def run():
gdc_1 = main.load_gold_data(Config1_1)
gdc_1 = main.transform_gold_data(Config1_1, gdc_1)
gdc_1 = main.transform_gold_data(Config1_2, gdc_1)
gdc = GoldDataContainer(cats_list=gdc_1.cats_list)
gdc = gold_data_manager.merge_assuming_identical_categories(gdc, gdc_1)
gdc_2 = main.load_gold_data(Config2)
gdc_2 = main.transform_gold_data(Config2, gdc_2)
gdc = gold_data_manager.merge_assuming_identical_categories(gdc, gdc_2)
gdc_3 = main.load_gold_data(Config3)
gdc_3 = main.transform_gold_data(Config3, gdc_3)
gdc = gold_data_manager.merge_assuming_identical_categories(gdc, gdc_3)
gdc_4 = main.load_gold_data(Config4)
gdc_4 = main.transform_gold_data(Config4, gdc_4)
gdc = gold_data_manager.merge_assuming_identical_categories(gdc, gdc_4)
gdc_5 = main.load_gold_data(Config5)
gdc_5 = main.transform_gold_data(Config5, gdc_5)
gdc = gold_data_manager.merge_assuming_identical_categories(gdc, gdc_5)
gdc_6 = main.load_gold_data(Config6)
gdc_6 = main.transform_gold_data(Config6, gdc_6)
gdc = gold_data_manager.merge_assuming_identical_categories(gdc, gdc_6)
trainer = main.init_trainer(ConfigTrain, cats_list=gdc.cats_list)
main.run_training(config=ConfigTrain,
trainer=trainer,
gold_data_container=gdc)
embed()
def run():
gdc = main.load_gold_data(ConfigBase)
gdc = main.transform_gold_data(ConfigBase, gdc)
trainer = main.init_trainer(ConfigTdc100, cats_list=gdc.cats_list)
main.run_training(config=ConfigTdc100, trainer=trainer, gold_data_container=gdc)
trainer = main.init_trainer(ConfigTdc80, cats_list=gdc.cats_list)
main.run_training(config=ConfigTdc80, trainer=trainer, gold_data_container=gdc)
def run():
gdc = main.load_gold_data(ConfigSub)
gdc = main.transform_gold_data(ConfigSub, gdc)
for i in range(30):
if i == 0:
ConfigSub.should_load_model = False
ConfigSub.should_create_model = True
else:
ConfigSub.should_load_model = True
ConfigSub.should_create_model = False
trainer = main.init_trainer(config=ConfigSub, cats_list=gdc.cats_list)
main.run_training(ConfigSub, trainer, gdc)
def train(trainer1, trainer2):
gdc = main.load_gold_data(ConfigTrainCompareBase)
gdc = main.transform_gold_data(ConfigTrainCompareBase, gdc)
if trainer1 is None:
ConfigTrainCompareBase.should_load_model = False
ConfigTrainCompareBase.should_create_model = True
trainer1 = main.init_trainer(ConfigTrainCompare1,
cats_list=gdc.cats_list)
trainer2 = main.init_trainer(ConfigTrainCompare2,
cats_list=gdc.cats_list)
main.run_training(ConfigTrainCompare1, trainer1, gdc)
main.run_training(ConfigTrainCompare2, trainer2, gdc)
return trainer1, trainer2
def test_nec():
env = ENV()
key_size = 4
seed = 1
np.random.seed(seed)
torch.manual_seed(seed)
net = nn.Linear(env.observation_space.shape[0], key_size)
config = {
"env": env,
"env_name": "test_env",
"exp_name": "test",
"device": torch.device("cpu"),
"max_steps": 40,
"initial_epsilon": 1,
"final_epsilon": 0.5,
"epsilon_anneal_start": 1,
"epsilon_anneal_end": 2,
"start_learning_step": 1,
"replay_frequency": 1,
"eval_frequency": 1000000, # no eval for now
###### NEC AGENT CONFIG #################
"train_eps": 1, # initializing agent to be fully exploratory
"eval_eps": 0,
"num_actions": env.action_space.n,
"observation_shape": env.observation_space.shape[0],
"replay_buffer_size": 20,
"batch_size": 3,
"discount": 1,
"horizon": 1,
"learning_rate": 0.1,
###### NEC CONFIG #######################
"embedding_net": net,
###### DND CONFIG #######################
"dnd_capacity": 15,
"num_neighbours": 1,
"key_size": key_size,
"alpha": 0.99,
}
# perform experiment
agent = run_training(config, True)
# check learned q_values for all states
q_values = agent.get_q_values(env._obs[[0, 1, 2, 0, 1, 2]],
[0, 0, 0, 1, 1, 1])
expected_values = np.array([5.0, 2.0, 3.0, 3.0, 5.0, 1.0])
print(f'Expected: {expected_values}')
print(f'Got: {q_values}')
assert np.allclose(q_values, expected_values, atol=0.2)
"""
Created on Sat Jul 11 00:09:38 2020
@author: btayart
"""
#%% Redo all training with ignore_unlabeled set to True
#Train the ENet model with vanilia CamVid
from main import run_training
from custom import CustomArgs
import torch
args = CustomArgs(resume=False, batch_size=6, print_step=False,
ignore_unlabeled = True,
model_type="ENet", name="ENet_allclasses")
run_training(args)
torch.cuda.empty_cache()
# BEST VALIDATION
# Epoch: 170
# Mean IoU: 0.651799197633767
args = CustomArgs(resume=False, batch_size=6, print_step=False, weight_decay=2e-3,
ignore_unlabeled = True,
model_type="ENet", name="ENet_allclasses_wd2")
run_training(args)
torch.cuda.empty_cache()
# BEST VALIDATION
# Epoch: 270
# Mean IoU: 0.6653864847519912
# Train the ENet model on CamVid with 'people' classes dropped
def run():
gdc = main.load_gold_data(ConfigSub)
gdc = main.transform_gold_data(ConfigSub, gdc)
trainer = main.init_trainer(config=ConfigSub, cats_list=gdc.cats_list)
main.run_training(ConfigSub, trainer, gdc)
#! /usr/bin/python3 # -*- coding: utf-8 -*- # @Time : 2018/6/7 0007 14:51 # @Author : jsz # @Software: PyCharm import main main.rename() name_test = 'testrandomtrain' name_test1 = 'testrandomval' tfrecords_file = '.\\testrandomtrain.tfrecords' tfrecords_file1 = '.\\testrandomval.tfrecords' test_dir = '.\\data\\train\\' save_dir = '.\\' test_dir1 = '.\\data\\val\\' save_dir1= '.\\' images, labels = main.get_file(test_dir) main.convert_to_tfrecord(images, labels, save_dir, name_test) images1, labels1 = main.get_file(test_dir1) main.convert_to_tfrecord(images1, labels1, save_dir1, name_test1) main.run_training(tfrecords_file,tfrecords_file1)