def forward(self, frame, policy):
# x: [B,2,84,84]
self.B = frame.size()[0]
# policy = policies[0]
# print (frame.size())
# fds
# print (frame.size())
#Predict mask
mask = self.predict_mask(frame) #[B,2,210,160]
# print (mask.size())
mask = mask.repeat(1,3,1,1)
# print (mask.size())
# fsad
#frame: [B,6,210,160]
masked_frame = frame * mask
log_dist_mask = policy.action_logdist(masked_frame)
log_dist_true = policy.action_logdist(frame)
action_dist_kl = torch.sum((log_dist_true - log_dist_mask)*torch.exp(log_dist_true), dim=1) #[B]
action_dist_kl = torch.mean(action_dist_kl) # * 1000
mask = mask.view(self.B, -1)
mask_sum = torch.mean(torch.sum(mask, dim=1)) * .000001
loss = action_dist_kl + mask_sum
return loss, action_dist_kl, mask_sum
def forward(self, frame, DQNs):
# x: [B,2,84,84]
self.B = frame.size()[0]
blurred_frame = self.blur_frame(frame)
#Predict mask
blur_weighting = self.predict_precision(frame) #[B,1,480,640]
blur_weighting = blur_weighting.repeat(1,3,1,1)
mixed_frame = frame * blur_weighting + (1.-blur_weighting)*blurred_frame
difs= []
for i in range(len(DQNs)):
q_mask = DQNs[i](mixed_frame)
q_real = DQNs[i](frame)
dif = torch.mean((q_mask-q_real)**2) #[B,A]
difs.append(dif)
difs = torch.stack(difs)
dif = torch.mean(difs)
blur_weighting = blur_weighting.view(self.B, -1)
mask_sum = torch.mean(torch.sum(blur_weighting, dim=1)) * .0000001
loss = dif + mask_sum
return loss, dif, mask_sum
def calculate_loss(self, x, beta=1., average=False):
'''
:param x: input image(s)
:param beta: a hyperparam for warmup
:param average: whether to average loss or not
:return: value of a loss function
'''
# pass through VAE
x_mean, x_logvar, z_q, z_q_mean, z_q_logvar = self.forward(x)
# RE
if self.args.input_type == 'binary':
RE = log_Bernoulli(x, x_mean, dim=1)
elif self.args.input_type == 'gray' or self.args.input_type == 'continuous':
RE = -log_Logistic_256(x, x_mean, x_logvar, dim=1)
else:
raise Exception('Wrong input type!')
# KL
log_p_z = self.log_p_z(z_q)
log_q_z = log_Normal_diag(z_q, z_q_mean, z_q_logvar, dim=1)
KL = -(log_p_z - log_q_z)
loss = - RE + beta * KL
if average:
loss = torch.mean(loss)
RE = torch.mean(RE)
KL = torch.mean(KL)
return loss, RE, KL
def prepare_model():
since = time.time()
num_epochs = 1
for epoch in range(num_epochs):
print('Epoch {}/{}'.format(epoch, num_epochs - 1))
print('-' * 10)
# Each epoch has a training and validation phase
for phase in ['train']:
mean = torch.zeros(3)
std = torch.zeros(3)
# Iterate over data.
for data in dataloaders[phase]:
# get the inputs
inputs, labels = data
now_batch_size,c,h,w = inputs.shape
mean += torch.sum(torch.mean(torch.mean(inputs,dim=3),dim=2),dim=0)
std += torch.sum(torch.std(inputs.view(now_batch_size,c,h*w),dim=2),dim=0)
print(mean/dataset_sizes['train'])
print(std/dataset_sizes['train'])
time_elapsed = time.time() - since
print('Training complete in {:.0f}m {:.0f}s'.format(
time_elapsed // 60, time_elapsed % 60))
return
def evaluate(model):
model.eval()
running_loss = [0., 0., 0.]
epoch_loss = 0.
display_step = 100
for batch_idx, (x, c) in enumerate(test_loader):
x, c = x.to(device), c.to(device)
log_p, logdet = model(x, c)
log_p, logdet = torch.mean(log_p), torch.mean(logdet)
loss = -(log_p + logdet)
running_loss[0] += loss.item() / display_step
running_loss[1] += log_p.item() / display_step
running_loss[2] += logdet.item() / display_step
epoch_loss += loss.item()
if (batch_idx + 1) % 100 == 0:
print('Global Step : {}, [{}, {}] [Log pdf, Log p(z), Log Det] : {}'
.format(global_step, epoch, batch_idx + 1, np.array(running_loss)))
running_loss = [0., 0., 0.]
del x, c, log_p, logdet, loss
del running_loss
epoch_loss /= len(test_loader)
print('Evaluation Loss : {:.4f}'.format(epoch_loss))
return epoch_loss
def forward(self, frame, DQNs):
# x: [B,2,84,84]
self.B = frame.size()[0]
#Predict mask
mask = self.predict_mask(frame) #[B,2,210,160]
# print (mask.size())
mask = mask.repeat(1,3,1,1)
#frame: [B,6,210,160]
masked_frame = frame * mask
difs= []
for i in range(len(DQNs)):
q_mask = DQNs[i](masked_frame)
q_real = DQNs[i](frame)
dif = torch.mean((q_mask-q_real)**2) #[B,A]
difs.append(dif)
difs = torch.stack(difs)
dif = torch.mean(difs)
mask = mask.view(self.B, -1)
mask_sum = torch.mean(torch.sum(mask, dim=1)) * .0000001
loss = dif + mask_sum
return loss, dif, mask_sum
def predictive_elbo(self, x, k, s):
# No pW or qW
self.B = x.size()[0] #batch size
# self.k = k #number of z samples aka particles P
# self.s = s #number of W samples
elbo1s = []
for i in range(s):
Ws, logpW, logqW = self.sample_W() #_ , [1], [1]
mu, logvar = self.encode(x) #[B,Z]
z, logpz, logqz = self.sample_z(mu, logvar, k=k) #[P,B,Z], [P,B]
x_hat = self.decode(Ws, z) #[P,B,X]
logpx = log_bernoulli(x_hat, x) #[P,B]
elbo = logpx + logpz - logqz #[P,B]
if k>1:
max_ = torch.max(elbo, 0)[0] #[B]
elbo = torch.log(torch.mean(torch.exp(elbo - max_), 0)) + max_ #[B]
# elbo1 = elbo1 #+ (logpW - logqW)*.00000001 #[B], logp(x|W)p(w)/q(w)
elbo1s.append(elbo)
elbo1s = torch.stack(elbo1s) #[S,B]
if s>1:
max_ = torch.max(elbo1s, 0)[0] #[B]
elbo1 = torch.log(torch.mean(torch.exp(elbo1s - max_), 0)) + max_ #[B]
elbo = torch.mean(elbo1s) #[1]
return elbo#, logprobs2[0], logprobs2[1], logprobs2[2], logprobs2[3], logprobs2[4]
def forward(self, z_seq, a_seq, term_seq):
# x: [B,2,84,84]
# T = x.size()[0]
h = torch.zeros(1,self.h_size).cuda()
z_losses = []
term_losses = []
for t in range(len(term_seq)-1):
inter = self.encode_az(a_seq[t], z_seq[t])
h = self.update_h(h, inter)
z_pred, term_pred = self.predict_output(h, inter)
z_loss = torch.mean((z_seq[t+1] - z_pred)**2)
term_loss = F.binary_cross_entropy_with_logits(input=term_pred, target=term_seq[t+1])
z_losses.append(z_loss)
term_losses.append(term_loss)
z_loss = torch.mean(torch.stack(z_losses))
term_loss = torch.mean(torch.stack(term_losses))
loss = z_loss + term_loss
return loss, z_loss, term_loss
def forward(self, x, k=1):
self.B = x.size()[0]
mu, logvar = self.encode(x)
z, logpz, logqz = self.sample(mu, logvar, k=k) #[P,B,Z]
x_hat = self.decode(z) #[PB,X]
x_hat = x_hat.view(k, self.B, -1)
# print x_hat.size()
# print x_hat.size()
# print x.size()
logpx = log_bernoulli(x_hat, x) #[P,B]
elbo = logpx + logpz - logqz #[P,B]
if k>1:
max_ = torch.max(elbo, 0)[0] #[B]
elbo = torch.log(torch.mean(torch.exp(elbo - max_), 0)) + max_ #[B]
elbo = torch.mean(elbo) #[1]
#for printing
logpx = torch.mean(logpx)
logpz = torch.mean(logpz)
logqz = torch.mean(logqz)
self.x_hat_sigmoid = F.sigmoid(x_hat)
return elbo, logpx, logpz, logqz
def get_paf_and_heatmap(model, img_raw, scale_search, param_stride=8, box_size=368):
multiplier = [scale * box_size / img_raw.shape[0] for scale in scale_search]
heatmap_avg = torch.zeros((len(multiplier), 19, img_raw.shape[0], img_raw.shape[1])).cuda()
paf_avg = torch.zeros((len(multiplier), 38, img_raw.shape[0], img_raw.shape[1])).cuda()
for i, scale in enumerate(multiplier):
img_test = cv2.resize(img_raw, (0, 0), fx=scale, fy=scale, interpolation=cv2.INTER_CUBIC)
img_test_pad, pad = pad_right_down_corner(img_test, param_stride, param_stride)
img_test_pad = np.transpose(np.float32(img_test_pad[:, :, :, np.newaxis]), (3, 2, 0, 1)) / 256 - 0.5
feed = Variable(torch.from_numpy(img_test_pad)).cuda()
output1, output2 = model(feed)
print(output1.size())
print(output2.size())
heatmap = nn.UpsamplingBilinear2d((img_raw.shape[0], img_raw.shape[1])).cuda()(output2)
paf = nn.UpsamplingBilinear2d((img_raw.shape[0], img_raw.shape[1])).cuda()(output1)
heatmap_avg[i] = heatmap[0].data
paf_avg[i] = paf[0].data
heatmap_avg = torch.transpose(torch.transpose(torch.squeeze(torch.mean(heatmap_avg, 0)), 0, 1), 1, 2).cuda()
heatmap_avg = heatmap_avg.cpu().numpy()
paf_avg = torch.transpose(torch.transpose(torch.squeeze(torch.mean(paf_avg, 0)), 0, 1), 1, 2).cuda()
paf_avg = paf_avg.cpu().numpy()
return paf_avg, heatmap_avg
def forward(self, x, k):
self.B = x.size()[0] #batch size
#Encode
