def test_check_status_running_iter(self):
g_conf = GlobalConfig()
g_conf.param.NAME = 'experiment_running_iter'
# TODO: this merge is weird.
g_conf.merge_with_yaml(
'configs/monitor_test/experiment_running_iter.yaml')
# JUST A TRICK TO CONTAIN THE CURRENT LIMITATIONS
g_conf.set_type_of_process('train')
coil_logger.add_message(
'Loading',
{"Keys_Division": [1, 123, 1, 1, 2, 12, 3, 12, 31, 2, 1, 1]})
coil_logger.add_message('Loading', {
"Models_loaded": ' VUALA ',
"Checkpoint": "988765"
})
for i in range(0, 10):
coil_logger.add_message('Reading', {
"Iteration": i,
"ReadKeys": [1, 123, 5, 1, 34, 1, 23]
})
coil_logger.add_message('Model', {
"Iteration": i,
"Output": ["output"]
})
# TODO: Check how the alias will work.
status = monitorer.get_status('monitor_test',
'experiment_running_iter',
g_conf.param.PROCESS_NAME)
self.assertEqual(status[0], "Iterating")
def test_check_status_not_existent(self):
# Check if status could be check for unexistent experiments
g_conf = GlobalConfig()
status = monitorer.get_status('monitor_test', 'experiment_25',
g_conf.param.PROCESS_NAME)
self.assertEqual(status[0], "Does Not Exist")
def test_check_status_not_existent(self):
# Check if status could be check for unexistent experiments
g_conf.immutable(False)
status = monitorer.get_status('monitor_test', 'experiment_25.yaml',
g_conf.PROCESS_NAME)
self.assertEqual(status[0], "Does Not Exist")
def test_check_status_to_run(self):
# Check for an experiment that exists in the config files but has not been started
g_conf = GlobalConfig()
g_conf.param.NAME = 'experiment_to_run'
status = monitorer.get_status('monitor_test', 'experiment_to_run',
g_conf.param.PROCESS_NAME)
self.assertEqual(status[0], "Not Started")
def test_check_status_to_run(self):
# Check for an experiment that exists in the config files but has not been started
g_conf.immutable(False)
g_conf.NAME = 'experiment_to_run'
status = monitorer.get_status('monitor_test', 'experiment_to_run.yaml',
g_conf.PROCESS_NAME)
self.assertEqual(status[0], "Not Started")
def mount_experiment_heap(folder, experiments_list, is_training, validation_datasets, drive_environments):
tasks_queue = []
is_training = True
for experiment in experiments_list:
# Train is always priority. # TODO: some system to check priority depending on iterations
# TODO: One thing is error other thing is stop. However at a first step we can try to restart all error things
if is_training:
# if monitorer.get_status(folder, experiment, 'train')[0] == "Not Started" or \
# monitorer.get_status(folder, experiment, 'train')[0] == "Error":
heapq.heappush(tasks_queue, (0, experiment+'_train' ,
{'type': 'train', 'folder': folder,
'experiment': experiment}))
for val_data in validation_datasets:
if monitorer.get_status(folder, experiment, 'validation_' + val_data)[0] == "Not Started" or \
monitorer.get_status(folder, experiment, 'validation_'+ val_data)[0] == "Error":
heapq.heappush(tasks_queue, (2, experiment+'_validation_' + val_data,
{'type': 'validation', 'folder': folder,
'experiment': experiment, 'dataset': val_data}))
for drive_env in drive_environments:
if monitorer.get_status(folder, experiment, 'drive_' + drive_env)[0] == "Not Started" or \
monitorer.get_status(folder, experiment, 'drive_' + drive_env)[0] == "Error":
heapq.heappush(tasks_queue, (1, experiment+'_drive_' + drive_env,
{'type': 'drive', 'folder': folder,
'experiment': experiment, 'environment': drive_env}))
return tasks_queue
def test_check_status_error(self):
g_conf.immutable(False)
# TODO: THe error ? How do nicely merge with the other parts ??
g_conf.NAME = 'experiment_running_error'
# TODO: this merge is weird.
merge_with_yaml('configs/monitor_test/experiment_running_error.yaml')
# JUST A TRICK TO CONTAIN THE CURRENT LIMITATIONS
set_type_of_process('train')
coil_logger.add_message(
'Loading',
{"Keys_Division": [1, 123, 1, 1, 2, 12, 3, 12, 31, 2, 1, 1]})
coil_logger.add_message('Loading', {
"Models_loaded": ' VUALA ',
"Checkpoint": "988765"
})
for i in range(0, 10):
coil_logger.add_message('Iterating', {
"Iteration": i,
"ReadKeys": [1, 123, 5, 1, 34, 1, 23]
})
coil_logger.add_message('Iterating', {
"Iteration": i,
"Output": ["output"]
})
coil_logger.add_message('Error', {
"Iteration": 10,
"Message": " Some data integrity problems ! "
})
# TODO: Check how the alias will work.
status = monitorer.get_status('monitor_test',
'experiment_running_error.yaml',
g_conf.PROCESS_NAME)
self.assertEqual(status[0], "Error")
print(status[1])
def test_check_status_finished(self):
g_conf.immutable(False)
g_conf.NAME = 'experiment_finished'
# TODO: this merge is weird.
merge_with_yaml('configs/monitor_test/experiment_finished.yaml')
g_conf.NUMBER_ITERATIONS = 20
# JUST A TRICK TO CONTAIN THE CURRENT LIMITATIONS
set_type_of_process('train')
# We set the number of iterations as
coil_logger.add_message(
'Loading',
{"Keys_Division": [1, 123, 1, 1, 2, 12, 3, 12, 31, 2, 1, 1]})
coil_logger.add_message('Loading', {
"Models_loaded": ' VUALA ',
"Checkpoint": "988765"
})
for i in range(0, 21):
coil_logger.add_message('Iterating', {
"Iteration": i,
"ReadKeys": [1, 123, 5, 1, 34, 1, 23]
}, i)
coil_logger.add_message('Iterating', {
"Iteration": i,
"Output": ["output"]
}, i)
# TODO: Check how the alias will work.
status = monitorer.get_status('monitor_test',
'experiment_finished.yaml',
g_conf.PROCESS_NAME)
self.assertEqual(status[0], "Finished")
def get_gpu_resources(gpu_resources, executing_processes, allocation_params):
"""
Args:
allocated_gpus:
executing_processes:
allocation_params:
Returns:
"""
still_executing_processes = []
for process_specs in executing_processes:
# Make process name:
if process_specs['type'] == 'drive':
name = 'drive_' + process_specs['environment']
elif process_specs['type'] == 'validation':
name = 'validation_' + process_specs['dataset']
else:
name = process_specs['type']
status = monitorer.get_status(process_specs['folder'], process_specs['experiment'],
name)[0]
if status == "Finished" or status == 'Error':
if isinstance(process_specs['gpu'], list):
for gpu in process_specs['gpu']:
gpu_resources[gpu] += allocation_params[process_specs['type']+'_cost']
else:
gpu_resources[process_specs['gpu']] += allocation_params[process_specs['type']+'_cost']
else:
still_executing_processes.append(process_specs)
return gpu_resources, max(gpu_resources.values()), still_executing_processes
def get_gpu_resources(gpu_resources, executing_processes, allocation_params):
"""
Args:
allocated_gpus:
executing_processes:
allocation_params:
Returns:
"""
for process_specs in executing_processes:
status = monitorer.get_status(process_specs['folder'], process_specs['experiment'],
process_specs['type'])[0]
if status == "Finished" or status == 'Error':
gpu_resources[process_specs['gpu']] += allocation_params[process_specs['type']+'_cost']
return gpu_resources, max(gpu_resources.values())
def test_check_status_running_loading(self):
g_conf.immutable(False)
g_conf.NAME = 'experiment_running_loading'
# TODO: this merge is weird.
merge_with_yaml('configs/monitor_test/experiment_running_loading.yaml')
# JUST A TRICK TO CONTAIN THE CURRENT LIMITATIONS
set_type_of_process('train')
coil_logger.add_message(
'Loading',
{"Keys_Division": [1, 123, 1, 1, 2, 12, 3, 12, 31, 2, 1, 1]})
coil_logger.add_message('Loading', {
"Models_loaded": ' VUALA ',
"Checkpoint": "988765"
})
# TODO: Check how the alias will work.
status = monitorer.get_status('monitor_test',
'experiment_running_loading.yaml',
g_conf.PROCESS_NAME)
self.assertEqual(status[0], "Loading")
def execute(gpu, exp_batch, exp_alias):
os.environ["CUDA_VISIBLE_DEVICES"] = gpu
merge_with_yaml(os.path.join('configs', exp_batch, exp_alias + '.yaml'))
set_type_of_process('train')
coil_logger.add_message('Loading', {'GPU': gpu})
if not os.path.exists('_output_logs'):
os.mkdir('_output_logs')
sys.stdout = open(os.path.join(
'_output_logs', g_conf.PROCESS_NAME + '_' + str(os.getpid()) + ".out"),
"a",
buffering=1)
if monitorer.get_status(exp_batch, exp_alias + '.yaml',
g_conf.PROCESS_NAME)[0] == "Finished":
return
full_dataset = os.path.join(os.environ["COIL_DATASET_PATH"],
g_conf.TRAIN_DATASET_NAME)
dataset = CoILDataset(full_dataset,
transform=transforms.Compose([transforms.ToTensor()
]))
sampler = BatchSequenceSampler(
splitter.control_steer_split(dataset.measurements,
dataset.meta_data), g_conf.BATCH_SIZE,
g_conf.NUMBER_IMAGES_SEQUENCE, g_conf.SEQUENCE_STRIDE)
data_loader = torch.utils.data.DataLoader(dataset,
batch_sampler=sampler,
shuffle=False,
num_workers=6,
pin_memory=True)
l1weight = 1.0
image_size = tuple([88, 200])
testmode = 1
# print("helllooooo", g_conf.MODEL_NAME)
if g_conf.GANMODEL_NAME == 'LSDcontrol':
# netD = ganmodels._netD().cuda()
netG = ganmodels._netG(skip=g_conf.SKIP).cuda()
# else:
# netD = ganmodels._oldnetD().cuda()
# netG = ganmodels._oldnetG().cuda()
# init_weights(netD)
init_weights(netG)
# print(netD)
print(netG)
# optimD = torch.optim.Adam(netD.parameters(), lr=0.0002, betas=(0.5, 0.999))
optimG = torch.optim.Adam(netG.parameters(), lr=0.0002, betas=(0.7, 0.999))
MSE_loss = torch.nn.MSELoss().cuda()
L1_loss = torch.nn.L1Loss().cuda()
iteration = 0
best_loss_iter = 0
best_lossD = 1000000.0
best_lossG = 1000000.0
accumulated_time = 0
netG.eval()
# netD.eval()
capture_time = time.time()
for data in data_loader:
val = 0.5
input_data, float_data = data
inputs = input_data['rgb'].cuda()
inputs = inputs.squeeze(1)
inputs_in = inputs - val
#forward pass
# print(inputs[0][0][0][0], inputs_in[0][0][0][0])
fake_inputs = netG(inputs_in) #subtracted by 0.5
fake_inputs_in = fake_inputs
# print(fake_inputs[0][0][0][0], fake_inputs_in[0][0][0][0])
if iteration % 200 == 0:
imgs_to_save = torch.cat((inputs_in[:2] + val, fake_inputs_in[:2]),
0).cpu().data
vutils.save_image(imgs_to_save,
'./noganimgs/' + str(iteration) +
'noganreal_samples.png',
normalize=True)
coil_logger.add_image("Images", imgs_to_save, iteration)
optimG.zero_grad()
print("~~~~~~~~~__________")
print(inputs_in[0][0][0][0])
print(fake_inputs[0][0][0][0])
lossG_mse = MSE_loss(fake_inputs, inputs)
print(lossG_mse)
lossG_mse /= len(inputs_in)
print("~~~~~~~~~__________--------------")
lossG_mse.backward() #retain_graph=True needed?
optimG.step()
coil_logger.add_scalar('MSE LossG', lossG_mse.data / len(inputs_in),
iteration)
#optimization for one iter done!
position = random.randint(0, len(float_data) - 1)
# if lossD.data < best_lossD:
# best_lossD = lossD.data.tolist()
if lossG_mse.data < best_lossG:
best_lossG = lossG_mse.data.tolist()
best_loss_iter = iteration
accumulated_time += time.time() - capture_time
capture_time = time.time()
# print("LossD", lossD.data.tolist(), "LossG", lossG.data.tolist(), "BestLossD", best_lossD, "BestLossG", best_lossG, "Iteration", iteration, "Best Loss Iteration", best_loss_iter)
# coil_logger.add_message('Iterating',
# {'Iteration': iteration,
# 'LossD': lossD.data.tolist(),
# 'LossG': lossG.data.tolist(),
# 'Images/s': (iteration*g_conf.BATCH_SIZE)/accumulated_time,
# 'BestLossD': best_lossD, 'BestLossIteration': best_loss_iter,
# 'BestLossG': best_lossG, 'BestLossIteration': best_loss_iter,
# 'GroundTruth': dataset.extract_targets(float_data)[position].data.tolist(),
# 'Inputs': dataset.extract_inputs(float_data)[position].data.tolist()},
# iteration)
# if is_ready_to_save(iteration):
#
# state = {
# 'iteration': iteration,
# 'stateD_dict': netD.state_dict(),
# 'stateG_dict': netG.state_dict(),
# 'best_lossD': best_lossD,
# 'best_lossG': best_lossG,
# 'total_time': accumulated_time,
# 'best_loss_iter': best_loss_iter
#
# }
# torch.save(state, os.path.join('/datatmp/Experiments/rohitgan/_logs', exp_batch, exp_alias
# , 'checkpoints', str(iteration) + '.pth'))
# if iteration == best_loss_iter:
#
# state = {
# 'iteration': iteration,
# 'stateD_dict': netD.state_dict(),
# 'stateG_dict': netG.state_dict(),
# 'best_lossD': best_lossD,
# 'best_lossG': best_lossG,
# 'total_time': accumulated_time,
# 'best_loss_iter': best_loss_iter
#
# }
# torch.save(state, os.path.join('/datatmp/Experiments/rohitgan/_logs', exp_batch, exp_alias
# , 'best_modelG' + '.pth'))
#
iteration += 1
def mount_experiment_heap(folder,
experiments_list,
is_training,
executing_processes,
old_tasks_queue,
validation_datasets,
drive_environments,
restart_error=True):
"""
Function that will add all the experiments to a heap. These experiments will
be consumed when there is enough resources
Args:
folder: The folder with the experiments
experiments_list: The list of all experiments to be executed
is_training: If Training is being add also ( NOT IMPLEMENTED)
executing_processes: The current processes being executed
old_tasks_queue: Current process on the task queue
validation_datasets: The validation datasets to be evaluated
drive_environments: All the driving environments where the models are going to be tested.
restart_error: If you are going to restart experiments that are not working (NOT implemented)
Returns:
"""
tasks_queue = []
exec_name_vec = execvec_to_names(executing_processes)
for experiment in experiments_list:
# Train is always priority.
task_to_add = None
if is_training:
if monitorer.get_status(folder, experiment,
'train')[0] == "Not Started":
task_to_add = (0, experiment + '_train', {
'type': 'train',
'folder': folder,
'experiment': experiment
})
elif restart_error and monitorer.get_status(folder, experiment, 'train')[0] \
== "Error":
task_to_add = (0, experiment + '_train', {
'type': 'train',
'folder': folder,
'experiment': experiment
})
if task_to_add is not None:
task_name_vec = dict_to_namevec(task_to_add[2])
if task_name_vec in exec_name_vec:
continue
if task_to_add is not None and task_to_add not in old_tasks_queue:
heapq.heappush(tasks_queue, task_to_add)
for val_data in validation_datasets:
task_to_add = None
if monitorer.get_status(folder, experiment, 'validation_' +
val_data)[0] == "Not Started":
task_to_add = (1, experiment + '_validation_' + val_data, {
'type': 'validation',
'folder': folder,
'experiment': experiment,
'dataset': val_data
})
elif restart_error and monitorer.get_status(
folder, experiment,
'validation_' + val_data)[0] == "Error":
task_to_add = (1, experiment + '_validation_' + val_data, {
'type': 'validation',
'folder': folder,
'experiment': experiment,
'dataset': val_data
})
if task_to_add is not None:
task_name_vec = dict_to_namevec(task_to_add[2])
if task_name_vec in exec_name_vec:
continue
if task_to_add is not None and task_to_add not in old_tasks_queue:
heapq.heappush(tasks_queue, task_to_add)
for drive_env in drive_environments:
task_to_add = None
if monitorer.get_status(folder, experiment,
'drive_' + drive_env)[0] == "Not Started":
task_to_add = (2, experiment + '_drive_' + drive_env, {
'type': 'drive',
'folder': folder,
'experiment': experiment,
'environment': drive_env
})
elif restart_error and monitorer.get_status(folder, experiment, 'drive_' + drive_env)\
[0] == "Error":
task_to_add = (2, experiment + '_drive_' + drive_env, {
'type': 'drive',
'folder': folder,
'experiment': experiment,
'environment': drive_env
})
if task_to_add is not None:
task_name_vec = dict_to_namevec(task_to_add[2])
if task_name_vec in exec_name_vec:
continue
if task_to_add is not None and task_to_add not in old_tasks_queue:
heapq.heappush(tasks_queue, task_to_add)
return tasks_queue
def execute(gpu, exp_batch, exp_alias):
os.environ["CUDA_VISIBLE_DEVICES"] = gpu
merge_with_yaml(os.path.join('configs', exp_batch, exp_alias + '.yaml'))
set_type_of_process('train')
coil_logger.add_message('Loading', {'GPU': gpu})
if not os.path.exists('_output_logs'):
os.mkdir('_output_logs')
sys.stdout = open(os.path.join('_output_logs',
g_conf.PROCESS_NAME + '_' + str(os.getpid()) + ".out"), "a", buffering=1)
if monitorer.get_status(exp_batch, exp_alias + '.yaml', g_conf.PROCESS_NAME)[0] == "Finished":
return
full_dataset = os.path.join(os.environ["COIL_DATASET_PATH"], g_conf.TRAIN_DATASET_NAME)
dataset = CoILDataset(full_dataset, transform=transforms.Compose([transforms.ToPILImage(), transforms.Resize(128, 128), transforms.ToTensor(), transforms.Normalize([ 0.5, 0.5, 0.5], [ 1.0, 1.0, 1.0])]))
sampler = BatchSequenceSampler(splitter.control_steer_split(dataset.measurements, dataset.meta_data),
g_conf.BATCH_SIZE, g_conf.NUMBER_IMAGES_SEQUENCE, g_conf.SEQUENCE_STRIDE)
data_loader = torch.utils.data.DataLoader(dataset, batch_sampler=sampler,
shuffle=False, num_workers=6, pin_memory=True)
transform = transforms.Compose([transforms.Resize((88, 200))])
l1weight = 1.0
image_size = tuple([88, 200])
testmode = 1
# print("helllooooo", g_conf.MODEL_NAME)
if g_conf.GANMODEL_NAME == 'LSDcontrol':
netD = ganmodels._netD().cuda()
netG = ganmodels._netG(skip=g_conf.SKIP).cuda()
# else:
# netD = ganmodels._oldnetD().cuda()
# netG = ganmodels._oldnetG().cuda()
init_weights(netD)
init_weights(netG)
print(netD)
print(netG)
optimD = torch.optim.Adam(netD.parameters(), lr=0.0002, betas=(0.5, 0.999))
optimG = torch.optim.Adam(netG.parameters(), lr=0.0002, betas=(0.5, 0.999))
MSE_loss = torch.nn.MSELoss().cuda()
L1_loss = torch.nn.L1Loss().cuda()
iteration = 0
best_loss_iter = 0
best_lossD = 1000000.0
best_lossG = 1000000.0
accumulated_time = 0
netG.eval()
netD.eval()
capture_time = time.time()
for data in data_loader:
input_data, float_data = data
inputs = input_data['rgb'].cuda()
inputs = inputs.squeeze(1)
print ("Inputs", i)
#forward pass
fake_inputs = netG(inputs)
if iteration % 1000 == 0:
coil_logger.add_image("Images", torch.cat((inputs[:3], fake_inputs[:3]), 0), iteration)
##--------------------Discriminator part!!!!!!!!!!-----------------------
set_requires_grad(netD, True)
optimD.zero_grad()
##fake
outputsD_fake_forD = netD(fake_inputs.detach())
labsize = outputsD_fake_forD.size()
#Create labels of patchgan style with label smoothing
labels_fake = torch.zeros(labsize[0], labsize[1], labsize[2], labsize[3]) #Fake labels
label_fake_noise = torch.rand(labels_fake.size()) * 0.5 - 0.25 #Label smoothing
labels_fake = labels_fake + label_fake_noise
labels_fake = Variable(labels_fake).cuda()
lossD_fake = MSE_loss(outputsD_fake_forD, labels_fake)
##real
outputsD_real = netD(inputs)
labsize = outputsD_real.size()
#Create labels of patchgan style with label smoothing
labels_real = torch.ones(labsize[0], labsize[1], labsize[2], labsize[3]) #Real labels
label_real_noise = torch.rand(labels_real.size()) * 0.5 - 0.25 #Label smoothing
labels_real = labels_real + label_real_noise
labels_real = Variable(labels_real).cuda()
lossD_real = MSE_loss(outputsD_real, labels_real)
#Discriminator updates
lossD = (lossD_real + lossD_fake) * 0.5
lossD /= len(inputs)
lossD.backward() #retain_graph=True needed?
optimD.step()
coil_logger.add_scalar('Total LossD', lossD.data, iteration)
coil_logger.add_scalar('Real LossD', lossD_real.data / len(inputs), iteration)
coil_logger.add_scalar('Fake LossD', lossD_fake.data / len(inputs), iteration)
##--------------------Generator part!!!!!!!!!!-----------------------
#TODO change decoder architecture
#TODO check norms of gradients later
#TODO add auxiliary regression loss for steering
set_requires_grad(netD, False)
optimG.zero_grad()
outputsD_fake_forG = netD(fake_inputs)
#Generator updates
lossG_adv = MSE_loss(outputsD_fake_forG, labels_real)
lossG_smooth = L1_loss(fake_inputs, inputs)
lossG = lossG_adv + l1weight * lossG_smooth
lossG /= len(inputs)
lossG.backward() #retain_graph=True needed?
optimG.step()
coil_logger.add_scalar('Total LossG', lossG.data, iteration)
coil_logger.add_scalar('Adv LossG', lossG_adv.data / len(inputs), iteration)
coil_logger.add_scalar('Smooth LossG', lossG_smooth.data / len(inputs), iteration)
#optimization for one iter done!
position = random.randint(0, len(float_data)-1)
if lossD.data < best_lossD:
best_lossD = lossD.data.tolist()
if lossG.data < best_lossG:
best_lossG = lossG.data.tolist()
best_loss_iter = iteration
accumulated_time += time.time() - capture_time
capture_time = time.time()
print("LossD", lossD.data.tolist(), "LossG", lossG.data.tolist(), "BestLossD", best_lossD, "BestLossG", best_lossG, "Iteration", iteration, "Best Loss Iteration", best_loss_iter)
coil_logger.add_message('Iterating',
{'Iteration': iteration,
'LossD': lossD.data.tolist(),
'LossG': lossG.data.tolist(),
'Images/s': (iteration*g_conf.BATCH_SIZE)/accumulated_time,
'BestLossD': best_lossD, 'BestLossIteration': best_loss_iter,
'BestLossG': best_lossG, 'BestLossIteration': best_loss_iter,
'GroundTruth': dataset.extract_targets(float_data)[position].data.tolist(),
'Inputs': dataset.extract_inputs(float_data)[position].data.tolist()},
iteration)
if is_ready_to_save(iteration):
state = {
'iteration': iteration,
'stateD_dict': netD.state_dict(),
'stateG_dict': netG.state_dict(),
'best_lossD': best_lossD,
'best_lossG': best_lossG,
'total_time': accumulated_time,
'best_loss_iter': best_loss_iter
}
torch.save(state, os.path.join('/datatmp/Experiments/rohitgan/_logs', exp_batch, exp_alias
, 'checkpoints', str(iteration) + '.pth'))
if iteration == best_loss_iter:
state = {
'iteration': iteration,
'stateD_dict': netD.state_dict(),
'stateG_dict': netG.state_dict(),
'best_lossD': best_lossD,
'best_lossG': best_lossG,
'total_time': accumulated_time,
'best_loss_iter': best_loss_iter
}
torch.save(state, os.path.join('/datatmp/Experiments/rohitgan/_logs', exp_batch, exp_alias
, 'best_modelG' + '.pth'))
iteration += 1
def execute(gpu, exp_batch, exp_alias):
# We set the visible cuda devices
os.environ["CUDA_VISIBLE_DEVICES"] = gpu
# At this point the log file with the correct naming is created.
merge_with_yaml(os.path.join(exp_batch, exp_alias + '.yaml'))
set_type_of_process('validation')
sys.stdout = open(str(os.getpid()) + ".out", "a", buffering=1)
if monitorer.get_status(exp_batch, exp_alias,
g_conf.PROCESS_NAME)[0] == "Finished":
# TODO: print some cool summary or not ?
return
#Define the dataset. This structure is has the __get_item__ redefined in a way
#that you can access the HDFILES positions from the root directory as a in a vector.
full_dataset = os.path.join(os.environ["COIL_DATASET_PATH"],
g_conf.DATASET_NAME)
dataset = CoILDataset(full_dataset,
transform=transforms.Compose([transforms.ToTensor()
]))
# Creates the sampler, this part is responsible for managing the keys. It divides
# all keys depending on the measurements and produces a set of keys for each bach.
# The data loader is the multi threaded module from pytorch that release a number of
# workers to get all the data.
# TODO: batch size an number of workers go to some configuration file
data_loader = torch.utils.data.DataLoader(dataset,
batch_size=120,
shuffle=False,
num_workers=12,
pin_memory=True)
# TODO: here there is clearly a posibility to make a cool "conditioning" system.
model = CoILModel(g_conf.MODEL_NAME)
model.cuda()
# TODO: The checkpoint will continue, so the logs should restart ??? OR continue were it was
latest = get_latest_evaluated_checkpoint()
if latest is None: # When nothing was tested, get latest returns none, we fix that.
latest = 0
print(dataset.meta_data)
while not maximun_checkpoint_reach(latest, g_conf.TEST_SCHEDULE):
if is_next_checkpoint_ready(g_conf.TEST_SCHEDULE):
latest = get_next_checkpoint(g_conf.TEST_SCHEDULE)
checkpoint = torch.load(
os.path.join('_logs', exp_batch, exp_alias, 'checkpoints',
str(latest) + '.pth'))
checkpoint_iteration = checkpoint['iteration']
print("Validation loaded ", checkpoint_iteration)
for data in data_loader:
input_data, labels = data
control_position = np.where(
dataset.meta_data[:, 0] == 'control')[0][0]
speed_position = np.where(
dataset.meta_data[:, 0] == 'speed_module')[0][0]
print(torch.squeeze(input_data['rgb']).shape)
print(control_position)
print(speed_position)
# Obs : Maybe we could also check for other branches ??
output = model.forward_branch(
torch.squeeze(input_data['rgb']).cuda(),
labels[:, speed_position, :].cuda(),
labels[:, control_position, :].cuda())
# TODO: clean this squeeze and dimension things
for i in range(input_data['rgb'].shape[0]):
coil_logger.write_on_csv(
checkpoint_iteration,
[output[i][0], output[i][1], output[i][2]])
#loss = criterion(output, labels)
#loss.backward()
#optimizer.step()
#shutil.copyfile(filename, 'model_best.pth.tar')
else:
time.sleep(1)
print("Waiting for the next Validation")
def execute(gpu, exp_batch, exp_alias):
manualSeed = 123
torch.cuda.manual_seed(manualSeed)
os.environ["CUDA_VISIBLE_DEVICES"] = gpu
merge_with_yaml(os.path.join('configs', exp_batch, exp_alias + '.yaml'))
set_type_of_process('train')
coil_logger.add_message('Loading', {'GPU': gpu})
if not os.path.exists('_output_logs'):
os.mkdir('_output_logs')
sys.stdout = open(os.path.join('_output_logs',
g_conf.PROCESS_NAME + '_' + str(os.getpid()) + ".out"), "a", buffering=1)
if monitorer.get_status(exp_batch, exp_alias + '.yaml', g_conf.PROCESS_NAME)[0] == "Finished":
return
full_dataset = os.path.join(os.environ["COIL_DATASET_PATH"], g_conf.TRAIN_DATASET_NAME)
dataset = CoILDataset(full_dataset, transform=transforms.Compose([transforms.ToTensor()]))
sampler = BatchSequenceSampler(splitter.control_steer_split(dataset.measurements, dataset.meta_data),
g_conf.BATCH_SIZE, g_conf.NUMBER_IMAGES_SEQUENCE, g_conf.SEQUENCE_STRIDE)
data_loader = torch.utils.data.DataLoader(dataset, batch_sampler=sampler,
shuffle=False, num_workers=6, pin_memory=True)
l1weight = g_conf.L1
image_size = tuple([88, 200])
testmode = 1
if g_conf.GANMODEL_NAME == 'LSDcontrol':
netD = ganmodels._netD().cuda()
netG = ganmodels._netG(skip=g_conf.SKIP).cuda()
if g_conf.GANMODEL_NAME == 'LSDcontrol_acgan_nopatch':
netD = acganmodels_nopatch._netD().cuda()
netG = acganmodels_nopatch._netG(skip=g_conf.SKIP).cuda()
init_weights(netD)
init_weights(netG)
print(netD)
print(netG)
optimD = torch.optim.Adam(netD.parameters(), lr=0.0002, betas=(0.5, 0.999))
optimG = torch.optim.Adam(netG.parameters(), lr=0.0002, betas=(0.5, 0.999))
MSE_loss = torch.nn.MSELoss().cuda()
L1_loss = torch.nn.L1Loss().cuda()
iteration = 0
best_loss_iter = 0
best_lossD = 1000000.0
best_lossG = 1000000.0
accumulated_time = 0
netG.train()
netD.train()
capture_time = time.time()
if not os.path.exists('./imgs_' + exp_alias):
os.mkdir('./imgs_' + exp_alias)
for data in data_loader:
input_data, float_data = data
inputs = input_data['rgb'].cuda()
inputs = inputs.squeeze(1)
fake_inputs = netG(inputs)
if iteration % 200 == 0:
imgs_to_save = torch.cat((inputs[:2], fake_inputs[:2]), 0).cpu().data
vutils.save_image(imgs_to_save, './imgs_' + exp_alias + '/' + str(iteration) + '_real_and_fake.png', normalize=True)
coil_logger.add_image("Images", imgs_to_save, iteration)
controls = float_data[:, dataset.controls_position(), :]
steer = controls[:, 0].cuda()
##--------------------Discriminator part!!!!!!!!!!-----------------------
##fake
set_requires_grad(netD, True)
optimD.zero_grad()
outputsD_fake_forD, fakeD_steer = netD(fake_inputs.detach())
labsize = outputsD_fake_forD.size()
labels_fake = torch.zeros(labsize) #Fake labels
label_fake_noise = torch.rand(labels_fake.size()) * 0.1 #Label smoothing
labels_fake = labels_fake + label_fake_noise
labels_fake = Variable(labels_fake).cuda()
lossD_fake_aux = MSE_loss(fakeD_steer, steer)
lossD_fake_total = lossD_fake + lossD_fake_aux
lossD_fake_total.backward()
optimD.step()
##real
set_requires_grad(netD, True)
optimD.zero_grad()
outputsD_real_forD, realD_steer = netD(inputs)
labsize = outputsD_real_forD.size()
labels_real = torch.ones(labsize) #Real labels
label_real_noise = torch.rand(labels_real.size()) * 0.1 #Label smoothing
labels_real = labels_real - label_real_noise
labels_real = Variable(labels_real).cuda()
lossD_real = torch.mean(outputsD_real_forD)
lossD_real_aux = MSE_loss(realD_steer, steer)
#Discriminator updates
lossD_real_total = lossD_real + lossD_real_aux
lossD_real_total.backward()
optimD.step()
lossD = lossD_real_total + lossD_fake_total
coil_logger.add_scalar('Aux Real LossD', lossD_real_aux.data, iteration)
coil_logger.add_scalar('Aux Fake LossD', lossD_fake_aux.data, iteration)
coil_logger.add_scalar('Total Real LossD', lossD_real_total.data , iteration)
coil_logger.add_scalar('Total Fake LossD', lossD_fake_total.data , iteration)
coil_logger.add_scalar('Real LossD', lossD_real.data , iteration)
coil_logger.add_scalar('Fake LossD', lossD_fake.data , iteration)
##--------------------Generator part!!!!!!!!!!-----------------------
set_requires_grad(netD, False)
optimG.zero_grad()
outputsD_fake_forG, G_steer = netD(fake_inputs)
#Generator updates
lossG_smooth = L1_loss(fake_inputs, inputs)
lossG_aux = MSE_loss(G_steer, steer)
lossG = lossG_adv + lossG_aux + l1weight * lossG_smooth
lossG.backward()
optimG.step()
coil_logger.add_scalar('Total LossG', lossG.data / len(inputs), iteration)
coil_logger.add_scalar('Adv LossG', lossG_adv.data , iteration)
coil_logger.add_scalar('Smooth LossG', lossG_smooth.data , iteration)
coil_logger.add_scalar('Aux LossG', lossG_aux.data , iteration)
#optimization for one iter done!
