def test(epoch, iter, log_file_semantic_val, log_file_scan_val, val_file,
log_file_2d_val):
test_loss_semantic = [] # To store semantic loss at each iteration
test_loss_scan = [] # To store scan loss at each iteration
test_loss_2d = []
model.eval()
model2d_fixed.eval()
model2d_trainable.eval()
if opt.use_proxy_loss:
model2d_classifier.eval()
start = time.time()
# h5py has too much data. 10000 samples are too much to use. Divide by 10 and pick 1000 at a time
print('Validating on %s' % val_file)
for h5py_index in range(10):
volumes, labels, frames, world_to_grids = data_util.load_hdf5_data(
val_file, num_classes, h5py_index)
frames = frames[:, :2 + num_images]
volumes = volumes.permute(0, 1, 4, 3, 2)
labels = labels.permute(0, 1, 4, 3, 2)
labels = labels[:, 0, :, grid_centerX,
grid_centerY] # center columns as targets
# Filter out the scenes not available
available_frames_index = data_util.get_available_frames_id(
opt.data_path_2d, frames)
if len(available_frames_index) < batch_size:
continue
volumes = volumes[available_frames_index]
labels = labels[available_frames_index]
frames = frames[available_frames_index]
world_to_grids = world_to_grids[available_frames_index]
num_samples = volumes.shape[0]
# shuffle
indices = torch.randperm(num_samples).long().split(batch_size)
# remove last mini-batch so that all the batches have equal size
indices = indices[:-1]
with torch.no_grad():
if CUDA_AVAILABLE:
mask_semantic = torch.cuda.LongTensor(batch_size *
column_height)
depth_images = torch.cuda.FloatTensor(batch_size * num_images,
proj_image_dims[1],
proj_image_dims[0])
color_images = torch.cuda.FloatTensor(batch_size * num_images,
3, input_image_dims[1],
input_image_dims[0])
camera_poses = torch.cuda.FloatTensor(batch_size * num_images,
4, 4)
label_images = torch.cuda.LongTensor(batch_size * num_images,
proj_image_dims[1],
proj_image_dims[0])
else:
mask_semantic = torch.LongTensor(batch_size * column_height)
depth_images = torch.FloatTensor(batch_size * num_images,
proj_image_dims[1],
proj_image_dims[0])
color_images = torch.FloatTensor(batch_size * num_images, 3,
input_image_dims[1],
input_image_dims[0])
camera_poses = torch.FloatTensor(batch_size * num_images, 4, 4)
label_images = torch.LongTensor(batch_size * num_images,
proj_image_dims[1],
proj_image_dims[0])
for t, v in enumerate(indices):
# print(t, v)
if CUDA_AVAILABLE:
targets_semantic = labels[v].cuda()
else:
targets_semantic = labels[v]
# Ignore Invalid targets for semantic
mask_semantic = targets_semantic.view(-1).data.clone()
for k in range(num_classes):
if criterion_weights_semantic[k] == 0:
mask_semantic[mask_semantic.eq(k)] = 0
mask_indices_semantic = mask_semantic.nonzero().squeeze()
if len(mask_indices_semantic.shape) == 0:
continue
# Ignore Invalid targets for scan
# Create mask from current volume where 1 represents voxel is known-free or known-occupied.
# 0 input should target 0, 1 should 1 and 2(from before voxel discarding) should 2.
if opt.train_scan_completion:
mask_scan = targets_semantic.view(-1).data.clone()
mask_scan[:] = 1
# Ignore Unknown Voxels from before.
mask_scan[targets_semantic.view(-1).eq(opt.num_classes -
1)] = 0
mask_scan_indices = mask_scan.nonzero().squeeze()
if len(mask_scan_indices.shape) == 0:
continue
# ToDo: What if you generate targets_scan from volumetric grid?
# ToDo: You should get the same result but confirm.
targets_scan = targets_semantic.view(-1).data.clone()
targets_scan[torch.ge(targets_scan, 1) *
torch.lt(targets_scan, num_classes - 1)] = 1
targets_scan[torch.eq(targets_scan, num_classes -
1)] = 2 # Label 41 with class 2
transforms = world_to_grids[v].unsqueeze(1)
transforms = transforms.expand(batch_size, num_images, 4,
4).contiguous().view(-1, 4, 4)
if CUDA_AVAILABLE:
transforms = transforms.cuda()
# get 2d data
is_load_success = data_util.load_frames_multi(
opt.data_path_2d, frames[v], depth_images, color_images,
camera_poses, color_mean, color_std)
if not is_load_success:
continue
# 3d Input
volume = volumes[v]
# Get indices of voxels to be removed if training scan completion
random_center_voxel_indices = torch.Tensor() # Empty Tensor
if opt.train_scan_completion:
# ToDo: For all sample in each batch, same random voxels are removed.
# ToDo: Voxel already unknown also gets removed.
random_center_voxel_indices = projection.get_random_center_voxels_index(
opt.voxel_removal_fraction)
# Mark the 3D voxels as Unknown and
volume[:, :, random_center_voxel_indices,
projection.volume_dims[0] // 2,
projection.volume_dims[1] // 2] = 0
# Compute projection mapping and mark center voxels as Unknown if training for scan completion
proj_mapping = [
projection.compute_projection(d, c, t,
random_center_voxel_indices)
for d, c, t in zip(depth_images, camera_poses, transforms)
]
if None in proj_mapping:
print('No mapping in proj_mapping')
continue
proj_mapping = list(zip(*proj_mapping))
proj_ind_3d = torch.stack(proj_mapping[0])
proj_ind_2d = torch.stack(proj_mapping[1])
if opt.use_proxy_loss:
data_util.load_label_frames(opt.data_path_2d, frames[v],
label_images, num_classes)
mask2d = label_images.view(-1).clone()
for k in range(num_classes):
if criterion_weights_semantic[k] == 0:
mask2d[mask2d.eq(k)] = 0
mask2d = mask2d.nonzero().squeeze()
if len(mask2d.shape) == 0:
continue # nothing to optimize for here
# 2d
imageft_fixed = model2d_fixed(color_images)
imageft = model2d_trainable(imageft_fixed)
if opt.use_proxy_loss:
ft2d = model2d_classifier(imageft)
ft2d = ft2d.permute(0, 2, 3, 1).contiguous()
# 2d/3d
if CUDA_AVAILABLE:
input3d = volume.cuda()
else:
input3d = volume
# Forward Pass Only
output_semantic, output_scan = model(input3d, imageft,
proj_ind_3d, proj_ind_2d,
grid_dims)
# Compute Scan and semantic Loss
loss_semantic = criterion_semantic(
output_semantic.view(-1, num_classes),
targets_semantic.view(-1))
test_loss_semantic.append(loss_semantic.item())
if opt.train_scan_completion:
loss_scan = criterion_scan(
output_scan.view(-1, _NUM_OCCUPANCY_STATES),
targets_scan.view(-1))
test_loss_scan.append(loss_scan.item())
if opt.use_proxy_loss:
loss2d = criterion2d(ft2d.view(-1, num_classes),
label_images.view(-1))
test_loss_2d.append(loss2d.item())
# Confusion
y = ft2d.data
y = y.view(-1, num_classes)[:, :-1]
_, predictions = y.max(1)
predictions = predictions.view(-1)
k = label_images.view(-1)
confusion2d_val.add(
torch.index_select(predictions, 0, mask2d),
torch.index_select(k, 0, mask2d))
# Confusion for Semantic
y = output_semantic.data
y = y.view(y.nelement() // y.size(2), num_classes)[:, :-1]
_, predictions = y.max(1)
predictions = predictions.view(-1)
k = targets_semantic.data.view(-1)
confusion_val.add(
torch.index_select(predictions, 0, mask_indices_semantic),
torch.index_select(k, 0, mask_indices_semantic))
# Confusion for Scan completion
if opt.train_scan_completion:
y = output_scan.data
# Discard semantic prediction of Unknown Voxels in target_scan
y = y.view(y.nelement() // y.size(2),
