def main(model,
train_params,
data_set_params,
cont_int_scale,
base_results_store_dir='./results'):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)
# -----------------------------------------------------------------------------------
# Sanity Checks
# -----------------------------------------------------------------------------------
# Validate Data set parameters
# ----------------------------
required_data_set_params = ['data_set_dir']
for key in required_data_set_params:
assert key in data_set_params, 'data_set_params does not have required key {}'.format(
key)
data_set_dir = data_set_params['data_set_dir']
# Validate training parameters
# ----------------------------
required_training_params = \
['train_batch_size', 'test_batch_size', 'learning_rate', 'num_epochs']
for key in required_training_params:
assert key in train_params, 'training_params does not have required key {}'.format(
key)
train_batch_size = train_params['train_batch_size']
test_batch_size = train_params['test_batch_size']
learning_rate = train_params['learning_rate']
num_epochs = train_params['num_epochs']
# Optional
lambda1 = train_params.get('gaussian_reg_weight', 0)
gaussian_kernel_sigma = train_params.get('gaussian_reg_sigma', 0)
use_gaussian_reg_on_lateral_kernels = False
if lambda1 is not 0 and gaussian_kernel_sigma is not 0:
use_gaussian_reg_on_lateral_kernels = True
# -----------------------------------------------------------------------------------
# Model
# -----------------------------------------------------------------------------------
print("====> Loading Model ")
print("Name: {}".format(model.__class__.__name__))
print(model)
# Get name of contour integration layer
temp = vars(model) # Returns a dictionary.
layers = temp['_modules'] # Returns all top level modules (layers)
cont_int_layer_type = ''
if 'contour_integration_layer' in layers:
cont_int_layer_type = model.contour_integration_layer.__class__.__name__
results_store_dir = os.path.join(
base_results_store_dir, model.__class__.__name__ + '_' +
cont_int_layer_type + datetime.now().strftime("_%Y%m%d_%H%M%S"))
if not os.path.exists(results_store_dir):
os.makedirs(results_store_dir)
# -------------------------------------------------------------------------------
# Data Loader
# -------------------------------------------------------------------------------
print("====> Setting up data loaders ")
data_load_start_time = datetime.now()
print("Data Source: {}".format(data_set_dir))
# Imagenet Mean and STD
ch_mean = [0.485, 0.456, 0.406]
ch_std = [0.229, 0.224, 0.225]
# Pre-processing
transforms_list = [
transforms.Normalize(mean=ch_mean, std=ch_std),
# utils.PunctureImage(n_bubbles=200, fwhm=np.array([7, 9, 11, 13, 15, 17]))
]
pre_process_transforms = transforms.Compose(transforms_list)
train_set = dataset_pathfinder.PathfinderNaturalImages(
data_dir=os.path.join(data_set_dir, 'train'),
transform=pre_process_transforms,
)
train_batch_size = min(train_batch_size, len(train_set))
train_data_loader = DataLoader(dataset=train_set,
num_workers=4,
batch_size=train_batch_size,
shuffle=True,
pin_memory=True)
val_set = dataset_pathfinder.PathfinderNaturalImages(
data_dir=os.path.join(data_set_dir, 'test'),
transform=pre_process_transforms,
)
test_batch_size = min(test_batch_size, len(val_set))
val_data_loader = DataLoader(dataset=val_set,
num_workers=4,
batch_size=test_batch_size,
shuffle=True,
pin_memory=True)
print("Data loading Took {}. # Train {}, # Test {}".format(
datetime.now() - data_load_start_time,
len(train_data_loader) * train_batch_size,
len(val_data_loader) * test_batch_size))
# -------------------------------------------------------------------------------
# Loss / optimizer
# -------------------------------------------------------------------------------
optimizer = optim.Adam(filter(lambda params: params.requires_grad,
model.parameters()),
lr=learning_rate)
lr_scheduler = optim.lr_scheduler.StepLR(optimizer,
step_size=30,
gamma=0.1)
criterion = nn.BCEWithLogitsLoss().to(device)
if use_gaussian_reg_on_lateral_kernels:
gaussian_mask_e = 1 - utils.get_2d_gaussian_kernel(
model.contour_integration_layer.lateral_e.weight.shape[2:],
sigma=gaussian_kernel_sigma)
gaussian_mask_i = 1 - utils.get_2d_gaussian_kernel(
model.contour_integration_layer.lateral_i.weight.shape[2:],
sigma=gaussian_kernel_sigma)
gaussian_mask_e = torch.from_numpy(gaussian_mask_e).float().to(device)
gaussian_mask_i = torch.from_numpy(gaussian_mask_i).float().to(device)
def inverse_gaussian_regularization(weight_e, weight_i):
loss1 = (gaussian_mask_e * weight_e).abs().sum() + \
(gaussian_mask_i * weight_i).abs().sum()
# print("Loss1: {:0.4f}".format(loss1))
return loss1
# ---------------------------------------------------------------------------
# Training Validation Routines
# ---------------------------------------------------------------------------
def binary_acc(y_pred, y_target):
y_pred_tag = torch.round(torch.sigmoid(y_pred))
correct_results_sum = (y_pred_tag == y_target.float()).sum().float()
acc = correct_results_sum / y_target.shape[0]
acc = torch.round(acc * 100)
return acc
def train():
""" Train for one Epoch over the train data set """
model.train()
e_loss = 0
e_acc = 0
for iteration, data_loader_out in enumerate(train_data_loader, 1):
optimizer.zero_grad() # zero the parameter gradients
img, label, _, _, _ = data_loader_out
img = img.to(device)
label = label.to(device)
