removing torchhub access through master
This commit is contained in:
Krzysztof Kudrynski
parent
d9f925cb9c
commit
ff16b6c649
383
hubconf.py
383
hubconf.py
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@ -1,372 +1,35 @@
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import urllib.request
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import torch
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import os
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import sys
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def relocated():
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raise ValueError(
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"NVIDIA entrypoints moved to branch torchhub \n"
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"Use torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', ...) to access the models"
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)
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# from https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/SpeechSynthesis/Tacotron2/inference.py
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def checkpoint_from_distributed(state_dict):
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def nvidia_ncf(**kwargs):
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"""Entrypoints moved to branch torchhub
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"""
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Checks whether checkpoint was generated by DistributedDataParallel. DDP
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wraps model in additional "module.", it needs to be unwrapped for single
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GPU inference.
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:param state_dict: model's state dict
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relocated()
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def nvidia_tacotron2(**kwargs):
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"""Entrypoints moved to branch torchhub
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"""
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ret = False
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for key, _ in state_dict.items():
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if key.find('module.') != -1:
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ret = True
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break
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return ret
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relocated()
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# from https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/SpeechSynthesis/Tacotron2/inference.py
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def unwrap_distributed(state_dict):
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def nvidia_waveglow(**kwargs):
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"""Entrypoints moved to branch torchhub
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"""
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Unwraps model from DistributedDataParallel.
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DDP wraps model in additional "module.", it needs to be removed for single
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GPU inference.
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:param state_dict: model's state dict
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"""
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new_state_dict = {}
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for key, value in state_dict.items():
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new_key = key.replace('module.1.', '')
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new_key = new_key.replace('module.', '')
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new_state_dict[new_key] = value
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return new_state_dict
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relocated()
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dependencies = ['torch']
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def nvidia_ncf(pretrained=True, **kwargs):
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"""Constructs an NCF 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 ml-20m dataset.
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model_math (str, 'fp32'): returns a model in given precision ('fp32' or 'fp16')
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nb_users (int): number of users
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nb_items (int): number of items
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mf_dim (int, 64): dimension of latent space in matrix factorization
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mlp_layer_sizes (list, [256,256,128,64]): sizes of layers of multi-layer-perceptron
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dropout (float, 0.5): dropout
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"""
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from PyTorch.Recommendation.NCF import neumf as ncf
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fp16 = "model_math" in kwargs and kwargs["model_math"] == "fp16"
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force_reload = "force_reload" in kwargs and kwargs["force_reload"]
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config = {'nb_users': None, 'nb_items': None, 'mf_dim': 64, 'mf_reg': 0.,
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'mlp_layer_sizes': [256, 256, 128, 64], 'mlp_layer_regs':[0, 0, 0, 0], 'dropout': 0.5}
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if pretrained:
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if fp16:
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checkpoint = 'https://developer.nvidia.com/joc-ncf-fp16-pyt-20190225'
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else:
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checkpoint = 'https://developer.nvidia.com/joc-ncf-fp32-pyt-20190225'
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ckpt_file = os.path.basename(checkpoint)
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if not os.path.exists(ckpt_file) or force_reload:
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sys.stderr.write('Downloading checkpoint from {}\n'.format(checkpoint))
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urllib.request.urlretrieve(checkpoint, ckpt_file)
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ckpt = torch.load(ckpt_file)
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if checkpoint_from_distributed(ckpt):
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ckpt = unwrap_distributed(ckpt)
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config['nb_users'] = ckpt['mf_user_embed.weight'].shape[0]
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config['nb_items'] = ckpt['mf_item_embed.weight'].shape[0]
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config['mf_dim'] = ckpt['mf_item_embed.weight'].shape[1]
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mlp_shapes = [ckpt[k].shape for k in ckpt.keys() if 'mlp' in k and 'weight' in k and 'embed' not in k]
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config['mlp_layer_sizes'] = [mlp_shapes[0][1], mlp_shapes[1][1], mlp_shapes[2][1], mlp_shapes[2][0]]
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config['mlp_layer_regs'] = [0] * len(config['mlp_layer_sizes'])
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else:
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if 'nb_users' not in kwargs:
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raise ValueError("Missing 'nb_users' argument.")
