forked from mrq/DL-Art-School
422 lines
21 KiB
Python
422 lines
21 KiB
Python
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import os
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import torch
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import torchvision
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from torch import nn
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import torch.nn.functional as F
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import functools
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from collections import OrderedDict
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from torch.nn import init
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from models.archs.arch_util import ConvBnLelu, ConvGnSilu
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from utils.util import checkpoint
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def initialize_weights(net_l, scale=1):
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if not isinstance(net_l, list):
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net_l = [net_l]
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for net in net_l:
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for m in net.modules():
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if isinstance(m, nn.Conv2d) or isinstance(m, nn.Conv3d):
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init.kaiming_normal_(m.weight, a=0, mode='fan_in')
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m.weight.data *= scale # for residual block
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if m.bias is not None:
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m.bias.data.zero_()
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elif isinstance(m, nn.Linear):
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init.kaiming_normal_(m.weight, a=0, mode='fan_in')
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m.weight.data *= scale
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if m.bias is not None:
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m.bias.data.zero_()
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elif isinstance(m, nn.BatchNorm2d):
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init.constant_(m.weight, 1)
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init.constant_(m.bias.data, 0.0)
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class AttentionNorm(nn.Module):
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def __init__(self, group_size, accumulator_size=128):
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super(AttentionNorm, self).__init__()
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self.accumulator_desired_size = accumulator_size
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self.group_size = group_size
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# These are all tensors so that they get saved with the graph.
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self.accumulator = nn.Parameter(torch.zeros(accumulator_size, group_size), requires_grad=False)
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self.accumulator_index = nn.Parameter(torch.zeros(1, dtype=torch.long, device='cpu'), requires_grad=False)
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self.accumulator_filled = nn.Parameter(torch.zeros(1, dtype=torch.bool, device='cpu'), requires_grad=False)
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# Returns tensor of shape (group,) with a normalized mean across the accumulator in the range [0,1]. The intent
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# is to divide your inputs by this value.
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def compute_buffer_norm(self):
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if self.accumulator_filled:
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return torch.mean(self.accumulator, dim=0)
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else:
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return torch.ones(self.group_size, device=self.accumulator.device)
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def add_norm_to_buffer(self, x):
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flat = x.sum(dim=[0, 1, 2], keepdim=True)
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norm = flat / torch.mean(flat)
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# This often gets reset in GAN mode. We *never* want gradient accumulation in this parameter.
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self.accumulator.requires_grad = False
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self.accumulator[self.accumulator_index] = norm.detach()
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self.accumulator_index += 1
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if self.accumulator_index >= self.accumulator_desired_size:
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self.accumulator_index *= 0
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self.accumulator_filled |= True
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# Input into forward is an attention tensor of shape (batch,width,height,groups)
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def forward(self, x: torch.Tensor):
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assert len(x.shape) == 4
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# Push the accumulator to the right device on the first iteration.
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if self.accumulator.device != x.device:
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self.accumulator = self.accumulator.to(x.device)
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self.add_norm_to_buffer(x)
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norm = self.compute_buffer_norm()
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x = x / norm
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# Need to re-normalize x so that the groups dimension sum to 1, just like when it was fed in.
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groups_sum = x.sum(dim=3, keepdim=True)
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return x / groups_sum
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class BareConvSwitch(nn.Module):
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"""
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Initializes the ConvSwitch.
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initial_temperature: The initial softmax temperature of the attention mechanism. For training from scratch, this
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should be set to a high number, for example 30.
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attention_norm: If specified, the AttentionNorm layer applied immediately after Softmax.
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"""
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def __init__(
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self,
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initial_temperature=1,
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attention_norm=None
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):
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super(BareConvSwitch, self).__init__()
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self.softmax = nn.Softmax(dim=-1)
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self.temperature = initial_temperature
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self.attention_norm = attention_norm
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initialize_weights(self)
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def set_attention_temperature(self, temp):
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self.temperature = temp
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# SwitchedConv.forward takes these arguments;
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# conv_group: List of inputs (len=n) to the switch, each with shape (b,f,w,h)
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# conv_attention: Attention computation as an output from a conv layer, of shape (b,n,w,h). Before softmax
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# output_attention_weights: If True, post-softmax attention weights are returned.
