def forward(self, X):
G1 = Variable(torch.randn(1, 3, 10, 10))
G2 = Variable(torch.randn(1, 3, 10, 10))
G3 = Variable(torch.randn(1, 3, 10, 10))
X = X.cuda()
G1 = G1.cuda()
G2 = G2.cuda()
G3 = G3.cuda()
sum_abs = G1.abs() + G2.abs() + G3.abs()
mask_need_norm = sum_abs.ge(1)
mask_need_norm = mask_need_norm.float()
G1_norm = torch.div(G1, sum_abs)
G2_norm = torch.div(G2, sum_abs)
G3_norm = torch.div(G3, sum_abs)
G1 = torch.add(-mask_need_norm, 1) * G1 + mask_need_norm * G1_norm
G2 = torch.add(-mask_need_norm, 1) * G2 + mask_need_norm * G2_norm
G3 = torch.add(-mask_need_norm, 1) * G3 + mask_need_norm * G3_norm
output = self.Propagator.forward(X, G1, G2, G3)
return output
def test_basic_op_grad(self):
"""Grad output might need to be reshaped to match the second argument."""
x = Variable(torch.randn(4, 6), requires_grad=True)
b = Variable(torch.rand(12, 1) + 1e-2, requires_grad=True)
def y():
# .mm() depends on the grad_output being of correct size
return b.mm(Variable(torch.rand(1, 2) + 1e-2))
(x + y()).sum().backward()
(x - y()).sum().backward()
(x * y()).sum().backward()
(x / y()).sum().backward()
(x.abs() ** y()).sum().backward()
def mse(nanobots):
import numpy as np
coordinates = np.asarray([[n.x, n.y, n.z] for n in nanobots], dtype=np.int)
# coordinates.shape = (#nanobots, 3)
radius = np.asarray([n.radius for n in nanobots], dtype=np.int)
# radius.shape = (#nanobots, )
me = np.asarray([12, 12, 12], dtype=np.int)
# me.shape = (3, )
me = np.expand_dims(me, axis=0)
# me.shape = (1, 3)
distances = np.sum(np.abs(coordinates - me), axis=1)
# distances.shape = (#nanobots,)
t = radius - distances.T
in_range = np.sum((t > 0).astype(np.int))
# in_range.shape = ()
dist = np.sum(np.abs(me))
# dist.shape = ()
loss = -in_range - dist
print(coordinates)
print(radius)
print(distances)
print(t)
print(in_range)
print(loss)
import torch
from torch.autograd import Variable
coordinates = Variable(torch.Tensor(coordinates))
radius = Variable(torch.Tensor(radius))
me = Variable(torch.Tensor(me), requires_grad=True)
for t in range(10):
distances = (coordinates - me).abs().sum(1)
t = radius - distances
in_range = (t > 0).sum()
dist = me.abs().sum()
loss = -in_range - dist - distances.mean()
print('loss:', loss)
loss.backward()
print(distances)
print(t)
print(in_range)
print(dist)
print('me:', me)
print(me.grad.data)
me.data -= torch.round(me.grad.data)
me.grad.data.zero_()
def test_local_var_unary_methods(self):
''' Unit tests for methods mentioned on issue 1385
https://github.com/OpenMined/PySyft/issues/1385'''
x = Var(torch.FloatTensor([1, 2, -3, 4, 5]))
assert torch.equal(x.abs(), Var(torch.FloatTensor([1, 2, 3, 4, 5])))
assert torch.equal(x.abs_(), Var(torch.FloatTensor([1, 2, 3, 4, 5])))
x = Var(torch.FloatTensor([1, 2, -3, 4, 5]))
assert torch.equal(x.cos().int(), Var(torch.IntTensor([0, 0, 0, 0,
0])))
x = Var(torch.FloatTensor([1, 2, -3, 4, 5]))
assert torch.equal(x.cos_().int(), Var(torch.IntTensor([0, 0, 0, 0,
0])))
x = Var(torch.FloatTensor([1, 2, -3, 4, 5]))
assert torch.equal(x.ceil(), x)
assert torch.equal(x.ceil_(), x)
assert torch.equal(x.cpu(), x)
def test_local_var_unary_methods(self):
''' Unit tests for methods mentioned on issue 1385
https://github.com/OpenMined/PySyft/issues/1385'''
x = Var(torch.FloatTensor([1, 2, -3, 4, 5]))
assert torch.equal(x.abs(), Var(torch.FloatTensor([1, 2, 3, 4, 5])))
assert torch.equal(x.abs_(), Var(torch.FloatTensor([1, 2, 3, 4, 5])))
x = Var(torch.FloatTensor([1, 2, -3, 4, 5]))
assert torch.equal(x.cos().int(), Var(torch.IntTensor(
[0, 0, 0, 0, 0])))
x = Var(torch.FloatTensor([1, 2, -3, 4, 5]))
assert torch.equal(x.cos_().int(), Var(torch.IntTensor(
[0, 0, 0, 0, 0])))
x = Var(torch.FloatTensor([1, 2, -3, 4, 5]))
assert torch.equal(x.ceil(), x)
assert torch.equal(x.ceil_(), x)
assert torch.equal(x.cpu(), x)
def test_remote_var_unary_methods(self):
''' Unit tests for methods mentioned on issue 1385
https://github.com/OpenMined/PySyft/issues/1385'''
hook = TorchHook(verbose=False)
local = hook.local_worker
remote = VirtualWorker(id=2,hook=hook)
local.add_worker(remote)
x = Var(torch.FloatTensor([1, 2, -3, 4, 5])).send(remote)
assert torch.equal(x.abs().get(), Var(torch.FloatTensor([1, 2, 3, 4, 5])))
assert torch.equal(x.abs_().get(), Var(torch.FloatTensor([1, 2, 3, 4, 5])))
assert torch.equal(x.cos().int().get(), Var(torch.IntTensor(
[0, 0, 0, 0, 0])))
assert torch.equal(x.cos_().int().get(), Var(torch.IntTensor(
[0, 0, 0, 0, 0])))
x = Var(torch.FloatTensor([1, 2, -3, 4, 5])).send(remote)
assert torch.equal(x.ceil().get(), Var(torch.FloatTensor([1, 2, -3, 4, 5])))
assert torch.equal(x.ceil_().get(), Var(torch.FloatTensor([1, 2, -3, 4, 5])))
assert torch.equal(x.cpu().get(), Var(torch.FloatTensor([1, 2, -3, 4, 5])))
def test_remote_var_unary_methods(self):
''' Unit tests for methods mentioned on issue 1385
https://github.com/OpenMined/PySyft/issues/1385'''
hook = TorchHook(verbose=False)
local = hook.local_worker
remote = VirtualWorker(id=2,hook=hook)
local.add_worker(remote)
x = Var(torch.FloatTensor([1, 2, -3, 4, 5])).send(remote)
assert torch.equal(x.abs().get(), Var(torch.FloatTensor([1, 2, 3, 4, 5])))
assert torch.equal(x.abs_().get(), Var(torch.FloatTensor([1, 2, 3, 4, 5])))
assert torch.equal(x.cos().int().get(), Var(torch.IntTensor(
[0, 0, 0, 0, 0])))
assert torch.equal(x.cos_().int().get(), Var(torch.IntTensor(
[0, 0, 0, 0, 0])))
x = Var(torch.FloatTensor([1, 2, -3, 4, 5])).send(remote)
assert torch.equal(x.ceil().get(), Var(torch.FloatTensor([1, 2, -3, 4, 5])))
assert torch.equal(x.ceil_().get(), Var(torch.FloatTensor([1, 2, -3, 4, 5])))
assert torch.equal(x.cpu().get(), Var(torch.FloatTensor([1, 2, -3, 4, 5])))
def doForwardPass(numericAndLineNumbers,
surprisalTable=None,
doDropout=True,
batchSizeHere=1):
global counter
global crossEntropy
global printHere
global devLosses
global hidden
global beginning
if hidden is not None:
hidden = Variable(hidden.data).detach()
forRestart = bernoulli.sample()
#print(forRestart)
sampled = startHidden(zeroHidden)
hiddenNew = sampleToHidden(sampled).unsqueeze(0)
# hidden = forRestart.unsqueeze(0).unsqueeze(2) * hiddenNew + (1-forRestart).unsqueeze(0).unsqueeze(2) * hidden
# print(torch.where)
hidden = torch.where(
forRestart.unsqueeze(0).unsqueeze(2) == 1, hiddenNew, hidden)
beginning = torch.where(
forRestart.unsqueeze(0) == 1, zeroBeginning, beginning)
# beginning = forRestart.unsqueeze(0).unsqueeze(2) * zeroBeginning + (1-forRestart).unsqueeze(0).unsqueeze(2) * beginning
else:
sampled = startHidden(zeroHidden)
hiddenNew = sampleToHidden(sampled).unsqueeze(0)
hidden = hiddenNew
beginning = zeroBeginning
numeric, lineNumbers = numericAndLineNumbers
numeric = torch.cat([beginning, numeric], dim=0)
beginning = numeric[numeric.size()[0] - 1].view(1, args.batchSize)
loss = 0
wordNum = 0
lossWords = 0
policyGradientLoss = 0
baselineLoss = 0
optimizer.zero_grad()
for c in components:
c.zero_grad()
# for q in parameters_made:
# for p in q:
# if p.grad is not None:
# p.grad.fill_(0)
totalQuality = 0.0
if True:
inputTensor = numeric # so it will be sequence_length x args.batchSizeHere
# print inputTensor
# quit()
inputTensorIn = inputTensor[:-1]
inputTensorOut = inputTensor[1:]
inputEmbeddings = word_pos_morph_embeddings(
inputTensorIn.view(args.horizon, batchSizeHere))
if doDropout:
inputEmbeddings = inputDropout(inputEmbeddings)
if args.dropout_rate > 0:
inputEmbeddings = dropout(inputEmbeddings)
lossesWordTotal = []
sampled_vectors = []
logProbConditionals = []
for i in range(inputEmbeddings.size()[0]):
# print(i, hidden.abs().max())
torch.clamp(hidden, min=-10, max=10)
output1, hidden = rnn_both(inputEmbeddings[i].unsqueeze(0), hidden)
assert args.rnn_layers == 1
meanHidden = cellToMean(hidden[0])
klLoss = [None for _ in inputEmbeddings]
logStandardDeviationHidden = hiddenToLogSDHidden(hidden[0])
# print(torch.exp(logStandardDeviationHidden))
scaleForDist = torch.log(1 + torch.exp(logStandardDeviationHidden))
memoryDistribution = torch.distributions.Normal(loc=meanHidden,
scale=scaleForDist)
# sampled = memoryDistribution.rsample()
encodedEpsilon = standardNormalPerStep.sample()
sampled = meanHidden + scaleForDist * encodedEpsilon
sampled_vectors.append(sampled)
logProbConditional = memoryDistribution.log_prob(sampled).sum(
dim=1) # TODO not clear whether back-prob through sampled?
