def drop_exp_2(r_feat_val, r_feat_train, pred):
# i1ep_score, mode_score, fid
n_mode = len(Counter(pred))
scores = np.zeros((n_mode, 3))
t_feat = r_feat_train.clone()
collapsed_order = torch.randperm(n_mode).long()
index = torch.arange(0, r_feat_train.size(0)).long()
collapsed = torch.zeros(r_feat_train.size(0)).byte()
Mxx = dista1e(r_feat_val, r_feat_val, sqrt=True)
for i in range(n_mode):
# Compute Score
Mxy = dista1e(r_feat_val, t_feat, sqrt=True)
Myy = dista1e(t_feat, t_feat, sqrt=True)
scores[i, 0] = i1eption_score(t_feat)
scores[i, 1] = mode_score(t_feat, r_feat_val)
scores[i, 2] = fid(t_feat, r_feat_val)
# Do drop -- fill dropped slots with remaining samples
c = collapsed_order[i]
collapsed[pred.eq(c)] = 1
cidx = index[collapsed.eq(1)]
1idx = index[collapsed.ne(1)]
if 1idx.dim() == 0 or cidx.dim() == 0 or 1idx.size(0) == 0:
continue
for j in cidx:
copy_idx = np.random.randint(0, 1idx.size(0))
t_feat[j] = t_feat[1idx[copy_idx]]
def drop_exp_2(r_feat_val, r_feat_train, pred):
# incep_score, mode_score, fid
n_mode = len(Counter(pred))
scores = np.zeros((n_mode, 3))
t_feat = r_feat_train.clone()
collapsed_order = torch.randperm(n_mode).long()
index = torch.arange(0, r_feat_train.size(0)).long()
collapsed = torch.zeros(r_feat_train.size(0)).byte()
Mxx = distance(r_feat_val, r_feat_val, sqrt=True)
for i in range(n_mode):
# Compute Score
Mxy = distance(r_feat_val, t_feat, sqrt=True)
Myy = distance(t_feat, t_feat, sqrt=True)
scores[i, 0] = inception_score(t_feat)
scores[i, 1] = mode_score(t_feat, r_feat_val)
scores[i, 2] = fid(t_feat, r_feat_val)
# Do drop -- fill dropped slots with remaining samples
c = collapsed_order[i]
collapsed[pred.eq(c)] = 1
cidx = index[collapsed.eq(1)]
ncidx = index[collapsed.ne(1)]
if ncidx.dim() == 0 or cidx.dim() == 0 or ncidx.size(0) == 0:
continue
for j in cidx:
copy_idx = np.random.randint(0, ncidx.size(0))
t_feat[j] = t_feat[ncidx[copy_idx]]
return scores
def overfit_exp_2(r_feat_val, r_feat_train, step=200):
# i1ep_score, mode_score, fid
n_mode = r_feat_train.size(0) // step
scores = np.zeros((n_mode+1, 3))
t_feat = r_feat_train.clone()
collapsed_order = torch.randperm(n_mode).long()
index = torch.arange(0, r_feat_train.size(0)).long()
collapsed = torch.zeros(r_feat_train.size(0)).byte()
Mxx = dista1e(r_feat_val, r_feat_val, sqrt=True)
for i in range(n_mode+1):
# Compute Score
Mxy = dista1e(r_feat_val, t_feat, sqrt=True)
Myy = dista1e(t_feat, t_feat, sqrt=True)
scores[i, 0] = i1eption_score(t_feat)
scores[i, 1] = mode_score(t_feat, r_feat_val)
scores[i, 2] = fid(t_feat, r_feat_val)
# Copy samples so as to overfit
if i == n_mode: break
t_feat[i*step:(i+1)*step] = r_feat_val[i*step:(i+1)*step]
return scores
def overfit_exp_2(r_feat_val, r_feat_train, step=200):
