def generate_triplets(df, ps, num_triplets, features, preserved_features,
margin):
def make_dictionary_for_face_class(df):
'''
- face_classes = {'class0': [class0_id0, ...], 'class1': [class1_id0, ...], ...}
'''
face_classes = dict()
for idx, label in enumerate(df['class']):
if label not in face_classes:
face_classes[label] = []
face_classes[label].append(df.iloc[idx, 0])
return face_classes
triplets = []
classes = ps['class'].unique()
face_classes = make_dictionary_for_face_class(df)
preserved_image = make_dictionary_for_face_class(ps)
i = 0
now_time = datetime.datetime.now()
while i < num_triplets + 1:
'''
- all three image is selected from new train set
'''
pos_class = np.random.choice([7, 8, 9])
neg_class = np.random.choice([7, 8, 9])
while len(face_classes[pos_class]) < 2:
pos_class = np.random.choice([7, 8, 9])
while pos_class == neg_class:
neg_class = np.random.choice([7, 8, 9])
pos_name = df.loc[df['class'] == pos_class, 'name'].values[0]
neg_name = df.loc[df['class'] == neg_class, 'name'].values[0]
if len(face_classes[pos_class]) == 2:
ianc, ipos = np.random.choice(2, size=2, replace=False)
else:
ianc = np.random.randint(0, len(face_classes[pos_class]))
ipos = np.random.randint(0, len(face_classes[pos_class]))
while ianc == ipos:
ipos = np.random.randint(0, len(face_classes[pos_class]))
ineg = np.random.randint(0, len(face_classes[neg_class]))
triplets.append([
face_classes[pos_class][ianc], face_classes[pos_class][ipos],
face_classes[neg_class][ineg], pos_class, neg_class, pos_name,
neg_name, 1, 1, 1
])
i += 1
i = 0
last_time = now_time
now_time = datetime.datetime.now()
print(now_time - last_time)
while i < num_triplets + 1:
'''
- all three images are selected from preserved images
'''
pos_class = np.random.choice(classes)
neg_class = np.random.choice(classes)
while len(preserved_image[pos_class]) < 2:
pos_class = np.random.choice(classes)
while pos_class == neg_class:
neg_class = np.random.choice(classes)
pos_name = ps.loc[ps['class'] == pos_class, 'name'].values[0]
neg_name = ps.loc[ps['class'] == neg_class, 'name'].values[0]
if len(preserved_image[pos_class]) == 2:
ianc, ipos = np.random.choice(2, size=2, replace=False)
else:
ianc = np.random.randint(0, len(preserved_image[pos_class]))
ipos = np.random.randint(0, len(preserved_image[pos_class]))
while ianc == ipos:
ipos = np.random.randint(0,
len(preserved_image[pos_class]))
ineg = np.random.randint(0, len(preserved_image[neg_class]))
triplets.append([
preserved_image[pos_class][ianc],
preserved_image[pos_class][ipos],
preserved_image[neg_class][ineg], pos_class, neg_class,
pos_name, neg_name, 2, 2, 2
])
i += 1
i = 0
last_time = now_time
now_time = datetime.datetime.now()
print(now_time - last_time)
while i < num_triplets + 1:
'''
-a and p are from preserved images
-n is from new train set
'''
pos_class = np.random.choice(classes)
neg_class = np.random.choice([7, 8, 9])
while len(preserved_image[pos_class]) < 2:
pos_class = np.random.choice(classes)
while pos_class == neg_class:
neg_class = np.random.choice([7, 8, 9])
pos_name = ps.loc[ps['class'] == pos_class, 'name'].values[0]
neg_name = df.loc[df['class'] == neg_class, 'name'].values[0]
if len(preserved_image[pos_class]) == 2:
ianc, ipos = np.random.choice(2, size=2, replace=False)
else:
ianc = np.random.randint(0, len(preserved_image[pos_class]))
ipos = np.random.randint(0, len(preserved_image[pos_class]))
while ianc == ipos:
ipos = np.random.randint(0,
len(preserved_image[pos_class]))
