def main(init_epoch):
parameter_list = list(
model_CNN.parameters()) + list(model_avg_fc.parameters()) + list(
model_reg.parameters()) #+list(model_class.parameters())
parameters_to_optimize = parameter_list
# print(len(parameter_list)," $$$$$4")
#exit()
# optimizer = optim.Adam(parameters_to_optimize,lr=0.01)
#torch.optim.Adadelta(params, lr=1.0, rho=0.9, eps=1e-06, weight_decay=0)
# optimizer = optim.Adadelta(parameters_to_optimize,lr=0.0001,weight_decay=0.5)
optimizer = optim.Adam(parameters_to_optimize, lr=0.0001)
l1 = nn.L1Loss()
l2 = nn.MSELoss()
c1 = nn.CrossEntropyLoss()
c2 = nn.CrossEntropyLoss()
c3 = nn.CrossEntropyLoss()
c4 = nn.CrossEntropyLoss()
c5 = nn.CrossEntropyLoss()
action_criterions = (c1, c2, c3, c4, c5)
train_dataset = VideoDataset('train')
test_dataset = VideoDataset('test')
train_dataloader = DataLoader(train_dataset,
batch_size=train_batch_size,
shuffle=True)
test_dataloader = DataLoader(test_dataset,
batch_size=test_batch_size,
shuffle=False)
grph = graph()
for epoch in range(init_epoch, num_epochs):
start = timeit.default_timer()
print(
'-------------------------------------------------------------------------------------------------------'
)
#train_phase(train_dataloader, optimizer, criterion, epoch)
tr_loss = train_phase(train_dataloader, optimizer, epoch, l1, l2,
action_criterions)
ts_loss = test_phase(test_dataloader, epoch, l1, l2, action_criterions)
print(" average training loss:{} , average test loss:{}".format(
tr_loss, ts_loss))
grph.update_graph(tr_loss, ts_loss)
#if epoch == 0: # save models every 5 epochs
grph.draw_and_save()
# if epoch%4==0 or epoch==num_epochs-1 :
save_model(model_CNN, 'model_CNN', model_saving_dir, epoch)
save_model(model_avg_fc, 'model_avg_fc', model_saving_dir, epoch)
save_model(model_reg, 'model_reg', model_saving_dir, epoch)
save_model(model_class, 'model_class', model_saving_dir, epoch)
stop = timeit.default_timer()
print("time taken each epoch {} seconds".format(stop - start))
grph.draw_and_save()
def main():
parameters_2_optimize = (list(model_lstm.parameters()))
optimizer = optim.Adam(parameters_2_optimize, lr=0.0001)
criterion = nn.MSELoss()
train_dataset = VideoDataset('train')
test_dataset = VideoDataset('test')
train_dataloader = DataLoader(train_dataset,
batch_size=train_batch_size,
shuffle=True)
test_dataloader = DataLoader(test_dataset,
batch_size=test_batch_size,
shuffle=False)
# actual training, testing loops
for epoch in range(10):
saving_dir = '...'
if epoch == 0: # save models every 5 epochs
save_model(model_lstm, 'model_my_lstm', epoch, saving_dir)
print(
'-------------------------------------------------------------------------------------------------------'
)
train_phase(train_dataloader, optimizer, criterion, epoch)
test_phase(test_dataloader)
if (epoch +
1) % model_ckpt_interval == 0: # save models every 5 epochs
save_model(model_lstm, 'model_my_lstm', epoch, saving_dir)
# lr updates
if (epoch + 1) % global_lr_stepsize == 0:
learning_rate = learning_rate * global_lr_gamma
for param_group in optimizer.param_groups:
param_group['lr'] = learning_rate
def main():
#parameter_list=list(model_CNN.parameters())+list(model_avg_fc.parameters())+list(model_reg.parameters())+list(model_class.parameters())
#parameters_to_optimize = parameter_list
# print(len(parameter_list)," $$$$$4")
#exit()
# optimizer = optim.Adam(parameters_to_optimize,lr=0.01)
#torch.optim.Adadelta(params, lr=1.0, rho=0.9, eps=1e-06, weight_decay=0)
#optimizer = optim.Adadelta(parameters_to_optimize,lr=0.0001,weight_decay=0.5)
l1 = nn.L1Loss()
l2 = nn.MSELoss()
c1 = nn.CrossEntropyLoss()
c2 = nn.CrossEntropyLoss()
c3 = nn.CrossEntropyLoss()
c4 = nn.CrossEntropyLoss()
c5 = nn.CrossEntropyLoss()
action_criterions = (c1, c2, c3, c4, c5)
#train_dataset = VideoDataset('train')
test_dataset = VideoDataset('test')
#train_dataloader = DataLoader(train_dataset, batch_size=train_batch_size, shuffle=True)
test_dataloader = DataLoader(test_dataset,
batch_size=test_batch_size,
shuffle=False)
#grph = graph()
for epoch in range(1):
saving_dir = "saved_models"
print(
'-------------------------------------------------------------------------------------------------------'
)
#train_phase(train_dataloader, optimizer, criterion, epoch)
#tr_loss=train_phase(train_dataloader, optimizer, epoch,l1,l2,action_criterions)
ts_loss = test_phase(test_dataloader, epoch, l1, l2, action_criterions)