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def forward(self, conv_group, conv_attention, output_attention_weights=False):
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# Stack up the conv_group input first and permute it to (batch, width, height, filter, groups)
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conv_outputs = torch.stack(conv_group, dim=0).permute(1, 3, 4, 2, 0)
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conv_attention = conv_attention.permute(0, 2, 3, 1)
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conv_attention = self.softmax(conv_attention / self.temperature)
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if self.attention_norm:
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conv_attention = self.attention_norm(conv_attention)
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# conv_outputs shape: (batch, width, height, filters, groups)
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# conv_attention shape: (batch, width, height, groups)
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# We want to format them so that we can matmul them together to produce:
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# desired shape: (batch, width, height, filters)
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# Note: conv_attention will generally be cast to float32 regardless of the input type, so cast conv_outputs to
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# float32 as well to match it.
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if self.training:
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# Doing it all in one op is substantially faster - better for training.
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attention_result = torch.einsum(
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"...ij,...j->...i", [conv_outputs.float(), conv_attention]
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)
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else:
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# eval_mode substantially reduces the GPU memory required to compute the attention result by performing the
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# attention multiplications one at a time. This is probably necessary for large images and attention breadths.
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attention_result = conv_outputs[:, :, :, :, 0] * conv_attention[:, :, :, 0].unsqueeze(dim=-1)
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for i in range(1, conv_attention.shape[-1]):
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attention_result += conv_outputs[:, :, :, :, i] * conv_attention[:, :, :, i].unsqueeze(dim=-1)
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# Remember to shift the filters back into the expected slot.
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if output_attention_weights:
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return attention_result.permute(0, 3, 1, 2), conv_attention
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else:
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return attention_result.permute(0, 3, 1, 2)
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class MultiConvBlock(nn.Module):
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def __init__(self, filters_in, filters_mid, filters_out, kernel_size, depth, scale_init=1.0, norm=False, weight_init_factor=1):
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assert depth >= 2
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super(MultiConvBlock, self).__init__()
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self.noise_scale = nn.Parameter(torch.full((1,), fill_value=.01))
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self.bnconvs = nn.ModuleList([ConvBnLelu(filters_in, filters_mid, kernel_size, norm=norm, bias=False, weight_init_factor=weight_init_factor)] +
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[ConvBnLelu(filters_mid, filters_mid, kernel_size, norm=norm, bias=False, weight_init_factor=weight_init_factor) for i in range(depth - 2)] +
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[ConvBnLelu(filters_mid, filters_out, kernel_size, activation=False, norm=False, bias=False, weight_init_factor=weight_init_factor)])
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self.scale = nn.Parameter(torch.full((1,), fill_value=scale_init))
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self.bias = nn.Parameter(torch.zeros(1), requires_grad=False)
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def forward(self, x, noise=None):
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if noise is not None:
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noise = noise * self.noise_scale
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x = x + noise
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for m in self.bnconvs:
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x = m.forward(x)
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return x * self.scale + self.bias
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# VGG-style layer with Conv(stride2)->BN->Activation->Conv->BN->Activation
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# Doubles the input filter count.
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class HalvingProcessingBlock(nn.Module):
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def __init__(self, filters):
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super(HalvingProcessingBlock, self).__init__()
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self.bnconv1 = ConvGnSilu(filters, filters * 2, stride=2, norm=False, bias=False)
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self.bnconv2 = ConvGnSilu(filters * 2, filters * 2, norm=True, bias=False)
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def forward(self, x):
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x = self.bnconv1(x)
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return self.bnconv2(x)
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# Block that upsamples 2x and reduces incoming filters by 2x. It preserves structure by taking a passthrough feed
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# along with the feature representation.
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class ExpansionBlock(nn.Module):
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def __init__(self, filters_in, filters_out=None, block=ConvGnSilu):
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super(ExpansionBlock, self).__init__()
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if filters_out is None:
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filters_out = filters_in // 2
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self.decimate = block(filters_in, filters_out, kernel_size=1, bias=False, activation=False, norm=True)
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self.process_passthrough = block(filters_out, filters_out, kernel_size=3, bias=True, activation=False, norm=True)
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self.conjoin = block(filters_out*2, filters_out, kernel_size=3, bias=False, activation=True, norm=False)
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self.process = block(filters_out, filters_out, kernel_size=3, bias=False, activation=True, norm=True)
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# input is the feature signal with shape (b, f, w, h)
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# passthrough is the structure signal with shape (b, f/2, w*2, h*2)
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# output is conjoined upsample with shape (b, f/2, w*2, h*2)
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def forward(self, input, passthrough):
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x = F.interpolate(input, scale_factor=2, mode="nearest")
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x = self.decimate(x)
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p = self.process_passthrough(passthrough)
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x = self.conjoin(torch.cat([x, p], dim=1))
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return self.process(x)
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# This is a classic u-net architecture with the goal of assigning each individual pixel an individual transform
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# switching set.
