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added is_verbose flag

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Brett Kuprel 2022-07-01 20:17:20 -04:00
parent 35e97768a5
commit cf9656baa2
3 changed files with 23 additions and 19 deletions
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3
image_from_text.py
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@ -44,7 +44,8 @@ def generate_image(
is_mega=is_mega, is_mega=is_mega,
models_root='pretrained', models_root='pretrained',
is_reusable=False, is_reusable=False,
sample_token_count=token_count sample_token_count=token_count,
is_verbose=True
) )
if token_count < 256: if token_count < 256:

34
min_dalle/min_dalle.py
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@ -21,11 +21,12 @@ class MinDalle:
is_mega: bool, is_mega: bool,
is_reusable: bool = True, is_reusable: bool = True,
models_root: str = 'pretrained', models_root: str = 'pretrained',
sample_token_count: int = 256 sample_token_count: int = 256,
is_verbose = True
): ):
print("initializing MinDalleTorch")
self.is_mega = is_mega self.is_mega = is_mega
self.is_reusable = is_reusable self.is_reusable = is_reusable
self.is_verbose = is_verbose
self.sample_token_count = sample_token_count self.sample_token_count = sample_token_count
self.batch_count = 2 self.batch_count = 2
self.text_token_count = 64 self.text_token_count = 64
@ -37,6 +38,7 @@ class MinDalle:
self.text_vocab_count = 50272 if is_mega else 50264 self.text_vocab_count = 50272 if is_mega else 50264
self.image_vocab_count = 16415 if is_mega else 16384 self.image_vocab_count = 16415 if is_mega else 16384
if self.is_verbose: print("initializing MinDalle")
model_name = 'dalle_bart_{}'.format('mega' if is_mega else 'mini') model_name = 'dalle_bart_{}'.format('mega' if is_mega else 'mini')
dalle_path = os.path.join(models_root, model_name) dalle_path = os.path.join(models_root, model_name)
vqgan_path = os.path.join(models_root, 'vqgan') vqgan_path = os.path.join(models_root, 'vqgan')
@ -56,7 +58,7 @@ class MinDalle:
def download_tokenizer(self): def download_tokenizer(self):
print("downloading tokenizer params") if self.is_verbose: print("downloading tokenizer params")
suffix = '' if self.is_mega else '_mini' suffix = '' if self.is_mega else '_mini'
vocab = requests.get(MIN_DALLE_REPO + 'vocab{}.json'.format(suffix)) vocab = requests.get(MIN_DALLE_REPO + 'vocab{}.json'.format(suffix))
merges = requests.get(MIN_DALLE_REPO + 'merges{}.txt'.format(suffix)) merges = requests.get(MIN_DALLE_REPO + 'merges{}.txt'.format(suffix))
@ -65,21 +67,21 @@ class MinDalle:
def download_encoder(self): def download_encoder(self):
print("downloading encoder params") if self.is_verbose: print("downloading encoder params")
suffix = '' if self.is_mega else '_mini' suffix = '' if self.is_mega else '_mini'
params = requests.get(MIN_DALLE_REPO + 'encoder{}.pt'.format(suffix)) params = requests.get(MIN_DALLE_REPO + 'encoder{}.pt'.format(suffix))
with open(self.encoder_params_path, 'wb') as f: f.write(params.content) with open(self.encoder_params_path, 'wb') as f: f.write(params.content)
def download_decoder(self): def download_decoder(self):
print("downloading decoder params") if self.is_verbose: print("downloading decoder params")
suffix = '' if self.is_mega else '_mini' suffix = '' if self.is_mega else '_mini'
params = requests.get(MIN_DALLE_REPO + 'decoder{}.pt'.format(suffix)) params = requests.get(MIN_DALLE_REPO + 'decoder{}.pt'.format(suffix))
with open(self.decoder_params_path, 'wb') as f: f.write(params.content) with open(self.decoder_params_path, 'wb') as f: f.write(params.content)
def download_detokenizer(self): def download_detokenizer(self):
print("downloading detokenizer params") if self.is_verbose: print("downloading detokenizer params")
params = requests.get(MIN_DALLE_REPO + 'detoker.pt') params = requests.get(MIN_DALLE_REPO + 'detoker.pt')
with open(self.detoker_params_path, 'wb') as f: f.write(params.content) with open(self.detoker_params_path, 'wb') as f: f.write(params.content)
@ -88,18 +90,18 @@ class MinDalle:
is_downloaded = os.path.exists(self.vocab_path) is_downloaded = os.path.exists(self.vocab_path)
is_downloaded &= os.path.exists(self.merges_path) is_downloaded &= os.path.exists(self.merges_path)
if not is_downloaded: self.download_tokenizer() if not is_downloaded: self.download_tokenizer()
print("intializing TextTokenizer") if self.is_verbose: print("intializing TextTokenizer")
with open(self.vocab_path, 'r', encoding='utf8') as f: with open(self.vocab_path, 'r', encoding='utf8') as f:
vocab = json.load(f) vocab = json.load(f)
with open(self.merges_path, 'r', encoding='utf8') as f: with open(self.merges_path, 'r', encoding='utf8') as f:
merges = f.read().split("\n")[1:-1] merges = f.read().split("\n")[1:-1]
self.tokenizer = TextTokenizer(vocab, merges) self.tokenizer = TextTokenizer(vocab, merges, is_verbose=self.is_verbose)
def init_encoder(self): def init_encoder(self):