logProbConditionals.append(logProbConditional)
hiddenNew = sampleToHidden(sampled).unsqueeze(0)
# this also serves as the output for prediction
hidden = hiddenNew
# print(hidden.abs().max())
# output, _ = rnn_both(torch.cat([word_pos_morph_embeddings(torch.cuda.LongTensor([[2 for _ in range(args.batchSizeHere)]])), inputEmbeddings[halfSeqLen+1:]], dim=0), (hiddenNew, cellNew))
# output = torch.cat([output1[:halfSeqLen], output], dim=0)
output = hiddenNew
if doDropout:
output = dropout(output)
word_logits = decoder(output)
word_logits = word_logits.view(batchSizeHere, outVocabSize)
word_softmax = logsoftmax(word_logits)
lossesWord = lossModuleTest(word_softmax,
inputTensorOut[i].view(batchSizeHere))
lossesWordTotal.append(lossesWord)
lossesWord = torch.stack(lossesWordTotal, dim=0)
lossWords = lossesWord.sum(dim=0).sum(dim=0)
loss = lossesWord.sum()
klLoss = 0
batchSizeInflatedHere = args.batchSize * len(sampled_vectors)
sampledTotal = torch.stack(sampled_vectors,
dim=0).view(batchSizeInflatedHere, -1)
logProbConditionalsTotal = torch.stack(
logProbConditionals, dim=0).view(batchSizeInflatedHere)
# print(sampledTotal.size(), logProbConditionalsTotal.size())
#for sampled, logProbConditional in zip(sampled_vectors, logProbConditionals):
adjustment = []
epsilon = sampledTotal
logdet = torch.autograd.Variable(
torch.from_numpy(
np.zeros(batchSizeInflatedHere).astype('float32')).cuda())
# n=1
context = torch.autograd.Variable(
torch.from_numpy(
np.zeros((batchSizeInflatedHere,
context_dim)).astype('float32')).cuda())
for flowStep in range(args.flow_length):
epsilon, logdet, context = flows[flowStep](
(epsilon, logdet, context))
if flowStep + 1 < args.flow_length:
epsilon, logdet, context = torchkit.flows.FlipFlow(1)(
(epsilon, logdet, context))
plainPriorLogProb = standardNormal.log_prob(epsilon).sum(
dim=1) #- (0.5 * torch.sum(sampled * sampled, dim=1))
logProbMarginal = plainPriorLogProb + logdet
klLoss = (logProbConditionalsTotal - logProbMarginal)
# print(logProbConditional, logProbMarginal)
# print(logStandardDeviationHidden)
# klLoss = 0.5 * (-1 - 2 * (logStandardDeviationHidden) + torch.pow(meanHidden, 2) + torch.exp(2*logStandardDeviationHidden))
# klLoss = klLoss.sum(1)
klLossSum = klLoss.sum()
if counter % 10 == 0:
klLossMean = klLoss.mean()
print(args.beta, args.flow_length, klLossMean, lossesWord.mean(),
args.beta * klLoss.mean() + lossesWord.mean())
if float(klLossMean) != float(klLossMean):
print(hidden.abs().max())
assert False, "got NA, abort"
loss = loss + args.beta * klLossSum
# print lossesWord
if surprisalTable is not None or True:
lossesCPU = lossesWord.data.cpu().view((args.horizon),
batchSizeHere).numpy()
if True:
for i in range(0, args.horizon
): #range(1,maxLength+1): # don't include i==0
j = 0
lineNum = int(lineNumbers[i][j])
print(i, itos_total[numeric[i + 1][j]], lossesCPU[i][j],
lineNum)
while lineNum >= len(completeData):
completeData.append([[], 0])
completeData[lineNum][0].append(itos_total[numeric[i +
1][j]])
completeData[lineNum][1] += lossesCPU[i][j]
if surprisalTable is not None:
if printHere:
print surprisalTable
for j in range(batchSizeHere):
for r in range(args.horizon):
surprisalTable[r] += lossesCPU[
r, j] #.data.cpu().numpy()[0]
wordNum = (args.horizon - 1) * batchSizeHere
if wordNum == 0:
print input_words
print batchOrdered
return 0, 0, 0, 0, 0
if printHere:
print loss / wordNum
print lossWords / wordNum
print["CROSS ENTROPY", crossEntropy, exp(crossEntropy)]
print("beta", args.beta)
crossEntropy = 0.99 * crossEntropy + 0.01 * (lossWords /
wordNum).data.cpu().numpy()
totalQuality = loss.data.cpu().numpy(
) # consists of lossesWord + lossesPOS
numberOfWords = wordNum
# probabilities = torch.sigmoid(dhWeights)
# neg_entropy = torch.sum( probabilities * torch.log(probabilities) + (1-probabilities) * torch.log(1-probabilities))
# policy_related_loss = lr_policy * (entropy_weight * neg_entropy + policyGradientLoss) # lives on CPU
loss = loss / batchSizeHere
return loss, None, None, totalQuality, numberOfWords, klLoss.mean()
Note: 1. G1~G3 constitute the affinity, they can be a bounch of output maps coming from any CNN, with the input of any useful known information (e.g., RGB images) 2. for any pixel i, |G1(i)| + |G2(i)| + |G3(i)| <= 1 is a sufficent condition for model stability (see paper) """ import torch from torch.autograd import Variable from pytorch_spn.modules.gaterecurrent2dnoind import GateRecurrent2dnoind Propagator = GateRecurrent2dnoind(True, False) X = Variable(torch.randn(1, 3, 10, 10)) G1 = Variable(torch.randn(1, 3, 10, 10)) G2 = Variable(torch.randn(1, 3, 10, 10)) G3 = Variable(torch.randn(1, 3, 10, 10)) sum_abs = G1.abs() + G2.abs() + G3.abs() mask_need_norm = sum_abs.ge(1) mask_need_norm = mask_need_norm.float() G1_norm = torch.div(G1, sum_abs) G2_norm = torch.div(G2, sum_abs) G3_norm = torch.div(G3, sum_abs) G1 = torch.add(-mask_need_norm, 1) * G1 + mask_need_norm * G1_norm G2 = torch.add(-mask_need_norm, 1) * G2 + mask_need_norm * G2_norm G3 = torch.add(-mask_need_norm, 1) * G3 + mask_need_norm * G3_norm X = X.cuda() G1 = G1.cuda() G2 = G2.cuda() G3 = G3.cuda()
def evaluate_fnet_multiround(inet,
fnet,
dataloader,
nactors,
nchannels=3,
targets=None,
concat_input=False):
print('Evaluate fusion network')
inet.eval()
fnet.eval()
total = 0
nbatches = 0
corrects = [0, 0, 0]
runtimes = [0, 0, 0]
sum_mae = 0
sum_mse = 0
criterionMSE = nn.MSELoss(reduction='mean')
for batch_idx, (inputs, targets) in enumerate(dataloader):
nbatches += 1
targets = Variable(targets).cuda()
inputs = Variable(inputs).cuda()
batch_size, C, H, W = inputs.shape
total += batch_size
x = inputs.unsqueeze(0).expand(nactors, batch_size, C, H,
W).contiguous().view(
nactors * batch_size, C, H, W)
x = x[torch.randperm(x.shape[0]), ...]
_, z_c, _ = inet(x)
z_c = z_c.view(nactors, batch_size, z_c.shape[1], z_c.shape[2],
z_c.shape[3])
sum_z = z_c.sum(dim=0)
x = x.view(nactors, batch_size, C, H, W)
x_g = x[torch.randperm(nactors), ...].permute(1, 0, 2, 3, 4)
img_fused = fnet(x_g)
_, z_fused, _ = inet(img_fused)
for k in range(nactors):
z = sum_z - z_c[k, ...]
z_hat = nactors * z_fused - z
out_hat = inet.module.classifier(z_hat)
inputs = x[k, ...]
out, _, _ = inet(inputs)
_, y = torch.max(out.data, 1)
_, y_hat = torch.max(out_hat.data, 1)
corrects[2] += y_hat.eq(y.data).sum().cpu().item() / nactors
input_hat = inet.module.inverse(z_hat)
mae = torch.norm(
(input_hat - inputs).view(batch_size, -1),
dim=1) / torch.norm(inputs.view(batch_size, -1), dim=1)
sum_mae += mae.mean().cpu().item() / nactors
sum_mse += criterionMSE(input_hat,
inputs).mean().cpu().item() / nactors
max = inputs.abs().max().cpu().item()
min = (input_hat - inputs).abs().max().cpu().item()
del _, z_c, z_hat, z_fused, inputs, targets, img_fused, out, out_hat, y, y_hat
# Evaluate time and classification performance
corrects = 100 * np.asarray(corrects) / (total)
print('\t == Correctly classified: X {:.4f} X_hat {:.4f} Match {:.4f}'.
