class User():
def __init__(self,
ARCH,
DATA,
datadir,
logdir,
modeldir,
split,
uncertainty,
mc=30):
# parameters
self.ARCH = ARCH
self.DATA = DATA
self.datadir = datadir
self.logdir = logdir
self.modeldir = modeldir
self.uncertainty = uncertainty
self.split = split
self.mc = mc
# get the data
parserModule = imp.load_source(
"parserModule", booger.TRAIN_PATH + '/tasks/semantic/dataset/' +
self.DATA["name"] + '/parser.py')
self.parser = parserModule.Parser(
root=self.datadir,
train_sequences=self.DATA["split"]["train"],
valid_sequences=self.DATA["split"]["valid"],
test_sequences=self.DATA["split"]["test"],
labels=self.DATA["labels"],
color_map=self.DATA["color_map"],
learning_map=self.DATA["learning_map"],
learning_map_inv=self.DATA["learning_map_inv"],
sensor=self.ARCH["dataset"]["sensor"],
max_points=self.ARCH["dataset"]["max_points"],
batch_size=1,
workers=self.ARCH["train"]["workers"],
gt=True,
shuffle_train=False)
# concatenate the encoder and the head
with torch.no_grad():
torch.nn.Module.dump_patches = True
if self.uncertainty:
self.model = SalsaNextUncertainty(self.parser.get_n_classes())
self.model = nn.DataParallel(self.model)
w_dict = torch.load(modeldir + "/SalsaNext",
map_location=lambda storage, loc: storage)
self.model.load_state_dict(w_dict['state_dict'], strict=True)
else:
self.model = SalsaNext(self.parser.get_n_classes())
# 遇到平行化(一堆.module報錯)的問題時，註解下面那行
self.model = nn.DataParallel(self.model)
w_dict = torch.load(modeldir + "/SalsaNext_valid_best",
map_location=lambda storage, loc: storage)
self.model.load_state_dict(w_dict['state_dict'], strict=True)
# use knn post processing?
self.post = None
if self.ARCH["post"]["KNN"]["use"]:
self.post = KNN(self.ARCH["post"]["KNN"]["params"],
self.parser.get_n_classes())
# GPU?
self.gpu = False
self.model_single = self.model
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
print("Infering in device: ", self.device)
if torch.cuda.is_available() and torch.cuda.device_count() > 0:
cudnn.benchmark = True
cudnn.fastest = True
self.gpu = True
self.model.cuda()
def infer(self):
cnn = []
knn = []
if self.split == None:
self.infer_subset(loader=self.parser.get_train_set(),
to_orig_fn=self.parser.to_original,
cnn=cnn,
knn=knn)
# do valid set
self.infer_subset(loader=self.parser.get_valid_set(),
to_orig_fn=self.parser.to_original,
cnn=cnn,
knn=knn)
# do test set
self.infer_subset(loader=self.parser.get_test_set(),
to_orig_fn=self.parser.to_original,
cnn=cnn,
knn=knn)
elif self.split == 'valid':
self.infer_subset(loader=self.parser.get_valid_set(),
to_orig_fn=self.parser.to_original,
cnn=cnn,
knn=knn)
elif self.split == 'train':
self.infer_subset(loader=self.parser.get_train_set(),
to_orig_fn=self.parser.to_original,
cnn=cnn,
knn=knn)
else:
self.infer_subset(loader=self.parser.get_test_set(),
to_orig_fn=self.parser.to_original,
cnn=cnn,
knn=knn)
print("Mean CNN inference time:{}\t std:{}".format(
np.mean(cnn), np.std(cnn)))
print("Mean KNN inference time:{}\t std:{}".format(
np.mean(knn), np.std(knn)))
print("Total Frames:{}".format(len(cnn)))
print("Finished Infering")
return
def infer_subset(self, loader, to_orig_fn, cnn, knn):
# switch to evaluate mode
self.model.eval()
total_time = 0
total_frames = 0
# empty the cache to infer in high res
if self.gpu:
torch.cuda.empty_cache()
with torch.no_grad():
end = time.time()
for i, (proj_in, proj_mask, _, _, path_seq, path_name, p_x, p_y,
proj_range, unproj_range, _, _, _, _,
npoints) in enumerate(loader):
# first cut to rela size (batch size one allows it)
p_x = p_x[0, :npoints]
p_y = p_y[0, :npoints]
proj_range = proj_range[0, :npoints]
unproj_range = unproj_range[0, :npoints]
path_seq = path_seq[0]
path_name = path_name[0]
if self.gpu:
proj_in = proj_in.cuda()
p_x = p_x.cuda()
p_y = p_y.cuda()
if self.post:
proj_range = proj_range.cuda()
unproj_range = unproj_range.cuda()
