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import os
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 import pad_reflect, split_image_into_overlapping_patches, stich_together, \
unpad_image
HF_MODELS = {
2: dict(
repo_id='sberbank-ai/Real-ESRGAN',
filename='RealESRGAN_x2.pth',
),
4: dict(
repo_id='sberbank-ai/Real-ESRGAN',
filename='RealESRGAN_x4.pth',
),
8: dict(
repo_id='sberbank-ai/Real-ESRGAN',
filename='RealESRGAN_x8.pth',
),
}
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, download=True):
if not os.path.exists(model_path) and download:
from huggingface_hub import hf_hub_url, cached_download
assert self.scale in [2,4,8], 'You can download models only with scales: 2, 4, 8'
config = HF_MODELS[self.scale]
cache_dir = os.path.dirname(model_path)
local_filename = os.path.basename(model_path)
config_file_url = hf_hub_url(repo_id=config['repo_id'], filename=config['filename'])
cached_download(config_file_url, cache_dir=cache_dir, force_filename=local_filename)
print('Weights downloaded to:', os.path.join(cache_dir, local_filename))
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 |