alexcheng88
65 lines
1.9 KiB
Python
65 lines
1.9 KiB
Python
"""
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acl backend
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"""
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import dnmetis_backend as dnmetis_backend
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import backend.backend as backend
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import numpy as np
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import os
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import pdb
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class AclBackend(backend.Backend):
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def __init__(self):
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super(AclBackend, self).__init__()
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self.ACL=5
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self.outputs = ""
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self.inputs = ""
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self.model_path = ""
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self.cfg_path = ""
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def version(self):
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return "1.0"
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def name(self):
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return "AclBackend"
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def image_format(self):
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# By default tensorflow uses NHWC (and the cpu implementation only does NHWC)
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return "NHWC"
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def load(self, args):
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# there is no input/output meta data i the graph so it need to come from config.
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if not args.inputs:
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raise ValueError("AclBackend needs inputs")
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if not args.outputs:
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raise ValueError("AclBackend needs outputs")
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self.outputs = args.outputs
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self.inputs = args.inputs
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self.model_path = args.model
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self.cfg_path = args.cfg_path
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#s.path.join(args.pwd, 'backend_cfg/built-in_config.txt')
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dnmetis_backend.backend_setconfig(self.cfg_path)
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dnmetis_backend.backend_load(self.ACL,self.model_path,"")
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return self
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def predict(self, feed):
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#fed=feed[self.inputs[0]]
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result_list=[]
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result = dnmetis_backend.backend_predict(self.ACL,self.model_path,feed)
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for _ in range(len(self.outputs)):
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#resnet50 tf & caffe
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if 'softmax_tensor' in self.outputs[_] or 'prob' in self.outputs[_]:
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result_list.append(np.argmax(result[_],1))
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# resnet50 tf
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if 'ArgMax' in self.outputs[_]:
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result_list.append(result[_])
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if result_list == []:
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# ssd-resnet34 tf
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result_list = result
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return result_list
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def unload(self):
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return dnmetis_backend.backend_unload(self.ACL,self.model_path,"")
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