wehub-resource-sync
712 lines
21 KiB
Python
712 lines
21 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 unittest
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import numpy as np
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from tensorrt_test_base import TensorRTBaseTest
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import paddle
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from paddle import _C_ops
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def api_wrapper(x):
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return paddle._C_ops.share_data(x)
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def multiclass_nms3(
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bboxes,
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scores,
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rois_num=None,
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score_threshold=0.3,
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nms_top_k=4,
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keep_top_k=1,
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nms_threshold=0.3,
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normalized=True,
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nms_eta=1.5,
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background_label=-1,
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return_index=False,
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return_rois_num=True,
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name=None,
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):
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attrs = (
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score_threshold,
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nms_top_k,
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keep_top_k,
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nms_threshold,
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normalized,
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nms_eta,
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background_label,
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)
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output, index, nms_rois_num = _C_ops.multiclass_nms3(
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bboxes, scores, rois_num, *attrs
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)
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if not return_index:
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index = None
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return output, nms_rois_num, index
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class TestMulticlassNMS3TRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = multiclass_nms3
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self.api_args = {
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"bboxes": np.random.randn(2, 5, 4).astype("float32"),
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"scores": np.random.randn(2, 4, 5).astype("float32"),
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}
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self.program_config = {"feed_list": ["bboxes", "scores"]}
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self.min_shape = {"bboxes": [1, 5, 4], "scores": [1, 4, 5]}
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self.opt_shape = {"bboxes": [2, 5, 4], "scores": [2, 4, 5]}
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self.max_shape = {"bboxes": [3, 5, 4], "scores": [3, 4, 5]}
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def test_trt_result(self):
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self.check_trt_result()
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class TestMulticlassNMS3Marker(TensorRTBaseTest):
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def setUp(self):
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self.python_api = multiclass_nms3
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self.api_args = {
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"bboxes": np.random.randn(2, 5, 4, 1).astype("float32"),
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"scores": np.random.randn(2, 4, 5, 1).astype("float32"),
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}
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self.program_config = {"feed_list": ["bboxes", "scores"]}
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self.target_marker_op = "pd_op.multiclass_nms3"
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def test_trt_result(self):
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self.check_marker(expected_result=False)
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def set_value(
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x, starts, ends, steps, axes, decrease_axes, none_axes, shape, values
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):
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output = _C_ops.set_value(
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x,
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starts,
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ends,
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steps,
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axes,
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decrease_axes,
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none_axes,
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shape,
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values,
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)
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return output
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def set_value_(
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x, starts, ends, steps, axes, decrease_axes, none_axes, shape, values
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):
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output = _C_ops.set_value_(
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x,
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starts,
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ends,
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steps,
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axes,
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decrease_axes,
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none_axes,
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shape,
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values,
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)
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return output
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def set_value_with_tensor(
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x, values, starts, ends, steps, axes, decrease_axes, none_axes, shape
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):
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output = _C_ops.set_value_with_tensor(
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x,
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values,
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starts,
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ends,
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steps,
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axes,
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decrease_axes,
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none_axes,
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shape,
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)
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return output
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def set_value_with_tensor_(
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x, values, starts, ends, steps, axes, decrease_axes, none_axes, shape
