from PIL import Image
This is used in get_grid. suptitle, sharex, sharey, squeeze, subplot_kw and gridspec_kw are all passed down to plt.subplots.
show_image can show PIL images...
im = Image.open(TEST_IMAGE_BW)
ax = show_image(im, cmap="Greys")
...and color images with standard CHW dim order...
im2 = np.array(Image.open(TEST_IMAGE))
ax = show_image(im2, figsize=(2,2))
...and color images with HWC dim order...
im3 = torch.as_tensor(im2).permute(2,0,1)
ax = show_image(im3, figsize=(2,2))
show_titled_image((im3,'A puppy'), figsize=(2,2))
Show all images ims as subplots with rows using titles. suptitle provides a way to create a figure title for all images. If you use suptitle, constrained_layout is used unless you set constrained_layout to False.
show_images((im,im3),titles=('number','puppy'),suptitle='Number Puppy', imsize=3)
ArrayImage, ArrayImageBW and ArrayMask are subclasses of ndarray that know how to show themselves.
im = Image.open(TEST_IMAGE)
im_t = cast(im, ArrayImage)
test_eq(type(im_t), ArrayImage)
ax = im_t.show(figsize=(2,2))
test_fig_exists(ax)
test_eq(tensor(torch.tensor([1,2,3])), torch.tensor([1,2,3]))
test_eq(tensor(array([1,2,3])), torch.tensor([1,2,3]))
test_eq(tensor(1,2,3), torch.tensor([1,2,3]))
test_eq_type(tensor(1.0), torch.tensor(1.0))
[`set_seed`](/torch_core.html#set_seed) is useful for reproducibility between runs. It is important to remember that certain classes such as Dataloaders have internal random number generators that is not effected by this function, so this must be run before such objects are created in order to guarantee reproducibility.
Here is an example of how [`set_seed`](/torch_core.html#set_seed) can be used to reset the state of random number generators.
set_seed(2*33)
a1 = np.random.random()
a2 = torch.rand(())
a3 = random.random()
set_seed(2*33)
b1 = np.random.random()
b2 = torch.rand(())
b3 = random.random()
print('a\'s: {0:3.3f} {1:3.3f} {2:3.3f}'.format(a1,a2,a3))
print('b\'s: {0:3.3f} {1:3.3f} {2:3.3f}'.format(b1,b2,a3))
test_eq(a1,b1)
test_eq(a2,b2)
test_eq(a3,b3)
[`get_random_states`](/torch_core.html#get_random_states) and [`set_random_states`](/torch_core.html#set_random_states) are useful for storing a state so you can go back to it later.
Below notice that the old values and rewinded values are the same because we were able to return to the previous state.
old_states = get_random_states()
olds = (random.random(),np.random.random(),torch.rand(()))
news = (random.random(),np.random.random(),torch.rand(()))
set_random_states(**old_states)
rewinds = (random.random(),np.random.random(),torch.rand(()))
print('olds: {0:3.3f} {1:3.3f} {2:3.3f}'.format(*olds))
print('news: {0:3.3f} {1:3.3f} {2:3.3f}'.format(*news))
print('rewinds: {0:3.3f} {1:3.3f} {2:3.3f}'.format(*rewinds))
test_ne(olds,news)
test_eq(olds,rewinds)
In [`no_random`](/torch_core.html#no_random) we combine the ideas of rewinding state with [`get_random_states`](/torch_core.html#get_random_states) and [`set_random_states`](/torch_core.html#set_random_states) with the ability to [`set_seed`](/torch_core.html#set_seed) and create a context manager that can allow us to control randomness in a portion of our code.
