class EvaluateConfiguration:
dataset_name = '' # Any folder in the <<root_of_datasets>> directory.
weights_file = r''
lr_scheduler_opts = LRSchedulerOpts(LRPolicies.onCommand)
data_aug_opts = DataAugOpts()
crodnet_opts = CrodnetOptions.CrodnetOptions()
single_cell_opts = CrodnetOptions.SingleCellOptions()
image_cropper_opts = ImageCropperOptions()
##################################
######## DISPLAYING OPTS #########
# If recompute_train is false, the metrics and loss shown for a training epoch, are computed with the results
# obtained with the training batches (thus, not reflecting the performance at the end of the epoch, but during it).
# Otherwise, we go through all the training data again to compute its loss and metrics. This is more time consuming.
recompute_train = False
nsteps_display = 20
nepochs_save = 100
nepochs_checktrain = 1
nepochs_checkval = 1
##################################
##################################
########### OTHER OPTS ###########
percent_of_data = 100 # For debbuging. Percentage of data to use. Put 100 if not debbuging
num_workers = 8 # Number of parallel processes to read the data.
if os.name == 'nt': # Windows
root_of_datasets = r'D:\datasets'
experiments_folder = r'.\experiments'
elif os.name == 'posix': # Linux
root_of_datasets = r'/home/xian/datasets'
experiments_folder = r'./experiments'
else:
raise Exception('Unexpected OS')
random_seed = None # An integer number, or None in order not to set the random seed.
tf_log_level = 'ERROR'
buffer_size = 1000 # For shuffling data.
max_image_size = 600
gpu_memory_fraction = -1.0
write_network_input = False
shuffle_data = True
##################################
##################################
##################################
# The following code should not be touched:
outdir = None
initialization_mode = 'load-pretrained' # 'load-pretrained', 'scratch'
modified_scopes = []
restore_optimizer = False
class UpdateTrainConfiguration(TrainConfiguration):
##################################
######### TRAINING OPTS ##########
num_epochs = 25
optimizer_name = 'momentum' # 'sgd', 'adam', 'rmsprop'
learning_rate = 1e-3
#learning_rate = 0
momentum = 0.9
# l2_regularization = 5e-4
l2_regularization = 2e-4
# l2_regularization = 0
#nbatches_accum = 2
train_selected_layers = False
layers_list = ['vgg_16/conv1/', 'vgg_16/conv2/', 'vgg_16/conv3/', 'vgg_16/conv4/', 'vgg_16/conv5/']
# layers_list = []
##################################
lr_scheduler_opts = LRSchedulerOpts(LRPolicies.scheduled)
lr_scheduler_opts.scheduledPolicyOpts.epochsLRDict = {10: 1e-4, 20: 1e-5, 24: 1e-6}
#lr_scheduler_opts.scheduledPolicyOpts.epochsLRDict = {15: 1e-5}
single_cell_opts = CrodnetOptions.SingleCellOptions()
# single_cell_opts.debug_train = True
# single_cell_opts.debug_eval = True
single_cell_opts.loc_loss_factor = 1.2
single_cell_opts.cm_loss_factor = 0.1
single_cell_opts.n_comparisons_inter = 0
single_cell_opts.n_comparisons_intra = 0
single_cell_opts.threshold_ar_low = 0.05
single_cell_opts.threshold_ar_low_neutral = 0.04
single_cell_opts.threshold_ar_high = 0.9
single_cell_opts.threshold_ar_high_neutral = 0.9
single_cell_opts.threshold_dc = 0.3
single_cell_opts.threshold_dc_neutral = 0.4
single_cell_opts.cm_same_class = True
single_cell_opts.th_cm_background = 0.15
single_cell_opts.th_cm_neutral = 0.20
single_cell_opts.n_images_per_batch = 16
single_cell_opts.n_crops_per_image = 8
image_cropper_opts = ImageCropperOptions()
image_cropper_opts.max_dc = 0.9
image_cropper_opts.min_ar = 0.02
image_cropper_opts.max_ar = 1
image_cropper_opts.probability_focus = 0.45
image_cropper_opts.probability_pair = 0.3
image_cropper_opts.probability_inside = 0
image_cropper_opts.max_dc_pair = 0.5
#multi_cell_opts = CrodnetOptions.MultiCellOptions()
#multi_cell_opts.grid_levels_size_pad = [
# (256, 64),
# (288, 96),
# # (352, 64),
# (416, 64),
# # (512, 64),
# (608, 64),
# (704, 64),
# (832, 64),
# (896, 64)
#]
#multi_cell_opts.cm_same_class = True
#multi_cell_opts.n_images_per_batch = 1
#multi_cell_opts.threshold_ar_low = 0.05
#multi_cell_opts.threshold_ar_high = 0.9
#dataset_name = 'VOC0712_filtered' # Any folder in the <<root_of_datasets>> directory.