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class ConvBasisMultiplexer(nn.Module):
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def __init__(self, input_channels, base_filters, reductions, processing_depth, multiplexer_channels, use_gn=True):
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super(ConvBasisMultiplexer, self).__init__()
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self.filter_conv = ConvGnSilu(input_channels, base_filters, bias=True)
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self.reduction_blocks = nn.ModuleList([HalvingProcessingBlock(base_filters * 2 ** i) for i in range(reductions)])
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reduction_filters = base_filters * 2 ** reductions
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self.processing_blocks = nn.Sequential(OrderedDict([('block%i' % (i,), ConvGnSilu(reduction_filters, reduction_filters, bias=False)) for i in range(processing_depth)]))
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self.expansion_blocks = nn.ModuleList([ExpansionBlock(reduction_filters // (2 ** i)) for i in range(reductions)])
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gap = base_filters - multiplexer_channels
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cbl1_out = ((base_filters - (gap // 2)) // 4) * 4 # Must be multiples of 4 to use with group norm.
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self.cbl1 = ConvGnSilu(base_filters, cbl1_out, norm=use_gn, bias=False, num_groups=4)
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cbl2_out = ((base_filters - (3 * gap // 4)) // 4) * 4
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self.cbl2 = ConvGnSilu(cbl1_out, cbl2_out, norm=use_gn, bias=False, num_groups=4)
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self.cbl3 = ConvGnSilu(cbl2_out, multiplexer_channels, bias=True, norm=False)
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def forward(self, x):
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x = self.filter_conv(x)
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reduction_identities = []
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for b in self.reduction_blocks:
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reduction_identities.append(x)
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x = b(x)
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x = self.processing_blocks(x)
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for i, b in enumerate(self.expansion_blocks):
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x = b(x, reduction_identities[-i - 1])
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x = self.cbl1(x)
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x = self.cbl2(x)
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x = self.cbl3(x)
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return x
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class ConfigurableSwitchComputer(nn.Module):
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def __init__(self, base_filters, multiplexer_net, pre_transform_block, transform_block, transform_count, init_temp=20,
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add_scalable_noise_to_transforms=False):
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super(ConfigurableSwitchComputer, self).__init__()
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tc = transform_count
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self.multiplexer = multiplexer_net(tc)
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self.pre_transform = pre_transform_block()
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self.transforms = nn.ModuleList([transform_block() for _ in range(transform_count)])
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self.add_noise = add_scalable_noise_to_transforms
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self.noise_scale = nn.Parameter(torch.full((1,), float(1e-3)))
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# And the switch itself, including learned scalars
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self.switch = BareConvSwitch(initial_temperature=init_temp, attention_norm=AttentionNorm(transform_count, accumulator_size=16 * transform_count))
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self.switch_scale = nn.Parameter(torch.full((1,), float(1)))
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self.post_switch_conv = ConvBnLelu(base_filters, base_filters, norm=False, bias=True)
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# The post_switch_conv gets a low scale initially. The network can decide to magnify it (or not)
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# depending on its needs.
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self.psc_scale = nn.Parameter(torch.full((1,), float(.1)))
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def forward(self, x, output_attention_weights=True):
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identity = x
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if self.add_noise:
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rand_feature = torch.randn_like(x) * self.noise_scale
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x = x + rand_feature
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x = self.pre_transform(x)
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xformed = [t.forward(x) for t in self.transforms]
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m = self.multiplexer(identity)
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outputs, attention = self.switch(xformed, m, True)
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outputs = identity + outputs * self.switch_scale
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outputs = outputs + self.post_switch_conv(outputs) * self.psc_scale
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if output_attention_weights:
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return outputs, attention
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else:
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return outputs
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def set_temperature(self, temp):
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self.switch.set_attention_temperature(temp)
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def compute_attention_specificity(att_weights, topk=3):
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att = att_weights.detach()
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vals, indices = torch.topk(att, topk, dim=-1)
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avg = torch.sum(vals, dim=-1)
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avg = avg.flatten().mean()
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return avg.item(), indices.flatten().detach()
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# Copied from torchvision.utils.save_image. Allows specifying pixel format.
