def train_khorrami_aug(wdecay,
lr,
route_iter,
folds=[4, 9],
model_name='vgg_capsule_disfa',
epoch_stuff=[30, 60],
res=False,
class_weights=False,
reconstruct=False,
loss_weights=None,
model_to_test=None,
oulu=False,
dropout=0):
out_dirs = []
out_dir_meta = '../experiments/showing_overfitting_justhflip_' + model_name + str(
route_iter)
num_epochs = epoch_stuff[1]
if model_to_test is None:
model_to_test = num_epochs - 1
epoch_start = 0
dec_after = ['step', epoch_stuff[0], 0.1]
lr = lr
im_resize = 110
im_size = 96
save_after = num_epochs
if not oulu:
type_data = 'train_test_files'
n_classes = 8
train_pre = os.path.join('../data/ck_96', type_data)
pre_pend = 'ck_96_' + type_data + '_'
else:
type_data = 'three_im_no_neutral_just_strong_False'
n_classes = 6
# 'train_test_files'; n_classes = 8;
train_pre = os.path.join(
'../data/Oulu_CASIA/train_test_files_preprocess_vl', type_data)
pre_pend = 'oulu_96_' + type_data + '_'
criterion = 'margin'
criterion_str = criterion
init = False
strs_append_list = [
'reconstruct', reconstruct, class_weights, 'all_aug', criterion_str,
init, 'wdecay', wdecay, num_epochs
] + dec_after + lr + [dropout]
if loss_weights is not None:
strs_append_list = strs_append_list + ['lossweights'] + loss_weights
strs_append = '_' + '_'.join([str(val) for val in strs_append_list])
lr_p = lr[:]
for split_num in folds:
model_file = None
margin_params = None
out_dir_train = os.path.join(out_dir_meta,
pre_pend + str(split_num) + strs_append)
final_model_file = os.path.join(out_dir_train,
'model_' + str(num_epochs - 1) + '.pt')
if os.path.exists(final_model_file):
print 'skipping', final_model_file
continue
else:
print 'not skipping', final_model_file
train_file = os.path.join(train_pre,
'train_' + str(split_num) + '.txt')
test_file = os.path.join(train_pre, 'test_' + str(split_num) + '.txt')
mean_file = os.path.join(train_pre,
'train_' + str(split_num) + '_mean.png')
std_file = os.path.join(train_pre,
'train_' + str(split_num) + '_std.png')
mean_im = scipy.misc.imread(mean_file).astype(np.float32)
std_im = scipy.misc.imread(std_file).astype(np.float32)
class_weights = util.get_class_weights(
util.readLinesFromFile(train_file))
list_of_to_dos = ['flip']
data_transforms = {}
data_transforms['train'] = transforms.Compose([
lambda x: augmenters.augment_image(x, list_of_to_dos, mean_im,
std_im, im_size),
transforms.ToTensor(), lambda x: x * 255.
])
data_transforms['val'] = transforms.Compose(
[transforms.ToTensor(), lambda x: x * 255.])
train_data = dataset.CK_96_Dataset(train_file, mean_file, std_file,
data_transforms['train'])
test_data = dataset.CK_96_Dataset(test_file, mean_file, std_file,
data_transforms['val'])
network_params = dict(n_classes=n_classes,
pool_type='max',
r=route_iter,
init=init,
class_weights=class_weights,
reconstruct=reconstruct,
loss_weights=loss_weights,
dropout=dropout)
batch_size = 128
batch_size_val = None
util.makedirs(out_dir_train)
train_params = dict(out_dir_train=out_dir_train,
train_data=train_data,
test_data=test_data,
batch_size=batch_size,
batch_size_val=batch_size_val,
num_epochs=num_epochs,
save_after=save_after,
disp_after=1,
plot_after=100,
test_after=1,
lr=lr,
dec_after=dec_after,
model_name=model_name,
criterion=criterion,
gpu_id=0,
num_workers=2,
model_file=model_file,
epoch_start=epoch_start,
margin_params=margin_params,
network_params=network_params,
weight_decay=wdecay)
test_params = dict(
out_dir_train=out_dir_train,
model_num=model_to_test,
# num_epochs-1,
train_data=train_data,
test_data=test_data,
gpu_id=0,
model_name=model_name,
batch_size_val=batch_size_val,
criterion=criterion,
margin_params=margin_params,
network_params=network_params)
print train_params
param_file = os.path.join(out_dir_train, 'params.txt')
all_lines = []
for k in train_params.keys():
str_print = '%s: %s' % (k, train_params[k])
print str_print
all_lines.append(str_print)
util.writeFile(param_file, all_lines)
if reconstruct:
train_model_recon(**train_params)
# test_model_recon(**test_params)
else:
train_model(**train_params)
def khorrami_full_exp():
for split_num in range(0,1):
out_dir_meta = '../experiments/khorrami_full_capsule/'
num_epochs = 100
epoch_start = 0
dec_after = ['exp',0.96,200,1e-6]
# dec_after = ['step',50,0.5]
lr = [0.001]
pool_type = 'max'
im_size = 96
model_name = 'khorrami_full_capsule'
save_after = 50
model_file=None
strs_append = '_'.join([str(val) for val in ['justflip',pool_type,num_epochs]+dec_after+lr])
out_dir_train = os.path.join(out_dir_meta,'ck_'+str(split_num)+'_'+strs_append)
print out_dir_train
train_file = '../data/ck_96/train_test_files/train_'+str(split_num)+'.txt'
test_file = '../data/ck_96/train_test_files/test_'+str(split_num)+'.txt'
mean_file = '../data/ck_96/train_test_files/train_'+str(split_num)+'_mean.png'
std_file = '../data/ck_96/train_test_files/train_'+str(split_num)+'_std.png'
mean_im = scipy.misc.imread(mean_file).astype(np.float32)
std_im = scipy.misc.imread(std_file).astype(np.float32)
std_im[std_im==0]=1.
list_of_to_dos = ['flip']
# ,'rotate','scale_translate','pixel_augment']
# 'flip','rotate','scale_translate']
data_transforms = {}
data_transforms['train']= transforms.Compose([
lambda x: augmenters.augment_image(x,list_of_to_dos,mean_im,std_im,im_size),
transforms.ToTensor(),
lambda x: x*255.
])
data_transforms['val']= transforms.Compose([
transforms.ToTensor(),
lambda x: x*255.
