From d6e057f0ccc95ec7cd574b8073b0ec3087f8f404 Mon Sep 17 00:00:00 2001 From: Philipp Date: Wed, 15 Feb 2023 07:03:58 -0800 Subject: [PATCH] rm unused imports --- src/lava/magma/core/model/model.py | 2 +- src/lava/magma/core/model/py/model.py | 8 ++++---- tests/lava/magma/core/learning/test_learning_rule.py | 4 ++-- 3 files changed, 7 insertions(+), 7 deletions(-) diff --git a/src/lava/magma/core/model/model.py b/src/lava/magma/core/model/model.py index 04cfd24bb..8dcb5017a 100644 --- a/src/lava/magma/core/model/model.py +++ b/src/lava/magma/core/model/model.py @@ -6,7 +6,7 @@ import typing as ty import logging -from abc import ABC, abstractmethod +from abc import ABC if ty.TYPE_CHECKING: from lava.magma.core.process.process import AbstractProcess diff --git a/src/lava/magma/core/model/py/model.py b/src/lava/magma/core/model/py/model.py index 0ce273200..ccaac9c50 100644 --- a/src/lava/magma/core/model/py/model.py +++ b/src/lava/magma/core/model/py/model.py @@ -116,13 +116,13 @@ def _get_var(self): data_port = self.process_to_service # Header corresponds to number of values # Data is either send once (for int) or one by one (array) - if isinstance(var, int) or isinstance(var, np.integer): + if isinstance(var, int) or isinstance(var, np.int32): data_port.send(enum_to_np(1)) data_port.send(enum_to_np(var)) elif isinstance(var, np.ndarray): # FIXME: send a whole vector (also runtime_service.py) var_iter = np.nditer(var, order="C") - num_items: np.integer = np.prod(var.shape) + num_items: np.int32 = np.prod(var.shape) data_port.send(enum_to_np(num_items)) for value in var_iter: data_port.send(enum_to_np(value, np.float64)) @@ -130,7 +130,7 @@ def _get_var(self): encoded_str = list(var.encode("ascii")) data_port.send(enum_to_np(len(encoded_str))) for ch in encoded_str: - data_port.send(enum_to_np(ch, d_type=np.integer)) + data_port.send(enum_to_np(ch, d_type=np.int32)) def _set_var(self): """Handles the set Var command from runtime service.""" @@ -141,7 +141,7 @@ def _set_var(self): # 2. Receive Var data data_port = self.service_to_process - if isinstance(var, int) or isinstance(var, np.integer): + if isinstance(var, int) or isinstance(var, np.int32): # First item is number of items (1) - not needed data_port.recv() # Data to set diff --git a/tests/lava/magma/core/learning/test_learning_rule.py b/tests/lava/magma/core/learning/test_learning_rule.py index 623c8885e..7b1384f00 100644 --- a/tests/lava/magma/core/learning/test_learning_rule.py +++ b/tests/lava/magma/core/learning/test_learning_rule.py @@ -60,7 +60,7 @@ def create_network( dense_inp = Dense(weights=np.eye(size, size) * 2.0) lif_0 = LIF( - shape=(size,), du=du, dv=dv, vth=vth, bias_mant=0.0, name="lif_pre" + shape=(size,), du=du, dv=dv, vth=vth, bias_mant=0, name="lif_pre" ) dense = LearningDense( @@ -68,7 +68,7 @@ def create_network( ) lif_1 = LIF( - shape=(size,), du=du, dv=dv, vth=vth, bias_mant=0.0, name="lif_post" + shape=(size,), du=du, dv=dv, vth=vth, bias_mant=0, name="lif_post" ) spike_gen.s_out.connect(dense_inp.s_in)