import os import torch import torchvision from matplotlib import cm from torch import nn import torch.nn.functional as F import functools from collections import OrderedDict from models.archs.SwitchedResidualGenerator_arch import HalvingProcessingBlock, ConfigurableSwitchComputer from models.archs.arch_util import ConvBnLelu, ConvGnSilu, ExpansionBlock, MultiConvBlock from switched_conv.switched_conv import BareConvSwitch, AttentionNorm from utils.util import checkpoint # This is a classic u-net architecture with the goal of assigning each individual pixel an individual transform # switching set. class ConvBasisMultiplexer(nn.Module): def __init__(self, input_channels, base_filters, reductions, processing_depth, multiplexer_channels, use_gn=True): super(ConvBasisMultiplexer, self).__init__() self.filter_conv = ConvGnSilu(input_channels, base_filters, bias=True) self.reduction_blocks = nn.ModuleList([HalvingProcessingBlock(base_filters * 2 ** i) for i in range(reductions)]) reduction_filters = base_filters * 2 ** reductions self.processing_blocks = nn.Sequential(OrderedDict([('block%i' % (i,), ConvGnSilu(reduction_filters, reduction_filters, bias=False)) for i in range(processing_depth)])) self.expansion_blocks = nn.ModuleList([ExpansionBlock(reduction_filters // (2 ** i)) for i in range(reductions)]) gap = base_filters - multiplexer_channels cbl1_out = ((base_filters - (gap // 2)) // 4) * 4 # Must be multiples of 4 to use with group norm. self.cbl1 = ConvGnSilu(base_filters, cbl1_out, norm=use_gn, bias=False, num_groups=4) cbl2_out = ((base_filters - (3 * gap // 4)) // 4) * 4 self.cbl2 = ConvGnSilu(cbl1_out, cbl2_out, norm=use_gn, bias=False, num_groups=4) self.cbl3 = ConvGnSilu(cbl2_out, multiplexer_channels, bias=True, norm=False) def forward(self, x): x = self.filter_conv(x) reduction_identities = [] for b in self.reduction_blocks: reduction_identities.append(x) x = b(x) x = self.processing_blocks(x) for i, b in enumerate(self.expansion_blocks): x = b(x, reduction_identities[-i - 1]) x = self.cbl1(x) x = self.cbl2(x) x = self.cbl3(x) return x def compute_attention_specificity(att_weights, topk=3): att = att_weights.detach() vals, indices = torch.topk(att, topk, dim=-1) avg = torch.sum(vals, dim=-1) avg = avg.flatten().mean() return avg.item(), indices.flatten().detach() # Copied from torchvision.utils.save_image. Allows specifying pixel format. def save_image(tensor, fp, nrow=8, padding=2, normalize=False, range=None, scale_each=False, pad_value=0, format=None, pix_format=None): from PIL import Image grid = torchvision.utils.make_grid(tensor, nrow=nrow, padding=padding, pad_value=pad_value, normalize=normalize, range=range, scale_each=scale_each) # Add 0.5 after unnormalizing to [0, 255] to round to nearest integer ndarr = grid.mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).to('cpu', torch.uint8).numpy() im = Image.fromarray(ndarr, mode=pix_format).convert('RGB') im.save(fp, format=format) def save_attention_to_image(folder, attention_out, attention_size, step, fname_part="map", l_mult=1.0): magnitude, indices = torch.topk(attention_out, 1, dim=-1) magnitude = magnitude.squeeze(3) indices = indices.squeeze(3) # indices is an integer tensor (b,w,h) where values are on the range [0,attention_size] # magnitude is a float tensor (b,w,h) [0,1] representing the magnitude of that attention. # Use HSV colorspace to show this. Hue is mapped to the indices, Lightness is mapped to intensity, # Saturation is left fixed. hue = indices.float() / attention_size saturation = torch.full_like(hue, .8) value = magnitude * l_mult hsv_img = torch.stack([hue, saturation, value], dim=1) output_path=os.path.join(folder, "attention_maps", fname_part) os.makedirs(output_path, exist_ok=True) save_image(hsv_img, os.path.join(output_path, "attention_map_%i.png" % (step,)), pix_format="HSV") def save_attention_to_image_rgb(output_folder, attention_out, attention_size, file_prefix, step, cmap_discrete_name='viridis'): magnitude, indices = torch.topk(attention_out, 3, dim=-1) magnitude = magnitude.cpu() indices = indices.cpu() magnitude /= torch.max(torch.abs(torch.min(magnitude)), torch.abs(torch.max(magnitude))) colormap = cm.get_cmap(cmap_discrete_name, attention_size) colormap_mag = cm.get_cmap(cmap_discrete_name) os.makedirs(os.path.join(output_folder), exist_ok=True) for i in range(3): img = torch.tensor(colormap(indices[:,:,:,i].detach().numpy())) img = img.permute((0, 3, 1, 2)) save_image(img, os.path.join(output_folder, file_prefix + "_%i_%s.png" % (step, "rgb_%i" % (i,))), pix_format="RGBA") mag_image = torch.tensor(colormap_mag(magnitude[:,:,:,i].detach().numpy())) mag_image = mag_image.permute((0, 3, 1, 2)) 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): def __init__(self, switch_depth, switch_filters, switch_reductions, switch_processing_layers, trans_counts, trans_kernel_sizes, trans_layers, transformation_filters, initial_temp=20, final_temperature_step=50000, heightened_temp_min=1, heightened_final_step=50000, upsample_factor=1, add_scalable_noise_to_transforms=False, for_video=False): super(ConfigurableSwitchedResidualGenerator2, self).__init__() switches = [] self.for_video = for_video if for_video: self.initial_conv = ConvBnLelu(6, transformation_filters, stride=upsample_factor, norm=False, activation=False, bias=True) else: 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, attention_norm=True, 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, ref=None): if self.for_video: x_lg = F.interpolate(x, scale_factor=self.upsample_factor, mode="bicubic") if ref is None: ref = torch.zeros_like(x_lg) x_lg = torch.cat([x_lg, ref], dim=1) else: x_lg = x x = self.initial_conv(x_lg) 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, mode="nearest"): super(Interpolate, self).__init__() self.factor = factor self.mode = mode def forward(self, x): return F.interpolate(x, scale_factor=self.factor, mode=self.mode)