def write(text_processor: TextProcessor,
output_file: str,
txt_file: str,
output_txt: bool = False):
with open(txt_file, "r") as fp, open(output_file, "w") as writer:
for line in fp:
if len(line.strip()) == 0 or len(line.strip()) == 0: continue
tok_line = text_processor.tokenize_one_line(line.strip(),
ignore_middle_eos=True)
if output_txt:
tokenized = [text_processor.id2token(tok)
for tok in tok_line][1:-1]
tokenized = list(
map(lambda tok: tok
if tok != "<unk>" else "unk", tokenized))
else:
tokenized = [str(tok) for tok in tok_line]
writer.write(" ".join(tokenized) + "\n")
def write(text_processor: TextProcessor, output_file: str, src_txt_file: str, src_lang: int,
dst_txt_file: str = None, dst_lang: int = None, min_len: int = 1, max_len: int = 175):
examples = {}
line_num = 0
src_lang_str = text_processor.languages[text_processor.id2token(src_lang)]
lens = {}
if dst_txt_file is not None:
dst_lang_str = text_processor.languages[text_processor.id2token(dst_lang)]
with open(src_txt_file, "r") as s_fp, open(dst_txt_file, "r") as d_fp:
for src_line, dst_line in zip(s_fp, d_fp):
if len(src_line.strip()) == 0 or len(dst_line.strip()) == 0: continue
src_tok_line = text_processor.tokenize_one_sentence_with_langid(src_line.strip(), src_lang)
dst_tok_line = text_processor.tokenize_one_sentence_with_langid(dst_line.strip(), dst_lang)
if min_len <= len(src_tok_line) <= max_len and min_len <= len(dst_tok_line) <= max_len:
examples[line_num] = (src_tok_line, dst_tok_line, src_lang_str, dst_lang_str)
lens[line_num] = len(dst_tok_line)
line_num += 1
if line_num % 1000 == 0:
print(line_num, end="\r")
print("\nSorting")
sorted_lens = sorted(lens.items(), key=lambda item: item[1])
sorted_examples = []
print("Sorted examples")
for len_item in sorted_lens:
line_num = len(sorted_examples)
sorted_examples.append(examples[len_item[0]])
print("Dumping")
with open(output_file, "wb") as fw:
marshal.dump(sorted_examples, fw)
else:
part_num = 0
# Used for MASS training where we only have source sentences.
with open(src_txt_file, "r") as s_fp:
for src_line in s_fp:
if len(src_line.strip()) == 0: continue
src_tok_line = text_processor.tokenize_one_sentence_with_langid(src_line.strip(), src_lang)
if min_len <= len(src_tok_line) <= max_len:
examples[line_num] = (src_tok_line, src_lang_str)
lens[line_num] = len(src_tok_line)
line_num += 1
if line_num % 1000 == 0:
print(line_num, "\r", end="")
if len(examples) >= 6000000:
print(datetime.datetime.now(), "Sorting and writing", part_num)
sorted_lens = sorted(lens.items(), key=lambda item: item[1])
sorted_examples = list(map(lambda len_item: examples[len_item[0]], sorted_lens))
with open(output_file + "." + str(part_num), "wb") as fw:
marshal.dump(sorted_examples, fw)
examples = {}
lens = {}
part_num += 1
if len(examples) > 0:
print(datetime.datetime.now(), "Sorting and writing", part_num)
sorted_lens = sorted(lens.items(), key=lambda item: item[1])
sorted_examples = list(map(lambda len_item: examples[len_item[0]], sorted_lens))
with open(output_file + "." + str(part_num), "wb") as fw:
marshal.dump(sorted_examples, fw)
def __init__(self,
root_img_dir: str,
data_bin_file: str,
max_capacity: int,
text_processor: TextProcessor,
max_img_per_batch: int,
lex_dict=None,
ngpu=1,
use_neg_samples: bool = False):
self.ngpu = ngpu
self.lex_dict = lex_dict
self.size_transform = transforms.Resize(256)
self.crop = transforms.CenterCrop(224)
self.to_tensor = transforms.ToTensor()
self.img_normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
self.pad_idx = text_processor.pad_token_id()
self.batches = []
self.root_img_dir = root_img_dir
max_capacity *= 1000000
self.image_batches = []
self.lang_ids = set()
self.all_captions = []
self.use_neg_samples = use_neg_samples
print("Start", datetime.datetime.now())
cur_batch, cur_imgs, cur_lex_cand_batch = [], [], []
cur_max_len = 0
with open(data_bin_file, "rb") as fp:
self.unique_images, captions = marshal.load(fp)
lang_id = text_processor.id2token(captions[0][1][0])
self.lang_ids.add(int(captions[0][1][0]))
self.lang = text_processor.languages[
