Initial commit

fbshipit-source-id: da6be2f26e3a1202f4bffde8cb980e2dcb851294

sam3/agent/helpers/roi_align.py

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+# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
+
+from torch import nn
+from torchvision.ops import roi_align
+
+
+# NOTE: torchvision's RoIAlign has a different default aligned=False
+class ROIAlign(nn.Module):
+    def __init__(self, output_size, spatial_scale, sampling_ratio, aligned=True):
+        """
+        Args:
+            output_size (tuple): h, w
+            spatial_scale (float): scale the input boxes by this number
+            sampling_ratio (int): number of inputs samples to take for each output
+                sample. 0 to take samples densely.
+            aligned (bool): if False, use the legacy implementation in
+                Detectron. If True, align the results more perfectly.
+
+        Note:
+            The meaning of aligned=True:
+
+            Given a continuous coordinate c, its two neighboring pixel indices (in our
+            pixel model) are computed by floor(c - 0.5) and ceil(c - 0.5). For example,
+            c=1.3 has pixel neighbors with discrete indices [0] and [1] (which are sampled
+            from the underlying signal at continuous coordinates 0.5 and 1.5). But the original
+            roi_align (aligned=False) does not subtract the 0.5 when computing neighboring
+            pixel indices and therefore it uses pixels with a slightly incorrect alignment
+            (relative to our pixel model) when performing bilinear interpolation.
+
+            With `aligned=True`,
+            we first appropriately scale the ROI and then shift it by -0.5
+            prior to calling roi_align. This produces the correct neighbors; see
+            detectron2/tests/test_roi_align.py for verification.
+
+            The difference does not make a difference to the model's performance if
+            ROIAlign is used together with conv layers.
+        """
+        super().__init__()
+        self.output_size = output_size
+        self.spatial_scale = spatial_scale
+        self.sampling_ratio = sampling_ratio
+        self.aligned = aligned
+
+        from torchvision import __version__
+
+        version = tuple(int(x) for x in __version__.split(".")[:2])
+        # https://github.com/pytorch/vision/pull/2438
+        assert version >= (0, 7), "Require torchvision >= 0.7"
+
+    def forward(self, input, rois):
+        """
+        Args:
+            input: NCHW images
+            rois: Bx5 boxes. First column is the index into N. The other 4 columns are xyxy.
+        """
+        assert rois.dim() == 2 and rois.size(1) == 5
+        if input.is_quantized:
+            input = input.dequantize()
+        return roi_align(
+            input,
+            rois.to(dtype=input.dtype),
+            self.output_size,
+            self.spatial_scale,
+            self.sampling_ratio,
+            self.aligned,
+        )
+
+    def __repr__(self):
+        tmpstr = self.__class__.__name__ + "("
+        tmpstr += "output_size=" + str(self.output_size)
+        tmpstr += ", spatial_scale=" + str(self.spatial_scale)
+        tmpstr += ", sampling_ratio=" + str(self.sampling_ratio)
+        tmpstr += ", aligned=" + str(self.aligned)
+        tmpstr += ")"
+        return tmpstr