def get_reinforce_ps_loss(phi, p0, reinforce = False):
# returns pseudoloss: loss whose gradient is unbiased for the
# true gradient
d = len(p0)
e_b = sigmoid(phi)
bn_rv = Bernoulli(probs = torch.ones(d) * e_b)
binary_samples = bn_rv.sample().detach()
# binary_samples = (torch.rand(d) > e_b).float().detach()
if reinforce:
binary_samples_ = bn_rv.sample().detach()
baseline = torch.sum((binary_samples_ - p0)**2)
else:
baseline = 0.0
sampled_loss = torch.sum((binary_samples - p0)**2)
# probs, draw_array = get_all_probs(e_b, d)
# losses_array = get_losses_from_draw_array(draw_array, p0)
#
# cat_rv = Categorical(probs)
# indx = cat_rv.sample()
# binary_samples = draw_array[indx]
# sampled_loss = losses_array[indx]
#
sampled_log_q = get_bernoulli_log_prob(e_b, binary_samples)
ps_loss = (sampled_loss - baseline).detach() * sampled_log_q
return ps_loss
def forward(self, image):
latent_means = self.encoder.forward(image)
bernoulli_rv = Bernoulli(latent_means)
bernoulli_samples = bernoulli_rv.sample().detach()
image_mean = self.decoder.forward(bernoulli_samples)
return image_mean, latent_means, bernoulli_samples
def sample_from_prior(self, bs: int, **kwargs):
pz = Bernoulli(logits=self.prior.expand(bs, *self.prior.shape[1:]))
z = pz.sample()
px = Bernoulli(logits=self.generative_model(z))
return {'px': px, 'z': [z], 'pz': [pz]}
#########
#check
rebar = 0
if __name__ == "__main__":
approx_net = RELAX_Net()
parameters = toy_theta.repeat(batch_size)
if rebar == 1:
rebar_net = REBAR_Net(init_temperature, toy_func, scale_param)
approx_net = rebar_net
optimizer = torch.optim.SGD(approx_net.parameters(), lr=lr1)
u = Variable(torch.arange(0.001, 0.999, 0.001))
for i in range(iterations):
parameters_grad = RELAX(toy_func, approx_net, Bernoulli(parameters),
parameters)
## Updating parameters
#parameters.data += lr2* parameters_grad.data / batch_size
for parameter in approx_net.parameters():
if parameter.grad is not None:
parameter.data -= lr1 * parameter.grad.data / batch_size
approx_net.zero_grad()
parameters.data += lr2 * torch.mean(parameters_grad.data) / batch_size
relaxed_samples = relaxed_input(u, parameters[0])
print(parameters[0])
out = approx_net(relaxed_samples)
#print(rebar_net.temp)
from torch import nn, Tensor, zeros
# gradient estimator
from ovis.estimators.config import parse_estimator_id
Estimator, config = parse_estimator_id("ovis-gamma1")
estimator = Estimator(iw=16, **config)
# dataset: sample x ~ Bernoulli(0.5)
from torch.distributions import Bernoulli
dset = Bernoulli(logits=zeros((1000, 10))).sample()
# define a simple Bernoulli VAE
from ovis.models import TemplateModel
class SimpleModel(TemplateModel):
def __init__(self, xdim, zdim):
super().__init__()
self.inference_network = nn.Linear(xdim, zdim)
self.generative_model = nn.Linear(zdim, xdim)
self.register_buffer('prior', zeros((1, zdim,)))
def forward(self, x: Tensor, zgrads: bool = False, **kwargs):
# q(z|x)
qz = Bernoulli(logits=self.inference_network(x))
# z ~ q(z | x)
z = qz.rsample() if zgrads else qz.sample()
# p(x)
pz = Bernoulli(logits=self.prior)
# p(x|z)
px = Bernoulli(logits=self.generative_model(z))
# store z, pz, qz as list for hierarchical models
return {'px': px, 'z': [z], 'qz': [qz], 'pz': [pz]}
def call_rsample():
return Bernoulli(r).rsample()
def test_bernoulli_3d(self):
p = Variable(torch.Tensor(2, 3, 5).fill_(0.5), requires_grad=True)
self.assertEqual(Bernoulli(p).sample().size(), (2, 3, 5))
self.assertEqual(Bernoulli(p).sample_n(2).size(), (2, 2, 3, 5))
def proba_distribution(self, action_logits: th.Tensor) -> 'BernoulliDistribution':
self.distribution = Bernoulli(logits=action_logits)
return self
def call_sample_wshape_gt_2():
return Bernoulli(r).sample((1, 2))
def observation_model(self, z:Tensor) -> Distribution:
"""return the distribution `p(x|z)`"""
px_logits = self.decoder(z)
px_logits = px_logits.view(-1, *self.input_shape) # reshape the output to input_shape number of columns (rows are unspecified)
