def main():
feature_train_file = cfg.feature_train_file
feature_test_file = cfg.feature_test_file
train_dataset = PlaceDateset(feature_train_file)
test_dataset = PlaceDateset(feature_test_file)
train_loader = DataLoader(train_dataset,
batch_size=cfg.batch_size,
shuffle=True)
test_loader = DataLoader(test_dataset,
batch_size=cfg.batch_size,
shuffle=False)
model = Feature_class(cfg).cuda()
optimizer = optim.__dict__[cfg.optim.name](model.parameters(),
**cfg.optim.setting)
#在指定的epoch对其进行衰减
scheduler = optim.lr_scheduler.__dict__[cfg.stepper.name](
optimizer, **cfg.stepper.setting)
criterion1 = nn.CrossEntropyLoss()
distance = CosineSimilarity()
criterion2 = losses.TripletMarginLoss(distance=distance)
total_loss = list()
total_epoch = list()
total_ap = list()
total_acc = list()
max_ap = 0
for epoch in range(0, cfg.epoch):
train(cfg, model, train_loader, optimizer, scheduler, epoch,
criterion1, criterion2)
loss, ap, acc = test(cfg, model, test_loader, criterion1, criterion2)
total_loss.append(loss)
total_ap.append(ap)
total_epoch.append(epoch)
total_acc.append(acc)
print('Test Epoch: {} \tloss: {:.6f}\tap: {:.6f}\t acc: {:.6f}'.format(
epoch, loss, ap, acc))
if ap > max_ap:
best_model = model
save_path = cfg.store + '.pth'
torch.save(best_model.state_dict(), save_path)
plt.figure()
plt.plot(total_epoch, total_loss, 'b-', label=u'loss')
plt.legend()
loss_path = cfg.store + "_loss.png"
plt.savefig(loss_path)
plt.figure()
plt.plot(total_epoch, total_ap, 'b-', label=u'AP')
plt.legend()
AP_path = cfg.store + "_AP.png"
plt.savefig(AP_path)
plt.figure()
plt.plot(total_epoch, total_acc, 'b-', label=u'acc')
plt.legend()
acc_path = cfg.store + "_acc.png"
plt.savefig(acc_path)
def __init__(
self,
model,
margin=0.2,
lr=1e-3,
lr_patience=2,
lr_decay_ratio=0.5,
memory_batch_max_num=2048,
):
super().__init__()
self.save_hyperparameters()
self.model = model
self.margin = margin
self.lr = lr
self.lr_patience = lr_patience
self.lr_decay_ratio = lr_decay_ratio
self.memory_batch_max_num = memory_batch_max_num
self.loss_func = losses.CrossBatchMemory(
losses.ContrastiveLoss(pos_margin=1,
neg_margin=0,
distance=CosineSimilarity()),
self.model.feature_dim,
memory_size=self.memory_batch_max_num,
miner=miners.MultiSimilarityMiner(epsilon=self.margin))
def setUpClass(self):
self.device = torch.device('cuda')
self.dist_miner = BatchHardMiner(distance=LpDistance(normalize_embeddings=False))
self.normalized_dist_miner = BatchHardMiner(distance=LpDistance(normalize_embeddings=True))
self.normalized_dist_miner_squared = BatchHardMiner(distance=LpDistance(normalize_embeddings=True, power=2))
self.sim_miner = BatchHardMiner(distance=CosineSimilarity())
self.labels = torch.LongTensor([0, 0, 1, 1, 0, 2, 1, 1, 1])
self.correct_a = torch.LongTensor([0, 1, 2, 3, 4, 6, 7, 8]).to(self.device)
self.correct_p = torch.LongTensor([4, 4, 8, 8, 0, 2, 2, 2]).to(self.device)
self.correct_n = [torch.LongTensor([2, 2, 1, 4, 3, 5, 5, 5]).to(self.device), torch.LongTensor([2, 2, 1, 4, 5, 5, 5, 5]).to(self.device)]