#compute output
if self.uncertainty:
log_var_r, proj_output_r = self.model(proj_in)
for i in range(self.mc):
log_var, proj_output = self.model(proj_in)
log_var_r = torch.cat((log_var, log_var_r))
proj_output_r = torch.cat((proj_output, proj_output_r))
log_var2, proj_output2 = self.model(proj_in)
proj_output = proj_output_r.var(dim=0,
keepdim=True).mean(dim=1)
log_var2 = log_var_r.var(dim=0, keepdim=True).mean(dim=1)
if self.post:
# knn postproc
unproj_argmax = self.post(proj_range, unproj_range,
proj_argmax, p_x, p_y)
else:
# put in original pointcloud using indexes
unproj_argmax = proj_argmax[p_y, p_x]
# measure elapsed time
if torch.cuda.is_available():
torch.cuda.synchronize()
frame_time = time.time() - end
print("Infered seq", path_seq, "scan", path_name, "in",
frame_time, "sec")
total_time += frame_time
total_frames += 1
end = time.time()
# save scan
# get the first scan in batch and project scan
pred_np = unproj_argmax.cpu().numpy()
pred_np = pred_np.reshape((-1)).astype(np.int32)
# log_var2 = log_var2[0][p_y, p_x]
# log_var2 = log_var2.cpu().numpy()
# log_var2 = log_var2.reshape((-1)).astype(np.float32)
log_var2 = log_var2[0][p_y, p_x]
log_var2 = log_var2.cpu().numpy()
log_var2 = log_var2.reshape((-1)).astype(np.float32)
# assert proj_output.reshape((-1)).shape == log_var2.reshape((-1)).shape == pred_np.reshape((-1)).shape
# map to original label
pred_np = to_orig_fn(pred_np)
# save scan
path = os.path.join(self.logdir, "sequences", path_seq,
"predictions", path_name)
pred_np.tofile(path)
path = os.path.join(self.logdir, "sequences", path_seq,
"log_var", path_name)
if not os.path.exists(
os.path.join(self.logdir, "sequences", path_seq,
"log_var")):
os.makedirs(
os.path.join(self.logdir, "sequences", path_seq,
"log_var"))
log_var2.tofile(path)
proj_output = proj_output[0][p_y, p_x]
proj_output = proj_output.cpu().numpy()
proj_output = proj_output.reshape((-1)).astype(np.float32)
path = os.path.join(self.logdir, "sequences", path_seq,
"uncert", path_name)
if not os.path.exists(
os.path.join(self.logdir, "sequences", path_seq,
"uncert")):
os.makedirs(
os.path.join(self.logdir, "sequences", path_seq,
"uncert"))
proj_output.tofile(path)
print(total_time / total_frames)
else:
proj_output = self.model(proj_in)
proj_argmax = proj_output[0].argmax(dim=0)
if torch.cuda.is_available():
torch.cuda.synchronize()
res = time.time() - end
print("Network seq", path_seq, "scan", path_name, "in",
res, "sec")
end = time.time()
cnn.append(res)
if torch.cuda.is_available():
torch.cuda.synchronize()
res = time.time() - end
print("Network seq", path_seq, "scan", path_name, "in",
res, "sec")
end = time.time()
cnn.append(res)
if self.post:
# knn postproc
unproj_argmax = self.post(proj_range, unproj_range,
proj_argmax, p_x, p_y)
else:
# put in original pointcloud using indexes
unproj_argmax = proj_argmax[p_y, p_x]
# measure elapsed time
if torch.cuda.is_available():
torch.cuda.synchronize()
res = time.time() - end
print("KNN Infered seq", path_seq, "scan", path_name, "in",
res, "sec")
knn.append(res)
end = time.time()
# save scan
# get the first scan in batch and project scan
pred_np = unproj_argmax.cpu().numpy()
pred_np = pred_np.reshape((-1)).astype(np.int32)
# map to original label
pred_np = to_orig_fn(pred_np)
# save scan
path = os.path.join(self.logdir, "sequences", path_seq,
"predictions", path_name)
pred_np.tofile(path)
class Trainer():
def __init__(self,
ARCH,
DATA,
datadir,
logdir,
path=None,
uncertainty=False):
# parameters
self.ARCH = ARCH
self.DATA = DATA
self.datadir = datadir
self.log = logdir
self.path = path
self.uncertainty = uncertainty
self.batch_time_t = AverageMeter()
self.data_time_t = AverageMeter()
self.batch_time_e = AverageMeter()
self.epoch = 0
# put logger where it belongs
self.info = {
"train_update": 0,
"train_loss": 0,
"train_acc": 0,
"train_iou": 0,