# incep_score, mode_score, fid
n_mode = r_feat_train.size(0) // step
scores = np.zeros((n_mode+1, 3))
t_feat = r_feat_train.clone()
collapsed_order = torch.randperm(n_mode).long()
index = torch.arange(0, r_feat_train.size(0)).long()
collapsed = torch.zeros(r_feat_train.size(0)).byte()
Mxx = distance(r_feat_val, r_feat_val, sqrt=True)
for i in range(n_mode+1):
# Compute Score
Mxy = distance(r_feat_val, t_feat, sqrt=True)
Myy = distance(t_feat, t_feat, sqrt=True)
scores[i, 0] = inception_score(t_feat)
scores[i, 1] = mode_score(t_feat, r_feat_val)
scores[i, 2] = fid(t_feat, r_feat_val)
# Copy samples so as to overfit
if i == n_mode: break
t_feat[i*step:(i+1)*step] = r_feat_val[i*step:(i+1)*step]
return scores
def collapse_exp_2(r_feat_val, r_feat, c_feat, pred):
# i1ep_score, mode_score, fid
n_mode = c_feat.size(0)
c_feat_repeat = c_feat[pred]
scores = np.zeros((n_mode, 3))
t_feat = r_feat.clone()
index = torch.arange(0, 2000).long()
collapsed_order = torch.randperm(n_mode).long()
Mxx = dista1e(r_feat_val, r_feat_val, sqrt=False)
for i in range(n_mode):
# Compute Score
Mxy = dista1e(r_feat_val, t_feat, sqrt=False)
Myy = dista1e(t_feat, t_feat, sqrt=False)
scores[i, 0] = i1eption_score(t_feat)
scores[i, 1] = mode_score(t_feat, r_feat_val)
scores[i, 2] = fid(t_feat, r_feat_val)
# Do collapse
c = collapsed_order[i]
cidx = index[pred.eq(c)]
t_feat[cidx] = c_feat_repeat[cidx]
return scores
def collapse_exp_2(r_feat_val, r_feat, c_feat, pred):
# incep_score, mode_score, fid
n_mode = c_feat.size(0)
c_feat_repeat = c_feat[pred]
scores = np.zeros((n_mode, 3))
t_feat = r_feat.clone()
index = torch.arange(0, 2000).long()
collapsed_order = torch.randperm(n_mode).long()
Mxx = distance(r_feat_val, r_feat_val, sqrt=False)
for i in range(n_mode):
# Compute Score
Mxy = distance(r_feat_val, t_feat, sqrt=False)
Myy = distance(t_feat, t_feat, sqrt=False)
scores[i, 0] = inception_score(t_feat)
scores[i, 1] = mode_score(t_feat, r_feat_val)
scores[i, 2] = fid(t_feat, r_feat_val)
# Do collapse
c = collapsed_order[i]
cidx = index[pred.eq(c)]
t_feat[cidx] = c_feat_repeat[cidx]
return scores
def run_combine(t):
print_prop(t)
if t.mode == "Mix" or t.mode == "Entropy":
mkdir(g.default_repo_dir + "samples/Mix")
print("running mix mod..")
mkdir(g.default_repo_dir + "rlt")
for sp in range(0, 5 if t.mixOnly else 1):
def getName():
ans = 'mix_' if t.mode == "Mix" else 'entropy_'
ans = ans + t.data + '_' + t.featureType + '_'
for entry in t.dataList:
ans = ans + entry.data + '_' + entry.folder + '_' + \
('%.2f' % entry.fraction) + '_' + \
str(entry.dup) + '_' + str(entry.imageMode) + '_'
return ans + str(t.mixSize) + '_' + str(sp)
saveFile = g.default_repo_dir + "rlt/" + getName()
if os.path.exists(saveFile):
print(saveFile + " already generated. Continue.")
