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Describe the bugI'm trying to train an EfficientAD model (tried medium and small) but am running into this issue. Have used similar config for other models successfuly (except model specific configs). DatasetFolder ModelOther (please specify in the field below) Steps to reproduce the behaviortrain efficientAD using my config in anomalib 0.6 OS informationOS information:
Expected behaviorTraining should continue normally ScreenshotsNo response Pip/GitHubpip What version/branch did you use?0.6.0 Configuration YAMLdataset:
abnormal_dir: bad
center_crop: null
extensions: null
format: folder
image_size: 256 # mandatory
mask: ground_truth
name: vespa
normal_dir: good
normal_test_dir: null
normalization: imagenet
num_workers: 8
path: ./../data/images/data_structure_anomalib
split_ratio: 0.2
task: classification
test_batch_size: 16
test_split_mode: from_dir
test_split_ratio: 0.2
tiling:
apply: false
random_tile_count: 16
remove_border_count: 0
stride: null
tile_size: null
use_random_tiling: false
train_batch_size: 16
transform_config:
eval: c:\Users\benedict\dev\VespaUseCase\machine_learning\src\d01_config\transform_config_1.yaml
train: null
val_split_mode: from_test
val_split_ratio: 0.5
logging:
log_graph: false
logger: []
metrics:
image:
- F1Score
- AUROC
pixel:
- F1Score
- AUROC
threshold:
manual_image: null
manual_pixel: null
method: adaptive
model:
lr: 0.0001
model_size: small
name: efficientad
normalization_method: min_max
padding: false
teacher_out_channels: 384
weight_decay: 1.0e-05
optimization:
export_mode: onnx
project:
path: ./results
seed: 42
trainer:
accelerator: auto
accumulate_grad_batches: 1
auto_lr_find: false
auto_scale_batch_size: false
benchmark: false
check_val_every_n_epoch: 1
default_root_dir: null
detect_anomaly: false
deterministic: false
devices: 1
enable_checkpointing: true
enable_model_summary: true
enable_progress_bar: true
fast_dev_run: false
gradient_clip_algorithm: norm
gradient_clip_val: 0
limit_predict_batches: 1.0
limit_test_batches: 1.0
limit_train_batches: 1.0
limit_val_batches: 1.0
log_every_n_steps: 50
max_epochs: 200
max_steps: -1
max_time: null
min_epochs: null
min_steps: null
move_metrics_to_cpu: false
multiple_trainloader_mode: max_size_cycle
num_nodes: 1
num_sanity_val_steps: 0
overfit_batches: 0.0
plugins: null
precision: 32
profiler: null
reload_dataloaders_every_n_epochs: 0
replace_sampler_ddp: true
strategy: null
sync_batchnorm: false
track_grad_norm: -1
val_check_interval: 1.0
visualization:
image_save_path: null
log_images: false
mode: full
save_images: true
show_images: falseAlbumentations Config YAMLLogsRuntimeError Traceback (most recent call last)
Cell In[16], line 1
----> 1 train_results = trainer.fit(model=model, datamodule=datamodule)
File c:\Users\benedict\anaconda3\envs\anomalib_env\lib\site-packages\pytorch_lightning\trainer\trainer.py:608, in Trainer.fit(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)
606 model = self._maybe_unwrap_optimized(model)
607 self.strategy._lightning_module = model
--> 608 call._call_and_handle_interrupt(
609 self, self._fit_impl, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path
610 )
File c:\Users\benedict\anaconda3\envs\anomalib_env\lib\site-packages\pytorch_lightning\trainer\call.py:38, in _call_and_handle_interrupt(trainer, trainer_fn, *args, **kwargs)
36 return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
37 else:
---> 38 return trainer_fn(*args, **kwargs)
40 except _TunerExitException:
41 trainer._call_teardown_hook()