position = random.randint(0, len(float_data)-1)
if lossD.data < best_lossD:
best_lossD = lossD.data.tolist()
if lossG.data < best_lossG:
best_lossG = lossG.data.tolist()
best_loss_iter = iteration
accumulated_time += time.time() - capture_time
capture_time = time.time()
print("LossD", lossD.data.tolist(), "LossG", lossG.data.tolist(), "BestLossD", best_lossD, "BestLossG", best_lossG, "Iteration", iteration, "Best Loss Iteration", best_loss_iter)
coil_logger.add_message('Iterating',
{'Iteration': iteration,
'LossD': lossD.data.tolist(),
'LossG': lossG.data.tolist(),
'Images/s': (iteration*g_conf.BATCH_SIZE)/accumulated_time,
'BestLossD': best_lossD, 'BestLossIteration': best_loss_iter,
'BestLossG': best_lossG, 'BestLossIteration': best_loss_iter,
'GroundTruth': dataset.extract_targets(float_data)[position].data.tolist(),
'Inputs': dataset.extract_inputs(float_data)[position].data.tolist()},
iteration)
if is_ready_to_save(iteration):
state = {
'iteration': iteration,
'stateD_dict': netD.state_dict(),
'stateG_dict': netG.state_dict(),
'best_lossD': best_lossD,
'best_lossG': best_lossG,
'total_time': accumulated_time,
'best_loss_iter': best_loss_iter
}
torch.save(state, os.path.join('/datatmp/Experiments/rohitgan/_logs', exp_batch, exp_alias
, 'checkpoints', str(iteration) + '.pth'))
if iteration == best_loss_iter:
state = {
'iteration': iteration,
'stateD_dict': netD.state_dict(),
'stateG_dict': netG.state_dict(),
'best_lossD': best_lossD,
'best_lossG': best_lossG,
'total_time': accumulated_time,
'best_loss_iter': best_loss_iter
}
torch.save(state, os.path.join('/datatmp/Experiments/rohitgan/_logs', exp_batch, exp_alias
, 'best_modelG' + '.pth'))
iteration += 1
def execute(gpu, exp_batch, exp_alias):
os.environ["CUDA_VISIBLE_DEVICES"] = gpu
merge_with_yaml(os.path.join('configs', exp_batch, exp_alias + '.yaml'))
set_type_of_process('train')
coil_logger.add_message('Loading', {'GPU': gpu})
if not os.path.exists('_output_logs'):
os.mkdir('_output_logs')
sys.stdout = open(os.path.join(
'_output_logs', g_conf.PROCESS_NAME + '_' + str(os.getpid()) + ".out"),
"a",
buffering=1)
if monitorer.get_status(exp_batch, exp_alias + '.yaml',
g_conf.PROCESS_NAME)[0] == "Finished":
return
full_dataset = os.path.join(os.environ["COIL_DATASET_PATH"],
g_conf.TRAIN_DATASET_NAME)
dataset = CoILDataset(full_dataset,
transform=transforms.Compose([transforms.ToTensor()
]))
sampler = BatchSequenceSampler(
splitter.control_steer_split(dataset.measurements,
dataset.meta_data), g_conf.BATCH_SIZE,
g_conf.NUMBER_IMAGES_SEQUENCE, g_conf.SEQUENCE_STRIDE)
data_loader = torch.utils.data.DataLoader(dataset,
batch_sampler=sampler,
shuffle=False,
num_workers=6,
pin_memory=True)
l1weight = g_conf.L1_WEIGHT
image_size = tuple([88, 200])
if g_conf.TRAIN_TYPE == 'WGAN':
clamp_value = g_conf.CLAMP
n_critic = g_conf.N_CRITIC
print("Configurations of ", exp_alias)
print("GANMODEL_NAME", g_conf.GANMODEL_NAME)
print("LOSS_FUNCTION", g_conf.LOSS_FUNCTION)
print("LR_G, LR_D, LR", g_conf.LR_G, g_conf.LR_D, g_conf.LEARNING_RATE)
print("SKIP", g_conf.SKIP)
print("TYPE", g_conf.TYPE)
print("L1 WEIGHT", g_conf.L1_WEIGHT)
print("LAB SMOOTH", g_conf.LABSMOOTH)
if g_conf.GANMODEL_NAME == 'LSDcontrol':
netD = ganmodels._netD(loss=g_conf.LOSS_FUNCTION).cuda()
netG = ganmodels._netG(loss=g_conf.LOSS_FUNCTION,
skip=g_conf.SKIP).cuda()
elif g_conf.GANMODEL_NAME == 'LSDcontrol_nopatch':
netD = ganmodels_nopatch._netD(loss=g_conf.LOSS_FUNCTION).cuda()
netG = ganmodels_nopatch._netG(loss=g_conf.LOSS_FUNCTION).cuda()
elif g_conf.GANMODEL_NAME == 'LSDcontrol_nopatch_smaller':
netD = ganmodels_nopatch_smaller._netD(
loss=g_conf.LOSS_FUNCTION).cuda()
netG = ganmodels_nopatch_smaller._netG(
loss=g_conf.LOSS_FUNCTION).cuda()
elif g_conf.GANMODEL_NAME == 'LSDcontrol_task':
netD = ganmodels_task._netD(loss=g_conf.LOSS_FUNCTION).cuda()
netG = ganmodels_task._netG(loss=g_conf.LOSS_FUNCTION).cuda()
netF = ganmodels_task._netF(loss=g_conf.LOSS_FUNCTION).cuda()
if g_conf.PRETRAINED == 'RECON':
netF_statedict = torch.load('netF_GAN_Pretrained.wts')
netF.load_state_dict(netF_statedict)
elif g_conf.PRETRAINED == 'IL':
model_IL = torch.load('best_loss_20-06_EpicClearWeather.pth')
model_IL_state_dict = model_IL['state_dict']
netF_state_dict = netF.state_dict()
for i, keys in enumerate(
zip(netF_state_dict.keys(), model_IL_state_dict.keys())):
newkey, oldkey = keys
if newkey.split('.')[0] == "branch" and oldkey.split(
'.')[0] == "branches":
print("No Transfer of ", newkey, " to ", oldkey)
else:
print("Transferring ", newkey, " to ", oldkey)
netF_state_dict[newkey] = model_IL_state_dict[oldkey]
netF.load_state_dict(netF_state_dict)
init_weights(netD)
init_weights(netG)
#do init for netF also later but now it is in the model code itself
print(netD)
print(netF)
print(netG)
optimD = torch.optim.Adam(netD.parameters(),
lr=g_conf.LR_D,
betas=(0.5, 0.999))
optimG = torch.optim.Adam(netG.parameters(),
lr=g_conf.LR_G,
betas=(0.5, 0.999))
if g_conf.TYPE == 'task':
optimF = torch.optim.Adam(netF.parameters(), lr=g_conf.LEARNING_RATE)
Task_Loss = TaskLoss()
if g_conf.LOSS_FUNCTION == 'LSGAN':
Loss = torch.nn.MSELoss().cuda()
elif g_conf.LOSS_FUNCTION == 'NORMAL':
Loss = torch.nn.BCEWithLogitsLoss().cuda()
L1_loss = torch.nn.L1Loss().cuda()
iteration = 0
best_loss_iter_F = 0
best_loss_iter_G = 0
best_lossF = 1000000.0
best_lossD = 1000000.0
best_lossG = 1000000.0
accumulated_time = 0
lossF = Variable(torch.Tensor([100.0]))
lossG_adv = Variable(torch.Tensor([100.0]))
lossG_smooth = Variable(torch.Tensor([100.0]))
lossG = Variable(torch.Tensor([100.0]))
netG.train()
netD.train()
netF.train()
capture_time = time.time()
if not os.path.exists('./imgs_' + exp_alias):
os.mkdir('./imgs_' + exp_alias)
#TODO put family for losses
fake_img_pool = ImagePool(50)
for data in data_loader:
set_requires_grad(netD, True)
set_requires_grad(netF, True)
set_requires_grad(netG, True)
# print("ITERATION:", iteration)
val = 0.0
input_data, float_data = data
inputs = input_data['rgb'].cuda()
inputs = inputs.squeeze(1)
inputs_in = inputs - val #subtracted by 0.5
#TODO: make sure the F network does not get optimized by G optim
controls = float_data[:, dataset.controls_position(), :]
embed, branches = netF(inputs_in,
dataset.extract_inputs(float_data).cuda())
print("Branch Outputs:::", branches[0][0])
embed_inputs = embed
fake_inputs = netG(embed_inputs)
fake_inputs_in = fake_inputs
if iteration % 500 == 0:
imgs_to_save = torch.cat(
(inputs_in[:2] + val, fake_inputs_in[:2] + val), 0).cpu().data
vutils.save_image(imgs_to_save,
'./imgs_' + exp_alias + '/' + str(iteration) +
'_real_and_fake.png',
normalize=True)
coil_logger.add_image("Images", imgs_to_save, iteration)
##--------------------Discriminator part!!!!!!!!!!-------------------##
set_requires_grad(netD, True)
set_requires_grad(netF, False)
set_requires_grad(netG, False)
optimD.zero_grad()
##fake
# fake_inputs_forD = fake_img_pool.query(fake_inputs)
outputsD_fake_forD = netD(fake_inputs)
labsize = outputsD_fake_forD.size()
labels_fake = torch.zeros(labsize) #Fake labels
label_fake_noise = torch.rand(
labels_fake.size()) * 0.1 #Label smoothing
if g_conf.LABSMOOTH == 1:
labels_fake = labels_fake + label_fake_noise
labels_fake = Variable(labels_fake).cuda()
lossD_fake = torch.mean(
outputsD_fake_forD) #Loss(outputsD_fake_forD, labels_fake)
##real
outputsD_real = netD(inputs_in)
labsize = outputsD_real.size()
labels_real = torch.ones(labsize) #Real labels
label_real_noise = torch.rand(
labels_real.size()) * 0.1 #Label smoothing
if g_conf.LABSMOOTH == 1:
labels_real = labels_real - label_real_noise
labels_real = Variable(labels_real).cuda()
lossD_real = -1.0 * torch.mean(
outputsD_real) #Loss(outputsD_real, labels_real)
### Gradient Penalty ###
gradient_penalty = calc_gradient_penalty(netD, inputs, fake_inputs)
# alpha = torch.rand((g_conf.BATCH_SIZE, 1, 1, 1))
# alpha = alpha.cuda()
#
# x_hat = alpha * inputs.data + (1 - alpha) * fake_inputs.data
# x_hat.requires_grad = True
#
# pred_hat = netD(x_hat)
# gradients = grad(outputs=pred_hat, inputs=x_hat, grad_outputs=torch.ones(pred_hat.size()).cuda(),
# create_graph=True, retain_graph=True, only_inputs=True)[0]
#
# gradient_penalty = 10 * ((gradients.view(gradients.size()[0], -1).norm(2, 1) - 1) ** 2).mean()
#Discriminator updates
lossD = torch.mean(
outputsD_fake_forD -
outputsD_real) + gradient_penalty #(lossD_real + lossD_fake) * 0.5
# lossD /= len(inputs)
print("Loss d", lossD)
lossD.backward(retain_graph=True)
optimD.step()
# if g_conf.TRAIN_TYPE == 'WGAN':
# for p in netD.parameters():
# p.data.clamp_(-clamp_value, clamp_value)
coil_logger.add_scalar('Total LossD', lossD.data, iteration)
coil_logger.add_scalar('Real LossD', lossD_real.data / len(inputs),
iteration)
coil_logger.add_scalar('Fake LossD', lossD_fake.data / len(inputs),
iteration)
##--------------------Generator part!!!!!!!!!!-----------------------##
set_requires_grad(netD, False)
set_requires_grad(netF, False)
set_requires_grad(netG, True)
if ((iteration + 1) % n_critic) == 0:
optimG.zero_grad()
outputsD_fake_forG = netD(fake_inputs)
#Generator updates
lossG_adv = -1.0 * torch.mean(
outputsD_fake_forG) #Loss(outputsD_fake_forG, labels_real)
lossG_smooth = L1_loss(fake_inputs, inputs)
lossG = (lossG_adv + l1weight * lossG_smooth) / (1.0 + l1weight)
# lossG /= len(inputs)
print(lossG)
lossG.backward(retain_graph=True)
optimG.step()
#####Task network updates##########################
set_requires_grad(netD, False)
set_requires_grad(netF, True)
set_requires_grad(netG, False)
optimF.zero_grad()
lossF = Variable(torch.Tensor())
lossF = Task_Loss.MSELoss(
branches,
dataset.extract_targets(float_data).cuda(), controls.cuda(),
dataset.extract_inputs(float_data).cuda())
coil_logger.add_scalar('Task Loss', lossF.data, iteration)
lossF.backward()
optimF.step()
coil_logger.add_scalar('Total LossG', lossG.data, iteration)
coil_logger.add_scalar('Adv LossG', lossG_adv.data / len(inputs),
iteration)
coil_logger.add_scalar('Smooth LossG', lossG_smooth.data / len(inputs),
iteration)
#optimization for one iter done!
position = random.randint(0, len(float_data) - 1)
if lossD.data < best_lossD:
best_lossD = lossD.data.tolist()
# print (lossG.item(), best_lossG)
if lossG.item() < best_lossG:
best_lossG = lossG.item()
best_loss_iter_G = iteration
if lossF.item() < best_lossF:
best_lossF = lossF.item()
best_loss_iter_F = iteration
accumulated_time += time.time() - capture_time
capture_time = time.time()
print("LossD", lossD.data.tolist(), "LossG", lossG.data.tolist(),
"BestLossD", best_lossD, "BestLossG", best_lossG, "LossF", lossF,
"BestLossF", best_lossF, "Iteration", iteration,
"Best Loss Iteration G", best_loss_iter_G,
"Best Loss Iteration F", best_loss_iter_F)
coil_logger.add_message(
'Iterating', {
'Iteration':
iteration,
'LossD':
lossD.data.tolist(),
'LossG':
lossG.data.tolist(),
'Images/s': (iteration * g_conf.BATCH_SIZE) / accumulated_time,
'BestLossD':
best_lossD,
'BestLossG':
best_lossG,
'BestLossIterationG':
best_loss_iter_G,
'BestLossF':
best_lossF,
'BestLossIterationF':
best_loss_iter_F,
'GroundTruth':
dataset.extract_targets(float_data)[position].data.tolist(),
'Inputs':
dataset.extract_inputs(float_data)[position].data.tolist()
}, iteration)
if is_ready_to_save(iteration):
state = {
'iteration': iteration,
'stateD_dict': netD.state_dict(),
'stateG_dict': netG.state_dict(),
'stateF_dict': netF.state_dict(),
'best_lossD': best_lossD,
'best_lossG': best_lossG,
'total_time': accumulated_time,
'best_loss_iter_G': best_loss_iter_G,
'best_loss_iter_F': best_loss_iter_F
}
torch.save(
state,
os.path.join('/datatmp/Experiments/rohitgan/_logs', exp_batch,
exp_alias, 'checkpoints',
str(iteration) + '.pth'))
if iteration == best_loss_iter_G and iteration > 10000:
state = {
'iteration': iteration,
'stateD_dict': netD.state_dict(),
'stateG_dict': netG.state_dict(),
'stateF_dict': netF.state_dict(),
'best_lossD': best_lossD,
'best_lossG': best_lossG,
'total_time': accumulated_time,
'best_loss_iter_G': best_loss_iter_G
}
torch.save(
state,
os.path.join('/datatmp/Experiments/rohitgan/_logs', exp_batch,
exp_alias, 'best_modelG' + '.pth'))
if iteration == best_loss_iter_F and iteration > 10000:
state = {
'iteration': iteration,
'stateD_dict': netD.state_dict(),
'stateG_dict': netG.state_dict(),
'stateF_dict': netF.state_dict(),
'best_lossD': best_lossD,
'best_lossG': best_lossG,
'best_lossF': best_lossF,
'total_time': accumulated_time,
'best_loss_iter_F': best_loss_iter_F
}
torch.save(
state,
os.path.join('/datatmp/Experiments/rohitgan/_logs', exp_batch,
exp_alias, 'best_modelF' + '.pth'))
iteration += 1
def mount_experiment_heap(folder,
experiments_list,
is_training,
executing_processes,
old_tasks_queue,
validation_datasets,
drive_environments,
restart_error=True):
tasks_queue = []
for experiment in experiments_list:
if experiment in executing_processes:
continue
# Train is always priority. # TODO: some system to check priority depending on iterations
task_to_add = None
# TODO: One thing is error other thing is stop. However at a first step we can try to restart all error things
if is_training:
if monitorer.get_status(folder, experiment,
'train')[0] == "Not Started":
task_to_add = (0, experiment + '_train', {
'type': 'train',
'folder': folder,
'experiment': experiment
})
elif restart_error and monitorer.get_status(folder, experiment, 'train')[0] \
== "Error":
task_to_add = (0, experiment + '_train', {
'type': 'train',
'folder': folder,
'experiment': experiment
})
if task_to_add is not None and task_to_add not in old_tasks_queue:
heapq.heappush(tasks_queue, task_to_add)
task_to_add = None
for val_data in validation_datasets:
if monitorer.get_status(folder, experiment, 'validation_' +
val_data)[0] == "Not Started":
task_to_add = (2, experiment + '_validation_' + val_data, {
'type': 'validation',
'folder': folder,
'experiment': experiment,
'dataset': val_data
})
elif restart_error and monitorer.get_status(
folder, experiment,
'validation_' + val_data)[0] == "Error":
task_to_add = (2, experiment + '_validation_' + val_data, {
'type': 'validation',
'folder': folder,
'experiment': experiment,
'dataset': val_data
})
if task_to_add is not None and task_to_add not in old_tasks_queue:
heapq.heappush(tasks_queue, task_to_add)
task_to_add = None
for drive_env in drive_environments:
if monitorer.get_status(folder, experiment,
'drive_' + drive_env)[0] == "Not Started":
task_to_add = (1, experiment + '_drive_' + drive_env, {
'type': 'drive',
'folder': folder,
'experiment': experiment,
'environment': drive_env
})
elif restart_error and monitorer.get_status(folder, experiment, 'drive_' + drive_env)\
[0] == "Error":
task_to_add = (1, experiment + '_drive_' + drive_env, {
'type': 'drive',
'folder': folder,
'experiment': experiment,
'environment': drive_env
})
if task_to_add is not None and task_to_add not in old_tasks_queue:
heapq.heappush(tasks_queue, task_to_add)
return tasks_queue
def execute(gpu, exp_batch, exp_alias):
from time import gmtime, strftime
manualSeed = g_conf.SEED
torch.cuda.manual_seed(manualSeed)
os.environ["CUDA_VISIBLE_DEVICES"] = gpu
merge_with_yaml(os.path.join('configs', exp_batch, exp_alias + '.yaml'))
set_type_of_process('train')
coil_logger.add_message('Loading', {'GPU': gpu})
if not os.path.exists('_output_logs'):
os.mkdir('_output_logs')
sys.stdout = open(os.path.join(
'_output_logs', g_conf.PROCESS_NAME + '_' + str(os.getpid()) + ".out"),
"a",
buffering=1)
if monitorer.get_status(exp_batch, exp_alias + '.yaml',
g_conf.PROCESS_NAME)[0] == "Finished":
return
full_dataset = os.path.join(os.environ["COIL_DATASET_PATH"],
g_conf.TRAIN_DATASET_NAME)
real_dataset = g_conf.TARGET_DOMAIN_PATH
# real_dataset = os.path.join(os.environ["COIL_DATASET_PATH"], "FinalRealWorldDataset")
#main data loader
dataset = CoILDataset(full_dataset,
transform=transforms.Compose([transforms.ToTensor()
]))
sampler = BatchSequenceSampler(
splitter.control_steer_split(dataset.measurements,
dataset.meta_data), g_conf.BATCH_SIZE,
g_conf.NUMBER_IMAGES_SEQUENCE, g_conf.SEQUENCE_STRIDE)
data_loader = torch.utils.data.DataLoader(dataset,
batch_sampler=sampler,
shuffle=False,
num_workers=6,
pin_memory=True)
real_dl = real_dataloader.RealDataset(real_dataset, g_conf.BATCH_SIZE)
st = lambda aug: iag.Sometimes(aug, 0.4)
oc = lambda aug: iag.Sometimes(aug, 0.3)
rl = lambda aug: iag.Sometimes(aug, 0.09)
augmenter = iag.Augmenter([iag.ToGPU()] + [
rl(iag.GaussianBlur(
(0, 1.5))), # blur images with a sigma between 0 and 1.5
rl(iag.AdditiveGaussianNoise(loc=0, scale=(
0.0, 0.05), per_channel=0.5)), # add gaussian noise to images
oc(iag.Dropout((0.0, 0.10), per_channel=0.5)
), # randomly remove up to X% of the pixels
oc(
iag.CoarseDropout(
(0.0, 0.10), size_percent=(0.08, 0.2),
per_channel=0.5)), # randomly remove up to X% of the pixels
oc(iag.Add((-40, 40), per_channel=0.5)
), # change brightness of images (by -X to Y of original value)
st(iag.Multiply((0.10, 2), per_channel=0.2)
), # change brightness of images (X-Y% of original value)
rl(iag.ContrastNormalization(
(0.5, 1.5), per_channel=0.5)), # improve or worsen the contrast
rl(iag.Grayscale((0.0, 1))), # put grayscale
] # do all of the above in random order
)
l1weight = g_conf.L1_WEIGHT
task_adv_weight = g_conf.TASK_ADV_WEIGHT
image_size = tuple([88, 200])
print(strftime("%Y-%m-%d %H:%M:%S", gmtime()))
print("Configurations of ", exp_alias)
print("GANMODEL_NAME", g_conf.GANMODEL_NAME)
print("LOSS_FUNCTION", g_conf.LOSS_FUNCTION)
print("LR_G, LR_D, LR", g_conf.LR_G, g_conf.LR_D, g_conf.LEARNING_RATE)
print("SKIP", g_conf.SKIP)
print("TYPE", g_conf.TYPE)
print("L1 WEIGHT", g_conf.L1_WEIGHT)
print("TASK ADV WEIGHT", g_conf.TASK_ADV_WEIGHT)
print("LAB SMOOTH", g_conf.LABSMOOTH)
if g_conf.GANMODEL_NAME == 'LSDcontrol':
netD = ganmodels._netD(loss=g_conf.LOSS_FUNCTION).cuda()
netG = ganmodels._netG(loss=g_conf.LOSS_FUNCTION,
skip=g_conf.SKIP).cuda()
elif g_conf.GANMODEL_NAME == 'LSDcontrol_nopatch':
netD = ganmodels_nopatch._netD(loss=g_conf.LOSS_FUNCTION).cuda()
netG = ganmodels_nopatch._netG(loss=g_conf.LOSS_FUNCTION).cuda()
elif g_conf.GANMODEL_NAME == 'LSDcontrol_nopatch_smaller':
netD = ganmodels_nopatch_smaller._netD(
loss=g_conf.LOSS_FUNCTION).cuda()
netG = ganmodels_nopatch_smaller._netG(
loss=g_conf.LOSS_FUNCTION).cuda()
elif g_conf.GANMODEL_NAME == 'LSDcontrol_task':
netD = ganmodels_task._netD(loss=g_conf.LOSS_FUNCTION).cuda()
netG = ganmodels_task._netG(loss=g_conf.LOSS_FUNCTION).cuda()
netF = ganmodels_task._netF(loss=g_conf.LOSS_FUNCTION).cuda()
if g_conf.PRETRAINED == 'RECON':
netF_statedict = torch.load('netF_GAN_Pretrained.wts')
netF.load_state_dict(netF_statedict)
elif g_conf.PRETRAINED == 'IL':
print("Loading IL")
model_IL = torch.load('best_loss_20-06_EpicClearWeather.pth')
model_IL_state_dict = model_IL['state_dict']
netF_state_dict = netF.state_dict()
print(len(netF_state_dict.keys()), len(model_IL_state_dict.keys()))
for i, keys in enumerate(
zip(netF_state_dict.keys(), model_IL_state_dict.keys())):
newkey, oldkey = keys
# if newkey.split('.')[0] == "branch" and oldkey.split('.')[0] == "branches":
# print("No Transfer of ", newkey, " to ", oldkey)
# else:
print("Transferring ", newkey, " to ", oldkey)
netF_state_dict[newkey] = model_IL_state_dict[oldkey]
netF.load_state_dict(netF_state_dict)
print("IL Model Loaded!")