ineg = np.random.randint(0, len(face_classes[neg_class]))
a_position = pos_class * 3 + ianc
p_position = pos_class * 3 + ipos
n_position = \
df.loc[df['class'] == neg_class, 'id'].ix[df['id'] == face_classes[neg_class][ineg]].index.values[0]
l2_dist = PairwiseDistance(2)
dp = l2_dist.forward_val(preserved_features[a_position],
preserved_features[p_position])
dn = l2_dist.forward_val(preserved_features[a_position],
features[n_position])
if dp - dn + margin > 0:
triplets.append([
preserved_image[pos_class][ianc],
preserved_image[pos_class][ipos],
face_classes[neg_class][ineg], pos_class, neg_class,
pos_name, neg_name, 2, 2, 1
])
i += 1
i = 0
last_time = now_time
now_time = datetime.datetime.now()
print(now_time - last_time)
while i < num_triplets + 1:
'''
-a is from preserved images
-p and n are from new train set
'''
pos_class = np.random.choice([7, 8, 9])
neg_class = np.random.choice([7, 8, 9])
while len(face_classes[pos_class]) < 2:
pos_class = np.random.choice([7, 8, 9])
while pos_class == neg_class:
neg_class = np.random.choice([7, 8, 9])
pos_name = ps.loc[ps['class'] == pos_class, 'name'].values[0]
neg_name = df.loc[df['class'] == neg_class, 'name'].values[0]
if len(face_classes[pos_class]) == 2:
ianc, ipos = np.random.choice(2, size=2, replace=False)
else:
ianc = np.random.randint(0, len(preserved_image[pos_class]))
ipos = np.random.randint(0, len(face_classes[pos_class]))
ineg = np.random.randint(0, len(face_classes[neg_class]))
a_position = \
ps.loc[ps['class'] == pos_class, 'id'].ix[ps['id'] == preserved_image[pos_class][ianc]].index.values[0]
p_position = \
df.loc[df['class'] == pos_class, 'id'].ix[df['id'] == face_classes[pos_class][ipos]].index.values[0]
n_position = \
df.loc[df['class'] == neg_class, 'id'].ix[df['id'] == face_classes[neg_class][ineg]].index.values[0]
l2_dist = PairwiseDistance(2)
dp = l2_dist.forward_val(preserved_features[a_position],
features[p_position])
dn = l2_dist.forward_val(preserved_features[a_position],
features[n_position])
if dp - dn + margin > 0:
triplets.append([
preserved_image[pos_class][ianc],
face_classes[pos_class][ipos],
face_classes[neg_class][ineg], pos_class, neg_class,
pos_name, neg_name, 2, 1, 1
])
i += 1
i = 0
last_time = now_time
now_time = datetime.datetime.now()
print(now_time - last_time)
while i < num_triplets:
'''
-a and p are from new train set
-n is from pre
'''
pos_class = np.random.choice([7, 8, 9])
neg_class = np.random.choice(classes)
while len(face_classes[pos_class]) < 2:
pos_class = np.random.choice([7, 8, 9])
while pos_class == neg_class:
neg_class = np.random.choice(classes)
pos_name = df.loc[df['class'] == pos_class, 'name'].values[0]
neg_name = ps.loc[ps['class'] == neg_class, 'name'].values[0]
if len(face_classes[pos_class]) == 2:
ianc, ipos = np.random.choice(2, size=2, replace=False)
else:
ianc = np.random.randint(0, len(face_classes[pos_class]))
ipos = np.random.randint(0, len(face_classes[pos_class]))
while ianc == ipos:
ipos = np.random.randint(0, len(face_classes[pos_class]))
ineg = np.random.randint(0, len(preserved_image[neg_class]))
a_position = \
df.loc[df['class'] == pos_class, 'id'].ix[df['id'] == face_classes[pos_class][ianc]].index.values[0]
p_position = \
df.loc[df['class'] == pos_class, 'id'].ix[df['id'] == face_classes[pos_class][ipos]].index.values[0]
n_position = \
ps.loc[ps['class'] == neg_class, 'id'].ix[ps['id'] == preserved_image[neg_class][ineg]].index.values[0]
l2_dist = PairwiseDistance(2)
dp = l2_dist.forward_val(features[a_position],