is_downloaded = os.path.exists(self.encoder_params_path) is_downloaded = os.path.exists(self.encoder_params_path)
if not is_downloaded: self.download_encoder() if not is_downloaded: self.download_encoder()
print("initializing DalleBartEncoderTorch") if self.is_verbose: print("initializing DalleBartEncoder")
self.encoder = DalleBartEncoder( self.encoder = DalleBartEncoder(
attention_head_count = self.attention_head_count, attention_head_count = self.attention_head_count,
embed_count = self.embed_count, embed_count = self.embed_count,
@ -117,7 +119,7 @@ class MinDalle:
def init_decoder(self): def init_decoder(self):
is_downloaded = os.path.exists(self.decoder_params_path) is_downloaded = os.path.exists(self.decoder_params_path)
if not is_downloaded: self.download_decoder() if not is_downloaded: self.download_decoder()
print("initializing DalleBartDecoderTorch") if self.is_verbose: print("initializing DalleBartDecoder")
self.decoder = DalleBartDecoder( self.decoder = DalleBartDecoder(
sample_token_count = self.sample_token_count, sample_token_count = self.sample_token_count,
image_token_count = self.image_token_count, image_token_count = self.image_token_count,
@ -138,7 +140,7 @@ class MinDalle:
def init_detokenizer(self): def init_detokenizer(self):
is_downloaded = os.path.exists(self.detoker_params_path) is_downloaded = os.path.exists(self.detoker_params_path)
if not is_downloaded: self.download_detokenizer() if not is_downloaded: self.download_detokenizer()
print("initializing VQGanDetokenizer") if self.is_verbose: print("initializing VQGanDetokenizer")
self.detokenizer = VQGanDetokenizer() self.detokenizer = VQGanDetokenizer()
params = torch.load(self.detoker_params_path) params = torch.load(self.detoker_params_path)
self.detokenizer.load_state_dict(params) self.detokenizer.load_state_dict(params)
@ -147,9 +149,9 @@ class MinDalle:
def generate_image_tokens(self, text: str, seed: int) -> LongTensor: def generate_image_tokens(self, text: str, seed: int) -> LongTensor:
print("tokenizing text") if self.is_verbose: print("tokenizing text")
tokens = self.tokenizer.tokenize(text) tokens = self.tokenizer.tokenize(text)
print("text tokens", tokens) if self.is_verbose: print("text tokens", tokens)
text_tokens = numpy.ones((2, 64), dtype=numpy.int32) text_tokens = numpy.ones((2, 64), dtype=numpy.int32)
text_tokens[0, :2] = [tokens[0], tokens[-1]] text_tokens[0, :2] = [tokens[0], tokens[-1]]
text_tokens[1, :len(tokens)] = tokens text_tokens[1, :len(tokens)] = tokens
@ -158,12 +160,12 @@ class MinDalle:
if torch.cuda.is_available(): text_tokens = text_tokens.cuda() if torch.cuda.is_available(): text_tokens = text_tokens.cuda()
if not self.is_reusable: self.init_encoder() if not self.is_reusable: self.init_encoder()
print("encoding text tokens") if self.is_verbose: print("encoding text tokens")
encoder_state = self.encoder.forward(text_tokens) encoder_state = self.encoder.forward(text_tokens)
if not self.is_reusable: del self.encoder if not self.is_reusable: del self.encoder
if not self.is_reusable: self.init_decoder() if not self.is_reusable: self.init_decoder()
print("sampling image tokens") if self.is_verbose: print("sampling image tokens")
if seed < 0: seed = random.randint(0, 2 ** 31) if seed < 0: seed = random.randint(0, 2 ** 31)
torch.manual_seed(seed) torch.manual_seed(seed)
image_tokens = self.decoder.forward(text_tokens, encoder_state) image_tokens = self.decoder.forward(text_tokens, encoder_state)
@ -174,7 +176,7 @@ class MinDalle:
def generate_image(self, text: str, seed: int) -> Image.Image: def generate_image(self, text: str, seed: int) -> Image.Image:
image_tokens = self.generate_image_tokens(text, seed) image_tokens = self.generate_image_tokens(text, seed)
if not self.is_reusable: self.init_detokenizer() if not self.is_reusable: self.init_detokenizer()
print("detokenizing image") if self.is_verbose: print("detokenizing image")
image = self.detokenizer.forward(image_tokens).to(torch.uint8) image = self.detokenizer.forward(image_tokens).to(torch.uint8)
if not self.is_reusable: del self.detokenizer if not self.is_reusable: del self.detokenizer
image = Image.fromarray(image.to('cpu').detach().numpy()) image = Image.fromarray(image.to('cpu').detach().numpy())

5
min_dalle/text_tokenizer.py
View File

@ -3,7 +3,8 @@ from typing import List, Tuple
class TextTokenizer: class TextTokenizer:
def __init__(self, vocab: dict, merges: List[str]): def __init__(self, vocab: dict, merges: List[str], is_verbose: bool = True):
self.is_verbose = is_verbose
self.token_from_subword = vocab self.token_from_subword = vocab
pairs = [tuple(pair.split()) for pair in merges] pairs = [tuple(pair.split()) for pair in merges]
self.rank_from_pair = dict(zip(pairs, range(len(pairs)))) self.rank_from_pair = dict(zip(pairs, range(len(pairs))))
@ -36,5 +37,5 @@ class TextTokenizer:
(subwords[i + 2:] if i + 2 < len(subwords) else []) (subwords[i + 2:] if i + 2 < len(subwords) else [])
) )
print(subwords) if self.is_verbose: print(subwords)
return subwords return subwords