============================================ Activated conda environment: da2 CUDA_HOME: /home/ywan0794/miniconda3/envs/da2 ============================================ === GPU Info === Tue May 12 18:06:35 2026 +-----------------------------------------------------------------------------------------+ | NVIDIA-SMI 550.163.01 Driver Version: 550.163.01 CUDA Version: 12.4 | |-----------------------------------------+------------------------+----------------------+ | GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. | | | | MIG M. | |=========================================+========================+======================| | 0 NVIDIA H100 NVL Off | 00000000:E1:00.0 Off | 0 | | N/A 36C P0 60W / 400W | 14MiB / 95830MiB | 0% Default | | | | Disabled | +-----------------------------------------+------------------------+----------------------+ +-----------------------------------------------------------------------------------------+ | Processes: | | GPU GI CI PID Type Process name GPU Memory | | ID ID Usage | |=========================================================================================| | 0 N/A N/A 4274 G /usr/lib/xorg/Xorg 4MiB | +-----------------------------------------------------------------------------------------+ CUDA available: True GPU count: 1 GPU name: NVIDIA H100 NVL ============================================ Starting MoGe Subset Sanity Eval (DA2 public vitb) at Tue May 12 06:06:55 PM AEST 2026 Config: /home/ywan0794/MoGe/configs/eval/subset_benchmarks.json Output: eval_output/da2_public_vitb_subset_20260512_180655.json ============================================ xFormers not available xFormers not available Traceback (most recent call last): File "/home/ywan0794/MoGe/moge/scripts/eval_baseline.py", line 165, in main() File "/home/ywan0794/miniconda3/envs/da2/lib/python3.10/site-packages/click/core.py", line 1485, in __call__ return self.main(*args, **kwargs) File "/home/ywan0794/miniconda3/envs/da2/lib/python3.10/site-packages/click/core.py", line 1406, in main rv = self.invoke(ctx) File "/home/ywan0794/miniconda3/envs/da2/lib/python3.10/site-packages/click/core.py", line 1269, in invoke return ctx.invoke(self.callback, **ctx.params) File "/home/ywan0794/miniconda3/envs/da2/lib/python3.10/site-packages/click/core.py", line 824, in invoke return callback(*args, **kwargs) File "/home/ywan0794/miniconda3/envs/da2/lib/python3.10/site-packages/click/decorators.py", line 34, in new_func return f(get_current_context(), *args, **kwargs) File "/home/ywan0794/MoGe/moge/scripts/eval_baseline.py", line 42, in main baseline : MGEBaselineInterface = baseline_cls.load.main(ctx.args, standalone_mode=False) File "/home/ywan0794/miniconda3/envs/da2/lib/python3.10/site-packages/click/core.py", line 1406, in main rv = self.invoke(ctx) File "/home/ywan0794/miniconda3/envs/da2/lib/python3.10/site-packages/click/core.py", line 1269, in invoke return ctx.invoke(self.callback, **ctx.params) File "/home/ywan0794/miniconda3/envs/da2/lib/python3.10/site-packages/click/core.py", line 824, in invoke return callback(*args, **kwargs) File "/home/ywan0794/MoGe/baselines/da_v2.py", line 50, in load return Baseline(repo_path, backbone, num_tokens, device) File "/home/ywan0794/MoGe/baselines/da_v2.py", line 36, in __init__ model.load_state_dict(checkpoint) File "/home/ywan0794/miniconda3/envs/da2/lib/python3.10/site-packages/torch/nn/modules/module.py", line 2629, in load_state_dict raise RuntimeError( RuntimeError: Error(s) in loading state_dict for DepthAnythingV2: size mismatch for depth_head.projects.0.weight: copying a param with shape torch.Size([96, 768, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 768, 1, 1]). size mismatch for depth_head.projects.0.bias: copying a param with shape torch.Size([96]) from checkpoint, the shape in current model is torch.Size([256]). size mismatch for depth_head.projects.1.weight: copying a param with shape torch.Size([192, 768, 1, 1]) from checkpoint, the shape in current model is torch.Size([512, 768, 1, 1]). size mismatch for depth_head.projects.1.bias: copying a param with shape torch.Size([192]) from checkpoint, the shape in current model is torch.Size([512]). size mismatch for depth_head.projects.2.weight: copying a param with shape torch.Size([384, 768, 1, 1]) from checkpoint, the shape in current model is torch.Size([1024, 768, 1, 1]). size mismatch for depth_head.projects.2.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]). size mismatch for depth_head.projects.3.weight: copying a param with shape torch.Size([768, 768, 1, 1]) from checkpoint, the shape in current model is torch.Size([1024, 768, 1, 1]). size mismatch for depth_head.projects.3.bias: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([1024]). size mismatch for depth_head.resize_layers.0.weight: copying a param with shape torch.Size([96, 96, 4, 4]) from checkpoint, the shape in current model is torch.Size([256, 256, 4, 4]). size mismatch for depth_head.resize_layers.0.bias: copying a param with shape torch.Size([96]) from checkpoint, the shape in current model is torch.Size([256]). size mismatch for depth_head.resize_layers.1.weight: copying a param with shape torch.Size([192, 192, 2, 2]) from checkpoint, the shape in current model is torch.Size([512, 512, 2, 2]). size mismatch for depth_head.resize_layers.1.bias: copying a param with shape torch.Size([192]) from checkpoint, the shape in current model is torch.Size([512]). size mismatch for depth_head.resize_layers.3.weight: copying a param with shape torch.Size([768, 768, 3, 3]) from checkpoint, the shape in current model is torch.Size([1024, 1024, 3, 3]). size mismatch for depth_head.resize_layers.3.bias: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([1024]). size mismatch for depth_head.scratch.layer1_rn.weight: copying a param with shape torch.Size([128, 96, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]). size mismatch for depth_head.scratch.layer2_rn.weight: copying a param with shape torch.Size([128, 192, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 512, 3, 3]). size mismatch for depth_head.scratch.layer3_rn.weight: copying a param with shape torch.Size([128, 384, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 1024, 3, 3]). size mismatch for depth_head.scratch.layer4_rn.weight: copying a param with shape torch.Size([128, 768, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 1024, 3, 3]). size mismatch for depth_head.scratch.refinenet1.out_conv.weight: copying a param with shape torch.Size([128, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 256, 1, 1]). size mismatch for depth_head.scratch.refinenet1.out_conv.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]). size mismatch for depth_head.scratch.refinenet1.resConfUnit1.conv1.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]). size mismatch for depth_head.scratch.refinenet1.resConfUnit1.conv1.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]). size mismatch for depth_head.scratch.refinenet1.resConfUnit1.conv2.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]). size mismatch for depth_head.scratch.refinenet1.resConfUnit1.conv2.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]). size mismatch for depth_head.scratch.refinenet1.resConfUnit2.conv1.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]). size mismatch for depth_head.scratch.refinenet1.resConfUnit2.conv1.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]). size mismatch for depth_head.scratch.refinenet1.resConfUnit2.conv2.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]). size mismatch for depth_head.scratch.refinenet1.resConfUnit2.conv2.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]). size mismatch for depth_head.scratch.refinenet2.out_conv.weight: copying a param with shape torch.Size([128, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 256, 1, 1]). size mismatch for depth_head.scratch.refinenet2.out_conv.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]). size mismatch for depth_head.scratch.refinenet2.resConfUnit1.conv1.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]). size mismatch for depth_head.scratch.refinenet2.resConfUnit1.conv1.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]). size mismatch for depth_head.scratch.refinenet2.resConfUnit1.conv2.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]). size mismatch for depth_head.scratch.refinenet2.resConfUnit1.conv2.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]). size mismatch for depth_head.scratch.refinenet2.resConfUnit2.conv1.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]). size mismatch for depth_head.scratch.refinenet2.resConfUnit2.conv1.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]). size mismatch for depth_head.scratch.refinenet2.resConfUnit2.conv2.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]). size mismatch for depth_head.scratch.refinenet2.resConfUnit2.conv2.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]). size mismatch for depth_head.scratch.refinenet3.out_conv.weight: copying a param with shape torch.Size([128, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 256, 1, 1]). size mismatch for depth_head.scratch.refinenet3.out_conv.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]). size mismatch for depth_head.scratch.refinenet3.resConfUnit1.conv1.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]). size mismatch for depth_head.scratch.refinenet3.resConfUnit1.conv1.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]). size mismatch for depth_head.scratch.refinenet3.resConfUnit1.conv2.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]). size mismatch for depth_head.scratch.refinenet3.resConfUnit1.conv2.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]). size mismatch for depth_head.scratch.refinenet3.resConfUnit2.conv1.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]). size mismatch for depth_head.scratch.refinenet3.resConfUnit2.conv1.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]). size mismatch for depth_head.scratch.refinenet3.resConfUnit2.conv2.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]). size mismatch for depth_head.scratch.refinenet3.resConfUnit2.conv2.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]). size mismatch for depth_head.scratch.refinenet4.out_conv.weight: copying a param with shape torch.Size([128, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 256, 1, 1]). size mismatch for depth_head.scratch.refinenet4.out_conv.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]). size mismatch for depth_head.scratch.refinenet4.resConfUnit1.conv1.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]). size mismatch for depth_head.scratch.refinenet4.resConfUnit1.conv1.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]). size mismatch for depth_head.scratch.refinenet4.resConfUnit1.conv2.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]). size mismatch for depth_head.scratch.refinenet4.resConfUnit1.conv2.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]). size mismatch for depth_head.scratch.refinenet4.resConfUnit2.conv1.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]). size mismatch for depth_head.scratch.refinenet4.resConfUnit2.conv1.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]). size mismatch for depth_head.scratch.refinenet4.resConfUnit2.conv2.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]). size mismatch for depth_head.scratch.refinenet4.resConfUnit2.conv2.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]). size mismatch for depth_head.scratch.output_conv1.weight: copying a param with shape torch.Size([64, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 256, 3, 3]). size mismatch for depth_head.scratch.output_conv1.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for depth_head.scratch.output_conv2.0.weight: copying a param with shape torch.Size([32, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([32, 128, 3, 3]). ============================================ Evaluation completed at Tue May 12 06:07:22 PM AEST 2026 ============================================