217 lines
8.1 KiB
Python
217 lines
8.1 KiB
Python
import argparse
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import logging
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import math
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import os
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import random
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from glob import glob
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import torch
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import torch.nn.functional as F
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import torchvision
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from PIL import Image
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from tqdm import tqdm
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import utils.options as option
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import utils
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from data import create_dataset, create_dataloader
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from data.image_corruptor import ImageCorruptor
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from models.ExtensibleTrainer import ExtensibleTrainer
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from utils import util
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def image_2_tensor(impath, desired_size):
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img = Image.open(impath)
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if desired_size is not None:
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factor = max(desired_size[0] / img.width, desired_size[1] / img.height)
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new_size = (int(math.ceil(img.width * factor)), int(math.ceil(img.height * factor)))
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img = img.resize(new_size, Image.BICUBIC)
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h_gap = img.height - desired_size[1]
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w_gap = img.width - desired_size[0]
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assert h_gap >= 0 and w_gap >= 0
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ht = h_gap // 2
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hb = desired_size[1] + ht
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wl = w_gap // 2
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wr = desired_size[1] + wl
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timg = torchvision.transforms.ToTensor()(img).unsqueeze(0)
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if desired_size is not None:
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timg = timg[:, :3, ht:hb, wl:wr]
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assert timg.shape[2] == desired_size[1] and timg.shape[3] == desired_size[0]
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else:
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# Enforce that the input must have a input dimension that is a factor of 16.
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b, c, h, w = timg.shape
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h = (h // 16) * 16
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w = (w // 16) * 16
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timg = timg[:, :3, :h, :w]
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return timg
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def interpolate_lr(hr, scale):
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return F.interpolate(hr, scale_factor=1 / scale, mode="area")
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def fetch_latents_for_image(gen, img, scale, lr_infer=interpolate_lr):
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z, _, _ = gen(gt=img,
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lr=lr_infer(img, scale),
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epses=[],
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reverse=False,
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add_gt_noise=False)
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return z
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def fetch_latents_for_images(gen, imgs, scale, lr_infer=interpolate_lr):
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latents = []
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for img in tqdm(imgs):
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z, _, _ = gen(gt=img,
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lr=lr_infer(img, scale),
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epses=[],
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reverse=False,
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add_gt_noise=False)
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latents.append(z)
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return latents
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def fetch_spatial_metrics_for_latents(latents):
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dt_scales = []
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dt_biases = []
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for i in range(len(latents)):
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latent = torch.stack(latents[i], dim=-1).squeeze(0)
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s = latent.std(dim=[1, 2, 3]).view(1,-1,1,1)
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b = latent.mean(dim=[1, 2, 3]).view(1,-1,1,1)
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dt_scales.append(s)
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dt_biases.append(b)
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return dt_scales, dt_biases
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def spatial_norm(latents):
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nlatents = []
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for i in range(len(latents)):
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latent = latents[i]
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b, c, h, w = latent.shape
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s = latent.std(dim=[2, 3]).view(1,c,1,1)
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b = latent.mean(dim=[2, 3]).view(1,c,1,1)
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nlatents.append((latents[i] - b) / s)
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return nlatents
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def local_norm(latents):
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nlatents = []
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for i in range(len(latents)):
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latent = latents[i]
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b, c, h, w = latent.shape
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s = latent.std(dim=[1]).view(1,1,h,w)
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b = latent.mean(dim=[1]).view(1,1,h,w)
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nlatents.append((latents[i] - b) / s)
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return nlatents
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if __name__ == "__main__":
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#### options
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torch.backends.cudnn.benchmark = True
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srg_analyze = False
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parser = argparse.ArgumentParser()
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parser.add_argument('-opt', type=str, help='Path to options YAML file.', default='../../experiments/train_exd_imgset_srflow/train_exd_imgset_srflow.yml')
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opt = option.parse(parser.parse_args().opt, is_train=False)
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opt = option.dict_to_nonedict(opt)
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utils.util.loaded_options = opt
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util.mkdirs(
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(path for key, path in opt['path'].items()
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if not key == 'experiments_root' and 'pretrain_model' not in key and 'resume' not in key))
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util.setup_logger('base', opt['path']['log'], 'test_' + opt['name'], level=logging.INFO,
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screen=True, tofile=True)
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logger = logging.getLogger('base')
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logger.info(option.dict2str(opt))
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model = ExtensibleTrainer(opt)
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gen = model.networks['generator']
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gen.eval()
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mode = "latent_transfer"
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imgs_to_resample_pattern = "F:\\4k6k\\datasets\\ns_images\\adrianna\\val2\\lr\\*"
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desired_size = None # (640,640) # <- Required when doing style transfer.