label_out = model(img)
bce_loss = criterion(label_out, label.float())
reg_loss = 0
if use_gaussian_reg_on_lateral_kernels:
reg_loss = \
inverse_gaussian_regularization(
model.contour_integration_layer.lateral_e.weight,
model.contour_integration_layer.lateral_i.weight
)
total_loss = bce_loss + lambda1 * reg_loss
acc = binary_acc(label_out, label)
# print("Loss: {:0.4f}, bce_loss {:0.4f}, lateral kernels reg_loss {:0.4f}, "
# "acc {:0.4f}".format(total_loss, bce_loss, lambda1 * reg_loss, acc))
total_loss.backward()
optimizer.step()
e_loss += total_loss.item()
e_acc += acc.item()
e_loss = e_loss / len(train_data_loader)
e_acc = e_acc / len(train_data_loader)
# iou_arr = ["{:0.2f}".format(item) for item in e_iou]
# print("Train Epoch {} Loss = {:0.4f}, Acc = {}".format(epoch, e_loss, e_acc))
return e_loss, e_acc
def validate():
""" Get loss over validation set """
model.eval()
e_loss = 0
e_acc = 0
with torch.no_grad():
for iteration, data_loader_out in enumerate(val_data_loader, 1):
img, label, _, _, _ = data_loader_out
img = img.to(device)
label = label.to(device)
label_out = model(img)
bce_loss = criterion(label_out, label.float())
reg_loss = 0
if use_gaussian_reg_on_lateral_kernels:
reg_loss = \
inverse_gaussian_regularization(
model.contour_integration_layer.lateral_e.weight,
model.contour_integration_layer.lateral_i.weight
)
total_loss = bce_loss + lambda1 * reg_loss
acc = binary_acc(label_out, label)
e_loss += total_loss.item()
e_acc += acc.item()
e_loss = e_loss / len(val_data_loader)
e_acc = e_acc / len(val_data_loader)
# print("Val Loss = {:0.4f}, Accuracy={}".format(e_loss, e_acc))
return e_loss, e_acc
# -----------------------------------------------------------------------------------
# Main Loop
# -----------------------------------------------------------------------------------
print("====> Starting Training ")
training_start_time = datetime.now()
train_history = []
val_history = []
lr_history = []
best_acc = 0
# Summary file
summary_file = os.path.join(results_store_dir, 'summary.txt')
file_handle = open(summary_file, 'w+')
file_handle.write("Data Set Parameters {}\n".format('-' * 60))
file_handle.write("Source : {}\n".format(data_set_dir))
# file_handle.write("Train Set Mean {}, std {}\n".format(
# train_set.data_set_mean, train_set.data_set_std))
# file_handle.write("Validation Set Mean {}, std {}\n".format(
# val_set.data_set_mean, train_set.data_set_std))
file_handle.write("Training Parameters {}\n".format('-' * 60))
file_handle.write("Random Seed : {}\n".format(random_seed))
file_handle.write("Train images : {}\n".format(len(train_set.images)))
file_handle.write("Val images : {}\n".format(len(val_set.images)))
file_handle.write("Train batch size : {}\n".format(train_batch_size))
file_handle.write("Val batch size : {}\n".format(test_batch_size))
file_handle.write("Epochs : {}\n".format(num_epochs))
file_handle.write("Optimizer : {}\n".format(
optimizer.__class__.__name__))
file_handle.write("learning rate : {}\n".format(learning_rate))
file_handle.write("Loss Fcn : {}\n".format(
criterion.__class__.__name__))
file_handle.write(
"Use Gaussian Regularization on lateral kernels: {}\n".format(
use_gaussian_reg_on_lateral_kernels))
if use_gaussian_reg_on_lateral_kernels:
file_handle.write("Gaussian Regularization sigma : {}\n".format(
gaussian_kernel_sigma))
file_handle.write(
"Gaussian Regularization weight : {}\n".format(lambda1))
# file_handle.write("IoU Threshold : {}\n".format(detect_thres))
file_handle.write("Image pre-processing :\n")
print(pre_process_transforms, file=file_handle)
file_handle.write("Model Parameters {}\n".format('-' * 63))
file_handle.write("Model Name : {}\n".format(
model.__class__.__name__))
file_handle.write("\n")
print(model, file=file_handle)
temp = vars(model) # Returns a dictionary.
file_handle.write("Model Parameters:\n")
p = [item for item in temp if not item.startswith('_')]
for var in sorted(p):
file_handle.write("{}: {}\n".format(var, getattr(model, var)))
layers = temp['_modules'] # Returns all top level modules (layers)
if 'contour_integration_layer' in layers:
file_handle.write("Contour Integration Layer: {}\n".format(
model.contour_integration_layer.__class__.__name__))
# print fixed hyper parameters
file_handle.write("Hyper parameters\n")
cont_int_layer_vars = \
[item for item in vars(model.contour_integration_layer) if not item.startswith('_')]
for var in sorted(cont_int_layer_vars):
file_handle.write("\t{}: {}\n".format(
var, getattr(model.contour_integration_layer, var)))
# print parameter names and whether they are trainable
file_handle.write("Contour Integration Layer Parameters\n")
layer_params = vars(model.contour_integration_layer)['_parameters']
for k, v in sorted(layer_params.items()):
file_handle.write("\t{}: requires_grad {}\n".format(
k, v.requires_grad))
file_handle.write("{}\n".format('-' * 80))
file_handle.write("Training details\n")
file_handle.write("Epoch, train_loss, train_acc, val_loss, val_acc, lr\n")
print("train_batch_size={}, test_batch_size= {}, lr={}, epochs={}".format(
train_batch_size, test_batch_size, learning_rate, num_epochs))
for epoch in range(0, num_epochs):
epoch_start_time = datetime.now()
train_history.append(train())
val_history.append(validate())
lr_history.append(get_lr(optimizer))
lr_scheduler.step(epoch)
print(
"Epoch [{}/{}], Train: loss={:0.4f}, Acc={:0.2f}. Val: loss={:0.4f}, Acc={:0.2f}."
