def relax_grad2(x, logits, b, surrogate, mixtureweights):
B = logits.shape[0]
C = logits.shape[1]
cat = Categorical(logits=logits)
# u = torch.rand(B,C).clamp(1e-10, 1.-1e-10).cuda()
u = myclamp(torch.rand(B,C).cuda())
gumbels = -torch.log(-torch.log(u))
z = logits + gumbels
# b = torch.argmax(z, dim=1) #.view(B,1)
logq = cat.log_prob(b).view(B,1)
surr_input = torch.cat([z, x, logits.detach()], dim=1)
cz = surrogate.net(surr_input)
z_tilde = sample_relax_given_b(logits, b)
surr_input = torch.cat([z_tilde, x, logits.detach()], dim=1)
cz_tilde = surrogate.net(surr_input)
logpx_given_z = logprob_undercomponent(x, component=b)
logpz = torch.log(mixtureweights[b]).view(B,1)
logpxz = logpx_given_z + logpz #[B,1]
f = logpxz - logq
net_loss = - torch.mean( (f.detach() - cz_tilde.detach()) * logq - logq + cz - cz_tilde )
grad = torch.autograd.grad([net_loss], [logits], create_graph=True, retain_graph=True)[0] #[B,C]
pb = torch.exp(logq)
return grad, pb
def sample_relax(logits, surrogate):
cat = Categorical(logits=logits)
u = torch.rand(B,C).clamp(1e-10, 1.-1e-10).cuda()
gumbels = -torch.log(-torch.log(u))
z = logits + gumbels
b = torch.argmax(z, dim=1) #.view(B,1)
logprob = cat.log_prob(b).view(B,1)
# czs = []
# for j in range(1):
# z = sample_relax_z(logits)
# surr_input = torch.cat([z, x, logits.detach()], dim=1)
# cz = surrogate.net(surr_input)
# czs.append(cz)
# czs = torch.stack(czs)
# cz = torch.mean(czs, dim=0)#.view(1,1)
surr_input = torch.cat([z, x, logits.detach()], dim=1)
cz = surrogate.net(surr_input)
cz_tildes = []
for j in range(1):
z_tilde = sample_relax_given_b(logits, b)
surr_input = torch.cat([z_tilde, x, logits.detach()], dim=1)
cz_tilde = surrogate.net(surr_input)
cz_tildes.append(cz_tilde)
cz_tildes = torch.stack(cz_tildes)
cz_tilde = torch.mean(cz_tildes, dim=0) #.view(B,1)
return b, logprob, cz, cz_tilde
def sample_relax(logits): #, k=1):
# u = torch.rand(B,C).clamp(1e-8, 1.-1e-8) #.cuda()
u = torch.rand(B,C).clamp(1e-12, 1.-1e-12) #.cuda()
gumbels = -torch.log(-torch.log(u))
z = logits + gumbels
b = torch.argmax(z, dim=1)
cat = Categorical(logits=logits)
logprob = cat.log_prob(b).view(B,1)
v_k = torch.rand(B,1).clamp(1e-12, 1.-1e-12)
z_tilde_b = -torch.log(-torch.log(v_k))
#this way seems biased even tho it shoudlnt be
# v_k = torch.gather(input=u, dim=1, index=b.view(B,1))
# z_tilde_b = torch.gather(input=z, dim=1, index=b.view(B,1))
v = torch.rand(B,C).clamp(1e-12, 1.-1e-12) #.cuda()
probs = torch.softmax(logits,dim=1).repeat(B,1)
# print (probs.shape, torch.log(v_k).shape, torch.log(v).shape)
# fasdfa
# print (v.shape)
# print (v.shape)
z_tilde = -torch.log((- torch.log(v) / probs) - torch.log(v_k))
# print (z_tilde)
# print (z_tilde_b)
z_tilde.scatter_(dim=1, index=b.view(B,1), src=z_tilde_b)
# print (z_tilde)
# fasdfs
return z, b, logprob, z_tilde
def sample_relax_given_class(logits, samp):
cat = Categorical(logits=logits)
u = torch.rand(B,C).clamp(1e-8, 1.-1e-8)
gumbels = -torch.log(-torch.log(u))
z = logits + gumbels
b = samp #torch.argmax(z, dim=1)
logprob = cat.log_prob(b).view(B,1)
u_b = torch.gather(input=u, dim=1, index=b.view(B,1))
z_tilde_b = -torch.log(-torch.log(u_b))
z_tilde = -torch.log((- torch.log(u) / torch.softmax(logits, dim=1)) - torch.log(u_b))
z_tilde.scatter_(dim=1, index=b.view(B,1), src=z_tilde_b)
z = z_tilde
u_b = torch.gather(input=u, dim=1, index=b.view(B,1))
z_tilde_b = -torch.log(-torch.log(u_b))
u = torch.rand(B,C).clamp(1e-8, 1.-1e-8)
z_tilde = -torch.log((- torch.log(u) / torch.softmax(logits, dim=1)) - torch.log(u_b))
z_tilde.scatter_(dim=1, index=b.view(B,1), src=z_tilde_b)
return z, z_tilde, logprob
def sample_true2():
cat = Categorical(probs= torch.tensor(true_mixture_weights))
cluster = cat.sample()
# print (cluster)
# fsd
norm = Normal(torch.tensor([cluster*10.]).float(), torch.tensor([5.0]).float())
samp = norm.sample()
# print (samp)
return samp,cluster
def sample_gmm(batch_size, mixture_weights):
cat = Categorical(probs=mixture_weights)
cluster = cat.sample([batch_size]) # [B]
mean = (cluster*10.).float().cuda()
std = torch.ones([batch_size]).cuda() *5.
norm = Normal(mean, std)
samp = norm.sample()
samp = samp.view(batch_size, 1)
return samp
class OneHotCategorical(Distribution):
r"""
Creates a one-hot categorical distribution parameterized by `probs`.
Samples are one-hot coded vectors of size probs.size(-1).
See also: :func:`torch.distributions.Categorical`
Example::
>>> m = OneHotCategorical(torch.Tensor([ 0.25, 0.25, 0.25, 0.25 ]))
>>> m.sample() # equal probability of 0, 1, 2, 3
0
0
1
0
[torch.FloatTensor of size 4]
Args:
probs (Tensor or Variable): event probabilities
"""
params = {'probs': constraints.simplex}
support = constraints.simplex
has_enumerate_support = True
def __init__(self, probs=None, logits=None):
self._categorical = Categorical(probs, logits)
batch_shape = self._categorical.probs.size()[:-1]
event_shape = self._categorical.probs.size()[-1:]
super(OneHotCategorical, self).__init__(batch_shape, event_shape)
def sample(self, sample_shape=torch.Size()):
sample_shape = torch.Size(sample_shape)
probs = self._categorical.probs
one_hot = probs.new(self._extended_shape(sample_shape)).zero_()
indices = self._categorical.sample(sample_shape)
if indices.dim() < one_hot.dim():
indices = indices.unsqueeze(-1)
return one_hot.scatter_(-1, indices, 1)
def log_prob(self, value):
indices = value.max(-1)[1]
return self._categorical.log_prob(indices)
def entropy(self):
return self._categorical.entropy()
def enumerate_support(self):
probs = self._categorical.probs
n = self.event_shape[0]
if isinstance(probs, Variable):
values = Variable(torch.eye(n, out=probs.data.new(n, n)))
else:
values = torch.eye(n, out=probs.new(n, n))
values = values.view((n,) + (1,) * len(self.batch_shape) + (n,))
return values.expand((n,) + self.batch_shape + (n,))
def test_gmm_loss(self):
""" Test case 1 """
n_samples = 10000
means = torch.Tensor([[0., 0.],
[1., 1.],
[-1., 1.]])
stds = torch.Tensor([[.03, .05],
[.02, .1],
[.1, .03]])
pi = torch.Tensor([.2, .3, .5])
cat_dist = Categorical(pi)
indices = cat_dist.sample((n_samples,)).long()
rands = torch.randn(n_samples, 2)
samples = means[indices] + rands * stds[indices]
class _model(nn.Module):
def __init__(self, gaussians):
super().__init__()
self.means = nn.Parameter(torch.Tensor(1, gaussians, 2).normal_())
self.pre_stds = nn.Parameter(torch.Tensor(1, gaussians, 2).normal_())
self.pi = nn.Parameter(torch.Tensor(1, gaussians).normal_())
def forward(self, *inputs):
return self.means, torch.exp(self.pre_stds), f.softmax(self.pi, dim=1)
model = _model(3)
optimizer = torch.optim.Adam(model.parameters())
iterations = 100000
log_step = iterations // 10
pbar = tqdm(total=iterations)
cum_loss = 0
for i in range(iterations):
batch = samples[torch.LongTensor(128).random_(0, n_samples)]
m, s, p = model.forward()
loss = gmm_loss(batch, m, s, p)
cum_loss += loss.item()
optimizer.zero_grad()
loss.backward()
optimizer.step()
pbar.set_postfix_str("avg_loss={:10.6f}".format(
cum_loss / (i + 1)))
pbar.update(1)
if i % log_step == log_step - 1:
print(m)
print(s)
print(p)
def sample_true(batch_size):
# print (true_mixture_weights.shape)
cat = Categorical(probs=torch.tensor(true_mixture_weights))
cluster = cat.sample([batch_size]) # [B]
mean = (cluster*10.).float()
std = torch.ones([batch_size]) *5.
# print (cluster.shape)
# fsd
# norm = Normal(torch.tensor([cluster*10.]).float(), torch.tensor([5.0]).float())
norm = Normal(mean, std)
samp = norm.sample()
# print (samp.shape)
# fadsf
samp = samp.view(batch_size, 1)
return samp
def reinforce_baseline(surrogate, x, logits, mixtureweights, k=1, get_grad=False):
B = logits.shape[0]
probs = torch.softmax(logits, dim=1)
outputs = {}
cat = Categorical(probs=probs)
grads =[]
# net_loss = 0
for jj in range(k):
cluster_H = cat.sample()
outputs['logq'] = logq = cat.log_prob(cluster_H).view(B,1)
outputs['logpx_given_z'] = logpx_given_z = logprob_undercomponent(x, component=cluster_H)
outputs['logpz'] = logpz = torch.log(mixtureweights[cluster_H]).view(B,1)
logpxz = logpx_given_z + logpz #[B,1]
surr_pred = surrogate.net(x)
outputs['f'] = f = logpxz - logq - 1.
