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import torch
from torch.nn import functional as F
from PIL import Image
import numpy as np
import cv2
from rrdbnet_arch import RRDBNet
from utils_sr import *
class RealESRGAN:
def __init__(self, device, scale=4):
self.device = device
self.scale = scale
self.model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=scale)
def load_weights(self, model_path):
loadnet = torch.load(model_path)
if 'params' in loadnet:
self.model.load_state_dict(loadnet['params'], strict=True)
elif 'params_ema' in loadnet:
self.model.load_state_dict(loadnet['params_ema'], strict=True)
else:
self.model.load_state_dict(loadnet, strict=True)
self.model.eval()
self.model.to(self.device)
@torch.cuda.amp.autocast()
def predict(self, lr_image, batch_size=4, patches_size=192,
padding=24, pad_size=15):
scale = self.scale
device = self.device
lr_image = np.array(lr_image)
lr_image = pad_reflect(lr_image, pad_size)
patches, p_shape = split_image_into_overlapping_patches(lr_image, patch_size=patches_size,
padding_size=padding)
img = torch.FloatTensor(patches/255).permute((0,3,1,2)).to(device).detach()
with torch.no_grad():
res = self.model(img[0:batch_size])
for i in range(batch_size, img.shape[0], batch_size):
res = torch.cat((res, self.model(img[i:i+batch_size])), 0)
sr_image = res.permute((0,2,3,1)).clamp_(0, 1).cpu()
np_sr_image = sr_image.numpy()
padded_size_scaled = tuple(np.multiply(p_shape[0:2], scale)) + (3,)
scaled_image_shape = tuple(np.multiply(lr_image.shape[0:2], scale)) + (3,)
np_sr_image = stich_together(np_sr_image, padded_image_shape=padded_size_scaled,
target_shape=scaled_image_shape, padding_size=padding * scale)
sr_img = (np_sr_image*255).astype(np.uint8)
sr_img = unpad_image(sr_img, pad_size*scale)
sr_img = Image.fromarray(sr_img)
return sr_img |