diff --git a/python/oneflow/nn/modules/padding.py b/python/oneflow/nn/modules/padding.py
index 5283c5c32b5ac40ad13fc42e0497ba06820af246..24031436630823fc649648c9744ceac2ac200007 100644
--- a/python/oneflow/nn/modules/padding.py
+++ b/python/oneflow/nn/modules/padding.py
@@ -17,6 +17,8 @@ from typing import Union
import oneflow as flow
from oneflow.nn.module import Module
+from oneflow.nn.common_types import _size_4_t
+from oneflow.nn.modules.utils import _quadruple
class ReplicationPad2d(Module):
@@ -77,30 +79,19 @@ class ReplicationPad2d(Module):
"""
- def __init__(self, padding: Union[int, tuple, list]):
+ def __init__(self, padding: _size_4_t):
super().__init__()
if isinstance(padding, (tuple, list)):
assert len(padding) == 4, ValueError("Length of padding must be 4")
- boundary = [padding[0], padding[1], padding[2], padding[3]]
+ boundary = [*padding]
elif isinstance(padding, int):
- boundary = [padding, padding, padding, padding]
+ boundary = _quadruple(padding)
else:
raise ValueError("padding must be int or list or tuple!")
self.padding = boundary
def forward(self, x):
- (_, _, h, w) = x.shape
- if (
- self.padding[2] < h
- and self.padding[3] < h
- and (self.padding[0] < w)
- and (self.padding[1] < w)
- ):
- return flow.F.pad(x, pad=self.padding, mode="replicate")
- else:
- raise AssertionError(
- "Padding size should be less than the corresponding input dimension. Please check."
- )
+ return flow.F.pad(x, pad=self.padding, mode="replicate")
def extra_repr(self) -> str:
return "{}".format(self.padding)
@@ -152,13 +143,13 @@ class ReflectionPad2d(Module):
"""
- def __init__(self, padding: Union[int, tuple]) -> None:
+ def __init__(self, padding: _size_4_t) -> None:
super().__init__()
if isinstance(padding, tuple):
assert len(padding) == 4, ValueError("Padding length must be 4")
- boundary = [padding[0], padding[1], padding[2], padding[3]]
+ boundary = [*padding]
elif isinstance(padding, int):
- boundary = [padding, padding, padding, padding]
+ boundary = _quadruple(padding)
else:
raise ValueError("padding must be in or list or tuple!")
self.padding = boundary
diff --git a/python/oneflow/test/modules/test_arange.py b/python/oneflow/test/modules/test_arange.py
index 9c4b92ffaafd5e1f2fa82f62b5c09454cb2c087f..e62fbd2ffe9661244f4bb7b8dd7ab498e71ddb54 100644
--- a/python/oneflow/test/modules/test_arange.py
+++ b/python/oneflow/test/modules/test_arange.py
@@ -22,6 +22,7 @@ from test_util import GenArgList
import oneflow as flow
import oneflow.unittest
+from automated_test_util import *
def _test_arange(test_case, device):
@@ -53,7 +54,7 @@ def _test_arange_backward(test_case, device):
@flow.unittest.skip_unless_1n1d()
class TestArange(flow.unittest.TestCase):
- def test_transpose(test_case):
+ def test_arange(test_case):
arg_dict = OrderedDict()
arg_dict["function_test"] = [
_test_arange,
@@ -65,6 +66,16 @@ class TestArange(flow.unittest.TestCase):
for arg in GenArgList(arg_dict):
arg[0](test_case, *arg[1:])
+ @autotest(n=5, auto_backward=False, rtol=1e-5, atol=1e-5)
+ def test_arange_with_random_data(test_case):
+ start = random().to(int)
+ end = start + random().to(int)
+ step = random(0, end - start).to(int)
+ x = torch.arange(start=start, end=end, step=step)
+ device = random_device()
+ x.to(device)
+ return x
+
if __name__ == "__main__":
unittest.main()
diff --git a/python/oneflow/test/modules/test_argmax.py b/python/oneflow/test/modules/test_argmax.py
index d1299eaa8cf764d8529236045a99d03431a43d30..316edd4a4d4be23b5e42356f4770f92cdd27b0d7 100644
--- a/python/oneflow/test/modules/test_argmax.py
+++ b/python/oneflow/test/modules/test_argmax.py
@@ -22,6 +22,7 @@ from test_util import GenArgList
import oneflow as flow
import oneflow.unittest
+from automated_test_util import *
def _test_argmax_aixs_negative(test_case, device):
@@ -91,6 +92,14 @@ class TestArgmax(flow.unittest.TestCase):
for arg in GenArgList(arg_dict):
arg[0](test_case, *arg[1:])
+ @autotest(n=5, auto_backward=False, rtol=1e-5, atol=1e-5)
+ def test_argmax_with_random_data(test_case):
+ device = random_device()
+ ndim = random(1, 6).to(int)
+ x = random_pytorch_tensor(ndim=ndim).to(device)
+ y = torch.argmax(x, dim=random(0, ndim).to(int), keepdim=random().to(bool))
+ return y
+
if __name__ == "__main__":
unittest.main()
diff --git a/python/oneflow/test/modules/test_argwhere.py b/python/oneflow/test/modules/test_argwhere.py
index 375c1f52660d90c530ba43a4d15b4e4b16043d76..ac7cd37b511d0dfc53ac5ad84784bfa8dd99b9c3 100644
--- a/python/oneflow/test/modules/test_argwhere.py
+++ b/python/oneflow/test/modules/test_argwhere.py
@@ -22,6 +22,7 @@ from test_util import GenArgList
import oneflow as flow
import oneflow.unittest
+from automated_test_util import *
def _test_argwhere(test_case, shape, device):
@@ -43,6 +44,14 @@ class TestArgwhere(flow.unittest.TestCase):
for arg in GenArgList(arg_dict):
arg[0](test_case, *arg[1:])
+ @unittest.skip("pytorch do not have argwhere fn/module yet!")
