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import torch
from abc import ABC
import torch.nn as nn
class ContrastLoss(nn.Module, ABC):
def __init__(self, hyp_param):
super(ContrastLoss, self).__init__()
self.param = hyp_param
_defaults = {
"temperature": 0.10,
"ignore_idx": 255,
"ood_idx": 254,
"max_views": 512,
"proj_dim": 512,
"sample_limits": 64,
"total_limits": 15240,
}
_raw = getattr(hyp_param, "contrastive_learning", None) or {}
_cfg = {**_defaults, **_raw}
self.temperature = _cfg["temperature"]
self.ignore_idx = _cfg["ignore_idx"]
self.ood_idx = _cfg["ood_idx"]
self.max_views = _cfg["max_views"]
self.proj_dim = _cfg["proj_dim"]
self.sample_limits = _cfg["sample_limits"]
self.total_limits = _cfg["total_limits"]
def select_class_wise_samples(self, embeddings, audio_embeddings, predictions, masks, batch_idx):
embedding_sample_list = []
label_list = []
embedding_sample_list_a = []
label_list_a = []
class_index_list = torch.unique(masks)
# means not silence
if len(class_index_list) > 1:
for class_index in class_index_list[1:]:
embedding_sample_list_a.append(audio_embeddings.unsqueeze(0))
label_list_a.append(class_index.unsqueeze(0) + batch_idx * 1e3)
else:
embedding_sample_list_a.append(audio_embeddings.unsqueeze(0))
label_list_a.append(torch.zeros([1], device=embeddings.device) + batch_idx * 1e3)
# contras_list = []
# contras_label_list = []
sample_limits = self.sample_limits
# we only have 0, 1
embeddings = embeddings.permute(1, 0)
for class_index in class_index_list:
hard_indices = embeddings[((masks != predictions) & (masks == class_index)).nonzero()]
easy_indices = embeddings[((masks == predictions) & (masks == class_index)).nonzero()]
hard_indices_num, easy_indices_num = hard_indices.shape[0], easy_indices.shape[0]
# the number that is selected to the contrastive learning.
selective_num_hard = min(sample_limits, hard_indices_num)
selective_num_easy = min(sample_limits, easy_indices_num)
if (selective_num_hard + selective_num_easy) < sample_limits * 2:
if selective_num_hard > selective_num_easy:
selective_num_hard += sample_limits * 2 - selective_num_easy
else:
selective_num_easy += sample_limits * 2 - selective_num_hard
# skip if contains too limited samples.
# if selective_num_hard < 10 and selective_num_easy < 10:
# continue
hard_chosen_indices = torch.randperm(hard_indices_num)[:selective_num_hard]
embedding_sample_list.append(hard_indices[hard_chosen_indices])
label_list.append(masks[hard_chosen_indices] + batch_idx * 1e3)
# add negative features to list.
easy_chosen_indices = torch.randperm(easy_indices_num)[:selective_num_easy]
embedding_sample_list.append(easy_indices[easy_chosen_indices])
label_list.append(masks[easy_chosen_indices] + batch_idx * 1e3)
return embedding_sample_list, label_list, embedding_sample_list_a, label_list_a
def forward_audio_visual(self, visual_embeddings, audio_embeddings, masks, predictions):
masks = masks.flatten(start_dim=1)
predictions = predictions.flatten(start_dim=1)
visual_embeddings = visual_embeddings.flatten(start_dim=-2)
visual_embedding_sample_list = []
visual_label_list = []
audio_embedding_sample_list = []
audio_label_list = []
for frame_idx in range(masks.shape[0]):
current_vision_feats = visual_embeddings[frame_idx]
current_masks = masks[frame_idx]
current_predictions = predictions[frame_idx]
current_audio_feats = audio_embeddings[frame_idx]
for layer_idx in range(3):
(selected_vision_embeddings, selected_vision_labels,
selected_audio_embeddings, selected_audio_labels) = self.select_class_wise_samples(current_vision_feats[layer_idx],
current_audio_feats[layer_idx],
current_predictions,
current_masks,
0)
visual_embedding_sample_list += selected_vision_embeddings
visual_label_list += selected_vision_labels
audio_embedding_sample_list += selected_audio_embeddings
audio_label_list += selected_audio_labels
if len(visual_embedding_sample_list) == 0: return 0.
