Merge branch 'master' into python3.8

# Conflicts:
#	models/hpm.py
#	models/rgb_part_net.py
#	utils/configuration.py
#	utils/triplet_loss.py

2 files changed, 12 insertions, 50 deletions

diff --git a/utils/configuration.py b/utils/configuration.py
index 376ae0f..8ee08f2 100644
--- a/utils/configuration.py
+++ b/utils/configuration.py
@@ -33,16 +33,11 @@ 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
+    tfa_squeeze_ratio: int
+    embedding_dims: Tuple[int]
     triplet_is_hard: bool
     triplet_is_mean: bool
     triplet_margins: Tuple[float, float]
@@ -63,14 +58,21 @@ class OptimizerHPConfiguration(TypedDict):
     weight_decay: float
     amsgrad: bool
     auto_encoder: SubOptimizerHPConfiguration
-    part_net: SubOptimizerHPConfiguration
     hpm: SubOptimizerHPConfiguration
-    fc: SubOptimizerHPConfiguration
+    part_net: SubOptimizerHPConfiguration
+
+
+class SubSchedulerHPConfiguration(TypedDict):
+    start_step: int
+    final_gamma: float
 
 
 class SchedulerHPConfiguration(TypedDict):
     start_step: int
     final_gamma: float
+    auto_encoder: SubSchedulerHPConfiguration
+    hpm: SubSchedulerHPConfiguration
+    part_net: SubSchedulerHPConfiguration
 
 
 class HyperparameterConfiguration(TypedDict):
diff --git a/utils/triplet_loss.py b/utils/triplet_loss.py
index ae899ec..03fff21 100644
--- a/utils/triplet_loss.py
+++ b/utils/triplet_loss.py
@@ -1,4 +1,4 @@
-from typing import Optional, Tuple
+from typing import Optional
 
 import torch
 import torch.nn as nn
@@ -85,43 +85,3 @@ class BatchTripletLoss(nn.Module):
         non_zero_mean = losses.sum(1) / non_zero_counts
         non_zero_mean[non_zero_counts == 0] = 0
         return non_zero_mean
-
-
-class JointBatchTripletLoss(BatchTripletLoss):
-    def __init__(
-            self,
-            hpm_num_parts: int,
-            is_hard: bool = True,
-            is_mean: bool = True,
-            margins: Tuple[float, float] = (0.2, 0.2)
-    ):
-        super().__init__(is_hard, is_mean)
-        self.hpm_num_parts = hpm_num_parts
-        self.margin_hpm, self.margin_pn = margins
-
-    def forward(self, x, y):
-        p, n, c = x.size()
-        dist = self._batch_distance(x)
-        flat_dist_mask = torch.tril_indices(n, n, offset=-1, device=dist.device)
-        flat_dist = dist[:, flat_dist_mask[0], flat_dist_mask[1]]
-
-        if self.is_hard:
-            positive_negative_dist = self._hard_distance(dist, y, p, n)
-        else:  # is_all
-            positive_negative_dist = self._all_distance(dist, y, p, n)
-
-        hpm_part_loss = F.relu(
-            self.margin_hpm + positive_negative_dist[:self.hpm_num_parts]
-        )
-        pn_part_loss = F.relu(
-            self.margin_pn + positive_negative_dist[self.hpm_num_parts:]
-        )
-        losses = torch.cat((hpm_part_loss, pn_part_loss)).view(p, -1)
-
-        non_zero_counts = (losses != 0).sum(1).float()
-        if self.is_mean:
-            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