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# 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 os
import sys
sys.path.insert(1, os.path.join(sys.path[0], ".."))
import numpy as np
import matplotlib.pyplot as plt
from argparse import ArgumentParser
import torch
import data_utils.utils as data_utils
import inference.utils as inference_utils
import BigGAN_PyTorch.utils as biggan_utils
from data_utils.datasets_common import pil_loader
import torchvision.transforms as transforms
import time
def get_data(root_path, model, resolution, which_dataset, visualize_instance_images):
data_path = os.path.join(root_path, "stored_instances")
if model == "cc_icgan":
feature_extractor = "classification"
else:
feature_extractor = "selfsupervised"
filename = "%s_res%i_rn50_%s_kmeans_k1000_instance_features.npy" % (
which_dataset,
resolution,
feature_extractor,
)
# Load conditioning instances from files
data = np.load(os.path.join(data_path, filename), allow_pickle=True).item()
transform_list = None
if visualize_instance_images:
# Transformation used for ImageNet images.
transform_list = transforms.Compose(
[data_utils.CenterCropLongEdge(), transforms.Resize(resolution)]
)
return data, transform_list
def get_model(exp_name, root_path, backbone, device="cuda"):
parser = biggan_utils.prepare_parser()
parser = biggan_utils.add_sample_parser(parser)
parser = inference_utils.add_backbone_parser(parser)
args = ["--experiment_name", exp_name]
args += ["--base_root", root_path]
args += ["--model_backbone", backbone]
config = vars(parser.parse_args(args=args))
# Load model and overwrite configuration parameters if stored in the model
config = biggan_utils.update_config_roots(config, change_weight_folder=False)
generator, config = inference_utils.load_model_inference(config, device=device)
biggan_utils.count_parameters(generator)
generator.eval()
return generator
def get_conditionings(test_config, generator, data):
# Obtain noise vectors
z = torch.empty(
test_config["num_imgs_gen"] * test_config["num_conditionings_gen"],
generator.z_dim if config["model_backbone"] == "stylegan2" else generator.dim_z,
).normal_(mean=0, std=test_config["z_var"])
# Subsampling some instances from the 1000 k-means centers file
if test_config["num_conditionings_gen"] > 1:
total_idxs = np.random.choice(
range(1000), test_config["num_conditionings_gen"], replace=False
)
# Obtain features, labels and ground truth image paths
all_feats, all_img_paths, all_labels = [], [], []
for counter in range(test_config["num_conditionings_gen"]):
# Index in 1000 k-means centers file
if test_config["index"] is not None:
idx = test_config["index"]
else:
idx = total_idxs[counter]
# Image paths to visualize ground-truth instance
if test_config["visualize_instance_images"]:
all_img_paths.append(data["image_path"][idx])
# Instance features
all_feats.append(
torch.FloatTensor(data["instance_features"][idx : idx + 1]).repeat(
test_config["num_imgs_gen"], 1
)
)
# Obtain labels
if test_config["swap_target"] is not None:
# Swap label for a manually specified one
label_int = test_config["swap_target"]
else:
# Use the label associated to the instance feature
label_int = int(data["labels"][idx])
# Format labels according to the backbone
labels = None
if test_config["model_backbone"] == "stylegan2":
dim_labels = 1000
labels = torch.eye(dim_labels)[torch.LongTensor([label_int])].repeat(
test_config["num_imgs_gen"], 1
)
else:
if test_config["model"] == "cc_icgan":
labels = torch.LongTensor([label_int]).repeat(
test_config["num_imgs_gen"]
)
all_labels.append(labels)
# Concatenate all conditionings
all_feats = torch.cat(all_feats)
if all_labels[0] is not None:
all_labels = torch.cat(all_labels)
else:
all_labels = None
return z, all_feats, all_labels, all_img_paths
def main(test_config):
suffix = (
"_nofeataug"
if test_config["resolution"] == 256
and test_config["trained_dataset"] == "imagenet"
else ""
)
exp_name = "%s_%s_%s_res%i%s" % (
test_config["model"],
test_config["model_backbone"],
test_config["trained_dataset"],
test_config["resolution"],
suffix,
)
device = "cuda"
### -- Data -- ###
data, transform_list = get_data(
test_config["root_path"],
test_config["model"],
test_config["resolution"],
test_config["which_dataset"],
test_config["visualize_instance_images"],
)
### -- Model -- ###
generator = get_model(
exp_name, test_config["root_path"], test_config["model_backbone"], device=device
)
### -- Generate images -- ###
# Prepare input and conditioning: different noise vector per sample but the same conditioning
# Sample noise vector
z, all_feats, all_labels, all_img_paths = get_conditionings(
test_config, generator, data
)
## Generate the images
all_generated_images = []
with torch.no_grad():
num_batches = 1 + (z.shape[0]) // test_config["batch_size"]
for i in range(num_batches):
start = test_config["batch_size"] * i
end = min(
test_config["batch_size"] * i + test_config["batch_size"], z.shape[0]
)
if all_labels is not None:
labels_ = all_labels[start:end].to(device)
else:
labels_ = None
gen_img = generator(
z[start:end].to(device), labels_, all_feats[start:end].to(device)
)
if test_config["model_backbone"] == "biggan":
gen_img = ((gen_img * 0.5 + 0.5) * 255).int()
elif test_config["model_backbone"] == "stylegan2":
gen_img = torch.clamp((gen_img * 127.5 + 128), 0, 255).int()
all_generated_images.append(gen_img.cpu())
all_generated_images = torch.cat(all_generated_images)
all_generated_images = all_generated_images.permute(0, 2, 3, 1).numpy()
big_plot = []
for i in range(0, test_config["num_conditionings_gen"]):
row = []
for j in range(0, test_config["num_imgs_gen"]):
subplot_idx = (i * test_config["num_imgs_gen"]) + j
row.append(all_generated_images[subplot_idx])
row = np.concatenate(row, axis=1)
big_plot.append(row)
big_plot = np.concatenate(big_plot, axis=0)
# (Optional) Show ImageNet ground-truth conditioning instances
if test_config["visualize_instance_images"]:
all_gt_imgs = []
for i in range(0, len(all_img_paths)):
all_gt_imgs.append(
np.array(
transform_list(
pil_loader(
os.path.join(test_config["dataset_path"], all_img_paths[i])
)
)
).astype(np.uint8)
)
all_gt_imgs = np.concatenate(all_gt_imgs, axis=0)
white_space = (
np.ones((all_gt_imgs.shape[0], 20, all_gt_imgs.shape[2])) * 255
).astype(np.uint8)
big_plot = np.concatenate([all_gt_imgs, white_space, big_plot], axis=1)
plt.figure(
figsize=(
5 * test_config["num_imgs_gen"],
5 * test_config["num_conditionings_gen"],
)
)
plt.imshow(big_plot)
plt.axis("off")
fig_path = "%s_Generations_with_InstanceDataset_%s%s%s_zvar%0.2f.png" % (
exp_name,
test_config["which_dataset"],
"_index" + str(test_config["index"])
if test_config["index"] is not None
else "",
"_class_idx" + str(test_config["swap_target"])
if test_config["swap_target"] is not None
else "",
test_config["z_var"],
)
plt.savefig(fig_path, dpi=600, bbox_inches="tight", pad_inches=0)
print("Done! Figure saved as %s" % (fig_path))
if __name__ == "__main__":
parser = ArgumentParser(
description="Generate and save images using pre-trained models"
)
parser.add_argument(
"--root_path",
type=str,
required=True,
help="Path where pretrained models + instance features have been downloaded.",
)
parser.add_argument(
"--which_dataset",
type=str,
default="imagenet",
choices=["imagenet", "coco"],
help="Dataset to sample instances from.",
)
parser.add_argument(
"--trained_dataset",
type=str,
default="imagenet",
choices=["imagenet", "coco"],
help="Dataset in which the model has been trained on.",
)
parser.add_argument(
"--model",
type=str,
default="icgan",
choices=["icgan", "cc_icgan"],
help="Model type.",
)
parser.add_argument(
"--model_backbone",
type=str,
default="biggan",
choices=["biggan", "stylegan2"],
help="Model backbone type.",
)
parser.add_argument(
"--resolution",
type=int,
default=256,
help="Resolution to generate images with " "(default: %(default)s)",
)
parser.add_argument(
"--z_var", type=float, default=1.0, help="Noise variance: %(default)s)"
)
parser.add_argument("--batch_size", type=int, default=16, help="Batch size.")
parser.add_argument(
"--num_imgs_gen",
type=int,
default=5,
help="Number of images to generate with different noise vectors, "
"given an input conditioning.",
)
parser.add_argument(
"--num_conditionings_gen",
type=int,
default=5,
help="Number of conditionings to generate with."
" Use `num_imgs_gen` to control the number of generated samples per conditioning",
)
parser.add_argument(
"--index",
type=int,
default=None,
help="Index of the stored instance to use as conditioning [0,1000)."
" Mutually exclusive with `num_conditionings_gen!=1`",
)
parser.add_argument(
"--swap_target",
type=int,
default=None,
help="For class-conditional IC-GAN, we can choose to swap the target for a different one."
" If swap_target=None, the original label from the instance is used. "
"If swap_target is in [0,1000), a specific ImageNet class is used instead.",
)
parser.add_argument(
"--visualize_instance_images",
action="store_true",
default=False,
help="Also visualize the ground-truth image corresponding to the instance conditioning "
"(requires a path to the ImageNet dataset)",
)
parser.add_argument(
"--dataset_path",
type=str,
default="",
help="Only needed if visualize_instance_images=True."
" Folder where to find the dataset ground-truth images.",
)
config = vars(parser.parse_args())
if config["index"] is not None and config["num_conditionings_gen"] != 1:
raise ValueError(
"If a specific feature vector (specificed by --index) "
"wants to be used to sample images from, num_conditionings_gen"
" needs to be set to 1"
)
if config["swap_target"] is not None and config["model"] == "icgan":
raise ValueError(
'Cannot specify a class label for IC-GAN! Only use "swap_target" with --model=cc_igan. '
)
main(config)
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