forked from PulseFocusPlatform/PulseFocusPlatform
125 lines
4.5 KiB
Python
125 lines
4.5 KiB
Python
import paddle
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import paddle.nn as nn
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import paddle.nn.functional as F
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from paddle import ParamAttr
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from paddle.regularizer import L2Decay
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from ppdet.core.workspace import register
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def _de_sigmoid(x, eps=1e-7):
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x = paddle.clip(x, eps, 1. / eps)
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x = paddle.clip(1. / x - 1., eps, 1. / eps)
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x = -paddle.log(x)
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return x
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@register
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class YOLOv3Head(nn.Layer):
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__shared__ = ['num_classes', 'data_format']
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__inject__ = ['loss']
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def __init__(self,
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in_channels=[1024, 512, 256],
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anchors=[[10, 13], [16, 30], [33, 23], [30, 61], [62, 45],
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[59, 119], [116, 90], [156, 198], [373, 326]],
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anchor_masks=[[6, 7, 8], [3, 4, 5], [0, 1, 2]],
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num_classes=80,
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loss='YOLOv3Loss',
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iou_aware=False,
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iou_aware_factor=0.4,
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data_format='NCHW'):
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"""
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Head for YOLOv3 network
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Args:
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num_classes (int): number of foreground classes
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anchors (list): anchors
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anchor_masks (list): anchor masks
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loss (object): YOLOv3Loss instance
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iou_aware (bool): whether to use iou_aware
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iou_aware_factor (float): iou aware factor
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data_format (str): data format, NCHW or NHWC
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"""
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super(YOLOv3Head, self).__init__()
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assert len(in_channels) > 0, "in_channels length should > 0"
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self.in_channels = in_channels
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self.num_classes = num_classes
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self.loss = loss
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self.iou_aware = iou_aware
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self.iou_aware_factor = iou_aware_factor
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self.parse_anchor(anchors, anchor_masks)
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self.num_outputs = len(self.anchors)
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self.data_format = data_format
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self.yolo_outputs = []
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for i in range(len(self.anchors)):
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if self.iou_aware:
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num_filters = len(self.anchors[i]) * (self.num_classes + 6)
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else:
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num_filters = len(self.anchors[i]) * (self.num_classes + 5)
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name = 'yolo_output.{}'.format(i)
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conv = nn.Conv2D(
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in_channels=self.in_channels[i],
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out_channels=num_filters,
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kernel_size=1,
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stride=1,
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padding=0,
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data_format=data_format,
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bias_attr=ParamAttr(regularizer=L2Decay(0.)))
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conv.skip_quant = True
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yolo_output = self.add_sublayer(name, conv)
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self.yolo_outputs.append(yolo_output)
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def parse_anchor(self, anchors, anchor_masks):
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self.anchors = [[anchors[i] for i in mask] for mask in anchor_masks]
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self.mask_anchors = []
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anchor_num = len(anchors)
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for masks in anchor_masks:
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self.mask_anchors.append([])
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for mask in masks:
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assert mask < anchor_num, "anchor mask index overflow"
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self.mask_anchors[-1].extend(anchors[mask])
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def forward(self, feats, targets=None):
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assert len(feats) == len(self.anchors)
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yolo_outputs = []
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for i, feat in enumerate(feats):
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yolo_output = self.yolo_outputs[i](feat)
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if self.data_format == 'NHWC':
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yolo_output = paddle.transpose(yolo_output, [0, 3, 1, 2])
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yolo_outputs.append(yolo_output)
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if self.training:
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return self.loss(yolo_outputs, targets, self.anchors)
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else:
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if self.iou_aware:
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y = []
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for i, out in enumerate(yolo_outputs):
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na = len(self.anchors[i])
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ioup, x = out[:, 0:na, :, :], out[:, na:, :, :]
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b, c, h, w = x.shape
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no = c // na
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x = x.reshape((b, na, no, h * w))
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ioup = ioup.reshape((b, na, 1, h * w))
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obj = x[:, :, 4:5, :]
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ioup = F.sigmoid(ioup)
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obj = F.sigmoid(obj)
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obj_t = (obj**(1 - self.iou_aware_factor)) * (
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ioup**self.iou_aware_factor)
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obj_t = _de_sigmoid(obj_t)
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loc_t = x[:, :, :4, :]
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cls_t = x[:, :, 5:, :]
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y_t = paddle.concat([loc_t, obj_t, cls_t], axis=2)
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y_t = y_t.reshape((b, c, h, w))
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y.append(y_t)
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return y
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else:
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return yolo_outputs
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@classmethod
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def from_config(cls, cfg, input_shape):
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return {'in_channels': [i.channels for i in input_shape], }
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