_NUM_OCCUPANCY_STATES)[:, :-1]
_, predictions_scan = y.max(1)
predictions_scan = predictions_scan.view(-1)
k = targets_scan.data.view(-1)
confusion_scan_val.add(
torch.index_select(predictions_scan, 0,
mask_scan_indices),
torch.index_select(k, 0, mask_scan_indices))
end = time.time()
took = end - start
evaluate_confusion(confusion_val, test_loss_semantic, epoch, iter, took,
'ValidationSemantic', log_file_semantic_val,
num_classes)
if opt.train_scan_completion:
evaluate_confusion(confusion_scan_val, test_loss_scan, epoch, iter,
took, 'ValidationScan', log_file_scan_val,
_NUM_OCCUPANCY_STATES)
if opt.use_proxy_loss:
evaluate_confusion(confusion2d_val, test_loss_2d, epoch, iter, took,
'Validation2d', log_file_2d_val, num_classes)
return test_loss_semantic, test_loss_scan, test_loss_2d
def test(epoch, iter, log_file, val_file, log_file_2d):
test_loss = []
test_loss_2d = []
model.eval()
model2d_fixed.eval()
model2d_trainable.eval()
if opt.use_proxy_loss:
model2d_classifier.eval()
start = time.time()
volumes, labels, frames, world_to_grids = data_util.load_hdf5_data(val_file, num_classes)
frames = frames[:, :2+num_images]
volumes = volumes.permute(0, 1, 4, 3, 2)
labels = labels.permute(0, 1, 4, 3, 2)
labels = labels[:, 0, :, grid_centerX, grid_centerY] # center columns as targets
num_samples = volumes.shape[0]
# shuffle
indices = torch.randperm(num_samples).long().split(batch_size)
# remove last mini-batch so that all the batches have equal size
indices = indices[:-1]
with torch.no_grad():
mask = torch.cuda.LongTensor(batch_size*column_height)
depth_images = torch.cuda.FloatTensor(batch_size * num_images, proj_image_dims[1], proj_image_dims[0])
color_images = torch.cuda.FloatTensor(batch_size * num_images, 3, input_image_dims[1], input_image_dims[0])
camera_poses = torch.cuda.FloatTensor(batch_size * num_images, 4, 4)
label_images = torch.cuda.LongTensor(batch_size * num_images, proj_image_dims[1], proj_image_dims[0])
for t,v in enumerate(indices):
targets = labels[v].cuda()
# valid targets
mask = targets.view(-1).data.clone()
for k in range(num_classes):
if criterion_weights[k] == 0:
mask[mask.eq(k)] = 0
maskindices = mask.nonzero().squeeze()
if len(maskindices.shape) == 0:
continue
transforms = world_to_grids[v].unsqueeze(1)
transforms = transforms.expand(batch_size, num_images, 4, 4).contiguous().view(-1, 4, 4).cuda()
# get 2d data
data_util.load_frames_multi(opt.data_path_2d, frames[v], depth_images, color_images, camera_poses, color_mean, color_std)
if opt.use_proxy_loss:
data_util.load_label_frames(opt.data_path_2d, frames[v], label_images, num_classes)
mask2d = label_images.view(-1).clone()
for k in range(num_classes):
if criterion_weights[k] == 0:
mask2d[mask2d.eq(k)] = 0
mask2d = mask2d.nonzero().squeeze()
if (len(mask2d.shape) == 0):
continue # nothing to optimize for here
# compute projection mapping
proj_mapping = [projection.compute_projection(d, c, t) for d, c, t in zip(depth_images, camera_poses, transforms)]
if None in proj_mapping: #invalid sample
#print '(invalid sample)'
continue
proj_mapping = zip(*proj_mapping)
proj_ind_3d = torch.stack(proj_mapping[0])
proj_ind_2d = torch.stack(proj_mapping[1])
# 2d
imageft_fixed = model2d_fixed(color_images)
imageft = model2d_trainable(imageft_fixed)
if opt.use_proxy_loss:
ft2d = model2d_classifier(imageft)
ft2d = ft2d.permute(0, 2, 3, 1).contiguous()
# 2d/3d
input3d = volumes[v].cuda()
output = model(input3d, imageft, proj_ind_3d, proj_ind_2d, grid_dims)
loss = criterion(output.view(-1, num_classes), targets.view(-1))
test_loss.append(loss.item())
if opt.use_proxy_loss:
loss2d = criterion2d(ft2d.view(-1, num_classes), label_images.view(-1))
test_loss_2d.append(loss2d.item())
# confusion
y = ft2d.data
y = y.view(-1, num_classes)[:, :-1]
_, predictions = y.max(1)
predictions = predictions.view(-1)
k = label_images.view(-1)
confusion2d_val.add(torch.index_select(predictions, 0, mask2d), torch.index_select(k, 0, mask2d))
# confusion
y = output.data
y = y.view(y.nelement()/y.size(2), num_classes)[:, :-1]
_, predictions = y.max(1)
predictions = predictions.view(-1)
k = targets.data.view(-1)
confusion_val.add(torch.index_select(predictions, 0, maskindices), torch.index_select(k, 0, maskindices))
end = time.time()
took = end - start
evaluate_confusion(confusion_val, test_loss, epoch, iter, took, 'Test', log_file)
if opt.use_proxy_loss:
evaluate_confusion(confusion2d_val, test_loss_2d, epoch, iter, took, 'Test2d', log_file_2d)
return test_loss, test_loss_2d
def train(epoch, iter, log_file_semantic, log_file_scan, train_file,
log_file_2d):
train_loss_semantic = [] # To store semantic loss at each iteration
train_loss_scan = [] # To store scan loss at each iteration
train_loss_2d = []
model.train()
start = time.time()
model2d_trainable.train()
if opt.use_proxy_loss:
model2d_classifier.train()
# h5py has too much data. 10000 samples are too much to use. Divide by 10 and pick 1000 at a time
print('Training on %s' % train_file)
for h5py_index in range(10):
volumes, labels, frames, world_to_grids = data_util.load_hdf5_data(
train_file, num_classes, h5py_index)
frames = frames[:, :2 + num_images]
volumes = volumes.permute(0, 1, 4, 3, 2)
labels = labels.permute(0, 1, 4, 3, 2)
labels = labels[:, 0, :, grid_centerX,
grid_centerY] # center columns as targets
# Filter out the scenes not available
available_frames_index = data_util.get_available_frames_id(
opt.data_path_2d, frames)
if len(available_frames_index) < batch_size:
continue
volumes = volumes[available_frames_index]
labels = labels[available_frames_index]
frames = frames[available_frames_index]
world_to_grids = world_to_grids[available_frames_index]
num_samples = volumes.shape[0]
# shuffle
indices = torch.randperm(num_samples).long().split(batch_size)
# remove last mini-batch so that all the batches have equal size
indices = indices[:-1]
if CUDA_AVAILABLE:
mask = torch.cuda.LongTensor(batch_size * column_height)
depth_images = torch.cuda.FloatTensor(batch_size * num_images,
proj_image_dims[1],
proj_image_dims[0])
color_images = torch.cuda.FloatTensor(batch_size * num_images, 3,
input_image_dims[1],
input_image_dims[0])
camera_poses = torch.cuda.FloatTensor(batch_size * num_images, 4,
4)
label_images = torch.cuda.LongTensor(batch_size * num_images,
proj_image_dims[1],
proj_image_dims[0])
else:
mask = torch.LongTensor(batch_size * column_height)
depth_images = torch.FloatTensor(batch_size * num_images,
proj_image_dims[1],
proj_image_dims[0])
color_images = torch.FloatTensor(batch_size * num_images, 3,
input_image_dims[1],
input_image_dims[0])
camera_poses = torch.FloatTensor(batch_size * num_images, 4, 4)
label_images = torch.LongTensor(batch_size * num_images,
proj_image_dims[1],
proj_image_dims[0])
for t, v in enumerate(indices):
iter_start = time.time()
# print(t, v)
if CUDA_AVAILABLE:
targets_semantic = torch.autograd.Variable(labels[v].cuda())
else:
targets_semantic = torch.autograd.Variable(labels[v])
# Ignore Invalid targets for semantic
mask_semantic = targets_semantic.view(-1).data.clone()
for k in range(num_classes):
if criterion_weights_semantic[k] == 0:
mask_semantic[mask_semantic.eq(k)] = 0
mask_semantic_indices = mask_semantic.nonzero().squeeze(
) # Used in confusion matrix
if len(mask_semantic_indices.shape) == 0:
continue
# Ignore Invalid targets for scan
# occ[0] = np.less_equal(np.abs(sdfs), 1) # occupied space - 1, empty space - 0
# occ[1] = np.greater_equal(sdfs, -1) # known space = 1, unknown space - 0
# Known-Free Space : 1, 0(2). - Target = 0
# Known-Occupied Space : 1, 1 (3) - Target = 1
# Unknown Space: 0, 0 - (0) - Target = 2
# Create mask from current volume where 1 represents voxel is known-free or known-occupied.
# ToDo: Ask tutor: What if I don't use a mask?
# 0 input should target 0, 1 should 1 and 2(from before voxel discarding) should 2.
if opt.train_scan_completion:
mask_scan = targets_semantic.view(-1).data.clone()
mask_scan[:] = 1
# Ignore Unknown Voxels from before.
mask_scan[targets_semantic.view(-1).eq(opt.num_classes -
1)] = 0
mask_scan_indices = mask_scan.nonzero().squeeze()
if len(mask_scan_indices.shape) == 0:
continue
# ToDo: What if you generate targets_scan from volumetric grid?
# ToDo: You should get the same result but confirm.
targets_scan = targets_semantic.view(-1).data.clone()
targets_scan[torch.ge(targets_scan, 1) *
torch.lt(targets_scan, num_classes - 1)] = 1
targets_scan[torch.eq(targets_scan, num_classes -
1)] = 2 # Label 41 with class 2
transforms = world_to_grids[v].unsqueeze(1)
transforms = transforms.expand(batch_size, num_images, 4,
4).contiguous().view(-1, 4, 4)
if CUDA_AVAILABLE:
transforms = transforms.cuda()
# Load the data
# print("loading the data")
is_load_success = data_util.load_frames_multi(
opt.data_path_2d, frames[v], depth_images, color_images,
camera_poses, color_mean, color_std)
if not is_load_success:
continue
# 3d Input
volume = volumes[v]
# Get indices of voxels to be removed if training scan completion
random_center_voxel_indices = torch.Tensor() # Empty Tensor
if opt.train_scan_completion:
# ToDo: For all sample in each batch, same random voxels are removed.
# ToDo: Voxel already unknown also gets removed.
random_center_voxel_indices = projection.get_random_center_voxels_index(
opt.voxel_removal_fraction)
# Mark the 3D voxels as Unknown and
volume[:, :, random_center_voxel_indices,
projection.volume_dims[0] // 2,
projection.volume_dims[1] // 2] = 0
# Compute projection mapping and mark center voxels as Unknown if training for scan completion
proj_mapping = [
projection.compute_projection(d, c, t,
random_center_voxel_indices)
for d, c, t in zip(depth_images, camera_poses, transforms)
]
if None in proj_mapping: # Invalid sample
print('No mapping in proj_mapping')
continue
proj_mapping = list(zip(*proj_mapping))
proj_ind_3d = torch.stack(proj_mapping[0])
proj_ind_2d = torch.stack(proj_mapping[1])
if opt.use_proxy_loss:
data_util.load_label_frames(opt.data_path_2d, frames[v],
label_images, num_classes)
mask2d = label_images.view(-1).clone()
for k in range(num_classes):
if criterion_weights_semantic[k] == 0:
mask2d[mask2d.eq(k)] = 0
mask2d = mask2d.nonzero().squeeze()
if len(mask2d.shape) == 0:
continue # nothing to optimize for here
# 2d
imageft_fixed = model2d_fixed(
torch.autograd.Variable(color_images))
imageft = model2d_trainable(imageft_fixed)
if opt.use_proxy_loss:
ft2d = model2d_classifier(imageft)
ft2d = ft2d.permute(0, 2, 3, 1).contiguous()
# 2d/3d
input3d = torch.autograd.Variable(volume)
if CUDA_AVAILABLE:
input3d = input3d.cuda()
# Forward Pass
output_semantic, output_scan = model(
input3d, imageft, torch.autograd.Variable(proj_ind_3d),
torch.autograd.Variable(proj_ind_2d), grid_dims)
# Display Once GPU memory usage - Be Sure of GPU usage. Collab is a bit unpredictable
check_gpu_memory_usage_once()
# Compute Scan and semantic Loss
loss_semantic = criterion_semantic(
output_semantic.view(-1, num_classes),
targets_semantic.view(-1))
train_loss_semantic.append(loss_semantic.item())
if opt.train_scan_completion:
loss_scan = criterion_scan(
output_scan.view(-1, _NUM_OCCUPANCY_STATES),
targets_scan.view(-1))
train_loss_scan.append(loss_scan.item())
loss = loss_scan + loss_semantic
else:
loss = loss_semantic
# Backpropagate total loss.
# ToDo: Note using same optimizer for both branches. Is there a need for different optimizers?
optimizer.zero_grad()
optimizer2d.zero_grad()
if opt.use_proxy_loss:
loss.backward(retain_graph=True)
else:
loss.backward()
optimizer.step()
# optimizer2d.step is probably required even when use_proxy_loss is False, since backprojection layer is
# differentiable, allowing us to backpropagate the gradients to 2d model from model(3D).
optimizer2d.step()
# ToDo: Check if proxy loss is required. If optimizer2d is injecting gradients, proxy loss may be needed.
if opt.use_proxy_loss:
loss2d = criterion2d(
ft2d.view(-1, num_classes),
torch.autograd.Variable(label_images.view(-1)))
train_loss_2d.append(loss2d.item())
optimizer2d.zero_grad()
optimizer2dc.zero_grad()
loss2d.backward()
optimizer2dc.step()
optimizer2d.step()
# confusion
y = ft2d.data
y = y.view(-1, num_classes)[:, :-1]
_, predictions = y.max(1)
predictions = predictions.view(-1)
k = label_images.view(-1)
confusion2d.add(torch.index_select(predictions, 0, mask2d),
torch.index_select(k, 0, mask2d))
# Confusion for Semantic
y = output_semantic.data
# Discard semantic prediction of class num_classes-1[Unknown Voxel]
y = y.view(y.nelement() // y.size(2), num_classes)[:, :-1]
_, predictions = y.max(1)
predictions = predictions.view(-1)
k = targets_semantic.data.view(-1)
confusion.add(
torch.index_select(predictions, 0, mask_semantic_indices),
torch.index_select(k, 0, mask_semantic_indices))
# Confusion for Scan completion
if opt.train_scan_completion:
y = output_scan.data
# Discard semantic prediction of Unknown Voxels in target_scan
y = y.view(y.nelement() // y.size(2),
_NUM_OCCUPANCY_STATES)[:, :-1]
_, predictions_scan = y.max(1)
predictions_scan = predictions_scan.view(-1)
k = targets_scan.data.view(-1)
confusion_scan.add(
torch.index_select(predictions_scan, 0, mask_scan_indices),
torch.index_select(k, 0, mask_scan_indices))
# Log loss for current iteration and print every 20th turn.
msg1 = _SPLITTER.join(
[str(f)
for f in [epoch, iter, loss_semantic.item()]])
log_file_semantic.write(msg1 + '\n')
if opt.train_scan_completion:
msg2 = _SPLITTER.join(
[str(f)
for f in [epoch, iter, loss_scan.item()]])
log_file_scan.write(msg2 + '\n')
# InFrequent logging stops chrome from crash[Colab] and also less strain on jupyter.
if iter % (64 // batch_size) == 0:
print("Semantic: %s, %0.6f" % (msg1, time.time() - iter_start))
if opt.train_scan_completion:
print("Scan : %s" % msg2)
iter += 1
if iter % (
10000 // batch_size
) == 0: # Save more frequently, since its Google Collaboratory.
