def post_process(qFrom, args, img_paths, streamQ, fpgaOutputs):
numProcessed = 0
labels = xdnn_io.get_labels(args['labels'])
zmqPub = None
if args['zmqpub']:
zmqPub = ZmqResultPublisher(args['deviceID'])
goldenMap = None
if args['golden']:
goldenMap = xdnn_io.getGoldenMap(args['golden'])
top5Count = 0
top1Count = 0
(fcWeight, fcBias) = xdnn_io.loadFCWeightsBias(args)
bsz = args['batch_sz']
fcOutput = np.empty((
bsz,
args['outsz'],
), dtype=np.float32, order='C')
start = 0
while True:
(sId, img_idx) = qFrom.get()
if numProcessed == 0:
start = timeit.default_timer()
if sId is None or img_idx is None:
break
imgList = []
for x in np.nditer(img_idx):
if x >= 0:
imgList.append(img_paths[x])
numProcessed += 1
npout_view = np.frombuffer(fpgaOutputs[sId].get_obj(),
dtype=np.float32)
xdnn.computeFC(fcWeight, fcBias, npout_view, bsz, args['outsz'],
args['fpgaoutsz'], fcOutput)
streamQ.put(sId)
smaxOutput = xdnn.computeSoftmax(fcOutput)
if args['golden']:
for i, p in enumerate(imgList):
top1Count += xdnn_io.isTopK(smaxOutput[i], goldenMap, p,
labels, 1)
top5Count += xdnn_io.isTopK(smaxOutput[i], goldenMap, p,
labels, 5)
if zmqPub is not None:
predictMsg = xdnn_io.getClassification(smaxOutput,
imgList,
labels,
zmqPub=True)
zmqPub.send(predictMsg)
print("%g images/s" % (float(numProcessed) / (time.time() - start)))
if args['golden']:
print ("\nAverage accuracy (n=%d) Top-1: %.1f%%, Top-5: %.1f%%\n") \
% (numProcessed,
float(top1Count)/float(numProcessed)*100.,
float(top5Count)/float(numProcessed)*100.)
def main(argv):
args = xdnn_io.processCommandLine(argv)
ret, handles = xdnn.createHandle(args['xclbin'], "kernelSxdnn_0")
# ret = xdnn.createHandle(g_xclbin, "kernelSxdnn_0", g_xdnnLib)
if ret != 0:
sys.exit(1)
labels = xdnn_io.get_labels(args['labels'])
# TODO dict of tuples instead?
fpgaRT = {}
fpgaOutputs = {}
fcWeights = {}
fcBiases = {}
netFiles = {}
confNames = []
args = args['jsoncfg'] # we do not use other args' keys
for netconf_args in args:
confName = str(netconf_args['name'])
confNames += [confName]
# netconf_args['netcfg'] = './data/{}_{}.json'.format(netconf_args['net'], netconf_args['dsp'])
fpgaRT[confName] = xdnn.XDNNFPGAOp(handles, netconf_args)
netconf_args['in_shape'] = tuple((netconf_args['batch_sz'],) + tuple(fpgaRT[confName].getInputDescriptors().itervalues().next()[1:] ))
(fcWeights[confName],
fcBiases[confName]) = xdnn_io.loadFCWeightsBias(netconf_args)
fpgaOutputs[confName] = np.empty ((netconf_args['batch_sz'], int(netconf_args['fpgaoutsz']),), dtype=np.float32, order='C')
netFiles[confName] = str(netconf_args['netcfg'])
batchArrays = []
for streamId, netconf_args in enumerate(args):
batchArrays.append(np.empty(netconf_args['in_shape'], dtype=np.float32, order='C'))
pl = []
img_paths = xdnn_io.getFilePaths(netconf_args['images'])
for j, p in enumerate(img_paths[:netconf_args['batch_sz']]):
batchArrays[-1][j, ...], _ = xdnn_io.loadImageBlobFromFile(p, netconf_args['img_raw_scale'],
netconf_args['img_mean'],
netconf_args['img_input_scale'],
netconf_args['in_shape'][2],
netconf_args['in_shape'][3])
pl.append(p)
confName = str(netconf_args['name'])
firstInputName = fpgaRT[confName].getInputs().iterkeys().next()
firstOutputName = fpgaRT[confName].getOutputs().iterkeys().next()
fpgaRT[confName].exec_async({ firstInputName : batchArrays[-1] }, { firstOutputName : fpgaOutputs[confName] }, streamId)