File c:\Users\benedict\anaconda3\envs\anomalib_env\lib\site-packages\pytorch_lightning\trainer\trainer.py:650, in Trainer._fit_impl(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)
643 ckpt_path = ckpt_path or self.resume_from_checkpoint
644 self._ckpt_path = self._checkpoint_connector._set_ckpt_path(
645 self.state.fn,
646 ckpt_path, # type: ignore[arg-type]
647 model_provided=True,
648 model_connected=self.lightning_module is not None,
649 )
--> 650 self._run(model, ckpt_path=self.ckpt_path)
652 assert self.state.stopped
653 self.training = False
File c:\Users\benedict\anaconda3\envs\anomalib_env\lib\site-packages\pytorch_lightning\trainer\trainer.py:1112, in Trainer._run(self, model, ckpt_path)
1108 self._checkpoint_connector.restore_training_state()
1110 self._checkpoint_connector.resume_end()
-> 1112 results = self._run_stage()
1114 log.detail(f"{self.__class__.__name__}: trainer tearing down")
1115 self._teardown()
File c:\Users\benedict\anaconda3\envs\anomalib_env\lib\site-packages\pytorch_lightning\trainer\trainer.py:1191, in Trainer._run_stage(self)
1189 if self.predicting:
1190 return self._run_predict()
-> 1191 self._run_train()
File c:\Users\benedict\anaconda3\envs\anomalib_env\lib\site-packages\pytorch_lightning\trainer\trainer.py:1214, in Trainer._run_train(self)
1211 self.fit_loop.trainer = self
1213 with torch.autograd.set_detect_anomaly(self._detect_anomaly):
-> 1214 self.fit_loop.run()
File c:\Users\benedict\anaconda3\envs\anomalib_env\lib\site-packages\pytorch_lightning\loops\loop.py:199, in Loop.run(self, *args, **kwargs)
197 try:
198 self.on_advance_start(*args, **kwargs)
--> 199 self.advance(*args, **kwargs)
200 self.on_advance_end()
201 self._restarting = False
File c:\Users\benedict\anaconda3\envs\anomalib_env\lib\site-packages\pytorch_lightning\loops\fit_loop.py:267, in FitLoop.advance(self)
265 self._data_fetcher.setup(dataloader, batch_to_device=batch_to_device)
266 with self.trainer.profiler.profile("run_training_epoch"):
--> 267 self._outputs = self.epoch_loop.run(self._data_fetcher)
File c:\Users\benedict\anaconda3\envs\anomalib_env\lib\site-packages\pytorch_lightning\loops\loop.py:199, in Loop.run(self, *args, **kwargs)
197 try:
198 self.on_advance_start(*args, **kwargs)
--> 199 self.advance(*args, **kwargs)
200 self.on_advance_end()
201 self._restarting = False
File c:\Users\benedict\anaconda3\envs\anomalib_env\lib\site-packages\pytorch_lightning\loops\epoch\training_epoch_loop.py:213, in TrainingEpochLoop.advance(self, data_fetcher)
210 self.batch_progress.increment_started()
212 with self.trainer.profiler.profile("run_training_batch"):
--> 213 batch_output = self.batch_loop.run(kwargs)
215 self.batch_progress.increment_processed()
217 # update non-plateau LR schedulers
218 # update epoch-interval ones only when we are at the end of training epoch
File c:\Users\benedict\anaconda3\envs\anomalib_env\lib\site-packages\pytorch_lightning\loops\loop.py:199, in Loop.run(self, *args, **kwargs)
197 try:
198 self.on_advance_start(*args, **kwargs)
--> 199 self.advance(*args, **kwargs)
200 self.on_advance_end()
201 self._restarting = False
File c:\Users\benedict\anaconda3\envs\anomalib_env\lib\site-packages\pytorch_lightning\loops\batch\training_batch_loop.py:88, in TrainingBatchLoop.advance(self, kwargs)
84 if self.trainer.lightning_module.automatic_optimization:
85 optimizers = _get_active_optimizers(
86 self.trainer.optimizers, self.trainer.optimizer_frequencies, kwargs.get("batch_idx", 0)
87 )
---> 88 outputs = self.optimizer_loop.run(optimizers, kwargs)
