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a3c_train.py
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import torch.optim as optim
import env.env as grounding_env
import torchvision
from models.models import *
import matplotlib.pyplot as plt
import cv2
from ae.auto_encoder import Auto_Encoder_Model_PReLu224
log_file = 'train_easy_diff_convolve.log'
device = 'cpu'
def ensure_shared_grads(model, shared_model):
for param, shared_param in zip(model.parameters(),
shared_model.parameters()):
if shared_param.grad is not None:
return
shared_param._grad = param.grad
def train(rank, args, shared_model):
torch.manual_seed(args.seed + rank)
env = grounding_env.GroundingEnv(args)
env.game_init()
ae_model = None
if args.auto_encoder:
ae_model = Auto_Encoder_Model_PReLu224()
model = A3C_LSTM_GA(args, ae_model).to(device)
if (args.load != "0"):
print(str(rank) + " Loading model ... "+args.load)
model.load_state_dict(
torch.load(args.load, map_location=lambda storage, loc: storage))
model.train()
optimizer = optim.SGD(shared_model.parameters(), lr=args.lr)
# optimizer = torch.optim.Adamax(filter(lambda p: p.requires_grad, shared_model.parameters()))
# optimizer = torch.optim.Adam(shared_model.parameters(), lr=0.0001)
# scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_0=10000, T_mult=1, eta_min=0.00001, last_epoch=-1)
if args.auto_encoder:
ae_criterion = torch.nn.MSELoss()
p_losses = []
v_losses = []
ae_losses = []
(image, _, instruction), _, _ = env.reset()
instruction_idx = []
for word in instruction.split(" "):
instruction_idx.append(env.word_to_idx[word])
instruction_idx = np.array(instruction_idx).astype(float)
original_image = image
image = torch.from_numpy(image).float()/255
instruction_idx = torch.from_numpy(instruction_idx).view(1, -1).float()
done = True
episode_length = 0
num_iters = 0
entropy_coef = 0.01 #0.005
decrease_factor = 0 #1e-6
final_entropy_coeff = 0.01#0.005
while True:
# Sync with the shared model
model.load_state_dict(shared_model.state_dict())
if done:
episode_length = 0
cx = torch.Tensor(torch.zeros(1, 256)).to(device)
hx = torch.Tensor(torch.zeros(1, 256)).to(device)
else:
cx = torch.Tensor(cx.data).to(device)
hx = torch.Tensor(hx.data).to(device)
values = []
log_probs = []
rewards = []
entropies = []
for step in range(args.num_steps):
episode_length += 1
tx = torch.Tensor(torch.from_numpy(np.array([episode_length])).float()).to(device) #.float()
instruction_idx = instruction_idx.to(device).float()
if args.auto_encoder:
# if original_image.shape == (3, 168, 300):
if original_image.shape == (3, 224, 224):
original_image = np.moveaxis(original_image, 0, 2)
ae_input = original_image / 255.0
ae_input = torch.Tensor(ae_input)
ae_input = ae_input.permute(-1,0,1)
ae_input = ae_input.unsqueeze(0)
value, logit, (hx, cx), decoder = model((ae_input, torch.Tensor(image.unsqueeze(0)),
torch.Tensor(instruction_idx),
(tx, hx, cx)))
decoder_image = decoder.squeeze().permute(1, 2, 0).detach().numpy()
# plt.imshow(decoder_image)
# plt.savefig('foo.png')
# # plt.show()
ae_loss = ae_criterion(decoder, ae_input)
else:
value, logit, (hx, cx) = model((torch.Tensor(image.unsqueeze(0)),
torch.Tensor(instruction_idx),
(tx, hx, cx)))
prob = F.softmax(logit,dim=-1)
log_prob = F.log_softmax(logit,dim=-1)
entropy = -(log_prob * prob).sum(1)
entropies.append(entropy)
action = prob.multinomial(num_samples=1).data.float()
