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lut_reproduce
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Vladimir Protsenko 8 months ago
parent 17bb7aa882
commit 5e08fb3eef
4 changed files with 56 additions and 71 deletions
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2
readme.md
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@ -4,7 +4,7 @@ Example
python train.py --model SRNetRot90
python validate.py --val_datasets Set5,Set14,B100,Urban100,Manga109 --model_path /wd/lut_reproduce/models/last_trained_net.pth
python transfer_to_lut.py --model_path /wd/lut_reproduce/models/last_trained_net.pth
python transfer_to_lut.py
python train.py --model_path /wd/lut_reproduce/models/last_transfered_lut.pth --total_iter 2000
python validate.py --val_datasets Set5,Set14,B100,Urban100,Manga109 --model_path /wd/lut_reproduce/models/last_trained_lut.pth

1
src/models/__init__.py
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@ -20,7 +20,6 @@ AVAILABLE_MODELS = {
'SDYNetx1': sdynet.SDYNetx1,
'RCNetx2Unlutable': rcnet.RCNetx2Unlutable,
'RCNetx2CenteredUnlutable': rcnet.RCNetx2CenteredUnlutable,
'RCNetCentered_7x7v2': rcnet.RCNetCentered_7x7v2
}
def SaveCheckpoint(model, path):

122
src/models/rcnet.py
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@ -443,7 +443,33 @@ class RCNetx2Unlutable(nn.Module):
self.stage2_7x7 = RCBlockRot90Unlutable(hidden_dim=hidden_dim, dense_conv_layer_count=layers_count, upscale_factor=scale, window_size=7)
def get_lut_model(self, quantization_interval=16, batch_size=2**10):
raise NotImplementedError
s1_rc_conv_luts_3x3 = lut.transfer_rc_conv(self.stage1_3x3.rc_conv, quantization_interval=quantization_interval).reshape(3,3,-1)
s1_dense_conv_lut_3x3 = lut.transfer_2x2_input_SxS_output(self.stage1_3x3.dense_conv_block, quantization_interval=quantization_interval, batch_size=batch_size)
s1_rc_conv_luts_5x5 = lut.transfer_rc_conv(self.stage1_5x5.rc_conv, quantization_interval=quantization_interval).reshape(5,5,-1)
s1_dense_conv_lut_5x5 = lut.transfer_2x2_input_SxS_output(self.stage1_5x5.dense_conv_block, quantization_interval=quantization_interval, batch_size=batch_size)
s1_rc_conv_luts_7x7 = lut.transfer_rc_conv(self.stage1_7x7.rc_conv, quantization_interval=quantization_interval).reshape(7,7,-1)
s1_dense_conv_lut_7x7 = lut.transfer_2x2_input_SxS_output(self.stage1_7x7.dense_conv_block, quantization_interval=quantization_interval, batch_size=batch_size)
s2_rc_conv_luts_3x3 = lut.transfer_rc_conv(self.stage2_3x3.rc_conv, quantization_interval=quantization_interval).reshape(3,3,-1)
s2_dense_conv_lut_3x3 = lut.transfer_2x2_input_SxS_output(self.stage2_3x3.dense_conv_block, quantization_interval=quantization_interval, batch_size=batch_size)
s2_rc_conv_luts_5x5 = lut.transfer_rc_conv(self.stage2_5x5.rc_conv, quantization_interval=quantization_interval).reshape(5,5,-1)
s2_dense_conv_lut_5x5 = lut.transfer_2x2_input_SxS_output(self.stage2_5x5.dense_conv_block, quantization_interval=quantization_interval, batch_size=batch_size)
s2_rc_conv_luts_7x7 = lut.transfer_rc_conv(self.stage2_7x7.rc_conv, quantization_interval=quantization_interval).reshape(7,7,-1)
s2_dense_conv_lut_7x7 = lut.transfer_2x2_input_SxS_output(self.stage2_7x7.dense_conv_block, quantization_interval=quantization_interval, batch_size=batch_size)
lut_model = rclut.RCLutx2.init_from_lut(
