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【手把手反内卷】开创全新AI多模态任务一视听分割:代码实践、优化教程(二)

前言

理论部分请看上一篇文章:

简要概述:我们要知道图像中哪个物体在发声如下视频演示:

gif 不能发出声音,大家脑补一下场景中有很多车,只有这辆120在发出声音,所以分割出发出声音的物体。

这是一位歌手时而唱歌,时而弹琴场景,只弹琴时,不分割人体,唱歌时,分割人体。

代码相对路径介绍(我的版本,非官方)

大家可以通过下载我的百度网盘(附带全部数据和代码),也可以下载官方代码,但不含数据,只能申请得到。

训练

先看train.py

看下面代码的help里面。

parser.add_argument("--session_name", default="MS3", type=str, help="使用MS3是对数据里的Multi-sources下的数据进行训练,是多声源数据,也就是,可能同时有多个物体发声")
parser.add_argument("--visual_backbone", default="resnet", type=str,
                    help="use resnet50 or pvt-v2 as the visual backbone")
​
parser.add_argument("--train_batch_size", default=4, type=int)
parser.add_argument("--val_batch_size", default=1, type=int)
parser.add_argument("--max_epoches", default=5, type=int)
parser.add_argument("--lr", default=0.0001, type=float)
parser.add_argument("--num_workers", default=0, type=int)
parser.add_argument("--wt_dec", default=5-4, type=float)
​
parser.add_argument('--masked_av_flag', action='store_true', default=True,
                    help='使用作者论文里说的loss: sa/masked_va loss')
parser.add_argument("--lambda_1", default=0.5, type=float, help='均衡系数weight for balancing l4 loss')
parser.add_argument("--masked_av_stages", default=[0, 1, 2, 3], nargs='+', type=int,
                    help='作者的设置compute sa/masked_va loss in which stages: [0, 1, 2, 3]')
parser.add_argument('--threshold_flag', action='store_true', default=False,
                    help='whether thresholding the generated masks')
parser.add_argument("--mask_pooling_type", default='avg', type=str, help='the manner to downsample predicted masks')
parser.add_argument('--norm_fea_flag', action='store_true', default=False, help='音频标准化normalize audio-visual features')
parser.add_argument('--closer_flag', action='store_true', default=False, help='use closer loss for masked_va loss')
parser.add_argument('--euclidean_flag', action='store_true', default=False,
                    help='use euclidean distance for masked_va loss')
parser.add_argument('--kl_flag', action='store_true', default=True, help='KL散度 use kl loss for masked_va loss')
​
parser.add_argument("--load_s4_params", action='store_true', default=False,
                    help='use S4 parameters for initilization')
parser.add_argument("--trained_s4_model_path", type=str, default='', help='pretrained S4 model')
​
parser.add_argument("--tpavi_stages", default=[0, 1, 2, 3], nargs='+', type=int,
                    help='tpavi模块 add tpavi block in which stages: [0, 1, 2, 3]')
parser.add_argument("--tpavi_vv_flag", action='store_true', default=False, help='视觉自注意visual-visual self-attention')
parser.add_argument("--tpavi_va_flag", action='store_true', default=True, help='视听交叉注意visual-audio cross-attention')
​
parser.add_argument("--weights", type=str, default='', help='初始训练预训练模型,可以不写path of trained model')
parser.add_argument('--log_dir', default='./train_logs', type=str)

大家根据train.sh就可以训练

代码细节

接下来会根据设置你要的视觉特征提取backbone,语音的默认使用vggish特征提取。

if (args.visual_backbone).lower() == "resnet":
    from model import ResNet_AVSModel as AVSModel
​
    print('==> Use ResNet50 as the visual backbone...')
elif (args.visual_backbone).lower() == "pvt":
    from model import PVT_AVSModel as AVSModel
​
    print('==> Use pvt-v2 as the visual backbone...')
else:
    raise NotImplementedError("only support the resnet50 and pvt-v2")

