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maxmustermann:torchhub
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maxmustermann:origin/byshiue-patch-1
maxmustermann:torchhub_legacy
maxmustermann:nvpstr/b31d09
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maxmustermann:master
maxmustermann:torchhub
maxmustermann:pribalta-mrcnntf2-patch
maxmustermann:origin/byshiue-patch-1
maxmustermann:torchhub_legacy
maxmustermann:nvpstr/b31d09

15 commits
master ... torchhub_l

Author SHA1 Message Date
nv-kkudrynski b7596093f0
Update hubconf.py with valid NGC checkpoints 2020-11-02 16:26:38 +01:00
nv-kkudrynski dfe30b6baf
Temporary fallback to backup checkpoints 2020-10-30 22:18:37 +01:00
PrzemekS 3e961c3a14
Merge pull request #521 from mwawrzos/torchhub 
[SSD][hubconf] contiguous input tensor
 2020-05-26 19:31:18 +02:00
Marek Wawrzos ffe84ae70a [SSD][hubconf] contiguous input tensor 2020-05-20 16:00:47 +02:00
GrzegorzKarchNV 9189f07093
Merge pull request #463 from GrzegorzKarchNV/torchhub-taco2-infer-fix 
fixed device in tacotron2 inference
 2020-04-21 22:08:26 +02:00
gkarch 46758dadf8 fixed device in tacotron2 inference 2020-04-21 22:07:11 +02:00
skierat db408837c2
Merge pull request #418 from bilalsal/patch-1 
Fix skimage import issue, for NVIDIA's PyTorch SSD
 2020-03-13 12:03:16 +01:00
Bilal Alsallakh 8e3ef240b8
Fix skimage import issue, for NVIDIA's PyTorch SSD 
The PyTorch SSD implemented by NVIDIA relies on the nvidia_ssd_processing_utils() routines in this module
(see https://pytorch.org/hub/nvidia_deeplearningexamples_ssd/).
Python 3 has issues accessing skimage's io and transform in the original code (issue reported in issue https://github.com/pytorch/hub/issues/77).
 2020-03-10 23:29:34 -07:00
nv-kkudrynski e0b6d222b4
Merge pull request #325 from nv-kkudrynski/torchhub 
Torchhub
 2019-12-02 17:57:42 +01:00
Krzysztof Kudrynski 4c12145030 tacotron2 and waveglow checkpoints hosted on ngc 2019-12-02 17:21:50 +01:00
Krzysztof Kudrynski 5639257b22 switching ssd to ngc hosting 2019-11-21 13:53:33 +01:00
nv-kkudrynski 0cabbfb67c
Merge pull request #150 from ailzhang/torchhub_fix 
Fix urllib error on colab hub example
 2019-08-14 13:40:46 +02:00
Ailing Zhang b1d9921414 fix urllib error on colab 2019-08-08 18:57:39 -07:00
nvpstr 9e28f35158
Merge pull request #133 from nv-kkudrynski/torchhub 
ckpt download location changed to torchhub home
 2019-07-31 17:14:04 +02:00
Krzysztof Kudrynski 21d7520e14 ckpt download location changed to torchhub home 2019-07-30 18:37:31 +02:00
2 changed files with 32 additions and 30 deletions
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4
PyTorch/SpeechSynthesis/Tacotron2/tacotron2/model.py
View file

@ -530,8 +530,8 @@ class Decoder(nn.Module):
self.initialize_decoder_states(memory, mask=None)
mel_lengths = torch.zeros([memory.size(0)], dtype=torch.int32).cuda()
not_finished = torch.ones([memory.size(0)], dtype=torch.int32).cuda()
mel_lengths = torch.zeros([memory.size(0)], dtype=torch.int32).to(memory.device)
not_finished = torch.ones([memory.size(0)], dtype=torch.int32).to(memory.device)
mel_outputs, gate_outputs, alignments = [], [], []
while True:
decoder_input = self.prenet(decoder_input)

