mirror of
https://github.com/pytorch/pytorch.git
synced 2025-10-20 21:14:14 +08:00
93d5503f34
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/20116 Differential Revision: D15256899 Pulled By: houseroad fbshipit-source-id: cf0dfd679d528fbb77f483e23071f4a96fb27091
624 lines
22 KiB
Python
624 lines
22 KiB
Python
from test_pytorch_common import TestCase, run_tests, flatten
|
|
|
|
import torch
|
|
import torch.onnx
|
|
from torch.autograd import Variable, Function
|
|
from torch.nn import Module, functional
|
|
import torch.nn as nn
|
|
|
|
import itertools
|
|
import io
|
|
import inspect
|
|
import glob
|
|
import os
|
|
import shutil
|
|
import common_utils as common
|
|
|
|
|
|
'''Usage: python test/onnx/test_operators.py [--no-onnx] [--produce-onnx-test-data]
|
|
--no-onnx: no onnx python dependence
|
|
--produce-onnx-test-data: generate onnx test data
|
|
'''
|
|
|
|
_onnx_test = False # flag to produce onnx test cases.
|
|
_onnx_dep = True # flag to import onnx package.
|
|
|
|
|
|
def export_to_pbtxt(model, inputs, *args, **kwargs):
|
|
return torch.onnx.export_to_pretty_string(
|
|
model, inputs, None, verbose=False, google_printer=True,
|
|
*args, **kwargs)
|
|
|
|
|
|
def export_to_pb(model, inputs, *args, **kwargs):
|
|
kwargs['operator_export_type'] = torch.onnx.OperatorExportTypes.ONNX
|
|
f = io.BytesIO()
|
|
with torch.no_grad():
|
|
torch.onnx.export(model, inputs, f, *args, **kwargs)
|
|
return f.getvalue()
|
|
|
|
|
|
class FuncModule(Module):
|
|
def __init__(self, f, params=None):
|
|
if params is None:
|
|
params = ()
|
|
super(FuncModule, self).__init__()
|
|
self.f = f
|
|
self.params = nn.ParameterList(list(params))
|
|
|
|
def forward(self, *args):
|
|
return self.f(*itertools.chain(args, self.params))
|
|
|
|
|
|
class TestOperators(TestCase):
|
|
|
|
def assertONNX(self, f, args, params=None, **kwargs):
|
|
if params is None:
|
|
params = ()
|
|
if isinstance(f, nn.Module):
|
|
m = f
|
|
else:
|
|
m = FuncModule(f, params)
|
|
m.eval()
|
|
onnx_model_pbtxt = export_to_pbtxt(m, args, **kwargs)
|
|
subname = kwargs.pop('subname', None)
|
|
self.assertExpected(onnx_model_pbtxt, subname)
|
|
if _onnx_dep:
|
|
onnx_model_pb = export_to_pb(m, args, **kwargs)
|
|
import onnx
|
|
import onnx.checker
|
|
import onnx.numpy_helper
|
|
import test_onnx_common
|
|
model_def = onnx.ModelProto.FromString(onnx_model_pb)
|
|
onnx.checker.check_model(model_def)
|
|
if _onnx_test:
|
|
test_function = inspect.stack()[1][0].f_code.co_name
|
|
test_name = test_function[0:4] + "_operator" + test_function[4:]
|
|
output_dir = os.path.join(test_onnx_common.pytorch_operator_dir, test_name)