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if 'nb_items' not in kwargs:
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raise ValueError("Missing 'nb_items' argument.")
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for k,v in kwargs.items():
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if k in config.keys():
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config[k] = v
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config['mlp_layer_regs'] = [0] * len(config['mlp_layer_sizes'])
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m = ncf.NeuMF(**config)
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if fp16:
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m.half()
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if pretrained:
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m.load_state_dict(ckpt)
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return m
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def nvidia_tacotron2(pretrained=True, **kwargs):
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"""Constructs a Tacotron 2 model (nn.module with additional infer(input) method).
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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 (type[, default value]):
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pretrained (bool, True): If True, returns a model pretrained on LJ Speech dataset.
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model_math (str, 'fp32'): returns a model in given precision ('fp32' or 'fp16')
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n_symbols (int, 148): Number of symbols used in a sequence passed to the prenet, see
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https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/SpeechSynthesis/Tacotron2/tacotron2/text/symbols.py
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p_attention_dropout (float, 0.1): dropout probability on attention LSTM (1st LSTM layer in decoder)
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p_decoder_dropout (float, 0.1): dropout probability on decoder LSTM (2nd LSTM layer in decoder)
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max_decoder_steps (int, 1000): maximum number of generated mel spectrograms during inference
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"""
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from PyTorch.SpeechSynthesis.Tacotron2.tacotron2 import model as tacotron2
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from PyTorch.SpeechSynthesis.Tacotron2.models import lstmcell_to_float, batchnorm_to_float
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from PyTorch.SpeechSynthesis.Tacotron2.tacotron2.text import text_to_sequence
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fp16 = "model_math" in kwargs and kwargs["model_math"] == "fp16"
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force_reload = "force_reload" in kwargs and kwargs["force_reload"]
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if pretrained:
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if fp16:
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checkpoint = 'https://developer.nvidia.com/joc-tacotron2-fp16-pyt-20190306'
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else:
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checkpoint = 'https://developer.nvidia.com/joc-tacotron2-fp32-pyt-20190306'
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ckpt_file = os.path.basename(checkpoint)
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if not os.path.exists(ckpt_file) or force_reload:
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sys.stderr.write('Downloading checkpoint from {}\n'.format(checkpoint))
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urllib.request.urlretrieve(checkpoint, ckpt_file)
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ckpt = torch.load(ckpt_file)
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state_dict = ckpt['state_dict']
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if checkpoint_from_distributed(state_dict):
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state_dict = unwrap_distributed(state_dict)
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config = ckpt['config']
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else:
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config = {'mask_padding': False, 'n_mel_channels': 80, 'n_symbols': 148,
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'symbols_embedding_dim': 512, 'encoder_kernel_size': 5,
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'encoder_n_convolutions': 3, 'encoder_embedding_dim': 512,
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'attention_rnn_dim': 1024, 'attention_dim': 128,
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'attention_location_n_filters': 32,
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'attention_location_kernel_size': 31, 'n_frames_per_step': 1,
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'decoder_rnn_dim': 1024, 'prenet_dim': 256,
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'max_decoder_steps': 1000, 'gate_threshold': 0.5,
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'p_attention_dropout': 0.1, 'p_decoder_dropout': 0.1,
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'postnet_embedding_dim': 512, 'postnet_kernel_size': 5,
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'postnet_n_convolutions': 5, 'decoder_no_early_stopping': False}
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for k,v in kwargs.items():
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if k in config.keys():
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config[k] = v
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m = tacotron2.Tacotron2(**config)
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if fp16:
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m = batchnorm_to_float(m.half())
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m = lstmcell_to_float(m)
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if pretrained:
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m.load_state_dict(state_dict)
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m.text_to_sequence = text_to_sequence
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return m
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def nvidia_waveglow(pretrained=True, **kwargs):
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"""Constructs a WaveGlow model (nn.module with additional infer(input) method).
