app.py

import gradio as gr
from torchvision.transforms import Compose, Resize, ToTensor, Normalize
import matplotlib.pyplot as plt 
from PIL import Image
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch

from pathlib import Path
from re import TEMPLATE
from typing import Optional, Union
import os

from huggingface_hub import PyTorchModelHubMixin, HfApi, HfFolder, Repository

TEMPLATE_MODEL_CARD_PATH = "dummy"

class HugGANModelHubMixin(PyTorchModelHubMixin):
    """A mixin to push PyTorch Models to the Hugging Face Hub. This
    mixin was adapted from the PyTorchModelHubMixin to also push a template
    README.md for the HugGAN sprint.
    """

    def push_to_hub(
        self,
        repo_path_or_name: Optional[str] = None,
        repo_url: Optional[str] = None,
        commit_message: Optional[str] = "Add model",
        organization: Optional[str] = None,
        private: Optional[bool] = None,
        api_endpoint: Optional[str] = None,
        use_auth_token: Optional[Union[bool, str]] = None,
        git_user: Optional[str] = None,
        git_email: Optional[str] = None,
        config: Optional[dict] = None,
        skip_lfs_files: bool = False,
        default_model_card: Optional[str] = TEMPLATE_MODEL_CARD_PATH
    ) -> str:
        """
        Upload model checkpoint or tokenizer files to the Hub while
        synchronizing a local clone of the repo in `repo_path_or_name`.
        Parameters:
            repo_path_or_name (`str`, *optional*):
                Can either be a repository name for your model or tokenizer in
                the Hub or a path to a local folder (in which case the
                repository will have the name of that local folder). If not
                specified, will default to the name given by `repo_url` and a
                local directory with that name will be created.
            repo_url (`str`, *optional*):
                Specify this in case you want to push to an existing repository
                in the hub. If unspecified, a new repository will be created in
                your namespace (unless you specify an `organization`) with
                `repo_name`.
            commit_message (`str`, *optional*):
                Message to commit while pushing. Will default to `"add config"`,
                `"add tokenizer"` or `"add model"` depending on the type of the
                class.
            organization (`str`, *optional*):
                Organization in which you want to push your model or tokenizer
                (you must be a member of this organization).
            private (`bool`, *optional*):
                Whether the repository created should be private.
            api_endpoint (`str`, *optional*):
                The API endpoint to use when pushing the model to the hub.
            use_auth_token (`bool` or `str`, *optional*):
                The token to use as HTTP bearer authorization for remote files.
                If `True`, will use the token generated when running
                `transformers-cli login` (stored in `~/.huggingface`). Will
                default to `True` if `repo_url` is not specified.
            git_user (`str`, *optional*):
                will override the `git config user.name` for committing and
                pushing files to the hub.
            git_email (`str`, *optional*):
                will override the `git config user.email` for committing and
                pushing files to the hub.
            config (`dict`, *optional*):
                Configuration object to be saved alongside the model weights.
            default_model_card (`str`, *optional*):
                Path to a markdown file to use as your default model card.
        Returns:
            The url of the commit of your model in the given repository.
        """

        if repo_path_or_name is None and repo_url is None:
            raise ValueError(
                "You need to specify a `repo_path_or_name` or a `repo_url`."
            )

        if use_auth_token is None and repo_url is None:
            token = HfFolder.get_token()
            if token is None:
                raise ValueError(
                    "You must login to the Hugging Face hub on this computer by typing `huggingface-cli login` and "
                    "entering your credentials to use `use_auth_token=True`. Alternatively, you can pass your own "
                    "token as the `use_auth_token` argument."
                )
        elif isinstance(use_auth_token, str):
            token = use_auth_token
        else:
            token = None

        if repo_path_or_name is None:
            repo_path_or_name = repo_url.split("/")[-1]

        # If no URL is passed and there's no path to a directory containing files, create a repo
        if repo_url is None and not os.path.exists(repo_path_or_name):
            repo_id = Path(repo_path_or_name).name
            if organization:
                repo_id = f"{organization}/{repo_id}"
            repo_url = HfApi(endpoint=api_endpoint).create_repo(
                repo_id=repo_id,
                token=token,
                private=private,
                repo_type=None,
                exist_ok=True,
            )

        repo = Repository(
            repo_path_or_name,
            clone_from=repo_url,
            use_auth_token=use_auth_token,
            git_user=git_user,
            git_email=git_email,
            skip_lfs_files=skip_lfs_files
        )
        repo.git_pull(rebase=True)

        # Save the files in the cloned repo
        self.save_pretrained(repo_path_or_name, config=config)

        model_card_path = Path(repo_path_or_name) / 'README.md'
        if not model_card_path.exists():
            model_card_path.write_text(TEMPLATE_MODEL_CARD_PATH.read_text())

