TypeError: can't assign a numpy.int64 to a torch.FloatTensor

[goodproj13]

Guys,
Did anyone know what caused this issue below? Many thanks in advance.
Torch: 0.40
Python: 3.6

We use voc format of KITTI dataset. The only thing I changed for the original code from this repo is to change the picture format from "jpg" to "png" which is the format for KITTI dataset. When I run ''python trainvel_net.py", I got error below:

Traceback (most recent call last):
File "trainval_net.py", line 209, in
imdb.num_classes, training=True)
File "/home/NewPartion/pycharm/faster-rcnn.pytorch/lib/roi_data_layer/roibatchLoader.py", line 54, in init
self.ratio_list_batch[left_idx:(right_idx+1)] = target_ratio
TypeError: can't assign a numpy.int64 to a torch.FloatTensor

[EMCP]

please paste all the output from your training run.. so we can see if you enabled CUDA, etc...

[ma-xu]

Did you solve this problem? I have the same issue ...

[goodproj13]

I didn't solve the problem and I ended up using other's. Sorry!

…

________________________________ From: Xu Ma <notifications@github.com> Sent: Friday, April 12, 2019 11:26:30 AM To: jwyang/faster-rcnn.pytorch Cc: Dongfang Liu; Author Subject: Re: [jwyang/faster-rcnn.pytorch] TypeError: can't assign a numpy.int64 to a torch.FloatTensor (#452) Did you solve this problem? I have the same issue ... — You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub<#452 (comment)>, or mute the thread<https://github.com/notifications/unsubscribe-auth/ALO7KPSBVIN5LS4PTJWHQJDPQCS2NANCNFSM4G25NUQQ>.

[ma-xu]

Maybe I should try mmdetection or Detectron ...

[Weizhongjin]

Change original code to
temp = torch.ones(batch_size)*target_ratio
self.ratio_list_batch[left_idx:(right_idx+1)] = temp

[benedictflorance]

@Weizhongjin

Change original code to
temp = torch.ones(batch_size)*target_ratio
self.ratio_list_batch[left_idx:(right_idx+1)] = temp

Doing the above fix throws this error:

**TypeError: mul() received an invalid combination of arguments - got (numpy.int64), but expected one of:
 * (Tensor other)
      didn't match because some of the arguments have invalid types: (numpy.int64)
 * (float other)
      didn't match because some of the arguments have invalid types: (numpy.int64)

**

Has anyone found a fix for this?

[benedictflorance]

