Total image pairs: 200
Total image pairs: 200
Running on CPU

Training config:

APPEND_VALID_MASK: False
CKPT_DIR: ./checkpoints
CKPT_INTERVAL: 1
CRITERION:
  CUSTOM: True
  NAME: MultiScaleLoss
  PARAMS:
    weights: [1, 0.5, 0.25, 0.125, 0.0625]
DATA:
  AUGMENTATION:
    PARAMS:
      CROP_SIZE: [256, 256]
      TRAINING:
        SPATIAL_AUG_PARAMS:
          flip: True
          max_scale: 1.0
          min_scale: -0.1
      VALIDATION:
        COLOR_AUG_PARAMS:
          aug_prob: 0.0
        ERASER_AUG_PARAMS:
          aug_prob: 0.0
        SPATIAL_AUG_PARAMS:
          aug_prob: 0.0
    USE: True
  BATCH_SIZE: 16
  NUM_WORKERS: 1
  PIN_MEMORY: True
  TRAIN_CROP_SIZE: [256, 256]
  TRAIN_DATASET:
    NAME: flyingchairs
    ROOT_DIR: ../../../Datasets/FlyingChairs_release/data
  VAL_CROP_SIZE: [256, 256]
  VAL_DATASET:
    NAME: flyingchairs
    ROOT_DIR: ../../../Datasets/FlyingChairs_release/data
DEVICE: cpu
DISTRIBUTED:
  BACKEND: nccl
  MASTER_ADDR: localhost
  MASTER_PORT: 12355
  RANK: 0
  USE: False
  WORLD_SIZE: 2
EPOCHS: 100
GRAD_CLIP:
  USE: True
  VALUE: 1.0
LOG_DIR: ./logs
LOG_ITERATIONS_INTERVAL: 100
MIXED_PRECISION: False
NUM_STEPS: None
OPTIMIZER:
  LR: 0.0004
  NAME: AdamW
  PARAMS:
    eps: 1e-08
    weight_decay: 0.0001
RESUME_TRAINING:
  CONSOLIDATED_CKPT: None
  EPOCHS: 100
  START_EPOCH: None
SCHEDULER:
  NAME: OneCycleLR
  PARAMS:
    anneal_strategy: linear
    cycle_momentum: False
    epochs: 100
    max_lr: 0.0004
    pct_start: 0.05
    steps_per_epoch: 2300
  USE: True
TARGET_SCALE_FACTOR: 1
VALIDATE_INTERVAL: 1
VALIDATE_ON: metric
--------------------------------------------------------------------------------

Epoch 1 of 100
--------------------------------------------------------------------------------
Traceback (most recent call last):
  File "myMain.py", line 67, in <module>
    trainer.train()
  File "/home/s/FlowNets/ezflow/ezflow/engine/trainer.py", line 599, in train
    best_model = self._trainer(total_iterations, start_iteration)
  File "/home/s/FlowNets/ezflow/ezflow/engine/trainer.py", line 141, in _epoch_trainer
    for iteration, (inp, target) in enumerate(self.train_loader):
  File "/home/s/.local/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 681, in __next__
    data = self._next_data()
  File "/home/s/.local/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 1376, in _next_data
    return self._process_data(data)
  File "/home/s/.local/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 1402, in _process_data
    data.reraise()
  File "/home/s/.local/lib/python3.7/site-packages/torch/_utils.py", line 461, in reraise
    raise exception
RuntimeError: Caught RuntimeError in DataLoader worker process 0.
Original Traceback (most recent call last):
  File "/home/s/.local/lib/python3.7/site-packages/torch/utils/data/_utils/worker.py", line 302, in _worker_loop
    data = fetcher.fetch(index)
  File "/home/s/.local/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 52, in fetch
    return self.collate_fn(data)
  File "/home/s/.local/lib/python3.7/site-packages/torch/utils/data/_utils/collate.py", line 175, in default_collate
    return [default_collate(samples) for samples in transposed]  # Backwards compatibility.
  File "/home/s/.local/lib/python3.7/site-packages/torch/utils/data/_utils/collate.py", line 175, in <listcomp>
    return [default_collate(samples) for samples in transposed]  # Backwards compatibility.
  File "/home/s/.local/lib/python3.7/site-packages/torch/utils/data/_utils/collate.py", line 175, in default_collate
    return [default_collate(samples) for samples in transposed]  # Backwards compatibility.
  File "/home/s/.local/lib/python3.7/site-packages/torch/utils/data/_utils/collate.py", line 175, in <listcomp>
    return [default_collate(samples) for samples in transposed]  # Backwards compatibility.
  File "/home/s/.local/lib/python3.7/site-packages/torch/utils/data/_utils/collate.py", line 141, in default_collate
    return torch.stack(batch, 0, out=out)
RuntimeError: stack expects each tensor to be equal size, but got [3, 375, 384] at entry 0 and [3, 384, 384] at entry 2