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385 lines
14 KiB
Python
385 lines
14 KiB
Python
import logging
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import math
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import os
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import queue
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import threading
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import cv2
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import numpy as np
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import torch
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from torch.nn import functional as F
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from imaginairy.utils.downloads import get_cached_url_path
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ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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logger = logging.getLogger(__name__)
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class RealESRGANer:
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"""A helper class for upsampling images with RealESRGAN.
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Args:
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scale (int): Upsampling scale factor used in the networks. It is usually 2 or 4.
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model_path (str): The path to the pretrained model. It can be urls (will first download it automatically).
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model (nn.Module): The defined network. Default: None.
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tile (int): As too large images result in the out of GPU memory issue, so this tile option will first crop
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input images into tiles, and then process each of them. Finally, they will be merged into one image.
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0 denotes for do not use tile. Default: 0.
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tile_pad (int): The pad size for each tile, to remove border artifacts. Default: 10.
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pre_pad (int): Pad the input images to avoid border artifacts. Default: 10.
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half (float): Whether to use half precision during inference. Default: False.
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"""
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def __init__(
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self,
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scale,
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model_path,
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dni_weight=None,
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model=None,
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tile=0,
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tile_pad=10,
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pre_pad=10,
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half=False,
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device=None,
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gpu_id=None,
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):
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self.scale = scale
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self.tile_size = tile
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self.tile_pad = tile_pad
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self.pre_pad = pre_pad
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self.mod_scale = None
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self.half = half
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# initialize model
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if gpu_id:
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self.device = (
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torch.device(f"cuda:{gpu_id}" if torch.cuda.is_available() else "cpu")
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if device is None
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else device
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)
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else:
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self.device = (
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torch.device("cuda" if torch.cuda.is_available() else "cpu")
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if device is None
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else device
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)
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if isinstance(model_path, list):
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# dni
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assert len(model_path) == len(
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dni_weight
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), "model_path and dni_weight should have the save length."
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loadnet = self.dni(model_path[0], model_path[1], dni_weight)
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else:
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# if the model_path starts with https, it will first download models to the folder: weights
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if model_path.startswith("https://"):
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model_path = get_cached_url_path(model_path)
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loadnet = torch.load(model_path, map_location=torch.device("cpu"))
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# prefer to use params_ema
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if "params_ema" in loadnet:
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keyname = "params_ema"
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loadnet = loadnet[keyname]
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elif "params" in loadnet:
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keyname = "params"
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loadnet = loadnet[keyname]
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else:
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loadnet = convert_realesrgan_state_dict(loadnet)
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model.load_state_dict(loadnet, strict=True)
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model.eval()
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self.model = model.to(self.device)
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if self.half:
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self.model = self.model.half()
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def dni(self, net_a, net_b, dni_weight, key="params", loc="cpu"):
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"""Deep network interpolation.
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``Paper: Deep Network Interpolation for Continuous Imagery Effect Transition``
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"""
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net_a = torch.load(net_a, map_location=torch.device(loc))
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net_b = torch.load(net_b, map_location=torch.device(loc))
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for k, v_a in net_a[key].items():
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net_a[key][k] = dni_weight[0] * v_a + dni_weight[1] * net_b[key][k]
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return net_a
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def pre_process(self, img):
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"""Pre-process, such as pre-pad and mod pad, so that the images can be divisible."""
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img = torch.from_numpy(np.transpose(img, (2, 0, 1))).float()
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self.img = img.unsqueeze(0).to(self.device)
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if self.half:
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self.img = self.img.half()
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# pre_pad
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if self.pre_pad != 0:
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self.img = F.pad(self.img, (0, self.pre_pad, 0, self.pre_pad), "reflect")
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# mod pad for divisible borders
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if self.scale == 2:
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self.mod_scale = 2
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elif self.scale == 1:
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self.mod_scale = 4
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if self.mod_scale is not None:
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self.mod_pad_h, self.mod_pad_w = 0, 0
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_, _, h, w = self.img.size()
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if h % self.mod_scale != 0:
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self.mod_pad_h = self.mod_scale - h % self.mod_scale
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if w % self.mod_scale != 0:
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self.mod_pad_w = self.mod_scale - w % self.mod_scale
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self.img = F.pad(
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self.img, (0, self.mod_pad_w, 0, self.mod_pad_h), "reflect"
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)
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def process(self):
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# model inference
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self.output = self.model(self.img)
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def tile_process(self):
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"""It will first crop input images to tiles, and then process each tile.
