Add test tutorial (#9)

* add test tutorial * remote torch/torchvision from requirements * update getting started * rename drop_out_ratio -> dropout_ratio
2025-06-03 22:03:48 +08:00 · 2020-07-12 23:53:56 +08:00 · 2020-07-12 23:53:56 +08:00 · b975d3b72a
commit b975d3b72a
parent b72a6d00ef
34 changed files with 81 additions and 70 deletions
--- a/configs/_base_/models/ann_r50-d8.py
+++ b/configs/_base_/models/ann_r50-d8.py
@ -22,7 +22,7 @@ model = dict(
        project_channels=256,
        query_scales=(1, ),
        key_pool_scales=(1, 3, 6, 8),
-        drop_out_ratio=0.1,
+        dropout_ratio=0.1,
        num_classes=19,
        norm_cfg=norm_cfg,
        align_corners=False,
@ -35,7 +35,7 @@ model = dict(
        channels=256,
        num_convs=1,
        concat_input=False,
-        drop_out_ratio=0.1,
+        dropout_ratio=0.1,
        num_classes=19,
        norm_cfg=norm_cfg,
        align_corners=False,
--- a/configs/_base_/models/ccnet_r50-d8.py
+++ b/configs/_base_/models/ccnet_r50-d8.py
@ -20,7 +20,7 @@ model = dict(
        in_index=3,
        channels=512,
        recurrence=2,
-        drop_out_ratio=0.1,
+        dropout_ratio=0.1,
        num_classes=19,
        norm_cfg=norm_cfg,
        align_corners=False,
@ -33,7 +33,7 @@ model = dict(
        channels=256,
        num_convs=1,
        concat_input=False,
-        drop_out_ratio=0.1,
+        dropout_ratio=0.1,
        num_classes=19,
        norm_cfg=norm_cfg,
        align_corners=False,
--- a/configs/_base_/models/danet_r50-d8.py
+++ b/configs/_base_/models/danet_r50-d8.py
@ -20,7 +20,7 @@ model = dict(
        in_index=3,
        channels=512,
        pam_channels=64,
-        drop_out_ratio=0.1,
+        dropout_ratio=0.1,
        num_classes=19,
        norm_cfg=norm_cfg,
        align_corners=False,
@ -33,7 +33,7 @@ model = dict(
        channels=256,
        num_convs=1,
        concat_input=False,
-        drop_out_ratio=0.1,
+        dropout_ratio=0.1,
        num_classes=19,
        norm_cfg=norm_cfg,
        align_corners=False,
--- a/configs/_base_/models/deeplabv3_r50-d8.py
+++ b/configs/_base_/models/deeplabv3_r50-d8.py
@ -20,7 +20,7 @@ model = dict(
        in_index=3,
        channels=512,
        dilations=(1, 12, 24, 36),
-        drop_out_ratio=0.1,
+        dropout_ratio=0.1,
        num_classes=19,
        norm_cfg=norm_cfg,
        align_corners=False,
@ -33,7 +33,7 @@ model = dict(
        channels=256,
        num_convs=1,
        concat_input=False,
-        drop_out_ratio=0.1,
+        dropout_ratio=0.1,
        num_classes=19,
        norm_cfg=norm_cfg,
        align_corners=False,
--- a/configs/_base_/models/deeplabv3plus_r50-d8.py
+++ b/configs/_base_/models/deeplabv3plus_r50-d8.py
@ -22,7 +22,7 @@ model = dict(
        dilations=(1, 12, 24, 36),
        c1_in_channels=256,
        c1_channels=48,
-        drop_out_ratio=0.1,
+        dropout_ratio=0.1,
        num_classes=19,
        norm_cfg=norm_cfg,
        align_corners=False,
@ -35,7 +35,7 @@ model = dict(
        channels=256,
        num_convs=1,
        concat_input=False,
-        drop_out_ratio=0.1,
+        dropout_ratio=0.1,
        num_classes=19,
        norm_cfg=norm_cfg,
        align_corners=False,
--- a/configs/_base_/models/encnet_r50-d8.py
