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train_obs_generation.py
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import torch
import datetime
import os
import time
import json
import math
import numpy as np
from os.path import join as pjoin
from observation_generation_dataset import ObservationGenerationData
from agent import Agent
import generic
import evaluate
def train():
time_1 = datetime.datetime.now()
config = generic.load_config()
env = ObservationGenerationData(config)
env.split_reset("train")
agent = Agent(config)
agent.zero_noise()
ave_train_loss = generic.HistoryScoreCache(capacity=500)
# visdom
if config["general"]["visdom"]:
import visdom
viz = visdom.Visdom()
plt_win = None
eval_plt_win = None
viz_loss, viz_eval_loss, viz_eval_f1 = [], [], []
episode_no = 0
batch_no = 0
output_dir = "."
data_dir = "."
json_file_name = agent.experiment_tag.replace(" ", "_")
best_eval_loss_so_far, best_training_loss_so_far = 10000.0, 10000.0
# load model from checkpoint
if agent.load_pretrained:
if os.path.exists(output_dir + "/" + agent.experiment_tag + "_model.pt"):
agent.load_pretrained_model(output_dir + "/" + agent.experiment_tag + "_model.pt", load_partial_graph=False)
elif os.path.exists(data_dir + "/" + agent.load_graph_generation_model_from_tag + ".pt"):
agent.load_pretrained_model(data_dir + "/" + agent.load_graph_generation_model_from_tag + ".pt", load_partial_graph=False)
try:
while(True):
if episode_no > agent.max_episode:
break
agent.train()
observation_strings, prev_action_strings = env.get_batch()
curr_batch_size = len(observation_strings)
lens = [len(elem) for elem in observation_strings]
max_len = max(lens)
padded_observation_strings = [elem + ["<pad>"]*(max_len - len(elem)) for elem in observation_strings]
padded_prev_action_strings = [elem + ["<pad>"]*(max_len - len(elem)) for elem in prev_action_strings]
masks = torch.zeros((curr_batch_size, max_len), dtype=torch.float).cuda() if agent.use_cuda else torch.zeros((curr_batch_size, max_len), dtype=torch.float)
for i in range(curr_batch_size):
masks[i, :lens[i]] = 1
preds_last_batch = []
last_k_batches_loss = []
prev_h = None
for i in range(max_len):
batch_obs_string = [elem[i] for elem in padded_observation_strings]
batch_prev_action_string = [elem[i] for elem in padded_prev_action_strings]
loss, pred, prev_h = agent.observation_generation_teacher_force(batch_obs_string, batch_prev_action_string, masks[:, i], prev_h)
last_k_batches_loss.append(loss)
ave_train_loss.push(generic.to_np(loss))
preds_last_batch.append(pred[-1])
if ((i + 1) % agent.backprop_frequency == 0 or i == max_len - 1): # and i > 0:
agent.optimizer.zero_grad()
ave_k_loss = torch.mean(torch.stack(last_k_batches_loss))
ave_k_loss.backward()
agent.optimizer.step()
last_k_batches_loss = []
prev_h = prev_h.detach()
k = 0
ep_string = []
while(masks[-1][k] > 0):
step_string = []
regen_strings = preds_last_batch[k].argmax(-1)
for l in range(len(regen_strings)):
step_string.append(agent.word_vocab[regen_strings[l]])
ep_string.append((' '.join(step_string).split("<eos>")[0]))
k += 1
if k == len(masks[-1]):
break
if len(ep_string) >= 3:
print(' | '.join(ep_string[:3]))
#####
# lr schedule
# learning_rate = 1.0 * (generic.power(agent.model.block_hidden_dim, -0.5) * min(generic.power(batch_no, -0.5), batch_no * generic.power(agent.learning_rate_warmup_until, -1.5)))
if batch_no < agent.learning_rate_warmup_until:
cr = agent.init_learning_rate / math.log2(agent.learning_rate_warmup_until)
