Lots of SSG work
- Checkpointed pretty much the entire model - enabling recurrent inputs - Added two new models for test - adding depth (again) and removing SPSR (in lieu of the new losses)
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James Betker
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aca2c7ab41
commit
ffd069fd97
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@ -222,7 +222,7 @@ class SSGr1(nn.Module):
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if step % 200 == 0:
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output_path = os.path.join(experiments_path, "attention_maps")
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prefix = "amap_%i_a%i_%%i.png"
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[save_attention_to_image_rgb(output_path, self.attentions[i], self.transformation_counts, prefix % (step, i), step, output_mag=False) for i in range(len(self.attentions))]
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[save_attention_to_image_rgb(output_path, self.attentions[i], self.nf, prefix % (step, i), step, output_mag=False) for i in range(len(self.attentions))]
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torchvision.utils.save_image(self.lr, os.path.join(experiments_path, "attention_maps", "amap_%i_base_image.png" % (step,)))
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@ -239,3 +239,165 @@ class SSGr1(nn.Module):
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val["switch_%i_histogram" % (i,)] = hists[i]
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return val
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class StackedSwitchGenerator(nn.Module):
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def __init__(self, in_nc, out_nc, nf, xforms=8, upscale=4, init_temperature=10):
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super(StackedSwitchGenerator, self).__init__()
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n_upscale = int(math.log(upscale, 2))
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self.nf = nf
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# processing the input embedding
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self.reference_embedding = ReferenceImageBranch(nf)
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# Feature branch
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self.model_fea_conv = ConvGnLelu(in_nc, nf, kernel_size=3, norm=False, activation=False)
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self.sw1 = SwitchWithReference(nf, xforms, init_temperature, has_ref=False)
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self.sw2 = SwitchWithReference(nf, xforms, init_temperature, has_ref=False)
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self.sw3 = SwitchWithReference(nf, xforms, init_temperature, has_ref=False)
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self.switches = [self.sw1.switch, self.sw2.switch, self.sw3.switch]
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self.final_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True)
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self.upsample = UpconvBlock(nf, nf // 2, block=ConvGnLelu, norm=False, activation=True, bias=True)
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self.final_hr_conv1 = ConvGnLelu(nf // 2, nf // 2, kernel_size=3, norm=False, activation=False, bias=True)
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self.final_hr_conv2 = ConvGnLelu(nf // 2, out_nc, kernel_size=3, norm=False, activation=False, bias=False)
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self.attentions = None
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self.lr = None
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self.init_temperature = init_temperature
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self.final_temperature_step = 10000
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def forward(self, x, ref, ref_center):
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# The attention_maps debugger outputs <x>. Save that here.
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self.lr = x.detach().cpu()
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ref_code = checkpoint(self.reference_embedding, ref, ref_center)
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ref_embedding = ref_code.view(-1, ref_code.shape[1], 1, 1).repeat(1, 1, x.shape[2] // 8, x.shape[3] // 8)
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x = self.model_fea_conv(x)
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x1, a1 = checkpoint(self.sw1, x, ref_embedding)
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x2, a2 = checkpoint(self.sw2, x1, ref_embedding)
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x3, a3 = checkpoint(self.sw3, x2, ref_embedding)
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x_out = checkpoint(self.final_lr_conv, x3)
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x_out = checkpoint(self.upsample, x_out)
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x_out = checkpoint(self.final_hr_conv2, x_out)
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self.attentions = [a1, a3, a3]
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return x_out
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def set_temperature(self, temp):
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[sw.set_temperature(temp) for sw in self.switches]
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def update_for_step(self, step, experiments_path='.'):
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if self.attentions:
