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utils.py
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utils.py
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import numpy as np
import torch
import pathlib
from tqdm import tqdm
import matplotlib as mpl
import matplotlib.pyplot as plt
mpl.use('Agg')
def split_equal(total: int, num: int):
quotient = int(total / num)
remainder = total % num
start = list()
end = list()
for i in range(num):
if i < remainder:
start.append(i*(quotient+1))
end.append((i+1)*(quotient+1))
else:
start.append(i*quotient+remainder)
end.append((i+1)*quotient+remainder)
return start, end
class Logger:
def __init__(self, logfile_path: pathlib.Path, train_log_name=None, test_log_name=None):
self.training_logfile = None if train_log_name is None else open(logfile_path / train_log_name, 'w')
self.testing_logfile = None if test_log_name is None else open(logfile_path / test_log_name, 'w')
def __del__(self):
if self.training_logfile is not None: self.training_logfile.close()
if self.testing_logfile is not None: self.testing_logfile.close()
def training_log(self, *strs):
string = ' '.join(strs)
self.training_logfile.write(string + '\n')
tqdm.write(string)
def testing_log(self, *strs):
string = ' '.join(strs)
self.testing_logfile.write(string + '\n')
tqdm.write(string)
class ReplayBuffer(object):
def __init__(self, state_dim, action_dim, max_size=int(1e6)):
self.max_size = max_size
self.ptr = 0
self.size = 0
self.state = np.zeros((max_size, state_dim))
self.action = np.zeros((max_size, action_dim))
self.next_state = np.zeros((max_size, state_dim))
self.reward = np.zeros((max_size, 1))
self.not_done = np.zeros((max_size, 1))
self.p = np.zeros((max_size, 1))
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def add(self, state, action, next_state, reward, done):
self.state[self.ptr] = state
self.action[self.ptr] = action
self.next_state[self.ptr] = next_state
self.reward[self.ptr] = reward
self.not_done[self.ptr] = 1. - done
self.ptr = (self.ptr + 1) % self.max_size
self.size = min(self.size + 1, self.max_size)
def sample(self, batch_size):
ind = np.random.randint(0, self.size, size=batch_size)
ind[0] = np.argmax(self.reward)
return (
torch.FloatTensor(self.state[ind]).to(self.device),
torch.FloatTensor(self.action[ind]).to(self.device),
torch.FloatTensor(self.next_state[ind]).to(self.device),
torch.FloatTensor(self.reward[ind]).to(self.device),
torch.FloatTensor(self.not_done[ind]).to(self.device)
)
class Picture_Drawer:
def __init__(self):
pass
@staticmethod
def draw_sopf_train_test(
data_path: pathlib.Path,
n_processor: int,
total_step: int,
interval: int,
size=12,
):
font1 = {'size': size}
mpl.rcParams['xtick.labelsize'] = size
mpl.rcParams['ytick.labelsize'] = size
training_data = np.load(data_path, allow_pickle=True)
train = training_data['train'].reshape(-1,n_processor)
eval = training_data['eval']
x = np.arange(1, total_step+1)
plt.xlim((0, total_step+1))
plt.ylim((np.min(eval), 1000))
plt.xlabel('Training Step', fontdict=font1)
plt.ylabel('Average Reward', fontdict=font1)
plt.tick_params(labelsize=size)
avg_train_reward = np.sum(train, axis=1) / n_processor
plt.scatter(x, avg_train_reward,
s = 5,
label="training",
c = 'b',
)
# for i in range(n_processor):
# plt.scatter(x, train[:, i],
# s = 5,
# label=f"training_processor{i}",
# c = 'b',
# )
x = np.arange(0, total_step+1, interval)
avg_test_reward = np.average(eval[:x.shape[0]], axis=1)
plt.plot(x, avg_test_reward,
label="evaluation",
color = 'r',
)
plt.legend(loc='best')
plt.savefig(data_path.parent / 'training.jpg', dpi=300, bbox_inches='tight', format='jpg')
plt.close()