elif g_conf.GANMODEL_NAME == 'LSDcontrol_task_2d':
netD = ganmodels_taskAC_shared._netD().cuda()
netG = ganmodels_taskAC_shared._netG().cuda()
netF = ganmodels_taskAC_shared._netF().cuda()
if g_conf.PRETRAINED == 'IL':
print("Loading IL")
model_IL = torch.load(g_conf.IL_AGENT_PATH)
model_IL_state_dict = model_IL['state_dict']
netF_state_dict = netF.state_dict()
print(len(netF_state_dict.keys()), len(model_IL_state_dict.keys()))
for i, keys in enumerate(
zip(netF_state_dict.keys(), model_IL_state_dict.keys())):
newkey, oldkey = keys
print("Transferring ", newkey, " to ", oldkey)
netF_state_dict[newkey] = model_IL_state_dict[oldkey]
netF.load_state_dict(netF_state_dict)
print("IL Model Loaded!")
#####
if g_conf.IF_AUG:
print("Loading Aug Decoder")
model_dec = torch.load(g_conf.DECODER_RECON_PATH)
else:
print("Loading Decoder")
model_dec = torch.load(g_conf.DECODER_RECON_PATH)
model_dec_state_dict = model_dec['stateG_dict']
netG_state_dict = netG.state_dict()
print(len(netG_state_dict.keys()),
len(model_dec_state_dict.keys()))
for i, keys in enumerate(
zip(netG_state_dict.keys(), model_dec_state_dict.keys())):
newkey, oldkey = keys
print("Transferring ", newkey, " to ", oldkey)
netG_state_dict[newkey] = model_dec_state_dict[oldkey]
netG.load_state_dict(netG_state_dict)
print("Decoder Model Loaded!")
init_weights(netD)
print(netD)
print(netF)
print(netG)
optimD = torch.optim.Adam(netD.parameters(),
lr=g_conf.LR_D,
betas=(0.5, 0.999))
optimG = torch.optim.Adam(netG.parameters(),
lr=g_conf.LR_G,
betas=(0.5, 0.999))
if g_conf.TYPE == 'task':
optimF = torch.optim.Adam(netF.parameters(), lr=g_conf.LEARNING_RATE)
Task_Loss = TaskLoss()
if g_conf.GANMODEL_NAME == 'LSDcontrol_task_2d':
print("Using cross entropy!")
Loss = torch.nn.CrossEntropyLoss().cuda()
L1_loss = torch.nn.L1Loss().cuda()
iteration = 0
best_loss_iter_F = 0
best_loss_iter_G = 0
best_lossF = 1000000.0
best_lossD = 1000000.0
best_lossG = 1000000.0
accumulated_time = 0
n_critic = g_conf.N_CRITIC
lossF = Variable(torch.Tensor([100.0]))
lossG_adv = Variable(torch.Tensor([100.0]))
lossG_smooth = Variable(torch.Tensor([100.0]))
lossG = Variable(torch.Tensor([100.0]))
netG.train()
netD.train()
netF.train()
capture_time = time.time()
if not os.path.exists('./imgs_' + exp_alias):
os.mkdir('./imgs_' + exp_alias)
fake_img_pool_src = ImagePool(50)
fake_img_pool_tgt = ImagePool(50)
for data in data_loader:
set_requires_grad(netD, True)
set_requires_grad(netF, True)
set_requires_grad(netG, True)
input_data, float_data = data
tgt_imgs = real_dl.get_imgs()
if g_conf.IF_AUG:
inputs = augmenter(0, input_data['rgb'])
else:
inputs = input_data['rgb'].cuda()
tgt_imgs = tgt_imgs.cuda()
inputs = inputs.squeeze(1)
inputs = inputs
tgt_imgs = tgt_imgs
controls = float_data[:, dataset.controls_position(), :]
camera_angle = float_data[:, 26, :]
camera_angle = camera_angle.cuda()
steer = float_data[:, 0, :]
steer = steer.cuda()
speed = float_data[:, 10, :]
speed = speed.cuda()
time_use = 1.0
car_length = 3.0
extra_factor = 2.5
threshold = 1.0
pos = camera_angle > 0.0
pos = pos.type(torch.FloatTensor)
neg = camera_angle <= 0.0
neg = neg.type(torch.FloatTensor)
pos = pos.cuda()
neg = neg.cuda()
rad_camera_angle = math.pi * (torch.abs(camera_angle)) / 180.0
val = extra_factor * (torch.atan((rad_camera_angle * car_length) /
(time_use * speed + 0.05))) / 3.1415
steer -= pos * torch.min(val, torch.Tensor([0.6]).cuda())
steer += neg * torch.min(val, torch.Tensor([0.6]).cuda())
steer = steer.cpu()
float_data[:, 0, :] = steer
float_data[:, 0, :][float_data[:, 0, :] > 1.0] = 1.0
float_data[:, 0, :][float_data[:, 0, :] < -1.0] = -1.0
src_embed_inputs, src_branches = netF(
inputs,
dataset.extract_inputs(float_data).cuda())
tgt_embed_inputs = netF(tgt_imgs, None)
src_fake_inputs = netG(src_embed_inputs.detach())
tgt_fake_inputs = netG(tgt_embed_inputs.detach())
if iteration % 100 == 0:
imgs_to_save = torch.cat((inputs[:1], src_fake_inputs[:1],
tgt_imgs[:1], tgt_fake_inputs[:1]),
0).cpu().data
coil_logger.add_image("Images", imgs_to_save, iteration)
imgs_to_save = imgs_to_save.clamp(0.0, 1.0)
vutils.save_image(imgs_to_save,
'./imgs_' + exp_alias + '/' + str(iteration) +
'_real_and_fake.png',
normalize=False)
##--------------------Discriminator part!!!!!!!!!!-------------------##
##source fake
if g_conf.IF_POOL:
src_fake_inputs_forD = fake_img_pool_src.query(src_fake_inputs)
tgt_fake_inputs_forD = fake_img_pool_tgt.query(tgt_fake_inputs)
else:
src_fake_inputs_forD = src_fake_inputs
tgt_fake_inputs_forD = tgt_fake_inputs
set_requires_grad(netD, True)
set_requires_grad(netF, False)
set_requires_grad(netG, False)
optimD.zero_grad()
outputsD_fake_src_bin, __ = netD(src_fake_inputs_forD.detach())
outputsD_fake_tgt_bin, __ = netD(tgt_fake_inputs_forD.detach())
outputsD_real_src_bin, __ = netD(inputs)
outputsD_real_tgt_bin, __ = netD(tgt_imgs)
gradient_penalty_src = calc_gradient_penalty(netD, inputs,
src_fake_inputs_forD,
"recon")
lossD_bin_src = torch.mean(
outputsD_fake_src_bin -
outputsD_real_src_bin) + gradient_penalty_src
gradient_penalty_tgt = calc_gradient_penalty(netD, tgt_imgs,
tgt_fake_inputs_forD,
"recon")
lossD_bin_tgt = torch.mean(
outputsD_fake_tgt_bin -
outputsD_real_tgt_bin) + gradient_penalty_tgt
lossD = (lossD_bin_src + lossD_bin_tgt) * 0.5
lossD.backward(retain_graph=True)
optimD.step()
coil_logger.add_scalar('Total LossD Bin', lossD.data, iteration)
coil_logger.add_scalar('Src LossD Bin', lossD_bin_src.data, iteration)
coil_logger.add_scalar('Tgt LossD Bin', lossD_bin_tgt.data, iteration)
##--------------------Generator part!!!!!!!!!!-----------------------##
set_requires_grad(netD, False)
set_requires_grad(netF, False)
set_requires_grad(netG, True)
optimG.zero_grad()
#fake outputs for bin
outputsD_bin_src_fake_forG, __ = netD(src_fake_inputs)
outputsD_bin_tgt_fake_forG, __ = netD(tgt_fake_inputs)
#Generator updates
if ((iteration + 1) % n_critic) == 0:
#for netD_bin
optimG.zero_grad()
outputsD_bin_fake_forG = netD(tgt_imgs)
#Generator updates
lossG_src_smooth = L1_loss(
src_fake_inputs, inputs) # L1 loss with real domain image
lossG_tgt_smooth = L1_loss(
tgt_fake_inputs, tgt_imgs) # L1 loss with real domain image
lossG_src_adv_bin = -1.0 * torch.mean(outputsD_bin_src_fake_forG)
lossG_tgt_adv_bin = -1.0 * torch.mean(outputsD_bin_tgt_fake_forG)
lossG_adv_bin = 0.5 * (lossG_src_adv_bin + lossG_tgt_adv_bin)
lossG_Adv = lossG_adv_bin
lossG_L1 = 0.5 * (lossG_src_smooth + lossG_tgt_smooth)
lossG = (lossG_Adv + l1weight * lossG_L1) / (1.0 + l1weight)
lossG.backward(retain_graph=True)
optimG.step()
coil_logger.add_scalar('Total LossG', lossG.data, iteration)
coil_logger.add_scalar('LossG Adv', lossG_Adv.data, iteration)
coil_logger.add_scalar('Adv Bin LossG', lossG_adv_bin.data,
iteration)
coil_logger.add_scalar('Smooth LossG', lossG_L1.data, iteration)
#####Task network updates##########################
set_requires_grad(netD, False)
set_requires_grad(netF, True)
set_requires_grad(netG, False)
optimF.zero_grad()
lossF_task = Task_Loss.MSELoss(
src_branches,
dataset.extract_targets(float_data).cuda(), controls.cuda(),
dataset.extract_inputs(float_data).cuda())
__, outputsD_fake_src_da = netD(src_fake_inputs_forD.detach())
__, outputsD_real_tgt_da = netD(tgt_imgs)
__, outputsD_fake_tgt_da = netD(tgt_fake_inputs_forD.detach())
__, outputsD_real_src_da = netD(inputs)
gradient_penalty_da_1 = calc_gradient_penalty(
netD, tgt_imgs, src_fake_inputs_forD, "da")
lossF_da_1 = torch.mean(outputsD_fake_src_da - outputsD_real_tgt_da
) + gradient_penalty_da_1
gradient_penalty_da_2 = calc_gradient_penalty(
netD, inputs, tgt_fake_inputs_forD, "da")
lossF_da_2 = torch.mean(outputsD_fake_tgt_da - outputsD_real_src_da
) + gradient_penalty_da_2
lossF_da = 0.5 * (lossF_da_1 + lossF_da_2)
lossF = (lossF_task +
task_adv_weight * lossF_da) / (1.0 + task_adv_weight)
coil_logger.add_scalar('Total Task Loss', lossF.data, iteration)
coil_logger.add_scalar('Adv Task Loss', lossF_da.data, iteration)
coil_logger.add_scalar('Only Task Loss', lossF_task.data,
iteration)
lossF.backward(retain_graph=True)
optimF.step()
if lossG.data < best_lossG:
best_lossG = lossG.data.tolist()
best_loss_iter_G = iteration
if lossF.data < best_lossF:
best_lossF = lossF.data.tolist()
best_loss_iter_F = iteration
#optimization for one iter done!