"valid_loss": 0,
"valid_acc": 0,
"valid_iou": 0,
"best_train_iou": 0,
"best_val_iou": 0
}
# get the data
parserModule = imp.load_source(
"parserModule", booger.TRAIN_PATH + '/tasks/semantic/dataset/' +
self.DATA["name"] + '/parser.py')
self.parser = parserModule.Parser(
root=self.datadir,
train_sequences=self.DATA["split"]["train"],
valid_sequences=self.DATA["split"]["valid"],
test_sequences=None,
labels=self.DATA["labels"],
color_map=self.DATA["color_map"],
learning_map=self.DATA["learning_map"],
learning_map_inv=self.DATA["learning_map_inv"],
sensor=self.ARCH["dataset"]["sensor"],
max_points=self.ARCH["dataset"]["max_points"],
batch_size=self.ARCH["train"]["batch_size"],
workers=self.ARCH["train"]["workers"],
gt=True,
# 想要在 show_scan=True 時看到連續畫面，就把這邊改False即可
shuffle_train=True)
# weights for loss (and bias)
epsilon_w = self.ARCH["train"]["epsilon_w"]
content = torch.zeros(self.parser.get_n_classes(), dtype=torch.float)
for cl, freq in DATA["content"].items():
x_cl = self.parser.to_xentropy(
cl) # map actual class to xentropy class
content[x_cl] += freq
self.loss_w = 1 / (content + epsilon_w) # get weights
for x_cl, w in enumerate(
self.loss_w): # ignore the ones necessary to ignore
if DATA["learning_ignore"][x_cl]:
# don't weigh
self.loss_w[x_cl] = 0
print("Loss weights from content: ", self.loss_w.data)
with torch.no_grad():
self.model = SalsaNext(self.parser.get_n_classes())
self.discriminator = Discriminator()
self.tb_logger = Logger(self.log + "/tb")
# GPU?
self.gpu = False
self.multi_gpu = False
self.n_gpus = 0
self.model_single = self.model
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
print("Training in device: ", self.device)
if torch.cuda.is_available() and torch.cuda.device_count() > 0:
cudnn.benchmark = True
cudnn.fastest = True
self.gpu = True
self.n_gpus = 1
self.model.cuda()
self.discriminator.cuda()
# loss function
# optimizer_C = optim.Adam(label_predictor.parameters())
# optimizer_D = optim.Adam(domain_classifier.parameters())
self.criterion = nn.NLLLoss(weight=self.loss_w).to(self.device)
self.ls = Lovasz_softmax(ignore=0).to(self.device)
self.SoftmaxHeteroscedasticLoss = SoftmaxHeteroscedasticLoss().to(
self.device)
self.criterion_pixelwise = torch.nn.SmoothL1Loss().to(self.device)
self.criterion_GAN = torch.nn.BCEWithLogitsLoss().to(self.device)
self.optimizer = optim.SGD([{
'params': self.model.parameters()
}],
lr=self.ARCH["train"]["lr"],
momentum=self.ARCH["train"]["momentum"],
weight_decay=self.ARCH["train"]["w_decay"])
self.optimizer_D = optim.SGD(
[{
'params': self.discriminator.parameters()
}],
lr=self.ARCH["train"]["lr"],
momentum=self.ARCH["train"]["momentum"],
weight_decay=self.ARCH["train"]["w_decay"])
# Use warmup learning rate
# post decay and step sizes come in epochs and we want it in steps
steps_per_epoch = self.parser.get_train_size()
up_steps = int(self.ARCH["train"]["wup_epochs"] * steps_per_epoch)
final_decay = self.ARCH["train"]["lr_decay"]**(1 / steps_per_epoch)
self.scheduler = warmupLR(optimizer=self.optimizer,
lr=self.ARCH["train"]["lr"],
warmup_steps=up_steps,
momentum=self.ARCH["train"]["momentum"],
decay=final_decay)
self.scheduler_D = warmupLR(optimizer_D=self.optimizer_D,
lr=self.ARCH["train"]["lr"],
warmup_steps=up_steps,
momentum=self.ARCH["train"]["momentum"],
decay=final_decay)
if self.path is not None:
# generator
torch.nn.Module.dump_patches = True
w_dict = torch.load(path + "/SalsaNext",
map_location=lambda storage, loc: storage)
self.model.load_state_dict(w_dict['state_dict'], strict=True)
self.optimizer.load_state_dict(w_dict['optimizer'])
self.epoch = w_dict['epoch'] + 1
self.scheduler.load_state_dict(w_dict['scheduler'])
print("dict epoch:", w_dict['epoch'])
self.info = w_dict['info']
print("info", w_dict['info'])
# discriminator
w_dict_D = torch.load(path + "/SalsaNext_D",
map_location=lambda storage, loc: storage)
