def test_chainer_np():
for tensor in tensors:
# regular variable
assert isinstance(x2num.makenp(tensor), np.ndarray)
# python primitive type
assert (isinstance(x2num.makenp(0), np.ndarray))
assert (isinstance(x2num.makenp(0.1), np.ndarray))
def test_scalar():
res = x2num.makenp(1.1)
assert isinstance(res, np.ndarray) and res.shape == (1,)
res = x2num.makenp(1000000000000000000000)
assert isinstance(res, np.ndarray) and res.shape == (1,)
res = x2num.makenp(np.float16(1.00000087))
assert isinstance(res, np.ndarray) and res.shape == (1,)
res = x2num.makenp(np.float128(1.00008+9))
assert isinstance(res, np.ndarray) and res.shape == (1,)
res = x2num.makenp(np.int64(100000000000))
assert isinstance(res, np.ndarray) and res.shape == (1,)
def image(tag, tensor):
"""Outputs a `Summary` protocol buffer with images.
The summary has up to `max_images` summary values containing images. The
images are built from `tensor` which must be 3-D with shape `[height, width,
channels]` and where `channels` can be:
* 1: `tensor` is interpreted as Grayscale.
* 3: `tensor` is interpreted as RGB.
* 4: `tensor` is interpreted as RGBA.
The `name` in the outputted Summary.Value protobufs is generated based on the
name, with a suffix depending on the max_outputs setting:
* If `max_outputs` is 1, the summary value tag is '*name*/image'.
* If `max_outputs` is greater than 1, the summary value tags are
generated sequentially as '*name*/image/0', '*name*/image/1', etc.
Args:
tag: A name for the generated node. Will also serve as a series name in
TensorBoard.
tensor: A 3-D `uint8` or `float32` `Tensor` of shape `[height, width,
channels]` where `channels` is 1, 3, or 4.
Returns:
A scalar `Tensor` of type `string`. The serialized `Summary` protocol
buffer.
"""
tag = _clean_tag(tag)
tensor = makenp(tensor, 'IMG')
tensor = tensor.astype(np.float32)
tensor = (tensor * 255).astype(np.uint8)
image = make_image(tensor)
return Summary(value=[Summary.Value(tag=tag, image=image)])
def test_pytorch_np():
for tensor in tensors:
# regular tensor
assert isinstance(x2num.makenp(tensor), np.ndarray)
# CUDA tensor
if torch.cuda.device_count()>0:
assert isinstance(x2num.makenp(tensor.cuda()), np.ndarray)
# regular variable
assert isinstance(x2num.makenp(torch.autograd.variable.Variable(tensor)), np.ndarray)
# CUDA variable
if torch.cuda.device_count()>0:
assert isinstance(x2num.makenp(torch.autograd.variable.Variable(tensor).cuda()), np.ndarray)
# python primitive type
assert(isinstance(x2num.makenp(0), np.ndarray))
assert(isinstance(x2num.makenp(0.1), np.ndarray))
def test_pytorch_img():
for s in shapes:
x = torch.Tensor(np.random.random_sample(s))
assert x2num.makenp(x, 'IMG').shape[2] == 3
def test_chainer_img():
for s in shapes:
x = chainer.Variable(np.random.random_sample(s))
assert x2num.makenp(x, 'IMG').shape[2] == 3
