use augs functions from sly lib

serve/README.md

@@ -44,6 +44,8 @@ Supported backbones:
 - [x] [Vision Transformer (ICLR'2021)](https://github.com/open-mmlab/mmsegmentation/tree/master/configs/vit)
 - [x] [Swin Transformer (ICCV'2021)](https://github.com/open-mmlab/mmsegmentation/tree/master/configs/swin)
 - [x] [Twins (NeurIPS'2021)](https://github.com/open-mmlab/mmsegmentation/tree/master/configs/twins)
+- [x] [BEiT (ICLR'2022)](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/beit)
+- [x] [ConvNeXt (CVPR'2022)](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/convnext)
 
 Supported methods:
 
@@ -80,6 +82,7 @@ Supported methods:
 - [x] [DPT (ArXiv'2021)](https://github.com/open-mmlab/mmsegmentation/tree/master/configs/dpt)
 - [x] [Segmenter (ICCV'2021)](https://github.com/open-mmlab/mmsegmentation/tree/master/configs/segmenter)
 - [x] [SegFormer (NeurIPS'2021)](https://github.com/open-mmlab/mmsegmentation/tree/master/configs/segformer)
+- [x] [K-Net (NeurIPS'2021)](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/knet)
 
 
 # How to Run

serve/config.json

@@ -7,7 +7,7 @@
     "semantic segmentation"
   ],
   "description": "Deploy model as REST API service",
-  "docker_image": "supervisely/mmseg:1.2.0",
+  "docker_image": "supervisely/mmseg:1.3.0",
   "main_script": "serve/src/sly_serve.py",
   "gui_template": "serve/src/gui.html",
   "task_location": "application_sessions",

train/README.md

@@ -50,6 +50,8 @@ Supported backbones:
 - [x] [Vision Transformer (ICLR'2021)](https://github.com/open-mmlab/mmsegmentation/tree/master/configs/vit)
 - [x] [Swin Transformer (ICCV'2021)](https://github.com/open-mmlab/mmsegmentation/tree/master/configs/swin)
 - [x] [Twins (NeurIPS'2021)](https://github.com/open-mmlab/mmsegmentation/tree/master/configs/twins)
+- [x] [BEiT (ICLR'2022)](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/beit)
+- [x] [ConvNeXt (CVPR'2022)](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/convnext)
 
 Supported methods:
 
@@ -86,6 +88,7 @@ Supported methods:
 - [x] [DPT (ArXiv'2021)](https://github.com/open-mmlab/mmsegmentation/tree/master/configs/dpt)
 - [x] [Segmenter (ICCV'2021)](https://github.com/open-mmlab/mmsegmentation/tree/master/configs/segmenter)
 - [x] [SegFormer (NeurIPS'2021)](https://github.com/open-mmlab/mmsegmentation/tree/master/configs/segformer)
+- [x] [K-Net (NeurIPS'2021)](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/knet)
 
 
 # How to Run

train/config.json

@@ -7,7 +7,7 @@
     "semantic segmentation"
   ],
   "description": "Dashboard to configure, start and monitor training",
-  "docker_image": "supervisely/mmseg:1.2.0",
+  "docker_image": "supervisely/mmseg:1.3.0",
   "main_script": "train/src/main.py",
   "gui_template": "train/src/gui.html",
   "task_location": "workspace_tasks",

train/src/sly_imgaugs.py

@@ -1,84 +1,7 @@
 import supervisely as sly
 from mmseg.datasets.builder import PIPELINES
-from supervisely.sly_logger import logger
-import imgaug.augmenters as iaa
 
 
-def get_function(category_name, aug_name):
-    try:
-        submodule = getattr(iaa, category_name)
-        aug_f = getattr(submodule, aug_name)
-        return aug_f
-    except Exception as e:
-        logger.error(repr(e))
-        # raise e
-        return None
-
-def build_pipeline(aug_infos, random_order=False):
-    pipeline = []
-    for aug_info in aug_infos:
-        category_name = aug_info["category"]
-        aug_name = aug_info["name"]
-        params = aug_info["params"]
-        for param_name, param_val in params.items():
-            if isinstance(param_val, dict):
-                if "x" in param_val.keys() and "y" in param_val.keys():
-                    param_val["x"] = tuple(param_val["x"])
-                    param_val["y"] = tuple(param_val["y"])
-            elif isinstance(param_val, list):
-                params[param_name] = tuple(param_val)
-
-        aug_func = get_function(category_name, aug_name)
-
-        aug = aug_func(**params)
-
-        sometimes = aug_info.get("sometimes", None)
-        if sometimes is not None:
-            aug = iaa.meta.Sometimes(sometimes, aug)
-        pipeline.append(aug)
-    augs = iaa.Sequential(pipeline, random_order=random_order)
-    return augs
-
-
-def aug_to_python(aug_info):
-    pstr = ""
-    for name, value in aug_info["params"].items():
-        v = value
-        if type(v) is list:  #name != 'nb_iterations' and
-            v = (v[0], v[1])
-        elif type(v) is dict and "x" in v.keys() and "y" in v.keys():
-            v = {"x": (v["x"][0], v["x"][1]), "y": (v["y"][0], v["y"][1])}
-
-        if type(value) is str:
-            pstr += f"{name}='{v}', "
-        else:
-            pstr += f"{name}={v}, "
-    method_py = f"iaa.{aug_info['category']}.{aug_info['name']}({pstr[:-2]})"
-
-    res = method_py
-    if "sometimes" in aug_info:
-        res = f"iaa.Sometimes({aug_info['sometimes']}, {method_py})"
-    return res
-
-
-def pipeline_to_python(aug_infos, random_order=False):
-    template = \
-"""import imgaug.augmenters as iaa
-seq = iaa.Sequential([
-{}
-], random_order={})
-"""
-    py_lines = []
-    for info in aug_infos:
-        line = aug_to_python(info)
-        _validate = info["python"]
-        if line != _validate:
-            raise ValueError("Generated python line differs from the one from config: \n\n{!r}\n\n{!r}"
-                             .format(line, _validate))
-        py_lines.append(line)
-    res = template.format('\t' + ',\n\t'.join(py_lines), random_order)
-    return res
-
 @PIPELINES.register_module()
 class SlyImgAugs(object):
     def __init__(self, config_path):

train/src/ui/augs.py

@@ -3,7 +3,6 @@
 import sly_globals as g
 from supervisely.app.v1.widgets.compare_gallery import CompareGallery
 import input_project
-from sly_imgaugs import build_pipeline, pipeline_to_python
 
 
 _templates = [
@@ -46,8 +45,8 @@
 
 def _load_template(json_path):
     config = sly.json.load_json_file(json_path)
-    pipeline = build_pipeline(config["pipeline"], random_order=config["random_order"])  # to validate
-    py_code = pipeline_to_python(config["pipeline"], config["random_order"])
+    pipeline = sly.imgaug_utils.build_pipeline(config["pipeline"], random_order=config["random_order"])  # to validate
+    py_code = sly.imgaug_utils.pipeline_to_python(config["pipeline"], config["random_order"])
 
     return pipeline, py_code, config
 

train/src/ui/monitoring.py

@@ -171,7 +171,6 @@ def prepare_segmentation_data(state, img_dir, ann_dir, palette):
             shutil.move(os.path.join(temp_project_seg_dir, dataset, img_dir, filename),
                         os.path.join(g.project_seg_dir, img_dir))
 
-    shutil.rmtree(g.project_dir)
     shutil.rmtree(temp_project_seg_dir)
     g.api.app.set_field(g.task_id, "state.preparingData", False)