features[p_position])
dn = l2_dist.forward_val(features[a_position],
preserved_features[n_position])
if dp - dn + margin > 0:
triplets.append([
face_classes[pos_class][ianc],
face_classes[pos_class][ipos],
preserved_image[neg_class][ineg], pos_class, neg_class,
pos_name, neg_name, 1, 1, 2
])
i += 1
i = 0
last_time = now_time
now_time = datetime.datetime.now()
print(now_time - last_time)
while i < num_triplets:
'''
-a is from new train set
-p and n is from pre
'''
pos_class = np.random.choice([7, 8, 9])
neg_class = np.random.choice(classes)
while len(face_classes[pos_class]) < 2:
pos_class = np.random.choice([7, 8, 9])
while pos_class == neg_class:
neg_class = np.random.choice(classes)
pos_name = df.loc[df['class'] == pos_class, 'name'].values[0]
neg_name = ps.loc[ps['class'] == neg_class, 'name'].values[0]
if len(face_classes[pos_class]) == 2:
ianc, ipos = np.random.choice(2, size=2, replace=False)
else:
ianc = np.random.randint(0, len(face_classes[pos_class]))
ipos = np.random.randint(0, len(preserved_image[pos_class]))
ineg = np.random.randint(0, len(preserved_image[neg_class]))
a_position = \
df.loc[df['class'] == pos_class, 'id'].ix[df['id'] == face_classes[pos_class][ianc]].index.values[0]
p_position = \
ps.loc[ps['class'] == pos_class, 'id'].ix[ps['id'] == preserved_image[pos_class][ipos]].index.values[0]
n_position = \
ps.loc[ps['class'] == neg_class, 'id'].ix[ps['id'] == preserved_image[neg_class][ineg]].index.values[0]
l2_dist = PairwiseDistance(2)
dp = l2_dist.forward_val(features[a_position],
preserved_features[p_position])
dn = l2_dist.forward_val(features[a_position],
preserved_features[n_position])
if dp - dn + margin > 0:
triplets.append([
face_classes[pos_class][ianc],
preserved_image[pos_class][ipos],
preserved_image[neg_class][ineg], pos_class, neg_class,
pos_name, neg_name, 1, 2, 2
])
i += 1
last_time = now_time
now_time = datetime.datetime.now()
print(now_time - last_time)
return triplets
def select_three_sample(model, args, epoch, writer):
model.eval()
num_each_class = [500, 500, 500, 500, 500, 500, 500, 50, 50, 50]
# num_each_class = [500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500]
transform = transforms.Compose([
transforms.Resize(32),
transforms.ToTensor(),
transforms.Normalize(mean=np.array([0.485, 0.456, 0.406]),
std=np.array([0.229, 0.224, 0.225])),
])
train_set = torchvision.datasets.ImageFolder(root=args.train_set,
transform=transform)
train_loader = torch.utils.data.DataLoader(train_set,
batch_size=args.test_batch_size,
shuffle=False)
NearestCentroid, KNeighbors, features, label, labels = [], [], [], [], []
for i, (data, target) in enumerate(train_loader):
data, target = data.cuda(), target.cuda()
data, target = Variable(data), Variable(target)
output = model(data)
features.extend(output.data)
KNeighbors.extend(output.data.cpu().numpy())
labels.extend(target.data.cpu().numpy())
count = 0
l2_dist = PairwiseDistance(2)
destination = os.path.join(args.check_path, 'epoch' + str(epoch))
if not os.path.exists(destination):
os.mkdir(destination)
for i in range(len(num_each_class)):
num_sample = features[count:count + num_each_class[i]]
m = torch.tensor(np.zeros(args.embedding_size)).float().cuda()
for x in num_sample:
m += x
m /= num_each_class[i]
sample1 = min(num_sample, key=lambda x: l2_dist.forward_val(x, m))
sample2 = max(num_sample,
key=lambda x: l2_dist.forward_val(x, sample1))
sample3 = max(num_sample,
key=lambda x: l2_dist.forward_val(x, sample2))