Note: Similar to torch.random.fork_rng, but also with numpy and random
Here are some examples on how we can use [`no_random`](/torch_core.html#no_random) to control the randomness within a block of code.
states=get_random_states()
olds = (random.random(),np.random.random(),torch.rand(()))
set_random_states(**states) #rewinding above random calls
with no_random():
new1 = (random.random(),np.random.random(),torch.rand(()))
with no_random():
new2 = (random.random(),np.random.random(),torch.rand(()))
with no_random(seed=100):
seeded1 = (random.random(),np.random.random(),torch.rand(()))
with no_random(seed=100):
seeded2 = (random.random(),np.random.random(),torch.rand(()))
rewinds = (random.random(),np.random.random(),torch.rand(()))
print('olds: {0:3.3f} {1:3.3f} {2:3.3f}'.format(*olds))
print('new1: {0:3.3f} {1:3.3f} {2:3.3f}'.format(*new1))
print('new2: {0:3.3f} {1:3.3f} {2:3.3f}'.format(*new2))
print('seeded1: {0:3.3f} {1:3.3f} {2:3.3f}'.format(*seeded1))
print('seeded2: {0:3.3f} {1:3.3f} {2:3.3f}'.format(*seeded2))
print('rewinds: {0:3.3f} {1:3.3f} {2:3.3f}'.format(*rewinds))
Notice that olds, and rewinds are alos both equal to each other. From this we can see that everything in the with blocks did not update the state outside of the block. Inside of the block, the state is reset for any particular seed, so for the same seed you should get the same random number generator results.
Note: It is important to remember that classes like Dataloader have internal random number generators, and [`no_random`](/torch_core.html#no_random) will have no effect on those random number generators.
test_ne(olds,new1)
test_eq(new1,new2)
test_ne(new1,seeded1)
test_eq(seeded1,seeded2)
test_eq(olds,rewinds)
t = tensor([1])
t2 = unsqueeze(t, n=2)
test_eq(t2,t[:,None,None])
t = tensor([1])
unsqueeze_(t, n=2)
test_eq(t, tensor([1]).view(1,1,1))
gather only applies during distributed training and the result tensor will be the one gathered across processes if gather=True (as a result, the batch size will be multiplied by the number of processes).
if torch.cuda.is_available():
_td = torch.device(torch.cuda.current_device())
test_eq(default_device(None), _td)
test_eq(default_device(True), _td)
else:
test_eq(default_device(False), torch.device('cpu'))
default_device(None);
t = to_device((3,(tensor(3),tensor(2))))
t1,(t2,t3) = t
if torch.cuda.is_available():
test_eq_type(t,(3,(tensor(3).cuda(),tensor(2).cuda())))
test_eq(t2.type(), "torch.cuda.LongTensor")
test_eq(t3.type(), "torch.cuda.LongTensor")
t3 = to_cpu(t3)
test_eq(t3.type(), "torch.LongTensor")
test_eq(t3, 2)
t3 = to_np(t3)
test_eq(type(t3), np.ndarray)
test_eq(t3, 2)
test_eq(to_concat([tensor([1,2]), tensor([3,4])]), tensor([1,2,3,4]))
test_eq(to_concat([tensor([[1,2]]), tensor([[3,4]])], dim=1), tensor([[1,2,3,4]]))
test_eq_type(to_concat([(tensor([1,2]), tensor([3,4])), (tensor([3,4]), tensor([5,6]))]), (tensor([1,2,3,4]), tensor([3,4,5,6])))
test_eq_type(to_concat([[tensor([1,2]), tensor([3,4])], [tensor([3,4]), tensor([5,6])]]), [tensor([1,2,3,4]), tensor([3,4,5,6])])
test_eq_type(to_concat([(tensor([1,2]),), (tensor([3,4]),)]), (tensor([1,2,3,4]),))
test_eq(to_concat([tensor([[1,2]]), tensor([[3,4], [5,6]])], dim=1), [tensor([1]),tensor([3, 5]),tensor([4, 6])])
test_eq(type(to_concat([dict(foo=tensor([1,2]), bar=tensor(3,4))])), dict)
Tensor.set_meta and Tensor.as_subclass work together to maintain __dict__ after casting.
class _T(Tensor): pass
t = tensor(1.).requires_grad_()
t.img_size = 1
t2 = t.as_subclass(_T)
test_eq(t.img_size, t2.img_size)
test_eq(t2.img_size, 1)
assert(t2.requires_grad_)
TensorBase hooks into __torch_function__ to ensure metadata is not lost. To see all functions being called, set debug.
a = TensorBase(1)
a.debug=True
1/(a+1)
class _TImage(TensorBase): pass
class _TImage2(_TImage): pass
t1 = _TImage([1.])
t2 = _TImage2([1.])