# outputs['net_loss'] = net_loss = net_loss - torch.mean((f.detach() ) * logq)
outputs['net_loss'] = net_loss = - torch.mean((f.detach() - surr_pred.detach()) * logq)
# net_loss += - torch.mean( -logq.detach()*logq)
# surr_loss = torch.mean(torch.abs(f.detach() - surr_pred))
grad_logq = torch.autograd.grad([torch.mean(logq)], [logits], create_graph=True, retain_graph=True)[0]
surr_loss = torch.mean(((f.detach() - surr_pred) * grad_logq )**2)
if get_grad:
grad = torch.autograd.grad([net_loss], [logits], create_graph=True, retain_graph=True)[0]
grads.append(grad)
# net_loss = net_loss/ k
if get_grad:
grads = torch.stack(grads)
# print (grads.shape)
outputs['grad_avg'] = torch.mean(torch.mean(grads, dim=0),dim=0)
outputs['grad_std'] = torch.std(grads, dim=0)[0]
outputs['surr_loss'] = surr_loss
# return net_loss, f, logpx_given_z, logpz, logq
return outputs
def sample_relax(probs):
cat = Categorical(probs=probs)
#Sample z
u = torch.rand(B,C).cuda()
u = u.clamp(1e-8, 1.-1e-8)
gumbels = -torch.log(-torch.log(u))
z = torch.log(probs) + gumbels
b = torch.argmax(z, dim=1)
logprob = cat.log_prob(b).view(B,1)
#Sample z_tilde
u_b = torch.rand(B,1).cuda()
u_b = u_b.clamp(1e-8, 1.-1e-8)
z_tilde_b = -torch.log(-torch.log(u_b))
u = torch.rand(B,C).cuda()
u = u.clamp(1e-8, 1.-1e-8)
z_tilde = -torch.log((- torch.log(u) / probs) - torch.log(u_b))
z_tilde[:,b] = z_tilde_b
return z, b, logprob, z_tilde, gumbels
def sample_relax_given_class_k(logits, samp, k):
cat = Categorical(logits=logits)
b = samp #torch.argmax(z, dim=1)
logprob = cat.log_prob(b).view(B,1)
zs = []
z_tildes = []
for i in range(k):
u = torch.rand(B,C).clamp(1e-8, 1.-1e-8)
gumbels = -torch.log(-torch.log(u))
z = logits + gumbels
u_b = torch.gather(input=u, dim=1, index=b.view(B,1))
z_tilde_b = -torch.log(-torch.log(u_b))
z_tilde = -torch.log((- torch.log(u) / torch.softmax(logits, dim=1)) - torch.log(u_b))
z_tilde.scatter_(dim=1, index=b.view(B,1), src=z_tilde_b)
z = z_tilde
u_b = torch.gather(input=u, dim=1, index=b.view(B,1))
z_tilde_b = -torch.log(-torch.log(u_b))
u = torch.rand(B,C).clamp(1e-8, 1.-1e-8)
z_tilde = -torch.log((- torch.log(u) / torch.softmax(logits, dim=1)) - torch.log(u_b))
z_tilde.scatter_(dim=1, index=b.view(B,1), src=z_tilde_b)
zs.append(z)
z_tildes.append(z_tilde)
zs= torch.stack(zs)
z_tildes= torch.stack(z_tildes)
z = torch.mean(zs, dim=0)
z_tilde = torch.mean(z_tildes, dim=0)
return z, z_tilde, logprob
def reinforce(x, logits, mixtureweights, k=1):
B = logits.shape[0]
probs = torch.softmax(logits, dim=1)
cat = Categorical(probs=probs)
net_loss = 0
for jj in range(k):
cluster_H = cat.sample()
logq = cat.log_prob(cluster_H).view(B,1)
logpx_given_z = logprob_undercomponent(x, component=cluster_H)
logpz = torch.log(mixtureweights[cluster_H]).view(B,1)
logpxz = logpx_given_z + logpz #[B,1]
f = logpxz - logq
net_loss += - torch.mean((f.detach() - 1.) * logq)
# net_loss += - torch.mean( -logq.detach()*logq)
net_loss = net_loss/ k
return net_loss, f, logpx_given_z, logpz, logq
def relax_grad(x, logits, b, surrogate, mixtureweights):
B = logits.shape[0]
C = logits.shape[1]
cat = Categorical(logits=logits)
# u = torch.rand(B,C).clamp(1e-10, 1.-1e-10).cuda()
u = myclamp(torch.rand(B,C).cuda())
gumbels = -torch.log(-torch.log(u))
z = logits + gumbels
# b = torch.argmax(z, dim=1) #.view(B,1)
logq = cat.log_prob(b).view(B,1)
surr_input = torch.cat([z, x, logits.detach()], dim=1)
cz = surrogate.net(surr_input)
z_tilde = sample_relax_given_b(logits, b)
surr_input = torch.cat([z_tilde, x, logits.detach()], dim=1)
cz_tilde = surrogate.net(surr_input)
logpx_given_z = logprob_undercomponent(x, component=b)
logpz = torch.log(mixtureweights[b]).view(B,1)
logpxz = logpx_given_z + logpz #[B,1]
f = logpxz - logq
grad_logq = torch.autograd.grad([torch.mean(logq)], [logits], create_graph=True, retain_graph=True)[0]
grad_surr_z = torch.autograd.grad([torch.mean(cz)], [logits], create_graph=True, retain_graph=True)[0]
grad_surr_z_tilde = torch.autograd.grad([torch.mean(cz_tilde)], [logits], create_graph=True, retain_graph=True)[0]
# surr_loss = torch.mean(((f.detach() - cz_tilde) * grad_logq - grad_logq + grad_surr_z - grad_surr_z_tilde)**2, dim=1, keepdim=True)
surr_loss = ((f.detach() - cz_tilde) * grad_logq - grad_logq + grad_surr_z - grad_surr_z_tilde)**2
# print (surr_loss.shape)
# print (logq.shape)
# fasda
# print (surr_loss, torch.exp(logq))
return surr_loss, torch.exp(logq)
def _distribution(self, obs):
"""Takes the observation and outputs a distribution over actions."""
logits = self.logits_net(obs)
return Categorical(logits=logits)
def compute_run(self, obs):
embedding = self.encode(obs)
x_dist = self.actor(embedding)
dist = Categorical(logits=F.log_softmax(x_dist, dim=1))
value = self.critic(embedding).squeeze(1)
return dist, value
def forward(self,
audio_feature,
decode_step,
tf_rate=0.0,
teacher=None,
state_len=None):
bs = audio_feature.shape[0]
# Encode
encode_feature, encode_len = self.encoder(audio_feature, state_len)
ctc_output = None
att_output = None
att_maps = None
# CTC based decoding
if self.joint_ctc:
ctc_output = self.ctc_layer(encode_feature)
# Attention based decoding
if self.joint_att:
if teacher is not None:
teacher = self.embed(teacher)
# Init (init char = <SOS>, reset all rnn state and cell)
self.decoder.init_rnn(encode_feature)
self.attention.reset_enc_mem()
last_char = self.embed(
torch.zeros((bs), dtype=torch.long).to(
next(self.decoder.parameters()).device))
output_char_seq = []
output_att_seq = [[]] * self.attention.num_head
# Decode
for t in range(decode_step):
# Attend (inputs current state of first layer, encoded features)
attention_score, context = self.attention(
self.decoder.state_list[0], encode_feature, encode_len)
# Spell (inputs context + embedded last character)
decoder_input = torch.cat([last_char, context], dim=-1)
dec_out = self.decoder(decoder_input)
# To char
cur_char = self.char_trans(dec_out)
# Teacher forcing
if (teacher is not None):
if random.random() <= tf_rate:
last_char = teacher[:, t + 1, :]
else:
sampled_char = Categorical(F.softmax(cur_char,
dim=-1)).sample()
last_char = self.embed(sampled_char)
else:
last_char = self.embed(torch.argmax(cur_char, dim=-1))
output_char_seq.append(cur_char)
for head, a in enumerate(attention_score):
output_att_seq[head].append(a.cpu())
att_output = torch.stack(output_char_seq, dim=1)
att_maps = [torch.stack(att, dim=1) for att in output_att_seq]
return ctc_output, encode_len, att_output, att_maps
class OneHotCategorical(Distribution):
r"""
Creates a one-hot categorical distribution parameterized by :attr:`probs` or
:attr:`logits`.
Samples are one-hot coded vectors of size ``probs.size(-1)``.
.. note:: The `probs` argument must be non-negative, finite and have a non-zero sum,
and it will be normalized to sum to 1 along the last dimension. attr:`probs`
will return this normalized value.
The `logits` argument will be interpreted as unnormalized log probabilities
and can therefore be any real number. It will likewise be normalized so that
the resulting probabilities sum to 1 along the last dimension. attr:`logits`
will return this normalized value.
See also: :func:`torch.distributions.Categorical` for specifications of
:attr:`probs` and :attr:`logits`.
Example::
>>> m = OneHotCategorical(torch.tensor([ 0.25, 0.25, 0.25, 0.25 ]))
>>> m.sample() # equal probability of 0, 1, 2, 3
tensor([ 0., 0., 0., 1.])
Args:
probs (Tensor): event probabilities
logits (Tensor): event log probabilities (unnormalized)
"""
arg_constraints = {'probs': constraints.simplex,
'logits': constraints.real_vector}
support = constraints.one_hot
has_enumerate_support = True
def __init__(self, probs=None, logits=None, validate_args=None):
self._categorical = Categorical(probs, logits)
batch_shape = self._categorical.batch_shape
event_shape = self._categorical.param_shape[-1:]
super(OneHotCategorical, self).__init__(batch_shape, event_shape, validate_args=validate_args)
def expand(self, batch_shape, _instance=None):
new = self._get_checked_instance(OneHotCategorical, _instance)
batch_shape = torch.Size(batch_shape)
new._categorical = self._categorical.expand(batch_shape)
super(OneHotCategorical, new).__init__(batch_shape, self.event_shape, validate_args=False)
new._validate_args = self._validate_args
return new
def _new(self, *args, **kwargs):
return self._categorical._new(*args, **kwargs)
@property
def _param(self):
return self._categorical._param
@property
def probs(self):
return self._categorical.probs
@property
def logits(self):
return self._categorical.logits
@property
def mean(self):
return self._categorical.probs
@property
def variance(self):
return self._categorical.probs * (1 - self._categorical.probs)
@property
def param_shape(self):
return self._categorical.param_shape
def sample(self, sample_shape=torch.Size()):
sample_shape = torch.Size(sample_shape)
probs = self._categorical.probs
num_events = self._categorical._num_events
indices = self._categorical.sample(sample_shape)
return torch.nn.functional.one_hot(indices, num_events).to(probs)
def log_prob(self, value):
if self._validate_args:
self._validate_sample(value)
indices = value.max(-1)[1]
return self._categorical.log_prob(indices)
def entropy(self):
return self._categorical.entropy()
def enumerate_support(self, expand=True):
n = self.event_shape[0]
values = torch.eye(n, dtype=self._param.dtype, device=self._param.device)
values = values.view((n,) + (1,) * len(self.batch_shape) + (n,))
if expand:
values = values.expand((n,) + self.batch_shape + (n,))
return values
n_steps = 100000
L2_losses = []
steps_list = []
for step in range(n_steps):
optim.zero_grad()
loss = 0
net_loss = 0
for i in range(batch_size):
x = sample_true()
logits = encoder.net(x)
# print (logits.shape)
# print (torch.softmax(logits, dim=0))
# fsfd
cat = Categorical(probs=torch.softmax(logits, dim=0))
cluster = cat.sample()
