def test_image_with_3_channel_batched(self):
self.assertTrue(
compare_proto(
summary.image('dummy',
np.random.rand(2, 3, 8,
8).astype(np.float32),
dataformats='NCHW'), self)) # noqa E127
def save_img_results(imgs_tcpu, fake_imgs, num_imgs, count, image_dir,
summary_writer):
num = cfg.TRAIN.VIS_COUNT
# The range of real_img (i.e., self.imgs_tcpu[i][0:num])
# is changed to [0, 1] by function vutils.save_image
real_img = imgs_tcpu[-1][0:num]
vutils.save_image(real_img,
'%s/real_samples.png' % (image_dir),
normalize=True)
real_img_set = vutils.make_grid(real_img).numpy()
# print("////////before transpose///////////////\n",real_img_set.shape, len(real_img_set.shape))
real_img_set = np.transpose(real_img_set, (1, 2, 0))
# print("//////////after transpose/////////////\n",real_img_set.shape, len(real_img_set.shape),"\n///////////////////////")
real_img_set = real_img_set * 255
real_img_set = real_img_set.astype(np.uint8)
#doge
#sup_real_img = summary.image('real_img', real_img_set)
sup_real_img = summary.image('real_img', real_img_set, dataformats='HWC')
summary_writer.add_summary(sup_real_img, count)
for i in range(num_imgs):
fake_img = fake_imgs[i][0:num]
# The range of fake_img.data (i.e., self.fake_imgs[i][0:num])
# is still [-1. 1]...
vutils.save_image(fake_img.data,
'%s/count_%09d_fake_samples%d.png' %
(image_dir, count, i),
normalize=True)
fake_img_set = vutils.make_grid(fake_img.data).cpu().numpy()
fake_img_set = np.transpose(fake_img_set, (1, 2, 0))
fake_img_set = (fake_img_set + 1) * 255 / 2
fake_img_set = fake_img_set.astype(np.uint8)
#doge
#sup_fake_img = summary.image('fake_img%d' % i, fake_img_set)
sup_fake_img = summary.image('fake_img%d' % i,
fake_img_set,
dataformats='HWC')
summary_writer.add_summary(sup_fake_img, count)
summary_writer.flush()
def add_images(self,
tag,
img_tensor,
global_step=None,
walltime=None,
dataformats='NCHW'):
torch._C._log_api_usage_once("tensorboard.logging.add_images")
if self._check_caffe2_blob(img_tensor):
img_tensor = workspace.FetchBlob(img_tensor)
self._get_file_writer().add_summary(
image(tag, img_tensor, dataformats=dataformats), global_step,
walltime)
def add_image(self, tag, img_tensor, global_step=None, walltime=None):
"""Add image data to summary.
Note that this requires the ``pillow`` package.
Args:
tag (string): Data identifier
img_tensor (torch.Tensor, numpy.array, or string/blobname): Image data
global_step (int): Global step value to record
walltime (float): Optional override default walltime (time.time())
seconds after epoch of event
Shape:
img_tensor: Default is :math:`(3, H, W)`. You can use ``torchvision.utils.make_grid()`` to
convert a batch of tensor into 3xHxW format or call ``add_images`` and let us do the job.
Tensor with :math:`(1, H, W)`, :math:`(H, W)`, :math:`(H, W, 3)` is also suitable as long as
corresponding ``dataformats`` argument is passed, e.g. ``CHW``, ``HWC``, ``HW``.
Examples::
from torch.utils.tensorboard import SummaryWriter
import numpy as np
img = np.zeros((3, 100, 100))
img[0] = np.arange(0, 10000).reshape(100, 100) / 10000
img[1] = 1 - np.arange(0, 10000).reshape(100, 100) / 10000
img_HWC = np.zeros((100, 100, 3))
img_HWC[:, :, 0] = np.arange(0, 10000).reshape(100, 100) / 10000
img_HWC[:, :, 1] = 1 - np.arange(0, 10000).reshape(100, 100) / 10000
writer = SummaryWriter()
writer.add_image('my_image', img, 0)
# If you have non-default dimension setting, set the dataformats argument.
writer.add_image('my_image_HWC', img_HWC, 0, dataformats='HWC')
writer.close()
Expected result:
.. image:: _static/img/tensorboard/add_image.png
:scale: 50 %
"""
self._get_file_writer().add_summary(
image(tag, img_tensor, dataformats='HWC'), global_step, walltime)
def add_images(self, tag, img_tensor, global_step=None, walltime=None):
"""Add batched image data to summary.
Note that this requires the ``pillow`` package.
Args:
tag (string): Data identifier
img_tensor (torch.Tensor, numpy.array, or string/blobname): Image data
global_step (int): Global step value to record
walltime (float): Optional override default walltime (time.time())
seconds after epoch of event
Shape:
img_tensor: Default is :math:`(N, 3, H, W)`. If ``dataformats`` is specified, other shape will be
accepted. e.g. NCHW or NHWC.
Examples::
from torch.utils.tensorboard import SummaryWriter
import numpy as np
img_batch = np.zeros((16, 3, 100, 100))
for i in range(16):
img_batch[i, 0] = np.arange(0, 10000).reshape(100, 100) / 10000 / 16 * i
img_batch[i, 1] = (1 - np.arange(0, 10000).reshape(100, 100) / 10000) / 16 * i
writer = SummaryWriter()
writer.add_images('my_image_batch', img_batch, 0)
writer.close()
Expected result:
.. image:: _static/img/tensorboard/add_images.png
:scale: 30 %
"""
self._get_file_writer().add_summary(
image(tag, img_tensor, dataformats='NHWC'), global_step, walltime)
def test_image_without_channel(self):
self.assertTrue(
compare_proto(
summary.image('dummy',
tensor_N(shape=(8, 8)),
dataformats='HW'), self)) # noqa E127
def test_image_with_3_channel_batched(self):
self.assertTrue(
compare_proto(
summary.image('dummy',
tensor_N(shape=(2, 3, 8, 8)),
dataformats='NCHW'), self)) # noqa E127
def test_image_with_one_channel(self):
self.assertTrue(compare_image_proto(
summary.image('dummy',
tensor_N(shape=(1, 8, 8)),
dataformats='CHW'),
self)) # noqa: E131
def test_image_without_channel(self):
self.assertTrue(
compare_proto(
summary.image('dummy',
np.random.rand(8, 8).astype(np.float32),
dataformats='HW'), self)) # noqa E127