数据读取部分:

class MS3Dataset(Dataset):
    """Dataset for multiple sound source segmentation"""
    def __init__(self, split='train'):
        super(MS3Dataset, self).__init__()
        self.split = split
        self.mask_num = 5
        df_all = pd.read_csv(cfg.DATA.ANNO_CSV, sep=',')
        self.df_split = df_all[df_all['split'] == split]
        print("{}/{} videos are used for {}".format(len(self.df_split), len(df_all), self.split))
        self.img_transform = transforms.Compose([
            transforms.ToTensor(),
            transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
        ])
        self.mask_transform = transforms.Compose([
            transforms.ToTensor(),
        ])
​
​
​
    def __getitem__(self, index):
        df_one_video = self.df_split.iloc[index]
        video_name = df_one_video[0]
        img_base_path =  os.path.join(cfg.DATA.DIR_IMG, video_name)
        audio_lm_path = os.path.join(cfg.DATA.DIR_AUDIO_LOG_MEL, self.split, video_name + '.pkl')
        mask_base_path = os.path.join(cfg.DATA.DIR_MASK, self.split, video_name)
        audio_log_mel = load_audio_lm(audio_lm_path)
        # audio_lm_tensor = torch.from_numpy(audio_log_mel)
        imgs, masks = [], []
        for img_id in range(1, 6):
            img = load_image_in_PIL_to_Tensor(os.path.join(img_base_path, "%s.mp4_%d.png"%(video_name, img_id)), transform=self.img_transform)
            imgs.append(img)
        for mask_id in range(1, self.mask_num + 1):
            mask = load_image_in_PIL_to_Tensor(os.path.join(mask_base_path, "%s_%d.png"%(video_name, mask_id)), transform=self.mask_transform, mode='P')
            masks.append(mask)
        imgs_tensor = torch.stack(imgs, dim=0)
        masks_tensor = torch.stack(masks, dim=0)
​
        return imgs_tensor, audio_log_mel, masks_tensor, video_name
​
    def __len__(self):
        return len(self.df_split)

可以看到,一次读取5张图,我看了视频,都是5秒的,说明作者一次训练一个视频,每个视频每秒的帧和GT和语音合并训练。

for n_iter, batch_data in enumerate(train_dataloader):
    imgs, audio, mask, _ = batch_data  # [bs, 5, 3, 224, 224], [bs, 5, 1, 96, 64], [bs, 5 or 1, 1, 224, 224]
​
    imgs = imgs.cuda()
    audio = audio.cuda()
    mask = mask.cuda()
    B, frame, C, H, W = imgs.shape
    imgs = imgs.view(B * frame, C, H, W)
    mask_num = 5
    mask = mask.view(B * mask_num, 1, H, W)
    audio = audio.view(-1, audio.shape[2], audio.shape[3], audio.shape[4])  # [B*T, 1, 96, 64]
    with torch.no_grad():
        audio_feature = audio_backbone(audio)  # [B*T, 128]
​
    output, v_map_list, a_fea_list = model(imgs, audio_feature)  # [bs*5, 1, 224, 224]
    loss, loss_dict = IouSemanticAwareLoss(output, mask, a_fea_list, v_map_list, \
                                           sa_loss_flag=args.masked_av_flag, lambda_1=args.lambda_1,
                                           count_stages=args.masked_av_stages, \
                                           mask_pooling_type=args.mask_pooling_type,
                                           threshold=args.threshold_flag, norm_fea=args.norm_fea_flag, \
                                           closer_flag=args.closer_flag, euclidean_flag=args.euclidean_flag,
                                           kl_flag=args.kl_flag)
​
    avg_meter_total_loss.add({'total_loss': loss.item()})
    avg_meter_iou_loss.add({'iou_loss': loss_dict['iou_loss']})
    avg_meter_sa_loss.add({'sa_loss': loss_dict['sa_loss']})
​
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
​
    global_step += 1
    if (global_step - 1) % 20 == 0:
        train_log = 'Iter:%5d/%5d, Total_Loss:%.4f, iou_loss:%.4f, sa_loss:%.4f, lr: %.4f' % (
            global_step - 1, max_step, avg_meter_total_loss.pop('total_loss'),
            avg_meter_iou_loss.pop('iou_loss'), avg_meter_sa_loss.pop('sa_loss'),
            optimizer.param_groups[0]['lr'])

