#cnnp = CNN('./cnn_config_1.5TP_optimal.json', 0)
#this part is for 5-runs accuracy
#--------------------------------
if False:
oris = []
gens = []
ps1s = []
rs1s = []
fs1s = []
ps2s = []
rs2s = []
fs2s = []
for seed in range(5):
cnn = CNN('./cnn_config_1.5T_optimal.json', 0)
cnn.train(verbose=1)
print(cnn.epoch)
cnn.model.load_state_dict(torch.load('{}CNN_{}.pth'.format(cnn.checkpoint_dir, cnn.epoch)))
cnnp = CNN('./cnn_config_1.5TP_optimal.json', 0)
cnnp.train(verbose=1)
print(cnnp.epoch)
cnnp.model.load_state_dict(torch.load('{}CNN_{}.pth'.format(cnnp.checkpoint_dir, cnnp.epoch)))
print('iter', seed, 'testing accuracy:', cnn.test(), cnnp.test())
ori, gen = eval_cnns(cnn, cnnp)
ps_1, rs_1, fs_1, ps_2, rs_2, fs_2 = PRF_cnns(cnn, cnnp)
ps1s += [ps_1]
rs1s += [rs_1]
fs1s += [fs_1]
ps2s += [ps_2]
rs2s += [rs_2]
def pre_train(dataloader,
test_loader,
dict_loader,
dataloader_test,
mask_labels,
total_epochs=50,
learning_rate=1e-4,
use_gpu=True,
seed=123):
args = parser.parse_args()
pprint(args)
num_bits = args.num_bits
model = CNN(model_name='alexnet', bit=num_bits, class_num=args.num_class)
criterion = custom_loss(num_bits=num_bits)
arch = 'cnn_'
filename = arch + args.dataset + '_' + str(num_bits) + "bits"
checkpoint_filename = os.path.join(args.checkpoint, filename + '.pt')
if use_gpu:
model = model.cuda()
model = torch.nn.DataParallel(model,
device_ids=range(
torch.cuda.device_count()))
criterion = criterion.cuda()
torch.cuda.manual_seed(seed)
running_loss = 0.0
start_epoch = 0
batch_time = AverageMeter()
data_time = AverageMeter()
end = time.time()
best_prec = -99999
k = 10500
n_samples = 200000
alpha = 0.4
alpha_1 = 0.99
mask_labels = torch.from_numpy(mask_labels).long().cuda()
Z_h1 = torch.zeros(n_samples,
num_bits).float().cuda() # intermediate values
z_h1 = torch.zeros(n_samples, num_bits).float().cuda() # temporal outputs
h1 = torch.zeros(n_samples, num_bits).float().cuda() # current outputs
Z_h2 = torch.zeros(args.anchor_num,
num_bits).float().cuda() # intermediate values
z_h2 = torch.zeros(args.anchor_num,
num_bits).float().cuda() # temporal outputs
h2 = torch.zeros(args.anchor_num,
num_bits).float().cuda() # current outputs
for epoch in range(start_epoch, total_epochs):
model.train(True)
rampup_value = rampup(epoch)
rampdown_value = rampdown(epoch)
learning_rate = rampup_value * rampdown_value * 0.00005
adam_beta1 = rampdown_value * 0.9 + (1.0 - rampdown_value) * 0.5
adam_beta2 = step_rampup(epoch) * 0.99 + (1 -
step_rampup(epoch)) * 0.999
if epoch == 0:
u_w = 0.0
else:
u_w = rampup_value
u_w_m = u_w * 5
u_w_m = torch.autograd.Variable(torch.FloatTensor([u_w_m]).cuda(),
requires_grad=False)
optimizer = Adam(model.parameters(),
lr=learning_rate,
betas=(adam_beta1, adam_beta2),
eps=1e-8,
amsgrad=True)
anchors_data, anchor_Label = generate_anchor_vectors(dict_loader)
for iteration, data in enumerate(dataloader, 0):
anchor_index = np.arange(args.anchor_num)
np.random.shuffle(anchor_index)
anchor_index = anchor_index[:100]
anchor_index = torch.from_numpy(anchor_index).long().cuda()
anchor_inputs = anchors_data[anchor_index, :, :, :]
anchor_labels = anchor_Label[anchor_index, :]
inputs, labels, index = data['image'], data['labels'], data[
'index']
labels = labels.float()
mask_flag = Variable(mask_labels[index], requires_grad=False)
idx = (mask_flag > 0)
if index.shape[0] == args.batch_size:
anchor_batch_S, anchor_batch_W = CalcSim(
labels[idx, :].cuda(), anchor_labels.cuda())
if inputs.size(3) == 3:
inputs = inputs.permute(0, 3, 1, 2)
inputs = inputs.type(torch.FloatTensor)
zcomp_h1 = z_h1[index.cuda(), :]
zcomp_h2 = z_h2[anchor_index, :]
labeled_batch_S, labeled_batch_W = CalcSim(
labels[idx, :].cuda(), labels[idx, :].cuda())
if use_gpu:
inputs = Variable(inputs.cuda(), requires_grad=False)
anchor_batch_S = Variable(anchor_batch_S.cuda(),
requires_grad=False)
anchor_batch_W = Variable(anchor_batch_W.cuda(),