visual_embedding_sample_list = torch.cat(visual_embedding_sample_list, dim=0).squeeze()
visual_label_list = torch.cat(visual_label_list, dim=0).unsqueeze(-1)
audio_embedding_sample_list = torch.cat(audio_embedding_sample_list, dim=0).squeeze()
audio_label_list = torch.cat(audio_label_list).unsqueeze(1)
# print(visual_embedding_sample_list.shape, visual_label_list.shape)
# print(audio_embedding_sample_list.shape, audio_label_list.shape)
# exit(1)
total_limits = self.total_limits
if visual_embedding_sample_list.shape[0] > total_limits:
rand_index = torch.randperm(visual_embedding_sample_list.shape[0])[total_limits]
visual_embedding_sample_list = visual_embedding_sample_list[:rand_index]
visual_label_list = visual_label_list[:rand_index]
loss = self.info_nce(visual_embedding_sample_list, visual_label_list, audio_embedding_sample_list,
audio_label_list)
return loss
# proof the q-project CAN BE the projector head of the contrastive learning.
# At the moment, I do believe the ATTENTION is the another format of the contrastive learning.
# First experiment: ignore the sound, only work on the projected vision mask.
def forward(self, embeddings, output_dicts, masks):
predictions = torch.cat([i['multistep_pred_masks'] for i in output_dicts])
predictions = torch.nn.functional.interpolate(predictions, size=(int(self.param.image_size/16), int(self.param.image_size/16)),
mode='bilinear', align_corners=False).squeeze(1)
masks = torch.nn.functional.interpolate(masks.unsqueeze(1), size=(int(self.param.image_size/16), int(self.param.image_size/16)),
mode='nearest').squeeze(1)
visual_embeddings, audio_embeddings = embeddings
# if len(predictions.shape) < 3 and len(masks.shape) < 3:
# predictions = predictions.unsqueeze(0)
# masks = masks.unsqueeze(0)
visual_embeddings = torch.cat([torch.cat([visual_embeddings[0][i].unsqueeze(0),
visual_embeddings[1][i].unsqueeze(0),
visual_embeddings[2][i].unsqueeze(0)]).unsqueeze(0)
for i in range(masks.shape[0])])
audio_embeddings = torch.cat([torch.cat([audio_embeddings[0][i].unsqueeze(0),
audio_embeddings[1][i].unsqueeze(0),
audio_embeddings[2][i].unsqueeze(0)]).unsqueeze(0)
for i in range(masks.shape[0])])
# dict_keys(['point_inputs', 'mask_inputs', 'multistep_pred_masks', 'multistep_pred_masks_high_res',
# 'multistep_pred_multimasks', 'multistep_pred_multimasks_high_res', 'multistep_pred_ious',
# 'multistep_point_inputs', 'multistep_object_score_logits', 'pred_masks', 'pred_masks_high_res',
# 'maskmem_features', 'maskmem_pos_enc'])
return self.forward_audio_visual(visual_embeddings, audio_embeddings.squeeze(-1), masks, predictions)