可以看到,训练很简单,先load图像5帧view合并在一起,再获取语音特征,送入模型。然后计算损失和Iou得分。

输入模型的数据分为两部分,图像帧【bs*5, 3, 224, 224】,乘以5意思是每个视频有5帧,第二部分是语音帧,维度相似。

class Pred_endecoder(nn.Module):
    # resnet based encoder decoder
    def __init__(self, channel=256, config=None, tpavi_stages=[], tpavi_vv_flag=False, tpavi_va_flag=True):
        super(Pred_endecoder, self).__init__()
        self.cfg = config
        self.tpavi_stages = tpavi_stages
        self.tpavi_vv_flag = tpavi_vv_flag
        self.tpavi_va_flag = tpavi_va_flag
​
        self.resnet = B2_ResNet()
        self.relu = nn.ReLU(inplace=True)
​
        self.conv4 = self._make_pred_layer(Classifier_Module, [3, 6, 12, 18], [3, 6, 12, 18], channel, 2048)
        self.conv3 = self._make_pred_layer(Classifier_Module, [3, 6, 12, 18], [3, 6, 12, 18], channel, 1024)
        self.conv2 = self._make_pred_layer(Classifier_Module, [3, 6, 12, 18], [3, 6, 12, 18], channel, 512)
        self.conv1 = self._make_pred_layer(Classifier_Module, [3, 6, 12, 18], [3, 6, 12, 18], channel, 256)
​
        self.path4 = FeatureFusionBlock(channel)
        self.path3 = FeatureFusionBlock(channel)
        self.path2 = FeatureFusionBlock(channel)
        self.path1 = FeatureFusionBlock(channel)
​
        for i in self.tpavi_stages:
            setattr(self, f"tpavi_b{i + 1}", TPAVIModule(in_channels=channel, mode='dot'))
            print("==> Build TPAVI block...")
​
        self.output_conv = nn.Sequential(
            nn.Conv2d(channel, 128, kernel_size=3, stride=1, padding=1),
            Interpolate(scale_factor=2, mode="bilinear"),
            nn.Conv2d(128, 32, kernel_size=3, stride=1, padding=1),
            nn.ReLU(True),
            nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
        )
​
        if self.training:
            self.initialize_weights()
​
    def pre_reshape_for_tpavi(self, x):
        # x: [B*5, C, H, W]
        _, C, H, W = x.shape
        x = x.reshape(-1, 5, C, H, W)
        x = x.permute(0, 2, 1, 3, 4).contiguous()  # [B, C, T, H, W]
        return x
​
    def post_reshape_for_tpavi(self, x):
        # x: [B, C, T, H, W]
        # return: [B*T, C, H, W]
        _, C, _, H, W = x.shape
        x = x.permute(0, 2, 1, 3, 4)  # [B, T, C, H, W]
        x = x.view(-1, C, H, W)
        return x
​
    def tpavi_vv(self, x, stage):
        # x: visual, [B*T, C=256, H, W]
        tpavi_b = getattr(self, f'tpavi_b{stage + 1}')
        x = self.pre_reshape_for_tpavi(x)  # [B, C, T, H, W]
        x, _ = tpavi_b(x)  # [B, C, T, H, W]
        x = self.post_reshape_for_tpavi(x)  # [B*T, C, H, W]
        return x
​
    def tpavi_va(self, x, audio, stage):
        # x: visual, [B*T, C=256, H, W]
        # audio: [B*T, 128]
        # ra_flag: return audio feature list or not
        tpavi_b = getattr(self, f'tpavi_b{stage + 1}')
        audio = audio.view(-1, 5, audio.shape[-1])  # [B, T, 128]
        x = self.pre_reshape_for_tpavi(x)  # [B, C, T, H, W]
        x, a = tpavi_b(x, audio)  # [B, C, T, H, W], [B, T, C]
        x = self.post_reshape_for_tpavi(x)  # [B*T, C, H, W]
        return x, a
​
    def _make_pred_layer(self, block, dilation_series, padding_series, NoLabels, input_channel):
        return block(dilation_series, padding_series, NoLabels, input_channel)
​
    def forward(self, x, audio_feature=None):
        x = self.resnet.conv1(x)
        x = self.resnet.bn1(x)
        x = self.resnet.relu(x)
        x = self.resnet.maxpool(x)
