Table Of Contents
Table Of Contents

Source code for gluoncv.data.transforms.presets.yolo

"""Transforms for YOLO series."""
from __future__ import absolute_import
import copy
import numpy as np
import mxnet as mx
from mxnet import autograd
from .. import bbox as tbbox
from .. import image as timage
from .. import experimental

__all__ = ['transform_test', 'load_test', 'YOLO3DefaultTrainTransform', 'YOLO3DefaultValTransform']

[docs]def transform_test(imgs, short=416, max_size=1024, stride=1, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)): """A util function to transform all images to tensors as network input by applying normalizations. This function support 1 NDArray or iterable of NDArrays. Parameters ---------- imgs : NDArray or iterable of NDArray Image(s) to be transformed. short : int, default=416 Resize image short side to this `short` and keep aspect ratio. Note that yolo network max_size : int, optional Maximum longer side length to fit image. This is to limit the input image shape. Aspect ratio is intact because we support arbitrary input size in our YOLO implementation. stride : int, optional, default is 1 The stride constraint due to precise alignment of bounding box prediction module. Image's width and height must be multiples of `stride`. Use `stride = 1` to relax this constraint. mean : iterable of float Mean pixel values. std : iterable of float Standard deviations of pixel values. Returns ------- (mxnet.NDArray, numpy.ndarray) or list of such tuple A (1, 3, H, W) mxnet NDArray as input to network, and a numpy ndarray as original un-normalized color image for display. If multiple image names are supplied, return two lists. You can use `zip()`` to collapse it. """ if isinstance(imgs, mx.nd.NDArray): imgs = [imgs] for im in imgs: assert isinstance(im, mx.nd.NDArray), "Expect NDArray, got {}".format(type(im)) tensors = [] origs = [] for img in imgs: img = timage.resize_short_within(img, short, max_size, mult_base=stride) orig_img = img.asnumpy().astype('uint8') img = mx.nd.image.to_tensor(img) img = mx.nd.image.normalize(img, mean=mean, std=std) tensors.append(img.expand_dims(0)) origs.append(orig_img) if len(tensors) == 1: return tensors[0], origs[0] return tensors, origs
[docs]def load_test(filenames, short=416, max_size=1024, stride=1, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)): """A util function to load all images, transform them to tensor by applying normalizations. This function support 1 filename or list of filenames. Parameters ---------- filenames : str or list of str Image filename(s) to be loaded. short : int, default=416 Resize image short side to this `short` and keep aspect ratio. Note that yolo network max_size : int, optional Maximum longer side length to fit image. This is to limit the input image shape. Aspect ratio is intact because we support arbitrary input size in our YOLO implementation. stride : int, optional, default is 1 The stride constraint due to precise alignment of bounding box prediction module. Image's width and height must be multiples of `stride`. Use `stride = 1` to relax this constraint. mean : iterable of float Mean pixel values. std : iterable of float Standard deviations of pixel values. Returns ------- (mxnet.NDArray, numpy.ndarray) or list of such tuple A (1, 3, H, W) mxnet NDArray as input to network, and a numpy ndarray as original un-normalized color image for display. If multiple image names are supplied, return two lists. You can use `zip()`` to collapse it. """ if isinstance(filenames, str): filenames = [filenames] imgs = [mx.image.imread(f) for f in filenames] return transform_test(imgs, short, max_size, stride, mean, std)
[docs]class YOLO3DefaultTrainTransform(object): """Default YOLO training transform which includes tons of image augmentations. Parameters ---------- width : int Image width. height : int Image height. net : mxnet.gluon.HybridBlock, optional The yolo network. .. hint:: If net is ``None``, the transformation will not generate training targets. Otherwise it will generate training targets to accelerate the training phase since we push some workload to CPU workers instead of GPUs. mean : array-like of size 3 Mean pixel values to be subtracted from image tensor. Default is [0.485, 0.456, 0.406]. std : array-like of size 3 Standard deviation to be divided from image. Default is [0.229, 0.224, 0.225]. iou_thresh : float IOU overlap threshold for maximum matching, default is 0.5. box_norm : array-like of size 4, default is (0.1, 0.1, 0.2, 0.2) Std value to be divided from encoded values. """ def __init__(self, width, height, net=None, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225), mixup=False, **kwargs): self._width = width self._height = height self._mean = mean self._std = std self._mixup = mixup self._target_generator = None if net is None: return # in case network has reset_ctx to gpu self._fake_x = mx.nd.zeros((1, 3, height, width)) net = copy.deepcopy(net) net.collect_params().reset_ctx(None) with autograd.train_mode(): _, self._anchors, self._offsets, self._feat_maps, _, _, _, _ = net(self._fake_x) from ....model_zoo.yolo.yolo_target import YOLOV3PrefetchTargetGenerator self._target_generator = YOLOV3PrefetchTargetGenerator( num_class=len(net.classes), **kwargs) def __call__(self, src, label): """Apply transform to training image/label.""" # random color jittering img = experimental.image.random_color_distort(src) # random expansion with prob 0.5 if np.random.uniform(0, 1) > 0.5: img, expand = timage.random_expand(img, fill=[m * 255 for m in self._mean]) bbox = tbbox.translate(label, x_offset=expand[0], y_offset=expand[1]) else: img, bbox = img, label # random cropping h, w, _ = img.shape bbox, crop = experimental.bbox.random_crop_with_constraints(bbox, (w, h)) x0, y0, w, h = crop img = mx.image.fixed_crop(img, x0, y0, w, h) # resize with random interpolation h, w, _ = img.shape interp = np.random.randint(0, 5) img = timage.imresize(img, self._width, self._height, interp=interp) bbox = tbbox.resize(bbox, (w, h), (self._width, self._height)) # random horizontal flip h, w, _ = img.shape img, flips = timage.random_flip(img, px=0.5) bbox = tbbox.flip(bbox, (w, h), flip_x=flips[0]) # to tensor img = mx.nd.image.to_tensor(img) img = mx.nd.image.normalize(img, mean=self._mean, std=self._std) if self._target_generator is None: return img, bbox.astype(img.dtype) # generate training target so cpu workers can help reduce the workload on gpu gt_bboxes = mx.nd.array(bbox[np.newaxis, :, :4]) gt_ids = mx.nd.array(bbox[np.newaxis, :, 4:5]) if self._mixup: gt_mixratio = mx.nd.array(bbox[np.newaxis, :, -1:]) else: gt_mixratio = None objectness, center_targets, scale_targets, weights, class_targets = self._target_generator( self._fake_x, self._feat_maps, self._anchors, self._offsets, gt_bboxes, gt_ids, gt_mixratio) return (img, objectness[0], center_targets[0], scale_targets[0], weights[0], class_targets[0], gt_bboxes[0])
[docs]class YOLO3DefaultValTransform(object): """Default YOLO validation transform. Parameters ---------- width : int Image width. height : int Image height. mean : array-like of size 3 Mean pixel values to be subtracted from image tensor. Default is [0.485, 0.456, 0.406]. std : array-like of size 3 Standard deviation to be divided from image. Default is [0.229, 0.224, 0.225]. """ def __init__(self, width, height, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)): self._width = width self._height = height self._mean = mean self._std = std def __call__(self, src, label): """Apply transform to validation image/label.""" # resize h, w, _ = src.shape img = timage.imresize(src, self._width, self._height, interp=9) bbox = tbbox.resize(label, in_size=(w, h), out_size=(self._width, self._height)) img = mx.nd.image.to_tensor(img) img = mx.nd.image.normalize(img, mean=self._mean, std=self._std) return img, bbox.astype(img.dtype)