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[PIR]Open uts for AdaptiveAvgPool3D (#60636)
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@0x45f
0x45f authored Jan 9, 2024
1 parent 44062f5 commit b1daab4
Showing 1 changed file with 104 additions and 94 deletions.
198 changes: 104 additions & 94 deletions test/legacy_test/test_adaptive_avg_pool3d.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,8 +17,8 @@
import numpy as np

import paddle
from paddle import base
from paddle.base import core
from paddle.pir_utils import test_with_pir_api


def adaptive_start_index(index, input_size, output_size):
Expand Down Expand Up @@ -134,56 +134,62 @@ def setUp(self):
x=self.x_np, output_size=[None, 3, None], pool_type="avg"
)

@test_with_pir_api
def test_static_graph(self):
for use_cuda in (
[False, True] if core.is_compiled_with_cuda() else [False]
):
place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace()
paddle.enable_static()
x = paddle.static.data(
name="x", shape=[2, 3, 5, 7, 7], dtype="float32"
)

out_1 = paddle.nn.functional.adaptive_avg_pool3d(
x=x, output_size=[3, 3, 3]
)

out_2 = paddle.nn.functional.adaptive_avg_pool3d(x=x, output_size=5)

out_3 = paddle.nn.functional.adaptive_avg_pool3d(
x=x, output_size=[2, 3, 5]
)

out_4 = paddle.nn.functional.adaptive_avg_pool3d(
x=x, output_size=[3, 3, 3], data_format="NDHWC"
)

out_5 = paddle.nn.functional.adaptive_avg_pool3d(
x=x, output_size=[None, 3, None]
)

exe = paddle.static.Executor(place=place)
[res_1, res_2, res_3, res_4, res_5] = exe.run(
base.default_main_program(),
feed={"x": self.x_np},
fetch_list=[out_1, out_2, out_3, out_4, out_5],
)

np.testing.assert_allclose(
res_1, self.res_1_np, rtol=1e-5, atol=1e-8
)
np.testing.assert_allclose(
res_2, self.res_2_np, rtol=1e-5, atol=1e-8
)
np.testing.assert_allclose(
res_3, self.res_3_np, rtol=1e-5, atol=1e-8
)
np.testing.assert_allclose(
res_4, self.res_4_np, rtol=1e-5, atol=1e-8
)
np.testing.assert_allclose(
res_5, self.res_5_np, rtol=1e-5, atol=1e-8
)
with paddle.static.program_guard(
paddle.static.Program(), paddle.static.Program()
):
x = paddle.static.data(
name="x", shape=[2, 3, 5, 7, 7], dtype="float32"
)

out_1 = paddle.nn.functional.adaptive_avg_pool3d(
x=x, output_size=[3, 3, 3]
)

out_2 = paddle.nn.functional.adaptive_avg_pool3d(
x=x, output_size=5
)

out_3 = paddle.nn.functional.adaptive_avg_pool3d(
x=x, output_size=[2, 3, 5]
)

out_4 = paddle.nn.functional.adaptive_avg_pool3d(
x=x, output_size=[3, 3, 3], data_format="NDHWC"
)

out_5 = paddle.nn.functional.adaptive_avg_pool3d(
x=x, output_size=[None, 3, None]
)

exe = paddle.static.Executor(place=place)
[res_1, res_2, res_3, res_4, res_5] = exe.run(
paddle.static.default_main_program(),
feed={"x": self.x_np},
fetch_list=[out_1, out_2, out_3, out_4, out_5],
)

np.testing.assert_allclose(
res_1, self.res_1_np, rtol=1e-5, atol=1e-8
)
np.testing.assert_allclose(
res_2, self.res_2_np, rtol=1e-5, atol=1e-8
)
np.testing.assert_allclose(
res_3, self.res_3_np, rtol=1e-5, atol=1e-8
)
np.testing.assert_allclose(
res_4, self.res_4_np, rtol=1e-5, atol=1e-8
)
np.testing.assert_allclose(
res_5, self.res_5_np, rtol=1e-5, atol=1e-8
)

def test_dynamic_graph(self):
for use_cuda in (
Expand Down Expand Up @@ -261,61 +267,65 @@ def setUp(self):
x=self.x_np, output_size=[None, 3, None], pool_type="avg"
)