logprob_cluster = cat.log_prob(cluster.detach())
# print (logprob_cluster)
pxz = logprob_undercomponent(
x,
component=cluster,
needsoftmax_mixtureweight=needsoftmax_mixtureweight,
cuda=False)
f = pxz - logprob_cluster
# print (f)
# logprob = logprob_givenmixtureeweights(x, needsoftmax_mixtureweight)
net_loss += -f.detach() * logprob_cluster
loss += -f
loss = loss / batch_size
net_loss = net_loss / batch_size
#REINFORCE
print ('REINFORCE')
# def sample_reinforce_given_class(logits, samp):
# return logprob
grads = []
for i in range (N):
dist = Categorical(logits=logits)
samp = dist.sample()
logprob = dist.log_prob(samp)
reward = f(samp)
gradlogprob = torch.autograd.grad(outputs=logprob, inputs=(logits), retain_graph=True)[0]
grads.append(reward*gradlogprob)
print ()
grads = torch.stack(grads).view(N,C)
# print (grads.shape)
grad_mean_reinforce = torch.mean(grads,dim=0)
grad_std_reinforce = torch.std(grads,dim=0)
print ('REINFORCE')
print ('mean:', grad_mean_reinforce)
print ('std:', grad_std_reinforce)
n_steps = 100000
L2_losses = []
steps_list = []
for step in range(n_steps):
optim.zero_grad()
loss = 0
net_loss = 0
for i in range(batch_size):
x = sample_true()
logits = encoder.net(x)
# print (logits.shape)
# print (torch.softmax(logits, dim=0))
# fsfd
cat = Categorical(probs= torch.softmax(logits, dim=0))
cluster = cat.sample()
logprob_cluster = cat.log_prob(cluster.detach())
# print (logprob_cluster)
pxz = logprob_undercomponent(x, component=cluster, needsoftmax_mixtureweight=needsoftmax_mixtureweight, cuda=False)
f = pxz - logprob_cluster
# print (f)
# logprob = logprob_givenmixtureeweights(x, needsoftmax_mixtureweight)
net_loss += -f.detach() * logprob_cluster
loss += -f
loss = loss / batch_size
net_loss = net_loss / batch_size
# print (loss, net_loss)
loss.backward(retain_graph=True)
def get_policy(self, obs):
logits = self.policy(obs)
if self.discrete_action:
return Categorical(logits=logits)
else:
return Normal(logits, self.log_std.exp())
def valor(args):
if not hasattr(args, "get"):
args.get = args.__dict__.get
env_fn = args.get('env_fn', lambda: gym.make('HalfCheetah-v2'))
actor_critic = args.get('actor_critic', ActorCritic)
ac_kwargs = args.get('ac_kwargs', {})
disc = args.get('disc', Discriminator)
dc_kwargs = args.get('dc_kwargs', {})
seed = args.get('seed', 0)
episodes_per_epoch = args.get('episodes_per_epoch', 40)
epochs = args.get('epochs', 50)
gamma = args.get('gamma', 0.99)
pi_lr = args.get('pi_lr', 3e-4)
vf_lr = args.get('vf_lr', 1e-3)
dc_lr = args.get('dc_lr', 2e-3)
train_v_iters = args.get('train_v_iters', 80)
train_dc_iters = args.get('train_dc_iters', 50)
train_dc_interv = args.get('train_dc_interv', 2)
lam = args.get('lam', 0.97)
max_ep_len = args.get('max_ep_len', 1000)
logger_kwargs = args.get('logger_kwargs', {})
context_dim = args.get('context_dim', 4)
max_context_dim = args.get('max_context_dim', 64)
save_freq = args.get('save_freq', 10)
k = args.get('k', 1)
logger = EpochLogger(**logger_kwargs)
logger.save_config(locals())
# seed += 10000 * proc_id()
torch.manual_seed(seed)
np.random.seed(seed)
env = env_fn()
obs_dim = env.observation_space.shape
act_dim = env.action_space.shape
ac_kwargs['action_space'] = env.action_space
# Model
actor_critic = actor_critic(input_dim=obs_dim[0] + max_context_dim,
**ac_kwargs)
disc = disc(input_dim=obs_dim[0], context_dim=max_context_dim, **dc_kwargs)
# Buffer
local_episodes_per_epoch = episodes_per_epoch # int(episodes_per_epoch / num_procs())
buffer = Buffer(max_context_dim, obs_dim[0], act_dim[0],
local_episodes_per_epoch, max_ep_len, train_dc_interv)
# Count variables
var_counts = tuple(
count_vars(module)
for module in [actor_critic.policy, actor_critic.value_f, disc.policy])
logger.log('\nNumber of parameters: \t pi: %d, \t v: %d, \t d: %d\n' %
var_counts)
# Optimizers
#Optimizer for RL Policy
train_pi = torch.optim.Adam(actor_critic.policy.parameters(), lr=pi_lr)
#Optimizer for value function (for actor-critic)
train_v = torch.optim.Adam(actor_critic.value_f.parameters(), lr=vf_lr)
#Optimizer for decoder
train_dc = torch.optim.Adam(disc.policy.parameters(), lr=dc_lr)
#pdb.set_trace()
# Parameters Sync
#sync_all_params(actor_critic.parameters())
#sync_all_params(disc.parameters())
'''
Training function
'''
def update(e):
obs, act, adv, pos, ret, logp_old = [
torch.Tensor(x) for x in buffer.retrieve_all()
]
# Policy
#pdb.set_trace()
_, logp, _ = actor_critic.policy(obs, act, batch=False)
#pdb.set_trace()
entropy = (-logp).mean()
# Policy loss
pi_loss = -(logp * (k * adv + pos)).mean()
# Train policy (Go through policy update)
train_pi.zero_grad()
pi_loss.backward()
# average_gradients(train_pi.param_groups)
train_pi.step()
# Value function
v = actor_critic.value_f(obs)
v_l_old = F.mse_loss(v, ret)
for _ in range(train_v_iters):
v = actor_critic.value_f(obs)
v_loss = F.mse_loss(v, ret)
# Value function train
train_v.zero_grad()
v_loss.backward()
# average_gradients(train_v.param_groups)
train_v.step()
# Discriminator
if (e + 1) % train_dc_interv == 0:
print('Discriminator Update!')
con, s_diff = [torch.Tensor(x) for x in buffer.retrieve_dc_buff()]
_, logp_dc, _ = disc(s_diff, con)
d_l_old = -logp_dc.mean()
# Discriminator train
for _ in range(train_dc_iters):
_, logp_dc, _ = disc(s_diff, con)
d_loss = -logp_dc.mean()
train_dc.zero_grad()
d_loss.backward()
# average_gradients(train_dc.param_groups)
train_dc.step()
_, logp_dc, _ = disc(s_diff, con)
dc_l_new = -logp_dc.mean()
else:
d_l_old = 0
dc_l_new = 0
# Log the changes
_, logp, _, v = actor_critic(obs, act)
pi_l_new = -(logp * (k * adv + pos)).mean()
v_l_new = F.mse_loss(v, ret)
kl = (logp_old - logp).mean()
logger.store(LossPi=pi_loss,
LossV=v_l_old,
KL=kl,
Entropy=entropy,
DeltaLossPi=(pi_l_new - pi_loss),
DeltaLossV=(v_l_new - v_l_old),
LossDC=d_l_old,
DeltaLossDC=(dc_l_new - d_l_old))
# logger.store(Adv=adv.reshape(-1).numpy().tolist(), Pos=pos.reshape(-1).numpy().tolist())
start_time = time.time()
#Resets observations, rewards, done boolean
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
#Creates context distribution where each logit is equal to one (This is first place to make change)
context_dim_prob_dict = {
i: 1 / context_dim if i < context_dim else 0
for i in range(max_context_dim)
}
last_phi_dict = {i: 0 for i in range(context_dim)}
context_dist = Categorical(
probs=torch.Tensor(list(context_dim_prob_dict.values())))
total_t = 0
for epoch in range(epochs):
#Sets actor critic and decoder (discriminator) into eval mode
actor_critic.eval()
disc.eval()
#Runs the policy local_episodes_per_epoch before updating the policy
for index in range(local_episodes_per_epoch):
# Sample from context distribution and one-hot encode it (Step 2)
# Every time we run the policy we sample a new context
c = context_dist.sample()
c_onehot = F.one_hot(c, max_context_dim).squeeze().float()
for _ in range(max_ep_len):
concat_obs = torch.cat(
[torch.Tensor(o.reshape(1, -1)),
c_onehot.reshape(1, -1)], 1)
'''
Feeds in observation and context into actor_critic which spits out a distribution
Label is a sample from the observation
pi is the action sampled
logp is the log probability of some other action a
logp_pi is the log probability of pi
v_t is the value function
'''
a, _, logp_t, v_t = actor_critic(concat_obs)
#Stores context and all other info about the state in the buffer
buffer.store(c,
concat_obs.squeeze().detach().numpy(),
a.detach().numpy(), r, v_t.item(),
logp_t.detach().numpy())
logger.store(VVals=v_t)
o, r, d, _ = env.step(a.detach().numpy()[0])
ep_ret += r
ep_len += 1
total_t += 1
terminal = d or (ep_len == max_ep_len)
if terminal:
# Key stuff with discriminator
dc_diff = torch.Tensor(buffer.calc_diff()).unsqueeze(0)
#Context
con = torch.Tensor([float(c)]).unsqueeze(0)
#Feed in differences between each state in your trajectory and a specific context
#Here, this is just the log probability of the label it thinks it is
_, _, log_p = disc(dc_diff, con)
buffer.end_episode(log_p.detach().numpy())
logger.store(EpRet=ep_ret, EpLen=ep_len)
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
if (epoch % save_freq == 0) or (epoch == epochs - 1):
logger.save_state({'env': env}, [actor_critic, disc], None)
# Sets actor_critic and discriminator into training mode
actor_critic.train()
disc.train()
update(epoch)
#Need to implement curriculum learning here to update context distribution
'''
#Psuedocode:
Loop through each of d episodes taken in local_episodes_per_epoch and check log probability from discrimantor
If >= 0.86, increase k in the following manner: k = min(int(1.5*k + 1), Kmax)
Kmax = 64
'''
decoder_accs = []
stag_num = 10
stag_pct = 0.05
if (epoch + 1) % train_dc_interv == 0 and epoch > 0:
#pdb.set_trace()
con, s_diff = [torch.Tensor(x) for x in buffer.retrieve_dc_buff()]
print("Context: ", con)
print("num_contexts", len(con))
_, logp_dc, _ = disc(s_diff, con)
log_p_context_sample = logp_dc.mean().detach().numpy()
print("Log Probability context sample", log_p_context_sample)
decoder_accuracy = np.exp(log_p_context_sample)
print("Decoder Accuracy", decoder_accuracy)
logger.store(LogProbabilityContext=log_p_context_sample,
DecoderAccuracy=decoder_accuracy)
'''
Create score (phi(i)) = -log_p_context_sample.mean() for each specific context
Assign phis to each specific context
Get p(i) in the following manner: (phi(i) + epsilon)
Get Probabilities by doing p(i)/sum of all p(i)'s
'''
logp_np = logp_dc.detach().numpy()
con_np = con.detach().numpy()
phi_dict = {i: 0 for i in range(context_dim)}
count_dict = {i: 0 for i in range(context_dim)}
for i in range(len(logp_np)):
current_con = con_np[i]
phi_dict[current_con] += logp_np[i]
count_dict[current_con] += 1
print(phi_dict)
phi_dict = {
k: last_phi_dict[k] if count_dict[k] == 0 else
(-1) * v / count_dict[k]
for (k, v) in phi_dict.items()
}
sorted_dict = dict(
sorted(phi_dict.items(),
key=lambda item: item[1],
