New branch with auto-encoder only

2 files changed, 0 insertions, 60 deletions

diff --git a/utils/configuration.py b/utils/configuration.py
index 435d815..1b7c8d3 100644
--- a/utils/configuration.py
+++ b/utils/configuration.py
@@ -32,26 +32,6 @@ class DataloaderConfiguration(TypedDict):
 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
-    fpfe_kernel_sizes: tuple[tuple, ...]
-    fpfe_paddings: tuple[tuple, ...]
-    fpfe_halving: tuple[int, ...]
-    tfa_squeeze_ratio: int
-    tfa_num_parts: int
-    embedding_dims: int
-    triplet_margins: tuple[float, float]
-
-
-class SubOptimizerHPConfiguration(TypedDict):
-    lr: int
-    betas: tuple[float, float]
-    eps: float
-    weight_decay: float
-    amsgrad: bool
 
 
 class OptimizerHPConfiguration(TypedDict):
@@ -61,10 +41,6 @@ class OptimizerHPConfiguration(TypedDict):
     eps: float
     weight_decay: float
     amsgrad: bool
-    auto_encoder: SubOptimizerHPConfiguration
-    part_net: SubOptimizerHPConfiguration
-    hpm: SubOptimizerHPConfiguration
-    fc: SubOptimizerHPConfiguration
 
 
 class SchedulerHPConfiguration(TypedDict):
diff --git a/utils/triplet_loss.py b/utils/triplet_loss.py
deleted file mode 100644
index 954def2..0000000
--- a/utils/triplet_loss.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-class BatchAllTripletLoss(nn.Module):
-    def __init__(self, margin: float = 0.2):
-        super().__init__()
-        self.margin = margin
-
-    def forward(self, x, y):
-        p, n, c = x.size()
-
-        # Euclidean distance p x n x n
-        x_squared_sum = torch.sum(x ** 2, dim=2)
-        x1_squared_sum = x_squared_sum.unsqueeze(2)
-        x2_squared_sum = x_squared_sum.unsqueeze(1)
-        x1_times_x2_sum = x @ x.transpose(1, 2)
-        dist = torch.sqrt(
-            F.relu(x1_squared_sum - 2 * x1_times_x2_sum + x2_squared_sum)
-        )
-
-        hard_positive_mask = y.unsqueeze(1) == y.unsqueeze(2)
-        hard_negative_mask = y.unsqueeze(1) != y.unsqueeze(2)
-        all_hard_positive = dist[hard_positive_mask].view(p, n, -1, 1)
-        all_hard_negative = dist[hard_negative_mask].view(p, n, 1, -1)
-        positive_negative_dist = all_hard_positive - all_hard_negative
-        all_loss = F.relu(self.margin + positive_negative_dist).view(p, -1)
-
-        # Non-zero parted mean
-        non_zero_counts = (all_loss != 0).sum(1)
-        parted_loss_mean = all_loss.sum(1) / non_zero_counts
-        parted_loss_mean[non_zero_counts == 0] = 0
-
-        loss = parted_loss_mean.mean()
-        return loss