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https://github.com/PaddlePaddle/FastDeploy.git
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6343b0db47
* Add notes for tensors * Optimize some apis * move some warnings * Support build with Paddle2ONNX * Add protobuf support * Fix compile on mac * add clearn package script * Add paddle2onnx code * remove submodule * Add onnx ocde * remove softlink * add onnx code * fix error * Add cmake file * fix patchelf * update paddle2onnx * Delete .gitmodules --------- Co-authored-by: PaddleCI <paddle_ci@example.com> Co-authored-by: pangyoki <pangyoki@126.com> Co-authored-by: jiangjiajun <jiangjiajun@baidu.lcom>
126 lines
4.9 KiB
Python
126 lines
4.9 KiB
Python
# Copyright (c) 2020 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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from __future__ import absolute_import
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import math
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import numpy as np
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from paddle2onnx.legacy.constant import dtypes
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from paddle2onnx.legacy.op_mapper import OpMapper as op_mapper
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from paddle2onnx.utils import require_fixed_shape
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@op_mapper('density_prior_box')
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class DensityPriorBox():
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"""
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In this function, use the attribute to get the prior box, because we do not use
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the image data and feature map, wo could the python code to create the varaible,
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and to create the onnx tensor as output.
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"""
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support_opset_verison_range = (1, 12)
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@classmethod
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def opset_9(cls, graph, node, **kw):
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clip = bool(node.attr('clip'))
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densities = node.attr('densities')
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fixed_ratios = node.attr('fixed_ratios')
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fixed_sizes = node.attr('fixed_sizes')
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flatten_to_2d = bool(node.attr('flatten_to_2d'))
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offset = node.attr('offset')
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step_h = node.attr('step_h')
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step_w = node.attr('step_w')
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variances = node.attr('variances')
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input_shape = node.input_shape('Input', 0)
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image_shape = node.input_shape('Image', 0)
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img_width = image_shape[3]
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img_height = image_shape[2]
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feature_width = input_shape[3]
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feature_height = input_shape[2]
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assert img_width > 0 and img_height > 0, require_fixed_shape(
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cls.__name__)
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if step_w == 0.0 or step_h == 0.0:
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step_w = float(img_width / feature_width)
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step_h = float(img_height / feature_height)
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num_priors = 0
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if len(fixed_sizes) > 0 and len(densities) > 0:
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for density in densities:
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if len(fixed_ratios) > 0:
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num_priors += len(fixed_ratios) * (pow(density, 2))
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out_dim = (feature_height, feature_width, num_priors, 4)
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out_boxes = np.zeros(out_dim).astype('float32')
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out_var = np.zeros(out_dim).astype('float32')
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step_average = int((step_w + step_h) * 0.5)
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for h in range(feature_height):
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for w in range(feature_width):
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c_x = (w + offset) * step_w
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c_y = (h + offset) * step_h
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idx = 0
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for density, fixed_size in zip(densities, fixed_sizes):
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if (len(fixed_ratios) > 0):
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for ar in fixed_ratios:
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shift = int(step_average / density)
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box_width_ratio = fixed_size * math.sqrt(ar)
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box_height_ratio = fixed_size / math.sqrt(ar)
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for di in range(density):
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for dj in range(density):
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c_x_temp = c_x - step_average / 2.0 + shift / 2.0 + dj * shift
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c_y_temp = c_y - step_average / 2.0 + shift / 2.0 + di * shift
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out_boxes[h, w, idx, :] = [
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max((c_x_temp - box_width_ratio / 2.0) /
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img_width, 0),
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max((c_y_temp - box_height_ratio / 2.0)
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/ img_height, 0),
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min((c_x_temp + box_width_ratio / 2.0) /
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img_width, 1),
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min((c_y_temp + box_height_ratio / 2.0)
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/ img_height, 1)
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]
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idx += 1
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if clip:
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out_boxes = np.clip(out_boxes, 0.0, 1.0)
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# set the variance.
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out_var = np.tile(variances,
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(feature_height, feature_width, num_priors, 1))
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if flatten_to_2d:
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out_boxes = out_boxes.reshape((-1, 4))
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out_var = out_var.reshape((-1, 4))
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#make node that
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node_boxes = graph.make_node(
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'Constant',
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inputs=[],
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outputs=node.output('Boxes'),
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dtype=dtypes.ONNX.FLOAT,
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dims=out_boxes.shape,
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value=out_boxes.flatten().tolist())
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node_vars = graph.make_node(
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'Constant',
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inputs=[],
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outputs=node.output('Variances'),
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dtype=dtypes.ONNX.FLOAT,
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dims=out_var.shape,
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value=out_var.flatten().tolist())
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