" Time {}".format(epoch + 1, num_epochs, train_history[epoch][0],
train_history[epoch][1], val_history[epoch][0],
val_history[epoch][1],
datetime.now() - epoch_start_time))
# Save best val accuracy weights
max_val_acc = val_history[epoch][1]
if max_val_acc > best_acc:
best_acc = max_val_acc
torch.save(model.state_dict(),
os.path.join(results_store_dir, 'best_accuracy.pth'))
# Save Last epoch weights
torch.save(model.state_dict(),
os.path.join(results_store_dir, 'last_epoch.pth'))
file_handle.write(
"[{}, {:0.4f}, {:0.2f}, {:0.4f}, {:0.2f}, {}],\n".format(
epoch + 1, train_history[epoch][0], train_history[epoch][1],
val_history[epoch][0], val_history[epoch][1],
lr_history[epoch]))
training_time = datetime.now() - training_start_time
print('Finished Training. Training took {}'.format(training_time))
file_handle.write("{}\n".format('-' * 80))
file_handle.write("Train Duration : {}\n".format(training_time))
file_handle.close()
# -------------------------------------------------------------------
# PLots
# -------------------------------------------------------------------
train_history = np.array(train_history)
val_history = np.array(val_history)
f, ax_arr = plt.subplots(1, 2)
ax_arr[0].plot(np.arange(1, num_epochs + 1),
train_history[:, 0],
label='train')
ax_arr[0].plot(np.arange(1, num_epochs + 1),
val_history[:, 0],
label='val')
ax_arr[0].set_xlabel('Epoch')
ax_arr[0].set_title("Loss Vs Time")
ax_arr[0].grid(True)
ax_arr[0].legend()
ax_arr[1].plot(np.arange(1, num_epochs + 1),
train_history[:, 1],
label='train')
ax_arr[1].plot(np.arange(1, num_epochs + 1),
val_history[:, 1],
label='val')
ax_arr[1].set_xlabel('Epoch')
ax_arr[1].set_title("Accuracy Vs Time")
ax_arr[1].grid(True)
ax_arr[1].legend()
f.savefig(os.path.join(results_store_dir, 'loss_and accuracy.jpg'),
format='jpg')
# -----------------------------------------------------------------------------------
# Run Li 2006 experiments
# -----------------------------------------------------------------------------------
dataset_parameters = {
'biped_dataset_dir': './data/BIPED/edges',
'biped_dataset_type': 'train',
'n_biped_imgs': 20000,
'n_epochs': 1 # Total images = n_epochs * n_biped_images
}
experiment_gain_vs_spacing_natural_images.main(
model,
results_store_dir,
data_set_params=dataset_parameters,
cont_int_scale=cont_int_scale)
def main(model,
train_params,
data_set_params,
cont_int_scale,
base_results_store_dir='./results',
n_imgs_for_exp=20000):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)
# -----------------------------------------------------------------------------------
# Sanity Checks
# -----------------------------------------------------------------------------------
# Validate Data set parameters
# ----------------------------
required_data_set_params = ['data_set_dir']
for key in required_data_set_params:
assert key in data_set_params, 'data_set_params does not have required key {}'.format(
key)
data_set_dir = data_set_params['data_set_dir']
# Validate training parameters
# ----------------------------
required_training_params = \
['train_batch_size', 'test_batch_size', 'learning_rate', 'num_epochs']
for key in required_training_params:
assert key in train_params, 'training_params does not have required key {}'.format(
key)
train_batch_size = train_params['train_batch_size']
test_batch_size = train_params['test_batch_size']
learning_rate = train_params['learning_rate']
num_epochs = train_params['num_epochs']
# Optional
clip_negative_lateral_weights = train_params.get(
'clip_negative_lateral_weights', False)
if 'lr_sched_step_size' not in train_params:
train_params['lr_sched_step_size'] = 30
if 'lr_sched_gamma' not in train_params:
train_params['lr_sched_gamma'] = 0.1
if 'random_seed' not in train_params:
train_params['random_seed'] = 1
if 'punc_n_bubbles' not in train_params:
train_params['punc_n_bubbles'] = 200
if 'punc_fwhm' not in train_params:
train_params['punc_fwhm'] = np.array([7, 9, 11, 13, 15, 17])
torch.manual_seed(train_params['random_seed'])
np.random.seed(train_params['random_seed'])
# -----------------------------------------------------------------------------------
# Model
# -----------------------------------------------------------------------------------
print("====> Loading Model ")
print("Name: {}".format(model.__class__.__name__))
print(model)
# Get name of contour integration layer
temp = vars(model) # Returns a dictionary.
layers = temp['_modules'] # Returns all top level modules (layers)
cont_int_layer_type = ''
if 'contour_integration_layer' in layers:
cont_int_layer_type = model.contour_integration_layer.__class__.__name__
results_store_dir = os.path.join(
base_results_store_dir, model.__class__.__name__ + '_' +
cont_int_layer_type + datetime.now().strftime("_%Y%m%d_%H%M%S"))
if not os.path.exists(results_store_dir):
os.makedirs(results_store_dir)
# -------------------------------------------------------------------------------
# Data Loader
# -------------------------------------------------------------------------------
print("====> Setting up data loaders ")
data_load_start_time = datetime.now()
print("Data Source: {}".format(data_set_dir))
# Imagenet Mean and STD
ch_mean = [0.485, 0.456, 0.406]
ch_std = [0.229, 0.224, 0.225]
# Pre-processing
transforms_list = [
transforms.Normalize(mean=ch_mean, std=ch_std),
utils.PunctureImage(n_bubbles=train_params['punc_n_bubbles'],
fwhm=train_params['punc_fwhm'])
]
pre_process_transforms = transforms.Compose(transforms_list)
train_set = dataset_pathfinder.PathfinderNaturalImages(
data_dir=os.path.join(data_set_dir, 'train'),
transform=pre_process_transforms,
re_add_end_stops=True,
)
train_batch_size = min(train_batch_size, len(train_set))
train_data_loader = DataLoader(dataset=train_set,
num_workers=30,
batch_size=train_batch_size,
shuffle=True,
pin_memory=True)
val_set = dataset_pathfinder.PathfinderNaturalImages(
data_dir=os.path.join(data_set_dir, 'test'),
transform=pre_process_transforms,
re_add_end_stops=True,
)
test_batch_size = min(test_batch_size, len(val_set))
val_data_loader = DataLoader(dataset=val_set,
num_workers=30,
batch_size=test_batch_size,
shuffle=True,
