def create_training_dataset(self, event):
"""
"""
num_shuffles = self.shuffles.GetValue()
userfeedback_option = self.userfeedback.GetStringSelection()
if userfeedback_option == 'Yes':
userfeedback = True
else:
userfeedback = False
trainindex = self.trainingindex.GetValue()
if self.model_comparison_choice.GetStringSelection() == 'No':
deeplabcut.create_training_dataset(
self.config,
num_shuffles,
Shuffles=[self.shuffle.GetValue()],
userfeedback=userfeedback,
net_type=self.net_choice.GetValue(),
augmenter_type=self.aug_choice.GetValue())
if self.model_comparison_choice.GetStringSelection() == 'Yes':
deeplabcut.create_training_model_comparison(
self.config,
trainindex=trainindex,
num_shuffles=num_shuffles,
userfeedback=userfeedback,
net_types=self.net_type,
augmenter_types=self.aug_type)
def create_training_dataset(self, event):
"""
"""
num_shuffles = self.shuffles.GetValue()
if self.model_comparison_choice.GetStringSelection() == 'No':
deeplabcut.create_training_dataset(
self.config,
num_shuffles,
net_type=self.net_choice.GetValue(),
augmenter_type=self.aug_choice.GetValue())
if self.model_comparison_choice.GetStringSelection() == 'Yes':
deeplabcut.create_training_model_comparison(
self.config,
num_shuffles,
net_types=self.net_type,
augmenter_types=self.aug_type)
def create_training_dataset(self, event):
"""
"""
num_shuffles = self.shuffle.GetValue()
config_file = auxiliaryfunctions.read_config(self.config)
trainindex = self.trainingindex.GetValue()
if self.userfeedback.GetStringSelection() == "Yes":
userfeedback = True
else:
userfeedback = False
if config_file.get("multianimalproject", False):
if self.cropandlabel.GetStringSelection() == "Yes":
n_crops, height, width = [
int(text.GetValue()) for _, text in self.crop_widgets
]
deeplabcut.cropimagesandlabels(
self.config, n_crops, (height, width), userfeedback
)
else:
random = False
deeplabcut.create_multianimaltraining_dataset(
self.config,
num_shuffles,
Shuffles=[self.shuffle.GetValue()],
net_type=self.net_choice.GetValue(),
)
else:
if self.model_comparison_choice.GetStringSelection() == "No":
deeplabcut.create_training_dataset(
self.config,
num_shuffles,
Shuffles=[self.shuffle.GetValue()],
userfeedback=userfeedback,
net_type=self.net_choice.GetValue(),
augmenter_type=self.aug_choice.GetValue(),
)
if self.model_comparison_choice.GetStringSelection() == "Yes":
deeplabcut.create_training_model_comparison(
self.config,
trainindex=trainindex,
num_shuffles=num_shuffles,
userfeedback=userfeedback,
net_types=self.net_type,
augmenter_types=self.aug_type,
)
def create_training_dataset(self, event):
"""
"""
num_shuffles = self.shuffle.GetValue()
config_file = auxiliaryfunctions.read_config(self.config)
trainindex = self.trainingindex.GetValue()
if self.userfeedback.GetStringSelection() == "Yes":
userfeedback = True
else:
userfeedback = False
if config_file.get("multianimalproject", False):
deeplabcut.create_multianimaltraining_dataset(
self.config,
num_shuffles,
Shuffles=[self.shuffle.GetValue()],
net_type=self.net_choice.GetValue(),
)
else:
if self.model_comparison_choice.GetStringSelection() == "No":
deeplabcut.create_training_dataset(
self.config,
num_shuffles,
Shuffles=[self.shuffle.GetValue()],
userfeedback=userfeedback,
net_type=self.net_choice.GetValue(),
augmenter_type=self.aug_choice.GetValue(),
)
if self.model_comparison_choice.GetStringSelection() == "Yes":
deeplabcut.create_training_model_comparison(
self.config,
trainindex=trainindex,
num_shuffles=num_shuffles,
userfeedback=userfeedback,
net_types=self.net_type,
augmenter_types=self.aug_type,
)
import deeplabcut
import numpy as np
# Loading example data set
path_config_file = os.path.join(os.getcwd(),
"openfield-Pranav-2018-10-30/config.yaml")
cfg = deeplabcut.auxiliaryfunctions.read_config(path_config_file)
maxiters = 10000
deeplabcut.load_demo_data(path_config_file)
## Create one split and make Shuffle 2 and 3 have the same split.
###Note that the new function in DLC 2.1 simplifies network/augmentation comparisons greatly:
deeplabcut.create_training_model_comparison(
path_config_file,
num_shuffles=1,
net_types=["resnet_50", "efficientnet-b3"],
augmenter_types=["imgaug", "scalecrop", "tensorpack"],
)
## here is an "old way" to do this
"""
trainIndices, testIndices=deeplabcut.mergeandsplit(path_config_file,trainindex=0,uniform=True)
deeplabcut.create_training_dataset(path_config_file,Shuffles=[2],trainIndices=trainIndices,testIndices=testIndices)
deeplabcut.create_training_dataset(path_config_file,Shuffles=[3],trainIndices=trainIndices,testIndices=testIndices)
for shuffle in [2,3]:
if shuffle==3:
posefile=os.path.join(cfg['project_path'],'dlc-models/iteration-'+str(cfg['iteration'])+'/'+ cfg['Task'] + cfg['date'] + '-trainset' + str(int(cfg['TrainingFraction'][0] * 100)) + 'shuffle' + str(shuffle),'train/pose_cfg.yaml')
DLC_config=deeplabcut.auxiliaryfunctions.read_plainconfig(posefile)
DLC_config['dataset_type']='tensorpack'
deeplabcut.auxiliaryfunctions.write_plainconfig(posefile,DLC_config)
import deeplabcut
import numpy as np
# Loading example data set
path_config_file = os.path.join(os.getcwd(),
"openfield-Pranav-2018-10-30/config.yaml")
cfg = deeplabcut.auxiliaryfunctions.read_config(path_config_file)
maxiters = 10000
deeplabcut.load_demo_data(path_config_file)