format(corrects[0], corrects[1], corrects[2]))
print('\t == MAE: {:.4f}, MSE: {:.4f} in {:.4f} -> {:.4f}'.format(
sum_mae / nbatches, sum_mse / nbatches, min, max))
runtimes = np.asarray(runtimes) / nbatches
print(
'Average time G: {:.4f}, E: {:.4f}, C: {:.4f} over {} batches of size {}'
.format(runtimes[0], runtimes[1], runtimes[2], nbatches,
batch_size))
def main ():
# Generator
if generator_name == 'sine':
generator = generate_sine
elif generator_name == 'spikes':
generator = generate_spikes
else:
raise "Generator {} not supported.".format(generator_name)
# Reproducibility
np.random.seed(seed)
torch.manual_seed(seed)
# Learnable, universal filter coefficients
params = np.random.randn(num_params)
params = transform_params(params)
# Pytorch variables
params = Variable(torch.FloatTensor(params), requires_grad=True)
indices = Variable(torch.arange(num_params).type(dtype) / np.float(num_params - 1), requires_grad=False)
x = Variable(torch.randn(input_shape).type(dtype), requires_grad=False)
# Wavenet instance
w = Wavenet(params, input_shape)
# Optimiser
optimiser = torch.optim.Adam([params], lr=1e-02)
lambda_reg = 1.0E+02
num_steps = 5000
# Regularisation
reg = Regularisation(params)
# Training loop
print "Initial parameters:", params.data.numpy()
loss_dict = lambda : {'sparsity': 0, 'regularisation': 0, 'combined': 0, 'compactness': 0}
losses = {'sparsity': [0], 'regularisation': [0], 'combined': [0], 'compactness': [0]}
print "=" * 80
print "START RUNNING"
print "-" * 80
start = time.time()
for step, x_ in enumerate(generator(**gen_opts)):
# Stop condition
if step >= num_steps:
break
# Set input
x.data = torch.from_numpy(x_).type(dtype)
# Get wavelet coefficients
c = w.forward(x)
# Sparsity loss
sparsity = 1. - gini(c)
# Regularisation loss
regularisation = reg.forward()
# Compactness loss
compactness = torch.sum(torch.dot(indices - 0.5, params.abs() / params.abs().sum()))
# Combined loss
combined = sparsity + lambda_reg * (regularisation) + compactness
# Perform backpropagation
combined.backward()
# Parameter update
if step % batch_size == 0:
optimiser.step()
optimiser.zero_grad()
pass
# Non-essential stuff below
# -------------------------------------------------------------------------
# Log
if step % 1000 == 0:
print "Step {}/{}".format(step, num_steps)
pass
# Logging loss history
losses['sparsity'][-1] += np.float(sparsity)
losses['regularisation'][-1] += np.float(regularisation)
losses['compactness'][-1] += np.float(compactness)
losses['combined'][-1] += np.float(combined)
if step % batch_size == 0:
for key in losses:
losses[key][-1] /= float(batch_size)
losses[key].append(0.)
pass
pass
# Draw model diagram
if step == 0:
from torchviz import make_dot
dot = make_dot(sparsity, params={'params': params, 'input': x})
dot.format = 'pdf'
dot.render('output/model')
pass
pass
end = time.time()
print "-" * 80
print "Took {:.1f} sec.".format(end - start)
print "=" * 80
# Clean-up
for key in losses:
losses[key].pop(-1)
pass
print "Final parameters:", params.data.numpy()
# Save to file
tag = '{}__N{}__{}'.format('x'.join(map(str, input_shape)), num_params, generator_name)
# -- Model
torch.save(w, 'output/model__{}.pt'.format(tag))
# -- Loss
with open('output/loss__{}.json'.format(tag), 'w') as f:
json.dump(losses, f)
pass
return
def train(self):
# Set up training.
real_o = Variable(torch.FloatTensor(self.batch_size, 3, 64, 64).cuda(),
requires_grad=False)
real_o_next = Variable(torch.FloatTensor(self.batch_size, 3, 64,
64).cuda(),
requires_grad=False)
label = Variable(torch.FloatTensor(self.batch_size).cuda(),
requires_grad=False)
z = Variable(torch.FloatTensor(self.batch_size,
self.rand_z_dim).cuda(),
requires_grad=False)
criterionD = nn.BCELoss().cuda()
optimD = optim.Adam([{
'params': self.D.parameters()
}],
lr=self.lr_d,
betas=(0.5, 0.999))
optimG = optim.Adam([{
'params': self.G.parameters()
}, {
'params': self.Q.parameters()
}, {
'params': self.T.parameters()
}],
lr=self.lr_g,
betas=(0.5, 0.999))
############################################
# Load rope dataset and apply transformations
rope_path = os.path.realpath(self.data_dir)
def filter_background(x):
x[:, (x < 0.3).any(dim=0)] = 0.0
return x
def dilate(x):
x = x.squeeze(0).numpy()
x = grey_dilation(x, size=3)
x = x[None, :, :]
return torch.from_numpy(x)
trans = [
transforms.Resize(64),
transforms.CenterCrop(64),
transforms.ToTensor(),
filter_background,
# transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
]
if not self.fcn:
# If fcn it will do the transformation to gray
# and normalize in the loop.
# trans.append(transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)))
if self.gray:
# Apply grayscale transformation.
trans.append(lambda x: x.mean(dim=0)[None, :, :])
trans.append(dilate)
trans.append(transforms.Normalize((0.5, ), (0.5, )))
trans_comp = transforms.Compose(trans)
# Image 1 and image 2 are k steps apart.
dataset = ImagePairs(root=rope_path,
transform=trans_comp,
n_frames_apart=self.k)
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=self.batch_size,
shuffle=True,
num_workers=2,
drop_last=True)
from torchvision.utils import save_image
imgs = next(iter(dataloader))[0][0]
save_image(imgs * 0.5 + 0.5, 'train_img.png')
############################################
# Load eval plan dataset
planning_data_dir = self.planning_data_dir
dataset_start = dset.ImageFolder(root=os.path.join(
planning_data_dir, 'start'),
transform=trans_comp)
dataset_goal = dset.ImageFolder(root=os.path.join(
planning_data_dir, 'goal'),
transform=trans_comp)
data_start_loader = torch.utils.data.DataLoader(dataset_start,
batch_size=1,
shuffle=False,
num_workers=1,
drop_last=True)
data_goal_loader = torch.utils.data.DataLoader(dataset_goal,
batch_size=1,
shuffle=False,
num_workers=1,
drop_last=True)
############################################
for epoch in range(self.n_epochs + 1):
self.G.train()
self.D.train()
self.Q.train()
self.T.train()
for num_iters, batch_data in enumerate(dataloader, 0):
# Real data
o = batch_data[0]
o_next = batch_data[1]
bs = o.size(0)
real_o.data.resize_(o.size())
real_o_next.data.resize_(o_next.size())
label.data.resize_(bs)
real_o.data.copy_(o)
real_o_next.data.copy_(o_next)
if self.fcn:
real_o = self.apply_fcn_mse(o)
real_o_next = self.apply_fcn_mse(o_next)
if real_o.abs().max() > 1:
import ipdb
ipdb.set_trace()
assert real_o.abs().max() <= 1
if epoch == 0:
break
############################################
# D Loss (Update D)
optimD.zero_grad()
# Real data
probs_real = self.D(real_o, real_o_next)
label.data.fill_(1)
loss_real = criterionD(probs_real, label)
loss_real.backward()
# Fake data
z, c, c_next = self._noise_sample(z, bs)
fake_o, fake_o_next = self.G(z, c, c_next)
probs_fake = self.D(fake_o.detach(), fake_o_next.detach())
label.data.fill_(0)
loss_fake = criterionD(probs_fake, label)
loss_fake.backward()
D_loss = loss_real + loss_fake
optimD.step()
############################################
# G loss (Update G)
optimG.zero_grad()
probs_fake_2 = self.D(fake_o, fake_o_next)
label.data.fill_(1)
G_loss = criterionD(probs_fake_2, label)
# Q loss (Update G, T, Q)
ent_loss = -self.P.log_prob(c).mean(0)
crossent_loss = -self.Q.log_prob(fake_o, c).mean(0)
crossent_loss_next = -self.Q.log_prob(fake_o_next,
c_next).mean(0)
# trans_prob = self.T.get_prob(Variable(torch.eye(self.dis_c_dim).cuda()))
ent_loss_next = -self.T.log_prob(c, None, c_next).mean(0)
mi_loss = crossent_loss - ent_loss
mi_loss_next = crossent_loss_next - ent_loss_next
Q_loss = mi_loss + mi_loss_next
# T loss (Update T)
Q_c_given_x, Q_c_given_x_var = (
i.detach() for i in self.Q.forward(real_o))
t_mu, t_variance = self.T.get_mu_and_var(c)