return Bernoulli(logits=px_logits)
def predict_option_termination(self, state, current_option):
termination = self.terminations(state)[:, current_option].sigmoid()
option_termination = Bernoulli(termination).sample()
Q = self.get_Q(state)
next_option = Q.argmax(dim=-1)
return bool(option_termination.item()), next_option.item()
def _select_variable_reinforce_multi(self, data):
logit = self.policy(data)
prob = torch.sigmoid(logit)
dist = Bernoulli(prob.view(-1))
vs = dist.sample()
return vs.nonzero(), dist.log_prob(vs).sum()
def learn(model,
model_args,
device,
k=5,
batch_size=32,
seed=666,
smt_epoch=100,
rl_epoch=1000):
torch.manual_seed(seed)
np.random.seed(seed)
random.seed(seed)
# Le probleme vient du count vectorizer qui vire certains mots
print("Load Dataset")
dataset = Robust2004.torch_dataset()
dataclasses = Robust2004.dataclasses()
dataclasses = {qt._id: qt for qt in dataclasses}
engine = get_engine()
collate_fn = embedding_collate_decorator(sequence_collate_fn)
indices = list(range(len(dataset)))
random.shuffle(indices)
for i, (trainindices, testindices) in enumerate(all_but_one(indices, k=k)):
trainindices = chain(*trainindices)
trainset = Subset(dataset, list(trainindices))
testset = Subset(dataset, list(testindices))
trainloader = DataLoader(trainset, 1, True, collate_fn=collate_fn)
testloader = DataLoader(testset, 1, True, collate_fn=collate_fn)
print("Build model")
model = model(*model_args)
try:
model = model.to(device)
except RuntimeError:
print("cudnn error")
model = model.to(device)
optimizer = optim.Adam(model.parameters())
loss_function = nn.BCELoss()
print("Train")
best_model = 0
delay = 0
max_delay = 5
print("Supervised Machine Translation")
for epoch in range(smt_epoch):
model.train()
n, mean = 0, 0
train_predictions = []
train_ids = []
for x, y, q_id, qrels, _ in trainloader:
x = x.to(device)
y = y.to(device)
pred = model(x)
pred__ = pred > 0.5
pred_ = pred__.detach().cpu().long().t().numpy().tolist()
train_predictions.extend(pred_)
train_ids.extend(map(lambda x: x.long().tolist(), q_id))
loss = loss_function(pred, y.float())
n += 1
mean = ((n - 1) * mean + loss.item()) / n
print(f"\rFold {i}, Epoch {epoch}\tTrain : {mean}", end="")
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_queries = {
id_: dataclasses[str(id_)].get_text(pred)
for id_, pred in zip(train_ids, train_predictions)
}
train_qrel = {
id_: dataclasses[str(id_)].qrels
for id_, pred in zip(train_ids, train_predictions)
}
train_map = eval_queries(train_queries, train_qrel, engine)
print(
f"\rFold {i}, Epoch {epoch}\tTrain Loss: {mean}, Train MAP {train_map}",
end="")
model.eval()
train_mean = mean
n, mean = 0, 0
test_predictions = []
test_ids = []
for x, y, q_id, qrels, _ in testloader:
x = x.to(device)
y = y.to(device)
pred = model(x)
pre__ = pred > 0.5
pred_ = pred__.detach().cpu().long().t().numpy().tolist()
test_predictions.extend(pred_)
test_ids.extend(map(lambda x: x.long().tolist(), q_id))
loss = loss_function(pred, y.float())
n += 1
mean = ((n - 1) * mean + loss.item()) / n
print(
f"\rFold {i}, Epoch {epoch}\tTrain Loss: {train_mean}\tTest : {mean}",
end="")
test_queries = {
id_: dataclasses[str(id_)].get_text(pred)
for id_, pred in zip(test_ids, test_predictions)
}
test_qrel = {
id_: dataclasses[str(id_)].qrels
for id_, pred in zip(test_ids, test_predictions)
}
test_map = eval_queries(test_queries, test_qrel, engine)
dataset_queries = {**train_queries, **test_queries}
dataset_qrel = {**train_qrel, **test_qrel}
dataset_map = eval_queries(dataset_queries, dataset_qrel, engine)
print(
"\b" * 500 +
f"\nFold {i}, Epoch {epoch}\tTrain MAP {train_map}\tTest MAP : {test_map}\tDataset MAP : {dataset_map}"
)
if test_map > best_model:
best_model = test_map
delay = 0
elif test_map < best_model:
delay += 1
if delay > max_delay:
print(best_model)
break
print("Reinforcement Learning")
mean_maps = {id_: [] for id_ in dataclasses.keys()}
for epoch in range(rl_epoch):
model.train()
