wehub-resource-sync
365 lines
12 KiB
Python
365 lines
12 KiB
Python
# Copyright (c) 2024 PaddlePaddle 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 os
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import tempfile
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import unittest
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import numpy as np
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import paddle
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import paddle.inference as paddle_infer
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import paddle.nn.functional as F
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from paddle import Tensor, nn
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from paddle.static import InputSpec
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from paddle.tensorrt.export import (
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Input,
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TensorRTConfig,
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_convert_,
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)
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from paddle.tensorrt.util import (
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predict_program,
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)
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class LeNetMultiInput(nn.Layer):
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"""LeNet model modified to accept two inputs."""
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def __init__(self, num_classes: int = 10) -> None:
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super().__init__()
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self.num_classes = num_classes
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# Convolution layers for the first input
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self.features1 = nn.Sequential(
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nn.Conv2D(1, 6, 3, stride=1, padding=1),
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nn.ReLU(),
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nn.MaxPool2D(2, 2),
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nn.Conv2D(6, 16, 5, stride=1, padding=0),
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nn.ReLU(),
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nn.MaxPool2D(2, 2),
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)
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# Convolution layers for the second input
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self.features2 = nn.Sequential(
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nn.Conv2D(1, 6, 3, stride=1, padding=1),
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nn.ReLU(),
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nn.MaxPool2D(2, 2),
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nn.Conv2D(6, 16, 5, stride=1, padding=0),
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nn.ReLU(),
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nn.MaxPool2D(2, 2),
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)
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# Fully connected layers
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if num_classes > 0:
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self.fc = nn.Sequential(
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nn.Linear(400 * 2, 120), # Adjusted for two inputs
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nn.Linear(120, 84),
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nn.Linear(84, num_classes),
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)
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def forward(self, input1: Tensor, input2: Tensor) -> Tensor:
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# Apply feature extraction on both inputs
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x1 = self.features1(input1)
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x2 = self.features2(input2)
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# Flatten both feature maps
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x1 = paddle.flatten(x1, 1)
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x2 = paddle.flatten(x2, 1)
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# Concatenate the features from both inputs
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x = paddle.concat([x1, x2], axis=1)
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if self.num_classes > 0:
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x = self.fc(x)
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return x
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class CumsumModel(nn.Layer):
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def __init__(self, input_dim):
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super().__init__()
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self.linear = nn.Linear(input_dim, input_dim)
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def forward(self, x):
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linear_out = self.linear(x)
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relu_out = F.relu(linear_out)
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axis = paddle.full([1], 2, dtype='int64')
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out = paddle.cumsum(relu_out, axis=axis)
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return out
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class TestConvert(unittest.TestCase):
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def setUp(self):
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paddle.seed(2024)
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self.temp_dir = tempfile.TemporaryDirectory()
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self.save_path = os.path.join(self.temp_dir.name, 'tensor_axis_cumsum')
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self.place = (
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paddle.CUDAPlace(0)
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if paddle.is_compiled_with_cuda()
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else paddle.CPUPlace()
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)
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def test_paddle_to_tensorrt_conversion_cumsum(self):
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paddle.enable_static()
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np_x = np.random.randn(9, 10, 11).astype('float32')
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with paddle.pir_utils.IrGuard():
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main_prog = paddle.static.Program()
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startup_prog = paddle.static.Program()
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with paddle.static.program_guard(main_prog, startup_prog):
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x = paddle.static.data(
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shape=np_x.shape, name='x', dtype=np_x.dtype
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)
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model = CumsumModel(input_dim=np_x.shape[-1])
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out = model(x)
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loss = paddle.mean(out)
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sgd = paddle.optimizer.SGD(learning_rate=0.0)
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sgd.minimize(paddle.mean(out))
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exe = paddle.static.Executor(self.place)
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exe.run(startup_prog)
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static_out = exe.run(feed={'x': np_x}, fetch_list=[out])
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# run infer
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paddle.static.save_inference_model(
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self.save_path, [x], [out], exe
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)
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config = paddle_infer.Config(
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self.save_path + '.json', self.save_path + '.pdiparams'
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)
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config.enable_new_ir()
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config.enable_new_executor()
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config.use_optimized_model(True)
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# Set input
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input_config = Input(
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min_input_shape=(9, 10, 11),
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optim_input_shape=(9, 10, 11),
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max_input_shape=(9, 10, 11),
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)
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# Create a TensorRTConfig with inputs as a required field.
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trt_config = TensorRTConfig(inputs=[input_config])
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trt_save_path = os.path.join(self.temp_dir.name, 'trt')
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trt_config.save_model_dir = trt_save_path
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trt_config.refit_params_path = self.save_path + '.pdiparams'
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model_dir = self.save_path
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# Obtain tensorrt_engine_op by passing the model path and trt_config.(converted_program)
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program_with_trt = paddle.tensorrt.convert(model_dir, trt_config)
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# Create a config for inference.