self.discriminator.load_state_dict(w_dict_D['state_dict'],
strict=True)
self.optimizer_D.load_state_dict(w_dict_D['optimizer_D'])
self.epoch = w_dict_D['epoch'] + 1
self.scheduler_D.load_state_dict(w_dict_D['scheduler_D'])
print("dict epoch:", w_dict_D['epoch'])
self.info = w_dict_D['info']
print("info", w_dict_D['info'])
def calculate_estimate(self, epoch, iter):
estimate = int((self.data_time_t.avg + self.batch_time_t.avg) * \
(self.parser.get_train_size() * self.ARCH['train']['max_epochs'] - (
iter + 1 + epoch * self.parser.get_train_size()))) + \
int(self.batch_time_e.avg * self.parser.get_valid_size() * (
self.ARCH['train']['max_epochs'] - (epoch)))
return str(datetime.timedelta(seconds=estimate))
@staticmethod
def get_mpl_colormap(cmap_name):
cmap = plt.get_cmap(cmap_name)
# Initialize the matplotlib color map
sm = plt.cm.ScalarMappable(cmap=cmap)
# Obtain linear color range
color_range = sm.to_rgba(np.linspace(0, 1, 256), bytes=True)[:, 2::-1]
return color_range.reshape(256, 1, 3)
@staticmethod
def make_log_img(depth, mask, pred, gt, color_fn):
# input should be [depth, pred, gt]
# make range image (normalized to 0,1 for saving)
depth = (cv2.normalize(depth,
None,
alpha=0,
beta=1,
norm_type=cv2.NORM_MINMAX,
dtype=cv2.CV_32F) * 255.0).astype(np.uint8)
out_img = cv2.applyColorMap(
depth, Trainer.get_mpl_colormap('viridis')) * mask[..., None]
# make label prediction
pred_color = color_fn((pred * mask).astype(np.int32))
out_img = np.concatenate([out_img, pred_color], axis=0)
# make label gt
gt_color = color_fn(gt)
out_img = np.concatenate([out_img, gt_color], axis=0)
return (out_img).astype(np.uint8)
@staticmethod
def save_to_log(logdir,
logger,
info,
epoch,
w_summary=False,
model=None,
img_summary=False,
imgs=[]):
# save scalars
for tag, value in info.items():
logger.scalar_summary(tag, value, epoch)
# save summaries of weights and biases
if w_summary and model:
for tag, value in model.named_parameters():
tag = tag.replace('.', '/')
logger.histo_summary(tag, value.data.cpu().numpy(), epoch)
if value.grad is not None:
logger.histo_summary(tag + '/grad',
value.grad.data.cpu().numpy(), epoch)
if img_summary and len(imgs) > 0:
directory = os.path.join(logdir, "predictions")
if not os.path.isdir(directory):
os.makedirs(directory)
for i, img in enumerate(imgs):
name = os.path.join(directory, str(i) + ".png")
cv2.imwrite(name, img)
def train(self):
self.ignore_class = []
for i, w in enumerate(self.loss_w):
if w < 1e-10:
self.ignore_class.append(i)
print("Ignoring class ", i, " in IoU evaluation")
self.evaluator = iouEval(self.parser.get_n_classes(), self.device,
self.ignore_class)
# train for n epochs
for epoch in range(self.epoch, self.ARCH["train"]["max_epochs"]):
# train for 1 epoch
acc, iou, loss, update_mean, hetero_l = self.train_epoch(
train_loader=self.parser.get_train_set(),
model=self.model,
discriminator=self.discriminator,
criterion=self.criterion,
optimizer=self.optimizer,
optimizer_D=self.optimizer_D,
epoch=epoch,
evaluator=self.evaluator,
scheduler=self.scheduler,
scheduler_D=self.scheduler_D,
color_fn=self.parser.to_color,
report=self.ARCH["train"]["report_batch"],
show_scans=self.ARCH["train"]["show_scans"],
save_bins=self.ARCH["train"]["save_bins"],
epoch_max=self.ARCH["train"]["max_epochs"])
# update info
self.info["train_update"] = update_mean
self.info["train_loss"] = loss
self.info["train_acc"] = acc
self.info["train_iou"] = iou
self.info["train_hetero"] = hetero_l
# remember best iou and save checkpoint
state = {
'epoch': epoch,
'state_dict': self.model.state_dict(),
'optimizer': self.optimizer.state_dict(),
'info': self.info,
'scheduler': self.scheduler.state_dict()
}
save_checkpoint(state, self.log, suffix="")
if self.info['train_iou'] > self.info['best_train_iou']:
print("Best mean iou in training set so far, save model!")