mu, logvar = self.encode(x) #[B,Z]
z, logpz, logqz = self.sample(mu, logvar, k=k) #[P,B,Z], [P,B]
#Decode
x_hat = self.decode(z) #[P,B,X]
logpx = log_bernoulli(x_hat, x) #[P,B]
#Compute elbo
elbo = logpx + logpz - logqz #[P,B]
if k>1:
max_ = torch.max(elbo, 0)[0] #[B]
elbo = torch.log(torch.mean(torch.exp(elbo - max_), 0)) + max_ #[B]
elbo = torch.mean(elbo) #[1]
logpx = torch.mean(logpx)
logpz = torch.mean(logpz)
logqz = torch.mean(logqz)
return elbo, logpx, logpz, logqz
def train_step(self, state_batch, mcts_probs, winner_batch, lr):
"""perform a training step"""
# wrap in Variable
if self.use_gpu:
state_batch = Variable(torch.FloatTensor(state_batch).cuda())
mcts_probs = Variable(torch.FloatTensor(mcts_probs).cuda())
winner_batch = Variable(torch.FloatTensor(winner_batch).cuda())
else:
state_batch = Variable(torch.FloatTensor(state_batch))
mcts_probs = Variable(torch.FloatTensor(mcts_probs))
winner_batch = Variable(torch.FloatTensor(winner_batch))
# zero the parameter gradients
self.optimizer.zero_grad()
# set learning rate
set_learning_rate(self.optimizer, lr)
# forward
log_act_probs, value = self.policy_value_net(state_batch)
# define the loss = (z - v)^2 - pi^T * log(p) + c||theta||^2
# Note: the L2 penalty is incorporated in optimizer
value_loss = F.mse_loss(value.view(-1), winner_batch)
policy_loss = -torch.mean(torch.sum(mcts_probs*log_act_probs, 1))
loss = value_loss + policy_loss
# backward and optimize
loss.backward()
self.optimizer.step()
# calc policy entropy, for monitoring only
entropy = -torch.mean(
torch.sum(torch.exp(log_act_probs) * log_act_probs, 1)
)
return loss.data[0], entropy.data[0]
def forward(self, x, k=1):
self.k = k
self.B = x.size()[0]
mu, logvar = self.encode(x)
z, logpz, logqz = self.sample(mu, logvar, k=k)
x_hat, logpW, logqW = self.decode(z)
logpx = log_bernoulli(x_hat, x) #[P,B]
elbo = logpx + logpz - logqz + (logpW - logqW)*.00000001 #[P,B]
if k>1:
max_ = torch.max(elbo, 0)[0] #[B]
elbo = torch.log(torch.mean(torch.exp(elbo - max_), 0)) + max_ #[B]
elbo = torch.mean(elbo) #[1]
#for printing
logpx = torch.mean(logpx)
logpz = torch.mean(logpz)
logqz = torch.mean(logqz)
self.x_hat_sigmoid = F.sigmoid(x_hat)
return elbo, logpx, logpz, logqz, logpW, logqW
def forward(self, x, k=1):
self.B = x.size()[0]
mu, logvar = self.encode(x)
z, logpz, logqz = self.sample(mu, logvar, k=k)
# x_hat = self.decode(z)
x_mean, x_logvar = self.decode(z) #[P,B,1]
# logpx = log_bernoulli(x_hat, x) #[P,B]
logpx = lognormal_decoder(x, x_mean, x_logvar) #[P,B]
# elbo = logpx + .00000001*logpz - logqz #[P,B]
elbo = logpx + logpz - logqz #[P,B]
if k>1:
max_ = torch.max(elbo, 0)[0] #[B]
elbo = torch.log(torch.mean(torch.exp(elbo - max_), 0)) + max_ #[B]
elbo = torch.mean(elbo) #[1]
#for printing
logpx = torch.mean(logpx)
logpz = torch.mean(logpz)
logqz = torch.mean(logqz)
# self.x_hat_sigmoid = F.sigmoid(x_hat)
return elbo, logpx, logpz, logqz
def singleTagLoss(pred_tag, keypoints):
"""
associative embedding loss for one image
"""
eps = 1e-6
tags = []
pull = 0
for i in keypoints:
tmp = []
for j in i:
if j[1]>0:
tmp.append(pred_tag[j[0]])
if len(tmp) == 0:
continue
tmp = torch.stack(tmp)
tags.append(torch.mean(tmp, dim=0))
pull = pull + torch.mean((tmp - tags[-1].expand_as(tmp))**2)
if len(tags) == 0:
return make_input(torch.zeros([1]).float()), make_input(torch.zeros([1]).float())
tags = torch.stack(tags)[:,0]
num = tags.size()[0]
size = (num, num, tags.size()[1])
A = tags.unsqueeze(dim=1).expand(*size)
B = A.permute(1, 0, 2)
diff = A - B
diff = torch.pow(diff, 2).sum(dim=2)[:,:,0]
push = torch.exp(-diff)
push = (torch.sum(push) - num)
return push/((num - 1) * num + eps) * 0.5, pull/(num + eps)
def global_pooling(x): # input x [n, c, h, w] # output l [n, c] s = torch.mean(x, dim=-1) s = torch.mean(s, dim=-1) return s
def angle_length_loss(y_pred, y_true, weights):
y_true = y_true.permute(0, 2, 3, 1)
y_pred = y_pred.permute(0, 2, 3, 1)
weights = weights.permute(0, 2, 3, 1)
# Single threshold
# score_per_bundle = {}
# bundles = ExpUtils.get_bundle_names(HP.CLASSES)[1:]
nr_of_classes = int(y_true.shape[-1] / 3.)
scores = torch.zeros(nr_of_classes)
for idx in range(nr_of_classes):
y_pred_bund = y_pred[:, :, :, (idx * 3):(idx * 3) + 3].contiguous()
y_true_bund = y_true[:, :, :, (idx * 3):(idx * 3) + 3].contiguous() # [x,y,z,3]
weights_bund = weights[:, :, :, (idx * 3)].contiguous() # [x,y,z]
angles = PytorchUtils.angle_last_dim(y_pred_bund, y_true_bund)
angles_weighted = angles / weights_bund
#norm lengths to 0-1 to be more equal to angles?? -> peaks are already around 1 -> ok
lengths = (torch.norm(y_pred_bund, 2., -1) - torch.norm(y_true_bund, 2, -1)) ** 2
lenghts_weighted = lengths * weights_bund
# Divide by weights.max otherwise lens would be way bigger
# Flip angles to make it a minimization problem
combined = -angles_weighted + lenghts_weighted / weights_bund.max()
scores[idx] = torch.mean(combined)
return torch.mean(scores)
def forward(self, frame, policies):
# x: [B,2,84,84]
self.B = frame.size()[0]
#Predict mask
pre_mask = self.predict_mask_nosigmoid(frame)
mask = F.sigmoid(pre_mask)
masked_frame = frame * mask
kls = []
for i in range(len(policies)):
policy = policies[i]
log_dist_mask = policy.action_logdist(masked_frame)
log_dist_true = policy.action_logdist(frame)
action_dist_kl = torch.sum((log_dist_true - log_dist_mask)*torch.exp(log_dist_true), dim=1) #[B]
action_dist_kl = torch.mean(action_dist_kl) # * 1000
kls.append(action_dist_kl)
kls = torch.stack(kls) #[policies, B]
action_dist_kl = torch.mean(action_dist_kl) #[1] #over batch and over policies
pre_mask = pre_mask.view(self.B, -1)
mask_cost = torch.abs(pre_mask + 20)
# mask_sum = torch.mean(torch.sum(mask_cost, dim=1)) * .00001
# mask_cost = torch.mean(mask_cost) * .00001
mask_cost = torch.mean(mask_cost) * .01
loss = action_dist_kl + mask_cost
return loss, action_dist_kl, mask_cost
def encode_and_logprob(self, x):
for i in range(len(self.first_half_weights)-1):
x = self.act_func(self.first_half_weights[i](x))
# pre_act = self.first_half_weights[i](x) #[B,D]
# # pre_act_with_noise = Variable(torch.randn(1, self.arch_2[i][1]).type(self.dtype)) * pre_act
# probs = torch.ones(1, self.arch_2[i][1]) * .5
# pre_act_with_noise = Variable(torch.bernoulli(probs).type(self.dtype)) * pre_act
# x = self.act_func(pre_act_with_noise)
mean = self.first_half_weights[-1](x)
logvar = self.q_logvar(x)
# print (logvar)
#Sample
eps = Variable(torch.randn(1, self.z_size)) #.type(self.dtype))
# x = (torch.sqrt(torch.exp(W_logvars)) * eps) + W_means
x = (torch.exp(.5*logvar) * eps) + mean
logq = -torch.mean( logvar.sum(1) + ((x - mean).pow(2)/torch.exp(logvar)).sum(1))
logp = torch.mean( x.pow(2).sum(1))
return x, logq+logp
def calculate_loss(self, x, beta=1., average=False):
# pass through VAE
x_mean, x_logvar, z1_q, z1_q_mean, z1_q_logvar, z2_q, z2_q_mean, z2_q_logvar, z1_p_mean, z1_p_logvar = self.forward(x)
# RE
if self.args.input_type == 'binary':
RE = log_Bernoulli(x, x_mean, dim=1)
elif self.args.input_type == 'gray' or self.args.input_type == 'continuous':
RE = -log_Logistic_256(x, x_mean, x_logvar, dim=1)
else:
raise Exception('Wrong input type!')
# KL
log_p_z1 = log_Normal_diag(z1_q, z1_p_mean, z1_p_logvar, dim=1)
log_q_z1 = log_Normal_diag(z1_q, z1_q_mean, z1_q_logvar, dim=1)
log_p_z2 = self.log_p_z2(z2_q)
log_q_z2 = log_Normal_diag(z2_q, z2_q_mean, z2_q_logvar, dim=1)
KL = -(log_p_z1 + log_p_z2 - log_q_z1 - log_q_z2)
# full loss
loss = -RE + beta * KL
if average:
loss = torch.mean(loss)
RE = torch.mean(RE)
KL = torch.mean(KL)
return loss, RE, KL
def setUp(self, length=3, factor=10, count=1000000,
seed=None, dtype=torch.float64, device=None):
'''Set up the test values.
Args:
length: Size of the vector.
factor: To multiply the mean and standard deviation.
count: Number of samples for Monte-Carlo estimation.
seed: Seed for the random number generator.
dtype: The data type.
device: In which device.