log_prob = log_prob.gather(1, torch.Tensor(action).long())
action = action.numpy()[0, 0]
(image, _, _), reward, done = env.step(action)
original_image = image
# reward += (math.exp(0.99*dist/1000))
done = done or episode_length >= args.max_episode_length
if done:
(image, _, instruction), _, _ = env.reset()
instruction_idx = []
for word in instruction.split(" "):
instruction_idx.append(env.word_to_idx[word])
instruction_idx = np.array(instruction_idx)
instruction_idx = torch.from_numpy(
instruction_idx).view(1, -1)
original_image = image
image = torch.from_numpy(image).float()/255
values.append(value)
log_probs.append(log_prob)
rewards.append(reward)
if done:
break
R = torch.zeros(1, 1)
if not done:
tx = torch.Tensor(torch.from_numpy(np.array([episode_length])).float()).to(device)
if args.auto_encoder:
original_image = np.moveaxis(original_image, 0, 2)
ae_input = original_image / 255.0
ae_input = torch.Tensor(ae_input)
ae_input = ae_input.permute(-1,0,1)
ae_input = ae_input.unsqueeze(0)
value, _, _, decoder = model((ae_input, torch.Tensor(image.unsqueeze(0)),
torch.Tensor(instruction_idx), (tx, hx, cx)))
ae_loss = ae_criterion(decoder, ae_input)
else:
value, _, _ = model((torch.Tensor(image.unsqueeze(0)),
torch.Tensor(instruction_idx), (tx, hx, cx)))
R = value.data
values.append(torch.Tensor(R))
policy_loss = 0
value_loss = 0
R = torch.Tensor(R)
gae = torch.zeros(1, 1)
if entropy_coef < final_entropy_coeff:
entropy_coef = (1+decrease_factor)*entropy_coef
else:
entropy_coef = final_entropy_coeff
for i in reversed(range(len(rewards))):
R = args.gamma * R + rewards[i]
advantage = R - values[i]
value_loss = value_loss + 0.5 * advantage.pow(2)
# Generalized Advantage Estimataion
delta_t = rewards[i] + args.gamma * \
values[i + 1].data - values[i].data
gae = gae * args.gamma * args.tau + delta_t
policy_loss = policy_loss - \
log_probs[i] * torch.Tensor(gae) - entropy_coef * entropies[i]
optimizer.zero_grad()
p_losses.append(policy_loss.data[0, 0])
v_losses.append(value_loss.data[0, 0])
if args.auto_encoder:
ae_losses.append(ae_loss.item())
if(len(p_losses) > 1000):
num_iters += 1
if args.auto_encoder:
print(" ".join([
"Training thread: {}".format(rank),
"Num iters: {}K".format(num_iters),
"Avg policy loss: {}".format(np.mean(p_losses)),
"Avg value loss: {}".format(np.mean(v_losses)),
"Avg AE loss: {}".format(np.mean(ae_losses))]))
with open(log_file, "a+") as f:
f.write(" ".join([
"Training thread: {}".format(rank),
"Num iters: {}K".format(num_iters),
"Avg policy loss: {}".format(np.mean(p_losses)),
"Avg value loss: {}".format(np.mean(v_losses)),
"Avg AE loss: {}\n".format(np.mean(ae_losses))]))
else:
print(" ".join([
"Training thread: {}".format(rank),
"Num iters: {}K".format(num_iters),
"Avg policy loss: {}".format(np.mean(p_losses)),
"Avg value loss: {}".format(np.mean(v_losses))]))
with open(log_file, "a+") as f:
f.write(" ".join([
"Training thread: {}".format(rank),
"Num iters: {}K".format(num_iters),
"Avg policy loss: {}".format(np.mean(p_losses)),
"Avg value loss: {}\n".format(np.mean(v_losses))]))
p_losses = []
v_losses = []
ae_losses = []
if args.auto_encoder:
(policy_loss + 0.5 * value_loss + ae_loss).backward()
else:
(policy_loss + 0.5*value_loss).backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 40)
ensure_shared_grads(model, shared_model)
optimizer.step()
# scheduler.step()