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temp = max(1, 1 + self.init_temperature *
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(self.final_temperature_step - step) / self.final_temperature_step)
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self.set_temperature(temp)
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if step % 200 == 0:
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output_path = os.path.join(experiments_path, "attention_maps")
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prefix = "amap_%i_a%i_%%i.png"
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[save_attention_to_image_rgb(output_path, self.attentions[i], self.nf, prefix % (step, i), step, output_mag=False) for i in range(len(self.attentions))]
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torchvision.utils.save_image(self.lr, os.path.join(experiments_path, "attention_maps", "amap_%i_base_image.png" % (step,)))
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def get_debug_values(self, step, net_name):
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temp = self.switches[0].switch.temperature
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mean_hists = [compute_attention_specificity(att, 2) for att in self.attentions]
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means = [i[0] for i in mean_hists]
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hists = [i[1].clone().detach().cpu().flatten() for i in mean_hists]
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val = {"switch_temperature": temp}
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for i in range(len(means)):
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val["switch_%i_specificity" % (i,)] = means[i]
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val["switch_%i_histogram" % (i,)] = hists[i]
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return val
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class SSGDeep(nn.Module):
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def __init__(self, in_nc, out_nc, nf, xforms=8, upscale=4, init_temperature=10):
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super(SSGDeep, self).__init__()
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n_upscale = int(math.log(upscale, 2))
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self.nf = nf
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# processing the input embedding
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self.reference_embedding = ReferenceImageBranch(nf)
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# Feature branch
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self.model_fea_conv = ConvGnLelu(in_nc, nf, kernel_size=7, norm=False, activation=False)
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self.sw1 = SwitchWithReference(nf, xforms, init_temperature, has_ref=False)
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# Grad branch. Note - groupnorm on this branch is REALLY bad. Avoid it like the plague.
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self.get_g_nopadding = ImageGradientNoPadding()
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self.grad_conv = ConvGnLelu(in_nc, nf, kernel_size=7, norm=False, activation=False, bias=False)
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self.sw_grad = SwitchWithReference(nf, xforms // 2, init_temperature, has_ref=True)
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self.grad_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True)
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self.upsample_grad = UpconvBlock(nf, nf // 2, block=ConvGnLelu, norm=False, activation=True, bias=False)
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self.grad_branch_output_conv = ConvGnLelu(nf // 2, out_nc, kernel_size=1, norm=False, activation=False, bias=True)
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# Join branch (grad+fea)
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self.conjoin_sw = SwitchWithReference(nf, xforms, init_temperature, has_ref=True)
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self.sw3 = SwitchWithReference(nf, xforms, init_temperature, has_ref=False)
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self.sw4 = SwitchWithReference(nf, xforms, init_temperature, has_ref=False)
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self.final_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True)
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self.upsample = UpconvBlock(nf, nf // 2, block=ConvGnLelu, norm=False, activation=True, bias=True)
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self.final_hr_conv1 = ConvGnLelu(nf // 2, nf // 2, kernel_size=3, norm=False, activation=False, bias=True)
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self.final_hr_conv2 = ConvGnLelu(nf // 2, out_nc, kernel_size=3, norm=False, activation=False, bias=False)
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self.switches = [self.sw1.switch, self.sw_grad.switch, self.conjoin_sw.switch, self.sw3.switch, self.sw4.switch]
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self.attentions = None
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self.lr = None
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self.init_temperature = init_temperature
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self.final_temperature_step = 10000
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def forward(self, x, ref, ref_center):
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# The attention_maps debugger outputs <x>. Save that here.
