Fixed slide inference (open-mmlab#90)

docs/getting_started.md

@@ -229,7 +229,7 @@ To trade speed with GPU memory, you may pass in `--options model.backbone.with_c
 python tools/train.py ${CONFIG_FILE} [optional arguments]
 ```
 
-If you want to specify the working directory in the command, you can add an argument `--work_dir ${YOUR_WORK_DIR}`.
+If you want to specify the working directory in the command, you can add an argument `--work-dir ${YOUR_WORK_DIR}`.
 
 ### Train with multiple GPUs
 
@@ -253,7 +253,7 @@ Difference between `resume-from` and `load-from`:
 If you run MMSegmentation on a cluster managed with [slurm](https://slurm.schedmd.com/), you can use the script `slurm_train.sh`. (This script also supports single machine training.)
 
 ```shell
-[GPUS=${GPUS}] ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} ${CONFIG_FILE} ${WORK_DIR}
+[GPUS=${GPUS}] ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} ${CONFIG_FILE} --work-dir ${WORK_DIR}
 ```
 
 Here is an example of using 16 GPUs to train PSPNet on the dev partition.

mmseg/apis/test.py

@@ -30,7 +30,7 @@ def single_gpu_test(model, data_loader, show=False, out_dir=None):
  prog_bar = mmcv.ProgressBar(len(dataset))
  for i, data in enumerate(data_loader):
  with torch.no_grad():
- result = model(return_loss=False, rescale=not show, **data)
+ result = model(return_loss=False, **data)
  if isinstance(results, list):
  results.extend(result)
  else:

mmseg/datasets/cityscapes.py

@@ -173,7 +173,7 @@ def _evaluate_cityscapes(self, results, logger, imgfile_prefix):
  try:
  import cityscapesscripts.evaluation.evalPixelLevelSemanticLabeling as CSEval # noqa
  except ImportError:
- raise ImportError('Please run "pip install citscapesscripts" to '
+ raise ImportError('Please run "pip install cityscapesscripts" to '
  'install cityscapesscripts first.')
  msg = 'Evaluating in Cityscapes style'
  if logger is None:

mmseg/models/segmentors/encoder_decoder.py

@@ -195,8 +195,10 @@ def slide_inference(self, img, img_meta, rescale):
 
  count_mat[:, :, y1:y2, x1:x2] += 1
  assert (count_mat == 0).sum() == 0
- # We want to regard count_mat as a constant while exporting to ONNX
- count_mat = torch.from_numpy(count_mat.detach().numpy())
+ if torch.onnx.is_in_onnx_export():
+ # cast count_mat to constant while exporting to ONNX
+ count_mat = torch.from_numpy(
+ count_mat.cpu().detach().numpy()).to(device=img.device)
  preds = preds / count_mat
  if rescale:
  preds = resize(

tools/train.py

@@ -19,7 +19,7 @@
 def parse_args():
  parser = argparse.ArgumentParser(description='Train a segmentor')
  parser.add_argument('config', help='train config file path')
- parser.add_argument('--work_dir', help='the dir to save logs and models')
+ parser.add_argument('--work-dir', help='the dir to save logs and models')
  parser.add_argument(
  '--load-from', help='the checkpoint file to load weights from')
  parser.add_argument(