pin_memory=True)
print("Data loading Took {}. # Train {}, # Test {}".format(
datetime.now() - data_load_start_time,
len(train_data_loader) * train_batch_size,
len(val_data_loader) * test_batch_size))
# -------------------------------------------------------------------------------
# Loss / optimizer
# -------------------------------------------------------------------------------
optimizer = optim.Adam(filter(lambda params: params.requires_grad,
model.parameters()),
lr=learning_rate)
lr_scheduler = optim.lr_scheduler.StepLR(
optimizer,
step_size=train_params['lr_sched_step_size'],
gamma=train_params['lr_sched_gamma'])
# -----------------------------------------------------------------------------------
# Loss Functions
# -----------------------------------------------------------------------------------
criterion = torch.nn.BCEWithLogitsLoss()
# criterion = train_utils.ClassBalancedCrossEntropy()
# criterion = train_utils.ClassBalancedCrossEntropyAttentionLoss()
criterion_loss_sigmoid_outputs = False
# Lateral Weights sparsity constraint
lateral_sparsity_loss = train_utils.InvertedGaussianL1Loss(
model.contour_integration_layer.lateral_e.weight.shape[2:],
model.contour_integration_layer.lateral_i.weight.shape[2:],
train_params['lateral_w_reg_gaussian_sigma'])
# lateral_sparsity_loss = train_utils.WeightNormLoss(norm=1) # vanilla L1 Loss
loss_function = train_utils.CombinedLoss(
criterion=criterion,
sigmoid_predictions=criterion_loss_sigmoid_outputs,
sparsity_loss_fcn=lateral_sparsity_loss,
sparsity_loss_weight=train_params['lateral_w_reg_weight'],
# negative_weights_loss_fcn=negative_lateral_weights_penalty,
# negative_weights_loss_weight=negative_lateral_weights_penalty_weight
).to(device)
# -----------------------------------------------------------------------------------
# Main Loop
# -----------------------------------------------------------------------------------
print("====> Starting Training ")
training_start_time = datetime.now()
train_history = []
val_history = []
lr_history = []
best_acc = 0
# Summary file
summary_file = os.path.join(results_store_dir, 'summary.txt')
file_handle = open(summary_file, 'w+')
file_handle.write("Data Set Parameters {}\n".format('-' * 60))
file_handle.write("Source : {}\n".format(data_set_dir))
# file_handle.write("Train Set Mean {}, std {}\n".format(
# train_set.data_set_mean, train_set.data_set_std))
# file_handle.write("Validation Set Mean {}, std {}\n".format(
# val_set.data_set_mean, train_set.data_set_std))
file_handle.write("Training Parameters {}\n".format('-' * 60))
file_handle.write("Random Seed : {}\n".format(
train_params['random_seed']))
file_handle.write("Train images : {}\n".format(len(train_set.images)))
file_handle.write("Val images : {}\n".format(len(val_set.images)))
file_handle.write("Train batch size : {}\n".format(train_batch_size))
file_handle.write("Val batch size : {}\n".format(test_batch_size))
file_handle.write("Epochs : {}\n".format(num_epochs))
file_handle.write("Optimizer : {}\n".format(
optimizer.__class__.__name__))
file_handle.write("learning rate : {}\n".format(learning_rate))
for key in train_params.keys():
if 'lr_sched' in key:
print(" {}: {}".format(key, train_params[key]), file=file_handle)
file_handle.write("Loss Fcn : {}\n".format(
loss_function.__class__.__name__))
print(loss_function, file=file_handle)
file_handle.write("clip negative lateral weights: {}\n".format(
clip_negative_lateral_weights))
file_handle.write("Image pre-processing :\n")
print(pre_process_transforms, file=file_handle)
file_handle.write("Model Parameters {}\n".format('-' * 63))
file_handle.write("Model Name : {}\n".format(
model.__class__.__name__))
temp = vars(model) # Returns a dictionary.
p1 = [item for item in temp if not item.startswith('_')]
for var in sorted(p1):
file_handle.write("{}: {}\n".format(var, getattr(model, var)))
file_handle.write("\n")
file_handle.write("\n")
print(model, file=file_handle)
temp = vars(model) # Returns a dictionary.
layers = temp['_modules'] # Returns all top level modules (layers)
if 'contour_integration_layer' in layers:
# print fixed hyper parameters
file_handle.write("Contour Integration Layer:\n")
file_handle.write("Type : {}\n".format(
model.contour_integration_layer.__class__.__name__))
cont_int_layer_vars = \
[item for item in vars(model.contour_integration_layer) if not item.startswith('_')]
for var in sorted(cont_int_layer_vars):
file_handle.write("\t{}: {}\n".format(
var, getattr(model.contour_integration_layer, var)))
# print parameter names and whether they are trainable
file_handle.write("Contour Integration Layer Parameters\n")
layer_params = vars(model.contour_integration_layer)['_parameters']
for k, v in sorted(layer_params.items()):
file_handle.write("\t{}: requires_grad {}\n".format(
k, v.requires_grad))
file_handle.write("{}\n".format('-' * 80))
file_handle.write("Training details\n")
file_handle.write("Epoch, train_loss, train_acc, val_loss, val_acc, lr\n")
print("train_batch_size={}, test_batch_size= {}, lr={}, epochs={}".format(
train_batch_size, test_batch_size, learning_rate, num_epochs))
# Track evolution of these variables during training
# (Must be a parameter of the contour integration layer)
track_var_dict = {
'a': [],
'b': [],
'j_xy': [],
'j_yx': [],
'i_bias': [],
'e_bias': []
}
for epoch in range(0, num_epochs):
epoch_start_time = datetime.now()
train_history.append(
iterate_epoch(
model=model,
data_loader=train_data_loader,
loss_fcn=loss_function,
optimizer1=optimizer,
device=device,
is_train=True,
clip_negative_lateral_weights=clip_negative_lateral_weights))
val_history.append(
iterate_epoch(model=model,
data_loader=val_data_loader,
loss_fcn=loss_function,
optimizer1=optimizer,
device=device,
is_train=False))
lr_history.append(train_utils.get_lr(optimizer))
lr_scheduler.step()
# Track parameters
cont_int_layer_params = model.contour_integration_layer.state_dict()
for param in track_var_dict:
if param in cont_int_layer_params:
track_var_dict[param].append(
cont_int_layer_params[param].cpu().detach().numpy())
print(
"Epoch [{}/{}], Train: loss={:0.4f}, Acc={:0.2f}. Val: loss={:0.4f}, Acc={:0.2f}."