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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): If True, returns a model pretrained on LJ Speech dataset.
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model_math (str, 'fp32'): returns a model in given precision ('fp32' or 'fp16')
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"""
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from PyTorch.SpeechSynthesis.Tacotron2.waveglow import model as waveglow
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from PyTorch.SpeechSynthesis.Tacotron2.models import batchnorm_to_float
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fp16 = "model_math" in kwargs and kwargs["model_math"] == "fp16"
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force_reload = "force_reload" in kwargs and kwargs["force_reload"]
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if pretrained:
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if fp16:
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checkpoint = 'https://developer.nvidia.com/joc-waveglow-fp16-pyt-20190306'
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else:
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checkpoint = 'https://developer.nvidia.com/joc-waveglow-fp32-pyt-20190306'
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ckpt_file = os.path.basename(checkpoint)
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if not os.path.exists(ckpt_file) or force_reload:
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sys.stderr.write('Downloading checkpoint from {}\n'.format(checkpoint))
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urllib.request.urlretrieve(checkpoint, ckpt_file)
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ckpt = torch.load(ckpt_file)
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state_dict = ckpt['state_dict']
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if checkpoint_from_distributed(state_dict):
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state_dict = unwrap_distributed(state_dict)
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config = ckpt['config']
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else:
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config = {'n_mel_channels': 80, 'n_flows': 12, 'n_group': 8,
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'n_early_every': 4, 'n_early_size': 2,
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'WN_config': {'n_layers': 8, 'kernel_size': 3,
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'n_channels': 512}}
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for k,v in kwargs.items():
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if k in config.keys():
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config[k] = v
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elif k in config['WN_config'].keys():
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config['WN_config'][k] = v
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m = waveglow.WaveGlow(**config)
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if fp16:
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m = batchnorm_to_float(m.half())
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for mat in m.convinv:
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mat.float()
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if pretrained:
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m.load_state_dict(state_dict)
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return m
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def nvidia_ssd_processing_utils():
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import numpy as np
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import skimage
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from PyTorch.Detection.SSD.src.utils import dboxes300_coco, Encoder
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class Processing:
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@staticmethod
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def load_image(image_path):
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"""Code from Loading_Pretrained_Models.ipynb - a Caffe2 tutorial"""
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img = skimage.img_as_float(skimage.io.imread(image_path))
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if len(img.shape) == 2:
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img = np.array([img, img, img]).swapaxes(0, 2)
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return img
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@staticmethod
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def rescale(img, input_height, input_width):
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"""Code from Loading_Pretrained_Models.ipynb - a Caffe2 tutorial"""
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aspect = img.shape[1] / float(img.shape[0])
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if (aspect > 1):
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# landscape orientation - wide image
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res = int(aspect * input_height)
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imgScaled = skimage.transform.resize(img, (input_width, res))
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if (aspect < 1):
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# portrait orientation - tall image
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res = int(input_width / aspect)
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imgScaled = skimage.transform.resize(img, (res, input_height))
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if (aspect == 1):
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imgScaled = skimage.transform.resize(img, (input_width, input_height))
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return imgScaled
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@staticmethod
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def crop_center(img, cropx, cropy):
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"""Code from Loading_Pretrained_Models.ipynb - a Caffe2 tutorial"""
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y, x, c = img.shape
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startx = x // 2 - (cropx // 2)
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starty = y // 2 - (cropy // 2)
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return img[starty:starty + cropy, startx:startx + cropx]
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@staticmethod
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def normalize(img, mean=128, std=128):
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img = (img * 256 - mean) / std
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return img
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@staticmethod
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def prepare_tensor(inputs, fp16=False):
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NHWC = np.array(inputs)
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NCHW = np.swapaxes(np.swapaxes(NHWC, 1, 3), 2, 3)
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tensor = torch.from_numpy(NCHW)
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tensor = tensor.cuda()
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tensor = tensor.float()
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if fp16:
