Beispiel #1
0
 def test_const_floats(self, tf_dtype, np_dtype):
     shape = [1, 1, 50, 50]
     values = np.random.choice(a=[True, False], size=shape, p=[0.5, 0.5])
     tensor_proto = tf.make_tensor_proto(values=values,
                                         dtype=tf_dtype,
                                         shape=shape)
     pb = PB({
         "attr":
         PB({
             "value":
             PB({
                 "tensor":
                 PB({
                     "dtype": tensor_proto.dtype,
                     "tensor_shape": tensor_proto.tensor_shape,
                     "bool_val": values.tolist()
                 })
             })
         })
     })
     self.expected = {
         'data_type': np_dtype,
         'shape': np.asarray(shape, dtype=np.int),
         'value': values
     }
     self.res = tf_const_ext(pb=pb)
     self.res["infer"](None)
     self.call_args = self.infer_mock.call_args
     self.expected_call_args = None
     self.compare()
Beispiel #2
0
 def test_const_uints(self, tf_dtype, np_dtype):
     shape = [1, 1, 200, 50]
     values = np.random.randint(low=np.iinfo(np_dtype).min, high=np.iinfo(np_dtype).max, size=shape, dtype=np_dtype)
     tensor_proto = tf.make_tensor_proto(values=values, dtype=tf_dtype, shape=shape)
     pb = PB({"attr": PB({
         "value": PB({
             "tensor": PB({
                 "dtype": tensor_proto.dtype,
                 "tensor_shape": tensor_proto.tensor_shape,
             })
         })
     })})
     if tf_dtype == tf.uint16:
         setattr(pb.attr.value.tensor, "int_val", values.tolist())
     else:
         setattr(pb.attr.value.tensor, "tensor_content", tensor_proto.tensor_content)
     self.expected = {
         'data_type': np_dtype,
         'shape': np.asarray(shape, dtype=np.int),
         'value': values
     }
     self.res = tf_const_ext(pb=pb)
     self.res["infer"](None)
     self.call_args = self.infer_mock.call_args
     self.expected_call_args = None
     self.compare()
Beispiel #3
0
 def test_const_floats(self, tf_dtype, np_dtype):
     shape = [1, 1, 200, 50]
     values = np.random.uniform(low=np.finfo(np.float32).min,
                                high=np.finfo(np.float32).max,
                                size=shape).astype(np_dtype)
     tensor_proto = tf.make_tensor_proto(values=values,
                                         dtype=tf_dtype,
                                         shape=shape)
     pb = PB({
         "attr":
         PB({
             "value":
             PB({
                 "tensor":
                 PB({
                     "dtype": tensor_proto.dtype,
                     "tensor_shape": tensor_proto.tensor_shape,
                     "tensor_content": tensor_proto.tensor_content
                 })
             })
         })
     })
     self.expected = {
         'data_type': np_dtype,
         'shape': np.asarray(shape, dtype=np.int),
         'value': values
     }
     self.res = tf_const_ext(pb=pb)
     self.res["infer"](None)
     self.call_args = self.infer_mock.call_args
     self.expected_call_args = None
     self.compare()