t_diff = t_mu - c
# Keep the variance small.
# TODO: add loss on t_diff
T_loss = (t_variance**2).sum(1).mean(0)
(G_loss + self.infow * Q_loss +
self.transw * T_loss).backward()
optimG.step()
#############################################
# Logging (iteration)
if num_iters % 100 == 0:
self.log_dict['Dloss'] = D_loss.item()
self.log_dict['Gloss'] = G_loss.item()
self.log_dict['Qloss'] = Q_loss.item()
self.log_dict['Tloss'] = T_loss.item()
self.log_dict['mi_loss'] = mi_loss.item()
self.log_dict['mi_loss_next'] = mi_loss_next.item()
self.log_dict['ent_loss'] = ent_loss.item()
self.log_dict['ent_loss_next'] = ent_loss_next.item()
self.log_dict['crossent_loss'] = crossent_loss.item()
self.log_dict[
'crossent_loss_next'] = crossent_loss_next.item()
self.log_dict['D(real)'] = probs_real.data.mean()
self.log_dict['D(fake)_before'] = probs_fake.data.mean()
self.log_dict['D(fake)_after'] = probs_fake_2.data.mean()
write_stats_from_var(self.log_dict, Q_c_given_x,
'Q_c_given_real_x_mu')
write_stats_from_var(self.log_dict,
Q_c_given_x,
'Q_c_given_real_x_mu',
idx=0)
write_stats_from_var(self.log_dict, Q_c_given_x_var,
'Q_c_given_real_x_variance')
write_stats_from_var(self.log_dict,
Q_c_given_x_var,
'Q_c_given_real_x_variance',
idx=0)
write_stats_from_var(self.log_dict, t_mu, 't_mu')
write_stats_from_var(self.log_dict, t_mu, 't_mu', idx=0)
write_stats_from_var(self.log_dict, t_diff, 't_diff')
write_stats_from_var(self.log_dict,
t_diff,
't_diff',
idx=0)
write_stats_from_var(self.log_dict, t_variance,
't_variance')
write_stats_from_var(self.log_dict,
t_variance,
't_variance',
idx=0)
print('\n#######################'
'\nEpoch/Iter:%d/%d; '
'\nDloss: %.3f; '
'\nGloss: %.3f; '
'\nQloss: %.3f, %.3f; '
'\nT_loss: %.3f; '
'\nEnt: %.3f, %.3f; '
'\nCross Ent: %.3f, %.3f; '
'\nD(x): %.3f; '
'\nD(G(z)): b %.3f, a %.3f;'
'\n0_Q_c_given_rand_x_mean: %.3f'
'\n0_Q_c_given_rand_x_std: %.3f'
'\n0_Q_c_given_fixed_x_std: %.3f'
'\nt_diff_abs_mean: %.3f'
'\nt_std_mean: %.3f' % (
epoch,
num_iters,
D_loss.item(),
G_loss.item(),
mi_loss.item(),
mi_loss_next.item(),
T_loss.item(),
ent_loss.item(),
ent_loss_next.item(),
crossent_loss.item(),
crossent_loss_next.item(),
probs_real.data.mean(),
probs_fake.data.mean(),
probs_fake_2.data.mean(),
Q_c_given_x[:, 0].cpu().numpy().mean(),
Q_c_given_x[:, 0].cpu().numpy().std(),
np.sqrt(Q_c_given_x_var[:,
0].cpu().numpy().mean()),
t_diff.data.abs().mean(),
t_variance.data.sqrt().mean(),
))
#############################################
# Start evaluation from here.
self.G.eval()
self.D.eval()
self.Q.eval()
self.T.eval()
#############################################
# Save images
# Plot fake data
x_save, x_next_save = self.G(*self.eval_input,
self.get_c_next(epoch))
save_image(x_save.data,
os.path.join(self.out_dir, 'gen',
'curr_samples_%03d.png' % epoch),
nrow=self.test_num_codes,
normalize=True)
save_image(x_next_save.data,
os.path.join(self.out_dir, 'gen',
'next_samples_%03d.png' % epoch),
nrow=self.test_num_codes,
normalize=True)
save_image((x_save - x_next_save).data,
os.path.join(self.out_dir, 'gen',
'diff_samples_%03d.png' % epoch),
nrow=self.test_num_codes,
normalize=True)
# Plot real data.
if epoch % 10 == 0:
save_image(real_o.data,
os.path.join(self.out_dir, 'real',
'real_samples_%d.png' % epoch),
nrow=self.test_num_codes,
normalize=True)
save_image(real_o_next.data,
os.path.join(self.out_dir, 'real',
'real_samples_next_%d.png' % epoch),
nrow=self.test_num_codes,
normalize=True)
#############################################
# Save parameters
if epoch % 5 == 0:
if not os.path.exists('%s/var' % self.out_dir):
os.makedirs('%s/var' % self.out_dir)
for i in [self.G, self.D, self.Q, self.T]:
torch.save(
i.state_dict(),
os.path.join(self.out_dir, 'var', '%s_%d' % (
i.__class__.__name__,
epoch,
)))
#############################################
# Logging (epoch)
for k, v in self.log_dict.items():
log_value(k, v, epoch)
if epoch > 0:
# tf logger
# log_value('avg|x_next - x|', (x_next_save.data - x_save.data).abs().mean(dim=0).sum(), epoch + 1)
# self.logger.histo_summary("Q_c_given_x", Q_c_given_x.data.cpu().numpy().reshape(-1), step=epoch)
# self.logger.histo_summary("Q_c0_given_x", Q_c_given_x[:, 0].data.cpu().numpy(), step=epoch)
# self.logger.histo_summary("Q_c_given_x_var", Q_c_given_x_var.cpu().numpy().reshape(-1), step=epoch)
# self.logger.histo_summary("Q_c0_given_x_var", Q_c_given_x_var[:, 0].data.cpu().numpy(), step=epoch)
# csv log
with open(os.path.join(self.out_dir, 'progress.csv'),
'a') as csv_file:
writer = csv.writer(csv_file)
if epoch == 1:
writer.writerow(["epoch"] + list(self.log_dict.keys()))
writer.writerow([
"%.3f" % _tmp
for _tmp in [epoch] + list(self.log_dict.values())
])
#############################################
# Do planning?
if self.plan_length <= 0 or epoch not in self.planning_epoch:
continue
print("\n#######################" "\nPlanning")
#############################################
# Showing plans on real images using best code.
# Min l2 distance from start and goal real images.
self.plan_hack(data_start_loader, data_goal_loader, epoch, 'L2')
# Min classifier distance from start and goal real images.
self.plan_hack(data_start_loader, data_goal_loader, epoch,
'classifier')
def train(self):
# Set up training.
real_o = Variable(torch.FloatTensor(self.batch_size, 3, 64, 64).cuda(), requires_grad=False)
real_o_next = Variable(torch.FloatTensor(self.batch_size, 3, 64, 64).cuda(), requires_grad=False)
label = Variable(torch.FloatTensor(self.batch_size).cuda(), requires_grad=False)
z = Variable(torch.FloatTensor(self.batch_size, self.rand_z_dim).cuda(), requires_grad=False)
criterionD = nn.BCELoss().cuda()
optimD = optim.Adam([{'params': self.D.parameters()}], lr=self.lr_d,
betas=(0.5, 0.999))
optimG = optim.Adam([{'params': self.G.parameters()},
{'params': self.Q.parameters()},
{'params': self.T.parameters()}], lr=self.lr_g,
betas=(0.5, 0.999))
############################################
# Load rope dataset and apply transformations
rope_path = os.path.realpath(self.data_dir)
trans = [
transforms.Resize(64),
transforms.CenterCrop(64),
transforms.ToTensor(),
# transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
]
if not self.fcn:
# If fcn it will do the transformation to gray
# and normalize in the loop.
trans.append(transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)))
if self.gray:
# Apply grayscale transformation.
trans.append(lambda x: x.mean(dim=0)[None, :, :])
trans_comp = transforms.Compose(trans)
# Image 1 and image 2 are k steps apart.
dataset = ImagePairs(root=rope_path,
transform=trans_comp,
n_frames_apart=self.k)
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=self.batch_size,
shuffle=True,
num_workers=2,
drop_last=True)
############################################
# Load eval plan dataset
planning_data_dir = self.planning_data_dir
dataset_start = dset.ImageFolder(root=os.path.join(planning_data_dir, 'start'),
transform=trans_comp)
dataset_goal = dset.ImageFolder(root=os.path.join(planning_data_dir, 'goal'),
transform=trans_comp)
data_start_loader = torch.utils.data.DataLoader(dataset_start,
batch_size=1,
shuffle=False,
num_workers=1,
drop_last=True)
data_goal_loader = torch.utils.data.DataLoader(dataset_goal,
batch_size=1,
shuffle=False,
num_workers=1,
drop_last=True)
############################################
for epoch in range(self.n_epochs + 1):
self.G.train()
self.D.train()
self.Q.train()
self.T.train()
for num_iters, batch_data in enumerate(dataloader, 0):
#print('going to sleep')
#time.sleep(2)
#print('waking up')
# Real data
o, _ = batch_data[0]
o_next, _ = batch_data[1]
bs = o.size(0)
real_o.data.resize_(o.size())
real_o_next.data.resize_(o_next.size())
label.data.resize_(bs)
real_o.data.copy_(o)
real_o_next.data.copy_(o_next)
# Plot real data:
if epoch % 10 == 0:
save_image(real_o.data,
os.path.join(self.out_dir, 'real', 'real_preFcn_samples_%d.png' % epoch),
nrow=self.test_num_codes,
normalize=True)
save_image(real_o_next.data,
os.path.join(self.out_dir, 'real', 'real_preFcn_samples_next_%d.png' % epoch),
nrow=self.test_num_codes,
normalize=True)
if self.fcn:
real_o = self.apply_fcn_mse(o) # a grey scale img [-1,1]. each pixel has the probability of being a part of the object
real_o_next = self.apply_fcn_mse(o_next)
if real_o.abs().max() > 1:
import ipdb;
ipdb.set_trace()
assert real_o.abs().max() <= 1
if epoch == 0:
break
############################################
# D Loss (Update D)
optimD.zero_grad()
# Real data
probs_real = self.D(real_o, real_o_next)
label.data.fill_(1) # label of real data is 1
loss_real = criterionD(probs_real, label)
loss_real.backward() # weight gradCalc of descriminator only (realData -> Descriminator -> Loss)
# Fake data
z, c, c_next = self._noise_sample(z, bs) # z,c have normal distribution; c_next has a gaussian distribution with mean = c and some default variance value
fake_o, fake_o_next = self.G(z, c, c_next)
probs_fake = self.D(fake_o.detach(), fake_o_next.detach())
label.data.fill_(0) # label of fake data is 0
loss_fake = criterionD(probs_fake, label)
loss_fake.backward() # weight gradCalc of descriminator only (because of detach (randNoise -> generator -> detach -> fakeData -> Descriminator -> Loss)
D_loss = loss_real + loss_fake
optimD.step() # weight update of D
############################################
# G loss (Update G)
optimG.zero_grad()
probs_fake_2 = self.D(fake_o, fake_o_next)
label.data.fill_(1) # the generator should make the discriminator output an 1 (i.e real)
G_loss = criterionD(probs_fake_2, label)
# Q loss (Update G, T, Q)
ent_loss = -self.P.log_prob(c).mean(0) # always equals log(2), only size(c) is used
crossent_loss = -self.Q.log_prob(fake_o, c).mean(0)