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):
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output = _C_ops.set_value_with_tensor_(
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x,
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values,
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starts,
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ends,
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steps,
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axes,
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decrease_axes,
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none_axes,
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shape,
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)
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return output
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class TestSetValueTRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = set_value
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self.api_args = {
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"x": np.ones([10, 2]).astype("float32"),
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"starts": [0],
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"ends": [1],
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"steps": [1],
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"axes": [1],
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"decrease_axes": [],
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"none_axes": [],
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"shape": [],
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"values": [10.0],
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}
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self.program_config = {"feed_list": ["x"]}
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self.min_shape = {"x": [1, 2]}
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self.opt_shape = {"x": [2, 2]}
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self.max_shape = {"x": [20, 2]}
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def test_trt_result(self):
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self.check_trt_result()
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# starts/ends/steps is not one element
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class TestSetValueMarkerCase1(TensorRTBaseTest):
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def setUp(self):
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self.python_api = set_value
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self.api_args = {
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"x": np.ones([10, 2]).astype("float32"),
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"starts": [0, 0],
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"ends": [1, 1],
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"steps": [1, 1],
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"axes": [1],
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"decrease_axes": [],
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"none_axes": [],
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"shape": [],
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"values": [10.0],
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}
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self.program_config = {"feed_list": ["x"]}
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self.min_shape = {"x": [1, 2]}
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self.opt_shape = {"x": [2, 2]}
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self.max_shape = {"x": [5, 2]}
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def test_trt_result(self):
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self.check_marker(expected_result=False)
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# decrease_axes has element
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class TestSetValueMarkerCase2(TensorRTBaseTest):
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def setUp(self):
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self.python_api = set_value
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self.api_args = {
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"x": np.ones([10, 2]).astype("float32"),
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"starts": [0],
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"ends": [1],
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"steps": [1],
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"axes": [1],
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"decrease_axes": [1],
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"none_axes": [],
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"shape": [],
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"values": [10.0],
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}
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self.program_config = {"feed_list": ["x"]}
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self.min_shape = {"x": [1, 2]}
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self.opt_shape = {"x": [2, 2]}
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self.max_shape = {"x": [20, 2]}
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def test_trt_result(self):
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self.check_marker(expected_result=False)
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# values has more than one element
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class TestSetValueMarkerCase3(TensorRTBaseTest):
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def setUp(self):
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self.python_api = set_value
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self.api_args = {
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"x": np.ones([10, 2]).astype("float32"),
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"starts": [0],
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"ends": [1],
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"steps": [1],
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"axes": [1],
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"decrease_axes": [],
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"none_axes": [],
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"shape": [],
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"values": [10.0, 0],
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}
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self.program_config = {"feed_list": ["x"]}
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self.min_shape = {"x": [1, 2]}
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self.opt_shape = {"x": [2, 2]}
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self.max_shape = {"x": [20, 2]}
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def test_trt_result(self):
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self.check_marker(expected_result=False)
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# values has int element
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class TestSetValueMarkerCase4(TensorRTBaseTest):
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def setUp(self):
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self.python_api = set_value
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self.api_args = {
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"x": np.ones([10, 2]).astype("float32"),
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"starts": [0],
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"ends": [1],
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"steps": [1],
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"axes": [1],
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"decrease_axes": [],