# Save 3d model
if not opt.train_scan_completion:
torch.save(
model.state_dict(),
os.path.join(
opt.output,
'model-semantic-epoch%s-iter%s-Sem%s.pth' %
(epoch, iter, str(loss_semantic.item()))))
else:
torch.save(
model.state_dict(),
os.path.join(
opt.output,
'model-semantic_and_scan-epoch%s-iter%s-sem%s-scan%s.pth'
% (epoch, iter, str(
loss_semantic.item()), str(loss_scan.item()))))
# Save 2d model
# Important ToDo: Do we need to retrain on model2d_trainable
torch.save(
model2d_trainable.state_dict(),
os.path.join(opt.output,
'model2d-iter%s-epoch%s.pth' % (iter, epoch)))
if opt.use_proxy_loss:
torch.save(
model2d_classifier.state_dict(),
os.path.join(
opt.output,
'model2dc-iter%s-epoch%s.pth' % (iter, epoch)))
if iter == 1:
torch.save(model2d_fixed.state_dict(),
os.path.join(opt.output, 'model2dfixed.pth'))
if iter % 100 == 0:
evaluate_confusion(confusion, train_loss_semantic, epoch, iter,
-1, 'TrainSemantic', log_file_semantic,
num_classes)
if opt.train_scan_completion:
evaluate_confusion(confusion_scan, train_loss_scan, epoch,
iter, -1, 'TrainScan', log_file_scan,
_NUM_OCCUPANCY_STATES)
if opt.use_proxy_loss:
evaluate_confusion(confusion2d, train_loss_2d, epoch, iter,
-1, 'Train2d', log_file_2d, num_classes)
end = time.time()
took = end - start
evaluate_confusion(confusion, train_loss_semantic, epoch, iter, took,
'TrainSemantic', log_file_semantic, num_classes)
if opt.train_scan_completion:
evaluate_confusion(confusion_scan, train_loss_scan, epoch, iter, took,
'TrainScan', log_file_scan, _NUM_OCCUPANCY_STATES)
if opt.use_proxy_loss:
evaluate_confusion(confusion2d, train_loss_2d, epoch, iter, took,
'Train2d', log_file_2d, num_classes)
return train_loss_semantic, train_loss_scan, iter, train_loss_2d
def train(epoch, iter, log_file, train_file, log_file_2d):
train_loss = []
train_loss_2d = []
model.train()
start = time.time()
model2d_trainable.train()
if opt.use_proxy_loss:
model2d_classifier.train()
volumes, labels, frames, world_to_grids = data_util.load_hdf5_data(train_file, num_classes)
frames = frames[:, :2+num_images]
volumes = volumes.permute(0, 1, 4, 3, 2)
labels = labels.permute(0, 1, 4, 3, 2)
labels = labels[:, 0, :, grid_centerX, grid_centerY] # center columns as targets
num_samples = volumes.shape[0]
# shuffle
indices = torch.randperm(num_samples).long().split(batch_size)
# remove last mini-batch so that all the batches have equal size
indices = indices[:-1]
mask = torch.cuda.LongTensor(batch_size*column_height)
depth_images = torch.cuda.FloatTensor(batch_size * num_images, proj_image_dims[1], proj_image_dims[0])
color_images = torch.cuda.FloatTensor(batch_size * num_images, 3, input_image_dims[1], input_image_dims[0])
camera_poses = torch.cuda.FloatTensor(batch_size * num_images, 4, 4)
label_images = torch.cuda.LongTensor(batch_size * num_images, proj_image_dims[1], proj_image_dims[0])
for t,v in enumerate(indices):
targets = torch.autograd.Variable(labels[v].cuda())
# valid targets
mask = targets.view(-1).data.clone()
for k in range(num_classes):
if criterion_weights[k] == 0:
mask[mask.eq(k)] = 0
maskindices = mask.nonzero().squeeze()
if len(maskindices.shape) == 0:
continue
transforms = world_to_grids[v].unsqueeze(1)
transforms = transforms.expand(batch_size, num_images, 4, 4).contiguous().view(-1, 4, 4).cuda()