lang_id] if lang_id in text_processor.languages else 0
for caption_info in captions:
image_id, caption = caption_info
if self.unique_images[image_id].lower().endswith(".png"):
continue
caption = torch.LongTensor(caption)
cur_batch.append(caption)
self.all_captions.append(caption)
if self.lex_dict is not None:
lex_cands = get_lex_suggestions(
self.lex_dict, caption, text_processor.pad_token_id())
cur_lex_cand_batch.append(lex_cands)
cur_imgs.append(image_id)
cur_max_len = max(cur_max_len, len(caption))
batch_capacity_size = 2 * (cur_max_len**3) * len(cur_batch)
if (len(cur_imgs) > max_img_per_batch
or batch_capacity_size > max_capacity
) and len(
cur_batch[:-1]) >= self.ngpu and len(cur_batch) > 1:
batch_tensor = pad_sequence(cur_batch[:-1],
batch_first=True,
padding_value=self.pad_idx)
lex_cand_batch = None
if self.lex_dict is not None:
lex_cand_batch = pad_sequence(
cur_lex_cand_batch[:-1],
batch_first=True,
padding_value=self.pad_idx)
cur_lex_cand_batch = [cur_lex_cand_batch[-1]]
pads = batch_tensor != self.pad_idx
pad_indices = [int(pads.size(1)) - 1] * int(pads.size(0))
pindices = torch.nonzero(~pads)
for (r, c) in pindices:
pad_indices[r] = min(pad_indices[r], int(c))
self.batches.append(
(batch_tensor, pads, torch.LongTensor(pad_indices),
lex_cand_batch))
self.image_batches.append(cur_imgs[:-1])
cur_batch = [cur_batch[-1]]
cur_imgs = [cur_imgs[-1]]
cur_max_len = len(cur_batch[0])
if len(cur_batch) > 0:
batch_tensor = pad_sequence(cur_batch,
batch_first=True,
padding_value=self.pad_idx)
pads = batch_tensor != self.pad_idx
pad_indices = [int(pads.size(1)) - 1] * int(pads.size(0))
lex_cand_batch = None
if self.lex_dict is not None:
lex_cand_batch = pad_sequence(cur_lex_cand_batch,
batch_first=True,
padding_value=self.pad_idx)
pindices = torch.nonzero(~pads)
for (r, c) in pindices:
pad_indices[r] = min(pad_indices[r], int(c))
self.batches.append(
(batch_tensor, pads, torch.LongTensor(pad_indices),
lex_cand_batch))
self.image_batches.append(cur_imgs)
print(
"Loaded %d image batches of %d unique images and %d all captions!"
% (len(self.batches), len(
self.unique_images), len(self.all_captions)))
print("End", datetime.datetime.now())
def write(text_processor: TextProcessor,
cache_dir: str,
seq_len: int,
txt_file: str,
sen_block_size: int = 10000):
sen_block_size = sen_block_size
current_cache, cur_cache_langs = [], []
examples = {}
line_num, file_count = 0, 0
text_processor.max_len = seq_len
with open(txt_file, "r") as fp:
for ln, line in enumerate(fp):
if len(line.strip()) == 0: continue
tok_lines = text_processor.tokenize_lines(line.strip(),
blind_split=True,
split_len=seq_len)
current_cache += list(tok_lines)
cur_cache_langs += [
text_processor.languages[text_processor.id2token(tok_lines[0,
0])]
] * tok_lines.shape[0]
if len(current_cache) >= 100000:
for tok_line, lang in zip(current_cache, cur_cache_langs):
# assuming that every list has same length due to correct padding.
examples[line_num] = (tok_line.tolist(), lang)
line_num += 1
if len(examples) >= sen_block_size:
with open(
os.path.join(cache_dir,
str(file_count) + ".pkl"),
"wb") as fw:
marshal.dump(examples, fw)
examples, file_count = {}, file_count + 1
current_cache, cur_cache_langs = [], []
print(
f"from {ln} actual documents, dumped {line_num} big vectors into {file_count} files"
)
if len(current_cache) > 0:
for tok_line, lang in zip(current_cache, cur_cache_langs):
# assuming that every list has same length due to correct padding.
examples[line_num] = (tok_line.tolist(), lang)
line_num += 1
if len(examples) >= sen_block_size:
with open(os.path.join(cache_dir,
str(file_count) + ".pkl"), "wb") as fw:
marshal.dump(examples, fw)
examples, file_count = {}, file_count + 1
if len(examples) >= 0:
with open(os.path.join(cache_dir,
str(file_count) + ".pkl"), "wb") as fw:
marshal.dump(examples, fw)
examples, file_count = {}, file_count + 1
print(
f"from {ln} actual documents, dumped {line_num} big vectors into {file_count} files"
)
with open(os.path.join(cache_dir, "info.txt"), "w") as fw:
fw.write(
str(sen_block_size) + "\t" + str(line_num) + "\t" +
str(file_count))