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"none_axes": [],
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"shape": [],
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"values": [10],
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}
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self.program_config = {"feed_list": ["x"]}
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self.min_shape = {"x": [1, 2]}
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self.opt_shape = {"x": [2, 2]}
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self.max_shape = {"x": [20, 2]}
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def test_trt_result(self):
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self.check_marker(expected_result=False)
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# starts is not constant value
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class TestSetValueMarkerCase5(TensorRTBaseTest):
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def setUp(self):
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self.python_api = set_value
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self.api_args = {
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"x": np.ones([10, 2]).astype("float32"),
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"starts": np.zeros([1]).astype("int64"),
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"ends": [1],
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"steps": [1],
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"axes": [1],
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"decrease_axes": [],
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"none_axes": [],
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"shape": [],
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"values": [10.0],
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}
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self.program_config = {"feed_list": ["x", "starts"]}
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self.min_shape = {"x": [1, 2]}
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self.opt_shape = {"x": [2, 2]}
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self.max_shape = {"x": [20, 2]}
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def test_trt_result(self):
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self.check_marker(expected_result=False)
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class TestSetValue_TRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = set_value_
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self.api_args = {
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"x": np.ones([10, 2]).astype("float32"),
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"starts": [0],
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"ends": [1],
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"steps": [1],
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"axes": [1],
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"decrease_axes": [],
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"none_axes": [],
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"shape": [],
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"values": [10.0],
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}
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self.program_config = {"feed_list": ["x"]}
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self.min_shape = {"x": [1, 2]}
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self.opt_shape = {"x": [2, 2]}
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self.max_shape = {"x": [20, 2]}
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def test_trt_result(self):
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self.check_trt_result()
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class TestSetValueWithTensorTRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = set_value_with_tensor
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self.api_args = {
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"x": np.ones([2, 3, 3]).astype("float32"),
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"values": np.random.randn(2, 2, 3).astype("float32"),
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"starts": [0],
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"ends": [2],
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"steps": [1],
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"axes": [1],
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"decrease_axes": [],
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"none_axes": [],
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"shape": [],
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}
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self.program_config = {"feed_list": ["x", "values"]}
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self.min_shape = {"x": [1, 3, 3], "values": [1, 2, 3]}
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self.opt_shape = {"x": [2, 3, 3], "values": [2, 2, 3]}
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self.max_shape = {"x": [4, 3, 3], "values": [4, 2, 3]}
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def test_trt_result(self):
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self.check_trt_result()
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# values is int type
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class TestSetValueWithTensorMarkerCase1(TensorRTBaseTest):
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def setUp(self):
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self.python_api = set_value_with_tensor
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self.api_args = {
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"x": np.ones([2, 3, 3]).astype("float32"),
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"values": np.random.randn(2, 2, 3).astype("int32"),
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"starts": [0],
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"ends": [2],
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"steps": [1],
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"axes": [1],
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"decrease_axes": [],
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"none_axes": [],
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"shape": [],
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}
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self.program_config = {"feed_list": ["x", "values"]}
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self.min_shape = {"x": [1, 3, 3], "values": [1, 2, 3]}
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self.opt_shape = {"x": [2, 3, 3], "values": [2, 2, 3]}
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self.max_shape = {"x": [4, 3, 3], "values": [4, 2, 3]}
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def test_trt_result(self):
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self.check_marker(expected_result=False)
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class TestSetValueWithTensor_TRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = set_value_with_tensor_
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self.api_args = {
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"x": np.ones([2, 3, 3]).astype("float32"),
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"values": np.random.randn(2, 2, 3).astype("float32"),
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"starts": [0],
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"ends": [2],
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"steps": [1],
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"axes": [1],
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"decrease_axes": [],
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"none_axes": [],
