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3.2 YOLOv3 为了节俭篇幅，这一节只挑局部贴代码，更多的代码和包的导入请参考 附件 \model\yolo.py。（参考：https://gitee.com/mindspore/m…）class YoloBlock 是 Darknet53 输入后的解决模块，包含 YOLOv3 结构图（参见 YOLOv3 人体指标检测模型实现（一））中的 Convolutional Set 以及前面的卷积：class YoloBlock(nn.Cell):
"""
YoloBlock for YOLOv3.
Args:
in_channels: Integer. Input channel.
out_chls: Integer. Middle channel.
out_channels: Integer. Output channel.
Returns:
Tuple, tuple of output tensor,(f1,f2,f3).
Examples:
YoloBlock(1024, 512, 255)
"""
def __init__(self, in_channels, out_chls, out_channels):
super(YoloBlock, self).__init__()
out_chls_2 = out_chls*2
self.conv0 = _conv_bn_relu(in_channels, out_chls, ksize=1)
self.conv1 = _conv_bn_relu(out_chls, out_chls_2, ksize=3)
self.conv2 = _conv_bn_relu(out_chls_2, out_chls, ksize=1)
self.conv3 = _conv_bn_relu(out_chls, out_chls_2, ksize=3)
self.conv4 = _conv_bn_relu(out_chls_2, out_chls, ksize=1)
self.conv5 = _conv_bn_relu(out_chls, out_chls_2, ksize=3)
self.conv6 = nn.Conv2d(out_chls_2, out_channels, kernel_size=1, stride=1, has_bias=True)
def construct(self, x):
c1 = self.conv0(x)
c2 = self.conv1(c1)
c3 = self.conv2(c2)
c4 = self.conv3(c3)
c5 = self.conv4(c4)
c6 = self.conv5(c5)
out = self.conv6(c6)
return c5, outclass YOLOv3 则将骨干网络和 YoloBlock 组合起来（蕴含上采样）成为结构图中残缺的 YOLOv3 模型：class YOLOv3(nn.Cell):
"""
YOLOv3 Network.
Note:
backbone = darknet53
Args:
backbone_shape: List. Darknet output channels shape.
backbone: Cell. Backbone Network.
out_channel: Integer. Output channel.
Returns:
Tensor, output tensor.
Examples:
YOLOv3(backbone_shape=[64, 128, 256, 512, 1024]
backbone=darknet53(),
out_channel=255)
"""
def __init__(self, backbone_shape, backbone, out_channel):
super(YOLOv3, self).__init__()
self.out_channel = out_channel
self.backbone = backbone
self.backblock0 = YoloBlock(backbone_shape[-1], out_chls=backbone_shape[-2], out_channels=out_channel)
self.conv1 = _conv_bn_relu(in_channel=backbone_shape[-2], out_channel=backbone_shape[-2]//2, ksize=1)
self.backblock1 = YoloBlock(in_channels=backbone_shape[-2]+backbone_shape[-3],
out_chls=backbone_shape[-3],
out_channels=out_channel)
self.conv2 = _conv_bn_relu(in_channel=backbone_shape[-3], out_channel=backbone_shape[-3]//2, ksize=1)
self.backblock2 = YoloBlock(in_channels=backbone_shape[-3]+backbone_shape[-4],
out_chls=backbone_shape[-4],
out_channels=out_channel)
self.concat = P.Concat(axis=1)
def construct(self, x):
# input_shape of x is (batch_size, 3, h, w)
# feature_map1 is (batch_size, backbone_shape[2], h/8, w/8)
# feature_map2 is (batch_size, backbone_shape[3], h/16, w/16)
# feature_map3 is (batch_size, backbone_shape[4], h/32, w/32)
img_hight = P.Shape()(x)[2]
img_width = P.Shape()(x)[3]
feature_map1, feature_map2, feature_map3 = self.backbone(x)
con1, big_object_output = self.backblock0(feature_map3)
con1 = self.conv1(con1)
ups1 = P.ResizeNearestNeighbor((img_hight // 16, img_width // 16))(con1)
con1 = self.concat((ups1, feature_map2))
con2, medium_object_output = self.backblock1(con1)
con2 = self.conv2(con2)
ups2 = P.ResizeNearestNeighbor((img_hight // 8, img_width // 8))(con2)
con3 = self.concat((ups2, feature_map1))
_, small_object_output = self.backblock2(con3)
return big_object_output, medium_object_output, small_object_outputclass DetectionBlock 负责对 YOLOv3 的输入下图中的计算：class DetectionBlock(nn.Cell):
"""
YOLOv3 detection Network. It will finally output the detection result.
Args:
scale: Character.
config: Configuration.
is_training: Bool, Whether train or not, default True.
Returns:
Tuple, tuple of output tensor,(f1,f2,f3).