89 else:
90 outputs = self.manual_loop.run(kwargs)
File c:\Users\benedict\anaconda3\envs\anomalib_env\lib\site-packages\pytorch_lightning\loops\loop.py:199, in Loop.run(self, *args, **kwargs)
197 try:
198 self.on_advance_start(*args, **kwargs)
--> 199 self.advance(*args, **kwargs)
200 self.on_advance_end()
201 self._restarting = False
File c:\Users\benedict\anaconda3\envs\anomalib_env\lib\site-packages\pytorch_lightning\loops\optimization\optimizer_loop.py:202, in OptimizerLoop.advance(self, optimizers, kwargs)
199 def advance(self, optimizers: List[Tuple[int, Optimizer]], kwargs: OrderedDict) -> None:
200 kwargs = self._build_kwargs(kwargs, self.optimizer_idx, self._hiddens)
--> 202 result = self._run_optimization(kwargs, self._optimizers[self.optim_progress.optimizer_position])
203 if result.loss is not None:
204 # automatic optimization assumes a loss needs to be returned for extras to be considered as the batch
205 # would be skipped otherwise
206 self._outputs[self.optimizer_idx] = result.asdict()
File c:\Users\benedict\anaconda3\envs\anomalib_env\lib\site-packages\pytorch_lightning\loops\optimization\optimizer_loop.py:249, in OptimizerLoop._run_optimization(self, kwargs, optimizer)
241 closure()
243 # ------------------------------
244 # BACKWARD PASS
245 # ------------------------------
246 # gradient update with accumulated gradients
247 else:
248 # the `batch_idx` is optional with inter-batch parallelism
--> 249 self._optimizer_step(optimizer, opt_idx, kwargs.get("batch_idx", 0), closure)
251 result = closure.consume_result()
253 if result.loss is not None:
254 # if no result, user decided to skip optimization
255 # otherwise update running loss + reset accumulated loss
256 # TODO: find proper way to handle updating running loss
File c:\Users\benedict\anaconda3\envs\anomalib_env\lib\site-packages\pytorch_lightning\loops\optimization\optimizer_loop.py:370, in OptimizerLoop._optimizer_step(self, optimizer, opt_idx, batch_idx, train_step_and_backward_closure)
362 rank_zero_deprecation(
363 "The NVIDIA/apex AMP implementation has been deprecated upstream. Consequently, its integration inside"
364 " PyTorch Lightning has been deprecated in v1.9.0 and will be removed in v2.0.0."
(...)
367 " return True."
368 )
369 kwargs["using_native_amp"] = isinstance(self.trainer.precision_plugin, MixedPrecisionPlugin)
--> 370 self.trainer._call_lightning_module_hook(
371 "optimizer_step",
372 self.trainer.current_epoch,
373 batch_idx,
374 optimizer,
375 opt_idx,
376 train_step_and_backward_closure,
377 on_tpu=isinstance(self.trainer.accelerator, TPUAccelerator),
378 **kwargs, # type: ignore[arg-type]
379 using_lbfgs=is_lbfgs,
380 )
382 if not should_accumulate:
383 self.optim_progress.optimizer.step.increment_completed()
File c:\Users\benedict\anaconda3\envs\anomalib_env\lib\site-packages\pytorch_lightning\trainer\trainer.py:1356, in Trainer._call_lightning_module_hook(self, hook_name, pl_module, *args, **kwargs)
1353 pl_module._current_fx_name = hook_name
1355 with self.profiler.profile(f"[LightningModule]{pl_module.__class__.__name__}.{hook_name}"):
-> 1356 output = fn(*args, **kwargs)
1358 # restore current_fx when nested context
1359 pl_module._current_fx_name = prev_fx_name
File c:\Users\benedict\anaconda3\envs\anomalib_env\lib\site-packages\pytorch_lightning\core\module.py:1754, in LightningModule.optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx, optimizer_closure, on_tpu, using_lbfgs)
1675 def optimizer_step(
1676 self,
1677 epoch: int,
(...)
