VISSL initial integration

.github/workflows/ci-testing.yml

@@ -136,7 +136,8 @@ jobs:
     - name: Install vissl
       if: matrix.topic[1] == 'image_extras'
       run: |
-        pip install git+https://github.com/facebookresearch/vissl.git@master
+        pip install git+https://github.com/facebookresearch/ClassyVision.git
+        pip install git+https://github.com/facebookresearch/vissl.git
 
     - name: Install graph test dependencies
       if: matrix.topic[0] == 'graph'

CHANGELOG.md

@@ -10,6 +10,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Added support `learn2learn` training_strategy for `ImageClassifier` ([#737](https://github.com/PyTorchLightning/lightning-flash/pull/737))
 
+- Added `vissl` training_strategies for `ImageEmbedder` ([#682](https://github.com/PyTorchLightning/lightning-flash/pull/682))
+
 ### Changed
 
 - Changed the default `num_workers` on linux to `0` (matching the default for other OS) ([#759](https://github.com/PyTorchLightning/lightning-flash/pull/759))

flash/core/registry.py

@@ -111,7 +111,11 @@ def _register_function(
         if not callable(fn):
             raise MisconfigurationException(f"You can only register a callable, found: {fn}")
 
-        name = name or fn.__name__
+        if name is None:
+            if hasattr(fn, "func"):
+                name = fn.func.__name__
+            else:
+                name = fn.__name__
 
         if self._verbose:
             rank_zero_info(f"Registering: {fn.__name__} function with name: {name} and metadata: {metadata}")

flash/core/utilities/providers.py

@@ -45,3 +45,4 @@ def __str__(self):
 _FAIRSEQ = Provider("PyTorch/fairseq", "https://github.com/pytorch/fairseq")
 _OPEN3D_ML = Provider("Intelligent Systems Lab Org/Open3D-ML", "https://github.com/isl-org/Open3D-ML")
 _PYTORCHVIDEO = Provider("Facebook Research/PyTorchVideo", "https://github.com/facebookresearch/pytorchvideo")
+_VISSL = Provider("Facebook Research/vissl", "https://github.com/facebookresearch/vissl")

flash/image/embedding/backbones/__init__.py

@@ -0,0 +1,5 @@
+from flash.core.registry import FlashRegistry  # noqa: F401
+from flash.image.embedding.backbones.vissl_backbones import register_vissl_backbones  # noqa: F401
+
+IMAGE_EMBEDDER_BACKBONES = FlashRegistry("embedder_backbones")
+register_vissl_backbones(IMAGE_EMBEDDER_BACKBONES)

