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How to use log_dir method in Slash
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Run_save_experiment.py
Source:Run_save_experiment.py 
1import sys2sys.path.append('..')3from src import ML4from src import config5from src.Standard import SpectralAverage6import os7from tqdm import tqdm8import argparse9from datetime import datetime10import numpy as np11import xlsxwriter12import csv13def main():14 parser = argparse.ArgumentParser(15 description="Options for saving experiments to an .xlsx sheet ")16 # parser.add_argument('data_type',17 # type=str,18 # help="Input data type: contigs, erps, or spectra")19 parser.add_argument('--log_dirs',20 dest='log_dirs',21 nargs='+',22 default=["logs/fit"],23 help="(Default: ['logs/fit']) Parent directory for "24 + "checkpoints.")25 parser.add_argument('--checkpoint_dirs',26 dest='checkpoint_dirs',27 nargs='+',28 default=None,29 help="(Default: None) Checkpoint directory (most "30 + "likely found in logs/fit) containing saved model.")31 # save the variables in 'args'32 args = parser.parse_args()33 # data_type = args.data_type34 log_dirs = args.log_dirs35 checkpoint_dirs = args.checkpoint_dirs36 lrs = [1e-05,37 1.2742749857031348e-05,38 1.623776739188721e-05,39 2.06913808111479e-05,40 2.6366508987303556e-05,41 3.359818286283781e-05,42 4.281332398719396e-05,43 5.4555947811685143e-05,44 6.951927961775606e-05,45 8.858667904100833e-05,46 0.00011288378916846884,47 0.0001438449888287663,48 0.00018329807108324357,49 0.00023357214690901214,50 0.00029763514416313193,51 0.000379269019073225,52 0.0004832930238571752,53 0.0006158482110660261,54 0.0007847599703514606,55 0.001]56 # # ERROR HANDLING57 # if data_type not in ["erps", "spectra", "contigs"]:58 # print(59 # "Invalid entry for data_type. "60 # + "Must be one of ['erps', 'contigs', 'spectra']")61 # raise ValueError62 # sys.exit(3)63 #64 # for log_dir in log_dirs:65 #66 # if checkpoint_dirs is not None:67 # for checkpoint_dir in checkpoint_dirs:68 # if not os.path.isdir(checkpoint_dir):69 # if not os.path.isdir(log_dir+checkpoint_dir):70 # print(71 # "Invalid entry for checkpoint directory, "72 # + "path does not exist as directory.")73 # raise FileNotFoundError74 # sys.exit(3)75 # else:76 # checkpoint_dir = log_dir+checkpoint_dir77 #78 # if checkpoint_dir is None:79 # checkpoint_dirs = [80 # log_dir + folder81 # for folder in os.listdir(log_dir)82 # if "_"+data_type in folder]83 # else:84 # checkpoint_dirs = [checkpoint_dir]85 workbook = xlsxwriter.Workbook('Experiments.xlsx')86 for z, log_dir in enumerate(log_dirs):87 # if checkpoint_dirs == None:88 checkpoint_dirs = os.listdir(log_dir)89 checkpoint_dirs.sort()90 worksheet = workbook.add_worksheet(str(z))91 # worksheet.set_default_row(200)92 # worksheet.set_column(6, 21, 200)93 # headers94 # model config / training95 worksheet.write('A1', 'Training Studies')96 worksheet.write('B1', 'Learning Rate')97 worksheet.write('C1', 'Final Accuracy')98 worksheet.write('D1', 'Final Loss')99 worksheet.write('E1', 'Final Validation Accuracy')100 worksheet.write('F1', 'Final Validation Loss')101 # training image headers102 worksheet.write('G1', 'Training / Validation Accuracy')103 worksheet.write('H1', 'Training / Validation Loss')104 worksheet.write('I1', 'ROC Curve')105 # testing image headers106 worksheet.write('J1', 'Confusion Matrix')107 worksheet.write('K1', 