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trace = torch.jit.trace(model_alexnet, args_alexnet) | |
graph = trace.graph | |
print(graph) | |
============================================ | |
.graph(%input.1 : Double(2, 3, 224, 224), | |
%196 : Tensor, | |
%197 : Tensor, | |
%198 : Tensor, | |
%199 : Tensor, | |
%200 : Tensor, |
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graph(%0 : Float(1, 3) | |
%1 : Float(5, 3) | |
%2 : Float(5)) { | |
%3 : Dynamic = onnx::Constant[value={0}]() | |
%4 : Dynamic = onnx::Gemm[alpha=1, beta=0, transB=1](%0, %1, %3) | |
%5 : Float(1, 5) = onnx::Add(%2, %4) | |
return (%5); | |
} | |
%3 : Dynamic = onnx::Constant[value={0}]() |
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running install | |
running bdist_egg | |
running egg_info | |
writing tensorboardX.egg-info/PKG-INFO | |
writing dependency_links to tensorboardX.egg-info/dependency_links.txt | |
writing requirements to tensorboardX.egg-info/requires.txt | |
writing top-level names to tensorboardX.egg-info/top_level.txt | |
reading manifest file 'tensorboardX.egg-info/SOURCES.txt' | |
reading manifest template 'MANIFEST.in' | |
writing manifest file 'tensorboardX.egg-info/SOURCES.txt' |
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running install | |
Done generating tensorboardX/proto/*pb2*.py | |
running build | |
running build_py | |
copying tensorboardX/proto/types_pb2.py -> build/lib/tensorboardX/proto | |
copying tensorboardX/proto/resource_handle_pb2.py -> build/lib/tensorboardX/proto | |
copying tensorboardX/proto/event_pb2.py -> build/lib/tensorboardX/proto | |
copying tensorboardX/proto/summary_pb2.py -> build/lib/tensorboardX/proto | |
copying tensorboardX/proto/graph_pb2.py -> build/lib/tensorboardX/proto | |
copying tensorboardX/proto/layout_pb2.py -> build/lib/tensorboardX/proto |
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--------------------------- --------------- --------------- --------------- --------------- --------------- | |
Name CPU time CUDA time Calls CPU total CUDA total | |
--------------------------- --------------- --------------- --------------- --------------- --------------- | |
conv2d 462.645us 0.000us 1 462.645us 0.000us | |
convolution 461.316us 0.000us 1 461.316us 0.000us | |
_convolution 459.809us 0.000us 1 459.809us 0.000us | |
tensor 2.967us 0.000us 1 2.967us 0.000us | |
_convolution_nogroup 445.746us 0.000us 1 445.746us 0.000us | |
thnn_conv2d 441.713us 0.000us 1 441.713us 0.000us |
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import torch.nn as nn | |
import torch | |
class SimpleModel(nn.Module): | |
def __init__(self): | |
super().__init__() | |
self.fc = nn.Linear(6,9) | |
def forward(self, x): | |
return self.fc(x.view(-1, 6)) |
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class Net1(nn.Module): | |
def __init__(self): | |
super(Net1, self).__init__() | |
self.conv1 = nn.Conv2d(1, 10, kernel_size=5) | |
self.conv2 = nn.Conv2d(10, 20, kernel_size=5) | |
self.conv2_drop = nn.Dropout2d() | |
self.fc1 = nn.Linear(320, 50) | |
self.fc2 = nn.Linear(50, 10) | |
self.bn = nn.BatchNorm2d(20) |
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class Net1(nn.Module): | |
def __init__(self): | |
super(Net1, self).__init__() | |
self.conv1 = nn.Conv2d(1, 10, kernel_size=5) | |
self.conv2 = nn.Conv2d(10, 20, kernel_size=5) | |
self.conv2_drop = nn.Dropout2d() | |
self.fc1 = nn.Linear(320, 50) | |
self.fc2 = nn.Linear(50, 10) | |
self.bn = nn.BatchNorm2d(20) |
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from __future__ import print_function | |
import argparse | |
import torch | |
import torch.nn as nn | |
import torch.nn.functional as F | |
import torch.optim as optim | |
from torchvision import datasets, transforms | |
from torch.autograd import Variable | |
from tensorboardX import SummaryWriter | |
# Training settings |
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with open ('data.csv') as f: | |
lines = f.readlines() | |
valid = [] | |
for line in lines: | |
a, b = line.strip('\n').split(',') | |
if a=='1' or a=='2' or a=='3': | |
print(a, b) | |
valid.append((int(a), int(b))) |
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