'''
if seed is not None:
torch.manual_seed(seed)
# input mean and covariance
self.mu = torch.randn(length, dtype=dtype, device=device) * factor
self.cov = rand.definite(length, dtype=dtype, device=device,
positive=True, semi=False, norm=factor**2)
self.var = self.cov.diag()
# Monte-Carlo estimation of the output mean and variance
normal = torch.distributions.MultivariateNormal(self.mu, self.cov)
out_samples = normal.sample((count,)).clamp_(min=0.0)
self.mc_mu = torch.mean(out_samples, dim=0)
self.mc_var = torch.var(out_samples, dim=0)
normal = torch.distributions.MultivariateNormal(self.mu * 0, self.cov)
out_samples = normal.sample((count,)).clamp_(min=0.0)
mean = torch.mean(out_samples, dim=0)
self.mc_zm_cov = cov(out_samples)
self.mc_zm_corr = self.mc_zm_cov + outer(mean)
def cos_sim(in0,in1):
in0_norm = normalize_tensor(in0)
in1_norm = normalize_tensor(in1)
N = in0.size()[0]
X = in0.size()[2]
Y = in0.size()[3]
return torch.mean(torch.mean(torch.sum(in0_norm*in1_norm,dim=1).view(N,1,X,Y),dim=2).view(N,1,1,Y),dim=3).view(N)
def torch_pearsonr(x, y): # https://github.com/pytorch/pytorch/issues/1254
mean_x = torch.mean(x)
mean_y = torch.mean(y)
xm = x.sub(mean_x)
ym = y.sub(mean_y)
r_num = xm.dot(ym)
r_den = torch.norm(xm, 2) * torch.norm(ym, 2)
r_val = r_num / r_den
return r_val
def forward(self, x=None, warmup=1., inf_net=None): #, k=1): #, marginf_type=0):
# x: [B,3,112,112]
# q: [B,L]
# inf type: 0 is both, 1 is only x, 2 is only y
# dec type: 0 is both, 1 is only x, 2 is only y
outputs = {}
if inf_net is None:
mu, logvar = self.inference_net(x)
else:
mu, logvar = inf_net.inference_net(x)
z, logpz, logqz = self.sample(mu, logvar)
z_dec = self.z_to_dec(z)
B = z_dec.shape[0]
# Decode Image
x_hat = self.image_decoder(z_dec)
alpha = torch.sigmoid(x_hat)
beta = Beta(alpha*self.beta_scale, (1.-alpha)*self.beta_scale)
x_noise = torch.clamp(x + torch.FloatTensor(x.shape).uniform_(0., 1./256.).cuda(), min=1e-5, max=1-1e-5)
# logpx = beta.log_prob(x + torch.FloatTensor(x.shape).uniform_(0., 1./256.).cuda()) #[120,3,112,112] # add uniform noise here
logpx = beta.log_prob(x_noise) #[120,3,112,112] # add uniform noise here
logpx = torch.sum(logpx.view(B, -1),1) # [PB] * self.w_logpx
# logpx = logpx * self.w_logpx
log_ws = logpx + logpz - logqz
outputs['logpx'] = torch.mean(logpx)
outputs['x_recon'] = alpha
outputs['welbo'] = torch.mean(logpx + warmup*( logpz - logqz))
outputs['elbo'] = torch.mean(log_ws)
outputs['logws'] = log_ws
outputs['z'] = z
outputs['logpz'] = torch.mean(logpz)
outputs['logqz'] = torch.mean(logqz)
outputs['logvar'] = logvar
# print (outputs['elbo'], outputs['welbo'], outputs['logpz'], outputs['logqz'])
# fafs
# if generate:
# # word_preds, sampled_words = self.text_generator.teacher_force(z_dec, generate=generate, embeder=self.encoder_embed)
# # if dec_type == 2:
# alpha = torch.sigmoid(self.image_decoder(z_dec))
# return outputs, alpha #, word_preds, sampled_words
return outputs
def ganLossG(self, d_fake):
if config.GAN_SETTING == "WGAN":
loss = -torch.mean(d_fake)
elif config.GAN_SETTING == "LSGAN":
loss = 0.5 * torch.mean((d_fake - 1)**2)
else:
real_labels = Variable(torch.ones(d_fake.size(0)).cuda())
fake_labels = Variable(torch.zeros(d_fake.size(0)).cuda())
loss = self.bce_loss(d_fake, real_labels)
return loss
def MVNError(output, gt):
outMean = torch.mean(output)
outStd = torch.std(output)
output = (output - outMean)/outStd
gtMean = torch.mean(gt)
gtStd = torch.std(gt)
gt = (gt - gtMean)/gtStd
d = output - gt
diff = torch.sqrt(torch.mean(d * d))
return diff
def forward(self, x=None, warmup=1., inf_net=None): #, k=1): #, marginf_type=0):
outputs = {}
B = x.shape[0]
if inf_net is None:
# mu, logvar = self.inference_net(x)
z, logits = self.q.sample(x)
else:
# mu, logvar = inf_net.inference_net(x)
z, logqz = inf_net.sample(x)
# print (z[0])
# b = harden(z)
# print (b[0])
# logpz = torch.sum( self.prior.log_prob(b), dim=1)
# print (logpz[0])
# print (logpz.shape)
# fdasf
probs_q = torch.sigmoid(logits)
probs_q = torch.clamp(probs_q, min=.00000001, max=.9999999)
probs_p = torch.ones(B, self.z_size).cuda() *.5
KL = probs_q*torch.log(probs_q/probs_p) + (1-probs_q)*torch.log((1-probs_q)/(1-probs_p))
KL = torch.sum(KL, dim=1)
# print (z.shape)
# Decode Image
x_hat = self.generator.forward(z)
alpha = torch.sigmoid(x_hat)
beta = Beta(alpha*self.beta_scale, (1.-alpha)*self.beta_scale)
x_noise = torch.clamp(x + torch.FloatTensor(x.shape).uniform_(0., 1./256.).cuda(), min=1e-5, max=1-1e-5)
logpx = beta.log_prob(x_noise) #[120,3,112,112] # add uniform noise here
logpx = torch.sum(logpx.view(B, -1),1) # [PB] * self.w_logpx
# print (logpx.shape,logpz.shape,logqz.shape)
# fsdfda
log_ws = logpx - KL #+ logpz - logqz
outputs['logpx'] = torch.mean(logpx)
outputs['x_recon'] = alpha
# outputs['welbo'] = torch.mean(logpx + warmup*( logpz - logqz))
outputs['welbo'] = torch.mean(logpx + warmup*(KL))
outputs['elbo'] = torch.mean(log_ws)
outputs['logws'] = log_ws
outputs['z'] = z
outputs['logpz'] = torch.zeros(1) #torch.mean(logpz)
outputs['logqz'] = torch.mean(KL)
# outputs['logvar'] = logvar
return outputs
def compute_accuracy(self, x, y, dataset):
if dataset == 'CelebA':
x = F.sigmoid(x)
predicted = self.threshold(x)
correct = (predicted == y).float()
accuracy = torch.mean(correct, dim=0) * 100.0
else:
_, predicted = torch.max(x, dim=1)
correct = (predicted == y).float()
accuracy = torch.mean(correct) * 100.0
return accuracy
def n_mpjpe(predicted, target):
"""
Normalized MPJPE (scale only), adapted from:
https://github.com/hrhodin/UnsupervisedGeometryAwareRepresentationLearning/blob/master/losses/poses.py
"""
assert predicted.shape == target.shape
norm_predicted = torch.mean(torch.sum(predicted**2, dim=3, keepdim=True), dim=2, keepdim=True)
norm_target = torch.mean(torch.sum(target*predicted, dim=3, keepdim=True), dim=2, keepdim=True)
scale = norm_target / norm_predicted
return mpjpe(scale * predicted, target)
def get_image_mean(img_tensor):
"""
:param img_tensor: torch.FloatTensor with shape [3, 480, 640]
:type img_tensor:
:return: torch.FloatTensor with shape [3]
:rtype:
"""
img_mean = torch.mean(img_tensor, 1)
img_mean = torch.mean(img_mean, 1)
return img_mean
def mse(self, pred, label, weight):
loss = weight * (pred - label) ** 2
return torch.mean(loss)
def train_NAVAR(data,
maxlags=5,
hidden_nodes=256,
dropout=0,
epochs=200,
learning_rate=1e-4,
batch_size=300,
lambda1=0,
val_proportion=0.0,
weight_decay=0,
check_every=1000,
hidden_layers=1,
normalize=True,
split_timeseries=False,
lstm=False):
"""
Trains a Neural Additive Vector Autoregression (NAVAR) model on time series data and scores the
potential causal links between variables.
Args:
data: ndarray
T (time points) x N (variables) input data
maxlags: int
Maximum number of time lags
hidden_nodes: int
Number of hidden nodes in each layers
dropout: float
Dropout probability in the hidden layers
epochs: int
Number of training epochs
learning_rate: float
Learning rate for Adam optimizer
batch_size: int
The size of the training batches
lambda1: float
Parameter for penalty to the contributions
val_proportion: float
Proportion of the dataset used for validation
weight_decay: float
Weight decay used in neural networks
check_every: int
Every 'check_every'th epoch we print training progress
hidden_layers: int
Number of hidden layers in the neural networks
normalize: bool
Indicates whether we should should normalize every variable
split_timeseries: int
If the original time series consists of multiple shorter time series, this argument should indicate the
original time series length. Otherwise should be zero.
lstm: bool
Indicates whether we should use the LSTM model (instead of MLP).
Returns:
causal_matrix: ndarray
N (variables) x N (variables) array containing the scores for every causal link.
causal_matrix[i, j] indicates the score for potential link i -> j
contributions: ndarray
N^2 x training_examples array containing the contributions from and to every variable
for every sample in the training_set
loss_val: float
Validation loss of the model after training
"""
# T is the number of time steps, N the number of variables
T, N = data.shape
# initialize the NAVAR model
if lstm:
model = NAVARLSTM(N,
hidden_nodes,
maxlags,
dropout=dropout,
hidden_layers=hidden_layers)
else:
model = NAVAR(N,
hidden_nodes,
maxlags,
dropout=dropout,
hidden_layers=hidden_layers)
# use Mean Squared Error and the Adam optimzer
criterion = torch.nn.MSELoss(reduction='mean')
optimizer = torch.optim.Adam(model.parameters(),
lr=learning_rate,
weight_decay=weight_decay)
# obtain the training and validation data
dataset = DataLoader(data,
maxlags,
normalize=normalize,
val_proportion=val_proportion,
split_timeseries=split_timeseries,
lstm=lstm)
X_train, Y_train = dataset.train_Xs, dataset.train_Ys
X_val, Y_val = dataset.val_Xs, dataset.val_Ys
# push model and data to GPU if available
if torch.cuda.is_available():
model = model.cuda()
X_train = X_train.cuda()
Y_train = Y_train.cuda()
if X_val is not None:
X_val = X_val.cuda()
Y_val = Y_val.cuda()
num_training_samples = X_train.shape[0]
total_loss = 0
loss_val = 0
# start of training loop
batch_counter = 0
for t in range(1, epochs + 1):
#obtain batches
batch_indeces_list = []
if batch_size < num_training_samples:
batch_perm = np.random.choice(num_training_samples,
size=num_training_samples,
replace=False)
for i in range(int(num_training_samples / batch_size) + 1):
start = i * batch_size
batch_i = batch_perm[start:start + batch_size]
if len(batch_i) > 0:
batch_indeces_list.append(batch_perm[start:start +
batch_size])
else:
batch_indeces_list = [np.arange(num_training_samples)]
for batch_indeces in batch_indeces_list:
batch_counter += 1
X_batch = X_train[batch_indeces]
Y_batch = Y_train[batch_indeces]
# forward pass to calculate predictions and contributions
predictions, contributions = model(X_batch)
# calculate the loss
loss_pred = criterion(predictions, Y_batch)
loss_l1 = (lambda1 / N) * torch.mean(
torch.sum(torch.abs(contributions), dim=1))
loss = loss_pred + loss_l1
total_loss += loss
# Zero gradients, perform a backward pass, and update the weights.
optimizer.zero_grad()
loss.backward()
optimizer.step()
# every 'check_every' epochs we calculate and print the validation loss
if t % check_every == 0:
model.eval()
if val_proportion > 0.0:
val_pred, val_contributions = model(X_val)
loss_val = criterion(val_pred, Y_val)
model.train()
print(
f'iteration {t}. Loss: {total_loss/batch_counter} Val loss: {loss_val}'
)
total_loss = 0
batch_counter = 0
# use the trained model to calculate the causal scores
model.eval()
# X_train can be too big to fit in the GPU, then this call raises "RuntimeError: CUDA out of memory."
if split_timeseries:
y_pred, contributions = model(X_train[:batch_size])
else:
y_pred, contributions = model(X_train)
causal_matrix = torch.std(contributions,
dim=0).view(N, N).detach().cpu().numpy()
return causal_matrix, contributions, loss_val
generator=generator1,
discriminator=discriminator1,
EP=EP,
arguments=arguments,
criterion=criterion,
conditional_gen=False,
source_num=1)
elif tr_method == 'ML':
if loss == 'Euclidean':
criterion = nn.MSELoss()
elif loss == 'Poisson':
eps = 1e-20
criterion = lambda lam, tar: torch.mean(-tar*torch.log(lam+eps) + lam)
generative_trainer(loader_mix=loader_mix,
train_loader=loader1,
generator=generator1,
EP=EP,
arguments=arguments,
criterion=criterion,
conditional_gen=False)
# save models
savepath = os.path.join(os.getcwd(), 'model_parameters')
if not os.path.exists(savepath):
os.mkdir(savepath)
ut.save_models([generator1], [discriminator1], exp_info,
savepath, arguments)
def top2gating(logits: Tensor, capacity_factor: float,
min_capacity: int) -> Tuple[Tensor, Tensor, Tensor, Tensor]:
"""Implements Top2Gating on logits."""