# def forward_visual_only(self, visual_embeddings, masks, predictions):
# masks = masks.flatten(start_dim=1)
# predictions = predictions.flatten(start_dim=1)
# visual_embeddings = visual_embeddings.flatten(start_dim=-2)
#
# visual_embedding_sample_list = []
# visual_label_list = []
# audio_embedding_sample_list = []
# audio_label_list = []
#
# for frame_idx in range(masks.shape[0]):
# current_vision_feats = visual_embeddings[frame_idx]
# current_masks = masks[frame_idx]
# current_predictions = predictions[frame_idx]
# for layer_idx in range(3):
# current_select_embeddings, current_select_labels = self.select_class_wise_samples(current_vision_feats[layer_idx],
# None,
# current_predictions,
# current_masks,
# frame_idx)
# visual_embedding_sample_list += current_select_embeddings
# visual_label_list += current_select_labels
#
#
#
# if len(embedding_sample_list) == 0: return 0.
# embedding_sample_list = torch.cat(embedding_sample_list, dim=0).squeeze()
# label_list = torch.cat(label_list, dim=0).unsqueeze(-1)
# total_limits = 15240
# if embedding_sample_list.shape[0] > total_limits:
# rand_index = torch.randperm(embedding_sample_list.shape[0])[total_limits]
# embedding_sample_list = embedding_sample_list[:rand_index]
# label_list = label_list[:rand_index]
# loss = self.info_nce(embedding_sample_list, label_list, embedding_sample_list,
# label_list)
# return loss
"""
# embeddings_size = (int(self.param.image_size/16), int(self.param.image_size/16))
# masks = torch.nn.functional.interpolate(masks.float(), embeddings_size, mode='nearest')
# masks = masks.flatten(start_dim=1)
# predictions = torch.nn.functional.interpolate(predictions.float(), embeddings_size, mode='nearest')
# predictions = predictions.flatten(start_dim=1)
#
# embedding_sample_list = []
# label_list = []
# contras_sample_list = []
# contras_label_list = []
# temp3.
# embedding_visual, embedding_audio = embeddings
# embedding_visual = torch.nn.functional.normalize(embedding_visual, p=2, dim=1)
# embedding_audio = torch.nn.functional.normalize(embedding_audio, p=2, dim=1)
# embedding_visual = embedding_visual.reshape(self.param.batch_size, int(embedding_visual.shape[0]/self.param.batch_size),
# *embedding_visual.shape[-2:])
#
# embedding_audio = embedding_audio.reshape(self.param.batch_size, int(embedding_audio.shape[0]/self.param.batch_size),
# *embedding_audio.shape[-2:])
# masks = masks.reshape(self.param.batch_size, int(masks.shape[0]/self.param.batch_size),
# masks.shape[-1])
# predictions = predictions.reshape(self.param.batch_size, int(predictions.shape[0]/self.param.batch_size),
# predictions.shape[-1])
#
# for batch_idx in range(masks.shape[0]):
# current_video_clip_embed = embedding_visual[batch_idx]
# current_video_clip_masks = masks[batch_idx]
# current_video_clip_preds = predictions[batch_idx]
# current_audio_clip_embed = embedding_audio[batch_idx]
# # print(current_video_clip_embed.shape, current_audio_clip_embed.shape, current_video_clip_masks.shape, current_video_clip_preds.shape)
