Fix: add mmcv fallback in flow_comp.py, remove mmcv from Dockerfile

Dockerfile

@@ -27,9 +27,7 @@ COPY model_version.json .
 RUN mkdir -p resources/checkpoint output working_dir logs data frontend/dist/assets
 
 # Install Python dependencies
-RUN pip install --no-cache-dir setuptools && \
-    pip install --no-cache-dir --no-build-isolation mmcv && \
-    uv pip install --system --no-cache \
+RUN uv pip install --system --no-cache \
     aiofiles \
     aiosqlite \
     diffusers \

sorawm/models/model/modules/flow_comp.py

@@ -2,8 +2,34 @@ import numpy as np
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from mmcv.cnn import ConvModule
-from mmcv.runner import load_checkpoint
+
+try:
+    from mmcv.cnn import ConvModule
+    from mmcv.runner import load_checkpoint
+except ImportError:
+    from loguru import logger
+    logger.warning("mmcv is not available in flow_comp, using fallback implementation")
+
+    class ConvModule(nn.Module):
+        """Minimal fallback for mmcv.cnn.ConvModule (Conv2d + optional activation)."""
+        def __init__(self, in_channels, out_channels, kernel_size, stride=1,
+                     padding=0, norm_cfg=None, act_cfg=dict(type="ReLU")):
+            super().__init__()
+            layers = [nn.Conv2d(in_channels, out_channels, kernel_size,
+                                stride=stride, padding=padding, bias=True)]
+            if act_cfg is not None:
+                act_type = act_cfg.get("type", "ReLU")
+                layers.append(nn.ReLU(inplace=True) if act_type == "ReLU" else nn.Identity())
+            self.block = nn.Sequential(*layers)
+
+        def forward(self, x):
+            return self.block(x)
+
+    def load_checkpoint(model, filename, strict=False, **kwargs):
+        state = torch.load(filename, map_location="cpu")
+        if "state_dict" in state:
+            state = state["state_dict"]
+        model.load_state_dict(state, strict=strict)
 
 from sorawm.configs import PHY_NET_CHECKPOINT_PATH, PHY_NET_CHECKPOINT_REMOTE_URL
 from sorawm.utils.download_utils import ensure_model_downloaded