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