# # exit(1)
# for sample_idx in range(masks.shape[1]):
# current_vision_feats = current_video_clip_embed[batch_idx]
# current_audio_feats = current_audio_clip_embed[batch_idx]
# current_masks = current_video_clip_masks[batch_idx]
# current_predictions = current_video_clip_preds[batch_idx]
# current_select_embeddings, current_select_labels = self.select_class_wise_samples(current_vision_feats,
# current_audio_feats,
# current_predictions,
# current_masks,
# batch_idx)
# temp2.
embeddings = torch.nn.functional.normalize(embeddings, p=2, dim=1)
embeddings = embeddings.reshape(self.param.batch_size, int(embeddings.shape[0]/self.param.batch_size),
*embeddings.shape[-2:])
masks = masks.reshape(self.param.batch_size, int(masks.shape[0]/self.param.batch_size),
masks.shape[-1])
predictions = predictions.reshape(self.param.batch_size, int(predictions.shape[0]/self.param.batch_size),
predictions.shape[-1])
for batch_idx in range(masks.shape[0]):
current_video_clip_embed = embeddings[batch_idx]
current_video_clip_masks = masks[batch_idx]
current_video_clip_preds = predictions[batch_idx]
# current_audio_clip_feats =
for sample_idx in range(masks.shape[1]):
current_vision_feats = current_video_clip_embed[batch_idx]
current_masks = current_video_clip_masks[batch_idx]
current_predictions = current_video_clip_preds[batch_idx]
current_select_embeddings, current_select_labels = self.select_class_wise_samples(current_vision_feats,
current_predictions,
current_masks,
batch_idx)
embedding_sample_list += current_select_embeddings
label_list += current_select_labels
# hard_indices = current_vision_feats[(current_masks != current_predictions).nonzero()]
# easy_indices = current_vision_feats[(current_masks == current_predictions).nonzero()]
#
# hard_indices_num, easy_indices_num = hard_indices.shape[0], easy_indices.shape[0]
#
# # the number that is selected to the contrastive learning.
# selective_num_hard = min(sample_limits, hard_indices_num)
# selective_num_easy = min(sample_limits, easy_indices_num)
# # skip if contains too limited samples.
# if selective_num_hard < 10 or selective_num_easy < 10:
# continue
#
# hard_chosen_indices = torch.randperm(hard_indices_num)[:selective_num_hard]
# embedding_sample_list.append(hard_indices[hard_chosen_indices])
# label_list.append(current_masks[hard_chosen_indices] + batch_idx * 1e3)
#
# # add negative features to list.
# easy_chosen_indices = torch.randperm(easy_indices_num)[:selective_num_easy]
# embedding_sample_list.append(easy_indices[easy_chosen_indices])
# label_list.append(current_masks[easy_chosen_indices] + batch_idx * 1e3)
if len(embedding_sample_list) == 0: return 0.
embedding_sample_list = torch.cat(embedding_sample_list, dim=0).squeeze()
label_list = torch.cat(label_list, dim=0).unsqueeze(-1)
total_limits = self.total_limits
if embedding_sample_list.shape[0] > total_limits:
rand_index = torch.randperm(embedding_sample_list.shape[0])[total_limits]
embedding_sample_list = embedding_sample_list[:rand_index]
label_list = label_list[:rand_index]
loss = self.info_nce(embedding_sample_list, label_list, embedding_sample_list,
label_list)
