def save_progress(self):
gpu_id = self.gpu_ids[0]
epoch = self.get_current_epoch()
data_provider = self.data_provider
enc = self.enc
dec = self.dec
enc.train(False)
dec.train(False)
###############
#TRAINING DATA
###############
train_classes = data_provider.get_classes(
np.arange(0, data_provider.get_n_dat('train', override=True)),
'train')
_, train_inds = np.unique(train_classes.numpy(), return_index=True)
x = data_provider.get_images(train_inds, 'train').cuda(gpu_id)
with torch.no_grad():
xHat = dec(enc(x))
imgX = tensor2img(x.data.cpu())
imgXHat = tensor2img(xHat.data.cpu())
imgTrainOut = np.concatenate((imgX, imgXHat), 0)
###############
#TESTING DATA
###############
test_classes = data_provider.get_classes(
np.arange(0, data_provider.get_n_dat('test')), 'test')
_, test_inds = np.unique(test_classes.numpy(), return_index=True)
x = data_provider.get_images(test_inds, 'test').cuda(gpu_id)
with torch.no_grad():
xHat = dec(enc(x))
z = list()
if enc.n_classes > 0:
class_var = torch.Tensor(
data_provider.get_classes(test_inds, 'test',
'one_hot').float()).cuda(gpu_id)
class_var = (class_var - 1) * 25
z.append(class_var)
if enc.n_ref > 0:
ref_var = torch.Tensor(data_provider.get_n_classes(),
enc.n_ref).normal_(0, 1).cuda(gpu_id)
z.append(ref_var)
loc_var = torch.Tensor(data_provider.get_n_classes(),
enc.n_latent_dim).normal_(0, 1).cuda(gpu_id)
z.append(loc_var)
with torch.no_grad():
x_z = dec(z)
imgX = tensor2img(x.data.cpu())
imgXHat = tensor2img(xHat.data.cpu())
imgX_z = tensor2img(x_z.data.cpu())
imgTestOut = np.concatenate((imgX, imgXHat, imgX_z), 0)
imgOut = np.concatenate((imgTrainOut, imgTestOut))
scipy.misc.imsave(
'{0}/progress_{1}.png'.format(self.save_dir, int(epoch - 1)),
imgOut)
enc.train(True)
dec.train(True)
# pdb.set_trace()
# zAll = torch.cat(zAll,0).cpu().numpy()
embedding = torch.cat(self.zAll, 0).cpu().numpy()
pickle.dump(embedding,
open('{0}/embedding_tmp.pkl'.format(self.save_dir), 'wb'))
pickle.dump(self.logger,
open('{0}/logger_tmp.pkl'.format(self.save_dir), 'wb'))
### History
plots.history(self.logger, '{0}/history.png'.format(self.save_dir))
### Short History
plots.short_history(self.logger,
'{0}/history_short.png'.format(self.save_dir))
### Embedding figure
plots.embeddings(embedding, '{0}/embedding.png'.format(self.save_dir))
xHat = None
x = None
def save_progress(self):
gpu_id = self.gpu_ids[0]
epoch = self.get_current_epoch()
data_provider = self.data_provider
enc = self.enc
dec = self.dec
enc.train(False)
dec.train(False)
###############
# TRAINING DATA
###############
img_inds = np.arange(self.n_display_imgs)
x, _, _ = data_provider.get_sample("train", img_inds)
x = x.cuda(gpu_id)
def xHat2sample(xHat, x):
if xHat.shape[1] == x.shape[1]:
pass
else:
mu = xHat[:, 0::2, :, :]
log_var = torch.log(xHat[:, 1::2, :, :])
xHat = self.reparameterize(mu, log_var, add_noise=True)
return xHat
with torch.no_grad():
z_mu, _ = enc(x)
xHat = dec(z_mu)
xHat = xHat2sample(xHat, x)
imgX = tensor2im(x.data.cpu())
imgXHat = tensor2im(xHat.data.cpu())
imgTrainOut = np.concatenate((imgX, imgXHat), 0)
