From fb222870bb3355a9c8ee898f27dc63e00d454019 Mon Sep 17 00:00:00 2001
From: Manon blanco <blanco@teklia.com>
Date: Mon, 22 May 2023 11:00:32 +0000
Subject: [PATCH] Remove coverage_vector from predict.py
---
README.md | 3 ++-
dan/predict/prediction.py | 5 -----
2 files changed, 2 insertions(+), 6 deletions(-)
diff --git a/README.md b/README.md
index 3f50eb52..0c78c71c 100644
--- a/README.md
+++ b/README.md
@@ -4,7 +4,6 @@
For more details about this package, make sure to see the documentation available at https://teklia.gitlab.io/atr/dan/.
-
## Installation
To use DAN in your own scripts, install it using pip:
@@ -55,7 +54,9 @@ See the [dedicated section](https://teklia.gitlab.io/atr/dan/usage/datasets/form
See the [dedicated section](https://teklia.gitlab.io/atr/dan/usage/train/) on the official DAN documentation.
### Synthetic data generation
+
See the [dedicated section](https://teklia.gitlab.io/atr/dan/usage/generate/) on the official DAN documentation.
### Model prediction
+
See the [dedicated section](https://teklia.gitlab.io/atr/dan/usage/predict/) on the official DAN documentation.
diff --git a/dan/predict/prediction.py b/dan/predict/prediction.py
index 9c8de085..f288fbbf 100644
--- a/dan/predict/prediction.py
+++ b/dan/predict/prediction.py
@@ -148,10 +148,6 @@ class DAN:
features = self.encoder(input_tensor.float())
features_size = features.size()
- coverage_vector = torch.zeros(
- (features.size(0), 1, features.size(2), features.size(3)),
- device=self.device,
- )
pos_features = self.decoder.features_updater.get_pos_features(features)
features = torch.flatten(pos_features, start_dim=2, end_dim=3).permute(
2, 0, 1
@@ -179,7 +175,6 @@ class DAN:
confidence_scores.append(
torch.max(torch.softmax(pred[:, :], dim=1), dim=1).values
)
- coverage_vector = torch.clamp(coverage_vector + weights, 0, 1)
predicted_tokens = torch.cat(
[
predicted_tokens,
--
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