# temp.
# sample_limits = 500
# for batch_idx in range(masks.shape[0]):
# # go through 3 layers embeddings.
# for j in range(len(embeddings)):
# current_vision_feats_list = embeddings[j]
# current_vision_feats = torch.nn.functional.normalize(current_vision_feats_list[batch_idx], p=2, dim=1)
# current_masks = masks[batch_idx]
# positive_indices = current_vision_feats[current_masks > 0, ...]
# negative_indices = current_vision_feats[current_masks == 0, ...]
# positive_indices_num, negative_indices_num = positive_indices.shape[0], negative_indices.shape[0]
#
# # the number that is selected to the contrastive learning.
# selective_num = min(sample_limits, positive_indices_num, negative_indices_num)
# if selective_num < 50: continue # skip if contains too limited samples.
#
# embedding_sample_list.append(positive_indices[torch.randperm(positive_indices_num)[:selective_num]])
# label_list.append(torch.tensor([batch_idx + (self.param.local_rank * 100)] * selective_num,
# device=positive_indices.device))
#
# # add negative features to list.
# negative_sample_list.append(negative_indices[torch.randperm(negative_indices_num)[:selective_num]])
# negative_label_list.append(torch.tensor([-1] * selective_num, device=negative_indices.device))
#
# if len(embedding_sample_list) == 0: return 0.
# embedding_sample_list = torch.cat(embedding_sample_list, dim=0)
# negative_sample_list = torch.cat(negative_sample_list, dim=0)
# label_list = torch.cat(label_list)
# negative_label_list = torch.cat(negative_label_list)
#
# loss = self.info_nce(embedding_sample_list, label_list.unsqueeze(-1),
# torch.cat([embedding_sample_list, negative_sample_list], dim=0),
# torch.cat([label_list, negative_label_list]).unsqueeze(-1))
# output_list_embeddings = [torch.zeros_like(embedding_sample_list) for _ in range(torch.distributed.get_world_size())]
# output_list_labels = [torch.zeros_like(label_list) for _ in range(torch.distributed.get_world_size())]
#
# torch.distributed.all_gather(output_list_embeddings, embedding_sample_list)
# torch.distributed.all_gather(output_list_labels, label_list)
#
# output_list_embeddings = torch.cat(output_list_embeddings)
# output_list_labels = torch.cat(output_list_labels, dim=1)
# loss = self.info_nce(output_list_embeddings, output_list_labels, output_list_embeddings, output_list_labels)
return loss
"""
# q_max.
# def forward(self, embeddings, masks):
# # for single-sounding obj. only, with first idx mask.
# masks = torch.nn.functional.interpolate(masks.float(), (64, 64), mode='bilinear', align_corners=False)
# masks = masks.flatten(start_dim=1)
# # embedding_sample_list = torch.zeros([masks.shape[0], 128]).to(self.param.local_rank)
# embedding_sample_list = []
# label_list = []
#
# negative_sample_list = []
# negative_label_list = []
# sample_limits = 20
# for batch_idx in range(masks.shape[0]):
# # go through 3 layers embeddings.
# for j in range(len(embeddings)):
# current_vision_feats_list, current_audio_feats_list = embeddings[j]
# current_audio_feats = torch.nn.functional.normalize(current_audio_feats_list[batch_idx], p=2, dim=1)
# current_vision_feats = torch.nn.functional.normalize(current_vision_feats_list[batch_idx], p=2, dim=1)
# current_masks = masks[batch_idx]
#
# # add following features to list.
# embedding_sample_list.append(current_vision_feats[current_masks > 0, ...].max(dim=0)[0].unsqueeze(0))
# label_list.append(batch_idx + (self.param.local_rank * 100))
#
# embedding_sample_list.append(current_audio_feats)
# label_list.append(batch_idx + (self.param.local_rank * 100))
#
# # add negative features to list.
# negative_num = min(current_vision_feats[current_masks == 0, ...].shape[0], sample_limits)
# if negative_num < 5: continue # skip if contains too limited samples.
# rand_idx = torch.randperm(current_vision_feats[current_masks == 0, ...].shape[0])[:negative_num]
# negative_sample_list.append(current_vision_feats[current_masks == 0, ...][rand_idx])
# negative_label_list.append(torch.tensor([-1] * negative_num, device=current_vision_feats.device))
#
# embedding_sample_list = torch.cat(embedding_sample_list)
# label_list = torch.tensor(label_list, device=masks.device)
# negative_sample_list = torch.cat(negative_sample_list, dim=0)
# negative_label_list = torch.cat(negative_label_list)
#
# loss = self.info_nce(embedding_sample_list, label_list.unsqueeze(-1),
# torch.cat([embedding_sample_list, negative_sample_list], dim=0),
# torch.cat([label_list, negative_label_list]).unsqueeze(-1))
#
# # output_list_embeddings = [torch.zeros_like(embedding_sample_list) for _ in range(torch.distributed.get_world_size())]
