Spaces:
Runtime error
Runtime error
import os | |
os.system("gdown https://drive.google.com/uc?id=14pXWwB4Zm82rsDdvbGguLfx9F8aM7ovT") | |
import clip | |
import os | |
from torch import nn | |
import numpy as np | |
import torch | |
import torch.nn.functional as nnf | |
import sys | |
from typing import Tuple, List, Union, Optional | |
from transformers import GPT2Tokenizer, GPT2LMHeadModel, AdamW, get_linear_schedule_with_warmup | |
from tqdm import tqdm, trange | |
import skimage.io as io | |
import PIL.Image | |
N = type(None) | |
V = np.array | |
ARRAY = np.ndarray | |
ARRAYS = Union[Tuple[ARRAY, ...], List[ARRAY]] | |
VS = Union[Tuple[V, ...], List[V]] | |
VN = Union[V, N] | |
VNS = Union[VS, N] | |
T = torch.Tensor | |
TS = Union[Tuple[T, ...], List[T]] | |
TN = Optional[T] | |
TNS = Union[Tuple[TN, ...], List[TN]] | |
TSN = Optional[TS] | |
TA = Union[T, ARRAY] | |
D = torch.device | |
CPU = torch.device('cpu') | |
model_path = 'model_wieghts.pt' | |
def get_device(device_id: int) -> D: | |
if not torch.cuda.is_available(): | |
return CPU | |
device_id = min(torch.cuda.device_count() - 1, device_id) | |
return torch.device(f'cuda:{device_id}') | |
CUDA = get_device | |
class MLP(nn.Module): | |
def forward(self, x: T) -> T: | |
return self.model(x) | |
def __init__(self, sizes: Tuple[int, ...], bias=True, act=nn.Tanh): | |
super(MLP, self).__init__() | |
layers = [] | |
for i in range(len(sizes) -1): | |
layers.append(nn.Linear(sizes[i], sizes[i + 1], bias=bias)) | |
if i < len(sizes) - 2: | |
layers.append(act()) | |
self.model = nn.Sequential(*layers) | |
class ClipCaptionModel(nn.Module): | |
#@functools.lru_cache #FIXME | |
def get_dummy_token(self, batch_size: int, device: D) -> T: | |
return torch.zeros(batch_size, self.prefix_length, dtype=torch.int64, device=device) | |
def forward(self, tokens: T, prefix: T, mask: Optional[T] = None, labels: Optional[T] = None): | |
embedding_text = self.gpt.transformer.wte(tokens) | |
prefix_projections = self.clip_project(prefix).view(-1, self.prefix_length, self.gpt_embedding_size) | |
#print(embedding_text.size()) #torch.Size([5, 67, 768]) | |
#print(prefix_projections.size()) #torch.Size([5, 1, 768]) | |
embedding_cat = torch.cat((prefix_projections, embedding_text), dim=1) | |
if labels is not None: | |
dummy_token = self.get_dummy_token(tokens.shape[0], tokens.device) | |
labels = torch.cat((dummy_token, tokens), dim=1) | |
out = self.gpt(inputs_embeds=embedding_cat, labels=labels, attention_mask=mask) | |
return out | |
def __init__(self, prefix_length: int, prefix_size: int = 512): | |
super(ClipCaptionModel, self).__init__() | |
self.prefix_length = prefix_length | |
self.gpt = GPT2LMHeadModel.from_pretrained('gpt2') | |
self.gpt_embedding_size = self.gpt.transformer.wte.weight.shape[1] | |
if prefix_length > 10: # not enough memory | |
self.clip_project = nn.Linear(prefix_size, self.gpt_embedding_size * prefix_length) | |
else: | |
self.clip_project = MLP((prefix_size, (self.gpt_embedding_size * prefix_length) // 2, self.gpt_embedding_size * prefix_length)) | |
class ClipCaptionPrefix(ClipCaptionModel): | |
def parameters(self, recurse: bool = True): | |
return self.clip_project.parameters() | |
def train(self, mode: bool = True): | |
super(ClipCaptionPrefix, self).train(mode) | |
self.gpt.eval() | |
return self | |
#@title Caption prediction | |
def generate_beam(model, tokenizer, beam_size: int = 5, prompt=None, embed=None, | |
entry_length=67, temperature=1., stop_token: str = '.'): | |
model.eval() | |
stop_token_index = tokenizer.encode(stop_token)[0] | |
tokens = None | |
scores = None | |
device = next(model.parameters()).device | |
seq_lengths = torch.ones(beam_size, device=device) | |
is_stopped = torch.zeros(beam_size, device=device, dtype=torch.bool) | |
with torch.no_grad(): | |
if embed is not None: | |
generated = embed | |
else: | |
if tokens is None: | |
tokens = torch.tensor(tokenizer.encode(prompt)) | |
tokens = tokens.unsqueeze(0).to(device) | |
generated = model.gpt.transformer.wte(tokens) | |
for i in range(entry_length): | |
outputs = model.gpt(inputs_embeds=generated) | |
logits = outputs.logits | |
logits = logits[:, -1, :] / (temperature if temperature > 0 else 1.0) | |
logits = logits.softmax(-1).log() | |
if scores is None: | |
scores, next_tokens = logits.topk(beam_size, -1) | |
generated = generated.expand(beam_size, *generated.shape[1:]) | |
next_tokens, scores = next_tokens.permute(1, 0), scores.squeeze(0) | |
if tokens is None: | |
tokens = next_tokens | |
else: | |
tokens = tokens.expand(beam_size, *tokens.shape[1:]) | |
tokens = torch.cat((tokens, next_tokens), dim=1) | |
else: | |
logits[is_stopped] = -float(np.inf) | |
