wehub-resource-sync
124 lines
4.2 KiB
Python
Executable File
124 lines
4.2 KiB
Python
Executable File
#!/usr/bin/env python3
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# Copyright (c) 2021 CINN Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import sys
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import unittest
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import numpy as np
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import paddle
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from paddle import static
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from paddle.cinn.common import DefaultHostTarget, DefaultNVGPUTarget, Float
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from paddle.cinn.frontend import NetBuilder
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enable_gpu = sys.argv.pop()
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class TestNetBuilder(unittest.TestCase):
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def setUp(self):
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if enable_gpu == "ON":
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self.target = DefaultNVGPUTarget()
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else:
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self.target = DefaultHostTarget()
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def paddle_verify_basic(self, result):
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paddle.enable_static()
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a = static.data(name='A', shape=[1, 24, 56, 56], dtype='float32')
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b = static.data(name='B', shape=[1, 24, 56, 56], dtype='float32')
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c = paddle.add(a, b)
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d = paddle.nn.initializer.NumpyArrayInitializer(
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np.array(result[2]).reshape((144, 24, 1, 1)).astype('float32')
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)
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res = paddle.nn.Conv2D(
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in_channels=24,
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out_channels=144,
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kernel_size=1,
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stride=1,
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dilation=1,
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padding=0,
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weight_attr=d,
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)(c)
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exe = static.Executor(paddle.CPUPlace())
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exe.run(static.default_startup_program())
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x = np.array(result[0]).reshape((1, 24, 56, 56)).astype("float32")
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y = np.array(result[1]).reshape((1, 24, 56, 56)).astype("float32")
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output = exe.run(feed={"A": x, "B": y}, fetch_list=[res])
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output = np.array(output).reshape(-1)
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print("result in paddle_verify: \n")
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for i in range(0, output.shape[0]):
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if np.abs(output[i] - result[len(result) - 1][i]) > 1e-4:
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print(
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"Error! ",
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i,
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"-th data has diff with target data:\n",
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output[i],
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" vs: ",
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result[len(result) - 1][i],
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". Diff is: ",
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output[i] - result[len(result) - 1][i],
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)
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np.testing.assert_allclose(result[len(result) - 1], output, atol=1e-4)
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def test_basic(self):
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builder = NetBuilder("test_basic")
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a = builder.create_input(Float(32), (1, 24, 56, 56), "A")
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b = builder.create_input(Float(32), (1, 24, 56, 56), "B")
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c = builder.add(a, b)
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d = builder.create_input(Float(32), (144, 24, 1, 1), "D")
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e = builder.conv2d(c, d)
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prog = builder.build()
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self.assertEqual(prog.size(), 2)
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# print program
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for i in range(prog.size()):
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print(prog[i])
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tensor_data = [
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np.random.random([1, 24, 56, 56]).astype("float32"),
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np.random.random([1, 24, 56, 56]).astype("float32"),
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np.random.random([144, 24, 1, 1]).astype("float32"),
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]
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result = prog.build_and_get_output(
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self.target, [a, b, d], tensor_data, [e]
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)
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result = result[0].numpy(self.target).reshape(-1)
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tensor_data.append(result)
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self.paddle_verify_basic(tensor_data)
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class TestNetBuilderOp(unittest.TestCase):
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def setUp(self):
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if enable_gpu == "ON":
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self.target = DefaultNVGPUTarget()
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else:
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self.target = DefaultHostTarget()
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def test_basic(self):
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builder = NetBuilder("testmul")
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a = builder.create_input(Float(32), (4, 4), "A")
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tensor_data = [np.random.random([4, 4]).astype("float32")]
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print(tensor_data[0])
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b = builder.add(a, a)
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prog = builder.build()
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result = prog.build_and_get_output(self.target, [a], tensor_data, [b])
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res = result[0].numpy(self.target)
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print(res)
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if __name__ == "__main__":
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unittest.main()
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