File size: 11,582 Bytes
a00ee36 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import sys
import os
import tempfile
import warnings
from pathlib import Path
import nltk
import torch
from torch import nn
import torchvision.transforms as transforms
import numpy as np
import imageio
from PIL import Image as Image_PIL
from scipy.stats import truncnorm
from nltk.corpus import wordnet as wn
import cma
import sklearn.metrics
import cog
sys.path.insert(0, "stylegan2_ada_pytorch")
from pytorch_pretrained_biggan import convert_to_images, utils
import inference.utils as inference_utils
import data_utils.utils as data_utils
NORM_MEAN = torch.Tensor([0.485, 0.456, 0.406]).view(3, 1, 1)
NORM_STD = torch.Tensor([0.229, 0.224, 0.225]).view(3, 1, 1)
nltk.download("wordnet")
IND2NAME = {
index: wn.of2ss("%08dn" % offset).lemma_names()[0]
for offset, index in utils.IMAGENET.items()
}
NAME2IND = dict([(value, key) for key, value in IND2NAME.items()])
CLASS_NAMES = sorted(list(IND2NAME.values()))
class Predictor(cog.Predictor):
def setup(self):
torch.manual_seed(np.random.randint(sys.maxsize))
warnings.simplefilter("ignore", cma.evolution_strategy.InjectionWarning)
self.last_gen_model = None
self.last_feature_extractor = None
self.model = None
self.feature_extractor = None
self.noise_size = 128
self.batch_size = 4
self.size = 256
@cog.input("image", type=Path, help="Input image Instance")
@cog.input("gen_model", type=str, options=["icgan", "cc_icgan"], default="icgan",
help='Select type of IC-GAN model. "icgan" is conditioned on the input image; '
'"cc_icgan" is conditioned on both input image and a conditional_class')
@cog.input("conditional_class", type=str, default=None, options=CLASS_NAMES,
help="Choose conditional class. Only valid for gen_model=cc_icgan")
@cog.input("num_samples", type=int, default=1, options=[1, 4, 9, 16],
help="number of samples generated")
@cog.input("seed", type=int, default=0, help="seed=0 means no seed")
def predict(self, image, gen_model="icgan", conditional_class=None, num_samples=1, seed=0):
assert isinstance(seed, int), "seed should be an integer"
if gen_model == 'cc_icgan':
assert conditional_class is not None, 'please set conditional_class for cc_icgan'
num_samples_ranked = num_samples
experiment_name = (
"icgan_biggan_imagenet_res256"
if gen_model == "icgan"
else "cc_icgan_biggan_imagenet_res256"
)
num_samples_total = num_samples * 10
truncation = 0.7
if conditional_class is not None:
class_index = NAME2IND[conditional_class]
input_image_instance = str(image)
if gen_model == "icgan":
class_index = None
if seed == 0:
seed = None
state = None if not seed else np.random.RandomState(seed)
np.random.seed(seed)
feature_extractor_name = ("classification" if gen_model == "cc_icgan" else "selfsupervised")
# Load feature extractor (outlier filtering and optionally input image feature extraction)
self.feature_extractor, self.last_feature_extractor = load_feature_extractor(
gen_model, self.last_feature_extractor, self.feature_extractor)
# Load features
if input_image_instance not in ["None", "", None]:
print("Obtaining instance features from input image!")
input_feature_index = None
input_image_tensor = preprocess_input_image(input_image_instance, self.size)
with torch.no_grad():
input_features, _ = self.feature_extractor(input_image_tensor.cuda())
input_features /= torch.linalg.norm(input_features, dim=-1, keepdims=True)
elif input_feature_index is not None:
print("Selecting an instance from pre-extracted vectors!")