self.info['best_train_iou'] = self.info['train_iou']
state = {
'epoch': epoch,
'state_dict': self.model.state_dict(),
'optimizer': self.optimizer.state_dict(),
'info': self.info,
'scheduler': self.scheduler.state_dict()
}
save_checkpoint(state, self.log, suffix="_train_best")
state_D = {
'epoch': epoch,
'state_dict': self.discriminator.state_dict(),
'optimizer_D': self.optimizer.state_dict(),
'info': self.info,
'scheduler_D': self.scheduler_D.state_dict()
}
save_checkpoint(state_D, self.log, suffix="_train_best_D")
if epoch % self.ARCH["train"]["report_epoch"] == 0:
# evaluate on validation set
print("*" * 80)
acc, iou, loss, rand_img, hetero_l = self.validate(
val_loader=self.parser.get_valid_set(),
model=self.model,
discriminator=self.discriminator,
criterion=self.criterion,
evaluator=self.evaluator,
class_func=self.parser.get_xentropy_class_string,
color_fn=self.parser.to_color,
save_scans=self.ARCH["train"]["save_scans"],
save_bins=self.ARCH["train"]["save_bins"],
epoch_now=self.epoch)
# update info
self.info["valid_loss"] = loss
self.info["valid_acc"] = acc
self.info["valid_iou"] = iou
self.info['valid_heteros'] = hetero_l
# remember best iou and save checkpoint
if self.info['valid_iou'] > self.info['best_val_iou']:
print("Best mean iou in validation so far, save model!")
print("*" * 80)
self.info['best_val_iou'] = self.info['valid_iou']
# save the weights!
state = {
'epoch': epoch,
'state_dict': self.model.state_dict(),
'optimizer': self.optimizer.state_dict(),
'info': self.info,
'scheduler': self.scheduler.state_dict()
}
save_checkpoint(state, self.log, suffix="_valid_best")
state_D = {
'epoch': epoch,
'state_dict': self.discriminator.state_dict(),
'optimizer_D': self.optimizer_D.state_dict(),
'info': self.info,
'scheduler_D': self.scheduler_D.state_dict()
}
save_checkpoint(state_D, self.log, suffix="_valid_best_D")
print("*" * 80)
# save to log
Trainer.save_to_log(logdir=self.log,
logger=self.tb_logger,
info=self.info,
epoch=epoch,
w_summary=self.ARCH["train"]["save_summary"],
model=self.model_single,
img_summary=self.ARCH["train"]["save_scans"],
imgs=rand_img)
print('Finished Training')
return
def train_epoch(self,
train_loader,
model,
discriminator,
criterion,
optimizer,
optimizer_D,
epoch,
evaluator,
scheduler,
scheduler_D,
color_fn,
report=10,
show_scans=False,
save_bins=False,
epoch_max=15):
def get_lambda(epoch, max_epoch):
p = epoch / max_epoch
return 2. / (1 + np.exp(-10. * p)) - 1.
