def __init__(self, opt, conv=common.default_conv):
super(DRN, self).__init__()
self.opt = opt
self.scale = opt.scale
self.phase = len(opt.scale)
n_blocks = opt.n_blocks
n_feats = opt.n_feats
kernel_size = 3
act = nn.ReLU(True)
self.upsample = nn.Upsample(scale_factor=max(opt.scale),
mode='bicubic',
align_corners=False)
rgb_mean = (0.4488, 0.4371, 0.4040)
rgb_std = (1.0, 1.0, 1.0)
self.sub_mean = common.MeanShift(opt.rgb_range, rgb_mean, rgb_std)
self.head = conv(opt.n_colors, n_feats, kernel_size)
self.head_two = conv(n_feats, n_feats * 4, kernel_size)
self.down = [
common.DownBlock(opt, 2, n_feats * pow(2, p), n_feats * pow(2, p),
n_feats * pow(2, p + 1))
for p in range(self.phase)
]
self.down = nn.ModuleList(self.down)
up_body_blocks = [[
common.RCAB(conv, n_feats * pow(2, p), kernel_size, act=act)
for _ in range(n_blocks)
] for p in range(self.phase, 1, -1)]
up_body_blocks.insert(0, [
common.RCAB(
conv, n_feats * pow(2, self.phase), kernel_size, act=act)
for _ in range(n_blocks)
])
# The fisrt upsample block
up = [[
common.Upsampler(conv, 2, n_feats * pow(2, self.phase), act=False),
conv(n_feats * pow(2, self.phase),
n_feats * pow(2, self.phase - 1),
kernel_size=1)
]]
# The rest upsample blocks
for p in range(self.phase - 1, 0, -1):
up.append([
# common.Upsampler(conv, 2, 2 * n_feats * pow(2, p), act=False),
# conv(2 * n_feats * pow(2, p), n_feats * pow(2, p - 1), kernel_size=1)
common.Upsampler(conv,
2,
n_feats * pow(2, self.phase),
act=False),
conv(n_feats * pow(2, self.phase),
n_feats * pow(2, self.phase - 1),
kernel_size=1)
])
self.up_blocks = nn.ModuleList()
for idx in range(self.phase):
self.up_blocks.append(nn.Sequential(*up_body_blocks[idx],
*up[idx]))
# tail conv that output sr imgs
tail = [conv(n_feats * pow(2, self.phase), opt.n_colors, kernel_size)]
for p in range(self.phase, 0, -1):
tail.append(conv(n_feats * pow(2, p), opt.n_colors, kernel_size))
self.tail = nn.ModuleList(tail)
self.add_mean = common.MeanShift(opt.rgb_range, rgb_mean, rgb_std, 1)
self.RCAB_block_1 = common.RCAB(conv,
n_feats * pow(2, 2),
kernel_size,
act=act)
self.up_block_1 = [[
common.Upsampler(conv, 2, n_feats * pow(2, 2), act=False),
conv(n_feats * pow(2, self.phase),
n_feats * pow(2, self.phase - 1),
kernel_size=1)
]]
self.RCAB_block_2 = common.RCAB(conv,
n_feats * pow(2, 1),
kernel_size,
act=act)
self.up_block_2 = [[
common.Upsampler(conv, 2, n_feats * pow(2, 1), act=False),
conv(n_feats * pow(2, 1), n_feats * pow(2, 0), kernel_size=1)
]]
self.head_3 = conv(n_feats * pow(2, 0), 8, kernel_size)
self.head_4 = conv(8, opt.n_colors, kernel_size)
def __init__(self, opt, conv=common.default_conv):
super(DRN, self).__init__()
self.opt = opt
self.scale = opt.scale
self.phase = len(opt.scale)
n_blocks = opt.n_blocks
n_feats = opt.n_feats
kernel_size = 3
act = nn.ReLU(True) # 加True的意思就是将计算的值覆盖之前的值，不用重新赋给新的变量
self.upsample = nn.Upsample(scale_factor=max(opt.scale),
mode='bicubic',
align_corners=False)
# 当align_corners = True时，像素被视为网格的格子上的点, 拐角处的像素对齐.可知是点之间是等间距的
# 当align_corners = False时, 像素被视为网格的交叉线上的点, 拐角处的点依然是原图像的拐角像素, 但是差值的点间却按照上图的取法取, 导致点与点之间是不等距的
rgb_mean = (0.4488, 0.4371, 0.4040)
rgb_std = (1.0, 1.0, 1.0)
self.sub_mean = common.MeanShift(opt.rgb_range, rgb_mean, rgb_std)
self.head = conv(opt.n_colors, n_feats, kernel_size)