###############
# TESTING DATA
###############
x, _, _ = data_provider.get_sample("validate", img_inds)
x = x.cuda(gpu_id)
with torch.no_grad():
z_mu, _ = enc(x)
xHat = dec(z_mu)
xHat = xHat2sample(xHat, x)
z_mu.normal_()
with torch.no_grad():
xHat_z = dec(z_mu)
xHat_z = xHat2sample(xHat_z, x)
imgX = tensor2im(x.data.cpu())
imgXHat = tensor2im(xHat.data.cpu())
imgXHat_z = tensor2im(xHat_z.data.cpu())
imgTestOut = np.concatenate((imgX, imgXHat, imgXHat_z), 0)
imgOut = np.concatenate((imgTrainOut, imgTestOut))
scipy.misc.imsave(
"{0}/progress_{1}.png".format(self.save_dir, int(epoch - 1)),
imgOut)
embeddings_train = np.concatenate(self.zAll, 0)
pickle.dump(embeddings_train,
open("{0}/embedding.pth".format(self.save_dir), "wb"))
pickle.dump(
embeddings_train,
open(
"{0}/embedding_{1}.pth".format(self.save_dir,
self.get_current_iter()),
"wb",
),
)
pickle.dump(self.logger,
open("{0}/logger_tmp.pkl".format(self.save_dir), "wb"))
# History
plots.history(self.logger, "{0}/history.png".format(self.save_dir))
# Short History
plots.short_history(self.logger,
"{0}/history_short.png".format(self.save_dir))
# Embedding figure
plots.embeddings(embeddings_train,
"{0}/embedding.png".format(self.save_dir))
def sampler(mode, inds):
return data_provider.get_sample(mode, inds)[0]
embeddings_validate = embeddings.get_latent_embeddings(
enc,
dec,
dp=self.data_provider,
recon_loss=self.crit_recon,
modes=["validate"],
batch_size=self.data_provider.batch_size,
sampler=sampler,
)
embeddings_validate["iteration"] = self.get_current_iter()
embeddings_validate["epoch"] = self.get_current_epoch()
torch.save(
embeddings_validate,
"{}/embeddings_validate_{}.pth".format(self.save_dir,
self.get_current_iter()),
)
xHat = None
x = None
enc.train(True)
dec.train(True)
def save_progress(self):
gpu_id = self.gpu_ids[0]
epoch = self.get_current_epoch()
data_provider = self.data_provider
enc = self.enc
dec = self.dec
enc.train(False)
dec.train(False)
###############
# TRAINING DATA
###############
train_classes = data_provider.get_classes(
np.arange(0, data_provider.get_n_dat("train", override=True)), "train"
)
_, train_inds = np.unique(train_classes.numpy(), return_index=True)
x, classes, x_mesh = data_provider.get_sample(
"train", train_inds, patched=False
)
x = x.cuda(gpu_id)
classes = classes.type_as(x).long()
classes_onehot = utils.index_to_onehot(
classes, self.data_provider.get_n_classes()
)
x_mesh = x_mesh.type_as(x)
def xHat2sample(xHat, x):
if xHat.shape[1] == x.shape[1]:
pass
else:
mu = xHat[:, 0::2, :, :]
log_var = torch.log(xHat[:, 1::2, :, :])
xHat = self.reparameterize(mu, log_var, add_noise=True)
return xHat
with torch.no_grad():
z = enc(x, classes_onehot, x_mesh)
for i in range(len(z)):
z[i] = z[i][0]
xHat = dec([classes_onehot] + z, x_mesh)
xHat = xHat2sample(xHat, x)
imgX = tensor2img(x.data.cpu())
imgXHat = tensor2img(xHat.data.cpu())
imgTrainOut = np.concatenate((imgX, imgXHat), 0)
###############
# TESTING DATA
###############
test_classes = data_provider.get_classes(
np.arange(0, data_provider.get_n_dat("test")), "test"
)
