MyPipe.py

import torch, os
import torch.nn.functional as F
from torchvision.transforms.functional import normalize
import numpy as np
from transformers import Pipeline
from skimage import io
from PIL import Image


class RMBGPipe(Pipeline):
    def __init__(self, **kwargs):
        Pipeline.__init__(self, **kwargs)
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.model.to(self.device)
        self.model.eval()

    def _sanitize_parameters(self, **kwargs):
        # parse parameters
        preprocess_kwargs = {}
        postprocess_kwargs = {}
        if "model_input_size" in kwargs:
            preprocess_kwargs["model_input_size"] = kwargs["model_input_size"]
        if "out_name" in kwargs:
            postprocess_kwargs["out_name"] = kwargs["out_name"]
        return preprocess_kwargs, {}, postprocess_kwargs

    def preprocess(self, orig_im: Image, model_input_size: list = [1024, 1024]):
        # preprocess the input
        orig_im_size = orig_im.shape[0:2]
        image = self.preprocess_image(orig_im, model_input_size).to(self.device)
        inputs = {
            "orig_im": orig_im,
            "image": image,
            "orig_im_size": orig_im_size,
        }
        return inputs

    def _forward(self, inputs):
        result = self.model(inputs.pop("image"))
        inputs["result"] = result
        return inputs

    def postprocess(self, inputs, out_name=""):
        result = inputs.pop("result")
        orig_im_size = inputs.pop("orig_im_size")
        orig_image = inputs.pop("orig_im")
        result_image = self.postprocess_image(result[0][0], orig_im_size)
        if out_name != "":
            # if out_name is specified we save the image using that name
            pil_im = Image.fromarray(result_image)
            no_bg_image = Image.new("RGBA", pil_im.size, (0, 0, 0, 0))
            no_bg_image.paste(orig_image, mask=pil_im)
            no_bg_image.save(out_name)
        else:
            return result_image

    # utilities functions
    def preprocess_image(
        self, im: np.ndarray, model_input_size: list = [1024, 1024]
    ) -> torch.Tensor:
        # same as utilities.py with minor modification
        if len(im.shape) < 3:
            im = im[:, :, np.newaxis]
        # orig_im_size=im.shape[0:2]
        im_tensor = torch.tensor(im, dtype=torch.float32).permute(2, 0, 1)
        im_tensor = F.interpolate(
            torch.unsqueeze(im_tensor, 0), size=model_input_size, mode="bilinear"
        ).type(torch.uint8)
        image = torch.divide(im_tensor, 255.0)
        image = normalize(image, [0.5, 0.5, 0.5], [1.0, 1.0, 1.0])
        return image

    def postprocess_image(self, result: torch.Tensor, im_size: list) -> np.ndarray:
        result = torch.squeeze(F.interpolate(result, size=im_size, mode="bilinear"), 0)
        ma = torch.max(result)
        mi = torch.min(result)
        result = (result - mi) / (ma - mi)
        im_array = (result * 255).permute(1, 2, 0).cpu().data.numpy().astype(np.uint8)
        im_array = np.squeeze(im_array)
        return im_array