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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from tensorboardX import x2num, SummaryWriter
import torch
import numpy as np
import unittest
class PyTorchNumpyTest(unittest.TestCase):
def test_pytorch_np(self):
tensors = [torch.rand(3, 10, 10), torch.rand(1), torch.rand(1, 2, 3, 4, 5)]
for tensor in tensors:
# regular tensor
assert isinstance(x2num.make_np(tensor), np.ndarray)
# CUDA tensor
if torch.cuda.device_count() > 0:
assert isinstance(x2num.make_np(tensor.cuda()), np.ndarray)
# regular variable
assert isinstance(x2num.make_np(torch.autograd.Variable(tensor)), np.ndarray)
# CUDA variable
if torch.cuda.device_count() > 0:
assert isinstance(x2num.make_np(torch.autograd.Variable(tensor).cuda()), np.ndarray)
# python primitive type
assert(isinstance(x2num.make_np(0), np.ndarray))
assert(isinstance(x2num.make_np(0.1), np.ndarray))
def test_pytorch_write(self):
with SummaryWriter() as w:
w.add_scalar('scalar', torch.autograd.Variable(torch.rand(1)), 0)
def test_pytorch_histogram(self):
with SummaryWriter() as w:
w.add_histogram('float histogram', torch.rand((50,)))
w.add_histogram('int histogram', torch.randint(0, 100, (50,)))
def test_pytorch_histogram_raw(self):
with SummaryWriter() as w:
num = 50
floats = x2num.make_np(torch.rand((num,)))
bins = [0.0, 0.25, 0.5, 0.75, 1.0]
counts, limits = np.histogram(floats, bins)
sum_sq = floats.dot(floats).item()
w.add_histogram_raw('float histogram raw',
min=floats.min().item(),
max=floats.max().item(),
num=num,
sum=floats.sum().item(),
sum_squares=sum_sq,
bucket_limits=limits[1:].tolist(),
bucket_counts=counts.tolist())
ints = x2num.make_np(torch.randint(0, 100, (num,)))
bins = [0, 25, 50, 75, 100]
counts, limits = np.histogram(ints, bins)
sum_sq = ints.dot(ints).item()
w.add_histogram_raw('int histogram raw',
min=ints.min().item(),
max=ints.max().item(),
num=num,
sum=ints.sum().item(),
sum_squares=sum_sq,
bucket_limits=limits[1:].tolist(),
bucket_counts=counts.tolist())
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