n, mean = 0, 0
train_predictions = []
train_ids = []
for x, y, q_id, qrels, seq_lens in trainloader:
x = x.to(device)
y = y.to(device)
pred = model(x)
sampler = Bernoulli(pred)
batch_pred = sampler.sample()
log_probs = sampler.log_prob(batch_pred)
loss = log_probs.sum()
batch_ids = list(map(lambda x: x.long().tolist(), q_id))
batch_queries = {
id_: dataclasses[str(id_)].get_text(pred)
for id_, pred in zip(batch_ids, batch_pred)
}
batch_qrel = {
id_: dataclasses[str(id_)].qrels
for id_, pred in zip(batch_ids, batch_pred)
}
batch_map = eval_queries(batch_queries, batch_qrel, engine)
n += 1
mean = ((n - 1) * mean + batch_map) / n
print(f"\rTrain Map : {mean: .3f}", end="")
loss = -batch_map * loss
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_mean = mean
n, mean = 0, 0
test_predictions = []
test_ids = []
print()
for x, y, q_id, qrels, seq_lens in testloader:
x = x.to(device)
y = y.to(device)
pred = model(x)
sampler = Bernoulli(pred)
batch_pred = sampler.sample()
log_probs = sampler.log_prob(batch_pred)
loss = log_probs.sum()
batch_qrel = {
id_: dataclasses[str(id_)].qrels
for id_, pred in zip(batch_ids, batch_pred)
}
batch_map = eval_queries(batch_queries, batch_qrel, engine)
n += 1
mean = ((n - 1) * mean + batch_map) / n
print(
f"\rTrain MAP : {train_mean: .3f}\tTest Map : {mean: .3f}",
end="")
print()
def forward(self, inputs, targets):
return Bernoulli(0.5 * torch.ones_like(targets))
def forward(self, x):
logits = self.logit_layer(x)
return Bernoulli(logits = logits)
def main():
if not args.evaluate:
sys.stdout = Logger(osp.join(args.save_dir, 'log_train.txt'))
else:
sys.stdout = Logger(osp.join(args.save_dir, 'log_test.txt'))
print("==========\nArgs:{}\n==========".format(args))
if use_gpu:
print("Currently using GPU {}".format(args.gpu))
cudnn.benchmark = True
torch.cuda.manual_seed_all(args.seed)
else:
print("Currently using CPU")
print("Initialize dataset {}".format(args.dataset))
if args.dataset is None:
datasets = [
'datasets/eccv16_dataset_summe_google_pool5.h5',
'datasets/eccv16_dataset_tvsum_google_pool5.h5',
'datasets/eccv16_dataset_ovp_google_pool5.h5',
'datasets/eccv16_dataset_youtube_google_pool5.h5'
]
dataset = {}
for name in datasets:
_, base_filename = os.path.split(name)
base_filename = os.path.splitext(base_filename)
dataset[base_filename[0]] = h5py.File(name, 'r')
# Load split file
splits = read_json(args.split)
assert args.split_id < len(
splits), "split_id (got {}) exceeds {}".format(
args.split_id, len(splits))
split = splits[args.split_id]
train_keys = split['train_keys']
test_keys = split['test_keys']
print("# train videos {}. # test videos {}".format(
len(train_keys), len(test_keys)))
else:
dataset = h5py.File(args.dataset, 'r')
num_videos = len(dataset.keys())
splits = read_json(args.split)
assert args.split_id < len(
splits), "split_id (got {}) exceeds {}".format(
args.split_id, len(splits))
split = splits[args.split_id]
train_keys = split['train_keys']
test_keys = split['test_keys']
print("# total videos {}. # train videos {}. # test videos {}".format(
num_videos, len(train_keys), len(test_keys)))
#### Set User Score Dataset ####
userscoreset = h5py.File(args.userscore, 'r')
print("Initialize model")
model = DSRRL(in_dim=args.input_dim,
hid_dim=args.hidden_dim,
num_layers=args.num_layers,
cell=args.rnn_cell)
optimizer = torch.optim.Adam(model.parameters(),
betas=(0.5, 0.999),
lr=args.lr,
weight_decay=args.weight_decay)
if args.stepsize > 0:
scheduler = lr_scheduler.StepLR(optimizer,
step_size=args.stepsize,
gamma=args.gamma)
if args.resume:
print("Loading checkpoint from '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
model.load_state_dict(checkpoint)
else:
start_epoch = 0
if use_gpu:
model = nn.DataParallel(model).cuda()
if args.evaluate:
print("Evaluate only")
evaluate(model, dataset, test_keys, use_gpu)
return
if args.dataset is None:
print("==> Start training")
start_time = time.time()
model.train()
baselines = {key: 0.