+++ b/configs/_base_/models/encnet_r50-d8.py
@ -22,7 +22,7 @@ model = dict(
        num_codes=32,
        use_se_loss=True,
        add_lateral=False,
-        drop_out_ratio=0.1,
+        dropout_ratio=0.1,
        num_classes=19,
        norm_cfg=norm_cfg,
        align_corners=False,
@ -37,7 +37,7 @@ model = dict(
        channels=256,
        num_convs=1,
        concat_input=False,
-        drop_out_ratio=0.1,
+        dropout_ratio=0.1,
        num_classes=19,
        norm_cfg=norm_cfg,
        align_corners=False,
--- a/configs/_base_/models/fcn_hr18.py
+++ b/configs/_base_/models/fcn_hr18.py
@ -41,7 +41,7 @@ model = dict(
        kernel_size=1,
        num_convs=1,
        concat_input=False,
-        drop_out_ratio=-1,
+        dropout_ratio=-1,
        num_classes=19,
        norm_cfg=norm_cfg,
        align_corners=False,
--- a/configs/_base_/models/fcn_r50-d8.py
+++ b/configs/_base_/models/fcn_r50-d8.py
@ -21,7 +21,7 @@ model = dict(
        channels=512,
        num_convs=2,
        concat_input=True,
-        drop_out_ratio=0.1,
+        dropout_ratio=0.1,
        num_classes=19,
        norm_cfg=norm_cfg,
        align_corners=False,
@ -34,7 +34,7 @@ model = dict(
        channels=256,
        num_convs=1,
        concat_input=False,
-        drop_out_ratio=0.1,
+        dropout_ratio=0.1,
        num_classes=19,
        norm_cfg=norm_cfg,
        align_corners=False,
--- a/configs/_base_/models/gcnet_r50-d8.py
+++ b/configs/_base_/models/gcnet_r50-d8.py
@ -22,7 +22,7 @@ model = dict(
        ratio=1 / 4.,
        pooling_type='att',
        fusion_types=('channel_add', ),
-        drop_out_ratio=0.1,
+        dropout_ratio=0.1,
        num_classes=19,
        norm_cfg=norm_cfg,
        align_corners=False,
@ -35,7 +35,7 @@ model = dict(
        channels=256,
        num_convs=1,
        concat_input=False,
-        drop_out_ratio=0.1,
+        dropout_ratio=0.1,
        num_classes=19,
        norm_cfg=norm_cfg,
        align_corners=False,
--- a/configs/_base_/models/nonlocal_r50-d8.py
+++ b/configs/_base_/models/nonlocal_r50-d8.py
@ -19,7 +19,7 @@ model = dict(
        in_channels=2048,
        in_index=3,
        channels=512,
-        drop_out_ratio=0.1,
+        dropout_ratio=0.1,
        reduction=2,
        use_scale=True,
        mode='embedded_gaussian',
@ -35,7 +35,7 @@ model = dict(
        channels=256,
        num_convs=1,
        concat_input=False,
-        drop_out_ratio=0.1,
+        dropout_ratio=0.1,
        num_classes=19,
        norm_cfg=norm_cfg,
        align_corners=False,
--- a/configs/_base_/models/ocrnet_hr18.py
+++ b/configs/_base_/models/ocrnet_hr18.py
@ -43,7 +43,7 @@ model = dict(
            kernel_size=1,
            num_convs=1,
            concat_input=False,
-            drop_out_ratio=-1,
+            dropout_ratio=-1,
            num_classes=19,
            norm_cfg=norm_cfg,
            align_corners=False,
@ -56,7 +56,7 @@ model = dict(
            input_transform='resize_concat',
            channels=512,
            ocr_channels=256,
-            drop_out_ratio=-1,
+            dropout_ratio=-1,
            num_classes=19,
            norm_cfg=norm_cfg,
            align_corners=False,
--- a/configs/_base_/models/psanet_r50-d8.py