58
hubconf.py
View file

@ -3,6 +3,7 @@ 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):
"""
@ -35,6 +36,17 @@ def unwrap_distributed(state_dict):
return new_state_dict
def _download_checkpoint(checkpoint, force_reload):
model_dir = os.path.join(torch.hub._get_torch_home(), 'checkpoints')
if not os.path.exists(model_dir):
os.makedirs(model_dir)
ckpt_file = os.path.join(model_dir, 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)
return ckpt_file
dependencies = ['torch']
@ -66,10 +78,7 @@ def nvidia_ncf(pretrained=True, **kwargs):
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_file = _download_checkpoint(checkpoint, force_reload)
ckpt = torch.load(ckpt_file)
if checkpoint_from_distributed(ckpt):
@ -127,13 +136,10 @@ def nvidia_tacotron2(pretrained=True, **kwargs):
if pretrained:
if fp16:
checkpoint = 'https://developer.nvidia.com/joc-tacotron2-fp16-pyt-20190306'
checkpoint = 'https://api.ngc.nvidia.com/v2/models/nvidia/tacotron2_pyt_ckpt_amp/versions/19.09.0/files/nvidia_tacotron2pyt_fp16_20190427'
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)
checkpoint = 'https://api.ngc.nvidia.com/v2/models/nvidia/tacotron2_pyt_ckpt_fp32/versions/19.09.0/files/nvidia_tacotron2pyt_fp32_20190427'
ckpt_file = _download_checkpoint(checkpoint, force_reload)
ckpt = torch.load(ckpt_file)
state_dict = ckpt['state_dict']
if checkpoint_from_distributed(state_dict):
@ -187,13 +193,10 @@ def nvidia_waveglow(pretrained=True, **kwargs):
if pretrained:
if fp16:
checkpoint = 'https://developer.nvidia.com/joc-waveglow-fp16-pyt-20190306'
checkpoint = 'https://api.ngc.nvidia.com/v2/models/nvidia/waveglow_ckpt_amp/versions/19.09.0/files/nvidia_waveglowpyt_fp16_20190427'
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)
checkpoint = 'https://api.ngc.nvidia.com/v2/models/nvidia/waveglow_ckpt_fp32/versions/19.09.0/files/nvidia_waveglowpyt_fp32_20190427'
ckpt_file = _download_checkpoint(checkpoint, force_reload)
ckpt = torch.load(ckpt_file)
state_dict = ckpt['state_dict']
if checkpoint_from_distributed(state_dict):
@ -225,13 +228,15 @@ def nvidia_waveglow(pretrained=True, **kwargs):
def nvidia_ssd_processing_utils():
import numpy as np
import skimage
from skimage import io, transform
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))
img = skimage.img_as_float(io.imread(image_path))
if len(img.shape) == 2:
img = np.array([img, img, img]).swapaxes(0, 2)
return img
@ -243,13 +248,13 @@ def nvidia_ssd_processing_utils():
if (aspect > 1):
# landscape orientation - wide image
res = int(aspect * input_height)
imgScaled = skimage.transform.resize(img, (input_width, res))
imgScaled = 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))
imgScaled = transform.resize(img, (res, input_height))
if (aspect == 1):
imgScaled = skimage.transform.resize(img, (input_width, input_height))
imgScaled = transform.resize(img, (input_width, input_height))
return imgScaled
@staticmethod
@ -270,6 +275,7 @@ def nvidia_ssd_processing_utils():
NHWC = np.array(inputs)
NCHW = np.swapaxes(np.swapaxes(NHWC, 1, 3), 2, 3)
tensor = torch.from_numpy(NCHW)
tensor = tensor.contiguous()
tensor = tensor.cuda()
tensor = tensor.float()
if fp16:
@ -326,6 +332,7 @@ def nvidia_ssd_processing_utils():
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
@ -356,14 +363,9 @@ def nvidia_ssd(pretrained=True, **kwargs):
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)
checkpoint = 'https://api.ngc.nvidia.com/v2/models/nvidia/ssd_pyt_ckpt_amp/versions/19.09.0/files/nvidia_ssdpyt_fp16_190826.pt'
# ckpt = torch.hub.load_state_dict_from_url(checkpoint, progress=True, check_hash=False)
ckpt_file = _download_checkpoint(checkpoint, force_reload)
ckpt = torch.load(ckpt_file)
ckpt = ckpt['model']
if checkpoint_from_distributed(ckpt):