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Joshua Z. Zhang authoredJoshua Z. Zhang authored
test_forward.py 43.40 KiB
import numpy as np
import math
import topi
import topi.testing
import tvm
from tvm import relay
from tvm.contrib import graph_runtime
from nnvm.testing.config import ctx_list
import onnx
from onnx import helper, TensorProto
import unittest
def get_tvm_output(graph_def, input_data, target, ctx, output_shape=None, output_dtype='float32'):
""" Generic function to execute and get tvm output"""
target = 'llvm'
if isinstance(input_data, list):
input_names = {}
shape_dict = {}
dtype_dict = {}
for i, _ in enumerate(input_data):
input_names[i] = graph_def.graph.input[i].name
shape_dict[input_names[i]] = input_data[i].shape
dtype_dict[input_names[i]] = input_data[i].dtype
else:
input_names = graph_def.graph.input[0].name
shape_dict = {input_names: input_data.shape}
dtype_dict = {input_names: input_data.dtype}
sym, params = relay.frontend.from_onnx(graph_def, shape_dict)
with relay.build_config(opt_level=1):
graph, lib, params = relay.build(sym, target, params=params)
ctx = tvm.cpu(0)
from tvm.contrib import graph_runtime
m = graph_runtime.create(graph, lib, ctx)
# set inputs
if isinstance(input_data, list):
for i, e in enumerate(input_names):
m.set_input(input_names[i], tvm.nd.array(input_data[i].astype(input_data[i].dtype)))
else:
m.set_input(input_names, tvm.nd.array(input_data.astype(input_data.dtype)))
m.set_input(**params)
# execute
m.run()
# get outputs
if isinstance(output_shape, list) and isinstance(output_dtype, list):
tvm_output_list = []
for i, _ in enumerate(output_shape):
tvm_output = m.get_output(i)
tvm_output_list.append(tvm_output.asnumpy())
return tvm_output_list
else:
tvm_output = m.get_output(0)
return tvm_output.asnumpy()
def get_caffe2_output(model, x, dtype='float32'):
import caffe2.python.onnx.backend
prepared_backend = caffe2.python.onnx.backend.prepare(model)
W = {model.graph.input[0].name: x.astype(dtype)}
c2_out = prepared_backend.run(W)[0]
return c2_out
def verify_onnx_forward_impl(graph_file, data_shape, out_shape):
dtype = 'float32'
x = np.random.uniform(size=data_shape)
model = onnx.load_model(graph_file)
c2_out = get_caffe2_output(model, x, dtype)
for target, ctx in ctx_list():
tvm_out = get_tvm_output(model, x, target, ctx, out_shape, dtype)
tvm.testing.assert_allclose(c2_out, tvm_out, rtol=1e-5, atol=1e-5)
def verify_super_resolution_example():
verify_onnx_forward_impl(super_resolution, (1, 1, 224, 224), (1, 1, 672, 672))
def verify_squeezenet1_1():
verify_onnx_forward_impl(squeezenet1_1, (1, 3, 224, 224), (1, 1000))
def verify_lenet():
verify_onnx_forward_impl(lenet, (1, 1, 28, 28), (1, 10))
def verify_resnet18():
verify_onnx_forward_impl(resnet18_1_0, (1, 3, 224, 224), (1, 1000))
def test_reshape():
in_shape = (4, 3, 3, 4)
ref_shape = (6, 2, 4, 3)
ref_array = np.array(ref_shape)
ref_node = onnx.helper.make_node('Constant',
inputs=[],
outputs=['ref_in'],
value=onnx.helper.make_tensor(name = 'const_tensor',
data_type = onnx.TensorProto.INT32,
dims = ref_array.shape,
vals = ref_array.flatten().astype(int)))
reshape_node = helper.make_node("Reshape", ["in", "ref_in"], ["out"])
graph = helper.make_graph([ref_node, reshape_node],
"reshape_test",
inputs = [helper.make_tensor_value_info("in",
TensorProto.FLOAT, list(in_shape))],
outputs = [helper.make_tensor_value_info("out",
TensorProto.FLOAT, list(ref_shape))])
model = helper.make_model(graph, producer_name='reshape_test')
for target, ctx in ctx_list():
x = np.random.uniform(size=in_shape).astype('int32')
tvm_out = get_tvm_output(model, x, target, ctx, ref_shape, 'float32')
tvm.testing.assert_allclose(ref_shape, tvm_out.shape)
def test_reshape_like():
in_shape = (4, 3, 3, 4)
ref_shape = (3, 4, 4, 3)
ref_array = np.random.uniform(size=ref_shape).astype('float32')
ref_node = onnx.helper.make_node('Constant',
inputs=[],
outputs=['ref_in'],
value=onnx.helper.make_tensor(name = 'const_tensor',
data_type = onnx.TensorProto.FLOAT,
dims = ref_array.shape,
vals = ref_array.flatten().astype(float)))
copy_node = helper.make_node("Identity", ["ref_in"], ["copy_in"])
