def main_program(path_data,nb_row_per_classe,
use_gpu,
nb_epoch,batch_size,learning_rate,
path_save_model):
# Label encoding and decoding dicts
enc_dict, dec_dict = create_encoding_deconding_dict(path_data)
#Data_set
size_image_train = 224
data_train=create_huge_data_set(path_data,nb_rows=nb_row_per_classe,size_image=size_image_train,encoding_dict=enc_dict)
data_valid=create_huge_data_set(path_data,nb_rows=100,size_image=size_image_train,skip_rows=range(1,nb_row_per_classe),encoding_dict=enc_dict)
# Model
model = create_model(use_gpu)
if use_gpu:
model.cuda()
#Loss
criterion = nn.CrossEntropyLoss()
#Optimiser
optimizer = optim.SGD(model.parameters(), lr=learning_rate)
# Scheduler LR
scheduler = LambdaLR(optimizer, lr_lambda=LRPolicy(start_lr=learning_rate))
#Data loader
train_loader=DataLoader(data_train,batch_size=batch_size,shuffle=True)
valid_loader=DataLoader(data_valid,batch_size=batch_size)
#Train
train_model(model,train_loader,valid_loader,nb_epoch,
scheduler,optimizer,criterion,use_gpu,
path_save=path_save_model)
pass
def main_program(path_data, nb_row_per_classe, use_gpu, do_training,
do_testing, nb_epoch, batch_size, learning_rate,
path_save_model):
# Label encoding and decoding dicts
enc_dict, dec_dict = create_encoding_deconding_dict(path_data)
#Data_set
size_image_train = 224
data_train = create_huge_data_set(path_data,
nb_rows=nb_row_per_classe,
size_image=size_image_train,
encoding_dict=enc_dict)
data_valid = create_huge_data_set(path_data,
nb_rows=100,
size_image=size_image_train,
skip_rows=range(1, nb_row_per_classe),
encoding_dict=enc_dict)
# Model
model = create_model(use_gpu)
if use_gpu:
model.cuda()
#Loss
criterion = nn.CrossEntropyLoss()
#Optimiser
optimizer = optim.SGD(model.parameters(), lr=learning_rate)
# Scheduler LR
scheduler = LambdaLR(optimizer, lr_lambda=LRPolicy(start_lr=learning_rate))
#Data loader
train_loader = DataLoader(data_train, batch_size=batch_size, shuffle=True)
valid_loader = DataLoader(data_valid, batch_size=batch_size)
#Train
if do_training:
train_model(model,
train_loader,
valid_loader,
nb_epoch,
scheduler,
optimizer,
criterion,
use_gpu,
path_save=path_save_model)
score = validate(model, valid_loader, use_gpu=use_gpu)[0]
print(score)
#Test
if do_testing:
print("BIDON")
model_final, history = load_model_weights(model,
path_save_model,
type="best",
use_gpu=use_gpu,
get_history=True)
history.display()
score = validate(model, valid_loader, use_gpu=use_gpu)[0]
print(score)
pass