s1_rc_conv_luts_3x3=s1_rc_conv_luts_3x3, s1_dense_conv_lut_3x3=s1_dense_conv_lut_3x3,
s1_rc_conv_luts_5x5=s1_rc_conv_luts_5x5, s1_dense_conv_lut_5x5=s1_dense_conv_lut_5x5,
s1_rc_conv_luts_7x7=s1_rc_conv_luts_7x7, s1_dense_conv_lut_7x7=s1_dense_conv_lut_7x7,
s2_rc_conv_luts_3x3=s2_rc_conv_luts_3x3, s2_dense_conv_lut_3x3=s2_dense_conv_lut_3x3,
s2_rc_conv_luts_5x5=s2_rc_conv_luts_5x5, s2_dense_conv_lut_5x5=s2_dense_conv_lut_5x5,
s2_rc_conv_luts_7x7=s2_rc_conv_luts_7x7, s2_dense_conv_lut_7x7=s2_dense_conv_lut_7x7
)
return lut_model
def forward(self, x):
b,c,h,w = x.shape
@ -522,7 +548,33 @@ class RCNetx2CenteredUnlutable(nn.Module):
self.stage2_7x7 = RCBlockCenteredUnlutable(hidden_dim=hidden_dim, dense_conv_layer_count=layers_count, upscale_factor=scale, window_size=7)
def get_lut_model(self, quantization_interval=16, batch_size=2**10):
raise NotImplementedError
s1_rc_conv_luts_3x3 = lut.transfer_rc_conv(self.stage1_3x3.rc_conv, quantization_interval=quantization_interval).reshape(3,3,-1)
s1_dense_conv_lut_3x3 = lut.transfer_2x2_input_SxS_output(self.stage1_3x3.dense_conv_block, quantization_interval=quantization_interval, batch_size=batch_size)
s1_rc_conv_luts_5x5 = lut.transfer_rc_conv(self.stage1_5x5.rc_conv, quantization_interval=quantization_interval).reshape(5,5,-1)
s1_dense_conv_lut_5x5 = lut.transfer_2x2_input_SxS_output(self.stage1_5x5.dense_conv_block, quantization_interval=quantization_interval, batch_size=batch_size)
s1_rc_conv_luts_7x7 = lut.transfer_rc_conv(self.stage1_7x7.rc_conv, quantization_interval=quantization_interval).reshape(7,7,-1)
s1_dense_conv_lut_7x7 = lut.transfer_2x2_input_SxS_output(self.stage1_7x7.dense_conv_block, quantization_interval=quantization_interval, batch_size=batch_size)
s2_rc_conv_luts_3x3 = lut.transfer_rc_conv(self.stage2_3x3.rc_conv, quantization_interval=quantization_interval).reshape(3,3,-1)
s2_dense_conv_lut_3x3 = lut.transfer_2x2_input_SxS_output(self.stage2_3x3.dense_conv_block, quantization_interval=quantization_interval, batch_size=batch_size)
s2_rc_conv_luts_5x5 = lut.transfer_rc_conv(self.stage2_5x5.rc_conv, quantization_interval=quantization_interval).reshape(5,5,-1)
s2_dense_conv_lut_5x5 = lut.transfer_2x2_input_SxS_output(self.stage2_5x5.dense_conv_block, quantization_interval=quantization_interval, batch_size=batch_size)
s2_rc_conv_luts_7x7 = lut.transfer_rc_conv(self.stage2_7x7.rc_conv, quantization_interval=quantization_interval).reshape(7,7,-1)
s2_dense_conv_lut_7x7 = lut.transfer_2x2_input_SxS_output(self.stage2_7x7.dense_conv_block, quantization_interval=quantization_interval, batch_size=batch_size)
lut_model = rclut.RCLutx2Centered.init_from_lut(
s1_rc_conv_luts_3x3=s1_rc_conv_luts_3x3, s1_dense_conv_lut_3x3=s1_dense_conv_lut_3x3,
s1_rc_conv_luts_5x5=s1_rc_conv_luts_5x5, s1_dense_conv_lut_5x5=s1_dense_conv_lut_5x5,
s1_rc_conv_luts_7x7=s1_rc_conv_luts_7x7, s1_dense_conv_lut_7x7=s1_dense_conv_lut_7x7,
s2_rc_conv_luts_3x3=s2_rc_conv_luts_3x3, s2_dense_conv_lut_3x3=s2_dense_conv_lut_3x3,
s2_rc_conv_luts_5x5=s2_rc_conv_luts_5x5, s2_dense_conv_lut_5x5=s2_dense_conv_lut_5x5,
s2_rc_conv_luts_7x7=s2_rc_conv_luts_7x7, s2_dense_conv_lut_7x7=s2_dense_conv_lut_7x7
)
return lut_model
def forward(self, x):
b,c,h,w = x.shape
@ -545,69 +597,3 @@ class RCNetx2CenteredUnlutable(nn.Module):
output = output.view(b, c, h*self.scale, w*self.scale)
