Revert "Update calls to torch.hub.* to use trust_repo=True. (#281)" (#286)

This reverts commit 5c690fa.

Author: NicolasHug

datvuthanh_hybridnets.md

@@ -77,7 +77,7 @@ This example loads the pretrained **HybridNets** model and passes an image for i
 import torch
 
 # load model
-model = torch.hub.load('datvuthanh/hybridnets', 'hybridnets', pretrained=True, trust_repo=True)
+model = torch.hub.load('datvuthanh/hybridnets', 'hybridnets', pretrained=True)
 
 #inference
 img = torch.randn(1,3,640,384)

huggingface_pytorch-transformers.md

@@ -71,7 +71,7 @@ The tokenizer object allows the conversion from character strings to tokens unde
 
 ```py
 import torch
-tokenizer = torch.hub.load('huggingface/pytorch-transformers', trust_repo=True, 'tokenizer', 'bert-base-uncased')    # Download vocabulary from S3 and cache.
+tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'tokenizer', 'bert-base-uncased')    # Download vocabulary from S3 and cache.
 tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'tokenizer', './test/bert_saved_model/')  # E.g. tokenizer was saved using `save_pretrained('./test/saved_model/')`
 ```
 

hustvl_yolop.md

@@ -117,7 +117,7 @@ This example loads the pretrained **YOLOP** model and passes an image for infere
 import torch
 
 # load model
-model = torch.hub.load('hustvl/yolop', 'yolop', trust_repo=True, pretrained=True)
+model = torch.hub.load('hustvl/yolop', 'yolop', pretrained=True)
 
 #inference
 img = torch.randn(1,3,640,640)

intelisl_midas_v2.md

@@ -48,7 +48,7 @@ model_type = "DPT_Large"     # MiDaS v3 - Large     (highest accuracy, slowest i
 #model_type = "DPT_Hybrid"   # MiDaS v3 - Hybrid    (medium accuracy, medium inference speed)
 #model_type = "MiDaS_small"  # MiDaS v2.1 - Small   (lowest accuracy, highest inference speed)
 
-midas = torch.hub.load("intel-isl/MiDaS", model_type, trust_repo=True)
+midas = torch.hub.load("intel-isl/MiDaS", model_type)
 ```
 Move model to GPU if available
 ```python

mateuszbuda_brain-segmentation-pytorch_unet.md

@@ -18,7 +18,7 @@ demo-model-link: https://huggingface.co/spaces/pytorch/U-NET-for-brain-MRI
 
 ```python
 import torch
-model = torch.hub.load('mateuszbuda/brain-segmentation-pytorch', 'unet', trust_repo=True,
+model = torch.hub.load('mateuszbuda/brain-segmentation-pytorch', 'unet',
     in_channels=3, out_channels=1, init_features=32, pretrained=True)
 
 ```

nicolalandro_ntsnet-cub200_ntsnet.md

@@ -18,7 +18,7 @@ demo-model-link: https://huggingface.co/spaces/pytorch/NTSNET
 
 ```python
 import torch
-model = torch.hub.load('nicolalandro/ntsnet-cub200', 'ntsnet', trust_repo=True, pretrained=True,
+model = torch.hub.load('nicolalandro/ntsnet-cub200', 'ntsnet', pretrained=True,
                        **{'topN': 6, 'device':'cpu', 'num_classes': 200})
 ```
 

nvidia_deeplearningexamples_efficientnet.md

@@ -68,7 +68,7 @@ You can choose among the following models:
 
 There are also quantized version of the models, but they require nvidia container. See [quantized models](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Classification/ConvNets/efficientnet#quantization)
 ```python
-efficientnet = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_efficientnet_b0', trust_repo=True, pretrained=True)
+efficientnet = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_efficientnet_b0', pretrained=True)
 utils = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_convnets_processing_utils')
 
 efficientnet.eval().to(device)

nvidia_deeplearningexamples_resnet50.md

@@ -61,7 +61,7 @@ print(f'Using {device} for inference')
 
 Load the model pretrained on IMAGENET dataset.
 ```python
-resnet50 = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_resnet50', pretrained=True, trust_repo=True)
+resnet50 = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_resnet50', pretrained=True)
 utils = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_convnets_processing_utils')
 
 resnet50.eval().to(device)

nvidia_deeplearningexamples_resnext.md

@@ -66,7 +66,7 @@ print(f'Using {device} for inference')
 