_, test_inds = np.unique(test_classes.numpy(), return_index=True)
x, classes, ref = data_provider.get_sample("test", test_inds, patched=False)
x = x.cuda(gpu_id)
classes = classes.type_as(x).long()
ref = ref.type_as(x)
with torch.no_grad():
z = enc(x, classes_onehot, x_mesh)
for i in range(len(z)):
z[i] = z[i][0]
xHat = dec([classes_onehot] + z, x_mesh)
xHat = xHat2sample(xHat, x)
for z_sub in z:
z_sub.normal_()
with torch.no_grad():
xHat_z = dec([classes_onehot] + z, x_mesh)
xHat_z = xHat2sample(xHat_z, x)
imgX = tensor2img(x.data.cpu())
imgXHat = tensor2img(xHat.data.cpu())
imgXHat_z = tensor2img(xHat_z.data.cpu())
imgTestOut = np.concatenate((imgX, imgXHat, imgXHat_z), 0)
imgOut = np.concatenate((imgTrainOut, imgTestOut))
scipy.misc.imsave(
"{0}/progress_{1}.png".format(self.save_dir, int(epoch - 1)), imgOut
)
embeddings_train = np.concatenate(self.zAll, 0)
# if we've done this convolutionally, randomly select a subset of points
embeddings_shape = embeddings_train.shape
if len(embeddings_shape) > 2:
# it's convolutional, and we dont really want to save EVERYTHING
# so we take the first slice
slices = [slice(0, 1) for i in range(len(embeddings_shape) - 2)]
slices = tuple(
[slice(0, embeddings_shape[0]), slice(0, embeddings_shape[1])] + slices
)
embeddings_train = np.squeeze(embeddings_train[slices])
pickle.dump(
embeddings_train, open("{0}/embedding.pth".format(self.save_dir), "wb")
)
pickle.dump(
embeddings_train,
open(
"{0}/embedding_{1}.pth".format(self.save_dir, self.get_current_iter()),
"wb",
),
)
pickle.dump(self.logger, open("{0}/logger_tmp.pkl".format(self.save_dir), "wb"))
# History
plots.history(self.logger, "{0}/history.png".format(self.save_dir))
# Short History
plots.short_history(self.logger, "{0}/history_short.png".format(self.save_dir))
# Embedding figure
plots.embeddings(embeddings_train, "{0}/embedding.png".format(self.save_dir))
# embeddings_validate = embeddings.get_latent_embeddings(
# enc,
# dec,
# dp=self.data_provider,
# recon_loss=self.crit_recon,
# modes=["validate"],
# batch_size=self.data_provider.batch_size,
# )
# embeddings_validate["iteration"] = self.get_current_iter()
# embeddings_validate["epoch"] = self.get_current_epoch()
# torch.save(
# embeddings_validate,
# "{}/embeddings_validate_{}.pth".format(
# self.save_dir, self.get_current_iter()
# ),
# )
xHat = None
x = None
enc.train(True)
dec.train(True)
def save_progress(self):
gpu_id = self.gpu_ids[0]
epoch = self.get_current_epoch()
data_provider = self.data_provider
enc = self.enc
dec = self.dec
enc.train(False)
dec.train(False)
###############
# TRAINING DATA
###############
train_classes = data_provider.get_classes(
np.arange(0, data_provider.get_n_dat("train", override=True)),
"train")
_, train_inds = np.unique(train_classes.numpy(), return_index=True)
x, classes, ref = data_provider.get_sample("train", train_inds)
x = x.cuda(gpu_id)
classes = classes.type_as(x).long()
classes_onehot = utils.index_to_onehot(
classes, self.data_provider.get_n_classes())
ref = ref.type_as(x)
def xHat2sample(xHat, x):
if xHat.shape[1] == x.shape[1]:
pass
