Catch URLError (#237)

* Catch URLError

* Updates

* Update CHANGELOG.md

* Update CHANGELOG.md

* Fix error

CHANGELOG.md

@@ -23,6 +23,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Fixed classification softmax ([#169](https://github.com/PyTorchLightning/lightning-flash/pull/169))
 
+- Fixed a bug where loading from a local checkpoint that had `pretrained=True` without an internet connection would sometimes raise an error ([#237](https://github.com/PyTorchLightning/lightning-flash/pull/237))
+
 ### Removed
 
 

flash/vision/backbones.py

@@ -11,14 +11,15 @@
 # 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 functools
 import os
+import urllib.error
 import warnings
 from functools import partial
 from typing import Tuple
 
 from pytorch_lightning import LightningModule
-from pytorch_lightning.utilities import _BOLTS_AVAILABLE
+from pytorch_lightning.utilities import _BOLTS_AVAILABLE, rank_zero_warn
 from torch import nn as nn
 from torchvision.models.detection.backbone_utils import resnet_fpn_backbone
 
@@ -51,6 +52,24 @@
 OBJ_DETECTION_BACKBONES = FlashRegistry("backbones")
 
 
+def catch_url_error(fn):
+
+    @functools.wraps(fn)
+    def wrapper(pretrained=False, **kwargs):
+        try:
+            return fn(pretrained=pretrained, **kwargs)
+        except urllib.error.URLError:
+            result = fn(pretrained=False, **kwargs)
+            rank_zero_warn(
+                "Failed to download pretrained weights for the selected backbone. The backbone has been created with"
+                " `pretrained=False` instead. If you are loading from a local checkpoint, this warning can be safely"
+                " ignored.", UserWarning
+            )
+            return result
+
+    return wrapper
+
+
 @IMAGE_CLASSIFIER_BACKBONES(name="simclr-imagenet", namespace="vision", package="bolts")
 def load_simclr_imagenet(path_or_url: str = f"{ROOT_S3_BUCKET}/simclr/bolts_simclr_imagenet/simclr_imagenet.ckpt", **_):
     simclr: LightningModule = SimCLR.load_from_checkpoint(path_or_url, strict=False)
@@ -83,7 +102,7 @@ def _fn_mobilenet_vgg(model_name: str, pretrained: bool = True) -> Tuple[nn.Modu
         _type = "mobilenet" if model_name in MOBILENET_MODELS else "vgg"
 
         IMAGE_CLASSIFIER_BACKBONES(
-            fn=partial(_fn_mobilenet_vgg, model_name),
+            fn=catch_url_error(partial(_fn_mobilenet_vgg, model_name)),
             name=model_name,
             namespace="vision",
             package="torchvision",
@@ -99,7 +118,7 @@ def _fn_resnet(model_name: str, pretrained: bool = True) -> Tuple[nn.Module, int
             return backbone, num_features
 
         IMAGE_CLASSIFIER_BACKBONES(
-            fn=partial(_fn_resnet, model_name),
+            fn=catch_url_error(partial(_fn_resnet, model_name)),
             name=model_name,
             namespace="vision",
             package="torchvision",
@@ -118,7 +137,10 @@ def _fn_resnet_fpn(
             return backbone, 256
 
         OBJ_DETECTION_BACKBONES(
-            fn=partial(_fn_resnet_fpn, model_name), name=model_name, package="torchvision", type="resnet-fpn"
+            fn=catch_url_error(partial(_fn_resnet_fpn, model_name)),
+            name=model_name,
+            package="torchvision",
+            type="resnet-fpn"
         )
 
     for model_name in DENSENET_MODELS:
@@ -130,7 +152,7 @@ def _fn_densenet(model_name: str, pretrained: bool = True) -> Tuple[nn.Module, i
             return backbone, num_features
 
         IMAGE_CLASSIFIER_BACKBONES(
-            fn=partial(_fn_densenet, model_name),
+            fn=catch_url_error(partial(_fn_densenet, model_name)),
             name=model_name,
             namespace="vision",
             package="torchvision",
@@ -156,5 +178,5 @@ def _fn_timm(
             return backbone, num_features
 
         IMAGE_CLASSIFIER_BACKBONES(
-            fn=partial(_fn_timm, model_name), name=model_name, namespace="vision", package="timm"
+            fn=catch_url_error(partial(_fn_timm, model_name)), name=model_name, namespace="vision", package="timm"
         )

flash/vision/detection/model.py

@@ -137,8 +137,8 @@ def get_model(
                 )
         else:
             backbone_model, num_features = ObjectDetector.backbones.get(backbone)(
-                pretrained_backbone,
-                trainable_backbone_layers,
+                pretrained=pretrained_backbone,
+                trainable_layers=trainable_backbone_layers,
                 **kwargs,
             )
             backbone_model.out_channels = num_features

tests/vision/test_backbones.py

@@ -1,8 +1,10 @@
+import urllib.error
+
 import pytest
 from pytorch_lightning.utilities import _BOLTS_AVAILABLE, _TORCHVISION_AVAILABLE
 
 from flash.utils.imports import _TIMM_AVAILABLE
-from flash.vision.backbones import IMAGE_CLASSIFIER_BACKBONES
+from flash.vision.backbones import catch_url_error, IMAGE_CLASSIFIER_BACKBONES
 
 
 @pytest.mark.parametrize(["backbone", "expected_num_features"], [
@@ -17,3 +19,13 @@ def test_image_classifier_backbones_registry(backbone, expected_num_features):
     backbone_model, num_features = backbone_fn(pretrained=False)
     assert backbone_model
     assert num_features == expected_num_features
+
+
+def test_pretrained_backbones_catch_url_error():
+
+    def raise_error_if_pretrained(pretrained=False):
+        if pretrained:
+            raise urllib.error.URLError('Test error')
+
+    with pytest.warns(UserWarning, match="Failed to download pretrained weights"):
+        catch_url_error(raise_error_if_pretrained)(pretrained=True)