t2+t1
class _T(TensorBase): pass
t = _T(range(5))
test_eq(t[0], 0)
test_eq_type(t+1, _T(range(1,6)))
test_eq(repr(t), '_T([0, 1, 2, 3, 4])')
test_eq_type(t[_T([False,False,True,True,True])], _T([2,3,4]))
test_eq_type(t[_T([2,3,4])], _T([2,3,4]))
test_eq(type(pickle.loads(pickle.dumps(t))), _T)
test_eq_type(t.new_ones(1), _T([1]))
test_eq_type(t.new_tensor([1,2]), _T([1,2]))
t = tensor([1,2,3])
m = TensorBase([False,True,True])
test_eq(t[m], tensor([2,3]))
t = tensor([[1,2,3],[1,2,3]])
m = cast(tensor([[False,True,True],
[False,True,True]]), TensorBase)
test_eq(t[m], tensor([2,3,2,3]))
t = tensor([[1,2,3],[1,2,3]])
t.img_size = 1
t2 = cast(t, TensorBase)
test_eq(t2.img_size, t.img_size)
x = retain_type(tensor([4,5,6]), t2)
test_eq(x.img_size, t.img_size)
t3 = TensorBase([[1,2,3],[1,2,3]], img_size=1)
test_eq(t3.img_size, t.img_size)
t4 = t2+1
t4.img_size = 2
test_eq(t2.img_size, 1)
test_eq(t4.img_size, 2)
# this will fail with `Tensor` but works with `TensorBase`
test_eq(pickle.loads(pickle.dumps(t2)).img_size, t2.img_size)
im = Image.open(TEST_IMAGE)
im_t = cast(array(im), TensorImage)
test_eq(type(im_t), TensorImage)
im_t2 = cast(tensor(1), TensorMask)
test_eq(type(im_t2), TensorMask)
test_eq(im_t2, tensor(1))
ax = im_t.show(figsize=(2,2))
_ =(im_t == im_t2)
test_fig_exists(ax)
Operations between TensorMask and TensorImageBase objects return the type of the TensorImageBase object:
a = TensorMask([1,2])
test_eq_type(TensorImage(1)+a, TensorImage([2,3]))
test_eq_type(1-a, TensorMask([0,-1]))
test_eq_type(to_concat([TensorImage([1,2]), TensorImage([3,4])]), TensorImage([1,2,3,4]))
t1 = TensorImage([1.]).view(1,1,1,1)
t2 = TensorFlowField([1.,1.]).view(1,1,1,2)
test_eq_type(F.grid_sample(t1, t2), TensorImage([[[[0.25]]]]))
tc = TensorCategory([1,2,3])
mask_t = TensorMask([0,2,4,5])
im_t = TensorImage([0,2,4,5])
test_eq(mask_t[tc], tensor([2,4,5]))
test_eq(im_t[tc], tensor([2,4,5]))
There are shortcuts for torch.stack and torch.cat if your L contains tensors or something convertible. You can manually convert with tensored.
t = L(([1,2],[3,4]))
test_eq(t.tensored(), [tensor(1,2),tensor(3,4)])
test_eq(t.stack(), tensor([[1,2],[3,4]]))
test_eq(t.cat(), tensor([1,2,3,4]))
a,b,c = [1],[1,2],[1,1,2]
test_eq(concat(a,b), c)
test_eq_type(concat(tuple (a),tuple (b)), tuple (c))
test_eq_type(concat(array (a),array (b)), array (c))
test_eq_type(concat(tensor(a),tensor(b)), tensor(c))
test_eq_type(concat(TensorBase(a),TensorBase(b)), TensorBase(c))
test_eq_type(concat([1,1],1), [1,1,1])
test_eq_type(concat(1,1,1), L(1,1,1))
test_eq_type(concat(L(1,2),1), L(1,2,1))
docs = L(list(string.ascii_lowercase[a:b]) for a,b in ((0,3),(3,7),(7,8),(8,16),(16,24),(24,26)))
b = Chunks(docs)
test_eq([b[ o] for o in range(0,5)], ['a','b','c','d','e'])
test_eq([b[-o] for o in range(1,6)], ['z','y','x','w','v'])
test_eq(b[6:13], 'g,h,i,j,k,l,m'.split(','))
test_eq(b[20:77], 'u,v,w,x,y,z'.split(','))