|
|
# Assume:
|
|
# 1) the old test should be delete before the test.
|
|
# 2) only one assertONNX in each test, otherwise will override the data.
|
|
assert not os.path.exists(output_dir), "{} should not exist!".format(output_dir)
|
|
os.makedirs(output_dir)
|
|
with open(os.path.join(output_dir, "model.onnx"), 'wb') as file:
|
|
file.write(model_def.SerializeToString())
|
|
data_dir = os.path.join(output_dir, "test_data_set_0")
|
|
os.makedirs(data_dir)
|
|
if isinstance(args, Variable):
|
|
args = (args,)
|
|
for index, var in enumerate(flatten(args)):
|
|
tensor = onnx.numpy_helper.from_array(var.data.numpy())
|
|
with open(os.path.join(data_dir, "input_{}.pb".format(index)), 'wb') as file:
|
|
file.write(tensor.SerializeToString())
|
|
outputs = m(*args)
|
|
if isinstance(outputs, Variable):
|
|
outputs = (outputs,)
|
|
for index, var in enumerate(flatten(outputs)):
|
|
tensor = onnx.numpy_helper.from_array(var.data.numpy())
|
|
with open(os.path.join(data_dir, "output_{}.pb".format(index)), 'wb') as file:
|
|
file.write(tensor.SerializeToString())
|
|
|
|
def assertONNXRaises(self, err, f, args, params=None, **kwargs):
|
|
if params is None:
|
|
params = ()
|
|
if isinstance(f, nn.Module):
|
|
m = f
|
|
else:
|
|
m = FuncModule(f, params)
|
|
self.assertExpectedRaises(err, lambda: export_to_pbtxt(m, args, **kwargs))
|
|
|
|
def assertONNXRaisesRegex(self, err, reg, f, args, params=None, **kwargs):
|
|
if params is None:
|
|
params = ()
|
|
if isinstance(f, nn.Module):
|
|
m = f
|
|
else:
|
|
m = FuncModule(f, params)
|
|
with self.assertRaisesRegex(err, reg):
|
|
export_to_pbtxt(m, args, **kwargs)
|
|
|
|
def test_basic(self):
|
|
x = torch.tensor([0.4], requires_grad=True)
|
|
y = torch.tensor([0.7], requires_grad=True)
|
|
self.assertONNX(lambda x, y: -torch.sigmoid(torch.tanh(x * (x + y))), (x, y))
|
|
|
|
def test_view(self):
|
|
x = torch.tensor([0.0], requires_grad=True)
|
|
self.assertONNX(lambda x: x.view(1, 1), x)
|
|
|
|
def test_index(self):
|
|
x = torch.tensor([[0.0]], requires_grad=True)
|
|
self.assertONNX(lambda x: x[0], x)
|
|
|
|
def test_type_as(self):
|
|
x = torch.tensor([0.0], requires_grad=True)
|
|
self.assertONNX(lambda x: x.type_as(x), x)
|
|
|
|
def test_addconstant(self):
|
|
x = torch.randn(2, 3, requires_grad=True).double()
|
|
self.assertONNX(lambda x: x + 1, x)
|
|
|
|
def test_add_broadcast(self):
|
|
x = torch.randn(2, 3, requires_grad=True).double()
|
|
y = torch.randn(3, requires_grad=True).double()
|
|
self.assertONNX(lambda x, y: x + y, (x, y))
|
|
|
|
def test_add_left_broadcast(self):
|
|
x = torch.randn(3, requires_grad=True).double()
|
|
y = torch.randn(2, 3, requires_grad=True).double()
|
|
self.assertONNX(lambda x, y: x + y, (x, y))
