Remove deprecated collate functions, model wrappers, and arguments

docs/source/getting_started/advanced.rst

@@ -165,10 +165,6 @@ There are three ways how you can customize augmentations in Lightly\ **SSL**:
 Previewing Augmentations
 ^^^^^^^^^^^^^^^^^^^^^^^^
 
-.. note::
-  This section is outdated and still uses the old collate functions which are deprecated
-  since v1.4.0. We will update this section soon.
-
 It often can be very useful to understand how the image augmentations we pick affect
 the input dataset. We provide a few helper methods that make it very easy to 
 preview augmentations using Lightly\ **SSL**.
@@ -183,7 +179,7 @@ well as their augmentations next to them.
     :align: center
     :alt: SimCLR augmentations example
 
-    Example augmentations of the `SimCLRCollateFunction` function on images
+    Example augmentations of the `SimCLRTransform` transform on images
     from the clothing dataset.
 
 The images seem rather blurry! However, we don't want our model to ignore small
@@ -194,17 +190,17 @@ details. Let's disable Gaussian Blur and check again:
     :align: center
     :alt: SimCLR augmentations example
 
-    Example augmentations of the `SimCLRCollateFunction` function on images
+    Example augmentations of the `SimCLRTransform` transform on images
     from the clothing dataset.
 
-We can also repeat the experiment for the `DINOCollateFunction` to see what 
+We can also repeat the experiment for the `DINOTransform` to see what 
 our DINO model would see during training.
 
 .. figure:: images/dino_augmentations.jpg
     :align: center
     :alt: DINO augmentations example
 
-    Example augmentations of the `DINOCollateFunction` function on images
+    Example augmentations of the `DINOTransform` transform on images
     from the clothing dataset.
 
 

docs/source/getting_started/code_examples/plot_image_augmentations.py

@@ -11,16 +11,16 @@
 # load the first two images using pillow
 input_images = [Image.open(fname) for fname in fnames[:2]]
 
-# create our colalte function
-collate_fn_simclr = lightly.data.SimCLRCollateFunction()
+# create our transform
+transform_simclr = lightly.transforms.SimCLRTransform()
 
 # plot the images
-fig = lightly.utils.debug.plot_augmented_images(input_images, collate_fn_simclr)
+fig = lightly.utils.debug.plot_augmented_images(input_images, transform_simclr)
 
 # let's disable blur
-collate_fn_simclr_no_blur = lightly.data.SimCLRCollateFunction()
-fig = lightly.utils.debug.plot_augmented_images(input_images, collate_fn_simclr_no_blur)
+transform_simclr_no_blur = lightly.transforms.SimCLRTransform(gaussian_blur=0.0)
+fig = lightly.utils.debug.plot_augmented_images(input_images, transform_simclr_no_blur)
 
-# we can also use the DINO collate function instead
-collate_fn_dino = lightly.data.DINOCollateFunction()
-fig = lightly.utils.debug.plot_augmented_images(input_images, collate_fn_dino)
+# we can also use the DINO transform instead
+transform_dino = lightly.transforms.DINOTransform()
+fig = lightly.utils.debug.plot_augmented_images(input_images, transform_dino)

docs/source/lightly.data.rst

@@ -18,15 +18,9 @@ lightly.data
 --------------
 
 .. note::
-   ``IJEPAMaskCollator`` used to live in ``lightly.data.collate``. That import path
-   still works but warns, and is removed in v1.7.0. Use
-   ``from lightly.data import IJEPAMaskCollator``.
+   ``IJEPAMaskCollator`` used to live in ``lightly.data.collate``, which has been
+   removed. Use ``from lightly.data import IJEPAMaskCollator``.
 
 .. autoclass:: lightly.data.ijepa_collate.IJEPAMaskCollator
    :members:
    :special-members: __call__
-
-.collate:
----------
-.. automodule:: lightly.data.collate
-   :members:

docs/source/lightly.loss.rst

@@ -77,9 +77,6 @@ lightly.loss
 .. autoclass:: lightly.loss.swav_loss.SwaVLoss
    :members:
 
-.. autoclass:: lightly.loss.sym_neg_cos_sim_loss.SymNegCosineSimilarityLoss
-   :members:
-
 .. autoclass:: lightly.loss.tico_loss.TiCoLoss
    :members:
 

lightly/__init__.py

@@ -21,8 +21,7 @@
 - **data**:
 
   The lightly.data module provides a dataset wrapper and collate functions. The
-  collate functions are in charge of the data augmentations which are crucial for
-  self-supervised learning.
+  collate functions combine the views produced by a transform into a batch.
 
