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## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(echo = TRUE,eval = FALSE,echo = T)
## -----------------------------------------------------------------------------
# URLs_PETS()
#
# path = 'oxford-iiit-pet'
# path_hr = paste(path, 'images', sep = '/')
# path_lr = paste(path, 'crappy', sep = '/')
## -----------------------------------------------------------------------------
# # run this only for the first time, then skip
# items = get_image_files(path_hr)
# parallel(crappifier(path_lr, path_hr), items)
## -----------------------------------------------------------------------------
# bs = 10
# size = 64
# arch = resnet34()
#
# get_dls = function(bs, size) {
# dblock = DataBlock(blocks = list(ImageBlock, ImageBlock),
# get_items = get_image_files,
# get_y = function(x) {paste(path_hr, as.character(x$name), sep = '/')},
# splitter = RandomSplitter(),
# item_tfms = Resize(size),
# batch_tfms = list(
# aug_transforms(max_zoom = 2.),
# Normalize_from_stats( imagenet_stats() )
# ))
# dls = dblock %>% dataloaders(path_lr, bs = bs, path = path)
# dls$c = 3L
# dls
# }
#
# dls_gen = get_dls(bs, size)
## -----------------------------------------------------------------------------
# dls_gen %>% show_batch(max_n = 4, dpi = 150)
## -----------------------------------------------------------------------------
# wd = 1e-3
#
# y_range = c(-3.,3.)
#
# loss_gen = MSELossFlat()
#
# create_gen_learner = function() {
# unet_learner(dls_gen, arch, loss_func = loss_gen,
# config = unet_config(blur=TRUE, norm_type = "Weight",
# self_attention = TRUE, y_range = y_range))
# }
#
#
# learn_gen = create_gen_learner()
#
# learn_gen %>% fit_one_cycle(2, pct_start = 0.8, wd = wd)
## -----------------------------------------------------------------------------
# learn_gen %>% show_results(max_n = 6, dpi = 200)
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