|
|
|
|
def test_add_size1_broadcast(self):
|
|
x = torch.randn(2, 3, requires_grad=True).double()
|
|
y = torch.randn(2, 1, requires_grad=True).double()
|
|
self.assertONNX(lambda x, y: x + y, (x, y))
|
|
|
|
def test_add_size1_right_broadcast(self):
|
|
x = torch.randn(2, 3, requires_grad=True).double()
|
|
y = torch.randn(3, requires_grad=True).double()
|
|
self.assertONNX(lambda x, y: x + y, (x, y))
|
|
|
|
def test_add_size1_singleton_broadcast(self):
|
|
x = torch.randn(2, 3, requires_grad=True).double()
|
|
y = torch.randn(1, 3, requires_grad=True).double()
|
|
self.assertONNX(lambda x, y: x + y, (x, y))
|
|
|
|
def test_rsub(self):
|
|
x = torch.randn(2, 3, requires_grad=True).double()
|
|
self.assertONNX(lambda x: 1 - x, (x,))
|
|
|
|
def test_transpose(self):
|
|
x = torch.tensor([[0.0, 1.0], [2.0, 3.0]], requires_grad=True)
|
|
self.assertONNX(lambda x: x.transpose(0, 1).transpose(1, 0), x)
|
|
|
|
def test_chunk(self):
|
|
x = torch.tensor([0.0, 1.0, 2.0], requires_grad=True)
|
|
self.assertONNX(lambda x: x.chunk(2), x)
|
|
|
|
def test_split(self):
|
|
x = torch.tensor([[0.0, 1.0, 1.0, 0.0, 2.0, 2.0], [2.0, 3.0, 3.0, 2.0, 1.0, 1.0]])
|
|
self.assertONNX(lambda x: torch.split(x, 2, 1), x)
|
|
|
|
def test_split_with_sizes(self):
|
|
x = torch.tensor([[0.0, 1.0, 1.0, 0.0, 2.0, 2.0], [2.0, 3.0, 3.0, 2.0, 1.0, 1.0]])
|
|
self.assertONNX(lambda x: torch.split(x, [2, 1, 3], 1), x)
|
|
|
|
def test_concat2(self):
|
|
x = torch.randn(2, 3)
|
|
y = torch.randn(2, 3)
|
|
self.assertONNX(lambda inputs: torch.cat(inputs, 1), ((x, y),))
|
|
|
|
def test_mm(self):
|
|
m1 = torch.randn(2, 3, requires_grad=True)
|
|
m2 = torch.randn(3, 4, requires_grad=True)
|
|
self.assertONNX(torch.mm, (m1, m2))
|
|
|
|
def test_addmm(self):
|
|
m1 = torch.randn(2, 3, requires_grad=True)
|
|
m2 = torch.randn(3, 4, requires_grad=True)
|
|
m3 = torch.randn(4, requires_grad=True)
|
|
self.assertONNX(lambda x, y, z: torch.addmm(torch.addmm(z, x, y), x, y), (m1, m2, m3))
|
|
|
|
def test_permute2(self):
|
|
x = torch.tensor([[[[[[0.0]]]]]], requires_grad=True)
|
|
self.assertONNX(lambda x: x.permute(0, 1, 4, 2, 5, 3), x)
|
|
|
|
def test_pad(self):
|
|
x = torch.tensor([[[[0.0, 1.0, 1.0, 1.0], [2.0, 3.0, 7.0, 7.0]]]], requires_grad=True)
|
|
self.assertONNX(nn.ReflectionPad2d((2, 3, 0, 1)), x)
|
|
|
|
def test_params(self):
|
|
x = torch.tensor([[1.0, 2.0], [3.0, 4.0]], requires_grad=True)
|
|
y = nn.Parameter(torch.tensor([[1.0, 2.0], [3.0, 4.0]], requires_grad=True))
|
|
self.assertONNX(lambda x, y: -torch.sigmoid(torch.tanh(x * (x + y))), x, params=(y, ))
|
|
|
|
def test_symbolic_mismatch(self):
|
|
class MyFun(Function):
|
|
@staticmethod
|
|
def symbolic(g, x):