dataset_name = 'VOC0712' # Any folder in the <<root_of_datasets>> directory.
debug_hnm = False
##################################
######### INITIALIZATION #########
initialization_mode = 'load-pretrained' # 'load-pretrained', 'scratch'
weights_file = r'C:\development\crodnet\experiments\2019\ssd_training_2019_01_10\model-240'
modified_scopes = ['new_layers', 'prediction', 'comparison']
restore_optimizer = False
#weights_file = '/home/xian/crodnet/experiments/2019/2019_03_14_2/model-18'
#modified_scopes = []
# restore_optimizer = True
#restore_optimizer = False
##################################
##################################
####### DATA AUGMENTATION ########
data_aug_opts = DataAugOpts()
data_aug_opts.apply_data_augmentation = True
data_aug_opts.horizontal_flip = True
data_aug_opts.random_brightness = True
data_aug_opts.random_contrast = True
data_aug_opts.random_saturation = True
data_aug_opts.random_hue = True
data_aug_opts.convert_to_grayscale_prob = 0.05
# data_aug_opts.write_image_after_data_augmentation = True
##################################
nepochs_save = 1
nepochs_checkval = 1
nsteps_display = 20
nepochs_hnm = 50
nepochs_mceval = 50
class TrainConfiguration:
##################################
######### TRAINING OPTS ##########
num_epochs = 5
optimizer_name = 'sgd' # 'sgd', 'adam', 'rmsprop'
learning_rate = 1e-3
momentum = 0.9
l2_regularization = 5e-4
vars_to_skip_l2_reg = ['scale', 'bias', 'BatchNorm'] # List with strings contained by the variables that you don't want to add to the L2 regularization loss.
nbatches_accum = 0 # 0 to not applyl batch accumulation.
# If train_selected_layers is true, the layers in layers_list are the only ones that are going to be trained.
# Otherwise, those are the only layers excluded for training.
# The elements of layers_list do not need to match exactly the layers names. It is enough if they are contained
# in the layer name. For instance, if we make layers_list = ['fc'] in vgg16, it will include layers fc6, fc7, fc8.
train_selected_layers = True
# layers_list = ['fc'] # If this is empy or none, all variables are trained.
layers_list = [] # If this is empy or none, all variables are trained.
##################################
dataset_name = '' # Any folder in the <<root_of_datasets>> directory.
##################################
######### INITIALIZATION #########
# Weights initialization:
# To start from sratch, choose 'scratch'
# To load pretrained weights, and start training with them, choose 'load-pretrained'
initialization_mode = 'load-pretrained' # 'load-pretrained', 'scratch'
weights_file = r''
modified_scopes = []
restore_optimizer = False
##################################
lr_scheduler_opts = LRSchedulerOpts(LRPolicies.onCommand)
data_aug_opts = DataAugOpts()
single_cell_opts = CrodnetOptions.SingleCellOptions()
image_cropper_opts = ImageCropperOptions()
multi_cell_opts = CrodnetOptions.MultiCellOptions()
mean_ap_opts = MeanAPOpts()
hard_negatives_factor = 0.25
detect_against_background = False
th_conf = 0.8
th_conf_eval = 0.1
write_results = False
threshold_nms = 0.5
threshold_pcs = 0.6
threshold_iou = 0.5
debug_hnm = False
##################################
######## DISPLAYING OPTS #########
# If recompute_train is false, the metrics and loss shown for a training epoch, are computed with the results
# obtained with the training batches (thus, not reflecting the performance at the end of the epoch, but during it).
# Otherwise, we go through all the training data again to compute its loss and metrics. This is more time consuming.
recompute_train = False
nsteps_display = 20
nepochs_save = 100
nepochs_checkval = 1
nepochs_hnm = 1
nepochs_mceval = 1
##################################
##################################
########### OTHER OPTS ###########
percent_of_data = 100 # For debbuging. Percentage of data to use. Put 100 if not debbuging
num_workers = 8 # Number of parallel processes to read the data.
