def load(config, dict_name=None):
""" Loading the dataloaders, net configs, optimiser and the head order"""
"""Loads data, net configs, optimiser and the head order
Params:
config: configuration for the training run
dict_name: name of dictionary, in case a previous run is resumed
Returns:
[type] -- [description]
"""
dataloaders_head_A, mapping_assignment_dataloader, mapping_test_dataloader = segmentation_create_dataloaders(
config)
dataloaders_head_B = dataloaders_head_A
net = archs.__dict__[config.arch](config)
if config.restart and dict_name is not None:
dict = torch.load(os.path.join(config.out_dir, dict_name),
map_location=lambda storage, loc: storage)
net.load_state_dict(dict["net"])
net.cuda() if not config.nocuda else None
net = torch.nn.DataParallel(net)
net.train()
optimiser = get_opt(config.opt)(net.module.parameters(), lr=config.lr)
if config.restart:
optimiser.load_state_dict(dict["optimiser"])
heads = ["A", "B"]
if hasattr(config, "head_B_first") and config.head_B_first:
heads = ["B", "A"]
return (dataloaders_head_A,
dataloaders_head_B,
mapping_assignment_dataloader,
mapping_test_dataloader,
net,
optimiser,
heads)
def train():
dataloaders_head_A, mapping_assignment_dataloader, mapping_test_dataloader = \
segmentation_create_dataloaders(config)
dataloaders_head_B = dataloaders_head_A # unlike for clustering datasets
net = archs.__dict__[config.arch](config)
if config.restart:
dict = torch.load(os.path.join(config.out_dir, dict_name),
map_location=lambda storage, loc: storage)
net.load_state_dict(dict["net"])
net.cuda()
net = torch.nn.DataParallel(net)
net.train()
optimiser = get_opt(config.opt)(net.module.parameters(), lr=config.lr)
if config.restart:
optimiser.load_state_dict(dict["optimiser"])
heads = ["A", "B"]
if hasattr(config, "head_B_first") and config.head_B_first:
heads = ["B", "A"]
# Results
# ----------------------------------------------------------------------
if config.restart:
next_epoch = config.last_epoch + 1
print("starting from epoch %d" % next_epoch)
config.epoch_acc = config.epoch_acc[:next_epoch] # in case we overshot
config.epoch_avg_subhead_acc = config.epoch_avg_subhead_acc[:
next_epoch]
config.epoch_stats = config.epoch_stats[:next_epoch]
config.epoch_loss_head_A = config.epoch_loss_head_A[:(next_epoch - 1)]
config.epoch_loss_no_lamb_head_A = config.epoch_loss_no_lamb_head_A[:(
next_epoch - 1)]
config.epoch_loss_head_B = config.epoch_loss_head_B[:(next_epoch - 1)]
config.epoch_loss_no_lamb_head_B = config.epoch_loss_no_lamb_head_B[:(
next_epoch - 1)]
else:
config.epoch_acc = []
config.epoch_avg_subhead_acc = []
config.epoch_stats = []
config.epoch_loss_head_A = []
config.epoch_loss_no_lamb_head_A = []
config.epoch_loss_head_B = []
config.epoch_loss_no_lamb_head_B = []
_ = segmentation_eval(
config,
net,
mapping_assignment_dataloader=mapping_assignment_dataloader,
mapping_test_dataloader=mapping_test_dataloader,
sobel=(not config.no_sobel),
using_IR=config.using_IR)
print("Pre: time %s: \n %s" %
(datetime.now(), nice(config.epoch_stats[-1])))
sys.stdout.flush()
next_epoch = 1
fig, axarr = plt.subplots(6, sharex=False, figsize=(20, 20))
if not config.use_uncollapsed_loss:
print("using condensed loss (default)")
loss_fn = IID_segmentation_loss
else:
print("using uncollapsed loss!")
loss_fn = IID_segmentation_loss_uncollapsed
# Train
# ------------------------------------------------------------------------
for e_i in xrange(next_epoch, config.num_epochs):
print("Starting e_i: %d %s" % (e_i, datetime.now()))
sys.stdout.flush()
if e_i in config.lr_schedule:
optimiser = update_lr(optimiser, lr_mult=config.lr_mult)
for head_i in range(2):
head = heads[head_i]
if head == "A":
dataloaders = dataloaders_head_A
epoch_loss = config.epoch_loss_head_A
epoch_loss_no_lamb = config.epoch_loss_no_lamb_head_A
lamb = config.lamb_A
elif head == "B":
dataloaders = dataloaders_head_B
epoch_loss = config.epoch_loss_head_B
epoch_loss_no_lamb = config.epoch_loss_no_lamb_head_B
lamb = config.lamb_B
iterators = (d for d in dataloaders)
b_i = 0
avg_loss = 0. # over heads and head_epochs (and subheads)
avg_loss_no_lamb = 0.
