[Feature] Add merge_heatmap_to_5

mmedit/models/extractors/__init__.py

@@ -1,4 +1,5 @@
-from .feedback_hour_glass import FeedbackHourglass, Hourglass
+from .feedback_hour_glass import (FeedbackHourglass, Hourglass,
+                                  reduce_to_five_heatmaps)
 from .lte import LTE
 
-__all__ = ['LTE', 'Hourglass', 'FeedbackHourglass']
+__all__ = ['LTE', 'Hourglass', 'FeedbackHourglass', 'reduce_to_five_heatmaps']

mmedit/models/extractors/feedback_hour_glass.py

@@ -157,3 +157,55 @@ def forward(self, x, last_hidden=None):
         heatmap = self.last(feature[:, :self.mid_channels])  # first half
         feedback = feature[:, self.mid_channels:]  # second half
         return heatmap, feedback
+
+
+def reduce_to_five_heatmaps(ori_heatmap, detach):
+    """Reduce facial landmark heatmaps to 5 heatmaps.
+
+    DIC realizes facial SR with the help of key points of the face.
+    The number of key points in datasets are different from each other.
+    This function reduces the input heatmaps into 5 heatmaps:
+        left eye
+        right eye
+        nose
+        mouse
+        face silhouette
+
+    Args:
+        ori_heatmap (Tensor): Input heatmap tensor. (B, N, 32, 32).
+        detach (bool): Detached from the current tensor or not.
+
+    returns:
+        Tensor: New heatmap tensor. (B, 5, 32, 32).
+    """
+
+    heatmap = ori_heatmap.clone()
+    max_heat = heatmap.max(dim=2, keepdim=True)[0].max(dim=3, keepdim=True)[0]
+    max_heat = max_heat.clamp_min_(0.05)
+    heatmap /= max_heat
+    if heatmap.size(1) == 5:
+        return heatmap.detach() if detach else heatmap
+    elif heatmap.size(1) == 68:
+        new_heatmap = torch.zeros_like(heatmap[:, :5])
+        new_heatmap[:, 0] = heatmap[:, 36:42].sum(1)  # left eye
+        new_heatmap[:, 1] = heatmap[:, 42:48].sum(1)  # right eye
+        new_heatmap[:, 2] = heatmap[:, 27:36].sum(1)  # nose
+        new_heatmap[:, 3] = heatmap[:, 48:68].sum(1)  # mouse
+        new_heatmap[:, 4] = heatmap[:, :27].sum(1)  # face silhouette
+        return new_heatmap.detach() if detach else new_heatmap
+    elif heatmap.size(1) == 194:  # Helen
+        new_heatmap = torch.zeros_like(heatmap[:, :5])
+        tmp_id = torch.cat((torch.arange(134, 153), torch.arange(174, 193)))
+        new_heatmap[:, 0] = heatmap[:, tmp_id].sum(1)  # left eye
+        tmp_id = torch.cat((torch.arange(114, 133), torch.arange(154, 173)))
+        new_heatmap[:, 1] = heatmap[:, tmp_id].sum(1)  # right eye
+        tmp_id = torch.arange(41, 57)
+        new_heatmap[:, 2] = heatmap[:, tmp_id].sum(1)  # nose
+        tmp_id = torch.arange(58, 113)
+        new_heatmap[:, 3] = heatmap[:, tmp_id].sum(1)  # mouse
+        tmp_id = torch.arange(0, 40)
+        new_heatmap[:, 4] = heatmap[:, tmp_id].sum(1)  # face silhouette
+        return new_heatmap.detach() if detach else new_heatmap
+    else:
+        raise NotImplementedError(
+            f'Face landmark number {heatmap.size(1)} not implemented!')

tests/test_extractors.py

@@ -3,7 +3,8 @@
 
 from mmedit.models import build_component
 from mmedit.models.extractors import Hourglass
-from mmedit.models.extractors.feedback_hour_glass import ResBlock
+from mmedit.models.extractors.feedback_hour_glass import (
+    ResBlock, reduce_to_five_heatmaps)
 
 
 def test_lte():
@@ -64,3 +65,27 @@ def test_feedback_hour_glass():
     heatmap, last_hidden = fhg.forward(x, last_hidden)
     assert heatmap.shape == (2, 20, 16, 16)
     assert last_hidden.shape == (2, 16, 16, 16)
+
+
+def test_reduce_to_five_heatmaps():
+    heatmap = torch.rand((2, 5, 64, 64))
+    new_heatmap = reduce_to_five_heatmaps(heatmap, False)
+    assert new_heatmap.shape == (2, 5, 64, 64)
+    new_heatmap = reduce_to_five_heatmaps(heatmap, True)
+    assert new_heatmap.shape == (2, 5, 64, 64)
+
+    heatmap = torch.rand((2, 68, 64, 64))
+    new_heatmap = reduce_to_five_heatmaps(heatmap, False)
+    assert new_heatmap.shape == (2, 5, 64, 64)
+    new_heatmap = reduce_to_five_heatmaps(heatmap, True)
+    assert new_heatmap.shape == (2, 5, 64, 64)
+
+    heatmap = torch.rand((2, 194, 64, 64))
+    new_heatmap = reduce_to_five_heatmaps(heatmap, False)
+    assert new_heatmap.shape == (2, 5, 64, 64)
+    new_heatmap = reduce_to_five_heatmaps(heatmap, True)
+    assert new_heatmap.shape == (2, 5, 64, 64)
+
+    with pytest.raises(NotImplementedError):
+        heatmap = torch.rand((2, 12, 64, 64))
+        reduce_to_five_heatmaps(heatmap, False)