From ff2841333f1e0f63d8bf9c8d430a62a21308db3d Mon Sep 17 00:00:00 2001
From: Lawrence <694869989@qq.com>
Date: Fri, 11 Oct 2019 13:19:39 +0800
Subject: [PATCH] Remove redundant data transforms (#1522)
* Delete transforms.py
* Delete extra_aug.py
* Update __init__.py
---
mmdet/datasets/__init__.py | 5 +-
mmdet/datasets/extra_aug.py | 163 ----------------------------------
mmdet/datasets/transforms.py | 166 -----------------------------------
3 files changed, 2 insertions(+), 332 deletions(-)
delete mode 100644 mmdet/datasets/extra_aug.py
delete mode 100644 mmdet/datasets/transforms.py
diff --git a/mmdet/datasets/__init__.py b/mmdet/datasets/__init__.py
index 8de55d2..7ad926d 100644
--- a/mmdet/datasets/__init__.py
+++ b/mmdet/datasets/__init__.py
@@ -3,7 +3,6 @@ from .cityscapes import CityscapesDataset
from .coco import CocoDataset
from .custom import CustomDataset
from .dataset_wrappers import ConcatDataset, RepeatDataset
-from .extra_aug import ExtraAugmentation
from .loader import DistributedGroupSampler, GroupSampler, build_dataloader
from .registry import DATASETS
from .voc import VOCDataset
@@ -13,6 +12,6 @@ from .xml_style import XMLDataset
__all__ = [
'CustomDataset', 'XMLDataset', 'CocoDataset', 'VOCDataset',
'CityscapesDataset', 'GroupSampler', 'DistributedGroupSampler',
- 'build_dataloader', 'ConcatDataset', 'RepeatDataset', 'ExtraAugmentation',
- 'WIDERFaceDataset', 'DATASETS', 'build_dataset'
+ 'build_dataloader', 'ConcatDataset', 'RepeatDataset', 'WIDERFaceDataset',
+ 'DATASETS', 'build_dataset'
]
diff --git a/mmdet/datasets/extra_aug.py b/mmdet/datasets/extra_aug.py
deleted file mode 100644
index c1bda3e..0000000
--- a/mmdet/datasets/extra_aug.py
+++ /dev/null
@@ -1,163 +0,0 @@
-import mmcv
-import numpy as np
-from numpy import random
-
-from mmdet.core.evaluation.bbox_overlaps import bbox_overlaps
-
-
-class PhotoMetricDistortion(object):
-
- def __init__(self,
- brightness_delta=32,
- contrast_range=(0.5, 1.5),
- saturation_range=(0.5, 1.5),
- hue_delta=18):
- self.brightness_delta = brightness_delta
- self.contrast_lower, self.contrast_upper = contrast_range
- self.saturation_lower, self.saturation_upper = saturation_range
- self.hue_delta = hue_delta
-
- def __call__(self, img, boxes, labels):
- # random brightness
- if random.randint(2):
- delta = random.uniform(-self.brightness_delta,
- self.brightness_delta)
- img += delta
-
- # mode == 0 --> do random contrast first
- # mode == 1 --> do random contrast last
- mode = random.randint(2)
- if mode == 1:
- if random.randint(2):
- alpha = random.uniform(self.contrast_lower,
- self.contrast_upper)
- img *= alpha
-
- # convert color from BGR to HSV
- img = mmcv.bgr2hsv(img)
-
- # random saturation
- if random.randint(2):
- img[..., 1] *= random.uniform(self.saturation_lower,
- self.saturation_upper)
-
- # random hue
- if random.randint(2):
- img[..., 0] += random.uniform(-self.hue_delta, self.hue_delta)
- img[..., 0][img[..., 0] > 360] -= 360
- img[..., 0][img[..., 0] < 0] += 360
-
- # convert color from HSV to BGR
- img = mmcv.hsv2bgr(img)
-
- # random contrast
- if mode == 0:
- if random.randint(2):
- alpha = random.uniform(self.contrast_lower,
- self.contrast_upper)
- img *= alpha
-
- # randomly swap channels
- if random.randint(2):
- img = img[..., random.permutation(3)]
-
- return img, boxes, labels
-
-
-class Expand(object):
-
- def __init__(self, mean=(0, 0, 0), to_rgb=True, ratio_range=(1, 4)):
- if to_rgb:
- self.mean = mean[::-1]
- else:
- self.mean = mean
- self.min_ratio, self.max_ratio = ratio_range
-
- def __call__(self, img, boxes, labels):
- if random.randint(2):
- return img, boxes, labels
-
- h, w, c = img.shape
- ratio = random.uniform(self.min_ratio, self.max_ratio)
- expand_img = np.full((int(h * ratio), int(w * ratio), c),
- self.mean).astype(img.dtype)