" Time {}".format(epoch + 1, num_epochs, train_history[epoch][0],
train_history[epoch][1], val_history[epoch][0],
val_history[epoch][1],
datetime.now() - epoch_start_time))
# Save best val accuracy weights
max_val_acc = val_history[epoch][1]
if max_val_acc > best_acc:
best_acc = max_val_acc
torch.save(model.state_dict(),
os.path.join(results_store_dir, 'best_accuracy.pth'))
# Save Last epoch weights
torch.save(model.state_dict(),
os.path.join(results_store_dir, 'last_epoch.pth'))
file_handle.write(
"[{}, {:0.4f}, {:0.2f}, {:0.4f}, {:0.2f}, {}],\n".format(
epoch + 1, train_history[epoch][0], train_history[epoch][1],
val_history[epoch][0], val_history[epoch][1],
lr_history[epoch]))
training_time = datetime.now() - training_start_time
print('Finished Training. Training took {}'.format(training_time))
# -----------------------------------------------------------------------------------
np.set_printoptions(precision=3,
linewidth=120,
suppress=True,
threshold=np.inf)
file_handle.write("{}\n".format('-' * 80))
file_handle.write("Train Duration : {}\n".format(training_time))
file_handle.close()
train_history = np.array(train_history)
val_history = np.array(val_history)
train_utils.store_tracked_variables(
track_var_dict,
results_store_dir,
n_ch=model.contour_integration_layer.edge_out_ch)
# Reload the model parameters that resulted in the best accuracy
# --------------------------------------------------------------
best_val_model_params = os.path.join(results_store_dir,
'best_accuracy.pth')
model.load_state_dict(
torch.load(best_val_model_params, map_location=device))
# -------------------------------------------------------------------
# PLots
# -------------------------------------------------------------------
f, ax_arr = plt.subplots(1, 2)
ax_arr[0].plot(np.arange(1, num_epochs + 1),
train_history[:, 0],
label='train')
ax_arr[0].plot(np.arange(1, num_epochs + 1),
val_history[:, 0],
label='val')
ax_arr[0].set_xlabel('Epoch')
ax_arr[0].set_title("Loss Vs Time")
ax_arr[0].grid(True)
ax_arr[0].legend()
ax_arr[1].plot(np.arange(1, num_epochs + 1),
train_history[:, 1],
label='train')
ax_arr[1].plot(np.arange(1, num_epochs + 1),
val_history[:, 1],
label='val')
ax_arr[1].set_xlabel('Epoch')
ax_arr[1].set_title("Accuracy Vs Time")
ax_arr[1].grid(True)
ax_arr[1].legend()
f.savefig(os.path.join(results_store_dir, 'loss_and accuracy.jpg'),
format='jpg')
# -----------------------------------------------------------------------------------
# Run Li 2006 equivalent experiments
# -----------------------------------------------------------------------------------
dataset_parameters = {
'biped_dataset_dir': './data/BIPED/edges',
'biped_dataset_type': 'train',
'n_biped_imgs': n_imgs_for_exp,
'n_epochs': 1 # Total images = n_epochs * n_biped_images
}
experiment_gain_vs_spacing_natural_images.main(
model,
results_store_dir,
data_set_params=dataset_parameters,
cont_int_scale=cont_int_scale)
# View trained kernels
# ------------------------------------------------------------------------------------
trained_kernels_store_dir = os.path.join(results_store_dir,
'trained_kernels')
if not os.path.exists(trained_kernels_store_dir):
os.makedirs(trained_kernels_store_dir)
utils.view_ff_kernels(
model.edge_extract.weight.data.cpu().detach().numpy(),
results_store_dir=trained_kernels_store_dir)
utils.view_spatial_lateral_kernels(
model.contour_integration_layer.lateral_e.weight.data.cpu().detach(
).numpy(),
model.contour_integration_layer.lateral_i.weight.data.cpu().detach(
).numpy(),
results_store_dir=trained_kernels_store_dir,
spatial_func=np.mean)
print("Data Source: {}".format(data_set_dir))
# Imagenet Mean and STD
ch_mean = [0.485, 0.456, 0.406]
ch_std = [0.229, 0.224, 0.225]
# Pre-processing
transforms_list = [
transforms.Normalize(mean=ch_mean, std=ch_std),
# utils.PunctureImage(n_bubbles=100, fwhm=20, peak_bubble_transparency=0)
]
pre_process_transforms = transforms.Compose(transforms_list)
data_set = dataset_pathfinder.PathfinderNaturalImages(
data_dir=data_set_dir,
# transform=pre_process_transforms,
)
data_loader = DataLoader(dataset=data_set,
num_workers=4,
batch_size=1,
shuffle=False,
pin_memory=True)
print("Data loading Took {}. # Images {}".format(
datetime.now() - data_load_start_time,
len(data_loader) * 1))
# -------------------------------------------------------------------------------
# Loss / optimizer
# -------------------------------------------------------------------------------
def main(model,
train_params,
data_set_params,
base_results_store_dir='./results'):
# -----------------------------------------------------------------------------------
# Validate Parameters
# -----------------------------------------------------------------------------------
print("====> Validating Parameters ")
# Pathfinder Dataset
# ------------------
pathfinder_required_data_set_params = ['pathfinder_data_set_dir']
for key in pathfinder_required_data_set_params:
assert key in data_set_params, 'data_set_params does not have required key {}'.format(
key)
pathfinder_data_set_dir = data_set_params['pathfinder_data_set_dir']
# Optional
pathfinder_train_subset_size = data_set_params.get(
'pathfinder_train_subset_size', None)
pathfinder_test_subset_size = data_set_params.get(
'pathfinder_test_subset_size', None)
# Contour Dataset
# ---------------
contour_required_data_set_params = ['contour_data_set_dir']
for key in contour_required_data_set_params:
assert key in data_set_params, 'data_set_params does not have required key {}'.format(
key)
contour_data_set_dir = data_set_params['contour_data_set_dir']
# Optional
contour_train_subset_size = data_set_params.get(
'contour_train_subset_size', None)
contour_test_subset_size = data_set_params.get('contour_test_subset_size',
None)
c_len_arr = data_set_params.get('c_len_arr', None)
beta_arr = data_set_params.get('beta_arr', None)
alpha_arr = data_set_params.get('alpha_arr', None)
gabor_set_arr = data_set_params.get('gabor_set_arr', None)
# Training
# --------
required_training_params = \
['train_batch_size', 'test_batch_size', 'learning_rate', 'num_epochs']
for key in required_training_params:
assert key in train_params, 'training_params does not have required key {}'.format(
key)
train_batch_size = train_params['train_batch_size']
test_batch_size = train_params['test_batch_size']
learning_rate = train_params['learning_rate']
num_epochs = train_params['num_epochs']
# Optional
lambda1 = train_params.get('gaussian_reg_weight', 0)
gaussian_kernel_sigma = train_params.get('gaussian_reg_sigma', 0)
use_gaussian_reg_on_lateral_kernels = False
if lambda1 is not 0 and gaussian_kernel_sigma is not 0:
use_gaussian_reg_on_lateral_kernels = True
# -----------------------------------------------------------------------------------
# Model
# -----------------------------------------------------------------------------------
print("====> Loading Model ")
print("Name: {}".format(model.__class__.__name__))
print(model)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)
# Get name of contour integration layer
temp = vars(model) # Returns a dictionary.