reverse=True))
sorted_dict_keys = list(sorted_dict.keys())
rank_dict = {
sorted_dict_keys[i]: 1 / (i + 1)
for i in range(len(sorted_dict_keys))
}
rank_dict_sum = sum(list(rank_dict.values()))
context_dim_prob_dict = {
k: rank_dict[k] / rank_dict_sum if k < context_dim else 0
for k in context_dim_prob_dict.keys()
}
print(context_dim_prob_dict)
decoder_accs.append(decoder_accuracy)
stagnated = (len(decoder_accs) > stag_num
and (decoder_accs[-stag_num - 1] - decoder_accuracy) /
stag_num < stag_pct)
if stagnated:
new_context_dim = max(int(0.75 * context_dim), 5)
elif decoder_accuracy >= 0.86:
new_context_dim = min(int(1.5 * context_dim + 1),
max_context_dim)
if stagnated or decoder_accuracy >= 0.86:
print("new_context_dim: ", new_context_dim)
new_context_prob_arr = np.array(
new_context_dim * [1 / new_context_dim] +
(max_context_dim - new_context_dim) * [0])
context_dist = Categorical(
probs=ptu.from_numpy(new_context_prob_arr))
context_dim = new_context_dim
for i in range(context_dim):
if i in phi_dict:
last_phi_dict[i] = phi_dict[i]
elif i not in last_phi_dict:
last_phi_dict[i] = max(phi_dict.values())
buffer.clear_dc_buff()
else:
logger.store(LogProbabilityContext=0, DecoderAccuracy=0)
# Log
logger.store(ContextDim=context_dim)
logger.log_tabular('Epoch', epoch)
logger.log_tabular('EpRet', with_min_and_max=True)
logger.log_tabular('EpLen', average_only=True)
logger.log_tabular('VVals', with_min_and_max=True)
logger.log_tabular('TotalEnvInteracts', total_t)
logger.log_tabular('LossPi', average_only=True)
logger.log_tabular('DeltaLossPi', average_only=True)
logger.log_tabular('LossV', average_only=True)
logger.log_tabular('DeltaLossV', average_only=True)
logger.log_tabular('LossDC', average_only=True)
logger.log_tabular('DeltaLossDC', average_only=True)
logger.log_tabular('Entropy', average_only=True)
logger.log_tabular('KL', average_only=True)
logger.log_tabular('Time', time.time() - start_time)
logger.log_tabular('LogProbabilityContext', average_only=True)
logger.log_tabular('DecoderAccuracy', average_only=True)
logger.log_tabular('ContextDim', average_only=True)
logger.dump_tabular()
def sample_reinforce_given_class(logits, samp):
dist = Categorical(logits=logits)
logprob = dist.log_prob(samp)
return logprob
# mylogprobgrad = torch.autograd.grad(outputs=mylogprob, inputs=(probs), retain_graph=True)[0]
print('rewards', rewards)
print('probs', probs)
print('logits', logits)
#REINFORCE
print('REINFORCE')
# def sample_reinforce_given_class(logits, samp):
# return logprob
grads = []
for i in range(N):
dist = Categorical(logits=logits)
samp = dist.sample()
logprob = dist.log_prob(samp)
reward = f(samp)
gradlogprob = torch.autograd.grad(outputs=logprob,
inputs=(logits),
retain_graph=True)[0]
grads.append(reward * gradlogprob)
print()
grads = torch.stack(grads).view(N, C)
# print (grads.shape)
grad_mean_reinforce = torch.mean(grads, dim=0)
grad_std_reinforce = torch.std(grads, dim=0)
print('REINFORCE')
#REINFORCE
# dist = LogitRelaxedBernoulli(torch.Tensor([1.]), bern_param)
# dist_bernoulli = Bernoulli(bern_param)
C= 2
n_components = C
B=1
probs = torch.ones(B,C)
bern_param = bern_param.view(B,1)
aa = 1 - bern_param
probs = torch.cat([aa, bern_param], dim=1)
cat = Categorical(probs= probs)
grads = []
for i in range(n):
b = cat.sample()
logprob = cat.log_prob(b.detach())
# b_ = torch.argmax(z, dim=1)
logprobgrad = torch.autograd.grad(outputs=logprob, inputs=(bern_param), retain_graph=True)[0]
grad = f(b) * logprobgrad
grads.append(grad[0][0].data.numpy())
print ('Grad Estimator: Reinfoce categorical')
print ('Grad mean', np.mean(grads))
print ('Grad std', np.std(grads))
def decode_where(self, input_coord_logits, input_attri_logits,
input_patch_vectors, sample_mode):
"""
Inputs:
where_states containing
- **coord_logits** (bsize, 1, grid_dim)
- **attri_logits** (bsize, 1, scale_ratio_dim, grid_dim)
- **patch_vectors** (bsize, 1, patch_feature_dim, grid_dim)
sample_mode
0: top 1, 1: multinomial
Outputs
- **sample_inds** (bsize, 3)
- **sample_vecs** (bsize, patch_feature_dim)
"""
##############################################################
# Sampling locations
##############################################################
coord_logits = input_coord_logits.squeeze(1)
if sample_mode == 0:
_, sample_coord_inds = torch.max(coord_logits + 1.0,
dim=-1,
keepdim=True)
else:
sample_coord_inds = Categorical(coord_logits).sample().unsqueeze(
-1)
##############################################################
# Sampling attributes and patch vectors
##############################################################
patch_vectors = input_patch_vectors.squeeze(1)
bsize, tsize, grid_dim = patch_vectors.size()
aux_pos_inds = sample_coord_inds.expand(bsize, tsize).unsqueeze(-1)
sample_patch_vectors = torch.gather(patch_vectors, -1,
aux_pos_inds).squeeze(-1)
attri_logits = input_attri_logits.squeeze(1)
bsize, tsize, grid_dim = attri_logits.size()
aux_pos_inds = sample_coord_inds.expand(bsize, tsize).unsqueeze(-1)
local_logits = torch.gather(attri_logits, -1, aux_pos_inds).squeeze(-1)
scale_logits = local_logits[:, :self.cfg.num_scales]
ratio_logits = local_logits[:, self.cfg.num_scales:]
if sample_mode == 0:
_, sample_scale_inds = torch.max(scale_logits + 1.0,
dim=-1,
keepdim=True)
_, sample_ratio_inds = torch.max(ratio_logits + 1.0,
dim=-1,
keepdim=True)
else:
sample_scale_inds = Categorical(scale_logits).sample().unsqueeze(
-1)
sample_ratio_inds = Categorical(ratio_logits).sample().unsqueeze(
-1)
sample_inds = torch.cat(
[sample_coord_inds, sample_scale_inds, sample_ratio_inds], -1)
return sample_inds, sample_patch_vectors
def forward(self, hidden_state=None):
"""
Performs a forward pass. If training, use Gumbel Softmax (hard) for sampling, else use
discrete sampling.
Hidden state here represents the encoded image/metadata - initializes the RNN from it.
"""
# hidden_state = self.input_module(hidden_state)
state, batch_size = self._init_state(hidden_state, type(self.rnn))
# Init output
if not (self.vqvae and not self.discrete_communication
and not self.rl):
output = [
torch.zeros(
(batch_size, self.vocab_size),
dtype=torch.float32,
device=self.device,
)
]
output[0][:, self.sos_id] = 1.0
else:
# In vqvae case, there is no sos symbol, since all words come from the unordered embedding table.
# It is not possible to index code words by sos or eos symbols, since the number of codewords
# is not necessarily the vocab size!
output = [
torch.zeros(
(batch_size, self.vocab_size),
dtype=torch.float32,
device=self.device,
)
]
# Keep track of sequence lengths
initial_length = self.output_len + 1 # add the sos token
seq_lengths = (
torch.ones([batch_size], dtype=torch.int64, device=self.device) *
initial_length
) # [initial_length, initial_length, ..., initial_length]. This gets reduced whenever it ends somewhere.
embeds = [] # keep track of the embedded sequence
sentence_probability = torch.zeros((batch_size, self.vocab_size),
device=self.device)
losses_2_3 = torch.empty(self.output_len, device=self.device)
entropy = torch.empty((batch_size, self.output_len),
device=self.device)
message_logits = torch.empty((batch_size, self.output_len),
device=self.device)
if self.vqvae:
distance_computer = EmbeddingtableDistances(self.e)
for i in range(self.output_len):
emb = torch.matmul(output[-1], self.embedding)
embeds.append(emb)
state = self.rnn.forward(emb, state)
if type(self.rnn) is nn.LSTMCell:
h, _ = state
else:
h = state
indices = [None] * batch_size
if not self.rl:
if not self.vqvae:
# That's the original baseline setting
p = F.softmax(self.linear_out(h), dim=1)
token, sentence_probability = self.calculate_token_gumbel_softmax(
p, self.tau, sentence_probability, batch_size)
else:
pre_quant = self.linear_out(h)
if not self.discrete_communication:
token = self.vq.apply(pre_quant, self.e, indices)
else:
distances = distance_computer(pre_quant)
softmin = F.softmax(-distances, dim=1)
if not self.gumbel_softmax:
token = self.hard_max.apply(
softmin, indices, self.discrete_latent_number
) # This also updates the indices
else:
_, indices[:] = torch.max(softmin, dim=1)
token, _ = self.calculate_token_gumbel_softmax(
softmin, self.tau, 0, batch_size)
else:
if not self.vqvae:
all_logits = F.log_softmax(self.linear_out(h) / self.tau,
dim=1)
else:
pre_quant = self.linear_out(h)
distances = distance_computer(pre_quant)
all_logits = F.log_softmax(-distances / self.tau, dim=1)
_, indices[:] = torch.max(all_logits, dim=1)
distr = Categorical(logits=all_logits)
entropy[:, i] = distr.entropy()
if self.training:
token_index = distr.sample()
token = to_one_hot(token_index, n_dims=self.vocab_size)
else:
token_index = all_logits.argmax(dim=1)
token = to_one_hot(token_index, n_dims=self.vocab_size)
message_logits[:, i] = distr.log_prob(token_index)
if not (self.vqvae and not self.discrete_communication
and not self.rl):
# Whenever we have a meaningful eos symbol, we prune the messages in the end
self._calculate_seq_len(seq_lengths,
token,
initial_length,
seq_pos=i + 1)
if self.vqvae:
loss_2 = torch.mean(
torch.norm(pre_quant.detach() - self.e[indices], dim=1)**2)
loss_3 = torch.mean(
torch.norm(pre_quant - self.e[indices].detach(), dim=1)**2)
loss_2_3 = (
loss_2 + self.beta * loss_3
) # This corresponds to the second and third loss term in VQ-VAE
losses_2_3[i] = loss_2_3
token = token.to(self.device)
output.append(token)
messages = torch.stack(output, dim=1)
loss_2_3_out = torch.mean(losses_2_3)
return (
messages,
seq_lengths,
entropy,
torch.stack(embeds, dim=1),
sentence_probability,
loss_2_3_out,
message_logits,
)
def evaluate_actions_sil(pi, actions):
cate_dist = Categorical(pi)
return cate_dist.log_prob(
actions.squeeze(-1)).unsqueeze(-1), cate_dist.entropy().unsqueeze(-1)
def select_actions(pi, deterministic=False):
cate_dist = Categorical(pi)
if deterministic:
return torch.argmax(pi, dim=1).item()
else:
return cate_dist.sample().unsqueeze(-1)
def forward(self, obs, memory, instr_embedding=None):
if self.use_desc and instr_embedding is None:
if self.enable_instr and self.arch == "fusion":
instr_embedding, instr_embedding2 = self._get_instr_embedding(
obs.instr)
else:
instr_embedding = self._get_instr_embedding(obs.instr)
if self.use_desc and self.lang_model == "attgru":
# outputs: B x L x D
# memory: B x M
mask = (obs.instr != 0).float()
instr_embedding = instr_embedding[:, :mask.shape[1]]
keys = self.memory2key(memory)
pre_softmax = (keys[:, None, :] *
instr_embedding).sum(2) + 1000 * mask
attention = F.softmax(pre_softmax, dim=1)
instr_embedding = (instr_embedding * attention[:, :, None]).sum(1)
x = torch.transpose(torch.transpose(obs.image, 1, 3), 2, 3)
if self.arch.startswith("expert_filmcnn"):
x = self.image_conv(x)
for controler in self.controllers:
x = controler(x, instr_embedding)
x = F.relu(self.film_pool(x))
elif self.arch == "fusion":
# old fusion model
'''
x = self.image_conv(x)
w = self.w_conv(x)
N,_,W,H = w.shape
w = w.view([N, self.instr_sents, -1])
w = F.softmax(w,dim=1)
y = torch.matmul(instr_embedding, w).view([N, 128, W, H])
'''
# new fusion model: separate cnns for image extractor and attention module input
x_feat = self.image_conv(x)
w = self.w_conv(x)
N, _, W, H = w.shape
w = w.view([N, self.instr_sents + 1, -1])
w = F.softmax(w, dim=1)
y = torch.matmul(instr_embedding, w[:, :-1]).view([N, 128, W, H])
x = torch.cat([x_feat, y], axis=1)
x = self.combined_conv(x)
x = x.view(x.shape[0], x.shape[1], 1, 1)
if self.enable_instr:
for controler in self.controllers:
x = controler(x, instr_embedding2)
x = F.relu(x)
else:
x = self.image_conv(x)
x = x.reshape(x.shape[0], -1)
if self.use_memory:
hidden = (memory[:, :self.semi_memory_size],
memory[:, self.semi_memory_size:])
hidden = self.memory_rnn(x, hidden)
embedding = hidden[0]
memory = torch.cat(hidden, dim=1)
else:
embedding = x
if self.use_desc and not "filmcnn" in self.arch and not "fusion" in self.arch:
embedding = torch.cat((embedding, instr_embedding), dim=1)
if hasattr(self, 'aux_info') and self.aux_info:
extra_predictions = {
info: self.extra_heads[info](embedding)
for info in self.extra_heads
}
else:
extra_predictions = dict()
x = self.actor(embedding)
dist = Categorical(logits=F.log_softmax(x, dim=1))
x = self.critic(embedding)
value = x.squeeze(1)
return {
'dist': dist,
'value': value,
'memory': memory,
'extra_predictions': extra_predictions
}
def update_parameters(self, exps):
# Collect experiences
for _ in range(self.epochs):
# Initialize log values
log_entropies = []
log_values = []
log_policy_losses = []
log_value_losses = []
log_grad_norms = []
for inds in self._get_batches_starting_indexes():
# Initialize batch values
batch_entropy = 0
batch_value = 0
batch_policy_loss = 0
batch_value_loss = 0
batch_loss = 0
# Initialize memory
if self.acmodel.recurrent:
memory = exps.memory[inds]
for i in range(self.recurrence):
# Create a sub-batch of experience
sb = exps[inds + i]
# Compute loss
if self.acmodel.recurrent:
if self.variable_view:
dist, gaze, value, memory = self.acmodel(
sb.obs, memory * sb.mask)
# Combine action and gaze distributions into single 1D distribution
# gaze_dist = gaze[0].probs.ger(gaze[1].probs)
# dist = Categorical(dist.probs.ger(gaze_dist.view(-1)))
full_dist = []
for j in range(inds.shape[0]):
gaze_dist = gaze[0].probs[j].ger(
gaze[1].probs[j]).view([-1])
full_action_space_dist = Categorical(
(dist.probs[j].ger(gaze_dist)).view(-1))
full_dist.append(full_action_space_dist.probs)
dist = Categorical(torch.stack(full_dist))
sb.action = sb.action.long(
) * 22 + sb.gaze[:, 0] * 11 + sb.gaze[:, 1]
else:
dist, value, memory = self.acmodel(
sb.obs, memory * sb.mask)
else:
dist, value = self.acmodel(sb.obs)
entropy = dist.entropy().mean()
ratio = torch.exp(dist.log_prob(sb.action) - sb.log_prob)
surr1 = ratio * sb.advantage
surr2 = torch.clamp(ratio, 1.0 - self.clip_eps,
1.0 + self.clip_eps) * sb.advantage
policy_loss = -torch.min(surr1, surr2).mean()
value_clipped = sb.value + torch.clamp(
value - sb.value, -self.clip_eps, self.clip_eps)
surr1 = (value - sb.returnn).pow(2)
surr2 = (value_clipped - sb.returnn).pow(2)
value_loss = torch.max(surr1, surr2).mean()
loss = policy_loss - self.entropy_coef * entropy + self.value_loss_coef * value_loss
# Update batch values
batch_entropy += entropy.item()
batch_value += value.mean().item()
batch_policy_loss += policy_loss.item()
batch_value_loss += value_loss.item()
batch_loss += loss
# Update memories for next epoch
if self.acmodel.recurrent and i < self.recurrence - 1:
exps.memory[inds + i + 1] = memory.detach()
# Update batch values
batch_entropy /= self.recurrence
batch_value /= self.recurrence
batch_policy_loss /= self.recurrence
batch_value_loss /= self.recurrence
batch_loss /= self.recurrence
# Update actor-critic
self.optimizer.zero_grad()
batch_loss.backward()
grad_norm = sum(
p.grad.data.norm(2).item()**2
for p in self.acmodel.parameters())**0.5
torch.nn.utils.clip_grad_norm_(self.acmodel.parameters(),
self.max_grad_norm)
self.optimizer.step()
# Update log values
log_entropies.append(batch_entropy)
log_values.append(batch_value)
log_policy_losses.append(batch_policy_loss)
log_value_losses.append(batch_value_loss)
log_grad_norms.append(grad_norm)
# Log some values
logs = {
"entropy": numpy.mean(log_entropies),
"value": numpy.mean(log_values),
"policy_loss": numpy.mean(log_policy_losses),
"value_loss": numpy.mean(log_value_losses),
"grad_norm": numpy.mean(log_grad_norms)
}
return logs
def forward(self, comm, obs, memory, instr_embedding=None):
message_embedding = torch.matmul(comm, self.comm_embed.weight)
_, hidden = self.comm_encoder_rnn(message_embedding)
message_encoded = hidden[-1]
if self.student_obs_type == "vision":
# Calculating instruction embedding
if self.use_instr and instr_embedding is None:
if self.lang_model == 'gru':
_, hidden = self.instr_rnn(self.word_embedding(obs.instr))
instr_embedding = hidden[-1]
# Calculating the image imedding
x = torch.transpose(torch.transpose(obs.image, 1, 3), 2, 3)
if self.arch.startswith("expert_filmcnn"):
image_embedding = self.image_conv(x)
# Calculating FiLM_embedding from image and instruction embedding
for controler in self.controllers:
x = controler(image_embedding, instr_embedding)
FiLM_embedding = F.relu(self.film_pool(x))
else:
FiLM_embedding = self.image_conv(x)
FiLM_embedding = FiLM_embedding.reshape(FiLM_embedding.shape[0],
-1)
# Going through the memory layer
if self.use_memory:
hidden = (memory[:, :self.semi_memory_size],
memory[:, self.semi_memory_size:])
hidden = self.memory_rnn(FiLM_embedding, hidden)
embedding = hidden[0]
memory = torch.cat(hidden, dim=1)
else:
embedding = x
if self.use_instr and not "filmcnn" in self.arch:
embedding = torch.cat((embedding, instr_embedding), dim=1)
if hasattr(self, 'aux_info') and self.aux_info:
extra_predictions = {
info: self.extra_heads[info](embedding)
for info in self.extra_heads
}
else:
extra_predictions = dict()
elif self.student_obs_type == "blind":
embedding = torch.zeros(message_embedding.shape[0],
self.semi_memory_size)
extra_predictions = {}
if torch.cuda.is_available():
embedding = embedding.cuda()
else:
raise ValueError(
"Student observation type must be either vision or blind")
#Policy part
policy_input = torch.cat((embedding, message_encoded), dim=1)
policy_input = self.dropout(policy_input)
x = self.actor(policy_input)
dist = Categorical(logits=F.log_softmax(x, dim=1))
x = self.critic(policy_input)
value = x.squeeze(1)
return {
'dist': dist,
'value': value,
'memory_student': memory,
'extra_predictions': extra_predictions
}
def __init__(self, probs=None, logits=None, validate_args=None):
self._categorical = Categorical(probs, logits)
batch_shape = self._categorical.batch_shape
event_shape = self._categorical.param_shape[-1:]
super(OneHotCategorical, self).__init__(batch_shape, event_shape, validate_args=validate_args)
def sample_action(self, state):
actions_logits = self(state)
distribution = Categorical(logits=actions_logits)
action = distribution.sample()
return action
def get_action_and_value(self, x, action=None):
logits = self.actor(x)
probs = Categorical(logits=logits)
if action is None:
action = probs.sample()
return action, probs.log_prob(action), probs.entropy(), self.critic(x)
def __init__(self, temperature, probs=None, logits=None, validate_args=None):
self._categorical = Categorical(probs, logits)
self.temperature = temperature
batch_shape = self._categorical.batch_shape
event_shape = self._categorical.param_shape[-1:]
super(ExpRelaxedCategorical, self).__init__(batch_shape, event_shape, validate_args=validate_args)
def trainAttention(model=NaiveAttention,dimbed=50,dimout=2,maxiter=epochs,epsilon=0.01,reg=None,verbose=True):
# Le paramètre "reg" permet de choisir de régulariser ou non avec un critère entropique.
writer = SummaryWriter("runs")
if model == NaiveAttention:
print("\n//////////////////// Attention-based LinNet : naive baseline /////////////////\n")
name = "attentionbase.pch"
etiq = "/Base_SGD"
elif model == SimpleAttention:
print("\n///////////////// Attention-based LinNet : basic implementation //////////////\n")
name = "attentionclassic.pch"
etiq = "/Classic_SGD"
elif model == FurtherAttention:
print("\n///////////////// Attention-based LinNet : further improvements //////////////\n")
name = "attentionfurther.pch"
etiq = "/Further_SGD_regul"
elif model == LSTMAttention:
print("\n//////////////////// Attention-based LinNet : adding an LSTM /////////////////\n")
name = "attentionlstm.pch"
etiq = "/LSTM_SGD"
elif model == BILSTMAttention:
print("\n//////////////////// Attention-based LinNet : adding an BiLSTM /////////////////\n")
name = "attentionbilstm.pch"
etiq = "/BiLSTM_SGD"
# Creating a checkpointed model
savepath = Path(name)
if savepath.is_file():
print("Restarting from previous state.")
