def __init__(self,
model,
task,
result,
report,
n_session,
n_block,
seed=None):
self.model_file = model
self.task_file = task
self.result_file = result
self.report_file = report
self.n_session = n_session
self.n_block = n_block
self.seed = seed
if self.seed is None:
self.seed = random.randint(0, 1000)
np.random.seed(seed), random.seed(seed)
self.model = Model(self.model_file)
self.task = Task(self.task_file)
self.n_trial = len(self.task)
import numpy as np
import matplotlib.pyplot as plt
from task_single import Task
from model_single import Model
import os
folder = "data/figures/"
if not os.path.exists(folder):
os.makedirs(folder)
seed = random.randint(0,1000)
np.random.seed(seed)
random.seed(seed)
# seed = 448,380,325
model = Model("model-topalidou.json")
task = Task("tasks/task-topalidou.json")
print("-"*30)
print("Seed: %d" % seed)
print("Model: %s" % model.filename)
print("Task: %s" % task.filename)
print("-"*30)
trial = task[0]
model.process(task, trial, stop=False, debug=False, cortical_activity=True)
cog = model["CTX"]["cog"].history[:3000]
mot = model["CTX"]["mot"].history[:3000]
import matplotlib.pyplot as plt
from task_single import Task
from model_single import Model
import os
folder = "data/figures/"
if not os.path.exists(folder):
os.makedirs(folder)
# seed = random.randint(0,1000)
# np.random.seed(seed)
# random.seed(seed)
seed = 749
model = Model("model-topalidou-thomas.json")
task = Task("tasks/task-topalidou.json")
print("-" * 30)
print("Seed: %d" % seed)
print("Model: %s" % model.filename)
print("Task: %s" % task.filename)
print("-" * 30)
model["CTX:cog → STR:cog"].gain = 0
model["CTX:mot → STR:mot"].gain = 0
model["CTX:cog → STR:ass"].gain = 0
model["CTX:mot → STR:ass"].gain = 0
trial = task[0]
model.process(task, trial, stop=False, debug=False)
import numpy as np
import matplotlib.pyplot as plt
from task_single import Task
from model_single import Model
import os
folder = "data/figures/"
if not os.path.exists(folder):
os.makedirs(folder)
seed = random.randint(0, 1000)
np.random.seed(seed)
random.seed(seed)
# seed = 448,380,325
model = Model("model-topalidou.json")
task = Task("tasks/task-topalidou.json")
print("-" * 30)
print("Seed: %d" % seed)
print("Model: %s" % model.filename)
print("Task: %s" % task.filename)
print("-" * 30)
trial = task[0]
model.process(task, trial, stop=False, debug=False, cortical_activity=True)
cog = model["CTX"]["cog"].history[:3000]
mot = model["CTX"]["mot"].history[:3000]
duration = 3.0
import matplotlib.pyplot as plt
from task_single import Task
from model_single import Model
import os
folder = "data/figures/"
if not os.path.exists(folder):
os.makedirs(folder)
# seed = random.randint(0,1000)
# np.random.seed(seed)
# random.seed(seed)
seed = 749
model = Model("model-topalidou-thomas.json")
task = Task("tasks/task-topalidou.json")
print("-"*30)
print("Seed: %d" % seed)
print("Model: %s" % model.filename)
print("Task: %s" % task.filename)
print("-"*30)
model["CTX:cog → STR:cog"].gain = 0
model["CTX:mot → STR:mot"].gain = 0
model["CTX:cog → STR:ass"].gain = 0
model["CTX:mot → STR:ass"].gain = 0
trial = task[0]
model.process(task, trial, stop=False, debug=False)
type=str,
help='pretrained model checkpoint')
parser.add_argument('--epochs', default=101, type=int, help='train epochs')
parser.add_argument('--train', default=True, type=bool, help='train')
args = parser.parse_args()
save_path = args.save_path + f'{args.message}_{time_str}'
if not os.path.exists(save_path):
os.mkdir(save_path)
logger = Logger(f'{save_path}/log.log')
logger.Print(args.message)
train_data, val_data, test_data = load_cisia_surf(train_size=args.batch_size,
test_size=args.test_size)
model = Model(pretrained=False, num_classes=2)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(),
lr=0.01,
momentum=0.9,
weight_decay=5e-4)
scheduler = lr_scheduler.ExponentialLR(optimizer, gamma=0.95)
if use_cuda:
model = model.cuda()
criterion = criterion.cuda()
loss_history = []
eval_history = []
