padding = int((kSize - 1)/2)

这是一个计算卷积核大小的公式，其中 kSize 是卷积核的大小，padding 是填充的大小。这个公式可以用来计算在进行卷积操作时需要进行的填充大小。具体的计算方法是将卷积核大小减去 1，然后除以 2，再向下取整即可得到填充大小。

相关问题

File "/root/Desktop/EAST-master/multigpu_train.py", line 180, in <module> tf.app.run() File "/root/miniconda3/envs/txy2/lib/python3.7/site-packages/tensorflow_core/python/platform/app.py", line 40, in run _run(main=main, argv=argv, flags_parser=_parse_flags_tolerate_undef) File "/root/miniconda3/envs/txy2/lib/python3.7/site-packages/absl/app.py", line 312, in run _run_main(main, args) File "/root/miniconda3/envs/txy2/lib/python3.7/site-packages/absl/app.py", line 258, in _run_main sys.exit(main(argv)) File "/root/Desktop/EAST-master/multigpu_train.py", line 110, in main total_loss, model_loss = tower_loss(iis, isms, igms, itms, reuse_variables) File "/root/Desktop/EAST-master/multigpu_train.py", line 30, in tower_loss f_score, f_geometry = model.model(images, is_training=True) File "/root/Desktop/EAST-master/model.py", line 77, in model spp_output = spp_layer(f[0]) File "/root/Desktop/EAST-master/model.py", line 44, in spp_layer strides=[1, strides[0], strides[1], 1], padding='VALID') File "/root/miniconda3/envs/txy2/lib/python3.7/site-packages/tensorflow_core/python/ops/nn_ops.py", line 3815, in max_pool name=name) File "/root/miniconda3/envs/txy2/lib/python3.7/site-packages/tensorflow_core/python/ops/gen_nn_ops.py", line 5662, in max_pool ksize = [_execute.make_int(_i, "ksize") for _i in ksize] File "/root/miniconda3/envs/txy2/lib/python3.7/site-packages/tensorflow_core/python/ops/gen_nn_ops.py", line 5662, in <listcomp> ksize = [_execute.make_int(_i, "ksize") for _i in ksize] File "/root/miniconda3/envs/txy2/lib/python3.7/site-packages/tensorflow_core/python/eager/execute.py", line 169, in make_int (arg_name, repr(v))) TypeError: Expected int for argument 'ksize' not <tf.Tensor 'model_0/feature_fusion/SpatialPyramidPooling/strided_slice_2:0' shape=() dtype=int32>. Process finished with exit code 1

这个错误是由于传递给函数的参数 ksize 需要是整数类型，但是你传递了一个 Tensor 对象。你需要确保将 Tensor 转换为整数类型后再传递给函数。你可以使用 TensorFlow 的 `tf.cast()` 函数将 Tensor 转换为整数类型，例如：`tf.cast(ksize_tensor, tf.int32)`。你需要找到代码中使用了 `tf.nn.max_pool()` 函数的部分，并检查是否在调用该函数时传递了一个 Tensor 类型的 ksize 参数，如果是，则需要将其转换为整数类型。例如，你可以将以下代码：

pool = tf.nn.max_pool(input, ksize=ksize_tensor, strides=[1, strides[0], strides[1], 1], padding='VALID')

修改为：

ksize = tf.cast(ksize_tensor, tf.int32)
pool = tf.nn.max_pool(input, ksize=[1, ksize, ksize, 1], strides=[1, strides[0], strides[1], 1], padding='VALID')

这应该可以解决你的问题。

ShuffleNet的代码

这里给出ShuffleNet v1和ShuffleNet v2的代码实现，供参考：

**ShuffleNet v1**

import torch.nn as nn
import torch.nn.functional as F
import math

class ShuffleBlock(nn.Module):
    def __init__(self, groups):
        super(ShuffleBlock, self).__init__()
        self.groups = groups
    
    def forward(self, x):
        n, c, h, w = x.size()
        g = self.groups
        assert c % g == 0, "Error: group number doesn't divide input channels"
        
        x = x.view(n, g, c//g, h, w).permute(0,2,1,3,4).contiguous()
        x = x.view(n, c, h, w)
        return x

class ShuffleNet(nn.Module):
    def __init__(self, groups=3, num_classes=1000):
        super(ShuffleNet, self).__init__()
        
        self.groups = groups
        self.conv1 = nn.Conv2d(3, 24, kernel_size=3, stride=2, padding=1, bias=False)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.stage2 = self._make_stage(24, 144, 3)
        self.stage3 = self._make_stage(144, 288, 7)
        self.stage4 = self._make_stage(288, 576, 3)
        self.conv5 = nn.Conv2d(576, 1024, kernel_size=1, stride=1, padding=0, bias=False)
        self.globalpool = nn.AdaptiveAvgPool2d((1,1))
        self.fc = nn.Linear(1024, num_classes)
        
    def _make_stage(self, inplanes, outplanes, blocks):
        layers = []
        layers.append(ShuffleBlock(self.groups))
        layers.append(nn.Conv2d(inplanes, outplanes//4, kernel_size=1, stride=1, padding=0, bias=False))
        layers.append(nn.BatchNorm2d(outplanes//4))
        layers.append(nn.ReLU(inplace=True))
        layers.append(nn.Conv2d(outplanes//4, outplanes//4, kernel_size=3, stride=1, padding=1, groups=outplanes//4, bias=False))
        layers.append(nn.BatchNorm2d(outplanes//4))
        layers.append(nn.Conv2d(outplanes//4, outplanes, kernel_size=1, stride=1, padding=0, bias=False))
        layers.append(nn.BatchNorm2d(outplanes))
        layers.append(nn.ReLU(inplace=True))
        