+++ b/configs/_base_/models/psanet_r50-d8.py
@ -25,7 +25,7 @@ model = dict(
        shrink_factor=2,
        normalization_factor=1.0,
        psa_softmax=True,
-        drop_out_ratio=0.1,
+        dropout_ratio=0.1,
        num_classes=19,
        norm_cfg=norm_cfg,
        align_corners=False,
@ -38,7 +38,7 @@ model = dict(
        channels=256,
        num_convs=1,
        concat_input=False,
-        drop_out_ratio=0.1,
+        dropout_ratio=0.1,
        num_classes=19,
        norm_cfg=norm_cfg,
        align_corners=False,
--- a/configs/_base_/models/pspnet_r50-d8.py
+++ b/configs/_base_/models/pspnet_r50-d8.py
@ -20,7 +20,7 @@ model = dict(
        in_index=3,
        channels=512,
        pool_scales=(1, 2, 3, 6),
-        drop_out_ratio=0.1,
+        dropout_ratio=0.1,
        num_classes=19,
        norm_cfg=norm_cfg,
        align_corners=False,
@ -33,7 +33,7 @@ model = dict(
        channels=256,
        num_convs=1,
        concat_input=False,
-        drop_out_ratio=0.1,
+        dropout_ratio=0.1,
        num_classes=19,
        norm_cfg=norm_cfg,
        align_corners=False,
--- a/configs/_base_/models/upernet_r50.py
+++ b/configs/_base_/models/upernet_r50.py
@ -20,7 +20,7 @@ model = dict(
        in_index=[0, 1, 2, 3],
        pool_scales=(1, 2, 3, 6),
        channels=512,
-        drop_out_ratio=0.1,
+        dropout_ratio=0.1,
        num_classes=19,
        norm_cfg=norm_cfg,
        align_corners=False,
@ -33,7 +33,7 @@ model = dict(
        channels=256,
        num_convs=1,
        concat_input=False,
-        drop_out_ratio=0.1,
+        dropout_ratio=0.1,
        num_classes=19,
        norm_cfg=norm_cfg,
        align_corners=False,
--- a/configs/ocrnet/ocrnet_hr18_512x512_160k_ade20k.py
+++ b/configs/ocrnet/ocrnet_hr18_512x512_160k_ade20k.py
@ -13,7 +13,7 @@ model = dict(decode_head=[
        kernel_size=1,
        num_convs=1,
        concat_input=False,
-        drop_out_ratio=-1,
+        dropout_ratio=-1,
        num_classes=150,
        norm_cfg=norm_cfg,
        align_corners=False,
@ -26,7 +26,7 @@ model = dict(decode_head=[
        input_transform='resize_concat',
        channels=512,
        ocr_channels=256,
-        drop_out_ratio=-1,
+        dropout_ratio=-1,
        num_classes=150,
        norm_cfg=norm_cfg,
        align_corners=False,
--- a/configs/ocrnet/ocrnet_hr18_512x512_20k_voc12aug.py
+++ b/configs/ocrnet/ocrnet_hr18_512x512_20k_voc12aug.py
@ -14,7 +14,7 @@ model = dict(decode_head=[
        kernel_size=1,
        num_convs=1,
        concat_input=False,
-        drop_out_ratio=-1,
+        dropout_ratio=-1,
        num_classes=21,
        norm_cfg=norm_cfg,
        align_corners=False,
@ -27,7 +27,7 @@ model = dict(decode_head=[
        input_transform='resize_concat',
        channels=512,
        ocr_channels=256,
-        drop_out_ratio=-1,
+        dropout_ratio=-1,
        num_classes=21,
        norm_cfg=norm_cfg,
        align_corners=False,
--- a/configs/ocrnet/ocrnet_hr18_512x512_40k_voc12aug.py
+++ b/configs/ocrnet/ocrnet_hr18_512x512_40k_voc12aug.py
@ -14,7 +14,7 @@ model = dict(decode_head=[
        kernel_size=1,
        num_convs=1,
        concat_input=False,
-        drop_out_ratio=-1,
+        dropout_ratio=-1,
