Cropping for semantic segmentation models

Summary: Cropping for semantic segmentation

Reviewed By: ppwwyyxx

Differential Revision: D19453097

fbshipit-source-id: a64d462e068d22b7e3be3703c16df3839474ea71

detectron2/data/transforms/transform_gen.py

@@ -342,7 +342,7 @@ def get_crop_size(self, image_size):
             ch, cw = crop_size + np.random.rand(2) * (1 - crop_size)
             return int(h * ch + 0.5), int(w * cw + 0.5)
         elif self.crop_type == "absolute":
-            return self.crop_size
+            return (min(self.crop_size[0], h), min(self.crop_size[1], w))
         else:
             NotImplementedError("Unknown crop type {}".format(self.crop_type))
 

projects/PointRend/point_rend/__init__.py

@@ -2,3 +2,4 @@
 from .config import add_pointrend_config
 from .coarse_mask_head import CoarseMaskHead
 from .roi_heads import PointRendROIHeads
+from .dataset_mapper import SemSegDatasetMapper

projects/PointRend/point_rend/config.py

@@ -8,6 +8,10 @@ def add_pointrend_config(cfg):
     """
     Add config for PointRend.
     """
+    # We retry random cropping until no single category in semantic segmentation GT occupies more
+    # than `SINGLE_CATEGORY_MAX_AREA` part of the crop.
+    cfg.INPUT.CROP.SINGLE_CATEGORY_MAX_AREA = 1.0
+
     # Names of the input feature maps to be used by a coarse mask head.
     cfg.MODEL.ROI_MASK_HEAD.IN_FEATURES = ("p2",)
     cfg.MODEL.ROI_MASK_HEAD.FC_DIM = 1024

projects/PointRend/point_rend/dataset_mapper.py

@@ -0,0 +1,113 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+import copy
+import logging
+import numpy as np
+import torch
+from fvcore.common.file_io import PathManager
+from fvcore.transforms.transform import CropTransform
+from PIL import Image
+
+from detectron2.data import detection_utils as utils
+from detectron2.data import transforms as T
+
+"""
+This file contains the mapping that's applied to "dataset dicts" for semantic segmentation models.
+Unlike the default DatasetMapper this mapper uses cropping as the last transformation.
+"""
+
+__all__ = ["SemSegDatasetMapper"]
+
+
+class SemSegDatasetMapper:
+    """
+    A callable which takes a dataset dict in Detectron2 Dataset format,
+    and map it into a format used by semantic segmentation models.
+
+    The callable currently does the following:
+
+    1. Read the image from "file_name"
+    2. Applies geometric transforms to the image and annotation
+    3. Find and applies suitable cropping to the image and annotation
+    4. Prepare image and annotation to Tensors
+    """
+
+    def __init__(self, cfg, is_train=True):
+        if cfg.INPUT.CROP.ENABLED and is_train:
+            self.crop_gen = T.RandomCrop(cfg.INPUT.CROP.TYPE, cfg.INPUT.CROP.SIZE)
+            logging.getLogger(__name__).info("CropGen used in training: " + str(self.crop_gen))
+        else:
+            self.crop_gen = None
+
+        self.tfm_gens = utils.build_transform_gen(cfg, is_train)
+
+        # fmt: off
+        self.img_format               = cfg.INPUT.FORMAT
+        self.single_category_max_area = cfg.INPUT.CROP.SINGLE_CATEGORY_MAX_AREA
+        self.ignore_value             = cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE
+        # fmt: on
+
+        self.is_train = is_train
+
+    def __call__(self, dataset_dict):
+        """
+        Args:
+            dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.
+
+        Returns:
+            dict: a format that builtin models in detectron2 accept
+        """
+        dataset_dict = copy.deepcopy(dataset_dict)  # it will be modified by code below
+        image = utils.read_image(dataset_dict["file_name"], format=self.img_format)
+        utils.check_image_size(dataset_dict, image)
+        assert "sem_seg_file_name" in dataset_dict
+
+        image, transforms = T.apply_transform_gens(self.tfm_gens, image)
+        if self.is_train:
+            with PathManager.open(dataset_dict.pop("sem_seg_file_name"), "rb") as f:
+                sem_seg_gt = Image.open(f)
+                sem_seg_gt = np.asarray(sem_seg_gt, dtype="uint8")
+            sem_seg_gt = transforms.apply_segmentation(sem_seg_gt)
+            if self.crop_gen:
+                image, sem_seg_gt = crop_transform(
+                    image,
+                    sem_seg_gt,
+                    self.crop_gen,
+                    self.single_category_max_area,
+                    self.ignore_value,
+                )
+            dataset_dict["sem_seg"] = torch.as_tensor(sem_seg_gt.astype("long"))
+
+        # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,
+        # but not efficient on large generic data structures due to the use of pickle & mp.Queue.
+        # Therefore it's important to use torch.Tensor.
+        dataset_dict["image"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))
+
+        if not self.is_train:
+            dataset_dict.pop("sem_seg_file_name", None)
+            return dataset_dict
+
+        return dataset_dict
+
+
+def crop_transform(image, sem_seg, crop_gen, single_category_max_area, ignore_value):
+    """
+    Find a cropping window such that no single category occupies more than
+        `single_category_max_area` in `sem_seg`. The function retries random cropping 10 times max.
+    """
+    if single_category_max_area >= 1.0:
+        crop_tfm = crop_gen.get_transform(image)
+        sem_seg_temp = crop_tfm.apply_segmentation(sem_seg)
+    else:
+        h, w = sem_seg.shape
+        crop_size = crop_gen.get_crop_size((h, w))
+        for _ in range(10):
+            y0 = np.random.randint(h - crop_size[0] + 1)
+            x0 = np.random.randint(w - crop_size[1] + 1)
+            sem_seg_temp = sem_seg[y0 : y0 + crop_size[0], x0 : x0 + crop_size[1]]
+            labels, cnt = np.unique(sem_seg_temp, return_counts=True)
+            cnt = cnt[labels != ignore_value]
+            if len(cnt) > 1 and np.max(cnt) / np.sum(cnt) < single_category_max_area:
+                break
+        crop_tfm = CropTransform(x0, y0, crop_size[1], crop_size[0])
+    image = crop_tfm.apply_image(image)
+    return image, sem_seg_temp

projects/PointRend/train_net.py

@@ -13,7 +13,7 @@
 import detectron2.utils.comm as comm
 from detectron2.checkpoint import DetectionCheckpointer
 from detectron2.config import get_cfg
-from detectron2.data import MetadataCatalog
+from detectron2.data import MetadataCatalog, build_detection_train_loader
 from detectron2.engine import DefaultTrainer, default_argument_parser, default_setup, launch
 from detectron2.evaluation import (
     CityscapesInstanceEvaluator,
@@ -24,7 +24,7 @@
     verify_results,
 )
 
-from point_rend import add_pointrend_config
+from point_rend import SemSegDatasetMapper, add_pointrend_config
 
 
 class Trainer(DefaultTrainer):
@@ -71,6 +71,14 @@ def build_evaluator(cls, cfg, dataset_name, output_folder=None):
             return evaluator_list[0]
         return DatasetEvaluators(evaluator_list)
 
+    @classmethod
+    def build_train_loader(cls, cfg):
+        if "SemanticSegmentor" in cfg.MODEL.META_ARCHITECTURE:
+            mapper = SemSegDatasetMapper(cfg, True)
+        else:
+            mapper = None
+        return build_detection_train_loader(cfg, mapper=mapper)
+
 
 def setup(args):
     """