+ @autotest(n=5, rtol=1e-5, atol=1e-5)
+ def test_argwhere_with_random_data(test_case):
+ device = random_device()
+ x = random_pytorch_tensor(ndim=random(2, 5).to(int)).to(device)
+ y = torch.argwhere(x)
+ return y
+
if __name__ == "__main__":
unittest.main()
diff --git a/python/oneflow/test/modules/test_instancenorm.py b/python/oneflow/test/modules/test_instancenorm.py
index 27760fa9a13eabf4bcf3befd0a488130673697c8..bb61600e709cf09b06a4f0647d2bad784d546475 100644
--- a/python/oneflow/test/modules/test_instancenorm.py
+++ b/python/oneflow/test/modules/test_instancenorm.py
@@ -22,6 +22,7 @@ from test_util import GenArgList
import oneflow as flow
import oneflow.unittest
+from automated_test_util import *
def _test_instancenorm1d(test_case, device):
@@ -418,6 +419,69 @@ class TestInstanceNorm(flow.unittest.TestCase):
for arg in GenArgList(arg_dict):
arg[0](test_case, *arg[1:])
+ @autotest(n=5, auto_backward=True, rtol=1e-4, atol=1e-4)
+ def test_instancenorm_with_random_data(test_case):
+ height = random(1, 6).to(int)
+ width = random(1, 6).to(int)
+ m = torch.nn.InstanceNorm1d(
+ num_features=height,
+ eps=random().to(float) | nothing(),
+ momentum=random().to(float) | nothing(),
+ affine=random().to(bool),
+ track_running_stats=random().to(bool),
+ )
+ m.train(random())
+ device = random_device()
+ m.to(device)
+ x = random_pytorch_tensor(ndim=3, dim1=height, dim2=width).to(device)
+ y = m(x)
+ return y
+
+ @autotest(n=5, auto_backward=True, rtol=1e-4, atol=1e-4)
+ def test_instancenorm_with_random_data(test_case):
+ channel = random(1, 6).to(int)
+ height = random(1, 6).to(int)
+ width = random(1, 6).to(int)
+ m = torch.nn.InstanceNorm2d(
+ num_features=channel,
+ eps=random().to(float) | nothing(),
+ momentum=random().to(float) | nothing(),
+ affine=random().to(bool),
+ track_running_stats=random().to(bool),
+ )
+ m.train(random())
+ device = random_device()
+ m.to(device)
+ x = random_pytorch_tensor(ndim=4, dim1=channel, dim2=height, dim3=width).to(
+ device
+ )
+ y = m(x)
+ return y
+
+ @autotest(n=5, auto_backward=False, rtol=1e-4, atol=1e-4)
+ def test_instancenorm_with_random_data(test_case):
+ channel = random(1, 6).to(int)
+ depth = random(1, 6).to(int)
+ height = random(1, 6).to(int)
+ width = random(1, 6).to(int)
+ # Set auto_backward=True will raise AssertionError: False is not true
+ # Set track_running_stats=True will raise error: Unexpected key(s) in state_dict: "num_batches_tracked".
