def calculate_fid_given_multi_list_files(list_files, batch_size, cuda, dims):
block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims]
model = InceptionV3([block_idx])
if cuda:
model.cuda()
m_array = []
s_array = []
for i in range(len(list_files)):
f_list = open(list_files[i], 'r')
one_list = f_list.readlines()
m, s = calculate_activation_statistics(one_list, model, batch_size,
dims, cuda)
print(list_files[i], ' finished')
m_array.append(m)
s_array.append(s)
fid_value_array = []
for i in range(len(paths)):
for j in range(len(paths)):
fid_value = calculate_frechet_distance(m_array[i], s_array[i],
m_array[j], s_array[j])
fid_value_array.append(fid_value)
return fid_value_array
def calculate_fid_given_multi_paths(paths, batch_size, cuda, dims):
"""Calculates the FID of multi paths"""
for p in paths:
if not os.path.exists(p):
raise RuntimeError('Invalid path: %s' % p)
block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims]
model = InceptionV3([block_idx])
if cuda:
model.cuda()
m_array = []
s_array = []
for i in range(len(paths)):
m, s = _compute_statistics_of_path(paths[i], model, batch_size, dims,
cuda)
print(paths[i], ' finished')
m_array.append(m)
s_array.append(s)
fid_value_array = []
for i in range(len(paths)):
for j in range(len(paths)):
fid_value = calculate_frechet_distance(m_array[i], s_array[i],
m_array[j], s_array[j])
fid_value_array.append(fid_value)
return fid_value_array
def calculate_fid_given_list_files(list_files, batch_size, cuda, dims):
f_list0 = open(list_files[0], 'r')
one_list0 = f_list0.readlines()
f_list0.close()
for i in range(len(one_list0)):
one_list0[i] = one_list0[i].split(",")[0]
f_list1 = open(list_files[1], 'r')
one_list1 = f_list1.readlines()
f_list1.close()
for i in range(len(one_list1)):
one_list1[i] = one_list1[i].split(",")[0]
block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims]
model = InceptionV3([block_idx])
if cuda:
model.cuda()
m1, s1 = calculate_activation_statistics(one_list0, model, batch_size,
dims, cuda)
m2, s2 = calculate_activation_statistics(one_list1, model, batch_size,
dims, cuda)
fid_value = calculate_frechet_distance(m1, s1, m2, s2)
return fid_value
def calculate_fid_given_paths(paths, batch_size, cuda, dims):
"""Calculates the FID of two paths"""
for p in paths:
if not os.path.exists(p):
raise RuntimeError('Invalid path: %s' % p)
block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims]
model = InceptionV3([block_idx])
if cuda:
model.cuda()
m1, s1 = _compute_statistics_of_path(paths[0], model, batch_size, dims,
cuda)
m2, s2 = _compute_statistics_of_path(paths[1], model, batch_size, dims,
cuda)
fid_value = calculate_frechet_distance(m1, s1, m2, s2)
return fid_value
def calculate_fid_given_list_files_csv(list_files_pair, data_folders_pair,
batch_size, cuda, dims):
one_list0 = []
for i in range(len(list_files_pair[0])):
list_file = list_files_pair[0][i]
f_list0 = open(list_file, 'r')
one_list0_ = f_list0.readlines()
f_list0.close()
for line in one_list0_:
one_list0.append(data_folders_pair[0][i] + line.split(",")[0])
#print(len(one_list0))
#one_list0 = one_list0[:-int(len(one_list0)/4)]
#print(len(one_list0))
one_list1 = []
for i in range(len(list_files_pair[1])):
list_file = list_files_pair[1][i]
f_list1 = open(list_file, 'r')
one_list1_ = f_list1.readlines()
f_list1.close()
for line in one_list1_:
one_list1.append(data_folders_pair[1][i] + line.split(",")[0])
#print(len(one_list1))
#one_list1 = one_list1[:-int(len(one_list1)/4)]
#print(len(one_list1))
block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims]
model = InceptionV3([block_idx])
if cuda:
model.cuda()
m1, s1 = calculate_activation_statistics(one_list0, model, batch_size,
dims, cuda)
m2, s2 = calculate_activation_statistics(one_list1, model, batch_size,
dims, cuda)
fid_value = calculate_frechet_distance(m1, s1, m2, s2)
return fid_value