'Output Nodes (3D)')108 # worksheet.write('L1', 'Eval lyons_pain (old)')109 # worksheet.write('M1', 'Eval lyons_pain (new)')110 # worksheet.write('N1', 'Eval glynn_pain')111 # worksheet.write('O1', 'Eval rehab')112 # worksheet.write('P1', 'Eval ref 24-30')113 # worksheet.write('Q1', 'Eval ref 31-40')114 # worksheet.write('R1', 'Eval ref 41-50')115 # worksheet.write('S1', 'Eval ref 51-60')116 # worksheet.write('T1', 'Eval ref 61-70')117 # worksheet.write('U1', 'Eval ref 71-80')118 # worksheet.write('V1', 'Eval ref 81+')119 # worksheet.write('J1', 'Eval CU_pain')120 # worksheet.write('K1', 'Eval CU_control')121 # worksheet.write('L1', 'Eval lyons_pain (old)')122 # worksheet.write('M1', 'Eval lyons_pain (new)')123 # worksheet.write('N1', 'Eval glynn_pain')124 # worksheet.write('O1', 'Eval rehab')125 # worksheet.write('P1', 'Eval ref 24-30')126 # worksheet.write('Q1', 'Eval ref 31-40')127 # worksheet.write('R1', 'Eval ref 41-50')128 # worksheet.write('S1', 'Eval ref 51-60')129 # worksheet.write('T1', 'Eval ref 61-70')130 # worksheet.write('U1', 'Eval ref 71-80')131 # worksheet.write('V1', 'Eval ref 81+')132 for i, checkpoint_dir in enumerate(checkpoint_dirs):133 with open(log_dir+"/"+checkpoint_dir+"/training.log", 'r') as f:134 for line in f:135 pass136 last_line = line.strip()137 training = last_line.split(',')138 training = [val.strip() for val in training]139 # if training[0] != '19':140 # continue141 if len(training) != 5:142 continue143 worksheet.write(i+1, 0, 'WD control vs. CU pain vs. Rehab')144 worksheet.write(i+1, 1, lrs[i//5])145 # write TF log output to the A-D cols146 worksheet.write(i+1, 2, training[1])147 worksheet.write(i+1, 3, training[2])148 worksheet.write(i+1, 4, training[3])149 worksheet.write(i+1, 5, training[4])150 # write training images151 worksheet.insert_image(i+1, 6, log_dir+"/"+checkpoint_dir+"/epoch_accuracy.png", {'y_scale': 0.35, 'x_scale': 0.7, 'object_position': 1})152 worksheet.insert_image(i+1, 7, log_dir+"/"+checkpoint_dir+"/epoch_loss.png", {'y_scale': 0.35, 'x_scale': 0.7, 'object_position': 1})153 # worksheet.insert_image(i+1, 8, "logs/"+log_dir+"/"+checkpoint_dir+"/ROC.png", {'y_scale': 0.75, 'x_scale': 0.75, 'object_position': 1})154 # worksheet.insert_image(i+1, 8, "logs/"+log_dir+"/"+checkpoint_dir+"/ROC.png", {'y_scale': 0.75, 'x_scale': 0.75, 'object_position': 1})155 # write testing images156 worksheet.insert_image(i+1, 9, log_dir+"/"+checkpoint_dir+"/confusion_matrix.png", {'y_scale': 0.5, 'x_scale': 0.7, 'object_position': 1})157 # worksheet.insert_image(i+1, 10, log_dir+"/"+checkpoint_dir+"/validation_3d_preds.gif", {'y_scale': 0.75, 'x_scale': 0.75, 'object_position': 1})158 # worksheet.insert_image(i+1, 11, "logs/"+log_dir+"/"+checkpoint_dir+"/lyons_pain.png", {'y_scale': 0.75, 'x_scale': 0.75, 'object_position': 1})159 # worksheet.insert_image(i+1, 9, "logs/"+log_dir+"/"+checkpoint_dir+"/CU_pain.png", {'y_scale': 0.75, 'x_scale': 0.75, 'object_position': 1})160 # worksheet.insert_image(i+1, 10, "logs/"+log_dir+"/"+checkpoint_dir+"/CU_control.png", {'y_scale': 0.75, 'x_scale': 0.75, 'object_position': 1})161 # worksheet.insert_image(i+1, 11, "logs/"+log_dir+"/"+checkpoint_dir+"/lyons_pain.png", {'y_scale': 0.75, 'x_scale': 0.75, 'object_position': 1})162 # worksheet.insert_image(i+1, 12, "logs/"+log_dir+"/"+checkpoint_dir+"/lyons_pain_2.png", {'y_scale': 0.75, 'x_scale': 