app.py

import torch

import gradio as gr

from PIL import Image

import torch.nn as nn
from torch.nn import functional as nnf
from transformers import GPT2Tokenizer, GPT2LMHeadModel
import cv2
from PIL import Image
from typing import Tuple, Optional, Union

import clip

gpt_model_name = 'sberbank-ai/rugpt3medium_based_on_gpt2'


class MLP(nn.Module):
    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)

    # @autocast()
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.model(x)


def freeze(
        model,
        freeze_emb=False,
        freeze_ln=False,
        freeze_attn=True,
        freeze_ff=True,
        freeze_other=False,
):
    for name, p in model.named_parameters():
        # freeze all parameters except the layernorm and positional embeddings
        name = name.lower()
        if 'ln' in name or 'norm' in name:
            p.requires_grad = not freeze_ln
        elif 'embeddings' in name:
            p.requires_grad = not freeze_emb
        elif 'mlp' in name:
            p.requires_grad = not freeze_ff
        elif 'attn' in name:
            p.requires_grad = not freeze_attn
        else:
            p.requires_grad = not freeze_other

    return model


class ClipCaptionModel(nn.Module):
    def __init__(self, prefix_length: int, prefix_size: int = 768):
        super(ClipCaptionModel, self).__init__()
        self.prefix_length = prefix_length
        """
        ru gpts shit
        """
        self.gpt = GPT2LMHeadModel.from_pretrained(gpt_model_name)

        self.gpt_embedding_size = self.gpt.transformer.wte.weight.shape[1]
        self.clip_project = MLP((prefix_size, (self.gpt_embedding_size * prefix_length) // 2,
                                 self.gpt_embedding_size * prefix_length))

    def get_dummy_token(self, batch_size: int, device: torch.device) -> torch.Tensor:
        return torch.zeros(batch_size, self.prefix_length, dtype=torch.int64, device=device)

    # @autocast()
    def forward(self, tokens: torch.Tensor, prefix: torch.Tensor, mask: Optional[torch.Tensor] = None,
                labels: Optional[torch.Tensor] = None):
        embedding_text = self.gpt.transformer.wte(tokens)

        prefix_projections = self.clip_project(prefix.float()).view(-1, self.prefix_length, self.gpt_embedding_size)

        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


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


def filter_ngrams(output_text):
    a_pos = output_text.find(' Ответ:')
    sec_a_pos = output_text.find(' Ответ:', a_pos + 1)
    return output_text[:sec_a_pos]


def generate2(
        model,
        tokenizer,
        tokens=None,
        prompt='',
        embed=None,
        entry_count=1,
        entry_length=67,  # maximum number of words
        top_p=0.98,
        temperature=1.,
        stop_token='.',
):
    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 range(entry_count):
            if not tokens:
                tokens = torch.tensor(tokenizer.encode(prompt))
                # print('tokens',tokens)
                tokens = tokens.unsqueeze(0).to(device)

            emb_tokens = model.gpt.transformer.wte(tokens)

            if embed is not None:
                generated = torch.cat((embed, emb_tokens), dim=1)
            else:
                generated = emb_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

                top_k = 2000
                top_p = 0.98
                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

                decoder_inputs_embeds = next_token_embed

            output_list = list(tokens.squeeze().cpu().numpy())

            output_text = tokenizer.decode(output_list)
            output_text = filter_ngrams(output_text)
            generated_list.append(output_text)

    return generated_list[0]


def read_image(path):
    image = cv2.imread(path)

    size = 196, 196
    image = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
    image.thumbnail(size, Image.Resampling.LANCZOS)

    return image


def create_emb(image):
    text = "Вопрос: что происходит на изображении? Ответ:  "
    image = preprocess(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)
    return (prefix, text)


def get_caption(prefix, prompt=''):
    prefix = prefix.to(device)
    with torch.no_grad():
        prefix_embed = model.clip_project(prefix).reshape(1, prefix_length, -1)
        if prompt:
            generated_text_prefix = generate2(model, tokenizer, prompt=prompt, embed=prefix_embed)
        else:
            generated_text_prefix = generate2(model, tokenizer, embed=prefix_embed)
    return generated_text_prefix.replace('\n', ' ')


def get_ans(clip_emb, prompt):
    output = get_caption(clip_emb, prompt=prompt)
    ans = output[len(prompt):].strip()
    return ans


device = 'cpu'
clip_model, preprocess = clip.load("ViT-L/14@336px", device=device, jit=False)
tokenizer = GPT2Tokenizer.from_pretrained('sberbank-ai/rugpt3medium_based_on_gpt2')
prefix_length = 30
model_path = 'prefix_small_latest_gpt2_medium.pt'
model = ClipCaptionPrefix(prefix_length)
model.load_state_dict(torch.load(model_path, map_location='cpu'))
model.to(device)
model.eval()



def classify_image(inp):
  print(type(inp))
  inp =  Image.fromarray(inp)
  prefix, text = create_emb(path_to_image)
  ans = get_ans(prefix, text)
  return texts

image = gr.inputs.Image(shape=(256, 256))
label = gr.outputs.Label(num_top_classes=3)


iface = gr.Interface(fn=classify_image, description="https://github.com/AlexWortega/ruImageCaptioning RuImage Captioning  trained for a image2text task to predict caption of image by https://t.me/lovedeathtransformers Alex Wortega", inputs=image, outputs="text",])
iface.launch()