def test_pair_margin_miner(self):
for dtype in TEST_DTYPES:
for distance in [LpDistance(), CosineSimilarity()]:
embedding_angles = torch.arange(0, 16)
embeddings = torch.tensor([c_f.angle_to_coord(a) for a in embedding_angles], requires_grad=True, dtype=dtype).to(self.device) #2D embeddings
labels = torch.randint(low=0, high=2, size=(16,))
mat = distance(embeddings)
pos_pairs = []
neg_pairs = []
for i in range(len(embeddings)):
anchor_label = labels[i]
for j in range(len(embeddings)):
if j == i:
continue
positive_label = labels[j]
if positive_label == anchor_label:
ap_dist = mat[i,j]
pos_pairs.append((i, j, ap_dist))
for i in range(len(embeddings)):
anchor_label = labels[i]
for j in range(len(embeddings)):
if j == i:
continue
negative_label = labels[j]
if negative_label != anchor_label:
an_dist = mat[i,j]
neg_pairs.append((i, j, an_dist))
for pos_margin_int in range(-1, 4):
pos_margin = float(pos_margin_int) * 0.05
for neg_margin_int in range(2, 7):
neg_margin = float(neg_margin_int) * 0.05
miner = PairMarginMiner(pos_margin, neg_margin, distance=distance)
correct_pos_pairs = []
correct_neg_pairs = []
for i,j,k in pos_pairs:
condition = (k < pos_margin) if distance.is_inverted else (k > pos_margin)
if condition:
correct_pos_pairs.append((i,j))
for i,j,k in neg_pairs:
condition = (k > neg_margin) if distance.is_inverted else (k < neg_margin)
if condition:
correct_neg_pairs.append((i,j))
correct_pos = set(correct_pos_pairs)
correct_neg = set(correct_neg_pairs)
a1, p1, a2, n2 = miner(embeddings, labels)
mined_pos = set([(a.item(),p.item()) for a,p in zip(a1,p1)])
mined_neg = set([(a.item(),n.item()) for a,n in zip(a2,n2)])
self.assertTrue(mined_pos == correct_pos)
self.assertTrue(mined_neg == correct_neg)
def test_backward(self):
loss_funcA = ContrastiveLoss()
loss_funcB = ContrastiveLoss(distance=CosineSimilarity())
for dtype in TEST_DTYPES:
for loss_func in [loss_funcA, loss_funcB]:
embedding_angles = [0]
embeddings = torch.tensor(
[c_f.angle_to_coord(a) for a in embedding_angles],
requires_grad=True,
dtype=dtype).to(self.device) #2D embeddings
labels = torch.LongTensor([0])
loss = loss_func(embeddings, labels)
loss.backward()
def test_with_no_valid_pairs(self):
loss_funcA = ContrastiveLoss()
loss_funcB = ContrastiveLoss(distance=CosineSimilarity())
for dtype in TEST_DTYPES:
embedding_angles = [0]
embeddings = torch.tensor(
[c_f.angle_to_coord(a) for a in embedding_angles],
requires_grad=True,
dtype=dtype).to(self.device) #2D embeddings
labels = torch.LongTensor([0])
lossA = loss_funcA(embeddings, labels)
lossB = loss_funcB(embeddings, labels)
self.assertEqual(lossA, 0)
self.assertEqual(lossB, 0)
def setUpClass(self):
self.device = torch.device('cuda')
self.dist_miner = HDCMiner(
filter_percentage=0.3,
distance=LpDistance(normalize_embeddings=False))
self.normalized_dist_miner = HDCMiner(
filter_percentage=0.3,
distance=LpDistance(normalize_embeddings=True))
self.normalized_dist_miner_squared = HDCMiner(
filter_percentage=0.3,
distance=LpDistance(normalize_embeddings=True, power=2))
