SRG3 work
Operates on top of a pre-trained SpineNET backbone (trained on CoCo 2017 with RetinaNet) This variant is extremely shallow.
This commit is contained in:
James Betker
parent
0acad81035
commit
8a4eb8241d
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@ -78,20 +78,36 @@ class ConvBasisMultiplexer(nn.Module):
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return x
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class SpineNetMultiplexer(nn.Module):
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def __init__(self, input_channels, transform_count):
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super(SpineNetMultiplexer, self).__init__()
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self.backbone = SpineNet('49', in_channels=input_channels)
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self.rdc1 = ConvBnSilu(256, 128, kernel_size=3, bias=False)
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self.rdc2 = ConvBnSilu(128, 64, kernel_size=3, bias=False)
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self.rdc3 = ConvBnSilu(64, transform_count, bias=False, bn=False, relu=False)
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class CachedBackboneWrapper:
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def __init__(self, backbone: nn.Module):
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self.backbone = backbone
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def __call__(self, *args):
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self.cache = self.backbone(*args)
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return self.cache
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def get_forward_result(self):
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return self.cache
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class BackboneMultiplexer(nn.Module):
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def __init__(self, backbone: CachedBackboneWrapper, transform_count):
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super(BackboneMultiplexer, self).__init__()
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self.backbone = backbone
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self.proc = nn.Sequential(ConvBnSilu(256, 256, kernel_size=3, bias=True),
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ConvBnSilu(256, 256, kernel_size=3, bias=False))
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self.up1 = nn.Sequential(ConvBnSilu(256, 128, kernel_size=3, bias=False, bn=False, silu=False),
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ConvBnSilu(128, 128, kernel_size=3, bias=False))
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self.up2 = nn.Sequential(ConvBnSilu(128, 64, kernel_size=3, bias=False, bn=False, silu=False),
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ConvBnSilu(64, 64, kernel_size=3, bias=False))
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self.final = ConvBnSilu(64, transform_count, bias=False, bn=False, silu=False)
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def forward(self, x):
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spine = self.backbone(x)
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feat = self.rdc1(spine[0])
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feat = self.rdc2(feat)
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feat = self.rdc3(feat)
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return feat
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spine = self.backbone.get_forward_result()
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feat = self.proc(spine[0])
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feat = self.up1(F.interpolate(feat, scale_factor=2, mode="nearest"))
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feat = self.up2(F.interpolate(feat, scale_factor=2, mode="nearest"))
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return self.final(feat)
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class ConfigurableSwitchComputer(nn.Module):
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@ -233,55 +249,56 @@ class Interpolate(nn.Module):
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class ConfigurableSwitchedResidualGenerator3(nn.Module):
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def __init__(self, trans_counts,
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trans_kernel_sizes,
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trans_layers, transformation_filters, initial_temp=20, final_temperature_step=50000,
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def __init__(self, base_filters, trans_count, initial_temp=20, final_temperature_step=50000,
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heightened_temp_min=1,
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heightened_final_step=50000, upsample_factor=1, enable_negative_transforms=False,
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add_scalable_noise_to_transforms=False):
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heightened_final_step=50000, upsample_factor=4):
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super(ConfigurableSwitchedResidualGenerator3, self).__init__()
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switches = []
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for trans_count, kernel, layers in zip(trans_counts, trans_kernel_sizes, trans_layers):
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multiplx_fn = functools.partial(SpineNetMultiplexer, 3)
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switches.append(ConfigurableSwitchComputer(base_filters=3, multiplexer_net=multiplx_fn,
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pre_transform_block=functools.partial(nn.Sequential,
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ConvBnLelu(3, transformation_filters, kernel_size=1, stride=4, bn=False, lelu=False, bias=False),
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ResidualDenseBlock_5C(
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transformation_filters),
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ResidualDenseBlock_5C(
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transformation_filters)),
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transform_block=functools.partial(nn.Sequential,
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ResidualDenseBlock_5C(transformation_filters),
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Interpolate(4),
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ConvBnLelu(transformation_filters, transformation_filters // 2, kernel_size=3, bias=False, bn=False),
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ConvBnLelu(transformation_filters // 2, 3, kernel_size=1, bias=False, bn=False, lelu=False)),
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transform_count=trans_count, init_temp=initial_temp,
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enable_negative_transforms=enable_negative_transforms,
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add_scalable_noise_to_transforms=add_scalable_noise_to_transforms,
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init_scalar=.01))
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self.initial_conv = ConvBnLelu(3, base_filters, bn=False, lelu=False, bias=True)
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self.sw_conv = ConvBnLelu(base_filters, base_filters, lelu=False, bias=True)
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self.upconv1 = ConvBnLelu(base_filters, base_filters, bn=False, bias=True)
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self.upconv2 = ConvBnLelu(base_filters, base_filters, bn=False, bias=True)