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def save_image(tensor, fp, nrow=8, padding=2,
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normalize=False, range=None, scale_each=False, pad_value=0, format=None, pix_format=None):
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from PIL import Image
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grid = torchvision.utils.make_grid(tensor, nrow=nrow, padding=padding, pad_value=pad_value,
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normalize=normalize, range=range, scale_each=scale_each)
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# Add 0.5 after unnormalizing to [0, 255] to round to nearest integer
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ndarr = grid.mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).to('cpu', torch.uint8).numpy()
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im = Image.fromarray(ndarr, mode=pix_format).convert('RGB')
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im.save(fp, format=format)
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def save_attention_to_image(folder, attention_out, attention_size, step, fname_part="map", l_mult=1.0):
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magnitude, indices = torch.topk(attention_out, 1, dim=-1)
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magnitude = magnitude.squeeze(3)
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indices = indices.squeeze(3)
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# indices is an integer tensor (b,w,h) where values are on the range [0,attention_size]
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# magnitude is a float tensor (b,w,h) [0,1] representing the magnitude of that attention.
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# Use HSV colorspace to show this. Hue is mapped to the indices, Lightness is mapped to intensity,
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# Saturation is left fixed.
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hue = indices.float() / attention_size
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saturation = torch.full_like(hue, .8)
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value = magnitude * l_mult
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hsv_img = torch.stack([hue, saturation, value], dim=1)
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|
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output_path=os.path.join(folder, "attention_maps", fname_part)
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os.makedirs(output_path, exist_ok=True)
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save_image(hsv_img, os.path.join(output_path, "attention_map_%i.png" % (step,)), pix_format="HSV")
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|
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||
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def save_attention_to_image_rgb(output_folder, attention_out, attention_size, file_prefix, step, cmap_discrete_name='viridis'):
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|
magnitude, indices = torch.topk(attention_out, 3, dim=-1)
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|
magnitude = magnitude.cpu()
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|
indices = indices.cpu()
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magnitude /= torch.max(torch.abs(torch.min(magnitude)), torch.abs(torch.max(magnitude)))
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colormap = cm.get_cmap(cmap_discrete_name, attention_size)
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colormap_mag = cm.get_cmap(cmap_discrete_name)
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os.makedirs(os.path.join(output_folder), exist_ok=True)
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for i in range(3):
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|
img = torch.tensor(colormap(indices[:,:,:,i].detach().numpy()))
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|
img = img.permute((0, 3, 1, 2))
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|
save_image(img, os.path.join(output_folder, file_prefix + "_%i_%s.png" % (step, "rgb_%i" % (i,))), pix_format="RGBA")
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|
|
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mag_image = torch.tensor(colormap_mag(magnitude[:,:,:,i].detach().numpy()))
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mag_image = mag_image.permute((0, 3, 1, 2))
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save_image(mag_image, os.path.join(output_folder, file_prefix + "_%i_%s.png" % (step, "mag_%i" % (i,))), pix_format="RGBA")
|
||
|
|
|
||
|
|
|
||
|
|
class ConfigurableSwitchedResidualGenerator2(nn.Module):
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|
|
def __init__(self, switch_depth, switch_filters, switch_reductions, switch_processing_layers, trans_counts, trans_kernel_sizes,
|
