Fix Dockerfile: extract mmcv.ops shim to separate Python file

Move inline Python code from Dockerfile RUN command into
setup_mmcv_shim.py to fix Docker parse errors. Use mmcv lite
instead of mmcv-full to avoid C++ compilation.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

Dockerfile

@@ -16,8 +16,12 @@ RUN pip install --no-cache-dir \
     torchvision==0.14.1+cpu \
     -f https://download.pytorch.org/whl/cpu/torch_stable.html
 
-# Install mmcv-full WITHOUT C++ ops (ops are patched to torchvision at runtime)
-RUN MMCV_WITH_OPS=0 FORCE_CUDA=0 pip install --no-cache-dir --no-build-isolation mmcv-full==1.7.2
+# Install mmcv (lite version - no C++ ops needed)
+RUN pip install --no-cache-dir mmcv==1.7.2
+
+# Copy and run mmcv.ops shim setup
+COPY setup_mmcv_shim.py .
+RUN python setup_mmcv_shim.py
 
 # Install remaining dependencies
 RUN pip install --no-cache-dir \

setup_mmcv_shim.py

@@ -0,0 +1,86 @@
+"""Create mmcv.ops compatibility shim using torchvision ops."""
+import os
+import mmcv
+
+ops_dir = os.path.join(os.path.dirname(mmcv.__file__), 'ops')
+os.makedirs(ops_dir, exist_ok=True)
+
+init_code = '''
+import torch
+import torchvision.ops as tv_ops
+
+class NMSop(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, bboxes, scores, iou_threshold, offset, score_threshold, max_num):
+        if score_threshold > 0:
+            valid_mask = scores > score_threshold
+            bboxes_f, scores_f = bboxes[valid_mask], scores[valid_mask]
+            valid_inds = torch.nonzero(valid_mask, as_tuple=False).squeeze(dim=1)
+        else:
+            bboxes_f, scores_f = bboxes, scores
+            valid_inds = None
+        if bboxes_f.numel() == 0:
+            return torch.zeros(0, dtype=torch.long, device=bboxes.device)
+        inds = tv_ops.nms(bboxes_f, scores_f, iou_threshold)
+        if max_num > 0:
+            inds = inds[:max_num]
+        if valid_inds is not None:
+            inds = valid_inds[inds]
+        return inds
+
+def batched_nms(boxes, scores, idxs, nms_cfg, class_agnostic=False):
+    nms_cfg_ = nms_cfg.copy()
+    class_agnostic = nms_cfg_.pop("class_agnostic", class_agnostic)
+    iou_thr = nms_cfg_.get("iou_threshold", nms_cfg_.get("iou_thr", 0.5))
+    if class_agnostic:
+        boxes_for_nms = boxes
+    else:
+        if boxes.numel() == 0:
+            return boxes.new_zeros((0, 5)), torch.zeros(0, dtype=torch.long, device=boxes.device)
+        max_coordinate = boxes.max()
+        offsets = idxs.to(boxes) * (max_coordinate + 1)
+        boxes_for_nms = boxes + offsets[:, None]
+    if boxes_for_nms.numel() == 0:
+        return boxes.new_zeros((0, 5)), torch.zeros(0, dtype=torch.long, device=boxes.device)
+    keep = tv_ops.nms(boxes_for_nms, scores, iou_thr)
+    max_num = nms_cfg_.get("max_num", -1)
+    if max_num > 0 and len(keep) > max_num:
+        keep = keep[:max_num]
+    dets = torch.cat([boxes[keep], scores[keep].unsqueeze(1)], dim=1)
+    return dets, keep
+
+def nms(boxes, scores, iou_threshold, offset=0, score_threshold=0, max_num=-1):
+    inds = NMSop.apply(boxes, scores, iou_threshold, offset, score_threshold, max_num)
+    dets = torch.cat([boxes[inds], scores[inds].unsqueeze(1)], dim=1)
+    return dets, inds
+
+def roi_align(input, rois, output_size, spatial_scale=1.0, sampling_ratio=-1, pool_mode="avg", aligned=True):
+    return tv_ops.roi_align(input, rois, output_size, spatial_scale=spatial_scale,
+                            sampling_ratio=sampling_ratio if sampling_ratio > 0 else 2, aligned=aligned)
+
+class RoIAlign(torch.nn.Module):
+    def __init__(self, output_size, spatial_scale=1.0, sampling_ratio=-1, pool_mode="avg", aligned=True, use_torchvision=False):
+        super().__init__()
+        self.output_size = output_size
+        self.spatial_scale = spatial_scale
+        self.sampling_ratio = sampling_ratio
+        self.aligned = aligned
+    def forward(self, input, rois):
+        return roi_align(input, rois, self.output_size, self.spatial_scale, self.sampling_ratio, aligned=self.aligned)
+'''
+
+with open(os.path.join(ops_dir, '__init__.py'), 'w') as f:
+    f.write(init_code)
+
+# Create nms submodule
+nms_dir = os.path.join(ops_dir, 'nms')
+os.makedirs(nms_dir, exist_ok=True)
+with open(os.path.join(nms_dir, '__init__.py'), 'w') as f:
+    f.write('from mmcv.ops import NMSop, batched_nms, nms\n')
+
+# Create carafe stub
+carafe_path = os.path.join(ops_dir, 'carafe.py')
+with open(carafe_path, 'w') as f:
+    f.write('# carafe stub - not needed for CPU inference\n')
+
+print('mmcv.ops shim created successfully')