])
train_data = dataset.CK_96_Dataset(train_file, mean_file, std_file, data_transforms['train'])
test_data = dataset.CK_96_Dataset(test_file, mean_file, std_file, data_transforms['val'])
network_params = dict(n_classes=8, conv_layers = [[64,5,2]],caps_layers=[[16,8,5,2],[32,8,7,3],[8,16,5,1]], r=3, init=False)
batch_size = 32
batch_size_val = 4
util.makedirs(out_dir_train)
train_params = dict(out_dir_train = out_dir_train,
train_data = train_data,
test_data = test_data,
batch_size = batch_size,
batch_size_val = batch_size_val,
num_epochs = num_epochs,
save_after = save_after,
disp_after = 1,
plot_after = 10,
test_after = num_epochs-1,
lr = lr,
dec_after = dec_after,
model_name = model_name,
criterion = 'margin',
gpu_id = 2,
num_workers = 0,
model_file = model_file,
epoch_start = epoch_start,
network_params = network_params)
print train_params
param_file = os.path.join(out_dir_train,'params.txt')
all_lines = []
for k in train_params.keys():
str_print = '%s: %s' % (k,train_params[k])
print str_print
all_lines.append(str_print)
util.writeFile(param_file,all_lines)
train_model(**train_params)
def train_khorrami_aug(wdecay,lr,route_iter,folds=[4,9],model_name='vgg_capsule_disfa',epoch_stuff=[30,60],res=False, class_weights = False, reconstruct = False, oulu = False, meta_data_dir = None,loss_weights = None, exp = False, non_peak = False, model_to_test = None, dropout = None):
out_dirs = []
out_dir_meta = '../experiments_dropout/'+model_name+str(route_iter)
num_epochs = epoch_stuff[1]
if model_to_test is None:
model_to_test = num_epochs -1
epoch_start = 0
if exp:
dec_after = ['exp',0.96,epoch_stuff[0],1e-6]
# dec_after = ['exp',0.96,epoch_stuff[0],1e-6]
else:
dec_after = ['step',epoch_stuff[0],0.1]
lr = lr
im_resize = 110
im_size = 96
save_after = 100
if non_peak:
type_data = 'train_test_files_non_peak_one_third'; n_classes = 8;
train_pre = os.path.join('../data/ck_96',type_data)
test_pre = os.path.join('../data/ck_96','train_test_files')
else:
type_data = 'train_test_files'; n_classes = 8;
train_pre = os.path.join('../data/ck_96',type_data)
test_pre = os.path.join('../data/ck_96',type_data)
if oulu:
type_data = 'three_im_no_neutral_just_strong_False'; n_classes = 6;
criterion = 'margin'
criterion_str = criterion
# criterion = nn.CrossEntropyLoss()
# criterion_str = 'crossentropy'
init = False
strs_append_list = ['reconstruct',reconstruct,class_weights,'flip',criterion_str,init,'wdecay',wdecay,num_epochs]+dec_after+lr+['dropout',dropout]
if loss_weights is not None:
strs_append_list = strs_append_list +['lossweights']+loss_weights
strs_append = '_'+'_'.join([str(val) for val in strs_append_list])
if oulu:
pre_pend = 'oulu_96_'+meta_data_dir+'_'
else:
pre_pend = 'ck_96_'+type_data+'_'
lr_p=lr[:]
for split_num in folds:
if res:
strs_appendc = '_'+'_'.join([str(val) for val in ['reconstruct',reconstruct,True,'all_aug',criterion_str,init,'wdecay',wdecay,600,'step',600,0.1]+lr_p])
out_dir_train = os.path.join(out_dir_meta,pre_pend+str(split_num)+strs_appendc)
model_file = os.path.join(out_dir_train,'model_599.pt')
epoch_start = 600
lr =[0.1*lr_curr for lr_curr in lr_p]
else:
model_file = None
margin_params = None
out_dir_train = os.path.join(out_dir_meta,pre_pend+str(split_num)+strs_append)
final_model_file = os.path.join(out_dir_train,'model_'+str(num_epochs-1)+'.pt')
if os.path.exists(final_model_file):
print 'skipping',final_model_file
# raw_input()
continue
else:
print 'not skipping', final_model_file
# raw_input()
# continue
if not oulu:
# train_file = '../data/ck_96/train_test_files/train_'+str(split_num)+'.txt'
# test_file = '../data/ck_96/train_test_files/test_'+str(split_num)+'.txt'
# mean_file = '../data/ck_96/train_test_files/train_'+str(split_num)+'_mean.png'
# std_file = '../data/ck_96/train_test_files/train_'+str(split_num)+'_std.png'
train_file = os.path.join(train_pre,'train_'+str(split_num)+'.txt')
test_file_easy = os.path.join(train_pre,'test_'+str(split_num)+'.txt')
test_file = os.path.join(test_pre,'test_'+str(split_num)+'.txt')
mean_file = os.path.join(train_pre,'train_'+str(split_num)+'_mean.png')
std_file = os.path.join(train_pre,'train_'+str(split_num)+'_std.png')
else:
train_file = os.path.join('../data/Oulu_CASIA',meta_data_dir, type_data, 'train_'+str(split_num)+'.txt')
test_file = os.path.join('../data/Oulu_CASIA',meta_data_dir, type_data, 'test_'+str(split_num)+'.txt')
mean_file = os.path.join('../data/Oulu_CASIA',meta_data_dir, type_data, 'train_'+str(split_num)+'_mean.png')
std_file = os.path.join('../data/Oulu_CASIA',meta_data_dir, type_data, 'train_'+str(split_num)+'_std.png')
mean_im = scipy.misc.imread(mean_file).astype(np.float32)
std_im = scipy.misc.imread(std_file).astype(np.float32)
class_weights = util.get_class_weights(util.readLinesFromFile(train_file))
# print std_im.shape
# print np.min(std_im),np.max(std_im)
# raw_input()
list_of_to_dos = ['flip']
# ,'rotate','scale_translate', 'pixel_augment']
data_transforms = {}
data_transforms['train']= transforms.Compose([
lambda x: augmenters.augment_image(x,list_of_to_dos,mean_im,std_im,im_size),
transforms.ToTensor(),
lambda x: x*255.
])
data_transforms['val']= transforms.Compose([
transforms.ToTensor(),
lambda x: x*255.
])
# train_data = dataset.CK_96_Dataset_Just_Mean(train_file, mean_file, data_transforms['train'])
# test_data = dataset.CK_96_Dataset_Just_Mean(test_file, mean_file, data_transforms['val'])
print train_file
print test_file
print std_file
print mean_file
# raw_input()
train_data = dataset.CK_96_Dataset(train_file, mean_file, std_file, data_transforms['train'])
train_data_no_t = dataset.CK_96_Dataset(test_file_easy, mean_file, std_file, data_transforms['val'])
test_data = dataset.CK_96_Dataset(test_file, mean_file, std_file, data_transforms['val'])
if dropout is not None:
network_params = dict(n_classes=n_classes,pool_type='max',r=route_iter,init=init,class_weights = class_weights, reconstruct = reconstruct,loss_weights = loss_weights, dropout = dropout)
else:
network_params = dict(n_classes=n_classes,pool_type='max',r=route_iter,init=init,class_weights = class_weights, reconstruct = reconstruct,loss_weights = loss_weights)
# if lr[0]==0:
batch_size = 128
batch_size_val = 128
# else:
# batch_size = 32
# batch_size_val = 16
util.makedirs(out_dir_train)
train_params = dict(out_dir_train = out_dir_train,
train_data = train_data,
test_data = test_data,
batch_size = batch_size,
batch_size_val = batch_size_val,
num_epochs = num_epochs,
save_after = save_after,
disp_after = 1,
plot_after = 100,
test_after = 1,
lr = lr,
dec_after = dec_after,
model_name = model_name,
criterion = criterion,
gpu_id = 0,
num_workers = 2,
model_file = model_file,
epoch_start = epoch_start,
margin_params = margin_params,
network_params = network_params,
weight_decay=wdecay)
test_params = dict(out_dir_train = out_dir_train,
model_num = model_to_test,
# num_epochs-1,
train_data = train_data,
test_data = test_data,
gpu_id = 0,
model_name = model_name,
batch_size_val = batch_size_val,
criterion = criterion,
margin_params = margin_params,
network_params = network_params)
test_params_train = dict(**test_params)
test_params_train['test_data'] = train_data_no_t
test_params_train['post_pend'] = '_easy'
print train_params
param_file = os.path.join(out_dir_train,'params.txt')
all_lines = []
for k in train_params.keys():
str_print = '%s: %s' % (k,train_params[k])
print str_print
all_lines.append(str_print)
util.writeFile(param_file,all_lines)
# if reconstruct:
train_model_recon(**train_params)
# test_model_recon(**test_params)
# else:
# train_model(**train_params)
# test_model(**test_params)
getting_accuracy.print_accuracy(out_dir_meta,pre_pend,strs_append,folds,log='log.txt')
def train_khorrami_aug_oulu(wdecay,lr,route_iter,folds=[4,9],model_name='vgg_capsule_disfa',epoch_stuff=[30,60],res=False,meta_data_dir = 'train_test_files_preprocess_vl'):
out_dirs = []
out_dir_meta = '../experiments/'+model_name+str(route_iter)
num_epochs = epoch_stuff[1]
epoch_start = 0
dec_after = ['exp',0.96,epoch_stuff[0],1e-6]
lr = lr
im_resize = 110
im_size = 96
save_after = num_epochs
type_data = 'three_im_no_neutral_just_strong_False'; n_classes = 6;
criterion = 'margin'
criterion_str = criterion
# criterion = nn.CrossEntropyLoss()
# criterion_str = 'crossentropy'
init = False
strs_append = '_'+'_'.join([str(val) for val in ['all_aug',criterion_str,init,'wdecay',wdecay,num_epochs]+dec_after+lr])
pre_pend = 'oulu_96_'+meta_data_dir+'_'+type_data+'_'
for split_num in folds:
if res:
strs_appendc = '_'.join([str(val) for val in ['all_aug','wdecay',wdecay,50,'step',50,0.1]+lr])
out_dir_train = os.path.join(out_dir_meta,'oulu_'+type_data+'_'+str(split_num)+'_'+strs_appendc)
model_file = os.path.join(out_dir_train,'model_49.pt')
epoch_start = 50
else:
model_file = None
margin_params = None
out_dir_train = os.path.join(out_dir_meta,pre_pend+str(split_num)+strs_append)
final_model_file = os.path.join(out_dir_train,'model_'+str(num_epochs-1)+'.pt')
if os.path.exists(final_model_file):
print 'skipping',final_model_file
continue
train_file = os.path.join('../data/Oulu_CASIA',meta_data_dir, type_data, 'train_'+str(split_num)+'.txt')
test_file = os.path.join('../data/Oulu_CASIA',meta_data_dir, type_data, 'test_'+str(split_num)+'.txt')
mean_file = os.path.join('../data/Oulu_CASIA',meta_data_dir, type_data, 'train_'+str(split_num)+'_mean.png')
std_file = os.path.join('../data/Oulu_CASIA',meta_data_dir, type_data, 'train_'+str(split_num)+'_std.png')
mean_im = scipy.misc.imread(mean_file).astype(np.float32)
std_im = scipy.misc.imread(std_file).astype(np.float32)
print std_im.shape
print np.min(std_im),np.max(std_im)
print mean_im.shape
print np.min(mean_im),np.max(mean_im)
class_weights = util.get_class_weights(util.readLinesFromFile(train_file))
# raw_input()
list_of_to_dos = ['flip','rotate','scale_translate']
data_transforms = {}
data_transforms['train']= transforms.Compose([
lambda x: augmenters.augment_image(x,list_of_to_dos,mean_im,std_im,im_size),
transforms.ToTensor(),
lambda x: x*255.