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tensor = tensor.half()
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return tensor
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@staticmethod
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def prepare_input(img_uri):
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img = Processing.load_image(img_uri)
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img = Processing.rescale(img, 300, 300)
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img = Processing.crop_center(img, 300, 300)
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img = Processing.normalize(img)
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return img
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@staticmethod
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def decode_results(predictions):
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dboxes = dboxes300_coco()
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encoder = Encoder(dboxes)
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ploc, plabel = [val.float() for val in predictions]
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results = encoder.decode_batch(ploc, plabel, criteria=0.5, max_output=20)
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return [[pred.detach().cpu().numpy() for pred in detections] for detections in results]
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@staticmethod
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def pick_best(detections, threshold=0.3):
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bboxes, classes, confidences = detections
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best = np.argwhere(confidences > threshold)[:, 0]
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return [pred[best] for pred in detections]
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@staticmethod
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def get_coco_object_dictionary():
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import os
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file_with_coco_names = "category_names.txt"
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if not os.path.exists(file_with_coco_names):
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print("Downloading COCO annotations.")
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import urllib
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import zipfile
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import json
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import shutil
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urllib.request.urlretrieve("http://images.cocodataset.org/annotations/annotations_trainval2017.zip", "cocoanno.zip")
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with zipfile.ZipFile("cocoanno.zip", "r") as f:
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f.extractall()
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print("Downloading finished.")
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with open("annotations/instances_val2017.json", 'r') as COCO:
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js = json.loads(COCO.read())
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class_names = [category['name'] for category in js['categories']]
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open("category_names.txt", 'w').writelines([c+"\n" for c in class_names])
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os.remove("cocoanno.zip")
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shutil.rmtree("annotations")
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else:
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class_names = open("category_names.txt").readlines()
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class_names = [c.strip() for c in class_names]
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return class_names
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return Processing()
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def nvidia_ssd(pretrained=True, **kwargs):
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"""Constructs an SSD300 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 COCO dataset.
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model_math (str, 'fp32'): returns a model in given precision ('fp32' or 'fp16')
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"""Entrypoints moved to branch torchhub
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"""
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relocated()
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from PyTorch.Detection.SSD.src import model as ssd
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fp16 = "model_math" in kwargs and kwargs["model_math"] == "fp16"
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force_reload = "force_reload" in kwargs and kwargs["force_reload"]
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m = ssd.SSD300()
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if fp16:
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m = m.half()
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def batchnorm_to_float(module):
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"""Converts batch norm to FP32"""
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if isinstance(module, torch.nn.modules.batchnorm._BatchNorm):
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module.float()
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for child in module.children():
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batchnorm_to_float(child)
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return module
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m = batchnorm_to_float(m)
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if pretrained:
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if fp16:
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checkpoint = 'https://developer.nvidia.com/joc-ssd-fp16-pyt-20190225'
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else:
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checkpoint = 'https://developer.nvidia.com/joc-ssd-fp32-pyt-20190225'
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ckpt_file = os.path.basename(checkpoint)
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if not os.path.exists(ckpt_file) or force_reload:
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sys.stderr.write('Downloading checkpoint from {}\n'.format(checkpoint))
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urllib.request.urlretrieve(checkpoint, ckpt_file)
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ckpt = torch.load(ckpt_file)
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ckpt = ckpt['model']
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if checkpoint_from_distributed(ckpt):
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ckpt = unwrap_distributed(ckpt)
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m.load_state_dict(ckpt)
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return m
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def nvidia_ssd(**kwargs):
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"""Entrypoints moved to branch torchhub
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"""
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relocated()
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