assert_less_equal(g1_input, g2_input)]):
tf.add(g1_input, g2_input, name = 'add1')
tf.train.write_graph(g1, '.', 'assert_less_equal_test.pb', as_text=False)
def batchmatmul_test(g1=tf.Graph()):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,2,8,4), name = '0')
g2_input = tf.placeholder(tf.float32, shape=(1,2,4,8), name = '1')
tf.matmul(g1_input, g2_input, transpose_a=True, transpose_b=True, name='batchmatmul1')
tf.train.write_graph(g1, '.', 'batchmatmul_test.pb', as_text=False)
def batchnorm_test(g1=tf.Graph()):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1, 16, 16, 32), name = '0')
g1_scale = tf.constant(1.0, dtype=tf.float32, shape=[32], name = '1')
g1_offset = tf.placeholder(tf.float32, shape=(32), name = '2')
g1_mean = tf.placeholder(tf.float32, shape=(32), name = '3')
g1_variance = tf.placeholder(tf.float32, shape=(32), name = '4')
tf.nn.fused_batch_norm(
g1_input, g1_scale, g1_offset, g1_mean, g1_variance,
epsilon=0.00001, is_training=False, name='batchnorm1')
tf.train.write_graph(g1, '.', 'batchnorm_test.pb', as_text=False)
def biasadd_test(g1=tf.Graph()):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,1,1,500), name = '0')
g2_input = tf.placeholder(tf.float32, shape=(500), name = '1')
tf.nn.bias_add(g1_input, g2_input, name = 'bias_add1')
tf.train.write_graph(g1, '.', 'biasadd_test.pb', as_text=False)
def cast_test(g1=tf.Graph()):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0')
tf.cast(g1_input, dtype=tf.int32, name='cast1')
tf.train.write_graph(g1, '.', 'cast_test.pb', as_text=False)
def concat_test(g1=tf.Graph()):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(4,7,3), name = '0')
g2_input = tf.placeholder(tf.float32, shape=(4,2,3), name = '1')
tf.concat([g1_input, g2_input], axis=1, name = 'concat1')
tf.train.write_graph(g1, '.', 'concat_test.pb', as_text=False)
def const_test(g1=tf.Graph()):
with g1.as_default():
tf.constant(1.0, dtype=tf.float32 ,name='constant1')
tf.train.write_graph(g1, '.', 'constant_test.pb', as_text=False)
def conv_test(g1=tf.Graph()):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,16,16,3), name = '0')
g1_weights = tf.constant(value=1.0, dtype=tf.float32, shape=(3,3,3,32), name = '1')
tf.nn.conv2d(g1_input, g1_weights, [1,1,1,1], "SAME", name = 'conv1')
tf.train.write_graph(g1, '.', 'conv_test.pb', as_text=False)
def depthwiseconv_test(g1=tf.Graph()):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,16,16,3), name = '0')
g1_weights = tf.constant(value=1.0, dtype=tf.float32, shape=(3,3,3,1), name = '1')
tf.nn.depthwise_conv2d_native(g1_input, g1_weights, [1,1,1,1], "SAME", name = 'depthwiseconv1')
tf.train.write_graph(g1, '.', 'depthwise_conv_test.pb', as_text=False)
def expanddims_test(g1=tf.Graph()):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(2,3,4), name = '0')
tf.expand_dims(g1_input, axis=-1, name='expanddims_neg')
tf.train.write_graph(g1, '.', 'expanddims_neg_test.pb', as_text=False)
def gather_test(g1=tf.Graph()):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(2,4), name = '0')
tf.gather(g1_input, [1,1], axis=1, name='gather1')
tf.train.write_graph(g1, '.', 'gather_test.pb', as_text=False)
def identity_test(g1=tf.Graph()):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0')
tf.identity(g1_input, 'identity')
tf.train.write_graph(g1, '.', 'identity_test.pb', as_text=False)
def matmul_test(g1=tf.Graph()):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(8,4), name = '0')
g2_input = tf.placeholder(tf.float32, shape=(4,8), name = '1')
tf.matmul(g1_input, g2_input, transpose_a=True, transpose_b=True, name='matmul1')
tf.train.write_graph(g1, '.', 'matmul_test.pb', as_text=False)
def mean_test(g1=tf.Graph()):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0')
tf.math.reduce_mean(
g1_input,
axis=(2,3),
keepdims=True,
name='mean1'
)
tf.math.reduce_mean(
g1_input,
axis=(2,3),
keepdims=False,
name='mean2'
)
tf.train.write_graph(g1, '.', 'mean_test.pb', as_text=False)
def mean_test_nhwc(g1=tf.Graph()):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,16,16,3), name = '0')
tf.math.reduce_mean(
g1_input,
axis=(1,2),
keepdims=True,
name='mean1'
)
tf.math.reduce_mean(
g1_input,
axis=(1,2),
keepdims=False,
name='mean2'
)
tf.train.write_graph(g1, '.', 'mean_test_nhwc.pb', as_text=False)
def mul_test(g1=tf.Graph()):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,1,1,16), name = '0')
g2_input = tf.placeholder(tf.float32, shape=(1,1,1,16), name = '1')
tf.multiply(g1_input, g2_input, name='mul1')
tf.train.write_graph(g1, '.', 'mul_test.pb', as_text=False)
