Merge branch 'xdev-prepare' into dev

disent/dataset/data/_groundtruth__dsprites_imagenet.py

@@ -62,9 +62,13 @@ def __getitem__(self, idx):
         return np.array(img)
 
 
+def _noop(x):
+    return x
+
+
 def load_imagenet_tiny_data(raw_data_dir):
     data = NumpyFolder(os.path.join(raw_data_dir, 'train'))
-    data = DataLoader(data, batch_size=64, num_workers=min(16, psutil.cpu_count(logical=False)), shuffle=False, drop_last=False, collate_fn=lambda x: x)
+    data = DataLoader(data, batch_size=64, num_workers=min(16, psutil.cpu_count(logical=False)), shuffle=False, drop_last=False, collate_fn=_noop)
     # load data - this is a bit memory inefficient doing it like this instead of with a loop into a pre-allocated array
     imgs = np.concatenate(list(tqdm(data, 'loading')), axis=0)
     assert imgs.shape == (100_000, 64, 64, 3)

disent/dataset/data/_groundtruth__xyobject.py

@@ -116,19 +116,7 @@ def __init__(
         rgb: bool = True,
         palette: str = 'rainbow_4',
         transform=None,
-        warn_: bool = True
     ):
-        if warn_:
-            warnings.warn(
-                '`XYObjectData` defaults were changed in disent v0.3.0, if you want `approx` <= v0.2.x behavior then use the following parameters. Pallets also changed slightly too.'
-                '\n\tgrid_size=64'
-                '\n\tgrid_spacing=1'
-                '\n\tmin_square_size=3'
-                '\n\tmax_square_size=9'
-                '\n\tsquare_size_spacing=2'
-                '\n\trgb=True'
-                '\n\tpalette="colors_1"'
-            )
         # generation
         self._rgb = rgb
         # check the pallete name
@@ -238,7 +226,6 @@ def __init__(
             rgb=rgb,
             palette=f'{palette}_1',
             transform=transform,
-            warn_=False,
         )
 
     def _get_observation(self, idx):

disent/dataset/transform/_augment.py

@@ -185,8 +185,10 @@ class FftKernel(DisentModule):
 
     def __init__(self, kernel: Union[torch.Tensor, str], normalize: bool = True):
         super().__init__()
-        # load the kernel
-        self._kernel = torch.nn.Parameter(get_kernel(kernel, normalize=normalize), requires_grad=False)
+        # load & save the kernel -- no gradients allowed
+        self._kernel: torch.Tensor
+        self.register_buffer('_kernel', get_kernel(kernel, normalize=normalize), persistent=True)
+        self._kernel.requires_grad = False
 
     def forward(self, obs):
         # add or remove batch dim

disent/dataset/util/stats.py

@@ -96,70 +96,113 @@ def main(progress=False):
             data.DSpritesData,
             data.SmallNorbData,
             data.Shapes3dData,
-            wrapped_partial(data.Mpi3dData, subset='toy',       in_memory=True),
-            wrapped_partial(data.Mpi3dData, subset='realistic', in_memory=True),
-            wrapped_partial(data.Mpi3dData, subset='real',      in_memory=True),
             # groundtruth -- impl synthetic
             data.XYObjectData,
             data.XYObjectShadedData,
+            # large datasets
+            (data.Mpi3dData, dict(subset='toy',       in_memory=True)),
+            (data.Mpi3dData, dict(subset='realistic', in_memory=True)),
+            (data.Mpi3dData, dict(subset='real',      in_memory=True)),
         ]:
             from disent.dataset.transform import ToImgTensorF32
+            # get arguments
+            if isinstance(data_cls, tuple):
+                data_cls, kwargs = data_cls
+            else:
+                data_cls, kwargs = data_cls, {}
             # Most common standardized way of computing the mean and std over observations
             # resized to 64px in size of dtype float32 in the range [0, 1].
-            data = data_cls(transform=ToImgTensorF32(size=64))
+            data = data_cls(transform=ToImgTensorF32(size=64), **kwargs)
             mean, std = compute_data_mean_std(data, progress=progress)
             # results!
-            print(f'{data.__class__.__name__} - {data.name}:\n    mean: {mean.tolist()}\n    std: {std.tolist()}')
+            print(f'{data.__class__.__name__} - {data.name} - {kwargs}:\n    mean: {mean.tolist()}\n    std: {std.tolist()}')
 
     # RUN!
     main()
 
 
 # ========================================================================= #
-# RESULTS: 2021-10-12                                                       #
+# RESULTS: 2021-11-12                                                       #
 # ========================================================================= #
 
