wehub-resource-sync
188 lines
6.6 KiB
Python
Executable File
188 lines
6.6 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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from paddle import base
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from paddle.cinn.common import DefaultHostTarget, DefaultNVGPUTarget
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from paddle.cinn.frontend import Interpreter
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assert len(sys.argv) == 1 + 2 + 1 # model and enable_gpu count
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enable_gpu = sys.argv.pop()
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multi_fc_model_dir = sys.argv.pop()
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naive_model_dir = sys.argv.pop()
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""" class TestFrontend(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(self, result):
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paddle.enable_static()
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a = static.data(name='A', shape=[24, 56, 56], dtype='float32')
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b = static.data(name='B', shape=[24, 56, 56], dtype='float32')
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c = paddle.add(a, b)
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d = paddle.nn.functional.relu(c)
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e = paddle.nn.initializer.NumpyArrayInitializer(
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np.array(result[2]).reshape((144, 24, 1, 1)).astype("float32"))
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f = static.nn.conv2d(
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input=d,
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num_filters=144,
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filter_size=1,
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stride=1,
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padding=0,
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dilation=1,
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param_attr=e)
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g = paddle.scale(f, scale=2.0, bias=0.5)
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res = paddle.nn.functional.softmax(g, axis=1)
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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("Error! ", i, "-th data has diff with target data:\n",
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output[i], " vs: ", result[len(result) - 1][i],
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". Diff is: ", output[i] - result[len(result) - 1][i])
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self.assertTrue(
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np.allclose(result[len(result) - 1], output, atol=1e-4))
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def test_basic(self):
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prog = Program()
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a = Variable("A").set_type(Float(32)).set_shape([1, 24, 56, 56])
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b = Variable("B").set_type(Float(32)).set_shape([1, 24, 56, 56])
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c = prog.add(a, b)
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d = prog.relu(c)
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e = Variable("E").set_type(Float(32)).set_shape([144, 24, 1, 1])
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f = prog.conv2d(d, e, {
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"stride": [1, 1],
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"dilation": [1, 1],
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"padding": [0, 0]
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})
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g = prog.scale(f, {"scale": 2.0, "bias": 0.5})
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h = prog.softmax(g, {"axis": 1})
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self.assertEqual(prog.size(), 5)
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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(self.target, [a, b, e], tensor_data,
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[h])
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result[0].set_type(Float(32))
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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(tensor_data) """
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class TestLoadPaddleModel_FC(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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self.model_dir = naive_model_dir
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def get_paddle_inference_result(self, model_dir, data):
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config = base.core.AnalysisConfig(model_dir)
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config.disable_gpu()
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config.switch_ir_optim(False)
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self.paddle_predictor = base.core.create_paddle_predictor(config)
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data = base.core.PaddleTensor(data)
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results = self.paddle_predictor.run([data])
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return results[0].as_ndarray()
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def test_model(self):
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np.random.seed(0)
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self.x_shape = [4, 30]
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x_data = (
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np.random.random(self.x_shape).astype("float16").astype("float32")
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)
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print('x_data', x_data)
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self.executor = Interpreter(["A"], [self.x_shape])
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self.executor.load_paddle_model(self.model_dir, self.target, False)
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a_t = self.executor.get_tensor("A")
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a_t.from_numpy(x_data, self.target)
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self.executor.run()
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out = self.executor.get_tensor("fc_0.tmp_2")
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target_data = self.get_paddle_inference_result(self.model_dir, x_data)
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print("target_data's shape is: ", target_data.shape)
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out_np = out.numpy(self.target)
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print("cinn data's shape is: ", out_np.shape)
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np.testing.assert_allclose(out_np, target_data, atol=1e-4)
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class TestLoadPaddleModel_MultiFC(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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self.model_dir = multi_fc_model_dir
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def get_paddle_inference_result(self, model_dir, data):
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config = base.core.AnalysisConfig(model_dir)
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config.disable_gpu()
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config.switch_ir_optim(False)
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self.paddle_predictor = base.core.create_paddle_predictor(config)
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data = base.core.PaddleTensor(data)
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results = self.paddle_predictor.run([data])
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return results[0].as_ndarray()
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def test_model(self):
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np.random.seed(0)
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self.x_shape = [8, 64]
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x_data = np.random.random(self.x_shape).astype("float32")
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self.executor = Interpreter(["A"], [self.x_shape])
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self.executor.load_paddle_model(self.model_dir, self.target, False)
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a_t = self.executor.get_tensor("A")
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a_t.from_numpy(x_data, self.target)
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self.executor.run()
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out = self.executor.get_tensor("fc_5.tmp_2")
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target = self.get_paddle_inference_result(self.model_dir, x_data)
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np.testing.assert_allclose(out.numpy(self.target), target, atol=1e-4)
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if __name__ == "__main__":
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unittest.main()
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