else:
mu = xHat[:, 0::2, :, :]
log_var = torch.log(xHat[:, 1::2, :, :])
xHat = bvae.reparameterize(mu, log_var, add_noise=True)
return xHat
with torch.no_grad():
z = enc(x, classes_onehot)
for i in range(len(z)):
z[i] = z[i][0]
xHat = dec([classes_onehot] + z)
xHat = xHat2sample(xHat, x)
imgX = tensor2img(x.data.cpu())
imgXHat = tensor2img(xHat.data.cpu())
imgTrainOut = np.concatenate((imgX, imgXHat), 0)
###############
# TESTING DATA
###############
test_classes = data_provider.get_classes(
np.arange(0, data_provider.get_n_dat("test")), "test")
_, test_inds = np.unique(test_classes.numpy(), return_index=True)
x, classes, ref = data_provider.get_sample("test", test_inds)
x = x.cuda(gpu_id)
classes = classes.type_as(x).long()
ref = ref.type_as(x)
with torch.no_grad():
z = enc(x, classes_onehot)
for i in range(len(z)):
z[i] = z[i][0]
xHat = dec([classes_onehot] + z)
xHat = xHat2sample(xHat, x)
for z_sub in z:
z_sub.normal_()
with torch.no_grad():
xHat_z = dec([classes_onehot] + z)
xHat_z = xHat2sample(xHat_z, x)
imgX = tensor2img(x.data.cpu())
imgXHat = tensor2img(xHat.data.cpu())
imgXHat_z = tensor2img(xHat_z.data.cpu())
imgTestOut = np.concatenate((imgX, imgXHat, imgXHat_z), 0)
imgOut = np.concatenate((imgTrainOut, imgTestOut))
scipy.misc.imsave(
"{0}/progress_{1}.png".format(self.save_dir, int(epoch - 1)),
imgOut)
# embeddings_test = embeddings.get_latent_embeddings(
# enc,
# dec,
# dp=self.data_provider,
# recon_loss=self.crit_recon,
# modes=["test"],
# batch_size=self.data_provider.batch_size,
# )
# embeddings_test["iteration"] = self.get_current_iter()
# embeddings_test["epoch"] = self.get_current_epoch()
# torch.save(
# embeddings_test,
# "{}/embeddings_test_{}.pth".format(
# self.save_dir, self.get_current_iter()
# ),
# )
embeddings_train = np.concatenate(self.zAll, 0)
embeddings_train = embeddings_train.reshape(embeddings_train.shape[0],
-1)
pickle.dump(embeddings_train,
open("{0}/embedding.pth".format(self.save_dir), "wb"))
pickle.dump(
embeddings_train,
open(
"{0}/embedding_{1}.pth".format(self.save_dir,
self.get_current_iter()),
"wb",
),
)
pickle.dump(self.logger,
open("{0}/logger_tmp.pkl".format(self.save_dir), "wb"))
# History
plots.history(self.logger, "{0}/history.png".format(self.save_dir))
# Short History
plots.short_history(self.logger,
"{0}/history_short.png".format(self.save_dir))
# Embedding figure
plots.embeddings(embeddings_train,
"{0}/embedding.png".format(self.save_dir))
embeddings_validate = embeddings.get_latent_embeddings(
enc,
dec,
dp=self.data_provider,
recon_loss=self.crit_recon,
modes=["validate"],
batch_size=self.data_provider.batch_size,
)
embeddings_validate["iteration"] = self.get_current_iter()
embeddings_validate["epoch"] = self.get_current_epoch()
torch.save(
embeddings_validate,
"{}/embeddings_validate_{}.pth".format(self.save_dir,
self.get_current_iter()),
)
xHat = None
x = None
enc.train(True)
dec.train(True)
def save_progress(self):
gpu_id = self.gpu_ids[0]
epoch = self.get_current_epoch()
data_provider = self.data_provider
enc = self.enc
dec = self.dec
enc.train(False)
