Cannot get the summary

[GuoQuanhao]

platform: win10 version: 1.6.0

net.py

import time
import torch
import torch.nn as nn
import torchvision.models._utils as _utils
import torchvision.models as models
import torch.nn.functional as F
from torch.autograd import Variable
from torchsummary import summary

def conv_bn(inp, oup, stride = 1, leaky = 0):
    return nn.Sequential(
        nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
        nn.BatchNorm2d(oup),
        nn.LeakyReLU(negative_slope=leaky, inplace=True)
    )

def conv_bn_no_relu(inp, oup, stride):
    return nn.Sequential(
        nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
        nn.BatchNorm2d(oup),
    )

def conv_bn1X1(inp, oup, stride, leaky=0):
    return nn.Sequential(
        nn.Conv2d(inp, oup, 1, stride, padding=0, bias=False),
        nn.BatchNorm2d(oup),
        nn.LeakyReLU(negative_slope=leaky, inplace=True)
    )

def conv_dw(inp, oup, stride, leaky=0.1):
    return nn.Sequential(
        nn.Conv2d(inp, inp, 3, stride, 1, groups=inp, bias=False),
        nn.BatchNorm2d(inp),
        nn.LeakyReLU(negative_slope= leaky,inplace=True),

        nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
        nn.BatchNorm2d(oup),
        nn.LeakyReLU(negative_slope= leaky,inplace=True),
    )

class SSH(nn.Module):
    def __init__(self, in_channel, out_channel):
        super(SSH, self).__init__()
        assert out_channel % 4 == 0
        leaky = 0
        if (out_channel <= 64):
            leaky = 0.1
        self.conv3X3 = conv_bn_no_relu(in_channel, out_channel//2, stride=1)

        self.conv5X5_1 = conv_bn(in_channel, out_channel//4, stride=1, leaky = leaky)
        self.conv5X5_2 = conv_bn_no_relu(out_channel//4, out_channel//4, stride=1)

        self.conv7X7_2 = conv_bn(out_channel//4, out_channel//4, stride=1, leaky = leaky)
        self.conv7x7_3 = conv_bn_no_relu(out_channel//4, out_channel//4, stride=1)

    def forward(self, input):
        conv3X3 = self.conv3X3(input)

        conv5X5_1 = self.conv5X5_1(input)
        conv5X5 = self.conv5X5_2(conv5X5_1)

        conv7X7_2 = self.conv7X7_2(conv5X5_1)
        conv7X7 = self.conv7x7_3(conv7X7_2)

        out = torch.cat([conv3X3, conv5X5, conv7X7], dim=1)
        out = F.relu(out)
        return out

class FPN(nn.Module):
    def __init__(self,in_channels_list,out_channels):
        super(FPN,self).__init__()
        leaky = 0
        if (out_channels <= 64):
            leaky = 0.1
        self.output1 = conv_bn1X1(in_channels_list[0], out_channels, stride = 1, leaky = leaky)
        self.output2 = conv_bn1X1(in_channels_list[1], out_channels, stride = 1, leaky = leaky)
        self.output3 = conv_bn1X1(in_channels_list[2], out_channels, stride = 1, leaky = leaky)

        self.merge1 = conv_bn(out_channels, out_channels, leaky = leaky)
        self.merge2 = conv_bn(out_channels, out_channels, leaky = leaky)

    def forward(self, input):
        # names = list(input.keys())
        input = list(input.values())

        output1 = self.output1(input[0])
        output2 = self.output2(input[1])
        output3 = self.output3(input[2])

        up3 = F.interpolate(output3, size=[output2.size(2), output2.size(3)], mode="nearest")
        output2 = output2 + up3
        output2 = self.merge2(output2)

        up2 = F.interpolate(output2, size=[output1.size(2), output1.size(3)], mode="nearest")
        output1 = output1 + up2
        output1 = self.merge1(output1)