+ m = torch.nn.InstanceNorm3d(
+ num_features=channel,
+ eps=random().to(float) | nothing(),
+ momentum=random().to(float) | nothing(),
+ affine=random().to(bool),
+ track_running_stats=False,
+ )
+ m.train(random())
+ device = random_device()
+ m.to(device)
+ x = random_pytorch_tensor(
+ ndim=5, dim1=channel, dim2=depth, dim3=height, dim4=width
+ ).to(device)
+ y = m(x)
+ return y
+
if __name__ == "__main__":
unittest.main()
diff --git a/python/oneflow/test/modules/test_normalization.py b/python/oneflow/test/modules/test_normalization.py
index 2542238376abd229b53c485dbe4e9abfcf869f10..f10851c85bf02f306b67c8aa322d9bf7ea7965c4 100644
--- a/python/oneflow/test/modules/test_normalization.py
+++ b/python/oneflow/test/modules/test_normalization.py
@@ -22,6 +22,7 @@ from test_util import GenArgList
import oneflow as flow
import oneflow.unittest
+from automated_test_util import *
input_arr = np.array(
[
@@ -136,6 +137,25 @@ class TestLayerNorm(flow.unittest.TestCase):
for arg in GenArgList(arg_dict):
arg[0](test_case, *arg[1:])
+ @autotest(n=5, auto_backward=False, rtol=1e-4, atol=1e-4)
+ def test_layernorm_with_random_data(test_case):
+ channel = random(1, 6).to(int)
+ height = random(1, 6).to(int)
+ width = random(1, 6).to(int)
+ m = torch.nn.LayerNorm(
+ normalized_shape=random(1, 6).to(int),
+ eps=random().to(float) | nothing(),
+ elementwise_affine=random().to(bool),
+ )
+ m.train(random())
+ device = random_device()
+ m.to(device)
+ x = random_pytorch_tensor(ndim=4, dim1=channel, dim2=height, dim3=width).to(
+ device
+ )
+ y = m(x)
+ return y
+
if __name__ == "__main__":
unittest.main()
diff --git a/python/oneflow/test/modules/test_replicationpad2d.py b/python/oneflow/test/modules/test_replicationpad2d.py
index b80fd8fcd810b5f52b382235d5ec000ecff22731..d93a832e24919f12249e686f83b61888437c34e0 100644
--- a/python/oneflow/test/modules/test_replicationpad2d.py
+++ b/python/oneflow/test/modules/test_replicationpad2d.py
@@ -22,6 +22,7 @@ from test_util import Array2Numpy, FlattenArray, GenArgList, Index2Coordinate
import oneflow as flow
import oneflow.unittest
+from automated_test_util import *
def _np_replication_pad2d_grad(src, dest, padding):
@@ -102,6 +103,19 @@ class TestReplicationPad2dModule(flow.unittest.TestCase):
for arg in GenArgList(arg_dict):
_test_ReplicationPad2d(test_case, *arg)
+ @autotest(n=5)
+ def test_replication_pad2d_with_random_data(test_case):
+ c = random(1, 6).to(int)
+ h = random(1, 6).to(int)
+ w = random(1, 6).to(int)
+ m = torch.nn.ReplicationPad2d(padding=random(low=0, high=7))
+ m.train(random())
+ device = random_device()
+ m.to(device)
+ x = random_pytorch_tensor(ndim=4, dim1=c, dim2=h, dim3=w).to(device)
+ y = m(x)
+ return y
+
if __name__ == "__main__":
unittest.main()
diff --git a/python/oneflow/test/tensor/test_tensor.py b/python/oneflow/test/tensor/test_tensor.py
index 4316349e12da9f9e2047766e3a64c048ce3d5e32..d941e8af0bbcccade0354f22edbed12c65edcb25 100644
--- a/python/oneflow/test/tensor/test_tensor.py
+++ b/python/oneflow/test/tensor/test_tensor.py
@@ -355,6 +355,24 @@ class TestTensor(flow.unittest.TestCase):
np_out = np.sum(input.numpy(), axis=(2, 1))
test_case.assertTrue(np.allclose(of_out.numpy(), np_out, 0.0001, 0.0001))
+ def test_argwhere(test_case):
+ shape = (2, 3, 4, 5)
+ precision = 1e-5
+ np_input = np.random.randn(*shape)
+ input = flow.Tensor(np_input)
+ of_out = input.argwhere()
+ np_out = np.argwhere(np_input)
+ test_case.assertTrue(np.allclose(of_out.numpy(), np_out, precision, precision))
+ test_case.assertTrue(np.array_equal(of_out.numpy().shape, np_out.shape))
+
+ @autotest(n=5, auto_backward=False)
+ def test_tensor_argmax_with_random_data(test_case):
+ device = random_device()
+ ndim = random(1, 6).to(int)
+ x = random_pytorch_tensor(ndim=ndim).to(device)
+ y = x.argmax(dim=random(0, ndim).to(int), keepdim=random().to(bool))
+ return y
+
@autotest()
def test_tensor_tanh_with_random_data(test_case):
device = random_device()