reshape_node = helper.make_node("Reshape", ["in", "copy_in"], ["out"])
graph = helper.make_graph([ref_node, copy_node, reshape_node],
"reshape_like_test",
inputs = [helper.make_tensor_value_info("in",
TensorProto.FLOAT, list(in_shape))],
outputs = [helper.make_tensor_value_info("out",
TensorProto.FLOAT, list(ref_shape))])
model = helper.make_model(graph, producer_name='reshape_like_test')
for target, ctx in ctx_list():
x = np.random.uniform(size=in_shape).astype('float32')
tvm_out = get_tvm_output(model, x, target, ctx, ref_shape, 'float32')
tvm.testing.assert_allclose(ref_shape, tvm_out.shape)
def _test_power_iteration(x_shape, y_shape):
if isinstance(y_shape, int):
y_shape = [y_shape]
x = np.random.uniform(size=x_shape).astype(np.float32)
y = np.random.uniform(size=y_shape).astype(np.float32)
np_res = np.power(x, y).astype(np.float32)
res = helper.make_node("Pow", ['x', 'y'], ['out'])
graph = helper.make_graph([res],
'power_test',
inputs = [helper.make_tensor_value_info("x",
TensorProto.FLOAT, list(x_shape)),
helper.make_tensor_value_info("y",
TensorProto.FLOAT, list(y_shape))],
outputs = [helper.make_tensor_value_info("out",
TensorProto.FLOAT, list(np_res.shape))])
model = helper.make_model(graph, producer_name='power_test')
for target, ctx in ctx_list():
tvm_out = get_tvm_output(model, [x, y], target, ctx, np_res.shape)
tvm.testing.assert_allclose(np_res, tvm_out, rtol=1e-5, atol=1e-5)
def test_power():
_test_power_iteration((1, 3), (1))
_test_power_iteration((2, 3), (2, 3))
_test_power_iteration((2, 3), (1, 3))
def test_squeeze():
in_shape = (1, 3, 1, 3, 1, 1)
out_shape = (3, 3)
y = helper.make_node("Squeeze", ['in'], ['out'], axes=[0, 2, 4, 5])
graph = helper.make_graph([y],
'squeeze_test',
inputs = [helper.make_tensor_value_info("in",
TensorProto.FLOAT, list(in_shape))],
outputs = [helper.make_tensor_value_info("out",
TensorProto.FLOAT, list(out_shape))])
model = helper.make_model(graph, producer_name='squeeze_test')
for target, ctx in ctx_list():
x = np.random.uniform(size=in_shape).astype('float32')
tvm_out = get_tvm_output(model, x, target, ctx, out_shape, 'float32')
tvm.testing.assert_allclose(out_shape, tvm_out.shape)
def test_unsqueeze():
in_shape = (3, 3)
axis = (0, 3, 4)
out_shape = (1, 3, 3, 1, 1)
y = helper.make_node("Unsqueeze", ['in'], ['out'], axes=list(axis))
graph = helper.make_graph([y],
'squeeze_test',
inputs = [helper.make_tensor_value_info("in",
TensorProto.FLOAT, list(in_shape))],
outputs = [helper.make_tensor_value_info("out",
TensorProto.FLOAT, list(out_shape))])
model = helper.make_model(graph, producer_name='squeeze_test')
for target, ctx in ctx_list():
x = np.random.uniform(size=in_shape).astype('float32')
tvm_out = get_tvm_output(model, x, target, ctx, out_shape, 'float32')
tvm.testing.assert_allclose(out_shape, tvm_out.shape)
def verify_gather(in_shape, indices, axis, dtype):
x = np.random.uniform(size=in_shape).astype(dtype)
indices = np.array(indices, dtype="int32")
out_np = np.take(x, indices, axis=axis)
y = helper.make_node("Gather", ['in', 'indices'], ['out'], axis=axis)
graph = helper.make_graph([y],
'gather_test',
inputs = [helper.make_tensor_value_info("in",
TensorProto.FLOAT, list(in_shape)),
helper.make_tensor_value_info("indices",
TensorProto.INT32, list(indices.shape))],
outputs = [helper.make_tensor_value_info("out",
TensorProto.FLOAT, list(out_np.shape))])
model = helper.make_model(graph, producer_name='gather_test')
for target, ctx in ctx_list():
tvm_out = get_tvm_output(model, [x, indices], target, ctx, out_np.shape)
tvm.testing.assert_allclose(out_np, tvm_out)
def test_gather():
verify_gather((4,), [1], 0, 'int32')
verify_gather((1,4), [0], 0, 'int32')
verify_gather((4,), [[[1,0],[0,1]]], 0, 'float32')
verify_gather((2,2), [[[1,0],[0,1]]], 1, 'int32')
verify_gather((3,3,3), [[[1,0]]], -1, 'int32')
verify_gather((4,3,5,6), [[2,1,0,0]], 0, 'float32')
def _test_slice_iteration(indata, outdata, starts, ends, axes=None):
if axes:
y = helper.make_node("Slice", ['in'], ['out'], axes=axes, starts=starts, ends=ends)
else:
y = helper.make_node("Slice", ['in'], ['out'], starts=starts, ends=ends)
graph = helper.make_graph([y],
'slice_test',
inputs = [helper.make_tensor_value_info("in",
TensorProto.FLOAT, list(indata.shape))],
outputs = [helper.make_tensor_value_info("out",
TensorProto.FLOAT, list(outdata.shape))])
model = helper.make_model(graph, producer_name='slice_test')
for target, ctx in ctx_list():
tvm_out = get_tvm_output(model, indata, target, ctx, outdata.shape, 'float32')
tvm.testing.assert_allclose(outdata, tvm_out)
def test_slice():
x = np.random.randn(20, 10, 5).astype(np.float32)
_test_slice_iteration(x, x[0:3, 0:10], (0, 0), (3, 10), (0, 1))
_test_slice_iteration(x, x[:, :, 3:4], (0, 0, 3), (20, 10, 4))
_test_slice_iteration(x, x[:, 1:1000], (1), (1000), (1))
_test_slice_iteration(x, x[:, 0:-1], (0), (-1), (1))
def _test_onnx_op_elementwise(inshape, outfunc, npargs, dtype, opname, kwargs):
indata = np.random.uniform(-1, 1, size=inshape).astype(dtype)
outdata = outfunc(indata, **npargs)
y = helper.make_node(opname, ['in'], ['out'], **kwargs)
graph = helper.make_graph([y],
opname+'_test',
inputs = [helper.make_tensor_value_info("in",
TensorProto.FLOAT, list(indata.shape))],
outputs = [helper.make_tensor_value_info("out",
TensorProto.FLOAT, list(outdata.shape))])
model = helper.make_model(graph, producer_name=opname+'_test')
for target, ctx in ctx_list():
tvm_out = get_tvm_output(model, indata, target, ctx, outdata.shape, dtype)
tvm.testing.assert_allclose(outdata, tvm_out)
def test_floor():
_test_onnx_op_elementwise((2, 4, 5, 6), np.floor, {}, 'float32', 'Floor', {})
def test_ceil():
_test_onnx_op_elementwise((2, 4, 5, 6), np.ceil, {}, 'float32', 'Ceil', {})
def test_clip():
_test_onnx_op_elementwise((2, 4, 5, 6),
np.clip,
{'a_min': -1.0, 'a_max': 1.0},
'float32',
'Clip',
{'min': -1.0, 'max': 1.0})
def test_matmul():
a_shape = (4, 3)
b_shape = (3, 4)
a_array = np.random.uniform(size=a_shape).astype('float32')
b_array = np.random.uniform(size=b_shape).astype('float32')
out_np = np.matmul(a_array, b_array)
mul_node = helper.make_node("MatMul", ["a", "b"], ["out"])
graph = helper.make_graph([mul_node],
"matmul_test",
inputs = [helper.make_tensor_value_info("a",
TensorProto.FLOAT, list(a_shape)),
helper.make_tensor_value_info("b",
TensorProto.FLOAT, list(b_shape))],
outputs = [helper.make_tensor_value_info("out",
TensorProto.FLOAT, list(out_np.shape))])
model = helper.make_model(graph, producer_name='matmul_test')
for target, ctx in ctx_list():
tvm_out = get_tvm_output(model, [a_array, b_array], target, ctx, out_np.shape)
tvm.testing.assert_allclose(out_np, tvm_out, rtol=1e-5, atol=1e-5)
def verify_lrn(shape, nsize, dtype, alpha=None, beta=None, bias=None):
in_array = np.random.uniform(size=shape).astype(dtype)
if alpha == None and beta == None and bias==None:
alpha = 0.0001
beta = 0.75
bias = 1.0
node = onnx.helper.make_node('LRN', inputs=['in'], outputs=['out'], size=nsize)
else:
node = onnx.helper.make_node('LRN', inputs=['in'], outputs=['out'], alpha=alpha,
beta=beta, bias=bias, size=nsize)
graph = helper.make_graph([node],
"lrn_test",
inputs = [helper.make_tensor_value_info("in", TensorProto.FLOAT, list(shape))],
outputs = [helper.make_tensor_value_info("out", TensorProto.FLOAT, list(shape))])
model = helper.make_model(graph, producer_name='lrn_test')
def _get_python_lrn():
square_sum = np.zeros(shape).astype(dtype)
for n, c, h, w in np.ndindex(in_array.shape):
square_sum[n, c, h, w] = sum(in_array[n,
max(0, c - int(math.floor((nsize - 1) / 2))): \
min(5, c + int(math.ceil((nsize - 1) / 2)) + 1),
h,
w] ** 2)
py_out = in_array / ((bias + (alpha / nsize) * square_sum) ** beta)
return py_out
for target, ctx in ctx_list():
input_name = model.graph.input[0].name
py_out = _get_python_lrn()
tvm_out = get_tvm_output(model, in_array, target, ctx, py_out.shape, 'float32')
tvm.testing.assert_allclose(py_out, tvm_out, rtol=1e-5, atol=1e-5)
def test_lrn():
verify_lrn((5, 5, 5, 5), 3, 'float32')
verify_lrn((5, 5, 5, 5), 3, 'float32', alpha=0.0002, beta=0.5, bias=2.0)
def _test_upsample_nearest():
scale = 2
in_shape = (1, 1, 3, 3)
out_shape = (1, 1, 3*scale, 3*scale)