1683 using_lbfgs: bool = False,
1684 ) -> None:
1685 r"""
1686 Override this method to adjust the default way the :class:`~pytorch_lightning.trainer.trainer.Trainer` calls
1687 each optimizer.
(...)
1752
1753 """
-> 1754 optimizer.step(closure=optimizer_closure)
File c:\Users\benedict\anaconda3\envs\anomalib_env\lib\site-packages\pytorch_lightning\core\optimizer.py:169, in LightningOptimizer.step(self, closure, **kwargs)
166 raise MisconfigurationException("When `optimizer.step(closure)` is called, the closure should be callable")
168 assert self._strategy is not None
--> 169 step_output = self._strategy.optimizer_step(self._optimizer, self._optimizer_idx, closure, **kwargs)
171 self._on_after_step()
173 return step_output
File c:\Users\benedict\anaconda3\envs\anomalib_env\lib\site-packages\pytorch_lightning\strategies\strategy.py:234, in Strategy.optimizer_step(self, optimizer, opt_idx, closure, model, **kwargs)
232 # TODO(fabric): remove assertion once strategy's optimizer_step typing is fixed
233 assert isinstance(model, pl.LightningModule)
--> 234 return self.precision_plugin.optimizer_step(
235 optimizer, model=model, optimizer_idx=opt_idx, closure=closure, **kwargs
236 )
File c:\Users\benedict\anaconda3\envs\anomalib_env\lib\site-packages\pytorch_lightning\plugins\precision\precision_plugin.py:119, in PrecisionPlugin.optimizer_step(self, optimizer, model, optimizer_idx, closure, **kwargs)
117 """Hook to run the optimizer step."""
118 closure = partial(self._wrap_closure, model, optimizer, optimizer_idx, closure)
--> 119 return optimizer.step(closure=closure, **kwargs)
File c:\Users\benedict\anaconda3\envs\anomalib_env\lib\site-packages\torch\optim\lr_scheduler.py:69, in LRScheduler.__init__..with_counter..wrapper(*args, **kwargs)
67 instance._step_count += 1
68 wrapped = func.__get__(instance, cls)
---> 69 return wrapped(*args, **kwargs)
File c:\Users\benedict\anaconda3\envs\anomalib_env\lib\site-packages\torch\optim\optimizer.py:280, in Optimizer.profile_hook_step..wrapper(*args, **kwargs)
276 else:
277 raise RuntimeError(f"{func} must return None or a tuple of (new_args, new_kwargs),"
278 f"but got {result}.")
--> 280 out = func(*args, **kwargs)
281 self._optimizer_step_code()
283 # call optimizer step post hooks
File c:\Users\benedict\anaconda3\envs\anomalib_env\lib\site-packages\torch\optim\optimizer.py:33, in _use_grad_for_differentiable.._use_grad(self, *args, **kwargs)
31 try:
32 torch.set_grad_enabled(self.defaults['differentiable'])
---> 33 ret = func(self, *args, **kwargs)
34 finally:
35 torch.set_grad_enabled(prev_grad)
File c:\Users\benedict\anaconda3\envs\anomalib_env\lib\site-packages\torch\optim\adamw.py:148, in AdamW.step(self, closure)
146 if closure is not None:
147 with torch.enable_grad():
--> 148 loss = closure()
150 for group in self.param_groups:
151 params_with_grad = []
File c:\Users\benedict\anaconda3\envs\anomalib_env\lib\site-packages\pytorch_lightning\plugins\precision\precision_plugin.py:105, in PrecisionPlugin._wrap_closure(self, model, optimizer, optimizer_idx, closure)
92 def _wrap_closure(
93 self,
94 model: "pl.LightningModule",
(...)