@EMCP

Called with args:
Namespace(batch_size=1, checkepoch=1, checkpoint=0, checkpoint_interval=10000, checksession=1, class_agnostic=False, cuda=True, dataset='hollywoodheads_scuta', disp_interval=100, lamda=0.1, large_scale=False, lr=0.002, lr_decay_gamma=0.1, lr_decay_step=6, mGPUs=True, max_epochs=5, net='vgg16', num_workers=0, optimizer='sgd', resume=False, save_dir='data/adaptation/experiments', session=1, start_epoch=1, use_tfboard=False)
loading our dataset...........
/export/livia/home/vision/bflorance/da-faster-rcnn-PyTorch/lib/model/utils/config.py:376: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.
  yaml_cfg = edict(yaml.load(f))
Using config:
{'ANCHOR_RATIOS': [0.5, 1, 2],
 'ANCHOR_SCALES': [4, 8, 16, 32],
 'CROP_RESIZE_WITH_MAX_POOL': False,
 'CUDA': False,
 'DATA_DIR': '/export/livia/home/vision/bflorance/da-faster-rcnn-PyTorch/data',
 'DEDUP_BOXES': 0.0625,
 'DSN_DIFF_WEIGHT': 100000,
 'EPS': 1e-14,
 'EXP_DIR': 'vgg16',
 'FEAT_STRIDE': [16],
 'GPU_ID': 0,
 'MATLAB': 'matlab',
 'MAX_NUM_GT_BOXES': 50,
 'MOBILENET': {'DEPTH_MULTIPLIER': 1.0,
               'FIXED_LAYERS': 5,
               'REGU_DEPTH': False,
               'WEIGHT_DECAY': 4e-05},
 'PIXEL_MEANS': array([[[102.9801, 115.9465, 122.7717]]]),
 'POOLING_MODE': 'align',
 'POOLING_SIZE': 7,
 'RESNET': {'FIXED_BLOCKS': 1, 'MAX_POOL': False},
 'RNG_SEED': 3,
 'ROOT_DIR': '/export/livia/home/vision/bflorance/da-faster-rcnn-PyTorch',
 'TEST': {'BBOX_REG': True,
          'HAS_RPN': True,
          'MAX_SIZE': 1000,
          'MODE': 'nms',
          'NMS': 0.3,
          'PROPOSAL_METHOD': 'gt',
          'RPN_MIN_SIZE': 16,
          'RPN_NMS_THRESH': 0.7,
          'RPN_POST_NMS_TOP_N': 300,
          'RPN_PRE_NMS_TOP_N': 6000,
          'RPN_TOP_N': 5000,
          'SCALES': [600],
          'SVM': False},
 'TRAIN': {'ASPECT_GROUPING': False,
           'BATCH_SIZE': 256,
           'BBOX_INSIDE_WEIGHTS': [1.0, 1.0, 1.0, 1.0],
           'BBOX_NORMALIZE_MEANS': [0.0, 0.0, 0.0, 0.0],
           'BBOX_NORMALIZE_STDS': [0.1, 0.1, 0.2, 0.2],
           'BBOX_NORMALIZE_TARGETS': True,
           'BBOX_NORMALIZE_TARGETS_PRECOMPUTED': True,
           'BBOX_REG': True,
           'BBOX_THRESH': 0.5,
           'BG_THRESH_HI': 0.5,
           'BG_THRESH_LO': 0.0,
           'BIAS_DECAY': False,
           'BN_TRAIN': False,
           'DISPLAY': 10,
           'DOUBLE_BIAS': True,
           'FG_FRACTION': 0.25,
           'FG_THRESH': 0.5,
           'GAMMA': 0.1,
           'HAS_RPN': True,
           'IMS_PER_BATCH': 1,
           'LEARNING_RATE': 0.01,
           'MAX_SIZE': 1000,
           'MOMENTUM': 0.9,
           'PROPOSAL_METHOD': 'gt',
           'RPN_BATCHSIZE': 256,
           'RPN_BBOX_INSIDE_WEIGHTS': [1.0, 1.0, 1.0, 1.0],
           'RPN_CLOBBER_POSITIVES': False,
           'RPN_FG_FRACTION': 0.5,
           'RPN_MIN_SIZE': 8,
           'RPN_NEGATIVE_OVERLAP': 0.3,
           'RPN_NMS_THRESH': 0.7,
           'RPN_POSITIVE_OVERLAP': 0.7,
           'RPN_POSITIVE_WEIGHT': -1.0,
           'RPN_POST_NMS_TOP_N': 2000,
           'RPN_PRE_NMS_TOP_N': 12000,
           'SCALES': [600],
           'SNAPSHOT_ITERS': 5000,
           'SNAPSHOT_KEPT': 3,
           'SNAPSHOT_PREFIX': 'res101_faster_rcnn',
           'STEPSIZE': [30000],
           'SUMMARY_INTERVAL': 180,
           'TRIM_HEIGHT': 600,
           'TRIM_WIDTH': 600,
           'TRUNCATED': False,
           'USE_ALL_GT': True,
           'USE_FLIPPED': True,
           'USE_GT': False,
           'WEIGHT_DECAY': 0.0005},
 'USE_GPU_NMS': True}
Loaded dataset `hollywoodheads_scuta_2007_train_s` for training
Set proposal method: gt
Appending horizontally-flipped training examples...
hollywoodheads_scuta_2007_train_s gt roidb loaded from /export/livia/home/vision/bflorance/da-faster-rcnn-PyTorch/data/cache/hollywoodheads_scuta_2007_train_s_gt_roidb.pkl
done
Preparing training data...
done
before filtering, there are 200 images...
after filtering, there are 200 images...
Source Train Size =  200
Loaded dataset `hollywoodheads_scuta_2007_train_t` for training
Set proposal method: gt
Appending horizontally-flipped training examples...
hollywoodheads_scuta_2007_train_t gt roidb loaded from /export/livia/home/vision/bflorance/da-faster-rcnn-PyTorch/data/cache/hollywoodheads_scuta_2007_train_t_gt_roidb.pkl
done
Preparing training data...
done
before filtering, there are 200 images...
after filtering, there are 200 images...
Target Train Size =  200
source 200 target 200 roidb entries
Traceback (most recent call last):
  File "da_trainval_net.py", line 257, in <module>
    s_imdb.num_classes, training=True)
  File "/export/livia/home/vision/bflorance/da-faster-rcnn-PyTorch/lib/roi_da_data_layer/roibatchLoader.py", line 55, in __init__
    self.ratio_list_batch[left_idx:(right_idx+1)] = target_ratio    # trainset ratio list ,each batch is same number
TypeError: can't assign a numpy.int64 to a torch.FloatTensor

[EMCP]

Can you possibly upgrade to PyTorch 1.0 ? I've used exclusively 1.x and had zero issues for months now

[benedictflorance]

@EMCP I'm working on domain adaptation (https://github.com/tiancity-NJU/da-faster-rcnn-PyTorch) and it uses the PyTorch 0.4 version of this repo. :(

[EMCP]

okay i am abroad from my deep learning rig, so I cannot test PyTorch .4 until mid June... but I would diff the two versions and ensure you've got any/all bug fixes pushed to the 1.x version... I never ran the Master branch so I cannot get a sense if it's been properly patched or not..

IMO , the repo owner should just do a release branch for older PyTorch versions and stick to the bleeding edge in Master root, instead of this 1.x off on the side strategy.

[benedictflorance]

self.ratio_list_batch[left_idx:(right_idx+1)] = torch.tensor(target_ratio.astype(np.float64)) # trainset ratio list ,each batch is same number
This fixed the issue for 0.4.0

[EMCP]

feel free to submit a PR and close @benedictflorance

[benedictflorance]

Yeah, I've submitted one.
#573