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Finally, all the processed tiles are merged into one images.
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Modified from: https://github.com/ata4/esrgan-launcher
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"""
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batch, channel, height, width = self.img.shape
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output_height = height * self.scale
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output_width = width * self.scale
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output_shape = (batch, channel, output_height, output_width)
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# start with black image
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self.output = self.img.new_zeros(output_shape)
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tiles_x = math.ceil(width / self.tile_size)
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tiles_y = math.ceil(height / self.tile_size)
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logger.debug(f"Tiling with {tiles_x}x{tiles_y} ({tiles_x*tiles_y}) tiles")
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# loop over all tiles
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for y in range(tiles_y):
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for x in range(tiles_x):
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# extract tile from input image
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ofs_x = x * self.tile_size
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ofs_y = y * self.tile_size
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# input tile area on total image
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input_start_x = ofs_x
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input_end_x = min(ofs_x + self.tile_size, width)
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input_start_y = ofs_y
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input_end_y = min(ofs_y + self.tile_size, height)
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# input tile area on total image with padding
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input_start_x_pad = max(input_start_x - self.tile_pad, 0)
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input_end_x_pad = min(input_end_x + self.tile_pad, width)
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input_start_y_pad = max(input_start_y - self.tile_pad, 0)
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input_end_y_pad = min(input_end_y + self.tile_pad, height)
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# input tile dimensions
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input_tile_width = input_end_x - input_start_x
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input_tile_height = input_end_y - input_start_y
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input_tile = self.img[
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:,
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:,
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input_start_y_pad:input_end_y_pad,
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input_start_x_pad:input_end_x_pad,
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]
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# upscale tile
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with torch.no_grad():
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output_tile = self.model(input_tile)
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# output tile area on total image
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output_start_x = input_start_x * self.scale
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output_end_x = input_end_x * self.scale
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output_start_y = input_start_y * self.scale
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output_end_y = input_end_y * self.scale
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# output tile area without padding
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output_start_x_tile = (input_start_x - input_start_x_pad) * self.scale
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output_end_x_tile = output_start_x_tile + input_tile_width * self.scale
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output_start_y_tile = (input_start_y - input_start_y_pad) * self.scale
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output_end_y_tile = output_start_y_tile + input_tile_height * self.scale
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# put tile into output image
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self.output[
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:, :, output_start_y:output_end_y, output_start_x:output_end_x
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] = output_tile[
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:,
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:,
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output_start_y_tile:output_end_y_tile,
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output_start_x_tile:output_end_x_tile,
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]
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def post_process(self):
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# remove extra pad
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if self.mod_scale is not None:
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_, _, h, w = self.output.size()
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self.output = self.output[
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:,
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:,
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0 : h - self.mod_pad_h * self.scale,
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0 : w - self.mod_pad_w * self.scale,
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]
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# remove prepad
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if self.pre_pad != 0:
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_, _, h, w = self.output.size()
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self.output = self.output[
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:,
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:,
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0 : h - self.pre_pad * self.scale,
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0 : w - self.pre_pad * self.scale,
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]
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return self.output
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@torch.no_grad()
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def enhance(self, img, outscale=None, alpha_upsampler="realesrgan"):
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h_input, w_input = img.shape[0:2]
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# img: numpy
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img = img.astype(np.float32)
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if np.max(img) > 256: # 16-bit image
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max_range = 65535
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print("\tInput is a 16-bit image")
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else:
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max_range = 255
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img = img / max_range
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if len(img.shape) == 2: # gray image
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img_mode = "L"
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img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
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elif img.shape[2] == 4: # RGBA image with alpha channel
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img_mode = "RGBA"
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alpha = img[:, :, 3]
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img = img[:, :, 0:3]
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img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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if alpha_upsampler == "realesrgan":
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alpha = cv2.cvtColor(alpha, cv2.COLOR_GRAY2RGB)
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else:
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img_mode = "RGB"
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img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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# ------------------- process image (without the alpha channel) ------------------- #
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self.pre_process(img)
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if self.tile_size > 0:
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self.tile_process()
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else:
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self.process()
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output_img = self.post_process()
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output_img = output_img.data.squeeze().float().cpu().clamp_(0, 1).numpy()
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output_img = np.transpose(output_img[[2, 1, 0], :, :], (1, 2, 0))
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if img_mode == "L":
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output_img = cv2.cvtColor(output_img, cv2.COLOR_BGR2GRAY)
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# ------------------- process the alpha channel if necessary ------------------- #
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if img_mode == "RGBA":
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if alpha_upsampler == "realesrgan":
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self.pre_process(alpha)
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if self.tile_size > 0:
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self.tile_process()
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else:
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self.process()
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output_alpha = self.post_process()
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output_alpha = (
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output_alpha.data.squeeze().float().cpu().clamp_(0, 1).numpy()
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)
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output_alpha = np.transpose(output_alpha[[2, 1, 0], :, :], (1, 2, 0))
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output_alpha = cv2.cvtColor(output_alpha, cv2.COLOR_BGR2GRAY)
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else: # use the cv2 resize for alpha channel
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h, w = alpha.shape[0:2]
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output_alpha = cv2.resize(
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alpha,
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(w * self.scale, h * self.scale),
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interpolation=cv2.INTER_LINEAR,
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)
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# merge the alpha channel
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output_img = cv2.cvtColor(output_img, cv2.COLOR_BGR2BGRA)
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output_img[:, :, 3] = output_alpha
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# ------------------------------ return ------------------------------ #
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if max_range == 65535: # 16-bit image
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output = (output_img * 65535.0).round().astype(np.uint16)
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else:
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output = (output_img * 255.0).round().astype(np.uint8)
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if outscale is not None and outscale != float(self.scale):
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output = cv2.resize(
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output,
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(
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int(w_input * outscale),
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int(h_input * outscale),
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),
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interpolation=cv2.INTER_LANCZOS4,
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)
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return output, img_mode
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class PrefetchReader(threading.Thread):
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"""Prefetch images.
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Args:
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img_list (list[str]): A image list of image paths to be read.
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num_prefetch_queue (int): Number of prefetch queue.
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"""
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def __init__(self, img_list, num_prefetch_queue):
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super().__init__()
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self.que = queue.Queue(num_prefetch_queue)
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self.img_list = img_list
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def run(self):
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for img_path in self.img_list:
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img = cv2.imread(img_path, cv2.IMREAD_UNCHANGED)
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self.que.put(img)
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self.que.put(None)
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def __next__(self):
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next_item = self.que.get()
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if next_item is None:
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raise StopIteration
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return next_item
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def __iter__(self):
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return self
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class IOConsumer(threading.Thread):
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def __init__(self, opt, que, qid):
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super().__init__()
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self._queue = que
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self.qid = qid
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self.opt = opt
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def run(self):
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while True:
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msg = self._queue.get()
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if isinstance(msg, str) and msg == "quit":
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break
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output = msg["output"]
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save_path = msg["save_path"]
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cv2.imwrite(save_path, output)
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print(f"IO worker {self.qid} is done.")
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def convert_realesrgan_state_dict(state_dict):
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new_state_dict = {}
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new_state_dict["conv_first.weight"] = state_dict.pop("model.0.weight")
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new_state_dict["conv_first.bias"] = state_dict.pop("model.0.bias")
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# "model.1.sub.21.RDB3.conv5.0.weight => body.21.rdb1.conv3.weight"
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for k, v in list(state_dict.items()):
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parts = k.split(".")
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if len(parts) == 8 and parts[0] == "model":
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new_parts = ["body", parts[3], parts[4].lower(), parts[5], parts[7]]
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new_k = ".".join(new_parts)
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new_state_dict[new_k] = state_dict.pop(k)
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new_state_dict["conv_body.weight"] = state_dict.pop("model.1.sub.23.weight")
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new_state_dict["conv_body.bias"] = state_dict.pop("model.1.sub.23.bias")
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new_state_dict["conv_up1.weight"] = state_dict.pop("model.3.weight")
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new_state_dict["conv_up1.bias"] = state_dict.pop("model.3.bias")
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new_state_dict["conv_up2.weight"] = state_dict.pop("model.6.weight")
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new_state_dict["conv_up2.bias"] = state_dict.pop("model.6.bias")
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new_state_dict["conv_hr.weight"] = state_dict.pop("model.8.weight")
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new_state_dict["conv_hr.bias"] = state_dict.pop("model.8.bias")
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new_state_dict["conv_last.weight"] = state_dict.pop("model.10.weight")
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new_state_dict["conv_last.bias"] = state_dict.pop("model.10.bias")
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return new_state_dict
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