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blender_combine.py
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import tensorflow as tf
import math
from tqdm import tqdm
from tensorflow.python.platform import flags
from torch.utils.data import DataLoader, Dataset
import imageio
from models import ResNetModel, CelebAModel
from utils import ReplayBuffer
import os.path as osp
import numpy as np
from logger import TensorBoardOutputFormat
from scipy.misc import imsave
from torchvision import transforms
import os
from itertools import product
from PIL import Image
import torch
from data import CelebAHQOverfit
from fid import get_fid_score
flags.DEFINE_integer('batch_size', 256, 'Size of inputs')
flags.DEFINE_integer('data_workers', 4, 'Number of workers to do things')
flags.DEFINE_string('logdir', 'cachedir', 'directory for logging')
flags.DEFINE_string('savedir', 'cachedir', 'location where log of experiments will be stored')
flags.DEFINE_integer('num_filters', 64, 'number of filters for conv nets -- 32 for miniimagenet, 64 for omniglot.')
flags.DEFINE_float('step_lr', 1000.0, 'size of gradient descent size')
flags.DEFINE_bool('cclass', True, 'not cclass')
flags.DEFINE_bool('proj_cclass', False, 'use for backwards compatibility reasons')
flags.DEFINE_bool('spec_norm', True, 'Whether to use spectral normalization on weights')
flags.DEFINE_bool('use_bias', True, 'Whether to use bias in convolution')
flags.DEFINE_bool('use_attention', False, 'Whether to use self attention in network')
flags.DEFINE_integer('num_steps', 80, 'number of steps to optimize the label')
flags.DEFINE_string('task', 'negation_figure', 'conceptcombine, combination_figure, negation_figure, or_figure, negation_eval')
flags.DEFINE_bool('eval', False, 'Whether to quantitively evaluate models')
flags.DEFINE_bool('latent_energy', False, 'latent energy in model')
flags.DEFINE_bool('proj_latent', False, 'Projection of latents')
# Whether to train for gentest
flags.DEFINE_bool('train', False, 'whether to train on generalization into multiple different predictions')
FLAGS = flags.FLAGS
def conceptcombineeval(model_list, select_idx):
dataset = CelebAHQOverfit()
dataloader = torch.utils.data.DataLoader(
dataset, batch_size=64, shuffle=True, num_workers=4)
n = 64
labels = []
for six in select_idx:
label_ix = np.eye(2)[six]
label_batch = np.tile(label_ix[None, :], (n, 1))
label = torch.Tensor(label_batch).cuda()
labels.append(label)
def get_color_distortion(s=1.0):
# s is the strength of color distortion.
color_jitter = transforms.ColorJitter(0.8*s, 0.8*s, 0.8*s, 0.4*s)
rnd_color_jitter = transforms.RandomApply([color_jitter], p=0.8)
rnd_gray = transforms.RandomGrayscale(p=0.2)
color_distort = transforms.Compose([
rnd_color_jitter,
rnd_gray])
return color_distort
color_transform = get_color_distortion()
im_size = 128
transform = transforms.Compose([transforms.RandomResizedCrop(im_size, scale=(0.4, 1.0)), transforms.RandomHorizontalFlip(), color_transform, transforms.ToTensor()])
gt_ims = []
fake_ims = []
for data, label in tqdm(dataloader):
gt_ims.extend(list((data.numpy().transpose((0, 2, 3, 1)) * 255).astype(np.uint8)))
im = torch.rand(n, 3, 128, 128).cuda()
im_noise = torch.randn_like(im).detach()