])
data_transforms['val']= transforms.Compose([
transforms.ToTensor(),
lambda x: x*255.
])
# train_data = dataset.CK_96_Dataset_Just_Mean(train_file, mean_file, data_transforms['train'])
# test_data = dataset.CK_96_Dataset_Just_Mean(test_file, mean_file, data_transforms['val'])
train_data = dataset.CK_96_Dataset(train_file, mean_file, std_file, data_transforms['train'])
test_data = dataset.CK_96_Dataset(test_file, mean_file, std_file, data_transforms['val'])
network_params = dict(n_classes=n_classes,pool_type='max',r=route_iter,init=init, class_weights = class_weights)
# if lr[0]==0:
batch_size = 128
batch_size_val = 128
# else:
# batch_size = 32
# batch_size_val = 16
util.makedirs(out_dir_train)
train_params = dict(out_dir_train = out_dir_train,
train_data = train_data,
test_data = test_data,
batch_size = batch_size,
batch_size_val = batch_size_val,
num_epochs = num_epochs,
save_after = save_after,
disp_after = 1,
plot_after = 100,
test_after = 1,
lr = lr,
dec_after = dec_after,
model_name = model_name,
criterion = criterion,
gpu_id = 0,
num_workers = 0,
model_file = model_file,
epoch_start = epoch_start,
margin_params = margin_params,
network_params = network_params,
weight_decay=wdecay)
print train_params
param_file = os.path.join(out_dir_train,'params.txt')
all_lines = []
for k in train_params.keys():
str_print = '%s: %s' % (k,train_params[k])
print str_print
all_lines.append(str_print)
util.writeFile(param_file,all_lines)
train_model(**train_params)
test_params = dict(out_dir_train = out_dir_train,
model_num = num_epochs-1,
train_data = train_data,
test_data = test_data,
gpu_id = 0,
model_name = model_name,
batch_size_val = batch_size_val,
criterion = criterion,
margin_params = margin_params,
network_params = network_params)
test_model(**test_params)
# getting_accuracy.print_accuracy(out_dir_meta,pre_pend,strs_append,folds,log='log.txt')
getting_accuracy.view_loss_curves(out_dir_meta,pre_pend,strs_append,folds,num_epochs-1)
def train_khorrami_aug(wdecay,
lr,
route_iter,
folds=[4, 9],
model_name='vgg_capsule_disfa',
epoch_stuff=[30, 60],
res=False):
out_dirs = []
out_dir_meta = '../experiments/ck_khcap_recon_r' + str(
route_iter) + '_noinit'
num_epochs = epoch_stuff[1]
epoch_start = 0
dec_after = ['step', epoch_stuff[0], 0.1]
# data/Oulu_CASIA/train_test_files_preprocess_vl/
lr = lr
im_resize = 110
im_size = 96
# model_name = 'vgg_capsule_disfa'
save_after = 10
# type_data = 'three_im_no_neutral_just_strong_False'; n_classes = 6;
type_data = 'train_test_files'
n_classes = 8
# ../data/Oulu_CASIA/train_test_files_preprocess_maheen_vl_gray/
strs_append = '_' + '_'.join([
str(val)
for val in [model_name, 'all_aug', 'wdecay', wdecay, num_epochs] +
dec_after + lr
])
pre_pend = 'ck_' + type_data + '_'
for split_num in folds:
if res:
strs_appendc = '_'.join([
str(val) for val in
[model_name, 'all_aug', 'wdecay', wdecay, 50, 'step', 50, 0.1
] + lr
])
out_dir_train = os.path.join(
out_dir_meta, 'oulu_' + type_data + '_' + str(split_num) +
'_' + strs_appendc)
model_file = os.path.join(out_dir_train, 'model_49.pt')
epoch_start = 50
else:
model_file = None
criterion = 'margin'
margin_params = None
out_dir_train = os.path.join(out_dir_meta,
pre_pend + str(split_num) + strs_append)
final_model_file = os.path.join(out_dir_train,
'model_' + str(num_epochs - 1) + '.pt')
if os.path.exists(final_model_file):
print 'skipping', final_model_file
continue
train_file = os.path.join('../data/ck_96', type_data,
'train_' + str(split_num) + '.txt')
test_file = os.path.join('../data/ck_96', type_data,
'test_' + str(split_num) + '.txt')
mean_file = os.path.join('../data/ck_96', type_data,
'train_' + str(split_num) + '_mean.png')
list_of_to_dos = ['flip', 'rotate', 'scale_translate']
data_transforms = {}
data_transforms['train'] = transforms.Compose([
lambda x: augmenters.augment_image(x, list_of_to_dos, mean_im, None
),
transforms.ToTensor(), lambda x: x * 255.
])
data_transforms['val'] = transforms.Compose(
[transforms.ToTensor(), lambda x: x * 255.])
train_data = dataset.CK_96_Dataset_Just_Mean(train_file, mean_file,
data_transforms['train'])
test_data = dataset.CK_96_Dataset_Just_Mean(test_file, mean_file,
data_transforms['val'])
network_params = dict(n_classes=n_classes,
loss=nn.CrossEntropyLoss(),
in_size=im_size,
r=route_iter,
init=False)
# if lr[0]==0:
batch_size = 128
batch_size_val = 128
# else:
# batch_size = 32
# batch_size_val = 16
util.makedirs(out_dir_train)
train_params = dict(out_dir_train=out_dir_train,
train_data=train_data,
test_data=test_data,
batch_size=batch_size,
batch_size_val=batch_size_val,
num_epochs=num_epochs,
save_after=save_after,
disp_after=1,
plot_after=10,
test_after=1,
lr=lr,
dec_after=dec_after,
model_name=model_name,
criterion=criterion,
gpu_id=0,
num_workers=0,
model_file=model_file,
epoch_start=epoch_start,
margin_params=margin_params,
network_params=network_params,
weight_decay=wdecay)
print train_params
param_file = os.path.join(out_dir_train, 'params.txt')
all_lines = []
for k in train_params.keys():
str_print = '%s: %s' % (k, train_params[k])
print str_print
all_lines.append(str_print)
util.writeFile(param_file, all_lines)
train_model(**train_params)
test_params = dict(out_dir_train=out_dir_train,
model_num=num_epochs - 1,
train_data=train_data,
test_data=test_data,
gpu_id=0,
model_name=model_name,
batch_size_val=batch_size_val,
criterion=criterion,
margin_params=margin_params,
network_params=network_params)
test_model(**test_params)
getting_accuracy.print_accuracy(out_dir_meta,
pre_pend,
strs_append,
folds,
log='log.txt')
def train_with_vgg(lr,
route_iter,
train_file_pre,
test_file_pre,
out_dir_pre,
n_classes,
folds=[4, 9],
model_name='vgg_capsule_disfa',
epoch_stuff=[30, 60],
res=False,
reconstruct=False,
loss_weights=None,
exp=False,
dropout=0,
gpu_id=0,
aug_more='flip',
model_to_test=None,
save_after=10,
batch_size=32,
batch_size_val=32,
criterion='marginmulti'):
# torch.setdefaulttensortype('torch.FloatTensor')
num_epochs = epoch_stuff[1]
if model_to_test is None:
model_to_test = num_epochs - 1
epoch_start = 0
if exp:
dec_after = ['exp', 0.96, epoch_stuff[0], 1e-6]
else:
dec_after = ['step', epoch_stuff[0], 0.1]
lr = lr
im_resize = 256
im_size = 224
model_file = None
margin_params = None
for split_num in folds:
# post_pend = [split_num,'reconstruct',reconstruct]+aug_more+[num_epochs]+dec_after+lr+[dropout]
# out_dir_train = '_'.join([str(val) for val in [out_dir_pre]+post_pend]);
out_dir_train = get_out_dir_train_name(out_dir_pre, lr, route_iter,
split_num, epoch_stuff,
reconstruct, exp, dropout,
aug_more)
print out_dir_train
# raw_input()
final_model_file = os.path.join(out_dir_train,
'model_' + str(num_epochs - 1) + '.pt')
if os.path.exists(final_model_file):
print 'skipping', final_model_file
# continue
else:
print 'not skipping', final_model_file
train_file = train_file_pre + str(split_num) + '.txt'
test_file = test_file_pre + str(split_num) + '.txt'
class_weights = util.get_class_weights_au(
util.readLinesFromFile(train_file))
# class_weights = None
mean_std = np.array([[93.5940, 104.7624, 129.1863], [1., 1.,
1.]]) #bgr
std_div = np.array([0.225 * 255, 0.224 * 255, 0.229 * 255])
bgr = True
list_of_to_dos = aug_more
print list_of_to_dos
data_transforms = {}
train_resize = None
list_transforms = []
if 'hs' in list_of_to_dos:
print '**********HS!!!!!!!'