- left = int(random.uniform(0, w * ratio - w))
- top = int(random.uniform(0, h * ratio - h))
- expand_img[top:top + h, left:left + w] = img
- img = expand_img
- boxes += np.tile((left, top), 2)
- return img, boxes, labels
-
-
-class RandomCrop(object):
-
- def __init__(self, min_ious=(0.1, 0.3, 0.5, 0.7, 0.9), min_crop_size=0.3):
- # 1: return ori img
- self.sample_mode = (1, *min_ious, 0)
- self.min_crop_size = min_crop_size
-
- def __call__(self, img, boxes, labels):
- h, w, c = img.shape
- while True:
- mode = random.choice(self.sample_mode)
- if mode == 1:
- return img, boxes, labels
-
- min_iou = mode
- for i in range(50):
- new_w = random.uniform(self.min_crop_size * w, w)
- new_h = random.uniform(self.min_crop_size * h, h)
-
- # h / w in [0.5, 2]
- if new_h / new_w < 0.5 or new_h / new_w > 2:
- continue
-
- left = random.uniform(w - new_w)
- top = random.uniform(h - new_h)
-
- patch = np.array(
- (int(left), int(top), int(left + new_w), int(top + new_h)))
- overlaps = bbox_overlaps(
- patch.reshape(-1, 4), boxes.reshape(-1, 4)).reshape(-1)
- if overlaps.min() < min_iou:
- continue
-
- # center of boxes should inside the crop img
- center = (boxes[:, :2] + boxes[:, 2:]) / 2
- mask = (center[:, 0] > patch[0]) * (
- center[:, 1] > patch[1]) * (center[:, 0] < patch[2]) * (
- center[:, 1] < patch[3])
- if not mask.any():
- continue
- boxes = boxes[mask]
- labels = labels[mask]
-
- # adjust boxes
- img = img[patch[1]:patch[3], patch[0]:patch[2]]
- boxes[:, 2:] = boxes[:, 2:].clip(max=patch[2:])
- boxes[:, :2] = boxes[:, :2].clip(min=patch[:2])
- boxes -= np.tile(patch[:2], 2)
-
- return img, boxes, labels
-
-
-class ExtraAugmentation(object):
-
- def __init__(self,
- photo_metric_distortion=None,
- expand=None,
- random_crop=None):
- self.transforms = []
- if photo_metric_distortion is not None:
- self.transforms.append(
- PhotoMetricDistortion(**photo_metric_distortion))
- if expand is not None:
- self.transforms.append(Expand(**expand))
- if random_crop is not None:
- self.transforms.append(RandomCrop(**random_crop))
-
- def __call__(self, img, boxes, labels):
- img = img.astype(np.float32)
- for transform in self.transforms:
- img, boxes, labels = transform(img, boxes, labels)
- return img, boxes, labels
diff --git a/mmdet/datasets/transforms.py b/mmdet/datasets/transforms.py
deleted file mode 100644
index 1e13d77..0000000
--- a/mmdet/datasets/transforms.py
+++ /dev/null
@@ -1,166 +0,0 @@
-import mmcv
-import numpy as np
-import torch
-
-__all__ = [
- 'ImageTransform', 'BboxTransform', 'MaskTransform', 'SegMapTransform',
- 'Numpy2Tensor'
-]
-
-
-class ImageTransform(object):
- """Preprocess an image.
-
- 1. rescale the image to expected size
- 2. normalize the image
- 3. flip the image (if needed)
- 4. pad the image (if needed)
- 5. transpose to (c, h, w)
- """
-
- def __init__(self,
- mean=(0, 0, 0),
- std=(1, 1, 1),
- to_rgb=True,
- size_divisor=None):
- self.mean = np.array(mean, dtype=np.float32)
- self.std = np.array(std, dtype=np.float32)
- self.to_rgb = to_rgb
- self.size_divisor = size_divisor
-
- def __call__(self, img, scale, flip=False, keep_ratio=True):
- if keep_ratio:
- img, scale_factor = mmcv.imrescale(img, scale, return_scale=True)
- else:
- img, w_scale, h_scale = mmcv.imresize(
- img, scale, return_scale=True)
- scale_factor = np.array([w_scale, h_scale, w_scale, h_scale],
- dtype=np.float32)
- img_shape = img.shape
- img = mmcv.imnormalize(img, self.mean, self.std, self.to_rgb)
- if flip:
- img = mmcv.imflip(img)
- if self.size_divisor is not None:
- img = mmcv.impad_to_multiple(img, self.size_divisor)
- pad_shape = img.shape
- else:
- pad_shape = img_shape
- img = img.transpose(2, 0, 1)
- return img, img_shape, pad_shape, scale_factor
-
-
-def bbox_flip(bboxes, img_shape, direction='horizontal'):
- """Flip bboxes horizontally or vertically.