logits[is_stopped, 0] = 0 | |
scores_sum = scores[:, None] + logits | |
seq_lengths[~is_stopped] += 1 | |
scores_sum_average = scores_sum / seq_lengths[:, None] | |
scores_sum_average, next_tokens = scores_sum_average.view(-1).topk(beam_size, -1) | |
next_tokens_source = next_tokens // scores_sum.shape[1] | |
seq_lengths = seq_lengths[next_tokens_source] | |
next_tokens = next_tokens % scores_sum.shape[1] | |
next_tokens = next_tokens.unsqueeze(1) | |
tokens = tokens[next_tokens_source] | |
tokens = torch.cat((tokens, next_tokens), dim=1) | |
generated = generated[next_tokens_source] | |
scores = scores_sum_average * seq_lengths | |
is_stopped = is_stopped[next_tokens_source] | |
next_token_embed = model.gpt.transformer.wte(next_tokens.squeeze()).view(generated.shape[0], 1, -1) | |
generated = torch.cat((generated, next_token_embed), dim=1) | |
is_stopped = is_stopped + next_tokens.eq(stop_token_index).squeeze() | |
if is_stopped.all(): | |
break | |
scores = scores / seq_lengths | |
output_list = tokens.cpu().numpy() | |
output_texts = [tokenizer.decode(output[:int(length)]) for output, length in zip(output_list, seq_lengths)] | |
order = scores.argsort(descending=True) | |
output_texts = [output_texts[i] for i in order] | |
return output_texts | |
def generate2( | |
model, | |
tokenizer, | |
tokens=None, | |
prompt=None, | |
embed=None, | |
entry_count=1, | |
entry_length=67, # maximum number of words | |
top_p=0.8, | |
temperature=1., | |
stop_token: str = '.', | |
): | |
model.eval() | |
generated_num = 0 | |
generated_list = [] | |
stop_token_index = tokenizer.encode(stop_token)[0] | |
filter_value = -float("Inf") | |
device = next(model.parameters()).device | |
with torch.no_grad(): | |
for entry_idx in trange(entry_count): | |
if embed is not None: | |
generated = embed | |
else: | |
if tokens is None: | |
tokens = torch.tensor(tokenizer.encode(prompt)) | |
tokens = tokens.unsqueeze(0).to(device) | |
generated = model.gpt.transformer.wte(tokens) | |
for i in range(entry_length): | |
outputs = model.gpt(inputs_embeds=generated) | |
logits = outputs.logits | |
logits = logits[:, -1, :] / (temperature if temperature > 0 else 1.0) | |
sorted_logits, sorted_indices = torch.sort(logits, descending=True) | |
cumulative_probs = torch.cumsum(nnf.softmax(sorted_logits, dim=-1), dim=-1) | |
sorted_indices_to_remove = cumulative_probs > top_p | |
sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[ | |
..., :-1 | |
].clone() | |
sorted_indices_to_remove[..., 0] = 0 | |
indices_to_remove = sorted_indices[sorted_indices_to_remove] | |
logits[:, indices_to_remove] = filter_value | |
next_token = torch.argmax(logits, -1).unsqueeze(0) | |
next_token_embed = model.gpt.transformer.wte(next_token) | |
if tokens is None: | |
tokens = next_token | |
else: | |
tokens = torch.cat((tokens, next_token), dim=1) | |
generated = torch.cat((generated, next_token_embed), dim=1) | |
if stop_token_index == next_token.item(): | |
break | |
output_list = list(tokens.squeeze().cpu().numpy()) | |
output_text = tokenizer.decode(output_list) | |
generated_list.append(output_text) | |
return generated_list[0] | |
is_gpu = False | |
device = CUDA(0) if is_gpu else "cpu" | |
clip_model, preprocess = clip.load("ViT-B/32", device=device, jit=False) | |
tokenizer = GPT2Tokenizer.from_pretrained("gpt2") | |
prefix_length = 10 | |
model = ClipCaptionModel(prefix_length) | |
model.load_state_dict(torch.load(model_path, map_location=CPU)) | |
model = model.eval() | |
device = CUDA(0) if is_gpu else "cpu" | |
model = model.to(device) | |
def inference(img): | |
use_beam_search = False | |
image = io.imread(img.name) | |
pil_image = PIL.Image.fromarray(image) | |
image = preprocess(pil_image).unsqueeze(0).to(device) | |
with torch.no_grad(): | |
prefix = clip_model.encode_image(image).to(device, dtype=torch.float32) | |
prefix_embed = model.clip_project(prefix).reshape(1, prefix_length, -1) | |
if use_beam_search: | |
generated_text_prefix = generate_beam(model, tokenizer, embed=prefix_embed)[0] | |
else: | |
generated_text_prefix = generate2(model, tokenizer, embed=prefix_embed) | |
return generated_text_prefix | |
title = "Anime2Sketch" | |
description = "demo for Anime2Sketch. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below." | |
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2104.05703'>Adversarial Open Domain Adaption for Sketch-to-Photo Synthesis</a> | <a href='https://github.com/Mukosame/Anime2Sketch'>Github Repo</a></p>" | |
gr.Interface( | |
inference, | |
gr.inputs.Image(type="file", label="Input"), | |
gr.outputs.Textbox(label="Output"), | |
title=title, | |
description=description, | |
article=article, | |
enable_queue=True | |
).launch(debug=True) |