def train(render_count=-1):
dataloaders_head_A, dataloaders_head_B, \
mapping_assignment_dataloader, mapping_test_dataloader = \
cluster_twohead_create_dataloaders(config)
net = archs.__dict__[config.arch](config)
if config.restart:
model_path = os.path.join(config.out_dir, net_name)
net.load_state_dict(
torch.load(model_path, map_location=lambda storage, loc: storage))
net.cuda()
net = torch.nn.DataParallel(net)
net.train()
optimiser = get_opt(config.opt)(net.module.parameters(), lr=config.lr)
if config.restart:
print("loading latest opt")
optimiser.load_state_dict(
torch.load(os.path.join(config.out_dir, opt_name)))
heads = ["B", "A"]
if config.head_A_first:
heads = ["A", "B"]
head_epochs = {}
head_epochs["A"] = config.head_A_epochs
head_epochs["B"] = config.head_B_epochs
# Results
# ----------------------------------------------------------------------
if config.restart:
if not config.restart_from_best:
next_epoch = config.last_epoch + 1 # corresponds to last saved model
else:
# sanity check
next_epoch = np.argmax(np.array(config.epoch_acc)) + 1
assert (next_epoch == config.last_epoch + 1)
print("starting from epoch %d" % next_epoch)
# in case we overshot without saving
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]
if config.double_eval:
config.double_eval_acc = config.double_eval_acc[:next_epoch]
config.double_eval_avg_subhead_acc = config.double_eval_avg_subhead_acc[:
next_epoch]
config.double_eval_stats = config.double_eval_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 = []
if config.double_eval:
config.double_eval_acc = []
config.double_eval_avg_subhead_acc = []
config.double_eval_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 = []
sub_head = None
if config.select_sub_head_on_loss:
sub_head = get_subhead_using_loss(config,
dataloaders_head_B,
net,
sobel=False,
lamb=config.lamb_B)
_ = cluster_eval(
config,
net,
mapping_assignment_dataloader=mapping_assignment_dataloader,
mapping_test_dataloader=mapping_test_dataloader,
sobel=False,
use_sub_head=sub_head)
print("Pre: time %s: \n %s" %
(datetime.now(), nice(config.epoch_stats[-1])))
if config.double_eval:
print("double eval: \n %s" % (nice(config.double_eval_stats[-1])))
sys.stdout.flush()
next_epoch = 1
fig, axarr = plt.subplots(6 + 2 * int(config.double_eval),
sharex=False,
figsize=(20, 20))
save_progression = hasattr(config, "save_progression") and \
config.save_progression
if save_progression:
save_progression_count = 0
save_progress(config,
net,
mapping_assignment_dataloader,
mapping_test_dataloader,
save_progression_count,
sobel=False,
render_count=render_count)
save_progression_count += 1
# Train
# ------------------------------------------------------------------------
for e_i in range(next_epoch, config.num_epochs):
print("Starting e_i: %d" % e_i)
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
avg_loss = 0. # over heads and head_epochs (and sub_heads)
avg_loss_no_lamb = 0.