for streamId, confName in enumerate(confNames):
fpgaRT[confName].get_result (streamId)
for netconf_args in args:
confName = str(netconf_args['name'])
fcOut = np.empty( (netconf_args['batch_sz'], netconf_args['outsz']), dtype=np.float32, order = 'C')
xdnn.computeFC (fcWeights[confName], fcBiases[confName], fpgaOutputs[confName], fcOut)
softmaxOut = xdnn.computeSoftmax(fcOut)
xdnn_io.printClassification(softmaxOut, netconf_args['images'], labels);
xdnn.closeHandle()
def main():
args = xdnn_io.processCommandLine()
ret, handles = xdnn.createHandle(args['xclbin'], "kernelSxdnn_0")
if ret != 0:
sys.exit(1)
fpgaRT = xdnn.XDNNFPGAOp(handles, args)
fcWeight, fcBias = xdnn_io.loadFCWeightsBias(args)
img_paths = xdnn_io.getFilePaths(args['images'])
fpgaOutput = np.empty((
args['batch_sz'],
args['fpgaoutsz'],
),
dtype=np.float32,
order='C')
fcOutput = np.empty((
args['batch_sz'],
args['outsz'],
),
dtype=np.float32,
order='C')
batch_array = np.empty(((args['batch_sz'], ) + args['in_shape']),
dtype=np.float32,
order='C')
labels = xdnn_io.get_labels(args['labels'])
if args['golden']:
goldenMap = xdnn_io.getGoldenMap(args['golden'])
top5Count = 0
top1Count = 0
for i in xrange(0, len(img_paths), args['batch_sz']):
pl = []
for j, p in enumerate(img_paths[i:i + args['batch_sz']]):
batch_array[j, ...], _ = xdnn_io.loadImageBlobFromFile(
p, args['img_raw_scale'], args['img_mean'],
args['img_input_scale'], args['in_shape'][2],
args['in_shape'][1])
pl.append(p)
fpgaRT.execute(batch_array, fpgaOutput)
xdnn.computeFC(fcWeight, fcBias, fpgaOutput, args['batch_sz'],
args['outsz'], args['fpgaoutsz'], fcOutput)
softmaxOut = xdnn.computeSoftmax(fcOutput)
xdnn_io.printClassification(softmaxOut, pl, labels)
if args['golden']:
for j, p in enumerate(img_paths[i:i + args['batch_sz']]):
top1Count += xdnn_io.isTopK(softmaxOut[j], goldenMap, p,
labels, 1)
top5Count += xdnn_io.isTopK(softmaxOut[j], goldenMap, p,
labels, 5)
xdnn.closeHandle()
if args['golden']:
print("\nAverage accuracy (n=%d) Top-1: %.1f%%, Top-5: %.1f%%\n") % (
len(img_paths), float(top1Count) / float(len(img_paths)) * 100.,
float(top5Count) / float(len(img_paths)) * 100.)
def main():
args = xdnn_io.processCommandLine()
ret = xdnn.createHandle(args['xclbin'], "kernelSxdnn_0", args['xlnxlib'])
if ret != 0:
sys.exit(1)
(weightsBlob, fcWeight, fcBias) = xdnn_io.loadWeights(args)
(fpgaInputs, batch_sz) = xdnn_io.prepareInput(args)
fpgaOutput = xdnn_io.prepareOutput(args['fpgaoutsz'], batch_sz)
for i in range(1):
startTime = timeit.default_timer()
xdnn.execute(
args['netcfg'],
weightsBlob,
fpgaInputs,
fpgaOutput,
batch_sz, # num batches
args['quantizecfg'],
args['scaleB'],
args['PE'])
elapsedTime = timeit.default_timer() - startTime
print "\nAfter FPGA (%f ms)" % (elapsedTime * 1000)
startTime = timeit.default_timer()
fcOut = xdnn.computeFC(fcWeight, fcBias, fpgaOutput, batch_sz,
args['outsz'], args['fpgaoutsz'], args['useblas'])
elapsedTime = timeit.default_timer() - startTime
print "\nAfter FC (%f ms)" % (elapsedTime * 1000)
#for i in range(10):
# print "%f" % fpgaOutput[i],
startTime = timeit.default_timer()
softmaxOut = xdnn.computeSoftmax(fcOut, batch_sz)
elapsedTime = timeit.default_timer() - startTime
print "\nAfter Softmax (%f ms)" % (elapsedTime * 1000)
#for i in range(10):
# print "%f" % fpgaOutput[i],
xdnn_io.printClassification(softmaxOut, args)