requires_grad=False)
labeled_batch_S = Variable(labeled_batch_S.cuda(),
requires_grad=False)
labeled_batch_W = Variable(labeled_batch_W.cuda(),
requires_grad=False)
# zero the parameter gradients
optimizer.zero_grad()
y_h1 = model(inputs)
y_h2 = model(anchor_inputs)
y = F.sigmoid(48 / num_bits * 0.4 *
torch.matmul(y_h1, y_h2.permute(1, 0)))
loss, l_batch_loss, m_loss = criterion(
y, y_h1, y_h2, anchor_batch_S, anchor_batch_W,
labeled_batch_S, labeled_batch_W, zcomp_h1, zcomp_h2,
mask_flag, u_w_m, epoch, num_bits)
h1[index, :] = y_h1.data.clone()
h2[anchor_index, :] = y_h2.data.clone()
# backward+optimize
loss.backward()
optimizer.step()
running_loss += loss.item()
Z_h2 = alpha_1 * Z_h2 + (1. - alpha_1) * h2
z_h2 = Z_h2 * (1. / (1. - alpha_1**(epoch + 1)))
print(
"Epoch[{}]({}/{}): Time:(data {:.3f}/ batch {:.3f}) Loss_H: {:.4f}/{:.4f}/{:.4f}"
.format(epoch, iteration, len(dataloader), data_time.val,
batch_time.val, loss.item(), l_batch_loss.item(),
m_loss.item()))
Z_h1 = alpha * Z_h1 + (1. - alpha) * h1
z_h1 = Z_h1 * (1. / (1. - alpha**(epoch + 1)))
if epoch % 1 == 0:
MAP = helpers.validate(model, dataloader_test, test_loader)
print("Test image map is:{}".format(MAP))
is_best = MAP > best_prec
best_prec = max(best_prec, MAP)
save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
},
is_best,
prefix=arch,
num_bits=num_bits,
filename=checkpoint_filename)
return model
class Mind:
def __init__(self,
n_actions,
input_shape,
save_path=None,
load_path=None,
action_inverses={},
update_interval=256 * 2,
save_interval=5):
self.n_actions = n_actions
self.save_path = save_path
self.network = CNN(n_out=n_actions, input_shape=input_shape)
if load_path != None:
self.network.load(load_path)
self.n_features = input_shape
self.data = []
self.current_episode_count = 1
self.random_actions = 0
self.last_action = None
self.last_action_random = False
self.action_inverses = action_inverses
self.lifetime = 1
self.update_interval = update_interval
self.save_interval = save_interval
self.n_updates = 1
def q(self, state):
return self.network.predict(np.expand_dims(np.array(state), axis=0))[0]
def should_explore(self, state):
if np.random.random() < 1000 / (1000 + self.lifetime):
return True
return False
def explore_action(self, state):
return np.random.randint(0, self.n_actions)
def action(self, state):
q = self.q(state)
# if self.last_action_random:
# if self.last_action in self.action_inverses:
# q[self.action_inverses[self.last_action]] = float('-inf')
action = np.argmax(q)
if self.should_explore(state):
self.random_actions += 1
action = self.explore_action(state)
self.last_action_random = True
else:
self.last_action_random = False
if self.lifetime % self.update_interval == 0:
self.update(alpha=0.9)
self.n_updates += 1
if self.n_updates % self.save_interval == 0:
if self.save_path != None:
self.save(self.save_path)
print('saved')
self.last_action = action
self.current_episode_count += 1
self.lifetime += 1
return action
def save(self, path):
self.network.save(path)
def reset(self):
self.count = 1
print('Random actions: ', self.random_actions)
self.random_actions = 0
def q_target(self, reward, best_next, alpha):
return reward + alpha * best_next
def feedback(self, old_action, old_state, reward, new_state):
self.data.append({
'Q_max': np.max(self.q(new_state)),
'reward': reward,
'old_state': old_state,
'old_action': old_action
})
def update(self, alpha=0.6):
np.random.shuffle(self.data)
samples = self.data
self.data = []
states = []
ys = []
for sample in samples:
y = self.q(sample['old_state'])
y[sample['old_action']] = self.q_target(sample['reward'],
best_next=sample['Q_max'],
alpha=alpha)
#y[sample['old_action']] = sarsa_target(sample['reward'], next_action = sample['Q_max'], alpha = alpha)
states.append(sample['old_state'])
ys.append(y)
self.network.train(np.array(states), np.array(ys))