-
- Args:
- bboxes(ndarray): shape (..., 4*k)
- img_shape(tuple): (height, width)
- """
- assert bboxes.shape[-1] % 4 == 0
- flipped = bboxes.copy()
- if direction == 'horizontal':
- w = img_shape[1]
- flipped[..., 0::4] = w - bboxes[..., 2::4] - 1
- flipped[..., 2::4] = w - bboxes[..., 0::4] - 1
- else:
- h = img_shape[0]
- flipped[..., 1::4] = h - bboxes[..., 3::4] - 1
- flipped[..., 3::4] = h - bboxes[..., 1::4] - 1
- return flipped
-
-
-class BboxTransform(object):
- """Preprocess gt bboxes.
-
- 1. rescale bboxes according to image size
- 2. flip bboxes (if needed)
- 3. pad the first dimension to `max_num_gts`
- """
-
- def __init__(self, max_num_gts=None):
- self.max_num_gts = max_num_gts
-
- def __call__(self, bboxes, img_shape, scale_factor, flip=False):
- gt_bboxes = bboxes * scale_factor
- if flip:
- gt_bboxes = bbox_flip(gt_bboxes, img_shape)
- gt_bboxes[:, 0::2] = np.clip(gt_bboxes[:, 0::2], 0, img_shape[1] - 1)
- gt_bboxes[:, 1::2] = np.clip(gt_bboxes[:, 1::2], 0, img_shape[0] - 1)
- if self.max_num_gts is None:
- return gt_bboxes
- else:
- num_gts = gt_bboxes.shape[0]
- padded_bboxes = np.zeros((self.max_num_gts, 4), dtype=np.float32)
- padded_bboxes[:num_gts, :] = gt_bboxes
- return padded_bboxes
-
-
-class MaskTransform(object):
- """Preprocess masks.
-
- 1. resize masks to expected size and stack to a single array
- 2. flip the masks (if needed)
- 3. pad the masks (if needed)
- """
-
- def __call__(self, masks, pad_shape, scale_factor, flip=False):
- # aspect ratio unchanged
- if isinstance(scale_factor, float):
- masks = [
- mmcv.imrescale(mask, scale_factor, interpolation='nearest')
- for mask in masks
- ]
- # aspect ratio changed
- else:
- w_ratio, h_ratio = scale_factor[:2]
- if masks:
- h, w = masks[0].shape[:2]
- new_h = int(np.round(h * h_ratio))
- new_w = int(np.round(w * w_ratio))
- new_size = (new_w, new_h)
- masks = [
- mmcv.imresize(mask, new_size, interpolation='nearest')
- for mask in masks
- ]
- if flip:
- masks = [mask[:, ::-1] for mask in masks]
- padded_masks = [
- mmcv.impad(mask, pad_shape[:2], pad_val=0) for mask in masks
- ]
- padded_masks = np.stack(padded_masks, axis=0)
- return padded_masks
-
-
-class SegMapTransform(object):
- """Preprocess semantic segmentation maps.
-
- 1. rescale the segmentation map to expected size
- 3. flip the image (if needed)
- 4. pad the image (if needed)
- """
-
- def __init__(self, size_divisor=None):
- self.size_divisor = size_divisor
-
- def __call__(self, img, scale, flip=False, keep_ratio=True):
- if keep_ratio:
- img = mmcv.imrescale(img, scale, interpolation='nearest')
- else:
- img = mmcv.imresize(img, scale, interpolation='nearest')
- if flip:
- img = mmcv.imflip(img)
- if self.size_divisor is not None:
- img = mmcv.impad_to_multiple(img, self.size_divisor)
- return img
-
-
-class Numpy2Tensor(object):
-
- def __init__(self):
- pass
-
- def __call__(self, *args):
- if len(args) == 1:
- return torch.from_numpy(args[0])
- else:
- return tuple([torch.from_numpy(np.array(array)) for array in args])
--
GitLab