eschmidbauer/DL-Art-School
1
0
Fork 0
forked from mrq/DL-Art-School
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Misc changes

Browse Source
This commit is contained in:
James Betker 2021-03-13 10:45:26 -07:00
parent 9fc3df3f5b
commit 94e069bced
2 changed files with 10 additions and 9 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

15
codes/scripts/byol/byol_resnet_playground.py
View File

@ -55,9 +55,9 @@ def im_norm(x):
def get_image_folder_dataloader(batch_size, num_workers, target_size=224, shuffle=True): def get_image_folder_dataloader(batch_size, num_workers, target_size=224, shuffle=True):
dataset_opt = dict_to_nonedict({ dataset_opt = dict_to_nonedict({
'name': 'amalgam', 'name': 'amalgam',
'paths': ['F:\\4k6k\\datasets\\ns_images\\imagesets\\pn_coven\\cropped2'], #'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_1024_square_with_new'],
#'paths': ['F:\\4k6k\\datasets\\ns_images\\imagesets\\imageset_256_full'], 'paths': ['F:\\4k6k\\datasets\\ns_images\\imagesets\\imageset_256_tiled_filtered_flattened'],
#'paths': ['F:\\4k6k\\datasets\\ns_images\\imagesets\\1024_test'], #'paths': ['F:\\4k6k\\datasets\\ns_images\\imagesets\\1024_test'],
'weights': [1], 'weights': [1],
'target_size': target_size, 'target_size': target_size,
@ -100,6 +100,7 @@ def get_latent_for_img(model, img):
img_t = img_t[:, :, :, dw:-dw] img_t = img_t[:, :, :, dw:-dw]
elif dh != 0: elif dh != 0:
img_t = img_t[:, :, dh:-dh, :] img_t = img_t[:, :, dh:-dh, :]
img_t = img_t[:,:3,:,:]
img_t = torch.nn.functional.interpolate(img_t, size=(224, 224), mode="area") img_t = torch.nn.functional.interpolate(img_t, size=(224, 224), mode="area")
model(img_t) model(img_t)
latent = layer_hooked_value latent = layer_hooked_value
@ -129,14 +130,14 @@ def produce_latent_dict(model):
def find_similar_latents(model, compare_fn=structural_euc_dist): def find_similar_latents(model, compare_fn=structural_euc_dist):
global layer_hooked_value global layer_hooked_value
img = 'F:\\4k6k\\datasets\\ns_images\\imagesets\\1024_test\\80692045.jpg.jpg' img = 'D:\\dlas\\results\\bobz.png'
#img = 'F:\\4k6k\\datasets\\ns_images\\adrianna\\analyze\\analyze_xx\\nicky_xx.jpg' #img = 'F:\\4k6k\\datasets\\ns_images\\adrianna\\analyze\\analyze_xx\\nicky_xx.jpg'
output_path = '../../../results/byol_resnet_similars' output_path = '../../../results/byol_resnet_similars'
os.makedirs(output_path, exist_ok=True) os.makedirs(output_path, exist_ok=True)
imglatent = get_latent_for_img(model, img).squeeze().unsqueeze(0) imglatent = get_latent_for_img(model, img).squeeze().unsqueeze(0)
_, c = imglatent.shape _, c = imglatent.shape
batch_size = 128 batch_size = 512
num_workers = 8 num_workers = 8
dataloader = get_image_folder_dataloader(batch_size, num_workers) dataloader = get_image_folder_dataloader(batch_size, num_workers)
id = 0 id = 0
@ -152,7 +153,7 @@ def find_similar_latents(model, compare_fn=structural_euc_dist):
result_paths.extend(batch['HQ_path']) result_paths.extend(batch['HQ_path'])
id += batch_size id += batch_size
if id > 10000: if id > 10000:
k = 500 k = 200
results = torch.cat(results, dim=0) results = torch.cat(results, dim=0)
vals, inds = torch.topk(results, k, largest=False) vals, inds = torch.topk(results, k, largest=False)
for i in inds: for i in inds:
@ -201,7 +202,7 @@ if __name__ == '__main__':
register_hook(model, 'avgpool') register_hook(model, 'avgpool')
with torch.no_grad(): with torch.no_grad():
#find_similar_latents(model, structural_euc_dist) find_similar_latents(model, structural_euc_dist)
#produce_latent_dict(model) #produce_latent_dict(model)
#build_kmeans() #build_kmeans()
use_kmeans() #use_kmeans()

4
codes/scripts/extract_square_images.py
View File

@ -19,8 +19,8 @@ def main():
# compression time. If read raw images during training, use 0 for faster IO speed. # compression time. If read raw images during training, use 0 for faster IO speed.
opt['dest'] = 'file' opt['dest'] = 'file'
opt['input_folder'] = ['F:\\4k6k\\datasets\\images\\lsun\\lsun\\cats'] opt['input_folder'] = ['E:\\4k6k\\datasets\\images\\faces\\CelebAMask-HQ\\CelebA-HQ-img']
opt['save_folder'] = 'F:\\4k6k\\datasets\\images\\lsun\\lsun\\cats\\cropped' opt['save_folder'] = 'E:\\4k6k\\datasets\\images\\faces\\CelebAMask-HQ\\256px'
opt['imgsize'] = 256 opt['imgsize'] = 256
opt['bottom_crop'] = 0 opt['bottom_crop'] = 0
opt['keep_folder'] = False opt['keep_folder'] = False