Structural latents checkpoint

codes/data/byol_attachment.py

@@ -4,7 +4,7 @@ from time import time
 import torch
 import torchvision
 from torch.utils.data import Dataset
-from kornia import augmentation as augs
+from kornia import augmentation as augs, kornia
 from kornia import filters
 import torch.nn as nn
 import torch.nn.functional as F
@@ -98,10 +98,11 @@ class RandomSharedRegionCrop(nn.Module):
         # 2. Pick a random width, height and top corner location for the first patch.
         # 3. Pick a random width, height and top corner location for the second patch.
         #    Note: All dims from (2) and (3) must contain at least half of the image, guaranteeing overlap.
-        # 6. Build patches from input images. Resize them appropriately. Apply translational jitter.
+        # 4. Build patches from input images. Resize them appropriately. Apply translational jitter.\
-        # 7. Compute the metrics needed to extract overlapping regions from the resized patches: top, left,
+        # 5. Randomly flip image 2 if needed.
+        # 5. Compute the metrics needed to extract overlapping regions from the resized patches: top, left,
         #    original_height, original_width.
-        # 8. Compute the "shared_view" from the above data.
+        # 6. Compute the "shared_view" from the above data.
 
         # Step 1
         c, d, _ = i1.shape
@@ -122,7 +123,7 @@ class RandomSharedRegionCrop(nn.Module):
         im2_t = random.randint(0, d-im2_h)
         im2_r, im2_b = im2_l+im2_w, im2_t+im2_h
 
-        # Step 6
+        # Step 4
         m = self.multiple
         jl, jt = random.randint(-self.jitter_range, self.jitter_range), random.randint(-self.jitter_range, self.jitter_range)
         jt = jt if base_t != 0 else abs(jt)  # If the top of a patch is zero, a negative jitter will cause it to go negative.
@@ -139,14 +140,22 @@ class RandomSharedRegionCrop(nn.Module):
         p2 = i2[:, im2_t*m+jt:(im2_t+im2_h)*m+jt, im2_l*m+jl:(im2_l+im2_w)*m+jl]
         p2_resized = no_batch_interpolate(p2, size=(d*m, d*m), mode="bilinear")
 
-        # Step 7
+        # Step 5
+        should_flip = random.random() < .5
+        if should_flip:
+            should_flip = 1
+            p2_resized = kornia.geometry.transform.hflip(p2_resized)
+        else:
+            should_flip = 0
+
+        # Step 6
         i1_shared_t, i1_shared_l = snap(base_t, im2_t), snap(base_l, im2_l)
         i2_shared_t, i2_shared_l = snap(im2_t, base_t), snap(im2_l, base_l)
         ix_h = min(base_b, im2_b) - max(base_t, im2_t)
         ix_w = min(base_r, im2_r) - max(base_l, im2_l)
-        recompute_package = torch.tensor([base_h, base_w, i1_shared_t, i1_shared_l, im2_h, im2_w, i2_shared_t, i2_shared_l, ix_h, ix_w], dtype=torch.long)
+        recompute_package = torch.tensor([base_h, base_w, i1_shared_t, i1_shared_l, im2_h, im2_w, i2_shared_t, i2_shared_l, should_flip, ix_h, ix_w], dtype=torch.long)
 