layers = temp['_modules'] # Returns all top level modules (layers)
cont_int_layer_type = ''
if 'contour_integration_layer' in layers:
cont_int_layer_type = model.contour_integration_layer.__class__.__name__
# Actual Results store directory
results_store_dir = os.path.join(
base_results_store_dir, model.__class__.__name__ + '_' +
cont_int_layer_type + datetime.now().strftime("_%Y%m%d_%H%M%S"))
if not os.path.exists(results_store_dir):
os.makedirs(results_store_dir)
# -----------------------------------------------------------------------------------
# Data Loaders
# -----------------------------------------------------------------------------------
print("====> Setting up data loaders ")
data_load_start_time = datetime.now()
# Pathfinder
# --------------------------------------
print("Setting up Pathfinder Data loaders... ")
print("Data Source: {}".format(pathfinder_data_set_dir))
# Pre-processing
# Imagenet Mean and STD
ch_mean = [0.485, 0.456, 0.406]
ch_std = [0.229, 0.224, 0.225]
pathfinder_transforms_list = [
transforms.Normalize(mean=ch_mean, std=ch_std),
utils.PunctureImage(n_bubbles=100, fwhm=np.array([7, 9, 11, 13, 15]))
]
pathfinder_pre_process_transforms = transforms.Compose(
pathfinder_transforms_list)
pathfinder_train_set = dataset_pathfinder.PathfinderNaturalImages(
data_dir=os.path.join(pathfinder_data_set_dir, 'train'),
transform=pathfinder_pre_process_transforms,
subset_size=pathfinder_train_subset_size,
)
pathfinder_train_data_loader = DataLoader(dataset=pathfinder_train_set,
num_workers=4,
batch_size=train_batch_size,
shuffle=True,
pin_memory=True)
pathfinder_val_set = dataset_pathfinder.PathfinderNaturalImages(
data_dir=os.path.join(pathfinder_data_set_dir, 'test'),
transform=pathfinder_pre_process_transforms,
subset_size=pathfinder_test_subset_size)
pathfinder_val_data_loader = DataLoader(dataset=pathfinder_val_set,
num_workers=4,
batch_size=test_batch_size,
shuffle=True,
pin_memory=True)
print("Pathfinder Data loading Took {}. # Train {}, # Test {}".format(
datetime.now() - data_load_start_time,
len(pathfinder_train_data_loader) * train_batch_size,
len(pathfinder_val_data_loader) * test_batch_size))
# Contour Dataset
# ---------------
print("Setting up Contour Data loaders... ")
data_load_start_time = datetime.now()
print("Data Source: {}".format(contour_data_set_dir))
print(
"\tRestrictions:\n\t clen={},\n\t beta={},\n\t alpha={},\n\t gabor_sets={},\n\t "
"train_subset={},\n\ttest subset={}\n".format(
c_len_arr, beta_arr, alpha_arr, gabor_set_arr,
contour_train_subset_size, contour_test_subset_size))
# Get mean/std of dataset
meta_data_file = os.path.join(contour_data_set_dir,
'dataset_metadata.pickle')
with open(meta_data_file, 'rb') as file_handle:
meta_data = pickle.load(file_handle)
# Pre-processing
contour_transforms_list = [
transforms.Normalize(mean=meta_data['channel_mean'],
std=meta_data['channel_std']),
utils.PunctureImage(n_bubbles=100, fwhm=np.array([7, 9, 11, 13, 15]))
]
contour_pre_process_transforms = transforms.Compose(
contour_transforms_list)
contour_train_set = dataset_contour.Fields1993(
data_dir=os.path.join(contour_data_set_dir, 'train'),
bg_tile_size=meta_data["full_tile_size"],
transform=contour_pre_process_transforms,
subset_size=contour_train_subset_size,
c_len_arr=c_len_arr,
beta_arr=beta_arr,
alpha_arr=alpha_arr,
gabor_set_arr=gabor_set_arr)
contour_train_data_loader = DataLoader(dataset=contour_train_set,
num_workers=4,
batch_size=train_batch_size,
shuffle=True,
pin_memory=True)
contour_val_set = dataset_contour.Fields1993(
data_dir=os.path.join(contour_data_set_dir, 'val'),
bg_tile_size=meta_data["full_tile_size"],
transform=contour_pre_process_transforms,
subset_size=contour_test_subset_size,
c_len_arr=c_len_arr,
beta_arr=beta_arr,
alpha_arr=alpha_arr,
gabor_set_arr=gabor_set_arr)
contour_val_data_loader = DataLoader(dataset=contour_val_set,
num_workers=4,
batch_size=test_batch_size,
shuffle=True,
pin_memory=True)
print("Contour Data loading Took {}. # Train {}, # Test {}".format(
datetime.now() - data_load_start_time,
len(contour_train_data_loader) * train_batch_size,
len(contour_val_data_loader) * test_batch_size))
# -----------------------------------------------------------------------------------
# Loss / optimizer
# -----------------------------------------------------------------------------------
detect_thres = 0.5
optimizer = optim.Adam(filter(lambda p1: p1.requires_grad,
model.parameters()),
lr=learning_rate)
lr_scheduler = optim.lr_scheduler.StepLR(optimizer,
step_size=30,
gamma=0.1)
criterion = nn.BCEWithLogitsLoss().to(device)
if use_gaussian_reg_on_lateral_kernels:
gaussian_mask_e = 1 - utils.get_2d_gaussian_kernel(
model.contour_integration_layer.lateral_e.weight.shape[2:],
sigma=gaussian_kernel_sigma)
gaussian_mask_i = 1 - utils.get_2d_gaussian_kernel(
model.contour_integration_layer.lateral_i.weight.shape[2:],
sigma=gaussian_kernel_sigma)
gaussian_mask_e = torch.from_numpy(gaussian_mask_e).float().to(device)
gaussian_mask_i = torch.from_numpy(gaussian_mask_i).float().to(device)
def inverse_gaussian_regularization(weight_e, weight_i):
loss1 = (gaussian_mask_e * weight_e).abs().sum() + \
(gaussian_mask_i * weight_i).abs().sum()
# print("Loss1: {:0.4f}".format(loss1))
return loss1
# -----------------------------------------------------------------------------------
# Training Validation Routines
# -----------------------------------------------------------------------------------
def train_pathfinder():
""" Train for one Epoch over the train data set """
model.train()
e_loss = 0
e_acc = 0
for iteration, data_loader_out in enumerate(
pathfinder_train_data_loader, 1):
optimizer.zero_grad() # zero the parameter gradients
img, label, _, _, _, = data_loader_out
img = img.to(device)
label = label.to(device)
# Second part is pathfinder out
_, label_out = model(img)
bce_loss = criterion(label_out, label.float())
reg_loss = 0
if use_gaussian_reg_on_lateral_kernels:
reg_loss = \
inverse_gaussian_regularization(
model.contour_integration_layer.lateral_e.weight,
model.contour_integration_layer.lateral_i.weight
)
total_loss = bce_loss + lambda1 * reg_loss