y = helper.make_node("Upsample", ['in'], ['out'], mode='nearest', scales=[1.0, 1.0, 2.0, 2.0])
in_array = np.random.uniform(size=in_shape).astype(np.float32)
out_array = topi.testing.upsampling_python(in_array, scale, "NCHW")
graph = helper.make_graph([y],
'upsample_nearest_test',
inputs = [helper.make_tensor_value_info("in", TensorProto.FLOAT, list(in_shape))],
outputs = [helper.make_tensor_value_info("out", TensorProto.FLOAT, list(out_shape))])
model = helper.make_model(graph, producer_name='upsample_nearest_test')
for target, ctx in ctx_list():
tvm_out = get_tvm_output(model, in_array, target, ctx, out_shape, 'float32')
tvm.testing.assert_allclose(out_array, tvm_out)
def _test_upsample_bilinear():
scale = 2
in_shape = (1, 1, 3, 3)
out_shape = (1, 1, 3*scale, 3*scale)
y = helper.make_node("Upsample", ['in'], ['out'], mode='linear', scales=[1.0, 1.0, 2.0, 2.0])
in_array = np.random.uniform(size=in_shape).astype(np.float32)
out_array = topi.testing.bilinear_resize_python(in_array, (3*scale, 3*scale), "NCHW")
graph = helper.make_graph([y],
'upsample_bilinear_test',
inputs = [helper.make_tensor_value_info("in", TensorProto.FLOAT, list(in_shape))],
outputs = [helper.make_tensor_value_info("out", TensorProto.FLOAT, list(out_shape))])
model = helper.make_model(graph, producer_name='upsample_bilinear_test')
for target, ctx in ctx_list():
tvm_out = get_tvm_output(model, in_array, target, ctx, out_shape, 'float32')
tvm.testing.assert_allclose(out_array, tvm_out, rtol=1e-5, atol=1e-5)
def test_upsample():
_test_upsample_nearest()
_test_upsample_bilinear()
def _test_softmax(inshape, axis):
opname = 'Softmax'
indata = np.random.uniform(size=inshape).astype(np.float32)
outshape = inshape
outdata = topi.testing.softmax_python(indata)
if isinstance(axis, int):
y = helper.make_node(opname, ['in'], ['out'], axis = axis)
elif axis is None:
y = helper.make_node(opname, ['in'], ['out'])
graph = helper.make_graph([y],
opname+'_test',
inputs = [helper.make_tensor_value_info("in",
TensorProto.FLOAT, list(indata.shape))],
outputs = [helper.make_tensor_value_info("out",
TensorProto.FLOAT, list(outdata.shape))])
model = helper.make_model(graph, producer_name=opname+'_test')
for target, ctx in ctx_list():
tvm_out = get_tvm_output(model, indata, target, ctx, outshape, 'float32')
tvm.testing.assert_allclose(outdata, tvm_out, rtol=1e-5, atol=1e-5)
def test_softmax():
_test_softmax((1, 10), None)
_test_softmax((1, 10), 1)
def verify_min(input_dim):
dtype = 'float32'
a_np1 = np.random.uniform(size=input_dim).astype(dtype)
a_np2 = np.random.uniform(size=input_dim).astype(dtype)
a_np3 = np.random.uniform(size=input_dim).astype(dtype)
b_np = np.min((a_np1, a_np2, a_np3), axis=0)
min_node = helper.make_node("Min", ["a_np1", "a_np2", "a_np3"], ["out"])
graph = helper.make_graph([min_node],
"Min_test",
inputs = [helper.make_tensor_value_info("a_np1",
TensorProto.FLOAT, list(input_dim)),
helper.make_tensor_value_info("a_np2",
TensorProto.FLOAT, list(input_dim)),
helper.make_tensor_value_info("a_np3",
TensorProto.FLOAT, list(input_dim))],
outputs = [helper.make_tensor_value_info("out",
TensorProto.FLOAT, list(b_np.shape))])
model = helper.make_model(graph, producer_name='Min_test')
for target, ctx in ctx_list():
tvm_out = get_tvm_output(model, [a_np1, a_np2, a_np3], target, ctx, b_np.shape)
tvm.testing.assert_allclose(b_np, tvm_out, rtol=1e-5, atol=1e-5)
def test_forward_min():
verify_min((1, 3, 20, 20))
verify_min((20, 20))
def verify_max(input_dim):
dtype = 'float32'
a_np1 = np.random.uniform(size=input_dim).astype(dtype)
a_np2 = np.random.uniform(size=input_dim).astype(dtype)
a_np3 = np.random.uniform(size=input_dim).astype(dtype)
b_np = np.max((a_np1, a_np2, a_np3), axis=0)
max_node = helper.make_node("Max", ["a_np1", "a_np2", "a_np3"], ["out"])
graph = helper.make_graph([max_node],
"Max_test",
inputs = [helper.make_tensor_value_info("a_np1",
TensorProto.FLOAT, list(input_dim)),
helper.make_tensor_value_info("a_np2",
TensorProto.FLOAT, list(input_dim)),
helper.make_tensor_value_info("a_np3",
TensorProto.FLOAT, list(input_dim))],
outputs = [helper.make_tensor_value_info("out",