with savepath.open("rb") as fp :
state = torch.load(fp)
else:
lin = model(dimbed,dimout).to(device)
optim = torch.optim.SGD(params=lin.parameters(),lr=epsilon)
state = State(lin,optim)
loss = nn.CrossEntropyLoss()
# Training the model
for epoch in tqdm(range(state.epoch,maxiter)):
state.model = state.model.train()
losstrain = 0
accytrain = 0
divtrain = 0
for x, y in train_loader:
state.optim.zero_grad()
y = y.to(device)
if model == NaiveAttention: preds = state.model(x)
else: preds, attns = state.model(x)
if model != NaiveAttention:
entropytrain = Categorical(probs = attns.squeeze(2).t()).entropy()
penalty = reg * torch.sum(entropytrain) if reg else 0
ltrain = loss(preds,y.long()) + penalty
ltrain.backward()
state.optim.step()
state.iteration += 1
acctr = sum((preds.argmax(1) == y)).item() / y.shape[0]
losstrain += ltrain
accytrain += acctr
divtrain += 1
#if model != NaiveAttention:
# entropytrain = Categorical(probs = attns.squeeze(2).t()).entropy()
state.model = state.model.eval()
losstest = 0
accytest = 0
divtest = 0
for x, y in test_loader:
with torch.no_grad():
y = y.to(device)
if model == NaiveAttention :
preds = state.model(x)
else :
preds, attns = state.model(x)
ltest = loss(preds,y.long())
accts = sum((preds.argmax(1) == y)).item() / y.shape[0]
losstest += ltest
accytest += accts
divtest += 1
# Saving the loss
writer.add_scalars('Attention/Loss'+etiq,{'train':losstrain/divtrain,'test':losstest/divtest},epoch)
writer.add_scalars('Attention/Accuracy'+etiq,{'train':accytrain/divtrain,'test':accytest/divtest},epoch)
if model != NaiveAttention :
entropytest = Categorical(probs = attns.squeeze(2).t()).entropy()
writer.add_histogram('Attention/EntropyTest'+etiq,entropytest,epoch)
writer.add_histogram('Attention/EntropyTrain'+etiq,entropytrain,epoch)
if verbose:
print('\nLOSS: \t\ttrain',(losstrain/divtrain).item(),'\t\ttest',(losstest/divtest).item())
print('ACCURACY: \ttrain',accytrain/divtrain,'\t\ttest',accytest/divtest)
# Saving the current state after each epoch
with savepath.open ("wb") as fp:
state.epoch = epoch+1
torch.save(state, fp)
print("\n\n\033[1mDone.\033[0m\n")
writer.flush()
writer.close()
# ////////////////////////////////////////////////////////////////////////////////////////////////// </training loop> ////
def __init__(self, probs=None, logits=None, validate_args=None):
self._categorical = Categorical(probs, logits)
batch_shape = self._categorical.batch_shape
event_shape = self._categorical.param_shape[-1:]
super(OneHotCategorical, self).__init__(batch_shape, event_shape, validate_args=validate_args)
def train(meta_decoder, decoder_optimizer, fclayers_for_hyper_params):
global moving_average
global moving_average_alpha
decoder_hidden = meta_decoder.initHidden()
decoder_optimizer.zero_grad()
output = torch.zeros([1, 1, meta_decoder.output_size], device=device)
softmax = nn.Softmax(dim=1)
softmax_outputs_stored = list()
loss = 0
#
for i in range(3):
output, decoder_hidden = meta_decoder(output, decoder_hidden)
#print(hyper_params[i])
softmax_outputs_stored.append(
softmax(fclayers_for_hyper_params[hyper_params[i][0]](output)))
#
output_interaction = softmax_outputs_stored[-1]
type_of_interaction = Categorical(output_interaction).sample().tolist()[0]
if type_of_interaction == 0:
# PairwiseEuDist
for i in range(3, 4):
output, decoder_hidden = meta_decoder(output, decoder_hidden)
softmax_outputs_stored.append(
softmax(fclayers_for_hyper_params[hyper_params[i][0]](output)))
elif type_of_interaction == 1:
# PairwiseLog
# no hyper-params for this interaction type
pass
else:
# PointwiseMLPCE
for i in range(4, 7):
output, decoder_hidden = meta_decoder(output, decoder_hidden)
softmax_outputs_stored.append(
softmax(fclayers_for_hyper_params[hyper_params[i][0]](output)))
#
resulted_str = []
for outputs in softmax_outputs_stored:
print("softmax_outputs: ", outputs)
idx = Categorical(outputs).sample()
resulted_str.append(idx.tolist()[0])
resulted_str[
2] = type_of_interaction # the type of interaction has already been sampled before
resulted_idx = resulted_str
resulted_str = "_".join(map(str, resulted_str))
print("resulted_str: " + resulted_str)
#
reward = calc_reward_given_descriptor(resulted_str)
if moving_average == -19013:
moving_average = reward
reward = 0.0
else:
tmp = reward
reward = reward - moving_average
moving_average = moving_average_alpha * tmp + (
1.0 - moving_average_alpha) * moving_average
#
print("current reward: " + str(reward))
print("current moving average: " + str(moving_average))
expectedReward = 0
for i in range(len(softmax_outputs_stored)):
logprob = torch.log(softmax_outputs_stored[i][0][resulted_idx[i]])
expectedReward += logprob * reward
loss = -expectedReward
print('loss:', loss)
# finally, backpropagate the loss according to the policy
loss.backward()
decoder_optimizer.step()
def HLAX(surrogate, surrogate2, x, logits, mixtureweights, k=1):
B = logits.shape[0]
probs = torch.softmax(logits, dim=1)
cat = RelaxedOneHotCategorical(probs=probs, temperature=torch.tensor([1.]).cuda())
cat_bernoulli = Categorical(probs=probs)
net_loss = 0
surr_loss = 0
for jj in range(k):
cluster_S = cat.rsample()
cluster_H = H(cluster_S)
logq_z = cat.log_prob(cluster_S.detach()).view(B,1)
logq_b = cat_bernoulli.log_prob(cluster_H.detach()).view(B,1)
logpx_given_z = logprob_undercomponent(x, component=cluster_H)
logpz = torch.log(mixtureweights[cluster_H]).view(B,1)
logpxz = logpx_given_z + logpz #[B,1]
f_z = logpxz - logq_z - 1.
f_b = logpxz - logq_b - 1.
surr_input = torch.cat([cluster_S, x], dim=1) #[B,21]
# surr_pred, alpha = surrogate.net(surr_input)
surr_pred = surrogate.net(surr_input)
alpha = torch.sigmoid(surrogate2.net(x))
net_loss += - torch.mean( alpha.detach()*(f_z.detach() - surr_pred.detach()) * logq_z
+ alpha.detach()*surr_pred
+ (1-alpha.detach())*(f_b.detach() ) * logq_b)
# surr_loss += torch.mean(torch.abs(f_z.detach() - surr_pred))
grad_logq_z = torch.mean( torch.autograd.grad([torch.mean(logq_z)], [logits], create_graph=True, retain_graph=True)[0], dim=1, keepdim=True)
grad_logq_b = torch.mean( torch.autograd.grad([torch.mean(logq_b)], [logits], create_graph=True, retain_graph=True)[0], dim=1, keepdim=True)
grad_surr = torch.mean( torch.autograd.grad([torch.mean(surr_pred)], [logits], create_graph=True, retain_graph=True)[0], dim=1, keepdim=True)
# print (alpha.shape, f_z.shape, surr_pred.shape, grad_logq_z.shape, grad_surr.shape)
# fsdfa
# grad_surr = torch.autograd.grad([surr_pred[0]], [logits], create_graph=True, retain_graph=True)[0]
# print (grad_surr)
# fsdfasd
surr_loss += torch.mean(
(alpha*(f_z.detach() - surr_pred) * grad_logq_z
+ alpha*grad_surr
+ (1-alpha)*(f_b.detach()) * grad_logq_b )**2
)
surr_dif = torch.mean(torch.abs(f_z.detach() - surr_pred))
# gradd = torch.autograd.grad([surr_loss], [alpha], create_graph=True, retain_graph=True)[0]
# print (gradd)
# fdsf
grad_path = torch.autograd.grad([torch.mean(surr_pred)], [logits], create_graph=True, retain_graph=True)[0]
grad_score = torch.autograd.grad([torch.mean((f_z.detach() - surr_pred.detach()) * logq_z)], [logits], create_graph=True, retain_graph=True)[0]
grad_path = torch.mean(torch.abs(grad_path))
grad_score = torch.mean(torch.abs(grad_score))
net_loss = net_loss/ k
surr_loss = surr_loss/ k
return net_loss, f_b, logpx_given_z, logpz, logq_b, surr_loss, surr_dif, grad_path, grad_score, torch.mean(alpha)
def _distribution(self, obs):
logits = self.logits_net(obs)
return Categorical(logits=logits)
def get_pi(self, x):
return Categorical(logits=self.actor(self.network(x.permute((0, 3, 1, 2)) / 255.0)))
def get_action(self, x, action=None):
logits = self.actor(self.forward(x))
probs = Categorical(logits=logits)
if action is None:
action = probs.sample()
return action, probs.log_prob(action), probs.entropy()
def get_policy(self, observations: np.array):
""" Turn an observation into a policy action distribution """
logits = self.policy_net(observations)
return Categorical(logits=logits)
def simplax():
def show_surr_preds():
batch_size = 1
rows = 3
cols = 1
fig = plt.figure(figsize=(10+cols,4+rows), facecolor='white') #, dpi=150)
for i in range(rows):
x = sample_true(1).cuda() #.view(1,1)
logits = encoder.net(x)
probs = torch.softmax(logits, dim=1)
cat = RelaxedOneHotCategorical(probs=probs, temperature=torch.tensor([1.]).cuda())
cluster_S = cat.rsample()
cluster_H = H(cluster_S)
logprob_cluster = cat.log_prob(cluster_S.detach()).view(batch_size,1)
check_nan(logprob_cluster)
z = cluster_S
n_evals = 40
x1 = np.linspace(-9,205, n_evals)
x = torch.from_numpy(x1).view(n_evals,1).float().cuda()
z = z.repeat(n_evals,1)
cluster_H = cluster_H.repeat(n_evals,1)
xz = torch.cat([z,x], dim=1)
logpxz = logprob_undercomponent(x, component=cluster_H, needsoftmax_mixtureweight=needsoftmax_mixtureweight, cuda=True)
f = logpxz #- logprob_cluster
surr_pred = surrogate.net(xz)
surr_pred = surr_pred.data.cpu().numpy()
f = f.data.cpu().numpy()
col =0
row = i
# print (row)
ax = plt.subplot2grid((rows,cols), (row,col), frameon=False, colspan=1, rowspan=1)
ax.plot(x1,surr_pred, label='Surr')
ax.plot(x1,f, label='f')
ax.set_title(str(cluster_H[0]))
ax.legend()
# save_dir = home+'/Documents/Grad_Estimators/GMM/'
plt_path = exp_dir+'gmm_surr.png'
plt.savefig(plt_path)
print ('saved training plot', plt_path)
plt.close()
def plot_dist():
mixture_weights = torch.softmax(needsoftmax_mixtureweight, dim=0)
rows = 1
cols = 1
fig = plt.figure(figsize=(10+cols,4+rows), facecolor='white') #, dpi=150)
col =0
row = 0
ax = plt.subplot2grid((rows,cols), (row,col), frameon=False, colspan=1, rowspan=1)
xs = np.linspace(-9,205, 300)
sum_ = np.zeros(len(xs))
# C = 20
for c in range(n_components):
m = Normal(torch.tensor([c*10.]).float(), torch.tensor([5.0]).float())
ys = []
for x in xs:
# component_i = (torch.exp(m.log_prob(x) )* ((c+5.) / 290.)).numpy()
component_i = (torch.exp(m.log_prob(x) )* mixture_weights[c]).detach().cpu().numpy()
ys.append(component_i)
ys = np.reshape(np.array(ys), [-1])
sum_ += ys
ax.plot(xs, ys, label='')
ax.plot(xs, sum_, label='')
# save_dir = home+'/Documents/Grad_Estimators/GMM/'
plt_path = exp_dir+'gmm_plot_dist.png'
plt.savefig(plt_path)
print ('saved training plot', plt_path)
plt.close()
def get_loss():
x = sample_true(batch_size).cuda() #.view(1,1)
logits = encoder.net(x)
probs = torch.softmax(logits, dim=1)
cat = RelaxedOneHotCategorical(probs=probs, temperature=torch.tensor([temp]).cuda())
cluster_S = cat.rsample()
cluster_H = H(cluster_S)
# cluster_onehot = torch.zeros(n_components)
# cluster_onehot[cluster_H] = 1.
logprob_cluster = cat.log_prob(cluster_S.detach()).view(batch_size,1)
check_nan(logprob_cluster)
logpxz = logprob_undercomponent(x, component=cluster_H, needsoftmax_mixtureweight=needsoftmax_mixtureweight, cuda=True)
f = logpxz - logprob_cluster
surr_input = torch.cat([cluster_S, x], dim=1) #[B,21]
surr_pred = surrogate.net(surr_input)
# net_loss = - torch.mean((f.detach()-surr_pred.detach()) * logprob_cluster + surr_pred)
# loss = - torch.mean(f)
surr_loss = torch.mean(torch.abs(logpxz.detach()-surr_pred))
return surr_loss
def plot_posteriors(needsoftmax_mixtureweight, name=''):
x = sample_true(1).cuda()
trueposterior = true_posterior(x, needsoftmax_mixtureweight).view(n_components)
logits = encoder.net(x)
probs = torch.softmax(logits, dim=1).view(n_components)
trueposterior = trueposterior.data.cpu().numpy()
qz = probs.data.cpu().numpy()
error = L2_mixtureweights(trueposterior,qz)
kl = KL_mixutreweights(p=trueposterior, q=qz)
rows = 1
cols = 1
fig = plt.figure(figsize=(8+cols,8+rows), facecolor='white') #, dpi=150)
col =0
row = 0
ax = plt.subplot2grid((rows,cols), (row,col), frameon=False, colspan=1, rowspan=1)
width = .3
ax.bar(range(len(qz)), trueposterior, width=width, label='True')
ax.bar(np.array(range(len(qz)))+width, qz, width=width, label='q')
# ax.bar(np.array(range(len(q_b)))+width+width, q_b, width=width)
ax.legend()
ax.grid(True, alpha=.3)
ax.set_title(str(error) + ' kl:' + str(kl))
ax.set_ylim(0.,1.)