list_transforms.append(
lambda x: augmenters.random_crop(x, im_size))
list_transforms.append(lambda x: augmenters.hide_and_seek(x))
if 'flip' in list_of_to_dos:
list_transforms.append(lambda x: augmenters.horizontal_flip(x))
list_transforms.append(transforms.ToTensor())
elif 'flip' in list_of_to_dos and len(list_of_to_dos) == 1:
train_resize = im_size
list_transforms.extend([
lambda x: augmenters.horizontal_flip(x),
transforms.ToTensor()
])
elif 'none' in list_of_to_dos:
train_resize = im_size
list_transforms.append(transforms.ToTensor())
# data_transforms['train']= transforms.Compose([
# # lambda x: augmenters.random_crop(x,im_size),
# transforms.ToTensor(),
# ])
else:
# data_transforms['train']= transforms.Compose([
list_transforms.append(
lambda x: augmenters.random_crop(x, im_size))
list_transforms.append(lambda x: augmenters.augment_image(
x, list_of_to_dos, color=True, im_size=im_size))
list_transforms.append(transforms.ToTensor())
# lambda x: x*255.
# ])
list_transforms_val = [transforms.ToTensor()]
if torch.version.cuda.startswith('9.1'):
list_transforms.append(lambda x: x.float())
else:
list_transforms.append(lambda x: x * 255.)
data_transforms['train'] = transforms.Compose(list_transforms)
data_transforms['val'] = transforms.Compose(list_transforms_val)
train_data = dataset.Bp4d_Dataset_with_mean_std_val(
train_file,
bgr=bgr,
binarize=False,
mean_std=mean_std,
transform=data_transforms['train'],
resize=train_resize)
test_data = dataset.Bp4d_Dataset_with_mean_std_val(
test_file,
bgr=bgr,
binarize=False,
mean_std=mean_std,
transform=data_transforms['val'],
resize=im_size)
network_params = dict(n_classes=n_classes,
pool_type='max',
r=route_iter,
init=False,
class_weights=class_weights,
reconstruct=reconstruct,
loss_weights=loss_weights,
std_div=std_div,
dropout=dropout)
util.makedirs(out_dir_train)
train_params = dict(out_dir_train=out_dir_train,
train_data=train_data,
test_data=test_data,
batch_size=batch_size,
batch_size_val=batch_size_val,
num_epochs=num_epochs,
save_after=save_after,
disp_after=1,
plot_after=100,
test_after=10,
lr=lr,
dec_after=dec_after,
model_name=model_name,
criterion=criterion,
gpu_id=gpu_id,
num_workers=0,
model_file=model_file,
epoch_start=epoch_start,
margin_params=margin_params,
network_params=network_params,
weight_decay=0)
test_params = dict(out_dir_train=out_dir_train,
model_num=model_to_test,
train_data=train_data,
test_data=test_data,
gpu_id=gpu_id,
model_name=model_name,
batch_size_val=batch_size_val,
criterion=criterion,
margin_params=margin_params,
network_params=network_params,
post_pend='',
barebones=True)
print train_params
param_file = os.path.join(out_dir_train, 'params.txt')
all_lines = []
for k in train_params.keys():
str_print = '%s: %s' % (k, train_params[k])
print str_print
all_lines.append(str_print)
train_model_recon(**train_params)
test_model_recon(**test_params)
def train_khorrami_aug(wdecay,lr,route_iter,folds=[4,9],model_name='vgg_capsule_disfa',epoch_stuff=[30,60],res=False, class_weights = False, reconstruct = False):
out_dirs = []
out_dir_meta = '../experiments/'+model_name+str(route_iter)
num_epochs = epoch_stuff[1]
epoch_start = 0
# dec_after = ['exp',0.96,epoch_stuff[0],1e-6]
dec_after = ['step',epoch_stuff[0],0.1]
lr = lr
im_resize = 110
im_size = 96
save_after = 100
type_data = 'train_test_files'; n_classes = 8;
criterion = 'margin'
criterion_str = criterion
# criterion = nn.CrossEntropyLoss()
# criterion_str = 'crossentropy'
init = False
loss_weights = [1.,0.5,0.5]
strs_append = '_'+'_'.join([str(val) for val in ['au_sup',loss_weights,'reconstruct',reconstruct,class_weights,'all_aug',criterion_str,init,'wdecay',wdecay,num_epochs]+dec_after+lr])
pre_pend = 'ck_96_'
lr_p=lr[:]
for split_num in folds:
if res:
strs_appendc = '_'+'_'.join([str(val) for val in ['reconstruct',reconstruct,True,'all_aug',criterion_str,init,'wdecay',wdecay,600,'step',600,0.1]+lr_p])
out_dir_train = os.path.join(out_dir_meta,pre_pend+str(split_num)+strs_appendc)
model_file = os.path.join(out_dir_train,'model_300.pt')
epoch_start = 300
lr =[0.1*lr_curr for lr_curr in lr_p]
else:
model_file = None
margin_params = None
out_dir_train = os.path.join(out_dir_meta,pre_pend+str(split_num)+strs_append)
final_model_file = os.path.join(out_dir_train,'model_'+str(num_epochs-1)+'.pt')
if os.path.exists(final_model_file):
print 'skipping',final_model_file
continue
train_file = '../data/ck_96/train_test_files/train_emofacscombo_'+str(split_num)+'.txt'
test_file = '../data/ck_96/train_test_files/test_emofacscombo_'+str(split_num)+'.txt'
mean_file = '../data/ck_96/train_test_files/train_'+str(split_num)+'_mean.png'
std_file = '../data/ck_96/train_test_files/train_'+str(split_num)+'_std.png'
mean_im = scipy.misc.imread(mean_file).astype(np.float32)
std_im = scipy.misc.imread(std_file).astype(np.float32)
if class_weights:
print class_weights
actual_class_weights,au_class_weights = util.get_class_weights(util.readLinesFromFile(train_file),au=True)
print actual_class_weights
print au_class_weights
# actual_class_weights = None
# au_class_weights = None
else:
actual_class_weights = None
au_class_weights = None
# print std_im.shape
# print np.min(std_im),np.max(std_im)
# raw_input()
list_of_to_dos = ['flip','rotate','scale_translate','pixel_augment']
data_transforms = {}
data_transforms['train']= transforms.Compose([
lambda x: augmenters.augment_image(x,list_of_to_dos,mean_im,std_im,im_size),
transforms.ToTensor(),
lambda x: x*255.
])
data_transforms['val']= transforms.Compose([
transforms.ToTensor(),
lambda x: x*255.
