def test_pprint():
paths = None
rgb_imgs = larray.lmap(test_pprint, paths)
rgb_imgs2 = larray.lmap(test_pprint, rgb_imgs)
s = larray.pprint_str(rgb_imgs2)
print s
assert s == """lmap(test_pprint, ...)
def test_pprint():
paths = None
rgb_imgs = larray.lmap(test_pprint, paths)
rgb_imgs2 = larray.lmap(test_pprint, rgb_imgs)
s = larray.pprint_str(rgb_imgs2)
print s
assert s == """lmap(test_pprint, ...)
def test_using_precompute():
np.random.seed(123)
# example library code starts here
def load_rgb(pth):
return pth + '_rgb'
def load_grey(pth):
return pth + '_grey'
def to_64x64(img):
return img + '_64x64'
paths = ['a', 'b', 'c', 'd'] # imagine some huge list of image paths
rgb_imgs = larray.lmap(load_rgb, paths)
grey_imgs = larray.lmap(load_grey, paths)
paths_64x64 = larray.lmap(to_64x64, grey_imgs)
train_set = larray.reindex(paths_64x64,
np.random.permutation(len(paths))).loop()
# example user code starts here.
# It is easy to memmap the __array__ of paths_64x64, but
# it is more difficult to compute derived things using that
# memmap.
# pretend this is a memmap of a precomputed quantity, for example.
use_paths_64x64 = ['stuff', 'i', 'saved', 'from', 'disk']
# the rest of the original graph (e.g. train_set)
# doesn't know about our new memmap
# or mongo-backed proxy, or whatever we're doing.
new_train_set = larray.clone(train_set,
given={paths_64x64: use_paths_64x64})
l10 = list(new_train_set[range(10)])
print l10
assert l10 == [
'from', 'stuff', 'i', 'saved', 'from', 'stuff', 'i', 'saved', 'from',
'stuff'
]
def test_usage():
np.random.seed(123)
def load_rgb(pth):
return pth + '_rgb'
def load_grey(pth):
return pth + '_grey'
def to_64x64(img):
return img + '_64x64'
paths = ['a', 'b', 'c', 'd'] # imagine some huge list of image paths
rgb_imgs = larray.lmap(load_rgb, paths)
grey_imgs = larray.lmap(load_grey, paths)
paths_64x64 = larray.lmap(to_64x64, grey_imgs)
train_set = larray.reindex(rgb_imgs, np.random.permutation(len(paths))).loop()
l10 = list(train_set[range(10)])
print l10
assert ['d', 'a', 'b', 'c'] == [l[0] for l in l10[:4]]
def test_using_precompute():
np.random.seed(123)
# example library code starts here
def load_rgb(pth):
return pth + '_rgb'
def load_grey(pth):
return pth + '_grey'
def to_64x64(img):
return img + '_64x64'
paths = ['a', 'b', 'c', 'd'] # imagine some huge list of image paths
rgb_imgs = larray.lmap(load_rgb, paths)
grey_imgs = larray.lmap(load_grey, paths)
paths_64x64 = larray.lmap(to_64x64, grey_imgs)
train_set = larray.reindex(paths_64x64, np.random.permutation(len(paths))).loop()
# example user code starts here.
# It is easy to memmap the __array__ of paths_64x64, but
# it is more difficult to compute derived things using that
# memmap.
# pretend this is a memmap of a precomputed quantity, for example.
use_paths_64x64 = ['stuff', 'i', 'saved', 'from', 'disk']
# the rest of the original graph (e.g. train_set)
# doesn't know about our new memmap
# or mongo-backed proxy, or whatever we're doing.
new_train_set = larray.clone(train_set, given={paths_64x64: use_paths_64x64})
l10 = list(new_train_set[range(10)])
print l10
assert l10 == [
'from', 'stuff', 'i', 'saved',
'from', 'stuff', 'i', 'saved',
'from', 'stuff']
def test_usage():
np.random.seed(123)
def load_rgb(pth):
return pth + '_rgb'
def load_grey(pth):
return pth + '_grey'
def to_64x64(img):
return img + '_64x64'
paths = ['a', 'b', 'c', 'd'] # imagine some huge list of image paths
rgb_imgs = larray.lmap(load_rgb, paths)
grey_imgs = larray.lmap(load_grey, paths)
paths_64x64 = larray.lmap(to_64x64, grey_imgs)
train_set = larray.reindex(rgb_imgs,
np.random.permutation(len(paths))).loop()
l10 = list(train_set[range(10)])
print l10
assert ['d', 'a', 'b', 'c'] == [l[0] for l in l10[:4]]