def pack_test(g1=tf.Graph()):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(2), name = '0')
g2_input = tf.placeholder(tf.float32, shape=(2), name = '1')
g3_input = tf.placeholder(tf.float32, shape=(2), name = '2')
tf.stack([g1_input, g2_input, g3_input], axis=1, name = 'pack1')
tf.train.write_graph(g1, '.', 'pack_test.pb', as_text=False)
def pack_test_nhwc(g1=tf.Graph()):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,1,1,2), name = '0')
g2_input = tf.placeholder(tf.float32, shape=(1,1,1,2), name = '1')
g3_input = tf.placeholder(tf.float32, shape=(1,1,1,2), name = '2')
tf.stack([g1_input, g2_input, g3_input], axis=3, name = 'pack1')
tf.train.write_graph(g1, '.', 'pack_test_nhwc.pb', as_text=False)
def pooling_test(g1=tf.Graph()):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,16,16,3), name = '0')
tf.nn.avg_pool(
value=g1_input,
ksize=(1,2,2,1),
strides=(1,2,2,1),
padding='VALID',
data_format='NHWC',
name='avg_pooling'
)
tf.nn.max_pool(
value=g1_input,
ksize=(1,2,2,1),
strides=(1,2,2,1),
padding='VALID',
data_format='NHWC',
name='max_pooling'
)
tf.train.write_graph(g1, '.', 'pooling_test.pb', as_text=False)
def pow_test(g1=tf.Graph()):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,2,2,3), name = '0')
g2_input = tf.placeholder(tf.float32, shape=(1,2,2,3), name = '1')
tf.pow(g1_input, g2_input, name = 'pow1')
tf.train.write_graph(g1, '.', 'pow_test.pb', as_text=False)
def relu_test(g1=tf.Graph()):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0')
tf.nn.relu(g1_input, 'relu')
tf.train.write_graph(g1, '.', 'relu_test.pb', as_text=False)
def relu6_test(g1=tf.Graph()):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0')
tf.nn.relu6(g1_input, 'relu6')
tf.train.write_graph(g1, '.', 'relu6_test.pb', as_text=False)
def reshape_test(g1=tf.Graph()):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(16), name = '0')
tf.reshape(g1_input, (1,1,1,16), 'reshape')
tf.train.write_graph(g1, '.', 'reshape_test.pb', as_text=False)
def rsqrt_test(g1=tf.Graph()):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0')
tf.math.rsqrt(g1_input, 'rsqrt')
tf.train.write_graph(g1, '.', 'rsqrt_test.pb', as_text=False)
def slice_test(g1=tf.Graph()):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(5,10), name = '0')
tf.slice(g1_input, [1, 0], [2, -1], name = 'slice1')
tf.train.write_graph(g1, '.', 'slice_test.pb', as_text=False)
def softmax_test(g1=tf.Graph()):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,3), name = '0')
tf.nn.softmax(g1_input, name='softmax')
tf.train.write_graph(g1, '.', 'softmax_test.pb', as_text=False)
def sqdiff_test(g1=tf.Graph()):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,2,2,3), name = '0')
g2_input = tf.placeholder(tf.float32, shape=(1,2,2,3), name = '1')
tf.squared_difference(g1_input, g2_input, name = 'sqdiff')
tf.train.write_graph(g1, '.', 'sqdiff_test.pb', as_text=False)
def squeeze_test(g1=tf.Graph()):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,2,3,1), name = '0')
tf.squeeze(g1_input, name='squeeze')
tf.train.write_graph(g1, '.', 'squeeze_test.pb', as_text=False)
def stopgradient_test(g1=tf.Graph()):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0')
tf.stop_gradient(g1_input, 'stopgradient')
tf.train.write_graph(g1, '.', 'stopgradient_test.pb', as_text=False)
def stridedslice_test(g1=tf.Graph()):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,1,1,10), name = '0')
tf.strided_slice(g1_input, [0, 0, 0, 0], [1, 1, 1, 5], [1,1,1,1], shrink_axis_mask=2, name = 'stridedslice1')
tf.train.write_graph(g1, '.', 'stridedslice_test.pb', as_text=False)
def stridedslice_masks_test(g1=tf.Graph()):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,3,3,10), name = '0')
tf.strided_slice(g1_input, [0, 1, 1, 0], [0, 0, 0, 0], [1,1,1,1], begin_mask=9, end_mask=15, name = 'stridedslice1')
tf.train.write_graph(g1, '.', 'stridedslice_masks_test.pb', as_text=False)
def sub_test(g1=tf.Graph()):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,2,2,3), name = '0')
g2_input = tf.placeholder(tf.float32, shape=(1,2,2,3), name = '1')
tf.subtract(g1_input, g2_input, name = 'sub1')
tf.train.write_graph(g1, '.', 'sub_test.pb', as_text=False)
def tanh_test(g1=tf.Graph()):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0')
tf.tanh(g1_input, 'tanh')
tf.train.write_graph(g1, '.', 'tanh_test.pb', as_text=False)
def transpose_test(g1=tf.Graph()):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0')
tf.transpose(g1_input, perm=[0,2,3,1], name = 'transpose')
tf.train.write_graph(g1, '.', 'transpose_test.pb', as_text=False)
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