])
train_data = dataset.CK_96_Dataset_WithAU(train_file, mean_file, std_file, data_transforms['train'])
test_data = dataset.CK_96_Dataset_WithAU(test_file, mean_file, std_file, data_transforms['val'])
network_params = dict(n_classes=n_classes,pool_type='max',r=route_iter,init=init,class_weights = actual_class_weights, reconstruct = reconstruct,au_sup = True, class_weights_au = au_class_weights,loss_weights = loss_weights)
# if lr[0]==0:
batch_size = 128
batch_size_val = 128
# else:
# batch_size = 32
# batch_size_val = 16
util.makedirs(out_dir_train)
train_params = dict(out_dir_train = out_dir_train,
train_data = train_data,
test_data = test_data,
batch_size = batch_size,
batch_size_val = batch_size_val,
num_epochs = num_epochs,
save_after = save_after,
disp_after = 1,
plot_after = 100,
test_after = 1,
lr = lr,
dec_after = dec_after,
model_name = model_name,
criterion = criterion,
gpu_id = 0,
num_workers = 0,
model_file = model_file,
epoch_start = epoch_start,
margin_params = margin_params,
network_params = network_params,
weight_decay=wdecay)
print train_params
param_file = os.path.join(out_dir_train,'params.txt')
all_lines = []
for k in train_params.keys():
str_print = '%s: %s' % (k,train_params[k])
print str_print
all_lines.append(str_print)
util.writeFile(param_file,all_lines)
# if reconstruct:
train_model_recon_au(**train_params)
# else:
# train_model(**train_params)
# test_params = dict(out_dir_train = out_dir_train,
# model_num = num_epochs-1,
# train_data = train_data,
# test_data = test_data,
# gpu_id = 0,
# model_name = model_name,
# batch_size_val = batch_size_val,
# criterion = criterion,
# margin_params = margin_params,
# network_params = network_params)
# test_model(**test_params)
getting_accuracy.print_accuracy(out_dir_meta,pre_pend,strs_append,folds,log='log.txt')
def khorrami_bl_exp(mmi=False, model_to_test=None):
out_dir_meta = '../experiments/khorrami_ck_96_caps_bl/'
# pre_pend = os.path.join(out_dir_meta,'ck_')
# post_pend = strs_append
num_epochs = 300
epoch_start = 0
# dec_after = ['exp',0.96,350,1e-6]
dec_after = ['exp', 0.96, 350, 1e-6]
# dec_after = ['step',num_epochs,0.1]
lr = [0.001, 0.001]
im_size = 96
model_name = 'khorrami_ck_96'
# model_name = 'khorrami_ck_96_caps_bl'
save_after = 10
# margin_params = {'step':1,'start':0.2}
# strs_append = '_'.join([str(val) for val in [model_name,300]+dec_after+lr])
# out_dir_train = os.path.join(out_dir_meta,'ck_'+str(split_num)+'_'+strs_append)
# model_file = os.path.join(out_dir_train,'model_299.pt')
model_file = None
if not mmi:
strs_append = '_'.join([
str(val) for val in
['train_test_files_non_peak_one_third', model_name, num_epochs] +
dec_after + lr
])
strs_append = '_' + strs_append
pre_pend = 'ck_'
folds = range(10)
else:
pre_pend = 'mmi_96_'
folds = range(2)
strs_append = '_'.join([
str(val) for val in ['train_test_files', model_name, num_epochs] +
dec_after + lr
])
strs_append = '_' + strs_append
if model_to_test is None:
model_to_test = num_epochs - 1
for split_num in folds:
out_dir_train = os.path.join(out_dir_meta,
pre_pend + str(split_num) + strs_append)
print out_dir_train
out_file_model = os.path.join(out_dir_train,
'model_' + str(num_epochs - 1) + '.pt')
if os.path.exists(out_file_model):
print 'skipping', out_file_model
# continue
else:
print 'not done', out_file_model
raw_input()
if not mmi:
train_file = '../data/ck_96/train_test_files_non_peak_one_third/train_' + str(
split_num) + '.txt'
test_file = '../data/ck_96/train_test_files/test_' + str(
split_num) + '.txt'
test_file_easy = '../data/ck_96/train_test_files_non_peak_one_third/test_' + str(
split_num) + '.txt'
mean_file = '../data/ck_96/train_test_files_non_peak_one_third/train_' + str(
split_num) + '_mean.png'
std_file = '../data/ck_96/train_test_files_non_peak_one_third/train_' + str(
split_num) + '_std.png'
else:
type_data = 'train_test_files'
n_classes = 6
train_pre = os.path.join('../data/mmi', type_data)
test_pre = train_pre
train_file = os.path.join(train_pre,
'train_' + str(split_num) + '.txt')
test_file_easy = os.path.join(
train_pre, 'test_front_' + str(split_num) + '.txt')
test_file = os.path.join(test_pre,
'test_side_' + str(split_num) + '.txt')
mean_file = os.path.join(train_pre,
'train_' + str(split_num) + '_mean.png')
std_file = os.path.join(train_pre,
'train_' + str(split_num) + '_std.png')
# train_file = '../data/ck_96/train_test_files/train_'+str(split_num)+'.txt'
# test_file = '../data/ck_96/train_test_files/test_'+str(split_num)+'.txt'
# mean_file = '../data/ck_96/train_test_files/train_'+str(split_num)+'_mean.png'
# std_file = '../data/ck_96/train_test_files/train_'+str(split_num)+'_std.png'
mean_im = scipy.misc.imread(mean_file).astype(np.float32)
std_im = scipy.misc.imread(std_file).astype(np.float32)
std_im[std_im == 0] = 1.
if not mmi:
list_of_to_dos = [
'pixel_augment', 'flip', 'rotate', 'scale_translate'
]
data_transforms = {}
data_transforms['train'] = transforms.Compose([
lambda x: augmenters.augment_image(x, list_of_to_dos, mean_im,
std_im, im_size),
transforms.ToTensor(), lambda x: x * 255.
])
data_transforms['val'] = transforms.Compose(
[transforms.ToTensor(), lambda x: x * 255.])
train_data = dataset.CK_96_Dataset(train_file, mean_file, std_file,
data_transforms['train'])
test_data = dataset.CK_96_Dataset(test_file, mean_file, std_file,
data_transforms['val'])
test_data_easy = dataset.CK_96_Dataset(test_file_easy, mean_file,
std_file,
data_transforms['val'])
else:
list_of_to_dos = ['flip', 'rotate', 'scale_translate']
data_transforms = {}
data_transforms['train'] = transforms.Compose([
lambda x: augmenters.random_crop(x, im_size),
lambda x: augmenters.augment_image(x, list_of_to_dos),
transforms.ToTensor(), lambda x: x * 255.
])
data_transforms['val'] = transforms.Compose(
[transforms.ToTensor(), lambda x: x * 255.])
