Skip to content

fix: atrous_rates for deeplabv3_mobilenet_v3_large (fixes #7956) #8019

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

Merged
NicolasHug merged 2 commits into from
Oct 11, 2023
Merged

fix: atrous_rates for deeplabv3_mobilenet_v3_large (fixes #7956) #8019

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
67dce75
feat: parameterize atrous_rates for deeplabv3 in `DeepLabHead`.
nvs-abhilash Oct 5, 2023
c65397d
Merge branch 'main' into mobilenet-fix
NicolasHug Oct 11, 2023
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
8 changes: 4 additions & 4 deletions torchvision/models/segmentation/deeplabv3.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,5 +1,5 @@
from functools import partial
from typing import Any, List, Optional
from typing import Any, Optional, Sequence

import torch
from torch import nn
Expand Down Expand Up @@ -46,9 +46,9 @@ class DeepLabV3(_SimpleSegmentationModel):


class DeepLabHead(nn.Sequential):
def __init__(self, in_channels: int, num_classes: int) -> None:
def __init__(self, in_channels: int, num_classes: int, atrous_rates: Sequence[int] = (12, 24, 36)) -> None:
super().__init__(
ASPP(in_channels, [12, 24, 36]),
ASPP(in_channels, atrous_rates),
nn.Conv2d(256, 256, 3, padding=1, bias=False),
nn.BatchNorm2d(256),
nn.ReLU(),
Expand Down Expand Up @@ -83,7 +83,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:


class ASPP(nn.Module):
def __init__(self, in_channels: int, atrous_rates: List[int], out_channels: int = 256) -> None:
def __init__(self, in_channels: int, atrous_rates: Sequence[int], out_channels: int = 256) -> None:
super().__init__()
modules = []
modules.append(
Expand Down