import argparse import logging import math import os from glob import glob import torch import torch.nn.functional as F import torchvision from PIL import Image from tqdm import tqdm import utils.options as option import utils from data.image_corruptor import ImageCorruptor from trainer.ExtensibleTrainer import ExtensibleTrainer from utils import util def image_2_tensor(impath, max_size=None): img = Image.open(impath) if max_size is not None: factor = min(max_size / img.width, max_size / img.height) new_size = (int(math.ceil(img.width * factor)), int(math.ceil(img.height * factor))) img = img.resize(new_size, Image.LANCZOS) ''' # Useful for setting an image to an exact size. h_gap = img.height - desired_size[1] w_gap = img.width - desired_size[0] assert h_gap >= 0 and w_gap >= 0 ht = h_gap // 2 hb = desired_size[1] + ht wl = w_gap // 2 wr = desired_size[1] + wl ''' timg = torchvision.transforms.ToTensor()(img).unsqueeze(0) #if desired_size is not None: # timg = timg[:, :3, ht:hb, wl:wr] # assert timg.shape[2] == desired_size[1] and timg.shape[3] == desired_size[0] #else: # Enforce that the input must have a input dimension that is a factor of 16. b, c, h, w = timg.shape h = (h // 16) * 16 w = (w // 16) * 16 timg = timg[:, :3, :h, :w] return timg def interpolate_lr(hr, scale): return F.interpolate(hr, scale_factor=1 / scale, mode="area") def fetch_latents_for_image(gen, img, scale, lr_infer=interpolate_lr): z, _, _ = gen(gt=img, lr=lr_infer(img, scale), epses=[], reverse=False, add_gt_noise=False) return z def fetch_latents_for_images(gen, imgs, scale, lr_infer=interpolate_lr): latents = [] for img in imgs: z, _, _ = gen(gt=img, lr=lr_infer(img, scale), epses=[], reverse=False, add_gt_noise=False) latents.append(z) return latents def fetch_spatial_metrics_for_latents(latents): dt_scales = [] dt_biases = [] for i in range(len(latents)): latent = torch.stack(latents[i], dim=-1).squeeze(0) s = latent.std(dim=[1, 2, 3]).view(1,-1,1,1) b = latent.mean(dim=[1, 2, 3]).view(1,-1,1,1) dt_scales.append(s) dt_biases.append(b) return dt_scales, dt_biases def spatial_norm(latents, exclusion_list=[]): nlatents = [] for i in range(len(latents)): latent = latents[i] if i in exclusion_list: nlatents.append(latent) else: b, c, h, w = latent.shape s = latent.std(dim=[2, 3]).view(1,c,1,1) b = latent.mean(dim=[2, 3]).view(1,c,1,1) nlatents.append((latents[i] - b) / s) return nlatents def local_norm(latents, exclusion_list=[]): nlatents = [] for i in range(len(latents)): latent = latents[i] if i in exclusion_list: nlatents.append(latent) else: b, c, h, w = latent.shape s = latent.std(dim=[1]).view(1,1,h,w) b = latent.mean(dim=[1]).view(1,1,h,w) nlatents.append((latents[i] - b) / s) return nlatents # Extracts a rectangle of the same shape as from and returns it. This is taken from the center of def extract_center_latent(ref, lat): _, _, h, w = lat.shape _, _, rh, rw = ref.shape dw = (rw - w) / 2 dh = (rh - h) / 2 return ref[:, :, math.floor(dh):-math.ceil(dh), math.floor(dw):-math.ceil(dw)] def linear_interpolation(latents1, latents2, proportion): return [l1*proportion+l2*(1-proportion) for l1, l2 in zip(latents1, latents2)] def slerp(latents1, latents2, proportion): res = [] for low, high in zip(latents1, latents2): low_norm = low / torch.norm(low, dim=[2,3], keepdim=True) high_norm = high / torch.norm(high, dim=[2,3], keepdim=True) omega = torch.acos((low_norm * high_norm).sum(1)) so = torch.sin(omega) res.append((torch.sin((1.0 - proportion) * omega) / so).unsqueeze(1) * low + (torch.sin(proportion * omega) / so).unsqueeze(1) * high) return res def create_interpolation_video(gen, lq, output_file, latents1, latents2, steps=10, prefix=''): # Outputs a series of images interpolated from [latents1] to [latents2]. image 0 biases towards latents2. for i in range(steps): proportion = i / (steps-1) lats = linear_interpolation(latents1, latents2, proportion) hr, _, _ = gen(lr=lq, z=lats[0], reverse=True, epses=lats, add_gt_noise=False) torchvision.transforms.ToPILImage()(hr.squeeze(0).cpu()) torchvision.utils.save_image(hr.cpu(), os.path.join(output_file, "%s_%i.png" % (prefix, i))) # Stopped using this because PILs animated gif output is total crap. #images[0].save(output_file, save_all=True, append_images=images[1:], duration=80, loop=0) if __name__ == "__main__": #### options torch.backends.cudnn.benchmark = True srg_analyze = False parser = argparse.ArgumentParser() parser.add_argument('-opt', type=str, help='Path to options YAML file.', default='../options/train_exd_imgsetext_srflow_bigboi_ganbase.yml') #parser.add_argument('-opt', type=str, help='Path