print train_file
print test_file
print std_file
print mean_file
# raw_input()
train_data = dataset.CK_96_Dataset_with_rs(
train_file, mean_file, std_file, data_transforms['train'])
test_data_easy = dataset.CK_96_Dataset_with_rs(
test_file_easy,
mean_file,
std_file,
data_transforms['val'],
resize=im_size)
test_data = dataset.CK_96_Dataset_with_rs(test_file,
mean_file,
std_file,
data_transforms['val'],
resize=im_size)
network_params = dict(n_classes=8, bn=False)
batch_size = 128
batch_size_val = 128
util.makedirs(out_dir_train)
train_params = dict(out_dir_train=out_dir_train,
train_data=train_data,
test_data=test_data,
batch_size=batch_size,
batch_size_val=batch_size_val,
num_epochs=num_epochs,
save_after=save_after,
disp_after=1,
plot_after=10,
test_after=1,
lr=lr,
dec_after=dec_after,
model_name=model_name,
criterion=nn.CrossEntropyLoss(),
gpu_id=1,
num_workers=0,
model_file=model_file,
epoch_start=epoch_start,
network_params=network_params)
test_params = dict(out_dir_train=out_dir_train,
model_num=model_to_test,
train_data=train_data,
test_data=test_data,
gpu_id=1,
model_name=model_name,
batch_size_val=batch_size_val,
criterion=nn.CrossEntropyLoss(),
margin_params=None,
network_params=network_params,
post_pend='',
model_nums=None)
test_params_easy = dict(out_dir_train=out_dir_train,
model_num=model_to_test,
train_data=train_data,
test_data=test_data_easy,
gpu_id=1,
model_name=model_name,
batch_size_val=batch_size_val,
criterion=nn.CrossEntropyLoss(),
margin_params=None,
network_params=network_params,
post_pend='_easy',
model_nums=None)
print train_params
param_file = os.path.join(out_dir_train, 'params.txt')
all_lines = []
for k in train_params.keys():
str_print = '%s: %s' % (k, train_params[k])
print str_print
all_lines.append(str_print)
util.writeFile(param_file, all_lines)
# train_model(**train_params)
test_model(**test_params)
# print test_params['test_data']
# print test_params['post_pend']
# # raw_input()
# print test_params_easy['test_data']
# print test_params_easy['post_pend']
test_model(**test_params_easy)
# print out_dir_train, model_to_test
# raw_input()
getting_accuracy.print_accuracy(out_dir_meta,
pre_pend,
strs_append,
folds,
log='log.txt')
getting_accuracy.view_loss_curves(out_dir_meta, pre_pend, strs_append,
folds, num_epochs - 1)
def train_gray(wdecay,
lr,
route_iter,
folds=[4, 9],
model_name='vgg_capsule_bp4d',
epoch_stuff=[30, 60],
res=False,
class_weights=False,
reconstruct=False,
loss_weights=None,
exp=False,
disfa=False,
vgg_base_file=None,
vgg_base_file_str=None,
mean_file=None,
std_file=None,
aug_more=False,
align=True):
out_dirs = []
out_dir_meta = '../experiments/' + model_name + str(route_iter)
num_epochs = epoch_stuff[1]
epoch_start = 0
if exp:
dec_after = ['exp', 0.96, epoch_stuff[0], 1e-6]
else:
dec_after = ['step', epoch_stuff[0], 0.1]
lr = lr
im_resize = 110
# 256
im_size = 96
save_after = 1
if disfa:
dir_files = '../data/disfa'
# type_data = 'train_test_10_6_method_110_gray_align'; n_classes = 10;
type_data = 'train_test_8_au_all_method_110_gray_align'
n_classes = 8
pre_pend = 'disfa_' + type_data + '_'
binarize = True
else:
dir_files = '../data/bp4d'
if align:
type_data = 'train_test_files_110_gray_align'
n_classes = 12
else:
type_data = 'train_test_files_110_gray_nodetect'
n_classes = 12
pre_pend = 'bp4d_' + type_data + '_'
binarize = False
criterion = 'marginmulti'
criterion_str = criterion
init = False
aug_str = aug_more
# if aug_more:
# aug_str = 'cropkhAugNoColor'
# else:
# aug_str = 'flipCrop'
strs_append = '_' + '_'.join([
str(val) for val in [
'reconstruct', reconstruct, class_weights, aug_str, criterion_str,
init, 'wdecay', wdecay, num_epochs
] + dec_after + lr + ['lossweights'] + loss_weights +
[vgg_base_file_str]
])
lr_p = lr[:]
for split_num in folds:
if res:
# strs_appendc = '_'+'_'.join([str(val) for val in ['reconstruct',reconstruct,True,'flipCrop',criterion_str,init,'wdecay',wdecay,10,'exp',0.96,350,1e-6]+['lossweights']+loss_weights])
# dec_afterc = dec_after
strs_appendc = '_' + '_'.join([
str(val) for val in [
'reconstruct', reconstruct, True, aug_str, criterion_str,
init, 'wdecay', wdecay, 10
] + dec_after + lr + ['lossweights'] + loss_weights +
[vgg_base_file_str]
])
out_dir_train = os.path.join(
out_dir_meta, pre_pend + str(split_num) + strs_appendc)
model_file = os.path.join(out_dir_train, 'model_9.pt')
epoch_start = 10
# lr =[0.1*lr_curr for lr_curr in lr_p]
else:
model_file = None
margin_params = None
out_dir_train = os.path.join(out_dir_meta,
pre_pend + str(split_num) + strs_append)
final_model_file = os.path.join(out_dir_train,
'model_' + str(num_epochs - 1) + '.pt')
if os.path.exists(final_model_file):
print 'skipping', final_model_file
# raw_input()
# continue
else:
print 'not skipping', final_model_file
# raw_input()
# continue
train_file = os.path.join(dir_files, type_data,
'train_' + str(split_num) + '.txt')
test_file = os.path.join(dir_files, type_data,
'test_' + str(split_num) + '.txt')
if vgg_base_file is None:
mean_file = os.path.join(dir_files, type_data,
'train_' + str(split_num) + '_mean.png')
std_file = os.path.join(dir_files, type_data,
'train_' + str(split_num) + '_std.png')
print train_file
print test_file
print mean_file
print std_file
# raw_input()
class_weights = util.get_class_weights_au(
util.readLinesFromFile(train_file))
data_transforms = {}
if aug_more == 'cropkhAugNoColor':
train_resize = None
print 'AUGING MORE'
list_of_todos = ['flip', 'rotate', 'scale_translate']
data_transforms['train'] = transforms.Compose([
lambda x: augmenters.random_crop(x, im_size),
lambda x: augmenters.augment_image(x, list_of_todos),
# lambda x: augmenters.horizontal_flip(x),
transforms.ToTensor(),
lambda x: x * 255,
])
elif aug_more == 'cropFlip':
train_resize = None
data_transforms['train'] = transforms.Compose([
lambda x: augmenters.random_crop(x, im_size),
lambda x: augmenters.horizontal_flip(x),
transforms.ToTensor(),
lambda x: x * 255,
])
elif aug_more == 'NONE':
train_resize = im_size
data_transforms['train'] = transforms.Compose([
transforms.ToTensor(),
lambda x: x * 255,
])
else:
raise ValueError('aug_more is problematic')
data_transforms['val'] = transforms.Compose([
transforms.ToTensor(),
lambda x: x * 255,
])
train_data = dataset.Bp4d_Dataset_Mean_Std_Im(
train_file,
mean_file,
std_file,
transform=data_transforms['train'],
binarize=binarize,
resize=train_resize)
test_data = dataset.Bp4d_Dataset_Mean_Std_Im(
test_file,
mean_file,
std_file,
resize=im_size,
transform=data_transforms['val'],
binarize=binarize)
# train_data = dataset.Bp4d_Dataset_Mean_Std_Im(test_file, mean_file, std_file, resize= im_size, transform = data_transforms['val'])
network_params = dict(n_classes=n_classes,
pool_type='max',
r=route_iter,
init=init,
class_weights=class_weights,
reconstruct=reconstruct,
loss_weights=loss_weights,
vgg_base_file=vgg_base_file)
batch_size = 128
batch_size_val = 128
util.makedirs(out_dir_train)
train_params = dict(out_dir_train=out_dir_train,
train_data=train_data,
test_data=test_data,
batch_size=batch_size,
batch_size_val=batch_size_val,
num_epochs=num_epochs,
save_after=save_after,
disp_after=1,
plot_after=10,
test_after=1,
lr=lr,
dec_after=dec_after,
model_name=model_name,
criterion=criterion,
gpu_id=0,
num_workers=0,
model_file=model_file,
epoch_start=epoch_start,
margin_params=margin_params,
network_params=network_params,
weight_decay=wdecay)
test_params = dict(out_dir_train=out_dir_train,
model_num=num_epochs - 1,
train_data=train_data,
test_data=test_data,
gpu_id=0,
model_name=model_name,
batch_size_val=batch_size_val,
criterion=criterion,
margin_params=margin_params,
network_params=network_params,
barebones=True)
# test_params_train = dict(**test_params)
# test_params_train['test_data'] = train_data_no_t
# test_params_train['post_pend'] = '_train'
print train_params
param_file = os.path.join(out_dir_train, 'params.txt')
all_lines = []
for k in train_params.keys():
str_print = '%s: %s' % (k, train_params[k])
print str_print
all_lines.append(str_print)