97 closure: Callable[[], Any],
98 ) -> Any:
99 """This double-closure allows makes sure the ``closure`` is executed before the
100 ``on_before_optimizer_step`` hook is called.
101
102 The closure (generally) runs ``backward`` so this allows inspecting gradients in this hook. This structure is
103 consistent with the ``PrecisionPlugin`` subclasses that cannot pass ``optimizer.step(closure)`` directly.
104 """
--> 105 closure_result = closure()
106 self._after_closure(model, optimizer, optimizer_idx)
107 return closure_result
File c:\Users\benedict\anaconda3\envs\anomalib_env\lib\site-packages\pytorch_lightning\loops\optimization\optimizer_loop.py:149, in Closure.__call__(self, *args, **kwargs)
148 def __call__(self, *args: Any, **kwargs: Any) -> Optional[Tensor]:
--> 149 self._result = self.closure(*args, **kwargs)
150 return self._result.loss
File c:\Users\benedict\anaconda3\envs\anomalib_env\lib\site-packages\pytorch_lightning\loops\optimization\optimizer_loop.py:135, in Closure.closure(self, *args, **kwargs)
134 def closure(self, *args: Any, **kwargs: Any) -> ClosureResult:
--> 135 step_output = self._step_fn()
137 if step_output.closure_loss is None:
138 self.warning_cache.warn("`training_step` returned `None`. If this was on purpose, ignore this warning...")
File c:\Users\benedict\anaconda3\envs\anomalib_env\lib\site-packages\pytorch_lightning\loops\optimization\optimizer_loop.py:419, in OptimizerLoop._training_step(self, kwargs)
410 """Performs the actual train step with the tied hooks.
411
412 Args:
(...)
416 A ``ClosureResult`` containing the training step output.
417 """
418 # manually capture logged metrics
--> 419 training_step_output = self.trainer._call_strategy_hook("training_step", *kwargs.values())
420 self.trainer.strategy.post_training_step()
422 model_output = self.trainer._call_lightning_module_hook("training_step_end", training_step_output)
File c:\Users\benedict\anaconda3\envs\anomalib_env\lib\site-packages\pytorch_lightning\trainer\trainer.py:1494, in Trainer._call_strategy_hook(self, hook_name, *args, **kwargs)
1491 return
1493 with self.profiler.profile(f"[Strategy]{self.strategy.__class__.__name__}.{hook_name}"):
-> 1494 output = fn(*args, **kwargs)
1496 # restore current_fx when nested context
1497 pl_module._current_fx_name = prev_fx_name
File c:\Users\benedict\anaconda3\envs\anomalib_env\lib\site-packages\pytorch_lightning\strategies\strategy.py:378, in Strategy.training_step(self, *args, **kwargs)
376 with self.precision_plugin.train_step_context():
377 assert isinstance(self.model, TrainingStep)
--> 378 return self.model.training_step(*args, **kwargs)
File c:\Users\benedict\anaconda3\envs\anomalib_env\lib\site-packages\anomalib\models\efficientad\lightning_model.py:217, in EfficientAD.training_step(***failed resolving arguments***)
214 self.imagenet_iterator = iter(self.imagenet_loader)
215 batch_imagenet = next(self.imagenet_iterator)[0]["image"].to(self.device)
--> 217 loss_st, loss_ae, loss_stae = self.model(batch=batch["image"], batch_imagenet=batch_imagenet)
219 loss = loss_st + loss_ae + loss_stae
220 self.log("train_st", loss_st.item(), on_epoch=True, prog_bar=True, logger=True)
File c:\Users\benedict\anaconda3\envs\anomalib_env\lib\site-packages\torch\nn\modules\module.py:1501, in Module._call_impl(self, *args, **kwargs)
1496 # If we don't have any hooks, we want to skip the rest of the logic in
1497 # this function, and just call forward.