avg_loss_count = 0
for tup in itertools.izip(*iterators):
net.module.zero_grad()
if not config.no_sobel:
pre_channels = config.in_channels - 1
else:
pre_channels = config.in_channels
all_img1 = torch.zeros(config.batch_sz, pre_channels,
config.input_sz, config.input_sz).to(
torch.float32).cuda()
all_img2 = torch.zeros(config.batch_sz, pre_channels,
config.input_sz, config.input_sz).to(
torch.float32).cuda()
all_affine2_to_1 = torch.zeros(config.batch_sz, 2,
3).to(torch.float32).cuda()
all_mask_img1 = torch.zeros(config.batch_sz, config.input_sz,
config.input_sz).to(
torch.float32).cuda()
curr_batch_sz = tup[0][0].shape[0]
for d_i in xrange(config.num_dataloaders):
img1, img2, affine2_to_1, mask_img1 = tup[d_i]
assert (img1.shape[0] == curr_batch_sz)
actual_batch_start = d_i * curr_batch_sz
actual_batch_end = actual_batch_start + curr_batch_sz
all_img1[
actual_batch_start:actual_batch_end, :, :, :] = img1
all_img2[
actual_batch_start:actual_batch_end, :, :, :] = img2
all_affine2_to_1[actual_batch_start:
actual_batch_end, :, :] = affine2_to_1
all_mask_img1[
actual_batch_start:actual_batch_end, :, :] = mask_img1
if not (curr_batch_sz
== config.dataloader_batch_sz) and (e_i == next_epoch):
print("last batch sz %d" % curr_batch_sz)
curr_total_batch_sz = curr_batch_sz * config.num_dataloaders # times 2
all_img1 = all_img1[:curr_total_batch_sz, :, :, :]
all_img2 = all_img2[:curr_total_batch_sz, :, :, :]
all_affine2_to_1 = all_affine2_to_1[:curr_total_batch_sz, :, :]
all_mask_img1 = all_mask_img1[:curr_total_batch_sz, :, :]
if (not config.no_sobel):
all_img1 = sobel_process(all_img1,
config.include_rgb,
using_IR=config.using_IR)
all_img2 = sobel_process(all_img2,
config.include_rgb,
using_IR=config.using_IR)
x1_outs = net(all_img1, head=head)
x2_outs = net(all_img2, head=head)
avg_loss_batch = None # avg over the heads
avg_loss_no_lamb_batch = None
for i in xrange(config.num_subheads):
loss, loss_no_lamb = loss_fn(
x1_outs[i],
x2_outs[i],
all_affine2_to_1=all_affine2_to_1,
all_mask_img1=all_mask_img1,
lamb=lamb,
half_T_side_dense=config.half_T_side_dense,
half_T_side_sparse_min=config.half_T_side_sparse_min,
half_T_side_sparse_max=config.half_T_side_sparse_max)
if avg_loss_batch is None:
avg_loss_batch = loss
avg_loss_no_lamb_batch = loss_no_lamb
else:
avg_loss_batch += loss
avg_loss_no_lamb_batch += loss_no_lamb
avg_loss_batch /= config.num_subheads
avg_loss_no_lamb_batch /= config.num_subheads
if ((b_i % 100) == 0) or (e_i == next_epoch):
print(
"Model ind %d epoch %d head %s batch: %d avg loss %f avg loss no "
"lamb %f "
"time %s" % \
(config.model_ind, e_i, head, b_i, avg_loss_batch.item(),
avg_loss_no_lamb_batch.item(), datetime.now()))
sys.stdout.flush()
if not np.isfinite(avg_loss_batch.item()):
print("Loss is not finite... %s:" % str(avg_loss_batch))
exit(1)
avg_loss += avg_loss_batch.item()
avg_loss_no_lamb += avg_loss_no_lamb_batch.item()
avg_loss_count += 1
avg_loss_batch.backward()
optimiser.step()
torch.cuda.empty_cache()
b_i += 1
if b_i == 2 and config.test_code:
break
avg_loss = float(avg_loss / avg_loss_count)
avg_loss_no_lamb = float(avg_loss_no_lamb / avg_loss_count)
epoch_loss.append(avg_loss)
epoch_loss_no_lamb.append(avg_loss_no_lamb)
# Eval
# -----------------------------------------------------------------------
is_best = segmentation_eval(
config,
net,
mapping_assignment_dataloader=mapping_assignment_dataloader,
mapping_test_dataloader=mapping_test_dataloader,
sobel=(not config.no_sobel),
using_IR=config.using_IR)
print("Pre: time %s: \n %s" %
(datetime.now(), nice(config.epoch_stats[-1])))
sys.stdout.flush()
axarr[0].clear()