0.75, 'object_position': 1})163 # worksheet.insert_image(i+1, 13, "logs/"+log_dir+"/"+checkpoint_dir+"/glynn_pain.png", {'y_scale': 0.75, 'x_scale': 0.75, 'object_position': 1})164 # worksheet.insert_image(i+1, 14, "logs/"+log_dir+"/"+checkpoint_dir+"/rehab.png", {'y_scale': 0.75, 'x_scale': 0.75, 'object_position': 1})165 # worksheet.insert_image(i+1, 15, "logs/"+log_dir+"/"+checkpoint_dir+"/ref 24-30.png", {'y_scale': 0.75, 'x_scale': 0.75, 'object_position': 1})166 # worksheet.insert_image(i+1, 16, "logs/"+log_dir+"/"+checkpoint_dir+"/ref 31-40.png", {'y_scale': 0.75, 'x_scale': 0.75, 'object_position': 1})167 # worksheet.insert_image(i+1, 17, "logs/"+log_dir+"/"+checkpoint_dir+"/ref 41-50.png", {'y_scale': 0.75, 'x_scale': 0.75, 'object_position': 1})168 # worksheet.insert_image(i+1, 18, "logs/"+log_dir+"/"+checkpoint_dir+"/ref 51-60.png", {'y_scale': 0.75, 'x_scale': 0.75, 'object_position': 1})169 # worksheet.insert_image(i+1, 19, "logs/"+log_dir+"/"+checkpoint_dir+"/ref 61-70.png", {'y_scale': 0.75, 'x_scale': 0.75, 'object_position': 1})170 # worksheet.insert_image(i+1, 20, "logs/"+log_dir+"/"+checkpoint_dir+"/ref 71-80.png", {'y_scale': 0.75, 'x_scale': 0.75, 'object_position': 1})171 # worksheet.insert_image(i+1, 21, "logs/"+log_dir+"/"+checkpoint_dir+"/ref 81+.png", {'y_scale': 0.75, 'x_scale': 0.75, 'object_position': 1})172 worksheet.set_row(i+1, 250)173 worksheet.set_column(6, 21, 80)174 workbook.close()175if __name__ == '__main__':...
main.py
Source:main.py
1import argparse2import json3import os4import gym5import gym_boxworld6from stable_baselines import A2C, ACKTR, ACER7from stable_baselines.common.policies import CnnPolicy, LstmPolicy8from relational_policies import RelationalPolicy, RelationalLstmPolicy # custom Policy9from stable_baselines.common.vec_env import SubprocVecEnv10from stable_baselines.common import set_global_seeds11from stable_baselines.bench import Monitor12from stable_baselines.common.atari_wrappers import FrameStack13from warehouse_env.warehouse_env import WarehouseEnv14import numpy as np15def saveInLearn(log_dir):16 # A unit of time saved17 unit_time = int(1e5)18 def callback(_locals, _globals):19 num_timesteps = _locals['self'].num_timesteps20 if num_timesteps >= 1 * unit_time and num_timesteps % unit_time == 0:21 _locals['self'].save(log_dir + 'model_{}.zip'.format(num_timesteps))22 return True23 return callback24def make_env(env_id, env_level, rank, log_dir, frame_stack=False, useMonitor=True, seed=0, map_file=None, render_as_observation=False,25 exponential_agent_training_curve=False):26 def _init():27 if env_id == "WarehouseEnv":28# if map_file is "None" or map_file is None:29 simple_agent = np.zeros((11,11)) 30 simple_agent[5,5] = 131# [[ 0, 1, 0, 0, 0, 0, 2, 0, 0],32# [ 0, 0, 0, 0, 0, 0, 0, 0, 0],33# [ 0, 0, 0, 0, 0, 0, 0, 0, 0],34# [ 0, 0, 0, 0, 0, 0, 0, 0, 0],35# [ 0, 0, 0, 0, 0, 0, 0, 0, 0],36# [ 0, 0, 0, 0, 0, 0, 0, 0, 0],37# [ 0, 0, 0, 0, 3, 0, 0, 0, 0]]38# simple_agent = \39# [[ 0, 1, 0, 0, 0, 0, 0, 0, 0],40# [ 0, 0, 0, 0, 0, 0, 0, 0, 0],41# [ 0, 0, 0, 0, 0, 0, 0, 0, 0],42# [ 0, 0, 0, 0, 0, 0, 0, 0, 0],43# [ 0, 0, 0, 0, 0, 0, 0, 0, 0],44# [ 0, 0, 0, 0, 0, 0, 0, 0, 0],45# [ 0, 0, 0, 0, 0, 0, 0, 0, 0]]46 simple_world = np.zeros((11,11))47# [[ 0, 0, 0, 0, 0, 0, 0, 0, 0],48# [ 0, 0, 0, 0, 0, 0, 0, 0, 0],49# [ 0, 0, 0, 0, 1, 0, 0, 0, 0],50# [ 0, 1, 0, 0, 0, 1, 0, 0, 0],51# [ 0, 