if os.name == 'nt': # Windows
root_of_datasets = r'D:\datasets'
experiments_folder = r'.\experiments'
elif os.name == 'posix': # Linux
root_of_datasets = r'/home/xian/datasets'
experiments_folder = r'./experiments'
else:
raise Exception('Unexpected OS')
random_seed = None # An integer number, or None in order not to set the random seed.
tf_log_level = 'ERROR'
buffer_size = 1000 # For shuffling data.
max_image_size = 600
gpu_memory_fraction = -1.0
shuffle_data = True
##################################
##################################
##################################
# The following code should not be touched:
outdir = None
class TrainConfiguration:
##################################
######### TRAINING OPTS ##########
num_epochs = 5
batch_size = 32
optimizer_name = 'sgd' # 'sgd', 'adam', 'rmsprop'
learning_rate = 1e-3
momentum = 0.9
l2_regularization = 5e-4
vars_to_skip_l2_reg = ['scale', 'biases', 'BatchNorm'] # List with strings contained by the variables that you don't want to add to the L2 regularization loss.
nbatches_accum = 0 # 0 to not applyl batch accumulation.
# If train_selected_layers is true, the layers in layers_list are the only ones that are going to be trained.
# Otherwise, those are the only layers excluded for training.
# The elements of layers_list do not need to match exactly the layers names. It is enough if they are contained
# in the layer name. For instance, if we make layers_list = ['fc'] in vgg16, it will include layers fc6, fc7, fc8.
train_selected_layers = True
# layers_list = ['fc'] # If this is empy or none, all variables are trained.
layers_list = [] # If this is empy or none, all variables are trained.
##################################
##################################
######### MODEL AND DATA #########
model_name = '' # 'vgg16', 'resnet50', 'yolo'
dataset_name = '' # Any folder in the <<root_of_datasets>> directory.
##################################
##################################
######### INITIALIZATION #########
# Weights initialization:
# To start from sratch, choose 'scratch'
# To load pretrained weights, and start training with them, choose 'load-pretrained'
initialization_mode = 'load-pretrained' # 'load-pretrained', 'scratch'
# To load pretrained weights:
# weights_file = r'.\weights\pretrained\YOLO_small.ckpt'
# weights_file = r'.\weights\pretrained\vgg_16.ckpt'
# weights_file = r'.\weights\pretrained\resnet_v1_50.ckpt'
weights_file = r''
modified_scopes = []
restore_optimizer = False
##################################
lr_scheduler_opts = LRSchedulerOpts(LRPolicies.onCommand)
##################################
####### DATA AUGMENTATION ########
data_aug_opts = DataAugOpts()
##################################
##################################
############ RESIZING ############
# Select the way to fit the image to the size required by the network.
# For DETECTION, use ONLY RESIZE_WARP.
# 'resize_warp': Resize both sides of the image to the required sizes. Aspect ratio may be changed.
# 'resize_pad_zeros': Scale the image until it totally fits inside the required shape. We pad with zeros the areas
# in which there is no image. Aspect ratio is preserved.
# 'resize_lose_part': Scale the image until it totally covers the area that the network will see. We may lose the
# upper and lower parts, or the left and right ones. Aspect ratio is preserved.
# 'centered_crop': Take a centered crop of the image. If any dimension of the image is smaller than the input
# size, we pad with zeros.
resize_method = 'resize_warp' # 'resize_warp', 'resize_pad_zeros', 'resize_lose_part', 'centered_crop'
##################################
##################################
######## DISPLAYING OPTS #########
# If recompute_train is false, the metrics and loss shown for a training epoch, are computed with the results
# obtained with the training batches (thus, not reflecting the performance at the end of the epoch, but during it).
# Otherwise, we go through all the training data again to compute its loss and metrics. This is more time consuming.
recompute_train = False
nsteps_display = 20
nepochs_save = 100
nepochs_checktrain = 1
nepochs_checkval = 1
##################################
##################################
########### OTHER OPTS ###########
percent_of_data = 100 # For debbuging. Percentage of data to use. Put 100 if not debbuging
num_workers = 8 # Number of parallel processes to read the data.
root_of_datasets = os.path.join(os.path.dirname(tools.get_base_dir()), 'datasets')
experiments_folder = os.path.join(tools.get_base_dir(), 'experiments')
random_seed = None # An integer number, or None in order not to set the random seed.
tf_log_level = 'ERROR'
buffer_size = 1000 # For shuffling data.
max_image_size = 600
gpu_memory_fraction = -1.0
write_image_after_data_augmentation = False
write_network_input = False
shuffle_data = True
##################################
##################################
##################################
# The following code should not be touched:
outdir = None