for i, data in enumerate(data_loader):
torch.cuda.empty_cache()
img_meta = data['img_meta'][0].data[0]
img_name = img_meta[0]['filename'].split('/')[-1]
with torch.no_grad():
result = model(return_loss=False, rescale=not show, **data)
Torchaudio is a library for audio and signal processing with PyTorch. It provides I/O, signal and data processing functions, datasets, model implementations and application components.
Recent breakthroughs in generative modeling of images have been predicated on the availability of high-quality and large-scale datasebts such as MNIST, CIFAR and ImageNet. We recognized the need for an audio dataset that was as approachable as those in the image domain.
Audio signals found in the wild contain multi-scale dependencies that prove particularly difficult to model, leading many previous efforts at data-driven audio synthesis to focus on more constrained domains such as texture synthesis or training small parametric models.
We encourage the broader community to use NSynth as a benchmark and entry point into audio machine learning. We also view NSynth as a building block for future datasets and envision a high-quality multi-note dataset for tasks like generation and transcription that involve learning complex language-like dependencies.
Description
NSynth is an audio dataset containing 305,979 musical notes, each with a unique pitch, timbre, and envelope. For 1,006 instruments from commercial sample libraries, we generated four second, monophonic 16kHz audio snippets, referred to as notes, by ranging over every pitch of a standard MIDI pian o (21-108) as well as five different velocities (25, 50, 75, 100, 127). The note was held for the first three seconds and allowed to decay for the final second.
Some instruments are not capable of producing all 88 pitches in this range, resulting in an average of 65.4 pitches per instrument. Furthermore, the commercial sample packs occasionally contain duplicate sounds across multiple velocities, leaving an average of 4.75 unique velocities per pitch.
We also annotated each of the notes with three additional pieces of information based on a combination of human evaluation and heuristic algorithms:
Source: The method of sound production for the note’s instrument. This can be one of acoustic or electronic for instruments that were recorded from acoustic or electronic instruments, respectively, or synthetic for synthesized instruments. See their frequencies below.
Family: The high-level family of which the note’s instrument is a member. Each instrument is a member of exactly one family. See the complete list and their frequencies below.
Qualities: Sonic qualities of the note. See the quality descriptions and their co-occurrences below. Each note is annotated with zero or more qualities.
Format
Files
The NSynth dataset can be download in two formats:
Train [tfrecord | json/wav]: A training set with 289,205 examples. Instruments do not overlap with valid or test.
Valid [tfrecord | json/wav]: A validation set with 12,678 examples. Instruments do not overlap with train.
Test [tfrecord | json/wav]: A test set with 4,096 examples. Instruments do not overlap with train.
Below we detail how the note features are encoded in the Example protocol buffers and JSON files.
Example Features
Each Example contains the following features.
Feature
Type
Description
note
int64
A unique integer identifier for the note.
note_str
bytes
A unique string identifier for the note in the format <instrument_str>-<pitch>-<velocity>.
instrument
int64
A unique, sequential identifier for the instrument the note was synthesized from.
instrument_str
bytes
A unique string identifier for the instrument this note was synthesized from in the format <instrument_family_str>-<instrument_production_str>-<instrument_name>.
pitch
int64
The 0-based MIDI pitch in the range [0, 127].
velocity
int64
The 0-based MIDI velocity in the range [0, 127].
sample_rate
int64
The samples per second for the audio feature.
audio*
[float]
A list of audio samples represented as floating point values in the range [-1,1].
qualities
[int64]
A binary vector representing which sonic qualities are present in this note.
qualities_str
[bytes]
A list IDs of which qualities are present in this note selected from the sonic qualities list.
在矢量量化编码中,关键是码本的建立和码字搜索算法,如果想对矢量量化有个整体的概览,强烈推荐《Handbook of Image and Video Processing》一书中Fundamentals of Vector Quantization章节。下面对矢量量化中两类典型的方法多阶段矢量量化、乘积量化以及乘积量化的改进做简单介绍。
如上图所示,对于待量化的向量x,经过一级量化器quantizer1后,得到的量化残差为r1 = x – C1b1,其中C1为一级量化器的码本,b1为x经过一级量化器quantizer1后的表示结果,将一级量化误差r1作为二级量化器的输入,后面过程与此类似。通过这种级联量化的量化方式,当构建的量化器为无穷个时,x可以被这无穷个码本精确表示。上图右侧子图比较直观的描绘了x被多个码本逐步近似的过程。
import os
import tensorrt as trt
os.environ["CUDA_VISIBLE_DEVICES"]='0'
TRT_LOGGER = trt.Logger()
onnx_file_path = 'Unet375-simple.onnx'
engine_file_path = 'Unet337.trt'
EXPLICIT_BATCH = 1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(network, TRT_LOGGER) as parser:
builder.max_workspace_size = 1 << 28 # 256MiB
builder.max_batch_size = 1
# Parse model file
if not os.path.exists(onnx_file_path):
print('ONNX file {} not found, please run yolov3_to_onnx.py first to generate it.'.format(onnx_file_path))
exit(0)
print('Loading ONNX file from path {}...'.format(onnx_file_path))
with open(onnx_file_path, 'rb') as model:
print('Beginning ONNX file parsing')
if not parser.parse(model.read()):
print ('ERROR: Failed to parse the ONNX file.')