@test_with_pir_api
def test_static_graph(self):
for use_cuda in (
[False, True] if core.is_compiled_with_cuda() else [False]
):
place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace()
paddle.enable_static()
x = paddle.static.data(
name="x", shape=[2, 3, 5, 7, 7], dtype="float32"
)

adaptive_avg_pool = paddle.nn.AdaptiveAvgPool3D(
output_size=[3, 3, 3]
)
out_1 = adaptive_avg_pool(x=x)

adaptive_avg_pool = paddle.nn.AdaptiveAvgPool3D(output_size=5)
out_2 = adaptive_avg_pool(x=x)

adaptive_avg_pool = paddle.nn.AdaptiveAvgPool3D(
output_size=[2, 3, 5]
)
out_3 = adaptive_avg_pool(x=x)

adaptive_avg_pool = paddle.nn.AdaptiveAvgPool3D(
output_size=[3, 3, 3], data_format="NDHWC"
)
out_4 = adaptive_avg_pool(x=x)

adaptive_avg_pool = paddle.nn.AdaptiveAvgPool3D(
output_size=[None, 3, None]
)
out_5 = adaptive_avg_pool(x=x)

exe = paddle.static.Executor(place=place)
[res_1, res_2, res_3, res_4, res_5] = exe.run(
base.default_main_program(),
feed={"x": self.x_np},
fetch_list=[out_1, out_2, out_3, out_4, out_5],
)

np.testing.assert_allclose(
res_1, self.res_1_np, rtol=1e-5, atol=1e-8
)
np.testing.assert_allclose(
res_2, self.res_2_np, rtol=1e-5, atol=1e-8
)
np.testing.assert_allclose(
res_3, self.res_3_np, rtol=1e-5, atol=1e-8
)
np.testing.assert_allclose(
res_4, self.res_4_np, rtol=1e-5, atol=1e-8
)
np.testing.assert_allclose(
res_5, self.res_5_np, rtol=1e-5, atol=1e-8
)
with paddle.static.program_guard(
paddle.static.Program(), paddle.static.Program()
):
x = paddle.static.data(
name="x", shape=[2, 3, 5, 7, 7], dtype="float32"
)

adaptive_avg_pool = paddle.nn.AdaptiveAvgPool3D(
output_size=[3, 3, 3]
)
out_1 = adaptive_avg_pool(x=x)

adaptive_avg_pool = paddle.nn.AdaptiveAvgPool3D(output_size=5)
out_2 = adaptive_avg_pool(x=x)

adaptive_avg_pool = paddle.nn.AdaptiveAvgPool3D(
output_size=[2, 3, 5]
)
out_3 = adaptive_avg_pool(x=x)

adaptive_avg_pool = paddle.nn.AdaptiveAvgPool3D(
output_size=[3, 3, 3], data_format="NDHWC"
)
out_4 = adaptive_avg_pool(x=x)

adaptive_avg_pool = paddle.nn.AdaptiveAvgPool3D(
output_size=[None, 3, None]
)
out_5 = adaptive_avg_pool(x=x)

exe = paddle.static.Executor(place=place)
[res_1, res_2, res_3, res_4, res_5] = exe.run(
paddle.static.default_main_program(),
feed={"x": self.x_np},
fetch_list=[out_1, out_2, out_3, out_4, out_5],
)

np.testing.assert_allclose(
res_1, self.res_1_np, rtol=1e-5, atol=1e-8
)
np.testing.assert_allclose(
res_2, self.res_2_np, rtol=1e-5, atol=1e-8
)
np.testing.assert_allclose(
res_3, self.res_3_np, rtol=1e-5, atol=1e-8
)
np.testing.assert_allclose(
res_4, self.res_4_np, rtol=1e-5, atol=1e-8
)
np.testing.assert_allclose(
res_5, self.res_5_np, rtol=1e-5, atol=1e-8
)

def test_dynamic_graph(self):
for use_cuda in (
Expand Down

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