From f87c70a9abda1d09b5c682ec734ebadc44fa0770 Mon Sep 17 00:00:00 2001 From: liuwenhao4 Date: Thu, 28 May 2020 11:09:43 +0800 Subject: [PATCH] Fixing some tiny faults about Pylint in my code(ops) --- .../_op_impl/_custom_op/batch_matmul_impl.py | 7 +- .../_custom_op/matmul_cube_dense_left_impl.py | 33 ++++--- .../matmul_cube_fracz_left_cast_impl.py | 14 +-- .../matmul_cube_fracz_right_mul_impl.py | 4 +- .../_op_impl/_custom_op/matmul_cube_impl.py | 20 ++-- tests/st/ops/ascend/test_ops_infer.py | 17 +--- tests/st/ops/ascend/test_tbe_ops/test_mul.py | 10 +- .../test_npu_clear_float_status.py | 6 +- .../test_tbe_ops/test_npu_get_float_status.py | 6 +- tests/st/ops/ascend/test_tbe_ops/test_pad.py | 4 +- .../ops/ascend/test_tbe_ops/test_realdiv.py | 10 +- .../ascend/test_tbe_ops/test_reciprocal.py | 6 +- .../ascend/test_tbe_ops/test_scatter_nd.py | 10 +- .../ops/ascend/test_tbe_ops/test_softmax.py | 6 +- .../st/ops/ascend/test_tbe_ops/test_split.py | 6 +- tests/st/ops/ascend/test_tbe_ops/test_sqrt.py | 6 +- .../st/ops/ascend/test_tbe_ops/test_square.py | 6 +- tests/st/ops/ascend/test_tbe_ops/test_sub.py | 10 +- tests/st/ops/ascend/test_tbe_ops/test_tile.py | 6 +- tests/st/ops/cpu/test_addn_op.py | 2 +- .../ops/cpu/test_conv2d_backprop_input_op.py | 2 +- tests/ut/python/ops/test_control_ops.py | 12 ++- tests/ut/python/ops/test_nn_ops.py | 14 +-- tests/ut/python/ops/test_ops_attr_infer.py | 99 ++++++++----------- .../python/pynative_mode/test_framstruct.py | 2 +- 25 files changed, 150 insertions(+), 168 deletions(-) diff --git a/mindspore/ops/_op_impl/_custom_op/batch_matmul_impl.py b/mindspore/ops/_op_impl/_custom_op/batch_matmul_impl.py index 97982c53cf5..c5129899060 100644 --- a/mindspore/ops/_op_impl/_custom_op/batch_matmul_impl.py +++ b/mindspore/ops/_op_impl/_custom_op/batch_matmul_impl.py @@ -164,9 +164,10 @@ def CusBatchMatMul(input_x1, input_x2, output, transpose_a=False, transpose_b=Tr matmul_hybrid_f_t_local_UB = tik_instance.Tensor(dtype, [64], name="matmul_hybrid_f_t_local_UB", scope=tik.scope_ubuf) - matmul_hybrid_f_t_local_UB_dst_tmp = tik_instance.Tensor(dtype, [64], - name="matmul_hybrid_f_t_local_UB_dst_tmp", - scope=tik.scope_ubuf) + matmul_hybrid_f_t_local_UB_dst_tmp = tik_instance.Tensor( + dtype, [64], + name="matmul_hybrid_f_t_local_UB_dst_tmp", + scope=tik.scope_ubuf) tik_instance.vector_dup(64, matmul_hybrid_f_t_local_UB, 0, 1, 1, 8) tik_instance.data_move(input_2_local_UB, input2[(block_idx // 6) * 16384 + thread_idx2 * 8192], 0, 1, diff --git a/mindspore/ops/_op_impl/_custom_op/matmul_cube_dense_left_impl.py b/mindspore/ops/_op_impl/_custom_op/matmul_cube_dense_left_impl.py index e5c380369d0..e95a9ba069a 100644 --- a/mindspore/ops/_op_impl/_custom_op/matmul_cube_dense_left_impl.py +++ b/mindspore/ops/_op_impl/_custom_op/matmul_cube_dense_left_impl.py @@ -127,7 +127,7 @@ def _shape_check(shape_a, shape_b, shape_bias, src_dtype, trans_a, trans_b): if n_shape % cce.BLOCK_IN != 0 and n_shape != 1: raise RuntimeError("input shape N should be 1 or multiple of %d" % cce.BLOCK_IN) - if len(shape_bias) != 0: + if shape_bias: if len(shape_bias) == 1: if is_gevm or is_gemv: if shape_bias[0] != m_shape * n_shape: @@ -189,7 +189,7 @@ def check_supported(input_x1, input_x2, bias=None, output_y={}, trans_a=False, t util.check_shape_size(shape_b, SHAPE_SIZE_LIMIT) try: trans_a_f = bool(1 - trans_a) - if src_dtype == "float32" or src_dtype == "int32": + if src_dtype in ("float32", "int32"): if len(shape_a) != 2 and len(shape_b) != 2: return False if trans_b: @@ -239,6 +239,7 @@ def check_supported(input_x1, input_x2, bias=None, output_y={}, trans_a=False, t return False except RuntimeError as e: + print(e) return False return True @@ -385,7 +386,7 @@ def CusMatMulCubeDenseLeft(input_x1, input_x2, bias=None, output_y={}, trans_a=F tensor_b = tvm.placeholder(shape_b_temp, name='tensor_b', dtype=src_dtype) - if len(shape_bias) > 0: + if shape_bias: tensor_bias = tvm.placeholder(shape_bias, name='tensor_bias', dtype=dst_dtype) @@ -449,20 +450,20 @@ def CusMatMulCubeDenseLeft(input_x1, input_x2, bias=None, output_y={}, trans_a=F resMatmul_local_UB, 0, 16, 224 // 2, 0, 56 * 16 * 2 // 2) tik_instance.BuildCCE(kernel_name=kernel_name, inputs=[input_x1, input_x2], outputs=[resMatmul]) return tik_instance - else: - print("come into tbe, shape is error!") - result = te.lang.cce.matmul(tensor_a, tensor_b, trans_a, trans_b, format_a=format_a, - format_b=format_b, dst_dtype=dst_dtype, tensor_bias=tensor_bias) - with tvm.target.cce(): - schedule = generic.auto_schedule(result) + print("come into tbe, shape is error!") + result = te.lang.cce.matmul(tensor_a, tensor_b, trans_a, trans_b, format_a=format_a, + format_b=format_b, dst_dtype=dst_dtype, tensor_bias=tensor_bias) - tensor_list = [tensor_a, tensor_b, result] - if len(shape_bias) > 0: - tensor_list = [tensor_a, tensor_b, tensor_bias, result] + with tvm.target.cce(): + schedule = generic.auto_schedule(result) - config = {"print_ir": False, - "name": kernel_name, - "tensor_list": tensor_list} + tensor_list = [tensor_a, tensor_b, result] + if shape_bias: + tensor_list = [tensor_a, tensor_b, tensor_bias, result] - te.lang.cce.cce_build_code(schedule, config) + config = {"print_ir": False, + "name": kernel_name, + "tensor_list": tensor_list} + + te.lang.cce.cce_build_code(schedule, config) diff --git a/mindspore/ops/_op_impl/_custom_op/matmul_cube_fracz_left_cast_impl.py b/mindspore/ops/_op_impl/_custom_op/matmul_cube_fracz_left_cast_impl.py index 11b668445ef..d6c0d850b92 100644 --- a/mindspore/ops/_op_impl/_custom_op/matmul_cube_fracz_left_cast_impl.py +++ b/mindspore/ops/_op_impl/_custom_op/matmul_cube_fracz_left_cast_impl.py @@ -124,7 +124,7 @@ src_dtype: str if n_shape % cce.BLOCK_IN != 0 and n_shape != 1: raise RuntimeError("input shape N should be 1 or multiple of %d" % cce.BLOCK_IN) - if len(shape_bias): + if shape_bias: if len(shape_bias) == 1: if is_gevm or is_gemv: if shape_bias[0] != m_shape * n_shape: @@ -144,11 +144,10 @@ def _get_bias(shape_bias): bias_length = shape_bias[0] if bias_length % 16 == 0: return shape_bias - else: - bias_length = (bias_length // 16) * 16 + 16 - shape_bias = [] - shape_bias.append(bias_length) - return shape_bias + bias_length = (bias_length // 16) * 16 + 16 + shape_bias = [] + shape_bias.append(bias_length) + return shape_bias def _get_input_shape(shape_x): @@ -184,7 +183,7 @@ def check_supported(input_x1, input_x2, bias=None, output_y={}, trans_a=False, t util.check_shape_size(shape_b, SHAPE_SIZE_LIMIT) try: trans_a_f = bool(1 - trans_a) - if src_dtype == "float32" or src_dtype == "int32": + if src_dtype in ("floate32", "int32"): if len(shape_a) != 2 and len(shape_b) != 2: return False if trans_b: @@ -234,6 +233,7 @@ def check_supported(input_x1, input_x2, bias=None, output_y={}, trans_a=False, t return False except RuntimeError as e: + print(e) return False return True diff --git a/mindspore/ops/_op_impl/_custom_op/matmul_cube_fracz_right_mul_impl.py b/mindspore/ops/_op_impl/_custom_op/matmul_cube_fracz_right_mul_impl.py index 79fab2c3cd2..e1ce6d4ede6 100644 --- a/mindspore/ops/_op_impl/_custom_op/matmul_cube_fracz_right_mul_impl.py +++ b/mindspore/ops/_op_impl/_custom_op/matmul_cube_fracz_right_mul_impl.py @@ -80,8 +80,8 @@ def CusMatMulCubeFraczRightMul(input_x1, input_x2, input_x3, bias=None, output_y ((32, 128, 16, 16), 'float16', (32, 32, 16, 16), 'float16', (1,), 'float32'), ((64, 32, 16, 16), 'float16', (64, 64, 16, 16), 'float16', (1,), 'float32'), ((16, 64, 16, 16), 'float16', (16, 16, 16, 16), 'float16', (1,), 'float32')] - input_shape = ( - tuple(input_x1_shape), input_x1_dtype, tuple(input_x2_shape), input_x2_dtype, tuple(input_x3_shape), input_x3_dtype) + input_shape = (tuple(input_x1_shape), input_x1_dtype, tuple(input_x2_shape), + input_x2_dtype, tuple(input_x3_shape), input_x3_dtype) if input_shape not in Supported: raise RuntimeError("input_shape %s is not supported" % str(input_shape)) diff --git a/mindspore/ops/_op_impl/_custom_op/matmul_cube_impl.py