continue
if t.mode == "Mix":
mixDat = getDat(t, t.dataList, g.default_repo_dir +
"samples/Mix/M1" + hostname, t.featureType, t.mixOnly)
dataList = [Ent(1, t.data, 'true', None)]
print('ftype', t.featureType)
trueDat = getDat(t, dataList, g.default_repo_dir +
"samples/Mix/M2" + hostname, t.featureType)
score = compute_score(trueDat, mixDat, 1, 1, True)
else:
t.mixSize = t.mixSize * 10
mixDat = getDat(t, t.dataList, g.default_repo_dir +
"samples/Mix/M1" + hostname, t.featureType, t.mixOnly)
t.mixSize = t.mixSize / 10
dataList = [Ent(1, t.data, 'true_test', None)]
trueDat = getDat(t, dataList, g.default_repo_dir +
"samples/Mix/M2" + hostname, t.featureType)
if t.featureType == 'pix':
epsilons = [1000]
elif t.featureType == 'smax':
epsilons = [0.001]
elif t.featureType == 'class':
epsilons = [0.1]
elif t.featureType == 'conv':
epsilons = [1000]
score = entropy_score(mixDat, trueDat, epsilons)
f = open(saveFile, "wb")
pickle.dump(score, f)
f.close()
elif t.mode == "Incep":
mkdir(g.default_repo_dir + "samples/Mix")
mkdir(g.default_repo_dir + "rlt")
for sp in range(0, 5):
def getIncepName():
ans = 'incep_' + t.data + '_' + t.featureType + '_'
for entry in t.dataList:
ans = ans + entry.data + '_' + entry.folder + '_' + \
('%.2f' % entry.fraction) + '_' + \
str(entry.dup) + '_' + str(entry.imageMode) + '_'
return ans + str(t.mixSize) + "_" + str(sp)
incep_file_name = g.default_repo_dir + "rlt/" + getIncepName()
if os.path.exists(incep_file_name):
print(incep_file_name + " already generated. Continue.")
return
X = getDat(t, t.dataList, g.default_repo_dir + "samples/Mix/M1" +
hostname, t.featureType) # get smax feature
incep_score = inception_score(X)
f = open(incep_file_name, "wb")
pickle.dump(incep_score, f)
f.close()
elif t.mode == "ModeScore":
mkdir(g.default_repo_dir + "samples/Mix")
mkdir(g.default_repo_dir + "rlt")
for sp in range(0, 5):
def getMSName():
ans = 'mode_score_' + t.data + '_' + t.featureType + '_'
for entry in t.dataList:
ans = ans + entry.data + '_' + entry.folder + '_' + \
('%.2f' % entry.fraction) + '_' + \
str(entry.dup) + '_' + str(entry.imageMode) + '_'
return ans + str(t.mixSize) + "_" + str(sp)
ms_file_name = g.default_repo_dir + "rlt/" + getMSName()
if os.path.exists(ms_file_name):
print(ms_file_name + " already generated. Continue.")
return
X = getDat(t, t.dataList, g.default_repo_dir +
"samples/Mix/M1" + hostname, t.featureType)
dataList = [Ent(1, t.data, 'true', None)]
Y = getDat(t, dataList, g.default_repo_dir +
"samples/Mix/M2" + hostname, t.featureType)
modeScore = mode_score(X, Y)
f = open(ms_file_name, "wb")
pickle.dump(modeScore, f)
f.close()
elif t.mode == "FID":
mkdir(g.default_repo_dir + "samples/Mix")
mkdir(g.default_repo_dir + "rlt")
for sp in range(0, 5):
def getFidName():
ans = 'fid_' + t.data + '_' + t.featureType + '_'
for entry in t.dataList:
ans = ans + entry.data + '_' + entry.folder + '_' + \
('%.2f' % entry.fraction) + '_' + \
str(entry.dup) + '_' + str(entry.imageMode) + '_'
return ans + str(t.mixSize) + "_" + str(sp)
fid_file_name = g.default_repo_dir + "rlt/" + getFidName()
if os.path.exists(fid_file_name):
print(fid_file_name + " already generated. Continue.")