0, 0, 0, 1, 0, 0, 0, 0],52# [ 0, 0, 0, 0, 0, 0, 0, 0, 0],53# [ 0, 0, 0, 0, 0, 0, 0, 0, 0]]54 env = WarehouseEnv(agent_map=simple_agent, obstacle_map=simple_world,55 render_as_observation=render_as_observation, 56 exponential_agent_training_curve=exponential_agent_training_curve)57 else:58 env = gym.make(env_id, level=env_level)59 if frame_stack:60 env = FrameStack(env, 4)61 if useMonitor:62 env = Monitor(env, log_dir + str(rank), allow_early_resets=True)63 return env64 set_global_seeds(seed)65 return _init66def set_logdir(config):67 log_dir = '{}/{}_{}_{}/log_0/'.format(config.log_dir, config.env_name, config.model_name, config.policy_name)68 # if log_dir exists,auto add new dir by order69 while os.path.exists(log_dir):70 lastdir_name = log_dir.split('/')[-2]71 order = int(lastdir_name.split('_')[-1])72 log_dir = log_dir.replace('_{}'.format(order), '_{}'.format(order + 1))73 os.makedirs(log_dir)74 with open(log_dir + 'config.txt', 'wt') as f:75 json.dump(config.__dict__, f, indent=2)76 print(("--------------------------Create dir:{} Successful!--------------------------\n").format(log_dir))77 return log_dir78def set_env(config, log_dir):79 env_id = "WarehouseEnv" if config.env_name == "WarehouseEnv" else config.env_name + 'NoFrameskip-v4'80 env = SubprocVecEnv([make_env(env_id, config.env_level, i, log_dir, frame_stack=config.frame_stack, map_file=config.map_file,81 render_as_observation=config.render_as_observation) for i in range(config.num_cpu)])82 return env83def set_model(config, env, log_dir):84 if config.timeline:85 from timeline_util import _train_step86 A2C.log_dir = log_dir87 A2C._train_step = _train_step88 policy = {'CnnPolicy': CnnPolicy, 'LstmPolicy': LstmPolicy, 'RelationalPolicy': RelationalPolicy, 'RelationalLstmPolicy': RelationalLstmPolicy}89 base_mode = {'A2C': A2C, "ACKTR": ACKTR, "ACER": ACER}90 # whether reduce oberservation91 policy[config.policy_name].reduce_obs = config.reduce_obs92 n_steps = config.env_steps93 policy_kwargs = dict(feature_extraction=(config.render_as_observation))94 model = base_mode[config.model_name](policy[config.policy_name], env, verbose=1, 95 tensorboard_log=log_dir, 96 n_steps=n_steps, policy_kwargs=policy_kwargs) 97# priming_steps=config.priming_steps, 98# coordinated_planner=config.coordinated_planner)99 print(("--------Algorithm:{} with {} num_cpu:{} total_timesteps:{} Start to train!--------\n")100 .format(config.model_name, config.policy_name, config.num_cpu, config.total_timesteps))101 return model102def run(config):103 log_dir = set_logdir(config)104 env = set_env(config, log_dir)105 model = set_model(config, env, log_dir)106 model.learn(total_timesteps=int(config.total_timesteps), callback=saveInLearn(log_dir) if config.save else None)107 if config.save:108 model.save(log_dir + 'model.zip')109if __name__ == '__main__':110 parser = argparse.ArgumentParser()111 parser.add_argument("env_name", choices=['BoxRandWorld', 'BoxWorld', "WarehouseEnv"], 112 help="Name of environment")113 parser.add_argument("-env_level", choices=['easy', 'medium', 'hard'], default='easy', 114 help="level of environment")115 116 parser.add_argument("-map_file", default='None', type=str, help="Map file")117 118 parser.add_argument("policy_name", choices=['RelationalPolicy', 'CnnPolicy', 'RelationalLstmPolicy', 'LstmPolicy'], 119 help="Name of policy")120 121 parser.add_argument("-model_name", choices=['A2C', 