Add SSN model.

mmaction/models/heads/__init__.py

@@ -1,7 +1,10 @@
 from .base import BaseHead
 from .i3d_head import I3DHead
 from .slowfast_head import SlowFastHead
+from .ssn_head import SSNHead
 from .tsm_head import TSMHead
 from .tsn_head import TSNHead
 
-__all__ = ['TSNHead', 'I3DHead', 'BaseHead', 'TSMHead', 'SlowFastHead']
+__all__ = [
+    'TSNHead', 'I3DHead', 'BaseHead', 'TSMHead', 'SlowFastHead', 'SSNHead'
+]

mmaction/models/localizers/__init__.py

@@ -1,5 +1,6 @@
 from .base import BaseLocalizer
 from .bmn import BMN
 from .bsn import PEM, TEM
+from .ssn import SSN
 
-__all__ = ['PEM', 'TEM', 'BMN', 'BaseLocalizer']
+__all__ = ['PEM', 'TEM', 'BMN', 'SSN', 'BaseLocalizer']

mmaction/models/localizers/base.py

@@ -5,6 +5,8 @@
 import torch.distributed as dist
 import torch.nn as nn
 
+from .. import builder
+
 
 class BaseLocalizer(nn.Module, metaclass=ABCMeta):
     """Base class for localizers.
@@ -14,12 +16,30 @@ class BaseLocalizer(nn.Module, metaclass=ABCMeta):
     Methods:``forward_test``, supporting to forward when testing.
     """
 
-    def __init__(self):
+    def __init__(self, backbone, cls_head, train_cfg=None, test_cfg=None):
         super().__init__()
+        self.backbone = builder.build_backbone(backbone)
+        self.cls_head = builder.build_head(cls_head)
+
+        self.train_cfg = train_cfg
+        self.test_cfg = test_cfg
+        self.init_weights()
 
     def init_weights(self):
         """Weight initialization for model."""
-        pass
+        self.backbone.init_weights()
+        self.cls_head.init_weights()
+
+    def extract_feat(self, imgs):
+        """Extract features through a backbone.
+
+        Args:
+            imgs (torch.Tensor): The input images.
+        Returns:
+            torch.tensor: The extracted features.
+        """
+        x = self.backbone(imgs)
+        return x
 
     @abstractmethod
     def forward_train(self, imgs, labels):
@@ -31,14 +51,12 @@ def forward_test(self, imgs):
         """Defines the computation performed at testing."""
         pass
 
-    def forward(self, imgs, label=None, return_loss=True):
+    def forward(self, imgs, return_loss=True, **kwargs):
         """Define the computation performed at every call."""
         if return_loss:
-            if label is None:
-                raise ValueError('Label should not be None.')
-            return self.forward_train(imgs, label)
+            return self.forward_train(imgs, **kwargs)
         else:
-            return self.forward_test(imgs)
+            return self.forward_test(imgs, **kwargs)
 
     @staticmethod
     def _parse_losses(losses):

mmaction/models/localizers/bmn.py

@@ -50,7 +50,7 @@ def __init__(self,
                  hidden_dim_1d=256,
                  hidden_dim_2d=128,
                  hidden_dim_3d=512):
-        super().__init__()
+        super(BaseLocalizer, self).__init__()
 
         self.tscale = temporal_dim
         self.boundary_ratio = boundary_ratio

mmaction/models/localizers/bsn.py

@@ -45,7 +45,8 @@ def __init__(self,
                  conv1_ratio=1,
                  conv2_ratio=1,
                  conv3_ratio=0.01):
-        super().__init__()
+        super(BaseLocalizer, self).__init__()
+
         self.temporal_dim = temporal_dim
         self.boundary_ratio = boundary_ratio
         self.feat_dim = tem_feat_dim
@@ -264,7 +265,7 @@ def __init__(self,
                  fc1_ratio=0.1,
                  fc2_ratio=0.1,
                  output_dim=1):
-        super().__init__()
+        super(BaseLocalizer, self).__init__()
 