util.writeFile(param_file, all_lines)
# if reconstruct:
train_model_recon(**train_params)
test_model_recon(**test_params)
# test_model_recon(**test_params_train)
# else:
# train_model(**train_params)
# test_params = dict(out_dir_train = out_dir_train,
# model_num = num_epochs-1,
# train_data = train_data,
# test_data = test_data,
# gpu_id = 0,
# model_name = model_name,
# batch_size_val = batch_size_val,
# criterion = criterion,
# margin_params = margin_params,
# network_params = network_params)
# test_model(**test_params)
getting_accuracy.print_accuracy(out_dir_meta,
pre_pend,
strs_append,
folds,
log='log.txt')
def train_vgg(wdecay,
lr,
route_iter,
folds=[4, 9],
model_name='vgg_capsule_bp4d',
epoch_stuff=[30, 60],
res=False,
class_weights=False,
reconstruct=False,
loss_weights=None,
exp=False,
align=False,
disfa=False,
more_aug=False,
dropout=None,
model_to_test=None,
gpu_id=0,
test_mode=False):
out_dirs = []
out_dir_meta = '../experiments/' + model_name + str(route_iter)
num_epochs = epoch_stuff[1]
if model_to_test is None:
model_to_test = num_epochs - 1
epoch_start = 0
if exp:
dec_after = ['exp', 0.96, epoch_stuff[0], 1e-6]
else:
dec_after = ['step', epoch_stuff[0], 0.1]
lr = lr
if model_name.startswith('vgg'):
im_resize = 256
im_size = 224
if not disfa:
dir_files = '../data/bp4d'
if align:
type_data = 'train_test_files_256_color_align'
n_classes = 12
else:
type_data = 'train_test_files_256_color_nodetect'
n_classes = 12
pre_pend = 'bp4d_256_' + type_data + '_'
binarize = False
else:
dir_files = '../data/disfa'
type_data = 'train_test_8_au_all_method_256_color_align'
n_classes = 8
pre_pend = 'disfa_' + type_data + '_'
binarize = True
pre_pend = 'disfa_256_' + type_data + '_'
else:
if not disfa:
im_resize = 110
im_size = 96
binarize = False
dir_files = '../data/bp4d'
type_data = 'train_test_files_110_color_align'
n_classes = 12
pre_pend = 'bp4d_110_'
else:
im_resize = 110
im_size = 96
dir_files = '../data/disfa'
type_data = 'train_test_8_au_all_method_110_color_align'
n_classes = 8
binarize = True
pre_pend = 'disfa_110_' + type_data + '_'
save_after = 1
criterion = 'marginmulti'
criterion_str = criterion
init = False
strs_append_list = [
'reconstruct', reconstruct, class_weights, 'all_aug', criterion_str,
init, 'wdecay', wdecay, num_epochs
] + dec_after + lr + [more_aug] + [dropout]
if loss_weights is not None:
strs_append_list = strs_append_list + ['lossweights'] + loss_weights
strs_append = '_' + '_'.join([str(val) for val in strs_append_list])
lr_p = lr[:]
for split_num in folds:
if res:
strs_append_list_c = [
'reconstruct', reconstruct, False, 'all_aug', criterion_str,
init, 'wdecay', wdecay, 10
] + ['step', 10, 0.1] + lr + [more_aug] + [dropout]
# print dec_after
# raw_input()
if loss_weights is not None:
strs_append_list_c = strs_append_list_c + ['lossweights'
] + loss_weights
strs_append_c = '_' + '_'.join(
[str(val) for val in strs_append_list_c])
out_dir_train = os.path.join(
out_dir_meta, pre_pend + str(split_num) + strs_append_c)
model_file = os.path.join(out_dir_train, 'model_4.pt')
epoch_start = 5
lr = [val * 0.1 for val in lr]
print 'FILE EXISTS', os.path.exists(
model_file), model_file, epoch_start
raw_input()
else:
model_file = None
margin_params = None
out_dir_train = os.path.join(out_dir_meta,
pre_pend + str(split_num) + strs_append)
final_model_file = os.path.join(out_dir_train,
'model_' + str(num_epochs - 1) + '.pt')
# final_model_file = os.path.join(out_dir_train,'results_model_'+str(model_to_test))
if os.path.exists(final_model_file) and not test_mode:
print 'skipping', final_model_file
# raw_input()
continue
else:
print 'not skipping', final_model_file
# raw_input()
# continue
train_file = os.path.join(dir_files, type_data,
'train_' + str(split_num) + '.txt')
test_file = os.path.join(dir_files, type_data,
'test_' + str(split_num) + '.txt')
data_transforms = None
if model_name.startswith('vgg_capsule_7_3_imagenet'
) or model_name.startswith('scratch_'):
# mean_std = np.array([[93.5940,104.7624,129.1863],[1.,1.,1.]]) #bgr
# std_div = np.array([0.225*255,0.224*255,0.229*255])
# print std_div
# raw_input()
mean_std = np.array([[0.485, 0.456, 0.406], [0.229, 0.224, 0.225]])
bgr = False
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
std_div = None
data_transforms = {}
data_transforms['train'] = [
transforms.ToPILImage(),
transforms.RandomCrop(im_size),
transforms.RandomHorizontalFlip(),
transforms.RandomRotation(15),
transforms.ColorJitter(),
transforms.ToTensor(), normalize
]
data_transforms['val'] = [
transforms.ToPILImage(),
transforms.Resize((im_size, im_size)),
transforms.ToTensor(), normalize
]
if torch.version.cuda.startswith('9'):
data_transforms['train'].append(lambda x: x.float())
data_transforms['val'].append(lambda x: x.float())
data_transforms['train'] = transforms.Compose(
data_transforms['train'])
data_transforms['val'] = transforms.Compose(data_transforms['val'])
train_data = dataset.Bp4d_Dataset(
train_file,
bgr=bgr,
binarize=binarize,
transform=data_transforms['train'])
test_data = dataset.Bp4d_Dataset(test_file,
bgr=bgr,
binarize=binarize,
transform=data_transforms['val'])
elif model_name.startswith('vgg'):
mean_std = np.array([[93.5940, 104.7624, 129.1863], [1., 1.,
1.]]) #bgr
std_div = np.array([0.225 * 255, 0.224 * 255, 0.229 * 255])
print std_div
# raw_input()
bgr = True
else:
mean_std = np.array([[0.485 * 255, 0.456 * 255, 0.406 * 255],
[0.229 * 255, 0.224 * 255, 0.225 * 255]])
bgr = False
print mean_std
class_weights = util.get_class_weights_au(
util.readLinesFromFile(train_file))
if data_transforms is None:
data_transforms = {}
if more_aug == 'MORE':
print more_aug
list_of_to_dos = ['flip', 'rotate', 'scale_translate']
# print torch.version.cuda
# raw_input()
if torch.version.cuda.startswith('9'):
# print 'HEYLO'
# raw_input()
data_transforms['train'] = transforms.Compose([
lambda x: augmenters.random_crop(x, im_size),
lambda x: augmenters.augment_image(
x, list_of_to_dos, color=True, im_size=im_size),
transforms.ToTensor(), lambda x: x.float()
])
data_transforms['val'] = transforms.Compose(
[transforms.ToTensor(), lambda x: x.float()])
else:
data_transforms['train'] = transforms.Compose([
lambda x: augmenters.random_crop(x, im_size),
lambda x: augmenters.augment_image(
x, list_of_to_dos, color=True, im_size=im_size),
transforms.ToTensor(),
lambda x: x * 255,
])
data_transforms['val'] = transforms.Compose([
transforms.ToTensor(),
lambda x: x * 255,
])
train_data = dataset.Bp4d_Dataset_with_mean_std_val(
train_file,
bgr=bgr,
binarize=binarize,
mean_std=mean_std,
transform=data_transforms['train'])
test_data = dataset.Bp4d_Dataset_with_mean_std_val(
test_file,
bgr=bgr,
binarize=binarize,
mean_std=mean_std,
transform=data_transforms['val'],
resize=im_size)
elif more_aug == 'LESS':
# std_div = None
data_transforms['train'] = transforms.Compose([
transforms.ToPILImage(),
# transforms.Resize((im_resize,im_resize)),
transforms.RandomCrop(im_size),
transforms.RandomHorizontalFlip(),
transforms.RandomRotation(15),
transforms.ColorJitter(),
transforms.ToTensor(),
lambda x: x * 255,
transforms.Normalize(mean_std[0, :], mean_std[1, :]),
])
data_transforms['val'] = transforms.Compose([
transforms.ToPILImage(),
transforms.Resize((im_size, im_size)),
transforms.ToTensor(),
lambda x: x * 255,
transforms.Normalize(mean_std[0, :], mean_std[1, :]),
])
train_data = dataset.Bp4d_Dataset(
train_file,
bgr=bgr,
binarize=binarize,
transform=data_transforms['train'])
test_data = dataset.Bp4d_Dataset(
test_file,
bgr=bgr,
binarize=binarize,
transform=data_transforms['val'])
elif more_aug == 'NONE':
print 'NO AUGING'
data_transforms['train'] = transforms.Compose(
[transforms.ToTensor(), lambda x: x * 255])
data_transforms['val'] = transforms.Compose(
[transforms.ToTensor(), lambda x: x * 255])
train_data = dataset.Bp4d_Dataset_with_mean_std_val(
train_file,
bgr=bgr,
binarize=binarize,
mean_std=mean_std,
transform=data_transforms['train'],
resize=im_size)
test_data = dataset.Bp4d_Dataset_with_mean_std_val(
test_file,