input_features = np.load(
"stored_instances/imagenet_res"
+ str(self.size)
+ "_rn50_"
+ feature_extractor_name
+ "_kmeans_k1000_instance_features.npy",
allow_pickle=True,
).item()["instance_features"][input_feature_index: input_feature_index + 1]
else:
input_features = None
# Load generative model
self.model, self.last_gen_model = load_generative_model(
gen_model, self.last_gen_model, experiment_name, self.model)
# Prepare other variables
replace_to_inplace_relu(self.model)
# Create noise, instance and class vector
noise_vector = truncnorm.rvs(
-2 * truncation,
2 * truncation,
size=(num_samples_total, self.noise_size),
random_state=state,
).astype(np.float32)
noise_vector = torch.tensor(noise_vector, requires_grad=False, device="cuda")
if input_features is not None:
instance_vector = torch.tensor(
input_features, requires_grad=False, device="cuda"
).repeat(num_samples_total, 1)
else:
instance_vector = None
if class_index is not None:
input_label = torch.LongTensor([class_index] * num_samples_total)
else:
input_label = None
if input_feature_index is not None:
print("Conditioning on instance with index: ", input_feature_index)
all_outs, all_dists = [], []
for i_bs in range(num_samples_total // self.batch_size + 1):
start = i_bs * self.batch_size
end = min(start + self.batch_size, num_samples_total)
if start == end:
break
out = get_output(
noise_vector[start:end],
input_label[start:end] if input_label is not None else None,
instance_vector[start:end] if instance_vector is not None else None,
self.model,
truncation,
channels=3,
)
if instance_vector is not None:
# Get features from generated images + feature extractor
out_ = preprocess_generated_image(out)
with torch.no_grad():
out_features, _ = self.feature_extractor(out_.cuda())
out_features /= torch.linalg.norm(out_features, dim=-1, keepdims=True)
dists = sklearn.metrics.pairwise_distances(
out_features.cpu(),
instance_vector[start:end].cpu(),
metric="euclidean",
n_jobs=-1,
)
all_dists.append(np.diagonal(dists))
all_outs.append(out.detach().cpu())
del out
all_outs = torch.cat(all_outs)
all_dists = np.concatenate(all_dists)
# Order samples by distance to conditioning feature vector and select only num_samples_ranked images
selected_idxs = np.argsort(all_dists)[:num_samples_ranked]
# Create figure
row_i, col_i, i_im = 0, 0, 0
all_images_mosaic = np.zeros(
(
3,
self.size * (int(np.sqrt(num_samples_ranked))),
self.size * (int(np.sqrt(num_samples_ranked))),
)
)
for j in selected_idxs:
all_images_mosaic[
:,
row_i * self.size: row_i * self.size + self.size,
col_i * self.size: col_i * self.size + self.size,
] = all_outs[j]
if row_i == int(np.sqrt(num_samples_ranked)) - 1:
row_i = 0
if col_i == int(np.sqrt(num_samples_ranked)) - 1:
col_i = 0
else:
col_i += 1
else:
row_i += 1
i_im += 1
out_path = Path(tempfile.mkdtemp()) / "out.png"
save(all_images_mosaic[np.newaxis, ...], str(out_path), torch_format=False)
return out_path
def replace_to_inplace_relu(model):
for child_name, child in model.named_children():
if isinstance(child, nn.ReLU):
setattr(model, child_name, nn.ReLU(inplace=False))
else:
replace_to_inplace_relu(child)
def save(out, name=None, torch_format=True):
if torch_format:
with torch.no_grad():
out = out.cpu().numpy()
img = convert_to_images(out)[0]
if name:
imageio.imwrite(name, np.asarray(img))
return img
def load_icgan(experiment_name, root_=""):
root = os.path.join(root_, experiment_name)
config = torch.load("%s/%s.pth" % (root, "state_dict_best0"))["config"]
config["weights_root"] = root_
config["model_backbone"] = "biggan"
config["experiment_name"] = experiment_name
G, config = inference_utils.load_model_inference(config)
G.cuda()
G.eval()
return G
def get_output(noise_vector, input_label, input_features, model, truncation, channels):
# stochastic_truncation = False as how it is set in colab
noise_vector = noise_vector.clamp(-2 * truncation, 2 * truncation)
if input_label is not None:
input_label = torch.LongTensor(input_label)
else:
input_label = None
out = model(
noise_vector,
input_label.cuda() if input_label is not None else None,
input_features.cuda() if input_features is not None else None,
)
if channels == 1:
out = out.mean(dim=1, keepdim=True)
out = out.repeat(1, 3, 1, 1)
return out
def load_generative_model(gen_model, last_gen_model, experiment_name, model):
# Load generative model
if gen_model != last_gen_model:
model = load_icgan(experiment_name, root_="./")
last_gen_model = gen_model
return model, last_gen_model
def load_feature_extractor(gen_model, last_feature_extractor, feature_extractor):
# Load feature extractor to obtain instance features
feat_ext_name = "classification" if gen_model == "cc_icgan" else "selfsupervised"
if last_feature_extractor != feat_ext_name:
if feat_ext_name == "classification":
feat_ext_path = ""
else:
feat_ext_path = "swav_pretrained.pth.tar"
last_feature_extractor = feat_ext_name
feature_extractor = data_utils.load_pretrained_feature_extractor(
feat_ext_path, feature_extractor=feat_ext_name
)
feature_extractor.eval()
return feature_extractor, last_feature_extractor
def preprocess_input_image(input_image_path, size):
pil_image = Image_PIL.open(input_image_path).convert("RGB")
transform_list = transforms.Compose(
[
data_utils.CenterCropLongEdge(),
transforms.Resize((size, size)),
transforms.ToTensor(),
transforms.Normalize(NORM_MEAN, NORM_STD),
]
)
tensor_image = transform_list(pil_image)
tensor_image = torch.nn.functional.interpolate(
tensor_image.unsqueeze(0), 224, mode="bicubic", align_corners=True
)
return tensor_image
def preprocess_generated_image(image):
transform_list = transforms.Normalize(NORM_MEAN, NORM_STD)
image = transform_list(image * 0.5 + 0.5)
image = torch.nn.functional.interpolate(
image, 224, mode="bicubic", align_corners=True
)
return image
|