'ACER', 'ACKTR'], 122 default='A2C', help="Name of model")123 parser.add_argument("-reduce_obs", action='store_true')124 parser.add_argument("-timeline", action='store_true', help='performance analysis,default=False')125 parser.add_argument("-frame_stack", action='store_true', help='whether use frame_stack, default=False')126 parser.add_argument("-cuda_device", default='1', help='which cuda device to run, default="1"')127 parser.add_argument("-num_cpu", default=4, type=int, help='number of CPUs')128 parser.add_argument("-total_timesteps", default=2e6, type=float, help='total train timesteps, default=2e6')129 parser.add_argument("-log_dir", default='exp_result', help='log_dir path, default="exp_result"')130 parser.add_argument("-save", action='store_true', help='whether save model to log_dir, default=False')131 132 parser.add_argument("-env_steps", default=50, type=int, help='num steps, default="50"')133 parser.add_argument("-priming_steps", default=1000, type=int, help='priming steps, default="1000"')134 parser.add_argument("-coordinated_planner", action='store_true', help='whether to use a coordinated_planner, default false')135 parser.add_argument("-render_as_observation", action='store_true', help='whether to use a render_as_observation, default false')136 parser.add_argument("-delta_tolling", action='store_true', help='whether to use a delta_tolling, default false')137 parser.add_argument("-random_agent_reset_location", action='store_true', help='whether to use a random_agent_reset_location, default false')138 parser.add_argument("-exponential_agent_training_curve", action='store_true', help='whether to use a exponential_agent_training_curve, default false')139 config = parser.parse_args()140 # print(config)141 os.environ['CUDA_VISIBLE_DEVICES'] = config.cuda_device142 run(config)...
train_nn.py
Source:train_nn.py
...39max_epochs = 8040batch_size = 12841def main():42 log_dir = LOG_DIR43 log_dir = init_log_dir(log_dir)44 data = spio.loadmat(DATA, squeeze_me=True)45 x_train = data['datasetInputs'][0]46 y_train = data['datasetTargets'][0]47 x_val = data['datasetInputs'][1]48 y_val = data['datasetTargets'][1]49 x_test = data['datasetInputs'][2]50 y_test = data['datasetTargets'][2]51 x_train = np.array(x_train)52 y_train = np.array(y_train)53 x_test = np.array(x_test)54 y_test = np.array(y_test)55 x_val = np.array(x_val)56 y_val = np.array(y_val)57 model = Sequential()58 model.add(Dense(layers[0], input_dim=900, activation=activation_fns[0]))59 model.add(Dropout(dropouts[0]))60 for i in range(1, len(layers)):61 model.add(Dropout(dropouts[i]))62 model.add(Dense(layers[i], kernel_regularizer=regularizer, activation=activation_fns[i]))63 model.add(Dense(7, activation='softmax'))64 print("[INFO] compiling model...")65 sgd = SGD(lr=learning_rate, momentum=momentum, decay=decay_lr, nesterov=nesterov)66 model.compile(loss='categorical_crossentropy',67 optimizer=sgd, metrics=['accuracy'])68 early_stop_callback = keras.callbacks.EarlyStopping(monitor='val_acc',69 min_delta=early_stop_min_delta, patience=patience, verbose=1, mode='max')70 append_to_callbacks(early_stop_callback)71 if lr_scheduler_fn:72 learning_rate_scheduler = keras.callbacks.LearningRateScheduler(73 lr_scheduler_fn, verbose=0)74 append_to_callbacks(learning_rate_scheduler)75 log_dir = append_params_to_log_dir(log_dir, ['lr_scheduler', lr_scheduler_fn.