print("========= train_epoch start =========")
losses = AverageMeter()
acc = AverageMeter()
iou = AverageMeter()
hetero_l = AverageMeter()
update_ratio_meter = AverageMeter()
lamb = get_lambda(epoch, epoch_max)
# empty the cache to train now
if self.gpu:
torch.cuda.empty_cache()
# switch to train mode
model.train()
discriminator.train()
end = time.time()
for i, (in_vol, proj_mask, proj_labels, _, path_seq, path_name, _, _,
_, _, proj_xyz, _, proj_remission, _,
_) in enumerate(train_loader):
loss_D, loss_S = 0.0, 0.0
# measure data loading time
self.data_time_t.update(time.time() - end)
if not self.multi_gpu and self.gpu:
in_vol = in_vol.cuda()
#proj_mask = proj_mask.cuda()
if self.gpu:
proj_labels = proj_labels.cuda().long()
# compute output
"Generator"
# print("========= Generator start =========")
output = model(
in_vol
) # output.shape = 2048 x 64 x 20, proj_labels.shape = 2048 x 64
patch = (1, 64, 2048)
Tensor = torch.cuda.FloatTensor
# proj_labels = ground truth
proj_labels = proj_labels.float()
proj_labels_dis = proj_labels.unsqueeze(1)
# Adversarial ground truths
valid = Variable(Tensor(np.ones((in_vol.size(0), 1))),
requires_grad=False)
fake = Variable(Tensor(np.zeros((in_vol.size(0), 1))),
requires_grad=False)
# semantic_answer = fake image
semantic_answer = Variable(output.argmax(dim=1).float(),
requires_grad=True)
semantic_answer = semantic_answer.unsqueeze(1)
# print("semantic_answer.grad: ", semantic_answer.grad)
loss_m = criterion(torch.log(output.clamp(min=1e-8)),
proj_labels.long()) + self.ls(
output, proj_labels.long())
optimizer.zero_grad()
loss_m.backward()
optimizer.step()
# ---------------------
# Train Discriminator, detach 是因為 train discriminator 時，Generator 要固定不變
# ---------------------
# print("============================ Train Generator ============================")
# fake
in_vol_cat_fake = torch.cat((in_vol, semantic_answer),
1) # [2048 x 64 x 6]
logit_fake = discriminator(
in_vol_cat_fake.detach()) # fake_logit = [1]
loss_fake = self.criterion_GAN(logit_fake, fake)
# real
in_vol_cat_real = torch.cat((in_vol, proj_labels_dis),
1) # [2048 x 64 x 6]
logit_real = discriminator(
in_vol_cat_real.detach()) # real_logit = [1]
loss_real = self.criterion_GAN(logit_real, valid)
#loss
loss = (loss_real + loss_fake) / 2
optimizer_D.zero_grad()
loss_D += loss.item()
loss.backward()
optimizer_D.step()
# ---------------------
# Train Generator (從頭到尾)
# ---------------------
# print("============================ Train Generator ============================")
# generate
output = model(
in_vol
) # output.shape = 2048 x 64 x 20, proj_labels.shape = 2048 x 64
# semantic_answer = fake image
semantic_answer = Variable(output.argmax(dim=1).float(),
requires_grad=True)
semantic_answer = semantic_answer.unsqueeze(1)
in_vol_cat_fake = torch.cat((in_vol, semantic_answer),
1) # [2048 x 64 x 6]
# 因為公式是 D(G(z)) ,所以這邊要做 discriminate，這邊不用detach，因為這邊D的過程必須影響G
f_logit = discriminator(in_vol_cat_fake)
loss_m = criterion(torch.log(
output.clamp(min=1e-8)), proj_labels.long()) + self.ls(
output, proj_labels.long()) + self.criterion_GAN(
f_logit, valid)
optimizer.zero_grad()
loss_m.backward()
optimizer.step()
# print("==================== pass ====================")
# ======================================================================================
# measure accuracy and record loss
loss = loss_m.mean()
with torch.no_grad():
evaluator.reset()
# output.shape: torch.Size([3, 20, 64, 2048])
argmax = output.argmax(dim=1)
evaluator.addBatch(argmax, proj_labels.long())
accuracy = evaluator.getacc()
jaccard, class_jaccard = evaluator.getIoU()
losses.update(loss.item(), in_vol.size(0))
acc.update(accuracy.item(), in_vol.size(0))