# save_dir = home+'/Documents/Grad_Estimators/GMM/'
plt_path = exp_dir+'posteriors'+name+'.png'
plt.savefig(plt_path)
print ('saved training plot', plt_path)
plt.close()
def inference_error(needsoftmax_mixtureweight):
error_sum = 0
kl_sum = 0
n=10
for i in range(n):
# if x is None:
x = sample_true(1).cuda()
trueposterior = true_posterior(x, needsoftmax_mixtureweight).view(n_components)
logits = encoder.net(x)
probs = torch.softmax(logits, dim=1).view(n_components)
error = L2_mixtureweights(trueposterior.data.cpu().numpy(),probs.data.cpu().numpy())
kl = KL_mixutreweights(trueposterior.data.cpu().numpy(), probs.data.cpu().numpy())
error_sum+=error
kl_sum += kl
return error_sum/n, kl_sum/n
# fsdfa
#SIMPLAX
needsoftmax_mixtureweight = torch.randn(n_components, requires_grad=True, device="cuda")#.cuda()
print ('current mixuture weights')
print (torch.softmax(needsoftmax_mixtureweight, dim=0))
print()
encoder = NN3(input_size=1, output_size=n_components).cuda()
surrogate = NN3(input_size=1+n_components, output_size=1).cuda()
# optim = torch.optim.Adam([needsoftmax_mixtureweight], lr=.00004)
# optim_net = torch.optim.Adam(encoder.parameters(), lr=.0004)
# optim_surr = torch.optim.Adam(surrogate.parameters(), lr=.004)
# optim = torch.optim.Adam([needsoftmax_mixtureweight], lr=.0001)
# optim_net = torch.optim.Adam(encoder.parameters(), lr=.0001)
optim_net = torch.optim.SGD(encoder.parameters(), lr=.0001)
# optim_surr = torch.optim.Adam(surrogate.parameters(), lr=.005)
temp = 1.
batch_size = 100
n_steps = 300000
surrugate_steps = 0
k = 1
L2_losses = []
inf_losses = []
inf_losses_kl = []
kl_losses_2 = []
surr_losses = []
steps_list =[]
grad_reparam_list =[]
grad_reinforce_list =[]
f_list = []
logpxz_list = []
logprob_cluster_list = []
logpx_list = []
# logprob_cluster_list = []
for step in range(n_steps):
for ii in range(surrugate_steps):
surr_loss = get_loss()
optim_surr.zero_grad()
surr_loss.backward()
optim_surr.step()
x = sample_true(batch_size).cuda() #.view(1,1)
logits = encoder.net(x)
probs = torch.softmax(logits, dim=1)
# print (probs)
# fsdafsa
# cat = RelaxedOneHotCategorical(probs=probs, temperature=torch.tensor([temp]).cuda())
cat = Categorical(probs=probs)
# cluster = cat.sample()
# logprob_cluster = cat.log_prob(cluster.detach())
net_loss = 0
loss = 0
surr_loss = 0
for jj in range(k):
# cluster_S = cat.rsample()
# cluster_H = H(cluster_S)
cluster_H = cat.sample()
# print (cluster_H.shape)
# print (cluster_H[0])
# fsad
# cluster_onehot = torch.zeros(n_components)
# cluster_onehot[cluster_H] = 1.
# logprob_cluster = cat.log_prob(cluster_S.detach()).view(batch_size,1)
logprob_cluster = cat.log_prob(cluster_H.detach()).view(batch_size,1)
# logprob_cluster = cat.log_prob(cluster_S).view(batch_size,1).detach()
# cat = RelaxedOneHotCategorical(probs=probs.detach(), temperature=torch.tensor([temp]).cuda())
# logprob_cluster = cat.log_prob(cluster_S).view(batch_size,1)
check_nan(logprob_cluster)
logpxz = logprob_undercomponent(x, component=cluster_H, needsoftmax_mixtureweight=needsoftmax_mixtureweight, cuda=True)
f = logpxz - logprob_cluster
# print (f)
# surr_input = torch.cat([cluster_S, x], dim=1) #[B,21]
surr_input = torch.cat([probs, x], dim=1) #[B,21]
surr_pred = surrogate.net(surr_input)
# print (f.shape)
# print (surr_pred.shape)
# print (logprob_cluster.shape)
# fsadfsa
# net_loss += - torch.mean((logpxz.detach()-surr_pred.detach()) * logprob_cluster + surr_pred - logprob_cluster)
# net_loss += - torch.mean((logpxz.detach() - surr_pred.detach() - 1.) * logprob_cluster + surr_pred)
net_loss += - torch.mean((logpxz.detach() - 1.) * logprob_cluster)
# net_loss += - torch.mean((logpxz.detach()) * logprob_cluster - logprob_cluster)
loss += - torch.mean(logpxz)
surr_loss += torch.mean(torch.abs(logpxz.detach()-surr_pred))
net_loss = net_loss/ k
loss = loss / k
surr_loss = surr_loss/ k
# if step %2==0:
# optim.zero_grad()
# loss.backward(retain_graph=True)
# optim.step()
optim_net.zero_grad()
net_loss.backward(retain_graph=True)
optim_net.step()
# optim_surr.zero_grad()
# surr_loss.backward(retain_graph=True)
# optim_surr.step()
# print (torch.mean(f).cpu().data.numpy())
# plot_posteriors(name=str(step))
# fsdf
# kl_batch = compute_kl_batch(x,probs)
if step%500==0:
print (step, 'f:', torch.mean(f).cpu().data.numpy(), 'surr_loss:', surr_loss.cpu().data.detach().numpy(),
'theta dif:', L2_mixtureweights(true_mixture_weights,torch.softmax(
needsoftmax_mixtureweight, dim=0).cpu().data.detach().numpy()))
# if step %5000==0:
# print (torch.softmax(needsoftmax_mixtureweight, dim=0).cpu().data.detach().numpy())
# # test_samp, test_cluster = sample_true2()
# # print (test_cluster.cpu().data.numpy(), test_samp.cpu().data.numpy(), torch.softmax(encoder.net(test_samp.cuda().view(1,1)), dim=1))
# print ()
if step > 0:
L2_losses.append(L2_mixtureweights(true_mixture_weights,torch.softmax(
needsoftmax_mixtureweight, dim=0).cpu().data.detach().numpy()))
steps_list.append(step)
surr_losses.append(surr_loss.cpu().data.detach().numpy())
inf_error, kl_error = inference_error(needsoftmax_mixtureweight)
inf_losses.append(inf_error)
inf_losses_kl.append(kl_error)
kl_batch = compute_kl_batch(x,probs,needsoftmax_mixtureweight)
kl_losses_2.append(kl_batch)
logpx = copmute_logpx(x, needsoftmax_mixtureweight)
logpx_list.append(logpx)
f_list.append(torch.mean(f).cpu().data.detach().numpy())
logpxz_list.append(torch.mean(logpxz).cpu().data.detach().numpy())
logprob_cluster_list.append(torch.mean(logprob_cluster).cpu().data.detach().numpy())
# i_feel_like_it = 1
# if i_feel_like_it:
if len(inf_losses) > 0:
print ('probs', probs[0])
print('logpxz', logpxz[0])
print('pred', surr_pred[0])
print ('dif', logpxz.detach()[0]-surr_pred.detach()[0])
print ('logq', logprob_cluster[0])
print ('dif*logq', (logpxz.detach()[0]-surr_pred.detach()[0])*logprob_cluster[0])
output= torch.mean((logpxz.detach()-surr_pred.detach()) * logprob_cluster, dim=0)[0]
output2 = torch.mean(surr_pred, dim=0)[0]
output3 = torch.mean(logprob_cluster, dim=0)[0]
# input_ = torch.mean(probs, dim=0) #[0]
# print (probs.shape)
# print (output.shape)
# print (input_.shape)
grad_reinforce = torch.autograd.grad(outputs=output, inputs=(probs), retain_graph=True)[0]
grad_reparam = torch.autograd.grad(outputs=output2, inputs=(probs), retain_graph=True)[0]
grad3 = torch.autograd.grad(outputs=output3, inputs=(probs), retain_graph=True)[0]
# print (grad)
# print (grad_reinforce.shape)
# print (grad_reparam.shape)
grad_reinforce = torch.mean(torch.abs(grad_reinforce))
grad_reparam = torch.mean(torch.abs(grad_reparam))
grad3 = torch.mean(torch.abs(grad3))
# print (grad_reinforce)
# print (grad_reparam)
# dfsfda
grad_reparam_list.append(grad_reparam.cpu().data.detach().numpy())
grad_reinforce_list.append(grad_reinforce.cpu().data.detach().numpy())
# grad_reinforce_list.append(grad_reinforce.cpu().data.detach().numpy())
print ('reparam:', grad_reparam.cpu().data.detach().numpy())
print ('reinforce:', grad_reinforce.cpu().data.detach().numpy())
print ('logqz grad:', grad3.cpu().data.detach().numpy())
print ('current mixuture weights')
print (torch.softmax(needsoftmax_mixtureweight, dim=0))
print()
# print ()
else:
grad_reparam_list.append(0.)
grad_reinforce_list.append(0.)
if len(surr_losses) > 3 and step %1000==0:
plot_curve(steps=steps_list, thetaloss=L2_losses,
infloss=inf_losses, surrloss=surr_losses,
grad_reinforce_list=grad_reinforce_list,
grad_reparam_list=grad_reparam_list,
f_list=f_list, logpxz_list=logpxz_list,
logprob_cluster_list=logprob_cluster_list,
inf_losses_kl=inf_losses_kl,
kl_losses_2=kl_losses_2,
logpx_list=logpx_list)
plot_posteriors(needsoftmax_mixtureweight)
plot_dist()
show_surr_preds()
# print (f)
# print (surr_pred)
#Understand surr preds
# if step %5000==0:
# if step ==0:
# fasdf
data_dict = {}
data_dict['steps'] = steps_list
data_dict['losses'] = L2_losses
# save_dir = home+'/Documents/Grad_Estimators/GMM/'
with open( exp_dir+"data_simplax.p", "wb" ) as f:
pickle.dump(data_dict, f)
print ('saved data')
def get_policy(obs):
logits = logits_net(obs)
return Categorical(logits=logits)
def __init__(self, probs=None, logits=None):
self._categorical = Categorical(probs, logits)
batch_shape = self._categorical.probs.size()[:-1]
event_shape = self._categorical.probs.size()[-1:]
super(OneHotCategorical, self).__init__(batch_shape, event_shape)
def sample_candidates(model,
n_samples: int,
encoder_output,
masks: Dict[str, Tensor],
max_output_length: int,
labels: dict = None):
"""
Sample n_samples sequences from the model
In each decoding step, find the k most likely partial hypotheses.
:param decoder:
:param size: size of the beam
:param encoder_output:
:param masks:
:param max_output_length:
:return:
- stacked_output: dim?,
- scores: dim?