 
-# Cars3dData - cars3d:
+# Cars3dData - cars3d - {}:
 #     mean: [0.8976676149976628, 0.8891658020067508, 0.885147515814868]
 #     std: [0.22503195531503034, 0.2399461278981261, 0.24792106319684404]
-# DSpritesData - dsprites:
+# DSpritesData - dsprites - {}:
 #     mean: [0.042494423521889584]
 #     std: [0.19516645880626055]
-# SmallNorbData - smallnorb:
+# SmallNorbData - smallnorb - {}:
 #     mean: [0.7520918401088603]
-#     std: [0.09563879016827262]
-# Shapes3dData - 3dshapes:
+#     std: [0.09563879016827263]
+# Shapes3dData - 3dshapes - {}:
 #     mean: [0.502584966788819, 0.5787597566089667, 0.6034499731859578]
-#     std: [0.2940814043555559, 0.3443979087517214, 0.3661685981524748]
-# Mpi3dData - mpi3d_toy:
-#     mean: [0.22681593831231503, 0.22353985202496676, 0.22666059934624702]
-#     std: [0.07854112062669572, 0.07319301658077378, 0.0790763900050426]
-# Mpi3dData - mpi3d_realistic:
-#     mean: [0.18240164396358813, 0.20723063241107917, 0.1820551008003256]
-#     std: [0.09511163559287175, 0.10128881101801782, 0.09428244469525177]
-# Mpi3dData - mpi3d_real:
-#     mean: [0.13111154099374112, 0.16746449372488892, 0.14051725201807627]
-#     std: [0.10137409845578041, 0.10087824338375781, 0.10534121043187629]
-# XYBlocksData - xyblocks:
+#     std: [0.2940814043555559, 0.34439790875172144, 0.3661685981524748]
+
+# XYBlocksData - xyblocks - {}:
 #     mean: [0.10040509259259259, 0.10040509259259259, 0.10040509259259259]
 #     std: [0.21689087652106678, 0.21689087652106676, 0.21689087652106678]
-# XYObjectData - xy_object:
+# XYObjectData - xy_object - {}:
 #     mean: [0.009818761549013288, 0.009818761549013288, 0.009818761549013288]
 #     std: [0.052632363725245844, 0.05263236372524584, 0.05263236372524585]
-# XYObjectShadedData - xy_object:
+# XYObjectShadedData - xy_object - {}:
 #     mean: [0.009818761549013288, 0.009818761549013288, 0.009818761549013288]
 #     std: [0.052632363725245844, 0.05263236372524584, 0.05263236372524585]
-# XYSquaresData - xy_squares:
+# XYSquaresData - xy_squares - {}:
 #     mean: [0.015625, 0.015625, 0.015625]
 #     std: [0.12403473458920855, 0.12403473458920854, 0.12403473458920854]
-# XYSquaresMinimalData - xy_squares:
+# XYSquaresMinimalData - xy_squares_minimal - {}:
 #     mean: [0.015625, 0.015625, 0.015625]
 #     std: [0.12403473458920855, 0.12403473458920854, 0.12403473458920854]
-# XColumnsData - x_columns:
+# XColumnsData - x_columns - {}:
 #     mean: [0.125, 0.125, 0.125]
 #     std: [0.33075929223788925, 0.3307592922378891, 0.3307592922378892]
 