self.sim_miner = HDCMiner(filter_percentage=0.3,
distance=CosineSimilarity())
self.labels = torch.LongTensor([0, 0, 1, 1, 1, 0])
correct_a1 = torch.LongTensor([0, 5, 1, 5])
correct_p = torch.LongTensor([5, 0, 5, 1])
self.correct_pos_pairs = torch.stack([correct_a1, correct_p],
dim=1).to(self.device)
correct_a2 = torch.LongTensor([1, 2, 4, 5, 0, 2])
correct_n = torch.LongTensor([2, 1, 5, 4, 2, 0])
self.correct_neg_pairs = torch.stack([correct_a2, correct_n],
dim=1).to(self.device)
default=3,
help='number of patches to compare with cam1')
parser.add_argument('--patches_to_compare_c2',
type=int,
default=5,
help='number of patches to compare with cam2')
return parser.parse_args(args)
if __name__ == '__main__':
args = parse_args()
trunk, embedder = load_trunk_embedder(args.trunk_model,
args.embedder_model)
match_finder = MatchFinder(distance=CosineSimilarity(), threshold=args.thr)
inference_model = InferenceModel(trunk,
embedder,
match_finder=match_finder,
batch_size=64)
labels = pd.read_csv(args.det_csv)
test_data, _ = get_data_loader('test', labels, args.det_patches)
indices_cameras = c_f.get_labels_to_indices(test_data.camera)
id_frames_cams = {}
for cam in ['c010', 'c011', 'c012', 'c013', 'c014', 'c015']:
id_frames_cams[cam] = get_id_frames_cam(test_data, indices_cameras,
cam)
id_frames_all = deepcopy(id_frames_cams['c010'])
def test_multi_similarity_miner(self):
epsilon = 0.1
for dtype in TEST_DTYPES:
for distance in [CosineSimilarity(), LpDistance()]:
miner = MultiSimilarityMiner(epsilon, distance=distance)
embedding_angles = torch.arange(0, 64)
embeddings = torch.tensor(
[c_f.angle_to_coord(a) for a in embedding_angles],
requires_grad=True,
dtype=dtype,
).to(TEST_DEVICE) # 2D embeddings
labels = torch.randint(low=0, high=10, size=(64, ))
mat = distance(embeddings)
pos_pairs = []
neg_pairs = []
for i in range(len(embeddings)):
anchor_label = labels[i]
for j in range(len(embeddings)):
if j != i:
other_label = labels[j]
if anchor_label == other_label:
pos_pairs.append((i, j, mat[i, j]))
if anchor_label != other_label:
neg_pairs.append((i, j, mat[i, j]))
correct_a1, correct_p = [], []
correct_a2, correct_n = [], []
for a1, p, ap_sim in pos_pairs:
most_difficult = (c_f.neg_inf(dtype)
if distance.is_inverted else
c_f.pos_inf(dtype))
for a2, n, an_sim in neg_pairs:
if a2 == a1:
condition = ((an_sim > most_difficult)
if distance.is_inverted else
(an_sim < most_difficult))
if condition:
most_difficult = an_sim
condition = ((ap_sim < most_difficult + epsilon)
if distance.is_inverted else
(ap_sim > most_difficult - epsilon))
if condition:
correct_a1.append(a1)
correct_p.append(p)
for a2, n, an_sim in neg_pairs:
most_difficult = (c_f.pos_inf(dtype)
if distance.is_inverted else
c_f.neg_inf(dtype))
for a1, p, ap_sim in pos_pairs:
if a2 == a1:
condition = ((ap_sim < most_difficult)
if distance.is_inverted else
(ap_sim > most_difficult))
if condition:
most_difficult = ap_sim
condition = ((an_sim > most_difficult - epsilon)
if distance.is_inverted else
(an_sim < most_difficult + epsilon))
if condition:
correct_a2.append(a2)
correct_n.append(n)
correct_pos_pairs = set([
(a, p) for a, p in zip(correct_a1, correct_p)
])
correct_neg_pairs = set([
(a, n) for a, n in zip(correct_a2, correct_n)
])
a1, p1, a2, n2 = miner(embeddings, labels)
pos_pairs = set([(a.item(), p.item()) for a, p in zip(a1, p1)])
neg_pairs = set([(a.item(), n.item()) for a, n in zip(a2, n2)])
self.assertTrue(pos_pairs == correct_pos_pairs)
self.assertTrue(neg_pairs == correct_neg_pairs)
def test_triplet_margin_miner(self):
for dtype in TEST_DTYPES:
for distance in [LpDistance(), CosineSimilarity()]:
embedding_angles = torch.arange(0, 16)
embeddings = torch.tensor(
[c_f.angle_to_coord(a) for a in embedding_angles],
requires_grad=True,
dtype=dtype,
).to(self.device) # 2D embeddings
labels = torch.randint(low=0, high=2, size=(16, ))
mat = distance(embeddings)
triplets = []
for i in range(len(embeddings)):
anchor_label = labels[i]
for j in range(len(embeddings)):
if j == i:
continue
positive_label = labels[j]
if positive_label == anchor_label:
ap_dist = mat[i, j]
for k in range(len(embeddings)):
if k == j or k == i:
continue
negative_label = labels[k]
if negative_label != positive_label:
an_dist = mat[i, k]
if distance.is_inverted:
triplets.append(
(i, j, k, ap_dist - an_dist))
else:
triplets.append(
(i, j, k, an_dist - ap_dist))
for margin_int in range(-1, 11):
margin = float(margin_int) * 0.05
minerA = TripletMarginMiner(margin,
type_of_triplets="all",
distance=distance)
minerB = TripletMarginMiner(margin,
type_of_triplets="hard",
distance=distance)
minerC = TripletMarginMiner(margin,
type_of_triplets="semihard",
distance=distance)
minerD = TripletMarginMiner(margin,
type_of_triplets="easy",
distance=distance)
correctA, correctB, correctC, correctD = [], [], [], []
for i, j, k, distance_diff in triplets:
if distance_diff > margin:
correctD.append((i, j, k))
else:
correctA.append((i, j, k))
if distance_diff > 0:
correctC.append((i, j, k))
if distance_diff <= 0:
correctB.append((i, j, k))
for correct, miner in [
(correctA, minerA),
(correctB, minerB),
(correctC, minerC),
(correctD, minerD),
]:
correct_triplets = set(correct)
a1, p1, n1 = miner(embeddings, labels)
mined_triplets = set([(a.item(), p.item(), n.item())
for a, p, n in zip(a1, p1, n1)])
self.assertTrue(mined_triplets == correct_triplets)
def test_triplet_margin_loss(self):
margin = 0.2
loss_funcA = TripletMarginLoss(margin=margin)
loss_funcB = TripletMarginLoss(margin=margin, reducer=MeanReducer())
loss_funcC = TripletMarginLoss(margin=margin,
distance=CosineSimilarity())
loss_funcD = TripletMarginLoss(margin=margin,
reducer=MeanReducer(),
distance=CosineSimilarity())
for dtype in TEST_DTYPES:
embedding_angles = [0, 20, 40, 60, 80]
embeddings = torch.tensor(
[c_f.angle_to_coord(a) for a in embedding_angles],
requires_grad=True,
dtype=dtype,
).to(self.device) # 2D embeddings
labels = torch.LongTensor([0, 0, 1, 1, 2])
lossA = loss_funcA(embeddings, labels)
lossB = loss_funcB(embeddings, labels)
lossC = loss_funcC(embeddings, labels)
lossD = loss_funcD(embeddings, labels)
triplets = [
(0, 1, 2),
(0, 1, 3),
(0, 1, 4),
(1, 0, 2),
(1, 0, 3),
(1, 0, 4),
(2, 3, 0),
(2, 3, 1),
(2, 3, 4),
(3, 2, 0),
(3, 2, 1),
(3, 2, 4),
]
correct_loss = 0
correct_loss_cosine = 0
num_non_zero_triplets = 0
num_non_zero_triplets_cosine = 0
for a, p, n in triplets:
anchor, positive, negative = embeddings[a], embeddings[
p], embeddings[n]
curr_loss = torch.relu(
torch.sqrt(torch.sum((anchor - positive)**2)) -
torch.sqrt(torch.sum((anchor - negative)**2)) + margin)
curr_loss_cosine = torch.relu(
torch.sum(anchor * negative) -
torch.sum(anchor * positive) + margin)
if curr_loss > 0:
num_non_zero_triplets += 1
if curr_loss_cosine > 0:
num_non_zero_triplets_cosine += 1
correct_loss += curr_loss
correct_loss_cosine += curr_loss_cosine
rtol = 1e-2 if dtype == torch.float16 else 1e-5
self.assertTrue(
torch.isclose(lossA,
correct_loss / num_non_zero_triplets,
rtol=rtol))
self.assertTrue(
torch.isclose(lossB, correct_loss / len(triplets), rtol=rtol))
self.assertTrue(
torch.isclose(lossC,
correct_loss_cosine /
num_non_zero_triplets_cosine,
rtol=rtol))
self.assertTrue(
torch.isclose(lossD,
correct_loss_cosine / len(triplets),
rtol=rtol))
def main():
feature_train_file = cfg.feature_train_file
feature_test_file = cfg.feature_test_file
train_dataset = PlaceDateset(feature_train_file)
test_dataset = PlaceDateset(feature_test_file)
train_loader = DataLoader(train_dataset,
batch_size=cfg.batch_size,
shuffle=True)
test_loader = DataLoader(test_dataset,
batch_size=cfg.batch_size,
shuffle=False)
model = ViT(cfg=cfg,
feature_seq=16,
num_classes=1,
dim=2048,
depth=8,
heads=8,
mlp_dim=1024,
dropout=0.1,
emb_dropout=0.1).cuda()
#model=ViT_cat(cfg=cfg,feature_seq=16,num_classes=2,dim=4096,depth=8,heads=8,mlp_dim=1024,dropout = 0.1,emb_dropout = 0.1).cuda()
optimizer = optim.__dict__[cfg.optim.name](model.parameters(),
**cfg.optim.setting)
#在指定的epoch对其进行衰减
scheduler = optim.lr_scheduler.__dict__[cfg.stepper.name](
optimizer, **cfg.stepper.setting)
#criterion1 = nn.CrossEntropyLoss(torch.Tensor(cfg.loss.weight).cuda())
#criterion1 = nn.BCEWithLogitsLoss()
criterion1 = FocalLoss(logits=True)
#加入对数损失
distance = CosineSimilarity()
criterion2 = losses.TripletMarginLoss(distance=distance)
total_loss = list()
total_epoch = list()
total_ap = list()
total_acc = list()
max_ap = 0
for epoch in range(0, cfg.epoch):
train(cfg, model, train_loader, optimizer, scheduler, epoch,
criterion1, criterion2)
loss, ap, acc = test(cfg, model, test_loader, criterion1, criterion2)
total_loss.append(loss)
total_ap.append(ap)
total_epoch.append(epoch)
total_acc.append(acc)
print('Test Epoch: {} \tloss: {:.6f}\tap: {:.6f}\tacc: {:.6f}'.format(
epoch, loss, ap, acc))
if ap > max_ap:
best_model = model
save_path = cfg.store + '.pth'
torch.save(best_model.state_dict(), save_path)
plt.figure()
plt.plot(total_epoch, total_loss, 'b-', label=u'loss')
plt.legend()
loss_path = cfg.store + "_loss.png"
plt.savefig(loss_path)
plt.figure()
plt.plot(total_epoch, total_ap, 'b-', label=u'AP')
plt.legend()
AP_path = cfg.store + "_AP.png"
plt.savefig(AP_path)
plt.figure()
plt.plot(total_epoch, total_acc, 'b-', label=u'acc')
plt.legend()
acc_path = cfg.store + "_acc.png"
plt.savefig(acc_path)
def get_pos_neg_vals(self, use_pairwise):
output = (0, 1, LpDistance(power=2))
if not use_pairwise:
return (1, 0, CosineSimilarity())
return output
def main():
muti_train_file = cfg.muti_train_file
muti_test_file = cfg.muti_test_file
train_dataset = MutiDateset(muti_train_file)
test_dataset = MutiDateset(muti_test_file)
train_loader = DataLoader(train_dataset,batch_size=cfg.batch_size,shuffle=True)
test_loader = DataLoader(test_dataset,batch_size=cfg.batch_size ,shuffle=False)
model1 = Dense_fenlei(num_classes=2,dim = 2048,dropout = 0.5).cuda()
model2 = ViT(cfg=cfg,feature_seq=16,num_classes=1,dim=2048,depth=8,heads=8,mlp_dim=1024,dropout = 0.1,emb_dropout = 0.1,batch_normalization=False).cuda()
model3 = ViT(cfg=cfg,feature_seq=16,num_classes=1,dim=2048,depth=8,heads=8,mlp_dim=1024,dropout = 0.1,emb_dropout = 0.1).cuda()
optimizer1 = optim.__dict__[cfg.optim1.name](model1.parameters(), **cfg.optim1.setting)
optimizer2 = optim.__dict__[cfg.optim2.name](model2.parameters(), **cfg.optim2.setting)
optimizer3 = optim.__dict__[cfg.optim3.name](model3.parameters(), **cfg.optim3.setting)
#在指定的epoch对其进行衰减
scheduler = optim.lr_scheduler.__dict__[cfg.stepper.name](optimizer1, **cfg.stepper.setting)
criterion3 = nn.CrossEntropyLoss(torch.Tensor(cfg.loss.weight).cuda())
#criterion1 = nn.BCEWithLogitsLoss()
criterion1 = FocalLoss(logits=True)
#加入对数损失
distance = CosineSimilarity()
criterion2 = losses.TripletMarginLoss(distance = distance)
total_loss, total_loss_place, total_loss_tea=list(), list(), list()
total_epoch=list()
total_ap, total_ap_place, total_ap_tea=list(),list(),list()
total_acc=list()
max_ap=0
for epoch in range(0,cfg.epoch):
train_mult(cfg, model1,model2,model3, train_loader, optimizer1,optimizer2, optimizer3, scheduler, epoch, criterion1,criterion2,criterion3)
loss,loss_place,loss_tea,ap,ap_place,ap_tea,acc=test_mult(cfg, model1, model2, model3, test_loader, criterion1,criterion2,criterion3)
total_loss.append(loss)
total_ap.append(ap)
total_loss_place.append(loss_place)
total_ap_place.append(ap_place)
total_loss_tea.append(loss_tea)
total_ap_tea.append(ap_tea)
total_epoch.append(epoch)
total_acc.append(acc)
print('Test Epoch: {} \tloss: {:.6f}\tap: {:.6f}\tacc: {:.6f}'.format(epoch, loss,ap,acc))
if ap>max_ap:
best_model=model3
save_path=cfg.store+'.pth'
torch.save(best_model.state_dict(), save_path)
plt.figure(figsize=(20, 20))
plt.plot(total_epoch,total_loss,'b^',label=u'loss')
plt.plot(total_epoch,total_loss_place,'y^',label=u'loss_place')
plt.plot(total_epoch,total_loss_tea,'r^',label=u'loss_tea')
plt.legend()
loss_path=cfg.store+"_loss.png"
plt.savefig(loss_path)
plt.figure(figsize=(20, 20))
plt.plot(total_epoch,total_ap,'b^',label=u'AP')
plt.plot(total_epoch,total_ap_place,'y^',label=u'AP_place')
plt.plot(total_epoch,total_ap_tea,'r^',label=u'AP_tea')
plt.legend()
AP_path=cfg.store+"_AP.png"