TensorProto.FLOAT, list(b_np.shape))])
model = helper.make_model(graph, producer_name='Max_test')
for target, ctx in ctx_list():
tvm_out = get_tvm_output(model, [a_np1, a_np2, a_np3], target, ctx, b_np.shape)
tvm.testing.assert_allclose(b_np, tvm_out, rtol=1e-5, atol=1e-5)
def test_forward_max():
verify_max((1, 3, 20, 20))
verify_max((20, 20))
def verify_mean(input_dim):
dtype = 'float32'
a_np1 = np.random.uniform(size=input_dim).astype(dtype)
a_np2 = np.random.uniform(size=input_dim).astype(dtype)
a_np3 = np.random.uniform(size=input_dim).astype(dtype)
b_np = np.mean((a_np1, a_np2, a_np3), axis=0)
mean_node = helper.make_node("Mean", ["a_np1", "a_np2", "a_np3"], ["out"])
graph = helper.make_graph([mean_node],
"Mean_test",
inputs = [helper.make_tensor_value_info("a_np1",
TensorProto.FLOAT, list(input_dim)),
helper.make_tensor_value_info("a_np2",
TensorProto.FLOAT, list(input_dim)),
helper.make_tensor_value_info("a_np3",
TensorProto.FLOAT, list(input_dim))],
outputs = [helper.make_tensor_value_info("out",
TensorProto.FLOAT, list(b_np.shape))])
model = helper.make_model(graph, producer_name='Mean_test')
for target, ctx in ctx_list():
tvm_out = get_tvm_output(model, [a_np1, a_np2, a_np3], target, ctx, b_np.shape)
tvm.testing.assert_allclose(b_np, tvm_out, rtol=1e-5, atol=1e-5)
def test_forward_mean():
verify_mean((1, 3, 20, 20))
verify_mean((20, 20))
def verify_hardsigmoid(input_dim, alpha, beta):
dtype = 'float32'
a_np1 = np.random.uniform(size=input_dim).astype(dtype)
b_np = np.clip(a_np1 * alpha + beta, 0, 1)
hardsigmoid_node = helper.make_node("HardSigmoid", ["a_np1"], ["out"], alpha=alpha, beta=beta)
graph = helper.make_graph([hardsigmoid_node],
"HardSigmoid_test",
inputs = [helper.make_tensor_value_info("a_np1",
TensorProto.FLOAT, list(input_dim))],
outputs = [helper.make_tensor_value_info("out",
TensorProto.FLOAT, list(b_np.shape))])
model = helper.make_model(graph, producer_name='HardSigmoid_test')
for target, ctx in ctx_list():
tvm_out = get_tvm_output(model, [a_np1], target, ctx, b_np.shape)
tvm.testing.assert_allclose(b_np, tvm_out, rtol=1e-5, atol=1e-5)
def test_forward_hardsigmoid():
verify_hardsigmoid((1, 3, 20, 20), 0.5, 0.6)
verify_hardsigmoid((20, 20), 0.3, 0.4)
def verify_argmin(input_dim, axis=None, keepdims=None):
def _argmin_numpy(data, axis=0, keepdims=True):
result = np.argmin(data, axis=axis)
if (keepdims == 1):
result = np.expand_dims(result, axis)
return result.astype(data.dtype)
a_np1 = np.random.uniform(-10, 10, input_dim).astype(np.int32)
if keepdims is None and axis is None:
b_np = _argmin_numpy(a_np1)
node = onnx.helper.make_node('ArgMin',
inputs=['a_np1'],
outputs=['out'])
elif axis is None:
b_np = _argmin_numpy(a_np1, keepdims=keepdims)
node = onnx.helper.make_node('ArgMin',
inputs=['a_np1'],
outputs=['out'],
keepdims=keepdims)
elif keepdims is None:
b_np = _argmin_numpy(a_np1, axis=axis)
node = onnx.helper.make_node('ArgMin',
inputs=['a_np1'],
outputs=['out'],
axis=axis)
else:
b_np = _argmin_numpy(a_np1, axis=axis, keepdims=keepdims)
node = onnx.helper.make_node('ArgMin',
inputs=['a_np1'],
outputs=['out'],
axis=axis,
keepdims=keepdims)
graph = helper.make_graph([node],
"argmin_test",
inputs = [helper.make_tensor_value_info("a_np1",
TensorProto.INT32, list(a_np1.shape))],
outputs = [helper.make_tensor_value_info("out",
TensorProto.INT32, list(b_np.shape))])
model = helper.make_model(graph, producer_name='argmin_test')
for target, ctx in ctx_list():
tvm_out = get_tvm_output(model, [a_np1], target, ctx, b_np.shape, b_np.dtype)
tvm.testing.assert_allclose(b_np, tvm_out, rtol=1e-5, atol=1e-5)
def verify_argmax(input_dim, axis=None, keepdims=None):
def _argmax_numpy(data, axis=0, keepdims=True):
result = np.argmax(data, axis=axis)
if (keepdims == 1):
result = np.expand_dims(result, axis)
return result.astype(data.dtype)
a_np1 = np.random.uniform(-10, 10, input_dim).astype(np.int32)
if keepdims is None and axis is None:
b_np = _argmax_numpy(a_np1)
node = onnx.helper.make_node('ArgMax',
inputs=['a_np1'],
outputs=['out'])
elif axis is None:
b_np = _argmax_numpy(a_np1, keepdims=keepdims)
node = onnx.helper.make_node('ArgMax',
inputs=['a_np1'],
outputs=['out'],
keepdims=keepdims)
elif keepdims is None:
b_np = _argmax_numpy(a_np1, axis=axis)
node = onnx.helper.make_node('ArgMax',
inputs=['a_np1'],
outputs=['out'],
axis=axis)
else:
b_np = _argmax_numpy(a_np1, axis=axis, keepdims=keepdims)
node = onnx.helper.make_node('ArgMax',
inputs=['a_np1'],
outputs=['out'],
axis=axis,
keepdims=keepdims)
graph = helper.make_graph([node],
"argmax_test",
inputs = [helper.make_tensor_value_info("a_np1",
TensorProto.INT32, list(a_np1.shape))],
outputs = [helper.make_tensor_value_info("out",
TensorProto.INT32, list(b_np.shape))])
model = helper.make_model(graph, producer_name='argmax_test')
for target, ctx in ctx_list():
tvm_out = get_tvm_output(model, [a_np1], target, ctx, b_np.shape, b_np.dtype)
tvm.testing.assert_allclose(b_np, tvm_out, rtol=1e-5, atol=1e-5)
def test_forward_arg_min_max():
'''Verify argmin and argmax'''
verify_argmin([3,4,4])
verify_argmax([3,4,4])
verify_argmin([3,4,4], axis=1)
verify_argmax([3,4,4], axis=0)
verify_argmin([3,4,4], keepdims=0)
verify_argmax([3,4,4], keepdims=1)
for axis in [None, 0,1,2]:
for keepdims in [None, True,False]:
verify_argmin([3,4,4], axis, keepdims)
verify_argmax([3,4,4], axis, keepdims)
def verify_constantfill(is_shape, input_dim, out_dim, value, dtype, **kwargs):
input_a = np.random.uniform(size=input_dim).astype(dtype)
out = np.empty(shape=out_dim, dtype=dtype)
out.fill(value)
if is_shape == True:
fill_node = helper.make_node("ConstantFill", [], ["out"], shape=input_dim, value=value, **kwargs)
else:
fill_node = helper.make_node("ConstantFill", ["input_a"], ["out"], value=value, dtype=dtype, **kwargs)
graph = helper.make_graph([fill_node],
"fill_test",
inputs = [helper.make_tensor_value_info("input_a",
TensorProto.FLOAT, list(input_dim))],
outputs = [helper.make_tensor_value_info("out",
TensorProto.FLOAT, list(out.shape))])
model = helper.make_model(graph, producer_name='fill_test')
for target, ctx in ctx_list():
if is_shape == True:
tvm_out = get_tvm_output(model, [], target, ctx, out.shape)
else:
tvm_out = get_tvm_output(model, [input_a], target, ctx, out.shape)
tvm.testing.assert_allclose(out, tvm_out, rtol=1e-5, atol=1e-5)
def test_constantfill():
verify_constantfill(True, (2, 3, 4, 5), (2, 3, 4, 5), 10, 'float32')
verify_constantfill(False, (2, 3, 4, 5), (2, 3, 4, 5), 10, 'float32')
verify_constantfill(True, (2, 3, 4, 5), (2, 3, 4, 5, 4, 5, 6), 10, 'float32', extra_shape=(4, 5, 6))
def verify_pad(indata, pads, value=0.0):
indata = np.array(indata).astype(np.float32)
# numpy expect result
len_dim = len(pads) // 2
np_pads = [(pads[i], pads[i+len_dim]) for i in range(len_dim)]
outdata = np.pad(indata, pad_width=np_pads, mode='constant', constant_values=value)
# onnx graph
node = helper.make_node(
'Pad',
inputs=['input'],
outputs=['output'],
mode='constant',
pads=pads,
value=value
)
graph = helper.make_graph([node],
'pad_test',
inputs = [helper.make_tensor_value_info("input",
TensorProto.FLOAT, list(indata.shape))],
outputs = [helper.make_tensor_value_info("output",
TensorProto.FLOAT, list(outdata.shape))])
model = helper.make_model(graph, producer_name='pad_test')
# tvm result
for target, ctx in ctx_list():
tvm_out = get_tvm_output(model, indata, target, ctx, outdata.shape, 'float32')
tvm.testing.assert_allclose(outdata, tvm_out, rtol=1e-5, atol=1e-5)
def test_pad():
verify_pad(np.random.randn(2, 2).astype(np.float32), [0, 1, 0, 0], 0.0)
verify_pad(np.random.randn(2, 3).astype(np.float32), [1, 0, 0, 1], 0.0)
verify_pad(np.random.randn(3, 2).astype(np.float32), [0, 0, 1, 0], 5.0)
def verify_reduce_x(name, indata, axis, keepdims):
indata = np.array(indata).astype(np.float32)
# numpy expect result
if name == 'ReduceMax':
outdata = np.maximum.reduce(indata, axis=axis, keepdims=keepdims == 1)
elif name == 'ReduceMin':
outdata = np.minimum.reduce(indata, axis=axis, keepdims=keepdims == 1)
elif name == 'ReduceSum':
outdata = np.sum(indata, axis=axis, keepdims=keepdims == 1)
elif name == 'ReduceMean':
outdata = np.mean(indata, axis=axis, keepdims=keepdims == 1)
else:
raise Exception('unsupport op: {}'.format(name))
if len(np.asarray(outdata).shape) == 0:
outdata = np.asarray([outdata])
# onnx graph
if axis is None:
node = helper.make_node(name, inputs=['input'], outputs=['output'],
keepdims=keepdims)
else:
node = helper.make_node(name, inputs=['input'], outputs=['output'],
axes=axis, keepdims=keepdims)
graph = helper.make_graph([node],
'{}_test'.format(name),
inputs = [helper.make_tensor_value_info("input",
TensorProto.FLOAT, list(indata.shape))],
outputs = [helper.make_tensor_value_info("output",
TensorProto.FLOAT, list(outdata.shape))])
model = helper.make_model(graph, producer_name='{}_test'.format(name))
# tvm result
for target, ctx in ctx_list():
tvm_out = get_tvm_output(model, indata, target, ctx, outdata.shape, 'float32')
tvm.testing.assert_allclose(outdata, tvm_out, rtol=1e-5, atol=1e-5)
def test_reduce_max():
verify_reduce_x("ReduceMax",
np.random.randn(3, 2, 2).astype(np.float32),
axis=None, keepdims=1)
verify_reduce_x("ReduceMax",
np.random.randn(3, 2, 3).astype(np.float32),
axis=None, keepdims=0)
verify_reduce_x("ReduceMax",
np.random.randn(3, 3, 3).astype(np.float32),
axis=(1,), keepdims=1)
def test_reduce_min():
verify_reduce_x("ReduceMin",
np.random.randn(3, 2, 2).astype(np.float32),
axis=None, keepdims=1)
verify_reduce_x("ReduceMin",
np.random.randn(3, 2, 3).astype(np.float32),
axis=None, keepdims=0)
verify_reduce_x("ReduceMin",
np.random.randn(3, 3, 3).astype(np.float32),
axis=(1,), keepdims=1)
def test_reduce_sum():
verify_reduce_x("ReduceSum",
np.random.randn(3, 2, 2).astype(np.float32),
axis=None, keepdims=1)
verify_reduce_x("ReduceSum",
np.random.randn(3, 2, 3).astype(np.float32),
axis=None, keepdims=0)
verify_reduce_x("ReduceSum",
np.random.randn(3, 3, 3).astype(np.float32),
axis=(1,), keepdims=1)
def test_reduce_mean():
verify_reduce_x("ReduceMean",
np.random.randn(3, 2, 2).astype(np.float32),
axis=None, keepdims=1)
verify_reduce_x("ReduceMean",
np.random.randn(3, 2, 3).astype(np.float32),
axis=None, keepdims=0)
verify_reduce_x("ReduceMean",
np.random.randn(3, 3, 3).astype(np.float32),
axis=(1,), keepdims=1)
def verify_split(indata, outdatas, split, axis=0):
indata = np.array(indata).astype(np.float32)
outdatas = [np.array(o).astype(np.float32) for o in outdatas]
node = helper.make_node(
'Split',
inputs=['input'],
outputs=['output_{}'.format(i) for i in range(len(split))],
axis=axis,
split=split
)
graph = helper.make_graph([node],
'split_test',
inputs = [helper.make_tensor_value_info("input",
TensorProto.FLOAT, list(indata.shape))],
outputs = [helper.make_tensor_value_info("output_{}".format(i),
TensorProto.FLOAT, list(outdatas[i].shape))
for i in range(len(split))
])
model = helper.make_model(graph, producer_name='split_test')
for target, ctx in ctx_list():
output_shape = [o.shape for o in outdatas]
output_type = ['float32', 'float32', 'float32']
tvm_out = get_tvm_output(model, indata, target, ctx, output_shape, output_type)
for o, t in zip(outdatas, tvm_out):
tvm.testing.assert_allclose(o, t)
def test_split():
# 1D
verify_split([1., 2., 3., 4., 5., 6.], [[1., 2.], [3., 4.], [5., 6.]], [2, 2, 2], 0)
verify_split([1., 2., 3., 4., 5., 6.], [[1., 2.], [3.], [4., 5., 6.]], [2, 1, 3], 0)
# 2D
verify_split([[1., 2., 3., 4.], [7., 8., 9., 10.]],
[[[1., 2.], [7., 8.]], [[3., 4.], [9., 10.]]], [2, 2], 1)
def test_binary_ops():
in_shape = (1, 2, 3, 3)
dtype = "float32"
out_shape = in_shape
def verify_binary_ops(op, x, y, out_np, broadcast=None):
if broadcast is None:
z = helper.make_node(op, ['in1', 'in2'], ['out'])
else:
z = helper.make_node(op, ['in1', 'in2'], ['out'], broadcast=1)
graph = helper.make_graph([z],
'_test',
inputs = [helper.make_tensor_value_info("in1",
TensorProto.FLOAT, list(in_shape)),
helper.make_tensor_value_info("in2",
TensorProto.FLOAT, list(in_shape))],
outputs = [helper.make_tensor_value_info("out",
TensorProto.FLOAT, list(out_shape))])
model = helper.make_model(graph, producer_name='_test')
for target, ctx in ctx_list():
tvm_out = get_tvm_output(model, [x, y], target, ctx)
tvm.testing.assert_allclose(out_np, tvm_out, rtol=1e-5, atol=1e-5)