flash/image/embedding/backbones/vissl_backbones.py

@@ -0,0 +1,117 @@
+# Copyright The PyTorch Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import torch.nn as nn
+
+from flash.core.registry import FlashRegistry
+from flash.core.utilities.imports import _VISSL_AVAILABLE
+
+if _VISSL_AVAILABLE:
+    from vissl.config.attr_dict import AttrDict
+    from vissl.models.model_helpers import RESNET_NORM_LAYER
+    from vissl.models.trunks import MODEL_TRUNKS_REGISTRY
+
+    from flash.image.embedding.vissl.adapter import VISSLAdapter
+else:
+    RESNET_NORM_LAYER = object
+
+
+def vision_transformer(
+    image_size: int = 224,
+    patch_size: int = 16,
+    hidden_dim: int = 384,
+    num_layers: int = 12,
+    num_heads: int = 6,
+    mlp_dim: int = 1532,
+    dropout_rate: float = 0,
+    attention_dropout_rate: float = 0,
+    drop_path_rate: float = 0,
+    qkv_bias: bool = True,
+    qk_scale: bool = False,
+    classifier: str = "token",
+    **kwargs,
+) -> nn.Module:
+
+    cfg = VISSLAdapter.get_model_config_template()
+    cfg.TRUNK = AttrDict(
+        {
+            "NAME": "vision_transformer",
+            "VISION_TRANSFORMERS": AttrDict(
+                {
+                    "IMAGE_SIZE": image_size,
+                    "PATCH_SIZE": patch_size,
+                    "HIDDEN_DIM": hidden_dim,
+                    "NUM_LAYERS": num_layers,
+                    "NUM_HEADS": num_heads,
+                    "MLP_DIM": mlp_dim,
+                    "DROPOUT_RATE": dropout_rate,
+                    "ATTENTION_DROPOUT_RATE": attention_dropout_rate,
+                    "DROP_PATH_RATE": drop_path_rate,
+                    "QKV_BIAS": qkv_bias,
+                    "QK_SCALE": qk_scale,
+                    "CLASSIFIER": classifier,
+                }
+            ),
+        }
+    )
+
+    trunk = MODEL_TRUNKS_REGISTRY["vision_transformer"](cfg, model_name="vision_transformer")
+    trunk.model_config = cfg
+
+    return trunk, trunk.num_features
+
+
+def resnet(
+    depth: int = 50,
+    width_multiplier: int = 1,
+    norm: RESNET_NORM_LAYER = None,
+    groupnorm_groups: int = 32,
+    standardize_convolutions: bool = False,
+    groups: int = 1,
+    zero_init_residual: bool = False,
+    width_per_group: int = 64,
+    layer4_stride: int = 2,
+    **kwargs,
+) -> nn.Module:
+    if norm is None:
+        norm = RESNET_NORM_LAYER.BatchNorm
+    cfg = VISSLAdapter.get_model_config_template()
+    cfg.TRUNK = AttrDict(
+        {
+            "NAME": "resnet",
+            "RESNETS": AttrDict(
+                {
+                    "DEPTH": depth,
+                    "WIDTH_MULTIPLIER": width_multiplier,
+                    "NORM": norm,
+                    "GROUPNORM_GROUPS": groupnorm_groups,
+                    "STANDARDIZE_CONVOLUTIONS": standardize_convolutions,
+                    "GROUPS": groups,
+                    "ZERO_INIT_RESIDUAL": zero_init_residual,
+                    "WIDTH_PER_GROUP": width_per_group,
+                    "LAYER4_STRIDE": layer4_stride,
+                }
+            ),
+        }
+    )
+
+    trunk = MODEL_TRUNKS_REGISTRY["resnet"](cfg, model_name="resnet")
+    trunk.model_config = cfg
+
+    return trunk, 2048
+
+
+def register_vissl_backbones(register: FlashRegistry):
+    if _VISSL_AVAILABLE:
+        for backbone in (vision_transformer, resnet):
+            register(backbone)

flash/image/embedding/heads/__init__.py

@@ -0,0 +1,5 @@
+from flash.core.registry import FlashRegistry  # noqa: F401
+from flash.image.embedding.heads.vissl_heads import register_vissl_heads  # noqa: F401
+
+IMAGE_EMBEDDER_HEADS = FlashRegistry("embedder_heads")
+register_vissl_heads(IMAGE_EMBEDDER_HEADS)

