image input channels issue

[taeyeopl]

Can I ask why the input channel issue??
RuntimeError: weight of size [64, 3, 7, 7], expected input[32, 256, 192, 3] to have 3 channels, but got 256 channels instead

(simple) user@user:/sdata1/workspace/simple-pose$ python pose_estimation/train.py     --cfg experiments/coco/resnet50/256x192_d256x3_adam_lr1e-3.yaml
/sdata1/workspace/simple-pose/pose_estimation/../lib/core/config.py:161: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.
  exp_config = edict(yaml.load(f))
=> creating output/coco/pose_resnet_50/256x192_d256x3_adam_lr1e-3
=> creating log/coco/pose_resnet_50/256x192_d256x3_adam_lr1e-3_2020-05-22-22-31
Namespace(cfg='experiments/coco/resnet50/256x192_d256x3_adam_lr1e-3.yaml', frequent=100, gpus=None, workers=None)
{'CUDNN': {'BENCHMARK': True, 'DETERMINISTIC': False, 'ENABLED': True},
 'DATASET': {'DATASET': 'coco',
             'DATA_FORMAT': 'jpg',
             'FLIP': True,
             'HYBRID_JOINTS_TYPE': '',
             'ROOT': './data/coco/',
             'ROT_FACTOR': 40,
             'SCALE_FACTOR': 0.3,
             'SELECT_DATA': False,
             'TEST_SET': 'val2017',
             'TRAIN_SET': 'train2017'},
 'DATA_DIR': '',
 'DEBUG': {'DEBUG': True,
           'SAVE_BATCH_IMAGES_GT': True,
           'SAVE_BATCH_IMAGES_PRED': True,
           'SAVE_HEATMAPS_GT': True,
           'SAVE_HEATMAPS_PRED': True},
 'GPUS': '0',
 'LOG_DIR': 'log',
 'LOSS': {'USE_TARGET_WEIGHT': True},
 'MODEL': {'EXTRA': {'DECONV_WITH_BIAS': False,
                     'FINAL_CONV_KERNEL': 1,
                     'HEATMAP_SIZE': array([48, 64]),
                     'NUM_DECONV_FILTERS': [256, 256, 256],
                     'NUM_DECONV_KERNELS': [4, 4, 4],
                     'NUM_DECONV_LAYERS': 3,
                     'NUM_LAYERS': 50,
                     'SIGMA': 2,
                     'TARGET_TYPE': 'gaussian'},
           'IMAGE_SIZE': array([192, 256]),
           'INIT_WEIGHTS': True,
           'NAME': 'pose_resnet',
           'NUM_JOINTS': 17,
           'PRETRAINED': 'models/pytorch/imagenet/resnet50-19c8e357.pth',
           'STYLE': 'pytorch'},
 'OUTPUT_DIR': 'output',
 'PRINT_FREQ': 100,
 'TEST': {'BATCH_SIZE': 1,
          'BBOX_THRE': 1.0,
          'COCO_BBOX_FILE': 'data/coco/person_detection_results/COCO_val2017_detections_AP_H_56_person.json',
          'FLIP_TEST': False,
          'IMAGE_THRE': 0.0,
          'IN_VIS_THRE': 0.2,
          'MODEL_FILE': '',
          'NMS_THRE': 1.0,
          'OKS_THRE': 0.9,
          'POST_PROCESS': True,
          'SHIFT_HEATMAP': True,
          'USE_GT_BBOX': True},
 'TRAIN': {'BATCH_SIZE': 32,
           'BEGIN_EPOCH': 0,
           'CHECKPOINT': '',
           'END_EPOCH': 140,
           'GAMMA1': 0.99,
           'GAMMA2': 0.0,
           'LR': 0.001,
           'LR_FACTOR': 0.1,
           'LR_STEP': [90, 120],
           'MOMENTUM': 0.9,
           'NESTEROV': False,
           'OPTIMIZER': 'adam',
           'RESUME': False,
           'SHUFFLE': True,
           'WD': 0.0001},
 'WORKERS': 4}
=> init deconv weights from normal distribution
=> init 0.weight as normal(0, 0.001)
=> init 0.bias as 0
=> init 1.weight as 1
=> init 1.bias as 0
=> init 3.weight as normal(0, 0.001)
=> init 3.bias as 0
=> init 4.weight as 1
=> init 4.bias as 0
=> init 6.weight as normal(0, 0.001)
=> init 6.bias as 0
=> init 7.weight as 1
=> init 7.bias as 0
=> init final conv weights from normal distribution
=> init 8.weight as normal(0, 0.001)
=> init 8.bias as 0
=> loading pretrained model models/pytorch/imagenet/resnet50-19c8e357.pth
/home/user/anaconda3/envs/simple/lib/python3.6/site-packages/torch/nn/functional.py:52: UserWarning: size_average and reduce args will be deprecated, please use reduction='elementwise_mean' instead.
  warnings.warn(warning.format(ret))
loading annotations into memory...
Done (t=7.44s)
creating index...
index created!
=> classes: ['__background__', 'person']
=> num_images: 118287
=> load 149813 samples
loading annotations into memory...
Done (t=0.22s)
creating index...
index created!
=> classes: ['__background__', 'person']
=> num_images: 5000
=> load 6352 samples
Traceback (most recent call last):
  File "pose_estimation/train.py", line 206, in <module>
    main()
  File "pose_estimation/train.py", line 174, in main
    final_output_dir, tb_log_dir, writer_dict)
  File "/sdata1/workspace/simple-pose/pose_estimation/../lib/core/function.py", line 45, in train
    output = model(input)
  File "/home/user/anaconda3/envs/simple/lib/python3.6/site-packages/torch/nn/modules/module.py", line 477, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/user/anaconda3/envs/simple/lib/python3.6/site-packages/torch/nn/parallel/data_parallel.py", line 121, in forward
    return self.module(*inputs[0], **kwargs[0])
  File "/home/user/anaconda3/envs/simple/lib/python3.6/site-packages/torch/nn/modules/module.py", line 477, in __call__
    result = self.forward(*input, **kwargs)
  File "/sdata1/workspace/simple-pose/pose_estimation/../lib/models/pose_resnet.py", line 235, in forward
    x = self.conv1(x)
  File "/home/user/anaconda3/envs/simple/lib/python3.6/site-packages/torch/nn/modules/module.py", line 477, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/user/anaconda3/envs/simple/lib/python3.6/site-packages/torch/nn/modules/conv.py", line 301, in forward
    self.padding, self.dilation, self.groups)
RuntimeError: Given groups=1, weight of size [64, 3, 7, 7], expected input[32, 256, 192, 3] to have 3 channels, but got 256 channels instead

[qinghuan007]

Do you know how to solve this problem now?