test_eq(b[:5], 'a,b,c,d,e'.split(','))
test_eq(b[:2], 'a,b'.split(','))
t = torch.arange(26)
docs = L(t[a:b] for a,b in ((0,3),(3,7),(7,8),(8,16),(16,24),(24,26)))
b = Chunks(docs)
test_eq([b[ o] for o in range(0,5)], range(0,5))
test_eq([b[-o] for o in range(1,6)], [25,24,23,22,21])
test_eq(b[6:13], torch.arange(6,13))
test_eq(b[20:77], torch.arange(20,26))
test_eq(b[:5], torch.arange(5))
test_eq(b[:2], torch.arange(2))
docs = L(TensorBase(t[a:b]) for a,b in ((0,3),(3,7),(7,8),(8,16),(16,24),(24,26)))
b = Chunks(docs)
test_eq_type(b[:2], TensorBase(range(2)))
test_eq_type(b[:5], TensorBase(range(5)))
test_eq_type(b[9:13], TensorBase(range(9,13)))
test_stdout(lambda: show_title("title"), "title")
# ensure that col names are unique when showing to a pandas series
assert show_title("title", ctx=pd.Series(dict(a=1)), label='a').equals(pd.Series(dict(a=1,a_='title')))
test_stdout(lambda: TitledStr('s').show(), 's')
test_stdout(lambda: TitledInt(1).show(), '1')
t2 = to_device(tensor(0))
dev = default_device()
test_eq(find_device(t2), dev)
test_eq(find_device([t2,t2]), dev)
test_eq(find_device({'a':t2,'b':t2}), dev)
test_eq(find_device({'a':[[t2],[t2]],'b':t2}), dev)
x = torch.randn(4,5)
test_eq(find_bs(x), 4)
test_eq(find_bs([x, x]), 4)
test_eq(find_bs({'a':x,'b':x}), 4)
test_eq(find_bs({'a':[[x],[x]],'b':x}), 4)
This decorator is particularly useful for using numpy functions as fastai metrics, for instance:
from sklearn.metrics import f1_score
@np_func
def f1(inp,targ): return f1_score(targ, inp)
a1,a2 = array([0,1,1]),array([1,0,1])
t = f1(tensor(a1),tensor(a2))
test_eq(f1_score(a1,a2), t)
assert isinstance(t,Tensor)
class _T(Module):
def __init__(self): self.f = nn.Linear(1,1)
def forward(self,x): return self.f(x)
t = _T()
t(tensor([1.]))
test_eq(one_hot([1,4], 5), tensor(0,1,0,0,1).byte())
test_eq(one_hot(torch.tensor([]), 5), tensor(0,0,0,0,0).byte())
test_eq(one_hot(2, 5), tensor(0,0,1,0,0).byte())
test_eq(one_hot_decode(tensor(0,1,0,0,1)), [1,4])
test_eq(one_hot_decode(tensor(0,0,0,0,0)), [ ])
test_eq(one_hot_decode(tensor(0,0,1,0,0)), [2 ])
m = nn.Linear(4,5)
test_eq(trainable_params(m), [m.weight, m.bias])
m.weight.requires_grad_(False)
test_eq(trainable_params(m), [m.bias])
for norm_func in [nn.BatchNorm1d, partial(nn.InstanceNorm1d, affine=True)]:
model = nn.Sequential(nn.Linear(10,20), norm_func(20), nn.Conv1d(3,4, 3))
test_eq(norm_bias_params(model), [model[0].bias, model[1].weight, model[1].bias, model[2].bias])
model = nn.ModuleList([nn.Linear(10,20, bias=False), nn.Sequential(norm_func(20), nn.Conv1d(3,4,3))])
test_eq(norm_bias_params(model), [model[1][0].weight, model[1][0].bias, model[1][1].bias])
model = nn.ModuleList([nn.Linear(10,20), nn.Sequential(norm_func(20), nn.Conv1d(3,4,3))])
test_eq(norm_bias_params(model, with_bias=False), [model[1][0].weight, model[1][0].bias])
t = tensor([1,2,3])
test_eq(batch_to_samples([t,t+1], max_n=2), ([1,2],[2,3]))