flash/image/embedding/heads/vissl_heads.py

@@ -0,0 +1,147 @@
+# Copyright The PyTorch Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import List, Union
+
+import torch
+import torch.nn as nn
+
+from flash.core.registry import FlashRegistry
+from flash.core.utilities.imports import _VISSL_AVAILABLE
+
+if _VISSL_AVAILABLE:
+    from vissl.config.attr_dict import AttrDict
+    from vissl.models.heads import MODEL_HEADS_REGISTRY, register_model_head
+
+    from flash.image.embedding.vissl.adapter import VISSLAdapter
+else:
+    AttrDict = object
+
+
+class SimCLRHead(nn.Module):
+    def __init__(
+        self,
+        model_config: AttrDict,
+        dims: List[int] = [2048, 2048, 128],
+        use_bn: bool = True,
+        **kwargs,
+    ) -> nn.Module:
+        super().__init__()
+
+        self.model_config = model_config
+        self.dims = dims
+        self.use_bn = use_bn
+
+        self.clf = self.create_mlp()
+
+    def create_mlp(self):
+        layers = []
+        last_dim = self.dims[0]
+
+        for dim in self.dims[1:-1]:
+            layers.append(nn.Linear(last_dim, dim))
+
+            if self.use_bn:
+                layers.append(
+                    nn.BatchNorm1d(
+                        dim,
+                        eps=self.model_config.HEAD.BATCHNORM_EPS,
+                        momentum=self.model_config.HEAD.BATCHNORM_MOMENTUM,
+                    )
+                )
+
+            layers.append(nn.ReLU(inplace=True))
+            last_dim = dim
+
+        layers.append(nn.Linear(last_dim, self.dims[-1]))
+        return nn.Sequential(*layers)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.clf(x)
+
+
+if _VISSL_AVAILABLE:
+    SimCLRHead = register_model_head("simclr_head")(SimCLRHead)
+
+
+def simclr_head(
+    dims: List[int] = [2048, 2048, 128],
+    use_bn: bool = True,
+    **kwargs,
+) -> nn.Module:
+    cfg = VISSLAdapter.get_model_config_template()
+    head_kwargs = {
+        "dims": dims,
+        "use_bn": use_bn,
+    }
+
+    cfg.HEAD.PARAMS.append(["simclr_head", head_kwargs])
+
+    head = MODEL_HEADS_REGISTRY["simclr_head"](cfg, **head_kwargs)
+    head.model_config = cfg
+
+    return head
+
+
+def swav_head(
+    dims: List[int] = [2048, 2048, 128],
+    use_bn: bool = True,
+    num_clusters: Union[int, List[int]] = [3000],
+    use_bias: bool = True,
+    return_embeddings: bool = True,
+    skip_last_bn: bool = True,
+    normalize_feats: bool = True,
+    activation_name: str = "ReLU",
+    use_weight_norm_prototypes: bool = False,
+    **kwargs,
+) -> nn.Module:
+    cfg = VISSLAdapter.get_model_config_template()
+    head_kwargs = {
+        "dims": dims,
+        "use_bn": use_bn,
+        "num_clusters": [num_clusters] if isinstance(num_clusters, int) else num_clusters,
+        "use_bias": use_bias,
+        "return_embeddings": return_embeddings,
+        "skip_last_bn": skip_last_bn,
+        "normalize_feats": normalize_feats,
+        "activation_name": activation_name,
+        "use_weight_norm_prototypes": use_weight_norm_prototypes,
+    }
+
+    cfg.HEAD.PARAMS.append(["swav_head", head_kwargs])
+
+    head = MODEL_HEADS_REGISTRY["swav_head"](cfg, **head_kwargs)
+    head.model_config = cfg
+
+    return head
+
+
+def barlow_twins_head(**kwargs) -> nn.Module:
+    return simclr_head(dims=[2048, 8192, 8192, 8192], **kwargs)
+
+
+def dino_head(**kwargs) -> nn.Module:
+    return swav_head(
+        dims=[384, 2048, 2048, 256],
+        use_bn=False,
+        return_embeddings=False,
+        activation_name="GELU",
+        num_clusters=[65536],
+        use_weight_norm_prototypes=True,
+        **kwargs,
+    )
+
+
+def register_vissl_heads(register: FlashRegistry):
+    for ssl_head in (swav_head, simclr_head, dino_head, barlow_twins_head):
+        register(ssl_head)

flash/image/embedding/losses/__init__.py

@@ -0,0 +1,5 @@
+from flash.core.registry import FlashRegistry  # noqa: F401
+from flash.image.embedding.losses.vissl_losses import register_vissl_losses  # noqa: F401
+
+IMAGE_EMBEDDER_LOSS_FUNCTIONS = FlashRegistry("embedder_losses")
+register_vissl_losses(IMAGE_EMBEDDER_LOSS_FUNCTIONS)