diff --git a/codes/models/archs/SwitchedResidualGenerator_arch.py b/codes/models/archs/SwitchedResidualGenerator_arch.py index bad60da7..9a472cae 100644 --- a/codes/models/archs/SwitchedResidualGenerator_arch.py +++ b/codes/models/archs/SwitchedResidualGenerator_arch.py @@ -4,8 +4,7 @@ from switched_conv import BareConvSwitch, compute_attention_specificity import torch.nn.functional as F import functools from models.archs.arch_util import initialize_weights -import torchvision -from torchvision import transforms +from switched_conv_util import save_attention_to_image class ConvBnLelu(nn.Module): @@ -90,7 +89,6 @@ class SwitchComputer(nn.Module): def set_temperature(self, temp): self.switch.set_attention_temperature(temp) - class SwitchedResidualGenerator(nn.Module): def __init__(self, switch_filters, initial_temp=20, final_temperature_step=50000): super(SwitchedResidualGenerator, self).__init__() @@ -137,33 +135,55 @@ class SwitchedResidualGenerator(nn.Module): self.switch3.set_temperature(temp) self.switch4.set_temperature(temp) - # Copied from torchvision.utils.save_image. Allows specifying pixel format. - def save_image(self, 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 get_debug_values(self, step): + # Take the chance to update the temperature here. + temp = max(1, int(self.init_temperature * (self.final_temperature_step - step) / self.final_temperature_step)) + self.set_temperature(temp) - def convert_attention_indices_to_image(self, 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) + if step % 250 == 0: + save_attention_to_image(self.a1, 4, step, "a1") + save_attention_to_image(self.a2, 8, step, "a2") + save_attention_to_image(self.a3, 16, step, "a3", 2) + save_attention_to_image(self.a4, 32, step, "a4", 4) - import os - os.makedirs("attention_maps/%s" % (fname_part,), exist_ok=True) - self.save_image(hsv_img, "attention_maps/%s/attention_map_%i.png" % (fname_part, step,), pix_format="HSV") + val = {"switch_temperature": temp} + for i in range(len(self.running_sum)): + val["switch_%i_specificity" % (i,)] = self.running_sum[i] / self.running_count + self.running_sum[i] = 0 + self.running_count = 0 + return val + + +class ConfigurableSwitchedResidualGenerator(nn.Module): + def __init__(self, switch_filters, switch_depths, trans_counts, trans_kernel_sizes, trans_layers, initial_temp=20, final_temperature_step=50000): + super(ConfigurableSwitchedResidualGenerator, self).__init__() + switches = [] + for filters, depth, trans_count, kernel, layers in zip(switch_filters, switch_depths, trans_counts, trans_kernel_sizes, trans_layers): + switches.append(SwitchComputer(3, filters, functools.partial(ResidualBranch, 3, 3, kernel_size=kernel, depth=layers), trans_count, depth, initial_temp)) + initialize_weights(switches, 1) + # Initialize the transforms with a lesser weight, since they are repeatedly added on to the resultant image. + initialize_weights([s.transforms for s in switches], .05) + self.switches = nn.ModuleList(switches) + self.transformation_counts = trans_counts + self.init_temperature = initial_temp + self.final_temperature_step = final_temperature_step + self.running_sum = [0 for i in range(len(switches))] + self.running_count = 0 + + def forward(self, x): + self.attentions = [] + for i, sw in enumerate(self.switches): + x, att = sw.forward(x, True) + self.attentions.append(att) + spec, _ = compute_attention_specificity(att, 2) + self.running_sum[i] += spec + + self.running_count += 1 + + return (x,) + + def set_temperature(self, temp): + [sw.set_temperature(temp) for sw in self.switches] def get_debug_values(self, step): # Take the chance to update the temperature here. @@ -171,10 +191,7 @@ class SwitchedResidualGenerator(nn.Module): self.set_temperature(temp) if step % 250 == 0: - self.convert_attention_indices_to_image(self.a1, 4, step, "a1") - self.convert_attention_indices_to_image(self.a2, 8, step, "a2") - self.convert_attention_indices_to_image(self.a3, 16, step, "a3", 2) - self.convert_attention_indices_to_image(self.a4, 32, step, "a4", 4) + [save_attention_to_image(self.attentions[i], self.transformation_counts[i], step, "a%i" % (i+1,), l_mult=float(self.transformation_counts[i]/4)) for i in range(len(self.switches))] val = {"switch_temperature": temp} for i in range(len(self.running_sum)): diff --git a/codes/models/networks.py b/codes/models/networks.py index f1ee145c..6c70fcef 100644 --- a/codes/models/networks.py +++ b/codes/models/networks.py @@ -72,6 +72,10 @@ def define_G(opt, net_key='network_G'): elif which_model == "SwitchedResidualGenerator": netG = SwitchedGen_arch.SwitchedResidualGenerator(switch_filters=opt_net['nf'], initial_temp=opt_net['temperature'], final_temperature_step=opt_net['temperature_final_step']) + elif which_model == "ConfigurableSwitchedResidualGenerator": + netG = SwitchedGen_arch.ConfigurableSwitchedResidualGenerator(switch_filters=opt_net['switch_filters'], switch_depths=opt_net['switch_depths'], trans_counts=opt_net['trans_counts'], + trans_kernel_sizes=opt_net['trans_kernel_sizes'], trans_layers=opt_net['trans_layers'], + initial_temp=opt_net['temperature'], final_temperature_step=opt_net['temperature_final_step']) # image corruption elif which_model == 'HighToLowResNet':