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| author | Jordan Gong <jordan.gong@protonmail.com> | 2021-02-08 18:11:25 +0800 |
|---|---|---|
| committer | Jordan Gong <jordan.gong@protonmail.com> | 2021-02-08 18:25:42 +0800 |
| commit | 99ddd7c142a4ec97cb8bd14b204651790b3cf4ee (patch) | |
| tree | a4ccbd08a7155e90df63aba60eb93ab2b7969c9b /utils | |
| parent | 507e1d163aaa6ea4be23e7f08ff6ce0ef58c830b (diff) | |
Code refactoring, modifications and new features
1. Decode features outside of auto-encoder
2. Turn off HPM 1x1 conv by default
3. Change canonical feature map size from `feature_channels * 8 x 4 x 2` to `feature_channels * 2 x 16 x 8`
4. Use mean of canonical embeddings instead of mean of static features
5. Calculate static and dynamic loss separately
6. Calculate mean of parts in triplet loss instead of sum of parts
7. Add switch to log disentangled images
8. Change default configuration
Diffstat (limited to 'utils')
| -rw-r--r-- | utils/configuration.py | 4 | ||||
| -rw-r--r-- | utils/triplet_loss.py | 2 |
2 files changed, 4 insertions, 2 deletions
diff --git a/utils/configuration.py b/utils/configuration.py index c4c4b4d..4ab1520 100644 --- a/utils/configuration.py +++ b/utils/configuration.py @@ -7,6 +7,7 @@ class SystemConfiguration(TypedDict): disable_acc: bool CUDA_VISIBLE_DEVICES: str save_dir: str + image_log_on: bool class DatasetConfiguration(TypedDict): @@ -31,6 +32,7 @@ class ModelHPConfiguration(TypedDict): ae_feature_channels: int f_a_c_p_dims: tuple[int, int, int] hpm_scales: tuple[int, ...] + hpm_use_1x1conv: bool hpm_use_avg_pool: bool hpm_use_max_pool: bool fpfe_feature_channels: int @@ -40,7 +42,7 @@ class ModelHPConfiguration(TypedDict): tfa_squeeze_ratio: int tfa_num_parts: int embedding_dims: int - triplet_margin: float + triplet_margins: tuple[float, float] class SubOptimizerHPConfiguration(TypedDict): diff --git a/utils/triplet_loss.py b/utils/triplet_loss.py index 8c143d6..d573ef4 100644 --- a/utils/triplet_loss.py +++ b/utils/triplet_loss.py @@ -34,5 +34,5 @@ class BatchAllTripletLoss(nn.Module): parted_loss_mean = all_loss.sum(1) / non_zero_counts parted_loss_mean[non_zero_counts == 0] = 0 - loss = parted_loss_mean.sum() + loss = parted_loss_mean.mean() return loss |