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import os
import math
import torch
import numpy as np
from rrdbnet_arch import RRDBNet
from torch.nn import functional as F
class RealESRNet(object):
def __init__(self, base_dir='./', model=None, scale=2, tile_size=0, tile_pad=10, device='cuda'):
self.base_dir = base_dir
self.scale = scale
self.tile_size = tile_size
self.tile_pad = tile_pad
self.device = device
self.load_srmodel(base_dir, model)
def load_srmodel(self, base_dir, model):
self.srmodel = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=32, num_block=23, num_grow_ch=32, scale=self.scale)
if model is None:
loadnet = torch.load(os.path.join(self.base_dir, 'weights', 'realesrnet_x%d.pth'%self.scale))
else:
loadnet = torch.load(os.path.join(self.base_dir, 'weights', model+'_x%d.pth'%self.scale))
#print(loadnet['params_ema'].keys)
self.srmodel.load_state_dict(loadnet['params_ema'], strict=True)
self.srmodel.eval()
self.srmodel = self.srmodel.to(self.device)
def tile_process(self, img):
"""It will first crop input images to tiles, and then process each tile.
Finally, all the processed tiles are merged into one images.
Modified from: https://github.com/ata4/esrgan-launcher
"""
batch, channel, height, width = img.shape
output_height = height * self.scale
output_width = width * self.scale
output_shape = (batch, channel, output_height, output_width)
# start with black image
output = img.new_zeros(output_shape)
tiles_x = math.ceil(width / self.tile_size)
tiles_y = math.ceil(height / self.tile_size)
# loop over all tiles
for y in range(tiles_y):
for x in range(tiles_x):
# extract tile from input image
ofs_x = x * self.tile_size
ofs_y = y * self.tile_size
# input tile area on total image
input_start_x = ofs_x
input_end_x = min(ofs_x + self.tile_size, width)
input_start_y = ofs_y
input_end_y = min(ofs_y + self.tile_size, height)
# input tile area on total image with padding
input_start_x_pad = max(input_start_x - self.tile_pad, 0)
input_end_x_pad = min(input_end_x + self.tile_pad, width)
input_start_y_pad = max(input_start_y - self.tile_pad, 0)
input_end_y_pad = min(input_end_y + self.tile_pad, height)
# input tile dimensions
input_tile_width = input_end_x - input_start_x
input_tile_height = input_end_y - input_start_y
tile_idx = y * tiles_x + x + 1
input_tile = img[:, :, input_start_y_pad:input_end_y_pad, input_start_x_pad:input_end_x_pad]
# upscale tile
try:
with torch.no_grad():
output_tile = self.srmodel(input_tile)
except RuntimeError as error:
print('Error', error)
return None
if tile_idx%10==0: print(f'\tTile {tile_idx}/{tiles_x * tiles_y}')
# output tile area on total image
output_start_x = input_start_x * self.scale
output_end_x = input_end_x * self.scale
output_start_y = input_start_y * self.scale
output_end_y = input_end_y * self.scale
# output tile area without padding
output_start_x_tile = (input_start_x - input_start_x_pad) * self.scale
output_end_x_tile = output_start_x_tile + input_tile_width * self.scale
output_start_y_tile = (input_start_y - input_start_y_pad) * self.scale
output_end_y_tile = output_start_y_tile + input_tile_height * self.scale
# put tile into output image
output[:, :, output_start_y:output_end_y,
output_start_x:output_end_x] = output_tile[:, :, output_start_y_tile:output_end_y_tile,
output_start_x_tile:output_end_x_tile]
return output
def process(self, img):
img = img.astype(np.float32) / 255.
img = torch.from_numpy(np.transpose(img[:, :, [2, 1, 0]], (2, 0, 1))).float()
img = img.unsqueeze(0).to(self.device)
if self.scale == 2:
mod_scale = 2
elif self.scale == 1:
mod_scale = 4
else:
mod_scale = None
if mod_scale is not None:
h_pad, w_pad = 0, 0
_, _, h, w = img.size()
if (h % mod_scale != 0):
h_pad = (mod_scale - h % mod_scale)
if (w % mod_scale != 0):
w_pad = (mod_scale - w % mod_scale)
img = F.pad(img, (0, w_pad, 0, h_pad), 'reflect')
try:
with torch.no_grad():
if self.tile_size > 0:
output = self.tile_process(img)
else:
output = self.srmodel(img)
del img
# remove extra pad
if mod_scale is not None:
_, _, h, w = output.size()
output = output[:, :, 0:h - h_pad, 0:w - w_pad]
output = output.data.squeeze().float().cpu().clamp_(0, 1).numpy()
output = np.transpose(output[[2, 1, 0], :, :], (1, 2, 0))
output = (output * 255.0).round().astype(np.uint8)
return output
except Exception as e:
print('sr failed:', e)
return None |