 Load the model pretrained on IMAGENET dataset.
 ```python
-resneXt = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_resneXt', trust_repo=True)
+resneXt = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_resneXt')
 utils = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_convnets_processing_utils')
 
 resneXt.eval().to(device)

nvidia_deeplearningexamples_se-resnext.md

@@ -66,7 +66,7 @@ print(f'Using {device} for inference')
 
 Load the model pretrained on IMAGENET dataset.
 ```python
-resneXt = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_se_resnext101_32x4d', trust_repo=True)
+resneXt = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_se_resnext101_32x4d')
 utils = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_convnets_processing_utils')
 
 resneXt.eval().to(device)

nvidia_deeplearningexamples_ssd.md

@@ -54,7 +54,7 @@ pip install numpy scipy scikit-image matplotlib
 Load an SSD model pretrained on COCO dataset, as well as a set of utility methods for convenient and comprehensive formatting of input and output of the model.
 ```python
 import torch
-ssd_model = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_ssd', trust_repo=True)
+ssd_model = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_ssd')
 utils = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_ssd_processing_utils')
 ```
 

nvidia_deeplearningexamples_tacotron2.md

@@ -43,7 +43,7 @@ apt-get install -y libsndfile1
 Load the Tacotron2 model pre-trained on [LJ Speech dataset](https://keithito.com/LJ-Speech-Dataset/) and prepare it for inference:
 ```python
 import torch
-tacotron2 = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_tacotron2', trust_repo=True, model_math='fp16')
+tacotron2 = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_tacotron2', model_math='fp16')
 tacotron2 = tacotron2.to('cuda')
 tacotron2.eval()
 ```

nvidia_deeplearningexamples_waveglow.md

@@ -41,7 +41,7 @@ apt-get install -y libsndfile1
 Load the WaveGlow model pre-trained on [LJ Speech dataset](https://keithito.com/LJ-Speech-Dataset/)
 ```python
 import torch
-waveglow = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_waveglow', trust_repo=True, model_math='fp32')
+waveglow = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_waveglow', model_math='fp32')
 ```
 
 Prepare the WaveGlow model for inference

pytorch_vision_ghostnet.md

@@ -19,7 +19,7 @@ demo-model-link: https://huggingface.co/spaces/pytorch/GhostNet
 
 ```python
 import torch
-model = torch.hub.load('huawei-noah/ghostnet', 'ghostnet_1x', pretrained=True, trust_repo=True)
+model = torch.hub.load('huawei-noah/ghostnet', 'ghostnet_1x', pretrained=True)
 model.eval()
 ```
 

pytorch_vision_hardnet.md

@@ -19,7 +19,7 @@ demo-model-link: https://huggingface.co/spaces/pytorch/HardNet
 
 ```python
 import torch
-model = torch.hub.load('PingoLH/Pytorch-HarDNet', 'hardnet68', pretrained=True, trust_repo=True)
+model = torch.hub.load('PingoLH/Pytorch-HarDNet', 'hardnet68', pretrained=True)
 # or any of these variants
 # model = torch.hub.load('PingoLH/Pytorch-HarDNet', 'hardnet85', pretrained=True)
 # model = torch.hub.load('PingoLH/Pytorch-HarDNet', 'hardnet68ds', pretrained=True)

pytorch_vision_ibnnet.md

@@ -19,7 +19,7 @@ demo-model-link: https://huggingface.co/spaces/pytorch/IBN-Net
 