# fake_o is forward through an NN Q that outputs (mu,var). creates a probability function into which c is placed.
# then we have the probability of each c given it's o_fake. we take a mean.
crossent_loss_next = -self.Q.log_prob(fake_o_next, c_next).mean(0)
# trans_prob = self.T.get_prob(Variable(torch.eye(self.dis_c_dim).cuda()))
ent_loss_next = -self.T.log_prob(c, None, c_next).mean(0)
mi_loss = crossent_loss - ent_loss
mi_loss_next = crossent_loss_next - ent_loss_next
Q_loss = mi_loss + mi_loss_next
# T loss (Update T)
Q_c_given_x, Q_c_given_x_var = (i.detach() for i in self.Q.forward(real_o))
t_mu, t_variance = self.T.get_mu_and_var(c)
t_diff = t_mu - c
# Keep the variance small.
# TODO: add loss on t_diff
T_loss = (t_variance ** 2).sum(1).mean(0)
(G_loss +
self.infow * Q_loss +
self.transw * T_loss).backward()
optimG.step()
#############################################
# Logging (iteration)
if num_iters % 100 == 0:
os.system('nvidia-settings -q gpucoretemp')
#print('going to sleep')
#time.sleep(20)
os.system('nvidia-settings -q gpucoretemp')
#print('waking up')
self.log_dict['Dloss'] = D_loss.data[0]
self.log_dict['Gloss'] = G_loss.data[0]
self.log_dict['Qloss'] = Q_loss.data[0]
self.log_dict['Tloss'] = T_loss.data[0]
self.log_dict['mi_loss'] = mi_loss.data[0]
self.log_dict['mi_loss_next'] = mi_loss_next.data[0]
self.log_dict['ent_loss'] = ent_loss.data[0]
self.log_dict['ent_loss_next'] = ent_loss_next.data[0]
self.log_dict['crossent_loss'] = crossent_loss.data[0]
self.log_dict['crossent_loss_next'] = crossent_loss_next.data[0]
self.log_dict['D(real)'] = probs_real.data.mean()
self.log_dict['D(fake)_before'] = probs_fake.data.mean()
self.log_dict['D(fake)_after'] = probs_fake_2.data.mean()
write_stats_from_var(self.log_dict, Q_c_given_x, 'Q_c_given_real_x_mu')
write_stats_from_var(self.log_dict, Q_c_given_x, 'Q_c_given_real_x_mu', idx=0)
write_stats_from_var(self.log_dict, Q_c_given_x_var, 'Q_c_given_real_x_variance')
write_stats_from_var(self.log_dict, Q_c_given_x_var, 'Q_c_given_real_x_variance', idx=0)
write_stats_from_var(self.log_dict, t_mu, 't_mu')
write_stats_from_var(self.log_dict, t_mu, 't_mu', idx=0)
write_stats_from_var(self.log_dict, t_diff, 't_diff')
write_stats_from_var(self.log_dict, t_diff, 't_diff', idx=0)
write_stats_from_var(self.log_dict, t_variance, 't_variance')
write_stats_from_var(self.log_dict, t_variance, 't_variance', idx=0)
print('\n#######################'
'\nEpoch/Iter:%d/%d; '
'\nDloss: %.3f; '
'\nGloss: %.3f; '
'\nQloss: %.3f, %.3f; '
'\nT_loss: %.3f; '
'\nEnt: %.3f, %.3f; '
'\nCross Ent: %.3f, %.3f; '
'\nD(x): %.3f; '
'\nD(G(z)): b %.3f, a %.3f;'
'\n0_Q_c_given_rand_x_mean: %.3f'
'\n0_Q_c_given_rand_x_std: %.3f'
'\n0_Q_c_given_fixed_x_std: %.3f'
'\nt_diff_abs_mean: %.3f'
'\nt_std_mean: %.3f'
% (epoch, num_iters,
D_loss.data[0],
G_loss.data[0],
mi_loss.data[0], mi_loss_next.data[0],
T_loss.data[0],
ent_loss.data[0], ent_loss_next.data[0],
crossent_loss.data[0], crossent_loss_next.data[0],
probs_real.data.mean(),
probs_fake.data.mean(), probs_fake_2.data.mean(),
Q_c_given_x[:, 0].data.mean(),
Q_c_given_x[:, 0].data.std(),
np.sqrt(Q_c_given_x_var[:, 0].data.mean()),
t_diff.data.abs().mean(),
t_variance.data.sqrt().mean(),
))
#############################################
# Start evaluation from here.
self.G.eval()
self.D.eval()
self.Q.eval()
self.T.eval()
#############################################
# Save images
# Plot fake data
x_save, x_next_save = self.G(*self.eval_input, self.get_c_next(epoch))
save_image(x_save.data,
os.path.join(self.out_dir, 'gen', 'curr_samples_%03d.png' % epoch),
nrow=self.test_num_codes,
normalize=True)
save_image(x_next_save.data,
os.path.join(self.out_dir, 'gen', 'next_samples_%03d.png' % epoch),
nrow=self.test_num_codes,
normalize=True)
save_image((x_save - x_next_save).data,
os.path.join(self.out_dir, 'gen', 'diff_samples_%03d.png' % epoch),
nrow=self.test_num_codes,
normalize=True)
# Plot real data.
if epoch % 10 == 0:
save_image(real_o.data,
os.path.join(self.out_dir, 'real', 'real_samples_%d.png' % epoch),
nrow=self.test_num_codes,
normalize=True)
save_image(real_o_next.data,
os.path.join(self.out_dir, 'real', 'real_samples_next_%d.png' % epoch),
nrow=self.test_num_codes,
normalize=True)
#############################################
# Save parameters
if epoch % 5 == 0:
if not os.path.exists('%s/var' % self.out_dir):
os.makedirs('%s/var' % self.out_dir)
for i in [self.G, self.D, self.Q, self.T]:
torch.save(i.state_dict(),
os.path.join(self.out_dir,
'var',
'%s_%d' % (i.__class__.__name__, epoch,
)))
#############################################
# Logging (epoch)
for k, v in self.log_dict.items():
log_value(k, v, epoch)
if epoch > 0:
# tf logger
# log_value('avg|x_next - x|', (x_next_save.data - x_save.data).abs().mean(dim=0).sum(), epoch + 1)
# self.logger.histo_summary("Q_c_given_x", Q_c_given_x.data.cpu().numpy().reshape(-1), step=epoch)
# self.logger.histo_summary("Q_c0_given_x", Q_c_given_x[:, 0].data.cpu().numpy(), step=epoch)
# self.logger.histo_summary("Q_c_given_x_var", Q_c_given_x_var.cpu().numpy().reshape(-1), step=epoch)
# self.logger.histo_summary("Q_c0_given_x_var", Q_c_given_x_var[:, 0].data.cpu().numpy(), step=epoch)
# csv log
with open(os.path.join(self.out_dir, 'progress.csv'), 'a') as csv_file:
writer = csv.writer(csv_file)
if epoch == 1:
writer.writerow(["epoch"] + list(self.log_dict.keys()))
writer.writerow(["%.3f" % _tmp for _tmp in [epoch] + list(self.log_dict.values())])
#############################################
# Do planning?
if self.plan_length <= 0 or epoch not in self.planning_epoch:
continue
print("\n#######################"
"\nPlanning")
#############################################
# Showing plans on real images using best code.
# Min l2 distance from start and goal real images.
self.plan_hack(data_start_loader,
data_goal_loader,
epoch,
'L2')