__name__])76 log_dir = append_params_to_log_dir(log_dir, ['lr_param', lr_param])77 else:78 log_dir = append_params_to_log_dir(log_dir, ['lr_scheduler', lr_scheduler_fn])79 log_dir = check_unique_log_dir(log_dir)80 if tensorboard:81 tensorboard_cb = TensorBoard(log_dir=log_dir, histogram_freq=0,82 write_graph=True, write_images=True)83 append_to_callbacks(tensorboard_cb)84 print("-------------------------------------------------------------------")85 print("Log Directory: " + log_dir)86 print("-------------------------------------------------------------------")87 if normalize_imgs:88 print("[INFO] Normalizing Images..")89 x_train = normalize_input(x_train)90 x_val = normalize_input(x_val)91 x_test = normalize_input(x_test)92 model.fit(x_train, y_train, epochs=max_epochs, batch_size=batch_size,93 validation_data=(x_val, y_val), callbacks=callbacks)94 # show the accuracy on the testing set95 print("[INFO] evaluating on testing set...")96 (loss, accuracy) = model.evaluate(x_test, y_test,97 batch_size=batch_size, verbose=1)98 print("[INFO] loss={:.4f}, accuracy: {:.4f}%".format(loss,99 accuracy * 100))100 y_pred = model.predict(x_test, batch_size=batch_size, verbose=1)101 y_pred = y_pred.argmax(1)102 y_true = y_test.argmax(1)103 conf_matrix = confusion_matrix(y_true, y_pred)104 f1_measure = f1_score(y_true, y_pred, average='weighted')105 class_report = classification_report(y_true, y_pred)106 print("F1 measure: " + str(f1_measure))107 print("------------------------------")108 print("Classification report: ")109 print(str(class_report))110 print("------------------------------")111 print_confusion_matrix(conf_matrix, True)112 print_confusion_matrix(conf_matrix, False)113 print ('Saving Model..')114 save_model(model)115def save_model(model):116 # Creates a HDF5 file117 model.save('facial_recognition_model' + datetime.datetime.now().strftime("%Y-%m-%d %H:%M") + '.h5')118def normalize_input(x):119 pool = multiprocessing.Pool(8)120 for i in range(len(x)):121 x[i] = pool.map(scale,[x[i]])[0]122 return x123def append_params_to_log_dir(log_dir, params):124 log_dir += str.join('_', [str(i) for i in params])125 log_dir += '_'126 return log_dir127def init_log_dir(log_dir):128 log_dir = append_params_to_log_dir(log_dir, activation_fns + layers)129 log_dir = append_params_to_log_dir(log_dir, ['lr', learning_rate])130 log_dir = append_params_to_log_dir(log_dir, ['dr', decay_lr])131 log_dir = append_params_to_log_dir(log_dir, ['m', momentum])132 log_dir = append_params_to_log_dir(log_dir, ['nest', nesterov])133 log_dir = append_params_to_log_dir(log_dir, ['patience', patience])134 log_dir = append_params_to_log_dir(log_dir, ['dropouts'] + dropouts)135 log_dir = append_params_to_log_dir(log_dir, ['reg', regularizer_type])136 return log_dir137def append_to_callbacks(callback):138 callbacks.append(callback)139def check_unique_log_dir(log_dir):140 if os.path.isdir(log_dir):141 print("Log Directory already exists! Appending date to log dir.")142 log_dir += datetime.datetime.now().strftime("%Y-%m-%d %H:%M")143 return log_dir144def print_confusion_matrix(cm, normalize_cm):145 if normalize_cm:146 cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]147 print("Normalized confusion matrix")148 else:149 print("Confusion matrix, without normalization")150 print(cm)151def scale(img):152 img = image_histogram_equalization(img)153 avg = np.average(img)...