|
|
# The inside of this function should never be invoked, because
|
|
# we will fail due to an argument mismatch first.
|
|
assert False
|
|
|
|
@staticmethod
|
|
def forward(ctx, x, y):
|
|
return x + y
|
|
|
|
x = torch.ones(2, 2)
|
|
y = torch.ones(2, 2)
|
|
# NB: Don't use expect test here, the type error wobbles depending
|
|
# on Python version
|
|
with self.assertRaisesRegex(TypeError, "occurred when translating MyFun"):
|
|
export_to_pbtxt(FuncModule(MyFun().apply), (x, y))
|
|
|
|
# TODO: Do an nn style test for these
|
|
def test_batchnorm(self):
|
|
x = torch.ones(2, 2, 2, 2, requires_grad=True)
|
|
self.assertONNX(nn.BatchNorm2d(2), x)
|
|
|
|
def test_batchnorm_1d(self):
|
|
x = torch.ones(2, 2, requires_grad=True)
|
|
self.assertONNX(nn.BatchNorm1d(2), x)
|
|
|
|
def test_batchnorm_training(self):
|
|
x = torch.ones(2, 2, 2, 2, requires_grad=True)
|
|
self.assertONNX(nn.BatchNorm2d(2), x, training=True)
|
|
|
|
def test_conv(self):
|
|
x = torch.ones(20, 16, 50, 40, requires_grad=True)
|
|
self.assertONNX(nn.Conv2d(16, 13, 3, bias=False), x)
|
|
|
|
def test_convtranspose(self):
|
|
x = torch.ones(2, 3, 4, 5, requires_grad=True)
|
|
self.assertONNX(nn.ConvTranspose2d(3, 3, 3, stride=3, bias=False,
|
|
padding=1, output_padding=2), x)
|
|
|
|
def test_maxpool(self):
|
|
x = torch.randn(20, 16, 50)
|
|
self.assertONNX(nn.MaxPool1d(3, stride=2), x)
|
|
|
|
def test_maxpool_dilations(self):
|
|
x = torch.randn(20, 16, 50)
|
|
self.assertONNX(nn.MaxPool1d(2, stride=1, dilation=2), x, opset_version=10)
|
|
|
|
def test_avg_pool2d(self):
|
|
x = torch.randn(20, 16, 50, 32)
|
|
self.assertONNX(nn.AvgPool2d(3, stride=2), x)
|
|
|
|
def test_maxpool_indices(self):
|
|
x = torch.randn(20, 16, 50)
|
|
self.assertONNX(nn.MaxPool1d(3, stride=2, return_indices=True), x)
|
|
|
|
def test_at_op(self):
|
|
x = torch.randn(3, 4)
|
|
|
|
class MyFun(Function):
|
|
|
|
@staticmethod
|
|
def symbolic(g, x):
|
|
return g.at("add", x, x)
|
|
|
|
@staticmethod
|
|
def forward(ctx, x):
|
|
return x + x
|
|
|
|
class MyModule(Module):
|
|
def forward(self, x):
|
|
return MyFun.apply(x)
|
|
|
|
self.assertONNX(MyModule(), x)
|
|
|
|
def test_clip(self):
|
|
x = torch.randn(3, 4, requires_grad=True)
|
|
self.assertONNX(lambda x: torch.clamp(x, min=-0.5, max=0.5), x)
|
|
|
|
def test_clip_min(self):
|
|
x = torch.randn(1, 2, 3, 4, requires_grad=True)
|
|
self.assertONNX(lambda x: x.clamp(min=-0.1), x)
|
|
|
|
def test_clip_max(self):
|
|
x = torch.randn(1, 2, 3, 4, requires_grad=True)
|
|
self.assertONNX(lambda x: x.clamp(max=0.1), x)
|
|
|
|
def test_hardtanh(self):
|
|
x = torch.randn(3, 4, requires_grad=True)
|
|
self.assertONNX(lambda x: torch.nn.Hardtanh(-0.5, 0.5)(x), x)
|