acc = binary_acc(label_out, label)
# print("Loss: {:0.4f}, bce_loss {:0.4f}, lateral kernels reg_loss {:0.4f}, "
# "acc {:0.4f}".format(total_loss, bce_loss, lambda1 * reg_loss, acc))
total_loss.backward()
optimizer.step()
e_loss += total_loss.item()
e_acc += acc.item()
e_loss = e_loss / len(pathfinder_train_data_loader)
e_acc = e_acc / len(pathfinder_train_data_loader)
# iou_arr = ["{:0.2f}".format(item) for item in e_iou]
# print("Train Epoch {} Loss = {:0.4f}, Acc = {}".format(epoch, e_loss, e_acc))
return e_loss, e_acc
def validate_pathfinder():
""" Get loss over validation set """
model.eval()
e_loss = 0
e_acc = 0
with torch.no_grad():
for iteration, data_loader_out in enumerate(
pathfinder_val_data_loader, 1):
img, label, _, _, _ = data_loader_out
img = img.to(device)
label = label.to(device)
_, label_out = model(img)
bce_loss = criterion(label_out, label.float())
reg_loss = 0
if use_gaussian_reg_on_lateral_kernels:
reg_loss = \
inverse_gaussian_regularization(
model.contour_integration_layer.lateral_e.weight,
model.contour_integration_layer.lateral_i.weight
)
total_loss = bce_loss + lambda1 * reg_loss
acc = binary_acc(label_out, label)
e_loss += total_loss.item()
e_acc += acc.item()
e_loss = e_loss / len(pathfinder_val_data_loader)
e_acc = e_acc / len(pathfinder_val_data_loader)
# print("Val Loss = {:0.4f}, Accuracy={}".format(e_loss, e_acc))
return e_loss, e_acc
def train_contour():
""" Train for one Epoch over the train data set """
model.train()
e_loss = 0
e_iou = 0
for iteration, (img, label) in enumerate(contour_train_data_loader, 1):
optimizer.zero_grad() # zero the parameter gradients
img = img.to(device)
label = label.to(device)
label_out, _ = model(img)
batch_loss = criterion(label_out, label.float())
kernel_loss = \
inverse_gaussian_regularization(
model.contour_integration_layer.lateral_e.weight,
model.contour_integration_layer.lateral_i.weight
)
total_loss = batch_loss + lambda1 * kernel_loss
# print("Total Loss: {:0.4f}, cross_entropy_loss {:0.4f}, kernel_loss {:0.4f}".format(
# total_loss, batch_loss, lambda1 * kernel_loss))
#
# import pdb
# pdb.set_trace()
total_loss.backward()
optimizer.step()
e_loss += total_loss.item()
preds = (torch.sigmoid(label_out) > detect_thres)
e_iou += utils.intersection_over_union(
preds.float(), label.float()).cpu().detach().numpy()
e_loss = e_loss / len(contour_train_data_loader)
e_iou = e_iou / len(contour_train_data_loader)
# print("Train Epoch {} Loss = {:0.4f}, IoU={:0.4f}".format(epoch, e_loss, e_iou))
return e_loss, e_iou
def validate_contour():
""" Get loss over validation set """
model.eval()
e_loss = 0
e_iou = 0
with torch.no_grad():
for iteration, (img, label) in enumerate(contour_val_data_loader,
1):
img = img.to(device)
label = label.to(device)
label_out, _ = model(img)
batch_loss = criterion(label_out, label.float())
kernel_loss = \
inverse_gaussian_regularization(
model.contour_integration_layer.lateral_e.weight,
model.contour_integration_layer.lateral_i.weight
)
total_loss = batch_loss + lambda1 * kernel_loss
e_loss += total_loss.item()
preds = (torch.sigmoid(label_out) > detect_thres)
e_iou += utils.intersection_over_union(
preds.float(), label.float()).cpu().detach().numpy()
e_loss = e_loss / len(contour_val_data_loader)
e_iou = e_iou / len(contour_val_data_loader)
# print("Val Loss = {:0.4f}, IoU={:0.4f}".format(e_loss, e_iou))
return e_loss, e_iou
def write_training_and_model_details(f_handle):
# Dataset Parameters:
f_handle.write("Data Set Parameters {}\n".format('-' * 60))
f_handle.write("CONTOUR DATASET \n")
f_handle.write(
"Source : {}\n".format(contour_data_set_dir))
f_handle.write("Restrictions :\n")
f_handle.write(" Lengths : {}\n".format(c_len_arr))
f_handle.write(" Beta : {}\n".format(beta_arr))
f_handle.write(" Alpha : {}\n".format(alpha_arr))
f_handle.write(" Gabor Sets : {}\n".format(gabor_set_arr))
f_handle.write(" Train subset size : {}\n".format(
contour_train_subset_size))
f_handle.write(
" Test subset size : {}\n".format(contour_test_subset_size))
f_handle.write("Number of Images : Train {}, Test {}\n".format(
len(contour_train_set.images), len(contour_val_set.images)))
f_handle.write("Train Set Mean {}, std {}\n".format(
contour_train_set.data_set_mean, contour_train_set.data_set_std))
f_handle.write("Val Set Mean {}, std {}\n".format(
contour_val_set.data_set_mean, contour_val_set.data_set_std))
f_handle.write("PATHFINDER DATASET\n")
f_handle.write(
"Source : {}\n".format(pathfinder_data_set_dir))
f_handle.write("Restrictions :\n")
f_handle.write(" Train subset size : {}\n".format(
pathfinder_train_subset_size))
f_handle.write(" Test subset size : {}\n".format(
pathfinder_test_subset_size))
f_handle.write("Number of Images : Train {}, Test {}\n".format(
len(pathfinder_train_set.images), len(pathfinder_val_set.images)))
# Training Parameters:
f_handle.write("Training Parameters {}\n".format('-' * 60))
f_handle.write(
"Train batch size : {}\n".format(train_batch_size))
f_handle.write(
"Val batch size : {}\n".format(test_batch_size))
f_handle.write("Epochs : {}\n".format(num_epochs))
f_handle.write("Optimizer : {}\n".format(
optimizer.__class__.__name__))
f_handle.write("learning rate : {}\n".format(learning_rate))
f_handle.write("Loss Fcn : {}\n".format(
criterion.__class__.__name__))
f_handle.write(
"Gaussian Regularization on lateral kernels: {}\n".format(
use_gaussian_reg_on_lateral_kernels))
if use_gaussian_reg_on_lateral_kernels:
f_handle.write(" Gaussian Reg. sigma : {}\n".format(
gaussian_kernel_sigma))
f_handle.write(" Gaussian Reg. weight : {}\n".format(lambda1))
f_handle.write("IoU Detection Threshold : {}\n".format(detect_thres))
f_handle.write("Image pre-processing :\n")
f_handle.write("Contour Dataset:\n")
print(contour_pre_process_transforms, file=f_handle)
f_handle.write("Pathfinder Dataset:\n")
print(pathfinder_pre_process_transforms, file=f_handle)
# Model Details
f_handle.write("Model Parameters {}\n".format('-' * 63))
f_handle.write("Model Name : {}\n".format(
model.__class__.__name__))
f_handle.write("\n")
print(model, file=file_handle)
tmp = vars(model) # Returns a dictionary.
