Fixed slide inference (#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 @@ class CityscapesDataset(CustomDataset):
         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 @@ class EncoderDecoder(BaseSegmentor):
 
                 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 @@ from mmseg.utils import collect_env, get_root_logger
 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(