# everything is in fp32 in this function
gates = F.softmax(logits, dim=1)
capacity = _capacity(gates, torch.tensor(capacity_factor * 2),
torch.tensor(min_capacity))
# Create a mask for 1st's expert per token
indices1_s = torch.argmax(gates, dim=1)
num_experts = int(gates.shape[1])
mask1 = F.one_hot(indices1_s, num_classes=num_experts)
# Create a mask for 2nd's expert per token using Gumbel-max trick
# https://timvieira.github.io/blog/post/2014/07/31/gumbel-max-trick/
logits_w_noise = logits + gumbel_rsample(logits.shape,
device=logits.device)
# Replace top-expert with min value
logits_except1 = logits_w_noise.masked_fill(mask1.bool(), float("-inf"))
indices2_s = torch.argmax(logits_except1, dim=1)
mask2 = F.one_hot(indices2_s, num_classes=num_experts)
# Compute locations in capacity buffer
locations1 = torch.cumsum(mask1, dim=0) - 1
locations2 = torch.cumsum(mask2, dim=0) - 1
# Update 2nd's location by accounting for locations of 1st
locations2 += torch.sum(mask1, dim=0, keepdim=True)
# gating decisions
exp_counts = torch.sum(mask1, dim=0).detach().to('cpu')
# Compute l_aux
me = torch.mean(gates, dim=0)
ce = torch.mean(mask1.float(), dim=0)
l_aux = torch.mean(me * ce) * num_experts * num_experts
# Remove locations outside capacity from mask
mask1 *= torch.lt(locations1, capacity)
mask2 *= torch.lt(locations2, capacity)
# Store the capacity location for each token
locations1_s = torch.sum(locations1 * mask1, dim=1)
locations2_s = torch.sum(locations2 * mask2, dim=1)
# Normalize gate probabilities
mask1_float = mask1.float()
mask2_float = mask2.float()
gates1_s = einsum("se,se->s", gates, mask1_float)
gates2_s = einsum("se,se->s", gates, mask2_float)
denom_s = gates1_s + gates2_s
# Avoid divide-by-zero
denom_s = torch.clamp(denom_s, min=torch.finfo(denom_s.dtype).eps)
gates1_s /= denom_s
gates2_s /= denom_s
# Calculate combine_weights and dispatch_mask
gates1 = einsum("s,se->se", gates1_s, mask1_float)
gates2 = einsum("s,se->se", gates2_s, mask2_float)
locations1_sc = _one_hot_to_float(locations1_s, capacity)
locations2_sc = _one_hot_to_float(locations2_s, capacity)
combine1_sec = einsum("se,sc->sec", gates1, locations1_sc)
combine2_sec = einsum("se,sc->sec", gates2, locations2_sc)
combine_weights = combine1_sec + combine2_sec
dispatch_mask = combine_weights.bool()
return l_aux, combine_weights, dispatch_mask, exp_counts
def _run(data_file_path,
dataset,
data_generator,
num_batches,
vocabulary_size,
context_size,
num_noise_words,
vec_dim,
num_epochs,
batch_size,
lr,
model_ver,
vec_combine_method,
save_all,
generate_plot,
model_ver_is_dbow,
gpu):
if torch.cuda.is_available():
torch.cuda.set_device(gpu)
print(torch.cuda.current_device())
if model_ver_is_dbow:
model = DBOW(vec_dim, num_docs=len(dataset), num_words=vocabulary_size)
else:
model = DM(vec_dim, num_docs=len(dataset), num_words=vocabulary_size)
cost_func = NegativeSampling()
optimizer = Adam(params=model.parameters(), lr=lr)
if torch.cuda.is_available():
model.cuda()
print("Dataset comprised of {:d} documents.".format(len(dataset)))
print("Vocabulary size is {:d}.\n".format(vocabulary_size))
print("Training started.")
best_loss = float("inf")
prev_model_file_path = None
for epoch_i in range(num_epochs):
epoch_start_time = time.time()
loss = []
for batch_i in range(num_batches):
batch = next(data_generator)
if torch.cuda.is_available():
batch.cuda_()
if model_ver_is_dbow:
x = model.forward(batch.doc_ids, batch.target_noise_ids)
else:
x = model.forward(
batch.context_ids,
batch.doc_ids,
batch.target_noise_ids)
x = cost_func.forward(x)
loss.append(x.item())
model.zero_grad()
x.backward()
optimizer.step()
_print_progress(epoch_i, batch_i, num_batches)
# end of epoch
loss = torch.mean(torch.FloatTensor(loss))
is_best_loss = loss < best_loss
best_loss = min(loss, best_loss)
state = {
'epoch': epoch_i + 1,
'model_state_dict': model.state_dict(),
'best_loss': best_loss,
'optimizer_state_dict': optimizer.state_dict()
}
prev_model_file_path = save_training_state(
data_file_path,
model_ver,
vec_combine_method,
context_size,
num_noise_words,
vec_dim,
batch_size,
lr,
epoch_i,
loss,
state,
save_all,
generate_plot,
is_best_loss,
prev_model_file_path,
model_ver_is_dbow)
epoch_total_time = round(time.time() - epoch_start_time)
print(" ({:d}s) - loss: {:.4f}".format(epoch_total_time, loss))
def get_loss(y_, y):
return torch.mean((y_ - y_train)**2) # torch.mean() 是一个均值函数
def log_test_results(self, n_epoch):
'''
Calculates errors on test dataset and logs them using TBVisualiser
'''
test_start_time = time.time()
# Set maximum number of batches to test
max_test_iters = min(self.cfg['General'][0]['max_test_iters'],
len(self.dataloader_test))
if max_test_iters == 0:
max_test_iters = 1
# Set model to evaluation mode
self.model.is_train = False
self.model.eval()
# Evaluate max_test_iters batches
test_errors = OrderedDict()
test_scalars = OrderedDict()
for i_test_batch, test_batch in enumerate(self.dataloader_test):
if i_test_batch == max_test_iters:
break
reconstr_image, _ = self.model.forward(test_batch)
errors = self.model.get_current_errors()
# Save errors from current batch
for label, error in errors.items():
if label in test_errors:
test_errors[label] += error
else:
test_errors[label] = error
scalars = self.model.get_current_scalars()
for label, scalar in scalars.items():
if label in test_scalars:
if not label in ['au_predictions', 'au_ground_truths']:
test_scalars[label] += scalar
else:
test_scalars[label] = torch.cat(
(test_scalars[label], scalar), dim=0)
else:
test_scalars[label] = scalar
# Log first image and reconstructed image
if i_test_batch == 0:
original_image = visualisation_utils.tensor2im(
test_batch[0][0])
reconstr_image = visualisation_utils.tensor2im(
reconstr_image[0])
self.tb_visualiser.log_images(
{
'Original Image': original_image,
'Reconstructed Image': reconstr_image
},
self.total_steps,
is_train=False)
# Normalise errors
for label in test_errors.keys():
test_errors[label] /= max_test_iters
# Log errors to tensorboard
test_duration = time.time() - test_start_time
self.tb_visualiser.plot_scalars(test_errors,
self.total_steps,
is_train=False)
visualisation_utils.print_current_test_errors(n_epoch, test_duration,
test_errors)
# Normalise scalars
for label in test_scalars.keys():
if not label in ['au_predictions', 'au_ground_truths']:
test_scalars[label] /= max_test_iters
# Calculate F1 scores
f1_denominator = 2 * test_scalars['true_pos'] + test_scalars['false_neg'] \
+ test_scalars['false_pos']
f1_denominator += torch.finfo(torch.float).tiny
individual_f1_scores = 2 * test_scalars['true_pos'] / f1_denominator
all_tp = torch.sum(test_scalars['true_pos'])
all_fn = torch.sum(test_scalars['false_neg'])
all_fp = torch.sum(test_scalars['false_pos']) + torch.finfo(
torch.float).tiny
average_f1_score = 2 * all_tp / (2 * all_tp + all_fn + all_fp)
f1_scores = {
'F1 Score for AU1': individual_f1_scores[0].item(),
'F1 Score for AU2': individual_f1_scores[1].item(),
'F1 Score for AU4': individual_f1_scores[2].item(),
'F1 Score for AU5': individual_f1_scores[3].item(),
'F1 Score for AU6': individual_f1_scores[4].item(),
'F1 Score for AU9': individual_f1_scores[5].item(),
'F1 Score for AU12': individual_f1_scores[6].item(),
'F1 Score for AU15': individual_f1_scores[7].item(),
'F1 Score for AU17': individual_f1_scores[8].item(),
'F1 Score for AU20': individual_f1_scores[9].item(),
'F1 Score for AU25': individual_f1_scores[10].item(),
'F1 Score for AU26': individual_f1_scores[11].item(),
'Average of F1 Scores': torch.mean(individual_f1_scores).item(),
'Overall F1 Score': average_f1_score.item()
}
# Calculate Accuracies
num_predictions = test_scalars['true_pos'] + test_scalars['true_neg'] \
+ test_scalars['false_pos'] + test_scalars['false_neg']
accuracy_tensor = (test_scalars['true_pos'] +
test_scalars['true_neg']) / num_predictions
accuracies = {
'Accuracy for AU1': accuracy_tensor[0].item(),
'Accuracy for AU2': accuracy_tensor[1].item(),
'Accuracy for AU4': accuracy_tensor[2].item(),
'Accuracy for AU5': accuracy_tensor[3].item(),
'Accuracy for AU6': accuracy_tensor[4].item(),
'Accuracy for AU9': accuracy_tensor[5].item(),
'Accuracy for AU12': accuracy_tensor[6].item(),
'Accuracy for AU15': accuracy_tensor[7].item(),
'Accuracy for AU17': accuracy_tensor[8].item(),
'Accuracy for AU20': accuracy_tensor[9].item(),
'Accuracy for AU25': accuracy_tensor[10].item(),
'Accuracy for AU26': accuracy_tensor[11].item(),
'Average Accuracy': torch.mean(accuracy_tensor).item()
}
# Calculate 2AFC Scores
component_2afc_tensor, average_2afc = metric_utils.compute_2AFC(
test_scalars['au_ground_truths'], test_scalars['au_predictions'])
values_2afc = {
'2AFC Score for AU1': component_2afc_tensor[0].item(),
'2AFC Score for AU2': component_2afc_tensor[1].item(),
'2AFC Score for AU4': component_2afc_tensor[2].item(),
'2AFC Score for AU5': component_2afc_tensor[3].item(),
'2AFC Score for AU6': component_2afc_tensor[4].item(),
'2AFC Score for AU9': component_2afc_tensor[5].item(),
'2AFC Score for AU12': component_2afc_tensor[6].item(),
'2AFC Score for AU15': component_2afc_tensor[7].item(),
'2AFC Score for AU17': component_2afc_tensor[8].item(),
'2AFC Score for AU20': component_2afc_tensor[9].item(),
'2AFC Score for AU25': component_2afc_tensor[10].item(),
'2AFC Score for AU26': component_2afc_tensor[11].item(),
'Overall 2AFC Score': average_2afc.item()
}
del test_scalars['true_pos']
del test_scalars['true_neg']
del test_scalars['false_pos']
del test_scalars['false_neg']
del test_scalars['au_ground_truths']
del test_scalars['au_predictions']
test_scalars['F1 Scores'] = f1_scores
test_scalars['Accuracies'] = accuracies
test_scalars['2AFC Scores'] = values_2afc
# Log Metrics
self.tb_visualiser.plot_scalars(test_scalars,
self.total_steps,
is_train=False)
# Set model back to training mode
self.model.is_train = True
self.model.train()
def train(fv, model_name, criterion, balance=False, batchsize=64, size=0):
if fv == "matlab":
dloader = matloader
else:
dloader = fvloader
train_data = dloader.load_train_data(size=size, balance=balance, fv=fv)
val_data = dloader.load_val_data(size=size, fv=fv)
