I try to implement paper association LSTM.
The model is created, but there is a error as follow:


RuntimeError                              Traceback (most recent call last)
~/experiment/ssd.pytorch-master/train_association_lstm.py in ()
271
272 if name == ‘main’:
–> 273     train()
~/experiment/ssd.pytorch-master/train_association_lstm.py in train()
196         loss_l, loss_c = criterion(out, targets)
197         loss = loss_l + loss_c
–> 198         loss.backward()
199         optimizer.step()
200         t1 = time.time()
~/anaconda2/envs/gluon/lib/python3.5/site-packages/torch/tensor.py in backward(self, gradient, retain_graph, create_graph)
91                 products. Defaults to False.
92         “”"
—> 93         torch.autograd.backward(self, gradient, retain_graph, create_graph)
94
95     def register_hook(self, hook):
~/anaconda2/envs/gluon/lib/python3.5/site-packages/torch/autograd/init.py in backward(tensors, grad_tensors, retain_graph, create_graph, grad_variables)
87     Variable._execution_engine.run_backward(
88         tensors, grad_tensors, retain_graph, create_graph,
—> 89         allow_unreachable=True)  # allow_unreachable flag
90
91
RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation

The complete Model code is:
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from layers import *
from data import vid
import os
from lib.model.roi_pooling.modules.roi_pool import _RoIPooling
from lib.model.roi_crop.modules.roi_crop import _RoICrop
from lib.model.roi_align.modules.roi_align import RoIAlignAvg
from lib.model.utils.net_utils import _affine_grid_gen
from utils.viz import plot_bbox, plot_image
from matplotlib import pyplot as plt
from bnlstm import BNLSTM
from layers.modules import MultiProjectLoss

class association_lstm(nn.Module):
    """Single Shot Multibox Architecture
    The network is composed of a base VGG network followed by the
    added multibox conv layers.  Each multibox layer branches into
        1) conv2d for class conf scores
        2) conv2d for localization predictions
        3) associated priorbox layer to produce default bounding
           boxes specific to the layer's feature map size.
    See: https://arxiv.org/pdf/1512.02325.pdf for more details.

    Args:
        phase: (string) Can be "test" or "train"
        size: input image size
        base: VGG16 layers for input, size of either 300 or 500
        extras: extra layers that feed to multibox loc and conf layers
        head: "multibox head" consists of loc and conf conv layers
    """

    def __init__(self, phase, cfg, size, base, extras, head, num_classes):
        super(association_lstm, self).__init__()
        self.phase = phase
        self.num_classes = num_classes
        self.cfg = vid
        self.priorbox = PriorBox(self.cfg)
        self.priors = Variable(self.priorbox.forward(), volatile=True)
        self.size = size

        # SSD network
        self.vgg = nn.ModuleList(base)
        # Layer learns to scale the l2 normalized features from conv4_3
        self.L2Norm = L2Norm(512, 20)
        self.extras = nn.ModuleList(extras)

        self.loc = nn.ModuleList(head[0])
        self.conf = nn.ModuleList(head[1])
        self.roi_pool = _RoIPooling(self.cfg['POOLING_SIZE'], self.cfg['POOLING_SIZE'], 1.0 / 16.0)
        self.roi_align = RoIAlignAvg(self.cfg['POOLING_SIZE'], self.cfg['POOLING_SIZE'], 1.0 / 16.0)

        self.grid_size = self.cfg['POOLING_SIZE'] * 2 if self.cfg['CROP_RESIZE_WITH_MAX_POOL'] else self.cfg['POOLING_SIZE']
        self.roi_crop = _RoICrop()
        self.img_shape = (self.cfg['min_dim'],self.cfg['min_dim'])
        self.tensor_len = 4+self.num_classes+49
        self.bnlstm1 = BNLSTM(input_size=84, hidden_size=150, batch_first=False, bidirectional=False)
        self.bnlstm2 = BNLSTM(input_size=150, hidden_size=300, batch_first=False, bidirectional=False)
        self.cls_pred = nn.Linear(300, self.num_classes)
        self.bbox_pred = nn.Linear(300, 4)
        self.association_pred = nn.Linear(300, 49)
        self.MultiProjectLoss = MultiProjectLoss(self.num_classes, 0, True, 3, 0.5 )
        if phase == 'vid_train':
            self.softmax = nn.Softmax(dim=-1)
            #self.detect = Trnsform_target(num_classes, 200, 0.5, 0.01, 0.45)
            self.detect = train_target(num_classes, 200, 0.5, 0.01, 0.45)
    def forward(self, x, targets):
        """Applies network layers and ops on input image(s) x.