for key in train_keys} # baseline rewards for videos
reward_writers = {key: []
for key in train_keys
} # record reward changes for each video
for epoch in range(start_epoch, args.max_epoch):
idxs = np.arange(len(train_keys))
np.random.shuffle(idxs) # shuffle indices
for idx in idxs:
key_parts = train_keys[idx].split('/')
name, key = key_parts
seq = dataset[name][key]['features'][
...] # sequence of features, (seq_len, dim)
seq = torch.from_numpy(seq).unsqueeze(
0) # input shape (1, seq_len, dim)
if use_gpu: seq = seq.cuda()
probs, out_feats, att_score = model(
seq) # output shape (1, seq_len, 1)
cost = args.beta * (
probs.mean() -
0.5)**2 # minimize summary length penalty term
m = Bernoulli(probs)
epis_rewards = []
for _ in range(args.num_episode):
actions = m.sample()
log_probs = m.log_prob(actions)
reward = compute_reward(seq, actions, use_gpu=use_gpu)
expected_reward = log_probs.mean() * (
reward - baselines[train_keys[idx]])
cost -= expected_reward
epis_rewards.append(reward.item())
recon_loss = reconstruction_loss(seq, out_feats)
spar_loss = sparsity_loss(att_score)
total_loss = cost + recon_loss + spar_loss
optimizer.zero_grad()
total_loss.backward()
optimizer.step()
torch.nn.utils.clip_grad_norm_(model.parameters(), 5.0)
baselines[train_keys[
idx]] = 0.9 * baselines[train_keys[idx]] + 0.1 * np.mean(
epis_rewards
) # update baseline reward via moving average
reward_writers[train_keys[idx]].append(np.mean(epis_rewards))
epoch_reward = np.mean(
[reward_writers[key][epoch] for key in train_keys])
#print("epoch {}/{}\t reward {}\t loss {}".format(epoch+1, args.max_epoch, epoch_reward, total_loss))
else:
print("==> Start training")
start_time = time.time()
model.train()
baselines = {key: 0.
for key in train_keys} # baseline rewards for videos
reward_writers = {key: []
for key in train_keys
} # record reward changes for each video
for epoch in range(start_epoch, args.max_epoch):
idxs = np.arange(len(train_keys))
np.random.shuffle(idxs) # shuffle indices
for idx in idxs:
key = train_keys[idx]
seq = dataset[key]['features'][
...] # sequence of features, (seq_len, dim)
seq = torch.from_numpy(seq).unsqueeze(
0) # input shape (1, seq_len, dim)
if use_gpu: seq = seq.cuda()
probs, out_feats, att_score = model(
seq) # output shape (1, seq_len, 1)
cost = args.beta * (
probs.mean() -
0.5)**2 # minimize summary length penalty term
m = Bernoulli(probs)
epis_rewards = []
for _ in range(args.num_episode):
actions = m.sample()
log_probs = m.log_prob(actions)
reward = compute_reward(seq, actions, use_gpu=use_gpu)
expected_reward = log_probs.mean() * (reward -
baselines[key])
cost -= expected_reward
epis_rewards.append(reward.item())
recon_loss = reconstruction_loss(seq, out_feats)
spar_loss = sparsity_loss(att_score)
total_loss = cost + recon_loss + spar_loss
#print(cost.item(), recon_loss.item(), spar_loss.item())
optimizer.zero_grad()
total_loss.backward()
optimizer.step()
torch.nn.utils.clip_grad_norm_(model.parameters(), 5.0)
baselines[key] = 0.9 * baselines[key] + 0.1 * np.mean(
epis_rewards) # update baseline reward via moving average
reward_writers[key].append(np.mean(epis_rewards))
epoch_reward = np.mean(
[reward_writers[key][epoch] for key in train_keys])
#print("epoch {}/{}\t reward {}\t loss {}".format(epoch+1, args.max_epoch, epoch_reward, total_loss))
write_json(reward_writers, osp.join(args.save_dir, 'rewards.json'))
evaluate(model, dataset, userscoreset, test_keys, use_gpu)
elapsed = round(time.time() - start_time)
elapsed = str(datetime.timedelta(seconds=elapsed))
print("Finished. Total elapsed time (h:m:s): {}".format(elapsed))
model_state_dict = model.module.state_dict(
) if use_gpu else model.state_dict()
model_save_path = osp.join(
args.save_dir,
args.metric + '_model_epoch_' + str(args.max_epoch) + '_split_id_' +
str(args.split_id) + '-' + str(args.rnn_cell) + '.pth.tar')
save_checkpoint(model_state_dict, model_save_path)
print("Model saved to {}".format(model_save_path))