+# XYSquaresData - xy_squares - {'grid_size': 8, 'grid_spacing': 8}:
+#     mean: [0.015625, 0.015625, 0.015625]
+#     std: [0.12403473458920855, 0.12403473458920854, 0.12403473458920854]
+# overlap between squares for reconstruction loss, 7 < 8
+# XYSquaresData - xy_squares - {'grid_size': 8, 'grid_spacing': 7}:
+#     mean: [0.015625, 0.015625, 0.015625]
+#     std: [0.12403473458920854, 0.12403473458920854, 0.12403473458920854]
+# overlap between squares for reconstruction loss, 6 < 8
+# XYSquaresData - xy_squares - {'grid_size': 8, 'grid_spacing': 6}:
+#     mean: [0.015625, 0.015625, 0.015625]
+#     std: [0.12403473458920854, 0.12403473458920854, 0.12403473458920855]
+# overlap between squares for reconstruction loss, 5 < 8
+# XYSquaresData - xy_squares - {'grid_size': 8, 'grid_spacing': 5}:
+#     mean: [0.015625, 0.015625, 0.015625]
+#     std: [0.12403473458920855, 0.12403473458920855, 0.12403473458920854]
+# overlap between squares for reconstruction loss, 4 < 8
+# XYSquaresData - xy_squares - {'grid_size': 8, 'grid_spacing': 4}:
+#     mean: [0.015625, 0.015625, 0.015625]
+#     std: [0.12403473458920855, 0.12403473458920854, 0.12403473458920854]
+# overlap between squares for reconstruction loss, 3 < 8
+# XYSquaresData - xy_squares - {'grid_size': 8, 'grid_spacing': 3}:
+#     mean: [0.015625, 0.015625, 0.015625]
+#     std: [0.12403473458920854, 0.12403473458920854, 0.12403473458920854]
+# overlap between squares for reconstruction loss, 2 < 8
+# XYSquaresData - xy_squares - {'grid_size': 8, 'grid_spacing': 2}:
+#     mean: [0.015625, 0.015625, 0.015625]
+#     std: [0.12403473458920854, 0.12403473458920854, 0.12403473458920854]
+# overlap between squares for reconstruction loss, 1 < 8
+# XYSquaresData - xy_squares - {'grid_size': 8, 'grid_spacing': 1}:
+#     mean: [0.015625, 0.015625, 0.015625]
+#     std: [0.12403473458920855, 0.12403473458920855, 0.12403473458920855]
+# XYSquaresData - xy_squares - {'rgb': False}:
+#     mean: [0.046146392822265625]
+#     std: [0.2096506119375896]
+
+# Mpi3dData - mpi3d_toy - {'subset': 'toy', 'in_memory': True}:
+#     mean: [0.22681593831231503, 0.22353985202496676, 0.22666059934624702]
+#     std: [0.07854112062669572, 0.07319301658077378, 0.0790763900050426]
+# Mpi3dData - mpi3d_realistic - {'subset': 'realistic', 'in_memory': True}:
+#     mean: [0.18240164396358813, 0.20723063241107917, 0.1820551008003256]
+#     std: [0.09511163559287175, 0.10128881101801782, 0.09428244469525177]
+# Mpi3dData - mpi3d_real - {'subset': 'real', 'in_memory': True}:
+#     mean: [0.13111154099374112, 0.16746449372488892, 0.14051725201807627]
+#     std: [0.10137409845578041, 0.10087824338375781, 0.10534121043187629]
+
 
 # ========================================================================= #
 # END                                                                       #

disent/frameworks/helper/reconstructions.py

@@ -241,7 +241,7 @@ def compute_unreduced_loss(self, x_recon, x_targ):
 
 class AugmentedReconLossHandler(ReconLossHandler):
 
-    def __init__(self, recon_loss_handler: ReconLossHandler, kernel: Union[str, torch.Tensor], kernel_weight=1.0):
+    def __init__(self, recon_loss_handler: ReconLossHandler, kernel: Union[str, torch.Tensor], wrap_weight=1.0, aug_weight=1.0):
         super().__init__(reduction=recon_loss_handler._reduction)
         # save variables
         self._recon_loss_handler = recon_loss_handler
@@ -251,16 +251,21 @@ def __init__(self, recon_loss_handler: ReconLossHandler, kernel: Union[str, torc
         # load the kernel
         self._kernel = FftKernel(kernel=kernel, normalize=True)
         # kernel weighting
-        assert 0 <= kernel_weight <= 1, f'kernel weight must be in the range [0, 1] but received: {repr(kernel_weight)}'
-        self._kernel_weight = kernel_weight
+        assert 0 <= wrap_weight, f'loss_weight must be in the range [0, inf) but received: {repr(wrap_weight)}'
+        assert 0 <= aug_weight, f'kern_weight must be in the range [0, inf) but received: {repr(aug_weight)}'
+        self._wrap_weight = wrap_weight
+        self._aug_weight = aug_weight
+        # disable gradients
+        for param in self.parameters():
+            param.requires_grad = False
 
     def activate(self, x_partial: torch.Tensor):
         return self._recon_loss_handler.activate(x_partial)
 
     def compute_unreduced_loss(self, x_recon: torch.Tensor, x_targ: torch.Tensor) -> torch.Tensor:
-        aug_loss = self._recon_loss_handler.compute_unreduced_loss(self._kernel(x_recon), self._kernel(x_targ))
-        loss = self._recon_loss_handler.compute_unreduced_loss(x_recon, x_targ)
-        return (1. - self._kernel_weight) * loss + self._kernel_weight * aug_loss
+        wrap_loss = self._recon_loss_handler.compute_unreduced_loss(x_recon, x_targ)
+        aug_loss  = self._recon_loss_handler.compute_unreduced_loss(self._kernel(x_recon), self._kernel(x_targ))
+        return (self._wrap_weight * wrap_loss) + (self._aug_weight * aug_loss)
 