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self.hr_conv = ConvBnLelu(base_filters, base_filters, bn=False, bias=True)
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self.final_conv = ConvBnLelu(base_filters, 3, bn=False, lelu=False, bias=True)
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self.switches = nn.ModuleList(switches)
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self.transformation_counts = trans_counts
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self.backbone = SpineNet('49', in_channels=3, use_input_norm=True)
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for p in self.backbone.parameters(recurse=True):
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p.requires_grad = False
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self.backbone_wrapper = CachedBackboneWrapper(self.backbone)
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multiplx_fn = functools.partial(BackboneMultiplexer, self.backbone_wrapper)
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pretransform_fn = functools.partial(nn.Sequential, ConvBnLelu(base_filters, base_filters, kernel_size=3, bn=False, lelu=False, bias=False))
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transform_fn = functools.partial(MultiConvBlock, base_filters, int(base_filters * 1.5), base_filters, kernel_size=3, depth=4)
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self.switch = ConfigurableSwitchComputer(base_filters, multiplx_fn, pretransform_fn, transform_fn, trans_count, init_temp=initial_temp,
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enable_negative_transforms=False, add_scalable_noise_to_transforms=True, init_scalar=.1)
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self.transformation_counts = trans_count
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self.init_temperature = initial_temp
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self.final_temperature_step = final_temperature_step
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self.heightened_temp_min = heightened_temp_min
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self.heightened_final_step = heightened_final_step
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self.attentions = None
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self.upsample_factor = upsample_factor
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self.backbone_forward = None
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def get_forward_results(self):
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return self.backbone_forward
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def forward(self, x):
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if self.upsample_factor > 1:
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x = F.interpolate(x, scale_factor=self.upsample_factor, mode="nearest")
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self.backbone_forward = self.backbone_wrapper(F.interpolate(x, scale_factor=2, mode="nearest"))
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x = self.initial_conv(x)
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self.attentions = []
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for i, sw in enumerate(self.switches):
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x, att = sw.forward(x, True)
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self.attentions.append(att)
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x, att = self.switch(x, output_attention_weights=True)
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self.attentions.append(att)
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return x,
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x = self.upconv1(F.interpolate(x, scale_factor=2, mode="nearest"))
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if self.upsample_factor > 2:
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x = F.interpolate(x, scale_factor=2, mode="nearest")
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x = self.upconv2(x)
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return self.final_conv(self.hr_conv(x)),
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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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self.switch.set_temperature(temp)
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def update_for_step(self, step, experiments_path='.'):
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if self.attentions:
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@ -299,11 +316,10 @@ class ConfigurableSwitchedResidualGenerator3(nn.Module):
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temp = 1 / temp
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self.set_temperature(temp)
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if step % 50 == 0:
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[save_attention_to_image(experiments_path, self.attentions[i], self.transformation_counts[i], step,
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"a%i" % (i + 1,)) for i in range(len(self.switches))]
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save_attention_to_image(experiments_path, self.attentions[0], self.transformation_counts, step, "a%i" % (1,), l_mult=10)
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def get_debug_values(self, step):
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temp = self.switches[0].switch.temperature
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temp = self.switch.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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@ -1,12 +1,48 @@
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# Taken and modified from https://github.com/lucifer443/SpineNet-Pytorch/blob/master/mmdet/models/backbones/spinenet.py
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch.nn.init import kaiming_normal
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from torchvision.models.resnet import BasicBlock, Bottleneck
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from torch.nn.modules.batchnorm import _BatchNorm
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from models.archs.arch_util import ConvBnRelu
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''' Convenience class with Conv->BN->ReLU. Includes weight initialization and auto-padding for standard
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kernel sizes. '''
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class ConvBnRelu(nn.Module):
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def __init__(self, filters_in, filters_out, kernel_size=3, stride=1, relu=True, bn=True, bias=True):
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super(ConvBnRelu, self).__init__()
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padding_map = {1: 0, 3: 1, 5: 2, 7: 3}
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assert kernel_size in padding_map.keys()
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self.conv = nn.Conv2d(filters_in, filters_out, kernel_size, stride, padding_map[kernel_size], bias=bias)
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if bn:
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self.bn = nn.BatchNorm2d(filters_out)
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else:
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self.bn = None
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if relu:
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self.relu = nn.ReLU()
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else:
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self.relu = None
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# Init params.
