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Revert big switch back

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This commit is contained in:
James Betker 2020-10-14 11:03:34 -06:00
parent 9815980329
commit 1f20d59c31
3 changed files with 11 additions and 43 deletions
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50
codes/models/archs/SwitchedResidualGenerator_arch.py
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@ -606,7 +606,7 @@ class SwitchModelBase(nn.Module):
from models.archs.spinenet_arch import make_res_layer, BasicBlock
class BigMultiplexer(nn.Module):
def __init__(self, in_nc, nf, mode, multiplexer_channels):
def __init__(self, in_nc, nf, multiplexer_channels):
super(BigMultiplexer, self).__init__()
self.spine = SpineNet(arch='96', output_level=[3], double_reduce_early=False)
@ -615,54 +615,28 @@ class BigMultiplexer(nn.Module):
self.tail_proc = make_res_layer(BasicBlock, nf, nf, 2)
self.tail_join = ReferenceJoinBlock(nf)
self.mode = mode
if mode == 0:
self.key_process = ConvGnSilu(nf, nf, kernel_size=1, activation=True, norm=False, bias=True)
self.query_key_combine = ConvGnSilu(nf*2, nf, kernel_size=3, activation=True, norm=False, bias=False)
self.cbl0 = ConvGnSilu(nf, nf, kernel_size=3, activation=True, norm=True, bias=False)
self.cbl1 = ConvGnSilu(nf, nf // 2, kernel_size=1, norm=True, bias=False, num_groups=4)
self.cbl2 = ConvGnSilu(nf // 2, 1, kernel_size=1, norm=False, bias=False)
else:
self.key_process = ConvGnSilu(nf, nf, kernel_size=3, activation=True, norm=False, bias=True)
self.query_key_combine = ConvGnSilu(nf*2, nf, kernel_size=1, activation=True, norm=True, bias=False)
self.cbl0 = ConvGnSilu(nf, nf, kernel_size=1, activation=True, norm=True, bias=False)
self.cbl1 = ConvGnSilu(nf, nf // 2, kernel_size=1, activation=True, norm=False, bias=False)
self.cbl2 = ConvGnSilu(nf // 2, 1, kernel_size=1, activation=False, norm=False, bias=False)
self.reduce = nn.Sequential(ConvGnSilu(nf, nf // 2, kernel_size=1, activation=True, norm=True, bias=False),
ConvGnSilu(nf // 2, multiplexer_channels, kernel_size=1, activation=False, norm=False, bias=False))
def forward(self, x, transformations):
s = self.spine(x)[0]
tail = self.fea_tail(x)
tail = self.tail_proc(tail)
if self.mode == 0:
q = F.interpolate(s, scale_factor=2, mode='bilinear')
else:
q = F.interpolate(s, scale_factor=2, mode='nearest')
q = F.interpolate(s, scale_factor=2, mode='nearest')
q = self.spine_red_proc(q)
q, _ = self.tail_join(q, tail)
b, t, f, h, w = transformations.shape
k = transformations.view(b * t, f, h, w)
k = self.key_process(k)
q = q.view(b, 1, f, h, w).repeat(1, t, 1, 1, 1).view(b * t, f, h, w)
v = self.query_key_combine(torch.cat([q, k], dim=1))
v = self.cbl0(v)
v = self.cbl1(v)
v = self.cbl2(v)
return v.view(b, t, h, w)
return self.reduce(q)
class TheBigSwitch(SwitchModelBase):
def __init__(self, in_nc, nf, xforms=16, upscale=2, mode=0, init_temperature=10):
def __init__(self, in_nc, nf, xforms=16, upscale=2, init_temperature=10):
super(TheBigSwitch, self).__init__(init_temperature, 10000)
self.nf = nf
self.transformation_counts = xforms
self.mode = mode
self.model_fea_conv = ConvGnLelu(in_nc, nf, kernel_size=7, norm=False, activation=False)
multiplx_fn = functools.partial(BigMultiplexer, in_nc, nf, mode)
multiplx_fn = functools.partial(BigMultiplexer, in_nc, nf)
transform_fn = functools.partial(MultiConvBlock, nf, int(nf * 1.5), nf, kernel_size=3, depth=4, weight_init_factor=.1)
self.switch = ConfigurableSwitchComputer(nf, multiplx_fn,
pre_transform_block=None, transform_block=transform_fn,
@ -686,20 +660,14 @@ class TheBigSwitch(SwitchModelBase):
sw.set_update_attention_norm(save_attentions)
x1 = self.model_fea_conv(x)
if self.mode == 0:
x1, a1 = self.switch(x1, att_in=x, do_checkpointing=True)
else:
x1, a1, attlogits = self.switch(x1, att_in=x, do_checkpointing=True, output_att_logits=True)
x1, a1 = self.switch(x1, att_in=x, do_checkpointing=True)
x_out = checkpoint(self.final_lr_conv, x1)
x_out = checkpoint(self.upsample, x_out)
x_out = checkpoint(self.final_hr_conv2, x_out)
if save_attentions:
self.attentions = [a1]
if self.mode == 0:
return x_out,
else:
return x_out, attlogits.permute(0,3,1,2)
return x_out,
if __name__ == '__main__':

2
codes/models/networks.py
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@ -111,7 +111,7 @@ def define_G(opt, net_key='network_G', scale=None):
netG = ssg.StackedSwitchGenerator2xTeco(nf=opt_net['nf'], xforms=opt_net['num_transforms'], init_temperature=opt_net['temperature'] if 'temperature' in opt_net.keys() else 10)
elif which_model == 'big_switch':
netG = SwitchedGen_arch.TheBigSwitch(opt_net['in_nc'], nf=opt_net['nf'], xforms=opt_net['num_transforms'], upscale=opt_net['scale'],
init_temperature=opt_net['temperature'], mode=opt_net['mode'])
init_temperature=opt_net['temperature'])
elif which_model == "flownet2":
from models.flownet2.models import FlowNet2
ld = torch.load(opt_net['load_path'])

2
codes/train2.py
View File

@ -30,7 +30,7 @@ def init_dist(backend='nccl', **kwargs):
def main():
#### options
parser = argparse.ArgumentParser()
parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_exd_imgset_bigswitch_att_invariance.yml')
parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_exd_imgset_ssgdeep.yml')
parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
parser.add_argument('--local_rank', type=int, default=0)
args = parser.parse_args()