 - **loss**:
 

lightly/cli/config/config.yaml

@@ -32,7 +32,7 @@ optimizer:
   lr: 1.                      # Learning rate of the optimizer.
   weight_decay: 0.00001       # L2 penalty.
 
-# collate namespace: Passed to lightly.data.ImageCollateFunction.
+# collate namespace: Passed to lightly.transforms.SimCLRTransform.
 collate:
   input_size: 64              # Size of the input images in pixels.
   cj_prob: 0.8                # Probability that color jitter is applied.
@@ -44,9 +44,6 @@ collate:
   random_gray_scale: 0.2      # Probability of converting image to gray scale.
   gaussian_blur: 0.5          # Probability of Gaussian blur.
   sigmas: [0.2, 2]            # Sigmas of Gaussian blur
-  kernel_size: null           # Will be deprecated in favor of `sigmas` argument. If set, the old behavior
-                              # applies and `sigmas` is ignored. Used to calculate sigma of gaussian blur
-                              # with kernel_size * input_size.
   vf_prob: 0.0                # Probability that vertical flip is applied.
   hf_prob: 0.5                # Probability that horizontal flip is applied.
   rr_prob: 0.0                # Probability that random rotation is applied.
@@ -71,8 +68,6 @@ trainer:
   max_epochs: 100             # Number of epochs to train for.
   precision: 32               # If set to 16, will use half-precision.
   enable_model_summary: True  # Whether to enable model summarisation.
-  weights_summary:            # [deprecated] Use enable_model_summary
-                              # and summary_callback.max_depth.
 
 # checkpoint_callback namespace: Modify the checkpoint callback
 checkpoint_callback:

lightly/cli/train_cli.py

@@ -26,11 +26,12 @@
     load_from_state_dict,
     load_state_dict_from_url,
 )
-from lightly.data import ImageCollateFunction, LightlyDataset
+from lightly.data import LightlyDataset, MultiViewCollate
 from lightly.embedding import SelfSupervisedEmbedding
 from lightly.loss import NTXentLoss
 from lightly.models import ResNetGenerator
 from lightly.models.batchnorm import get_norm_layer
+from lightly.transforms import SimCLRTransform
 from lightly.utils.hipify import bcolors
 
 
@@ -108,13 +109,12 @@ def _train_cli(cfg, is_cli_call=True):
     criterion = NTXentLoss(**cfg["criterion"])
     optimizer = torch.optim.SGD(model.parameters(), **cfg["optimizer"])
 
-    dataset = LightlyDataset(input_dir)
+    dataset = LightlyDataset(input_dir, transform=SimCLRTransform(**cfg["collate"]))
 
     cfg["loader"]["batch_size"] = min(cfg["loader"]["batch_size"], len(dataset))
 
-    collate_fn = ImageCollateFunction(**cfg["collate"])
     dataloader = torch.utils.data.DataLoader(
-        dataset, **cfg["loader"], collate_fn=collate_fn
+        dataset, **cfg["loader"], collate_fn=MultiViewCollate()
     )
 
     encoder = SelfSupervisedEmbedding(model, criterion, optimizer, dataloader)
@@ -177,7 +177,7 @@ def train_cli(cfg):
         >>> lightly-ssl-train input_dir=data/ trainer.max_epochs=10
         >>>
         >>> # print a full summary of the model
-        >>> lightly-ssl-train input_dir=data/ trainer.weights_summary=full
+        >>> lightly-ssl-train input_dir=data/ summary_callback.max_depth=-1
 
     """
     return _train_cli(cfg)

lightly/data/__init__.py

@@ -9,19 +9,6 @@
     UnseekableTimestampError,
     VideoError,
 )
-from lightly.data.collate import (
-    BaseCollateFunction,
-    DINOCollateFunction,
-    ImageCollateFunction,
-    MAECollateFunction,
-    MoCoCollateFunction,
-    MSNCollateFunction,
-    MultiCropCollateFunction,
-    PIRLCollateFunction,
-    SimCLRCollateFunction,
-    SwaVCollateFunction,
-    VICRegLCollateFunction,
-    imagenet_normalize,
-)
 from lightly.data.dataset import LightlyDataset
 from lightly.data.ijepa_collate import IJEPAMaskCollator
+from lightly.data.multi_view_collate import MultiViewCollate