iou.update(jaccard.item(), in_vol.size(0))
# measure elapsed time
self.batch_time_t.update(time.time() - end)
end = time.time()
# get gradient updates and weights, so I can print the relationship of
# their norms
update_ratios = []
for g in self.optimizer.param_groups:
lr = g["lr"]
for value in g["params"]:
if value.grad is not None:
w = np.linalg.norm(value.data.cpu().numpy().reshape(
(-1)))
update = np.linalg.norm(
-max(lr, 1e-10) * value.grad.cpu().numpy().reshape(
(-1)))
update_ratios.append(update / max(w, 1e-10))
update_ratios = np.array(update_ratios)
update_mean = update_ratios.mean()
update_std = update_ratios.std()
update_ratio_meter.update(update_mean) # over the epoch
#print("========= show_scans =========\ndepth_np\nmask_np\npred_np\ngt_np\ncolor_fn\n")
if show_scans:
#if True:
#print("========= show_scans =========")
# get the first scan in batch and project points
mask_np = proj_mask[0].cpu().numpy()
depth_np = in_vol[0][0].cpu().numpy()
pred_np = argmax[0].cpu().numpy()
# print("\pred_np.shape: ",pred_np.shape)
# print("pred_np: ",pred_np[0,:,1000])
gt_np = proj_labels[0].cpu().numpy()
out = Trainer.make_log_img(depth_np, mask_np, pred_np, gt_np,
color_fn)
out_check = Trainer.make_log_img(depth_np, mask_np, pred_np,
gt_np, color_fn)
mask_np = proj_mask[1].cpu().numpy()
depth_np = in_vol[1][0].cpu().numpy()
pred_np = argmax[1].cpu().numpy()
gt_np = proj_labels[1].cpu().numpy()
out2 = Trainer.make_log_img(depth_np, mask_np, pred_np, gt_np,
color_fn)
out = np.concatenate([out, out2], axis=0)
#有把out跟out2做連接，所以印出的圖應該只有三種，由上到下分別是depth_np, pred_np, gt_np，最後也有存在 logs/prediction 內
cv2.imshow("sample_training", out_check)
cv2.waitKey(1)
# JLL want to save pred to bin file
# 1. npy 先確認正確
# 2. 成功搞成 bin
if save_bins and epoch_now % 5 == 0:
#if True:
#pred_np = argmax[0].cpu().numpy()
#print("pred_np.shape: ", pred_np.shape)
batch_size = self.ARCH["train"]["batch_size"]
"""
print("\nseq: ", path_seq)
print("name: ", path_name)
print("proj_xyz.shape: ", proj_xyz.shape)
print("proj_i.shape: ", proj_remission.shape)
print("argmax.shape: ", argmax.cpu().numpy().shape)
print("\nbatch_size: ", batch_size)
"""
path_now = os.getcwd()
for x in range(0, batch_size):
bin_5dim = np.zeros(shape=(64, 2048, 5))
bin_5dim[:, :, 0:3] = proj_xyz[x, :, :, :]
bin_5dim[:, :, 3] = proj_remission[x, :, :]
bin_5dim[:, :, 4] = argmax[x, :, :].cpu().numpy()
np.save(
path_now + '/dataset/semantic_npy/' + path_seq[x] +
'/' + path_name[x].replace(".label", ".npy"), bin_5dim)
if i % self.ARCH["train"]["report_batch"] == 0:
print('Lr: {lr:.3e} | '
'Update: {umean:.3e} mean,{ustd:.3e} std | '
'Epoch: [{0}][{1}/{2}] | '
'Time {batch_time.val:.3f} ({batch_time.avg:.3f}) | '
'Data {data_time.val:.3f} ({data_time.avg:.3f}) | '
'Loss {loss.val:.4f} ({loss.avg:.4f}) | '
'acc {acc.val:.3f} ({acc.avg:.3f}) | '
'IoU {iou.val:.3f} ({iou.avg:.3f}) | [{estim}]'.format(
epoch,
i,
len(train_loader),
batch_time=self.batch_time_t,
data_time=self.data_time_t,
loss=losses,
acc=acc,
iou=iou,
lr=lr,
umean=update_mean,
ustd=update_std,
estim=self.calculate_estimate(epoch, i)))
print("loss_D: %.3f" % loss_D)
# print("loss_S: %.3f"%loss_S)
save_to_log(
self.log, 'log.txt', 'Lr: {lr:.3e} | '
'Update: {umean:.3e} mean,{ustd:.3e} std | '
'Epoch: [{0}][{1}/{2}] | '
'Time {batch_time.val:.3f} ({batch_time.avg:.3f}) | '
'Data {data_time.val:.3f} ({data_time.avg:.3f}) | '
'Loss {loss.val:.4f} ({loss.avg:.4f}) | '
'acc {acc.val:.3f} ({acc.avg:.3f}) | '
'IoU {iou.val:.3f} ({iou.avg:.3f}) | [{estim}]'.format(
epoch,
i,
len(train_loader),
batch_time=self.batch_time_t,
data_time=self.data_time_t,
loss=losses,
acc=acc,
iou=iou,
lr=lr,
umean=update_mean,
ustd=update_std,