1498 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
1499 or _global_backward_pre_hooks or _global_backward_hooks
1500 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1501 return forward_call(*args, **kwargs)
1502 # Do not call functions when jit is used
1503 full_backward_hooks, non_full_backward_hooks = [], []
File c:\Users\benedict\anaconda3\envs\anomalib_env\lib\site-packages\anomalib\models\efficientad\torch_model.py:286, in EfficientADModel.forward(self, batch, batch_imagenet)
284 # Autoencoder and Student AE Loss
285 aug_img = self.choose_random_aug_image(batch)
--> 286 ae_output_aug = self.ae(aug_img)
288 with torch.no_grad():
289 teacher_output_aug = self.teacher(aug_img)
File c:\Users\benedict\anaconda3\envs\anomalib_env\lib\site-packages\torch\nn\modules\module.py:1501, in Module._call_impl(self, *args, **kwargs)
1496 # If we don't have any hooks, we want to skip the rest of the logic in
1497 # this function, and just call forward.
1498 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
1499 or _global_backward_pre_hooks or _global_backward_hooks
1500 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1501 return forward_call(*args, **kwargs)
1502 # Do not call functions when jit is used
1503 full_backward_hooks, non_full_backward_hooks = [], []
File c:\Users\benedict\anaconda3\envs\anomalib_env\lib\site-packages\anomalib\models\efficientad\torch_model.py:182, in AutoEncoder.forward(self, x)
180 def forward(self, x):
181 x = imagenet_norm_batch(x)
--> 182 x = self.encoder(x)
183 x = self.decoder(x)
184 return x
File c:\Users\benedict\anaconda3\envs\anomalib_env\lib\site-packages\torch\nn\modules\module.py:1501, in Module._call_impl(self, *args, **kwargs)
1496 # If we don't have any hooks, we want to skip the rest of the logic in
1497 # this function, and just call forward.
1498 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
1499 or _global_backward_pre_hooks or _global_backward_hooks
1500 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1501 return forward_call(*args, **kwargs)
1502 # Do not call functions when jit is used
1503 full_backward_hooks, non_full_backward_hooks = [], []
File c:\Users\benedict\anaconda3\envs\anomalib_env\lib\site-packages\anomalib\models\efficientad\torch_model.py:112, in Encoder.forward(self, x)
110 x = F.relu(self.enconv4(x))
111 x = F.relu(self.enconv5(x))
--> 112 x = self.enconv6(x)
113 return x
File c:\Users\benedict\anaconda3\envs\anomalib_env\lib\site-packages\torch\nn\modules\module.py:1501, in Module._call_impl(self, *args, **kwargs)
1496 # If we don't have any hooks, we want to skip the rest of the logic in
1497 # this function, and just call forward.
