207 lines
11 KiB
Python
207 lines
11 KiB
Python
import os
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import torch
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import torchvision
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from matplotlib import cm
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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 models.archs.SwitchedResidualGenerator_arch import HalvingProcessingBlock, ConfigurableSwitchComputer
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from models.archs.arch_util import ConvBnLelu, ConvGnSilu, ExpansionBlock, MultiConvBlock
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from switched_conv.switched_conv import BareConvSwitch, AttentionNorm
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from utils.util import checkpoint
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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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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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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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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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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")
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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,
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add_scalable_noise_to_transforms=False, for_video=False):
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super(ConfigurableSwitchedResidualGenerator2, self).__init__()
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switches = []
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self.for_video = for_video
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if for_video:
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self.initial_conv = ConvBnLelu(6, transformation_filters, stride=upsample_factor, norm=False, activation=False, bias=True)
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else:
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self.initial_conv = ConvBnLelu(3, transformation_filters, norm=False, activation=False, bias=True)
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self.upconv1 = ConvBnLelu(transformation_filters, transformation_filters, norm=False, bias=True)
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self.upconv2 = ConvBnLelu(transformation_filters, transformation_filters, norm=False, bias=True)
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self.hr_conv = ConvBnLelu(transformation_filters, transformation_filters, norm=False, bias=True)
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self.final_conv = ConvBnLelu(transformation_filters, 3, norm=False, activation=False, bias=True)
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for _ in range(switch_depth):
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multiplx_fn = functools.partial(ConvBasisMultiplexer, transformation_filters, switch_filters, switch_reductions, switch_processing_layers, trans_counts)
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pretransform_fn = functools.partial(ConvBnLelu, transformation_filters, transformation_filters, norm=False, bias=False, weight_init_factor=.1)
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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)
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switches.append(ConfigurableSwitchComputer(transformation_filters, multiplx_fn, attention_norm=True,
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pre_transform_block=pretransform_fn, transform_block=transform_fn,
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transform_count=trans_counts, init_temp=initial_temp,
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add_scalable_noise_to_transforms=add_scalable_noise_to_transforms))
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self.switches = nn.ModuleList(switches)
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self.transformation_counts = trans_counts
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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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assert self.upsample_factor == 2 or self.upsample_factor == 4
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def forward(self, x, ref=None):
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if self.for_video:
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x_lg = F.interpolate(x, scale_factor=self.upsample_factor, mode="bicubic")
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if ref is None:
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ref = torch.zeros_like(x_lg)
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x_lg = torch.cat([x_lg, ref], dim=1)
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else:
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x_lg = x
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x = self.initial_conv(x_lg)
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self.attentions = []
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for i, sw in enumerate(self.switches):
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x, att = checkpoint(sw, x)
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self.attentions.append(att)
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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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x = self.final_conv(self.hr_conv(x))
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return 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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def update_for_step(self, step, experiments_path='.'):
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if self.attentions:
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temp = max(1,
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1 + self.init_temperature * (self.final_temperature_step - step) / self.final_temperature_step)
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if temp == 1 and self.heightened_final_step and step > self.final_temperature_step and \
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self.heightened_final_step != 1:
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# Once the temperature passes (1) it enters an inverted curve to match the linear curve from above.
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# without this, the attention specificity "spikes" incredibly fast in the last few iterations.
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h_steps_total = self.heightened_final_step - self.final_temperature_step
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h_steps_current = min(step - self.final_temperature_step, h_steps_total)
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# The "gap" will represent the steps that need to be traveled as a linear function.
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h_gap = 1 / self.heightened_temp_min
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temp = h_gap * h_steps_current / h_steps_total
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# Invert temperature to represent reality on this side of the curve
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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, step, "a%i" % (i+1,), l_mult=10) for i in range(len(self.attentions))]
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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 Interpolate(nn.Module):
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def __init__(self, factor, mode="nearest"):
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super(Interpolate, self).__init__()
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self.factor = factor
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self.mode = mode
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def forward(self, x):
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return F.interpolate(x, scale_factor=self.factor, mode=self.mode)
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