return output
class ReconstructedConvCenteredv2(nn.Module):
def __init__(self, hidden_dim, window_size=7):
super(ReconstructedConvCenteredv2, self).__init__()
self.window_size = window_size
self.projection1 = torch.nn.Parameter(torch.rand((window_size**2, hidden_dim))/window_size)
self.projection2 = torch.nn.Parameter(torch.rand((window_size**2, hidden_dim))/window_size)
def pixel_wise_forward(self, x):
x = (x-127.5)/127.5
out = torch.einsum('bwk,wh,wh -> bwk', x, self.projection1, self.projection2)
out = torch.tanh(out)
out = out*127.5 + 127.5
return out
def forward(self, x):
original_shape = x.shape
x = F.pad(x, pad=[self.window_size//2]*4, mode='replicate')
x = F.unfold(x, self.window_size)
x = self.pixel_wise_forward(x)
x = (x.mean(1) - (x.max().values - x.min().values))/2
x = x.reshape(*original_shape)
x = round_func(x)
return x
def __repr__(self):
return f"{self.__class__.__name__} projection1: {self.projection1.shape} projection2: {self.projection2.shape}"
class RCBlockCenteredv2(nn.Module):
def __init__(self, hidden_dim = 32, window_size=3, dense_conv_layer_count=4, upscale_factor=4):
super(RCBlockCenteredv2, self).__init__()
self.window_size = window_size
self.rc_conv = ReconstructedConvCenteredv2(hidden_dim=hidden_dim, window_size=window_size)
self.dense_conv_block = DenseConvUpscaleBlock(hidden_dim=hidden_dim, layers_count=dense_conv_layer_count, upscale_factor=upscale_factor)
def forward(self, x):
b,c,hs,ws = x.shape
x = self.rc_conv(x)
x = F.pad(x, pad=[0,1,0,1], mode='replicate')
x = self.dense_conv_block(x)
return x
class RCNetCentered_7x7v2(nn.Module):
def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4):
super(RCNetCentered_7x7v2, self).__init__()
self.hidden_dim = hidden_dim
self.layers_count = layers_count
self.scale = scale
window_size = 7
self.stage = RCBlockCenteredv2(hidden_dim=hidden_dim, dense_conv_layer_count=layers_count, upscale_factor=scale, window_size=window_size)
def forward(self, x):
b,c,h,w = x.shape
x = x.view(b*c, 1, h, w)
x = self.stage(x)
x = x.view(b, c, h*self.scale, w*self.scale)
return x
def get_lut_model(self, quantization_interval=16, batch_size=2**10):
window_size = self.stage.rc_conv.window_size
rc_conv_luts = lut.transfer_rc_conv(self.stage.rc_conv, quantization_interval=quantization_interval).reshape(window_size,window_size,-1)
dense_conv_lut = lut.transfer_2x2_input_SxS_output(self.stage.dense_conv_block, quantization_interval=quantization_interval, batch_size=batch_size)
lut_model = rclut.RCLutCentered_7x7.init_from_lut(rc_conv_luts, dense_conv_lut)
return lut_model

2
src/scripts/transfer_to_lut.py
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@ -19,7 +19,7 @@ import models
class TransferToLutOptions():
def __init__(self):
self.parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
self.parser.add_argument('--model_path', '-m', type=str, default='', help="model path folder")
self.parser.add_argument('--model_path', '-m', type=str, default='../../models/last_trained_net.pth', help="model path folder")
self.parser.add_argument('--quantization_bits', '-q', type=int, default=4, help="Number of 4DLUT buckets defined as 2**bits. Value is in range [1, 8].")
self.parser.add_argument('--batch_size', '-b', type=int, default=2**10, help="Size of the batch for the input domain values.")

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