context("Medical_predict")
source("utils.R")
test_succeeds('download URLs_SIIM_SMALL', {
if(!dir.exists('siim_small')) {
URLs_SIIM_SMALL()
}
})
test_succeeds('prepare dataloader and model', {
items = get_dicom_files("siim_small/train/")
df = data.table::fread("siim_small/labels.csv")
pneumothorax = DataBlock(blocks = list(ImageBlock(cls = Dicom()), CategoryBlock()),
get_x = function(x) {paste('siim_small', x[[1]], sep = '/')},
get_y = function(x) {paste(x[[2]])},
batch_tfms = aug_transforms(size = 224))
dls = pneumothorax %>% dataloaders(as.matrix(df))
dls %>% show_batch()
learn = cnn_learner(dls, resnet34(), metrics = accuracy)
})
test_succeeds('predict medical', {
#result = learn %>% predict(as.character(items[0]))
#test_dl = learn$dls$test_dl(as.character(items[0]))
#predictions = learn$get_preds(dl = test_dl, with_decoded = TRUE)
})
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