# First get good initializations for sampling
# for i in range(10):
# for i in range(20):
# im_noise.normal_()
# im = im + 0.001 * im_noise
# # im.requires_grad = True
# im.requires_grad_(requires_grad=True)
# energy = 0
# for model, label in zip(model_list, labels):
# energy = model.forward(im, label) + energy
# # print("step: ", i, energy.mean())
# im_grad = torch.autograd.grad([energy.sum()], [im])[0]
# im = im - FLAGS.step_lr * im_grad
# im = im.detach()
# im = torch.clamp(im, 0, 1)
# im = im.detach().cpu().numpy().transpose((0, 2, 3, 1))
# im = (im * 255).astype(np.uint8)
# ims = []
# for i in range(im.shape[0]):
# im_i = np.array(transform(Image.fromarray(np.array(im[i]))))
# ims.append(im_i)
# im = torch.Tensor(np.array(ims)).cuda()
# Then refine the images
for i in range(FLAGS.num_steps):
im_noise.normal_()
im = im + 0.001 * im_noise
# im.requires_grad = True
im.requires_grad_(requires_grad=True)
energy = 0
for model, label in zip(model_list, labels):
energy = model.forward(im, label) + energy
print("step: ", i, energy.mean())
im_grad = torch.autograd.grad([energy.sum()], [im])[0]
im = im - FLAGS.step_lr * im_grad
im = im.detach()
im = torch.clamp(im, 0, 1)
im = im.detach().cpu()
fake_ims.extend(list((im.numpy().transpose((0, 2, 3, 1)) * 255).astype(np.uint8)))
if len(gt_ims) > 10000:
break
get_fid_score(gt_ims, fake_ims)
fake_ims = np.array(fake_ims)
fake_ims_flat = fake_ims.reshape(fake_ims.shape[0], -1)
std_im = np.std(fake_ims, axis=0).mean()
print("standard deviation of image", std_im)
import pdb
pdb.set_trace()
print("here")
def conceptcombine(model_list, select_idx):
n = 16
labels = []
for six in select_idx:
label_batch = np.tile(six[None, :], (n, 1))
label = torch.Tensor(label_batch).cuda()
labels.append(label)
im = torch.rand(n, 3, 128, 128).cuda()
im_noise = torch.randn_like(im).detach()
def get_color_distortion(s=1.0):
# s is the strength of color distortion.
color_jitter = transforms.ColorJitter(0.8*s, 0.8*s, 0.8*s, 0.4*s)
rnd_color_jitter = transforms.RandomApply([color_jitter], p=0.8)
rnd_gray = transforms.RandomGrayscale(p=0.2)
color_distort = transforms.Compose([
rnd_color_jitter,
rnd_gray])
return color_distort
color_transform = get_color_distortion()
im_size = 128
transform = transforms.Compose([transforms.RandomResizedCrop(im_size, scale=(0.08, 1.0)), transforms.RandomHorizontalFlip(), color_transform, transforms.ToTensor()])
# First get good initializations for sampling
for i in range(2):
for i in range(20):
im_noise.normal_()
im = im + 0.001 * im_noise
# im.requires_grad = True
im.requires_grad_(requires_grad=True)
energy = 0
for model, label in zip(model_list, labels):
energy = model.forward(im, label) + energy
# print("step: ", i, energy.mean())
im_grad = torch.autograd.grad([energy.sum()], [im])[0]
im = im - FLAGS.step_lr * im_grad
im = im.detach()
im = torch.clamp(im, 0, 1)
im = im.detach().cpu().numpy().transpose((0, 2, 3, 1))
im = (im * 255).astype(np.uint8)
ims = []
for i in range(im.shape[0]):
im_i = np.array(transform(Image.fromarray(np.array(im[i]))))
ims.append(im_i)
im = torch.Tensor(np.array(ims)).cuda()
# Then refine the images
w = imageio.get_writer("blender_cond.mp4")
steps = [180, 180, 180, 180]
def get_color_distortion_small(s=0.3):
