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fix code_style
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@a162837
a162837 committed Nov 4, 2024
1 parent e2f56b1 commit 6ef4f1c
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Showing 2 changed files with 73 additions and 38 deletions.
12 changes: 8 additions & 4 deletions python/paddle/tensor/math.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3831,16 +3831,18 @@ def clip(

else:
if in_dynamic_or_pir_mode():
if isinstance(min, (Variable, paddle.pir.Value, paddle.Tensor)):
if isinstance(min, Variable):
min = min.item(0)
if isinstance(max, (Variable, paddle.pir.Value, paddle.Tensor)):
if isinstance(max, Variable):
max = max.item(0)
min = min_ if min is None else min
max = max_ if max is None else max
return _C_ops.clip(x, min, max)
else:
if min is not None:
check_type(min, 'min', (float, int, Variable, paddle.Tensor), 'clip')
check_type(
min, 'min', (float, int, Variable, paddle.Tensor), 'clip'
)
if isinstance(min, (Variable, paddle.Tensor)):
check_dtype(
min.dtype,
Expand All @@ -3850,7 +3852,9 @@ def clip(
'(When the type of min in clip is Variable.)',
)
if max is not None:
check_type(max, 'max', (float, int, Variable, paddle.Tensor), 'clip')
check_type(
max, 'max', (float, int, Variable, paddle.Tensor), 'clip'
)
if isinstance(max, (Variable, paddle.Tensor)):
check_dtype(
max.dtype,
Expand Down
99 changes: 65 additions & 34 deletions test/legacy_test/test_clip_tensor_op.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -28,9 +28,13 @@ def test_static_clip(self):
if base.core.is_compiled_with_cuda()
else base.CPUPlace()
)
data = np.random.random(data_shape).astype('float32')
min_data = np.random.random(data_shape[-2:]).astype('float32')
max_data = np.random.random(data_shape[-3:]).astype('float32')
data = np.random.uniform(-2.0, 2.0, data_shape).astype('float32')
min_data = np.random.uniform(-2.5, -1.5, data_shape[-2:]).astype(
'float32'
)
max_data = np.random.uniform(1.5, 2.5, data_shape[-3:]).astype(
'float32'
)
paddle.enable_static()
with paddle.static.program_guard(paddle.static.Program()):
x = paddle.static.data(name='x', shape=data_shape, dtype='float32')
Expand Down Expand Up @@ -60,9 +64,11 @@ def test_static_clip(self):
if base.core.is_compiled_with_cuda()
else base.CPUPlace()
)
data = np.random.random(data_shape).astype('float32')
min_data = np.random.random(data_shape[-2:]).astype('float32')
max_data = np.random.random(data_shape[-3:]).astype('float32')

data = np.random.uniform(-2.0, 7.0, data_shape).astype('float32')
min_data = np.random.uniform(-2.5, -1.5, data_shape[-2:]).astype(
'float32'
)
paddle.enable_static()
with paddle.static.program_guard(paddle.static.Program()):
x = paddle.static.data(name='x', shape=data_shape, dtype='float32')
Expand Down Expand Up @@ -90,8 +96,10 @@ def test_static_clip(self):
if base.core.is_compiled_with_cuda()
else base.CPUPlace()
)
data = np.random.random(data_shape).astype('float32')
min_data = np.random.random(data_shape[-2:]).astype('float32')
data = np.random.uniform(-2.0, 2.0, data_shape).astype('float32')
min_data = np.random.uniform(-2.5, -1.5, data_shape[-2:]).astype(
'float32'
)
max_data = float(np.finfo(np.float32).max)
paddle.enable_static()
with paddle.static.program_guard(paddle.static.Program()):
Expand All @@ -115,14 +123,14 @@ def test_static_clip(self):
if base.core.is_compiled_with_cuda()
else base.CPUPlace()
)
data = np.random.random(data_shape).astype('float32')
min_data = np.random.random([1]).astype('float32')
max_data = min_data + 10.0
data = np.random.uniform(-2.0, 2.0, data_shape).astype('float32')
min_data = np.random.uniform(-2.5, -1.5, []).astype('float32')
max_data = np.random.uniform(1.5, 2.5, []).astype('float32')
paddle.enable_static()
with paddle.static.program_guard(paddle.static.Program()):
x = paddle.static.data(name='x', shape=data_shape, dtype='float32')
min = paddle.static.data(name='min', shape=[1], dtype='float32')
max = paddle.static.data(name='max', shape=[1], dtype='float32')
min = paddle.static.data(name='min', shape=[], dtype='float32')
max = paddle.static.data(name='max', shape=[], dtype='float32')
out = paddle.clip(x, min, max)
exe = base.Executor(self.place)
res = exe.run(
Expand Down Expand Up @@ -169,9 +177,13 @@ def test_dygraph_clip(self):
)
paddle.disable_static(self.place)
data_shape = [1, 2, 3, 4]
data = np.random.random(data_shape).astype('float32')
min_data = np.random.random(data_shape[-2:]).astype('float32')
max_data = np.random.random(data_shape[-3:]).astype('float32')
data = np.random.uniform(-2.0, 2.0, data_shape).astype('float32')
min_data = np.random.uniform(-2.5, -1.5, data_shape[-2:]).astype(
'float32'
)
max_data = np.random.uniform(1.5, 2.5, data_shape[-3:]).astype(
'float32'
)
out_np = np.clip(data, min_data, max_data)
data = paddle.to_tensor(data)
min_data = paddle.to_tensor(min_data)
Expand All @@ -180,9 +192,13 @@ def test_dygraph_clip(self):
np.testing.assert_allclose(out.numpy(), out_np, rtol=1e-05)