        x1 = self.resnet.layer1(x)  # BF x 256  x 56 x 56
        x2 = self.resnet.layer2(x1)  # BF x 512  x 28 x 28
        x3 = self.resnet.layer3_1(x2)  # BF x 1024 x 14 x 14
        x4 = self.resnet.layer4_1(x3)  # BF x 2048 x  7 x  7
        # print(x1.shape, x2.shape, x3.shape, x4.shape)
​
        conv1_feat = self.conv1(x1)  # BF x 256 x 56 x 56
        conv2_feat = self.conv2(x2)  # BF x 256 x 28 x 28
        conv3_feat = self.conv3(x3)  # BF x 256 x 14 x 14
        conv4_feat = self.conv4(x4)  # BF x 256 x  7 x  7
        # print(conv1_feat.shape, conv2_feat.shape, conv3_feat.shape, conv4_feat.shape)
​
        feature_map_list = [conv1_feat, conv2_feat, conv3_feat, conv4_feat]
        a_fea_list = [None] * 4
​
        if len(self.tpavi_stages) > 0:
            if (not self.tpavi_vv_flag) and (not self.tpavi_va_flag):
                raise Exception('tpavi_vv_flag and tpavi_va_flag cannot be False at the same time if len(tpavi_stages)>0, \
                    tpavi_vv_flag is for video self-attention while tpavi_va_flag indicates the standard version (audio-visual attention)')
            for i in self.tpavi_stages:
                tpavi_count = 0
                conv_feat = torch.zeros_like(feature_map_list[i]).cuda()
                if self.tpavi_vv_flag:
                    conv_feat_vv = self.tpavi_vv(feature_map_list[i], stage=i)
                    conv_feat += conv_feat_vv
                    tpavi_count += 1
                if self.tpavi_va_flag:
                    conv_feat_va, a_fea = self.tpavi_va(feature_map_list[i], audio_feature, stage=i)
                    conv_feat += conv_feat_va
                    tpavi_count += 1
                    a_fea_list[i] = a_fea
                conv_feat /= tpavi_count
                feature_map_list[i] = conv_feat  # update features of stage-i which conduct TPAVI
​
        conv4_feat = self.path4(feature_map_list[3])  # BF x 256 x 14 x 14
        conv43 = self.path3(conv4_feat, feature_map_list[2])  # BF x 256 x 28 x 28
        conv432 = self.path2(conv43, feature_map_list[1])  # BF x 256 x 56 x 56
        conv4321 = self.path1(conv432, feature_map_list[0])  # BF x 256 x 112 x 112
        # print(conv4_feat.shape, conv43.shape, conv432.shape, conv4321.shape)
​
        pred = self.output_conv(conv4321)  # BF x 1 x 224 x 224
        # print(pred.shape)
​
        return pred, feature_map_list, a_fea_list
​
    def initialize_weights(self):
        res50 = models.resnet50(pretrained=False)
        resnet50_dict = torch.load(self.cfg.TRAIN.PRETRAINED_RESNET50_PATH)
        res50.load_state_dict(resnet50_dict)
        pretrained_dict = res50.state_dict()
        # print(pretrained_dict.keys())
        all_params = {}
        for k, v in self.resnet.state_dict().items():
            if k in pretrained_dict.keys():
                v = pretrained_dict[k]
                all_params[k] = v
            elif '_1' in k:
                name = k.split('_1')[0] + k.split('_1')[1]
                v = pretrained_dict[name]
                all_params[k] = v
            elif '_2' in k:
                name = k.split('_2')[0] + k.split('_2')[1]
                v = pretrained_dict[name]
                all_params[k] = v
        assert len(all_params.keys()) == len(self.resnet.state_dict().keys())
        self.resnet.load_state_dict(all_params)
        print(f'==> Load pretrained ResNet50 parameters from {self.cfg.TRAIN.PRETRAINED_RESNET50_PATH}')