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config = paddle_infer.Config(
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trt_config.save_model_dir + '.json',
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trt_config.save_model_dir + '.pdiparams',
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)
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if paddle.is_compiled_with_cuda():
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config.enable_use_gpu(100, 0)
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else:
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config.disable_gpu()
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predictor = paddle_infer.create_predictor(config)
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paddle.disable_static()
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for i, input_instance in enumerate(trt_config.inputs):
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min_data, _, max_data = input_instance.generate_input_data()
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model_inputs = paddle.to_tensor(min_data)
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output_converted = predictor.run([model_inputs])
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class TestConvert_(unittest.TestCase):
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def test_run(self):
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with paddle.pir_utils.IrGuard():
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input_config = Input(
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min_input_shape=(9, 10, 11),
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optim_input_shape=(9, 10, 11),
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max_input_shape=(10, 10, 11),
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)
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trt_config = TensorRTConfig(inputs=[input_config])
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for i, input_instance in enumerate(trt_config.inputs):
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min_data, _, max_data = input_instance.generate_input_data()
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paddle.disable_static()
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x = paddle.to_tensor(min_data)
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net = CumsumModel(input_dim=min_data.shape[-1])
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out = net(x)
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input_spec = [
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InputSpec(shape=[None, 10, 11], dtype='float32', name='x')
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]
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program_with_trt, scope = _convert_(
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net,
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input_spec=input_spec,
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config=trt_config,
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)
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output_var = program_with_trt.list_vars()[-1]
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output_converted = predict_program(
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program_with_trt,
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{"x": min_data},
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[output_var],
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scope=scope,
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)
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output_expected = out.numpy()
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output_converted_np = output_converted[0]
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# Check that the results are close to each other within a tolerance of 1e-2
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np.testing.assert_allclose(
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output_expected,
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output_converted_np,
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rtol=1e-2,
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atol=1e-2,
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err_msg="Outputs are not within the 1e-2 tolerance",
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)
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class TestConvertMultipleInputs(unittest.TestCase):
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def setUp(self):
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self.temp_dir = tempfile.TemporaryDirectory()
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self.save_path = os.path.join(
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self.temp_dir.name, 'tensor_axis_cumsum_multiple'
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)
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self.place = (
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paddle.CUDAPlace(0)
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if paddle.is_compiled_with_cuda()
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else paddle.CPUPlace()
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)
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def test_run(self):
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with paddle.pir_utils.IrGuard():
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input_config = Input(
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min_input_shape=(1, 1, 28, 28),
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optim_input_shape=(1, 1, 28, 28),
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max_input_shape=(1, 1, 28, 28),
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)
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input_config2 = Input(
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min_input_shape=(1, 1, 28, 28),
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optim_input_shape=(1, 1, 28, 28),
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max_input_shape=(1, 1, 28, 28),
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)
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trt_config = TensorRTConfig(inputs=[input_config, input_config2])
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trt_config.save_model_dir = os.path.join(self.temp_dir.name, 'trt')
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min_data_list = []
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max_data_list = []
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for i, input_instance in enumerate(trt_config.inputs):
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min_data, _, max_data = input_instance.generate_input_data()
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min_data_list.append(min_data)
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max_data_list.append(max_data)
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paddle.disable_static()
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x = [paddle.to_tensor(md) for md in min_data_list]
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net = LeNetMultiInput()
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out = net(*x)
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input_spec = [
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InputSpec(
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shape=min_data_list[0].shape, dtype='float32', name='input1'
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),
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InputSpec(
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shape=min_data_list[1].shape, dtype='float32', name='input2'
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),
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]
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program_with_trt, scope = _convert_(
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net,
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input_spec=input_spec,
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config=trt_config,
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full_graph=True,
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)
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config = paddle_infer.Config(
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trt_config.save_model_dir + '.json',
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trt_config.save_model_dir + '.pdiparams',
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)
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if paddle.is_compiled_with_cuda():
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config.enable_use_gpu(100, 0)
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else:
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config.disable_gpu()
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predictor = paddle_infer.create_predictor(config)
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output_converted = predictor.run(x)
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output_converted_np = output_converted[0]
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output_expected = out.numpy()
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np.testing.assert_allclose(
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output_expected,
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output_converted_np,
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rtol=1e-2,
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atol=1e-2,
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err_msg="Outputs are not within the 1e-2 tolerance",
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)
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class TestConvertPredictor(unittest.TestCase):
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def setUp(self):
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self.temp_dir = tempfile.TemporaryDirectory()
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self.save_path = os.path.join(self.temp_dir.name, 'tensor_axis_cumsum')
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self.place = (
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paddle.CUDAPlace(0)
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if paddle.is_compiled_with_cuda()
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else paddle.CPUPlace()
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)
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def test_run(self):
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input_config = Input(
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min_input_shape=(9, 10, 11),
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optim_input_shape=(9, 10, 11),
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max_input_shape=(10, 10, 11),
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)
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trt_config = TensorRTConfig(inputs=[input_config])
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trt_config.save_model_dir = os.path.join(self.temp_dir.name, 'trt')
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min_data, _, max_data = input_config.generate_input_data()
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net = CumsumModel(input_dim=min_data.shape[-1])
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x = paddle.to_tensor(min_data)
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out = net(x).numpy()
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input_spec = [
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InputSpec(shape=[None, 10, 11], dtype='float32', name='x')
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]
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program_with_trt, scope = _convert_(
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net,
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input_spec=input_spec,
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config=trt_config,
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)
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config = paddle_infer.Config(
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trt_config.save_model_dir + '.json',
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trt_config.save_model_dir + '.pdiparams',
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)
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if paddle.is_compiled_with_cuda():
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config.enable_use_gpu(100, 0)
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else:
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config.disable_gpu()
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predictor = paddle_infer.create_predictor(config)
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output_converted = predictor.run([x])
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output_converted_np = output_converted[0]
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np.testing.assert_allclose(
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out,
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output_converted_np,
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rtol=1e-2,
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atol=1e-2,
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err_msg="Outputs are not within the 1e-2 tolerance",
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)
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if __name__ == "__main__":
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unittest.main()
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