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authorJordan Gong <jordan.gong@protonmail.com>2020-12-30 11:14:26 +0800
committerJordan Gong <jordan.gong@protonmail.com>2020-12-30 11:14:26 +0800
commit0178b7722c63820d701b0442b5946d9a042074d7 (patch)
treeb41bb798c1ecd107bf273c611afcb0f1103197e2 /models/hpm.py
parent6cf6f7fb0e43b437c80f134c3eb1a1c5afffdba9 (diff)
Add pooling options in HPM
According to [1], we can use GAP and GMP together, or one of both in ablation study. [1]Y. Fu et al., “Horizontal pyramid matching for person re-identification,” in Proceedings of the AAAI Conference on Artificial Intelligence, 2019, vol. 33, pp. 8295–8302.
Diffstat (limited to 'models/hpm.py')
-rw-r--r--models/hpm.py5
1 files changed, 4 insertions, 1 deletions
diff --git a/models/hpm.py b/models/hpm.py
index 1773f56..85a4e58 100644
--- a/models/hpm.py
+++ b/models/hpm.py
@@ -10,13 +10,15 @@ class HorizontalPyramidMatching(nn.Module):
self,
scales: tuple[int, ...] = (1, 2, 4, 8),
out_channels: int = 256,
- use_avg_pool: bool = False,
+ use_avg_pool: bool = True,
+ use_max_pool: bool = True,
**kwargs
):
super().__init__()
self.scales = scales
self.out_channels = out_channels
self.use_avg_pool = use_avg_pool
+ self.use_max_pool = use_max_pool
self.backbone = resnet50(pretrained=True)
self.in_channels = self.backbone.layer4[-1].conv1.in_channels
@@ -29,6 +31,7 @@ class HorizontalPyramidMatching(nn.Module):
pyramid = [HorizontalPyramidPooling(self.in_channels,
self.out_channels,
use_avg_pool=self.use_avg_pool,
+ use_max_pool=self.use_max_pool,
**kwargs)
for _ in range(scale)]
return pyramid