Intro
The fastai library
simplifies training fast and accurate neural nets using modern best
practices. See the fastai website to get started. The library is based
on research into deep learning best practices undertaken at
fast.ai, and includes “out of the box” support for
vision, text, tabular, and
collab (collaborative filtering) models.
Dataset
Grab the pets dataset and Specify folders:
URLs_PETS()
path = 'oxford-iiit-pet'
path_hr = paste(path, 'images', sep = '/')
path_lr = paste(path, 'crappy', sep = '/')Prepare the input data by crappifying images:
# run this only for the first time, then skip
items = get_image_files(path_hr)
parallel(crappifier(path_lr, path_hr), items)Parameters and dataloader
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)See batch:
dls_gen %>% show_batch(max_n = 4, dpi = 150)
Fit
Define loss function and create unet_learner:
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)epoch train_loss valid_loss time
0 0.025911 0.035153 00:42
1 0.019524 0.019408 00:39