205 lines
7.0 KiB
Python
205 lines
7.0 KiB
Python
import os
|
|
import shutil
|
|
|
|
import torch
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
from PIL import Image
|
|
from torch.utils.data import DataLoader
|
|
from torchvision.transforms import ToTensor
|
|
from tqdm import tqdm
|
|
|
|
from data.images.image_folder_dataset import ImageFolderDataset
|
|
from models.classifiers.resnet_with_checkpointing import resnet50
|
|
|
|
# Computes the structural euclidean distance between [x,y]. "Structural" here means the [h,w] dimensions are preserved
|
|
# and the distance is computed across the channel dimension.
|
|
from utils.kmeans import kmeans, kmeans_predict
|
|
from utils.options import dict_to_nonedict
|
|
|
|
|
|
def structural_euc_dist(x, y):
|
|
diff = torch.square(x - y)
|
|
sum = torch.sum(diff, dim=-1)
|
|
return torch.sqrt(sum)
|
|
|
|
|
|
def cosine_similarity(x, y):
|
|
x = norm(x)
|
|
y = norm(y)
|
|
return -nn.CosineSimilarity()(x, y) # probably better to just use this class to perform the calc. Just left this here to remind myself.
|
|
|
|
|
|
def key_value_difference(x, y):
|
|
x = F.normalize(x, dim=-1, p=2)
|
|
y = F.normalize(y, dim=-1, p=2)
|
|
return 2 - 2 * (x * y).sum(dim=-1)
|
|
|
|
|
|
def norm(x):
|
|
sh = x.shape
|
|
sh_r = tuple([sh[i] if i != len(sh)-1 else 1 for i in range(len(sh))])
|
|
return (x - torch.mean(x, dim=-1).reshape(sh_r)) / torch.std(x, dim=-1).reshape(sh_r)
|
|
|
|
|
|
def im_norm(x):
|
|
return (((x - torch.mean(x, dim=(2,3)).reshape(-1,1,1,1)) / torch.std(x, dim=(2,3)).reshape(-1,1,1,1)) * .5) + .5
|
|
|
|
|
|
def get_image_folder_dataloader(batch_size, num_workers, target_size=224, shuffle=True):
|
|
dataset_opt = dict_to_nonedict({
|
|
'name': 'amalgam',
|
|
#'paths': ['F:\\4k6k\\datasets\\ns_images\\imagesets\\pn_coven\\cropped2'],
|
|
#'paths': ['F:\\4k6k\\datasets\\ns_images\\imagesets\\imageset_1024_square_with_new'],
|
|
#'paths': ['F:\\4k6k\\datasets\\ns_images\\imagesets\\imageset_256_tiled_filtered_flattened'],
|
|
#'paths': ['F:\\4k6k\\datasets\\ns_images\\imagesets\\1024_test'],
|
|
'paths': ['E:\\4k6k\\datasets\\ns_images\\imagesets\\imageset_256_full'],
|
|
'weights': [1],
|
|
'target_size': target_size,
|
|
'force_multiple': 32,
|
|
'scale': 1
|
|
})
|
|
dataset = ImageFolderDataset(dataset_opt)
|
|
return DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, shuffle=shuffle)
|
|
|
|
|
|
def _find_layer(net, layer_name):
|
|
if type(layer_name) == str:
|
|
modules = dict([*net.named_modules()])
|
|
return modules.get(layer_name, None)
|
|
elif type(layer_name) == int:
|
|
children = [*net.children()]
|
|
return children[layer_name]
|
|
return None
|
|
|
|
|
|
layer_hooked_value = None
|
|
def _hook(_, __, output):
|
|
global layer_hooked_value
|
|
layer_hooked_value = output
|
|
|
|
|
|
def register_hook(net, layer_name):
|
|
layer = _find_layer(net, layer_name)
|
|
assert layer is not None, f'hidden layer ({self.layer}) not found'
|
|
layer.register_forward_hook(_hook)
|
|
|
|
|
|
def get_latent_for_img(model, img):
|
|
img_t = ToTensor()(Image.open(img)).to('cuda').unsqueeze(0)
