import os
import shutil
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
from PIL import Image
from torch.utils.data import DataLoader
from torchvision.transforms import ToTensor, Resize
from tqdm import tqdm
import numpy as np
import utils
from data.image_folder_dataset import ImageFolderDataset
from models.spinenet_arch import SpineNet
# 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 import util
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):
dataset_opt = dict_to_nonedict({
'name': 'amalgam',
'paths': ['F:\\4k6k\\datasets\\ns_images\\imagesets\\imageset_256_full'],
'weights': [1],
'target_size': 256,
'force_multiple': 32,
'scale': 1
})
dataset = ImageFolderDataset(dataset_opt)
return DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, shuffle=True)
def create_latent_database(model, model_index=0, batch_size=8):
num_workers = 4
output_path = '../results/byol_latents/'
os.makedirs(output_path, exist_ok=True)
dataloader = get_image_folder_dataloader(batch_size, num_workers)
id = 0
dict_count = 1
latent_dict = {}
all_paths = []
for batch in tqdm(dataloader):
hq = batch['hq'].to('cuda')
latent = model(hq)
if isinstance(latent, tuple):
latent = latent[model_index]
for b in range(latent.shape[0]):
im_path = batch['HQ_path'][b]
all_paths.append(im_path)
latent_dict[id] = latent[b].detach().cpu()
if (id+1) % 1000 == 0:
print("Saving checkpoint..")
torch.save(latent_dict, os.path.join(output_path, "latent_dict_%i.pth" % (dict_count,)))
latent_dict = {}
torch.save(all_paths, os.path.join(output_path, "all_paths.pth"))
dict_count += 1
id += 1
def _get_mins_from_comparables(latent, comparables, batch_size, compare_fn):
_, c, h, w = latent.shape
clat = latent.reshape(1,-1,h*w).permute(2,0,1)
cpbl_chunked = torch.chunk(comparables, len(comparables) // batch_size)
assert len(comparables) % batch_size == 0 # The reconstruction logic doesn't work if this is not the case.
mins = []
min_offsets = []
for cpbl_chunk in tqdm(cpbl_chunked):
cpbl_chunk = cpbl_chunk.to('cuda')
dist = compare_fn(clat, cpbl_chunk.unsqueeze(0))
_min = torch.min(dist, dim=-1)
mins.append(_min[0])
min_offsets.append(_min[1])
mins = torch.min(torch.stack(mins, dim=-1), dim=-1)
# There's some way to do this in torch, I just can't figure it out..
for i in range(len(mins[1])):
mins[1][i] = mins[1][i] * batch_size + min_offsets[mins[1][i]][i]
return mins[0].cpu(), mins[1].cpu(), len(comparables)
def _get_mins_from_latent_dictionary(latent, hq_img_repo, ld_file_name, batch_size, compare_fn):
_, c, h, w = latent.shape
lat_dict = torch.load(os.path.join(hq_img_repo, ld_file_name))
comparables = torch.stack(list(lat_dict.values()), dim=0).permute(0,2,3,1)
cbl_shape = comparables.shape[:3]
comparables = comparables.reshape(-1, c)
return _get_mins_from_comparables(latent, comparables, batch_size, compare_fn)
def find_similar_latents(model, model_index=0, lat_patch_size=16, compare_fn=structural_euc_dist):
img = 'F:\\4k6k\\datasets\\ns_images\\adrianna\\analyze\\analyze_xx\\adrianna_xx.jpg'
#img = 'F:\\4k6k\\datasets\\ns_images\\adrianna\\analyze\\analyze_xx\\nicky_xx.jpg'
hq_img_repo = '../results/byol_latents'
output_path = '../results/byol_similars'
batch_size = 4096
num_maps = 1
lat_patch_mult = 512 // lat_patch_size
os.makedirs(output_path, exist_ok=True)
img_bank_paths = torch.load(os.path.join(hq_img_repo, "all_paths.pth"))
img_t = ToTensor()(Image.open(img)).to('cuda').unsqueeze(0)
_, _, h, w = img_t.shape
img_t = img_t[:, :, :128*(h//128), :128*(w//128)]
latent = model(img_t)
if not isinstance(latent, tuple):
latent = (latent,)
latent = latent[model_index]
_, c, h, w = latent.shape
mins, min_offsets = [], []
total_latents = -1
for d_id in range(1,num_maps+1):
mn, of, tl = _get_mins_from_latent_dictionary(latent, hq_img_repo, "latent_dict_%i.pth" % (d_id), batch_size, compare_fn)
if total_latents != -1:
assert total_latents == tl
else:
total_latents = tl
mins.append(mn)
min_offsets.append(of)
mins = torch.min(torch.stack(mins, dim=-1), dim=-1)
# There's some way to do this in torch, I just can't figure it out..
for i in range(len(mins[1])):
mins[1][i] = mins[1][i] * total_latents + min_offsets[mins[1][i]][i]
min_ids = mins[1]
print("Constructing image map..")