dec.train(False)
###############
# TRAINING DATA
###############
img_inds = np.arange(self.n_display_imgs)
x, _, _ = data_provider.get_sample("train", img_inds)
x = x.cuda(gpu_id)
with torch.no_grad():
z = enc(x)
xHat = dec(z)
imgX = tensor2im(x.data.cpu())
imgXHat = tensor2im(xHat.data.cpu())
imgTrainOut = np.concatenate((imgX, imgXHat), 0)
###############
# TESTING DATA
###############
x, _, _ = data_provider.get_sample("validate", img_inds)
x = x.cuda(gpu_id)
with torch.no_grad():
z = enc(x)
xHat = dec(z)
imgX = tensor2im(x.data.cpu())
imgXHat = tensor2im(xHat.data.cpu())
imgTestOut = np.concatenate((imgX, imgXHat), 0)
imgOut = np.concatenate((imgTrainOut, imgTestOut))
scipy.misc.imsave(
"{0}/progress_{1}.png".format(self.save_dir, int(epoch - 1)), imgOut
)
embeddings_train = np.concatenate(self.zAll, 0)
pickle.dump(
embeddings_train, open("{0}/embedding.pth".format(self.save_dir), "wb")
)
pickle.dump(
embeddings_train,
open(
"{0}/embedding_{1}.pth".format(self.save_dir, self.get_current_iter()),
"wb",
),
)
pickle.dump(self.logger, open("{0}/logger_tmp.pkl".format(self.save_dir), "wb"))
# History
plots.history(self.logger, "{0}/history.png".format(self.save_dir))
# Short History
plots.short_history(self.logger, "{0}/history_short.png".format(self.save_dir))
# Embedding figure
plots.embeddings(embeddings_train, "{0}/embedding.png".format(self.save_dir))
enc.train(True)
dec.train(True)
def save_progress(self):
gpu_id = self.gpu_ids[0]
epoch = self.get_current_epoch()
data_provider = self.data_provider
enc = self.enc
dec = self.dec
enc.train(False)
dec.train(False)
###############
# TRAINING DATA
###############
train_classes = data_provider.get_classes(
np.arange(0, data_provider.get_n_dat("train", override=True)), "train"
)
_, train_inds = np.unique(train_classes.numpy(), return_index=True)
x, classes, ref = data_provider.get_sample("train", train_inds)
x = x.cuda(gpu_id)
classes = classes.type_as(x).long()
classes_onehot = utils.index_to_onehot(
classes, self.data_provider.get_n_classes()
)
ref = ref.type_as(x)
with torch.no_grad():
z = enc(x, classes_onehot)
for i in range(len(z)):
z[i] = z[i][0]
xHat = dec([classes_onehot] + z)
imgX = tensor2img(x.data.cpu())
imgXHat = tensor2img(xHat.data.cpu())
imgTrainOut = np.concatenate((imgX, imgXHat), 0)
###############
# TESTING DATA
###############
test_classes = data_provider.get_classes(
np.arange(0, data_provider.get_n_dat("test")), "test"
)
_, test_inds = np.unique(test_classes.numpy(), return_index=True)
x, classes, ref = data_provider.get_sample("test", test_inds)
x = x.cuda(gpu_id)
classes = classes.type_as(x).long()
ref = ref.type_as(x)
x = data_provider.get_images(test_inds, "test").cuda(gpu_id)
with torch.no_grad():
z = enc(x, classes_onehot)
for i in range(len(z)):
z[i] = z[i][0]
xHat = dec([classes_onehot] + z)
for z_sub in z:
z_sub.normal_()
with torch.no_grad():
xHat_z = dec([classes_onehot] + z)
imgX = tensor2img(x.data.cpu())
imgXHat = tensor2img(xHat.data.cpu())
imgXHat_z = tensor2img(xHat_z.data.cpu())
imgTestOut = np.concatenate((imgX, imgXHat, imgXHat_z), 0)
imgOut = np.concatenate((imgTrainOut, imgTestOut))
scipy.misc.imsave(
"{0}/progress_{1}.png".format(self.save_dir, int(epoch - 1)), imgOut
)
enc.train(True)
dec.train(True)
embeddings = np.concatenate(self.zAll, 0)
# pickle.dump(embeddings, open("{0}/embedding.pth".format(self.save_dir), "wb"))
# pickle.dump(
# embeddings,
# open(
# "{0}/embedding_{1}.pth".format(self.save_dir, self.get_current_iter()),
# "wb",
# ),
# )
pickle.dump(self.logger, open("{0}/logger_tmp.pkl".format(self.save_dir), "wb"))
# History
plots.history(self.logger, "{0}/history.png".format(self.save_dir))
# Short History
plots.short_history(self.logger, "{0}/history_short.png".format(self.save_dir))
# Embedding figure
plots.embeddings(embeddings, "{0}/embedding.png".format(self.save_dir))
xHat = None
x = None
def save_progress(self, enc, dec, data_provider, logger, embedding, opt):
gpu_id = self.gpu_ids[0]
epoch = max(logger.log['epoch'])
enc.train(False)
dec.train(False)
# pdb.set_trace()
train_classes = data_provider.get_classes(np.arange(0, data_provider.get_n_dat('train')), 'train')
_, train_inds = np.unique(train_classes.numpy(), return_index=True)
x_in, x_out, y_in, y_out = self.get_data(data_provider, train_inds, train_or_test = 'train')
with torch.no_grad():
xHat = dec(enc(x_in, y_in), y_out)
imgX = tensor2img(x_out.data.cpu())
imgXHat = tensor2img(xHat.data.cpu())
imgTrainOut = np.concatenate((imgX, imgXHat), 0)
test_classes = data_provider.get_classes(np.arange(0, data_provider.get_n_dat('test')), 'test')
_, test_inds = np.unique(test_classes.numpy(), return_index=True)
x_in, x_out, y_in, y_out = self.get_data(data_provider, test_inds, train_or_test = 'test')
with torch.no_grad():
xHat = dec(enc(x_in, y_in), y_out)
z = list()
if self.n_classes > 0:
class_var = torch.Tensor(data_provider.get_classes(test_inds, 'test', 'one_hot').float()).cuda(gpu_id)
class_var = (class_var-1) * 25
z.append(class_var)
if self.n_ref > 0:
ref_var = torch.Tensor(data_provider.get_n_classes(), self.n_ref).normal_(0,1).cuda(gpu_id)
z.append(ref_var)
loc_var = torch.Tensor(data_provider.get_n_classes(), self.n_latent_dim).normal_(0,1).cuda(gpu_id)
z.append(loc_var)
# x_z = dec(z, y_out)
imgX = tensor2img(x_out.data.cpu())
imgXHat = tensor2img(xHat.data.cpu())
# imgX_z = tensor2img(x_z.data.cpu())
# imgTestOut = np.concatenate((imgX, imgXHat, imgX_z), 0)
imgTestOut = np.concatenate((imgX, imgXHat), 0)
imgOut = np.concatenate((imgTrainOut, imgTestOut))
scipy.misc.imsave('{0}/progress_{1}.png'.format(opt.save_dir, int(epoch)), imgOut)
enc.train(True)
dec.train(True)
# pdb.set_trace()
# zAll = torch.cat(zAll,0).cpu().numpy()
pickle.dump(embedding, open('{0}/embedding_tmp.pkl'.format(opt.save_dir), 'wb'))
pickle.dump(logger, open('{0}/logger_tmp.pkl'.format(opt.save_dir), 'wb'))
### History
plots.history(logger, '{0}/history.png'.format(opt.save_dir))
### Short History
plots.short_history(logger, '{0}/history_short.png'.format(opt.save_dir))
### Embedding figure
plots.embeddings(embedding, '{0}/embedding.png'.format(opt.save_dir))
xHat = None
x = None