p = [item for item in tmp if not item.startswith('_')]
for var in sorted(p):
f_handle.write("{}: {}\n".format(var, getattr(model, var)))
layers1 = tmp['_modules'] # Returns all top level modules (layers)
if 'contour_integration_layer' in layers1:
f_handle.write("Contour Integration Layer: {}\n".format(
model.contour_integration_layer.__class__.__name__))
# print fixed hyper parameters
f_handle.write("Hyper parameters\n")
cont_int_layer_vars = \
[item for item in vars(model.contour_integration_layer) if not item.startswith('_')]
for var in sorted(cont_int_layer_vars):
f_handle.write("\t{}: {}\n".format(
var, getattr(model.contour_integration_layer, var)))
# print parameter names and whether they are trainable
f_handle.write("Contour Integration Layer Parameters\n")
layer_params = vars(model.contour_integration_layer)['_parameters']
for k, v in sorted(layer_params.items()):
f_handle.write("\t{}: requires_grad {}\n".format(
k, v.requires_grad))
# Headers for columns in training details in summary file
f_handle.write("{}\n".format('-' * 80))
f_handle.write("Training details\n")
f_handle.write(
"[Epoch,\ncontour train_loss, train_iou, val_loss, val_iou\n")
f_handle.write(
"pathfinder train_loss, train_acc, val_loss, val_acc]\n")
# -----------------------------------------------------------------------------------
# Main Loop
# -----------------------------------------------------------------------------------
print("====> Starting Training ")
training_start_time = datetime.now()
pathfinder_train_history = []
pathfinder_val_history = []
contour_train_history = []
contour_val_history = []
lr_history = []
# Summary File
# ------------
summary_file = os.path.join(results_store_dir, 'summary.txt')
file_handle = open(summary_file, 'w+')
write_training_and_model_details(file_handle)
# Actual main loop start
# ----------------------
print("train_batch_size={}, test_batch_size= {}, lr={}, epochs={}".format(
train_batch_size, test_batch_size, learning_rate, num_epochs))
for epoch in range(0, num_epochs):
# Contour Dataset First
epoch_start_time = datetime.now()
contour_train_history.append(train_contour())
contour_val_history.append(validate_contour())
print(
"Epoch [{}/{}], Contour Train: loss={:0.4f}, IoU={:0.4f}. Val: "
"loss={:0.4f}, IoU={:0.4f}. Time {}".format(
epoch + 1, num_epochs, contour_train_history[epoch][0],
contour_train_history[epoch][1], contour_val_history[epoch][0],
contour_val_history[epoch][1],
datetime.now() - epoch_start_time))
# Pathfinder Dataset
epoch_start_time = datetime.now()
pathfinder_train_history.append(train_pathfinder())
pathfinder_val_history.append(validate_pathfinder())
print(
"Epoch [{}/{}], Pathfinder Train: loss={:0.4f}, Acc={:0.3f}. Val: "
"loss={:0.4f}, Acc={:0.3f}. Time {}".format(
epoch + 1, num_epochs, pathfinder_train_history[epoch][0],
pathfinder_train_history[epoch][1],
pathfinder_val_history[epoch][0],
pathfinder_val_history[epoch][1],
datetime.now() - epoch_start_time))
lr_history.append(get_lr(optimizer))
lr_scheduler.step(epoch)
# Save Last epoch weights
torch.save(model.state_dict(),
os.path.join(results_store_dir, 'last_epoch.pth'))
file_handle.write("[{}, {:0.4f}, {:0.4f}, {:0.4f}, {:0.4f}, "
"{:0.4f}, {:0.3f}, {:0.4f}, {:0.3f}],\n".format(
epoch + 1, contour_train_history[epoch][0],
contour_train_history[epoch][1],
contour_val_history[epoch][0],
contour_val_history[epoch][1],
pathfinder_train_history[epoch][0],
pathfinder_train_history[epoch][1],
pathfinder_val_history[epoch][0],
pathfinder_val_history[epoch][1]))
training_time = datetime.now() - training_start_time
print('Finished Training. Training took {}'.format(training_time))
file_handle.write("{}\n".format('-' * 80))
file_handle.write("Train Duration : {}\n".format(training_time))
file_handle.close()
# -----------------------------------------------------------------------------------
# Plots
# -----------------------------------------------------------------------------------
plot_pathfinder_results(pathfinder_train_history, pathfinder_val_history,
results_store_dir)
plot_contour_results(contour_train_history, contour_val_history,
results_store_dir)
# -----------------------------------------------------------------------------------
# Run Li 2006 experiments
# -----------------------------------------------------------------------------------
print("====> Running Experiments")
experiment_gain_vs_len.main(model,
base_results_dir=results_store_dir,
iou_results=False)
experiment_gain_vs_len.main(model,
results_store_dir,
iou_results=False,
frag_size=np.array([14, 14]))
experiment_gain_vs_spacing.main(model, base_results_dir=results_store_dir)
experiment_gain_vs_spacing.main(model,
results_store_dir,
frag_size=np.array([14, 14]))