        num_classes=21,
        norm_cfg=norm_cfg,
        align_corners=False,
@ -27,7 +27,7 @@ model = dict(decode_head=[
        input_transform='resize_concat',
        channels=512,
        ocr_channels=256,
-        drop_out_ratio=-1,
+        dropout_ratio=-1,
        num_classes=21,
        norm_cfg=norm_cfg,
        align_corners=False,
--- a/configs/ocrnet/ocrnet_hr18_512x512_80k_ade20k.py
+++ b/configs/ocrnet/ocrnet_hr18_512x512_80k_ade20k.py
@ -13,7 +13,7 @@ model = dict(decode_head=[
        kernel_size=1,
        num_convs=1,
        concat_input=False,
-        drop_out_ratio=-1,
+        dropout_ratio=-1,
        num_classes=150,
        norm_cfg=norm_cfg,
        align_corners=False,
@ -26,7 +26,7 @@ model = dict(decode_head=[
        input_transform='resize_concat',
        channels=512,
        ocr_channels=256,
-        drop_out_ratio=-1,
+        dropout_ratio=-1,
        num_classes=150,
        norm_cfg=norm_cfg,
        align_corners=False,
--- a/configs/ocrnet/ocrnet_hr48_512x1024_160k_cityscapes.py
+++ b/configs/ocrnet/ocrnet_hr48_512x1024_160k_cityscapes.py
@ -18,7 +18,7 @@ model = dict(
            num_convs=1,
            norm_cfg=norm_cfg,
            concat_input=False,
-            drop_out_ratio=-1,
+            dropout_ratio=-1,
            num_classes=19,
            align_corners=False,
            loss_decode=dict(
@ -31,7 +31,7 @@ model = dict(
            input_transform='resize_concat',
            in_index=(0, 1, 2, 3),
            norm_cfg=norm_cfg,
-            drop_out_ratio=-1,
+            dropout_ratio=-1,
            num_classes=19,
            align_corners=False,
            loss_decode=dict(
--- a/configs/ocrnet/ocrnet_hr48_512x1024_40k_cityscapes.py
+++ b/configs/ocrnet/ocrnet_hr48_512x1024_40k_cityscapes.py
@ -18,7 +18,7 @@ model = dict(
            num_convs=1,
            norm_cfg=norm_cfg,
            concat_input=False,
-            drop_out_ratio=-1,
+            dropout_ratio=-1,
            num_classes=19,
            align_corners=False,
            loss_decode=dict(
@ -31,7 +31,7 @@ model = dict(
            input_transform='resize_concat',
            in_index=(0, 1, 2, 3),
            norm_cfg=norm_cfg,
-            drop_out_ratio=-1,
+            dropout_ratio=-1,
            num_classes=19,
            align_corners=False,
            loss_decode=dict(
--- a/configs/ocrnet/ocrnet_hr48_512x1024_80k_cityscapes.py
+++ b/configs/ocrnet/ocrnet_hr48_512x1024_80k_cityscapes.py
@ -18,7 +18,7 @@ model = dict(
            num_convs=1,
            norm_cfg=norm_cfg,
            concat_input=False,
-            drop_out_ratio=-1,
+            dropout_ratio=-1,
            num_classes=19,
            align_corners=False,
            loss_decode=dict(
@ -31,7 +31,7 @@ model = dict(
            input_transform='resize_concat',
            in_index=(0, 1, 2, 3),
            norm_cfg=norm_cfg,
-            drop_out_ratio=-1,
+            dropout_ratio=-1,
            num_classes=19,
            align_corners=False,
            loss_decode=dict(
--- a/configs/ocrnet/ocrnet_hr48_512x512_160k_ade20k.py
+++ b/configs/ocrnet/ocrnet_hr48_512x512_160k_ade20k.py
@ -18,7 +18,7 @@ model = dict(
            num_convs=1,
            norm_cfg=norm_cfg,