bgr=bgr,
binarize=binarize,
mean_std=mean_std,
transform=data_transforms['val'],
resize=im_size)
else:
raise ValueError('more_aug not valid')
if dropout is not None:
print 'RECONS', reconstruct
network_params = dict(n_classes=n_classes,
pool_type='max',
r=route_iter,
init=init,
class_weights=class_weights,
reconstruct=reconstruct,
loss_weights=loss_weights,
std_div=std_div,
dropout=dropout)
else:
network_params = dict(n_classes=n_classes,
pool_type='max',
r=route_iter,
init=init,
class_weights=class_weights,
reconstruct=reconstruct,
loss_weights=loss_weights,
std_div=std_div)
batch_size = 32
batch_size_val = 32
util.makedirs(out_dir_train)
train_params = dict(out_dir_train=out_dir_train,
train_data=train_data,
test_data=test_data,
batch_size=batch_size,
batch_size_val=batch_size_val,
num_epochs=num_epochs,
save_after=save_after,
disp_after=1,
plot_after=100,
test_after=1,
lr=lr,
dec_after=dec_after,
model_name=model_name,
criterion=criterion,
gpu_id=gpu_id,
num_workers=0,
model_file=model_file,
epoch_start=epoch_start,
margin_params=margin_params,
network_params=network_params,
weight_decay=wdecay)
test_params = dict(out_dir_train=out_dir_train,
model_num=model_to_test,
train_data=train_data,
test_data=test_data,
gpu_id=gpu_id,
model_name=model_name,
batch_size_val=batch_size_val,
criterion=criterion,
margin_params=margin_params,
network_params=network_params,
barebones=True)
# test_params_train = dict(**test_params)
# test_params_train['test_data'] = train_data_no_t
# test_params_train['post_pend'] = '_train'
print train_params
param_file = os.path.join(out_dir_train, 'params.txt')
all_lines = []
for k in train_params.keys():
str_print = '%s: %s' % (k, train_params[k])
print str_print
all_lines.append(str_print)
# util.writeFile(param_file,all_lines)
# if reconstruct:
if not test_mode:
train_model_recon(**train_params)
test_model_recon(**test_params)
# test_params = dict(out_dir_train = out_dir_train,
# model_num = 4,
# train_data = train_data,
# test_data = test_data,
# gpu_id = gpu_id,
# model_name = model_name,
# batch_size_val = batch_size_val,
# criterion = criterion,
# margin_params = margin_params,
# network_params = network_params,barebones=True)
# test_model_recon(**test_params)
getting_accuracy.print_accuracy(out_dir_meta,
pre_pend,
strs_append,
folds,
log='log.txt')
def train_gray(wdecay,lr,route_iter,folds = [4,9],model_name='vgg_capsule_disfa',epoch_stuff=[30,60],res=False, reconstruct = False, oulu = False, meta_data_dir = 'train_test_files_preprocess_vl',loss_weights = None, exp = False, dropout = 0, gpu_id = 0, aug_more = 'flip', model_to_test = None):
out_dir_meta = '../experiments_dropout/'+model_name+'_'+str(route_iter)
num_epochs = epoch_stuff[1]
if model_to_test is None:
model_to_test = num_epochs -1
epoch_start = 0
if exp:
dec_after = ['exp',0.96,epoch_stuff[0],1e-6]
else:
dec_after = ['step',epoch_stuff[0],0.1]
lr = lr
im_resize = 110
im_size = 96
save_after = 100
type_data = 'train_test_files'; n_classes = 8;
train_pre = os.path.join('../data/ck_96',type_data)
test_pre = os.path.join('../data/ck_96',type_data)
if oulu:
type_data = 'three_im_no_neutral_just_strong_False'; n_classes = 6;
criterion = 'margin'
criterion_str = criterion
init = False
strs_append_list = ['reconstruct',reconstruct]+aug_more+[num_epochs]+dec_after+lr+[dropout]
if loss_weights is not None:
strs_append_list = strs_append_list +['lossweights']+loss_weights
strs_append = '_'+'_'.join([str(val) for val in strs_append_list])
if oulu:
pre_pend = 'oulu_96_'+meta_data_dir+'_'
else:
pre_pend = 'ck_96_'+type_data+'_'
lr_p=lr[:]
for split_num in folds:
if res:
print 'what to res?'
raw_input()
else:
model_file = None
margin_params = None
out_dir_train = os.path.join(out_dir_meta,pre_pend+str(split_num)+strs_append)
print out_dir_train
final_model_file = os.path.join(out_dir_train,'model_'+str(num_epochs-1)+'.pt')
if os.path.exists(final_model_file):
print 'skipping',final_model_file
# raw_input()
continue
else:
print 'not skipping', final_model_file
# raw_input()
# continue
if not oulu:
train_file = os.path.join(train_pre,'train_'+str(split_num)+'.txt')
test_file_easy = os.path.join(train_pre,'test_'+str(split_num)+'.txt')
test_file = os.path.join(test_pre,'test_'+str(split_num)+'.txt')
mean_file = os.path.join(train_pre,'train_'+str(split_num)+'_mean.png')
std_file = os.path.join(train_pre,'train_'+str(split_num)+'_std.png')
else:
train_file = os.path.join('../data/Oulu_CASIA',meta_data_dir, type_data, 'train_'+str(split_num)+'.txt')
test_file = os.path.join('../data/Oulu_CASIA',meta_data_dir, type_data, 'test_'+str(split_num)+'.txt')
mean_file = os.path.join('../data/Oulu_CASIA',meta_data_dir, type_data, 'train_'+str(split_num)+'_mean.png')
std_file = os.path.join('../data/Oulu_CASIA',meta_data_dir, type_data, 'train_'+str(split_num)+'_std.png')
mean_im = scipy.misc.imread(mean_file).astype(np.float32)
std_im = scipy.misc.imread(std_file).astype(np.float32)
class_weights = util.get_class_weights(util.readLinesFromFile(train_file))
list_of_to_dos = aug_more
print list_of_to_dos
# raw_input()
# aug_more.split('_')
# ['flip','rotate','scale_translate', 'pixel_augment']
data_transforms = {}
if 'hs' in list_of_to_dos:
print '**********HS!!!!!!!'
list_transforms = [lambda x: augmenters.hide_and_seek(x)]
if 'flip' in list_of_to_dos:
list_transforms.append(lambda x: augmenters.horizontal_flip(x))
list_transforms = list_transforms+ [transforms.ToTensor(),lambda x: x*255.]
print list_transforms
data_transforms['train']= transforms.Compose(list_transforms)
elif 'none' in list_of_to_dos:
print 'DOING NOTHING!!!!!!'
data_transforms['train']= transforms.Compose([
transforms.ToTensor(),
lambda x: x*255.
])
else:
data_transforms['train']= transforms.Compose([
lambda x: augmenters.augment_image(x,list_of_to_dos,mean_im,std_im,im_size),
transforms.ToTensor(),
lambda x: x*255.
])
data_transforms['val']= transforms.Compose([
transforms.ToTensor(),
lambda x: x*255.
])
print data_transforms['train']
# raw_input()
# train_data = dataset.CK_96_Dataset_Just_Mean(train_file, mean_file, data_transforms['train'])
# test_data = dataset.CK_96_Dataset_Just_Mean(test_file, mean_file, data_transforms['val'])
print train_file
print test_file
print std_file
print mean_file
# raw_input()
train_data = dataset.CK_96_Dataset(train_file, mean_file, std_file, data_transforms['train'])
test_data = dataset.CK_96_Dataset(test_file, mean_file, std_file, data_transforms['val'])
network_params = dict(n_classes=n_classes,pool_type='max',r=route_iter,init=init,class_weights = class_weights, reconstruct = reconstruct,loss_weights = loss_weights, dropout = dropout)
batch_size = 128
batch_size_val = 128
util.makedirs(out_dir_train)
train_params = dict(out_dir_train = out_dir_train,
train_data = train_data,
test_data = test_data,
batch_size = batch_size,
batch_size_val = batch_size_val,
num_epochs = num_epochs,
save_after = save_after,
disp_after = 1,
plot_after = 100,
test_after = 1,
lr = lr,
dec_after = dec_after,
model_name = model_name,
criterion = criterion,
gpu_id = gpu_id,
num_workers = 0,
model_file = model_file,
epoch_start = epoch_start,
margin_params = margin_params,
network_params = network_params,
weight_decay=wdecay)
test_params = dict(out_dir_train = out_dir_train,
model_num = model_to_test,
# num_epochs-1,
train_data = train_data,
test_data = test_data,
gpu_id = gpu_id,
model_name = model_name,
batch_size_val = batch_size_val,
criterion = criterion,
margin_params = margin_params,
network_params = network_params)
print train_params
param_file = os.path.join(out_dir_train,'params.txt')
all_lines = []
for k in train_params.keys():
str_print = '%s: %s' % (k,train_params[k])
print str_print
all_lines.append(str_print)
util.writeFile(param_file,all_lines)
# if reconstruct:
train_model_recon(**train_params)
test_model_recon(**test_params)
# else:
# train_model(**train_params)
# test_model(**test_params)
getting_accuracy.print_accuracy(out_dir_meta,pre_pend,strs_append,folds,log='log.txt')
