Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Update basicblock.py #185

Open
wants to merge 1 commit into
base: master
Choose a base branch
from
Open

Update basicblock.py #185

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
14 changes: 10 additions & 4 deletions models/basicblock.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -58,13 +58,19 @@ def sequential(*args):
# --------------------------------------------
# return nn.Sequantial of (Conv + BN + ReLU)
# --------------------------------------------
def conv(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1, bias=True, mode='CBR', negative_slope=0.2):
def conv(out_channels=64, kernel_size=3, stride=1, padding=1, bias=True, mode='CBR', negative_slope=0.2, in_channels=None):
# Changed convolution layers to lazy convolution layers.
# Lazy layers defer parameter initialization until input is passed through.
# This avoids the need to explicitly define input sizes, making the model more flexible.
# It also improves memory efficiency as memory is allocated only when the layers are used.
L = []
for t in mode:
if t == 'C':
L.append(nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride, padding=padding, bias=bias))
L.append(nn.LazyConv2d(out_channels=out_channels, kernel_size=kernel_size, stride=stride, padding=padding, bias=bias))
elif t == 'T':
L.append(nn.ConvTranspose2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride, padding=padding, bias=bias))
if in_channels is None:
raise ValueError("in_channels must be provided for ConvTranspose2d")
L.append(nn.LazyConvTranspose2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride, padding=padding, bias=bias))
elif t == 'B':
L.append(nn.BatchNorm2d(out_channels, momentum=0.9, eps=1e-04, affine=True))
elif t == 'I':
Expand Down Expand Up @@ -94,7 +100,7 @@ def conv(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1, bi
elif t == 'A':
L.append(nn.AvgPool2d(kernel_size=kernel_size, stride=stride, padding=0))
else:
raise NotImplementedError('Undefined type: '.format(t))
raise NotImplementedError(f'Undefined type: {t}')
return sequential(*L)


Expand Down