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example.py
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example.py
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from profiling import Profiling
# from profiling import record
import model.alexnet as alexnet
import torch
import torch.nn as nn
from torch.autograd import Variable
# Iteration number
iter = 3
#
# Create model
#
model = alexnet.alexnet()
#
# Use case 1: use it as context-manager
#
# profiler will measure the following 3 iterations.
with Profiling(model) as p:
for i in xrange(iter):
# Forward:
output = model.forward(Variable(torch.ones(2, 3, 224, 224), requires_grad=True))
# Backward:
grads = torch.ones(2, 1000)
output.backward(grads);
# profiler won't measure the following 2 extra iterations, since they are out of profiler's range.
for i in xrange(2):
# Forward:
output = model.forward(Variable(torch.ones(2, 3, 224, 224), requires_grad=True))
# Backward:
grads = torch.ones(2, 1000)
output.backward(grads);
# profiler will print the 3 iterations result rather than 5.
print(p)
#
# Use case 2: use it directly
#
# p = Profiling(model).start()
# for i in xrange(iter):
# # Forward:
# output = model.forward(Variable(torch.ones(2, 3, 224, 224), requires_grad=True))
# # Backward:
# grads = torch.ones(2, 1000)
# output.backward(grads);
# p.stop()
# # profiler won't measure the following 2 extra iterations, since they are out of profiler's range.
# for i in xrange(2):
# # Forward:
# output = model.forward(Variable(torch.ones(2, 3, 224, 224), requires_grad=True))
# # Backward:
# grads = torch.ones(2, 1000)
# output.backward(grads);
# print(p)