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scale = 2
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resample_factor = 2 # When != 1, the HR image is upsampled by this factor using a bicubic to get the local latents.
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temperature = .65
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output_path = "E:\\4k6k\\mmsr\\results\\latent_playground"
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# Data types <- used to perform latent transfer.
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data_path = "F:\\4k6k\\datasets\\ns_images\\imagesets\\images-half"
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data_type_filters = ["*alexa*", "*lanette*", "*80755*", "*x-art-1912*", "*joli_high*", "*stacy-cruz*"]
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#data_type_filters = ["*lanette*"]
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max_ref_datatypes = 30 # Only picks this many images from the above data types to sample from.
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interpolation_steps = 30
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with torch.no_grad():
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# Fetch the images to resample.
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resample_imgs = []
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img_files = glob(imgs_to_resample_pattern)
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for i, img_file in enumerate(img_files):
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if i > 5:
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break
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t = image_2_tensor(img_file, desired_size).to(model.env['device'])
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if resample_factor != 1:
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t = F.interpolate(t, scale_factor=resample_factor, mode="bicubic")
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resample_imgs.append(t)
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# Fetch the latent metrics & latents for each image we are resampling.
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latents = fetch_latents_for_images(gen, resample_imgs, scale)
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multiple_latents = False
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if mode == "restore":
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for i, latent_set in enumerate(latents):
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latents[i] = local_norm(spatial_norm(latent_set))
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latents[i] = [l * temperature for l in latents[i]]
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elif mode == "feed_through":
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latents = [torch.randn_like(l) * temperature for l in latents[i]]
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elif mode == "latent_transfer":
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# Just get the **one** result for each pattern and use that latent.
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dt_imgs = [glob(os.path.join(data_path, p))[-5] for p in data_type_filters]
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dt_transfers = [image_2_tensor(i, desired_size) for i in dt_imgs]
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# Downsample the images because they are often just too big to feed through the network (probably needs to be parameterized)
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for j in range(len(dt_transfers)):
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if min(dt_transfers[j].shape[2], dt_transfers[j].shape[3]) > 1600:
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dt_transfers[j] = F.interpolate(dt_transfers[j], scale_factor=1/2, mode='area')
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corruptor = ImageCorruptor({'fixed_corruptions':['jpeg-low', 'gaussian_blur_5']})
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def corrupt_and_downsample(img, scale):
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img = F.interpolate(img, scale_factor=1/scale, mode="area")
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from data.util import torch2cv, cv2torch
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cvimg = torch2cv(img)
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cvimg = corruptor.corrupt_images([cvimg])[0]
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img = cv2torch(cvimg)
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torchvision.utils.save_image(img, "corrupted_lq_%i.png" % (random.randint(0,100),))
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return img
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dt_latents = [fetch_latents_for_image(gen, i, scale, corrupt_and_downsample) for i in dt_transfers]
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tlatents = []
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for lat in latents:
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dts = []
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for slat in dt_latents:
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assert slat[0].shape[2] >= lat[0].shape[2]
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assert slat[0].shape[3] >= lat[0].shape[3]
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dts.append([sl[:,:,:l.shape[2],:l.shape[3]] * temperature for l, sl in zip(lat, slat)])
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tlatents.append(dts)
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latents = tlatents
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multiple_latents = True
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# Re-compute each image with the new metrics
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for i, img in enumerate(resample_imgs):
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if not multiple_latents:
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lats = [latents[i]]
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else:
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lats = latents[i]
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for j in range(len(lats)):
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hr, _ = gen(lr=F.interpolate(img, scale_factor=1/scale, mode="area"),
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z=lats[j][0],
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reverse=True,
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epses=lats[j],
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add_gt_noise=False)
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os.makedirs(os.path.join(output_path), exist_ok=True)
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torchvision.utils.save_image(hr, os.path.join(output_path, "%i_%i.png" % (i,j)))
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