test_data = dloader.load_test_data(size=size, fv=fv)
# model_name = "transformer_%s_size%d_bce" % (fv, size)
model_dir = os.path.join("./modeldir/%s" % model_name)
model_pth = os.path.join(model_dir, "model.pth")
writer = tensorboardX.SummaryWriter(model_dir)
if os.path.exists(model_pth):
print("------load model--------")
model = torch.load(model_pth)
else:
# model = Transformer(fv, NUM_HEADS=4, NUM_LAYERS=3).cuda()
model = Transformer(fv).cuda()
model = nn.DataParallel(model)
optimizer = torch.optim.Adam(model.parameters(),
lr=0.0001, weight_decay=0.001)
# scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
# optimizer, factor=0.5,
# patience=30, min_lr=1e-4)
epochs = 2000
step = 1
val_step = 1
max_f1 = 0.0
for e in range(epochs):
model.train()
print("------epoch--------", e)
st = time.time()
train_shuffle = fvloader.shuffle(train_data)
for item in fvloader.batch_fv(train_shuffle, batch=batchsize):
# for name, param in model.named_parameters():
# writer.add_histogram(
# name, param.clone().cpu().data.numpy(), step)
# writer.add_histogram(
# "grad/"+name, param.grad.clone().cpu().data.numpy(), step)
model.zero_grad()
genes, nimgs, labels, timesteps = item
inputs = torch.from_numpy(nimgs).type(torch.cuda.FloatTensor)
gt = torch.from_numpy(labels).type(torch.cuda.FloatTensor)
pd = model(inputs)
# loss = criterion(pd, gt)
all_loss = criterion(pd, gt)
label_loss = torch.mean(all_loss, dim=0)
loss = torch.mean(label_loss)
# for i in range(6):
# writer.add_scalar("train sl_%d_loss" % i,
# label_loss[i].item(), step)
train_pd = torch_util.threshold_tensor_batch(pd)
np_pd = train_pd.data.cpu().numpy()
torch_util.torch_metrics(
labels, np_pd, writer, step, mode="train")
writer.add_scalar("train loss", loss, step)
loss.backward()
optimizer.step()
step += 1
et = time.time()
writer.add_scalar("train time", et - st, e)
for param_group in optimizer.param_groups:
writer.add_scalar("lr", param_group['lr'], e)
# run_origin_train(model, imbtrain_data, writer, e, criterion)
if e % 1 == 0:
val_loss, val_f1 = run_val(
model, dloader, val_data, writer, val_step, criterion)
# scheduler.step(val_loss)
val_step += 1
if e == 0:
start_loss = val_loss
min_loss = start_loss
# if val_loss > 2 * min_loss:
# print("early stopping at %d" % e)
# break
# if e % 50 == 0:
# pt = os.path.join(model_dir, "%d.pt" % e)
# torch.save(model.state_dict(), pt)
# result = os.path.join(model_dir, "result_epoch%d.txt" % e)
# run_test(model, test_data, result)
if min_loss > val_loss or max_f1 < val_f1:
if min_loss > val_loss:
print("---------save best----------", "loss", val_loss)
min_loss = val_loss
if max_f1 < val_f1:
print("---------save best----------", "f1", val_f1)
max_f1 = val_f1
torch.save(model, model_pth)
result = os.path.join(model_dir, "result_epoch%d.txt" % e)
run_test(model, dloader, test_data, result)
def __init__(self, tensor):
"""tensor is taken as a sample to calculate the mean and std"""
self.mean = torch.mean(tensor)
self.std = torch.std(tensor)
def __call__(self, input, target):
# Average across channels in order to get the final score
class_idx = torch.arange(input.shape[1]).to(input.device)
input = torch.argmax(input, axis=1)==class_idx[:,None,None,None,None]
input = input.transpose(1,0)
return torch.mean(compute_per_channel_dice(input, target, epsilon=self.epsilon))
def main(args):
ts = time.strftime('%Y-%b-%d-%H:%M:%S', time.gmtime())
splits = ['train', 'valid'] + (['test'] if args.test else [])
datasets = OrderedDict()
for split in splits:
datasets[split] = PTB(data_dir=args.data_dir,
split=split,
create_data=args.create_data,
max_sequence_length=args.max_sequence_length,
min_occ=args.min_occ)
model = SentenceVAE(vocab_size=datasets['train'].vocab_size,
sos_idx=datasets['train'].sos_idx,
eos_idx=datasets['train'].eos_idx,
pad_idx=datasets['train'].pad_idx,
unk_idx=datasets['train'].unk_idx,
max_sequence_length=args.max_sequence_length,
embedding_size=args.embedding_size,
rnn_type=args.rnn_type,
hidden_size=args.hidden_size,
word_dropout=args.word_dropout,
embedding_dropout=args.embedding_dropout,
latent_size=args.latent_size,
num_layers=args.num_layers,
bidirectional=args.bidirectional)
if torch.cuda.is_available():
model = model.cuda()
print(model)
if args.tensorboard_logging:
writer = SummaryWriter(
os.path.join(args.logdir, experiment_name(args, ts)))
writer.add_text("model", str(model))
writer.add_text("args", str(args))
writer.add_text("ts", ts)
save_model_path = os.path.join(args.save_model_path, ts)
os.makedirs(save_model_path)
total_steps = (len(datasets["train"]) // args.batch_size) * args.epochs
print("Train dataset size", total_steps)
def kl_anneal_function(anneal_function, step):
if anneal_function == 'identity':
return 1
if anneal_function == 'linear':
if args.warmup is None:
return 1 - (total_steps - step) / total_steps
else:
warmup_steps = (total_steps / args.epochs) * args.warmup
return 1 - (warmup_steps - step
) / warmup_steps if step < warmup_steps else 1.0
ReconLoss = torch.nn.NLLLoss(size_average=False,
ignore_index=datasets['train'].pad_idx)
def loss_fn(logp, target, length, mean, logv, anneal_function, step):
# cut-off unnecessary padding from target, and flatten
target = target[:, :torch.max(length).data[0]].contiguous().view(-1)
logp = logp.view(-1, logp.size(2))
# Negative Log Likelihood
recon_loss = ReconLoss(logp, target)
# KL Divergence
KL_loss = -0.5 * torch.sum(1 + logv - mean.pow(2) - logv.exp())
KL_weight = kl_anneal_function(anneal_function, step)
return recon_loss, KL_loss, KL_weight
optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate)
tensor = torch.cuda.FloatTensor if torch.cuda.is_available(
) else torch.Tensor
step = 0
for epoch in range(args.epochs):
for split in splits:
data_loader = DataLoader(dataset=datasets[split],
batch_size=args.batch_size,
shuffle=split == 'train',
num_workers=cpu_count(),
pin_memory=torch.cuda.is_available())
tracker = defaultdict(tensor)
# Enable/Disable Dropout
if split == 'train':
model.train()
else:
model.eval()
for iteration, batch in enumerate(data_loader):
batch_size = batch['input'].size(0)
for k, v in batch.items():
if torch.is_tensor(v):
batch[k] = to_var(v)
# Forward pass
logp, mean, logv, z = model(batch['input'], batch['length'])
# loss calculation
recon_loss, KL_loss, KL_weight = loss_fn(
logp, batch['target'], batch['length'], mean, logv,
args.anneal_function, step)
if split == 'train':
loss = (recon_loss + KL_weight * KL_loss) / batch_size
else:
# report complete elbo when validation
loss = (recon_loss + KL_loss) / batch_size
# backward + optimization
if split == 'train':
optimizer.zero_grad()
loss.backward()
optimizer.step()
step += 1
# bookkeepeing
tracker['negELBO'] = torch.cat(
(tracker['negELBO'], loss.data.unsqueeze(0)))
if args.tensorboard_logging:
neg_elbo = (recon_loss + KL_loss) / batch_size
writer.add_scalar("%s/Negative_ELBO" % split.upper(),
neg_elbo.data[0],
epoch * len(data_loader) + iteration)
writer.add_scalar("%s/Recon_Loss" % split.upper(),
recon_loss.data[0] / batch_size,
epoch * len(data_loader) + iteration)
writer.add_scalar("%s/KL_Loss" % split.upper(),
KL_loss.data[0] / batch_size,
epoch * len(data_loader) + iteration)
writer.add_scalar("%s/KL_Weight" % split.upper(),
KL_weight,
epoch * len(data_loader) + iteration)
if iteration % args.print_every == 0 or iteration + 1 == len(
data_loader):
logger.info(
"%s Batch %04d/%i, Loss %9.4f, Recon-Loss %9.4f, KL-Loss %9.4f, KL-Weight %6.3f"
% (split.upper(), iteration, len(data_loader) - 1,
loss.data[0], recon_loss.data[0] / batch_size,
KL_loss.data[0] / batch_size, KL_weight))
if split == 'valid':
if 'target_sents' not in tracker:
tracker['target_sents'] = list()
tracker['target_sents'] += idx2word(
batch['target'].data,
i2w=datasets['train'].get_i2w(),
pad_idx=datasets['train'].pad_idx)
tracker['z'] = torch.cat((tracker['z'], z.data), dim=0)
logger.info("%s Epoch %02d/%i, Mean Negative ELBO %9.4f" %
(split.upper(), epoch, args.epochs,
torch.mean(tracker['negELBO'])))
if args.tensorboard_logging:
writer.add_scalar("%s-Epoch/NegELBO" % split.upper(),
torch.mean(tracker['negELBO']), epoch)
# save a dump of all sentences and the encoded latent space
if split == 'valid':
dump = {
'target_sents': tracker['target_sents'],
'z': tracker['z'].tolist()
}
if not os.path.exists(os.path.join('dumps', ts)):
os.makedirs('dumps/' + ts)
with open(
os.path.join('dumps/' + ts +
'/valid_E%i.json' % epoch),
'w') as dump_file:
json.dump(dump, dump_file)
# save checkpoint
if split == 'train':
checkpoint_path = os.path.join(save_model_path,
"E%i.pytorch" % (epoch))
torch.save(model.state_dict(), checkpoint_path)
logger.info("Model saved at %s" % checkpoint_path)
if args.num_samples:
torch.cuda.empty_cache()
model.eval()
with torch.no_grad():
print(f"Generating {args.num_samples} samples")
generations, _ = model.inference(n=args.num_samples)
vocab = datasets["train"].i2w
print(
"Sampled latent codes from z ~ N(0, I), generated sentences:")
for i, generation in enumerate(generations, start=1):
sentence = [vocab[str(word.item())] for word in generation]
print(f"{i}:", " ".join(sentence))
def message(self, x_j, pos_i, pos_j):
if x_j is None:
x_j = pos_j
# Get offsets from points
offsets = self.offset_mlp(x_j)
# Reshape offsets to shape [SUM n_neighbors(n_points), n_kpoints, kp_dim]
offsets = offsets.view((-1, self.num_points, self.kernel_dim))
# Rescale offset for this layer
offsets *= self.kp_extent
# Center every neighborhood [SUM n_neighbors(n_points), dim]
neighbors = pos_j - pos_i
# Number of points
n_points = neighbors.shape[0]
# Get points kernels and add offsets
K_points = self.kernel
K_points = K_points.float().view((-1, 3)).unsqueeze(0)
K_points_deformed = K_points + offsets
self.internal_losses["permissive_loss"] = permissive_loss(
K_points_deformed, self.radius)
# Get all difference matrices [SUM n_neighbors(n_points), n_kpoints, dim]
neighbors = neighbors.unsqueeze(1)
differences = neighbors - K_points_deformed
sq_distances = (differences**2).sum(-1)
# Get Kernel point influences [n_points, n_kpoints, n_neighbors]
if self.KP_influence == "constant":