b/mindspore/ops/_op_impl/_custom_op/matmul_cube_impl.py index 603ed287f6e..d14cb0d3c70 100644 --- a/mindspore/ops/_op_impl/_custom_op/matmul_cube_impl.py +++ b/mindspore/ops/_op_impl/_custom_op/matmul_cube_impl.py @@ -129,7 +129,7 @@ def _shape_check(shape_a, shape_b, shape_bias, src_dtype, trans_a, trans_b): if n_shape % cce.BLOCK_IN != 0 and n_shape != 1: raise RuntimeError("input shape N should be 1 or multiple of %d" % cce.BLOCK_IN) - if len(shape_bias): + if shape_bias: if len(shape_bias) == 1: if is_gevm or is_gemv: if shape_bias[0] != m_shape * n_shape: @@ -149,11 +149,10 @@ def _get_bias(shape_bias): bias_length = shape_bias[0] if bias_length % 16 == 0: return shape_bias - else: - bias_length = (bias_length // 16) * 16 + 16 - shape_bias = [] - shape_bias.append(bias_length) - return shape_bias + bias_length = (bias_length // 16) * 16 + 16 + shape_bias = [] + shape_bias.append(bias_length) + return shape_bias def _get_input_shape(shape_x): @@ -189,7 +188,7 @@ def check_supported(input_x1, input_x2, bias=None, output_y={}, trans_a=False, t util.check_shape_size(shape_b, SHAPE_SIZE_LIMIT) try: trans_a_f = bool(1 - trans_a) - if src_dtype == "float32" or src_dtype == "int32": + if src_dtype in ("float32", "int32"): if len(shape_a) != 2 and len(shape_b) != 2: return False if trans_b: @@ -239,6 +238,7 @@ def check_supported(input_x1, input_x2, bias=None, output_y={}, trans_a=False, t return False except RuntimeError as e: + print(e) return False return True @@ -314,7 +314,7 @@ def CusMatMulCube(input_x1, input_x2, bias=None, output_y={}, trans_a=False, tra src_dtype = input_x1.get("dtype").lower() dst_dtype = output_y.get("dtype").lower() - if src_dtype == "float32" or src_dtype == "int32": + if src_dtype in ("float32", "int32"): matmul_vector_cce(shape_a, shape_b, src_dtype, trans_a, trans_b, shape_bias, kernel_name) return _shape_check(shape_a, shape_b, shape_bias, src_dtype, trans_a, trans_b) @@ -377,7 +377,7 @@ def CusMatMulCube(input_x1, input_x2, bias=None, output_y={}, trans_a=False, tra tensor_b = tvm.placeholder(shape_b_temp, name='tensor_b', dtype=src_dtype) - if len(shape_bias) > 0: + if shape_bias: tensor_bias = tvm.placeholder(shape_bias, name='tensor_bias', dtype=dst_dtype) result = te.lang.cce.matmul(tensor_a, tensor_b, trans_a, trans_b, format_a=format_a, @@ -387,7 +387,7 @@ def CusMatMulCube(input_x1, input_x2, bias=None, output_y={}, trans_a=False, tra schedule = generic.auto_schedule(result) tensor_list = [tensor_a, tensor_b, result] - if len(shape_bias) > 0: + if shape_bias: tensor_list = [tensor_a, tensor_b, tensor_bias, result] config = {"print_ir": False, diff --git a/tests/st/ops/ascend/test_ops_infer.py b/tests/st/ops/ascend/test_ops_infer.py index 43d56c865c7..350116eb9f7 100644 --- a/tests/st/ops/ascend/test_ops_infer.py +++ b/tests/st/ops/ascend/test_ops_infer.py @@ -16,17 +16,10 @@ import functools import numpy as np import mindspore.nn as nn -import mindspore.context as context import mindspore.common.dtype as mstype -from mindspore import Tensor, Parameter -from mindspore.common.initializer import initializer -from mindspore.ops import Primitive -from mindspore.ops import composite as C +from mindspore import Tensor from mindspore.ops import operations as P -from mindspore.ops import functional as F -from mindspore.ops import prim_attr_register, PrimitiveWithInfer -from mindspore.ops.primitive import constexpr from mindspore import context context.set_context(mode=context.GRAPH_MODE, save_graphs=True) @@ -38,7 +31,7 @@ def test_cast_op_attr(): self.cast = P.Cast() def construct(self, x, t): return self.cast(x, t) - + class CastTypeTest(nn.Cell): def __init__(self, net): super(CastTypeTest, self).