网络部分很简单,模型的定义没什么亮点,我们看forward里面的代码:

def forward(self, x, audio_feature=None):  #  输入图像帧和音频梅尔图经过vggish 的特征图。
    x = self.resnet.conv1(x)
    x = self.resnet.bn1(x)
    x = self.resnet.relu(x)
    x = self.resnet.maxpool(x)
    x1 = self.resnet.layer1(x)  # BF x 256  x 56 x 56
    x2 = self.resnet.layer2(x1)  # BF x 512  x 28 x 28
    x3 = self.resnet.layer3_1(x2)  # BF x 1024 x 14 x 14
    x4 = self.resnet.layer4_1(x3)  # BF x 2048 x  7 x  7  先进行resnet特征提取
    # print(x1.shape, x2.shape, x3.shape, x4.shape)
​
    conv1_feat = self.conv1(x1)  # BF x 256 x 56 x 56   维度转换一下
    conv2_feat = self.conv2(x2)  # BF x 256 x 28 x 28
    conv3_feat = self.conv3(x3)  # BF x 256 x 14 x 14
    conv4_feat = self.conv4(x4)  # BF x 256 x  7 x  7
    # print(conv1_feat.shape, conv2_feat.shape, conv3_feat.shape, conv4_feat.shape)
​
    feature_map_list = [conv1_feat, conv2_feat, conv3_feat, conv4_feat]
    a_fea_list = [None] * 4
​
    if len(self.tpavi_stages) > 0:   # 做几次tpavi模块,论文中是4次
        if (not self.tpavi_vv_flag) and (not self.tpavi_va_flag):
            raise Exception('tpavi_vv_flag and tpavi_va_flag cannot be False at the same time if len(tpavi_stages)>0, \
                tpavi_vv_flag is for video self-attention while tpavi_va_flag indicates the standard version (audio-visual attention)')
        for i in self.tpavi_stages:
            tpavi_count = 0
            conv_feat = torch.zeros_like(feature_map_list[i]).cuda()
            if self.tpavi_vv_flag:
                conv_feat_vv = self.tpavi_vv(feature_map_list[i], stage=i)
                conv_feat += conv_feat_vv
                tpavi_count += 1
            if self.tpavi_va_flag:
                # tpavi模块
                conv_feat_va, a_fea = self.tpavi_va(feature_map_list[i], audio_feature, stage=i)  
                conv_feat += conv_feat_va
                tpavi_count += 1
                a_fea_list[i] = a_fea
            conv_feat /= tpavi_count
            feature_map_list[i] = conv_feat  # update features of stage-i which conduct TPAVI
​
    conv4_feat = self.path4(feature_map_list[3])  # BF x 256 x 14 x 14  # 解码
    conv43 = self.path3(conv4_feat, feature_map_list[2])  # BF x 256 x 28 x 28
    conv432 = self.path2(conv43, feature_map_list[1])  # BF x 256 x 56 x 56
    conv4321 = self.path1(conv432, feature_map_list[0])  # BF x 256 x 112 x 112
    # print(conv4_feat.shape, conv43.shape, conv432.shape, conv4321.shape)
​
    pred = self.output_conv(conv4321)  # BF x 1 x 224 x 224
    # print(pred.shape)
​
    return pred, feature_map_list, a_fea_list

可以看到要经过一个TPAVI模块,是蛮复杂的模块:

class TPAVIModule(nn.Module):
    def __init__(self, in_channels, inter_channels=None, mode='dot', 
                 dimension=3, bn_layer=True):
        """
        args:
            in_channels: original channel size (1024 in the paper)
            inter_channels: channel size inside the block if not specifed reduced to half (512 in the paper)
            mode: supports Gaussian, Embedded Gaussian, Dot Product, and Concatenation 
            dimension: can be 1 (temporal), 2 (spatial), 3 (spatiotemporal)
            bn_layer: whether to add batch norm
        """
        super(TPAVIModule, self).__init__()
​
        assert dimension in [1, 2, 3]
        
        if mode not in ['gaussian', 'embedded', 'dot', 'concatenate']:
            raise ValueError('`mode` must be one of `gaussian`, `embedded`, `dot` or `concatenate`')
            
        self.mode = mode
        self.dimension = dimension
​
        self.in_channels = in_channels
        self.inter_channels = inter_channels
​
        # the channel size is reduced to half inside the block
        if self.inter_channels is None:
            self.inter_channels = in_channels // 2
            if self.inter_channels == 0:
                self.inter_channels = 1
        
