def run(rundir, chanIdx, q, args):
xspub = xstream.Publisher()
xssub = xstream.Subscribe(chanIdx2Str(chanIdx))
runner = Runner(rundir)
inTensors = runner.get_input_tensors()
outTensors = runner.get_output_tensors()
q.put(1) # ready for work
fpgaBlobs = None
fcOutput = None
labels = xdnn_io.get_labels(args['labels'])
xdnnCPUOp = xdnn.XDNNCPUOp("%s/weights.h5" % rundir)
while True:
try:
payload = xssub.get()
if not payload:
break
(meta, buf) = payload
if fpgaBlobs == None:
# allocate buffers
fpgaBlobs = []
batchsz = meta['shape'][0] # inTensors[0].dims[0]
for io in [inTensors, outTensors]:
blobs = []
for t in io:
shape = (batchsz,) + tuple([t.dims[i] for i in range(t.ndims)][1:])
blobs.append(np.empty((shape), dtype=np.float32, order='C'))
fpgaBlobs.append(blobs)
fcOutput = np.empty((batchsz, args['outsz'],), dtype=np.float32, order='C')
fpgaInput = fpgaBlobs[0][0]
assert(tuple(meta['shape']) == fpgaInput.shape)
data = np.frombuffer(buf, dtype=np.float32).reshape(fpgaInput.shape)
np.copyto(fpgaInput, data)
jid = runner.execute_async(fpgaBlobs[0], fpgaBlobs[1])
runner.wait(jid)
xdnnCPUOp.computeFC(fpgaBlobs[1][0], fcOutput)
softmaxOut = xdnnCPUOp.computeSoftmax(fcOutput)
xdnn_io.printClassification(softmaxOut, meta['images'], labels)
sys.stdout.flush()
if meta['id'] % 1000 == 0:
print("Recvd query %d" % meta['id'])
sys.stdout.flush()
del data
del buf
del payload
xspub.send(meta['from'], "success")
except Exception as e:
logging.error("Worker exception " + str(e))
def __init__(self, args, img_paths, fpgaOutputs, output_shapes,shared_output_arrs):
self.args = args
self.img_paths = img_paths
self.fpgaOutputs = fpgaOutputs
self.output_shapes = output_shapes
self._shared_output_arrs = shared_output_arrs
self.numProcessed = 0
self.startTime = timeit.default_timer()
self.cpuOp = xdnn.XDNNCPUOp(self.args['weights']);
def main():
args = xdnn_io.processCommandLine()
runner = Runner(args['vitis_rundir'])
inTensors = runner.get_input_tensors()
outTensors = runner.get_output_tensors()
batch_sz = args['batch_sz']
if batch_sz == -1:
# use Runner's suggested batch size
batch_sz = inTensors[0].dims[0]
if args['golden']:
goldenMap = xdnn_io.getGoldenMap(args['golden'])
top5Count = 0
top1Count = 0
fpgaBlobs = []
for io in [inTensors, outTensors]:
blobs = []
for t in io:
shape = (batch_sz,) + tuple([t.dims[i] for i in range(t.ndims)][1:])
blobs.append(np.empty((shape), dtype=np.float32, order='C'))
fpgaBlobs.append(blobs)
img_paths = xdnn_io.getFilePaths(args['images'])
labels = xdnn_io.get_labels(args['labels'])
xdnnCPUOp = xdnn.XDNNCPUOp("%s/weights.h5" % args['vitis_rundir'])
fcOutput = np.empty((batch_sz, args['outsz'],), dtype=np.float32, order='C')
fpgaInput = fpgaBlobs[0][0]
for i in range(0, len(img_paths), batch_sz):
pl = []
# fill tensor input data from image file
for j, p in enumerate(img_paths[i:i + batch_sz]):
img, _ = xdnn_io.loadImageBlobFromFile(p,
args['img_raw_scale'], args['img_mean'], args['img_input_scale'],
fpgaInput.shape[2], fpgaInput.shape[3])
pl.append(p)
np.copyto(fpgaInput[j], img)
jid = runner.execute_async(fpgaBlobs[0], fpgaBlobs[1])
runner.wait(jid)
xdnnCPUOp.computeFC(fpgaBlobs[1][0], fcOutput)
softmaxOut = xdnnCPUOp.computeSoftmax(fcOutput)
if args['golden']:
for j,p in enumerate(img_paths[i:i + batch_sz]):
top1Count += xdnn_io.isTopK(softmaxOut[j], goldenMap, p, labels, 1)
top5Count += xdnn_io.isTopK(softmaxOut[j], goldenMap, p, labels, 5)
else:
xdnn_io.printClassification(softmaxOut, pl, labels)
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 post_process(self, image_paths, fpgaOutput):
global labels
global fcOutput
global cCpuRT
if cCpuRT is None:
cCpuRT = xdnn.XDNNCPUOp(self.args['weights'])
fcOutput = np.empty((
self.args['batch_sz'],
self.args['outsz'],
),
dtype=np.float32,
order='C')
labels = xdnn_io.get_labels(self.args['labels'])
input_buffer = list(fpgaOutput.values())[
0] # Assume the first network output will feed CPU layers
cCpuRT.computeFC(input_buffer, fcOutput)
softmaxOut = cCpuRT.computeSoftmax(fcOutput)
if self.args['golden']:
xdnn_io.printClassification(softmaxOut, image_paths, labels)