__init__() @@ -54,9 +47,9 @@ def test_cast_op_attr(): t5 = cast_net(z, mstype.float16) return (t1, t2, t3, t4, t5) net = CastTypeTest(CastNet()) - t1 = Tensor(np.ones([1,16,1,1918]).astype(np.int32)) - t2 = Tensor(np.ones([1,16,1,3840]).astype(np.float32)) - t3 = Tensor(np.ones([1,16,1,1918]).astype(np.int32)) + t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.int32)) + t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32)) + t3 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.int32)) out = net(t1, t2, t3) assert out[0].asnumpy().dtype == np.float32 assert out[1].asnumpy().dtype == np.int32 diff --git a/tests/st/ops/ascend/test_tbe_ops/test_mul.py b/tests/st/ops/ascend/test_tbe_ops/test_mul.py index 1018728872c..b4e84121355 100644 --- a/tests/st/ops/ascend/test_tbe_ops/test_mul.py +++ b/tests/st/ops/ascend/test_tbe_ops/test_mul.py @@ -33,13 +33,13 @@ class Net(nn.Cell): return self.mul(x1, x2) -x1 = np.random.randn(3, 4).astype(np.float32) -x2 = np.random.randn(3, 4).astype(np.float32) +arr_x1 = np.random.randn(3, 4).astype(np.float32) +arr_x2 = np.random.randn(3, 4).astype(np.float32) def test_net(): mul = Net() - output = mul(Tensor(x1), Tensor(x2)) - print(x1) - print(x2) + output = mul(Tensor(arr_x1), Tensor(arr_x2)) + print(arr_x1) + print(arr_x2) print(output.asnumpy()) diff --git a/tests/st/ops/ascend/test_tbe_ops/test_npu_clear_float_status.py b/tests/st/ops/ascend/test_tbe_ops/test_npu_clear_float_status.py index 3955c049eff..eaba73b100d 100644 --- a/tests/st/ops/ascend/test_tbe_ops/test_npu_clear_float_status.py +++ b/tests/st/ops/ascend/test_tbe_ops/test_npu_clear_float_status.py @@ -33,11 +33,11 @@ class Net(nn.Cell): return self.npu_clear_float_status(x1) -x1 = np.random.randn(8).astype(np.float32) +arr_x1 = np.random.randn(8).astype(np.float32) def test_net(): npu_clear_float_status = Net() - output = npu_clear_float_status(Tensor(x1)) - print(x1) + output = npu_clear_float_status(Tensor(arr_x1)) + print(arr_x1) print(output.asnumpy()) diff --git a/tests/st/ops/ascend/test_tbe_ops/test_npu_get_float_status.py b/tests/st/ops/ascend/test_tbe_ops/test_npu_get_float_status.py index 27bf9e3921f..78bc5a84101 100644 --- a/tests/st/ops/ascend/test_tbe_ops/test_npu_get_float_status.py +++ b/tests/st/ops/ascend/test_tbe_ops/test_npu_get_float_status.py @@ -33,11 +33,11 @@ class Net(nn.Cell): return self.npu_get_float_status(x1) -x1 = np.random.randn(8).astype(np.float32) +arr_x1 = np.random.randn(8).astype(np.float32) def test_net(): npu_get_float_status = Net() - output = npu_get_float_status(Tensor(x1)) - print(x1) + output = npu_get_float_status(Tensor(arr_x1)) + print(arr_x1) print(output.asnumpy()) diff --git a/tests/st/ops/ascend/test_tbe_ops/test_pad.py b/tests/st/ops/ascend/test_tbe_ops/test_pad.py index 113fcd0eef8..2c50d785b69 100644 --- a/tests/st/ops/ascend/test_tbe_ops/test_pad.py +++ b/tests/st/ops/ascend/test_tbe_ops/test_pad.py @@ -34,11 +34,11 @@ class Net(nn.Cell): return x -x = np.random.random(size=(2, 2)).astype(np.float32) +arr_x = np.random.random(size=(2, 2)).astype(np.float32) def test_net(): pad = Net() - output = pad(Tensor(x)) + output = pad(Tensor(arr_x)) print("=================output====================") print(output.asnumpy()) diff --git a/tests/st/ops/ascend/test_tbe_ops/test_realdiv.py b/tests/st/ops/ascend/test_tbe_ops/test_realdiv.py index f855c27b972..3d08aeee3a4 100644 --- a/tests/st/ops/ascend/test_tbe_ops/test_realdiv.py +++ b/tests/st/ops/ascend/test_tbe_ops/test_realdiv.py @@ -33,13 +33,13 @@ class Net(nn.Cell): return self.realdiv(x1, x2) -x1 = np.random.randn(3, 4).astype(np.float32) -x2 = np.random.randn(3, 4).astype(np.float32) +arr_x1 = np.random.randn(3, 4).astype(np.float32) +arr_x2 = np.random.randn(3, 4).astype(np.float32) def test_net(): realdiv = Net() - output = realdiv(Tensor(x1), Tensor(x2)) - print(x1) - print(x2) + output = realdiv(Tensor(arr_x1), Tensor(arr_x2)) + print(arr_x1) + print(arr_x2) print(output.asnumpy()) diff --git a/tests/st/ops/ascend/test_tbe_ops/test_reciprocal.py b/tests/st/ops/ascend/test_tbe_ops/test_reciprocal.py index 9249e24eefa..65aef4b207c 100644 --- a/tests/st/ops/ascend/test_tbe_ops/test_reciprocal.py +++ b/tests/st/ops/ascend/test_tbe_ops/test_reciprocal.py @@ -33,11 +33,11 @@ class