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nafssr.py
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nafssr.py
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from typing import Sequence
from layers import *
import jax
import flax.linen as nn
class NAFSSR(nn.Module):
n_filters: int = 48
stochastic_depth_rate: float = .1
n_blocks: int = 16
upscale_rate: int = 4
fusion_from: int = -1
fusion_to: int = 1000
train_size: List = None, 40, 100, 3
base_rate: float = 1.5
def setup(self):
self.intro = nn.Conv(
self.n_filters,
kernel_size=(3, 3),
strides=(1, 1),
padding='SAME'
)
kh, kw = int(self.train_size[1] * self.base_rate), int(self.train_size[2] * self.base_rate)
self.middles = nn.Sequential([
NAFBlockSR(
self.n_filters,
kh, kw,
(self.fusion_from <= i <= self.fusion_to),
1. - self.stochastic_depth_rate
) for i in range(self.n_blocks)
])
self.end = nn.Sequential([
nn.Conv(
3 * (self.upscale_rate ** 2),
(3, 3),
(1, 1),
padding='SAME'
),
PixelShuffle(self.upscale_rate)
])
def __call__(self, inputs: Sequence, training: bool = True):
B, H, W, C = inputs[0].shape
features = [
self.intro(i) for i in inputs
]
features = self.middles(features)
recons = [
self.end(f) for f in features
]
recons = [
jax.image.resize(i,
(B, H * self.upscale_rate, W * self.upscale_rate, C),
method='bilinear'
) + r for i, r in zip(inputs, recons)
]
return recons