utils.py
Source:utils.py
...50 composed = np.zeros((h, 2*w, ch))51 composed[:, :w, :] = ap52 composed[:, w:, :] = bp53 return composed54def get_log_dir(log_dir, expt_name):55 """Compose the log_dir with the experiment name."""56 if log_dir is None:57 raise Exception('log_dir can not be None.')58 if expt_name is not None:59 return os.path.join(log_dir, expt_name)60 return log_dir61def mkdir(mypath):62 """Create a directory if it does not exist."""63 try:64 os.makedirs(mypath)65 except OSError as exc:66 if exc.errno == EEXIST and os.path.isdir(mypath):67 pass68 else:69 raise70def create_expt_dir(params):71 """Create the experiment directory and return it."""72 expt_dir = get_log_dir(params.log_dir, params.expt_name)73 # Create directories if they do not exist74 mkdir(params.log_dir)75 mkdir(expt_dir)76 # Save the parameters77 with open(os.path.join(expt_dir, 'params.json'), 'w') as f:78 f.write(json.dumps(params, indent=4, sort_keys=True))79 80 return expt_dir81def plot_loss(loss, label, filename, log_dir):82 """Plot a loss function and save it in a file."""83 plt.figure(figsize=(5, 4))84 plt.plot(loss, label=label)85 plt.legend()86 plt.savefig(os.path.join(log_dir, filename))87 plt.clf()88def log(losses, atob, it_val, N=4, log_dir=DEFAULT_LOG_DIR, expt_name=None,89 is_a_binary=True, is_b_binary=False):90 """Log losses and atob results."""91 log_dir = get_log_dir(log_dir, expt_name)92 # Save the losses for further inspection93 pickle.dump(losses, open(os.path.join(log_dir, 'losses.pkl'), 'wb'))94 ###########################################################################95 # PLOT THE LOSSES #96 ###########################################################################97 plot_loss(losses['d'], 'discriminator', 'd_loss.png', log_dir)98 plot_loss(losses['d_val'], 'discriminator validation', 'd_val_loss.png', log_dir)99 plot_loss(losses['p2p'], 'Pix2Pix', 'p2p_loss.png', log_dir)100 plot_loss(losses['p2p_val'], 'Pix2Pix validation', 'p2p_val_loss.png', log_dir)101 ###########################################################################102 # PLOT THE A->B RESULTS #103 ###########################################################################104 plt.figure(figsize=(12, 8))105 for i in range(N*N):106 a, _ = next(it_val)107 bp = atob.predict(a)108 img = compose_imgs(a[0], bp[0], is_a_binary=is_a_binary, is_b_binary=is_b_binary)109 plt.subplot(N, N, i+1)110 plt.imshow(img)111 plt.axis('off')112 plt.savefig(os.path.join(log_dir, 'atob.png'))113 plt.clf()114 # Make sure all the figures are closed.115 plt.close('all')116def save_weights(models, log_dir=DEFAULT_LOG_DIR, expt_name=None):117 """Save the weights of the models into a file."""118 log_dir = get_log_dir(log_dir, expt_name)119 models.atob.save_weights(os.path.join(log_dir, ATOB_WEIGHTS_FILE), overwrite=True)120 models.d.save_weights(os.path.join(log_dir, D_WEIGHTS_FILE), overwrite=True)121def load_weights(atob, d, log_dir=DEFAULT_LOG_DIR, expt_name=None):122 """Load the weights into the corresponding models."""123 log_dir = get_log_dir(log_dir, expt_name)124 atob.load_weights(os.path.join(log_dir, ATOB_WEIGHTS_FILE))125 d.load_weights(os.path.join(log_dir, D_WEIGHTS_FILE))126def load_weights_of(m, weights_file, log_dir=DEFAULT_LOG_DIR, expt_name=None):127 """Load the weights of the model m."""128 log_dir = get_log_dir(log_dir, expt_name)129 m.load_weights(os.path.join(log_dir, weights_file))130def load_losses(log_dir=DEFAULT_LOG_DIR, expt_name=None):131 """Load the losses of the given experiment."""132 log_dir = get_log_dir(log_dir, expt_name)133 losses = pickle.load(open(os.path.join(log_dir, 'losses.pkl'), 'rb'))134 return losses135def load_params(params):136 """137 Load the parameters of an experiment and return them.138 The params passed as argument will be merged with the new params dict.139 If there is a conflict with a key, the params passed as argument prevails.140 """141 expt_dir = get_log_dir(params.log_dir, params.expt_name)142 expt_params = json.load(open(os.path.join(expt_dir, 'params.json'), 'rb'))143 # Update the loaded parameters with the current parameters. This will144 # override conflicting keys as expected.145 expt_params.update(params)...
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