         self.feat_dim = pem_feat_dim
         self.hidden_dim = pem_hidden_dim

mmaction/models/localizers/ssn.py

@@ -0,0 +1,129 @@
+import torch
+import torch.nn as nn
+
+from .. import builder
+from ..registry import LOCALIZERS
+from .base import BaseLocalizer
+
+
+@LOCALIZERS.register_module()
+class SSN(BaseLocalizer):
+    """Temporal Action Detection with Structured Segment Networks.
+
+    Args:
+        backbone (dict): Config for building backbone.
+        cls_head (dict): Config for building classification head.
+        in_channels (int): Number of channels for input data.
+            Default: 3.
+        spatial_type (str): Type of spatial pooling.
+            Default: 'avg'.
+        dropout_ratio (float): Ratio of dropout.
+            Default: 0.5.
+        loss_cls (dict): Config for building loss.
+            Default: ``dict(type='SSNLoss')``.
+        train_cfg (dict): Config for training. Default: None.
+        test_cfg (dict): Config for testing. Default: None.
+    """
+
+    def __init__(self,
+                 backbone,
+                 cls_head,
+                 in_channels=3,
+                 spatial_type='avg',
+                 dropout_ratio=0.5,
+                 loss_cls=dict(type='SSNLoss'),
+                 train_cfg=None,
+                 test_cfg=None):
+
+        super().__init__(backbone, cls_head, train_cfg, test_cfg)
+
+        self.is_test_prepared = False
+        self.in_channels = in_channels
+
+        self.spatial_type = spatial_type
+        if self.spatial_type == 'avg':
+            self.pool = nn.AvgPool2d((7, 7), stride=1, padding=0)
+        elif self.spatial_type == 'max':
+            self.pool = nn.MaxPool2d((7, 7), stride=1, padding=0)
+        else:
+            self.pool = None
+
+        self.dropout_ratio = dropout_ratio
+        if self.dropout_ratio != 0:
+            self.dropout = nn.Dropout(p=self.dropout_ratio)
+        else:
+            self.dropout = None
+        self.loss_cls = builder.build_loss(loss_cls)
+
+    def forward_train(self, imgs, proposal_scale_factor, proposal_type,
+                      proposal_labels, reg_targets, **kwargs):
+        """Define the computation performed at every call when training."""
+        imgs = imgs.reshape((-1, self.in_channels) + imgs.shape[4:])
+
+        x = self.extract_feat(imgs)
+
+        if self.pool:
+            x = self.pool(x)
+        if self.dropout is not None:
+            x = self.dropout(x)
+
+        activity_scores, completeness_scores, bbox_preds = self.cls_head(
+            (x, proposal_scale_factor))
+
+        loss = self.loss_cls(activity_scores, completeness_scores, bbox_preds,
+                             proposal_type, proposal_labels, reg_targets,
+                             self.train_cfg)
+        loss_dict = dict(**loss)
+
+        return loss_dict
+
+    def forward_test(self, imgs, relative_proposal_list, scale_factor_list,
+                     proposal_tick_list, reg_norm_consts, **kwargs):
+        """Define the computation performed at every call when testing."""
+        num_crops = imgs.shape[0]
+        imgs = imgs.reshape((num_crops, -1, self.in_channels) + imgs.shape[3:])
+        num_ticks = imgs.shape[1]
+
+        output = []
+        minibatch_size = self.test_cfg.ssn.sampler.batch_size
+        for idx in range(0, num_ticks, minibatch_size):
+            chunk = imgs[:, idx:idx +
+                         minibatch_size, :, :, :].view((-1, ) + imgs.shape[2:])
+            x = self.extract_feat(chunk)
+            if self.pool:
+                x = self.pool(x)
+            # Merge crop to save memory.
+            x = x.reshape((num_crops, x.size(0) // num_crops, -1)).mean(dim=0)
+            output.append(x)
+        output = torch.cat(output, dim=0)
+
+        relative_proposal_list = relative_proposal_list.squeeze(0)
+        proposal_tick_list = proposal_tick_list.squeeze(0)
+        scale_factor_list = scale_factor_list.squeeze(0)
+        reg_norm_consts = reg_norm_consts.squeeze(0)
+
+        if not self.is_test_prepared:
+            self.is_test_prepared = self.cls_head.prepare_test_fc(
+                self.cls_head.consensus.num_multipliers)
+
+        (output, activity_scores, completeness_scores,
+         bbox_preds) = self.cls_head(
+             (output, proposal_tick_list, scale_factor_list), test_mode=True)
+
+        if bbox_preds is not None:
+            bbox_preds = bbox_preds.view(-1, self.cls_head.num_classes, 2)
+            bbox_preds[:, :, 0] = (
+                bbox_preds[:, :, 0] * reg_norm_consts[1, 0] +
+                reg_norm_consts[0, 0])
+            bbox_preds[:, :, 1] = (
+                bbox_preds[:, :, 1] * reg_norm_consts[1, 1] +
+                reg_norm_consts[0, 1])
+
+            return (relative_proposal_list.cpu().numpy(),
+                    activity_scores.cpu().numpy(),
+                    completeness_scores.cpu().numpy(),
+                    bbox_preds.cpu().numpy())
+        else:
+            return (relative_proposal_list.cpu().numpy(),
+                    activity_scores.cpu().numpy(),
+                    completeness_scores.cpu().numpy(), None)