return
X = getDat(t, t.dataList, g.default_repo_dir +
"samples/Mix/M1" + hostname, t.featureType)
dataList = [Ent(1, t.data, 'true', None)]
Y = getDat(t, dataList, g.default_repo_dir +
"samples/Mix/M2" + hostname, t.featureType)
FID = fid(X, Y)
f = open(fid_file_name, "wb")
pickle.dump(FID, f)
f.close()
elif t.mode == 'collapse':
run_collapse_exp(t.data)
elif t.mode == 'drop':
run_drop_exp(t.data)
elif t.mode == 'overfit':
run_overfit_exp(t.data)
elif t.mode == 'Gen': # generate necessary data..
if t.data == 'noise':
print("\nSampling noise...")
noise_sampler(t)
elif t.data == 'mnistsub': # subclass
print("\nSampling subclass...")
t.data = 'mnist'
subclass_sampler(t)
else:
t.folderName = t.model + \
(str(t.epoch) if not t.model.startswith('true') else "")
if t.model.startswith('true'):
print("\nSampling " + t.folderName + " ...")
folder_sampler(t)
else:
print("\nSampling " + t.folderName + " ...")
generator_sampler(t)
elif t.mode == "Gan": # generate some fake images..
if t.model == "DCGAN":
DCGAN_main(t)
elif t.model == "WGAN":
WGAN_main(t)
elif t.model == "NNGAN":
NNGAN_main(t)
elif t.model == "MGGAN":
MGGAN_main(t)
elif t.model == "DCGAN_cluster":
DCGAN_cluster_main(t)
else:
print("error.. Unknown model.")
else:
print("not designed yet.")
def run_combine(t):
print_prop(t)
if t.mode == "Mix" or t.mode == "Entropy":
mkdir(g.default_repo_dir + "samples/Mix")
print("running mix mod..")
mkdir(g.default_repo_dir + "rlt")
for sp in range(0, 5 if t.mixOnly else 1):
def getName():
ans = 'mix_' if t.mode == "Mix" else 'entropy_'
ans = ans + t.data + '_' + t.featureType + '_'
for entry in t.dataList:
ans = ans + entry.data + '_' + entry.folder + '_' + \
('%.2f' % entry.fraction) + '_' + \
str(entry.dup) + '_' + str(entry.imageMode) + '_'
return ans + str(t.mixSize) + '_' + str(sp)
saveFile = g.default_repo_dir + "rlt/" + getName()
if os.path.exists(saveFile):
print(saveFile + " already generated. Continue.")
continue
if t.mode == "Mix":
mixDat = getDat(
t, t.dataList,
g.default_repo_dir + "samples/Mix/M1" + hostname,
t.featureType, t.mixOnly)
dataList = [Ent(1, t.data, 'true', None)]
print('ftype', t.featureType)
trueDat = getDat(
t, dataList,
g.default_repo_dir + "samples/Mix/M2" + hostname,
t.featureType)
score = compute_score(trueDat, mixDat, 1, 1, True)
else:
t.mixSize = t.mixSize * 10
mixDat = getDat(
t, t.dataList,
g.default_repo_dir + "samples/Mix/M1" + hostname,
t.featureType, t.mixOnly)
t.mixSize = t.mixSize / 10
dataList = [Ent(1, t.data, 'true_test', None)]
trueDat = getDat(
t, dataList,
g.default_repo_dir + "samples/Mix/M2" + hostname,
t.featureType)
if t.featureType == 'pix':
epsilons = [1000]
elif t.featureType == 'smax':
epsilons = [0.001]
elif t.featureType == 'class':
epsilons = [0.1]
elif t.featureType == 'conv':
epsilons = [1000]
score = entropy_score(mixDat, trueDat, epsilons)
f = open(saveFile, "wb")
pickle.dump(score, f)
f.close()
elif t.mode == "Incep":
mkdir(g.default_repo_dir + "samples/Mix")
mkdir(g.default_repo_dir + "rlt")
for sp in range(0, 5):
def getIncepName():
ans = 'incep_' + t.data + '_' + t.featureType + '_'
for entry in t.dataList:
ans = ans + entry.data + '_' + entry.folder + '_' + \
('%.2f' % entry.fraction) + '_' + \
str(entry.dup) + '_' + str(entry.imageMode) + '_'
return ans + str(t.mixSize) + "_" + str(sp)
incep_file_name = g.default_repo_dir + "rlt/" + getIncepName()
if os.path.exists(incep_file_name):
print(incep_file_name + " already generated. Continue.")
