[ConvNets/PyT] Exposing to PytorchHub
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# See the License for the specific language governing permissions and
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# limitations under the License.
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from .entrypoints import nvidia_efficientnet, nvidia_resneXt, nvidia_resnet50, nvidia_convnets_processing_utils
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from .resnet import resnet50, resnext101_32x4d, se_resnext101_32x4d
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from .resnet import resnet50, resnext101_32x4d, se_resnext101_32x4d
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from .efficientnet import (
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from .efficientnet import (
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efficientnet_b0,
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efficientnet_b0,
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@ -0,0 +1,138 @@
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# Copyright (c) 2018-2019, NVIDIA CORPORATION
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#
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# All rights reserved.
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are met:
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#
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# * Redistributions of source code must retain the above copyright notice, this
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# list of conditions and the following disclaimer.
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#
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# * Redistributions in binary form must reproduce the above copyright notice,
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# this list of conditions and the following disclaimer in the documentation
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# and/or other materials provided with the distribution.
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#
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# * Neither the name of the copyright holder nor the names of its
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# contributors may be used to endorse or promote products derived from
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# this software without specific prior written permission.
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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def nvidia_resnet50(pretrained=True, **kwargs):
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"""Constructs a ResNet50 model.
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For detailed information on model input and output, training recipies, inference and performance
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visit: github.com/NVIDIA/DeepLearningExamples and/or ngc.nvidia.com
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Args:
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pretrained (bool, True): If True, returns a model pretrained on IMAGENET dataset.
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"""
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from . import resnet50
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return resnet50(pretrained=pretrained)
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def nvidia_efficientnet(type='efficient-b0', pretrained=True, **kwargs):
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"""Constructs a EfficientNet model.
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For detailed information on model input and output, training recipies, inference and performance
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visit: github.com/NVIDIA/DeepLearningExamples and/or ngc.nvidia.com
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Args:
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pretrained (bool, True): If True, returns a model pretrained on IMAGENET dataset.
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"""
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from .efficientnet import _ce
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return _ce(type)(pretrained=pretrained, **kwargs)
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def nvidia_resneXt(pretrained=True, **kwargs):
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"""Constructs a ResNeXt model.
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For detailed information on model input and output, training recipies, inference and performance
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visit: github.com/NVIDIA/DeepLearningExamples and/or ngc.nvidia.com
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Args:
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pretrained (bool, True): If True, returns a model pretrained on IMAGENET dataset.
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"""
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from . import resnext101_32x4d
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return resnext101_32x4d(pretrained=pretrained)
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def nvidia_convnets_processing_utils():
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import numpy as np
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import torch
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from PIL import Image
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import torchvision.transforms as transforms
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import numpy as np
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import json
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import requests
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import validators
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class Processing:
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@staticmethod
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def prepare_input_from_uri(uri, cuda=False):
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img_transforms = transforms.Compose(
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[transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor()]
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)
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if (validators.url(uri)):
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img = Image.open(requests.get(uri, stream=True).raw)
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else:
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img = Image.open(uri)
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img = img_transforms(img)
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with torch.no_grad():
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# mean and std are not multiplied by 255 as they are in training script
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# torch dataloader reads data into bytes whereas loading directly
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# through PIL creates a tensor with floats in [0,1] range
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mean = torch.tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1)
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std = torch.tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1)
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img = img.float()
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if cuda:
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mean = mean.cuda()
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std = std.cuda()
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img = img.cuda()
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input = img.unsqueeze(0).sub_(mean).div_(std)
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return input
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@staticmethod
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def pick_n_best(predictions, n=5):
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predictions = predictions.float().cpu().numpy()
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topN = np.argsort(-1*predictions, axis=-1)[:,:n]
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imgnet_classes = Processing.get_imgnet_classes()
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results=[]
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for idx,case in enumerate(topN):
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r = []
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for c, v in zip(imgnet_classes[case], predictions[idx, case]):
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r.append((f"{c}", f"{100*v:.1f}%"))
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print(f"sample {idx}: {r}")
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results.append(r)
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return results
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@staticmethod
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def get_imgnet_classes():
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import os
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import json
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imgnet_classes_json = "LOC_synset_mapping.json"
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if not os.path.exists(imgnet_classes_json):
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print("Downloading Imagenet Classes names.")
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import urllib
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urllib.request.urlretrieve(
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"https://raw.githubusercontent.com/NVIDIA/DeepLearningExamples/master/PyTorch/Classification/ConvNets/LOC_synset_mapping.json",
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filename=imgnet_classes_json)
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print("Downloading finished.")
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imgnet_classes = np.array(json.load(open(imgnet_classes_json, "r")))
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return imgnet_classes
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return Processing()
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@ -47,7 +47,7 @@ class EntryPoint:
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if pretrained:
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if pretrained:
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assert self.model.checkpoint_url is not None
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assert self.model.checkpoint_url is not None
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state_dict = torch.hub.load_state_dict_from_url(
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state_dict = torch.hub.load_state_dict_from_url(
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self.model.checkpoint_url, map_location=torch.device("cpu")
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self.model.checkpoint_url, map_location=torch.device("cpu"), progress=True
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)
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)
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if pretrained_from_file is not None:
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if pretrained_from_file is not None:
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@ -4,6 +4,9 @@ import sys
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from PyTorch.Detection.SSD.src import nvidia_ssd, nvidia_ssd_processing_utils
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from PyTorch.Detection.SSD.src import nvidia_ssd, nvidia_ssd_processing_utils
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sys.path.append(os.path.join(sys.path[0], 'PyTorch/Detection/SSD'))
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sys.path.append(os.path.join(sys.path[0], 'PyTorch/Detection/SSD'))
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from PyTorch.Classification.ConvNets.image_classification.models import nvidia_efficientnet, nvidia_resneXt, nvidia_resnet50, nvidia_convnets_processing_utils
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sys.path.append(os.path.join(sys.path[0], 'PyTorch/Classification/ConvNets/image_classification'))
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from PyTorch.SpeechSynthesis.Tacotron2.tacotron2 import nvidia_tacotron2
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from PyTorch.SpeechSynthesis.Tacotron2.tacotron2 import nvidia_tacotron2
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from PyTorch.SpeechSynthesis.Tacotron2.tacotron2 import nvidia_tts_utils
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from PyTorch.SpeechSynthesis.Tacotron2.tacotron2 import nvidia_tts_utils
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from PyTorch.SpeechSynthesis.Tacotron2.waveglow import nvidia_waveglow
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from PyTorch.SpeechSynthesis.Tacotron2.waveglow import nvidia_waveglow
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