Examples:
DetectionBlock(scale='l',stride=32,config=config)
"""
def __init__(self, scale, config=None, is_training=True):
super(DetectionBlock, self).__init__()
self.config = config
if scale == 's':
idx = (0, 1, 2)
elif scale == 'm':
idx = (3, 4, 5)
elif scale == 'l':
idx = (6, 7, 8)
else:
raise KeyError("Invalid scale value for DetectionBlock")
self.anchors = Tensor([self.config.anchor_scales for i in idx], ms.float32)
self.num_anchors_per_scale = 3
self.num_attrib = 4+1+self.config.num_classes
self.lambda_coord = 1
self.sigmoid = nn.Sigmoid()
self.reshape = P.Reshape()
self.tile = P.Tile()
self.concat = P.Concat(axis=-1)
self.conf_training = is_training
def construct(self, x, input_shape):
num_batch = P.Shape()(x)[0]
grid_size = P.Shape()(x)[2:4]
# Reshape and transpose the feature to [n, grid_size[0], grid_size[1], 3, num_attrib]
prediction = P.Reshape()(x, (num_batch,
self.num_anchors_per_scale,
self.num_attrib,
grid_size[0],
grid_size[1]))
prediction = P.Transpose()(prediction, (0, 3, 4, 1, 2))
range_x = range(grid_size[1])
range_y = range(grid_size[0])
grid_x = P.Cast()(F.tuple_to_array(range_x), ms.float32)
grid_y = P.Cast()(F.tuple_to_array(range_y), ms.float32)
# Tensor of shape [grid_size[0], grid_size[1], 1, 1] representing the coordinate of x/y axis for each grid
# [batch, gridx, gridy, 1, 1]
grid_x = self.tile(self.reshape(grid_x, (1, 1, -1, 1, 1)), (1, grid_size[0], 1, 1, 1))
grid_y = self.tile(self.reshape(grid_y, (1, -1, 1, 1, 1)), (1, 1, grid_size[1], 1, 1))
# Shape is [grid_size[0], grid_size[1], 1, 2]
grid = self.concat((grid_x, grid_y))
box_xy = prediction[:, :, :, :, :2]
box_wh = prediction[:, :, :, :, 2:4]
box_confidence = prediction[:, :, :, :, 4:5]
box_probs = prediction[:, :, :, :, 5:]
# gridsize1 is x
# gridsize0 is y
box_xy = (self.sigmoid(box_xy) + grid) / P.Cast()(F.tuple_to_array((grid_size[1], grid_size[0])), ms.float32)
# box_wh is w->h
box_wh = P.Exp()(box_wh) * self.anchors / input_shape
box_confidence = self.sigmoid(box_confidence)
box_probs = self.sigmoid(box_probs)
if self.conf_training:
return grid, prediction, box_xy, box_wh
return self.concat((box_xy, box_wh, box_confidence, box_probs))class YoloLossBlock 用于计算模型推理失去的 3 个输入特色图的损失：class YoloLossBlock(nn.Cell):
"""Loss block cell of YOLOV3 network."""
def __init__(self, scale, config=None):
super(YoloLossBlock, self).__init__()
self.config = config
if scale == 's':
# anchor mask
idx = (0, 1, 2)
elif scale == 'm':
idx = (3, 4, 5)
elif scale == 'l':
idx = (6, 7, 8)
else:
raise KeyError("Invalid scale value for DetectionBlock")
self.anchors = Tensor([self.config.anchor_scales for i in idx], ms.float32)
self.ignore_threshold = Tensor(self.config.ignore_threshold, ms.float32)
self.concat = P.Concat(axis=-1)
self.iou = Iou()
self.reduce_max = P.ReduceMax(keep_dims=False)
self.xy_loss = XYLoss()
self.wh_loss = WHLoss()
self.confidenceLoss = ConfidenceLoss()
self.classLoss = ClassLoss()
def construct(self, grid, prediction, pred_xy, pred_wh, y_true, gt_box, input_shape):
# prediction : origin output from yolo
# pred_xy: (sigmoid(xy)+grid)/grid_size
# pred_wh: (exp(wh)*anchors)/input_shape
# y_true : after normalize
# gt_box: [batch, maxboxes, xyhw] after normalize
object_mask = y_true[:, :, :, :, 4:5]
class_probs = y_true[:, :, :, :, 5:]
grid_shape = P.Shape()(prediction)[1:3]
grid_shape = P.Cast()(F.tuple_to_array(grid_shape[::-1]), ms.float32)
pred_boxes = self.concat((pred_xy, pred_wh))
true_xy = y_true[:, :, :, :, :2] * grid_shape - grid
true_wh = y_true[:, :, :, :, 2:4]
true_wh = P.Select()(P.Equal()(true_wh, 0.0),
P.Fill()(P.DType()(true_wh),
P.Shape()(true_wh), 1.0),
true_wh)
true_wh = P.Log()(true_wh / self.anchors * input_shape)
# 2-w*h for large picture, use small scale, since small obj need more precise
box_loss_scale = 2 - y_true[:, :, :, :, 2:3] * y_true[:, :, :, :, 3:4]
gt_shape = P.Shape()(gt_box)
gt_box = P.Reshape()(gt_box, (gt_shape[0], 1, 1, 1, gt_shape[1], gt_shape[2]))
# add one more dimension for broadcast
iou = self.iou(P.ExpandDims()(pred_boxes, -2), gt_box)
# gt_box is x,y,h,w after normalize
# [batch, grid[0], grid[1], num_anchor, num_gt]
best_iou = self.reduce_max(iou, -1)
# [batch, grid[0], grid[1], num_anchor]
# ignore_mask IOU too small
ignore_mask = best_iou < self.ignore_threshold
ignore_mask = P.Cast()(ignore_mask, ms.float32)
ignore_mask = P.ExpandDims()(ignore_mask, -1)