# Min classifier distance from start and goal real images.
self.plan_hack(data_start_loader,
data_goal_loader,
epoch,
'classifier')
dhWeights = Variable(torch.FloatTensor([0.0] * len(itos_deps)),
requires_grad=True)
distanceWeights = Variable(torch.FloatTensor([0.0] * len(itos_deps)),
requires_grad=True)
for i, key in enumerate(itos_deps):
dhLogits[key] = 0.0
if key == ("VERB", "obj", "NOUN"):
dhLogits[key] = (10.0 if random() > 0.5 else -10.0)
dhWeights.data[i] = dhLogits[key]
originalDistanceWeights[key] = 0.0 #random()
distanceWeights.data[i] = originalDistanceWeights[key]
assert float(dhWeights.abs().max()) > 5
words = list(vocab.iteritems())
words = sorted(words, key=lambda x: x[1], reverse=True)
itos = map(lambda x: x[0], words)
stoi = dict(zip(itos, range(len(itos))))
if len(itos) > 6:
assert stoi[itos[5]] == 5
vocab_size = 50000
word_embeddings = torch.nn.Embedding(num_embeddings=vocab_size + 3,
embedding_dim=1) #.cuda()
pos_u_embeddings = torch.nn.Embedding(num_embeddings=len(posUni) + 3,
embedding_dim=1) #.cuda()
def forward(numericAndLineNumbers,
surprisalTable=None,
doDropout=True,
batchSizeHere=1):
global counter
global crossEntropy
global printHere
global devLosses
global hidden
global beginning
global beginning
global beginning_chars
if hidden is not None:
hidden = Variable(hidden.data).detach()
forRestart = bernoulli.sample()
#print(forRestart)
hiddenNew = startHidden(zeroHidden).unsqueeze(0)
# print(hiddenNew.size(), hidden.size())
hidden = torch.where(
forRestart.unsqueeze(0).unsqueeze(2) == 1, hiddenNew, hidden)
beginning = torch.where(
forRestart.unsqueeze(0) == 1, zeroBeginning, beginning)
# beginning = forRestart.unsqueeze(0).unsqueeze(2) * zeroBeginning + (1-forRestart).unsqueeze(0).unsqueeze(2) * beginning
beginning_chars = torch.where(
forRestart.unsqueeze(0).unsqueeze(2) == 1, zeroBeginning_chars,
beginning_chars)
else:
hidden = startHidden(zeroHidden).unsqueeze(0)
# print(hidden.size())
beginning = zeroBeginning
beginning_chars = zeroBeginning_chars
numeric, numeric_chars, lineNumbers = numericAndLineNumbers
numeric = torch.cat([beginning, numeric], dim=0)
numeric_chars = torch.cat([beginning_chars, numeric_chars], dim=0)
beginning = numeric[numeric.size()[0] - 1].view(1, args.batchSize)
beginning_chars = numeric_chars[numeric_chars.size()[0] - 1].view(
1, args.batchSize, 16)
loss = 0
wordNum = 0
lossWords = 0
policyGradientLoss = 0
baselineLoss = 0
optimizer.zero_grad()
for c in components:
c.zero_grad()
# for q in parameters_made:
# for p in q:
# if p.grad is not None:
# p.grad.fill_(0)
totalQuality = 0.0
if True:
inputTensor = numeric # so it will be horizon x args.batchSizeHere
# print inputTensor
# quit()
inputTensorIn = inputTensor[:-1]
inputTensorOut = inputTensor[1:]
input_tensor_chars = Variable(numeric_chars[:-1], requires_grad=False)
embedded_chars = input_tensor_chars.transpose(0, 2).transpose(2, 1)
embedded_chars = embedded_chars.contiguous().view(16, -1)
_, embedded_chars = char_composition(
character_embeddings(embedded_chars), None)
embedded_chars = embedded_chars[0].view(2, args.horizon,
args.batchSize,
args.char_enc_hidden_dim)
embedded_chars = char_composition_output(
torch.cat([embedded_chars[0], embedded_chars[1]], dim=2))
# embedded = word_embeddings(input_tensor)
inputEmbeddings = word_pos_morph_embeddings(
inputTensorIn.view(args.horizon, batchSizeHere))
#print(embedded.size())
# print("=========")
# print(numeric[:,5])
# print(embedded[:,5,:].mean(dim=1)[numeric[:-1,5] == 3])
# print(embedded_chars[:,5,:].mean(dim=1)[numeric[:-1,5] == 3])
inputEmbeddings = torch.cat([inputEmbeddings, embedded_chars], dim=2)
if doDropout:
if args.input_dropoutRate > 0:
inputEmbeddings = inputDropout(inputEmbeddings)
if args.dropout_rate > 0:
inputEmbeddings = dropout(inputEmbeddings)
lossesWordTotal = []
sampled_vectors = []
logProbConditionals = []
output_vectors = []
scales = []
means = []
encodedEpsilonForAllSteps = standardNormal.sample().view(
args.horizon, args.batchSize, -1)
for i in range(inputEmbeddings.size()[0]):
#print(i, hidden.abs().max())
output1, hidden = rnn_both(inputEmbeddings[i].unsqueeze(0), hidden)
assert args.rnn_layers == 1
hidden = torch.clamp(hidden, min=-5, max=5)
output = hidden
if doDropout:
if args.dropout_rate > 0:
output = dropout(output)
output_vectors.append(output)
output = torch.cat(output_vectors, dim=0)
# print(output.size())
word_logits = decoder(output)
# print(word_logits.size())
# word_logits = word_logits.view(args.horizon, batchSizeHere, outVocabSize)
word_softmax = logsoftmax(word_logits)
# print(word_softmax)
# print(word_softmax.size())
# print(torch.exp(word_softmax).sum(dim=2))
# print(word_softmax.size())
# print(word_logits.abs().max(), word_softmax.abs().max())
# print(word_softmax.size(), inputTensorOut.size())
lossesWord = lossModuleTest(word_softmax.view(-1, 50003),
inputTensorOut.view(-1))
# print(inputTensorOut)
# print(lossesWord.mean())
# lossesWordTotal.append(lossesWord)
# lossesWord = torch.stack(lossesWordTotal, dim=0)
lossWords = lossesWord.sum()
loss = lossWords
klLoss = 0
# print(sampledTotal.size(), logProbConditionalsTotal.size())
#for sampled, logProbConditional in zip(sampled_vectors, logProbConditionals):
# n=1
#print(loss, logProbConditionalsTotal.mean(), logProbMarginal.mean())
klLoss = 0
# print(logProbConditional, logProbMarginal)
# print(logStandardDeviationHidden)
# klLoss = 0.5 * (-1 - 2 * (logStandardDeviationHidden) + torch.pow(meanHidden, 2) + torch.exp(2*logStandardDeviationHidden))
# klLoss = klLoss.sum(1)
klLossSum = 0
if counter % 10 == 0:
klLossMean = 0
print(BETA, args.flow_length, klLossMean, lossesWord.mean(),
BETA * klLossMean + lossesWord.mean())
if float(klLossMean) != float(klLossMean):
print(hidden.abs().max())
assert False, "got NA, abort"
loss = loss + BETA * klLossSum
# print lossesWord
if surprisalTable is not None or True:
lossesCPU = lossesWord.data.cpu().view((args.horizon),
batchSizeHere).numpy()
if True:
for i in range(0, args.horizon
): #range(1,maxLength+1): # don't include i==0
j = 0
lineNum = int(lineNumbers[i][j])
print(i, itos_total[numeric[i + 1][j]], lossesCPU[i][j],
lineNum)
while lineNum >= len(completeData):
completeData.append([[], 0])
completeData[lineNum][0].append(itos_total[numeric[i +
1][j]])
completeData[lineNum][1] += lossesCPU[i][j]
if surprisalTable is not None:
if printHere:
print surprisalTable
for j in range(batchSizeHere):
for r in range(args.horizon):
surprisalTable[r] += lossesCPU[
r, j] #.data.cpu().numpy()[0]
wordNum = (args.horizon - 1) * batchSizeHere
if wordNum == 0:
print input_words
print batchOrdered
return 0, 0, 0, 0, 0
if printHere:
print loss / wordNum
print lossWords / wordNum
print["CROSS ENTROPY", crossEntropy, exp(crossEntropy)]
print("beta", BETA)
crossEntropy = 0.99 * crossEntropy + 0.01 * (lossWords /
wordNum).data.cpu().numpy()
totalQuality = loss.data.cpu().numpy(
) # consists of lossesWord + lossesPOS
numberOfWords = wordNum
# probabilities = torch.sigmoid(dhWeights)
# neg_entropy = torch.sum( probabilities * torch.log(probabilities) + (1-probabilities) * torch.log(1-probabilities))
# policy_related_loss = lr_policy * (entropy_weight * neg_entropy + policyGradientLoss) # lives on CPU
loss = loss / batchSizeHere
return loss, None, None, totalQuality, numberOfWords, 0
def forward(numeric, surprisalTable=None, doDropout=True, batchSizeHere=1):
global counter
global crossEntropy
global printHere
global devLosses
global hidden
global beginning
global beginning
global beginning_chars
if hidden is not None:
hidden = Variable(hidden.data).detach()
forRestart = bernoulli.sample()
#print(forRestart)
sampled = startHidden(zeroHidden)
hiddenNew = sampleToHidden(sampled).unsqueeze(0)
# hidden = forRestart.unsqueeze(0).unsqueeze(2) * hiddenNew + (1-forRestart).unsqueeze(0).unsqueeze(2) * hidden
# print(torch.where)
hidden = torch.where(
forRestart.unsqueeze(0).unsqueeze(2) == 1, hiddenNew, hidden)
beginning = torch.where(
forRestart.unsqueeze(0) == 1, zeroBeginning, beginning)
# beginning = forRestart.unsqueeze(0).unsqueeze(2) * zeroBeginning + (1-forRestart).unsqueeze(0).unsqueeze(2) * beginning
beginning_chars = torch.where(
forRestart.unsqueeze(0).unsqueeze(2) == 1, zeroBeginning_chars,
beginning_chars)
else:
sampled = startHidden(zeroHidden)