avg_loss_count = 0
for head_i_epoch in range(head_epochs[head]):
sys.stdout.flush()
iterators = (d for d in dataloaders)
b_i = 0
for tup in itertools.izip(*iterators):
net.module.zero_grad()
all_imgs = torch.zeros(
(config.batch_sz, config.in_channels, config.input_sz,
config.input_sz)).cuda()
all_imgs_tf = torch.zeros(
(config.batch_sz, config.in_channels, config.input_sz,
config.input_sz)).cuda()
imgs_curr = tup[0][0] # always the first
curr_batch_sz = imgs_curr.size(0)
for d_i in range(config.num_dataloaders):
imgs_tf_curr = tup[1 + d_i][0] # from 2nd to last
assert (curr_batch_sz == imgs_tf_curr.size(0))
actual_batch_start = d_i * curr_batch_sz
actual_batch_end = actual_batch_start + curr_batch_sz
all_imgs[actual_batch_start:actual_batch_end, :, :, :] = \
imgs_curr.cuda()
all_imgs_tf[actual_batch_start:actual_batch_end, :, :, :] = \
imgs_tf_curr.cuda()
if not (curr_batch_sz == config.dataloader_batch_sz):
print("last batch sz %d" % curr_batch_sz)
curr_total_batch_sz = curr_batch_sz * config.num_dataloaders #
# times 2
all_imgs = all_imgs[:curr_total_batch_sz, :, :, :]
all_imgs_tf = all_imgs_tf[:curr_total_batch_sz, :, :, :]
x_outs = net(all_imgs)
x_tf_outs = net(all_imgs_tf)
avg_loss_batch = None # avg over the heads
avg_loss_no_lamb_batch = None
for i in range(config.num_sub_heads):
loss, loss_no_lamb = IID_loss(x_outs[i],
x_tf_outs[i],
lamb=lamb)
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_sub_heads
avg_loss_no_lamb_batch /= config.num_sub_heads
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:" %
avg_loss_batch.item())
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()
if ((b_i % 50) == 0) and save_progression:
save_progress(config,
net,
mapping_assignment_dataloader,
mapping_test_dataloader,
save_progression_count,
sobel=False,
render_count=render_count)
save_progression_count += 1
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
# -----------------------------------------------------------------------
sub_head = None
if config.select_sub_head_on_loss:
sub_head = get_subhead_using_loss(config,
dataloaders_head_B,
net,
sobel=False,
lamb=config.lamb_B)
is_best = cluster_eval(
config,
net,
mapping_assignment_dataloader=mapping_assignment_dataloader,
mapping_test_dataloader=mapping_test_dataloader,
sobel=False,
use_sub_head=sub_head)
print("Pre: time %s: \n %s" %
(datetime.now(), nice(config.epoch_stats[-1])))
if config.double_eval:
print("double eval: \n %s" % (nice(config.double_eval_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")
if config.double_eval:
axarr[6].clear()
axarr[6].plot(config.double_eval_acc)
axarr[6].set_title("double eval acc (best), top: %f" %
max(config.double_eval_acc))
axarr[7].clear()
axarr[7].plot(config.double_eval_avg_subhead_acc)
axarr[7].set_title("double eval acc (avg)), top: %f" %
max(config.double_eval_avg_subhead_acc))
fig.tight_layout()
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()
if e_i % config.save_freq == 0:
torch.save(net.module.state_dict(),
os.path.join(config.out_dir, "latest_net.pytorch"))
torch.save(
optimiser.state_dict(),
os.path.join(config.out_dir, "latest_optimiser.pytorch"))
config.last_epoch = e_i # for last saved version
if is_best:
# also serves as backup if hardware fails - less likely to hit this
torch.save(net.module.state_dict(),
os.path.join(config.out_dir, "best_net.pytorch"))
torch.save(
optimiser.state_dict(),
os.path.join(config.out_dir, "best_optimiser.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)
# Model ------------------------------------------------------------------------
dataloaders, mapping_assignment_dataloader, mapping_test_dataloader = \
cluster_create_dataloaders(config)
net = archs.__dict__[config.arch](config)
if config.restart:
model_path = os.path.join(config.out_dir, net_name)
net.load_state_dict(
torch.load(model_path, map_location=lambda storage, loc: storage))
net.cuda()
net = torch.nn.DataParallel(net)
net.train()
optimiser = get_opt(config.opt)(net.module.parameters(), lr=config.lr)
if config.restart:
print("loading latest opt")
optimiser.load_state_dict(
torch.load(os.path.join(config.out_dir, opt_name)))
if config.restart:
if not config.restart_from_best:
next_epoch = config.last_epoch + 1 # corresponds to last saved model
else:
# sanity check
next_epoch = np.argmax(np.array(config.epoch_acc)) + 1
assert (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
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 range(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 sub_heads)
avg_loss_no_lamb = 0.
avg_loss_count = 0
for tup in zip(*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 range(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 range(config.num_sub_heads):
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_sub_heads
avg_loss_no_lamb_batch /= config.num_sub_heads
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)