data_util.load_frames_multi(opt.data_path_2d, frames[v], depth_images, color_images, camera_poses, color_mean, color_std)
# compute projection mapping
proj_mapping = [projection.compute_projection(d, c, t) for d, c, t in zip(depth_images, camera_poses, transforms)]
for d, c, t in zip(depth_images, camera_poses, transforms):
test = projection.compute_projection(d, c, t)
if None in proj_mapping: #invalid sample
#print '(invalid sample)'
continue
proj_mapping = list(zip(*proj_mapping))
proj_ind_3d = torch.stack(proj_mapping[0])
proj_ind_2d = torch.stack(proj_mapping[1])
if opt.use_proxy_loss:
data_util.load_label_frames(opt.data_path_2d, frames[v], label_images, num_classes)
mask2d = label_images.view(-1).clone()
for k in range(num_classes):
if criterion_weights[k] == 0:
mask2d[mask2d.eq(k)] = 0
mask2d = mask2d.nonzero().squeeze()
if (len(mask2d.shape) == 0):
continue # nothing to optimize for here
# 2d
imageft_fixed = model2d_fixed(torch.autograd.Variable(color_images))
imageft = model2d_trainable(imageft_fixed)
if opt.use_proxy_loss:
ft2d = model2d_classifier(imageft)
ft2d = ft2d.permute(0, 2, 3, 1).contiguous()
# 2d/3d
input3d = torch.autograd.Variable(volumes[v].cuda())
output = model(input3d, imageft, torch.autograd.Variable(proj_ind_3d), torch.autograd.Variable(proj_ind_2d), grid_dims)
loss = criterion(output.view(-1, num_classes), targets.view(-1))
train_loss.append(loss.item())
optimizer.zero_grad()
optimizer2d.zero_grad()
loss.backward(retain_graph=True)
optimizer.step()
optimizer2d.step()
if opt.use_proxy_loss:
loss2d = criterion2d(ft2d.view(-1, num_classes), torch.autograd.Variable(label_images.view(-1)))
train_loss_2d.append(loss2d.item())
optimizer2d.zero_grad()
optimizer2dc.zero_grad()
loss2d.backward()
optimizer2dc.step()
optimizer2d.step()
# confusion
y = ft2d.data
y = y.view(-1, num_classes)[:, :-1]
_, predictions = y.max(1)
predictions = predictions.view(-1)
k = label_images.view(-1)
confusion2d.add(torch.index_select(predictions, 0, mask2d), torch.index_select(k, 0, mask2d))
# confusion
y = output.data
y = y.view(y.nelement()/y.size(2), num_classes)[:, :-1]
_, predictions = y.max(1)
predictions = predictions.view(-1)
k = targets.data.view(-1)
confusion.add(torch.index_select(predictions, 0, maskindices), torch.index_select(k, 0, maskindices))
log_file.write(_SPLITTER.join([str(f) for f in [epoch, iter, loss.item()]]) + '\n')
iter += 1
if iter % 10000 == 0:
torch.save(model.state_dict(), os.path.join(opt.output, 'model-iter%s-epoch%s.pth' % (iter, epoch)))
torch.save(model2d_trainable.state_dict(), os.path.join(opt.output, 'model2d-iter%s-epoch%s.pth' % (iter, epoch)))
if opt.use_proxy_loss:
torch.save(model2d_classifier.state_dict(), os.path.join(opt.output, 'model2dc-iter%s-epoch%s.pth' % (iter, epoch)))
if iter == 1:
torch.save(model2d_fixed.state_dict(), os.path.join(opt.output, 'model2dfixed.pth'))
if iter % 100 == 0:
evaluate_confusion(confusion, train_loss, epoch, iter, -1, 'Train', log_file)
if opt.use_proxy_loss:
evaluate_confusion(confusion2d, train_loss_2d, epoch, iter, -1, 'Train2d', log_file_2d)
end = time.time()
took = end - start
evaluate_confusion(confusion, train_loss, epoch, iter, took, 'Train', log_file)
if opt.use_proxy_loss:
evaluate_confusion(confusion2d, train_loss_2d, epoch, iter, took, 'Train2d', log_file_2d)
return train_loss, iter, train_loss_2d