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Full checkpoint-ize SSG1

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This commit is contained in:
James Betker 2020-10-04 18:24:52 -06:00
parent fc396baf1a
commit aca2c7ab41
1 changed files with 36 additions and 40 deletions
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76
codes/models/archs/StructuredSwitchedGenerator.py
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@ -10,7 +10,7 @@ from switched_conv_util import save_attention_to_image_rgb
from switched_conv import compute_attention_specificity
import os
import torchvision
from utils.util import checkpoint
from torch.utils.checkpoint import checkpoint
# VGG-style layer with Conv(stride2)->BN->Activation->Conv->BN->Activation
# Doubles the input filter count.
@ -127,6 +127,29 @@ class ReferenceImageBranch(nn.Module):
x = self.features(x)
return gather_2d(x, center_point // 8) # Divide by 8 to scale the center_point down.
class SwitchWithReference(nn.Module):
def __init__(self, nf, num_transforms, init_temperature=10, has_ref=True):
super(SwitchWithReference, self).__init__()
self.nf = nf
self.transformation_counts = num_transforms
multiplx_fn = functools.partial(QueryKeyMultiplexer, nf)
transform_fn = functools.partial(MultiConvBlock, nf, int(nf * 1.25), nf, kernel_size=3, depth=4, weight_init_factor=.1)
if has_ref:
self.ref_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.3, final_norm=False, kernel_size=1, depth=2)
else:
self.ref_join = None
self.switch = ConfigurableSwitchComputer(nf, multiplx_fn,
pre_transform_block=None, transform_block=transform_fn,
attention_norm=True,
transform_count=self.transformation_counts, init_temp=init_temperature,
add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True)
def forward(self, x, mplex_ref=None, ref=None):
if self.ref_join is not None:
branch, ref_std = self.ref_join(x, ref)
return self.switch(branch, True, identity=x, att_in=(branch, mplex_ref)) + (ref_std,)
else:
return self.switch(x, True, identity=x, att_in=(x, mplex_ref))
class SSGr1(nn.Module):
def __init__(self, in_nc, out_nc, nf, xforms=8, upscale=4, init_temperature=10):
@ -137,47 +160,25 @@ class SSGr1(nn.Module):
# processing the input embedding
self.reference_embedding = ReferenceImageBranch(nf)
# switch options
transformation_filters = nf
self.transformation_counts = xforms
multiplx_fn = functools.partial(QueryKeyMultiplexer, transformation_filters)
transform_fn = functools.partial(MultiConvBlock, transformation_filters, int(transformation_filters * 1.25),
transformation_filters, kernel_size=3, depth=4,
weight_init_factor=.1)
# Feature branch
self.model_fea_conv = ConvGnLelu(in_nc, nf, kernel_size=3, norm=False, activation=False)
self.sw1 = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
pre_transform_block=None, transform_block=transform_fn,
attention_norm=True,
transform_count=self.transformation_counts, init_temp=init_temperature,
add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True)
self.sw1 = SwitchWithReference(nf, xforms, init_temperature, has_ref=False)
# Grad branch. Note - groupnorm on this branch is REALLY bad. Avoid it like the plague.
self.get_g_nopadding = ImageGradientNoPadding()
self.grad_conv = ConvGnLelu(in_nc, nf, kernel_size=3, norm=False, activation=False, bias=False)
self.grad_ref_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.3, final_norm=False, kernel_size=1, depth=2)
self.sw_grad = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
pre_transform_block=None, transform_block=transform_fn,
attention_norm=True,
transform_count=self.transformation_counts // 2, init_temp=init_temperature,
add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True)
self.sw_grad = SwitchWithReference(nf, xforms // 2, init_temperature, has_ref=True)
self.grad_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True)
self.upsample_grad = UpconvBlock(nf, nf // 2, block=ConvGnLelu, norm=False, activation=True, bias=False)
self.grad_branch_output_conv = ConvGnLelu(nf // 2, out_nc, kernel_size=1, norm=False, activation=False, bias=True)
# Join branch (grad+fea)
self.conjoin_ref_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.3, kernel_size=1, depth=2)
self.conjoin_sw = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
pre_transform_block=None, transform_block=transform_fn,
attention_norm=True,
transform_count=self.transformation_counts, init_temp=init_temperature,
add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True)
self.conjoin_sw = SwitchWithReference(nf, xforms, init_temperature, has_ref=True)
self.final_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True)
self.upsample = UpconvBlock(nf, nf // 2, block=ConvGnLelu, norm=False, activation=True, bias=True)
self.final_hr_conv1 = ConvGnLelu(nf // 2, nf // 2, kernel_size=3, norm=False, activation=False, bias=True)
self.final_hr_conv2 = ConvGnLelu(nf // 2, out_nc, kernel_size=3, norm=False, activation=False, bias=False)
self.switches = [self.sw1, self.sw_grad, self.conjoin_sw]
self.switches = [self.sw1.switch, self.sw_grad.switch, self.conjoin_sw.switch]
self.attentions = None
self.lr = None
self.init_temperature = init_temperature
@ -192,23 +193,18 @@ class SSGr1(nn.Module):
ref_embedding = ref_code.view(-1, ref_code.shape[1], 1, 1).repeat(1, 1, x.shape[2] // 8, x.shape[3] // 8)
x = self.model_fea_conv(x)
x1 = x
x1, a1 = self.sw1(x1, True, identity=x, att_in=(x1, ref_embedding))
x1, a1 = checkpoint(self.sw1, x, ref_embedding)
x_grad = self.grad_conv(x_grad)
x_grad_identity = x_grad
x_grad, grad_fea_std = self.grad_ref_join(x_grad, x1)
x_grad, a3 = self.sw_grad(x_grad, True, identity=x_grad_identity, att_in=(x_grad, ref_embedding))
x_grad = self.grad_lr_conv(x_grad)
x_grad_out = self.upsample_grad(x_grad)
x_grad_out = self.grad_branch_output_conv(x_grad_out)
x_grad, a3, grad_fea_std = checkpoint(self.sw_grad, x_grad, ref_embedding, x1)
x_grad = checkpoint(self.grad_lr_conv, x_grad)
x_grad_out = checkpoint(self.upsample_grad, x_grad)
x_grad_out = checkpoint(self.grad_branch_output_conv, x_grad_out)
x_out = x1
x_out, fea_grad_std = self.conjoin_ref_join(x_out, x_grad)
x_out, a4 = self.conjoin_sw(x_out, True, identity=x1, att_in=(x_out, ref_embedding))
x_out = self.final_lr_conv(x_out)
x_out, a4, fea_grad_std = checkpoint(self.conjoin_sw, x1, ref_embedding, x_grad)
x_out = checkpoint(self.final_lr_conv, x_out)
x_out = checkpoint(self.upsample, x_out)
x_out = self.final_hr_conv2(x_out)
x_out = checkpoint(self.final_hr_conv2, x_out)
self.attentions = [a1, a3, a4]
self.grad_fea_std = grad_fea_std.detach().cpu()