        for i in range(1, blocks):
            layers.append(ShuffleBlock(self.groups))
            layers.append(nn.Conv2d(outplanes, outplanes//4, kernel_size=1, stride=1, padding=0, bias=False))
            layers.append(nn.BatchNorm2d(outplanes//4))
            layers.append(nn.ReLU(inplace=True))
            layers.append(nn.Conv2d(outplanes//4, outplanes//4, kernel_size=3, stride=1, padding=1, groups=outplanes//4, bias=False))
            layers.append(nn.BatchNorm2d(outplanes//4))
            layers.append(nn.Conv2d(outplanes//4, outplanes, kernel_size=1, stride=1, padding=0, bias=False))
            layers.append(nn.BatchNorm2d(outplanes))
            layers.append(nn.ReLU(inplace=True))
        
        return nn.Sequential(*layers)
    
    def forward(self, x):
        x = self.conv1(x)
        x = self.maxpool(x)
        x = self.stage2(x)
        x = self.stage3(x)
        x = self.stage4(x)
        x = self.conv5(x)
        x = self.globalpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x

**ShuffleNet v2**

import torch.nn as nn
import torch.nn.functional as F
import math

class ShuffleBlock(nn.Module):
    def __init__(self, inp, oup, mid_channels, ksize, stride):
        super(ShuffleBlock, self).__init__()
        
        self.stride = stride
        self.ksize = ksize
        self.mid_channels = mid_channels
        
        self.conv1 = nn.Conv2d(inp, mid_channels, kernel_size=1, stride=1, padding=0, bias=False)
        self.bn1 = nn.BatchNorm2d(mid_channels)
        self.conv2 = nn.Conv2d(mid_channels, mid_channels, kernel_size=ksize, stride=stride, padding=(ksize-1)//2, groups=mid_channels, bias=False)
        self.bn2 = nn.BatchNorm2d(mid_channels)
        self.conv3 = nn.Conv2d(mid_channels, oup, kernel_size=1, stride=1, padding=0, bias=False)
        self.bn3 = nn.BatchNorm2d(oup)
        
        self.shortcut = nn.Sequential()
        if stride == 2:
            self.shortcut = nn.Sequential(
                nn.Conv2d(inp, inp, kernel_size=ksize, stride=stride, padding=(ksize-1)//2, groups=inp, bias=False),
                nn.BatchNorm2d(inp),
                nn.Conv2d(inp, oup, kernel_size=1, stride=1, padding=0, bias=False),
                nn.BatchNorm2d(oup)
            )
        
    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = F.relu(self.bn2(self.conv2(out)))
        out = self.bn3(self.conv3(out))
        
        if self.stride == 2:
            shortcut = self.shortcut(x)
        else:
            shortcut = x
        
        out = F.relu(torch.cat([out, shortcut], 1))
        out = self.channel_shuffle(out, 2)
        return out
    
    def channel_shuffle(self, x, groups):
        n, c, h, w = x.size()
        x = x.view(n, groups, c//groups, h, w).permute(0,2,1,3,4).contiguous()
        x = x.view(n, c, h, w)
        return x

class ShuffleNetV2(nn.Module):
    def __init__(self, num_classes=1000, input_size=224, width_mult=1.):
        super(ShuffleNetV2, self).__init__()
        
        self.stage_repeats = [4, 8, 4]
        self.stage_out_channels = [24, 116, 232, 464, 1024]
        if width_mult == 0.5:
            self.stage_out_channels = [int(x * width_mult) for x in self.stage_out_channels]
        
        self.conv1 = nn.Conv2d(3, self.stage_out_channels[0], kernel_size=3, stride=2, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(self.stage_out_channels[0])
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        
        self.stage2 = self._make_stage(1)
        self.stage3 = self._make_stage(2)
        self.stage4 = self._make_stage(3)
        self.conv5 = nn.Conv2d(self.stage_out_channels[3], self.stage_out_channels[4], kernel_size=1, stride=1, padding=0, bias=False)
        self.bn5 = nn.BatchNorm2d(self.stage_out_channels[4])
        self.avgpool = nn.AvgPool2d(kernel_size=7, stride=1)
        self.fc = nn.Linear(self.stage_out_channels[4], num_classes)
        
    def _make_stage(self, stage):
        modules = []
        
        stage_repeats = self.stage_repeats[stage-1]
        stage_out_channels = self.stage_out_channels[stage]
        for i in range(stage_repeats):
            if i == 0:
                modules.append(ShuffleBlock(self.stage_out_channels[stage-1], stage_out_channels, stage_out_channels//2, ksize=3, stride=2))
            else:
                modules.append(ShuffleBlock(stage_out_channels, stage_out_channels, stage_out_channels//2, ksize=3, stride=1))
        
        return nn.Sequential(*modules)
    
    def forward(self, x):
        x = F.relu(self.bn1(self.conv1(x)))
        x = self.maxpool(x)
        x = self.stage2(x)
        x = self.stage3(x)
        x = self.stage4(x)
        x = F.relu(self.bn5(self.conv5(x)))
        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x

以上给出的是PyTorch的实现代码，其中包括ShuffleBlock和ShuffleNet/ShuffleNetV2两个模型的实现。具体实现细节可以参考论文中的介绍。