axarr[0].plot(config.epoch_acc)
axarr[0].set_title("acc (best), top: %f" % max(config.epoch_acc))
axarr[1].clear()
axarr[1].plot(config.epoch_avg_subhead_acc)
axarr[1].set_title("acc (avg), top: %f" %
max(config.epoch_avg_subhead_acc))
axarr[2].clear()
axarr[2].plot(config.epoch_loss_head_A)
axarr[2].set_title("Loss head A")
axarr[3].clear()
axarr[3].plot(config.epoch_loss_no_lamb_head_A)
axarr[3].set_title("Loss no lamb head A")
axarr[4].clear()
axarr[4].plot(config.epoch_loss_head_B)
axarr[4].set_title("Loss head B")
axarr[5].clear()
axarr[5].plot(config.epoch_loss_no_lamb_head_B)
axarr[5].set_title("Loss no lamb head B")
fig.canvas.draw_idle()
fig.savefig(os.path.join(config.out_dir, "plots.png"))
if is_best or (e_i % config.save_freq == 0):
net.module.cpu()
save_dict = {
"net": net.module.state_dict(),
"optimiser": optimiser.state_dict()
}
if e_i % config.save_freq == 0:
torch.save(save_dict,
os.path.join(config.out_dir, "latest.pytorch"))
config.last_epoch = e_i # for last saved version
if is_best:
torch.save(save_dict,
os.path.join(config.out_dir, "best.pytorch"))
with open(os.path.join(config.out_dir, "best_config.pickle"),
'wb') as outfile:
pickle.dump(config, outfile)
with open(os.path.join(config.out_dir, "best_config.txt"),
"w") as text_file:
text_file.write("%s" % config)
net.module.cuda()
with open(os.path.join(config.out_dir, "config.pickle"),
'wb') as outfile:
pickle.dump(config, outfile)
with open(os.path.join(config.out_dir, "config.txt"),
"w") as text_file:
text_file.write("%s" % config)
if config.test_code:
exit(0)
if not hasattr(old_config, "num_sub_heads"):
old_config.num_sub_heads = old_config.num_heads
if not hasattr(old_config, "use_doersch_datasets"):
old_config.use_doersch_datasets = False
with open(os.path.join(old_config.out_dir, "config.pickle"), "wb") as outfile:
pickle.dump(old_config, outfile)
with open(os.path.join(old_config.out_dir, "config.txt"), "w") as text_file:
text_file.write("%s" % old_config)
# Model ------------------------------------------------------
dataloaders_head_A, mapping_assignment_dataloader, mapping_test_dataloader = segmentation_create_dataloaders(
old_config)
dataloaders_head_B = dataloaders_head_A # unlike for clustering datasets
net = archs.__dict__[old_config.arch](old_config) # type: ignore
net_state = torch.load(
os.path.join(old_config.out_dir, "best_net.pytorch"),
map_location=lambda storage, loc: storage,
)
net.load_state_dict(net_state)
net.cuda()
net = torch.nn.DataParallel(net)
stats_dict = segmentation_eval(
old_config,
net,
if not hasattr(old_config, "num_sub_heads"):
old_config.num_sub_heads = old_config.num_heads
if not hasattr(old_config, "use_doersch_datasets"):
old_config.use_doersch_datasets = False
with open(os.path.join(old_config.out_dir, "config.pickle"), 'wb') as outfile:
pickle.dump(old_config, outfile)
with open(os.path.join(old_config.out_dir, "config.txt"), "w") as text_file:
text_file.write("%s" % old_config)
# Model ------------------------------------------------------
dataloaders_head_A, mapping_assignment_dataloader, mapping_test_dataloader = \
segmentation_create_dataloaders(old_config)
dataloaders_head_B = dataloaders_head_A # unlike for clustering datasets
net = archs.__dict__[old_config.arch](old_config)
net_state = torch.load(os.path.join(old_config.out_dir, "best_net.pytorch"),
map_location=lambda storage, loc: storage)
net.load_state_dict(net_state)
net.cuda()
net = torch.nn.DataParallel(net)
stats_dict = segmentation_eval(
old_config,
net,
mapping_assignment_dataloader=mapping_assignment_dataloader,
mapping_test_dataloader=mapping_test_dataloader,