Net(nn.Cell): return self.reciprocal(x1) -x1 = np.random.randn(3, 4).astype(np.float32) +arr_x1 = np.random.randn(3, 4).astype(np.float32) def test_net(): reciprocal = Net() - output = reciprocal(Tensor(x1)) - print(x1) + output = reciprocal(Tensor(arr_x1)) + print(arr_x1) print(output.asnumpy()) diff --git a/tests/st/ops/ascend/test_tbe_ops/test_scatter_nd.py b/tests/st/ops/ascend/test_tbe_ops/test_scatter_nd.py index 982d7951fab..bd91d7cb440 100644 --- a/tests/st/ops/ascend/test_tbe_ops/test_scatter_nd.py +++ b/tests/st/ops/ascend/test_tbe_ops/test_scatter_nd.py @@ -31,13 +31,13 @@ class Net(nn.Cell): return self.scatternd(indices, update, (3, 3)) -indices = np.array([[0, 1], [1, 1]]).astype(np.int32) -update = np.array([3.2, 1.1]).astype(np.float32) +arr_indices = np.array([[0, 1], [1, 1]]).astype(np.int32) +arr_update = np.array([3.2, 1.1]).astype(np.float32) def test_net(): scatternd = Net() - print(indices) - print(update) - output = scatternd(Tensor(indices), Tensor(update)) + print(arr_indices) + print(arr_update) + output = scatternd(Tensor(arr_indices), Tensor(arr_update)) print(output.asnumpy()) diff --git a/tests/st/ops/ascend/test_tbe_ops/test_softmax.py b/tests/st/ops/ascend/test_tbe_ops/test_softmax.py index 07feff5be07..057f0e24652 100644 --- a/tests/st/ops/ascend/test_tbe_ops/test_softmax.py +++ b/tests/st/ops/ascend/test_tbe_ops/test_softmax.py @@ -31,11 +31,11 @@ class Net(nn.Cell): return self.Softmax(x) -x = np.array([[5, 1]]).astype(np.float32) +arr_x = np.array([[5, 1]]).astype(np.float32) def test_net(): softmax = Net() - output = softmax(Tensor(x)) - print(x) + output = softmax(Tensor(arr_x)) + print(arr_x) print(output.asnumpy()) diff --git a/tests/st/ops/ascend/test_tbe_ops/test_split.py b/tests/st/ops/ascend/test_tbe_ops/test_split.py index bed4fdae81a..de73a45d727 100644 --- a/tests/st/ops/ascend/test_tbe_ops/test_split.py +++ b/tests/st/ops/ascend/test_tbe_ops/test_split.py @@ -31,13 +31,13 @@ class Net(nn.Cell): return self.split(x) -x = np.random.randn(2, 4).astype(np.float32) +arr_x = np.random.randn(2, 4).astype(np.float32) def test_net(): split = Net() - output = split(Tensor(x)) + output = split(Tensor(arr_x)) print("====input========") - print(x) + print(arr_x) print("====output=======") print(output) diff --git a/tests/st/ops/ascend/test_tbe_ops/test_sqrt.py b/tests/st/ops/ascend/test_tbe_ops/test_sqrt.py index 5a61ae9c35d..6e06a0126d3 100644 --- a/tests/st/ops/ascend/test_tbe_ops/test_sqrt.py +++ b/tests/st/ops/ascend/test_tbe_ops/test_sqrt.py @@ -31,11 +31,11 @@ class Net(nn.Cell): return self.sqrt(x) -x = np.array([1.0, 4.0, 9.0]).astype(np.float32) +arr_x = np.array([1.0, 4.0, 9.0]).astype(np.float32) def test_net(): sqrt = Net() - output = sqrt(Tensor(x)) - print(x) + output = sqrt(Tensor(arr_x)) + print(arr_x) print(output.asnumpy()) diff --git a/tests/st/ops/ascend/test_tbe_ops/test_square.py b/tests/st/ops/ascend/test_tbe_ops/test_square.py index ab6c3a993dc..8695035ddf0 100644 --- a/tests/st/ops/ascend/test_tbe_ops/test_square.py +++ b/tests/st/ops/ascend/test_tbe_ops/test_square.py @@ -31,11 +31,11 @@ class Net(nn.Cell): return self.square(x) -x = np.array([1.0, 4.0, 9.0]).astype(np.float32) +arr_x = np.array([1.0, 4.0, 9.0]).astype(np.float32) def test_net(): square = Net() - output = square(Tensor(x)) - print(x) + output = square(Tensor(arr_x)) + print(arr_x) print(output.asnumpy()) diff --git a/tests/st/ops/ascend/test_tbe_ops/test_sub.py b/tests/st/ops/ascend/test_tbe_ops/test_sub.py index 77d5302fc53..ce564d3f456 100644 --- a/tests/st/ops/ascend/test_tbe_ops/test_sub.py +++ b/tests/st/ops/ascend/test_tbe_ops/test_sub.py @@ -31,13 +31,13 @@ class Net(nn.Cell): return self.sub(x, y) -x = np.random.randn(1, 3, 3, 4).astype(np.float32) -y = np.random.randn(1, 3, 3, 4).astype(np.float32) +arr_x = np.random.randn(1, 3, 3, 4).astype(np.float32) +arr_y = np.random.randn(1, 3, 3, 4).astype(np.float32) def test_net(): sub = Net() - output = sub(Tensor(x), Tensor(y)) - print(x) - print(y) + output = sub(Tensor(arr_x), Tensor(arr_y)) + print(arr_x) + print(arr_y) print(output.asnumpy()) diff --git a/tests/st/ops/ascend/test_tbe_ops/test_tile.py b/tests/st/ops/ascend/test_tbe_ops/test_tile.py index f2a5ed6a878..8dbc4ba3bc5 100644 --- a/tests/st/ops/ascend/test_tbe_ops/test_tile.py +++ b/tests/st/ops/ascend/test_tbe_ops/test_tile.py @@ -31,11 +31,11 @@ class Net(nn.Cell): return self.tile(x, (1, 4)) -x = np.array([[0], [1], [2], [3]]).astype(np.int32) +arr_x = np.array([[0], [1], [2], [3]]).astype(np.int32) def test_net(): tile = Net() - print(x) - output = tile(Tensor(x)) + print(arr_x) + output = tile(Tensor(arr_x)) print(output.asnumpy()) diff --git a/tests/st/ops/cpu/test_addn_op.py b/tests/st/ops/cpu/test_addn_op.py index f239313eefb..d8cffe09842 100644 --- a/tests/st/ops/cpu/test_addn_op.py +++ b/tests/st/ops/cpu/test_addn_op.py @@ -68,7 +68,7 @@ def test_net_3Input(): addn = Net3I() output = addn(Tensor(x, mstype.float32), Tensor(y, mstype.float32), Tensor(z, mstype.float32)) print("output:\n", output) - expect_result = [[0., 3., 6.], + expect_result = [[0., 3., 6.], [9., 12., 15]] assert (output.asnumpy() == expect_result).all() diff --git a/tests/st/ops/cpu/test_conv2d_backprop_input_op.py b/tests/st/ops/cpu/test_conv2d_backprop_input_op.py index 7945f3828fe..f57d24e849c 100644 --- a/tests/st/ops/cpu/test_conv2d_backprop_input_op.py +++ b/tests/st/ops/cpu/test_conv2d_backprop_input_op.py @@ -66,7 +66,7 @@ class Net5(nn.Cell): def test_conv2d_backprop_input(): conv2d_input = Net5() output = conv2d_input() - print("================================") + print("================================") # expect output: # [[[[ -5, -4, 5, 12, 0, -8] # [-15, -6, 17, 17, -2, -11] diff --git a/tests/ut/python/ops/test_control_ops.py b/tests/ut/python/ops/test_control_ops.py index 6d41d1cb5b1..8697e33fcbb 100644 --- a/tests/ut/python/ops/test_control_ops.py +++ b/tests/ut/python/ops/test_control_ops.py @@ -20,7 +20,6 @@ import mindspore as ms from mindspore import Tensor from mindspore import context from mindspore import nn -from mindspore.common.parameter import Parameter, ParameterTuple from mindspore.ops import composite as C from mindspore.ops import functional as F from mindspore.ops import operations as P @@ -447,11 +446,14 @@ def test_index_to_switch_layer(): def test_control_depend_check(): with pytest.raises(TypeError) as e: - depend = P.ControlDepend(0.0) + P.ControlDepend(0.0) + print(e) with pytest.raises(ValueError) as e: - depend = P.ControlDepend(2) + P.ControlDepend(2) + print(e) with pytest.raises(TypeError) as e: - depend = P.ControlDepend((2,)) + P.ControlDepend((2,)) + print(e) def test_if_nested_compile(): @@ -497,7 +499,7 @@ def test_if_inside_for(): c1 = Tensor(1, dtype=ms.int32) c2 = Tensor(1, dtype=ms.int32) net = Net() - out = net(c1, c2) + net(c1, c2) def test_while_in_while(): diff --git a/tests/ut/python/ops/test_nn_ops.py b/tests/ut/python/ops/test_nn_ops.py index 3e258dba306..15ff49e2c0e 100644 --- a/tests/ut/python/ops/test_nn_ops.py +++ b/tests/ut/python/ops/test_nn_ops.py @@ -31,7 +31,6 @@ from ....mindspore_test_framework.pipeline.forward.compile_forward \ import pipeline_for_compile_forward_ge_graph_for_case_by_case_config from ....mindspore_test_framework.pipeline.forward.verify_exception \ import pipeline_for_verify_exception_for_case_by_case_config -from mindspore import context context.set_context(mode=context.GRAPH_MODE, save_graphs=True) def conv3x3(in_channels, out_channels, stride=1, padding=1): @@ -382,17 +381,18 @@ def test_conv2d_same_primitive(): class Conv2DSameNet(nn.Cell): def __init__(self): super(Conv2DSameNet, self).