# # output_list_labels = [torch.zeros_like(label_list) for _ in range(torch.distributed.get_world_size())]
# #
# # torch.distributed.all_gather(output_list_embeddings, embedding_sample_list)
# # torch.distributed.all_gather(output_list_labels, label_list)
# #
# # output_list_embeddings = torch.cat(output_list_embeddings)
# # output_list_labels = torch.cat(output_list_labels, dim=1)
# # loss = self.info_nce(output_list_embeddings, output_list_labels, output_list_embeddings, output_list_labels)
# return loss
# attention mean.
# def forward(self, embeddings):
# embedding_sample_list = []
# label_list = []
# for layer_embeddings in embeddings:
# embedding_sample_list.append(torch.nn.functional.normalize(layer_embeddings, p=2, dim=1))
# # currently we only utilise single frame.
# label_list.append(torch.tensor(list(range(0, 1 + 1)) * self.param.batch_size) + (self.param.local_rank * 100))
# embedding_sample_list = torch.cat(embedding_sample_list).cuda(self.param.local_rank)
# label_list = torch.cat(label_list).cuda(self.param.local_rank).unsqueeze(0)
#
# """
# all gather implementation.
# """
# """
# output_list_embeddings = [torch.zeros_like(embedding_sample_list) for _ in range(torch.distributed.get_world_size())]
# output_list_labels = [torch.zeros_like(label_list) for _ in range(torch.distributed.get_world_size())]
#
# torch.distributed.all_gather(output_list_embeddings, embedding_sample_list)
# torch.distributed.all_gather(output_list_labels, label_list)
#
# output_list_embeddings = torch.cat(output_list_embeddings)
# output_list_labels = torch.cat(output_list_labels, dim=1)
# loss = self.info_nce(output_list_embeddings, output_list_labels, output_list_embeddings, output_list_labels)
# """
# loss = self.info_nce(embedding_sample_list, label_list, embedding_sample_list, label_list)
# # frame_token_semantic_attn = torch.nn.functional.normalize(frame_token_semantic_attn.squeeze(), p=2, dim=1)
# # audio_token_attn = torch.nn.functional.normalize(audio_token_attn, p=2, dim=1)
# # city_gt = torch.nn.functional.interpolate(city_gt.unsqueeze(1).float(), size=city_proj.shape[2:],
# # mode='nearest').squeeze().long()
# #
# # ood_gt = torch.nn.functional.interpolate(ood_gt.unsqueeze(1).float(), size=ood_proj.shape[2:],
# # mode='nearest').squeeze().long()
# #
# # # normalise the embed results
# # city_proj = torch.nn.functional.normalize(city_proj, p=2, dim=1)
# # ood_proj = torch.nn.functional.normalize(ood_proj, p=2, dim=1)
#
# # randomly extract embed samples within a batch
# # anchor_embeds, anchor_labels, contrs_embeds, contrs_labels = self.extraction_samples(city_proj, city_gt,
# # ood_proj, ood_gt)
# #
# # # calculate the CoroCL
# # loss = self.info_nce(anchors_=anchor_embeds, a_labels_=anchor_labels.unsqueeze(1), contras_=contrs_embeds,
# # c_labels_=contrs_labels.unsqueeze(1)) if anchor_embeds.nelement() > 0 else \
# # torch.tensor([.0], device=city_proj.device)
#
# return loss
@staticmethod
def manipulate_cover_mask(a_label, current_mask):
# shifting current visual index value
# background:=1, foreground:=2.
a_label = a_label + 1
visual_mask = torch.matmul(a_label, torch.transpose(a_label, 0, 1))
# kicked out the positive value in same visual class.
current_mask[:visual_mask.shape[1], :visual_mask.shape[0]][visual_mask == 1.] = 0
current_mask[:visual_mask.shape[1], :visual_mask.shape[0]][visual_mask == 4.] = 0
return current_mask
# The implementation of cross-image contrastive learning is based on:
# https://github.com/tfzhou/ContrastiveSeg/blob/287e5d3069ce6d7a1517ddf98e004c00f23f8f99/lib/loss/loss_contrast.py
def info_nce(self, anchors_, a_labels_, contras_, c_labels_):
c_labels_ = torch.cat([a_labels_, c_labels_])
contras_ = torch.cat([anchors_, contras_])
# calculates the binary mask: same category => 1, different categories => 0
mask = torch.eq(a_labels_, torch.transpose(c_labels_, 0, 1)).float()
# calculates the dot product
anchor_dot_contrast = torch.div(torch.matmul(anchors_, torch.transpose(contras_, 0, 1)),
self.temperature)
# for numerical stability
logits_max, _ = torch.max(anchor_dot_contrast, dim=1, keepdim=True)
logits = anchor_dot_contrast - logits_max.detach()
# calculates the negative mask
neg_mask = 1 - mask
# avoid the self duplicate issue
mask = self.manipulate_cover_mask(a_label=a_labels_, current_mask=mask)
mask = mask.fill_diagonal_(0.)