|
|
_, _, h, w = img_t.shape
|
|
# Center crop img_t and resize to 224.
|
|
d = min(h, w)
|
|
dh, dw = (h-d)//2, (w-d)//2
|
|
if dw != 0:
|
|
img_t = img_t[:, :, :, dw:-dw]
|
|
elif dh != 0:
|
|
img_t = img_t[:, :, dh:-dh, :]
|
|
img_t = img_t[:,:3,:,:]
|
|
img_t = torch.nn.functional.interpolate(img_t, size=(224, 224), mode="area")
|
|
model(img_t)
|
|
latent = layer_hooked_value
|
|
return latent
|
|
|
|
|
|
def produce_latent_dict(model):
|
|
batch_size = 32
|
|
num_workers = 4
|
|
dataloader = get_image_folder_dataloader(batch_size, num_workers)
|
|
id = 0
|
|
paths = []
|
|
latents = []
|
|
for batch in tqdm(dataloader):
|
|
hq = batch['hq'].to('cuda')
|
|
hq = F.interpolate(F.interpolate(hq, size=(16,16), mode='bilinear'), size=(224,244))
|
|
model(hq)
|
|
l = layer_hooked_value.cpu().split(1, dim=0)
|
|
latents.extend(l)
|
|
paths.extend(batch['HQ_path'])
|
|
id += batch_size
|
|
if id > 10000:
|
|
print("Saving checkpoint..")
|
|
torch.save((latents, paths), '../results_instance_resnet.pth')
|
|
id = 0
|
|
|
|
|
|
def find_similar_latents(model, compare_fn=structural_euc_dist):
|
|
global layer_hooked_value
|
|
|
|
img = 'D:\\dlas\\results\\bobz.png'
|
|
#img = 'F:\\4k6k\\datasets\\ns_images\\adrianna\\analyze\\analyze_xx\\nicky_xx.jpg'
|
|
output_path = '../../../results/byol_resnet_similars'
|
|
os.makedirs(output_path, exist_ok=True)
|
|
imglatent = get_latent_for_img(model, img).squeeze().unsqueeze(0)
|
|
_, c = imglatent.shape
|
|
|
|
batch_size = 512
|
|
num_workers = 8
|
|
dataloader = get_image_folder_dataloader(batch_size, num_workers)
|
|
id = 0
|
|
output_batch = 1
|
|
results = []
|
|
result_paths = []
|
|
for batch in tqdm(dataloader):
|
|
hq = batch['hq'].to('cuda')
|
|
model(hq)
|
|
latent = layer_hooked_value.clone().squeeze()
|
|
compared = compare_fn(imglatent.repeat(latent.shape[0], 1), latent)
|
|
results.append(compared.cpu())
|
|
result_paths.extend(batch['HQ_path'])
|
|
id += batch_size
|
|
if id > 10000:
|
|
k = 200
|
|
results = torch.cat(results, dim=0)
|
|
vals, inds = torch.topk(results, k, largest=False)
|
|
for i in inds:
|
|
mag = int(results[i].item() * 1000)
|
|
shutil.copy(result_paths[i], os.path.join(output_path, f'{mag:05}_{output_batch}_{i}.jpg'))
|
|
results = []
|
|
result_paths = []
|
|
id = 0
|
|
|
|
|
|
def build_kmeans():
|
|
latents, _ = torch.load('../results_instance_resnet.pth')
|
|
latents = torch.cat(latents, dim=0).squeeze().to('cuda')
|
|
cluster_ids_x, cluster_centers = kmeans(latents, num_clusters=8, distance="euclidean", device=torch.device('cuda:0'))
|
|
torch.save((cluster_ids_x, cluster_centers), '../k_means_instance_resnet.pth')
|
|
|
|
|
|
def use_kmeans():
|
|
output = "../results/k_means_instance_resnet/"
|
|
_, centers = torch.load('../k_means_instance_resnet.pth')
|
|
batch_size = 32
|
|
num_workers = 1
|
|
dataloader = get_image_folder_dataloader(batch_size, num_workers, target_size=224, shuffle=False)
|
|
for i, batch in enumerate(tqdm(dataloader)):
|
|
hq = batch['hq'].to('cuda')
|
|
model(hq)
|
|
l = layer_hooked_value.clone().squeeze()
|
|
pred = kmeans_predict(l, centers, device=l.device)
|
|
for b in range(pred.shape[0]):
|
|
if pred[b] == 3:
|
|
outpath = os.path.dirname(batch['HQ_path'][b]).replace('\\pn_coven\\cropped', '\\pn_coven\\modeling')
|
|
os.makedirs(outpath, exist_ok=True)
|
|
shutil.move(batch['HQ_path'][b], outpath)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
pretrained_path = '../../../experiments/resnet_byol_diffframe_115k.pth'
|
|
model = resnet50(pretrained=False).to('cuda')
|
|
sd = torch.load(pretrained_path)
|
|
resnet_sd = {}
|
|
for k, v in sd.items():
|
|
if 'target_encoder.net.' in k:
|
|
resnet_sd[k.replace('target_encoder.net.', '')] = v
|
|
model.load_state_dict(sd, strict=True)
|
|
model.eval()
|
|
register_hook(model, 'avgpool')
|
|
|
|
with torch.no_grad():
|
|
#find_similar_latents(model, structural_euc_dist)
|
|
produce_latent_dict(model)
|
|
#build_kmeans()
|
|
#use_kmeans()
|