# Every point get an influence of 1.
all_weights = torch.ones_like(sq_distances)
elif self.KP_influence == "linear":
# Influence decrease linearly with the distance, and get to zero when d = kp_extent.
all_weights = 1.0 - (torch.sqrt(sq_distances) / (self.kp_extent))
all_weights[all_weights < 0] = 0.0
elif self.KP_influence == "square":
# Influence decrease linearly with the distance, and get to zero when d = kp_extent.
all_weights = 1.0 - (sq_distances / (self.kp_extent**2))
all_weights[all_weights < 0] = 0.0
else:
raise ValueError(
"Unknown influence function type (config.KP_influence)")
neighbors_1nn = torch.argmin(sq_distances, dim=-1)
# Fitting Loss
sq_distances_min = sq_distances.gather(1, neighbors_1nn.unsqueeze(-1))
sq_distances_min /= self.radius**2 # To be independant of the layer
self.internal_losses["fitting_loss"] = torch.mean(sq_distances_min)
weights = all_weights.gather(1, neighbors_1nn.unsqueeze(-1))
K_weights = self.kernel_weight
K_weights = torch.index_select(K_weights, 0,
neighbors_1nn.view(-1)).view(
(n_points, self.in_features,
self.out_features))
# Get the features of each neighborhood [n_points, n_neighbors, in_fdim]
features = x_j
# Apply distance weights [n_points, n_kpoints, in_fdim]
weighted_features = torch.einsum("nb, nc -> nc", weights, features)
# Apply network weights [n_kpoints, n_points, out_fdim]
out_features = torch.einsum("na, nac -> nc", weighted_features,
K_weights)
return out_features
def permissive_loss(deformed_kpoints, radius):
"""This loss is responsible to penalize deformed_kpoints to
move outside from the radius defined for the convolution
"""
norm_deformed_normalized = F.normalize(deformed_kpoints) / float(radius)
return torch.mean(norm_deformed_normalized[norm_deformed_normalized > 1.0])
trainLoss = 0.
train_estimation_relative_error = 0
for batch, [trainX, trainY] in enumerate(tqdm(trainLoader, ncols=10)):
# break
nbatch += 1
trainX = trainX.to(device)
trainY = trainY.to(device)
trainX = torch.sin(trainX)
trainY = OneHotLabel(trainY, n_output)
batch_train_repeatX, batch_train_repeatY = KMeansRepeatX(trainX, repeat_n), KMeansRepeatY(trainY,
repeat_n)
pre = net.forward(batch_train_repeatX, train=True, BP=BP_train)
loss = CELoss(pre, batch_train_repeatY)
trainLoss += torch.mean(loss).detach().cpu().numpy()
if BP_train:
net.backward(batch_train_repeatY, BP_train)
net.update_params(learning_rate, BP_train)
else:
net.backward(loss, BP_train)
net.update_params(learning_rate, BP_train, method)
trainLoss /= nbatch
train_loss.append(trainLoss)
print('train epoch:{} loss:{}'.format(epoch, trainLoss))
if ((epoch + 1) % 100 == 0):
learning_rate *= 0.8
print('学习率衰减至{}'.format(learning_rate))
loss = 0.
N = 0.
n = 0.
def __call__(self, rnn_output):
if self.beta == .0:
return .0
l2 = torch.sqrt(torch.sum(torch.pow(rnn_output, 2), dim=-1))
l2 = self.beta * torch.mean(torch.pow(l2[:, 1:] - l2[:, :-1], 2))
return l2
# adjust learning rate
if i % DECAY_EPOCH == 0:
LR_D = LR_D / 2
LR_G = LR_G / 2
adjust_learning_rate(optim_D, LR_D)
adjust_learning_rate(optim_G, LR_G)
artist_paintings = artist_works()
G_ideas = torch.randn(BATCH_SIZE, N_IDEAS)
G_paintings = G(G_ideas)
prob_artist0 = D(artist_paintings) # D try to increase this
prob_artist1 = D(G_paintings) # D try to reduce this
D_loss = -torch.mean(torch.log(prob_artist0) + torch.log(1. - prob_artist1))
G_loss = -torch.mean(torch.log(prob_artist1))
if i % 3 == 0:
optim_D.zero_grad()
D_loss.backward(retain_graph = True) # reuse computational graph
optim_D.step()
optim_G.zero_grad()
G_loss.backward()
optim_G.step()
if i % 100 == 0: # plotting
plt.cla()
plt.plot(PAINT_POINTS[0], G_paintings.data.numpy()[0], c='#4AD631', lw=3, label='Generated painting',)
plt.plot(PAINT_POINTS[0], 2 * np.power(PAINT_POINTS[0], 2) + 1, c='#74BCFF', lw=3, label='upper bound')
def train(self):
svhn_iter = iter(self.svhn_loader)
mnist_iter = iter(self.mnist_loader)
iter_per_epoch = min(len(svhn_iter), len(mnist_iter))
fixed_svhn = self.np_to_var(svhn_iter.next()[0])
fixed_mnist = self.np_to_var(mnist_iter.next()[0])
for step in range(self.train_iters+1):
if (step+1) % iter_per_epoch == 0:
mnist_iter = iter(self.mnist_loader)
svhn_iter = iter(self.svhn_loader)
svhn, _ = svhn_iter.next()
svhn = self.np_to_var(svhn)
mnist, _ = mnist_iter.next()
mnist = self.np_to_var(mnist)
#============ train D ============#
# real images
self.reset_grad()
out = self.Dx(mnist)
Dx_loss = torch.mean((out-1)**2)
out = self.Dy(svhn)
Dy_loss = torch.mean((out-1)**2)
D_real_loss = Dx_loss + Dy_loss
D_real_loss.backward()
self.D_optim.step()
# fake images
self.reset_grad()
out = self.Dy(self.Gxy(mnist))
Dy_loss = torch.mean(out**2)
out = self.Dx(self.Gyx(svhn))
Dx_loss = torch.mean(out**2)
D_fake_loss = Dx_loss + Dy_loss
D_fake_loss.backward()
self.D_optim.step()
#============ train G ============#
# mnist-svhn-mnist cycle
self.reset_grad()
mnist_to_svhn = self.Gxy(mnist)
out = self.Dy(mnist_to_svhn)
mnist_reconst = self.Gyx(mnist_to_svhn)
# adversarial loss
G_loss = torch.mean((out-1)**2)
# cycle-consistency loss
G_loss += torch.mean((mnist - mnist_reconst)**2)
G_loss.backward()
self.G_optim.step()
# svhn-mnist-svhn cycle
self.reset_grad()
svhn_to_mnist = self.Gyx(svhn)
out = self.Dx(svhn_to_mnist)
svhn_reconst = self.Gxy(svhn_to_mnist)
# adversarial loss
G_loss = torch.mean((out-1)**2)
# cycle-consistency loss
G_loss += torch.mean((svhn - svhn_reconst)**2)
G_loss.backward()
self.G_optim.step()
# print logs
if (step+1) % self.log_step == 0:
print('Step [%d/%d], d_real_loss: %.4f, d_fake_loss: %.4f, g_loss: %.4f'
% (step+1, self.train_iters, D_real_loss.data[0], D_fake_loss.data[0], G_loss.data[0]))
if (step+1) % self.sample_step == 0:
fake_mnist = self.Gyx(fixed_svhn)
fake_svhn = self.Gxy(fixed_mnist)
mnist, fake_mnist = self.var_to_np(fixed_mnist), self.var_to_np(fake_mnist)
svhn , fake_svhn = self.var_to_np(fixed_svhn), self.var_to_np(fake_svhn)
merged = merge_images(mnist, fake_svhn)
path = os.path.join(self.sample_path, 'sample-%d-m-s.png' % (step+1))
scipy.misc.imsave(path, merged)
print('Saved %s' % path)
merged = merge_images(svhn, fake_mnist)
path = os.path.join(self.sample_path, 'sample-%d-s-m.png' % (step+1))
scipy.misc.imsave(path, merged)
print('Saved %s' % path)
if (step+1) % 5000 == 0:
Gxy_path = os.path.join(self.model_path, 'Gxy-%d.pkl' % (step+1))
Gyx_path = os.path.join(self.model_path, 'Gyx-%d.pkl' % (step+1))
Dx_path = os.path.join(self.model_path, 'Dx-%d.pkl' % (step+1))
Dy_path = os.path.join(self.model_path, 'Dy-%d.pkl' % (step+1))
torch.save(self.Gxy.state_dict(), Gxy_path)
torch.save(self.Gyx.state_dict(), Gyx_path)
torch.save(self.Dx.state_dict(), Dx_path)
torch.save(self.Dy.state_dict(), Dy_path)
def mean(input, dim):
return th.mean(input, dim=dim)
def train(self):
#torch.autograd.set_detect_anomaly(True)
"""
Main training loop
Helpful URL: https://github.com/balakg/posewarp-cvpr2018/blob/master/code/posewarp_gan_train.py
"""
for epoch in range(self.num_epochs):
num_batches = len(self.train_dataset_loader)
# Initialize running averages
disc_losses = AverageMeter()
train_disc_accuracies = AverageMeter()
tot_losses = AverageMeter()
train_accuracies = AverageMeter()
for batch_id, batch_data in enumerate(self.train_dataset_loader):
self.gan.train() # Set the model to train mode
self.vgg_loss_network.eval()
current_step = epoch * num_batches + batch_id
# Get data from dataset
src_img = batch_data['im'].cuda(async=True)
target_img = batch_data['target_im'].cuda(async=True)
src_iuv = batch_data['im_iuv'].cuda(async=True)
target_iuv = batch_data['target_iuv'].cuda(async=True)
#pdb.set_trace()
# ============
# Run predictive GAN on source image
_, classification_src = self.gan(src_img, src_iuv, target_iuv, use_gt=False)
# Run predictive GAN on target image
_ , classification_tgt = self.gan(target_img, src_iuv, target_iuv, use_gt=True)
# Create discriminator groundtruth
# For src, we create zeros
# For tgt, we create ones
disc_gt_src = torch.zeros(classification_src.shape[0], 1, dtype=torch.float32).cuda()
disc_gt_tgt = torch.ones(classification_src.shape[0], 1, dtype=torch.float32).cuda()
disc_gt = torch.cat((disc_gt_src, disc_gt_tgt), dim=0).cuda(async=True)
classification_all = torch.cat((classification_src, classification_tgt) , dim=0)
# Train Discriminator network
disc_loss = self._optimizeDiscriminator(classification_all, disc_gt)
disc_losses.update(disc_loss.item(), disc_gt.shape[0])
disc_acc = 100.0 * torch.mean( ( torch.round(F.softmax(classification_all, dim=1)) == disc_gt ).float() )
train_disc_accuracies.update(disc_acc.item(), disc_gt.shape[0])
print("Epoch: {}, Batch {}/{} has Discriminator loss {}, and acc {}".format(epoch, batch_id, num_batches, disc_losses.avg, train_disc_accuracies.avg))
