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Update swinir.py

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C43H66N12O12S2 2022-09-20 20:09:13 +03:00 committed by AUTOMATIC1111
parent 5f71ecfe6f
commit d8ed699839
1 changed files with 74 additions and 43 deletions
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modules/swinir.py
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@ -1,63 +1,87 @@
import sys
import traceback
import cv2
from collections import OrderedDict
import os
import requests
from collections import namedtuple
import contextlib
import numpy as np
from PIL import Image
import torch
import modules.images
from modules.shared import cmd_opts, opts, device
from modules.swinir_arch import SwinIR as net
precision_scope = torch.autocast if cmd_opts.precision == "autocast" else contextlib.nullcontext
def load_model(task = "realsr", large_model = True, model_path="C:/sd/ESRGANn/4x-large.pth", scale=4):
try:
modules.shared.sd_upscalers.append(UpscalerSwin("McSwinnySwin"))
except Exception:
print(f"Error loading ESRGAN model", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
if not large_model:
# use 'nearest+conv' to avoid block artifacts
model = net(upscale=scale, in_chans=3, img_size=64, window_size=8,
img_range=1., depths=[6, 6, 6, 6, 6, 6], embed_dim=180, num_heads=[6, 6, 6, 6, 6, 6],
mlp_ratio=2, upsampler='nearest+conv', resi_connection='1conv')
else:
# larger model size; use '3conv' to save parameters and memory; use ema for GAN training
model = net(upscale=scale, in_chans=3, img_size=64, window_size=8,
img_range=1., depths=[6, 6, 6, 6, 6, 6, 6, 6, 6], embed_dim=240,
num_heads=[8, 8, 8, 8, 8, 8, 8, 8, 8],
mlp_ratio=2, upsampler='nearest+conv', resi_connection='3conv')
pretrained_model = torch.load(model_path)
precision_scope = (
torch.autocast if cmd_opts.precision == "autocast" else contextlib.nullcontext
)
def load_model(filename, scale=4):
model = net(
upscale=scale,
in_chans=3,
img_size=64,
window_size=8,
img_range=1.0,
depths=[6, 6, 6, 6, 6, 6, 6, 6, 6],
embed_dim=240,
num_heads=[8, 8, 8, 8, 8, 8, 8, 8, 8],
mlp_ratio=2,
upsampler="nearest+conv",
resi_connection="3conv",
)
pretrained_model = torch.load(filename)
model.load_state_dict(pretrained_model["params_ema"], strict=True)
if not cmd_opts.no_half:
model = model.half()
return model
return model.half().to(device)
def upscale(img, tile=opts.ESRGAN_tile, tile_overlap=opts.ESRGAN_tile_overlap, window_size = 8, scale = 4):
def load_models(dirname):
for file in os.listdir(dirname):
path = os.path.join(dirname, file)
model_name, extension = os.path.splitext(file)
if extension != ".pt" and extension != ".pth":
continue
try:
modules.shared.sd_upscalers.append(UpscalerSwin(path, model_name))
except Exception:
print(f"Error loading SwinIR model: {path}", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
def upscale(
img,
model,
tile=opts.GAN_tile,
tile_overlap=opts.GAN_tile_overlap,
window_size=8,
scale=4,
):
img = np.array(img)
img = img[:, :, ::-1]
img = np.moveaxis(img, 2, 0) / 255
img = torch.from_numpy(img).float()
img = img.unsqueeze(0).to(device)
model = load_model()
with torch.no_grad(), precision_scope("cuda"):
_, _, h_old, w_old = img.size()
h_pad = (h_old // window_size + 1) * window_size - h_old
w_pad = (w_old // window_size + 1) * window_size - w_old
img = torch.cat([img, torch.flip(img, [2])], 2)[:, :, :h_old + h_pad, :]
img = torch.cat([img, torch.flip(img, [3])], 3)[:, :, :, :w_old + w_pad]
img = torch.cat([img, torch.flip(img, [2])], 2)[:, :, : h_old + h_pad, :]
img = torch.cat([img, torch.flip(img, [3])], 3)[:, :, :, : w_old + w_pad]
output = inference(img, model, tile, tile_overlap, window_size, scale)
output = output[..., :h_old * scale, :w_old * scale]
output = output[..., : h_old * scale, : w_old * scale]
output = output.data.squeeze().float().cpu().clamp_(0, 1).numpy()
if output.ndim == 3:
output = np.transpose(output[[2, 1, 0], :, :], (1, 2, 0)) # CHW-RGB to HCW-BGR
output = np.transpose(
output[[2, 1, 0], :, :], (1, 2, 0)
) # CHW-RGB to HCW-BGR
output = (output * 255.0).round().astype(np.uint8) # float32 to uint8
return Image.fromarray(output, 'RGB')
return Image.fromarray(output, "RGB")
def inference(img, model, tile, tile_overlap, window_size, scale):
# test the image tile by tile
b, c, h, w = img.size()
@ -66,27 +90,34 @@ def inference(img, model, tile, tile_overlap, window_size, scale):
sf = scale
stride = tile - tile_overlap
h_idx_list = list(range(0, h-tile, stride)) + [h-tile]
w_idx_list = list(range(0, w-tile, stride)) + [w-tile]
E = torch.zeros(b, c, h*sf, w*sf, dtype=torch.half, device=device).type_as(img)
h_idx_list = list(range(0, h - tile, stride)) + [h - tile]
w_idx_list = list(range(0, w - tile, stride)) + [w - tile]
E = torch.zeros(b, c, h * sf, w * sf, dtype=torch.half, device=device).type_as(img)
W = torch.zeros_like(E, dtype=torch.half, device=device)
for h_idx in h_idx_list:
for w_idx in w_idx_list:
in_patch = img[..., h_idx:h_idx+tile, w_idx:w_idx+tile]
in_patch = img[..., h_idx : h_idx + tile, w_idx : w_idx + tile]
out_patch = model(in_patch)
out_patch_mask = torch.ones_like(out_patch)
E[..., h_idx*sf:(h_idx+tile)*sf, w_idx*sf:(w_idx+tile)*sf].add_(out_patch)
W[..., h_idx*sf:(h_idx+tile)*sf, w_idx*sf:(w_idx+tile)*sf].add_(out_patch_mask)
E[
..., h_idx * sf : (h_idx + tile) * sf, w_idx * sf : (w_idx + tile) * sf
].add_(out_patch)
W[
..., h_idx * sf : (h_idx + tile) * sf, w_idx * sf : (w_idx + tile) * sf
].add_(out_patch_mask)
output = E.div_(W)
return output
class UpscalerSwin(modules.images.Upscaler):
def __init__(self, title):
def __init__(self, filename, title):
self.name = title
self.model = load_model(filename)
def do_upscale(self, img):
img = upscale(img)
model = self.model.to(device)
img = upscale(img, model)
return img