doc_out = '''
<html><body><img id="imgmap" src="output.png" usemap="#map">
<map name="map">%s</map><br>
<button onclick="if(imgmap.src.includes('output.png')){imgmap.src='source.png';}else{imgmap.src='output.png';}">Swap Images</button>
</body></html>
'''
img_map_areas = []
img_out = torch.zeros((1, 3, h * lat_patch_size, w * lat_patch_size))
for i, ind in enumerate(tqdm(min_ids)):
u = np.unravel_index(ind.item(), (num_maps * total_latents // (lat_patch_mult ** 2), lat_patch_mult, lat_patch_mult))
h_, w_ = np.unravel_index(i, (h, w))
img = ToTensor()(Resize((512, 512))(Image.open(img_bank_paths[u[0]])))
t = lat_patch_size * u[1]
l = lat_patch_size * u[2]
patch = img[:, t:t + lat_patch_size, l:l + lat_patch_size]
io_loc_t = h_ * lat_patch_size
io_loc_l = w_ * lat_patch_size
img_out[:,:,io_loc_t:io_loc_t+lat_patch_size,io_loc_l:io_loc_l+lat_patch_size] = patch
# Also save the image with a masked map
mask = torch.full_like(img, fill_value=.3)
mask[:, t:t + lat_patch_size, l:l + lat_patch_size] = 1
masked_img = img * mask
masked_src_img_output_file = os.path.join(output_path, "%i_%i__%i.png" % (io_loc_t, io_loc_l, u[0]))
torchvision.utils.save_image(masked_img, masked_src_img_output_file)
# Update the image map areas.
img_map_areas.append('<area shape="rect" coords="%i,%i,%i,%i" href="%s">' % (io_loc_l, io_loc_t,
io_loc_l + lat_patch_size, io_loc_t + lat_patch_size,
masked_src_img_output_file))
torchvision.utils.save_image(img_out, os.path.join(output_path, "output.png"))
torchvision.utils.save_image(img_t, os.path.join(output_path, "source.png"))
doc_out = doc_out % ('\n'.join(img_map_areas))
with open(os.path.join(output_path, 'map.html'), 'w') as f:
print(doc_out, file=f)
def explore_latent_results(model):
batch_size = 16
num_workers = 4
output_path = '../../results/byol_spinenet_explore_latents/'
os.makedirs(output_path, exist_ok=True)
dataloader = get_image_folder_dataloader(batch_size, num_workers)
id = 0
for batch in tqdm(dataloader):
hq = batch['hq'].to('cuda')
latent = model(hq)[1] # BYOL trainer only trains the '4' output, which is indexed at [1]. Confusing.
# This operation works by computing the distance of every structural index from the center and using that
# as a "heatmap".
b, c, h, w = latent.shape
center = latent[:, :, h//2, w//2].unsqueeze(-1).unsqueeze(-1)
centers = center.repeat(1, 1, h, w)
dist = cosine_similarity(latent, centers).unsqueeze(1)
dist = im_norm(dist)
torchvision.utils.save_image(dist, os.path.join(output_path, "%i.png" % id))
id += 1
class BYOLModelWrapper(nn.Module):
def __init__(self, wrap):
super().__init__()
self.wrap = wrap
def forward(self, img):
return self.wrap.get_projection(img)
if __name__ == '__main__':
pretrained_path = '../../../experiments/spinenet49_imgset_sbyol.pth'
model = SpineNet('49', in_channels=3, use_input_norm=True).to('cuda')
model.load_state_dict(torch.load(pretrained_path), strict=True)
model.eval()
#util.loaded_options = {'checkpointing_enabled': True}
#pretrained_path = '../../experiments/train_sbyol_512unsupervised_restart/models/48000_generator.pth'
#from models.byol.byol_structural import StructuralBYOL
#subnet = SpineNet('49', in_channels=3, use_input_norm=True).to('cuda')
#model = StructuralBYOL(subnet, image_size=256, hidden_layer='endpoint_convs.4.conv')
#model.load_state_dict(torch.load(pretrained_path), strict=True)
#model = BYOLModelWrapper(model)
#model.eval()
with torch.no_grad():
#create_latent_database(model, 1) # 0 = model output dimension to use for latent storage
find_similar_latents(model, 1, 16, structural_euc_dist) # 1 = model output dimension to use for latent predictor.