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navigation.py
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navigation.py
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import IPython
import numpy as np
import torch
import torch.nn as nn
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
import os
if 'FirstTextWorldProblems/ftwp/' in os.path.realpath(__file__):
# package imports
from ftwp.agents.GRU100.model.tokenizer import Tokenizer
from ftwp.utils import Saver, make_path
_FILE_PREFIX = os.path.join(os.path.realpath(__file__).split('ftwp/')[1].replace(os.path.basename(__file__), ''), '../')
else:
from .tokenizer import Tokenizer
from utils import Saver, make_path
_FILE_PREFIX = ''
class Navigation:
def __init__(self, device):
self.model = NavigationModel.initialize_trained_model(device=device)
def __call__(self, x):
transform = False
if not isinstance(x, list):
x = [x]
transform = True
_, _, doors, nsew = self.model(x)
if transform:
doors = doors[0]
nsew = nsew[0]
return doors, nsew
class NavigationModel(nn.Module):
nsew = ['north', 'south', 'east', 'west']
def __init__(self, device, encoder_hidden_dim=16, linear_hidden_dim=16):
super(NavigationModel, self).__init__()
self.tokenizer = Tokenizer(device=device)
self.embedding_dim = self.tokenizer.embedding_dim
self.embedding = nn.Embedding(self.tokenizer.vocab_len, self.embedding_dim)
if self.tokenizer.embedding_init is not None:
self.embedding.weight = nn.Parameter(self.tokenizer.embedding_init)
self.encoder = nn.GRU(self.embedding_dim, encoder_hidden_dim, batch_first=True, bidirectional=True)
self.device = device
self.nsew_scorer = nn.ModuleDict({
k: nn.Sequential(
nn.Linear(in_features=encoder_hidden_dim * 2,
out_features=linear_hidden_dim),
nn.ReLU(),
nn.Linear(in_features=linear_hidden_dim, out_features=1),
nn.Sigmoid())
for k in self.nsew
})
self.door_finder = nn.Sequential(
nn.Linear(in_features=encoder_hidden_dim * 2,
out_features=linear_hidden_dim),
nn.ReLU(),
nn.Linear(in_features=linear_hidden_dim, out_features=1),
nn.Sigmoid())
self.to(self.device)
def forward(self, x):
def unpadded_sequence_length(tensor):
return ((tensor == 0).type(torch.int) <= 0).sum(dim=1)
x = clean_description(x)
tokenized = self.tokenizer.process_cmds(x, pad=True)
lengths = unpadded_sequence_length(tokenized)
embedded = self.embedding(tokenized)
packed_sequence = pack_padded_sequence(input=embedded,
lengths=lengths,
batch_first=True,
enforce_sorted=False)
out, hidden = self.encoder(packed_sequence)
encoded = hidden.permute(1, 0, 2).reshape(hidden.size(1), -1) # correct for bididrectional
out = pad_packed_sequence(out)[0].permute(1, 0, 2)
nsew_scores = {k: self.nsew_scorer[k](encoded) for k in self.nsew}
door_scores = []
for b in range(len(x)):
new_score = self.door_finder(out[b, :, :]).squeeze(1)
door_scores.append(new_score)
door_scores = torch.stack(door_scores)
# Translate the scores to commands
nsew, doors = self.to_commands(nsew_scores, door_scores, x)
return door_scores, nsew_scores, doors, nsew
def to_commands(self, nsew_scores, door_scores, x):
nsew_thr = 0.5
door_thr = 0.5
nsew = []
doors = []
x_pad = np.array([['<PAD>'] * max([len(s.split()) for s in x])] * len(x)).astype('<U60')
for b in range(len(x)):
for word_idx, word in enumerate(x[b].split()):
x_pad[b, word_idx] = word
for b in range(len(x)):
nsew.append([k for k in self.nsew if nsew_scores[k][b] > nsew_thr])
cmd = ' '.join([word for word, score in zip(list(x_pad[b]), [v.item() for v in list(door_scores[b].detach())]) if score > door_thr and word != '<PAD>'])
if cmd == '':
doors.append([])
else:
doors.append([c.strip() + ' door' for c in cmd.split('door') if c.strip() != ''])
return nsew, doors
@classmethod
def initialize_trained_model(cls, device):
model = cls(device=device)
model_path = os.path.join(_FILE_PREFIX, 'weights/navigation_weights_16')
model.load_state_dict(torch.load(model_path, map_location=device), strict=True)
print('Loaded model from {}'.format(model_path))
return model
def cut_descriptions(descriptions):
possible_descriptions = descriptions.split('\n\n')
description = []
kwords = [' north', ' south', ' west', ' east', 'leading', 'try going', 'exit', 'door']
for d in possible_descriptions:
if any([word in d for word in kwords]):
description.append(d.strip())
return ' '.join(description)
def clean_description(descriptions):
clnd_descriptions = []
for description in descriptions:
short_description = cut_descriptions(description)
if not isinstance(short_description, str) or short_description == '':
clnd_descriptions.append('nothing')
else:
clnd_descriptions.append(short_description.lower().replace("don't", 'do not').replace("you're", 'you are').replace('.', ' <SEP>').replace(':', '').replace('?', ' <SEP>').replace(',', '').replace('!', ' <SEP>').replace(':',' <SEP>').strip('<SEP>').strip())
return clnd_descriptions