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|
|
trans_layers, transformation_filters, initial_temp=20, final_temperature_step=50000, heightened_temp_min=1,
|
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|
|
heightened_final_step=50000, upsample_factor=1,
|
||
|
|
add_scalable_noise_to_transforms=False):
|
||
|
|
super(ConfigurableSwitchedResidualGenerator2, self).__init__()
|
||
|
|
switches = []
|
||
|
|
self.initial_conv = ConvBnLelu(3, transformation_filters, norm=False, activation=False, bias=True)
|
||
|
|
self.upconv1 = ConvBnLelu(transformation_filters, transformation_filters, norm=False, bias=True)
|
||
|
|
self.upconv2 = ConvBnLelu(transformation_filters, transformation_filters, norm=False, bias=True)
|
||
|
|
self.hr_conv = ConvBnLelu(transformation_filters, transformation_filters, norm=False, bias=True)
|
||
|
|
self.final_conv = ConvBnLelu(transformation_filters, 3, norm=False, activation=False, bias=True)
|
||
|
|
for _ in range(switch_depth):
|
||
|
|
multiplx_fn = functools.partial(ConvBasisMultiplexer, transformation_filters, switch_filters, switch_reductions, switch_processing_layers, trans_counts)
|
||
|
|
pretransform_fn = functools.partial(ConvBnLelu, transformation_filters, transformation_filters, norm=False, bias=False, weight_init_factor=.1)
|
||
|
|
transform_fn = functools.partial(MultiConvBlock, transformation_filters, int(transformation_filters * 1.5), transformation_filters, kernel_size=trans_kernel_sizes, depth=trans_layers, weight_init_factor=.1)
|
||
|
|
switches.append(ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
|
||
|
|
pre_transform_block=pretransform_fn, transform_block=transform_fn,
|
||
|
|
transform_count=trans_counts, init_temp=initial_temp,
|
||
|
|
add_scalable_noise_to_transforms=add_scalable_noise_to_transforms))
|
||
|
|
|
||
|
|
self.switches = nn.ModuleList(switches)
|
||
|
|
self.transformation_counts = trans_counts
|
||
|
|
self.init_temperature = initial_temp
|
||
|
|
self.final_temperature_step = final_temperature_step
|
||
|
|
self.heightened_temp_min = heightened_temp_min
|
||
|
|
self.heightened_final_step = heightened_final_step
|
||
|
|
self.attentions = None
|
||
|
|
self.upsample_factor = upsample_factor
|
||
|
|
assert self.upsample_factor == 2 or self.upsample_factor == 4
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
x = self.initial_conv(x)
|
||
|
|
|
||
|
|
self.attentions = []
|
||
|
|
for i, sw in enumerate(self.switches):
|
||
|
|
x, att = checkpoint(sw, x)
|
||
|
|
self.attentions.append(att)
|
||
|
|
|
||
|
|
x = self.upconv1(F.interpolate(x, scale_factor=2, mode="nearest"))
|
||
|
|
if self.upsample_factor > 2:
|
||
|
|
x = F.interpolate(x, scale_factor=2, mode="nearest")
|
||
|
|
x = self.upconv2(x)
|
||
|
|
x = self.final_conv(self.hr_conv(x))
|
||
|
|
return x
|
||
|
|
|
||
|
|
def set_temperature(self, temp):
|
||
|
|
[sw.set_temperature(temp) for sw in self.switches]
|
||
|
|
|
||
|
|
def update_for_step(self, step, experiments_path='.'):
|
||
|
|
if self.attentions:
|
||
|
|
temp = max(1,
|
||
|
|
1 + self.init_temperature * (self.final_temperature_step - step) / self.final_temperature_step)
|
||
|
|
if temp == 1 and self.heightened_final_step and step > self.final_temperature_step and \
|
||
|
|
self.heightened_final_step != 1:
|
||
|
|
# Once the temperature passes (1) it enters an inverted curve to match the linear curve from above.
|
||
|
|
# without this, the attention specificity "spikes" incredibly fast in the last few iterations.
|
||
|
|
h_steps_total = self.heightened_final_step - self.final_temperature_step
|
||
|
|
h_steps_current = min(step - self.final_temperature_step, h_steps_total)
|
||
|
|
# The "gap" will represent the steps that need to be traveled as a linear function.
|
||
|
|
h_gap = 1 / self.heightened_temp_min
|
||
|
|
temp = h_gap * h_steps_current / h_steps_total
|
||
|
|
# Invert temperature to represent reality on this side of the curve
|
||
|
|
temp = 1 / temp
|
||
|
|
self.set_temperature(temp)
|
||
|
|
if step % 50 == 0:
|
||
|
|
[save_attention_to_image(experiments_path, self.attentions[i], self.transformation_counts, step, "a%i" % (i+1,), l_mult=10) for i in range(len(self.attentions))]
|
||
|
|
|
||
|
|
def get_debug_values(self, step, net_name):
|
||
|
|
temp = self.switches[0].switch.temperature
|
||
|
|
mean_hists = [compute_attention_specificity(att, 2) for att in self.attentions]
|
||
|
|
means = [i[0] for i in mean_hists]
|
||
|
|
hists = [i[1].clone().detach().cpu().flatten() for i in mean_hists]
|
||
|
|
val = {"switch_temperature": temp}
|
||
|
|
for i in range(len(means)):
|
||
|
|
val["switch_%i_specificity" % (i,)] = means[i]
|
||
|
|
val["switch_%i_histogram" % (i,)] = hists[i]
|
||
|
|
return val
|
||
|
|
|
||
|
|
|
||
|
|
class Interpolate(nn.Module):
|
||
|
|
def __init__(self, factor):
|
||
|
|
super(Interpolate, self).__init__()
|
||
|
|
self.factor = factor
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
return F.interpolate(x, scale_factor=self.factor)
|
||
|
|
|