-
Notifications
You must be signed in to change notification settings - Fork 3.1k
/
hubconf.py
376 lines (311 loc) · 15.2 KB
/
hubconf.py
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
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
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
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']
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 = _download_checkpoint(checkpoint, force_reload)
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://api.ngc.nvidia.com/v2/models/nvidia/tacotron2pyt_fp16/versions/1/files/nvidia_tacotron2pyt_fp16_20190306.pth'
else:
checkpoint = 'https://api.ngc.nvidia.com/v2/models/nvidia/tacotron2pyt_fp32/versions/1/files/nvidia_tacotron2pyt_fp32_20190306.pth'
ckpt_file = _download_checkpoint(checkpoint, force_reload)
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://api.ngc.nvidia.com/v2/models/nvidia/waveglowpyt_fp16/versions/1/files/nvidia_waveglowpyt_fp16_20190306.pth'
else:
checkpoint = 'https://api.ngc.nvidia.com/v2/models/nvidia/waveglowpyt_fp32/versions/1/files/nvidia_waveglowpyt_fp32_20190306.pth'
ckpt_file = _download_checkpoint(checkpoint, force_reload)
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 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(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 = transform.resize(img, (input_width, res))
if (aspect < 1):
# portrait orientation - tall image
res = int(input_width / aspect)
imgScaled = transform.resize(img, (res, input_height))
if (aspect == 1):
imgScaled = 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://api.ngc.nvidia.com/v2/models/nvidia/ssdpyt_fp16/versions/1/files/nvidia_ssdpyt_fp16_20190225.pt'
else:
checkpoint = 'https://api.ngc.nvidia.com/v2/models/nvidia/ssdpyt_fp32/versions/1/files/nvidia_ssdpyt_fp32_20190225.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):
ckpt = unwrap_distributed(ckpt)
m.load_state_dict(ckpt)
return m