position = random.randint(0, len(float_data) - 1)
if lossD.data < best_lossD:
best_lossD = lossD.data.tolist()
accumulated_time += time.time() - capture_time
capture_time = time.time()
if is_ready_to_save(iteration):
state = {
'iteration': iteration,
'stateD_dict': netD.state_dict(),
'stateG_dict': netG.state_dict(),
'stateF_dict': netF.state_dict(),
'best_lossD': best_lossD,
'best_lossG': best_lossG,
'total_time': accumulated_time,
'best_loss_iter_G': best_loss_iter_G,
'best_loss_iter_F': best_loss_iter_F
}
torch.save(
state,
os.path.join('/datatmp/Datasets/rohitgan/_logs', exp_batch,
exp_alias, 'checkpoints',
str(iteration) + '.pth'))
if iteration == best_loss_iter_F and iteration > 10000:
state = {
'iteration': iteration,
'stateD_dict': netD.state_dict(),
'stateG_dict': netG.state_dict(),
'stateF_dict': netF.state_dict(),
'best_lossD': best_lossD,
'best_lossG': best_lossG,
'best_lossF': best_lossF,
'total_time': accumulated_time,
'best_loss_iter_F': best_loss_iter_F
}
torch.save(
state,
os.path.join('/datatmp/Datasets/rohitgan/_logs', exp_batch,
exp_alias, 'best_modelF' + '.pth'))
iteration += 1
def execute(gpu, exp_batch, exp_alias):
os.environ["CUDA_VISIBLE_DEVICES"] = gpu
merge_with_yaml(os.path.join('configs', exp_batch, exp_alias + '.yaml'))
set_type_of_process('train')
coil_logger.add_message('Loading', {'GPU': gpu})
if not os.path.exists('_output_logs'):
os.mkdir('_output_logs')
sys.stdout = open(os.path.join(
'_output_logs', g_conf.PROCESS_NAME + '_' + str(os.getpid()) + ".out"),
"a",
buffering=1)
if monitorer.get_status(exp_batch, exp_alias + '.yaml',
g_conf.PROCESS_NAME)[0] == "Finished":
return
full_dataset = os.path.join(os.environ["COIL_DATASET_PATH"],
g_conf.TRAIN_DATASET_NAME)
dataset = CoILDataset(full_dataset,
transform=transforms.Compose([transforms.ToTensor()
]))
sampler = BatchSequenceSampler(
splitter.control_steer_split(dataset.measurements,
dataset.meta_data), g_conf.BATCH_SIZE,
g_conf.NUMBER_IMAGES_SEQUENCE, g_conf.SEQUENCE_STRIDE)
data_loader = torch.utils.data.DataLoader(dataset,
batch_sampler=sampler,
shuffle=False,
num_workers=6,
pin_memory=True)
l1weight = 1.0
image_size = tuple([88, 200])
testmode = 1
modelG = coil_ganmodules_task._netG()
modelF = coil_ganmodules_task._netF()
fstd = torch.load('netF_GAN_Pretrained.wts')
gstd = torch.load('netG_GAN_Pretrained.wts')
print(fstd.keys())
print("+++++++++")
print(gstd.keys())
print(modelG)
modelF.load_state_dict(fstd)
modelG.load_state_dict(gstd)
print(modelF)
print(modelG)
# netG.eval()
capture_time = time.time()
for data in data_loader:
val = 0.5
input_data, float_data = data
inputs = input_data['rgb'].cuda()
inputs = inputs.squeeze(1)
inputs_in = inputs - val #subtracted by 0.5
#forward pass
embeds, branches = modelF(inputs)
fake_inputs = modelG(embeds)
imgs_to_save = torch.cat((inputs_in[:2] + val, fake_inputs_in[:2]),
0).cpu().data
vutils.save_image(imgs_to_save,
'./imgs' + '/' + str(iteration) +
'_real_and_fake.png',
normalize=True)
coil_logger.add_image("Images", imgs_to_save, iteration)
def execute(gpu, exp_batch, exp_alias):
os.environ["CUDA_VISIBLE_DEVICES"] = gpu
merge_with_yaml(os.path.join('configs', exp_batch, exp_alias + '.yaml'))
set_type_of_process('train')
coil_logger.add_message('Loading', {'GPU': gpu})
if not os.path.exists('_output_logs'):
os.mkdir('_output_logs')
sys.stdout = open(os.path.join(
'_output_logs', g_conf.PROCESS_NAME + '_' + str(os.getpid()) + ".out"),
"a",
buffering=1)
if monitorer.get_status(exp_batch, exp_alias + '.yaml',
g_conf.PROCESS_NAME)[0] == "Finished":
return
full_dataset = os.path.join(os.environ["COIL_DATASET_PATH"],
g_conf.TRAIN_DATASET_NAME)
dataset = CoILDataset(full_dataset,
transform=transforms.Compose([transforms.ToTensor()
]))
sampler = BatchSequenceSampler(
splitter.control_steer_split(dataset.measurements,
dataset.meta_data), g_conf.BATCH_SIZE,
g_conf.NUMBER_IMAGES_SEQUENCE, g_conf.SEQUENCE_STRIDE)
data_loader = torch.utils.data.DataLoader(dataset,
batch_sampler=sampler,
shuffle=False,
num_workers=6,
pin_memory=True)
l1weight = g_conf.L1_WEIGHT
image_size = tuple([88, 200])
print("Configurations of ", exp_alias)
print("GANMODEL_NAME", g_conf.GANMODEL_NAME)
print("LOSS_FUNCTION", g_conf.LOSS_FUNCTION)
print("LR_G, LR_D, LR", g_conf.LR_G, g_conf.LR_D, g_conf.LEARNING_RATE)
print("SKIP", g_conf.SKIP)
print("TYPE", g_conf.TYPE)
print("L1 WEIGHT", g_conf.L1_WEIGHT)
print("LAB SMOOTH", g_conf.LABSMOOTH)
if g_conf.GANMODEL_NAME == 'LSDcontrol':
netD = ganmodels._netD(loss=g_conf.LOSS_FUNCTION).cuda()
netG = ganmodels._netG(loss=g_conf.LOSS_FUNCTION,
skip=g_conf.SKIP).cuda()
elif g_conf.GANMODEL_NAME == 'LSDcontrol_nopatch':
netD = ganmodels_nopatch._netD(loss=g_conf.LOSS_FUNCTION).cuda()
netG = ganmodels_nopatch._netG(loss=g_conf.LOSS_FUNCTION).cuda()
elif g_conf.GANMODEL_NAME == 'LSDcontrol_nopatch_smaller':
netD = ganmodels_nopatch_smaller._netD(
loss=g_conf.LOSS_FUNCTION).cuda()
netG = ganmodels_nopatch_smaller._netG(
loss=g_conf.LOSS_FUNCTION).cuda()
elif g_conf.GANMODEL_NAME == 'LSDcontrol_task':
netD = ganmodels_task._netD(loss=g_conf.LOSS_FUNCTION).cuda()
netG = ganmodels_task._netG(loss=g_conf.LOSS_FUNCTION).cuda()
netF = ganmodels_task._netG(loss=g_conf.LOSS_FUNCTION).cuda()
init_weights(netD)
init_weights(netG)
print(netD)
print(netG)
optimD = torch.optim.Adam(netD.parameters(),
lr=g_conf.LR_D,
betas=(0.7, 0.999))
optimG = torch.optim.Adam(netG.parameters(),
lr=g_conf.LR_G,
betas=(0.7, 0.999))
if g_conf.TYPE == 'task':
optimF = torch.optim.Adam(netG.parameters(),
lr=g_conf.LEARNING_RATE,
betas=(0.7, 0.999))
Task_Loss = TaskLoss()
if g_conf.LOSS_FUNCTION == 'LSGAN':
Loss = torch.nn.MSELoss().cuda()
elif g_conf.LOSS_FUNCTION == 'NORMAL':
Loss = torch.nn.BCELoss().cuda()
L1_loss = torch.nn.L1Loss().cuda()
iteration = 0
best_loss_iter = 0
best_lossD = 1000000.0
best_lossG = 1000000.0
accumulated_time = 0
netG.train()
netD.train()
capture_time = time.time()
if not os.path.exists('./imgs_' + exp_alias):
os.mkdir('./imgs_' + exp_alias)
#TODO add image queue
#TODO add auxiliary regression loss for steering
#TODO put family for losses
fake_img_pool = ImagePool(50)
for data in data_loader:
val = 0.5
input_data, float_data = data
inputs = input_data['rgb'].cuda()
inputs = inputs.squeeze(1)
inputs_in = inputs - val
fake_inputs = netG(inputs_in) #subtracted by 0.5
fake_inputs_in = fake_inputs
if iteration % 200 == 0:
imgs_to_save = torch.cat((inputs_in[:2] + val, fake_inputs_in[:2]),
0).cpu().data
vutils.save_image(imgs_to_save,
'./imgs_' + exp_alias + '/' + str(iteration) +
'_real_and_fake.png',
normalize=True)
coil_logger.add_image("Images", imgs_to_save, iteration)
##--------------------Discriminator part!!!!!!!!!!-------------------##
set_requires_grad(netD, True)
optimD.zero_grad()
##fake
fake_inputs_forD = fake_img_pool.query(fake_inputs)
outputsD_fake_forD = netD(fake_inputs_forD.detach())
labsize = outputsD_fake_forD.size()
labels_fake = torch.zeros(labsize) #Fake labels
label_fake_noise = torch.rand(
labels_fake.size()) * 0.05 - 0.025 #Label smoothing
if g_conf.LABSMOOTH == 1:
labels_fake = labels_fake + labels_fake_noise
labels_fake = Variable(labels_fake).cuda()
lossD_fake = Loss(outputsD_fake_forD, labels_fake)
##real
outputsD_real = netD(inputs)
print("some d outputs", outputsD_real[0])
labsize = outputsD_real.size()
labels_real = torch.ones(labsize) #Real labels
label_real_noise = torch.rand(
labels_real.size()) * 0.05 - 0.025 #Label smoothing
if g_conf.LABSMOOTH == 1:
labels_real = labels_real + labels_real_noise
labels_real = Variable(labels_real).cuda()
lossD_real = Loss(outputsD_real, labels_real)
#Discriminator updates
lossD = (lossD_real + lossD_fake) * 0.5
# lossD /= len(inputs)
lossD.backward()
optimD.step()
coil_logger.add_scalar('Total LossD', lossD.data, iteration)
coil_logger.add_scalar('Real LossD', lossD_real.data, iteration)
coil_logger.add_scalar('Fake LossD', lossD_fake.data, iteration)
##--------------------Generator part!!!!!!!!!!-----------------------
set_requires_grad(netD, False)
optimG.zero_grad()
outputsD_fake_forG = netD(fake_inputs)
#Generator updates
lossG_adv = Loss(outputsD_fake_forG, labels_real)
lossG_smooth = L1_loss(fake_inputs, inputs)
lossG = (lossG_adv + l1weight * lossG_smooth) / (1.0 + l1weight)
lossG
lossG.backward()
optimG.step()
coil_logger.add_scalar('Total LossG', lossG.data, iteration)
coil_logger.add_scalar('Adv LossG', lossG_adv.data, iteration)
coil_logger.add_scalar('Smooth LossG', lossG_smooth.data, iteration)
#optimization for one iter done!
position = random.randint(0, len(float_data) - 1)
if lossD.data < best_lossD:
best_lossD = lossD.data.tolist()
if lossG.data < best_lossG:
best_lossG = lossG.data.tolist()
best_loss_iter = iteration
accumulated_time += time.time() - capture_time
capture_time = time.time()
print("LossD", lossD.data.tolist(), "LossG", lossG.data.tolist(),
"BestLossD", best_lossD, "BestLossG", best_lossG, "Iteration",
iteration, "Best Loss Iteration", best_loss_iter)
coil_logger.add_message(
'Iterating', {
'Iteration':
iteration,
'LossD':
lossD.data.tolist(),
'LossG':
lossG.data.tolist(),
'Images/s': (iteration * g_conf.BATCH_SIZE) / accumulated_time,
'BestLossD':
best_lossD,
'BestLossIteration':
best_loss_iter,
'BestLossG':
best_lossG,
'BestLossIteration':
best_loss_iter,
'GroundTruth':
dataset.extract_targets(float_data)[position].data.tolist(),
'Inputs':
dataset.extract_inputs(float_data)[position].data.tolist()
}, iteration)
if is_ready_to_save(iteration):
state = {
'iteration': iteration,
'stateD_dict': netD.state_dict(),
'stateG_dict': netG.state_dict(),
'best_lossD': best_lossD,
'best_lossG': best_lossG,
'total_time': accumulated_time,
'best_loss_iter': best_loss_iter
}
torch.save(
state,
os.path.join('/datatmp/Experiments/rohitgan/_logs', exp_batch,
exp_alias, 'checkpoints',
str(iteration) + '.pth'))
if iteration == best_loss_iter:
state = {
'iteration': iteration,
'stateD_dict': netD.state_dict(),
'stateG_dict': netG.state_dict(),
'best_lossD': best_lossD,
'best_lossG': best_lossG,
'total_time': accumulated_time,
'best_loss_iter': best_loss_iter
}
torch.save(
state,
os.path.join('/datatmp/Experiments/rohitgan/_logs', exp_batch,
exp_alias, 'best_modelG' + '.pth'))
iteration += 1
def folder_execute(params=None):
"""
Execute a folder of experiments. It will execute trainings and
all the selected evaluations for each of the models present on the folder.
Args
params: a dictionary containing:
gpus: the gpu numbers that are going to be allocated for the experiment
gpu_value: the "value" of each gpu, depending on the value more or less experiments
will be allocated per GPU
folder: the folder where all the experiment configuration files are
validation_datasets: the validation datasets that are going to be validated
per experiment
driving_environments: The driving environments where the models are going to be tested.
"""
folder = params['folder']
allocated_gpus = params['gpus']
validation_datasets = params['validation_datasets']
driving_environments = params['driving_environments']
allocation_parameters = params['allocation_parameters']
experiments_list = os.listdir(os.path.join('configs', folder))
experiments_list = [
experiment.split('.')[-2] for experiment in experiments_list
]
allocated_gpus = {
gpu: allocation_parameters['gpu_value']
for gpu in allocated_gpus
}
executing_processes = []
free_gpus, resources_on_most_free_gpu, executing_processes = get_gpu_resources(
allocated_gpus, executing_processes, allocation_parameters)
# Is a queue of tasks to be executed. The priority is always train.
# then test then val.
tasks_queue = mount_experiment_heap(folder, experiments_list,
params['is_training'], [], [],
validation_datasets,
driving_environments)
# No process is executing right now.
while True:
# if not done or executing get to the list
# If amount of resources is smaller than a threshold.
while resources_on_most_free_gpu >= min([allocation_parameters['train_cost'],
allocation_parameters['validation_cost'],
allocation_parameters['drive_cost']]) \
and tasks_queue != []:
# Allocate all the gpus
popped_thing = heapq.heappop(tasks_queue)
process_specs = popped_thing[2] # To get directly the dict
# Get the train status, that will affect in scheduling a validation or drive process
train_status = monitorer.get_status(folder,
process_specs['experiment'],
'train')[0]
# ADD TRAIN TO EXECUTE
if process_specs['type'] == 'train' and resources_on_most_free_gpu >= \
allocation_parameters['train_cost']:
free_gpus, resources_on_most_free_gpu, gpu_number = allocate_gpu_resources(
free_gpus, allocation_parameters['train_cost'])
execute_train(gpu_number, process_specs['folder'],
process_specs['experiment'],
params['number_of_workers'])
process_specs.update({'gpu': gpu_number})
executing_processes.append(process_specs)
# ADD DRIVE TO EXECUTE
elif process_specs['type'] == 'drive' and resources_on_most_free_gpu >= \
allocation_parameters['drive_cost'] \
and (train_status == 'Iterating' or train_status == 'Loading' or
train_status == 'Finished'):
print(process_specs['type'])
free_gpus, resources_on_most_free_gpu, gpu_number = allocate_gpu_resources(
free_gpus, allocation_parameters['drive_cost'])
execute_drive(gpu_number, process_specs['folder'],
process_specs['experiment'],
process_specs['environment'],
params['driving_parameters'])
process_specs.update({'gpu': gpu_number})
executing_processes.append(process_specs)
# ADD VALIDATION TO EXECUTE
elif process_specs['type'] == 'validation' and resources_on_most_free_gpu >= \
allocation_parameters['validation_cost'] \
and (train_status == 'Iterating' or train_status == 'Loading' or
train_status == 'Finished'):
free_gpus, resources_on_most_free_gpu, gpu_number = allocate_gpu_resources(
free_gpus, allocation_parameters['validation_cost'])
execute_validation(gpu_number, process_specs['folder'],
process_specs['experiment'],
process_specs['dataset'])
process_specs.update({'gpu': gpu_number})
executing_processes.append(process_specs)
tasks_queue = mount_experiment_heap(folder, experiments_list,
params['is_training'],
executing_processes, tasks_queue,
validation_datasets,
driving_environments, False)
printer.plot_folder_summaries(folder, params['is_training'],
validation_datasets,
driving_environments)
# Check allocated process, and look which ones finished.
if len(tasks_queue) == 0 and len(executing_processes) == 0:
break
free_gpus, resources_on_most_free_gpu, executing_processes = get_gpu_resources(
allocated_gpus, executing_processes, allocation_parameters)
time.sleep(10)
print("ALL EXPERIMENTS EXECUTED")
def execute(gpu, exp_batch, exp_alias):
from time import gmtime, strftime
manualSeed = g_conf.SEED
torch.cuda.manual_seed(manualSeed)
os.environ["CUDA_VISIBLE_DEVICES"] = gpu
merge_with_yaml(os.path.join('configs', exp_batch, exp_alias + '.yaml'))
set_type_of_process('train')
coil_logger.add_message('Loading', {'GPU': gpu})
if not os.path.exists('_output_logs'):
os.mkdir('_output_logs')
sys.stdout = open(os.path.join(
'_output_logs', g_conf.PROCESS_NAME + '_' + str(os.getpid()) + ".out"),
"a",
buffering=1)
if monitorer.get_status(exp_batch, exp_alias + '.yaml',
g_conf.PROCESS_NAME)[0] == "Finished":
return
full_dataset = os.path.join(os.environ["COIL_DATASET_PATH"],
g_conf.TRAIN_DATASET_NAME)
real_dataset = g_conf.TARGET_DOMAIN_PATH
#main data loader
dataset = CoILDataset(full_dataset,
real_dataset,
transform=transforms.Compose([transforms.ToTensor()
]))
sampler = BatchSequenceSampler(
splitter.control_steer_split(dataset.measurements,
dataset.meta_data), g_conf.BATCH_SIZE,
g_conf.NUMBER_IMAGES_SEQUENCE, g_conf.SEQUENCE_STRIDE)
data_loader = torch.utils.data.DataLoader(dataset,
batch_sampler=sampler,
shuffle=False,
num_workers=6,
pin_memory=True)
st = lambda aug: iag.Sometimes(aug, 0.4)
oc = lambda aug: iag.Sometimes(aug, 0.3)
rl = lambda aug: iag.Sometimes(aug, 0.09)
augmenter = iag.Augmenter([iag.ToGPU()] + [
rl(iag.GaussianBlur(
(0, 1.5))), # blur images with a sigma between 0 and 1.5
rl(iag.AdditiveGaussianNoise(loc=0, scale=(
0.0, 0.05), per_channel=0.5)), # add gaussian noise to images
oc(iag.Dropout((0.0, 0.10), per_channel=0.5)
), # randomly remove up to X% of the pixels
oc(
iag.CoarseDropout(
(0.0, 0.10), size_percent=(0.08, 0.2),
per_channel=0.5)), # randomly remove up to X% of the pixels
oc(iag.Add((-40, 40), per_channel=0.5)
), # change brightness of images (by -X to Y of original value)
st(iag.Multiply((0.10, 2), per_channel=0.2)
), # change brightness of images (X-Y% of original value)
rl(iag.ContrastNormalization(
(0.5, 1.5), per_channel=0.5)), # improve or worsen the contrast
rl(iag.Grayscale((0.0, 1))), # put grayscale
] # do all of the above in random order
)
l1weight = g_conf.L1_WEIGHT
task_adv_weight = g_conf.TASK_ADV_WEIGHT
image_size = tuple([88, 200])
print(strftime("%Y-%m-%d %H:%M:%S", gmtime()))
print("GPU", gpu)
print("Configurations of ", exp_alias)
print("GANMODEL_NAME", g_conf.GANMODEL_NAME)
print("LOSS_FUNCTION", g_conf.LOSS_FUNCTION)
print("LR_G, LR_D, LR", g_conf.LR_G, g_conf.LR_D, g_conf.LEARNING_RATE)
print("SKIP", g_conf.SKIP)
print("TYPE", g_conf.TYPE)
print("L1 WEIGHT", g_conf.L1_WEIGHT)
print("TASK ADV WEIGHT", g_conf.TASK_ADV_WEIGHT)
print("LAB SMOOTH", g_conf.LABSMOOTH)
if g_conf.GANMODEL_NAME == 'LSDcontrol':
netD = ganmodels._netD(loss=g_conf.LOSS_FUNCTION).cuda()
netG = ganmodels._netG(loss=g_conf.LOSS_FUNCTION,
skip=g_conf.SKIP).cuda()
elif g_conf.GANMODEL_NAME == 'LSDcontrol_nopatch':
netD = ganmodels_nopatch._netD(loss=g_conf.LOSS_FUNCTION).cuda()
netG = ganmodels_nopatch._netG(loss=g_conf.LOSS_FUNCTION).cuda()
elif g_conf.GANMODEL_NAME == 'LSDcontrol_nopatch_smaller':
netD = ganmodels_nopatch_smaller._netD(
loss=g_conf.LOSS_FUNCTION).cuda()
netG = ganmodels_nopatch_smaller._netG(
loss=g_conf.LOSS_FUNCTION).cuda()
elif g_conf.GANMODEL_NAME == 'LSDcontrol_task':
netD_task = ganmodels_task._netD_task(loss=g_conf.LOSS_FUNCTION).cuda()
netD_img = ganmodels_task._netD_img(loss=g_conf.LOSS_FUNCTION).cuda()
netG = ganmodels_task._netG(loss=g_conf.LOSS_FUNCTION).cuda()
netF = ganmodels_task._netF(loss=g_conf.LOSS_FUNCTION).cuda()
if g_conf.PRETRAINED == 'RECON':
netF_statedict = torch.load('netF_GAN_Pretrained.wts')
netF.load_state_dict(netF_statedict)
elif g_conf.PRETRAINED == 'IL':
print("Loading IL")
model_IL = torch.load('best_loss_20-06_EpicClearWeather.pth')
model_IL_state_dict = model_IL['state_dict']
netF_state_dict = netF.state_dict()
print(len(netF_state_dict.keys()), len(model_IL_state_dict.keys()))
for i, keys in enumerate(
zip(netF_state_dict.keys(), model_IL_state_dict.keys())):
newkey, oldkey = keys
# if newkey.split('.')[0] == "branch" and oldkey.split('.')[0] == "branches":
# print("No Transfer of ", newkey, " to ", oldkey)
# else:
print("Transferring ", newkey, " to ", oldkey)
netF_state_dict[newkey] = model_IL_state_dict[oldkey]
netF.load_state_dict(netF_state_dict)
print("IL Model Loaded!")
elif g_conf.GANMODEL_NAME == 'LSDcontrol_task_2d':
netD_bin = ganmodels_task._netD_task().cuda()
netD_img = ganmodels_task._netD_img().cuda()
netG = ganmodels_task._netG().cuda()
netF = ganmodels_task._netF().cuda()
if g_conf.PRETRAINED == 'IL':
print("Loading IL")
model_IL = torch.load(g_conf.IL_AGENT_PATH)
model_IL_state_dict = model_IL['state_dict']
netF_state_dict = netF.state_dict()
print(len(netF_state_dict.keys()), len(model_IL_state_dict.keys()))
for i, keys in enumerate(
zip(netF_state_dict.keys(), model_IL_state_dict.keys())):
newkey, oldkey = keys
print("Transferring ", newkey, " to ", oldkey)
netF_state_dict[newkey] = model_IL_state_dict[oldkey]
netF.load_state_dict(netF_state_dict)
print("IL Model Loaded!")