# Start training GAN first for several iterations
if current_step < self.start_disc_iters:
print("Discriminator training only: {}/{}\n".format(current_step,self.start_disc_iters))
continue
# ============
# Optimize the GAN
# Note that now we use disc_gt_tgt which are 1's
generated_img, classification_src = self.gan(src_img, src_iuv, target_iuv, use_gt=False)
tot_loss = self._optimizeGAN(generated_img, target_img, classification_src, disc_gt_tgt)
tot_losses.update(tot_loss.item(), disc_gt_tgt.shape[0])
acc = 100.0 * torch.mean( ( torch.round(F.softmax(classification_src, dim=1)) == disc_gt_tgt ).float() )
tot_losses.update(tot_loss.item(), disc_gt_tgt.shape[0])
train_accuracies.update(acc.item(), disc_gt_tgt.shape[0])
# Not adjusting learning rate currently
# if epoch % 100 == 99:
# self._adjust_learning_rate(epoch)
# # Not Clipping Weights
# self._clip_weights()
if current_step % self.log_freq == 0:
print("Epoch: {}, Batch {}/{} has loss {}, and acc {}".format(epoch, batch_id, num_batches, tot_losses.avg, train_accuracies.avg))
# TODO: you probably want to plot something here
self.txwriter.add_scalar('train/discriminator_loss', disc_losses.avg, current_step)
self.txwriter.add_scalar('train/total_loss', tot_losses.avg, current_step)
self.txwriter.add_scalar('train/discriminator_acc', train_accuracies.avg, current_step)
"""
Visualize some images
"""
if current_step % self.display_freq == 0:
name1 = '{0}_{1}_{2}'.format(epoch, current_step, "image1")
name2 = '{0}_{1}_{2}'.format(epoch, current_step, "image2")
name3 = '{0}_{1}_{2}'.format(epoch, current_step, "gan_image")
im1 = denormalizeImage(src_img[0,:,:,:].cpu().numpy())
im2 = denormalizeImage(target_img[0,:,:,:].cpu().numpy())
im3 = denormalizeImage(generated_img[0,:,:,:].detach().cpu().numpy())
self.txwriter.add_image("Image1/"+name1,im1)
self.txwriter.add_image("Image2/"+name2,im2)
self.txwriter.add_image("GAN/"+name3,im3)
"""
TODO : Test accuracies
if current_step % self.test_freq == 0:#self._test_freq-1:
self._model.eval()
val_accuracy = self.validate()
print("Epoch: {} has val accuracy {}".format(epoch, val_accuracy))
self.txwriter.add_scalar('test/acc', val_accuracy, current_step)
"""
"""
Save Model periodically
"""
if (current_step % self.save_freq == 0) and current_step > 0:
save_name = 'model_checkpoint.pth'
torch.save(self.gan.state_dict(), save_name)
print('Saved model to {}'.format(save_name))
def learn(self):
self.learn_iter += 1
# for x in self.Actor_target.state_dict().keys():
# eval('self.Actor_target.' + x + '.data.mul_((1-TAU))')
# eval('self.Actor_target.' + x + '.data.add_(TAU*self.Actor_eval.' + x + '.data)')
# for x in self.Critic_target.state_dict().keys():
# eval('self.Critic_target.' + x + '.data.mul_((1-TAU))')
# eval('self.Critic_target.' + x + '.data.add_(TAU*self.Critic_eval.' + x + '.data)')
# for target_param, param in zip(net_target.parameters(), net.parameters()):
# target_param.data.copy_(target_param.data * (1.0 - tau) + param.data * tau)
# for k, v in self.eval_critic_net.state_dict().items():
# self.target_critic_net.state_dict()[k].copy_(self.tau * v + (1-self.tau) * self.target_critic_net.state_dict()[k])
# for k, v in self.eval_actor_net.state_dict().items():
# self.target_actor_net.state_dict()[k].copy_(self.tau * v + (1-self.tau) * self.target_actor_net.state_dict()[k])
batch_data = self.memory.sample(self.batch_size)
s0, a0, r1, s1 = zip(*batch_data)
s0 = torch.tensor(s0, dtype=torch.float)
a0 = torch.tensor(a0, dtype=torch.float).view(self.batch_size,
len(self.action_bounds))
r1 = torch.tensor(r1, dtype=torch.float).view(self.batch_size, -1)
s1 = torch.tensor(s1, dtype=torch.float)
# Select action according to policy and add clipped noise
# Input (s, a), output q
q_s0_a0_1 = self.eval_critic_net1(s0, a0)
q_s0_a0_2 = self.eval_critic_net2(s0, a0)
# Input (s_, a_), output q_ for q_target
# 得到a_
noise = (torch.randn_like(a0) * self.policy_noise_clip * 2).clamp(
-self.policy_noise_clip, self.policy_noise_clip)
a1 = self.target_actor_net(s1).detach() + noise
action_bound = self.action_bounds.expand_as(a1)
a1[a1 < -action_bound] = -action_bound[a1 < -action_bound]
a1[a1 > action_bound] = action_bound[a1 > action_bound]
q_s1_a1_1 = self.target_critic_net1(s1, a1).detach()
q_s1_a1_2 = self.target_critic_net2(s1, a1).detach()
q_s1_a1 = torch.min(q_s1_a1_1, q_s1_a1_2)
q_target = r1 + self.gamma * q_s1_a1
loss_critic = nn.MSELoss()(q_s0_a0_1, q_target) + nn.MSELoss()(
q_s0_a0_2, q_target)
# critic 学习过程
# # td_error=R + GAMMA * ct（bs_,at(bs_)）-ce(s,ba) 更新ce ,
# 但这个ae(s)是记忆中的ba，让ce得出的Q靠近Q_target,让评价更准确
# loss = (Q(st, at) - (rt + r*Q'(st+1, u'(st+1))))**2
self.optimizer_critic1.zero_grad()
self.optimizer_critic2.zero_grad()
loss_critic.backward()
self.optimizer_critic1.step()
self.optimizer_critic2.step()
loss_actor = 0
# actor 学习过程
# https://zhuanlan.zhihu.com/p/84321382
# Delayed policy updates
if self.learn_iter % self.policy_delay == 0:
actor_a = self.eval_actor_net(s0)
critic_q = self.eval_critic_net1(s0, actor_a)
# loss=-q=-ce（s,ae（s））更新ae ae（s）=a ae（s_）=a_
# 如果 a是一个正确的行为的话，那么它的Q应该更贴近0
loss_actor = -torch.mean(critic_q)
self.optimizer_actor.zero_grad()
loss_actor.backward()
self.optimizer_actor.step()
# Update the frozen target models
for param, target_param in zip(
self.eval_critic_net1.parameters(),
self.target_critic_net1.parameters()):
target_param.data.copy_(self.tau * param.data +
(1 - self.tau) * target_param.data)
for param, target_param in zip(
self.eval_critic_net2.parameters(),
self.target_critic_net2.parameters()):
target_param.data.copy_(self.tau * param.data +
(1 - self.tau) * target_param.data)
for param, target_param in zip(self.eval_actor_net.parameters(),
self.target_actor_net.parameters()):
target_param.data.copy_(self.tau * param.data +
(1 - self.tau) * target_param.data)
return loss_critic, loss_actor
def kl_loss(code):
return torch.mean(torch.pow(code, 2))
w_comp_p = int(w_p / 16) + 1
h_mask_p.append(h_comp_p)
w_mask_p.append(w_comp_p)
x = x.cuda()
y = y.cuda()
xp = xp.cuda()
# out is CNN featuremaps
output_highfeature = encoder(x)
# print("out: ", output_highfeature.size())
# print("output_highfeature: ", output_highfeature.size())
output_highfeature_p = encoder(xp)
x_mean = []
xp_mean = []
for i, j in zip(output_highfeature, output_highfeature_p):
x_mean.append(float(torch.mean(i)))
xp_mean.append(float(torch.mean(j)))
# x_mean = torch.mean(output_highfeature)
# x_mean = float(x_mean)
for i in range(batch_size):
decoder_hidden_init[i] = decoder_hidden_init[i] * x_mean[i]
decoder_hidden_init[i] = torch.tanh(decoder_hidden_init[i])
decoder_hidden_init_p[i] = decoder_hidden_init_p[i] * xp_mean[i]
decoder_hidden_init_p[i] = torch.tanh(decoder_hidden_init_p[i])
# decoder_hidden_init[]
# dense_input is height and output_area is width which is bb
output_area1 = output_highfeature.size()
output_area1_p = output_highfeature_p.size()
output_area = output_area1[3]
def top1gating(
logits: Tensor,
capacity_factor: float,
min_capacity: int,
used_token: Tensor = None,
noisy_gate_policy: Optional[str] = None,
drop_tokens: bool = True,
use_rts: bool = True,
use_tutel: bool = False) -> Tuple[Tensor, Tensor, Tensor, Tensor]:
"""Implements Top1Gating on logits."""
if noisy_gate_policy == 'RSample':
logits_w_noise = logits + gumbel_rsample(logits.shape,
device=logits.device)
# everything is in fp32 in this function
gates = F.softmax(logits, dim=1)
capacity = _capacity(gates, torch.tensor(capacity_factor),
torch.tensor(min_capacity))
# Create a mask for 1st's expert per token
# noisy gating
indices1_s = torch.argmax(
logits_w_noise if noisy_gate_policy == 'RSample' else gates, dim=1)
num_experts = int(gates.shape[1])
mask1 = F.one_hot(indices1_s, num_classes=num_experts)
# mask only used tokens
if used_token is not None:
mask1 = einsum("s,se->se", used_token, mask1)
# gating decisions
exp_counts = torch.sum(mask1, dim=0).detach().to('cpu')
# if we don't want to drop any tokens
if not drop_tokens:
new_capacity = torch.max(exp_counts).to(logits.device)
dist.all_reduce(new_capacity,
op=dist.ReduceOp.MAX,
group=dist.group.WORLD)
capacity = new_capacity
# Compute l_aux
me = torch.mean(gates, dim=0)
ce = torch.mean(mask1.float(), dim=0)
l_aux = torch.sum(me * ce) * num_experts
# Random Token Selection
if use_rts:
uniform = exp_selection_uniform_map.get(logits.device)
if uniform is None:
uniform = torch.distributions.uniform.Uniform(
low=torch.tensor(0.0, device=logits.device),
high=torch.tensor(1.0, device=logits.device)).rsample
exp_selection_uniform_map[logits.device] = uniform
mask1_rand = mask1 * uniform(mask1.shape)
else:
mask1_rand = mask1
assert logits.shape[
0] >= min_capacity, "No. of tokens (batch-size) should be greater than min_capacity. Either set min_capacity to 0 or increase your batch size."