        out = [output1, output2, output3]
        return out



class MobileNetV1(nn.Module):
    def __init__(self):
        super(MobileNetV1, self).__init__()
        self.stage1 = nn.Sequential(
            conv_bn(3, 8, 2, leaky = 0.1),    # 3
            conv_dw(8, 16, 1),   # 7
            conv_dw(16, 32, 2),  # 11
            conv_dw(32, 32, 1),  # 19
            conv_dw(32, 64, 2),  # 27
            conv_dw(64, 64, 1),  # 43
        )
        self.stage2 = nn.Sequential(
            conv_dw(64, 128, 2),  # 43 + 16 = 59
            conv_dw(128, 128, 1), # 59 + 32 = 91
            conv_dw(128, 128, 1), # 91 + 32 = 123
            conv_dw(128, 128, 1), # 123 + 32 = 155
            conv_dw(128, 128, 1), # 155 + 32 = 187
            conv_dw(128, 128, 1), # 187 + 32 = 219
        )
        self.stage3 = nn.Sequential(
            conv_dw(128, 256, 2), # 219 +3 2 = 241
            conv_dw(256, 256, 1), # 241 + 64 = 301
        )
        self.avg = nn.AdaptiveAvgPool2d((1,1))
        self.fc = nn.Linear(256, 1000)

    def forward(self, x):
        x = self.stage1(x)
        x = self.stage2(x)
        x = self.stage3(x)
        x = self.avg(x)
        # x = self.model(x)
        x = x.view(-1, 256)
        x = self.fc(x)
        return x

if __name__=='__main__':
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    net = MobileNetV1().to(device)
    summary(net, input_size=(3, 640, 640))

retinaface.py

import torch
import torch.nn as nn
import torchvision.models.detection.backbone_utils as backbone_utils
import torchvision.models._utils as _utils
import torch.nn.functional as F
from collections import OrderedDict
from torchsummary import summary
'''
from models.net import MobileNetV1 as MobileNetV1
from models.net import FPN as FPN
from models.net import SSH as SSH
'''
from net import MobileNetV1 as MobileNetV1
from net import FPN as FPN
from net import SSH as SSH



class ClassHead(nn.Module):
    def __init__(self,inchannels=512,num_anchors=3):
        super(ClassHead,self).__init__()
        self.num_anchors = num_anchors
        self.conv1x1 = nn.Conv2d(inchannels,self.num_anchors*2,kernel_size=(1,1),stride=1,padding=0)

    def forward(self,x):
        out = self.conv1x1(x)
        out = out.permute(0,2,3,1).contiguous()
        
        return out.view(out.shape[0], -1, 2)

class BboxHead(nn.Module):
    def __init__(self,inchannels=512,num_anchors=3):
        super(BboxHead,self).__init__()
        self.conv1x1 = nn.Conv2d(inchannels,num_anchors*4,kernel_size=(1,1),stride=1,padding=0)

    def forward(self,x):
        out = self.conv1x1(x)
        out = out.permute(0,2,3,1).contiguous()

        return out.view(out.shape[0], -1, 4)

class LandmarkHead(nn.Module):
    def __init__(self,inchannels=512,num_anchors=3):
        super(LandmarkHead,self).__init__()
        self.conv1x1 = nn.Conv2d(inchannels,num_anchors*10,kernel_size=(1,1),stride=1,padding=0)

    def forward(self,x):
        out = self.conv1x1(x)
        out = out.permute(0,2,3,1).contiguous()

        return out.view(out.shape[0], -1, 10)

class RetinaFace(nn.Module):
    def __init__(self, cfg = None, phase = 'train'):
        """
        :param cfg:  Network related settings.
        :param phase: train or test.
        """
        super(RetinaFace,self).__init__()
        self.phase = phase
        backbone = None
        if cfg['name'] == 'mobilenet0.25':
            backbone = MobileNetV1()
            if cfg['pretrain']:
                checkpoint = torch.load("./weights/mobilenetV1X0.25_pretrain.tar", map_location=torch.device('cpu'))
                from collections import OrderedDict
                new_state_dict = OrderedDict()
                for k, v in checkpoint['state_dict'].items():
                    name = k[7:]  # remove module.
                    new_state_dict[name] = v
                # load params
                backbone.load_state_dict(new_state_dict)
        elif cfg['name'] == 'Resnet50':
            import torchvision.models as models
            backbone = models.resnet50(pretrained=cfg['pretrain'])

        self.body = _utils.IntermediateLayerGetter(backbone, cfg['return_layers'])
        in_channels_stage2 = cfg['in_channel']
        in_channels_list = [
            in_channels_stage2 * 2,
            in_channels_stage2 * 4,
            in_channels_stage2 * 8,
        ]
        out_channels = cfg['out_channel']
        self.fpn = FPN(in_channels_list,out_channels)
        self.ssh1 = SSH(out_channels, out_channels)
        self.ssh2 = SSH(out_channels, out_channels)
        self.ssh3 = SSH(out_channels, out_channels)