|
|
|
def test_full(self):
|
|
x = torch.randn(3, 4, requires_grad=True)
|
|
self.assertONNX(lambda x: torch.full(x.shape, 2), x)
|
|
|
|
def test_full_like(self):
|
|
x = torch.randn(3, 4, requires_grad=True)
|
|
self.assertONNX(lambda x: torch.full_like(x, 2), x)
|
|
|
|
def test_max(self):
|
|
x = torch.randn(3, 4, requires_grad=True)
|
|
y = torch.randn(3, 4, requires_grad=True)
|
|
self.assertONNX(lambda x, y: torch.max(x, y), (x, y))
|
|
|
|
def test_min(self):
|
|
x = torch.randn(3, 4, requires_grad=True)
|
|
y = torch.randn(3, 4, requires_grad=True)
|
|
self.assertONNX(lambda x, y: torch.min(x, y), (x, y))
|
|
|
|
def test_mean(self):
|
|
x = torch.randn(1, 2, 3, 4, requires_grad=True)
|
|
self.assertONNX(lambda x: torch.mean(x), x)
|
|
|
|
def test_reduced_mean(self):
|
|
x = torch.randn(1, 2, 3, 4, requires_grad=True)
|
|
self.assertONNX(lambda x: torch.mean(x, dim=2), x)
|
|
|
|
def test_reduced_mean_keepdim(self):
|
|
x = torch.randn(1, 2, 3, 4, requires_grad=True)
|
|
self.assertONNX(lambda x: torch.mean(x, dim=2, keepdim=True), x)
|
|
|
|
def test_sum(self):
|
|
x = torch.randn(1, 2, 3, 4, requires_grad=True)
|
|
self.assertONNX(lambda x: torch.sum(x), x)
|
|
|
|
def test_reduced_sum(self):
|
|
x = torch.randn(1, 2, 3, 4, requires_grad=True)
|
|
self.assertONNX(lambda x: torch.sum(x, dim=2), x)
|
|
|
|
def test_reduced_sum_keepdim(self):
|
|
x = torch.randn(1, 2, 3, 4, requires_grad=True)
|
|
self.assertONNX(lambda x: torch.sum(x, dim=2, keepdim=True), x)
|
|
|
|
def test_prod(self):
|
|
x = torch.randn(1, 2, 3, 4, requires_grad=True)
|
|
self.assertONNX(lambda x: torch.prod(x), x)
|
|
|
|
def test_reduced_prod(self):
|
|
x = torch.randn(1, 2, 3, 4, requires_grad=True)
|
|
self.assertONNX(lambda x: torch.prod(x, dim=2), x)
|
|
|
|
def test_reduced_prod_keepdim(self):
|
|
x = torch.randn(1, 2, 3, 4, requires_grad=True)
|
|
self.assertONNX(lambda x: torch.prod(x, dim=2, keepdim=True), x)
|
|
|
|
def test_sqrt(self):
|
|
x = torch.randn(3, 4, requires_grad=True)
|
|
self.assertONNX(lambda x: torch.sqrt(x), x)
|
|
|
|
def test_equal(self):
|
|
x = torch.randn(1, 2, 3, 1, requires_grad=False).int()
|
|
y = torch.randn(1, 4, requires_grad=False).int()
|
|
self.assertONNX(lambda x, y: x == y, (x, y))
|
|
|
|
def test_lt(self):
|
|
x = torch.randn(1, 2, 3, 1, requires_grad=False).int()
|
|
y = torch.randn(1, 4, requires_grad=False).int()
|
|
self.assertONNX(lambda x, y: x < y, (x, y))
|
|
|
|
def test_gt(self):
|
|
x = torch.randn(1, 2, 3, 1, requires_grad=False).int()
|
|
y = torch.randn(1, 4, requires_grad=False).int()
|
|
self.assertONNX(lambda x, y: x > y, (x, y))
|
|
|
|
def test_le(self):
|
|
x = torch.randn(3, 4, requires_grad=False).int()
|
|