x = np.random.uniform(size=in_shape).astype(dtype)
y = np.random.uniform(size=in_shape).astype(dtype)
z = np.random.uniform(size=(3,)).astype(dtype)
verify_binary_ops("Add",x, y, x + y, broadcast=None)
verify_binary_ops("Add", x, z, x + z, broadcast=True)
verify_binary_ops("Sub", x, y, x - y, broadcast=None)
verify_binary_ops("Sub", x, z, x - z, broadcast=True)
verify_binary_ops("Mul",x, y, x * y, broadcast=None)
verify_binary_ops("Mul", x, z, x * z, broadcast=True)
verify_binary_ops("Div", x, y, x / y, broadcast=None)
verify_binary_ops("Div", x, z, x / z, broadcast=True)
verify_binary_ops("Sum", x, y, x + y, broadcast=None)
def test_single_ops():
in_shape = (1, 2, 3, 3)
dtype = "float32"
out_shape = in_shape
def verify_single_ops(op, x, out_np, rtol=1e-5, atol=1e-5):
z = helper.make_node(op, ['in1'], ['out'])
graph = helper.make_graph([z],
'_test',
inputs = [helper.make_tensor_value_info("in1",
TensorProto.FLOAT, list(in_shape)),],
outputs = [helper.make_tensor_value_info("out",
TensorProto.FLOAT, list(out_shape))])
model = helper.make_model(graph, producer_name='_test')
for target, ctx in ctx_list():
tvm_out = get_tvm_output(model, [x], target, ctx)
tvm.testing.assert_allclose(out_np, tvm_out, rtol=rtol, atol=atol)
x = np.random.uniform(size=in_shape).astype(dtype)
verify_single_ops("Neg",x, -x)
verify_single_ops("Abs",x, np.abs(x))
verify_single_ops("Reciprocal",x, 1/x)
verify_single_ops("Sqrt",x, np.sqrt(x))
verify_single_ops("Relu",x, np.maximum(x, 0))
verify_single_ops("Exp",x, np.exp(x))
verify_single_ops("Log",x, np.log(x))
verify_single_ops("Log",x, np.log(x))
verify_single_ops("Tanh",x, np.tanh(x))
verify_single_ops("Sigmoid",x, 1 / (1 + np.exp(-x)))
verify_single_ops("Softsign",x, x / (1 + np.abs(x)))
verify_single_ops("SoftPlus",x, np.log(1 + np.exp(x)))
def test_leaky_relu():
def leaky_relu_x(x, alpha):
return np.where(x >= 0, x, x * alpha)
_test_onnx_op_elementwise((2, 4, 5, 6),
leaky_relu_x,
{'alpha': 0.25},
'float32',
'LeakyRelu',
{'alpha': 0.25})
def test_elu():
def elu_x(x, alpha):
return np.where(x > 0, x, alpha * (np.exp(x) - 1.0))
_test_onnx_op_elementwise((2, 4, 5, 6),
elu_x,
{'alpha': 0.25},
'float32',
'Elu',
{'alpha': 0.25})
def test_selu():
def selu_x(x, alpha, gamma):
return gamma * np.where(x > 0, x, alpha * (np.exp(x) - 1.0))
_test_onnx_op_elementwise((2, 4, 5, 6),
selu_x,
{'alpha': 0.25, 'gamma': 0.3},
'float32',
'Selu',
{'alpha': 0.25, 'gamma': 0.3})
def test_ThresholdedRelu():
def ThresholdedRelu_x(x, alpha):
out_np = np.clip(x, alpha, np.inf)
out_np[out_np == alpha] = 0
return out_np
_test_onnx_op_elementwise((2, 4, 5, 6),
ThresholdedRelu_x,
{'alpha': 0.25},
'float32',
'ThresholdedRelu',
{'alpha': 0.25})
def test_ScaledTanh():
def ScaledTanh_x(x, alpha, beta):
return alpha * np.tanh(beta * x)
_test_onnx_op_elementwise((2, 4, 5, 6),
ScaledTanh_x,
{'alpha': 0.25, 'beta': 0.3},
'float32',
'ScaledTanh',
{'alpha': 0.25, 'beta': 0.3})
def test_ParametricSoftplus():
def ParametricSoftplus_x(x, alpha, beta):
return alpha * np.log(np.exp(beta * x) + 1)
_test_onnx_op_elementwise((2, 4, 5, 6),
ParametricSoftplus_x,
{'alpha': 0.25, 'beta': 0.3},
'float32',
'ParametricSoftplus',
{'alpha': 0.25, 'beta': 0.3})
def test_Scale():
def Scale_x(x, scale):
return scale * x
_test_onnx_op_elementwise((2, 4, 5, 6),
Scale_x,
{'scale': 0.25},
'float32',
'Scale',
{'scale': 0.25})
def test_LogSoftmax():
_test_onnx_op_elementwise((1, 4),
topi.testing.log_softmax_python,
{},
'float32',
'LogSoftmax',
{'axis': 1})
if __name__ == '__main__':
test_reshape()
test_reshape_like()
test_power()
test_squeeze()
test_unsqueeze()
test_slice()
test_floor()
test_ceil()
test_clip()
test_matmul()
test_gather()
test_lrn()
test_upsample()
test_forward_min()
test_forward_max()
test_forward_mean()
test_forward_hardsigmoid()
test_forward_arg_min_max()
test_softmax()
test_constantfill()
test_pad()
test_reduce_max()
test_reduce_min()
test_reduce_sum()
test_reduce_mean()
test_pad()
test_split()
test_binary_ops()
test_single_ops()
test_leaky_relu()
test_elu()
test_selu()
test_ThresholdedRelu()
test_ScaledTanh()
test_ParametricSoftplus()
test_Scale()
test_LogSoftmax()