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu' if self.relu else 'linear')
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elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
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nn.init.constant_(m.weight, 1)
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nn.init.constant_(m.bias, 0)
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def forward(self, x):
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x = self.conv(x)
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if self.bn:
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x = self.bn(x)
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if self.relu:
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return self.relu(x)
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else:
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return x
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def constant_init(module, val, bias=0):
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if hasattr(module, 'weight') and module.weight is not None:
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@ -213,7 +249,11 @@ class SpineNet(nn.Module):
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arch,
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in_channels=3,
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output_level=[3, 4, 5, 6, 7],
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zero_init_residual=True):
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conv_cfg=None,
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norm_cfg=dict(type='BN', requires_grad=True),
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zero_init_residual=True,
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activation='relu',
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use_input_norm=False):
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super(SpineNet, self).__init__()
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self._block_specs = build_block_specs()[2:]
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self._endpoints_num_filters = SCALING_MAP[arch]['endpoints_num_filters']
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@ -225,6 +265,7 @@ class SpineNet(nn.Module):
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self.zero_init_residual = zero_init_residual
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assert min(output_level) > 2 and max(output_level) < 8, "Output level out of range"
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self.output_level = output_level
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self.use_input_norm = use_input_norm
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self._make_stem_layer(in_channels)
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self._make_scale_permuted_network()
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@ -237,7 +278,8 @@ class SpineNet(nn.Module):
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in_channels,
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64,
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kernel_size=7,
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stride=2) # Original paper had stride=2 and a maxpool after.
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stride=2)
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self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
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# Build the initial level 2 blocks.
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self.init_block1 = make_res_layer(
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@ -286,12 +328,19 @@ class SpineNet(nn.Module):
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if self.zero_init_residual:
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for m in self.modules():
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if isinstance(m, Bottleneck):
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constant_init(m.norm3, 0)
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constant_init(m.bn3, 0)
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elif isinstance(m, BasicBlock):
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constant_init(m.norm2, 0)
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constant_init(m.bn2, 0)
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def forward(self, input):
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feat = self.conv1(input)
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# Spinenet is pretrained on the standard pytorch input norm. The image will need to
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# be normalized before feeding it through.
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if self.use_input_norm:
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mean = torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1).to(input.device)
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std = torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1).to(input.device)
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input = (input - mean) / std
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feat = self.maxpool(self.conv1(input))
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feat1 = self.init_block1(feat)
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feat2 = self.init_block2(feat1)
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block_feats = [feat1, feat2]
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@ -68,12 +68,7 @@ def define_G(opt, net_key='network_G'):
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heightened_temp_min=opt_net['heightened_temp_min'], heightened_final_step=opt_net['heightened_final_step'],
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upsample_factor=scale, add_scalable_noise_to_transforms=opt_net['add_noise'])
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elif which_model == "ConfigurableSwitchedResidualGenerator3":
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netG = SwitchedGen_arch.ConfigurableSwitchedResidualGenerator3(trans_counts=opt_net['trans_counts'],
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trans_kernel_sizes=opt_net['trans_kernel_sizes'], trans_layers=opt_net['trans_layers'],
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transformation_filters=opt_net['transformation_filters'],
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initial_temp=opt_net['temperature'], final_temperature_step=opt_net['temperature_final_step'],
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heightened_temp_min=opt_net['heightened_temp_min'], heightened_final_step=opt_net['heightened_final_step'],
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upsample_factor=scale, add_scalable_noise_to_transforms=opt_net['add_noise'])
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netG = SwitchedGen_arch.ConfigurableSwitchedResidualGenerator3(base_filters=opt_net['base_filters'], trans_count=opt_net['trans_count'])
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elif which_model == "NestedSwitchGenerator":
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netG = ng.NestedSwitchedGenerator(switch_filters=opt_net['switch_filters'],
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switch_reductions=opt_net['switch_reductions'],
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@ -33,7 +33,7 @@ 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_div2k_rrdb.yml')
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parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_div2k_srg3.yml')
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parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none',
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help='job launcher')
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parser.add_argument('--local_rank', type=int, default=0)
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@ -97,6 +97,7 @@ if __name__ == "__main__":
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torch.randn(1, 3, 64, 64),
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device='cuda')
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'''
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'''
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test_stability(functools.partial(srg.ConfigurableSwitchedResidualGenerator2,
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switch_filters=[32,32,32,32],
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switch_growths=[16,16,16,16],
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@ -110,6 +111,7 @@ if __name__ == "__main__":
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torch.randn(1, 3, 64, 64),
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device='cuda')
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'''
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'''
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test_stability(functools.partial(srg1.ConfigurableSwitchedResidualGenerator,
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switch_filters=[32,32,32,32],
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switch_growths=[16,16,16,16],
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@ -123,3 +125,7 @@ if __name__ == "__main__":
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torch.randn(1, 3, 64, 64),
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device='cuda')
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'''
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test_stability(functools.partial(srg.ConfigurableSwitchedResidualGenerator3,
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64, 16),
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torch.randn(1, 3, 64, 64),
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device='cuda')
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