# Ultralytics with custom module not working in other envs **URL:** \ **Category:** Support\ **Tags:** troubleshooting, yolo, support\ **Created:** [November 24, 2025, 3:56am UTC](https://community.ultralytics.com/t/ultralytics-with-custom-module-not-working-in-other-envs/1657 "2025-11-24T03:56:00Z")\ **Posts on this page:** 5\ **Page:** 1
**Author:** ![peetey101](https://sea1.discourse-cdn.com/flex001/user_avatar/community.ultralytics.com/peetey101/32/1108_2.png) [@peetey101](https://community.ultralytics.com/u/peetey101)\ **Post date:** [November 24, 2025, 3:56am UTC](https://community.ultralytics.com/t/ultralytics-with-custom-module-not-working-in-other-envs/1657/1 "2025-11-24T03:56:00Z")
I have forked the ultralytics repo and cloned to my local env. Then I followed below steps to setup. ```auto git clone https://github.com/ultralytics/ultralytics.git cd ultralytics pip install -e . ``` Then I have added my custom module it worked. But when I try running in Kaggle it is not working and gives me below error message. ```auto /usr/local/lib/python3.11/dist-packages/torch/nn/init.py:511: UserWarning: Initializing zero-element tensors is a no-op warnings.warn("Initializing zero-element tensors is a no-op") Traceback (most recent call last): File "/kaggle/working/ultralytics/tests/model_validity2.py", line 6, in model = YOLO("/kaggle/working/ultralytics/ultralytics/cfg/models/12/swin-t-12.yaml",task='detect') ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/kaggle/working/ultralytics/ultralytics/models/yolo/model.py", line 81, in __init__ super(). __init__ (model=model, task=task, verbose=verbose) File "/kaggle/working/ultralytics/ultralytics/engine/model.py", line 147, in __init__ self._new(model, task=task, verbose=verbose) File "/kaggle/working/ultralytics/ultralytics/engine/model.py", line 255, in _new self.model = (model or self._smart_load("model"))(cfg_dict, verbose=verbose and RANK == -1) # build model ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/kaggle/working/ultralytics/ultralytics/nn/tasks.py", line 387, in __init__ self.model, self.save = parse_model(deepcopy(self.yaml), ch=ch, verbose=verbose) # model, savelist ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/kaggle/working/ultralytics/ultralytics/nn/tasks.py", line 1650, in parse_model m_ = torch.nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args) # module ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/kaggle/working/ultralytics/ultralytics/nn/tasks.py", line 1650, in m_ = torch.nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args) # module ^^^^^^^^ File "/kaggle/working/ultralytics/ultralytics/nn/modules/swin_transformers.py", line 88, in __init__ self.cv3 = Conv(2 * c_, c2, 1, 1) ^^^^^^^^^^^^^^^^^^^^^^ File "/kaggle/working/ultralytics/ultralytics/nn/modules/conv.py", line 65, in __init__ self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p, d), groups=g, dilation=d, bias=False) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/torch/nn/modules/conv.py", line 521, in __init__ super(). __init__ ( File "/usr/local/lib/python3.11/dist-packages/torch/nn/modules/conv.py", line 166, in __init__ torch.empty( TypeError: empty() received an invalid combination of arguments - got (tuple, dtype=NoneType, device=NoneType), but expected one of: * (tuple of ints size, *, tuple of names names, torch.memory_format memory_format = None, torch.dtype dtype = None, torch.layout layout = None, torch.device device = None, bool pin_memory = False, bool requires_grad = False) * (tuple of ints size, *, torch.memory_format memory_format = None, Tensor out = None, torch.dtype dtype = None, torch.layout layout = None, torch.device device = None, bool pin_memory = False, bool requires_grad = False) ``` What I have done wrong in the setting up? forked repo : [GitHub - pawara101/ultralytics: Ultralytics YOLO 🚀](https://github.com/pawara101/ultralytics.git) when I run in kaggle I have done below steps ```auto !git clone https://github.com/pawara101/ultralytics.git %cd ultralytics !pip install -e . ``` ---
