Remove interpolate in favor of PyTorch's implementation

torchvision/models/detection/keypoint_rcnn.py

@@ -1,8 +1,6 @@
 import torch
 from torch import nn
 
-from torchvision.ops import misc as misc_nn_ops
-
 from torchvision.ops import MultiScaleRoIAlign
 
 from ..utils import load_state_dict_from_url
@@ -253,7 +251,7 @@ def __init__(self, in_channels, num_keypoints):
 
     def forward(self, x):
         x = self.kps_score_lowres(x)
-        x = misc_nn_ops.interpolate(
+        x = torch.nn.functional.interpolate(
             x, scale_factor=float(self.up_scale), mode="bilinear", align_corners=False
         )
         return x

torchvision/models/detection/roi_heads.py

@@ -5,7 +5,6 @@
 from torch import nn, Tensor
 
 from torchvision.ops import boxes as box_ops
-from torchvision.ops import misc as misc_nn_ops
 
 from torchvision.ops import roi_align
 
@@ -175,8 +174,8 @@ def _onnx_heatmaps_to_keypoints(maps, maps_i, roi_map_width, roi_map_height,
     width_correction = widths_i / roi_map_width
     height_correction = heights_i / roi_map_height
 
-    roi_map = torch.nn.functional.interpolate(
-        maps_i[None], size=(int(roi_map_height), int(roi_map_width)), mode='bicubic', align_corners=False)[0]
+    roi_map = F.interpolate(
+        maps_i[:, None], size=(int(roi_map_height), int(roi_map_width)), mode='bicubic', align_corners=False)[:, 0]
 
     w = torch.scalar_tensor(roi_map.size(2), dtype=torch.int64)
     pos = roi_map.reshape(num_keypoints, -1).argmax(dim=1)
@@ -256,8 +255,8 @@ def heatmaps_to_keypoints(maps, rois):
         roi_map_height = int(heights_ceil[i].item())
         width_correction = widths[i] / roi_map_width
         height_correction = heights[i] / roi_map_height
-        roi_map = torch.nn.functional.interpolate(
-            maps[i][None], size=(roi_map_height, roi_map_width), mode='bicubic', align_corners=False)[0]
+        roi_map = F.interpolate(
+            maps[i][:, None], size=(roi_map_height, roi_map_width), mode='bicubic', align_corners=False)[:, 0]
         # roi_map_probs = scores_to_probs(roi_map.copy())
         w = roi_map.shape[2]
         pos = roi_map.reshape(num_keypoints, -1).argmax(dim=1)
@@ -392,7 +391,7 @@ def paste_mask_in_image(mask, box, im_h, im_w):
     mask = mask.expand((1, 1, -1, -1))
 
     # Resize mask
-    mask = misc_nn_ops.interpolate(mask, size=(h, w), mode='bilinear', align_corners=False)
+    mask = F.interpolate(mask, size=(h, w), mode='bilinear', align_corners=False)
     mask = mask[0][0]
 
     im_mask = torch.zeros((im_h, im_w), dtype=mask.dtype, device=mask.device)
@@ -420,7 +419,7 @@ def _onnx_paste_mask_in_image(mask, box, im_h, im_w):
     mask = mask.expand((1, 1, mask.size(0), mask.size(1)))
 
     # Resize mask
-    mask = torch.nn.functional.interpolate(mask, size=(int(h), int(w)), mode='bilinear', align_corners=False)
+    mask = F.interpolate(mask, size=(int(h), int(w)), mode='bilinear', align_corners=False)
     mask = mask[0][0]
 
     x_0 = torch.max(torch.cat((box[0].unsqueeze(0), zero)))

torchvision/models/detection/transform.py

@@ -2,10 +2,10 @@
 import math
 import torch
 from torch import nn, Tensor
+from torch.nn import functional as F
 import torchvision
 from torch.jit.annotations import List, Tuple, Dict, Optional
 
-from torchvision.ops import misc as misc_nn_ops
 from .image_list import ImageList
 from .roi_heads import paste_masks_in_image
 
@@ -28,7 +28,7 @@ def _resize_image_and_masks_onnx(image, self_min_size, self_max_size, target):
 
     if "masks" in target:
         mask = target["masks"]
-        mask = misc_nn_ops.interpolate(mask[None].float(), scale_factor=scale_factor)[0].byte()
+        mask = F.interpolate(mask[:, None].float(), scale_factor=scale_factor)[:, 0].byte()
         target["masks"] = mask
     return image, target
 
@@ -50,7 +50,7 @@ def _resize_image_and_masks(image, self_min_size, self_max_size, target):
 
     if "masks" in target:
         mask = target["masks"]
-        mask = misc_nn_ops.interpolate(mask[None].float(), scale_factor=scale_factor)[0].byte()
+        mask = F.interpolate(mask[:, None].float(), scale_factor=scale_factor)[:, 0].byte()
         target["masks"] = mask
     return image, target
 

torchvision/ops/misc.py

@@ -1,7 +1,3 @@
-from collections import OrderedDict
-from torch.jit.annotations import Optional, List
-from torch import Tensor
-
 """
 helper class that supports empty tensors on some nn functions.
 
@@ -12,10 +8,8 @@
 is implemented
 """
 
-import math
 import warnings
 import torch
-from torchvision.ops import _new_empty_tensor
 
 
 class Conv2d(torch.nn.Conv2d):
@@ -42,51 +36,7 @@ def __init__(self, *args, **kwargs):
             "removed in future versions, use torch.nn.BatchNorm2d instead.", FutureWarning)
 
 
-def _check_size_scale_factor(dim, size, scale_factor):
-    # type: (int, Optional[List[int]], Optional[float]) -> None
-    if size is None and scale_factor is None:
-        raise ValueError("either size or scale_factor should be defined")
-    if size is not None and scale_factor is not None:
-        raise ValueError("only one of size or scale_factor should be defined")
-    if scale_factor is not None:
-        if isinstance(scale_factor, (list, tuple)):
-            if len(scale_factor) != dim:
-                raise ValueError(
-                    "scale_factor shape must match input shape. "
-                    "Input is {}D, scale_factor size is {}".format(dim, len(scale_factor))
-                )
-
-
-def _output_size(dim, input, size, scale_factor):
-    # type: (int, Tensor, Optional[List[int]], Optional[float]) -> List[int]
-    assert dim == 2
-    _check_size_scale_factor(dim, size, scale_factor)
-    if size is not None:
-        return size
-    # if dim is not 2 or scale_factor is iterable use _ntuple instead of concat
-    assert scale_factor is not None and isinstance(scale_factor, (int, float))
-    scale_factors = [scale_factor, scale_factor]
-    # math.floor might return float in py2.7
-    return [
-        int(math.floor(input.size(i + 2) * scale_factors[i])) for i in range(dim)
-    ]
-
-
-def interpolate(input, size=None, scale_factor=None, mode="nearest", align_corners=None):
-    # type: (Tensor, Optional[List[int]], Optional[float], str, Optional[bool]) -> Tensor
-    """
-    Equivalent to nn.functional.interpolate, but with support for empty batch sizes.
-    This will eventually be supported natively by PyTorch, and this
-    class can go away.
-    """
-    if input.numel() > 0:
-        return torch.nn.functional.interpolate(
-            input, size, scale_factor, mode, align_corners
-        )
-
-    output_shape = _output_size(2, input, size, scale_factor)
-    output_shape = list(input.shape[:-2]) + list(output_shape)
-    return _new_empty_tensor(input, output_shape)
+interpolate = torch.nn.functional.interpolate
 
 
 # This is not in nn