fix code typos mentioned in #5 and refactor

README.md

@@ -20,7 +20,7 @@ This repo is the official implementation of ["End-to-End Semi-Supervised Object
 
 ### Partial Labeled Data
 
-We followed STAC[1] to evalutate on 5 different data splits for each settings, and report the average performance of 5 splits. The results are shown in the following:
+We followed STAC[1] to evaluate on 5 different data splits for each settings, and report the average performance of 5 splits. The results are shown in the following:
 
 #### 1% labeled data
 | Method | mAP| Model Weights |Config Files|

configs/soft_teacher/base.py

@@ -180,7 +180,7 @@
 unsup_pipeline = [
     dict(type="LoadImageFromFile"),
     # dict(type="LoadAnnotations", with_bbox=True),
-    # generate fake labels for data format compability
+    # generate fake labels for data format compatibility
     dict(type="PseudoSamples", with_bbox=True),
     dict(
         type="MultiBranch", unsup_teacher=strong_pipeline, unsup_student=weak_pipeline
@@ -256,8 +256,7 @@
 custom_hooks = [
     dict(type="NumClassCheckHook"),
     dict(type="WeightSummary"),
-    dict(type="MeanTeacher", momentum=0.999, interval=1, warm_up=0),
-    dict(type="Weighter", steps=[-5000], vals=[4, 0], name="unsup_weight"),
+    dict(type="MeanTeacher", momentum=0.999, interval=1, warm_up=0)
 ]
 evaluation = dict(type="SubModulesDistEvalHook", interval=4000)
 optimizer = dict(type="SGD", lr=0.01, momentum=0.9, weight_decay=0.0001)

configs/soft_teacher/soft_teacher_faster_rcnn_r101_caffe_fpn_coco_full_1080k.py

@@ -1,36 +1,10 @@
-_base_ = "base.py"
+_base_ = "soft_teacher_faster_rcnn_r50_caffe_fpn_coco_full_720k.py"
 model = dict(
     backbone=dict(
         depth=101,
         init_cfg=dict(checkpoint="open-mmlab://detectron2/resnet101_caffe"),
     )
 )
 
-data = dict(
-    samples_per_gpu=8,
-    workers_per_gpu=8,
-    train=dict(
-        sup=dict(
-            ann_file="data/coco/annotations/instances_train2017.json",
-            img_prefix="data/coco/train2017/",
-        ),
-        unsup=dict(
-            ann_file="data/coco/annotations/instances_unlabeled2017.json",
-            img_prefix="data/coco/unlabeled2017/",
-        ),
-    ),
-    sampler=dict(
-        train=dict(
-            sample_ratio=[1, 1],
-        )
-    ),
-)
-
-semi_wrapper = dict(
-    train_cfg=dict(
-        unsup_weight=2.0,
-    )
-)
-
 lr_config = dict(step=[120000 * 6, 160000 * 6])
 runner = dict(_delete_=True, type="IterBasedRunner", max_iters=180000 * 6)

configs/soft_teacher/soft_teacher_faster_rcnn_r50_caffe_fpn_coco_full_1440k.py

@@ -1,30 +1,5 @@
-_base_ = "base.py"
+_base_ = "soft_teacher_faster_rcnn_r50_caffe_fpn_coco_full_720k.py"
 
-data = dict(
-    samples_per_gpu=8,
-    workers_per_gpu=8,
-    train=dict(
-        sup=dict(
-            ann_file="data/coco/annotations/instances_train2017.json",
-            img_prefix="data/coco/train2017/",
-        ),
-        unsup=dict(
-            ann_file="data/coco/annotations/instances_unlabeled2017.json",
-            img_prefix="data/coco/unlabeled2017/",
-        ),
-    ),
-    sampler=dict(
-        train=dict(
-            sample_ratio=[1, 1],
-        )
-    ),
-)
-
-semi_wrapper = dict(
-    train_cfg=dict(
-        unsup_weight=2.0,
-    )
-)
 
 lr_config = dict(step=[120000 * 8, 160000 * 8])
 runner = dict(_delete_=True, type="IterBasedRunner", max_iters=180000 * 8)

ssod/datasets/pipelines/__init__.py

@@ -1,2 +1,2 @@
-from .formating import *
+from .formatting import *
 from .rand_aug import *

ssod/datasets/pipelines/geo_utils.py

@@ -1,5 +1,5 @@
 """
-Recored the geometric transformation information used in the augmentation in a transformation matrix.
+Record the geometric transformation information used in the augmentation in a transformation matrix.
 """
 import numpy as np
 

ssod/datasets/pipelines/rand_aug.py

@@ -425,7 +425,7 @@ def _translate_bboxes(self, results, offset, direction="horizontal"):
                 min_y = np.maximum(0, min_y + offset)
                 max_y = np.minimum(h, max_y + offset)
 
-            # the boxs translated outside of image will be filtered along with
+            # the boxes translated outside of image will be filtered along with
             # the corresponding masks, by invoking ``_filter_invalid``.
             results[key] = np.concatenate([min_x, min_y, max_x, max_y], axis=-1)
 

ssod/datasets/samplers/semi_sampler.py

@@ -123,7 +123,7 @@ def __iter__(self):
                         ratio = [0] * len(shuffled_indice_per_dataset)
                         for u, c in zip(unique, counts):
                             ratio[u] = c
-                        assert len(ratio) == 2, "Only two set is suppoted"
+                        assert len(ratio) == 2, "Only two set is supported"
                         if ratio[0] == 0:
                             ratio[0] = 1
                             ratio[1] -= 1

ssod/utils/patch.py

@@ -76,6 +76,6 @@ def patch_config(cfg):
     if cfg.get("semi_wrapper", None) is not None:
         cfg.model = cfg.semi_wrapper
         cfg.pop("semi_wrapper")
-    # enable enviroment variables
+    # enable environment variables
     setup_env(cfg)
     return cfg