test_eq(batch_to_samples(tensor([1,2,3]), 10), [1, 2, 3])
test_eq(batch_to_samples([tensor([1,2,3]), tensor([4,5,6])], 10), [(1, 4), (2, 5), (3, 6)])
test_eq(batch_to_samples([tensor([1,2,3]), tensor([4,5,6])], 2), [(1, 4), (2, 5)])
test_eq(batch_to_samples([tensor([1,2,3]), [tensor([4,5,6]),tensor([7,8,9])]], 10),
[(1, (4, 7)), (2, (5, 8)), (3, (6, 9))])
test_eq(batch_to_samples([tensor([1,2,3]), [tensor([4,5,6]),tensor([7,8,9])]], 2), [(1, (4, 7)), (2, (5, 8))])
t = fastuple(tensor([1,2,3]),TensorBase([2,3,4]))
test_eq_type(batch_to_samples(t)[0][1], TensorBase(2))
test_eq(batch_to_samples(t).map(type), [fastuple]*3)
brks = tensor(0,1,2,4,8,64).float()
ys = tensor(range_of(brks)).float()
ys /= ys[-1].item()
pts = tensor(0.2,0.5,0.8,3,5,63)
preds = pts.interp_1d(brks, ys)
test_close(preds.numpy(), np.interp(pts.numpy(), brks.numpy(), ys.numpy()))
plt.scatter(brks,ys)
plt.scatter(pts,preds)
plt.legend(['breaks','preds']);
After calling this, ALL sub-processes in the pytorch process group must arrive here before proceeding.
Compression lib can be any of: blosclz, lz4, lz4hc, snappy, zlib or zstd.
x = tensor([0,1,2,3,4,5])
idxs = tensor([2,5,1,0,3,4])
test_eq_type(nested_reorder(([x], x), idxs), ([idxs], idxs))
y = L(0,1,2,3,4,5)
z = L(i.item() for i in idxs)
test_eq_type(nested_reorder((y, x), idxs), (z,idxs))
plt.imshow(make_cross_image(), cmap="Greys");
plt.imshow(make_cross_image(False).permute(1,2,0));
show_image_batch(([Image.open(TEST_IMAGE_BW),Image.open(TEST_IMAGE)],['bw','color']), items=2)
tst = nn.Linear(4,5)
assert requires_grad(tst)
for p in tst.parameters(): p.requires_grad_(False)
assert not requires_grad(tst)
tst = nn.Linear(4,5)
tst.weight.data.uniform_(-1,1)
tst.bias.data.uniform_(-1,1)
tst = init_default(tst, func = lambda x: x.data.fill_(1.))
test_eq(tst.weight, torch.ones(5,4))
test_eq(tst.bias, torch.zeros(5))
tst = nn.Linear(4,5)
tst.weight.data.uniform_(-1,1)
tst.bias.data.uniform_(-1,1)
cond_init(tst, func = lambda x: x.data.fill_(1.))
test_eq(tst.weight, torch.ones(5,4))
test_eq(tst.bias, torch.zeros(5))
tst = nn.BatchNorm2d(5)
init = [tst.weight.clone(), tst.bias.clone()]
cond_init(tst, func = lambda x: x.data.fill_(1.))
test_eq(tst.weight, init[0])
test_eq(tst.bias, init[1])
tst = nn.Sequential(nn.Linear(4,5), nn.Sequential(nn.Linear(4,5), nn.Linear(4,5)))
apply_leaf(tst, partial(init_default, func=lambda x: x.data.fill_(1.)))
for l in [tst[0], *tst[1]]: test_eq(l.weight, torch.ones(5,4))
for l in [tst[0], *tst[1]]: test_eq(l.bias, torch.zeros(5))
tst = nn.Sequential(nn.Linear(4,5), nn.Sequential(nn.Linear(4,5), nn.BatchNorm1d(5)))
init = [tst[1][1].weight.clone(), tst[1][1].bias.clone()]
apply_init(tst, func=lambda x: x.data.fill_(1.))
for l in [tst[0], tst[1][0]]: test_eq(l.weight, torch.ones(5,4))
for l in [tst[0], tst[1][0]]: test_eq(l.bias, torch.zeros(5))
test_eq(tst[1][1].weight, init[0])
test_eq(tst[1][1].bias, init[1])