estim=self.calculate_estimate(epoch, i)))
# step scheduler
scheduler.step()
scheduler_D.step()
return acc.avg, iou.avg, losses.avg, update_ratio_meter.avg, hetero_l.avg
def validate(self,
val_loader,
model,
discriminator,
criterion,
evaluator,
class_func,
color_fn,
save_scans,
save_bins,
epoch_now=0):
losses = AverageMeter()
jaccs = AverageMeter()
wces = AverageMeter()
acc = AverageMeter()
iou = AverageMeter()
hetero_l = AverageMeter()
rand_imgs = []
# switch to evaluate mode
model.eval()
evaluator.reset()
# empty the cache to infer in high res
if self.gpu:
torch.cuda.empty_cache()
with torch.no_grad():
end = time.time()
for i, (in_vol, proj_mask, proj_labels, _, path_seq, path_name, _,
_, _, _, proj_xyz, _, proj_remission, _,
_) in enumerate(val_loader):
if not self.multi_gpu and self.gpu:
in_vol = in_vol.cuda()
proj_mask = proj_mask.cuda()
if self.gpu:
proj_labels = proj_labels.cuda(non_blocking=True).long()
# compute output
output = model(in_vol)
log_out = torch.log(output.clamp(min=1e-8))
jacc = self.ls(output, proj_labels)
wce = criterion(log_out, proj_labels)
loss = wce + jacc
# measure accuracy and record loss
argmax = output.argmax(dim=1)
evaluator.addBatch(argmax, proj_labels)
losses.update(loss.mean().item(), in_vol.size(0))
jaccs.update(jacc.mean().item(), in_vol.size(0))
wces.update(wce.mean().item(), in_vol.size(0))
if save_scans:
# get the first scan in batch and project points
mask_np = proj_mask[0].cpu().numpy()
depth_np = in_vol[0][0].cpu().numpy()
pred_np = argmax[0].cpu().numpy()
gt_np = proj_labels[0].cpu().numpy()
out = Trainer.make_log_img(depth_np, mask_np, pred_np,
gt_np, color_fn)
rand_imgs.append(out)
# measure elapsed time
self.batch_time_e.update(time.time() - end)
end = time.time()
if save_bins and epoch_now % 5 == 0:
#if True:
#pred_np = argmax[0].cpu().numpy()
#print("pred_np.shape: ", pred_np.shape)
batch_size = self.ARCH["train"]["batch_size"]
"""
print("\nseq: ", path_seq)
print("name: ", path_name)
print("proj_xyz.shape: ", proj_xyz.shape)
print("proj_i.shape: ", proj_remission.shape)
print("argmax.shape: ", argmax.cpu().numpy().shape)
print("\nbatch_size: ", batch_size)
"""
path_now = os.getcwd()
for x in range(0, batch_size):
bin_5dim = np.zeros(shape=(64, 2048, 5))
bin_5dim[:, :, 0:3] = proj_xyz[x, :, :, :]
bin_5dim[:, :, 3] = proj_remission[x, :, :]
bin_5dim[:, :, 4] = argmax[x, :, :].cpu().numpy()
np.save(
path_now + '/dataset/semantic_npy/' + path_seq[x] +
'/' + path_name[x].replace(".label", ".npy"),
bin_5dim)
accuracy = evaluator.getacc()
jaccard, class_jaccard = evaluator.getIoU()
acc.update(accuracy.item(), in_vol.size(0))
iou.update(jaccard.item(), in_vol.size(0))
print('Validation set:\n'
'Time avg per batch {batch_time.avg:.3f}\n'
'Loss avg {loss.avg:.4f}\n'
'Jaccard avg {jac.avg:.4f}\n'
'WCE avg {wces.avg:.4f}\n'
'Acc avg {acc.avg:.3f}\n'
'IoU avg {iou.avg:.3f}'.format(batch_time=self.batch_time_e,
loss=losses,
jac=jaccs,
wces=wces,
acc=acc,
iou=iou))
save_to_log(
self.log, 'log.txt', 'Validation set:\n'
'Time avg per batch {batch_time.avg:.3f}\n'
'Loss avg {loss.avg:.4f}\n'
'Jaccard avg {jac.avg:.4f}\n'
'WCE avg {wces.avg:.4f}\n'
'Acc avg {acc.avg:.3f}\n'
'IoU avg {iou.avg:.3f}'.format(batch_time=self.batch_time_e,
loss=losses,
jac=jaccs,
wces=wces,
acc=acc,
iou=iou))
# print also classwise
for i, jacc in enumerate(class_jaccard):
print('IoU class {i:} [{class_str:}] = {jacc:.3f}'.format(
i=i, class_str=class_func(i), jacc=jacc))
save_to_log(
self.log, 'log.txt',
'IoU class {i:} [{class_str:}] = {jacc:.3f}'.format(
i=i, class_str=class_func(i), jacc=jacc))
self.info["valid_classes/" + class_func(i)] = jacc
return acc.avg, iou.avg, losses.avg, rand_imgs, hetero_l.avg