diff options
Diffstat (limited to 'utils')
| -rw-r--r-- | utils/configuration.py | 28 | ||||
| -rw-r--r-- | utils/triplet_loss.py | 92 |
2 files changed, 0 insertions, 120 deletions
diff --git a/utils/configuration.py b/utils/configuration.py index 5a5bc0c..fff3876 100644 --- a/utils/configuration.py +++ b/utils/configuration.py @@ -33,23 +33,6 @@ 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_avg_pool: bool - hpm_use_max_pool: bool - tfa_num_parts: int - tfa_squeeze_ratio: int - embedding_dims: tuple[int] - triplet_is_hard: bool - triplet_is_mean: bool - triplet_margins: tuple[float, float] - - -class SubOptimizerHPConfiguration(TypedDict): - lr: int - betas: tuple[float, float] - eps: float - weight_decay: float - amsgrad: bool class OptimizerHPConfiguration(TypedDict): @@ -58,22 +41,11 @@ class OptimizerHPConfiguration(TypedDict): eps: float weight_decay: float amsgrad: bool - auto_encoder: SubOptimizerHPConfiguration - hpm: 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 deleted file mode 100644 index 5e3a97a..0000000 --- a/utils/triplet_loss.py +++ /dev/null @@ -1,92 +0,0 @@ -from typing import Optional - -import torch -import torch.nn as nn -import torch.nn.functional as F - - -class BatchTripletLoss(nn.Module): - def __init__( - self, - is_hard: bool = True, - is_mean: bool = True, - margin: Optional[float] = 0.2, - ): - super().__init__() - self.is_hard = is_hard - self.is_mean = is_mean - self.margin = margin - - 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) - - 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() - if self.is_mean: - loss_metric = self._none_zero_mean(losses, non_zero_counts) - else: # is_sum - loss_metric = losses.sum(1) - 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': loss_metric, - 'dist': flat_dist, - 'counts': non_zero_counts - } - - @staticmethod - def _batch_distance(x): - # 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) - ) - return dist - - @staticmethod - def _hard_distance(dist, y, p, n): - positive_mask = y.unsqueeze(1) == y.unsqueeze(2) - negative_mask = y.unsqueeze(1) != y.unsqueeze(2) - hard_positive = dist[positive_mask].view(p, n, -1).max(-1).values - hard_negative = dist[negative_mask].view(p, n, -1).min(-1).values - positive_negative_dist = hard_positive - hard_negative - - return positive_negative_dist - - @staticmethod - def _all_distance(dist, y, p, n): - # Unmask identical samples - positive_mask = torch.eye( - n, dtype=torch.bool, device=y.device - ) ^ (y.unsqueeze(1) == y.unsqueeze(2)) - negative_mask = y.unsqueeze(1) != y.unsqueeze(2) - all_positive = dist[positive_mask].view(p, n, -1, 1) - all_negative = dist[negative_mask].view(p, n, 1, -1) - positive_negative_dist = all_positive - all_negative - - return positive_negative_dist - - @staticmethod - def _none_zero_mean(losses, non_zero_counts): - # Non-zero parted mean - non_zero_mean = losses.sum(1) / non_zero_counts - non_zero_mean[non_zero_counts == 0] = 0 - return non_zero_mean |