y = torch.randn(3, 4, requires_grad=False).int()
|
|
self.assertONNX(lambda x, y: x <= y, (x, y))
|
|
|
|
def test_ge(self):
|
|
x = torch.randn(3, 4, requires_grad=False).int()
|
|
y = torch.randn(3, 4, requires_grad=False).int()
|
|
self.assertONNX(lambda x, y: x >= y, (x, y))
|
|
|
|
def test_exp(self):
|
|
x = torch.randn(3, 4, requires_grad=True)
|
|
self.assertONNX(lambda x: x.exp(), x)
|
|
|
|
def test_sin(self):
|
|
x = torch.randn(3, 4, requires_grad=True)
|
|
self.assertONNX(lambda x: x.sin(), x)
|
|
|
|
def test_cos(self):
|
|
x = torch.randn(3, 4, requires_grad=True)
|
|
self.assertONNX(lambda x: x.cos(), x)
|
|
|
|
def test_tan(self):
|
|
x = torch.randn(3, 4, requires_grad=True)
|
|
self.assertONNX(lambda x: x.tan(), x)
|
|
|
|
def test_asin(self):
|
|
x = torch.rand(3, 4, requires_grad=True)
|
|
self.assertONNX(lambda x: x.asin(), x)
|
|
|
|
def test_acos(self):
|
|
x = torch.rand(3, 4, requires_grad=True)
|
|
self.assertONNX(lambda x: x.acos(), x)
|
|
|
|
def test_slice(self):
|
|
x = torch.rand(3, 4, requires_grad=True)
|
|
self.assertONNX(lambda x: x[:, 1:2], x)
|
|
|
|
def test_sign(self):
|
|
x = torch.rand(3, 4, requires_grad=True)
|
|
self.assertONNX(lambda x: x.sign(), x)
|
|
|
|
def test_narrow(self):
|
|
x = torch.randn(3, 3, requires_grad=True)
|
|
self.assertONNX(lambda x: torch.narrow(x, 0, 0, 2), x)
|
|
|
|
def test_atan(self):
|
|
x = torch.randn(3, 4, requires_grad=True)
|
|
self.assertONNX(lambda x: x.atan(), x)
|
|
|
|
def test_view_flatten(self):
|
|
x = torch.randn(1, 2, 3, 4, requires_grad=True)
|
|
self.assertONNX(lambda x: x.view(x.size()[0], x.numel() // x.size()[0]), x)
|
|
|
|
def test_flatten(self):
|
|
x = torch.randn(1, 2, 3, 4, requires_grad=True)
|
|
self.assertONNX(lambda x: torch.flatten(x), x)
|
|
|
|
def test_flatten2D(self):
|
|
x = torch.randn(1, 2, 3, 4, requires_grad=True)
|
|
self.assertONNX(lambda x: torch.flatten(x, 1), x)
|
|
|
|
def test_isnan(self):
|
|
x = torch.tensor([1, float('nan'), 2])
|
|
self.assertONNX(lambda x: torch.isnan(x), x)
|
|
|
|
def test_argmax(self):
|
|
x = torch.randn(4, 4, requires_grad=True)
|
|
self.assertONNX(lambda x: torch.argmax(x, dim=1), x)
|
|
|
|
def test_logsoftmax(self):
|
|
x = torch.randn(1, 2, 3, 4, requires_grad=True)
|
|
self.assertONNX(nn.LogSoftmax(dim=3), x)
|
|
|
|
def test_pow(self):
|
|
x = torch.randn(1, 2, 3, 4, requires_grad=True)
|
|
y = torch.randn(1, 2, 3, 4, requires_grad=True)
|
|
self.assertONNX(lambda x, y: x.pow(y), (x, y))
|
|
|
|
def test_elu(self):
|
|
x = torch.randn(1, 2, 3, 4, requires_grad=True)
|
|
self.assertONNX(nn.ELU(), x)
|
|
|
|
def test_selu(self):
|
|
x = torch.randn(1, 2, 3, 4, requires_grad=True)
|
|
self.assertONNX(nn.SELU(), x)
|
|
|
|