        ## add align channel
        self.align_channel = nn.Linear(128, in_channels)
        self.norm_layer=nn.LayerNorm(in_channels)
​
        # assign appropriate convolutional, max pool, and batch norm layers for different dimensions
        if dimension == 3:
            conv_nd = nn.Conv3d
            max_pool_layer = nn.MaxPool3d(kernel_size=(1, 2, 2))
            bn = nn.BatchNorm3d
        elif dimension == 2:
            conv_nd = nn.Conv2d
            max_pool_layer = nn.MaxPool2d(kernel_size=(2, 2))
            bn = nn.BatchNorm2d
        else:
            conv_nd = nn.Conv1d
            max_pool_layer = nn.MaxPool1d(kernel_size=(2))
            bn = nn.BatchNorm1d
​
        # function g in the paper which goes through conv. with kernel size 1
        self.g = conv_nd(in_channels=self.in_channels, out_channels=self.inter_channels, kernel_size=1)
​
        if bn_layer:
            self.W_z = nn.Sequential(
                    conv_nd(in_channels=self.inter_channels, out_channels=self.in_channels, kernel_size=1),
                    bn(self.in_channels)
                )
            nn.init.constant_(self.W_z[1].weight, 0)
            nn.init.constant_(self.W_z[1].bias, 0)
        else:
            self.W_z = conv_nd(in_channels=self.inter_channels, out_channels=self.in_channels, kernel_size=1)
​
            nn.init.constant_(self.W_z.weight, 0)
            nn.init.constant_(self.W_z.bias, 0)
​
        # define theta and phi for all operations except gaussian
        if self.mode == "embedded" or self.mode == "dot" or self.mode == "concatenate":
            self.theta = conv_nd(in_channels=self.in_channels, out_channels=self.inter_channels, kernel_size=1)
            self.phi = conv_nd(in_channels=self.in_channels, out_channels=self.inter_channels, kernel_size=1)
        
        if self.mode == "concatenate":
            self.W_f = nn.Sequential(
                    nn.Conv2d(in_channels=self.inter_channels * 2, out_channels=1, kernel_size=1),
                    nn.ReLU()
                )
​
            
    def forward(self, x, audio=None):
        """
        args:
            x: (N, C, T, H, W) for dimension=3; (N, C, H, W) for dimension 2; (N, C, T) for dimension 1
            audio: (N, T, C)
        """
​
        audio_temp = 0
        batch_size, C = x.size(0), x.size(1)
        if audio is not None:
            # print('==> audio.shape', audio.shape)
            H, W = x.shape[-2], x.shape[-1]
            audio_temp = self.align_channel(audio) # [bs, T, C]
            audio = audio_temp.permute(0, 2, 1) # [bs, C, T]
            audio = audio.unsqueeze(-1).unsqueeze(-1) # [bs, C, T, 1, 1]
            audio = audio.repeat(1, 1, 1, H, W) # [bs, C, T, H, W]
        else:
            audio = x
​
        # (N, C, THW)
        g_x = self.g(x).view(batch_size, self.inter_channels, -1) # [bs, C, THW]
        # print('g_x.shape', g_x.shape)
        # g_x = x.view(batch_size, C, -1)  # [bs, C, THW]
        g_x = g_x.permute(0, 2, 1) # [bs, THW, C]
​
        if self.mode == "gaussian":
            theta_x = x.view(batch_size, self.in_channels, -1)
            phi_x = audio.view(batch_size, self.in_channels, -1)
            theta_x = theta_x.permute(0, 2, 1)
            f = torch.matmul(theta_x, phi_x)
​
        elif self.mode == "embedded" or self.mode == "dot":
            theta_x = self.theta(x).view(batch_size, self.inter_channels, -1) # [bs, C', THW]
            phi_x = self.phi(audio).view(batch_size, self.inter_channels, -1) # [bs, C', THW]
            theta_x = theta_x.permute(0, 2, 1) # [bs, THW, C']
            f = torch.matmul(theta_x, phi_x) # [bs, THW, THW]
​
        elif self.mode == "concatenate":
            theta_x = self.theta(x).view(batch_size, self.inter_channels, -1, 1)
            phi_x = self.phi(audio).view(batch_size, self.inter_channels, 1, -1)
            
