GeezerVisonThe World Wide Web, restored to 800 pixels of glory. |
| Home|About|Gaming cabinet|Archives|How it works|Starter sites |
GeezerVison is showing github.com — DeepLearningExamples/hubconf.py at master · alierkan/DeepLearningExamples Expand file tree/ Copy pathhubconf.pyMore file actions 34 lines (27 loc) · 2.26 KB / Copy pathhubconf.pyFile metadata and controls34 lines (27 loc) · 2.26 KB 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 import os import sys from PyTorch.Detection.SSD.ssd 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 resnet50 as nvidia_resnet50 from PyTorch.Classification.ConvNets.image_classification.models import resnext101_32x4d as nvidia_resnext101_32x4d from PyTorch.Classification.ConvNets.image_classification.models import se_resnext101_32x4d as nvidia_se_resnext101_32x4d from PyTorch.Classification.ConvNets.image_classification.models import efficientnet_b0 as nvidia_efficientnet_b0 from PyTorch.Classification.ConvNets.image_classification.models import efficientnet_b4 as nvidia_efficientnet_b4 from PyTorch.Classification.ConvNets.image_classification.models import efficientnet_widese_b0 as nvidia_efficientnet_widese_b0 from PyTorch.Classification.ConvNets.image_classification.models import efficientnet_widese_b4 as nvidia_efficientnet_widese_b4 from PyTorch.Classification.ConvNets.image_classification.models import nvidia_convnets_processing_utils from PyTorch.Classification.ConvNets.image_classification.models import resnext101_32x4d as nvidia_resneXt from PyTorch.Classification.ConvNets.image_classification.models import nvidia_efficientnet sys.path.append(os.path.join(sys.path[0], 'PyTorch/Classification/ConvNets/image_classification')) from PyTorch.Classification.GPUNet.configs.gpunet_torchhub import nvidia_gpunet sys.path.append(os.path.join(sys.path[0], 'PyTorch/Classification/GPUNet/configs')) 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 sys.path.append(os.path.join(sys.path[0], 'PyTorch/SpeechSynthesis/Tacotron2')) from PyTorch.SpeechSynthesis.HiFiGAN.fastpitch import nvidia_fastpitch from PyTorch.SpeechSynthesis.HiFiGAN.fastpitch import nvidia_textprocessing_utils from PyTorch.SpeechSynthesis.HiFiGAN.hifigan import nvidia_hifigan sys.path.append(os.path.join(sys.path[0], 'PyTorch/SpeechSynthesis/HiFiGAN')) from PyTorch.Forecasting.TFT.tft_torchhub import nvidia_tft, nvidia_tft_data_utils sys.path.append(os.path.join(sys.path[0], 'PyTorch/Forecasting/TFT')) |
| You are visitor 01402520 — GeezerVison does not store the pages you look up. Best viewed with Netscape Navigator 4.7 or Internet Explorer 6 at 800×600. No cookies required. |