        Args:
            x: input image or batch of images. Shape: [batch,3,300,300].

        Return:
            Depending on phase:
            test:
                Variable(tensor) of output class label predictions,
                confidence score, and corresponding location predictions for
                each object detected. Shape: [batch,topk,7]

            train:
                list of concat outputs from:
                    1: confidence layers, Shape: [batch*num_priors,num_classes]
                    2: localization layers, Shape: [batch,num_priors*4]
                    3: priorbox layers, Shape: [2,num_priors*4]
        """
        sources = list()
        loc = list()
        conf = list()
        batch_size = x.data.size(0)
        print('input image size: ',x.size())
        display_img = x[0].clone().cpu().numpy().transpose((1,2,0))
        print('display_img size: ',display_img.shape)
        
        # apply vgg up to conv4_3 relu
        for k in range(23):
            x = self.vgg[k](x)

        s = self.L2Norm(x)
        sources.append(s)

        # apply vgg up to fc7
        for k in range(23, len(self.vgg)):
            x = self.vgg[k](x)
        sources.append(x)

        # apply extra layers and cache source layer outputs
        for k, v in enumerate(self.extras):
            x = F.relu(v(x), inplace=True)
            if k % 2 == 1:
                sources.append(x)

        # apply multibox head to source layers
        for (x, l, c) in zip(sources, self.loc, self.conf):
            loc.append(l(x).permute(0, 2, 3, 1).contiguous())
            conf.append(c(x).permute(0, 2, 3, 1).contiguous())

        roi_feat = sources[1]
        loc = torch.cat([o.view(o.size(0), -1) for o in loc], 1)
        conf = torch.cat([o.view(o.size(0), -1) for o in conf], 1)
        if self.phase == "vid_train":
            """
            output = self.detect(
                loc.view(loc.size(0), -1, 4),                   # loc preds
                self.softmax(conf.view(conf.size(0), -1, self.num_classes)),                # conf preds
                #conf.view(conf.size(0), -1, self.num_classes),
                self.priors.type(type(x.data)),                 # default boxes
                targets
            )
            """
            output = self.detect(
                loc.view(loc.size(0), -1, 4),                   # loc preds
                self.softmax(conf.view(conf.size(0), -1, self.num_classes)),                # conf preds
                #conf.view(conf.size(0), -1, self.num_classes),
                self.priors.type(type(x.data)),                 # default boxes
            )
        else:
            output = (
                loc.view(loc.size(0), -1, 4),
                conf.view(conf.size(0), -1, self.num_classes),
                self.priors
            )
            return output                            # 临时返回

        #rois, loc, conf, loc_t, conf_t = output  # rois size: batchsize, top_k, 5 
        rois, loc, conf, priors = output
        
        img_scale = torch.Tensor([self.img_shape[1], self.img_shape[0],
                          self.img_shape[1], self.img_shape[0]])

        rois[:,:,1:] = rois[:,:,1:]*img_scale
        if self.cfg['POOLING_MODE'] == 'crop':
            grid_xy = _affine_grid_gen(rois.view(-1, 5), roi_feat.size()[2:], self.grid_size)
            grid_yx = torch.stack([grid_xy.data[:,:,:,1], grid_xy.data[:,:,:,0]], 3).contiguous()
            pooled_feat = self.roi_crop(roi_feat, Variable(grid_yx).detach())
            if self.cfg['CROP_RESIZE_WITH_MAX_POOL']:
                pooled_feat = F.max_pool2d(pooled_feat, 2, 2)
        elif self.cfg['POOLING_MODE'] == 'align':
            pooled_feat = self.roi_align(roi_feat, rois.view(-1, 5))
        elif self.cfg['POOLING_MODE'] == 'pool':
            pooled_feat = self.roi_pool(roi_feat, rois.view(-1,5))

        Con1_1 = nn.Conv2d(1024, 1, kernel_size=1, padding=0, dilation=1)
        pooled_feat = Con1_1(pooled_feat)
        scale = L2Norm(1, 20)
        normlize_feat = scale(pooled_feat)
        feat = normlize_feat.view(normlize_feat.size(0), normlize_feat.size(1), -1)
        feat = feat.squeeze().view(batch_size, feat.size(0)/batch_size, -1)
        
        stacked_tensor = torch.cat((conf,loc,feat),-1)
        o1, _ = self.bnlstm1(stacked_tensor)

        o2, _ = self.bnlstm2(o1)
        cls_pred = self.cls_pred(o2)

        bbox_pred = self.bbox_pred(o2)

        association_pred = self.association_pred(o2)
        return bbox_pred, cls_pred, self.priors

    def load_weights(self, base_file):
        other, ext = os.path.splitext(base_file)
        if ext == '.pkl' or '.pth':
            print('Loading weights into state dict...')
            self.load_state_dict(torch.load(base_file,
                                 map_location=lambda storage, loc: storage))
            print('Finished!')
        else:
            print('Sorry only .pth and .pkl files supported.')


# This function is derived from torchvision VGG make_layers()
# https://github.com/pytorch/vision/blob/master/torchvision/models/vgg.py
def vgg(cfg, i, batch_norm=False):
    layers = []
    in_channels = i
    for v in cfg:
        if v == 'M':
            layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
        elif v == 'C':
            layers += [nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)]
        else:
            conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
            if batch_norm:
                layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]
            else:
                layers += [conv2d, nn.ReLU(inplace=True)]
            in_channels = v
    pool5 = nn.MaxPool2d(kernel_size=3, stride=1, padding=1)
    conv6 = nn.Conv2d(512, 1024, kernel_size=3, padding=6, dilation=6)
    conv7 = nn.Conv2d(1024, 1024, kernel_size=1)
    layers += [pool5, conv6,
               nn.ReLU(inplace=True), conv7, nn.ReLU(inplace=True)]
    return layers


def add_extras(cfg, i, batch_norm=False):
    # Extra layers added to VGG for feature scaling
    layers = []
    in_channels = i
    flag = False
    for k, v in enumerate(cfg):
        if in_channels != 'S':
            if v == 'S':
                layers += [nn.Conv2d(in_channels, cfg[k + 1],
                           kernel_size=(1, 3)[flag], stride=2, padding=1)]
            else:
                layers += [nn.Conv2d(in_channels, v, kernel_size=(1, 3)[flag])]
            flag = not flag
        in_channels = v
    return layers


def multibox(vgg, extra_layers, cfg, num_classes):
    loc_layers = []
    conf_layers = []
    vgg_source = [21, -2]
    for k, v in enumerate(vgg_source):
        loc_layers += [nn.Conv2d(vgg[v].out_channels,
                                 cfg[k] * 4, kernel_size=3, padding=1)]
        conf_layers += [nn.Conv2d(vgg[v].out_channels,
                        cfg[k] * num_classes, kernel_size=3, padding=1)]
    for k, v in enumerate(extra_layers[1::2], 2):
        loc_layers += [nn.Conv2d(v.out_channels, cfg[k]
                                 * 4, kernel_size=3, padding=1)]
        conf_layers += [nn.Conv2d(v.out_channels, cfg[k]
                                  * num_classes, kernel_size=3, padding=1)]
    return vgg, extra_layers, (loc_layers, conf_layers)


base = {
    '300': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'C', 512, 512, 512, 'M',
            512, 512, 512],
    '512': [],
}
extras = {
    '300': [256, 'S', 512, 128, 'S', 256, 128, 256, 128, 256],
    '512': [],
}
mbox = {
    '300': [4, 6, 6, 6, 4, 4],  # number of boxes per feature map location
    '512': [],
}


def build_association_lstm(phase, cfg, size=300, num_classes=21):
    if phase != "test" and phase != "vid_train":
        print("ERROR: Phase: " + phase + " not recognized")
        return
    if size != 300:
        print("ERROR: You specified size " + repr(size) + ". However, " +
              "currently only SSD300 (size=300) is supported!")
        return
    base_, extras_, head_ = multibox(vgg(base[str(size)], 3),
                                     add_extras(extras[str(size)], 1024),
                                     mbox[str(size)], num_classes)
    return association_lstm(phase, cfg, size, base_, extras_, head_, num_classes)