self.down = [
common.DownBlock(opt, 2, n_feats * pow(2, p), n_feats * pow(2, p),
n_feats * pow(2, p + 1))
for p in range(self.phase)
]
self.down = nn.ModuleList(self.down)
up_body_blocks = [[
common.RCAB(conv, n_feats * pow(2, p), kernel_size, act=act)
for _ in range(n_blocks)
] for p in range(self.phase, 1, -1)]
up_body_blocks.insert(0, [
common.RCAB(
conv, n_feats * pow(2, self.phase), kernel_size, act=act)
for _ in range(n_blocks)
])
# The fisrt upsample block
up = [[
common.Upsampler(conv, 2, n_feats * pow(2, self.phase), act=False),
conv(n_feats * pow(2, self.phase),
n_feats * pow(2, self.phase - 1),
kernel_size=1)
]]
# The rest upsample blocks
for p in range(self.phase - 1, 0, -1):
up.append([
common.Upsampler(conv, 2, 2 * n_feats * pow(2, p), act=False),
conv(2 * n_feats * pow(2, p),
n_feats * pow(2, p - 1),
kernel_size=1)
])
self.up_blocks = nn.ModuleList()
for idx in range(self.phase):
self.up_blocks.append(nn.Sequential(*up_body_blocks[idx],
*up[idx]))
# tail conv that output sr imgs
tail = [conv(n_feats * pow(2, self.phase), opt.n_colors, kernel_size)]
for p in range(self.phase, 0, -1):
tail.append(conv(n_feats * pow(2, p), opt.n_colors, kernel_size))
self.tail = nn.ModuleList(tail)
self.add_mean = common.MeanShift(opt.rgb_range, rgb_mean, rgb_std, 1)
def __init__(self, opt, conv=common.default_conv):
super(DRN, self).__init__()
self.opt = opt
self.scale = opt.scale
self.phase = len(opt.scale)
n_blocks = opt.n_blocks
n_feats = opt.n_feats
kernel_size = 3
sf = 0
if (self.scale[0] % 2) == 0:
sf = 2
elif self.scale[0] == 3:
sf = 3
print('DRN scale >> ' + str(sf))
act = nn.ReLU(True)
self.upsample = nn.Upsample(scale_factor=max(opt.scale),
mode='bicubic',
align_corners=False)
rgb_mean = (0.4488, 0.4371, 0.4040)
rgb_std = (1.0, 1.0, 1.0)
self.sub_mean = common.MeanShift(opt.rgb_range, rgb_mean, rgb_std)
self.head = conv(opt.n_colors, n_feats, kernel_size)
self.down = [
common.DownBlock(opt, sf, n_feats * pow(2, p), n_feats * pow(2, p),
n_feats * pow(2, p + 1))
for p in range(self.phase)
]
self.down = nn.ModuleList(self.down)
up_body_blocks = [[
common.RCAB(conv, n_feats * pow(2, p), kernel_size, act=act)
for _ in range(n_blocks)
] for p in range(self.phase, 1, -1)]
up_body_blocks.insert(0, [
common.RCAB(
conv, n_feats * pow(2, self.phase), kernel_size, act=act)
for _ in range(n_blocks)
])
# The first upsample block
up = [[
common.Upsampler(conv, sf, n_feats * pow(2, self.phase),
act=False),
conv(n_feats * pow(2, self.phase),
n_feats * pow(2, self.phase - 1),
kernel_size=1)
]]
# The rest upsample blocks
for p in range(self.phase - 1, 0, -1):
up.append([
common.Upsampler(conv, sf, 2 * n_feats * pow(2, p), act=False),
conv(2 * n_feats * pow(2, p),
n_feats * pow(2, p - 1),
kernel_size=1)
])
self.up_blocks = nn.ModuleList()
for idx in range(self.phase):
self.up_blocks.append(nn.Sequential(*up_body_blocks[idx],
*up[idx]))
# tail conv that output sr imgs
tail = [conv(n_feats * pow(2, self.phase), opt.n_colors, kernel_size)]
for p in range(self.phase, 0, -1):
tail.append(conv(n_feats * pow(2, p), opt.n_colors, kernel_size))
self.tail = nn.ModuleList(tail)
self.P2W = Pos2Weight(inC=opt.n_colors,
kernel_size=kernel_size,
outC=opt.n_colors)
self.add_mean = common.MeanShift(opt.rgb_range, rgb_mean, rgb_std, 1)