RuntimeError: The size of tensor a (256) must match the size of tensor b (255) at non-singleton dimension 3

[HuXinzhi1004]

Hello! Thanks for your great job. When I run

time CUDA_VISIBLE_DEVICES=0,4 python3 -m butterflydetector.train --lr=1e-3 --momentum=0.95 --epochs=150 --lr-decay 120 140 --batch-size=16 --basenet=hrnetw32det --head-quad=1 --headnets butterfly10 --square-edge=512 --lambdas 1 1 1 1 --dataset uavdt

I got the error.

(cluster) hxz@ubuntu16:/home/data/hxz/butterflydetector$ time CUDA_VISIBLE_DEVICES=0,4 python3 -m butterflydetector.train --lr=1e-3 --momentum=0.95 --epochs=150 --lr-decay 120 140 --batch-size=16 --basenet=hrnetw32det --head-quad=1 --headnets butterfly10 --square-edge=512 --lambdas 1 1 1 1 --dataset uavdt INFO:butterflydetector.logs:{'type': 'process', 'argv': ['/home/data/hxz/butterflydetector/butterflydetector/train.py', '--lr=1e-3', '--momentum=0.95', '--epochs=150', '--lr-decay', '120', '140', '--batch-size=16', '--basenet=hrnetw32det', '--head-quad=1', '--headnets', 'butterfly10', '--square-edge=512', '--lambdas', '1', '1', '1', '1', '--dataset', 'uavdt'], 'args': {'debug': False, 'checkpoint': None, 'basenet': 'hrnetw32det', 'headnets': ['butterfly10'], 'pretrained': True, 'cross_talk': 0.0, 'head_dropout': 0.0, 'head_quad': 1, 'lambdas': [1.0, 1.0, 1.0, 1.0], 'r_smooth': 0.0, 'regression_loss': 'laplace', 'background_weight': 1.0, 'margin_loss': False, 'auto_tune_mtl': False, 'butterfly_side_length': 1, 'momentum': 0.95, 'beta2': 0.999, 'adam_eps': 1e-06, 'nesterov': True, 'weight_decay': 0.0, 'adam': False, 'amsgrad': False, 'lr': 0.001, 'lr_decay': [120, 140], 'lr_burn_in_epochs': 2, 'lr_burn_in_factor': 0.001, 'lr_gamma': 0.1, 'dataset': 'uavdt', 'train_annotations': None, 'train_image_dir': None, 'val_annotations': None, 'val_image_dir': None, 'pre_n_images': 8000, 'n_images': None, 'duplicate_data': None, 'pre_duplicate_data': None, 'loader_workers': 2, 'batch_size': 16, 'output': 'outputs/hrnetw32det-butterfly10-edge512-211210-152433.pkl', 'stride_apply': 1, 'epochs': 150, 'freeze_base': 0, 'pre_lr': 0.0001, 'rescale_images': 1.0, 'orientation_invariant': False, 'update_batchnorm_runningstatistics': False, 'square_edge': 512, 'ema': 0.001, 'disable_cuda': False, 'augmentation': True, 'debug_fields_indices': [], 'profile': None, 'device': device(type='cuda'), 'pin_memory': True}, 'version': '0.0.1', 'hostname': 'ubuntu16'} INFO:butterflydetector.network.hrnet:=> init weights from normal distribution INFO:butterflydetector.network.hrnet:=> loading pretrained model pretrained/imagenet/hrnet_w32-36af842e.pth INFO:butterflydetector.network.basenetworks:stride = 4 INFO:butterflydetector.network.basenetworks:output features = 512 INFO:butterflydetector.network.heads:selected head CompositeField for butterfly10 Using multiple GPUs: 2 INFO:butterflydetector.network.losses:multihead loss: ['butterfly10.c', 'butterfly10.vec1', 'butterfly10.scales1', 'butterfly10.scales2'], [1.0, 1.0, 1.0, 1.0] /home/data/hxz/butterflydetector/butterflydetector/data_manager/uavdt.py:66: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. self.targets = np.asarray(self.targets) /home/data/hxz/butterflydetector/butterflydetector/data_manager/uavdt.py:67: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. self.targets_ignore = np.asarray(self.targets_ignore) Images: 40409 Images: 16580 Images: 8000 INFO:butterflydetector.optimize:SGD optimizer INFO:butterflydetector.network.trainer:{'type': 'config', 'field_names': ['butterfly10.c', 'butterfly10.vec1', 'butterfly10.scales1', 'butterfly10.scales2']} /home/hxz/anaconda3/envs/cluster/lib/python3.7/site-packages/torch/nn/functional.py:3635: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details. "See the documentation of nn.Upsample for details.".format(mode) Traceback (most recent call last): File "/home/hxz/anaconda3/envs/cluster/lib/python3.7/runpy.py", line 193, in _run_module_as_main "main", mod_spec) File "/home/hxz/anaconda3/envs/cluster/lib/python3.7/runpy.py", line 85, in _run_code exec(code, run_globals) File "/home/data/hxz/butterflydetector/butterflydetector/train.py", line 200, in main() File "/home/data/hxz/butterflydetector/butterflydetector/train.py", line 196, in main trainer.loop(train_loader, val_loader, args.epochs, start_epoch=start_epoch) File "/home/data/hxz/butterflydetector/butterflydetector/network/trainer.py", line 99, in loop self.train(train_scenes, epoch) File "/home/data/hxz/butterflydetector/butterflydetector/network/trainer.py", line 173, in train loss, head_losses = self.train_batch(data, target, meta, apply_gradients) File "/home/data/hxz/butterflydetector/butterflydetector/network/trainer.py", line 116, in train_batch loss, head_losses = self.loss(outputs, targets) File "/home/hxz/anaconda3/envs/cluster/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl return forward_call(*input, *kwargs) File "/home/data/hxz/butterflydetector/butterflydetector/network/losses.py", line 176, in forward for l, f, t in zip(self.losses, head_fields, head_targets) File "/home/data/hxz/butterflydetector/butterflydetector/network/losses.py", line 177, in for ll in l(f, t)] File "/home/hxz/anaconda3/envs/cluster/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl return forward_call(input, **kwargs) File "/home/data/hxz/butterflydetector/butterflydetector/network/losses.py", line 457, in forward ) / 100.0 / batch_size RuntimeError: The size of tensor a (256) must match the size of tensor b (255) at non-singleton dimension 3