data_shape = [1, 2, 3, 4]
data = np.random.random(data_shape).astype('float32')
min_data = np.random.random(data_shape[-2:]).astype('float32')
max_data = np.random.random(data_shape[-1:]).astype('float32')
data = np.random.uniform(-2.0, 2.0, data_shape).astype('float32')
min_data = np.random.uniform(-2.5, -1.5, data_shape[-2:]).astype(
'float32'
)
max_data = np.random.uniform(1.5, 2.5, data_shape[-1:]).astype(
'float32'
)
out_np = np.clip(data, min_data, max_data)
data = paddle.to_tensor(data)
min_data = paddle.to_tensor(min_data)
Expand All @@ -191,19 +207,23 @@ def test_dygraph_clip(self):
np.testing.assert_allclose(out.numpy(), out_np, rtol=1e-05)

data_shape = [1, 2, 3, 4]
data = np.random.random(data_shape).astype('int32')
min_data = np.random.random(data_shape[-2:]).astype('int32')
data = np.random.uniform(-2.0, 7.0, data_shape).astype('int32')
min_data = np.random.uniform(-2.5, -1.5, data_shape[-2:]).astype(
'int32'
)
max_data = 5
out_np = np.clip(data, min_data, max_data)
data = paddle.to_tensor(data)
min_data = paddle.to_tensor(min_data)
data = paddle.to_tensor(data, dtype='int32')
min_data = paddle.to_tensor(min_data, dtype='int32')
max_data = paddle.to_tensor([max_data], dtype='int32')
out = paddle.clip(data, min_data, max_data)
np.testing.assert_allclose(out.numpy(), out_np, rtol=1e-05)

data_shape = [1, 2, 3, 4]
data = np.random.random(data_shape).astype('float32')
min_data = np.random.random(data_shape[-2:]).astype('float32')
data = np.random.uniform(-2.0, 2.0, data_shape).astype('float32')
min_data = np.random.uniform(-2.5, -1.5, data_shape[-2:]).astype(
'float32'
)
max_data = float(np.finfo(np.float32).max)
out_np = np.clip(data, min_data, max_data)
data = paddle.to_tensor(data)
Expand All @@ -212,8 +232,10 @@ def test_dygraph_clip(self):
np.testing.assert_allclose(out.numpy(), out_np, rtol=1e-05)

data_shape = [1, 2, 3, 4]
data = np.random.random(data_shape).astype('float32')
min_data = np.random.random(data_shape[-2:]).astype('float32')
data = np.random.uniform(-2.0, 2.0, data_shape).astype('float32')
min_data = np.random.uniform(-2.5, -1.5, data_shape[-2:]).astype(
'float32'
)
max_data = 5
out_np = np.clip(data, min_data, max_data)
data = paddle.to_tensor(data)
Expand All @@ -223,9 +245,13 @@ def test_dygraph_clip(self):

if base.core.is_compiled_with_cuda():
data_shape = [1, 2, 3, 4]
data = np.random.random(data_shape).astype('float16')
min_data = np.random.random(data_shape[-2:]).astype('float16')
max_data = np.random.random(data_shape[-1:]).astype('float16')
data = np.random.uniform(-2.0, 2.0, data_shape).astype('float16')
min_data = np.random.uniform(-2.5, -1.5, data_shape[-2:]).astype(
'float16'
)
max_data = np.random.uniform(1.5, 2.5, data_shape[-1:]).astype(
'float16'
)
out_np = np.clip(data, min_data, max_data)
data = paddle.to_tensor(data)
min_data = paddle.to_tensor(min_data)
Expand Down Expand Up @@ -263,11 +289,16 @@ def test_shapeerror_clip(self):

def test_tensor_clip_(self):
data_shape = [1, 9, 9, 4]
data = paddle.to_tensor(np.random.random(data_shape).astype('float32'))
min = paddle.to_tensor(
np.random.random(data_shape[-2:]).astype('float32')
data = np.random.uniform(-2.0, 2.0, data_shape).astype('float32')
data = paddle.to_tensor(data)
min_data = np.random.uniform(-2.5, -1.5, data_shape[-2:]).astype(
'float32'
)
min = paddle.to_tensor(min_data)
max_data = np.random.uniform(1.5, 2.5, data_shape[-3:]).astype(
'float32'
)
max = min + 5
max = paddle.to_tensor(max_data)
data.clip_(min, max)


Expand Down

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