            concat_input=False,
-            drop_out_ratio=-1,
+            dropout_ratio=-1,
            num_classes=150,
            align_corners=False,
            loss_decode=dict(
@ -31,7 +31,7 @@ model = dict(
            input_transform='resize_concat',
            in_index=(0, 1, 2, 3),
            norm_cfg=norm_cfg,
-            drop_out_ratio=-1,
+            dropout_ratio=-1,
            num_classes=150,
            align_corners=False,
            loss_decode=dict(
--- a/configs/ocrnet/ocrnet_hr48_512x512_20k_voc12aug.py
+++ b/configs/ocrnet/ocrnet_hr48_512x512_20k_voc12aug.py
@ -18,7 +18,7 @@ model = dict(
            num_convs=1,
            norm_cfg=norm_cfg,
            concat_input=False,
-            drop_out_ratio=-1,
+            dropout_ratio=-1,
            num_classes=21,
            align_corners=False,
            loss_decode=dict(
@ -31,7 +31,7 @@ model = dict(
            input_transform='resize_concat',
            in_index=(0, 1, 2, 3),
            norm_cfg=norm_cfg,
-            drop_out_ratio=-1,
+            dropout_ratio=-1,
            num_classes=21,
            align_corners=False,
            loss_decode=dict(
--- a/configs/ocrnet/ocrnet_hr48_512x512_40k_voc12aug.py
+++ b/configs/ocrnet/ocrnet_hr48_512x512_40k_voc12aug.py
@ -18,7 +18,7 @@ model = dict(
            num_convs=1,
            norm_cfg=norm_cfg,
            concat_input=False,
-            drop_out_ratio=-1,
+            dropout_ratio=-1,
            num_classes=21,
            align_corners=False,
            loss_decode=dict(
@ -31,7 +31,7 @@ model = dict(
            input_transform='resize_concat',
            in_index=(0, 1, 2, 3),
            norm_cfg=norm_cfg,
-            drop_out_ratio=-1,
+            dropout_ratio=-1,
            num_classes=21,
            align_corners=False,
            loss_decode=dict(
--- a/configs/ocrnet/ocrnet_hr48_512x512_80k_ade20k.py
+++ b/configs/ocrnet/ocrnet_hr48_512x512_80k_ade20k.py
@ -18,7 +18,7 @@ model = dict(
            num_convs=1,
            norm_cfg=norm_cfg,
            concat_input=False,
-            drop_out_ratio=-1,
+            dropout_ratio=-1,
            num_classes=150,
            align_corners=False,
            loss_decode=dict(
@ -31,7 +31,7 @@ model = dict(
            input_transform='resize_concat',
            in_index=(0, 1, 2, 3),
            norm_cfg=norm_cfg,
-            drop_out_ratio=-1,
+            dropout_ratio=-1,
            num_classes=150,
            align_corners=False,
            loss_decode=dict(
--- a/docs/config.md
+++ b/docs/config.md
@ -224,8 +224,8 @@ log_config = dict(  # config to register logger hook
 dist_params = dict(backend='nccl')  # Parameters to setup distributed training, the port can also be set.
 log_level = 'INFO'  # The level of logging.
 load_from = None  # load models as a pre-trained model from a given path. This will not resume training.
-resume_from = None  # Resume checkpoints from a given path, the training will be resumed from the epoch when the checkpoint's is saved.
-workflow = [('train', 1)]  # Workflow for runner. [('train', 1)] means there is only one workflow and the workflow named 'train' is executed once. The workflow trains the model by 12 epochs according to the total_epochs.