        self.ClassHead = self._make_class_head(fpn_num=3, inchannels=cfg['out_channel'])
        self.BboxHead = self._make_bbox_head(fpn_num=3, inchannels=cfg['out_channel'])
        self.LandmarkHead = self._make_landmark_head(fpn_num=3, inchannels=cfg['out_channel'])

    def _make_class_head(self,fpn_num=3,inchannels=64,anchor_num=2):
        classhead = nn.ModuleList()
        for i in range(fpn_num):
            classhead.append(ClassHead(inchannels,anchor_num))
        return classhead
    
    def _make_bbox_head(self,fpn_num=3,inchannels=64,anchor_num=2):
        bboxhead = nn.ModuleList()
        for i in range(fpn_num):
            bboxhead.append(BboxHead(inchannels,anchor_num))
        return bboxhead

    def _make_landmark_head(self,fpn_num=3,inchannels=64,anchor_num=2):
        landmarkhead = nn.ModuleList()
        for i in range(fpn_num):
            landmarkhead.append(LandmarkHead(inchannels,anchor_num))
        return landmarkhead

    def forward(self,inputs):
        out = self.body(inputs)

        # FPN
        fpn = self.fpn(out)

        # SSH
        feature1 = self.ssh1(fpn[0])
        feature2 = self.ssh2(fpn[1])
        feature3 = self.ssh3(fpn[2])
        features = [feature1, feature2, feature3]

        bbox_regressions = torch.cat([self.BboxHead[i](feature) for i, feature in enumerate(features)], dim=1)
        classifications = torch.cat([self.ClassHead[i](feature) for i, feature in enumerate(features)],dim=1)
        ldm_regressions = torch.cat([self.LandmarkHead[i](feature) for i, feature in enumerate(features)], dim=1)

        if self.phase == 'train':
            output = (bbox_regressions, classifications, ldm_regressions)
        else:
            output = (bbox_regressions, F.softmax(classifications, dim=-1), ldm_regressions)
        return output


if __name__=='__main__':
    cfg_mnet = {
    'name': 'mobilenet0.25',
    'min_sizes': [[16, 32], [64, 128], [256, 512]],
    'steps': [8, 16, 32],
    'variance': [0.1, 0.2],
    'clip': False,
    'loc_weight': 2.0,
    'gpu_train': True,
    'batch_size': 1,
    'ngpu': 1,
    'epoch': 250,
    'decay1': 190,
    'decay2': 220,
    'image_size': 320,
    'pretrain': True,
    'return_layers': {'stage1': 1, 'stage2': 2, 'stage3': 3},
    'in_channel': 32,
    'out_channel': 64
    }
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    net = RetinaFace(cfg=cfg_mnet).to(device)
    net.eval()
    #print(net)
    summary(net, input_size=(3, 640, 640))
when I use python retinaface.py, I get the following error

Traceback (most recent call last):
  File ".\models\retinaface.py", line 159, in <module>
    summary(net, input_size=(3, 640, 640))
  File "E:\Anaconda3\envs\pytorch1.6.0\lib\site-packages\torchsummary\torchsummary.py", line 72, in summary
    model(*x)
  File "E:\Anaconda3\envs\pytorch1.6.0\lib\site-packages\torch\nn\modules\module.py", line 722, in _call_impl
    result = self.forward(*input, **kwargs)
  File ".\models\retinaface.py", line 113, in forward
    out = self.body(inputs)
  File "E:\Anaconda3\envs\pytorch1.6.0\lib\site-packages\torch\nn\modules\module.py", line 726, in _call_impl
    hook_result = hook(self, input, result)
  File "E:\Anaconda3\envs\pytorch1.6.0\lib\site-packages\torchsummary\torchsummary.py", line 26, in hook
    summary[m_key]["output_shape"] = list(output.size())
AttributeError: 'collections.OrderedDict' object has no attribute 'size'

so, how can I get the summary

[FrancescoSaverioZuppichini]

collections.OrderedDict is not a tensor, this is why torchsummary is not able to compute its size

[cainmagi]

One of your network outputs (including the hidden layer outputs) is a dict (IntermediateLayerGetter). Currently, torchsummary does not support this case. You could use my modification (#165) to handle this problem. There are also some other solutions in the pull requests.

---

Edit I find that your code also has dict input (FPN). Currently the input shape is recorded but not used in torchsummary. I have fixed the problem in an ugly way.

[FrancescoSaverioZuppichini]

Just switch to torchinfo much better!