-        # Step 8
+        # Step 7
         mask1 = torch.full((1, base_h*m, base_w*m), fill_value=.5)
         mask1[:, i1_shared_t*m:(i1_shared_t+ix_h)*m, i1_shared_l*m:(i1_shared_l+ix_w)*m] = 1
         masked1 = pad_to(p1 * mask1, d*m)
@@ -171,10 +180,14 @@ def reconstructed_shared_regions(fea1, fea2, recompute_package: torch.Tensor):
     # It'd be real nice if we could do this at the batch level, but I don't see a really good way to do that outside
     # of conforming the recompute_package across the entire batch.
     for b in range(package.shape[0]):
-        f1_h, f1_w, f1s_t, f1s_l, f2_h, f2_w, f2s_t, f2s_l, s_h, s_w = tuple(package[b].tolist())
+        f1_h, f1_w, f1s_t, f1s_l, f2_h, f2_w, f2s_t, f2s_l, should_flip, s_h, s_w = tuple(package[b].tolist())
+        # Unflip 2 if needed.
+        f2 = fea2[b]
+        if should_flip == 1:
+            f2 = kornia.geometry.transform.hflip(f2)
         # Resize the input features to match
         f1s = F.interpolate(fea1[b].unsqueeze(0), (f1_h, f1_w), mode="bilinear")
-        f2s = F.interpolate(fea2[b].unsqueeze(0), (f2_h, f2_w), mode="bilinear")
+        f2s = F.interpolate(f2.unsqueeze(0), (f2_h, f2_w), mode="bilinear")
         # Outputs must be padded so they can "get along" with each other.
         res1.append(pad_to(f1s[:, :, f1s_t:f1s_t+s_h, f1s_l:f1s_l+s_w], pad_dim))
         res2.append(pad_to(f2s[:, :, f2s_t:f2s_t+s_h, f2s_l:f2s_l+s_w], pad_dim))

codes/models/byol/byol_structural.py

@@ -176,3 +176,8 @@ class StructuralBYOL(nn.Module):
 
         loss = loss_one + loss_two
         return loss.mean()
+
+    def get_projection(self, image):
+        enc = self.online_encoder(image)
+        proj = self.online_predictor(enc)
+        return enc, proj

codes/scripts/byol_extract_wrapped_model.py

@@ -3,8 +3,8 @@ import torch
 from models.archs.spinenet_arch import SpineNet
 
 if __name__ == '__main__':
-    pretrained_path = '../../experiments/train_byol_512unsupervised/models/117000_generator.pth'
+    pretrained_path = '../../experiments/train_sbyol_512unsupervised/models/35000_generator.pth'
-    output_path = '../../experiments/spinenet49_imgset_byol.pth'
+    output_path = '../../experiments/spinenet49_imgset_sbyol.pth'
 
     wrap_key = 'online_encoder.net.'
     sd = torch.load(pretrained_path)

codes/scripts/byol_spinenet_playground.py

@@ -3,6 +3,7 @@ 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
@@ -10,12 +11,16 @@ 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.archs.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
+
+
 def structural_euc_dist(x, y):
     diff = torch.square(x - y)
     sum = torch.sum(diff, dim=-1)
@@ -28,6 +33,12 @@ def cosine_similarity(x, 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))])
@@ -41,8 +52,8 @@ def im_norm(x):
 def get_image_folder_dataloader(batch_size, num_workers):
     dataset_opt = {
         'name': 'amalgam',
-        #'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\\1024_test'],
+        #'paths': ['F:\\4k6k\\datasets\\ns_images\\imagesets\\1024_test'],
         'weights': [1],
         'target_size': 512,
         'force_multiple': 32,
@@ -52,7 +63,7 @@ def get_image_folder_dataloader(batch_size, num_workers):
     return DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, shuffle=True)
 
 
-def create_latent_database(model):
+def create_latent_database(model, model_index=0):
     batch_size = 8
     num_workers = 1
     output_path = '../../results/byol_spinenet_latents/'
@@ -65,7 +76,7 @@ def create_latent_database(model):
     all_paths = []
     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.
+        latent = model(hq)[model_index]   # BYOL trainer only trains the '4' output, which is indexed at [1]. Confusing.
         for b in range(latent.shape[0]):
             im_path = batch['HQ_path'][b]
             all_paths.append(im_path)
@@ -79,14 +90,8 @@ def create_latent_database(model):
             id += 1
 
