Propagate collate_fn to InputTransform in ImageEmbedder

CHANGELOG.md

@@ -20,9 +20,11 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Fixed
 
-- Fixed a bug where `pretraining_transforms` in the `ImageEmbedder` was never called. ([1196](https://github.com/PyTorchLightning/lightning-flash/pull/1196))
+- Fixed a bug where collate functions were never called in the `ImageEmbedder` class. ([#1217](https://github.com/PyTorchLightning/lightning-flash/pull/1217))
 
-- Fixed a bug where `BASE_MODEL_NAME` was not in the dict for dino and moco strategies. ([1196](https://github.com/PyTorchLightning/lightning-flash/pull/1196))
+- Fixed a bug where `pretraining_transforms` in the `ImageEmbedder` was never called. ([#1196](https://github.com/PyTorchLightning/lightning-flash/pull/1196))
+
+- Fixed a bug where `BASE_MODEL_NAME` was not in the dict for dino and moco strategies. ([#1196](https://github.com/PyTorchLightning/lightning-flash/pull/1196))
 
 - Fixed normalizing inputs to video classification ([#1213](https://github.com/PyTorchLightning/lightning-flash/pull/1213))
 

flash/core/adapter.py

@@ -89,6 +89,15 @@ def input_transform(self) -> Optional[INPUT_TRANSFORM_TYPE]:
     def input_transform(self, input_transform: INPUT_TRANSFORM_TYPE) -> None:
         self.adapter.input_transform = input_transform
 
+    @torch.jit.unused
+    @property
+    def collate_fn(self) -> Optional[Callable]:
+        return self.adapter.collate_fn
+
+    @collate_fn.setter
+    def collate_fn(self, collate_fn: Callable) -> None:
+        self.adapter.collate_fn = collate_fn
+
     @torch.jit.unused
     @property
     def backbone(self) -> nn.Module:

flash/core/data/io/input_transform.py

@@ -1060,7 +1060,7 @@ def create_or_configure_input_transform(
         )
         return transform(**transform_kwargs)
 
-    if isinstance(transform, partial) and transform.func.__name__ == "LambdaInputTransform":
+    if isinstance(transform, partial):
         return transform(**transform_kwargs)
 
     if isinstance(transform, Callable):

flash/image/embedding/heads/vissl_heads.py

@@ -89,13 +89,15 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 
 
 def simclr_head(
-    dims: List[int] = [2048, 2048, 256],
+    num_features: int = 2048,
+    embedding_dim: int = 128,
+    dims: List[int] = [2048],
     use_bn: bool = True,
     **kwargs,
 ) -> nn.Module:
     cfg = VISSLAdapter.get_model_config_template()
     head_kwargs = {
-        "dims": dims,
+        "dims": [num_features] + dims + [embedding_dim],
         "use_bn": use_bn,
     }
 
@@ -108,7 +110,9 @@ def simclr_head(
 
 
 def swav_head(
-    dims: List[int] = [2048, 2048, 128],
+    num_features: int = 2048,
+    embedding_dim: int = 128,
+    dims: List[int] = [2048],
     use_bn: bool = True,
     num_clusters: Union[int, List[int]] = [3000],
     use_bias: bool = True,
@@ -121,7 +125,7 @@ def swav_head(
 ) -> nn.Module:
     cfg = VISSLAdapter.get_model_config_template()
     head_kwargs = {
-        "dims": dims,
+        "dims": [num_features] + dims + [embedding_dim],
         "use_bn": use_bn,
         "num_clusters": [num_clusters] if isinstance(num_clusters, int) else num_clusters,
         "use_bias": use_bias,
@@ -140,8 +144,11 @@ def swav_head(
     return head
 
 
-def barlow_twins_head(**kwargs) -> nn.Module:
-    return simclr_head(**kwargs)
+def barlow_twins_head(
+    latent_embedding_dim: int = 8192,
+    **kwargs,
+) -> nn.Module:
+    return simclr_head(embedding_dim=latent_embedding_dim, **kwargs)
 
 
 def moco_head(**kwargs) -> nn.Module:

flash/image/embedding/losses/vissl_losses.py

@@ -13,6 +13,9 @@
 # limitations under the License.
 from typing import List, Union
 
+import torch.cuda
+from torch import nn
+
 from flash.core.registry import FlashRegistry
 from flash.core.utilities.imports import _VISSL_AVAILABLE
 
@@ -26,11 +29,21 @@
     ClassyLoss = object
 
 
+def _recursive_register(module):
+    for key, value in module.__dict__.items():
+        if isinstance(value, torch.Tensor):
+            delattr(module, key)
+            module.register_buffer(key, value)
+        if isinstance(value, nn.Module):
+            _recursive_register(value)
+
+
 def get_loss_fn(loss_name: str, cfg: AttrDict):
     set_cpu_device()
     loss_fn = LOSS_REGISTRY[loss_name](cfg)
     loss_fn.__dict__["loss_name"] = loss_name
 
+    _recursive_register(loss_fn)
     return loss_fn
 
 

flash/image/embedding/model.py

@@ -12,13 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import warnings
-from functools import partial
 from typing import Any, Dict, List, Optional
 
 from flash.core.adapter import AdapterTask
-from flash.core.data.io.input import DataKeys
-from flash.core.data.io.input_transform import LambdaInputTransform
-from flash.core.data.transforms import ApplyToKeys
 from flash.core.registry import FlashRegistry
 from flash.core.utilities.imports import _VISSL_AVAILABLE, requires
 from flash.core.utilities.types import LR_SCHEDULER_TYPE, OPTIMIZER_TYPE
@@ -92,10 +88,10 @@ def __init__(
         if pretraining_transform_kwargs is None:
             pretraining_transform_kwargs = {}
 
-        backbone, _ = self.backbones.get(backbone)(pretrained=pretrained, **backbone_kwargs)
+        backbone, num_features = self.backbones.get(backbone)(pretrained=pretrained, **backbone_kwargs)
 
         metadata = self.training_strategies.get(training_strategy, with_metadata=True)
-        loss_fn, head, hooks = metadata["fn"](head=head, **training_strategy_kwargs)
+        loss_fn, head, hooks = metadata["fn"](head=head, num_features=num_features, **training_strategy_kwargs)
 
         adapter = metadata["metadata"]["adapter"].from_task(
             self,
@@ -112,9 +108,7 @@ def __init__(
             learning_rate=learning_rate,
         )
 
-        input_transform, self.collate_fn = self.transforms.get(pretraining_transform)(**pretraining_transform_kwargs)
-        output = ApplyToKeys(DataKeys.INPUT, input_transform)
-        self.input_transform = partial(LambdaInputTransform, transform=output)
+        self.input_transform = self.transforms.get(pretraining_transform)(**pretraining_transform_kwargs)
 
         warnings.warn(
             "Warning: VISSL ImageEmbedder overrides any user provided transforms"

flash/image/embedding/transforms/vissl_transforms.py

@@ -17,11 +17,8 @@
 import torch.nn as nn
 
 from flash.core.registry import FlashRegistry
-from flash.core.utilities.imports import _VISSL_AVAILABLE
 from flash.image.embedding.vissl.transforms import moco_collate_fn, multicrop_collate_fn, simclr_collate_fn
-
-if _VISSL_AVAILABLE:
-    from classy_vision.dataset.transforms import TRANSFORM_REGISTRY
+from flash.image.embedding.vissl.transforms.multicrop import StandardMultiCropSSLTransform
 
 
 def simclr_transform(
@@ -33,19 +30,21 @@ def simclr_transform(
     jitter_strength: float = 1.0,
     normalize: Optional[nn.Module] = None,
     collate_fn: Callable = simclr_collate_fn,
-) -> nn.Module:
+) -> partial:
     """For simclr, barlow twins and moco."""
-    transform = TRANSFORM_REGISTRY["multicrop_ssl_transform"](
+    transform = partial(
+        StandardMultiCropSSLTransform,
         total_num_crops=total_num_crops,
         num_crops=num_crops,
         size_crops=size_crops,
         crop_scales=crop_scales,
         gaussian_blur=gaussian_blur,
         jitter_strength=jitter_strength,
         normalize=normalize,
+        collate_fn=collate_fn,
     )
 
-    return transform, collate_fn
+    return transform
 
 
 def swav_transform(
@@ -57,19 +56,21 @@ def swav_transform(
     jitter_strength: float = 1.0,
     normalize: Optional[nn.Module] = None,
     collate_fn: Callable = multicrop_collate_fn,
-) -> nn.Module:
+) -> partial:
     """For swav and dino."""
-    transform = TRANSFORM_REGISTRY["multicrop_ssl_transform"](
+    transform = partial(
+        StandardMultiCropSSLTransform,
         total_num_crops=total_num_crops,
         num_crops=num_crops,
         size_crops=size_crops,
         crop_scales=crop_scales,
         gaussian_blur=gaussian_blur,
         jitter_strength=jitter_strength,
         normalize=normalize,
+        collate_fn=collate_fn,
     )
 
-    return transform, collate_fn
+    return transform
 
 
 barlow_twins_transform = partial(simclr_transform, collate_fn=simclr_collate_fn)

flash/image/embedding/vissl/adapter.py

@@ -122,13 +122,6 @@ def from_task(
 
         return result
 
-    def on_epoch_start(self) -> None:
-        use_gpu = self.adapter_task.device != torch.device("cpu") and self.adapter_task.device != "cpu"
-        if hasattr(self.loss_fn, "info_criterion"):
-            self.loss_fn.info_criterion.use_gpu = use_gpu
-        if hasattr(self.loss_fn, "swav_criterion"):
-            self.loss_fn.swav_criterion.use_gpu = use_gpu
-
     @staticmethod
     def get_model_config_template():
         cfg = AttrDict(

flash/image/embedding/vissl/hooks.py

@@ -49,7 +49,11 @@ def on_start(self, task: "flash.image.embedding.vissl.adapter.MockVISSLTask") ->
 
         # get around vissl distributed training by setting MockTask flags
         num_nodes = lightning_module.trainer.num_nodes
-        accelerators_ids = accelerator_connector(lightning_module.trainer).parallel_device_ids
+        accelerators_ids = getattr(
+            lightning_module.trainer,
+            "device_ids",
+            getattr(accelerator_connector(lightning_module.trainer), "parallel_device_ids", None),
+        )
         accelerator_per_node = len(accelerators_ids) if accelerators_ids is not None else 1
         task.world_size = num_nodes * accelerator_per_node
 

flash/image/embedding/vissl/transforms/__init__.py

@@ -5,8 +5,3 @@
     multicrop_collate_fn,
     simclr_collate_fn,
 )
-
-if _VISSL_AVAILABLE:
-    from classy_vision.dataset.transforms import register_transform  # noqa: F401
-
-    register_transform("multicrop_ssl_transform")(StandardMultiCropSSLTransform)