# ignore_mask backpro will cause a lot maximunGrad and minimumGrad time consume.
# so we turn off its gradient
ignore_mask = F.stop_gradient(ignore_mask)
xy_loss = self.xy_loss(object_mask, box_loss_scale, prediction[:, :, :, :, :2], true_xy)
wh_loss = self.wh_loss(object_mask, box_loss_scale, prediction[:, :, :, :, 2:4], true_wh)
confidence_loss = self.confidenceLoss(object_mask, prediction[:, :, :, :, 4:5], ignore_mask)
class_loss = self.classLoss(object_mask, prediction[:, :, :, :, 5:], class_probs)
loss = xy_loss + wh_loss + confidence_loss + class_loss
batch_size = P.Shape()(prediction)[0]
return loss / batch_size 损失函数的实现可见 附件 \model\loss.py。最初，class YOLOV3DarkNet53 将后面的 class 组装在一起：class YOLOV3DarkNet53(nn.Cell):
"""
Darknet based YOLOV3 network.
Args:
is_training: Bool. Whether train or not.
Returns:
Cell, cell instance of Darknet based YOLOV3 neural network.
Examples:
YOLOV3DarkNet53(True)
"""
def __init__(self, is_training, config=None):
super(YOLOV3DarkNet53, self).__init__()
self.config = config
self.keep_detect = self.config.keep_detect
self.tenser_to_array = P.TupleToArray()
# YOLOv3 network
self.feature_map = YOLOv3(backbone=DarkNet(ResidualBlock, [1, 2, 8, 8, 4],
[32, 64, 128, 256, 512],
[64, 128, 256, 512, 1024],
detect=True),
backbone_shape=[64, 128, 256, 512, 1024],
out_channel=self.config.out_channel)
# prediction on the default anchor boxes
self.detect_1 = DetectionBlock('l', is_training=is_training, config=self.config)
self.detect_2 = DetectionBlock('m', is_training=is_training, config=self.config)
self.detect_3 = DetectionBlock('s', is_training=is_training, config=self.config)
def construct(self, x):
input_shape = F.shape(x)[2:4]
input_shape = F.cast(self.tenser_to_array(input_shape), ms.float32)
big_object_output, medium_object_output, small_object_output = self.feature_map(x)
if not self.keep_detect:
return big_object_output, medium_object_output, small_object_output
output_big = self.detect_1(big_object_output, input_shape)
output_me = self.detect_2(medium_object_output, input_shape)
output_small = self.detect_3(small_object_output, input_shape)
# big is the final output which has smallest feature map
return output_big, output_me, output_small 将 loss 的计算和网络组装在一起：class YoloWithLossCell(nn.Cell):
"""YOLOV3 loss."""
def __init__(self, network, config=None):
super(YoloWithLossCell, self).__init__()
self.yolo_network = network
self.config = config
self.tenser_to_array = P.TupleToArray()
self.loss_big = YoloLossBlock('l', self.config)
self.loss_me = YoloLossBlock('m', self.config)
self.loss_small = YoloLossBlock('s', self.config)
def construct(self, x, y_true_0, y_true_1, y_true_2, gt_0, gt_1, gt_2):
input_shape = F.shape(x)[2:4]
input_shape = F.cast(self.tenser_to_array(input_shape), ms.float32)
yolo_out = self.yolo_network(x)
loss_l = self.loss_big(*yolo_out[0], y_true_0, gt_0, input_shape)
loss_m = self.loss_me(*yolo_out[1], y_true_1, gt_1, input_shape)
loss_s = self.loss_small(*yolo_out[2], y_true_2, gt_2, input_shape)
return loss_l + loss_m + loss_s（未完，请见下一篇 YOLOv3 人体指标检测模型实现（三））正文完