hiddenNew = sampleToHidden(sampled).unsqueeze(0)
hidden = hiddenNew
beginning = zeroBeginning
beginning_chars = zeroBeginning_chars
numeric, numeric_chars = numeric
numeric = torch.cat([beginning, numeric], dim=0)
numeric_chars = torch.cat([beginning_chars, numeric_chars], dim=0)
beginning = numeric[numeric.size()[0] - 1].view(1, args.batchSize)
beginning_chars = numeric_chars[numeric_chars.size()[0] - 1].view(
1, args.batchSize, 16)
loss = 0
wordNum = 0
lossWords = 0
policyGradientLoss = 0
baselineLoss = 0
optimizer.zero_grad()
for c in components:
c.zero_grad()
# for q in parameters_made:
# for p in q:
# if p.grad is not None:
# p.grad.fill_(0)
totalQuality = 0.0
if True:
inputTensor = numeric # so it will be horizon x args.batchSizeHere
# print inputTensor
# quit()
inputTensorIn = inputTensor[:-1]
inputTensorOut = inputTensor[1:]
input_tensor_chars = Variable(numeric_chars[:-1], requires_grad=False)
embedded_chars = input_tensor_chars.transpose(0, 2).transpose(2, 1)
embedded_chars = embedded_chars.contiguous().view(16, -1)
_, embedded_chars = char_composition(
character_embeddings(embedded_chars), None)
embedded_chars = embedded_chars[0].view(2, args.horizon,
args.batchSize,
args.char_enc_hidden_dim)
embedded_chars = char_composition_output(
torch.cat([embedded_chars[0], embedded_chars[1]], dim=2))
# embedded = word_embeddings(input_tensor)
inputEmbeddings = word_pos_morph_embeddings(
inputTensorIn.view(args.horizon, batchSizeHere))
#print(embedded.size())
# print("=========")
# print(numeric[:,5])
# print(embedded[:,5,:].mean(dim=1)[numeric[:-1,5] == 3])
# print(embedded_chars[:,5,:].mean(dim=1)[numeric[:-1,5] == 3])
inputEmbeddings = torch.cat([inputEmbeddings, embedded_chars], dim=2)
if doDropout:
if args.input_dropoutRate > 0:
inputEmbeddings = inputDropout(inputEmbeddings)
if args.dropout_rate > 0:
inputEmbeddings = dropout(inputEmbeddings)
lossesWordTotal = []
sampled_vectors = []
logProbConditionals = []
output_vectors = []
scales = []
means = []
encodedEpsilonForAllSteps = standardNormal.sample().view(
args.horizon, args.batchSize, -1)
for i in range(inputEmbeddings.size()[0]):
#print(i, hidden.abs().max())
output1, hidden = rnn_both(inputEmbeddings[i].unsqueeze(0), hidden)
assert args.rnn_layers == 1
meanHidden = cellToMean(hidden[0])
klLoss = [None for _ in inputEmbeddings]
logStandardDeviationHidden = hiddenToLogSDHidden(hidden[0])
# print(torch.exp(logStandardDeviationHidden))
scaleForDist = 1e-8 + torch.log(
1 + torch.exp(logStandardDeviationHidden))
scales.append(scaleForDist)
means.append(meanHidden)
# sampled = memoryDistribution.rsample()
encodedEpsilon = encodedEpsilonForAllSteps[
i] #standardNormalPerStep.sample()
# encodedEpsilon = torch.clamp(encodedEpsilon, min=-10, max=10)
sampled = meanHidden + scaleForDist * encodedEpsilon
sampled_vectors.append(sampled)
# print(encodedEpsilon.abs().max())
hiddenNew = sampleToHidden(sampled).unsqueeze(0)
# this also serves as the output for prediction
hidden = hiddenNew
hidden = torch.clamp(hidden, min=-5, max=5)
# print(hidden.abs().max())
# output, _ = rnn_both(torch.cat([word_pos_morph_embeddings(torch.cuda.LongTensor([[2 for _ in range(args.batchSizeHere)]])), inputEmbeddings[halfSeqLen+1:]], dim=0), (hiddenNew, cellNew))
# output = torch.cat([output1[:halfSeqLen], output], dim=0)
output = hiddenNew
if doDropout:
if args.dropout_rate > 0:
output = dropout(output)
output_vectors.append(output)
meanHidden = torch.stack(means, dim=0)
scaleForDist = torch.stack(scales, dim=0)
sampled = torch.stack(sampled_vectors, dim=0)
# memoryDistribution = torch.distributions.Normal(loc=meanHidden, scale=scaleForDist)
# logProbConditional = memoryDistribution.log_prob(sampled).sum(dim=2) #
# print("============")
# print(logProbConditional)
# print(sampled.size(), meanHidden.size(), scaleForDist.size())
logProbConditional = -((
(sampled - meanHidden)**2) / (2 * (scaleForDist**2))) - math.log(
math.sqrt(2 * math.pi)) - torch.log(scaleForDist)
logProbConditional = logProbConditional.sum(dim=2)
# print(logProbConditional)
batchSizeInflatedHere = args.batchSize * len(sampled_vectors)
sampledTotal = sampled.view(batchSizeInflatedHere, -1)
#print(logProbConditional.size())
# print(output_vectors[0].size())
output = torch.cat(output_vectors, dim=0)
# print(output.size())
word_logits = decoder(output)
# print(word_logits.size())
# word_logits = word_logits.view(args.horizon, batchSizeHere, outVocabSize)
word_softmax = logsoftmax(word_logits)
# print(word_softmax)
# print(word_softmax.size())
# print(torch.exp(word_softmax).sum(dim=2))
# print(word_softmax.size())
# print(word_logits.abs().max(), word_softmax.abs().max())
# print(word_softmax.size(), inputTensorOut.size())
lossesWord = lossModuleTest(word_softmax.view(-1, 50003),
inputTensorOut.view(-1))
# print(inputTensorOut)
# print(lossesWord.mean())
# lossesWordTotal.append(lossesWord)
# lossesWord = torch.stack(lossesWordTotal, dim=0)
lossWords = lossesWord.sum()
loss = lossWords
klLoss = 0
logProbConditionalsTotal = logProbConditional.view(
batchSizeInflatedHere)
# print(sampledTotal.size(), logProbConditionalsTotal.size())
#for sampled, logProbConditional in zip(sampled_vectors, logProbConditionals):
adjustment = []
epsilon = sampledTotal
# n=1
plainPriorLogProb = standardNormal.log_prob(epsilon).sum(
dim=1) #- (0.5 * torch.sum(sampled * sampled, dim=1))
logProbMarginal = plainPriorLogProb
#print(loss, logProbConditionalsTotal.mean(), logProbMarginal.mean())
klLoss = (logProbConditionalsTotal - logProbMarginal)
# print(logProbConditional, logProbMarginal)
# print(logStandardDeviationHidden)
# klLoss = 0.5 * (-1 - 2 * (logStandardDeviationHidden) + torch.pow(meanHidden, 2) + torch.exp(2*logStandardDeviationHidden))
# klLoss = klLoss.sum(1)
klLossSum = klLoss.sum()
if counter % 10 == 0:
klLossMean = klLoss.mean()
print(BETA, args.flow_length, klLossMean, lossesWord.mean(),
BETA * klLossMean + lossesWord.mean())
if float(klLossMean) != float(klLossMean):
print(hidden.abs().max())
assert False, "got NA, abort"
loss = loss + BETA * klLossSum
# print lossesWord
if surprisalTable is not None or printHere:
lossesCPU = lossesWord.data.cpu().view((args.horizon),
batchSizeHere).numpy()
if printHere:
for i in range(0, args.horizon
): #range(1,maxLength+1): # don't include i==0
j = 0
print(i, itos_total[numeric[i + 1][j]], lossesCPU[i][j])
if surprisalTable is not None:
if printHere:
print surprisalTable
for j in range(batchSizeHere):
for r in range(args.horizon):
surprisalTable[r] += lossesCPU[
r, j] #.data.cpu().numpy()[0]
wordNum = (args.horizon - 1) * batchSizeHere
if wordNum == 0:
print input_words
print batchOrdered
return 0, 0, 0, 0, 0
if printHere:
print loss / wordNum
print lossWords / wordNum
print["CROSS ENTROPY", crossEntropy, exp(crossEntropy)]
print("beta", BETA)
crossEntropy = 0.99 * crossEntropy + 0.01 * (lossWords /
wordNum).data.cpu().numpy()
totalQuality = loss.data.cpu().numpy(
) # consists of lossesWord + lossesPOS
numberOfWords = wordNum
# probabilities = torch.sigmoid(dhWeights)
# neg_entropy = torch.sum( probabilities * torch.log(probabilities) + (1-probabilities) * torch.log(1-probabilities))
# policy_related_loss = lr_policy * (entropy_weight * neg_entropy + policyGradientLoss) # lives on CPU
loss = loss / batchSizeHere
return loss, None, None, totalQuality, numberOfWords, klLoss.mean(
) if not doDropout else None
def train(self):
# Set up training.
criterionD = nn.BCELoss().cuda()
optimD = optim.Adam([{'params': self.D.parameters()}], lr=self.lr_d,
betas=(0.5, 0.999))
optimG = optim.Adam([{'params': self.G.parameters()},
{'params': self.Q.parameters()},
{'params': self.T.parameters()}], lr=self.lr_g,
betas=(0.5, 0.999))
############################################
# Load rope dataset and apply transformations
rope_path = os.path.realpath(self.data_dir)
trans = [
transforms.Resize(64),
transforms.CenterCrop(64),
transforms.ToTensor(),
# transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
]
if not self.fcn:
# If fcn it will do the transformation to gray
# and normalize in the loop.
trans.append(transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)))
if self.gray:
# Apply grayscale transformation.
trans.append(lambda x: x.mean(dim=0)[None, :, :])
trans_comp = transforms.Compose(trans)