####
if g_conf.IF_AUG:
print("Loading Aug Decoder")
model_dec = torch.load(g_conf.DECODER_RECON_PATH)
else:
print("Loading Decoder")
model_dec = torch.load(g_conf.DECODER_RECON_PATH)
model_dec_state_dict = model_dec['stateG_dict']
netG_state_dict = netG.state_dict()
print(len(netG_state_dict.keys()),
len(model_dec_state_dict.keys()))
for i, keys in enumerate(
zip(netG_state_dict.keys(), model_dec_state_dict.keys())):
newkey, oldkey = keys
print("Transferring ", newkey, " to ", oldkey)
netG_state_dict[newkey] = model_dec_state_dict[oldkey]
netG.load_state_dict(netG_state_dict)
print("Decoder Model Loaded!")
init_weights(netD_bin)
init_weights(netD_img)
# init_weights(netG)
print(netD_bin)
print(netF)
optimD_bin = torch.optim.Adam(netD_bin.parameters(),
lr=g_conf.LR_D,
betas=(0.5, 0.999))
optimD_img = torch.optim.Adam(netD_img.parameters(),
lr=g_conf.LR_D,
betas=(0.5, 0.999))
optimG = torch.optim.Adam(netG.parameters(),
lr=g_conf.LR_D,
betas=(0.5, 0.999))
if g_conf.TYPE == 'task':
optimF = torch.optim.Adam(netF.parameters(), lr=g_conf.LEARNING_RATE)
Task_Loss = TaskLoss()
if g_conf.GANMODEL_NAME == 'LSDcontrol_task_2d':
print("Using cross entropy!")
Loss = torch.nn.CrossEntropyLoss().cuda()
L1_loss = torch.nn.L1Loss().cuda()
iteration = 0
best_loss_iter_F = 0
best_loss_iter_G = 0
best_lossF = 1000000.0
best_lossD = 1000000.0
best_lossG = 1000000.0
accumulated_time = 0
gen_iterations = 0
n_critic = g_conf.N_CRITIC
lossF = Variable(torch.Tensor([100.0]))
lossG_adv = Variable(torch.Tensor([100.0]))
lossG_smooth = Variable(torch.Tensor([100.0]))
lossG = Variable(torch.Tensor([100.0]))
netD_bin.train()
netD_img.train()
netG.train()
netF.train()
capture_time = time.time()
if not os.path.exists('./imgs_' + exp_alias):
os.mkdir('./imgs_' + exp_alias)
#TODO check how C network is optimized in LSDSEG
#TODO put family for losses
#IMPORTANT WHILE RUNNING THIS, CONV.PY MUST HAVE BATCHNORMS
fake_img_pool_src = ImagePool(50)
fake_img_pool_tgt = ImagePool(50)
for data in data_loader:
set_requires_grad(netD_bin, True)
set_requires_grad(netD_img, True)
set_requires_grad(netG, True)
set_requires_grad(netF, True)
# print("ITERATION:", iteration)
val = 0.0
input_data, float_data, tgt_imgs = data
if g_conf.IF_AUG:
inputs = augmenter(0, input_data['rgb'])
tgt_imgs = augmenter(0, tgt_imgs)
else:
inputs = input_data['rgb'].cuda()
tgt_imgs = tgt_imgs.cuda()
inputs = inputs.squeeze(1)
inputs = inputs - val #subtracted by 0.5
tgt_imgs = tgt_imgs - val #subtracted by 0.5
controls = float_data[:, dataset.controls_position(), :]
src_embed_inputs, src_branches = netF(
inputs,
dataset.extract_inputs(float_data).cuda())
tgt_embed_inputs = netF(tgt_imgs, None)
src_img_fake = netG(src_embed_inputs)
tgt_img_fake = netG(tgt_embed_inputs)
if iteration % 100 == 0:
imgs_to_save = torch.cat(
(inputs[:1] + val, src_img_fake[:1] + val, tgt_imgs[:1] + val,
tgt_img_fake[:1] + val), 0).cpu().data
coil_logger.add_image("Images", imgs_to_save, iteration)
imgs_to_save = imgs_to_save.clamp(0.0, 1.0)
vutils.save_image(imgs_to_save,
'./imgs_' + exp_alias + '/' + str(iteration) +
'_real_and_fake.png',
normalize=False)
##--------------------Discriminator part!!!!!!!!!!-------------------##
set_requires_grad(netD_bin, True)
set_requires_grad(netD_img, False)
set_requires_grad(netG, False)
set_requires_grad(netF, False)
optimD_bin.zero_grad()
outputsD_real_src_bin = netD_bin(src_embed_inputs)
outputsD_real_tgt_bin = netD_bin(tgt_embed_inputs)
gradient_penalty = calc_gradient_penalty(netD_bin, src_embed_inputs,
tgt_embed_inputs)
lossD_bin = torch.mean(outputsD_real_tgt_bin -
outputsD_real_src_bin) + gradient_penalty
lossD_bin.backward(retain_graph=True)
optimD_bin.step()
coil_logger.add_scalar('Total LossD Bin', lossD_bin.data, iteration)
#### Discriminator img update ####
set_requires_grad(netD_bin, False)
set_requires_grad(netD_img, True)
set_requires_grad(netG, False)
set_requires_grad(netF, False)
optimD_img.zero_grad()
outputsD_fake_src_img = netD_img(src_img_fake.detach())
outputsD_fake_tgt_img = netD_img(tgt_img_fake.detach())
outputsD_real_src_img = netD_img(inputs)
outputsD_real_tgt_img = netD_img(tgt_imgs)
gradient_penalty_src = calc_gradient_penalty(netD_img, inputs,
src_img_fake)
lossD_img_src = torch.mean(
outputsD_fake_src_img -
outputsD_real_src_img) + gradient_penalty_src
gradient_penalty_tgt = calc_gradient_penalty(netD_img, tgt_imgs,
tgt_img_fake)
lossD_img_tgt = torch.mean(
outputsD_fake_tgt_img -
outputsD_real_tgt_img) + gradient_penalty_tgt
lossD_img = (lossD_img_src + lossD_img_tgt) * 0.5
lossD_img.backward(retain_graph=True)
optimD_img.step()
coil_logger.add_scalar('Total LossD img', lossD_img.data, iteration)
if ((iteration + 1) % n_critic) == 0:
#####Generator updates#######
set_requires_grad(netD_bin, False)
set_requires_grad(netD_img, False)
set_requires_grad(netG, True)
set_requires_grad(netF, False)
outputsD_fake_src_img = netD_img(src_img_fake)
outputsD_real_tgt_img = netD_img(tgt_imgs)
outputsD_fake_tgt_img = netD_img(tgt_img_fake)
lossG_src_smooth = L1_loss(src_img_fake, inputs)
lossG_tgt_smooth = L1_loss(tgt_img_fake, tgt_imgs)
lossG_smooth = (lossG_src_smooth + lossG_tgt_smooth) * 0.5
lossG_adv = 0.5 * (-1.0 * outputsD_fake_src_img.mean() -
1.0 * outputsD_fake_tgt_img.mean())
lossG = (lossG_smooth + 0.0 * lossG_adv)
lossG.backward(retain_graph=True)
optimG.step()
coil_logger.add_scalar('Total LossG', lossG.data, iteration)
#####Task network updates##########################
set_requires_grad(netD_bin, False)
set_requires_grad(netD_img, False)
set_requires_grad(netG, False)
set_requires_grad(netF, True)
optimF.zero_grad()
src_embed_inputs, src_branches = netF(
inputs,
dataset.extract_inputs(float_data).cuda())
tgt_embed_inputs = netF(tgt_imgs, None)
src_img_fake = netG(src_embed_inputs)
tgt_img_fake = netG(tgt_embed_inputs)
outputsD_fake_src_img = netD_img(src_img_fake)
outputsD_real_tgt_img = netD_img(tgt_imgs)
lossF_task = Task_Loss.MSELoss(
src_branches,
dataset.extract_targets(float_data).cuda(), controls.cuda(),
dataset.extract_inputs(float_data).cuda())
lossF_adv_bin = netD_bin(src_embed_inputs).mean() - netD_bin(
tgt_embed_inputs).mean()
lossF_adv_img = outputsD_fake_src_img.mean(
) - outputsD_real_tgt_img.mean()
lossF_adv = 0.5 * (lossF_adv_bin + 0.1 * lossF_adv_img)
lossF = (lossF_task + task_adv_weight * lossF_adv)
coil_logger.add_scalar('Total Task Loss', lossF.data, iteration)
coil_logger.add_scalar('Adv Task Loss', lossF_adv.data, iteration)
coil_logger.add_scalar('Only Task Loss', lossF_task.data,
iteration)
lossF.backward(retain_graph=True)
optimF.step()
if lossF.data < best_lossF:
best_lossF = lossF.data.tolist()
best_loss_iter_F = iteration
#optimization for one iter done!
position = random.randint(0, len(float_data) - 1)
accumulated_time += time.time() - capture_time
capture_time = time.time()
if is_ready_to_save(iteration):
state = {
'iteration': iteration,
'stateD_bin_dict': netD_bin.state_dict(),
'stateF_dict': netF.state_dict(),
'best_lossD': best_lossD,
'total_time': accumulated_time,
'best_loss_iter_F': best_loss_iter_F
}
torch.save(
state,
os.path.join('/datatmp/Experiments/rohitgan/_logs', exp_batch,
exp_alias, 'checkpoints',
str(iteration) + '.pth'))
if iteration == best_loss_iter_F and iteration > 10000:
state = {
'iteration': iteration,
'stateD_bin_dict': netD_bin.state_dict(),
'stateF_dict': netF.state_dict(),
'best_lossD': best_lossD,
'best_lossF': best_lossF,
'total_time': accumulated_time,
'best_loss_iter_F': best_loss_iter_F
}
torch.save(
state,
os.path.join('/datatmp/Experiments/rohitgan/_logs', exp_batch,
exp_alias, 'best_modelF' + '.pth'))
iteration += 1
def execute(gpu, exp_batch, exp_alias):
# We set the visible cuda devices
try:
os.environ["CUDA_VISIBLE_DEVICES"] = gpu
# At this point the log file with the correct naming is created.
merge_with_yaml(os.path.join('configs', exp_batch,
exp_alias + '.yaml'))
set_type_of_process('train')
coil_logger.add_message('Loading', {'GPU': gpu})
if not os.path.exists('_output_logs'):
os.mkdir('_output_logs')
sys.stdout = open(os.path.join(
'_output_logs',
g_conf.PROCESS_NAME + '_' + str(os.getpid()) + ".out"),
"a",
buffering=1)
if monitorer.get_status(exp_batch, exp_alias + '.yaml',
g_conf.PROCESS_NAME)[0] == "Finished":
# TODO: print some cool summary or not ?
return
#Define the dataset. This structure is has the __get_item__ redefined in a way
#that you can access the HDFILES positions from the root directory as a in a vector.
full_dataset = os.path.join(os.environ["COIL_DATASET_PATH"],
g_conf.TRAIN_DATASET_NAME)
#augmenter_cpu = iag.AugmenterCPU(g_conf.AUGMENTATION_SUITE_CPU)
dataset = CoILDataset(full_dataset,
transform=transforms.Compose(
[transforms.ToTensor()]))
# Creates the sampler, this part is responsible for managing the keys. It divides
# all keys depending on the measurements and produces a set of keys for each bach.
sampler = BatchSequenceSampler(
splitter.control_steer_split(dataset.measurements,
dataset.meta_data), g_conf.BATCH_SIZE,
g_conf.NUMBER_IMAGES_SEQUENCE, g_conf.SEQUENCE_STRIDE)
# The data loader is the multi threaded module from pytorch that release a number of
# workers to get all the data.
# TODO: batch size an number of workers go to some configuration file
data_loader = torch.utils.data.DataLoader(dataset,
batch_sampler=sampler,
shuffle=False,
num_workers=12,
pin_memory=True)
# By instanciating the augmenter we get a callable that augment images and transform them
# into tensors.
augmenter = iag.Augmenter(g_conf.AUGMENTATION_SUITE)
# TODO: here there is clearly a posibility to make a cool "conditioning" system.
model = CoILModel(g_conf.MODEL_NAME)
model.cuda()
exit()
print(model)
criterion = Loss()
# TODO: DATASET SIZE SEEMS WEIRD
optimizer = optim.SGD(model.parameters(), lr=0.0001, momentum=0.9)
checkpoint_file = get_latest_saved_checkpoint()
if checkpoint_file != None:
checkpoint = torch.load(
os.path.join('_logs', exp_batch, exp_alias, 'checkpoints',
str(get_latest_saved_checkpoint())))
iteration = checkpoint['iteration']
accumulated_time = checkpoint['total_time']
best_loss = checkpoint['best_loss']
best_loss_iter = checkpoint['best_loss_iter']
else:
iteration = 0
best_loss = 10000.0
accumulated_time = 0 # We accumulate iteration time and keep the average speed
best_loss_iter = 0
# TODO: The checkpoint will continue, so it should erase everything up to the iteration
print(dataset.meta_data)
print(model)
capture_time = time.time()
for data in data_loader:
input_data, float_data = data
#TODO, ADD ITERATION SCHEDULE
input_rgb_data = augmenter(0, input_data['rgb'])
#coil_logger.add_images(input_rgb_data)
# get the control commands from float_data, size = [120,1]
controls = float_data[:, dataset.controls_position(), :]
print(" CONTROLS ", controls.shape)
# The output(branches) is a list of 5 branches results, each branch is with size [120,3]
model.zero_grad()
print('INPUTS', dataset.extract_inputs(float_data).shape)
branches = model(input_rgb_data,
dataset.extract_inputs(float_data).cuda())
#print ("len ",len(branches))
#targets = torch.cat([steer_gt, gas_gt, brake_gt], 1)
print("Extracted targets ",
dataset.extract_targets(float_data).shape[0])
loss = criterion.MSELoss(
branches,
dataset.extract_targets(float_data).cuda(), controls.cuda(),
dataset.extract_inputs(float_data).cuda())
# TODO: All these logging things could go out to clean up the main
if loss.data < best_loss:
best_loss = loss.data.tolist()
best_loss_iter = iteration
# Log a random position
position = random.randint(0, len(float_data) - 1)
output = model.extract_branch(torch.stack(branches[0:4]), controls)
error = torch.abs(output -
dataset.extract_targets(float_data).cuda())
# TODO: For now we are computing the error for just the correct branch, it could be multi- branch,
coil_logger.add_scalar('Loss', loss.data, iteration)
loss.backward()
optimizer.step()
accumulated_time += time.time() - capture_time
capture_time = time.time()
# TODO: Get only the float_data that are actually generating output
# TODO: itearation is repeating , and that is dumb
coil_logger.add_message(
'Iterating', {
'Iteration':
iteration,
'Loss':
loss.data.tolist(),
'Images/s':
(iteration * g_conf.BATCH_SIZE) / accumulated_time,
'BestLoss':
best_loss,
'BestLossIteration':
best_loss_iter,
'Output':
output[position].data.tolist(),
'GroundTruth':
dataset.extract_targets(
float_data)[position].data.tolist(),
'Error':
error[position].data.tolist(),
'Inputs':
dataset.extract_inputs(float_data)[position].data.tolist()
}, iteration)
# TODO: For now we are computing the error for just the correct branch, it could be multi-branch,
# TODO: save also the optimizer state dictionary
if is_ready_to_save(iteration):