**Author:** ![Toxite](https://sea1.discourse-cdn.com/flex001/user_avatar/community.ultralytics.com/toxite/32/123_2.png) [@Toxite](https://community.ultralytics.com/u/Toxite)\ **Post date:** [November 24, 2025, 5:37am UTC](https://community.ultralytics.com/t/ultralytics-with-custom-module-not-working-in-other-envs/1657/2 "2025-11-24T05:37:38Z")
Restart Kaggle runtime after installing ---
**Author:** ![peetey101](https://sea1.discourse-cdn.com/flex001/user_avatar/community.ultralytics.com/peetey101/32/1108_2.png) [@peetey101](https://community.ultralytics.com/u/peetey101)\ **Post date:** [November 24, 2025, 5:58am UTC](https://community.ultralytics.com/t/ultralytics-with-custom-module-not-working-in-other-envs/1657/3 "2025-11-24T05:58:10Z")
hi @Toxite I have restared the kernel but the issue still remains. ```auto m = YOLO("ultralytics/cfg/models/12/swin-t-12.yaml",verbose=True) ``` error log ```auto from n params module arguments 0 -1 1 1856 ultralytics.nn.modules.conv.Conv [3, 64, 3, 2] 1 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2] 2 -1 3 279808 ultralytics.nn.modules.block.C2f [128, 128, 3, True] 3 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2] --------------------------------------------------------------------------- TypeError Traceback (most recent call last) /tmp/ipykernel_125/4158778360.py in () ----> 1 m = YOLO("ultralytics/cfg/models/12/swin-t-12.yaml",verbose=True) /kaggle/working/ultralytics/ultralytics/models/yolo/model.py in __init__ (self, model, task, verbose) 79 else: 80 # Continue with default YOLO initialization ---> 81 super(). __init__ (model=model, task=task, verbose=verbose) 82 if hasattr(self.model, "model") and "RTDETR" in self.model.model[-1]._get_name(): # if RTDETR head 83 from ultralytics import RTDETR /kaggle/working/ultralytics/ultralytics/engine/model.py in __init__ (self, model, task, verbose) 145 __import__ ("os").environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8" # to avoid deterministic warnings 146 if str(model).endswith((".yaml", ".yml")): --> 147 self._new(model, task=task, verbose=verbose) 148 else: 149 self._load(model, task=task) /kaggle/working/ultralytics/ultralytics/engine/model.py in _new(self, cfg, task, model, verbose) 253 self.cfg = cfg 254 self.task = task or guess_model_task(cfg_dict) --> 255 self.model = (model or self._smart_load("model"))(cfg_dict, verbose=verbose and RANK == -1) # build model 256 self.overrides["model"] = self.cfg 257 self.overrides["task"] = self.task /kaggle/working/ultralytics/ultralytics/nn/tasks.py in __init__ (self, cfg, ch, nc, verbose) 385 LOGGER.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}") 386 self.yaml["nc"] = nc # override YAML value --> 387 self.model, self.save = parse_model(deepcopy(self.yaml), ch=ch, verbose=verbose) # model, savelist 388 self.names = {i: f"{i}" for i in range(self.yaml["nc"])} # default names dict 389 self.inplace = self.yaml.get("inplace", True) /kaggle/working/ultralytics/ultralytics/nn/tasks.py in parse_model(d, ch, verbose) 1648 c2 = ch[f] 1649 -> 1650 m_ = torch.nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args) # module 1651 t = str(m)[8:-2].replace(" __main__.", "") # module type 1652 m_.np = sum(x.numel() for x in m_.parameters()) # number params /kaggle/working/ultralytics/ultralytics/nn/tasks.py in (.0) 1648 c2 = ch[f] 1649 -> 1650 m_ = torch.nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args) # module 1651 t = str(m)[8:-2].replace(" __main__.", "") # module type 1652 m_.np = sum(x.numel() for x in m_.parameters()) # number params /kaggle/working/ultralytics/ultralytics/nn/modules/swin_transformers.py in __init__ (self, c1, c2, n, shortcut, g, e) 86 self.cv1 = Conv(c1, c_, 1, 1) 87 self.cv2 = Conv(c1, c_, 1, 1) ---> 88 self.cv3 = Conv(2 * c_, c2, 1, 1) 89 num_heads = c_ // 32 90 self.m = SwinTransformerBlock(c_, c_, num_heads, n) /kaggle/working/ultralytics/ultralytics/nn/modules/conv.py in __init__ (self, c1, c2, k, s, p, g, d, act) 63 """ 64 super(). __init__ () ---> 65 self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p, d), groups=g, dilation=d, bias=False) 