print "\nSuccess!\n"
xdnn.closeHandle()
def exec_post_fpga(self, image, streamId):
args = copy.deepcopy(self._config)
fpgaOutput = self._streamOutputs[streamId]
batch_sz = 1
fcOut = np.empty((
batch_sz,
args['outsz'],
),
dtype=np.float32,
order='C_CONTIGUOUS')
xdnn.computeFC(self._fcWeight, self._fcBias,
self._streamOutputs[streamId], batch_sz, args['outsz'],
args['fpgaoutsz'], fcOut)
softmaxOut = xdnn.computeSoftmax(fcOut)
result = xdnn_io.getClassification(softmaxOut, [image], self._labels)
result = result.strip().split("\n")
top5 = [x for x in result if "-------" not in x]
return top5
def main():
args = xdnn_io.processCommandLine()
# processCommandLine()
startTime = timeit.default_timer()
ret = xdnn.createHandle(args['xclbin'], "kernelSxdnn_0", args['xlnxlib'])
# ret = xdnn.createHandle(g_xclbin, "kernelSxdnn_0", g_xdnnLib)
if ret != 0:
sys.exit(1)
elapsedTime = timeit.default_timer() - startTime
print "\nAfter createHandle (%f ms):" % (elapsedTime * 1000)
startTime = timeit.default_timer()
# TODO dict of tuples instead?
fpgaInputs = {}
fpgaOutputs = {}
weightsBlobs = {}
fcWeights = {}
fcBiases = {}
batch_sizes = {}
fpgaOutputSizes = {}
PEs = {}
netFiles = {}
confNames = []
for netconf_args in args['jsoncfg']:
confName = str(netconf_args['name'])
confNames.append(confName)
# make a tuple instead
PE = [int(x) for x in netconf_args['PE'].split()]
# if cuMask in cuMaskList:
# raise Exception('cuMasks are non-disjoint')
datadir = str(netconf_args['datadir'])
fpgaoutsz = int(netconf_args['fpgaoutsz'])
netfile = str(netconf_args['netcfg'])
PEs[confName] = PE
(weightsBlobs[confName], fcWeights[confName],
fcBiases[confName]) = xdnn_io.loadWeights(netconf_args)
fpgaOutputSizes[confName] = fpgaoutsz
(fpgaInputs[confName],
batch_sz) = xdnn_io.prepareInput(netconf_args, PE)
batch_sizes[confName] = batch_sz
fpgaOutputs[confName] = xdnn_io.prepareOutput(
int(netconf_args['fpgaoutsz']), batch_sz)
netFiles[confName] = netfile
elapsedTime = timeit.default_timer() - startTime
print "\nAfter init (%f ms):" % (elapsedTime * 1000)
startTime = timeit.default_timer()
for netconf_args in args['jsoncfg']:
confName = str(netconf_args['name'])
xdnn.exec_async(netFiles[confName], weightsBlobs[confName],
fpgaInputs[confName], fpgaOutputs[confName],
int(batch_sizes[confName]),
netconf_args['quantizecfg'], netconf_args['scaleB'],
PEs[confName])
elapsedTime = timeit.default_timer() - startTime
print "\nAfter Execonly (%f ms):" % (elapsedTime * 1000)
startTime = timeit.default_timer()
for confName in confNames:
xdnn.get_result(PEs[confName])
elapsedTime = timeit.default_timer() - startTime
print "\nAfter wait (%f ms):" % (elapsedTime * 1000)
startTime = timeit.default_timer()
for netconf_args in args['jsoncfg']:
confName = str(netconf_args['name'])
fcOut = xdnn.computeFC(fcWeights[confName], fcBiases[confName],
fpgaOutputs[confName], batch_sizes[confName],
netconf_args['outsz'],
netconf_args['fpgaoutsz'],
netconf_args['useblas'])
elapsedTime = timeit.default_timer() - startTime
print "\nAfter FC (%f ms):" % (elapsedTime * 1000)
startTime = timeit.default_timer()
softmaxOut = xdnn.computeSoftmax(fcOut, batch_sizes[confName])
elapsedTime = timeit.default_timer() - startTime
print "\nAfter Softmax (%f ms):" % (elapsedTime * 1000)
xdnn_io.printClassification(softmaxOut, netconf_args)
print "\nSuccess!\n"