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self.lr = x.detach().cpu()
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x_grad = self.get_g_nopadding(x)
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ref_code = checkpoint(self.reference_embedding, ref, ref_center)
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ref_embedding = ref_code.view(-1, ref_code.shape[1], 1, 1).repeat(1, 1, x.shape[2] // 8, x.shape[3] // 8)
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x = self.model_fea_conv(x)
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x1, a1 = checkpoint(self.sw1, x, ref_embedding)
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x_grad = self.grad_conv(x_grad)
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x_grad, a3, grad_fea_std = checkpoint(self.sw_grad, x_grad, ref_embedding, x1)
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x_grad = checkpoint(self.grad_lr_conv, x_grad)
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x_grad_out = checkpoint(self.upsample_grad, x_grad)
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x_grad_out = checkpoint(self.grad_branch_output_conv, x_grad_out)
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x_out, a4, fea_grad_std = checkpoint(self.conjoin_sw, x1, ref_embedding, x_grad)
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x_out, a5 = checkpoint(self.sw3, x_out, ref_embedding)
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x_out, a6 = checkpoint(self.sw4, x_out, ref_embedding)
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x_out = checkpoint(self.final_lr_conv, x_out)
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x_out = checkpoint(self.upsample, x_out)
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x_out = checkpoint(self.final_hr_conv2, x_out)
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self.attentions = [a1, a3, a4, a5, a6]
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self.grad_fea_std = grad_fea_std.detach().cpu()
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self.fea_grad_std = fea_grad_std.detach().cpu()
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return x_grad_out, x_out, x_grad
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def set_temperature(self, temp):
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[sw.set_temperature(temp) for sw in self.switches]
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def update_for_step(self, step, experiments_path='.'):
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if self.attentions:
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temp = max(1, 1 + self.init_temperature *
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(self.final_temperature_step - step) / self.final_temperature_step)
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self.set_temperature(temp)
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if step % 200 == 0:
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output_path = os.path.join(experiments_path, "attention_maps")
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prefix = "amap_%i_a%i_%%i.png"
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[save_attention_to_image_rgb(output_path, self.attentions[i], self.nf, prefix % (step, i), step, output_mag=False) for i in range(len(self.attentions))]
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torchvision.utils.save_image(self.lr, os.path.join(experiments_path, "attention_maps", "amap_%i_base_image.png" % (step,)))
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def get_debug_values(self, step, net_name):
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temp = self.switches[0].switch.temperature
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mean_hists = [compute_attention_specificity(att, 2) for att in self.attentions]
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means = [i[0] for i in mean_hists]
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hists = [i[1].clone().detach().cpu().flatten() for i in mean_hists]
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val = {"switch_temperature": temp,
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"grad_branch_feat_intg_std_dev": self.grad_fea_std,
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"conjoin_branch_grad_intg_std_dev": self.fea_grad_std}
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for i in range(len(means)):
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val["switch_%i_specificity" % (i,)] = means[i]
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val["switch_%i_histogram" % (i,)] = hists[i]
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return val
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@ -82,13 +82,13 @@ def define_G(opt, net_key='network_G', scale=None):
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xforms = opt_net['num_transforms'] if 'num_transforms' in opt_net.keys() else 8
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netG = ssg.SSGr1(in_nc=3, out_nc=3, nf=opt_net['nf'], xforms=xforms, upscale=opt_net['scale'],
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init_temperature=opt_net['temperature'] if 'temperature' in opt_net.keys() else 10)
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elif which_model == 'ssg_no_embedding':
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elif which_model == 'stacked_switches':
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xforms = opt_net['num_transforms'] if 'num_transforms' in opt_net.keys() else 8
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netG = ssg.SSGNoEmbedding(in_nc=3, out_nc=3, nf=opt_net['nf'], xforms=xforms, upscale=opt_net['scale'],
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netG = ssg.StackedSwitchGenerator(in_nc=3, out_nc=3, nf=opt_net['nf'], xforms=xforms, upscale=opt_net['scale'],
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init_temperature=opt_net['temperature'] if 'temperature' in opt_net.keys() else 10)
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elif which_model == 'ssg_lite':
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elif which_model == 'ssg_deep':
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xforms = opt_net['num_transforms'] if 'num_transforms' in opt_net.keys() else 8
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netG = ssg.SSGLite(in_nc=3, out_nc=3, nf=opt_net['nf'], xforms=xforms, upscale=opt_net['scale'],
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netG = ssg.SSGDeep(in_nc=3, out_nc=3, nf=opt_net['nf'], xforms=xforms, upscale=opt_net['scale'],
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init_temperature=opt_net['temperature'] if 'temperature' in opt_net.keys() else 10)
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elif which_model == "backbone_encoder":
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netG = SwitchedGen_arch.BackboneEncoder(pretrained_backbone=opt_net['pretrained_spinenet'])
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@ -32,7 +32,7 @@ def init_dist(backend='nccl', **kwargs):
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def main():
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#### options
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parser = argparse.ArgumentParser()
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parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_exd_imgset_ssgr_constrained_gan.yml')
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parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_exd_imgset_ssgr_deep.yml')
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parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
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parser.add_argument('--local_rank', type=int, default=0)
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args = parser.parse_args()
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