 
-def _get_mins_from_latent_dictionary(latent, hq_img_repo, ld_file_name, batch_size):
+def _get_mins_from_comparables(latent, comparables, 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]
-    assert cbl_shape[1] == 32
-    comparables = comparables.reshape(-1, c)
-
     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.
@@ -94,11 +99,12 @@ def _get_mins_from_latent_dictionary(latent, hq_img_repo, ld_file_name, batch_si
     min_offsets = []
     for cpbl_chunk in tqdm(cpbl_chunked):
         cpbl_chunk = cpbl_chunk.to('cuda')
-        dist = structural_euc_dist(clat, cpbl_chunk.unsqueeze(0))
+        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]
@@ -106,26 +112,36 @@ def _get_mins_from_latent_dictionary(latent, hq_img_repo, ld_file_name, batch_si
     return mins[0].cpu(), mins[1].cpu(), len(comparables)
 
 
-def find_similar_latents(model):
+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_spinenet_latents'
     output_path = '../../results/byol_spinenet_similars'
-    batch_size = 1024
+    batch_size = 2048
-    num_maps = 8
+    num_maps = 4
+    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)[model_index]
-    latent = model(img_t)[1]
     _, 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)
+        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:
@@ -140,32 +156,37 @@ def find_similar_latents(model):
 
     print("Constructing image map..")
     doc_out = '''
-    <html><body><img id="imgmap" src="source.png" usemap="#map">
+    <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*16,w*16))
+    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//(32*32),32,32))
+        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 = 16 * u[1]
+        t = lat_patch_size * u[1]
-        l = 16 * u[2]
+        l = lat_patch_size * u[2]
-        patch = img[:, t:t+16, l:l+16]
+        patch = img[:, t:t + lat_patch_size, l:l + lat_patch_size]
-        img_out[:,:,h_*16:h_*16+16,w_*16:w_*16+16] = patch
+        img_out[:,:, h_ * lat_patch_size:h_ * lat_patch_size + lat_patch_size,
+        w_ * lat_patch_size:w_ * lat_patch_size + lat_patch_size] = patch
 
         # Also save the image with a masked map
         mask = torch.full_like(img, fill_value=.3)
-        mask[:, t:t+16, l:l+16] = 1
+        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" % (t, 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">' % (w_*16,h_*16,w_*16+16,h_*16+16,masked_src_img_output_file))
+        img_map_areas.append('<area shape="rect" coords="%i,%i,%i,%i" href="%s">' % (w_ * lat_patch_size,
+                                                                                     h_ * lat_patch_size,
+                                                                                     w_ * lat_patch_size + lat_patch_size,
+                                                                                     h_ * lat_patch_size + 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))
@@ -195,12 +216,30 @@ def explore_latent_results(model):
         id += 1
 
 
-if __name__ == '__main__':
+class BYOLModelWrapper(nn.Module):
-    pretrained_path = '../../experiments/spinenet49_imgset_byol.pth'
+    def __init__(self, wrap):
+        super().__init__()
+        self.wrap = wrap
 
+    def forward(self, img):
+        return self.wrap.get_projection(img)
+
+
+if __name__ == '__main__':
+    util.loaded_options = {'checkpointing_enabled': True}
+    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()
 
+    #pretrained_path = '../../experiments/train_sbyol_512unsupervised/models/35000_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.3.conv')
+    #model.load_state_dict(torch.load(pretrained_path), strict=True)
+    #model = BYOLModelWrapper(model)
+    #model.eval()
+
     with torch.no_grad():
-        find_similar_latents(model)
+        #create_latent_database(model, 0)    # 0 = model output dimension to use for latent storage
+        find_similar_latents(model, 0, 8, structural_euc_dist)  # 1 = model output dimension to use for latent predictor.

codes/train.py

@@ -292,7 +292,7 @@ class Trainer:
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
-    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_byol_512unsupervised.yml')
+    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_sbyol_512unsupervised.yml')
     parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
     parser.add_argument('--local_rank', type=int, default=0)
     args = parser.parse_args()