learning_rate = cr * math.log2(batch_no + 1)
else:
learning_rate = agent.init_learning_rate
for param_group in agent.optimizer.param_groups:
param_group['lr'] = learning_rate
episode_no += curr_batch_size
batch_no += 1
time_2 = datetime.datetime.now()
print("Episode: {:3d} | time spent: {:s} | loss: {:2.3f}".format(episode_no, str(time_2 - time_1).rsplit(".")[0], ave_train_loss.get_avg()))
if agent.report_frequency == 0 or (episode_no % agent.report_frequency > (episode_no - curr_batch_size) % agent.report_frequency):
continue
eval_loss, eval_f1 = 0.0, 0.0
if episode_no % agent.report_frequency <= (episode_no - curr_batch_size) % agent.report_frequency:
if agent.run_eval:
eval_loss = evaluate.evaluate_observation_generation_loss(env, agent, "valid")
eval_f1 = evaluate.evaluate_observation_generation_free_generation(env, agent, "valid")
env.split_reset("train")
# if run eval, then save model by eval accuracy
if eval_loss < best_eval_loss_so_far:
best_eval_loss_so_far = eval_loss
agent.save_model_to_path(output_dir + "/" + agent.experiment_tag + "_model.pt")
else:
if loss < best_training_loss_so_far:
best_training_loss_so_far = loss
agent.save_model_to_path(output_dir + "/" + agent.experiment_tag + "_model.pt")
time_2 = datetime.datetime.now()
print("Episode: {:3d} | time spent: {:s} | loss: {:2.3f} | valid loss: {:2.3f} | valid f1: {:2.3f}".format(episode_no, str(time_2 - time_1).rsplit(".")[0], loss, eval_loss, eval_f1))
# plot using visdom
if config["general"]["visdom"]:
viz_loss.append(ave_train_loss.get_avg())
viz_eval_loss.append(eval_loss)
viz_eval_f1.append(eval_f1)
viz_x = np.arange(len(viz_loss)).tolist()
if plt_win is None:
plt_win = viz.line(X=viz_x, Y=viz_loss,
opts=dict(title=agent.experiment_tag + "_loss"),
name="training loss")
viz.line(X=viz_x, Y=viz_eval_loss,
opts=dict(title=agent.experiment_tag + "_eval_loss"),
win=plt_win,
update='append', name="eval loss")
else:
viz.line(X=[len(viz_loss) - 1], Y=[viz_loss[-1]],
opts=dict(title=agent.experiment_tag + "_loss"),
win=plt_win,
update='append', name="training loss")
viz.line(X=[len(viz_eval_loss) - 1], Y=[viz_eval_loss[-1]],
opts=dict(title=agent.experiment_tag + "_eval_loss"),
win=plt_win,
update='append', name="eval loss")
if eval_plt_win is None:
eval_plt_win = viz.line(X=viz_x, Y=viz_eval_f1,
opts=dict(title=agent.experiment_tag + "_eval_f1"),
name="eval f1")
else:
viz.line(X=[len(viz_eval_f1) - 1], Y=[viz_eval_f1[-1]],
opts=dict(title=agent.experiment_tag + "_eval_f1"),
win=eval_plt_win,
update='append', name="eval f1")
# write accuracies down into file
_s = json.dumps({"time spent": str(time_2 - time_1).rsplit(".")[0],
"loss": str(ave_train_loss.get_avg()),
"eval loss": str(eval_loss),
"eval f1": str(eval_f1)})
with open(output_dir + "/" + json_file_name + '.json', 'a+') as outfile:
outfile.write(_s + '\n')
outfile.flush()
# At any point you can hit Ctrl + C to break out of training early.
except KeyboardInterrupt:
print('--------------------------------------------')
print('Exiting from training early...')
if agent.run_eval:
if os.path.exists(output_dir + "/" + agent.experiment_tag + "_model.pt"):
print('Evaluating on test set and saving log...')
agent.load_pretrained_model(output_dir + "/" + agent.experiment_tag + "_model.pt", load_partial_graph=False)
test_loss = evaluate.evaluate_observation_generation_loss(env, agent, "test")
test_f1 = evaluate.evaluate_observation_generation_free_generation(env, agent, "test")
print(test_loss, test_f1)
if __name__ == '__main__':
train()