## Create one split and make Shuffle 2 and 3 have the same split.
###Note that the new function in DLC 2.1 simplifies network/augmentation comparisons greatly:
deeplabcut.create_training_model_comparison(
path_config_file,
num_shuffles=1,
net_types=["mobilenet_v2_0.35", "resnet_50", "efficientnet-b3"],
augmenter_types=["imgaug"],
)
freezeencoder = False # True
for shuffle in 1 + np.arange(3):
posefile, _, _ = deeplabcut.return_train_network_path(path_config_file,
shuffle=shuffle)
# for EfficientNet
edits = {
"decay_steps": maxiters,
"lr_init": 0.0005 * 12,
"freezeencoder": freezeencoder,
}
import os
os.environ["DLClight"]="True"
import deeplabcut as dlc
config_path = "/usr/users/onur.serce/dlc_real-alja_onur-2020-04-06/config.yaml"
print("'config_path' is:", config_path)
print("'dlc.__version__' is:'", dlc.__version__)
print("\n")
dlc.create_training_model_comparison(config_path, num_shuffles=10, net_types=['resnet_152'], augmenter_types=['imgaug'], userfeedback=False, windows2linux=True)
print('\n', 'create_training_model_comparison completed!')
trainIndexes, testIndexes=deeplabcut.mergeandsplit(path_config_file,trainindex=0,uniform=True)
deeplabcut.create_training_dataset(path_config_file,Shuffles=[2],trainIndexes=trainIndexes,testIndexes=testIndexes)
deeplabcut.create_training_dataset(path_config_file,Shuffles=[3],trainIndexes=trainIndexes,testIndexes=testIndexes)
for shuffle in [2,3]:
if shuffle==3:
posefile=os.path.join(cfg['project_path'],'dlc-models/iteration-'+str(cfg['iteration'])+'/'+ cfg['Task'] + cfg['date'] + '-trainset' + str(int(cfg['TrainingFraction'][0] * 100)) + 'shuffle' + str(shuffle),'train/pose_cfg.yaml')
DLC_config=deeplabcut.auxiliaryfunctions.read_plainconfig(posefile)
DLC_config['dataset_type']='tensorpack'
deeplabcut.auxiliaryfunctions.write_plainconfig(posefile,DLC_config)
'''
###Note that the new function in DLC 2.1 does that much easier...
deeplabcut.create_training_model_comparison(
path_config_file,
num_shuffles=1,
net_types=['resnet_50'],
augmenter_types=['imgaug', 'default', 'tensorpack'])
for shuffle in [2, 3]:
print("TRAIN NETWORK", shuffle)
deeplabcut.train_network(path_config_file,
shuffle=shuffle,
saveiters=10000,
displayiters=200,
maxiters=5,
max_snapshots_to_keep=11)
print("EVALUATE")
deeplabcut.evaluate_network(path_config_file,
Shuffles=[shuffle],
trainIndices, testIndices=deeplabcut.mergeandsplit(path_config_file,trainindex=0,uniform=True)
deeplabcut.create_training_dataset(path_config_file,Shuffles=[2],trainIndices=trainIndices,testIndices=testIndices)
deeplabcut.create_training_dataset(path_config_file,Shuffles=[3],trainIndices=trainIndices,testIndices=testIndices)
for shuffle in [2,3]:
if shuffle==3:
posefile=os.path.join(cfg['project_path'],'dlc-models/iteration-'+str(cfg['iteration'])+'/'+ cfg['Task'] + cfg['date'] + '-trainset' + str(int(cfg['TrainingFraction'][0] * 100)) + 'shuffle' + str(shuffle),'train/pose_cfg.yaml')
DLC_config=deeplabcut.auxiliaryfunctions.read_plainconfig(posefile)
DLC_config['dataset_type']='tensorpack'
deeplabcut.auxiliaryfunctions.write_plainconfig(posefile,DLC_config)
"""
###Note that the new function in DLC 2.1 simplifies network/augmentation comparisons greatly:
deeplabcut.create_training_model_comparison(
path_config_file,
num_shuffles=1,
net_types=["resnet_50"],
augmenter_types=["imgaug", "default", "tensorpack"],
)
for shuffle in [1, 2, 3]:
if shuffle < 3:
posefile, _, _ = deeplabcut.return_train_network_path(path_config_file,
shuffle=shuffle)
if shuffle == 2: # Tensorpack:
edits = {"rotate_max_deg_abs": 180, "noise_sigma": 0.01}
elif shuffle == 1: # imgaug
edits = {"rotation": 180, "motion_blur": True}
DLC_config = deeplabcut.auxiliaryfunctions.edit_config(posefile, edits)
print("TRAIN NETWORK", shuffle)
deeplabcut.train_network(
