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[VTA] Producing simulation stats in the Relay to VTA inference tutorial #3481

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Jul 3, 2019
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[VTA] Producing simulation stats in the Relay to VTA inference tutorial #3481

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40 changes: 27 additions & 13 deletions vta/tutorials/frontend/deploy_resnet_on_vta.py
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Original file line number Diff line number Diff line change
Expand Up @@ -229,25 +229,39 @@
m.set_input(**params)
m.set_input('data', image)

# Perform inference: we run the module 4 times,
# and repeat 3 times to get error bounds
timer = m.module.time_evaluator("run", ctx, number=4, repeat=3)
tcost = timer()
# Perform inference and gather execution statistics
# More on: https://docs.tvm.ai/api/python/module.html#tvm.module.Module.time_evaluator
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NOTE: we can cross reference rst content in the same document by :any: reference

num = 4 # number of times we run module for a single measurement
rep = 3 # number of measurements (we derive std dev from this)
timer = m.module.time_evaluator("run", ctx, number=num, repeat=rep)

if env.TARGET == "sim":
simulator.clear_stats()
timer()
sim_stats = simulator.stats()
print("\nExecution statistics:")
for k, v in sim_stats.items():
# Since we execute the workload many times, we need to normalize stats
# Note that there is always one warm up run
# Therefore we divide the overall stats by (num * rep + 1)
print("\t{:<16}: {:>16}".format(k, v // (num * rep + 1)))
else:
tcost = timer()
std = np.std(tcost.results) * 1000 / env.BATCH
mean = tcost.mean * 1000 / env.BATCH
print("\nPerformed inference in %.2fms/sample (std = %.2f)" % (mean, std))

# Get classification results
tvm_output = m.get_output(0, tvm.nd.empty((env.BATCH, 1000), "float32", remote.cpu(0)))
top_categories = np.argsort(tvm_output.asnumpy()[0])

# Report top-5 classification results
std = np.std(tcost.results) * 1000 / env.BATCH
mean = tcost.mean * 1000 / env.BATCH
print("%s prediction" % model)
print(" #1:", synset[top_categories[-1]])
print(" #2:", synset[top_categories[-2]])
print(" #3:", synset[top_categories[-3]])
print(" #4:", synset[top_categories[-4]])
print(" #5:", synset[top_categories[-5]])
print("Performed inference in %.2fms/sample (std = %.2f)" % (mean, std))
print("\n%s prediction" % model)
print("\t#1:", synset[top_categories[-1]])
print("\t#2:", synset[top_categories[-2]])
print("\t#3:", synset[top_categories[-3]])
print("\t#4:", synset[top_categories[-4]])
print("\t#5:", synset[top_categories[-5]])

# This just checks that one of the 5 top categories
# is one variety of cat; this is by no means an accurate
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