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"shape": [],
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}
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self.program_config = {"feed_list": ["x", "values"]}
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self.min_shape = {"x": [1, 3, 3], "values": [1, 2, 3]}
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self.opt_shape = {"x": [2, 3, 3], "values": [2, 2, 3]}
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self.max_shape = {"x": [4, 3, 3], "values": [4, 2, 3]}
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def test_trt_result(self):
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self.check_trt_result()
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class TestShareDataTRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = api_wrapper
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self.api_args = {
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"x": np.random.rand(4, 3, 5).astype("float32"),
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}
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self.program_config = {"feed_list": ["x"]}
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self.min_shape = {"x": [4, 3, 5]}
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self.opt_shape = {"x": [5, 3, 5]}
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self.max_shape = {"x": [6, 3, 5]}
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def test_trt_result(self):
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self.check_trt_result()
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class TestTemporalShiftTRTPatternBasic(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.nn.functional.temporal_shift
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self.api_args = {
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"x": np.random.random([4, 9, 7, 7]).astype(np.float32),
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"seg_num": 2,
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"shift_ratio": 0.2,
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"data_format": "NCHW",
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}
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self.program_config = {"feed_list": ["x"]}
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self.min_shape = {"x": [2, 9, 7, 7]}
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self.opt_shape = {"x": [2, 9, 7, 7]}
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self.max_shape = {"x": [8, 9, 7, 7]}
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def test_trt_result_fp16(self):
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self.check_trt_result(precision_mode="fp16")
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def test_trt_result_fp32(self):
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self.check_trt_result()
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class TestTemporalShiftTRTPatternZeroSlice(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.nn.functional.temporal_shift
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self.api_args = {
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"x": np.random.random([4, 2, 7, 7]).astype(np.float32),
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"seg_num": 2,
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"shift_ratio": 0.2,
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"data_format": "NCHW",
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}
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self.program_config = {"feed_list": ["x"]}
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self.min_shape = {"x": [2, 2, 7, 7]}
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self.opt_shape = {"x": [2, 2, 7, 7]}
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self.max_shape = {"x": [8, 2, 7, 7]}
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def test_trt_result_fp16(self):
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self.check_trt_result(precision_mode="fp16")
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def test_trt_result_fp32(self):
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self.check_trt_result()
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class TestTemporalShiftTRTPatternDifferentSegNum(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.nn.functional.temporal_shift
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self.api_args = {
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"x": np.random.random([4, 9, 7, 7]).astype(np.float32),
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"seg_num": 4,
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"shift_ratio": 0.2,
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"data_format": "NCHW",
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}
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self.program_config = {"feed_list": ["x"]}
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self.min_shape = {"x": [4, 9, 7, 7]}
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self.opt_shape = {"x": [4, 9, 7, 7]}
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self.max_shape = {"x": [8, 9, 7, 7]}
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def test_trt_result_fp16(self):
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self.check_trt_result(precision_mode="fp16")
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def test_trt_result_fp32(self):
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self.check_trt_result()
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class TestTemporalShiftTRTPatternDifferentShiftRatio(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.nn.functional.temporal_shift
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self.api_args = {
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"x": np.random.random([4, 9, 7, 7]).astype(np.float32),
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"seg_num": 2,
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"shift_ratio": 0.4,
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"data_format": "NCHW",
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}
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self.program_config = {"feed_list": ["x"]}
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self.min_shape = {"x": [2, 9, 7, 7]}
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self.opt_shape = {"x": [2, 9, 7, 7]}
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self.max_shape = {"x": [8, 9, 7, 7]}
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def test_trt_result_fp16(self):
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self.check_trt_result(precision_mode="fp16")
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def test_trt_result_fp32(self):
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self.check_trt_result()
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class TestTemporalShiftTRTPatternDifferentDataFormat(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.nn.functional.temporal_shift
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self.api_args = {
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"x": np.random.random([4, 9, 7, 7]).astype(np.float32),
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"seg_num": 2,
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"shift_ratio": 0.2,
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"name": None,
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"data_format": "NHWC",
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}
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self.program_config = {"feed_list": ["x"]}
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self.min_shape = {"x": [2, 9, 7, 7]}
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self.opt_shape = {"x": [2, 9, 7, 7]}