xdnn.closeHandle()
def main():
processCommandLine()
ret = xdnn.createManager(g_xdnnLib)
if ret != True:
sys.exit(1)
(fcWeight, fcBias) = xdnn_io.loadFCWeightsBias(g_xdnnTestDataDir)
#
# Spawn the first 2 stages of our pipeline
# Stage 1: Process JPG
# Stage 2: Run FPGA "classify"
qPrep = Queue(maxsize=1)
qFpga = Queue(maxsize=1)
prepProc = Process(target=prep_process, args=(qPrep, ))
xdnnProc = Process(target=xdnn_process, args=(qPrep, qFpga))
prepProc.start()
xdnnProc.start()
#
# The rest of this function post-processes FPGA output:
# 1) Compute the final FC + Softmax layers
# 2) Print classification & accuracy
#
zmqPub = None
if g_zmqPub:
zmqPub = ZmqResultPublisher()
goldenMap = None
if g_goldenFile:
goldenMap = getGoldenMap(g_goldenFile, g_labelFile)
numProcessed = 0
allTop1 = 0
allTop5 = 0
while True:
loopTime = timeit.default_timer()
(fpgaOutput, inputImageFiles) = qFpga.get()
if type(fpgaOutput) == type(None) \
and type(inputImageFiles) == type(None):
break
startTime = timeit.default_timer()
fcOutput = xdnn.computeFC(fcWeight, fcBias, fpgaOutput, g_batchSize,
g_outputSize, g_fpgaOutputSize, g_useBlas)
elapsedTime = timeit.default_timer() - startTime
print "[time] FC (%.2f ms)" % (elapsedTime * 1000)
startTime = timeit.default_timer()
smaxOutput = xdnn.computeSoftmax(fcOutput, g_batchSize)
elapsedTime = timeit.default_timer() - startTime
#print "\nAfter Softmax (%.2f ms):" % (elapsedTime * 1000)
numProcessed += g_batchSize
(top1, top5) = printClassification(smaxOutput.flatten().tolist(),
g_outputSize,
inputImageFiles,
g_labelFile,
goldenMap,
zmqPub=zmqPub)
if goldenMap:
print "Accuracy (i=%d) Top-1: %d, Top-5: %d" \
% (numProcessed/g_batchSize, top1, top5)
allTop1 += top1
allTop5 += top5
print "Num processed: %d" % numProcessed
print "\n[time] Total loop (%.2f ms)" % (
(timeit.default_timer() - loopTime) * 1000)
if goldenMap and numProcessed:
print "\nAverage accuracy (n=%d) Top-1: %.1f%%, Top-5: %.1f%%\n" \
% (numProcessed,
float(allTop1)/float(numProcessed)*100.,
float(allTop5)/float(numProcessed)*100.)
prepProc.join()
xdnnProc.join()
def executeOnFPGA(sProtoBufPath, Qmode, Inference_Data, handle, name,
num_models):
TOTAL_IMAGES = 128
# Create handle for FPGA
ret, handle = xdnn.createHandle(
"../overlaybins/" + "aws" + "/overlay_1.xclbin", "kernelSxdnn_0")
#Initialize objects to store results
fpgaRT = {}
fpgaOutput = {}
fcWeight = {}
fcBias = {}
netFiles = {}
confNames = []
#Generate batch
batch_array = generateRandomBatch(TOTAL_IMAGES, None)
#Get Image batch to start inference
for i in range(0, num_models):
confNames += [str(i)]
#Generate batch 10 * batchsize
config = initializeFpgaModel(sProtoBufPath, Qmode)
config["PE"] = i
config["name"] = config["name"] + "_" + str(i)
# Load weights to FPGA
config = TransferWeightsFPGA(len(batch_array), config, handle, i)
fpgaRT[str(i)] = xdnn.XDNNFPGAOp(handle, config)
(fcWeight[str(i)], fcBias[str(i)]) = xdnn_io.loadFCWeightsBias(config)
fpgaOutput[str(i)], fcOutput, config = AllocateMemoryToHost(config)
start0 = time.time()
# Schedule FPGA execution asynchronously
for i in range(0, num_models):
fpgaRT[str(i)].exec_async(batch_array, fpgaOutput[str(i)], i)
start1 = time.time()
#Fetch results of all parallel executions
for i in range(0, num_models):
#Get FPGA output
ret = fpgaRT[str(i)].get_result(i)
#Compute Inner product - fully connected layer
