From b6e5972b64cc61fc967cf3d098fc629d781adce4 Mon Sep 17 00:00:00 2001
From: Jordan Gong <jordan.gong@protonmail.com>
Date: Mon, 22 Mar 2021 19:32:16 +0800
Subject: Add embedding visualization and validate on testing set
---
utils/configuration.py | 1 +
utils/triplet_loss.py | 9 +++++++--
2 files changed, 8 insertions(+), 2 deletions(-)
(limited to 'utils')
diff --git a/utils/configuration.py b/utils/configuration.py
index f6ac182..157d249 100644
--- a/utils/configuration.py
+++ b/utils/configuration.py
@@ -14,6 +14,7 @@ class DatasetConfiguration(TypedDict):
name: str
root_dir: str
train_size: int
+ val_size: int
num_sampled_frames: int
truncate_threshold: int
discard_threshold: int
diff --git a/utils/triplet_loss.py b/utils/triplet_loss.py
index 03fff21..5e3a97a 100644
--- a/utils/triplet_loss.py
+++ b/utils/triplet_loss.py
@@ -28,6 +28,7 @@ class BatchTripletLoss(nn.Module):
else: # is_all
positive_negative_dist = self._all_distance(dist, y, p, n)
+ non_zero_counts = None
if self.margin:
losses = F.relu(self.margin + positive_negative_dist).view(p, -1)
non_zero_counts = (losses != 0).sum(1).float()
@@ -35,14 +36,18 @@ class BatchTripletLoss(nn.Module):
loss_metric = self._none_zero_mean(losses, non_zero_counts)
else: # is_sum
loss_metric = losses.sum(1)
- return loss_metric, flat_dist, non_zero_counts
else: # Soft margin
losses = F.softplus(positive_negative_dist).view(p, -1)
if self.is_mean:
loss_metric = losses.mean(1)
else: # is_sum
loss_metric = losses.sum(1)
- return loss_metric, flat_dist, None
+
+ return {
+ 'loss': loss_metric,
+ 'dist': flat_dist,
+ 'counts': non_zero_counts
+ }
@staticmethod
def _batch_distance(x):
--
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