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3ce19a2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 | # PyTorch StudioGAN: https://github.com/POSTECH-CVLab/PyTorch-StudioGAN
# The MIT License (MIT)
# See license file or visit https://github.com/POSTECH-CVLab/PyTorch-StudioGAN for details
# src/metrics/generate.py
import math
from tqdm import tqdm
import torch
import numpy as np
import utils.sample as sample
import utils.losses as losses
def generate_images_and_stack_features(generator, discriminator, eval_model, num_generate, y_sampler, batch_size, z_prior,
truncation_factor, z_dim, num_classes, LOSS, RUN, MODEL, is_stylegan, generator_mapping,
generator_synthesis, quantize, world_size, DDP, device, logger, disable_tqdm):
eval_model.eval()
feature_holder, prob_holder, fake_label_holder = [], [], []
if device == 0 and not disable_tqdm:
logger.info("generate images and stack features ({} images).".format(num_generate))
num_batches = int(math.ceil(float(num_generate) / float(batch_size)))
if DDP: num_batches = num_batches//world_size + 1
for i in tqdm(range(num_batches), disable=disable_tqdm):
fake_images, fake_labels, _, _, _, _, _ = sample.generate_images(z_prior=z_prior,
truncation_factor=truncation_factor,
batch_size=batch_size,
z_dim=z_dim,
num_classes=num_classes,
y_sampler=y_sampler,
radius="N/A",
generator=generator,
discriminator=discriminator,
is_train=False,
LOSS=LOSS,
RUN=RUN,
MODEL=MODEL,
is_stylegan=is_stylegan,
generator_mapping=generator_mapping,
generator_synthesis=generator_synthesis,
style_mixing_p=0.0,
device=device,
stylegan_update_emas=False,
cal_trsp_cost=False)
with torch.no_grad():
features, logits = eval_model.get_outputs(fake_images, quantize=quantize)
probs = torch.nn.functional.softmax(logits, dim=1)
feature_holder.append(features)
prob_holder.append(probs)
fake_label_holder.append(fake_labels)
feature_holder = torch.cat(feature_holder, 0)
prob_holder = torch.cat(prob_holder, 0)
fake_label_holder = torch.cat(fake_label_holder, 0)
if DDP:
feature_holder = torch.cat(losses.GatherLayer.apply(feature_holder), dim=0)
prob_holder = torch.cat(losses.GatherLayer.apply(prob_holder), dim=0)
fake_label_holder = torch.cat(losses.GatherLayer.apply(fake_label_holder), dim=0)
return feature_holder, prob_holder, list(fake_label_holder.detach().cpu().numpy())
def sample_images_from_loader_and_stack_features(dataloader, eval_model, batch_size, quantize,
world_size, DDP, device, disable_tqdm):
eval_model.eval()
total_instance = len(dataloader.dataset)
num_batches = math.ceil(float(total_instance) / float(batch_size))
if DDP: num_batches = int(math.ceil(float(total_instance) / float(batch_size*world_size)))
data_iter = iter(dataloader)
if device == 0 and not disable_tqdm:
print("Sample images and stack features ({} images).".format(total_instance))
feature_holder, prob_holder, label_holder = [], [], []
for i in tqdm(range(0, num_batches), disable=disable_tqdm):
try:
images, labels = next(data_iter)
except StopIteration:
break
images, labels = images.to(device), labels.to(device)
with torch.no_grad():
features, logits = eval_model.get_outputs(images, quantize=quantize)
probs = torch.nn.functional.softmax(logits, dim=1)
feature_holder.append(features)
prob_holder.append(probs)
label_holder.append(labels.to("cuda"))
feature_holder = torch.cat(feature_holder, 0)
prob_holder = torch.cat(prob_holder, 0)
label_holder = torch.cat(label_holder, 0)
if DDP:
feature_holder = torch.cat(losses.GatherLayer.apply(feature_holder), dim=0)
prob_holder = torch.cat(losses.GatherLayer.apply(prob_holder), dim=0)
label_holder = torch.cat(losses.GatherLayer.apply(label_holder), dim=0)
return feature_holder, prob_holder, list(label_holder.detach().cpu().numpy())
def stack_features(data_loader, eval_model, num_feats, batch_size, quantize, world_size, DDP, device, disable_tqdm):
eval_model.eval()
data_iter = iter(data_loader)
num_batches = math.ceil(float(num_feats) / float(batch_size))
if DDP: num_batches = num_batches//world_size + 1
real_feats, real_probs, real_labels = [], [], []
for i in tqdm(range(0, num_batches), disable=disable_tqdm):
start = i * batch_size
end = start + batch_size
try:
images, labels = next(data_iter)
except StopIteration:
break
images, labels = images.to(device), labels.to(device)
with torch.no_grad():
embeddings, logits = eval_model.get_outputs(images, quantize=quantize)
probs = torch.nn.functional.softmax(logits, dim=1)
real_feats.append(embeddings)
real_probs.append(probs)
real_labels.append(labels)
real_feats = torch.cat(real_feats, dim=0)
real_probs = torch.cat(real_probs, dim=0)
real_labels = torch.cat(real_labels, dim=0)
if DDP:
real_feats = torch.cat(losses.GatherLayer.apply(real_feats), dim=0)
real_probs = torch.cat(losses.GatherLayer.apply(real_probs), dim=0)
real_labels = torch.cat(losses.GatherLayer.apply(real_labels), dim=0)
real_feats = real_feats.detach().cpu().numpy().astype(np.float64)
real_probs = real_probs.detach().cpu().numpy().astype(np.float64)
real_labels = real_labels.detach().cpu().numpy()
return real_feats, real_probs, real_labels
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