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Clean up of SRFlowNet_arch

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This commit is contained in:
James Betker 2020-12-16 10:27:38 -07:00
parent 42ac8e3eeb
commit c25b49bb12
2 changed files with 1 additions and 23 deletions
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3
codes/models/archs/srflow_orig/FlowActNorms.py
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@ -22,7 +22,6 @@ class _ActNorm(nn.Module):
self.num_features = num_features self.num_features = num_features
self.scale = float(scale) self.scale = float(scale)
self.inited = False self.inited = False
self.force_initialization = False
def _check_input_dim(self, input): def _check_input_dim(self, input):
return NotImplemented return NotImplemented
@ -77,8 +76,6 @@ class _ActNorm(nn.Module):
return input, logdet return input, logdet
def forward(self, input, logdet=None, reverse=False, offset_mask=None, logs_offset=None, bias_offset=None): def forward(self, input, logdet=None, reverse=False, offset_mask=None, logs_offset=None, bias_offset=None):
if self.force_initialization or not self.inited:
self.initialize_parameters(input)
self._check_input_dim(input) self._check_input_dim(input)
if offset_mask is not None: if offset_mask is not None:

21
codes/models/archs/srflow_orig/SRFlowNet_arch.py
View File

@ -34,8 +34,6 @@ class SRFlowNet(nn.Module):
self.flowUpsamplerNet = \ self.flowUpsamplerNet = \
FlowUpsamplerNet((self.patch_sz, self.patch_sz, 3), hidden_channels, K, FlowUpsamplerNet((self.patch_sz, self.patch_sz, 3), hidden_channels, K,
flow_coupling=opt['networks']['generator']['flow']['coupling'], opt=opt) flow_coupling=opt['networks']['generator']['flow']['coupling'], opt=opt)
self.force_act_norm_init_until = opt_get(self.opt, ['networks', 'generator', 'flow', 'act_norm_start_step'])
self.act_norm_always_init = False
self.i = 0 self.i = 0
self.dbg_logp = 0 self.dbg_logp = 0
self.dbg_logdet = 0 self.dbg_logdet = 0
@ -59,23 +57,6 @@ class SRFlowNet(nn.Module):
z = torch.normal(mean=0, std=heat, size=(batch_size, 3 * 8 * 8 * fac * fac, z_size, z_size)) z = torch.normal(mean=0, std=heat, size=(batch_size, 3 * 8 * 8 * fac * fac, z_size, z_size))
return z.to(device) return z.to(device)
def update_for_step(self, step, experiments_path='.'):
if self.act_norm_always_init and step > self.force_act_norm_init_until:
set_act_norm_always_init = True
set_value = False
self.act_norm_always_init = False
elif not self.act_norm_always_init and step < self.force_act_norm_init_until:
set_act_norm_always_init = True
set_value = True
self.act_norm_always_init = True
else:
set_act_norm_always_init = False
if set_act_norm_always_init:
for m in self.modules():
from models.archs.srflow_orig.FlowActNorms import _ActNorm
if isinstance(m, _ActNorm):
m.force_initialization = set_value
def forward(self, gt=None, lr=None, z=None, eps_std=None, reverse=False, epses=None, reverse_with_grad=False, def forward(self, gt=None, lr=None, z=None, eps_std=None, reverse=False, epses=None, reverse_with_grad=False,
lr_enc=None, lr_enc=None,
add_gt_noise=True, step=None, y_label=None): add_gt_noise=True, step=None, y_label=None):
@ -170,7 +151,7 @@ class SRFlowNet(nn.Module):
def reverse_flow(self, lr, z, y_onehot, eps_std, epses=None, lr_enc=None, add_gt_noise=True): def reverse_flow(self, lr, z, y_onehot, eps_std, epses=None, lr_enc=None, add_gt_noise=True):
logdet = torch.zeros_like(lr[:, 0, 0, 0]) logdet = torch.zeros_like(lr[:, 0, 0, 0])
pixels = thops.pixels(lr) * self.flow_scale ** 2 pixels = thops.pixels(lr) * self.opt['scale'] ** 2
if add_gt_noise: if add_gt_noise:
logdet = logdet - float(-np.log(self.quant) * pixels) logdet = logdet - float(-np.log(self.quant) * pixels)