def runValidation(modelName, net, params):
'''Feed the specified images into the model at any batch size; and return
an array of the image filename and the top-5 predictions'''
imageData = []
# Results will be accumulate here
results = []
imageFiles = getter.apiGetValidationInputs(modelName, cache=True)
t = len(imageFiles)
if modelName == models.MOBILENET or modelName == models.RESNET50:
log.info('Running prediction...')
bar = Bar('Running prediction...', max=t)
for i in range(t):
single_result = net.predict([imageFiles[i]], 1)
fn = single_result[0]
single_result_list = single_result[1:]
results.append([fn, single_result_list])
bar.next()
bar.finish()
elif modelName == models.SSDMOBILENET:
from . import parse_ssd_output
bar = Bar('Running prediction...', max=t)
for i in range(t):
out = net.predict([imageFiles[i]], 1)
bar.next()
bar.finish()
output_path = os.path.join(paths.TARGETS, "openvino_ubuntu",
"workloads", "ssdmobilenet",
"ssd_detection_output.csv")
results = parse_ssd_output.parse_ssd_detection(output_path)
return results
def runValidation(modelName, net, params):
imageFiles = getter.apiGetValidationInputs(modelName)
imageData = [] #empty list
# Results will be accumulate here
results = [] #empty list
t = len(imageFiles)
log.info('Running prediction...')
N = len(imageFiles)
B = 1
for i in range(N):
data = preprocessor.apiProcess(modelName, imageFiles[i])
res = net.predict(imageFiles[i], data, 5, 0, 1)
log.debug("%d predictions left" % (N - (i + 1)))
#for j in range(len(res['predictions'])):
results.append([imageFiles[i], res['predictions'][0]])
net.destructor()
return results
def runValidation(modelName, net, params):
'''Process all of the validation inputs from the apiGet* function
This is a reference example. Batch size can be set to whatever is optimal.
The important things to note are: [a] inputs come from the getter, and [b]
the results are returned in the form of a list of lists defined in the
report.py comments, depending on the workload/model.
Note: This function returns its value upstream to the harness through the
apiRun() API function. It is only an example, apiRun() can be implemented
however a developer chooses.'''
imageFiles = getter.apiGetValidationInputs(modelName)
imageData = []
# Results will be accumulate here
results = []
t = len(imageFiles)
# Feeding 10 at a time
log.info('Running prediction...')
N = len(imageFiles)
B = 1 # resnet is now batch 1?
m = 0
for i in range(t):
if params[const.PRECISION] == const.FP32:
data = preprocessor.apiProcess(modelName, imageFiles[i])
if params[const.PRECISION] == const.INT8:
data = preprocessor.apiProcess_int8(modelName, imageFiles[i])
imageData.append(data)
# Batch size of B; TODO test for one-off fail
if len(imageData) == B or (i + 1) == t:
res = net.predict(imageData, params)
log.debug("%d predictions left" % (N - (i + 1)))
imageData = []
for j in range(len(res['predictions'])):
results.append([imageFiles[m], res['predictions'][j]])
m = m + 1
# For debugging
tmpdir = tempfile.mkdtemp()
outputFn = os.path.join(tmpdir, 'outputs.json')
with open(outputFn, 'w') as fp:
json.dump(results, fp)
log.debug('Validation output file is {}'.format(outputFn))
#print(results)
return results
def run(modelName, modelFileName, params):
# TODO careful this over-writes (not appends to) original
if (params[const.HARDWARE] == 'gpu'):
os.environ['LD_LIBRARY_PATH'] = commonDir + ':' + os.path.join(
commonDir, 'GpuAcc')
else:
os.environ['LD_LIBRARY_PATH'] = commonDir + ':' + os.path.join(
commonDir, 'CpuAcc')
tmpdir = tempfile.mkdtemp()
inputFn = os.path.join(tmpdir, 'inputs.json')
outputFn = os.path.join(tmpdir, 'outputs.json')
if params[const.MODE] == const.ACCURACY:
imageFileNames = getter.apiGetValidationInputs(modelName, cache=True)
else:
imageFileNames = getter.apiGetTestInputs(modelName,
params[const.BATCH],
cache=True)
cxxParams = {
'images':
imageFileNames, # Note: this can be up to 5,000 filenames
'model':
os.path.join(paths.MODELS, 'tensorflow', modelName, 'frozen_graph.pb'),
'params':
params
}
with open(inputFn, 'w') as fp:
json.dump(cxxParams, fp)
exeCmd = os.path.join(commonDir, '..', modelName, modelName + '.exe')
cmd = [exeCmd, inputFn, outputFn]
log.info('Running prediction...')
log.debug(cmd)
ret = subprocess.call(cmd)
if ret:
log.error('Inference failed')
return None
log.info('Loading results file %s' % outputFn)
with open(outputFn) as fp:
returnData = json.load(fp)
if params[const.MODE] == const.ACCURACY:
return returnData['predictions']
else:
return returnData['times']