top_idx = _top_idx(mask1_rand, capacity)
new_mask1 = mask1 * torch.zeros_like(mask1).scatter_(0, top_idx, 1)
mask1 = new_mask1
if use_tutel:
# Tutel doesn't support index values masked with zero
# so we need to replace masked indices with -1
indices_mask = mask1.sum(dim=1) * num_experts - 1
indices1_s = torch.min(indices1_s, indices_mask)
# Compute locations in capacity buffer
if use_tutel:
locations1 = tutel_moe.fast_cumsum_sub_one(mask1)
else:
locations1 = torch.cumsum(mask1, dim=0) - 1
if use_tutel:
gates1_s = (gates * mask1).sum(dim=1)
locations1_s = torch.sum(locations1 * mask1, dim=1)
return l_aux, capacity, num_experts, [
indices1_s,
], [
locations1_s,
], [
gates1_s,
], exp_counts
# Store the capacity location for each token
locations1_s = torch.sum(locations1 * mask1, dim=1)
# Normalize gate probabilities
mask1_float = mask1.float()
gates = gates * mask1_float
locations1_sc = _one_hot_to_float(locations1_s, capacity)
combine_weights = einsum("se,sc->sec", gates, locations1_sc)
dispatch_mask = combine_weights.bool()
return l_aux, combine_weights, dispatch_mask, exp_counts
def __train_pass__(self, epoch, is_training=True):
st_epoch = time.time()
if (is_training):
self.G.train()
status = 'TRAIN'
else:
self.G.eval()
status = 'EVAL'
g_time = 0.0
for i, batch in enumerate(self.dataloader):
if (i >= len(self.dataloader) - 2):
break
st_batch = time.time()
if (self.opt_parser.comb_fan_awing):
image_in, image_out, fan_pred_landmarks = batch
fan_pred_landmarks = fan_pred_landmarks.reshape(
-1, 68, 3).detach().cpu().numpy()
elif (self.opt_parser.add_audio_in):
image_in, image_out, audio_in = batch
audio_in = audio_in.reshape(-1, 1, 256, 256).to(device)
else:
image_in, image_out = batch
with torch.no_grad():
# # online landmark (AwingNet)
image_in, image_out = \
image_in.reshape(-1, 3, 256, 256).to(device), image_out.reshape(-1, 3, 256, 256).to(device)
inputs = image_out
outputs, boundary_channels = self.fa_model(inputs)
pred_heatmap = outputs[-1][:, :-1, :, :].detach().cpu()
pred_landmarks, _ = get_preds_fromhm(pred_heatmap)
pred_landmarks = pred_landmarks.numpy() * 4
# online landmark (FAN) -> replace jaw + eye brow in AwingNet
if (self.opt_parser.comb_fan_awing):
fl_jaw_eyebrow = fan_pred_landmarks[:, 0:27, 0:2]
fl_rest = pred_landmarks[:, 51:, :]
pred_landmarks = np.concatenate([fl_jaw_eyebrow, fl_rest],
axis=1).astype(np.int)
# draw landmark on while bg
img_fls = []
for pred_fl in pred_landmarks:
img_fl = np.ones(shape=(256, 256, 3)) * 255.0
if (self.opt_parser.comb_fan_awing):
img_fl = vis_landmark_on_img74(img_fl, pred_fl) # 74x2
else:
img_fl = vis_landmark_on_img98(img_fl, pred_fl) # 98x2
img_fls.append(img_fl.transpose((2, 0, 1)))
img_fls = np.stack(img_fls, axis=0).astype(np.float32) / 255.0
image_fls_in = torch.tensor(img_fls,
requires_grad=False).to(device)
if (self.opt_parser.add_audio_in):
# print(image_fls_in.shape, image_in.shape, audio_in.shape)
image_in = torch.cat([image_fls_in, image_in, audio_in], dim=1)
else:
image_in = torch.cat([image_fls_in, image_in], dim=1)
# image_in, image_out = \
# image_in.reshape(-1, 6, 256, 256).to(device), image_out.reshape(-1, 3, 256, 256).to(device)
# image2image net fp
g_out = self.G(image_in)
g_out = torch.tanh(g_out)
loss_l1 = self.criterionL1(g_out, image_out)
loss_vgg, loss_style = self.criterionVGG(g_out,
image_out,
style=True)
loss_vgg, loss_style = torch.mean(loss_vgg), torch.mean(loss_style)
loss = loss_l1 + loss_vgg + loss_style
if (is_training):
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
# log
if (self.opt_parser.write):
self.writer.add_scalar('loss',
loss.cpu().detach().numpy(), self.count)
self.writer.add_scalar('loss_l1',
loss_l1.cpu().detach().numpy(),
self.count)
self.writer.add_scalar('loss_vgg',
loss_vgg.cpu().detach().numpy(),
self.count)
self.count += 1
# save image to track training process
if (i % self.opt_parser.jpg_freq == 0):
vis_in = np.concatenate([
image_in[0, 3:6].cpu().detach().numpy().transpose(
(1, 2, 0)),
image_in[0, 0:3].cpu().detach().numpy().transpose(
(1, 2, 0))
],
axis=1)
vis_out = np.concatenate([
image_out[0].cpu().detach().numpy().transpose(
(1, 2, 0)), g_out[0].cpu().detach().numpy().transpose(
(1, 2, 0))
],
axis=1)
vis = np.concatenate([vis_in, vis_out], axis=0)
try:
os.makedirs(
os.path.join(self.opt_parser.jpg_dir,
self.opt_parser.name))
except:
pass
cv2.imwrite(
os.path.join(self.opt_parser.jpg_dir, self.opt_parser.name,
'e{:03d}_b{:04d}.jpg'.format(epoch, i)),
vis * 255.0)
# save ckpt
if (i % self.opt_parser.ckpt_last_freq == 0):
self.__save_model__('last', epoch)
print(
"Epoch {}, Batch {}/{}, loss {:.4f}, l1 {:.4f}, vggloss {:.4f}, styleloss {:.4f} time {:.4f}"
.format(epoch, i,
len(self.dataset) // self.opt_parser.batch_size,
loss.cpu().detach().numpy(),
loss_l1.cpu().detach().numpy(),
loss_vgg.cpu().detach().numpy(),
loss_style.cpu().detach().numpy(),
time.time() - st_batch))
g_time += time.time() - st_batch
if (self.opt_parser.test_speed):
if (i >= 100):
break
print('Epoch time usage:',
time.time() - st_epoch, 'I/O time usage:',
time.time() - st_epoch - g_time, '\n=========================')
if (self.opt_parser.test_speed):
exit(0)
if (epoch % self.opt_parser.ckpt_epoch_freq == 0):
self.__save_model__('{:02d}'.format(epoch), epoch)
def optimize_model():
if len(memory) < BATCH_SIZE:
return
transitions = memory.sample(BATCH_SIZE)
batch = Transition(*zip(*transitions))
# Compute a mask of non-final states and concatenate the batch elements
non_final_mask = torch.tensor(tuple(
map(lambda s: s is not None, batch.next_state)),
device=device,
dtype=torch.uint8)
# Divide memory into different tensors
non_final_next_states = torch.cat(
[s for s in batch.next_state if s is not None])
state_batch = torch.cat(batch.state)
action_batch = torch.cat(batch.action).view(BATCH_SIZE, 1)
reward_batch = torch.cat(batch.reward)
# Create state-action (s,a) tensor for input into the critic network with taken actions
state_action = torch.cat([state_batch, action_batch], -1)
# Compute Q(s,a) using critic network
state_action_values = critic_nn(state_action)
# Compute deterministic next state action using actor target network
next_action = target_actor_nn(non_final_next_states).detach()
# Compute next timestep state-action (s,a) tensor for non-final next states
next_state_action = torch.zeros(BATCH_SIZE, 4, device=device)
next_state_action[non_final_mask, :] = torch.cat(
[non_final_next_states, next_action], -1)
# Compute next state values at t+1 using target critic network
next_state_values = target_critic_nn(next_state_action).detach()
# Compute expected state action values y[i]= r[i] + Q'(s[i+1], a[i+1])
expected_state_action_values = reward_batch.view(
BATCH_SIZE, 1) + GAMMA * next_state_values
# Critic loss by mean squared error
loss_critic = F.mse_loss(state_action_values, expected_state_action_values)
# Optimize the critic network
optimizer_critic.zero_grad()
loss_critic.backward()
for param in critic_nn.parameters():
param.grad.data.clamp_(-1, 1)
optimizer_critic.step()
#optimize actor
# Actor actions
state_actor_action = actor_nn(state_batch)
# State-actor-actions tensor
state_actor_action_values = torch.cat([state_batch, state_actor_action],
-1)
# Loss
loss_actor = -1 * torch.mean(critic_nn(state_actor_action_values))
optimizer_actor.zero_grad()
loss_actor.backward()
for param in actor_nn.parameters():
param.grad.data.clamp_(-1, 1)
optimizer_actor.step()
# Soft parameter update
update_targets(target_critic_nn, critic_nn)
update_targets(target_actor_nn, actor_nn)
def forward(self, true, pred):
loss = true - pred
return torch.mean(torch.log(torch.cosh(loss + 1e-12)))
def forward(self, x):
mean = torch.mean(x, 1, keepdim=True)
variance = torch.mean((x - mean)**2, 1, keepdim=True)
x = (x - mean) * torch.rsqrt(variance + self.eps)
x = x * self.gamma + self.beta
return x
def forward(self, true, pred):
return torch.mean(torch.abs(true - pred))
def forward(self, map_pred, map_gtd): # map_pred : input prediction saliency map, map_gtd : input ground truth density map
map_pred = map_pred.float()
map_gtd = map_gtd.float()
map_pred = map_pred.view(1, -1) # change the map_pred into a tensor with n rows and 1 cols
map_gtd = map_gtd.view(1, -1) # change the map_pred into a tensor with n rows and 1 cols
min1 = torch.min(map_pred)
max1 = torch.max(map_pred)
# print("min1 and max1 are (saliecny map):", min1, max1)
map_pred = (map_pred - min1) / (max1 - min1 + self.epsilon) # min-max normalization for keeping KL loss non-NAN
min2 = torch.min(map_gtd)
max2 = torch.max(map_gtd)
# print("min2 and max2 are (fixation points) :", min2, max2)
map_gtd = (map_gtd - min2) / (max2 - min2 + self.epsilon) # min-max normalization for keeping KL loss non-NAN
map_gtd_id_1 = torch.gt(map_gtd, 0.5)
map_gtd_id_0 = torch.lt(map_gtd, 0.5)
map_gtd_id_00 = torch.eq(map_gtd, 0.5)
map_gtd[map_gtd_id_1] = 1.0
map_gtd[map_gtd_id_0] = 0.0
map_gtd[map_gtd_id_00] = 0.0
map_pred_mean = torch.mean(map_pred) # calculating the mean value of tensor
map_pred_mean = map_pred_mean.item() # change the tensor into a number
map_pred_std = torch.std(map_pred) # calculate the standard deviation
map_pred_std = map_pred_std.item() # change the tensor into a number
map_pred = (map_pred - map_pred_mean) / (map_pred_std + self.epsilon) # normalization
NSS = map_pred * map_gtd
# print("early NSS is :", NSS)
'''
dim_NSS = NSS.size()
print("dim_NSS is :", dim_NSS)
dim_NSS = dim_NSS[1]
sum_nss = 0
dim_sum = 0
for idxnss in range(0, dim_NSS):
if (NSS[0, idxnss] > 0.05): # # should not be 0, because there are a lot of 0.00XXX in map1_NSS due to float format
sum_nss += NSS[0, idxnss]
dim_sum += 1
NSS = sum_nss / dim_sum
'''
# NSS = NSS # should not add anythin, because there are a lot of 0.00XXX in map1_NSS due to float format
# id = torch.nonzero(NSS)
id = torch.gt(NSS, 0.1) # find out the id of NSS > 0.1
bignss = NSS[id]
# print(bignss)
if(len(bignss) == 0): # NSS[id] is empty
id = torch.gt(NSS, -0.00000001) # decrease the threshold, because must set it as tensor not inter
bignss = NSS[id]
# NSS = torch.sum(NSS[id])
# NSS = torch.mean(NSS)
NSS = torch.mean(bignss)
NSS = -NSS # the bigger NSS the better
return NSS
def forward(self, input):
return input / torch.sqrt(
torch.mean(input**2, dim=1, keepdim=True) + 1e-8)