def test_repeat(self):
|
|
x = torch.randn(1, 2, 3, 4, requires_grad=True)
|
|
self.assertONNX(lambda x: x.repeat(1, 2, 3, 4), x)
|
|
|
|
def test_repeat_dim_overflow(self):
|
|
x = torch.randn(1, 2, requires_grad=True)
|
|
self.assertONNX(lambda x: x.repeat(1, 2, 3, 4), x)
|
|
|
|
def test_norm(self):
|
|
x = torch.randn(1, 2, 3, 4, requires_grad=True)
|
|
self.assertONNX(lambda x: x.norm(p=2, dim=2), (x))
|
|
|
|
def test_upsample_nearest(self):
|
|
x = torch.randn(1, 2, 3, 4, requires_grad=True)
|
|
self.assertONNX(lambda x: nn.functional.interpolate(x, scale_factor=2., mode='nearest'), x)
|
|
|
|
def test_upsample_bilinear(self):
|
|
x = torch.randn(1, 2, 3, 4, requires_grad=True)
|
|
self.assertONNX(lambda x: nn.functional.interpolate(x, scale_factor=2., mode='bilinear'), x)
|
|
|
|
def test_unsqueeze(self):
|
|
x = torch.randn(3, 4, requires_grad=True)
|
|
self.assertONNX(lambda x: x.unsqueeze(len(x.shape)), x)
|
|
|
|
def test_batchnorm_noaffine(self):
|
|
x = torch.randn(128, 128, 1, 1, requires_grad=True)
|
|
self.assertONNX(nn.BatchNorm2d(128, affine=False, momentum=0.3), x)
|
|
|
|
def test_embedding_bags(self):
|
|
emb_bag = nn.EmbeddingBag(10, 8)
|
|
input = torch.tensor([1, 2, 3, 4]).long()
|
|
offset = torch.tensor([0]).long()
|
|
self.assertONNX(emb_bag, (input, offset))
|
|
|
|
def test_implicit_expand(self):
|
|
x = torch.randn(3, 4, requires_grad=True)
|
|
self.assertONNX(lambda x: x + 1, x)
|
|
|
|
def test_reduce_sum_negative_indices(self):
|
|
x = torch.randn(3, 4, requires_grad=True)
|
|
self.assertONNX(lambda x: x.sum(-1), x)
|
|
|
|
def test_randn(self):
|
|
x = torch.randn(1, 2, 3, 4)
|
|
self.assertONNX(lambda x: torch.randn(1, 2, 3, 4) + x, x)
|
|
|
|
def test_rrelu(self):
|
|
x = torch.randn(1, 2, 3, 4)
|
|
self.assertONNX(torch.nn.RReLU(), x)
|
|
|
|
def test_log_sigmoid(self):
|
|
x = torch.randn(1, 2, 3, 4)
|
|
self.assertONNX(torch.nn.LogSigmoid(), x)
|
|
|
|
def test_linear(self):
|
|
x = torch.randn(3, 4)
|
|
self.assertONNX(torch.nn.Linear(4, 5, bias=True), x)
|
|
|
|
def test_zeros_like(self):
|
|
x = torch.randn(5, 8, requires_grad=True)
|
|
self.assertONNX(lambda x: torch.zeros_like(x), x)
|
|
|
|
def test_ones_like(self):
|
|
x = torch.randn(6, 10, requires_grad=True)
|
|
self.assertONNX(lambda x: torch.ones_like(x), x)
|
|
|
|
def test_expand(self):
|
|
x = torch.randn(6, 1, requires_grad=True)
|
|
self.assertONNX(lambda x: x.expand(4, 6, 2), x)
|
|
|
|
def test_ne(self):
|
|
x = torch.randn(1, 2, 3, 1, requires_grad=False).int()
|
|
y = torch.randn(1, 4, requires_grad=False).int()
|
|
self.assertONNX(lambda x, y: torch.ne(x, y), (x, y))
|
|
|
|
def test_reducemax(self):
|
|
x = torch.randn(1, 2, 3, 4)
|
|