1498 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
1499 or _global_backward_pre_hooks or _global_backward_hooks
1500 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1501 return forward_call(*args, **kwargs)
1502 # Do not call functions when jit is used
1503 full_backward_hooks, non_full_backward_hooks = [], []
File c:\Users\benedict\anaconda3\envs\anomalib_env\lib\site-packages\torch\nn\modules\conv.py:463, in Conv2d.forward(self, input)
462 def forward(self, input: Tensor) -> Tensor:
--> 463 return self._conv_forward(input, self.weight, self.bias)
File c:\Users\benedict\anaconda3\envs\anomalib_env\lib\site-packages\torch\nn\modules\conv.py:459, in Conv2d._conv_forward(self, input, weight, bias)
455 if self.padding_mode != 'zeros':
456 return F.conv2d(F.pad(input, self._reversed_padding_repeated_twice, mode=self.padding_mode),
457 weight, bias, self.stride,
458 _pair(0), self.dilation, self.groups)
--> 459 return F.conv2d(input, weight, bias, self.stride,
460 self.padding, self.dilation, self.groups)
RuntimeError: Calculated padded input size per channel: (7 x 7). Kernel size: (8 x 8). Kernel size can't be greater than actual input sizeCode of Conduct
Edit: Edit 2: |
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Replies: 34 comments 2 replies
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Whats inside c:\Users\benedict\dev\VespaUseCase\machine_learning\src\d01_config\transform_config_1.yaml ? |
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i've added the contents of it below the config section |
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sorry i've copied the wrong one, i'll update it in a minute Edit: Done |
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In the Configuration YAML you set image_size 256 and in the transform_config_1 you set CenterCrop to 224. Both should have the same value and both should be at least 256. |
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I think you're right, i didn't remove the center cropping correctly. I'll try it out again with the correct size of 256x256. Will respond whether it works now later, but thanks already! |
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the previous issue seems resolved, but now i'm facing another issue:
|
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when i set dataset.normalization to null i get this error: |
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sorry, you have to set it to
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i have applied the pr and set the config but i'm still facing the same issue sadly. Thanky you, anyways! |
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You are still seeing Thats the important part, is it really in your local repo? anomalib/src/anomalib/models/efficientad/lightning_model.py Lines 178 to 206 in efc7e60 |
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I get the error "RuntimeError: quantile() input tensor is too large" from the forward method of torch_model.py when I run it. |
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yeah exactly, but i applied the fixes manually. double checked for completeness, but will try once more with cloned repo to make sure it's applied correctly. |
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Tried again with the cloned and merged repo install but still the same error. |
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Hm strange. Can you do some debugging like printing |
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Just tried some print statements that aren't printed, both in the respective function itself and right before it's call, but they aren't printed. Edit: |
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Alright i'll try this out! |
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I installed it like you mentioned but i'm still getting the error Currently trying the print statements and debugging again |
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The debugger still doesn't stop at my set breakpoints and the print statements aren't printed.. |
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Oh sorry I just now realized that the issue is in I guess the problem is now caused by your |
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this is my config, with the batch_size fix already applied: currently running and waiting for results |
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Changing the seems to have fixed the issue. Thank you very much for helping me out here! Btw, greetings from berlin :) |
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Can the image size be set to something else？not 256? |
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I've run this model with 256x256 and 640x640 and had no issues. |
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I have a question that when I export to openvino, the metadata.json show : |
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We are currently working on OpenVINO C++ inference support. Once we finish the implementation we will update the documentation. |
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The model outputs smaller and larger numbers for normal and anomalous images. with the |
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I'm interested in this too. I'm getting noisy anomaly maps for normal images but great looking ones for anomalous ones. Thanks! |
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What do you mean by "noisy" and "great"? Are you interested in anomaly segmentation or just anomaly image classification? Do you train the model with segmenation ground truths of the anomalies? |
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By "noisy" I mean that for normal images it seems to be much more sensible to light distractions/ changes in contrast etc. (Btw., I'm currently not talking about results from efficient ad, but I guess this is applicable to all models (the normalization tweak).) I'm kinda interested in segmentation, I'm training it with ground truth masks on segmentation task. But I find the heat map to be visually more appealing (for demonstration purposes, under the presumption that the "noise" could be eliminated), that's why I would like to fix it, if possible. |
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The colors of the heat map are coming from here: anomalib/src/anomalib/post_processing/post_process.py Lines 85 to 103 in 17efdb5 If you use the task segmentation (which I'm not 100% sure because in your initial config.yaml you define |
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@alexriedel1 |
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The fastest and easiest would be to resize your images to 256x256. |
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Epoch 0: 1%| | 22/2160 [01:06<1:47:10, 3.01s/it, loss=601, v_num=30, train_st_step=57.90, train_ae_step=0.881, train_stae_step=4.980, train_loss_step=63.80]Changing the
seems to have fixed the issue.
Thank you very much for helping me out here!
Btw, greetings from berlin :)