# sum the negative odot results
neg_logits = torch.exp(logits) * neg_mask
neg_logits = neg_logits.sum(1, keepdim=True)
exp_logits = torch.exp(logits)
# log_prob -> log(exp(x))-log(exp(x) + exp(y))
# log_prob -> log{exp(x)/[exp(x)+exp(y)]}
log_prob = logits - torch.log(exp_logits + neg_logits)
# log_prob = logits - torch.log(exp_logits.sum(1, keepdim=True))
# calculate the info-nce based on the positive samples (under same categories)
mask_pos_pairs = mask.sum(1)
mask_pos_pairs = torch.where(mask_pos_pairs < 1e-6, 1, mask_pos_pairs)
# mean_log_prob_pos = (mask * log_prob).sum(1) / mask_pos_pairs.sum(1)
mean_log_prob_pos = (mask * log_prob).sum(1) / mask_pos_pairs
assert not torch.isnan(mean_log_prob_pos).any(), print(torch.isnan(log_prob).any())
return - mean_log_prob_pos.mean()
# def extraction_samples(self, city_embd, city_label, ood_embd, ood_label):
# # reformat the matrix
# city_embd = city_embd.flatten(start_dim=2).permute(0, 2, 1)
# city_label = city_label.flatten(start_dim=1)
# ood_embd = ood_embd.flatten(start_dim=2).permute(0, 2, 1)
# ood_label = ood_label.flatten(start_dim=1)
#
# # define different types of embeds
# city_positive = city_embd[city_label == self.ood_idx]
# city_negative = city_embd[(city_label != self.ood_idx) & (city_label != self.ignore_idx)]
# ood_positive = ood_embd[ood_label == self.ood_idx]
# ood_negative = ood_embd[(ood_label != self.ood_idx) & (ood_label != self.ignore_idx)]
#
# # define the number of choice
# sample_num = int(min(self.max_views, city_positive.shape[0], ood_positive.shape[0],
# city_negative.shape[0], ood_negative.shape[0]))
#
# # randomly extract the anchor set with {city_ood, city_inlier}
# city_positive_anchor = city_positive[torch.randperm(city_positive.shape[0])][:sample_num]
# city_negative_anchor = city_negative[torch.randperm(city_negative.shape[0])][:sample_num]
#
# anchor_embed = torch.cat([city_positive_anchor, city_negative_anchor], dim=0)
#
# anchor_label = torch.cat([torch.empty(city_positive_anchor.shape[0],
# device=city_positive_anchor.device).fill_(1.),
# torch.empty(city_negative_anchor.shape[0],
# device=city_negative_anchor.device).fill_(0.)])
#
# # randomly extract the contras set with {city_ood, city_inlier, coco_ood, coco_inlier}
# city_positive_contras = city_positive_anchor.clone()
# city_negative_contras = city_negative_anchor.clone()
# ood_positive_contras = ood_positive[torch.randperm(ood_positive.shape[0])][:sample_num]
# ood_negative_contras = ood_negative[torch.randperm(ood_negative.shape[0])][:sample_num]
#
# contrs_embed = torch.cat([city_positive_contras, city_negative_contras,
# ood_positive_contras, ood_negative_contras], dim=0)
#
# contrs_label = torch.cat([torch.empty(city_positive_contras.shape[0],
# device=city_positive_contras.device).fill_(1.),
# torch.empty(city_negative_contras.shape[0],
# device=city_negative_contras.device).fill_(0.),
# torch.empty(ood_positive_contras.shape[0],
# device=ood_positive_contras.device).fill_(1.),
# torch.empty(ood_negative_contras.shape[0],
# device=ood_negative_contras.device).fill_(0.)])
#
# return anchor_embed, anchor_label, contrs_embed, contrs_label
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