用mindspore写了一个decouplehead模块，在yolov5上使用时报错
问题描述：
1.平台：gpu
2.版本：1.2.0
decouplehead模块代码如下：
import mindspore as ms
import mindspore.nn as nn
from mindspore.common.tensor import Tensor
from mindspore import context
from mindspore.context import ParallelMode
from mindspore.parallel._auto_parallel_context import auto_parallel_context
from mindspore.communication.management import get_group_size
from mindspore.ops import operations as P
from mindspore.ops import functional as F
from mindspore.ops import composite as C
class BaseConv(nn.Cell):
"""A Conv2d -> Batchnorm -> silu/leaky relu block"""
def __init__(
self, in_channels, out_channels, ksize, stride, bias=True, act="leaky"
):
super(BaseConv,self).__init__()
# same padding
# pad = (ksize - 1) // 2
self.conv = nn.Conv2d(
in_channels,
out_channels,
kernel_size=ksize,
stride=stride,
# padding=pad, #默认 same padding
has_bias=bias
)
self.bn = nn.BatchNorm2d(out_channels)
self.act = nn.LeakyReLU(0.1)
def construct(self, x):
return self.act(self.bn(self.conv(x)))
class DecoupleHead(nn.Cell):
def __init__(
self,
num_classes,
width=1.0,
strides=[8, 16, 32],
in_channels=[128, 256, 512],
act="leaky",
):
"""
Args:
act (str): activation type of conv. Defalut value: "silu".
depthwise (bool): whether apply depthwise conv in conv branch. Defalut value: False.
"""
super(DecoupleHead,self).__init__()
self.n_anchors = 3
self.num_classes = num_classes
self.cls_convs = nn.CellList()
self.reg_convs = nn.CellList()
self.cls_preds = nn.CellList()
self.reg_preds = nn.CellList()
self.obj_preds = nn.CellList()
self.stems = nn.CellList()
self.concat = P.Concat(1)
for i in range(len(in_channels)):
self.stems.append(
BaseConv(
in_channels=int(in_channels[i] * width),
out_channels=int(256 * width),
ksize=1,
stride=1,
act=act,
)
)
self.cls_convs.append(
nn.SequentialCell(
*[
BaseConv(
in_channels=int(256 * width),
out_channels=int(256 * width),
ksize=3,
stride=1,
act=act,
),
BaseConv(
in_channels=int(256 * width),
out_channels=int(256 * width),
ksize=3,
stride=1,
act=act,
),
]
)
)
self.reg_convs.append(
nn.SequentialCell(
*[
BaseConv(
in_channels=int(256 * width),
out_channels=int(256 * width),
ksize=3,
stride=1,
act=act,
),
BaseConv(
in_channels=int(256 * width),
out_channels=int(256 * width),
ksize=3,
stride=1,
act=act,
),
]
)
)
# class
self.cls_preds.append(
nn.Conv2d(
in_channels=int(256 * width),
out_channels=self.n_anchors * self.num_classes,
kernel_size=1,
stride=1,
padding=0,
)
)
# x,y,w,h
self.reg_preds.append(
nn.Conv2d(
in_channels=int(256 * width),
out_channels=self.n_anchors * 4,
kernel_size=1,
stride=1,
padding=0,
)
)
#confidence
self.obj_preds.append(
nn.Conv2d(
in_channels=int(256 * width),
out_channels=self.n_anchors * 1,
kernel_size=1,
stride=1,
padding=0,
)
)
self.strides = strides
def construct(self, xin):
outputs = []
for k, (cls_conv, reg_conv, x) in enumerate(
zip(self.cls_convs, self.reg_convs, xin)
):
x = self.stems[k](x)
cls_x = x
reg_x = x
# print("ffffffffffffffffffffffffffffff")
# print(reg_x)
# print("jjjjjjjjjjjjjjjjjjjjjj")
cls_feat = cls_conv(cls_x)
cls_output = self.cls_preds[k](cls_feat)
reg_feat = reg_conv(reg_x)
reg_output = self.reg_preds[k](reg_feat)
obj_output = self.obj_preds[k](reg_feat)
output = self.concat((reg_output, obj_output, cls_output))
outputs.append(output)
return outputs在yolo中调用的代码如下：
class YOLO(nn.Cell):
def __init__(self, backbone, shape):
super(YOLO, self).__init__()
self.backbone = backbone
self.config = ConfigYOLOV5()
#neck , fpn pan
self.conv1 = Conv(shape[5], shape[4], k=1, s=1)
self.CSP5 = BottleneckCSP(shape[5], shape[4], n=1*shape[6], shortcut=False)
self.conv2 = Conv(shape[4], shape[3], k=1, s=1)
self.CSP6 = BottleneckCSP(shape[4], shape[3], n=1*shape[6], shortcut=False)
self.conv3 = Conv(shape[3], shape[3], k=3, s=2)
self.CSP7 = BottleneckCSP(shape[4], shape[4], n=1*shape[6], shortcut=False)
self.conv4 = Conv(shape[4], shape[4], k=3, s=2)
self.CSP8 = BottleneckCSP(shape[5], shape[5], n=1*shape[6], shortcut=False)
# yolovx head
input_channels = [shape[3],shape[4],shape[5]]
self.decouple_head = DecoupleHead(num_classes= self.config.num_classes, in_channels=input_channels)
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_height = P.Shape()(x)[2] * 2
img_width = P.Shape()(x)[3] * 2
feature_map1, feature_map2, feature_map3 = self.backbone(x)
c1 = self.conv1(feature_map3)
ups1 = P.ResizeNearestNeighbor((img_height // 16, img_width // 16))(c1)
c2 = self.concat((ups1, feature_map2))
c3 = self.CSP5(c2)
c4 = self.conv2(c3)
ups2 = P.ResizeNearestNeighbor((img_height // 8, img_width // 8))(c4)
c5 = self.concat((ups2, feature_map1))
# out
c6 = self.CSP6(c5)
c7 = self.conv3(c6)
c8 = self.concat((c7, c4))
# out
c9 = self.CSP7(c8)
c10 = self.conv4(c9)
c11 = self.concat((c10, c1))
# out
c12 = self.CSP8(c11)
# print("dddddddddddddd")
# print(c6)
small_object_output, medium_object_output, big_object_output=self.decouple_head([c6,c9,c12])
return small_object_output, medium_object_output, big_object_output使用的是静态图方式，在使用yolov5自带的head时运行没有问题，替换为这个decouplehead就出错了，结果下图中的错误，现在不知道如何定位，麻烦帮忙看一下
看报错信息是说少了一个参数，但是实在找不到在哪里出现的这个问题
【截图信息】
解答：
如果动态图可以，静态图不行的话，我怀疑
这块可能静态图的支持不够好，可以尝试用
for k in range(len(self.reg)):
cls_conv=self.cls[k]
reg_conv = convs[k]
x = xin[k]这种形式写