def __init__(self,in_channels, out_channels, num_features, bp_stages, upscale_factor=4, norm_type=None, act_type='prelu'):
super(DBPN, self).__init__()
if upscale_factor == 2:
stride = 2
padding = 2
projection_filter = 6
elif upscale_factor == 4:
stride = 4
padding = 2
projection_filter = 8
elif upscale_factor == 8:
stride = 8
padding = 2
projection_filter = 12
feature_extract_1 = B.ConvBlock(in_channels, 128, kernel_size=3, norm_type=norm_type, act_type=act_type)
feature_extract_2 = B.ConvBlock(128, num_features, kernel_size=1, norm_type=norm_type, act_type=act_type)
bp_units = []
for _ in range(bp_stages-1):
bp_units.extend([B.UpprojBlock(num_features, num_features, projection_filter, stride=stride, valid_padding=False,
padding=padding, norm_type=norm_type, act_type=act_type),
B.DownprojBlock(num_features, num_features, projection_filter, stride=stride, valid_padding=False,
padding=padding, norm_type=norm_type, act_type=act_type)])
last_bp_unit = B.UpprojBlock(num_features, num_features, projection_filter, stride=stride, valid_padding=False,
padding=padding, norm_type=norm_type, act_type=act_type)
conv_hr = B.ConvBlock(num_features, out_channels, kernel_size=1, norm_type=None, act_type=None)
self.network = B.sequential(feature_extract_1, feature_extract_2, *bp_units, last_bp_unit, conv_hr)
def __init__(self,in_channels, out_channels, num_features, bp_stages, upscale_factor=4, norm_type=None, act_type='prelu'):
super(D_DBPN, self).__init__()
if upscale_factor == 2:
stride = 2
padding = 2
projection_filter = 6
elif upscale_factor == 4:
stride = 4
padding = 2
projection_filter = 8
elif upscale_factor == 8:
stride = 8
padding = 2
projection_filter = 12
feature_extract_1 = B.ConvBlock(in_channels, 256, kernel_size=3, norm_type=norm_type, act_type=act_type)
feature_extract_2 = B.ConvBlock(256, num_features, kernel_size=1, norm_type=norm_type, act_type=act_type)
bp_units = B.DensebackprojBlock(num_features, num_features, projection_filter, bp_stages, stride=stride, valid_padding=False,
padding=padding, norm_type=norm_type, act_type=act_type)
conv_hr = B.ConvBlock(num_features*bp_stages, out_channels, kernel_size=3, norm_type=None, act_type=None)
self.network = B.sequential(feature_extract_1, feature_extract_2, bp_units, conv_hr)