# Image 1 and image 2 are k steps apart.
dataset = self.dataset(phase='train', mode='train')
self.plan_length = dataset.spec.max_seq_len - 3
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=self.batch_size,
shuffle=True,
num_workers=2,
drop_last=True)
############################################
# Load eval plan dataset
planning_data_dir = self.planning_data_dir
dataset_plan = self.dataset(phase='val', mode='plan')
data_plan_loader = torch.utils.data.DataLoader(dataset_plan,
batch_size=1,
shuffle=False,
num_workers=4,
drop_last=True)
# dataset_start = self.dataset(phase='val', mode='start')
# dataset_goal = self.dataset(phase='val', mode='goal')
# data_start_loader = torch.utils.data.DataLoader(dataset_start,
# batch_size=1,
# shuffle=False,
# num_workers=1,
# drop_last=True)
# data_goal_loader = torch.utils.data.DataLoader(dataset_goal,
# batch_size=1,
# shuffle=False,
# num_workers=1,
# drop_last=True)
############################################
real_o = Variable(torch.FloatTensor(self.batch_size, 3, dataset.img_sz, dataset.img_sz).cuda(), requires_grad=False)
real_o_next = Variable(torch.FloatTensor(self.batch_size, 3, dataset.img_sz, dataset.img_sz).cuda(), requires_grad=False)
label = Variable(torch.FloatTensor(self.batch_size).cuda(), requires_grad=False)
z = Variable(torch.FloatTensor(self.batch_size, self.rand_z_dim).cuda(), requires_grad=False)
for epoch in range(self.n_epochs + 1):
self.G.train()
self.D.train()
self.Q.train()
self.T.train()
for num_iters, batch_data in tqdm(enumerate(dataloader, 0)):
# break
# Real data
o, _ = batch_data[0]
o_next, _ = batch_data[1]
bs = o.size(0)
real_o.data.resize_(o.size())
real_o_next.data.resize_(o_next.size())
label.data.resize_(bs)
real_o.data.copy_(o)
real_o_next.data.copy_(o_next)
if self.fcn:
real_o = self.apply_fcn_mse(o)
real_o_next = self.apply_fcn_mse(o_next)
if real_o.abs().max() > 1:
import ipdb;
ipdb.set_trace()
assert real_o.abs().max() <= 1
if epoch == 0:
break
############################################
# D Loss (Update D)
optimD.zero_grad()
# Real data
probs_real = self.D(real_o, real_o_next)
label.data.fill_(1)
loss_real = criterionD(probs_real, label)
loss_real.backward()
# Fake data
z, c, c_next = self._noise_sample(z, bs)
fake_o, fake_o_next = self.G(z, c, c_next)
probs_fake = self.D(fake_o.detach(), fake_o_next.detach())
label.data.fill_(0)
loss_fake = criterionD(probs_fake, label)
loss_fake.backward()
D_loss = loss_real + loss_fake
optimD.step()
############################################
# G loss (Update G)
optimG.zero_grad()
probs_fake_2 = self.D(fake_o, fake_o_next)
label.data.fill_(1)
G_loss = criterionD(probs_fake_2, label)
# Q loss (Update G, T, Q)
ent_loss = -self.P.log_prob(c).mean(0)
crossent_loss = -self.Q.log_prob(fake_o, c).mean(0)
crossent_loss_next = -self.Q.log_prob(fake_o_next, c_next).mean(0)
# trans_prob = self.T.get_prob(Variable(torch.eye(self.dis_c_dim).cuda()))
ent_loss_next = -self.T.log_prob(c, None, c_next).mean(0)
mi_loss = crossent_loss - ent_loss
mi_loss_next = crossent_loss_next - ent_loss_next
Q_loss = mi_loss + mi_loss_next
# T loss (Update T)
Q_c_given_x, Q_c_given_x_var = (i.detach() for i in self.Q.forward(real_o))
t_mu, t_variance = self.T.get_mu_and_var(c)
t_diff = t_mu - c
# Keep the variance small.
# TODO: add loss on t_diff
T_loss = (t_variance ** 2).sum(1).mean(0)
(G_loss +
self.infow * Q_loss +
self.transw * T_loss).backward()
optimG.step()
#############################################
# Logging (iteration)
if num_iters % 100 == 0:
self.log_dict['Dloss'] = D_loss.item()
self.log_dict['Gloss'] = G_loss.item()
self.log_dict['Qloss'] = Q_loss.item()
self.log_dict['Tloss'] = T_loss.item()
self.log_dict['mi_loss'] = mi_loss.item()
self.log_dict['mi_loss_next'] = mi_loss_next.item()
self.log_dict['ent_loss'] = ent_loss.item()
self.log_dict['ent_loss_next'] = ent_loss_next.item()
self.log_dict['crossent_loss'] = crossent_loss.item()
self.log_dict['crossent_loss_next'] = crossent_loss_next.item()
self.log_dict['D(real)'] = probs_real.data.mean()
self.log_dict['D(fake)_before'] = probs_fake.data.mean()
self.log_dict['D(fake)_after'] = probs_fake_2.data.mean()
write_stats_from_var(self.log_dict, Q_c_given_x, 'Q_c_given_real_x_mu')
write_stats_from_var(self.log_dict, Q_c_given_x, 'Q_c_given_real_x_mu', idx=0)
write_stats_from_var(self.log_dict, Q_c_given_x_var, 'Q_c_given_real_x_variance')
write_stats_from_var(self.log_dict, Q_c_given_x_var, 'Q_c_given_real_x_variance', idx=0)
write_stats_from_var(self.log_dict, t_mu, 't_mu')
write_stats_from_var(self.log_dict, t_mu, 't_mu', idx=0)
write_stats_from_var(self.log_dict, t_diff, 't_diff')
write_stats_from_var(self.log_dict, t_diff, 't_diff', idx=0)
write_stats_from_var(self.log_dict, t_variance, 't_variance')
write_stats_from_var(self.log_dict, t_variance, 't_variance', idx=0)
print('\n#######################'
'\nEpoch/Iter:%d/%d; '
'\nDloss: %.3f; '
'\nGloss: %.3f; '
'\nQloss: %.3f, %.3f; '
'\nT_loss: %.3f; '
'\nEnt: %.3f, %.3f; '
'\nCross Ent: %.3f, %.3f; '
'\nD(x): %.3f; '
'\nD(G(z)): b %.3f, a %.3f;'
'\n0_Q_c_given_rand_x_mean: %.3f'
'\n0_Q_c_given_rand_x_std: %.3f'
'\n0_Q_c_given_fixed_x_std: %.3f'
'\nt_diff_abs_mean: %.3f'
'\nt_std_mean: %.3f'
% (epoch, num_iters,
D_loss.item(),
G_loss.item(),
mi_loss.item(), mi_loss_next.item(),
T_loss.item(),
ent_loss.item(), ent_loss_next.item(),
crossent_loss.item(), crossent_loss_next.item(),
probs_real.data.mean(),
probs_fake.data.mean(), probs_fake_2.data.mean(),
Q_c_given_x[:, 0].cpu().numpy().mean(),
Q_c_given_x[:, 0].cpu().numpy().std(),
np.sqrt(Q_c_given_x_var[:, 0].cpu().numpy().mean()),
t_diff.data.abs().mean(),
t_variance.data.sqrt().mean(),
))
#############################################
# Start evaluation from here.
self.G.eval()
self.D.eval()
self.Q.eval()
self.T.eval()
#############################################
# Save images
# Plot fake data
x_save, x_next_save = self.G(*self.eval_input, self.get_c_next(epoch))
save_image(x_save.data,
os.path.join(self.out_dir, 'gen', 'curr_samples_%03d.png' % epoch),
nrow=self.test_num_codes,
normalize=True)
save_image(x_next_save.data,
os.path.join(self.out_dir, 'gen', 'next_samples_%03d.png' % epoch),
nrow=self.test_num_codes,
normalize=True)
save_image((x_save - x_next_save).data,
os.path.join(self.out_dir, 'gen', 'diff_samples_%03d.png' % epoch),
nrow=self.test_num_codes,
normalize=True)
# Plot real data.
if epoch % 10 == 0:
save_image(real_o.data,
os.path.join(self.out_dir, 'real', 'real_samples_%d.png' % epoch),
nrow=self.test_num_codes,
normalize=True)
save_image(real_o_next.data,
os.path.join(self.out_dir, 'real', 'real_samples_next_%d.png' % epoch),
nrow=self.test_num_codes,
normalize=True)
#############################################
# Save parameters
if epoch % 5 == 0:
if not os.path.exists('%s/var' % self.out_dir):
os.makedirs('%s/var' % self.out_dir)
for i in [self.G, self.D, self.Q, self.T]:
torch.save(i.state_dict(),
os.path.join(self.out_dir,
'var',
'%s_%d' % (i.__class__.__name__, epoch,
)))
#############################################
# Logging (epoch)
for k, v in self.log_dict.items():
log_value(k, v, epoch)
if epoch > 0:
# tf logger
# log_value('avg|x_next - x|', (x_next_save.data - x_save.data).abs().mean(dim=0).sum(), epoch + 1)
# self.logger.histo_summary("Q_c_given_x", Q_c_given_x.data.cpu().numpy().reshape(-1), step=epoch)
# self.logger.histo_summary("Q_c0_given_x", Q_c_given_x[:, 0].data.cpu().numpy(), step=epoch)
# self.logger.histo_summary("Q_c_given_x_var", Q_c_given_x_var.cpu().numpy().reshape(-1), step=epoch)
# self.logger.histo_summary("Q_c0_given_x_var", Q_c_given_x_var[:, 0].data.cpu().numpy(), step=epoch)
# csv log
with open(os.path.join(self.out_dir, 'progress.csv'), 'a') as csv_file:
writer = csv.writer(csv_file)
if epoch == 1:
writer.writerow(["epoch"] + list(self.log_dict.keys()))
writer.writerow(["%.3f" % _tmp for _tmp in [epoch] + list(self.log_dict.values())])
#############################################
# Do planning?
if self.plan_length <= 0 or epoch not in self.planning_epoch:
continue
print("\n#######################"
"\nPlanning")
#############################################
# Showing plans on real images using best code.
# Min l2 distance from start and goal real images.
evaluator = EvalPSNR(2)
plans = []
datas = []
for i, data in enumerate(data_plan_loader):
plan = self.plan_hack(i, data[:, 0], data[:, -1], epoch, 'L2', data.shape[1] - 3, save=False)
evaluator(plan[None].cpu().numpy(), data.cpu().numpy())
print(evaluator.PSNR(), evaluator.SSIM())
# if i < 4:
# self.make_gif(torch.cat([data[0], plan.cpu()], dim=2), i, epoch)
plans.append(plan.cpu())
datas.append(data[0])
if i == 3:
for i in range(4): datas[i] = np.concatenate(
[datas[i], np.zeros([100 - datas[i].shape[0]] + list(datas[i].shape[1:]))], 0)
for i in range(4): plans[i] = np.concatenate([plans[i], torch.zeros([100 - plans[i].shape[0]] + list(plans[i].shape[1:]))], 0)
data = np.concatenate(datas, 3)
plan = np.concatenate(plans, 3)
self.make_gif(torch.from_numpy(np.concatenate([data, plan], 2)), i, epoch, fps=4)
import pdb; pdb.set_trace()
print(('Test: [{0}/{1}]\t'
'PSNR {PSNR:.3f}'
'SSIM {SSIM:.3f}'.format(
i,
len(data_plan_loader),
PSNR=evaluator.PSNR(),
SSIM=evaluator.SSIM())))