NearestCentroid.append(sample1.cpu().numpy())
label.append(i)
sample1_loc, sample2_loc, sample3_loc = -1, -1, -1
for j in range(num_sample.__len__()):
if (num_sample[j] == sample1).all():
sample1_loc = j
if (num_sample[j] == sample2).all():
sample2_loc = j
if (num_sample[j] == sample3).all():
sample3_loc = j
frame = pd.read_csv(args.train_set_csv)
destination_class = os.path.join(
destination, str(frame['name'][count + sample1_loc]))
if not os.path.exists(destination_class):
os.mkdir(destination_class)
sample1_source = os.path.join(
args.train_set, str(frame['name'][count + sample1_loc]),
str(frame['id'][count + sample1_loc]) + '.png')
sample2_source = os.path.join(
args.train_set, str(frame['name'][count + sample2_loc]),
str(frame['id'][count + sample2_loc]) + '.png')
sample3_source = os.path.join(
args.train_set, str(frame['name'][count + sample3_loc]),
str(frame['id'][count + sample3_loc]) + '.png')
shutil.copy(sample1_source, destination_class + '/sample1.png')
shutil.copy(sample2_source, destination_class + '/sample2.png')
shutil.copy(sample3_source, destination_class + '/sample3.png')
count += num_each_class[i]
clf = neighbors.NearestCentroid()
clf.fit(NearestCentroid, label)
return features, labels, clf, destination
def generate_triplets(df, num_triplets, features, margin):
def make_dictionary_for_face_class(df):
'''
- face_classes = {'class0': [class0_id0, ...], 'class1': [class1_id0, ...], ...}
'''
face_classes = dict()
for idx, label in enumerate(df['class']):
if label not in face_classes:
face_classes[label] = []
face_classes[label].append(df.iloc[idx, 0])
return face_classes
triplets = []
classes = df['class'].unique()
face_classes = make_dictionary_for_face_class(df)
i = 0
while i < num_triplets:
'''
- randomly choose anchor, positive and negative images for triplet loss
- anchor and positive images in pos_class
- negative image in neg_class
- at least, two images needed for anchor and positive images in pos_class
- negative image should have different class as anchor and positive images by definition
'''
pos_class = np.random.choice(classes)
neg_class = np.random.choice(classes)
while len(face_classes[pos_class]) < 2:
pos_class = np.random.choice(classes)
while pos_class == neg_class:
neg_class = np.random.choice(classes)
pos_name = df.loc[df['class'] == pos_class, 'name'].values[0]
neg_name = df.loc[df['class'] == neg_class, 'name'].values[0]
if len(face_classes[pos_class]) == 2:
ianc, ipos = np.random.choice(2, size=2, replace=False)
else:
ianc = np.random.randint(0, len(face_classes[pos_class]))
ipos = np.random.randint(0, len(face_classes[pos_class]))
while ianc == ipos:
ipos = np.random.randint(0, len(face_classes[pos_class]))
ineg = np.random.randint(0, len(face_classes[neg_class]))
a_position = df.loc[df['class'] == pos_class, 'id'].ix[
df['id'] == face_classes[pos_class][ianc]].index.values[0]
p_position = df.loc[df['class'] == pos_class, 'id'].ix[
df['id'] == face_classes[pos_class][ipos]].index.values[0]
n_position = df.loc[df['class'] == neg_class, 'id'].ix[
df['id'] == face_classes[neg_class][ineg]].index.values[0]
l2_dist = PairwiseDistance(2)
dp = l2_dist.forward_val(features[a_position],
features[p_position])
dn = l2_dist.forward_val(features[a_position],
features[n_position])
if dp - dn + margin > 0:
triplets.append([
face_classes[pos_class][ianc],
face_classes[pos_class][ipos],
face_classes[neg_class][ineg], pos_class, neg_class,
pos_name, neg_name
])
i += 1
return triplets