# s is the strength of color distortion.
color_jitter = transforms.ColorJitter(0.8*s, 0.8*s, 0.8*s, 0.4*s)
rnd_color_jitter = transforms.RandomApply([color_jitter], p=0.8)
# rnd_gray = transforms.RandomGrayscale(p=0.2)
# color_distort = transforms.Compose([
# rnd_color_jitter,
# rnd_gray])
return color_jitter
color_transform_small = get_color_distortion_small()
transform_small = transforms.Compose([transforms.RandomResizedCrop(im_size, scale=(0.7, 1.0)), color_transform_small, transforms.ToTensor()])
for it in range(4):
# for i in range(2):
# w.append_data(np.zeros_like(im[:16].detach().view(4, 4, 3, 128, 128).permute(0, 3, 1, 4, 2).cpu().numpy().reshape(4*128, 4*128, 3) * 255))
for i in range(steps[it]):
if i % 5 == 0:
w.append_data(im[:16].detach().view(4, 4, 3, 128, 128).permute(0, 3, 1, 4, 2).cpu().numpy().reshape(4*128, 4*128, 3) * 255)
im_noise.normal_()
im = im + 0.001 * im_noise
# im.requires_grad = True
im.requires_grad_(requires_grad=True)
energy = 0
print("on iteration: ", it)
for i, (model, label) in enumerate(zip(model_list, labels)):
# energy = -torch.logsumexp(torch.stack([-1 * scale * (model_list[0].forward(im, labels[0]) + model_list[1].forward(im, labels[1])), -1 * scale * (model_list[2].forward(im, labels[2]) - 0.01 * model_list[3].forward(im, labels[3]))], dim=0), dim=0) / scale
if i > it:
break
energy_eval = 4 / (it + 1) * model.forward(im, label)
energy = energy_eval + energy
# if it == i:
# energy = 4 * energy + energy_eval
# print("forward model ", energy_eval.mean())
# if it == 0:
# energy = energy_eval + energy
# elif it == 1:
# energy = energy_eval + energy
# else:
# energy = energy_eval
# energy = -0.02 * model_list[0].forward(im, labels[0]) -0.02 * model_list[1].forward(im, labels[1]) + model_list[2].forward(im, labels[2])
print("mean energy: ", energy.mean())
im_grad = torch.autograd.grad([energy.sum()], [im])[0]
im = im - FLAGS.step_lr * im_grad
im = im.detach()
im = torch.clamp(im, 0, 1)
im = im.detach().cpu().numpy().transpose((0, 2, 3, 1))
im = (im * 255).astype(np.uint8)
ims = []
for i in range(im.shape[0]):
im_i = np.array(transform_small(Image.fromarray(np.array(im[i]))))
ims.append(im_i)
im = torch.Tensor(np.array(ims)).cuda()
output = im.detach().cpu().numpy()
output = output.transpose((0, 2, 3, 1))
output = output.reshape((-1, 8, 128, 128, 3)).transpose((0, 2, 1, 3, 4)).reshape((-1, 128 * 8, 3))
imsave("debug.png", output)
def combine_main(models, resume_iters, select_idx):
model_list = []
for model, resume_iter in zip(models, resume_iters):
model_path = osp.join("cachedir", model, "model_{}.pth".format(resume_iter))
checkpoint = torch.load(model_path)
FLAGS_model = checkpoint['FLAGS']
print("step_lr: ", FLAGS_model.step_lr)
model_base = CelebAModel(FLAGS_model)
model_base.load_state_dict(checkpoint['ema_model_state_dict_0'])
model_base = model_base.cuda()
model_list.append(model_base)
conceptcombine(model_list, select_idx)
if __name__ == "__main__":
models_orig = ['object_cond_idx_1_noise_7', 'object_cond_idx_2_noise_8', 'object_cond_idx_3_noise_7', 'object_cond_idx_4_noise_7']
resume_iters_orig = ["10000", "10000", "6000", "25000"]
label = np.load("data/object/label.npy")
n = 45
label_pos = label[n, :2]
label_size = np.array([label[n, 2], label[n, 2]])
label_rotate = np.array([np.sin(label[n, 3]), np.cos(label[n, 3])])
label_type = np.eye(2)[int(label[n, 4])]
##################################
models = []
resume_iters = []
select_idx = []
# Settings for the composition_figure
models = models + [models_orig[2]]
resume_iters = resume_iters + [resume_iters_orig[2]]
select_idx = select_idx + [label_rotate]
models = [models_orig[0]]
resume_iters = [resume_iters_orig[0]]
select_idx = [label_pos]
models = models + [models_orig[3]]
resume_iters = resume_iters + [resume_iters_orig[3]]
select_idx = select_idx + [np.array([0, 1])]
models = models + [models_orig[1]]
resume_iters = resume_iters + [resume_iters_orig[1]]
select_idx = select_idx + [label_size]
FLAGS.step_lr = FLAGS.step_lr / len(models)
# List of 4 attributes that might be good
# Young -> Female -> Smiling -> Wavy
combine_main(models, resume_iters, select_idx)