for error in range(parser.num_errors):
print (parser.get_error(error))
network.get_input(0).shape = [1, 3, 300, 400]
print('Completed parsing of ONNX file')
print('Building an engine from file {}; this may take a while...'.format(onnx_file_path))
#network.mark_output(network.get_layer(network.num_layers-1).get_output(0))
engine = builder.build_cuda_engine(network)
print("Completed creating Engine")
with open(engine_file_path, "wb") as f:
f.write(engine.serialize())
trtexec --loadEngine=mnist16.trt --batch=1
打印输出:
trtexec会打印出很多时间,这里需要对每个时间的含义进行解释,然后大家各取所需,进行评测。总的打印如下:
[09/06/2021-13:50:34] [I] Average on 10 runs - GPU latency: 2.74553 ms - Host latency: 3.74192 ms (end to end 4.93066 ms, enqueue 0.624805 ms) # 跑了10次,GPU latency: GPU计算耗时, Host latency:GPU输入+计算+输出耗时,end to end:GPU端到端的耗时,eventout - eventin,enqueue:CPU异步耗时
[09/06/2021-13:50:34] [I] Host Latency
[09/06/2021-13:50:34] [I] min: 3.65332 ms (end to end 3.67603 ms)
[09/06/2021-13:50:34] [I] max: 5.95093 ms (end to end 6.88892 ms)
[09/06/2021-13:50:34] [I] mean: 3.71375 ms (end to end 5.30082 ms)
[09/06/2021-13:50:34] [I] median: 3.70032 ms (end to end 5.32935 ms)
[09/06/2021-13:50:34] [I] percentile: 4.10571 ms at 99% (end to end 6.11792 ms at 99%)
[09/06/2021-13:50:34] [I] throughput: 356.786 qps
[09/06/2021-13:50:34] [I] walltime: 3.00741 s
[09/06/2021-13:50:34] [I] Enqueue Time
[09/06/2021-13:50:34] [I] min: 0.248474 ms
[09/06/2021-13:50:34] [I] max: 2.12134 ms
[09/06/2021-13:50:34] [I] median: 0.273987 ms
[09/06/2021-13:50:34] [I] GPU Compute
[09/06/2021-13:50:34] [I] min: 2.69702 ms
[09/06/2021-13:50:34] [I] max: 4.99219 ms
[09/06/2021-13:50:34] [I] mean: 2.73299 ms
[09/06/2021-13:50:34] [I] median: 2.71875 ms
[09/06/2021-13:50:34] [I] percentile: 3.10791 ms at 99%
[09/06/2021-13:50:34] [I] total compute time: 2.93249 s
Host Latency gpu: 输入+计算+输出 三部分的耗时
Enqueue Time:CPU异步的时间(该时间不具有参考意义,因为GPU的计算可能还没有完成)
GPU Compute:GPU计算的耗时
综上,去了Enqueue Time时间都是有意义的
Input[0] on model model_static.onnx succeed.
Input[1] on model model_static.onnx error.
Input[2] on model model_static.onnx error.
Input[0] on model model_dynamic_0.onnx succeed.
Input[1] on model model_dynamic_0.onnx succeed.
Input[2] on model model_dynamic_0.onnx error.
Input[0] on model model_dynamic_23.onnx succeed.
Input[1] on model model_dynamic_23.onnx error.
Input[2] on model model_dynamic_23.onnx succeed.
print(exceptions[(1, 'model_static.onnx')])
# output # [ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Got invalid dimensions for input: in for the following indices index: 0 Got: 2 Expected: 1 Please fix either the inputs or the model.
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
x = x * x[0].item()
return x, torch.Tensor([i for i in x])
model = Model()
dummy_input = torch.rand(10)
torch.onnx.export(model, dummy_input, 'a.onnx')
return g.op("Resize",
input,
empty_roi,
empty_scales,
output_size,
coordinate_transformation_mode_s=coordinate_transformation_mode,
cubic_coeff_a_f=-0.75, # only valid when mode="cubic"
mode_s=interpolate_mode, # nearest, linear, or cubic
nearest_mode_s="floor") # only valid when mode="nearest"