add swav and simclr models to imageclassifier + backbone reorg

flash/vision/backbones.py

@@ -11,11 +11,71 @@
 # 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 contextlib import suppress
 from typing import Tuple
 
-import torch.nn as nn
 import torchvision
+from pytorch_lightning.utilities import _BOLTS_AVAILABLE
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
+from torch import nn as nn
+
+if _BOLTS_AVAILABLE:
+    with suppress(TypeError):
+        from pl_bolts.models.self_supervised import SimCLR, SwAV
+
+ROOT_S3_BUCKET = "https://pl-bolts-weights.s3.us-east-2.amazonaws.com"
+
+MOBILENET_MODELS = ["mobilenet_v2"]
+VGG_MODELS = ["vgg11", "vgg13", "vgg16", "vgg19"]
+RESNET_MODELS = ["resnet18", "resnet34", "resnet50", "resnet101", "resnet152", "resnext50_32x4d", "resnext101_32x8d"]
+DENSENET_MODELS = ["densenet121", "densenet169", "densenet161", "densenet161"]
+TORCHVISION_MODELS = MOBILENET_MODELS + VGG_MODELS + RESNET_MODELS + DENSENET_MODELS
+
+BOLTS_MODELS = ["simclr-imagenet", "swav-imagenet"]
+
+
+def backbone_and_num_features(model_name: str, *args, **kwargs) -> Tuple[nn.Module, int]:
+    if model_name in BOLTS_MODELS:
+        return bolts_backbone_and_num_features(model_name)
+
+    if model_name in TORCHVISION_MODELS:
+        return torchvision_backbone_and_num_features(model_name, *args, **kwargs)
+
+    raise ValueError(f"{model_name} is not supported yet.")
+
+
+def bolts_backbone_and_num_features(model_name: str) -> Tuple[nn.Module, int]:
+    """
+    >>> bolts_backbone_and_num_features('simclr-imagenet')  # doctest: +ELLIPSIS
+    (Sequential(...), 2048)
+    >>> bolts_backbone_and_num_features('swav-imagenet')  # doctest: +ELLIPSIS
+    (Sequential(...), 3000)
+    """
+
+    # TODO: maybe we should plain pytorch weights so we don't need to rely on bolts to load these
+    # also mabye just use torchhub for the ssl lib
+    def load_simclr_imagenet(path_or_url: str = f"{ROOT_S3_BUCKET}/simclr/bolts_simclr_imagenet/simclr_imagenet.ckpt"):
+        simclr = SimCLR.load_from_checkpoint(path_or_url, strict=False)
+        # remove the last two layers & turn it into a Sequential model
+        backbone = nn.Sequential(*list(simclr.encoder.children())[:-2])
+        return backbone, 2048
+
+    def load_swav_imagenet(path_or_url: str = f"{ROOT_S3_BUCKET}/swav/swav_imagenet/swav_imagenet.pth.tar"):
+        swav = SwAV.load_from_checkpoint(path_or_url, strict=True)
+        # remove the last two layers & turn it into a Sequential model
+        backbone = nn.Sequential(*list(swav.model.children())[:-2])
+        return backbone, 3000
+
+    models = {
+        'simclr-imagenet': load_simclr_imagenet,
+        'swav-imagenet': load_swav_imagenet,
+    }
+    if not _BOLTS_AVAILABLE:
+        raise MisconfigurationException("Bolts isn't installed. Please, use ``pip install lightning-bolts``.")
+    if model_name in models:
+        return models[model_name]()
+
+    raise ValueError(f"{model_name} is not supported yet.")
 
 
 def torchvision_backbone_and_num_features(model_name: str, pretrained: bool = True) -> Tuple[nn.Module, int]:
@@ -31,22 +91,20 @@ def torchvision_backbone_and_num_features(model_name: str, pretrained: bool = Tr
     if model is None:
         raise MisconfigurationException(f"{model_name} is not supported by torchvision")
 
-    if model_name in ["mobilenet_v2", "vgg11", "vgg13", "vgg16", "vgg19"]:
+    if model_name in MOBILENET_MODELS + VGG_MODELS:
         model = model(pretrained=pretrained)
         backbone = model.features
         num_features = model.classifier[-1].in_features
         return backbone, num_features
 
-    elif model_name in [
-        "resnet18", "resnet34", "resnet50", "resnet101", "resnet152", "resnext50_32x4d", "resnext101_32x8d"
-    ]:
+    elif model_name in RESNET_MODELS:
         model = model(pretrained=pretrained)
         # remove the last two layers & turn it into a Sequential model
         backbone = nn.Sequential(*list(model.children())[:-2])
         num_features = model.fc.in_features
         return backbone, num_features
 
-    elif model_name in ["densenet121", "densenet169", "densenet161", "densenet161"]:
+    elif model_name in DENSENET_MODELS:
         model = model(pretrained=pretrained)
         backbone = nn.Sequential(*model.features, nn.ReLU(inplace=True))
         num_features = model.classifier.in_features

flash/vision/classification/model.py

@@ -19,7 +19,7 @@
 from torch.nn import functional as F
 
 from flash.core.classification import ClassificationTask
-from flash.vision.backbones import torchvision_backbone_and_num_features
+from flash.vision.backbones import backbone_and_num_features
 from flash.vision.classification.data import ImageClassificationData, ImageClassificationDataPipeline
 
 
@@ -57,7 +57,7 @@ def __init__(
 
         self.save_hyperparameters()
 
-        self.backbone, num_features = torchvision_backbone_and_num_features(backbone, pretrained)
+        self.backbone, num_features = backbone_and_num_features(backbone, pretrained)
 
         self.head = nn.Sequential(
             nn.AdaptiveAvgPool2d((1, 1)),

flash/vision/embedding/image_embedder_model.py

@@ -24,9 +24,8 @@
 from flash.core import Task
 from flash.core.data import TaskDataPipeline
 from flash.core.data.utils import _contains_any_tensor
-from flash.vision.backbones import torchvision_backbone_and_num_features
+from flash.vision.backbones import backbone_and_num_features
 from flash.vision.classification.data import _default_valid_transforms, _pil_loader
-from flash.vision.embedding.model_map import _load_bolts_model, _models
 
 
 class ImageEmbedderDataPipeline(TaskDataPipeline):
@@ -115,12 +114,7 @@ def __init__(
         assert pooling_fn in [torch.mean, torch.max]
         self.pooling_fn = pooling_fn
 
-        if backbone in _models:
-            config = _load_bolts_model(backbone)
-            self.backbone = config['model']
-            num_features = config['num_features']
-        else:
-            self.backbone, num_features = torchvision_backbone_and_num_features(backbone, pretrained)
+        self.backbone, num_features = backbone_and_num_features(backbone, pretrained)
 
         if embedding_dim is None:
             self.head = nn.Identity()

tests/vision/classification/test_model.py

@@ -51,7 +51,7 @@ def test_init_train(tmpdir, backbone):
 
 
 def test_non_existent_backbone():
-    with pytest.raises(MisconfigurationException):
+    with pytest.raises(ValueError):
         ImageClassifier(2, "i am never going to implement this lol")
 
 

tests/vision/test_download.py

@@ -13,9 +13,9 @@
 # limitations under the License.
 import pytest
 
-from flash.vision.embedding.model_map import _load_bolts_model
+from flash.vision.backbones import bolts_backbone_and_num_features
 
 
 @pytest.mark.parametrize("name", ['simclr-imagenet', 'swav-imagenet'])
 def test_load_bolts(name):
-    _load_bolts_model(name)
+    bolts_backbone_and_num_features(name)