     def compute_unreduced_loss_from_partial(self, x_partial_recon: torch.Tensor, x_targ: torch.Tensor) -> torch.Tensor:
         return self.compute_unreduced_loss(self.activate(x_partial_recon), x_targ)

disent/frameworks/vae/_unsupervised__vae.py

@@ -158,6 +158,10 @@ def do_training_step(self, batch, batch_idx):
             'recon_loss': recon_loss,
             'reg_loss': reg_loss,
             'aug_loss': aug_loss,
+            # ratios
+            'ratio_reg': (reg_loss   / loss) if (loss != 0) else 0,
+            'ratio_rec': (recon_loss / loss) if (loss != 0) else 0,
+            'ratio_aug': (aug_loss   / loss) if (loss != 0) else 0,
         }
 
     # --------------------------------------------------------------------- #

disent/util/lightning/callbacks/_callbacks_vae.py

@@ -122,25 +122,25 @@ def _to_dmat(
     return dmat
 
 
-_AE_DIST_NAMES = ('x', 'z_l1', 'x_recon')
-_VAE_DIST_NAMES = ('x', 'z_l1', 'kl', 'x_recon')
+_AE_DIST_NAMES = ('x', 'z', 'x_recon')
+_VAE_DIST_NAMES = ('x', 'z', 'kl', 'x_recon')
 
 
 @torch.no_grad()
-def _get_dists_ae(ae: Ae, recon_loss: ReconLossHandler, x_a: torch.Tensor, x_b: torch.Tensor):
+def _get_dists_ae(ae: Ae, x_a: torch.Tensor, x_b: torch.Tensor):
     # feed forware
     z_a, z_b = ae.encode(x_a), ae.encode(x_b)
     r_a, r_b = ae.decode(z_a), ae.decode(z_b)
     # distances
     return [
-        recon_loss.compute_pairwise_loss(x_a, x_b),
-        torch.norm(z_a - z_b, p=1, dim=-1),  # l1 dist
-        recon_loss.compute_pairwise_loss(r_a, r_b),
+        ae.recon_handler.compute_pairwise_loss(x_a, x_b),
+        torch.norm(z_a - z_b, p=2, dim=-1),  # l2 dist
+        ae.recon_handler.compute_pairwise_loss(r_a, r_b),
     ]
 
 
 @torch.no_grad()
-def _get_dists_vae(vae: Vae, recon_loss: ReconLossHandler, x_a: torch.Tensor, x_b: torch.Tensor):
+def _get_dists_vae(vae: Vae, x_a: torch.Tensor, x_b: torch.Tensor):
     from torch.distributions import kl_divergence
     # feed forward
     (z_post_a, z_prior_a), (z_post_b, z_prior_b) = vae.encode_dists(x_a), vae.encode_dists(x_b)
@@ -150,19 +150,19 @@ def _get_dists_vae(vae: Vae, recon_loss: ReconLossHandler, x_a: torch.Tensor, x_
     kl_ab = 0.5 * kl_divergence(z_post_a, z_post_b) + 0.5 * kl_divergence(z_post_b, z_post_a)
     # distances
     return [
-        recon_loss.compute_pairwise_loss(x_a, x_b),
-        torch.norm(z_a - z_b, p=1, dim=-1),  # l1 dist
-        recon_loss._pairwise_reduce(kl_ab),
-        recon_loss.compute_pairwise_loss(r_a, r_b),
+        vae.recon_handler.compute_pairwise_loss(x_a, x_b),
+        torch.norm(z_a - z_b, p=2, dim=-1),  # l2 dist
+        vae.recon_handler._pairwise_reduce(kl_ab),
+        vae.recon_handler.compute_pairwise_loss(r_a, r_b),
     ]
 