66 self.bn = nn.BatchNorm2d(c2) 67 self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity() /usr/local/lib/python3.11/dist-packages/torch/nn/modules/conv.py in __init__ (self, in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias, padding_mode, device, dtype) 519 padding_ = padding if isinstance(padding, str) else _pair(padding) 520 dilation_ = _pair(dilation) --> 521 super(). __init__ ( 522 in_channels, 523 out_channels, /usr/local/lib/python3.11/dist-packages/torch/nn/modules/conv.py in __init__ (self, in_channels, out_channels, kernel_size, stride, padding, dilation, transposed, output_padding, groups, bias, padding_mode, device, dtype) 164 else: 165 self.weight = Parameter( --> 166 torch.empty( 167 (out_channels, in_channels // groups, *kernel_size), 168 **factory_kwargs, TypeError: empty() received an invalid combination of arguments - got (tuple, dtype=NoneType, device=NoneType), but expected one of: * (tuple of ints size, *, tuple of names names, torch.memory_format memory_format = None, torch.dtype dtype = None, torch.layout layout = None, torch.device device = None, bool pin_memory = False, bool requires_grad = False) * (tuple of ints size, *, torch.memory_format memory_format = None, Tensor out = None, torch.dtype dtype = None, torch.layout layout = None, torch.device device = None, bool pin_memory = False, bool requires_grad = False) ``` ---
**Author:** ![pderrenger](https://sea1.discourse-cdn.com/flex001/user_avatar/community.ultralytics.com/pderrenger/32/73_2.png) [@pderrenger](https://community.ultralytics.com/u/pderrenger)\ **Post date:** [November 25, 2025, 12:40am UTC](https://community.ultralytics.com/t/ultralytics-with-custom-module-not-working-in-other-envs/1657/4 "2025-11-25T00:40:05Z")
This isn’t a Kaggle setup issue, it’s a channel‑shapes issue inside your custom Swin module. From the traceback, the failure happens when PyTorch tries to build this layer in your `swin_transformers.py`: ```python self.cv3 = Conv(2 * c_, c2, 1, 1) ``` `nn.Conv2d` is complaining because one of the arguments that should be an `int` (typically `in_channels` or `out_channels`) is actually a non‑int (tuple/float/etc.), so `torch.empty((out_channels, in_channels // groups, *kernel_size))` gets an invalid size. That means in Kaggle at least, either `c_` or `c2` is not a plain integer when you hit that line. A few concrete things to try: 1. In `SwinTransformer. __init__ ` (or whatever class owns that line), add a quick debug print before `self.cv3`: ```python print("Swin debug:", c1, c2, type(c1), type(c2)) self.cv3 = Conv(2 * c_, c2, 1, 1) ``` and run the same YAML in Kaggle. You’ll see exactly what types are coming in. 1. Also check your `parse_model` changes in `ultralytics/nn/tasks.py` for this module. You must ensure `c1`/`c2` are set to integers from `ch[f]` and `args[0]`, similar to the pattern shown in the [model YAML configuration guide](https://docs.ultralytics.com/guides/model-yaml-config/): ```python if m is CustomBlock: # your custom module c1, c2 = ch[f], args[0] args = [c1, c2, *args[1:]] ``` Do something analogous for your Swin block if it needs special handling. 1. Run the exact same command and YAML locally: ```python from ultralytics import YOLO m = YOLO("ultralytics/cfg/models/12/swin-t-12.yaml", verbose=True) ``` If you add the same debug print, you’ll likely see the same bad type locally as well; you probably just hadn’t exercised this YAML there yet. Your `git clone` + `pip install -e .` flow on Kaggle is fine; the error is coming purely from how the custom module’s channels are being set up, not from the environment. ---
**Author:** ![peetey101](https://sea1.discourse-cdn.com/flex001/user_avatar/community.ultralytics.com/peetey101/32/1108_2.png) [@peetey101](https://community.ultralytics.com/u/peetey101)\ **Post date:** [November 25, 2025, 6:36am UTC](https://community.ultralytics.com/t/ultralytics-with-custom-module-not-working-in-other-envs/1657/5 "2025-11-25T06:36:35Z")
Seems your approach is working fine. Thanks