return
X = getDat(t, t.dataList,
g.default_repo_dir + "samples/Mix/M1" + hostname,
t.featureType) # get smax feature
incep_score = inception_score(X)
f = open(incep_file_name, "wb")
pickle.dump(incep_score, f)
f.close()
elif t.mode == "ModeScore":
mkdir(g.default_repo_dir + "samples/Mix")
mkdir(g.default_repo_dir + "rlt")
for sp in range(0, 5):
def getMSName():
ans = 'mode_score_' + t.data + '_' + t.featureType + '_'
for entry in t.dataList:
ans = ans + entry.data + '_' + entry.folder + '_' + \
('%.2f' % entry.fraction) + '_' + \
str(entry.dup) + '_' + str(entry.imageMode) + '_'
return ans + str(t.mixSize) + "_" + str(sp)
ms_file_name = g.default_repo_dir + "rlt/" + getMSName()
if os.path.exists(ms_file_name):
print(ms_file_name + " already generated. Continue.")
return
X = getDat(t, t.dataList,
g.default_repo_dir + "samples/Mix/M1" + hostname,
t.featureType)
dataList = [Ent(1, t.data, 'true', None)]
Y = getDat(t, dataList,
g.default_repo_dir + "samples/Mix/M2" + hostname,
t.featureType)
modeScore = mode_score(X, Y)
f = open(ms_file_name, "wb")
pickle.dump(modeScore, f)
f.close()
elif t.mode == "FID":
mkdir(g.default_repo_dir + "samples/Mix")
mkdir(g.default_repo_dir + "rlt")
for sp in range(0, 5):
def getFidName():
ans = 'fid_' + t.data + '_' + t.featureType + '_'
for entry in t.dataList:
ans = ans + entry.data + '_' + entry.folder + '_' + \
('%.2f' % entry.fraction) + '_' + \
str(entry.dup) + '_' + str(entry.imageMode) + '_'
return ans + str(t.mixSize) + "_" + str(sp)
fid_file_name = g.default_repo_dir + "rlt/" + getFidName()
if os.path.exists(fid_file_name):
print(fid_file_name + " already generated. Continue.")
return
X = getDat(t, t.dataList,
g.default_repo_dir + "samples/Mix/M1" + hostname,
t.featureType)
dataList = [Ent(1, t.data, 'true', None)]
Y = getDat(t, dataList,
g.default_repo_dir + "samples/Mix/M2" + hostname,
t.featureType)
FID = fid(X, Y)
f = open(fid_file_name, "wb")
pickle.dump(FID, f)
f.close()
elif t.mode == 'collapse':
run_collapse_exp(t.data)
elif t.mode == 'drop':
run_drop_exp(t.data)
elif t.mode == 'overfit':
run_overfit_exp(t.data)
elif t.mode == 'Gen': # generate necessary data..
if t.data == 'noise':
print("\nSampling noise...")
noise_sampler(t)
elif t.data == 'mnistsub': # subclass
print("\nSampling subclass...")
t.data = 'mnist'
subclass_sampler(t)
else:
t.folderName = t.model + \
(str(t.epoch) if not t.model.startswith('true') else "")
if t.model.startswith('true'):
print("\nSampling " + t.folderName + " ...")
folder_sampler(t)
else:
print("\nSampling " + t.folderName + " ...")
generator_sampler(t)
elif t.mode == "Gan": # generate some fake images..
if t.model == "DCGAN":
DCGAN_main(t)
elif t.model == "WGAN":
WGAN_main(t)
elif t.model == "NNGAN":
NNGAN_main(t)
elif t.model == "MGGAN":
MGGAN_main(t)
elif t.model == "DCGAN_cluster":
DCGAN_cluster_main(t)
else:
print("error.. Unknown model.")
else:
print("not designed yet.")