How to fix it ?

[TonyzBi]

@HuXinzhi1004 I got the same error with dataset Viddrone2019, Did you fix it ?

[HuXinzhi1004]

just change the image input size, but I don’t know if it’s the right thing to do

发自我的iPhone

------------------ Original ------------------ From: TonyzBi @.> Date: Wed,Dec 22,2021 10:39 AM To: vita-epfl/butterflydetector @.> Cc: HuXinzhi1004 @.>, Mention @.> Subject: Re: [vita-epfl/butterflydetector] RuntimeError: The size of tensor a (256) must match the size of tensor b (255) at non-singleton dimension 3 (Issue #5)

@HuXinzhi1004 I got the the same error with dataset of Visdrone2019, Did you fixed it ?

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[TonyzBi]

@HuXinzhi1004 Thanks, I have tried other size of input, Just report other size error, it seems no matching the target size when running loss.

[TonyzBi]

@HuXinzhi1004 I change the size of output feature by Heads module, It worked, But I am not sure if this modification has change the purpose of the author, I don't know if it’s the right thing to do too.

+++ b/butterflydetector/network/heads.py
@@ -190,15 +190,26 @@ class CompositeField(torch.nn.Module):
         scales_x = [torch.nn.functional.relu(scale_x) for scale_x in scales_x]

         # upscale
+        # for _ in range(self._quad):
+        #     classes_x = [self.dequad_op(class_x)[:, :, :-1, :-1]
+        #                  for class_x in classes_x]
+        #     regs_x = [self.dequad_op(reg_x)[:, :, :-1, :-1]
+        #               for reg_x in regs_x]
+        #     regs_x_spread = [self.dequad_op(reg_x_spread)[:, :, :-1, :-1]
+        #                      for reg_x_spread in regs_x_spread]
+        #     scales_x = [self.dequad_op(scale_x)[:, :, :-1, :-1]
+        #                 for scale_x in scales_x]
+
         for _ in range(self._quad):
-            classes_x = [self.dequad_op(class_x)[:, :, :-1, :-1]
+            classes_x = [self.dequad_op(class_x)
                          for class_x in classes_x]
-            regs_x = [self.dequad_op(reg_x)[:, :, :-1, :-1]
+            regs_x = [self.dequad_op(reg_x)
                       for reg_x in regs_x]
-            regs_x_spread = [self.dequad_op(reg_x_spread)[:, :, :-1, :-1]
+            regs_x_spread = [self.dequad_op(reg_x_spread)
                              for reg_x_spread in regs_x_spread]
-            scales_x = [self.dequad_op(scale_x)[:, :, :-1, :-1]
+            scales_x = [self.dequad_op(scale_x)
                         for scale_x in scales_x]
+
         # reshape regressions