state = {
'iteration': iteration,
'state_dict': model.state_dict(),
'best_loss': best_loss,
'total_time': accumulated_time,
'best_loss_iter': best_loss_iter
}
# TODO : maybe already summarize the best model ???
torch.save(
state,
os.path.join('_logs', exp_batch, exp_alias, 'checkpoints',
str(iteration) + '.pth'))
iteration += 1
except KeyboardInterrupt:
coil_logger.add_message('Error', {'Message': 'Killed By User'})
except:
traceback.print_exc()
coil_logger.add_message('Error', {'Message': 'Something Happened'})
def execute(gpu, exp_batch, exp_alias):
os.environ["CUDA_VISIBLE_DEVICES"] = gpu
merge_with_yaml(os.path.join('configs', exp_batch, exp_alias + '.yaml'))
set_type_of_process('train')
coil_logger.add_message('Loading', {'GPU': gpu})
if not os.path.exists('_output_logs'):
os.mkdir('_output_logs')
sys.stdout = open(os.path.join(
'_output_logs', g_conf.PROCESS_NAME + '_' + str(os.getpid()) + ".out"),
"a",
buffering=1)
if monitorer.get_status(exp_batch, exp_alias + '.yaml',
g_conf.PROCESS_NAME)[0] == "Finished":
return
full_dataset = os.path.join(os.environ["COIL_DATASET_PATH"],
g_conf.TRAIN_DATASET_NAME)
dataset = CoILDataset(full_dataset,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5],
[1.0, 1.0, 1.0])
]))
sampler = BatchSequenceSampler(
splitter.control_steer_split(dataset.measurements,
dataset.meta_data), g_conf.BATCH_SIZE,
g_conf.NUMBER_IMAGES_SEQUENCE, g_conf.SEQUENCE_STRIDE)
data_loader = torch.utils.data.DataLoader(dataset,
batch_sampler=sampler,
shuffle=False,
num_workers=6,
pin_memory=True)
l1weight = 1.0
image_size = tuple([88, 200])
testmode = 1
if g_conf.GANMODEL_NAME == 'LSDcontrol':
netD = ganmodels._netD().cuda()
netG = ganmodels._netG(skip=g_conf.SKIP).cuda()
checkpoint = torch.load(
os.path.join(
'/datatmp/Experiments/rohitgan/_logs/eccv/experiment_1/checkpoints/1010000.pth'
))
netG.load_state_dict(checkpoint['stateG_dict'])
netD.load_state_dict(checkpoint['stateD_dict'])
print(netD)
print(netG)
MSE_loss = torch.nn.MSELoss().cuda()
L1_loss = torch.nn.L1Loss().cuda()
iteration = 0
# netG.eval()
# netD.eval()
capture_time = time.time()
for data in data_loader:
input_data, float_data = data
inputs = input_data['rgb'].cuda()
inputs = inputs.squeeze(1)
#forward pass
fake_inputs = netG(inputs)
imgs_to_save = torch.cat((fake_inputs, inputs), 0).cpu().data
imgs_to_save = (imgs_to_save + 1.0) / 2.0
imgs = [img for img in imgs_to_save]
vutils.save_image(imgs, 'imgs/' + str(iteration) + '.png')
outputsD_fake_forD = netD(fake_inputs.detach())
labsize = outputsD_fake_forD.size()
labels_fake = torch.zeros(labsize[0], labsize[1], labsize[2],
labsize[3]) #Fake labels
label_fake_noise = torch.rand(
labels_fake.size()) * 0.5 - 0.25 #Label smoothing
labels_fake = labels_fake + label_fake_noise
labels_fake = Variable(labels_fake).cuda()
lossD_fake = MSE_loss(outputsD_fake_forD, labels_fake)
outputsD_real = netD(inputs)
labsize = outputsD_real.size()
labels_real = torch.ones(labsize[0], labsize[1], labsize[2],
labsize[3]) #Real labels
label_real_noise = torch.rand(
labels_real.size()) * 0.5 - 0.25 #Label smoothing
labels_real = labels_real + label_real_noise
labels_real = Variable(labels_real).cuda()
lossD_real = MSE_loss(outputsD_real, labels_real)
lossD = (lossD_real + lossD_fake) * 0.5
lossD /= len(inputs)
outputsD_fake_forG = netD(fake_inputs)
lossG_adv = MSE_loss(outputsD_fake_forG, labels_real)
lossG_smooth = L1_loss(fake_inputs, inputs)
lossG = lossG_adv + l1weight * lossG_smooth
lossG /= len(inputs)
# print("LossD", lossD.data.tolist(), "LossG", lossG.data.tolist(), "BestLossD", best_lossD, "BestLossG", best_lossG, "Iteration", iteration, "Best Loss Iteration", best_loss_iter)
iteration += 1
def folder_execute(params=None):
"""
On this mode the training software keeps all
It forks a process to run the monitor over the training logs.
Arguments
param, prioritize training, prioritize test, prioritize
"""
folder = params['folder']
allocated_gpus = params['gpus']
validation_datasets = params['validation_datasets']
driving_environments = params['driving_environments']
allocation_parameters = params['allocation_parameters']
experiments_list = os.listdir(os.path.join('configs', folder))
experiments_list = [experiment.split('.')[-2] for experiment in experiments_list]
# Each gpu has maximun 2 slots
allocated_gpus = {gpu: allocation_parameters['gpu_value'] for gpu in allocated_gpus}
executing_processes = []
free_gpus, resources_on_most_free_gpu, executing_processes = get_gpu_resources(allocated_gpus,
executing_processes,
allocation_parameters)
# Is a queue of tasks to be executed. The priority is always train.
# then test then val.
# TODO: change the priority to test the ones that have already been trained.
tasks_queue = mount_experiment_heap(folder, experiments_list, params['is_training'],
[], [],
validation_datasets, driving_environments)
# No process is executing right now.
print(tasks_queue)
# TODO: the while should go outside, so the monitorer process is independent of the type of execution
while True:
# if not done or executing get to the list
# If amount of resources is smaller than a threshold.
while resources_on_most_free_gpu >= min([allocation_parameters['train_cost'],
allocation_parameters['validation_cost'],
allocation_parameters['drive_cost']]) \
and tasks_queue != []:
# Allocate all the gpus
popped_thing = heapq.heappop(tasks_queue)
process_specs = popped_thing[2] # To get directly the dict
# Get the train status, that will affect in scheduling a validation or drive process
train_status = monitorer.get_status(folder, process_specs['experiment'], 'train')[0]
if process_specs['type'] == 'train' and resources_on_most_free_gpu >= \
allocation_parameters['train_cost']:
free_gpus, resources_on_most_free_gpu, gpu_number = allocate_gpu_resources(
free_gpus,
allocation_parameters['train_cost'])
execute_train(gpu_number, process_specs['folder'], process_specs['experiment'])
process_specs.update({'gpu': gpu_number})
executing_processes.append(process_specs)
elif process_specs['type'] == 'validation' and resources_on_most_free_gpu >= \
allocation_parameters['validation_cost'] \
and (train_status == 'Iterating' or train_status == 'Loading' or
train_status == 'Finished'):
free_gpus, resources_on_most_free_gpu, gpu_number = allocate_gpu_resources(
free_gpus, allocation_parameters['validation_cost'])
execute_validation(gpu_number, process_specs['folder'], process_specs['experiment'],
process_specs['dataset'])
process_specs.update({'gpu': gpu_number})
executing_processes.append(process_specs)
elif process_specs['type'] == 'drive' and resources_on_most_free_gpu >= \
allocation_parameters['drive_cost'] \
and (train_status == 'Iterating' or train_status == 'Loading' or
train_status == 'Finished'):
free_gpus, resources_on_most_free_gpu, gpu_number = allocate_gpu_resources(
free_gpus, allocation_parameters['drive_cost'])
execute_drive(gpu_number, process_specs['folder'], process_specs['experiment'],
process_specs['environment'], no_screen=params['no_screen'])
process_specs.update({'gpu': gpu_number})
executing_processes.append(process_specs)
tasks_queue = mount_experiment_heap(folder, experiments_list, params['is_training'],
executing_processes, tasks_queue,
validation_datasets, driving_environments, False)
printer.plot_folder_summaries(folder,
params['is_training'],
validation_datasets,
driving_environments)
# Check allocated process, and look which ones finished.
free_gpus, resources_on_most_free_gpu, executing_processes = get_gpu_resources(
allocated_gpus,
executing_processes,
allocation_parameters)
if len(tasks_queue) == 0 and len(executing_processes) == 0:
break
print ("Task queue", tasks_queue)
print ("")
print ("exec proc", executing_processes)
print("resources", free_gpus)
time.sleep(10)
print("ALL EXPERIMENTS EXECUTED")
def execute(gpu, exp_batch, exp_alias, dataset_name):
# We set the visible cuda devices
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
# At this point the log file with the correct naming is created.
merge_with_yaml(os.path.join('configs', exp_batch, exp_alias + '.yaml'))
set_type_of_process('validation', dataset_name)
if not os.path.exists('_output_logs'):
os.mkdir('_output_logs')
sys.stdout = open(os.path.join(
'_output_logs', g_conf.PROCESS_NAME + '_' + str(os.getpid()) + ".out"),
"a",
buffering=1)
if monitorer.get_status(exp_batch, exp_alias + '.yaml',
g_conf.PROCESS_NAME)[0] == "Finished":
# TODO: print some cool summary or not ?
return
#Define the dataset. This structure is has the __get_item__ redefined in a way
#that you can access the HDFILES positions from the root directory as a in a vector.
full_dataset = os.path.join(os.environ["COIL_DATASET_PATH"], dataset_name)
print(full_dataset)
dataset = CoILDataset(full_dataset,
transform=transforms.Compose([transforms.ToTensor()
]))
# Creates the sampler, this part is responsible for managing the keys. It divides
# all keys depending on the measurements and produces a set of keys for each bach.
# The data loader is the multi threaded module from pytorch that release a number of
# workers to get all the data.
# TODO: batch size an number of workers go to some configuration file
data_loader = torch.utils.data.DataLoader(dataset,
batch_size=120,
shuffle=False,
num_workers=12,
pin_memory=True)
# TODO: here there is clearly a posibility to make a cool "conditioning" system.
model = CoILModel(g_conf.MODEL_NAME)
model.cuda()
model.eval()
criterion = Loss()
latest = get_latest_evaluated_checkpoint()
if latest is None: # When nothing was tested, get latest returns none, we fix that.
latest = 0
latest = 200000
best_loss = 1000.0
best_error = 1000.0
best_loss_iter = 0
best_error_iter = 0
print(dataset.meta_data[0][0])
for k in dataset.meta_data:
k[0] = str(k[0], 'utf-8')
print(dataset.meta_data[0][0])
cpts = glob.glob(
'/home-local/rohitrishabh/coil_20-06/_logs/eccv/experiment_1/checkpoints/*.pth'
)
# while not maximun_checkpoint_reach(latest, g_conf.TEST_SCHEDULE):
for ckpt in cpts:
# if is_next_checkpoint_ready(g_conf.TEST_SCHEDULE):
# latest = get_next_checkpoint(g_conf.TEST_SCHEDULE)
latest = int(ckpt[-10:-4])
# checkpoint = torch.load(os.path.join('_logs', exp_batch, exp_alias
# , 'checkpoints', str(latest) + '.pth'))
checkpoint = torch.load(ckpt)
checkpoint_iteration = checkpoint['iteration']
print("Validation loaded ", checkpoint_iteration)
accumulated_loss = 0.0
accumulated_error = 0.0
iteration_on_checkpoint = 0
for data in data_loader:
input_data, float_data = data
control_position = np.where(
dataset.meta_data[:, 0] == 'control')[0][0]
speed_position = np.where(
dataset.meta_data[:, 0] == 'speed_module')[0][0]
# print (torch.squeeze(input_data['rgb']).shape)
# print (control_position)
# print (speed_position)
# Obs : Maybe we could also check for other branches ??
output = model.forward_branch(
torch.squeeze(input_data['rgb']).cuda(),
float_data[:, speed_position, :].cuda(),
float_data[:, control_position, :].cuda())
for i in range(input_data['rgb'].shape[0]):
coil_logger.write_on_csv(
checkpoint_iteration,
[output[i][0], output[i][1], output[i][2]])
# TODO: Change this a functional standard using the loss functions.
loss = torch.mean(
(output -
dataset.extract_targets(float_data).cuda())**2).data.tolist()
mean_error = torch.mean(
torch.abs(
output -
dataset.extract_targets(float_data).cuda())).data.tolist()
accumulated_error += mean_error
accumulated_loss += loss
error = torch.abs(output -
dataset.extract_targets(float_data).cuda())
# Log a random position
position = random.randint(0, len(float_data) - 1)
#print (output[position].data.tolist())
coil_logger.add_message(
'Iterating in Validation', {
'Checkpoint':
latest,
'Iteration': (str(iteration_on_checkpoint * 120) + '/' +
str(len(dataset))),
'MeanError':
mean_error,
'Loss':
loss,
'Output':
output[position].data.tolist(),
'GroundTruth':
dataset.extract_targets(float_data)
[position].data.tolist(),
'Error':
error[position].data.tolist(),
'Inputs':
dataset.extract_inputs(float_data)[position].data.tolist()
}, latest)
iteration_on_checkpoint += 1
checkpoint_average_loss = accumulated_loss / len(dataset)
checkpoint_average_error = accumulated_error / len(dataset)
coil_logger.add_scalar('Loss', checkpoint_average_loss, latest)
coil_logger.add_scalar('Error', checkpoint_average_error, latest)
print('Loss: ', checkpoint_average_loss, "----Error: ",
checkpoint_average_error)
if checkpoint_average_loss < best_loss:
best_loss = checkpoint_average_loss
best_loss_iter = latest
state = {
'state_dict': model.state_dict(),
'best_loss': best_loss,
'best_loss_iter': best_loss_iter
}
# TODO : maybe already summarize the best model ???
torch.save(
state,
os.path.join('_logs', exp_batch, exp_alias,
'best_model_l2' + '.pth'))
if checkpoint_average_error < best_error:
best_error = checkpoint_average_error
best_error_iter = latest
state = {
'state_dict': model.state_dict(),
'best_error': best_error,
'best_error_iter': best_error_iter
}
# TODO : maybe already summarize the best model ???
torch.save(
state,
os.path.join('_logs', exp_batch, exp_alias,
'best_model_l1' + '.pth'))
print('Best Loss: ', best_loss, "Checkpoint", best_loss_iter)
print('Best Error: ', best_error, "Checkpoint", best_error_iter)
coil_logger.add_message(
'Iterating in Validation', {
'Summary': {
'Error': checkpoint_average_error,
'Loss': checkpoint_average_loss,
'BestError': best_error,
'BestLoss': best_loss,
'BestLossCheckpoint': best_loss_iter,
'BestErrorCheckpoint': best_error_iter
},
'Checkpoint': latest
})
def execute(gpu, exp_batch, exp_alias, dataset_name, architecture,
suppress_output):
try:
# We set the visible cuda devices
torch.manual_seed(2)
os.environ["CUDA_VISIBLE_DEVICES"] = gpu
# Validation available for:
# coil_unit (UNIT + task combined)
# coil_icra (Also used for finetuned models)
# wgangp_lsd (Our architecture)
architecture_name = architecture
# At this point the log file with the correct naming is created.
if architecture_name == 'coil_unit':
pass
elif architecture_name == 'wgangp_lsd':
merge_with_yaml(
os.path.join('/home/rohitrishabh/CoilWGAN/configs', exp_batch,
exp_alias + '.yaml'))
set_type_of_process('validation', dataset_name)
elif architecture_name == 'coil_icra':
merge_with_yaml(
os.path.join(
'/home/adas/CleanedCode/CoIL_Codes/coil_20-06/configs',
exp_batch, exp_alias + '.yaml'))
set_type_of_process('validation', dataset_name)
if monitorer.get_status(exp_batch, exp_alias + '.yaml',
g_conf.PROCESS_NAME)[0] == "Finished":
# TODO: print some cool summary or not ?
return
if not os.path.exists('_output_logs'):
os.mkdir('_output_logs')
if suppress_output:
sys.stdout = open(os.path.join(
'_output_logs',
g_conf.PROCESS_NAME + '_' + str(os.getpid()) + ".out"),
"a",
buffering=1)
#Define the dataset. This structure is has the __get_item__ redefined in a way
#that you can access the HDFILES positions from the root directory as a in a vector.