+resume_from = None  # Resume checkpoints from a given path, the training will be resumed from the iteration when the checkpoint's is saved.
+workflow = [('train', 1)]  # Workflow for runner. [('train', 1)] means there is only one workflow and the workflow named 'train' is executed once. The workflow trains the model by 40000 iterations according to the total_iters.
 cudnn_benchmark = True  # Whether use cudnn_benchmark to speed up, which is fast for fixed input size.
 optimizer = dict(  # Config used to build optimizer, support all the optimizers in PyTorch whose arguments are also the same as those in PyTorch
    type='SGD',  # Type of optimizers, refer to https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/optimizer/default_constructor.py#L13 for more details
--- a/docs/getting_started.md
+++ b/docs/getting_started.md
@ -125,23 +125,34 @@ Assume that you have already downloaded the checkpoints to the directory `checkp
        --eval mAP
    ```

-4. Test PSPNet with 8 GPUs, and evaluate the standard mIoU and cityscapes metric.
+4. Test PSPNet with 4 GPUs, and evaluate the standard mIoU and cityscapes metric.

    ```shell
    ./tools/dist_test.sh configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py \
        checkpoints/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth \
-        8 --out results.pkl --eval mIoU cityscapes
+        4 --out results.pkl --eval mIoU cityscapes
    ```

-5. Test PSPNet on cityscapes test split with 8 GPUs, and generate the png files to be submit to the official evaluation server.
+5. Test PSPNet on cityscapes test split with 4 GPUs, and generate the png files to be submit to the official evaluation server.
+
+    First, add following to config file `configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py`,
+
+    ```python
+    data = dict(
+        test=dict(
+            img_dir='leftImg8bit/test',
+            ann_dir='gtFine/test'))
+    ```
+   Then run test.

    ```shell
    ./tools/dist_test.sh configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py \
        checkpoints/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth \
-        8 --format-only --options "imgfile_prefix=./pspnet_test_results"
+        4 --format-only --options "imgfile_prefix=./pspnet_test_results"
    ```

 You will get png files under `./pspnet_test_results` directory.
+You may run `zip -r results.zip pspnet_test_results/` and submit the zip file to [evaluation server](https://www.cityscapes-dataset.com/submit/).


 ### Image demo
@ -205,8 +216,10 @@ By default we evaluate the model on the validation set after some iterations, yo
 evaluation = dict(interval=4000)  # This evaluate the model per 4000 iterations.
 ```

-**\*Important\***: The default learning rate in config files is for 8 GPUs and 1 img/gpu (batch size = 8x1 = 8).
-Equivalently, you may also use 4 GPUs and 2 imgs/gpu since all models using cross-GPU SyncBN.
+**\*Important\***: The default learning rate in config files is for 4 GPUs and 2 img/gpu (batch size = 4x2 = 8).
+Equivalently, you may also use 8 GPUs and 1 imgs/gpu since all models using cross-GPU SyncBN.
+
+To trade speed with GPU memory, you may pass in `--options model.backbone.with_cp=True` to enable checkpoint in backbone.

 ### Train with a single GPU

--- a/mmseg/datasets/cityscapes.py
+++ b/mmseg/datasets/cityscapes.py
@ -61,6 +61,7 @@ class CityscapesDataset(CustomDataset):
            list[str: str]: result txt files which contains corresponding
            semantic segmentation images.
        """
+        mmcv.mkdir_or_exist(imgfile_prefix)
        result_files = []
        prog_bar = mmcv.ProgressBar(len(self))
        for idx in range(len(self)):
@ -135,9 +136,9 @@ class CityscapesDataset(CustomDataset):
                the prefix of filename, e.g., "a/b/prefix".
                If results are evaluated with cityscapes protocol, it would be
                the prefix of output png files. The output files would be
-                png images under folder "a/b/prefix/xxx/", where "xxx" is the
-                video name of cityscapes. If not specified, a temp file will
-                be created.