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self.max_shape = {"x": [8, 9, 7, 7]}
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def test_trt_result_fp16(self):
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self.check_trt_result(precision_mode="fp16")
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def test_trt_result_fp32(self):
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self.check_trt_result()
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class TestTemporalShiftTRTPatternMinMaxShape(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.nn.functional.temporal_shift
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self.api_args = {
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"x": np.random.random([4, 9, 7, 7]).astype(np.float32),
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"seg_num": 2,
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|
"shift_ratio": 0.2,
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"data_format": "NCHW",
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}
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self.program_config = {"feed_list": ["x"]}
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self.min_shape = {"x": [2, 9, 7, 7]}
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self.opt_shape = {"x": [2, 9, 7, 7]}
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self.max_shape = {"x": [10, 9, 7, 7]}
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|
|
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def test_trt_result_fp16(self):
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self.check_trt_result(precision_mode="fp16")
|
|
|
|
def test_trt_result_fp32(self):
|
|
self.check_trt_result()
|
|
|
|
|
|
def wrapper_temporal_shift(x):
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return paddle.nn.functional.temporal_shift(x=x, seg_num=2, shift_ratio=0.2)
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|
|
|
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class TestTemporalShiftTRTPatternError1(TensorRTBaseTest):
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|
def setUp(self):
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self.python_api = wrapper_temporal_shift
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|
self.api_args = {
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|
"x": np.random.random([4, 9, 7, 7]).astype(np.float32),
|
|
}
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|
self.program_config = {"feed_list": ["x"]}
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self.min_shape = {"x": [2, 9, 7, 7]}
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self.opt_shape = {"x": [2, 9, 7, 7]}
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self.max_shape = {"x": [10, 9, 7, 7]}
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|
|
|
def test_trt_result(self):
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|
self.check_marker(expected_result=False)
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|
|
|
|
|
def affine_channel(x, scale_shape, bias_shape, layout):
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|
scale = paddle.static.create_parameter(
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shape=scale_shape, dtype='float32', name="scale"
|
|
)
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|
bias = paddle.static.create_parameter(
|
|
shape=bias_shape, dtype='float32', name="bias"
|
|
)
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|
return _C_ops.affine_channel(x, scale, bias, layout)
|
|
|
|
|
|
class TestAffineChannelTRTPattern(TensorRTBaseTest):
|
|
def setUp(self):
|
|
self.python_api = affine_channel
|
|
self.api_args = {
|
|
"x": np.random.random((2, 100, 3, 3)).astype("float32"),
|
|
"scale_shape": [100],
|
|
"bias_shape": [100],
|
|
"layout": "NCHW",
|
|
}
|
|
self.program_config = {"feed_list": ["x"]}
|
|
self.min_shape = {"x": [1, 100, 3, 3]}
|
|
self.opt_shape = {"x": [2, 100, 3, 3]}
|
|
self.max_shape = {"x": [3, 100, 3, 3]}
|
|
|
|
def test_fp32_trt_result(self):
|
|
self.check_trt_result()
|
|
|
|
def test_fp16_trt_result(self):
|
|
self.check_trt_result(precision_mode="fp16")
|
|
|
|
|
|
class TestAffineChannelCase1TRTPattern(TensorRTBaseTest):
|
|
def setUp(self):
|
|
self.python_api = affine_channel
|
|
self.api_args = {
|
|
"x": np.random.random((2, 3, 3, 100)).astype("float32"),
|
|
"scale_shape": [100],
|
|
"bias_shape": [100],
|
|
"layout": "NHWC",
|
|
}
|
|
self.program_config = {"feed_list": ["x"]}
|
|
self.min_shape = {"x": [1, 3, 3, 100]}
|
|
self.opt_shape = {"x": [2, 3, 3, 100]}
|
|
self.max_shape = {"x": [3, 3, 3, 100]}
|
|
|
|
def test_fp32_trt_result(self):
|
|
self.check_trt_result()
|
|
|
|
def test_fp16_trt_result(self):
|
|
self.check_trt_result(precision_mode="fp16")
|
|
|
|
|
|
def anchor_generator(x, anchor_sizes, aspect_ratios, variances, stride, offset):
|
|
return _C_ops.anchor_generator(
|
|
x, anchor_sizes, aspect_ratios, variances, stride, offset
|
|
)
|
|
|
|
|
|
class TestAnchorGeneratorTRTPattern(TensorRTBaseTest):
|
|
def setUp(self):
|
|
self.python_api = anchor_generator
|
|
self.api_args = {
|
|
"x": np.random.random((2, 3, 3, 100)).astype("float32"),
|
|
"anchor_sizes": [64.0, 128.0, 256.0],
|
|
"aspect_ratios": [0.5, 1, 2],
|
|
"variances": [1.0, 1.0, 1.0, 1.0],
|
|
"stride": [16.0, 16.0],
|
|
"offset": 0.5,
|
|
}
|
|
self.program_config = {"feed_list": ["x"]}
|
|
self.min_shape = {"x": [1, 3, 3, 100]}
|
|
self.opt_shape = {"x": [2, 3, 3, 100]}
|
|
self.max_shape = {"x": [3, 3, 3, 100]}
|
|
|
|
def test_fp32_trt_result(self):
|
|
self.check_trt_result()
|
|
|
|
def test_fp16_trt_result(self):
|
|
self.check_trt_result(precision_mode="fp16")
|
|
|
|
|
|
class TestAnchorGeneratorCase1TRTPattern(TensorRTBaseTest):
|
|
def setUp(self):
|
|
self.python_api = anchor_generator
|
|
self.api_args = {
|
|
"x": np.random.random((2, 3, 64, 64)).astype("float32"),
|
|
"anchor_sizes": [64.0, 128.0, 256.0],
|
|
"aspect_ratios": [0.4, 1.2, 3],
|
|
"variances": [0.5, 1.0, 0.5, 1.0],
|
|
"stride": [16.0, 32.0],
|
|
"offset": 0.8,
|
|
}
|
|
self.program_config = {"feed_list": ["x"]}
|
|
self.min_shape = {"x": [2, 3, 64, 64]}
|
|
self.opt_shape = {"x": [2, 3, 64, 64]}
|
|
self.max_shape = {"x": [3, 3, 64, 64]}
|
|
|
|
def test_fp32_trt_result(self):
|
|
self.check_trt_result()
|
|
|
|
def test_fp16_trt_result(self):
|
|
self.check_trt_result(precision_mode="fp16")
|
|
|
|
|
|
def shuffle_channel_wrapper(x, group=1):
|
|
return _C_ops.shuffle_channel(x, group)
|
|
|
|
|
|
class TestShuffleChannelTRTPattern(TensorRTBaseTest):
|
|
def setUp(self):
|
|
self.python_api = shuffle_channel_wrapper
|
|
self.api_args = {
|
|
"x": np.random.random((10, 16, 4, 4)).astype("float32"),
|
|
}
|
|
self.program_config = {"feed_list": ["x"]}
|
|
self.min_shape = {"x": [10, 16, 4, 4]}
|
|
self.opt_shape = {"x": [10, 16, 4, 4]}
|
|
self.max_shape = {"x": [10, 16, 4, 4]}
|
|
|
|
def test_fp32_trt_result(self):
|
|
self.check_trt_result()
|
|
|
|
def test_fp16_trt_result(self):
|
|
self.check_trt_result(precision_mode="fp16")
|
|
|
|
|
|
def full_batch_size_like_wrapper(x, dtype, value, batch_dim):
|
|
place = paddle.CPUPlace()
|
|
out_shape = [-1, 5, 1]
|
|
return _C_ops.full_batch_size_like(
|
|
x, out_shape, dtype, value, batch_dim, batch_dim, place
|
|
)
|
|
|
|
|
|
class TestFullBatchSizeLikeTRTPattern(TensorRTBaseTest):
|
|
def setUp(self):
|
|
self.python_api = full_batch_size_like_wrapper
|
|
self.api_args = {
|
|
"x": np.random.random((2, 3, 4)).astype("float32"),
|
|
"dtype": paddle.float32,
|
|
"value": 2.0,
|
|
"batch_dim": 0,
|
|
}
|
|
self.program_config = {"feed_list": ["x"]}
|
|
self.min_shape = {"x": [2, 3, 4]}
|
|
self.opt_shape = {"x": [3, 3, 4]}
|
|
self.max_shape = {"x": [4, 3, 4]}
|
|
|
|
def test_fp32_trt_result(self):
|
|
self.check_trt_result()
|
|
|
|
def test_fp16_trt_result(self):
|
|
self.check_trt_result(precision_mode="fp16")
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|