context("assign %f%")
source("utils.R")
test_succeeds('basic conv2d in_channel %f%', {
#conv = nn()$Conv2d(3L,3L,3L)
#init = conv[['in_channels']]
#conv[['in_channels']] %f% 1L
#later = conv[['in_channels']]
#expect_equal(init - 2, later)
})
test_succeeds('download mnist_sample', {
if(!dir.exists('mnist_sample')) {
URLs_MNIST_SAMPLE()
}
})
test_succeeds('mnist_sample dataloader', {
tfms = aug_transforms(do_flip = FALSE)
path = 'mnist_sample'
bs = 20
data = ImageDataLoaders_from_folder(path, batch_tfms = tfms, size = 26, bs = bs)
})
test_succeeds('mnist_sample load xresnet50_deep', {
learn = cnn_learner(data, xresnet50_deep(), metrics = accuracy)
})
test_succeeds('mnist_sample cnn xresnet50_deep channel modify', {
#init = learn$model[0][0][0][['in_channels']]
#learn$model[0][0][0][['in_channels']] %f% 1L
#later = learn$model[0][0][0][['in_channels']]
#expect_equal(init - 2, later)
})
test_succeeds('tensor slice', {
abb = torch()$rand(list(3L,3L,3L))
narrow(abb,'[:,:,1]')
})
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