+                png images under folder "a/b/prefix/xxx.png", where "xxx" is
+                the image name of cityscapes. If not specified, a temp file
+                will be created for evaluation.
                Default: None.

        Returns:
--- a/mmseg/models/decode_heads/decode_head.py
+++ b/mmseg/models/decode_heads/decode_head.py
@ -17,7 +17,7 @@ class BaseDecodeHead(nn.Module, metaclass=ABCMeta):
        in_channels (int|Sequence[int]): Input channels.
        channels (int): Channels after modules, before conv_seg.
        num_classes (int): Number of classes.
-        drop_out_ratio (float): Ratio of dropout layer. Default: 0.1.
+        dropout_ratio (float): Ratio of dropout layer. Default: 0.1.
        conv_cfg (dict|None): Config of conv layers. Default: None.
        norm_cfg (dict|None): Config of norm layers. Default: None.
        act_cfg (dict): Config of activation layers.
@ -46,7 +46,7 @@ class BaseDecodeHead(nn.Module, metaclass=ABCMeta):
                 channels,
                 *,
                 num_classes,
-                 drop_out_ratio=0.1,
+                 dropout_ratio=0.1,
                 conv_cfg=None,
                 norm_cfg=None,
                 act_cfg=dict(type='ReLU'),
@ -63,7 +63,7 @@ class BaseDecodeHead(nn.Module, metaclass=ABCMeta):
        self._init_inputs(in_channels, in_index, input_transform)
        self.channels = channels
        self.num_classes = num_classes
-        self.drop_out_ratio = drop_out_ratio
+        self.dropout_ratio = dropout_ratio
        self.conv_cfg = conv_cfg
        self.norm_cfg = norm_cfg
        self.act_cfg = act_cfg
@ -77,8 +77,8 @@ class BaseDecodeHead(nn.Module, metaclass=ABCMeta):
            self.sampler = None

        self.conv_seg = nn.Conv2d(channels, num_classes, kernel_size=1)
-        if drop_out_ratio > 0:
-            self.dropout = nn.Dropout2d(drop_out_ratio)
+        if dropout_ratio > 0:
+            self.dropout = nn.Dropout2d(dropout_ratio)
        else:
            self.dropout = None

--- a/requirements/build.txt
+++ b/requirements/build.txt
@ -1,3 +1,2 @@
 # These must be installed before building mmsegmentation
 numpy
-# torch
--- a/requirements/runtime.txt
+++ b/requirements/runtime.txt
@ -1,4 +1,2 @@
 matplotlib
 numpy
-# torch
-# torchvision
--- a/tests/test_models/test_heads.py
+++ b/tests/test_models/test_heads.py
@ -71,7 +71,7 @@ def test_decode_head():
    assert hasattr(head, 'dropout') and head.dropout.p == 0.1

    # test set dropout
-    head = BaseDecodeHead(32, 16, num_classes=19, drop_out_ratio=0.2)
+    head = BaseDecodeHead(32, 16, num_classes=19, dropout_ratio=0.2)
    assert hasattr(head, 'dropout') and head.dropout.p == 0.2

    # test no input_transform
--- a/tools/dist_test.sh
+++ b/tools/dist_test.sh
@ -4,7 +4,6 @@ CONFIG=$1
 CHECKPOINT=$2
 GPUS=$3
 PORT=${PORT:-29500}
-$CONFIG\/$GPUS/
 PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \
 python -m torch.distributed.launch --nproc_per_node=$GPUS --master_port=$PORT \
    $(dirname "$0")/test.py $CONFIG $CHECKPOINT --launcher pytorch ${@:4}
--- a/tools/get_flops.py
+++ b/tools/get_flops.py
@ -31,6 +31,7 @@ def main():
        raise ValueError('invalid input shape')

    cfg = Config.fromfile(args.config)
+    cfg.model.pretrained = None
    model = build_segmentor(
        cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg).cuda()
    model.eval()