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## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(echo = TRUE,eval = FALSE,echo = T)
## -----------------------------------------------------------------------------
# URLs_COCO_TINY()
## -----------------------------------------------------------------------------
# c(images, lbl_bbox) %<-% get_annotations('coco_tiny/train.json')
#
# names(lbl_bbox) = images
#
# img2bbox = lbl_bbox
## -----------------------------------------------------------------------------
# get_y = list(function(o) img2bbox[[o$name]][[1]],
# function(o) as.list(img2bbox[[o$name]][[2]]))
#
# coco = DataBlock(blocks = list(ImageBlock(), BBoxBlock(), BBoxLblBlock()),
# get_items = get_image_files(),
# splitter = RandomSplitter(),
# get_y = get_y,
# item_tfms = Resize(128),
# batch_tfms = aug_transforms(),
# n_inp = 1)
#
# dls = coco %>% dataloaders('coco_tiny/train')
# dls %>% show_batch(max_n = 12)
## -----------------------------------------------------------------------------
# encoder = create_body(resnet34(), pretrained = TRUE)
#
# arch = RetinaNet(encoder, get_c(dls), final_bias=-4)
#
# ratios = c(1/2,1,2)
# scales = c(1,2**(-1/3), 2**(-2/3))
#
# crit = RetinaNetFocalLoss(scales = scales, ratios = ratios)
#
# nn = nn()
#
# retinanet_split = function(m) {
# L(m$encoder,nn$Sequential(m$c5top6, m$p6top7, m$merges,
# m$smoothers, m$classifier, m$box_regressor))$map(params())
# }
## -----------------------------------------------------------------------------
# learn = Learner(dls, arch, loss_func = crit, splitter = retinanet_split)
#
# learn$freeze()
#
# learn %>% fit_one_cycle(10, slice(1e-5, 1e-4))
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