to options YAML file.', default='../../options/train_exd_imgsetext_srflow_bigboi_frompsnr.yml') opt = option.parse(parser.parse_args().opt, is_train=False) opt = option.dict_to_nonedict(opt) utils.util.loaded_options = opt util.mkdirs( (path for key, path in opt['path'].items() if not key == 'experiments_root' and 'pretrain_model' not in key and 'resume' not in key)) util.setup_logger('base', opt['path']['log'], 'test_' + opt['name'], level=logging.INFO, screen=True, tofile=True) logger = logging.getLogger('base') logger.info(option.dict2str(opt)) model = ExtensibleTrainer(opt) gen = model.networks['generator'] gen.eval() mode = "feed_through" # temperature | restore | latent_transfer | feed_through #imgs_to_resample_pattern = "F:\\4k6k\\datasets\\ns_images\\adrianna\\val2\\lr\\*" imgs_to_resample_pattern = "F:\\4k6k\\datasets\\ns_images\\adrianna\\analyze\\analyze_xx\\*" #imgs_to_resample_pattern = "F:\\4k6k\\datasets\\ns_images\\imagesets\\images-half\\*lanette*" scale = 2 resample_factor = 2 # When != 1, the HR image is upsampled by this factor using a bicubic to get the local latents. E.g. set this to '2' to get 2x upsampling. temperature = 1 output_path = "..\\..\\results\\latent_playground" # Data types <- used to perform latent transfer. data_path = "F:\\4k6k\\datasets\\ns_images\\imagesets\\images-half" #data_type_filters = ["*alexa*", "*lanette*", "*80755*", "*joli_high*"] data_type_filters = ["*lanette*"] max_size = 1600 # Should be set to 2x the largest single dimension of the input space, otherwise an error will occur. max_ref_datatypes = 30 # Only picks this many images from the above data types to sample from. interpolation_steps = 30 with torch.no_grad(): # Compute latent variables for the reference images. if mode == "latent_transfer": # Just get the **one** result for each pattern and use that latent. dt_imgs = [glob(os.path.join(data_path, p))[-5] for p in data_type_filters] dt_transfers = [image_2_tensor(i, max_size) for i in dt_imgs] # Downsample the images because they are often just too big to feed through the network (probably needs to be parameterized) #for j in range(len(dt_transfers)): # if min(dt_transfers[j].shape[2], dt_transfers[j].shape[3]) > 1600: # dt_transfers[j] = F.interpolate(dt_transfers[j], scale_factor=1 / 2, mode='area') corruptor = ImageCorruptor({'fixed_corruptions': ['jpeg-medium', 'gaussian_blur_3']}) def corrupt_and_downsample(img, scale): img = F.interpolate(img, scale_factor=1 / scale, mode="area") from data.util import torch2cv, cv2torch cvimg = torch2cv(img) cvimg = corruptor.corrupt_images([cvimg])[0] img = cv2torch(cvimg) return img dt_latents = [fetch_latents_for_image(gen, i, scale, corrupt_and_downsample) for i in dt_transfers] # Fetch the images to resample. img_files = glob(imgs_to_resample_pattern) #random.shuffle(img_files) for im_it, img_file in enumerate(tqdm(img_files)): t = image_2_tensor(img_file).to(model.env['device']) if resample_factor > 1: t = F.interpolate(t, scale_factor=resample_factor, mode="bicubic") elif resample_factor < 1: t = F.interpolate(t, scale_factor=resample_factor, mode="area") # Ensure the input image is a factor of 16. _, _, h, w = t.shape h = 16 * (h // 16) w = 16 * (w // 16) t = t[:, :, :h, :w] resample_img = t # Fetch the latent metrics & latents for each image we are resampling. latents = fetch_latents_for_images(gen, [resample_img], scale)[0] multiple_latents = False if mode == "restore": latents = local_norm(spatial_norm(latents)) #latents = spatial_norm(latents) latents = [l * temperature for l in latents] elif mode == "feed_through": latents = [torch.randn_like(l) * temperature for l in latents] elif mode == "latent_transfer": dts = [] for slat in dt_latents: assert slat[0].shape[2] >= latents[0].shape[2] assert slat[0].shape[3] >= latents[0].shape[3] dts.append([extract_center_latent(sl, l) * temperature for l, sl in zip(latents, slat)]) latents = dts multiple_latents = True elif mode == "temperature": latents = [l * temperature for l in latents] # Re-compute each image with the new metrics if not multiple_latents: lats = [latents] else: lats = latents for j in range(len(lats)): path = os.path.join(output_path, "%i_%i" % (im_it, j)) os.makedirs(path, exist_ok=True) torchvision.utils.save_image(resample_img, os.path.join(path, "orig_%i.jpg" % (im_it))) create_interpolation_video(gen, F.interpolate(resample_img, scale_factor=1/scale, mode="area"), path, [torch.zeros_like(l) for l in lats[j]], lats[j], prefix=mode)