self.assertONNX(lambda x: torch.max(x), x)
|
|
|
|
def test_reducemin(self):
|
|
x = torch.randn(1, 2, 3, 4)
|
|
self.assertONNX(lambda x: torch.min(x), x)
|
|
|
|
def test_erf(self):
|
|
x = torch.randn(1, 2, 3, 4)
|
|
self.assertONNX(lambda x: x.erf(), x)
|
|
|
|
def test_dropout(self):
|
|
x = torch.randn(3, 4, requires_grad=True)
|
|
self.assertONNX(lambda x: torch.max(functional.dropout(x, training=False)), x)
|
|
|
|
def test_nonzero(self):
|
|
x = torch.tensor([[[2., 2.], [1., 0.]], [[0., 0.], [1., 1.]]], requires_grad=True)
|
|
self.assertONNX(lambda x: torch.nonzero(x), x)
|
|
|
|
def test_master_opset(self):
|
|
x = torch.randn(2, 3).float()
|
|
y = torch.randn(2, 3).float()
|
|
self.assertONNX(lambda x, y: x + y, (x, y), opset_version=10)
|
|
|
|
def test_retain_param_name_disabled(self):
|
|
class MyModule(Module):
|
|
def __init__(self):
|
|
super(MyModule, self).__init__()
|
|
self.fc1 = nn.Linear(4, 5, bias=False)
|
|
self.fc1.weight.data.fill_(2.)
|
|
self.fc2 = nn.Linear(5, 6, bias=False)
|
|
self.fc2.weight.data.fill_(3.)
|
|
|
|
def forward(self, x):
|
|
return self.fc2(self.fc1(x))
|
|
|
|
x = torch.randn(3, 4).float()
|
|
self.assertONNX(MyModule(), (x,), _retain_param_name=False)
|
|
|
|
def test_c2_op(self):
|
|
class MyModel(torch.nn.Module):
|
|
def __init__(self):
|
|
super(MyModel, self).__init__()
|
|
|
|
def forward(self, scores, bbox_deltas, im_info, anchors):
|
|
a, b = torch.ops._caffe2.GenerateProposals(
|
|
(scores), (bbox_deltas), (im_info), (anchors),
|
|
2.0, 6000, 300, 0.7, 16, True, -90, 90, 1.0, True,
|
|
)
|
|
return a, b
|
|
|
|
model = MyModel()
|
|
A = 4
|
|
H = 10
|
|
W = 8
|
|
img_count = 3
|
|
scores = torch.ones(img_count, A, H, W, dtype=torch.float32)
|
|
bbox_deltas = torch.linspace(0, 10, steps=img_count * 4 * A * H * W,
|
|
dtype=torch.float32)
|
|
bbox_deltas = bbox_deltas.view(img_count, 4 * A, H, W)
|
|
im_info = torch.ones(img_count, 3, dtype=torch.float32)
|
|
anchors = torch.ones(A, 4, dtype=torch.float32)
|
|
inputs = (scores, bbox_deltas, im_info, anchors)
|
|
self.assertONNX(model, inputs)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
no_onnx_dep_flag = '--no-onnx'
|
|
_onnx_dep = no_onnx_dep_flag not in common.UNITTEST_ARGS
|
|
if no_onnx_dep_flag in common.UNITTEST_ARGS:
|
|
common.UNITTEST_ARGS.remove(no_onnx_dep_flag)
|
|
onnx_test_flag = '--produce-onnx-test-data'
|
|
_onnx_test = onnx_test_flag in common.UNITTEST_ARGS
|
|
if onnx_test_flag in common.UNITTEST_ARGS:
|
|
common.UNITTEST_ARGS.remove(onnx_test_flag)
|
|
if _onnx_test:
|
|
_onnx_dep = True
|
|
import test_onnx_common
|
|
for d in glob.glob(os.path.join(test_onnx_common.pytorch_operator_dir, "test_operator_*")):
|
|
shutil.rmtree(d)
|
|
run_tests()
|