if dataset_name != []:
full_dataset = os.path.join(os.environ["COIL_DATASET_PATH"],
dataset_name)
else:
full_dataset = os.environ["COIL_DATASET_PATH"]
augmenter = Augmenter(None)
dataset = CoILDataset(full_dataset, transform=augmenter)
# Creates the sampler, this part is responsible for managing the keys. It divides
# all keys depending on the measurements and produces a set of keys for each bach.
# The data loader is the multi threaded module from pytorch that release a number of
# workers to get all the data.
# TODO: batch size an number of workers go to some configuration file
batchsize = 30
data_loader = torch.utils.data.DataLoader(dataset,
batch_size=batchsize,
shuffle=False,
num_workers=1,
pin_memory=True)
# TODO: here there is clearly a posibility to make a cool "conditioning" system.
if architecture_name == 'coil_unit':
model_task, model_gen = CoILModel('coil_unit')
model_task, model_gen = model_task.cuda(), model_gen.cuda()
else:
model = CoILModel(architecture_name)
model.cuda()
latest = 0
# print (dataset.meta_data)
best_loss = 1000
best_error = 1000
best_loss_mini = 1000
best_loss_iter = 0
best_error_iter = 0
batch_size = 30
best_loss_ckpt = ''
if architecture_name == 'coil_unit':
ckpts = glob.glob('/home/rohitrishabh/UNIT_DA/outputs/' +
exp_alias + '/checkpoints/gen*.pt')
else:
ckpts = glob.glob(
os.path.join(
'/home/adas/CleanedCode/CoIL_Codes/coil_20-06/_logs',
exp_batch, exp_alias) + '/*.pth')
if architecture_name == 'coil_unit':
model_task.eval()
model_gen.eval()
else:
model.eval()
ckpts = sorted(ckpts)
# TODO: refactor on the getting on the checkpoint organization needed
for ckpt in ckpts:
# if is_next_checkpoint_ready(g_conf.TEST_SCHEDULE):
# latest = get_next_checkpoint(g_conf.TEST_SCHEDULE)
# ckpt = os.path.join('/datatmp/Experiments/rohitgan/_logs', exp_batch, exp_alias
# , 'checkpoints', str(latest) + '.pth')
checkpoint = torch.load(ckpt)
print("Validation loaded ", ckpt)
if architecture_name == 'wgangp_lsd':
print(ckpt, checkpoint['best_loss_iter_F'])
model.load_state_dict(checkpoint['stateF_dict'])
model.eval()
elif architecture_name == 'coil_unit':
model_task.load_state_dict(checkpoint['task'])
model_gen.load_state_dict(checkpoint['b'])
model_task.eval()
model_gen.eval()
elif architecture_name == 'coil_icra':
model.load_state_dict(checkpoint['state_dict'])
model.eval()
accumulated_loss = 0
accumulated_error = 0
iteration_on_checkpoint = 0
datacount = 0
for data in data_loader:
input_data, float_data = data
controls = float_data[:, dataset.controls_position(), :]
camera_angle = float_data[:, 26, :]
camera_angle = camera_angle.cuda()
steer = float_data[:, 0, :]
steer = steer.cuda()
speed = float_data[:, 10, :]
speed = speed.cuda()
time_use = 1.0
car_length = 3.0
extra_factor = 2.5
threshold = 1.0
pos = camera_angle > 0.0
pos = pos.type(torch.FloatTensor)
neg = camera_angle <= 0.0
neg = neg.type(torch.FloatTensor)
pos = pos.cuda()
neg = neg.cuda()
rad_camera_angle = math.pi * (torch.abs(camera_angle)) / 180.0
val = extra_factor * (torch.atan(
(rad_camera_angle * car_length) /
(time_use * speed + 0.05))) / 3.1415
steer -= pos * torch.min(val, torch.Tensor([0.6]).cuda())
steer += neg * torch.min(val, torch.Tensor([0.6]).cuda())
steer = steer.cpu()
float_data[:, 0, :] = steer
float_data[:, 0, :][float_data[:, 0, :] > 1.0] = 1.0
float_data[:, 0, :][float_data[:, 0, :] < -1.0] = -1.0
datacount += 1
control_position = 24
speed_position = 10
if architecture_name == 'wgangp_lsd':
embed, output = model(
torch.squeeze(input_data['rgb']).cuda(),
float_data[:, speed_position, :].cuda())
loss = torch.sum(
(output[0] -
dataset.extract_targets(float_data).cuda()
)**2).data.tolist()
mean_error = torch.sum(
torch.abs(output[0] -
dataset.extract_targets(float_data).cuda())
).data.tolist()
elif architecture_name == 'coil_unit':
embed, n_b = model_gen.encode(
torch.squeeze(input_data['rgb']).cuda())
output = model_task(
embed,
Variable(float_data[:, speed_position, :]).cuda())
loss = torch.sum(
(output[0].data -
dataset.extract_targets(float_data).cuda())**2)
mean_error = torch.sum(
torch.abs(output[0].data -
dataset.extract_targets(float_data).cuda()))
elif architecture_name == 'coil_icra':
output = model.forward_branch(
torch.squeeze(input_data['rgb']).cuda(),
float_data[:, speed_position, :].cuda(),
float_data[:, control_position, :].cuda())
loss = torch.sum(
(output - dataset.extract_targets(float_data).cuda()
)**2).data.tolist()
mean_error = torch.sum(
torch.abs(output -
dataset.extract_targets(float_data).cuda())
).data.tolist()
if loss < best_loss_mini:
best_loss_mini = loss
accumulated_error += mean_error
accumulated_loss += loss
# error = torch.abs(output[0] - dataset.extract_targets(float_data).cuda())
# Log a random position
position = random.randint(0, len(float_data) - 1)
iteration_on_checkpoint += 1
print(datacount, len(data_loader), accumulated_loss)
checkpoint_average_loss = accumulated_loss / float(
datacount * batchsize)
checkpoint_average_error = accumulated_error / float(
datacount * batchsize)
if checkpoint_average_loss < best_loss:
best_loss = checkpoint_average_loss
best_loss_iter = latest
best_loss_ckpt = ckpt
if checkpoint_average_error < best_error:
best_error = checkpoint_average_error
best_error_iter = latest
print("current loss", checkpoint_average_loss)
print("best_loss", best_loss)
coil_logger.add_message(
'Iterating', {
'Summary': {
'Error': checkpoint_average_error,
'Loss': checkpoint_average_loss,
'BestError': best_error,
'BestLoss': best_loss,
'BestLossCheckpoint': best_loss_iter,
'BestErrorCheckpoint': best_error_iter
},
'Checkpoint': latest
}, latest)
latest += 2000
coil_logger.add_message('Finished', {})
print("Best Validation Loss ckpt:", best_loss_ckpt)
# TODO: DO ALL THE AMAZING LOGGING HERE, as a way to very the status in paralell.
# THIS SHOULD BE AN INTERELY PARALLEL PROCESS
except KeyboardInterrupt:
coil_logger.add_message('Error', {'Message': 'Killed By User'})
except:
traceback.print_exc()
coil_logger.add_message('Error', {'Message': 'Something Happened'})
def execute(gpu, exp_batch, exp_alias):
# We set the visible cuda devices
os.environ["CUDA_VISIBLE_DEVICES"] = gpu
# At this point the log file with the correct naming is created.
merge_with_yaml(os.path.join(exp_batch, exp_alias+'.yaml'))
set_type_of_process('train')
sys.stdout = open(str(os.getpid()) + ".out", "a", buffering=1)
if monitorer.get_status(exp_batch, exp_alias, g_conf.PROCESS_NAME)[0] == "Finished":
# TODO: print some cool summary or not ?
return
#Define the dataset. This structure is has the __get_item__ redefined in a way
#that you can access the HDFILES positions from the root directory as a in a vector.
full_dataset = os.path.join(os.environ["COIL_DATASET_PATH"], g_conf.DATASET_NAME)
dataset = CoILDataset(full_dataset, transform=transforms.Compose([transforms.ToTensor()]))
# Creates the sampler, this part is responsible for managing the keys. It divides
# all keys depending on the measurements and produces a set of keys for each bach.
sampler = CoILSampler(splitter.control_steer_split(dataset.measurements, dataset.meta_data))
# The data loader is the multi threaded module from pytorch that release a number of
# workers to get all the data.
# TODO: batch size an number of workers go to some configuration file
data_loader = torch.utils.data.DataLoader(dataset, sampler=sampler, batch_size=120,
shuffle=False, num_workers=12, pin_memory=True)
# By instanciating the augmenter we get a callable that augment images and transform them
# into tensors.
augmenter = iag.Augmenter(g_conf.AUGMENTATION_SUITE)
# TODO: here there is clearly a posibility to make a cool "conditioning" system.
model = CoILModel(g_conf.MODEL_NAME)
model.cuda()
print(model)
criterion = Loss()
# TODO: DATASET SIZE SEEMS WEIRD
optimizer = optim.SGD(model.parameters(), lr=0.0001, momentum=0.9)
checkpoint_file = get_latest_saved_checkpoint()
if checkpoint_file != None:
checkpoint = torch.load(os.path.join('_logs', exp_batch, exp_alias,
'checkpoints', str(get_latest_saved_checkpoint())))
iteration = checkpoint['iteration']
else:
iteration = 0
# TODO: The checkpoint will continue, so the logs should restart ??? OR continue were it was
print (dataset.meta_data)
print (model)
for data in data_loader:
input_data, labels = data
#TODO we have to divide the input with other data.
#TODO, ADD ITERATION SCHEDULE
input_rgb_data = augmenter(0, input_data['rgb'])
# get the control commands from labels, size = [120,1]
controls = labels[:, 24, :]
# The output(branches) is a list of 5 branches results, each branch is with size [120,3]
model.zero_grad()
branches = model(input_rgb_data, labels[:, 10, :].cuda())
#print ("len ",len(branches))
# get the steer, gas and brake ground truth from labels
steer_gt = labels[:, 0, :]
gas_gt = labels[:, 1, :]
brake_gt = labels[:, 2, :]
speed_gt = labels[:, 10, :]
targets = torch.cat([steer_gt, gas_gt, brake_gt], 1)
loss = criterion.MSELoss(branches, targets.cuda(), controls.cuda(), speed_gt.cuda())
loss.backward()
optimizer.step()
# TODO: save also the optimizer state dictionary
if is_ready_to_save(iteration):
state = {
'iteration': iteration,
'state_dict': model.state_dict()
}
# TODO : maybe already summarize the best model ???
torch.save(state, os.path.join('_logs', exp_batch, exp_alias
, 'checkpoints', str(iteration) + '.pth'))
iteration += 1
def execute(gpu, exp_batch, exp_alias):
# We set the visible cuda devices
try:
os.environ["CUDA_VISIBLE_DEVICES"] = gpu
# At this point the log file with the correct naming is created.
merge_with_yaml(os.path.join('configs', exp_batch,
exp_alias + '.yaml'))
set_type_of_process('train')
coil_logger.add_message('Loading', {'GPU': gpu})
if not os.path.exists('_output_logs'):
os.mkdir('_output_logs')
sys.stdout = open(os.path.join(
'_output_logs',
g_conf.PROCESS_NAME + '_' + str(os.getpid()) + ".out"),
"a",
buffering=1)
if monitorer.get_status(exp_batch, exp_alias + '.yaml',
g_conf.PROCESS_NAME)[0] == "Finished":
# TODO: print some cool summary or not ?
return
#Define the dataset. This structure is has the __get_item__ redefined in a way
#that you can access the HDFILES positions from the root directory as a in a vector.
full_dataset = os.path.join(os.environ["COIL_DATASET_PATH"],
g_conf.TRAIN_DATASET_NAME)
#augmenter_cpu = iag.AugmenterCPU(g_conf.AUGMENTATION_SUITE_CPU)
dataset = CoILDataset(full_dataset,
transform=transforms.Compose(
[transforms.ToTensor()]))
# Creates the sampler, this part is responsible for managing the keys. It divides
# all keys depending on the measurements and produces a set of keys for each bach.
sampler = BatchSequenceSampler(
splitter.control_steer_split(dataset.measurements,
dataset.meta_data), g_conf.BATCH_SIZE,
g_conf.NUMBER_IMAGES_SEQUENCE, g_conf.SEQUENCE_STRIDE)
# The data loader is the multi threaded module from pytorch that release a number of
# workers to get all the data.
# TODO: batch size an number of workers go to some configuration file
data_loader = torch.utils.data.DataLoader(dataset,
batch_sampler=sampler,
shuffle=False,
num_workers=12,
pin_memory=False)
# By instanciating the augmenter we get a callable that augment images and transform them
# into tensors.
st = lambda aug: iag.Sometimes(aug, 0.4)
oc = lambda aug: iag.Sometimes(aug, 0.3)
rl = lambda aug: iag.Sometimes(aug, 0.09)
augmenter = iag.Augmenter([iag.ToGPU()] + [
rl(iag.GaussianBlur(
(0, 1.5))), # blur images with a sigma between 0 and 1.5
rl(
iag.AdditiveGaussianNoise(
loc=0, scale=(0.0, 0.05),
per_channel=0.5)), # add gaussian noise to images
oc(iag.Dropout((0.0, 0.10), per_channel=0.5)
), # randomly remove up to X% of the pixels
oc(
iag.CoarseDropout(
(0.0, 0.10), size_percent=(0.08, 0.2), per_channel=0.5)
), # randomly remove up to X% of the pixels
oc(iag.Add((-40, 40), per_channel=0.5)
), # change brightness of images (by -X to Y of original value)
st(iag.Multiply((0.10, 2), per_channel=0.2)
), # change brightness of images (X-Y% of original value)
rl(iag.ContrastNormalization((
0.5, 1.5), per_channel=0.5)), # improve or worsen the contrast
rl(iag.Grayscale((0.0, 1))), # put grayscale
] # do all of the above in random order
)
# augmenter = iag.Augmenter(g_conf.AUGMENTATION_SUITE)
# TODO: here there is clearly a posibility to make a cool "conditioning" system.
model = CoILModel(g_conf.MODEL_NAME)
model.cuda()
print(model)
criterion = Loss()
# TODO: DATASET SIZE SEEMS WEIRD
optimizer = optim.Adam(model.parameters(), lr=0.0002)
checkpoint_file = get_latest_saved_checkpoint()
if checkpoint_file != None:
checkpoint = torch.load(
os.path.join('_logs', exp_batch, exp_alias, 'checkpoints',
str(get_latest_saved_checkpoint())))
iteration = checkpoint['iteration']
accumulated_time = checkpoint['total_time']
best_loss = checkpoint['best_loss']
best_loss_iter = checkpoint['best_loss_iter']
else:
iteration = 0
best_loss = 10000.0
accumulated_time = 0 # We accumulate iteration time and keep the average speed
best_loss_iter = 0
# TODO: The checkpoint will continue, so it should erase everything up to the iteration
best_loss_save = 10000.0
best_loss_save_iter = 0
curr_loss_save = 0.0
print(dataset.meta_data)
print(model)
capture_time = time.time()
model.train()
for data in data_loader:
input_data, float_data = data
#TODO, ADD ITERATION SCHEDULE
input_rgb_data = augmenter(0, input_data['rgb'])
augment_for_controls = 1
adjustlr = 1
if augment_for_controls: #and self._config.targets_names[j] == "Steer":
camera_angle = float_data[:, 26, :]
camera_angle = camera_angle.cuda(
) #self._config.variable_names.index('Angle'),i]
print("Camera angle", camera_angle[0])
steer = float_data[:, 0, :]
# print("Original", steer[0])
steer = steer.cuda()
speed = float_data[:, 10, :]
speed = speed.cuda()
# print (steer)
time_use = 1.0
car_length = 3.0
extra_factor = 2.5
threshold = 1.0
pos = camera_angle > 0.0
pos = pos.type(torch.FloatTensor)
neg = camera_angle <= 0.0
neg = neg.type(torch.FloatTensor)
pos = pos.cuda()
neg = neg.cuda()
rad_camera_angle = math.pi * (torch.abs(camera_angle)) / 180.0
val = extra_factor * (torch.atan(
(rad_camera_angle * car_length) /
(time_use * speed + 0.05))) / 3.1415
# print(val)
steer -= pos * torch.min(val, torch.tensor([0.6]).cuda())
steer += neg * torch.min(val, torch.tensor([0.6]).cuda())
print("val", val[0])
print("speed", speed[0])
steer = steer.cpu()
float_data[:, 0, :] = steer
float_data[:, 0, :][float_data[:, 0, :] > 1.0] = 1.0
float_data[:, 0, :][float_data[:, 0, :] < -1.0] = -1.0
#coil_logger.add_images(input_rgb_data)
# get the control commands from float_data, size = [120,1]
controls = float_data[:, dataset.controls_position(), :]
# print(" CONTROLS ", controls.shape)
# The output(branches) is a list of 5 branches results, each branch is with size [120,3]
model.zero_grad()
# print ( 'INPUTS', dataset.extract_inputs(float_data).shape )
branches = model(input_rgb_data,
dataset.extract_inputs(float_data).cuda())
#print ("len ",len(branches))
#targets = torch.cat([steer_gt, gas_gt, brake_gt], 1)
# print ("Extracted targets ", dataset.extract_targets(float_data).shape[0])
loss = criterion.MSELoss(
branches,
dataset.extract_targets(float_data).cuda(), controls.cuda(),
dataset.extract_inputs(float_data).cuda())
# TODO: All these logging things could go out to clean up the main
if loss.data < best_loss:
best_loss = loss.data.tolist()
best_loss_iter = iteration
curr_loss_save += loss.data
# Log a random position
position = random.randint(0, len(float_data) - 1)
output = model.extract_branch(torch.stack(branches[0:4]), controls)
error = torch.abs(output -
dataset.extract_targets(float_data).cuda())
# TODO: For now we are computing the error for just the correct branch, it could be multi- branch,
coil_logger.add_scalar('Loss', loss.data, iteration)
loss.backward()
optimizer.step()
accumulated_time += time.time() - capture_time
capture_time = time.time()
# TODO: Get only the float_data that are actually generating output
# TODO: itearation is repeating , and that is dumb
coil_logger.add_message(
'Iterating', {
'Iteration':
iteration,
'Loss':
loss.data.tolist(),
'Images/s':
(iteration * g_conf.BATCH_SIZE) / accumulated_time,
'BestLoss':
best_loss,
'BestLossIteration':
best_loss_iter,
'BestLossSave':
best_loss_save,
'Output':
output[position].data.tolist(),
'GroundTruth':
dataset.extract_targets(
float_data)[position].data.tolist(),
'Error':
error[position].data.tolist(),
'Inputs':
dataset.extract_inputs(float_data)[position].data.tolist()
}, iteration)
# TODO: For now we are computing the error for just the correct branch, it could be multi-branch,
# TODO: save also the optimizer state dictionary
if is_ready_to_save(iteration):
state = {
'iteration': iteration,
'state_dict': model.state_dict(),
'best_loss': best_loss,
'total_time': accumulated_time,
'best_loss_iter': best_loss_iter
}
# TODO : maybe already summarize the best model ???
torch.save(
state,
os.path.join('_logs', exp_batch, exp_alias, 'checkpoints',
str(iteration) + '.pth'))
print("before best save")
if iteration % 5 == 0 and iteration > 4:
curr_loss_save /= 5000.0
if curr_loss_save < best_loss_save:
best_loss_save = curr_loss_save
curr_loss_save = 0
state = {
'iteration': iteration,
'state_dict': model.state_dict(),
'best_loss': best_loss_save,
'total_time': accumulated_time,
'best_loss_iter': best_loss_save_iter
}
# TODO : maybe already summarize the best model ???
torch.save(
state,
os.path.join('_logs', exp_batch, exp_alias,
'best_loss_save' + '.pth'))
print("after best save")
if iteration == best_loss_iter:
state = {
'iteration': iteration,
'state_dict': model.state_dict(),
'best_loss': best_loss,
'total_time': accumulated_time,
'best_loss_iter': best_loss_iter
}
# TODO : maybe already summarize the best model ???
torch.save(
state,
os.path.join('_logs', exp_batch, exp_alias,
'best_loss' + '.pth'))
iteration += 1
if adjustlr and iteration % 1000:
adjust_learning_rate(optimizer, iteration)
except KeyboardInterrupt:
coil_logger.add_message('Error', {'Message': 'Killed By User'})
except:
traceback.print_exc()
coil_logger.add_message('Error', {'Message': 'Something Happened'})