xdnn.computeFC(fcWeight[str(i)], fcBias[str(i)], fpgaOutput[str(i)],
config['batch_sz'], config['outsz'],
config['fpgaoutsz'], fcOutput)
#Compute output softmax
softmaxOut = xdnn.computeSoftmax(fcOutput)
#xdnn_io.printClassification(softmaxOut, config['images'], labels);
end = time.time()
print("throughput", (num_models * len(batch_array) / (end - start0)),
"duration", end - start0)
Inference_result = []
#Append results
Inference_Data.append({
"experiment":
str(Qmode) + "_bit_mode",
"duration_overall":
end - start0,
"imgsPerSecAll":
num_models * len(batch_array) / (end - start0),
"num_models_parallel":
num_models
})
xdnn.closeHandle()
Inference_Data = pd.DataFrame(Inference_Data)
# Inference_Data.to_csv('multinet_results.csv')
result = pd.read_csv('multinet_results.csv')
result = result.append(Inference_Data)
result.to_csv('multinet_results.csv')
def img_classify(msg):
global g_inputs
global g_inputbuf
global g_fpgaOutput
global g_weightsBlob
global g_fcWeight
global g_fcBias
# message is a rowset, one col, a list of file names.
rs = msg.rowset
if len(rs.columns) == 0 or rs.columns[0].nrow == 0:
print("Img classify request size is 0.\n")
return None
print("Img classify request size is {0}.\n".format(rs.columns[0].nrow))
# Lock the fpga device. config is protected by this lock as well.
fpga_lock.acquire()
ret = None
for i in range(rs.columns[0].nrow):
fname = rs.columns[0].sdata[i]
print("Running classification for images: {0}\n".format(fname))
print("Prepare inputs ...\n")
# g_batchSize = 1, for now.
print "g_inputs", g_inputs
g_inputs[0] = xdnn_io.loadImageBlobFromFile(str(fname), g_mean,
g_img_h, g_img_w)
print("Quantize inputs ...\n")
quantizeInputs = xdnn.quantizeInputs(g_firstFpgaLayerName, g_inputs,
None, None, g_fpgaCfgFile,
g_scaleB)
print("Prepare inputs for fpga inputs ...\n")
fpgaInputs = xdnn.prepareInputsForFpga(quantizeInputs, g_fpgaCfgFile,
g_scaleB, -1,
g_firstFpgaLayerName)
print("Run FPGA commands ...\n")
xdnn.execute(g_netFile, g_weightsBlob, fpgaInputs, g_fpgaOutput,
g_batchSize, g_fpgaCfgFile, g_scaleB, g_PE)
print("Compute FC ...\n")
fcOutput = xdnn.computeFC(g_fcWeight, g_fcBias, g_fpgaOutput,
g_batchSize, g_outputSize, g_fpgaOutputSize,
g_useBlas)
print("Softmax ...\n")
softmaxOut = xdnn.computeSoftmax(fcOutput, g_batchSize)
ret = get_classification(softmaxOut, fname)
fpga_lock.release()
# Now construct return msg
if ret == None:
print("Return None: ???\n")
return None
retmsg = xdrive_pb2.XMsg()
rs = retmsg.rowset
# return 4 columns, (filename, ordinal, score, class)
col1 = rs.columns.add()
col2 = rs.columns.add()
col3 = rs.columns.add()
col4 = rs.columns.add()
col1.nrow = len(ret)
col2.nrow = len(ret)
col3.nrow = len(ret)
col4.nrow = len(ret)
for i in range(len(ret)):
(a, b, c, d) = ret[i]
# print("Return {0}, {1}, {2}, {3}.\n".format(a, b, c, d))
col1.nullmap.append(False)
col1.sdata.append(a)
col2.nullmap.append(False)
col2.i32data.append(b)
col3.nullmap.append(False)
col3.f64data.append(c)
col4.nullmap.append(False)
col4.sdata.append(d)
return retmsg
def img_classify(msg):
global g_args
global g_ctxt
# message is a rowset, one col, a list of file names.
rs = msg.rowset
if len(rs.columns) == 0 or rs.columns[0].nrow == 0:
print("Img classify request size is 0.\n")
return None
print("Img classify request size is {0}.\n".format(rs.columns[0].nrow))
