def test_conv2d():
"""Test 2D custom convolution module
We compare our custom reflection-padded convolution to the builtin
Torch convolution.
We check that:
- The shape of the output is equal
- The output values are equal in the center, where the
reflection-padding should not have influenced the result.
"""
batch_sz = 5
in_channels = 5
kernel_size = 3
padding_torch = 1
size = (45, 55)
# xi: for inplace
# xc: for normal torch convolution
xi = torch.randn(batch_sz, in_channels, *size).cuda()
xc = xi.data.clone()
output = torch.ones(batch_sz, 1, *size).cuda()
ci = conv_relu.ConvRelu2dInPlaceModule(output,
in_channels,
1,
kernel_size=kernel_size)
cc = nn.Sequential(
nn.Conv2d(in_channels,
1,
kernel_size=kernel_size,
padding=padding_torch),
nn.ReLU(),
)
for c in [ci, *cc.modules()]:
c.cuda()
try:
c.weight.data.fill_(1)
c.bias.data.zero_()
except AttributeError:
pass # skip relu (it does not have parameters)
xiv = Variable(xi, requires_grad=True)
xcv = Variable(xc, requires_grad=True)
yi = ci(xiv)
yc = cc(xcv)
assert yi.shape == yc.shape
# Check center of output, where the output should be equal.
d = 1
yi_ = yi[:, :, d:-d, d:-d]
yc_ = yc[:, :, d:-d, d:-d]
# Check that pytorch and own convolution agree in the center:
assert torch_equal(yi_, yc_)
assert torch_equal(yi.data, output)
def test_conv3d():
"""Test 3D custom convolution module
We compare our custom reflection-padded convolution to the builtin
Torch convolution.
We check that:
- The shape of the output is equal
- The output values are equal in the center, where the
reflection-padding should not have influenced the result.
"""
batch_sz = 5
in_channels = 5
kernel_size = 3
padding_torch = 1
size = (13, 7, 19)
# xi: for inplace
# xc: for normal torch convolution
xi = torch.ones(batch_sz, in_channels, *size).cuda()
xc = torch.ones(batch_sz, in_channels, *size).cuda()
output = torch.ones(batch_sz, 1, *size).cuda()
ci = Conv3DModule(output, in_channels, 1, kernel_size=kernel_size)
cc = nn.Conv3d(in_channels, 1, kernel_size=kernel_size, padding=padding_torch)
for c in [ci, cc]:
c.cuda()
c.weight.data.fill_(1)
c.bias.data.zero_()
xi.requires_grad = True
xc.requires_grad = True
yi = ci(xi)
yc = cc(xc)
assert yi.shape == yc.shape
# Check center of output, where the output should be equal.
d = 1
yi_ = yi[:, :, d:-d, d:-d, d:-d]
yc_ = yc[:, :, d:-d, d:-d, d:-d]
# Check that pytorch and own convolution agree in the center:
assert torch_equal(yi_, yc_)
assert torch_equal(yi.data, output)
#%%
import os, sys
class_path = os.path.dirname(os.path.abspath('__file__')) + "/make_class/"
class_path
# '/Users/user/Projects/neural_tutorial'
#%%
sys.path.insert(1, class_path)
print('module path was inserted.')
#%%
import CustomClass as cc
print(cc.introduce())
#%%
from CustomClass import CustomClass as cc
mycc = cc() ## jupyter 에서는 print 없이 출력가능
mycc.plus(1, 1)
#%%
from CustomClass import Mathmatics as math
math().square(2, 2, debug=True)