[ConvNets/PyT] Exposing to PytorchHub

PyTorch/Classification/ConvNets/image_classification/models/__init__.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from .entrypoints import nvidia_efficientnet, nvidia_resneXt, nvidia_resnet50, nvidia_convnets_processing_utils
 from .resnet import resnet50, resnext101_32x4d, se_resnext101_32x4d
 from .efficientnet import (
     efficientnet_b0,

PyTorch/Classification/ConvNets/image_classification/models/entrypoints.py

@@ -0,0 +1,138 @@
+# Copyright (c) 2018-2019, NVIDIA CORPORATION
+#
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# * Redistributions of source code must retain the above copyright notice, this
+#   list of conditions and the following disclaimer.
+#
+# * Redistributions in binary form must reproduce the above copyright notice,
+#   this list of conditions and the following disclaimer in the documentation
+#   and/or other materials provided with the distribution.
+#
+# * Neither the name of the copyright holder nor the names of its
+#   contributors may be used to endorse or promote products derived from
+#   this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+def nvidia_resnet50(pretrained=True, **kwargs):
+    """Constructs a ResNet50 model.
+    For detailed information on model input and output, training recipies, inference and performance
+    visit: github.com/NVIDIA/DeepLearningExamples and/or ngc.nvidia.com
+    Args:
+        pretrained (bool, True): If True, returns a model pretrained on IMAGENET dataset.
+    """
+
+    from . import resnet50
+
+    return resnet50(pretrained=pretrained)
+
+def nvidia_efficientnet(type='efficient-b0', pretrained=True, **kwargs):
+    """Constructs a EfficientNet model.
+    For detailed information on model input and output, training recipies, inference and performance
+    visit: github.com/NVIDIA/DeepLearningExamples and/or ngc.nvidia.com
+    Args:
+        pretrained (bool, True): If True, returns a model pretrained on IMAGENET dataset.
+    """
+
+    from .efficientnet import _ce
+
+    return _ce(type)(pretrained=pretrained, **kwargs)
+
+def nvidia_resneXt(pretrained=True, **kwargs):
+    """Constructs a ResNeXt model.
+    For detailed information on model input and output, training recipies, inference and performance
+    visit: github.com/NVIDIA/DeepLearningExamples and/or ngc.nvidia.com
+    Args:
+        pretrained (bool, True): If True, returns a model pretrained on IMAGENET dataset.
+    """
+
+    from . import resnext101_32x4d
+
+    return resnext101_32x4d(pretrained=pretrained)
+
+def nvidia_convnets_processing_utils():
+    import numpy as np
+    import torch
+    from PIL import Image
+    import torchvision.transforms as transforms
+    import numpy as np
+    import json
+    import requests
+    import validators
+
+    class Processing:
+
+        @staticmethod
+        def prepare_input_from_uri(uri, cuda=False):
+            img_transforms = transforms.Compose(
+                [transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor()]
+            )
+
+            if (validators.url(uri)):
+                img = Image.open(requests.get(uri, stream=True).raw)
+            else:
+                img = Image.open(uri)
+
+            img = img_transforms(img)
+            with torch.no_grad():
+                # mean and std are not multiplied by 255 as they are in training script
+                # torch dataloader reads data into bytes whereas loading directly
+                # through PIL creates a tensor with floats in [0,1] range
+                mean = torch.tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1)
+                std = torch.tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1)
+                img = img.float()
+                if cuda:
+                    mean = mean.cuda()
+                    std = std.cuda()
+                    img = img.cuda()
+                input = img.unsqueeze(0).sub_(mean).div_(std)
+
+            return input
+
+        @staticmethod
+        def pick_n_best(predictions, n=5):
+            predictions = predictions.float().cpu().numpy()
+            topN = np.argsort(-1*predictions, axis=-1)[:,:n]
+            imgnet_classes = Processing.get_imgnet_classes()
+            
+            results=[]
+            for idx,case in enumerate(topN):
+                r = []
+                for c, v in zip(imgnet_classes[case], predictions[idx, case]):
+                    r.append((f"{c}", f"{100*v:.1f}%"))
+                print(f"sample {idx}: {r}")
+                results.append(r)
+            
+            return results
+
+        @staticmethod
+        def get_imgnet_classes():
+            import os
+            import json
+            imgnet_classes_json = "LOC_synset_mapping.json"
+
+            if not os.path.exists(imgnet_classes_json):
+                print("Downloading Imagenet Classes names.")
+                import urllib
+                urllib.request.urlretrieve(
+                    "https://raw.githubusercontent.com/NVIDIA/DeepLearningExamples/master/PyTorch/Classification/ConvNets/LOC_synset_mapping.json", 
+                    filename=imgnet_classes_json)
+                print("Downloading finished.")
+            imgnet_classes = np.array(json.load(open(imgnet_classes_json, "r")))
+
+            return imgnet_classes
+
+    return Processing()

PyTorch/Classification/ConvNets/image_classification/models/model.py

@@ -47,7 +47,7 @@ class EntryPoint:
         if pretrained:
             assert self.model.checkpoint_url is not None
             state_dict = torch.hub.load_state_dict_from_url(
-                self.model.checkpoint_url, map_location=torch.device("cpu")
+                self.model.checkpoint_url, map_location=torch.device("cpu"), progress=True
             )
 
         if pretrained_from_file is not None:

hubconf.py

@@ -4,6 +4,9 @@ import sys
 from PyTorch.Detection.SSD.src import nvidia_ssd, nvidia_ssd_processing_utils
 sys.path.append(os.path.join(sys.path[0], 'PyTorch/Detection/SSD'))
 
+from PyTorch.Classification.ConvNets.image_classification.models import nvidia_efficientnet, nvidia_resneXt, nvidia_resnet50, nvidia_convnets_processing_utils
+sys.path.append(os.path.join(sys.path[0], 'PyTorch/Classification/ConvNets/image_classification'))
+
 from PyTorch.SpeechSynthesis.Tacotron2.tacotron2 import nvidia_tacotron2
 from PyTorch.SpeechSynthesis.Tacotron2.tacotron2 import nvidia_tts_utils
 from PyTorch.SpeechSynthesis.Tacotron2.waveglow import nvidia_waveglow