# Lock the fpga device. config is protected by this lock as well.
fpga_lock.acquire()
ret = []
if is_deploymode():
firstInput = g_ctxt['fpgaInput'].itervalues().next()
firstOutput = g_ctxt['fpgaOutput'].itervalues().next()
for i in xrange(0, rs.columns[0].nrow, g_args['batch_sz']):
pl = []
for j in range(g_args['batch_sz']):
fname = str(rs.columns[0].sdata[i + j])
print("Running classification for {0}-th images: {1}\n".format(
i + j, fname))
if is_deploymode():
firstInput[j, ...], _ = xdnn_io.loadImageBlobFromFile(
fname, g_args['img_raw_scale'], g_args['img_mean'],
g_args['img_input_scale'], g_ctxt['inShape'][2],
g_ctxt['inShape'][3])
else:
g_ctxt['batch_array'][j,
...], _ = xdnn_io.loadImageBlobFromFile(
fname, g_args['img_raw_scale'],
g_args['img_mean'],
g_args['img_input_scale'],
g_ctxt['in_shape'][2],
g_ctxt['in_shape'][1])
pl.append(fname)
if is_deploymode():
g_ctxt['fpgaRT'].execute(g_ctxt['fpgaInput'], g_ctxt['fpgaOutput'])
xdnn.computeFC(g_ctxt['fcWeight'], g_ctxt['fcBias'], firstOutput,
g_ctxt['fcOutput'])
else:
g_ctxt['fpgaRT'].execute(g_ctxt['batch_array'],
g_ctxt['fpgaOutput'])
xdnn.computeFC(g_ctxt['fcWeight'], g_ctxt['fcBias'],
g_ctxt['fpgaOutput'], g_args['batch_sz'],
g_args['outsz'], g_args['fpgaoutsz'],
g_ctxt['fcOutput'])
softmaxOut = xdnn.computeSoftmax(g_ctxt['fcOutput'])
ret = ret + get_classification(softmaxOut, pl, g_ctxt['labels'])
fpga_lock.release()
retmsg = xdrive_pb2.XMsg()
rs = retmsg.rowset
# return 4 columns, (filename, ordinal, score, class)
col1 = rs.columns.add()
col2 = rs.columns.add()
col3 = rs.columns.add()
col4 = rs.columns.add()
col1.nrow = len(ret)
col2.nrow = len(ret)
col3.nrow = len(ret)
col4.nrow = len(ret)
for i in range(len(ret)):
# print("Return {0}, {1}, {2}, {3}.\n".format(a, b, c, d))
col1.nullmap.append(False)
col1.sdata.append(ret[i][0])
col2.nullmap.append(False)
col2.i32data.append(ret[i][1])
col3.nullmap.append(False)
col3.f64data.append(ret[i][2])
col4.nullmap.append(False)
col4.sdata.append(ret[i][3])
return retmsg
def main(argv=None):
args = xdnn_io.processCommandLine(argv)
startTime = timeit.default_timer()
ret = xdnn.createHandle(args['xclbin'], "kernelSxdnn_0", args['xlnxlib'])
if ret != 0:
sys.exit(1)
elapsedTime = timeit.default_timer() - startTime
print "\nTime to createHandle (%f ms):" % (elapsedTime * 1000)
# we do not need other args keys except 'jsoncfg'
args = args['jsoncfg']
netCfgs = defaultdict(dict)
confNames = []
startTime = timeit.default_timer()
for streamId, netCfg_args in enumerate(args):
confName = str(netCfg_args['name'])
confNames += [confName]
netCfg_args['netcfg'] = './data/{}_{}.cmd'.format(
netCfg_args['net'], netCfg_args['dsp'])
netCfgs[confName]['streamId'] = streamId
netCfgs[confName]['args'] = netCfg_args
(netCfgs[confName]['weightsBlobs'], netCfgs[confName]['fcWeights'],
netCfgs[confName]['fcBiases']) = xdnn_io.loadWeights(netCfg_args)
netCfgs[confName]['batch_sz'] = 1
netCfgs[confName]['fpgaOutputs'] = xdnn_io.prepareOutput(
netCfg_args["fpgaoutsz"], netCfgs[confName]['batch_sz'])
elapsedTime = timeit.default_timer() - startTime
print "\nTime to init (%f ms):" % (elapsedTime * 1000)
## run YOLO
confName = 'yolo'
netCfg = netCfgs[confName]
startTime = timeit.default_timer()
(netCfg['fpgaInputs'], netCfg['batch_sz'],
