ax.text(0.4,
1.04,
'Recon',
transform=ax.transAxes,
family='serif',
size=6)
#Plot the grad
ax = plt.subplot2grid((rows, cols), (i, 3), frameon=False)
frame = dataset[traj_ind][start_ind + i][1] #[2,84,84]
x = Variable(torch.from_numpy(np.array([frame])).float(),
requires_grad=True).cuda()
# dist = policy.action_dist(x)
dist = policy.action_logdist(x)
grad = torch.autograd.grad(torch.sum(dist[:, 3]), x)[0]
grad = grad.data.cpu().numpy()[
0] #for the first one in teh batch -> [2,84,84]
grad = np.abs(grad)
state1 = np.concatenate([grad[0], grad[1]], axis=1)
# ax.imshow(state1, cmap='gray', norm=NoNorm())
ax.imshow(state1, cmap='gray')
ax.set_xticks([])
ax.set_yticks([])
ax.text(0.4,
1.04,
'Grad of Real',
transform=ax.transAxes,
losses = []
for m in range(mc_samples):
eps = Variable(torch.Tensor(1, 2, 84, 84).normal_(0, 1)).cuda()
noisy_frame = frame + eps * (torch.sqrt(torch.exp(logvar)))
noisy_frame = torch.clamp(noisy_frame, min=0., max=max_val)
# noisy_frame = noisy_frame - Variable(torch.min(noisy_frame).data)
# noisy_frame = noisy_frame / Variable(torch.max(noisy_frame).data)
# noisy_frame = noisy_frame * max_val
dist_noise = policy.action_dist(noisy_frame)
log_dist_noise = policy.action_logdist(noisy_frame)
log_dist_true = policy.action_logdist(frame)
action_dist_kl = torch.sum(
(log_dist_true - log_dist_noise) * torch.exp(log_dist_true),
dim=1) #[B]
action_dist_kl = torch.mean(action_dist_kl) # * 1000.
# std of 2 is the prior, which is 4 for var, which is log(4) for logvar
# loss = action_dist_kl + (logvar - torch.log(4))**2
logvar_dif = torch.mean((logvar - .6)**2) * .0001
loss = action_dist_kl + logvar_dif
losses.append(loss)
losses = []
for m in range(mc_samples):
eps = Variable(torch.Tensor(1,2,84,84).normal_(0,1)).cuda()
noisy_frame = frame + eps*(torch.sqrt(torch.exp(logvar)))
noisy_frame = torch.clamp(noisy_frame, min=0., max=max_val)
# noisy_frame = noisy_frame - Variable(torch.min(noisy_frame).data)
# noisy_frame = noisy_frame / Variable(torch.max(noisy_frame).data)
# noisy_frame = noisy_frame * max_val
dist_noise = policy.action_dist(noisy_frame)
log_dist_noise = policy.action_logdist(noisy_frame)
log_dist_true = policy.action_logdist(frame)
action_dist_kl = torch.sum((log_dist_true - log_dist_noise)*torch.exp(log_dist_true), dim=1) #[B]
action_dist_kl = torch.mean(action_dist_kl) # * 1000.
# std of 2 is the prior, which is 4 for var, which is log(4) for logvar
# loss = action_dist_kl + (logvar - torch.log(4))**2
logvar_dif = torch.mean((logvar - .6)**2) *.0001
loss = action_dist_kl + logvar_dif
losses.append(loss)
# losses = []
# for m in range(mc_samples):
# eps = Variable(torch.Tensor(1,2,84,84).normal_(0,1)).cuda()
# noisy_frame = frame + eps*(torch.sqrt(torch.exp(mask)))
# noisy_frame = torch.clamp(noisy_frame, min=0., max=max_val)
# print (fra)
# noisy_frame = noisy_frame - Variable(torch.min(noisy_frame).data)
# noisy_frame = noisy_frame / Variable(torch.max(noisy_frame).data)
# noisy_frame = noisy_frame * max_val
# dist_mask = policy.action_dist(masked_frame)
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
# std of 2 is the prior, which is 4 for var, which is log(4) for mask
# loss = action_dist_kl + (mask - torch.log(4))**2
# mask_dif = torch.mean((mask - .6)**2) *.0001
mask_sum = torch.sum(mask_sig) * .0001
loss = action_dist_kl + mask_sum