            h = theta_x.size(2)
            w = phi_x.size(3)
            theta_x = theta_x.repeat(1, 1, 1, w)
            phi_x = phi_x.repeat(1, 1, h, 1)
            
            concat = torch.cat([theta_x, phi_x], dim=1)
            f = self.W_f(concat)
            f = f.view(f.size(0), f.size(2), f.size(3))
        
        if self.mode == "gaussian" or self.mode == "embedded":
            f_div_C = F.softmax(f, dim=-1)
        elif self.mode == "dot" or self.mode == "concatenate":
            N = f.size(-1) # number of position in x
            f_div_C = f / N  # [bs, THW, THW]
        
        y = torch.matmul(f_div_C, g_x) # [bs, THW, C]
        
        # contiguous here just allocates contiguous chunk of memory
        y = y.permute(0, 2, 1).contiguous() # [bs, C, THW]
        y = y.view(batch_size, self.inter_channels, *x.size()[2:]) # [bs, C', T, H, W]
        
        W_y = self.W_z(y)  # [bs, C, T, H, W]
        # residual connection
        z = W_y + x #  # [bs, C, T, H, W]
​
        # add LayerNorm
        z =  z.permute(0, 2, 3, 4, 1) # [bs, T, H, W, C]
        z = self.norm_layer(z)
        z = z.permute(0, 4, 1, 2, 3) # [bs, C, T, H, W]
        
        return z, audio_temp

代码看着复杂,其实是作者做了很多的模块选择以及代码的通道转换,实际最后的操作就是几个1* 1 *1 3D卷积,咱不用想也知道,3d卷积来做时序的特征提取。然后做一些累乘累加操作。

if dimension == 3:
    conv_nd = nn.Conv3d
    max_pool_layer = nn.MaxPool3d(kernel_size=(1, 2, 2))
    bn = nn.BatchNorm3d
elif dimension == 2:
    conv_nd = nn.Conv2d
    max_pool_layer = nn.MaxPool2d(kernel_size=(2, 2))
    bn = nn.BatchNorm2d
else:
    conv_nd = nn.Conv1d
    max_pool_layer = nn.MaxPool1d(kernel_size=(2))
    bn = nn.BatchNorm1d

最后经过几个解码器,将特征图转为一维度:

conv4_feat = self.path4(feature_map_list[3])  # BF x 256 x 14 x 14
conv43 = self.path3(conv4_feat, feature_map_list[2])  # BF x 256 x 28 x 28
conv432 = self.path2(conv43, feature_map_list[1])  # BF x 256 x 56 x 56
conv4321 = self.path1(conv432, feature_map_list[0])  # BF x 256 x 112 x 112
# print(conv4_feat.shape, conv43.shape, conv432.shape, conv4321.shape)
​
pred = self.output_conv(conv4321)  # BF x 1 x 224 x 224

可以看到【BF x 1 x 224 x 224】这个1维度的变化,就是网络的一个回归预测部分。最后输出的bs *frame 张1 * 224 *224 的图,就是我们最后输出的图(经过argmax等操作后显示成0,1分类),就变成了预测的mask图,

大家可以看我的预测图:

测试

先看看ms3_meta_data.csv 的数据

可以看到,一共有三份数据:训练、验证和测试集,我们训练好模型后,可以使用test.py 进行测试,测试效果会放在test_log文件夹。会去测试,test文件夹里的数据。运行测试代码,改一下训练好的模型路径就可以看到结果。

测试某个视频

点开avsbench_data/det/det的raw_videos/里面放你想测试的videos,建议5s,因为要切5帧,除非你改代码。

然后运行preprocess_scripts/preprocess_ms3.py,这是为了生成语音的梅尔图,和切帧,会保存到raw_videos同级。

接着运行detect.py(在train.py 同级)就可以针对你的视频,推理了。

实时检测,这个代码我还在写,稍等。

代码所有的链接(本地文件不能上传,只能提供原始github):https://github.com/OpenNLPLab/AVSBench

最后

近期我会录制视频,过一遍原理和代码和训练推理,大家关注一下~


本文转载自: https://blog.csdn.net/qq_46098574/article/details/126255334
版权归原作者 cv君 所有, 如有侵权,请联系我们删除。

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