netCfg['shapes']) = xdnn_io.prepareInput(netCfg['args'],
netCfg['args']['PE'])
elapsedTime = timeit.default_timer() - startTime
print "\nTime to transfer input image to FPGA (%f ms):" % (elapsedTime *
1000)
startTime = timeit.default_timer()
xdnn.exec_async(netCfg['args']['netcfg'], netCfg['weightsBlobs'],
netCfg['fpgaInputs'], netCfg['fpgaOutputs'],
netCfg['batch_sz'], netCfg['args']['quantizecfg'],
netCfg['args']['scaleB'], netCfg['args']['PE'],
netCfg['streamId'])
elapsedTime = timeit.default_timer() - startTime
print "\nTime to execute Yolo on FPGA (%f ms):" % (elapsedTime * 1000)
startTime = timeit.default_timer()
xdnn.get_result(netCfg['args']['PE'], netCfg['streamId'])
elapsedTime = timeit.default_timer() - startTime
print "\nTime to retrieve yolo outputs from FPGA (%f ms):" % (elapsedTime *
1000)
startTime = timeit.default_timer()
out_h = \
out_w = netCfg['args']['in_shape'][1] / 32
anchor_boxes = 5
objectness = 1
coordinates = 4
classes = 80
out_c = objectness + coordinates + classes
# Reshape the fpgaOutputs into a 4D volume
yolo_outputs = netCfg['fpgaOutputs'].reshape(anchor_boxes, out_c, out_h,
out_w)
# Apply sigmoid to 1st, 2nd, 4th channel for all anchor boxes
yolo_outputs[:, 0:2, :, :] = sigmoid(
yolo_outputs[:, 0:2, :, :]) # (X,Y) Predictions
yolo_outputs[:, 4, :, :] = sigmoid(
yolo_outputs[:, 4, :, :]) # Objectness / Box Confidence
# Apply softmax on the class scores foreach anchor box
for box in range(anchor_boxes):
yolo_outputs[box, 5:, :, :] = softmax(yolo_outputs[box, 5:, :, :])
# Perform Non-Max Suppression
# Non-Max Suppression filters out detections with a score lesser than 0.24
# Additionally if there are two predections with an overlap > 30%, the prediction with the lower score will be filtered
scorethresh = 0.24
iouthresh = 0.3
bboxes = nms.do_baseline_nms(yolo_outputs.flat, netCfg['shapes'][0][1],
netCfg['shapes'][0][0],
netCfg['args']['in_shape'][2],
netCfg['args']['in_shape'][1], out_w, out_h,
anchor_boxes, classes, scorethresh, iouthresh)
with open(netCfg['args']['labels']) as f:
namez = f.readlines()
names = [x.strip() for x in namez]
# Lets print the detections our model made
for j in range(len(bboxes)):
print("Obj %d: %s" % (j, names[bboxes[j]['classid']]))
print("\t score = %f" % (bboxes[j]['prob']))
print("\t (xlo,ylo) = (%d,%d)" %
(bboxes[j]['ll']['x'], bboxes[j]['ll']['y']))
print("\t (xhi,yhi) = (%d,%d)" %
(bboxes[j]['ur']['x'], bboxes[j]['ur']['y']))
elapsedTime = timeit.default_timer() - startTime
print "\nTime to execute on CPU (%f ms):" % (elapsedTime * 1000)
startTime = timeit.default_timer()
img = cv2.imread(netCfg['args']['images'][0])
#img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # YOLO was trained with RGB, not BGR like Caffe
# choose one of the bounding boxes
obj_idx = 0
# specify a margin added to the selected bounding box
margin = 10
H_slice = slice(max(0, bboxes[obj_idx]['ur']['y'] - margin),
min(img.shape[0], bboxes[obj_idx]['ll']['y'] + margin))
W_slice = slice(max(0, bboxes[obj_idx]['ll']['x'] - margin),
min(img.shape[1], bboxes[obj_idx]['ur']['x'] + margin))
img = img[H_slice, W_slice, :]
print('pass obj {}: {} with size {} to googlenet'.format(
obj_idx, names[bboxes[obj_idx]['classid']], img.shape))