The loss compute as follow:
class MultiResLoss(nn.Module):
    """SSD Weighted Loss Function
    Compute Targets:
        1) Produce Confidence Target Indices by matching  ground truth boxes
           with (default) 'priorboxes' that have jaccard index > threshold parameter
           (default threshold: 0.5).
        2) Produce localization target by 'encoding' variance into offsets of ground
           truth boxes and their matched  'priorboxes'.
        3) Hard negative mining to filter the excessive number of negative examples
           that comes with using a large number of default bounding boxes.
           (default negative:positive ratio 3:1)
    Objective Loss:
        L(x,c,l,g) = (Lconf(x, c) + αLloc(x,l,g)) / N
        Where, Lconf is the CrossEntropy Loss and Lloc is the SmoothL1 Loss
        weighted by α which is set to 1 by cross val.
        Args:
            c: class confidences,
            l: predicted boxes,
            g: ground truth boxes
            N: number of matched default boxes
        See: https://arxiv.org/pdf/1512.02325.pdf for more details.
    """

    def __init__(self, num_classes, overlap_thresh, prior_for_matching,
                 bkg_label, neg_mining, neg_pos, neg_overlap, encode_target,
                 use_gpu=True):
        super(MultiResLoss, self).__init__()
        self.use_gpu = use_gpu
        self.num_classes = num_classes
        self.threshold = overlap_thresh
        self.background_label = bkg_label
        self.encode_target = encode_target
        self.use_prior_for_matching = prior_for_matching
        self.do_neg_mining = neg_mining
        self.negpos_ratio = neg_pos
        self.neg_overlap = neg_overlap
        self.variance = cfg['variance']

    def forward(self, predictions, targets):
        """Multibox Loss
        Args:
            predictions (tuple): A tuple containing loc preds, conf preds,
            and prior boxes from SSD net.
                conf shape: torch.size(batch_size,num_priors,num_classes)
                loc shape: torch.size(batch_size,num_priors,4)
                priors shape: torch.size(num_priors,4)

            targets (tensor): Ground truth boxes and labels for a batch,
                shape: [batch_size,num_objs,5] (last idx is the label).
        """

        loc_data, conf_data, priors = predictions
        num = loc_data.size(0)
        num_priors = (priors.size(1))
        num_classes = self.num_classes

        # match priors (default boxes) and ground truth boxes
        loc_t = torch.Tensor(num, num_priors, 4)
        conf_t = torch.LongTensor(num, num_priors)
        for idx in range(num):
            truths = targets[idx][:, :-1].data
            labels = targets[idx][:, -1].data
            #defaults = priors.data
            defaults = priors[idx].data
            match(self.threshold, truths, defaults, self.variance, labels,
                  loc_t, conf_t, idx)
        if self.use_gpu:
            loc_t = loc_t.cuda()
            conf_t = conf_t.cuda()
        # wrap targets
        loc_t = Variable(loc_t, requires_grad=False)
        conf_t = Variable(conf_t, requires_grad=False)
        print('conf_t dtype: ',conf_t.dtype)

        pos = conf_t > 0
        num_pos = pos.sum(dim=1, keepdim=True)

        pos_idx = pos.unsqueeze(pos.dim()).expand_as(loc_data)
        loc_p = loc_data[pos_idx].view(-1, 4)
        loc_t = loc_t[pos_idx].view(-1, 4)
        loss_l = F.smooth_l1_loss(loc_p, loc_t, size_average=False)

        # Compute max conf across batch for hard negative mining
        batch_conf = conf_data.view(-1, self.num_classes)
        loss_c = log_sum_exp(batch_conf) - batch_conf.gather(1, conf_t.view(-1, 1))

        # Hard Negative Mining
        # add for pytorch 0.4
        loss_c = loss_c.view(pos.size()[0], pos.size()[1])
        loss_c[pos] = 0  # filter out pos boxes for now
        loss_c = loss_c.view(num, -1)
        _, loss_idx = loss_c.sort(1, descending=True)
        _, idx_rank = loss_idx.sort(1)
        num_pos = pos.long().sum(1, keepdim=True)
        num_neg = torch.clamp(self.negpos_ratio*num_pos, max=pos.size(1)-1)
        neg = idx_rank < num_neg.expand_as(idx_rank)

        # Confidence Loss Including Positive and Negative Examples
        pos_idx = pos.unsqueeze(2).expand_as(conf_data)
        neg_idx = neg.unsqueeze(2).expand_as(conf_data)
        conf_p = conf_data[(pos_idx+neg_idx).gt(0)].view(-1, self.num_classes)
        targets_weighted = conf_t[(pos+neg).gt(0)]
        loss_c = F.cross_entropy(conf_p, targets_weighted, size_average=False)

        # Sum of losses: L(x,c,l,g) = (Lconf(x, c) + αLloc(x,l,g)) / N

        N = num_pos.data.sum()
        # add for pytorch 0.4
        N = num_pos.data.sum().double()
        loss_l = loss_l.double()
        loss_c = loss_c.double()
        ###
        loss_l /= N
        loss_c /= N
        return loss_l, loss_c

Hope someone can help me find where the error is.
Thank you very much!
​