 ```python
 import torch
-model = torch.hub.load('XingangPan/IBN-Net', 'resnet50_ibn_a', pretrained=True, trust_repo=True)
+model = torch.hub.load('XingangPan/IBN-Net', 'resnet50_ibn_a', pretrained=True)
 model.eval()
 ```
 

pytorch_vision_meal_v2.md

@@ -27,7 +27,7 @@ We require one additional Python dependency
 import torch
 # list of models: 'mealv1_resnest50', 'mealv2_resnest50', 'mealv2_resnest50_cutmix', 'mealv2_resnest50_380x380', 'mealv2_mobilenetv3_small_075', 'mealv2_mobilenetv3_small_100', 'mealv2_mobilenet_v3_large_100', 'mealv2_efficientnet_b0'
 # load pretrained models, using "mealv2_resnest50_cutmix" as an example
-model = torch.hub.load('szq0214/MEAL-V2','meal_v2', 'mealv2_resnest50_cutmix', pretrained=True, trust_repo=True)
+model = torch.hub.load('szq0214/MEAL-V2','meal_v2', 'mealv2_resnest50_cutmix', pretrained=True)
 model.eval()
 ```
 

pytorch_vision_proxylessnas.md

@@ -21,7 +21,7 @@ demo-model-link: https://huggingface.co/spaces/pytorch/ProxylessNAS
 import torch
 target_platform = "proxyless_cpu"
 # proxyless_gpu, proxyless_mobile, proxyless_mobile14 are also avaliable.
-model = torch.hub.load('mit-han-lab/ProxylessNAS', target_platform, pretrained=True, trust_repo=True)
+model = torch.hub.load('mit-han-lab/ProxylessNAS', target_platform, pretrained=True)
 model.eval()
 ```
 

pytorch_vision_resnest.md

@@ -20,9 +20,9 @@ demo-model-link: https://huggingface.co/spaces/pytorch/ResNeSt
 ```python
 import torch
 # get list of models
-torch.hub.list('zhanghang1989/ResNeSt', force_reload=True, trust_repo=True)
+torch.hub.list('zhanghang1989/ResNeSt', force_reload=True)
 # load pretrained models, using ResNeSt-50 as an example
-model = torch.hub.load('zhanghang1989/ResNeSt', 'resnest50', pretrained=True, trust_repo=True)
+model = torch.hub.load('zhanghang1989/ResNeSt', 'resnest50', pretrained=True)
 model.eval()
 ```
 

sigsep_open-unmix-pytorch_umx.md

@@ -26,7 +26,7 @@ pip install -q torchaudio
 import torch
 
 # loading umxhq four target separator
-separator = torch.hub.load('sigsep/open-unmix-pytorch', 'umxhq', trust_repo=True)
+separator = torch.hub.load('sigsep/open-unmix-pytorch', 'umxhq')
 
 # generate random audio
 # ... with shape (nb_samples, nb_channels, nb_timesteps)

snakers4_silero-models_stt.md

@@ -31,7 +31,6 @@ device = torch.device('cpu')  # gpu also works, but our models are fast enough f
 
 model, decoder, utils = torch.hub.load(repo_or_dir='snakers4/silero-models',
                                        model='silero_stt',
-                                       trust_repo=True,
                                        language='en', # also available 'de', 'es'
                                        device=device)
 (read_batch, split_into_batches,

snakers4_silero-models_tts.md

@@ -29,7 +29,6 @@ speaker = 'lj_16khz'
 device = torch.device('cpu')
 model, symbols, sample_rate, example_text, apply_tts = torch.hub.load(repo_or_dir='snakers4/silero-models',
                                                                       model='silero_tts',
-                                                                      trust_repo=True,
                                                                       language=language,
                                                                       speaker=speaker)
 model = model.to(device)  # gpu or cpu

snakers4_silero-vad_vad.md

@@ -33,8 +33,7 @@ torch.hub.download_url_to_file('https://models.silero.ai/vad_models/en.wav', 'en
 
 model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad',
                               model='silero_vad',
-                              force_reload=True,
-                              trust_repo=True)
+                              force_reload=True)
 
 (get_speech_timestamps,
  _, read_audio,

ultralytics_yolov5.md

@@ -71,7 +71,7 @@ This example loads a pretrained **YOLOv5s** model and passes an image for infere
 import torch
 
 # Model
-model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True, trust_repo=True)
+model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)
 
 # Images
 imgs = ['https://ultralytics.com/images/zidane.jpg']  # batch of images