cv2.imwrite('cropped_yolo_output.jpg', img)
'''
if img.shape[-1] == 1 or img.shape[-1] == 3:
# [H, W, C]
old_dims = np.array(img.shape[:2], dtype=float)
else:
# [C, H, W]
old_dims = np.array(img.shape[1:], dtype=float)
'''
## run GOOGLENET
confName = 'googlenet'
netCfg = netCfgs[confName]
'''
new_dims = netCfg['args']['in_shape']
if new_dims[-1] == 1 or new_dims[-1] == 3:
# [H, W, C]
new_dims = np.array(new_dims[:2], dtype=int)
else:
# [C, H, W]
new_dims = np.array(new_dims[1:], dtype=int)
scale_dims = new_dims.copy()
min_scale_idx = np.argmin(old_dims/new_dims)
if min_scale_idx == 0:
scale_dims[1] = scale_dims[0] * old_dims[1] / old_dims[0]
else:
scale_dims[0] = scale_dims[1] * old_dims[0] / old_dims[1]
scale_dims = scale_dims.astype(int)
# transform input image to match googlenet
# scale the image
print('scale image to {}'.format(scale_dims))
img = resize_image(img, list(scale_dims))
cv2.imwrite('rescaled_scaled.jpg', img)
# crop the image
crop_idxs = [np.arange(new_dims[i]) + int((scale_dims[i]-new_dims[i])/2) for i in range(2)]
if img.shape[-1] == 1 or img.shape[-1] == 3:
# [H, W, C]
img = img[crop_idxs[0].reshape(-1,1), crop_idxs[1], :]
else:
# [C, H, W]
img = img[:, crop_idxs[0].reshape(-1,1), crop_idxs[1]]
print('crop image to {}'.format(img.shape))
cv2.imwrite('rescaled_cropped.jpg', img)
#img = np.transpose(img, (2, 0, 1))
#cv2.imwrite('rescaled_transposed.jpg', img)
'''
netCfg['args']['images'] = [img]
elapsedTime = timeit.default_timer() - startTime
print "\nTime to prepare googlenet image on CPU (%f ms):" % (elapsedTime *
1000)
startTime = timeit.default_timer()
(netCfg['fpgaInputs'], netCfg['batch_sz'],
netCfg['shapes']) = xdnn_io.prepareInput(netCfg['args'],
netCfg['args']['PE'])
elapsedTime = timeit.default_timer() - startTime
print "\nTime to transfer input image to FPGA (%f ms):" % (elapsedTime *
1000)
startTime = timeit.default_timer()
xdnn.exec_async(netCfg['args']['netcfg'], netCfg['weightsBlobs'],
netCfg['fpgaInputs'], netCfg['fpgaOutputs'],
netCfg['batch_sz'], netCfg['args']['quantizecfg'],
netCfg['args']['scaleB'], netCfg['args']['PE'],
netCfg['streamId'])
elapsedTime = timeit.default_timer() - startTime
print "\nTime to execute googlenet on FPGA (%f ms):" % (elapsedTime * 1000)
startTime = timeit.default_timer()
xdnn.get_result(netCfg['args']['PE'], netCfg['streamId'])
elapsedTime = timeit.default_timer() - startTime
print "\nTime to retrieve googlenet outputs from FPGA (%f ms):" % (
elapsedTime * 1000)
startTime = timeit.default_timer()
fcOut = np.empty((netCfg['batch_sz'] * netCfg['args']['outsz']),
dtype=np.float32,
order='C')
xdnn.computeFC(netCfg['fcWeights'], netCfg['fcBiases'],
netCfg['fpgaOutputs'], netCfg['batch_sz'],
netCfg['args']['outsz'], netCfg['args']['fpgaoutsz'], fcOut)
elapsedTime = timeit.default_timer() - startTime
print "\nTime to run FC layers on CPU (%f ms):" % (elapsedTime * 1000)
startTime = timeit.default_timer()
softmaxOut = xdnn.computeSoftmax(fcOut, netCfg['batch_sz'])
elapsedTime = timeit.default_timer() - startTime
print "\nTime to run Softmax on CPU (%f ms):" % (elapsedTime * 1000)
xdnn_io.printClassification(softmaxOut, netCfg['args'])
print "\nSuccess!\n"
xdnn.closeHandle()
def softmax(fcOutput, g_batchSize):
return xdnn.computeSoftmax(fcOutput, g_batchSize)