 
-def _get_dists_fn(model, recon_loss: ReconLossHandler) -> Tuple[Optional[Tuple[str, ...]], Optional[Callable[[object, object], Sequence[Sequence[float]]]]]:
+def _get_dists_fn(model: Ae) -> Tuple[Optional[Tuple[str, ...]], Optional[Callable[[object, object], Sequence[Sequence[float]]]]]:
     # get aggregate function
     if isinstance(model, Vae):
-        dists_names, dists_fn = _VAE_DIST_NAMES, wrapped_partial(_get_dists_vae, model, recon_loss)
+        dists_names, dists_fn = _VAE_DIST_NAMES, wrapped_partial(_get_dists_vae, model)
     elif isinstance(model, Ae):
-        dists_names, dists_fn = _AE_DIST_NAMES, wrapped_partial(_get_dists_ae, model, recon_loss)
+        dists_names, dists_fn = _AE_DIST_NAMES, wrapped_partial(_get_dists_ae, model)
     else:
         dists_names, dists_fn = None, None
     return dists_names, dists_fn
@@ -303,7 +303,6 @@ def __init__(
         assert traversal_repeats > 0
         self._traversal_repeats = traversal_repeats
         self._seed = seed
-        self._recon_loss = make_reconstruction_loss('mse', 'mean')
         self._plt_block_size = plt_block_size
         self._plt_show = plt_show
         self._log_wandb = log_wandb
@@ -321,7 +320,7 @@ def do_step(self, trainer: pl.Trainer, pl_module: pl.LightningModule):
             log.warning(f'cannot run {self.__class__.__name__} over non-ground-truth data, skipping!')
             return
         # get aggregate function
-        dists_names, dists_fn = _get_dists_fn(vae, self._recon_loss)
+        dists_names, dists_fn = _get_dists_fn(vae)
         if (dists_names is None) or (dists_fn is None):
             log.warning(f'cannot run {self.__class__.__name__}, unsupported model type: {type(vae)}, must be {Ae.__name__} or {Vae.__name__}')
             return
@@ -488,6 +487,19 @@ def do_step(self, trainer: pl.Trainer, pl_module: pl.LightningModule):
             plt.show()
 
 
+def _normalized_numeric_metrics(items: dict):
+    results = {}
+    for k, v in items.items():
+        if isinstance(v, (float, int)):
+            results[k] = v
+        else:
+            try:
+                results[k] = float(v)
+            except:
+                log.warning(f'SKIPPED: metric with key: {repr(k)}, result has invalid type: {type(v)} with value: {repr(v)}')
+    return results
+
+
 class VaeMetricLoggingCallback(BaseCallbackPeriodic):
 
     def __init__(
@@ -521,10 +533,11 @@ def _compute_metrics_and_log(self, trainer: pl.Trainer, pl_module: pl.LightningM
                 scores = metric(dataset, lambda x: vae.encode(x.to(vae.device)))
             metric_results = ' '.join(f'{k}{c.GRY}={c.lMGT}{v:.3f}{c.RST}' for k, v in scores.items())
             log.info(f'| {metric.__name__:<{pad}} - time{c.GRY}={c.lYLW}{timer.pretty:<9}{c.RST} - {metric_results}')
+
             # log to trainer
             prefix = 'final_metric' if is_final else 'epoch_metric'
             prefixed_scores = {f'{prefix}/{k}': v for k, v in scores.items()}
-            log_metrics(trainer.logger, prefixed_scores)
+            log_metrics(trainer.logger, _normalized_numeric_metrics(prefixed_scores))
 
             # log summary for WANDB
             # this is kinda hacky... the above should work for parallel coordinate plots

disent/util/visualize/plot.py

@@ -135,7 +135,10 @@ def plt_subplots(
     assert isinstance(ncols, int)
     # check titles
     if titles is not None:
-        titles = np.array(titles).reshape([nrows, ncols])
+        titles = np.array(titles)
+        if titles.ndim == 1:
+            titles = np.array([titles] + ([[None]*ncols] * (nrows-1)))
+        assert titles.ndim == 2
     # get labels
     if (row_labels is None) or isinstance(row_labels, str):
         row_labels = [row_labels] * nrows
@@ -161,7 +164,8 @@ def plt_subplots(
                 ax.set_ylabel(row_labels[y], fontsize=label_size)
             # set title
             if titles is not None:
-                ax.set_title(titles[y][x], fontsize=titles_size)
+                if titles[y][x] is not None:
+                    ax.set_title(titles[y][x], fontsize=titles_size)
     # set title
     fig.suptitle(title, fontsize=title_size)
     # done!

experiment/config/run_action/skip.yaml

@@ -0,0 +1,2 @@
+# @package _global_
+action: skip

experiment/config/schedule/beta_cyclic_fast.yaml

@@ -1,4 +1,4 @@
-name: beta_cyclic
+name: beta_cyclic_fast
 
 schedule_items:
   beta:

experiment/config/schedule/beta_cyclic_slow.yaml

@@ -1,4 +1,4 @@
-name: beta_cyclic
+name: beta_cyclic_slow
 
 schedule_items:
   beta: