def calcola_confusion_matrix(pl):
batch_size = pl["batch_size"]
device = pl["device"]
dims = pl["dims"]
stats1 = {}
# calcolo le statistiche della directory
for ld1 in pl["dir1"]:
path_ld1 = pl["prefix1"] + ld1
print("Calcolo statistica directory: " + ld1)
m, s = calcola_statistiche_del_path(path_ld1, batch_size, dims, device)
stats1[ld1] = (m, s)
stats2 = {}
for ld2 in pl["dir2"]:
path_ld2 = pl["prefix2"] + ld2
print("Calcolo statistica directory: " + ld2)
m, s = calcola_statistiche_del_path(path_ld2, batch_size, dims, device)
stats2[ld2] = (m, s)
matrice = np.zeros((len(pl["dir1"]), len(pl["dir2"])))
for i in range(len(pl["dir1"])):
ld1 = pl["dir1"][i]
for j in range(i, len(pl["dir2"])):
ld2 = pl["dir2"][j]
dd = fid.calculate_frechet_distance(stats1[ld1][0], stats1[ld1][1],
stats2[ld2][0], stats2[ld2][1])
print(" distanza fra " + ld1 + " e " + ld2 + " = " + str(dd))
matrice[i][j] = dd
matrice[j][i] = dd
return matrice
def calc_average_fid_and_blur(model, dloader):
fid = 0
blur = 0
model.eval()
for batch_ind, (data, _) in enumerate(dloader):
if model.confs['CUDA']:
data = data.cuda()
data = torch.autograd.Variable(data.view(1, 1, 28, 28))
#data.unsqueeze(0)
print(data.shape)
recon_x, _, _ = model.forward(data)
blur += calc_blur(recon_x).data[0]
recon_x = recon_x.view(
1, recon_x.shape[1] * recon_x.shape[2] * recon_x.shape[3])
#recon_x.unsqueeze()
data = data.view(1, data.shape[1] * data.shape[2] * data.shape[3])
recon_acts_mean = np.mean(recon_x.data.cpu().numpy(), axis=0)
recon_acts_sig = np.cov(recon_x.data.cpu().numpy(), rowvar=False)
data_acts_mean = np.mean(data.data.cpu().numpy(), axis=0)
data_acts_sig = np.cov(data.data.cpu().numpy(), rowvar=False)
#print(recon_acts_sig)
# Source: https://github.com/mseitzer/pytorch-fid
fid += calculate_frechet_distance(data_acts_mean,
data_acts_sig,
recon_acts_mean,
recon_acts_sig,
eps=1e-8)
#print(fid)
print(blur)
print('Fid: {}'.format(fid / len(dloader.dataset)))
print('Blur: {}'.format(blur / len(dloader.dataset)))
mu_fake_vggface, sigma_fake_vggface = _compute_statistics_from_pred_arr(
fake_pred_arr_vggface)
mu_recon_vggface, sigma_recon_vggface = _compute_statistics_from_pred_arr(
recon_pred_arr_vggface)
mu_wrong_vggface, sigma_wrong_vggface = _compute_statistics_from_pred_arr(
wrong_pred_arr_vggface)
# here we use training stat for FID
with open('training_stat_FID.pickle', 'rb') as f:
training_stat = pickle.load(f)
mu_real, mu_real_aligned, mu_real_vggface, \
sigma_real, sigma_real_aligned, sigma_real_vggface = \
training_stat['mu_real'], training_stat['mu_real_aligned'], training_stat['mu_real_vggface'], \
training_stat['sigma_real'], training_stat['sigma_real_aligned'], training_stat['sigma_real_vggface']
fid_real_fake = calculate_frechet_distance(mu_real, sigma_real, mu_fake,
sigma_fake)
fid_real_recon = calculate_frechet_distance(mu_real, sigma_real, mu_recon,
sigma_recon)
fid_real_wrong = calculate_frechet_distance(mu_real, sigma_real, mu_wrong,
sigma_wrong)
fid_real_fake_aligned = calculate_frechet_distance(mu_real_aligned,
sigma_real_aligned,
mu_fake_aligned,
sigma_fake_aligned)
fid_real_recon_aligned = calculate_frechet_distance(mu_real_aligned,
sigma_real_aligned,
mu_recon_aligned,
sigma_recon_aligned)
fid_real_wrong_aligned = calculate_frechet_distance(mu_real_aligned,
sigma_real_aligned,
mu_real_aligned, sigma_real_aligned = _compute_statistics_from_pred_arr(
real_pred_arr_aligned)
mu_fake_aligned, sigma_fake_aligned = _compute_statistics_from_pred_arr(
fake_pred_arr_aligned)
mu_recon_aligned, sigma_recon_aligned = _compute_statistics_from_pred_arr(
recon_pred_arr_aligned)
with open('training_stat_FID.pickle', 'rb') as f:
training_stat = pickle.load(f)
mu_real, mu_real_aligned, mu_real_vggface, \
sigma_real, sigma_real_aligned, sigma_real_vggface = \
training_stat['mu_real'], training_stat['mu_real_aligned'], training_stat['mu_real_vggface'], \
training_stat['sigma_real'], training_stat['sigma_real_aligned'], training_stat['sigma_real_vggface']
fid_real_fake = calculate_frechet_distance(mu_real, sigma_real,
mu_fake, sigma_fake)
fid_real_recon = calculate_frechet_distance(mu_real, sigma_real,
mu_recon, sigma_recon)
fid_real_fake_aligned = calculate_frechet_distance(
mu_real_aligned, sigma_real_aligned, mu_fake_aligned,
sigma_fake_aligned)
fid_real_recon_aligned = calculate_frechet_distance(
mu_real_aligned, sigma_real_aligned, mu_recon_aligned,
sigma_recon_aligned)
predicts_fake = []
predicts_recon = []
predicts_fake_aligned = []
predicts_recon_aligned = []
for i in range(num_imgs):