def main():
from train import Trainer
net = Net(VinConfig)
net = net.cuda()
net = net.double()
trainer = Trainer(VinConfig, net)
trainer.train()
import numpy as np
from model import Net
import torch
import matplotlib.pyplot as plt
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import pdb
from tqdm import tqdm
import dataLoader as data
from torch.utils.tensorboard import SummaryWriter
net = Net().train().to(device)
net = net.double()
learning_rate = 0.01
writer = SummaryWriter()
g_checkpoint = torch.load(
"./training_weights.ckpt",
map_location=torch.device(device)) #the file to train
net.load_state_dict(g_checkpoint['model'])
#Will train from the same file every time, if you don't have yet make sure to just comment this out
optimizer = optim.SGD(
net.parameters(), lr=learning_rate,
momentum=0.5) #Not sure what the parameters do , just copying it
optimizer.load_state_dict(g_checkpoint['optimizer'])
criterion = nn.CrossEntropyLoss()
class LFTReward:
def __init__(self):
self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# self.device = torch.device('cpu')
self.encoder_net = Net()
self.encoder_net = self.encoder_net.to(self.device)
self.encoder_net.double()
self.encoder_net.load_state_dict(torch.load(model_path, map_location={'cuda:0': 'cpu'}))
# self.encoder_net.load_state_dict(torch.load(model_path))
self.encoder_net.eval()
img_g = self.load_data()
img_g = torch.tensor(img_g).to(self.device)
self.img_g_z = self.encoder_net(img_g)
@staticmethod
def distance(x1, x2):
diff = torch.abs(x1 - x2).contiguous()
return torch.pow(diff, 2)
@staticmethod
def load_data():
img_ds = []
for i in range(1, folder_size + 1):
img_d = cv2.imread(f'goal_abstract/{i}/bw_{i}.png')
img_ds.append(img_d / 255.0)
return img_ds
@staticmethod
def rgb2gray(rgb):
return np.dot(rgb[..., :3], [0.2989, 0.5870, 0.1140])
def reward(self, s, j):
# gray = self.rgb2gray(s)
# resized_img = cv2.resize(gray, (256, 256))
resized_img = s.cpu().detach().numpy()
resized_img = resized_img / 255.0
# resized_img = resized_img[:, :, :, None]
rgb_image = cv2.merge((resized_img, resized_img, resized_img))
rgb_image = rgb_image.reshape((-1, 84, 84, 3))
pixel_val = rgb_image[0, 10, 60, 0]
if pixel_val == 0:
goal_wall = True
elif abs(pixel_val - 0.75) < 0.01:
goal_wall = True
else:
goal_wall = False
img_s = torch.tensor(rgb_image).to(self.device)
img_s = img_s.double()
img_s_z = self.encoder_net(img_s)
rewards = self.calc_reward1(img_s_z, goal_wall)
return rewards
def calc_reward1(self, img_s_z, goal_wall):
reward_list = []
for frame in range(img_s_z.shape[0]):
distance_list = []
for i in range(self.img_g_z.shape[0]):
distance = torch.clamp(self.distance(img_s_z[frame], self.img_g_z[i]), max=5).cpu().detach().numpy()
distance = np.sum(distance)
distance_list.append(distance)
reward = 0
if distance_list[0] < 2.5:
reward += 50
if distance_list[1] < 3:
reward += 50
# if distance_list[2] < 3:
# reward -= 2
reward_list.append(reward)
return reward_list
def calc_reward2(self, img_s_z):
reward_list = []
for frame in range(img_s_z.shape[0]):
reward_sum_per_frame = 0
for i in range(self.img_g_z.shape[0]):
distance = torch.clamp(self.distance(img_s_z[frame], self.img_g_z[i]), max=5).cpu().detach().numpy()
reward = np.exp(-1 * distance / 10.0)
reward = np.sum(reward)
reward_sum_per_frame += reward
reward_list.append(reward_sum_per_frame)
return reward_list