plt.savefig(AP_path)
plt.figure()
plt.plot(total_epoch,total_acc,'b^',label=u'acc')
plt.legend()
acc_path=cfg.store+"_acc.png"
plt.savefig(acc_path)
def test_contrastive_loss(self):
loss_funcA = ContrastiveLoss(pos_margin=0.25,
neg_margin=1.5,
distance=LpDistance(power=2))
loss_funcB = ContrastiveLoss(pos_margin=1.5,
neg_margin=0.6,
distance=CosineSimilarity())
loss_funcC = ContrastiveLoss(pos_margin=0.25,
neg_margin=1.5,
distance=LpDistance(power=2),
reducer=MeanReducer())
loss_funcD = ContrastiveLoss(pos_margin=1.5,
neg_margin=0.6,
distance=CosineSimilarity(),
reducer=MeanReducer())
for dtype in TEST_DTYPES:
embedding_angles = [0, 20, 40, 60, 80]
embeddings = torch.tensor(
[c_f.angle_to_coord(a) for a in embedding_angles],
requires_grad=True,
dtype=dtype).to(self.device) #2D embeddings
labels = torch.LongTensor([0, 0, 1, 1, 2])
lossA = loss_funcA(embeddings, labels)
lossB = loss_funcB(embeddings, labels)
lossC = loss_funcC(embeddings, labels)
lossD = loss_funcD(embeddings, labels)
pos_pairs = [(0, 1), (1, 0), (2, 3), (3, 2)]
neg_pairs = [(0, 2), (0, 3), (0, 4), (1, 2), (1, 3), (1, 4),
(2, 0), (2, 1), (2, 4), (3, 0), (3, 1), (3, 4),
(4, 0), (4, 1), (4, 2), (4, 3)]
correct_pos_losses = [0, 0, 0, 0]
correct_neg_losses = [0, 0, 0, 0]
num_non_zero_pos = [0, 0, 0, 0]
num_non_zero_neg = [0, 0, 0, 0]
for a, p in pos_pairs:
anchor, positive = embeddings[a], embeddings[p]
correct_lossA = torch.relu(
torch.sum((anchor - positive)**2) - 0.25)
correct_lossB = torch.relu(1.5 -
torch.matmul(anchor, positive))
correct_pos_losses[0] += correct_lossA
correct_pos_losses[1] += correct_lossB
correct_pos_losses[2] += correct_lossA
correct_pos_losses[3] += correct_lossB
if correct_lossA > 0:
num_non_zero_pos[0] += 1
num_non_zero_pos[2] += 1
if correct_lossB > 0:
num_non_zero_pos[1] += 1
num_non_zero_pos[3] += 1
for a, n in neg_pairs:
anchor, negative = embeddings[a], embeddings[n]
correct_lossA = torch.relu(1.5 -
torch.sum((anchor - negative)**2))
correct_lossB = torch.relu(
torch.matmul(anchor, negative) - 0.6)
correct_neg_losses[0] += correct_lossA
correct_neg_losses[1] += correct_lossB
correct_neg_losses[2] += correct_lossA
correct_neg_losses[3] += correct_lossB
if correct_lossA > 0:
num_non_zero_neg[0] += 1
num_non_zero_neg[2] += 1
if correct_lossB > 0:
num_non_zero_neg[1] += 1
num_non_zero_neg[3] += 1
for i in range(2):
if num_non_zero_pos[i] > 0:
correct_pos_losses[i] /= num_non_zero_pos[i]
if num_non_zero_neg[i] > 0:
correct_neg_losses[i] /= num_non_zero_neg[i]
for i in range(2, 4):
correct_pos_losses[i] /= len(pos_pairs)
correct_neg_losses[i] /= len(neg_pairs)
correct_losses = [0, 0, 0, 0]
for i in range(4):
correct_losses[
i] = correct_pos_losses[i] + correct_neg_losses[i]
rtol = 1e-2 if dtype == torch.float16 else 1e-5
self.assertTrue(torch.isclose(lossA, correct_losses[0], rtol=rtol))
self.assertTrue(torch.isclose(lossB, correct_losses[1], rtol=rtol))
self.assertTrue(torch.isclose(lossC, correct_losses[2], rtol=rtol))
self.assertTrue(torch.isclose(lossD, correct_losses[3], rtol=rtol))