"""
# init
transformer = model.is_transformer
any_mask = next(iter(masks.values()))
batch_size = any_mask.size(0)
att_vectors = None # not used for Transformer
device = encoder_output.device
masks.pop("trg", None) # mutating one of the inputs is not good
# Recurrent models only: initialize RNN hidden state
if not transformer and model.decoder.bridge_layer is not None:
hidden = model.decoder.bridge_layer(encoder_output.hidden)
else:
hidden = None
# tile encoder states and decoder initial states beam_size times
if hidden is not None:
# layers x batch*k x dec_hidden_size
hidden = tile(hidden, n_samples, dim=1)
# encoder_output: batch*k x src_len x enc_hidden_size
encoder_output.tile(n_samples, dim=0)
masks = {k: tile(v, n_samples, dim=0) for k, v in masks.items()}
# Transformer only: create target mask
masks["trg"] = any_mask.new_ones([1, 1, 1]) if transformer else None
# the structure holding all batch_size * k partial hypotheses
alive_seq = torch.full((batch_size * n_samples, 1),
model.bos_index,
dtype=torch.long,
device=device)
# the structure indicating, for each hypothesis, whether it has
# encountered eos yet (if it has, stop updating the hypothesis
# likelihood)
is_finished = torch.zeros(batch_size * n_samples,
dtype=torch.bool,
device=device)
# for each (batch x n_samples) sequence, there is a log probability
seq_probs = torch.zeros(batch_size * n_samples, device=device)
for step in range(1, max_output_length + 1):
dec_input = alive_seq if transformer \
else alive_seq[:, -1].view(-1, 1)
# decode a step
probs, hidden, att_scores, att_vectors = model.decode(
trg_input=dec_input,
encoder_output=encoder_output,
masks=masks,
decoder_hidden=hidden,
prev_att_vector=att_vectors,
unroll_steps=1,
labels=labels,
generate="true")
# batch*k x trg_vocab
# probs = model.decoder.gen_func(logits[:, -1], dim=-1).squeeze(1)
next_ids = Categorical(probs).sample().unsqueeze(1) # batch*k x 1
next_scores = probs.gather(1, next_ids).squeeze(1) # batch*k
seq_probs = torch.where(is_finished, seq_probs,
seq_probs + next_scores.log())
# append latest prediction
# batch_size*k x hyp_len
alive_seq = torch.cat([alive_seq, next_ids], -1)
# update which hypotheses are finished
is_finished = is_finished | next_ids.eq(model.eos_index).squeeze(1)
if is_finished.all():
break
# final_outputs: batch x n_samples x len
final_outputs = alive_seq.view(batch_size, n_samples, -1)
seq_probs = seq_probs.view(batch_size, n_samples)
return final_outputs, seq_probs
def loss(actions_logits, action, discounted_reward):
distribution = Categorical(logits=actions_logits)
return (-distribution.log_prob(action) * discounted_reward).view(-1) # - (distribution.entropy() * (10 ** -2)) # TODO questo iperparametro va messo in un altro modo
def sample_reinforce_given_class(logits, samp):
dist = Categorical(logits=logits)
logprob = dist.log_prob(samp)
return logprob
class ExpRelaxedCategorical(Distribution):
r"""
Creates a ExpRelaxedCategorical parameterized by `probs` and `temperature`.
Returns the log of a point in the simplex. Based on the interface to OneHotCategorical.
Implementation based on [1].
See also: :func:`torch.distributions.OneHotCategorical`
Args:
temperature (Tensor): relaxation temperature
probs (Tensor): event probabilities
logits (Tensor): the log probability of each event.
[1] The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables
(Maddison et al, 2017)
[2] Categorical Reparametrization with Gumbel-Softmax
(Jang et al, 2017)
"""
arg_constraints = {'probs': constraints.simplex}
support = constraints.real
has_rsample = True
def __init__(self, temperature, probs=None, logits=None, validate_args=None):
self._categorical = Categorical(probs, logits)
self.temperature = temperature
batch_shape = self._categorical.batch_shape
event_shape = self._categorical.param_shape[-1:]
super(ExpRelaxedCategorical, self).__init__(batch_shape, event_shape, validate_args=validate_args)
def _new(self, *args, **kwargs):
return self._categorical._new(*args, **kwargs)
@property
def param_shape(self):
return self._categorical.param_shape
@property
def logits(self):
return self._categorical.logits
@property
def probs(self):
return self._categorical.probs
def rsample(self, sample_shape=torch.Size()):
sample_shape = torch.Size(sample_shape)
uniforms = clamp_probs(self.logits.new(self._extended_shape(sample_shape)).uniform_())
gumbels = -((-(uniforms.log())).log())
scores = (self.logits + gumbels) / self.temperature
return scores - _log_sum_exp(scores)
def log_prob(self, value):
K = self._categorical._num_events
if self._validate_args:
self._validate_sample(value)
logits, value = broadcast_all(self.logits, value)
log_scale = (self.temperature.new(self.temperature.shape).fill_(K).lgamma() -
self.temperature.log().mul(-(K - 1)))
score = logits - value.mul(self.temperature)
score = (score - _log_sum_exp(score)).sum(-1)
return score + log_scale
class DIPTransform:
def __init__(self,
image_shape,
num_iters,
stop_iters,
input_depth=32,
input_noise_std=0,
optimizer='adam',
lr=1e-2,
plot_every=0,
device='cpu'):
"""
:param image_shape: image shape (CxHxW)
:param num_iters: number of iterations to overfit
:param stop_iters: dict int->float specifying categorical distribution over percentages in (0, 100] representing the probability that the transform runs for KEY/100 * NUM_ITERS iters.
:param input_depth: depth of random input. Default value taken from paper.
:param input_noise_std: stdev of noise added to base random input at each iter. They say in the paper that this helps sometimes (seemingly using 0.03), but default is 0 (no noise).
:param optimizer: supported optimizers are 'adam' and 'LBFGS'
:param lr: learning rate. Per paper and code, 1e-2 works best.
:param plot_every: how often to save images. Doesn't save images if set to 0 (default).
:device: 'cuda' to use GPU if available.
For example, with NUM_ITERS = 100 and STOP_ITERS = {10: 0.5, 50: 0.3, 100: 0.2}, there is a 50% chance that the transform runs for 10 iterations, a 30% chance it runs for 50 iterations, and a 20% chance it runs for the full 100 iterations.
"""
self.iters, probs = zip(*stop_iters.items())
self.probs = Categorical(torch.tensor(probs))
self.num_iters = num_iters
self.image_shape = image_shape
self.input_depth = input_depth
self.input_noise_std = input_noise_std
self.plot_every = plot_every
self.device = device
self.optimizer = optimizer
self.lr = lr
self.loss = torch.nn.MSELoss()
# const strings from provided DIP code
self.opt_over = 'net'
self.const_input = 'noise'
# initialize network
self.net = skip(input_depth,
3,
num_channels_down=[8, 16, 32],
num_channels_up=[8, 16, 32],
num_channels_skip=[0, 0, 4],
upsample_mode='bilinear',
need_sigmoid=True,
need_bias=True,
pad='zeros',
act_fun='LeakyReLU').to(self.device)
def sample_iters(self):
return self.iters[self.probs.sample()] * self.num_iters // 100
def run(self, image, num_iters):
assert image.shape[
1:] == self.image_shape, 'Wrong shape. Expected {}, got {}.'.format(
self.image_shape, image.shape[1:])
# run net for num_iters iterations
net_input = get_noise(self.input_depth, self.const_input,
self.image_shape[1:]).to(self.device)
net_input_saved = net_input.detach().clone()
noise = net_input.detach().clone()
p = get_params(self.opt_over, self.net, net_input)
#def local_closure(iter_num):
# self.closure(net_input_saved, image, noise, iter_num)
lambda_closure = lambda iter_num: self.closure(net_input_saved, image,
noise, iter_num)
optimize(self.optimizer,
p,
lambda_closure,
self.lr,
num_iters,
pass_iter=True)
transformed = self.net(net_input)
return transformed
def closure(self, net_input_saved, image, noise, iter_num):
net_input = net_input_saved
if self.input_noise_std > 0:
net_input = net_input_saved + (noise.normal_() *
self.input_noise_std)
out = self.net(net_input)
total_loss = self.loss(out, image)
total_loss.backward()
#print(total_loss)
if self.plot_every > 0 and iter_num % self.plot_every == 0 and total_loss < 0.01:
out_np = torch_to_np(out)
plot_image_grid([np.clip(out_np, 0, 1)],
factor=4,
nrow=1,
show=False,
save_path=f'results_dip/imgs/{iter_num}.png')
# maybe log loss here?
def __call__(self, sample):
"""
Takes in, transforms, and returns PIL image given by SAMPLE. Transformation is a random number of iterations of DIP.
Distribution of number of iterations is specified when the transform is initialized.
"""
torch_img = np_to_torch(tmp := pil_to_np(sample)).to(self.device)
plot_image_grid([np.clip(tmp, 0, 1)],
factor=4,
nrow=1,
show=False,
save_path='results_dip/imgs/true.png') # TODO remove
num_iters = self.sample_iters()
transformed = self.run(torch_img, num_iters)
return np_to_pil(torch_to_np(transformed))
class OneHotCategorical(Distribution):
r"""
Creates a one-hot categorical distribution parameterized by :attr:`probs` or
:attr:`logits`.
Samples are one-hot coded vectors of size ``probs.size(-1)``.
.. note:: :attr:`probs` will be normalized to be summing to 1.
See also: :func:`torch.distributions.Categorical` for specifications of
:attr:`probs` and :attr:`logits`.
Example::
>>> m = OneHotCategorical(torch.tensor([ 0.25, 0.25, 0.25, 0.25 ]))
>>> m.sample() # equal probability of 0, 1, 2, 3
tensor([ 0., 0., 0., 1.])
Args:
probs (Tensor): event probabilities
logits (Tensor): event log probabilities
"""
arg_constraints = {'probs': constraints.simplex}
support = constraints.simplex
has_enumerate_support = True
def __init__(self, probs=None, logits=None, validate_args=None):
self._categorical = Categorical(probs, logits)
batch_shape = self._categorical.batch_shape
event_shape = self._categorical.param_shape[-1:]
super(OneHotCategorical, self).__init__(batch_shape, event_shape, validate_args=validate_args)
def _new(self, *args, **kwargs):
return self._categorical._new(*args, **kwargs)
@property
def probs(self):
return self._categorical.probs
@property
def logits(self):
return self._categorical.logits
@property
def mean(self):
return self._categorical.probs
@property
def variance(self):
return self._categorical.probs * (1 - self._categorical.probs)
@property
def param_shape(self):
return self._categorical.param_shape
def sample(self, sample_shape=torch.Size()):
sample_shape = torch.Size(sample_shape)
probs = self._categorical.probs
one_hot = probs.new(self._extended_shape(sample_shape)).zero_()
indices = self._categorical.sample(sample_shape)
if indices.dim() < one_hot.dim():
indices = indices.unsqueeze(-1)
return one_hot.scatter_(-1, indices, 1)
def log_prob(self, value):
if self._validate_args:
self._validate_sample(value)
indices = value.max(-1)[1]
return self._categorical.log_prob(indices)
def entropy(self):
return self._categorical.entropy()
def enumerate_support(self):
n = self.event_shape[0]
values = self._new((n, n))
torch.eye(n, out=values)
values = values.view((n,) + (1,) * len(self.batch_shape) + (n,))
return values.expand((n,) + self.batch_shape + (n,))
def get_pi_value_and_aux_value(self, x):
hidden = self.network(x.permute((0, 3, 1, 2)) / 255.0)
return Categorical(logits=self.actor(hidden)), self.critic(hidden.detach()), self.aux_critic(hidden)