        # Commit and push!
        repo.git_add()
        repo.git_commit(commit_message)
        return repo.git_push()


def weights_init_normal(m):
    classname = m.__class__.__name__
    if classname.find("Conv") != -1:
        torch.nn.init.normal_(m.weight.data, 0.0, 0.02)
    elif classname.find("BatchNorm2d") != -1:
        torch.nn.init.normal_(m.weight.data, 1.0, 0.02)
        torch.nn.init.constant_(m.bias.data, 0.0)


##############################
#           U-NET
##############################


class UNetDown(nn.Module):
    def __init__(self, in_size, out_size, normalize=True, dropout=0.0):
        super(UNetDown, self).__init__()
        layers = [nn.Conv2d(in_size, out_size, 4, 2, 1, bias=False)]
        if normalize:
            layers.append(nn.InstanceNorm2d(out_size))
        layers.append(nn.LeakyReLU(0.2))
        if dropout:
            layers.append(nn.Dropout(dropout))
        self.model = nn.Sequential(*layers)

    def forward(self, x):
        return self.model(x)


class UNetUp(nn.Module):
    def __init__(self, in_size, out_size, dropout=0.0):
        super(UNetUp, self).__init__()
        layers = [
            nn.ConvTranspose2d(in_size, out_size, 4, 2, 1, bias=False),
            nn.InstanceNorm2d(out_size),
            nn.ReLU(inplace=True),
        ]
        if dropout:
            layers.append(nn.Dropout(dropout))

        self.model = nn.Sequential(*layers)

    def forward(self, x, skip_input):
        x = self.model(x)
        x = torch.cat((x, skip_input), 1)

        return x


class GeneratorUNet(nn.Module, HugGANModelHubMixin):
    def __init__(self, in_channels=3, out_channels=3):
        super(GeneratorUNet, self).__init__()

        self.down1 = UNetDown(in_channels, 64, normalize=False)
        self.down2 = UNetDown(64, 128)
        self.down3 = UNetDown(128, 256)
        self.down4 = UNetDown(256, 512, dropout=0.5)
        self.down5 = UNetDown(512, 512, dropout=0.5)
        self.down6 = UNetDown(512, 512, dropout=0.5)
        self.down7 = UNetDown(512, 512, dropout=0.5)
        self.down8 = UNetDown(512, 512, normalize=False, dropout=0.5)

        self.up1 = UNetUp(512, 512, dropout=0.5)
        self.up2 = UNetUp(1024, 512, dropout=0.5)
        self.up3 = UNetUp(1024, 512, dropout=0.5)
        self.up4 = UNetUp(1024, 512, dropout=0.5)
        self.up5 = UNetUp(1024, 256)
        self.up6 = UNetUp(512, 128)
        self.up7 = UNetUp(256, 64)

        self.final = nn.Sequential(
            nn.Upsample(scale_factor=2),
            nn.ZeroPad2d((1, 0, 1, 0)),
            nn.Conv2d(128, out_channels, 4, padding=1),
            nn.Tanh(),
        )

    def forward(self, x):
        # U-Net generator with skip connections from encoder to decoder
        d1 = self.down1(x)
        d2 = self.down2(d1)
        d3 = self.down3(d2)
        d4 = self.down4(d3)
        d5 = self.down5(d4)
        d6 = self.down6(d5)
        d7 = self.down7(d6)
        d8 = self.down8(d7)
        u1 = self.up1(d8, d7)
        u2 = self.up2(u1, d6)
        u3 = self.up3(u2, d5)
        u4 = self.up4(u3, d4)
        u5 = self.up5(u4, d3)
        u6 = self.up6(u5, d2)
        u7 = self.up7(u6, d1)

        return self.final(u7)

def load_image_infer(image_file):
    # Configure dataloaders
    transform = Compose([
                Resize((args.image_size, args.image_size), Image.BICUBIC),
                ToTensor(),
                Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
            ])
    image_file = Image.fromarray(np.array(image_file)[:, ::-1, :], "RGB")
    image_file = transform(image_file)

    return image_file
  
def generate_images(test_input):
  test_input = load_image_infer(test_input)
  prediction = generator(test_input).data
  fig = plt.figure(figsize=(128, 128))
  title = ['Predicted Image']
  
  plt.title('Predicted Image')
  # Getting the pixel values in the [0, 1] range to plot.
  plt.imshow(prediction[0,:,:,:] * 0.5 + 0.5)
  plt.axis('off')
  return fig

generator = GeneratorUNet()
generator.from_pretrained("huggan/pix2pix-edge2shoes")

img = gr.inputs.Image(shape=(256,256))
plot = gr.outputs.Image(type="plot")

description = "Pix2pix model that translates image-to-image."
gr.Interface(generate_images, inputs = img, outputs = plot, 
title = "Pix2Pix Shoes Reconstructor", description = description).launch()