__init__() - self.conv1 = nn.Conv2d(16, 64, (1, 41), (1,4), "same", 0, 1, has_bias=True) - self.conv2 = nn.Conv2d(16, 64, (1, 41), (1,4), "same", 0, 1, has_bias=True) + self.conv1 = nn.Conv2d(16, 64, (1, 41), (1, 4), "same", 0, 1, has_bias=True) + self.conv2 = nn.Conv2d(16, 64, (1, 41), (1, 4), "same", 0, 1, has_bias=True) def construct(self, x, y): r1 = self.conv1(x) r2 = self.conv2(y) return (r1, r2) - t1 = Tensor(np.ones([1,16,1,1918]).astype(np.float32)) - t2 = Tensor(np.ones([1,16,1,3840]).astype(np.float32)) + t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.float32)) + t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32)) net = Conv2DSameNet() - out = net(t1, t2) - + net(t1, t2) + + class ComparisonNet(nn.Cell): def __init__(self): """ ComparisonNet definition """ diff --git a/tests/ut/python/ops/test_ops_attr_infer.py b/tests/ut/python/ops/test_ops_attr_infer.py index 95b66e8ff0b..92bd23245f2 100644 --- a/tests/ut/python/ops/test_ops_attr_infer.py +++ b/tests/ut/python/ops/test_ops_attr_infer.py @@ -13,30 +13,14 @@ # limitations under the License. # ============================================================================ """ test nn ops """ -import functools import numpy as np -import mindspore import mindspore.nn as nn import mindspore.context as context -import mindspore.common.dtype as mstype -from mindspore import Tensor, Parameter -from mindspore.common.initializer import initializer -from mindspore.ops import Primitive -from mindspore.ops import composite as C -from mindspore.ops import operations as P +from mindspore import Tensor from mindspore.ops import functional as F from mindspore.ops import prim_attr_register, PrimitiveWithInfer -from mindspore.ops.primitive import constexpr - -from ..ut_filter import non_graph_engine -from ....mindspore_test_framework.mindspore_test import mindspore_test -from ....mindspore_test_framework.pipeline.forward.compile_forward \ - import pipeline_for_compile_forward_ge_graph_for_case_by_case_config -from ....mindspore_test_framework.pipeline.forward.verify_exception \ - import pipeline_for_verify_exception_for_case_by_case_config -from mindspore import context context.set_context(mode=context.GRAPH_MODE, save_graphs=True) class FakeOp(PrimitiveWithInfer): @@ -57,16 +41,16 @@ def test_conv2d_same_primitive(): class Conv2DSameNet(nn.Cell): def __init__(self): super(Conv2DSameNet, self).__init__() - self.conv1 = nn.Conv2d(16, 64, (1, 41), (1,4), "same", 0, 1, has_bias=True) - self.conv2 = nn.Conv2d(16, 64, (1, 41), (1,4), "same", 0, 1, has_bias=True) + self.conv1 = nn.Conv2d(16, 64, (1, 41), (1, 4), "same", 0, 1, has_bias=True) + self.conv2 = nn.Conv2d(16, 64, (1, 41), (1, 4), "same", 0, 1, has_bias=True) def construct(self, x, y): r1 = self.conv1(x) r2 = self.conv2(y) return (r1, r2) - t1 = Tensor(np.ones([1,16,1,1918]).astype(np.float32)) - t2 = Tensor(np.ones([1,16,1,3840]).astype(np.float32)) + t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.float32)) + t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32)) net = Conv2DSameNet() - out = net(t1, t2) + net(t1, t2) # test cell as high order argument # The graph with free variables used as argument is not supported yet @@ -87,10 +71,10 @@ def Xtest_conv2d_op_with_arg(): a = self.opnet(conv_op, x) b = self.opnet(conv_op, y) return (a, b) - t1 = Tensor(np.ones([1,16,1,1918]).astype(np.float32)) - t2 = Tensor(np.ones([1,16,1,3840]).astype(np.float32)) + t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.float32)) + t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32)) net = OpsNet(Conv2dNet()) - out = net(t1, t2) + net(t1, t2) def test_conv2d_op_with_arg(): @@ -115,11 +99,10 @@ def test_conv2d_op_with_arg(): a = self.opnet(op, x, y) b = self.opnet(op, y, x) return (a, b) - t1 = Tensor(np.ones([1,16,1,1918]).astype(np.float32)) - t2 = Tensor(np.ones([1,16,1,3840]).astype(np.float32)) + t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.float32)) + t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32)) net = OpsNet(OpNet()) - out = net(t1, t2) - + net(t1, t2) def test_conv2d_op_with_arg_same_input(): @@ -144,10 +127,10 @@ def test_conv2d_op_with_arg_same_input(): a = self.opnet(op, x, x) b = self.opnet(op, y, x) return (a, b) - t1 = Tensor(np.ones([1,16,1,1918]).astype(np.float32)) - t2 = Tensor(np.ones([1,16,1,3840]).astype(np.float32)) + t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.float32)) + t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32)) net = OpsNet(OpNet()) - out = net(t1, t2) + net(t1, t2) # test op with partial def test_op_as_partial(): @@ -160,11 +143,11 @@ def test_op_as_partial(): a = partial_op(y) b = partial_op(z) return a, b - t1 = Tensor(np.ones([1,16,1,1918]).astype(np.float32)) - t2 = Tensor(np.ones([1,16,1,3840]).astype(np.float32)) - t3 = Tensor(np.ones([1,16,1,1234]).astype(np.float32)) + t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.float32)) + t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32)) + t3 = Tensor(np.ones([1, 16, 1, 1234]).astype(np.float32)) net = OpAsPartial() - out = net(t1, t2, t3) + net(t1, t2, t3) # test op with partial def test_op_as_partial_inside(): @@ -182,13 +165,14 @@ def test_op_as_partial_inside(): super(OuterNet, self).__init__() self.net = OpAsPartial() def construct(self, x, y, z): - a,b = self.net(x, y, z) + a, b = self.net(x, y, z) return a, b - t1 = Tensor(np.ones([1,16,1,1918]).astype(np.float32)) - t2 = Tensor(np.ones([1,16,1,3840]).astype(np.float32)) - t3 = Tensor(np.ones([1,16,1,1234]).astype(np.float32)) + t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.float32)) + t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32)) + t3 = Tensor(np.ones([1, 16, 1, 1234]).astype(np.float32)) net = OuterNet() - out = net(t1, t2, t3) + net(t1, t2, t3) + # test op with partial case 2 def test_op_as_partial_independent(): @@ -202,11 +186,12 @@ def test_op_as_partial_independent(): partial_op2 = F.partial(self.op, x) b = partial_op2(z) return a, b - t1 = Tensor(np.ones([1,16,1,1918]).astype(np.float32)) - t2 = Tensor(np.ones([1,16,1,3840]).astype(np.float32)) - t3 = Tensor(np.ones([1,16,1,1234]).astype(np.float32)) + t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.float32)) + t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32)) + t3 = Tensor(np.ones([1, 16, 1, 1234]).astype(np.float32)) net = OpAsPartial() - out = net(t1, t2, t3) + net(t1, t2, t3) + def test_nest_partial(): class NestPartial(nn.Cell): @@ -221,11 +206,11 @@ def test_nest_partial(): partial_op4 = F.partial(partial_op3, x) b = partial_op4(z) return a, b - t1 = Tensor(np.ones([1,16,1,1918]).astype(np.float32)) - t2 = Tensor(np.ones([1,16,1,3840]).astype(np.float32)) - t3 = Tensor(np.ones([1,16,1,1234]).astype(np.float32)) + t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.float32)) + t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32)) + t3 = Tensor(np.ones([1, 16, 1, 1234]).astype(np.float32)) net = NestPartial() - out = net(t1, t2, t3) + net(t1, t2, t3) # high order argument # op and op args as network arguments @@ -245,11 +230,11 @@ def test_op_with_arg_as_input(): a = self.opnet(op, x, z) b = self.opnet(op, x, y) return (a, b) - t1 = Tensor(np.ones([1,16,1,1918]).astype(np.float32)) - t2 = Tensor(np.ones([1,16,1,3840]).astype(np.float32)) - t3 = Tensor(np.ones([1,16,1,1234]).astype(np.float32)) + t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.float32)) + t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32)) + t3 = Tensor(np.ones([1, 16, 1, 1234]).astype(np.float32)) net = OpsNet(WithOpArgNet()) - out = net(t1, t2, t3) + net(t1, t2, t3) # The partial application used as argument is not supported yet # because of the limit of inference specialize system @@ -269,8 +254,8 @@ def Xtest_partial_as_arg(): a = self.partial_net(partial_op, z) b = self.partial_net(partial_op, y) return (a, b) - t1 = Tensor(np.ones([1,16,1,1918]).astype(np.float32)) - t2 = Tensor(np.ones([1,16,1,3840]).astype(np.float32)) - t3 = Tensor(np.ones([1,16,1,1234]).astype(np.float32)) + t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.float32)) + t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32)) + t3 = Tensor(np.ones([1, 16, 1, 1234]).astype(np.float32)) net = OpsNet(PartialArgNet()) - out = net(t1, t2, t3) + net(t1, t2, t3) diff --git a/tests/ut/python/pynative_mode/test_framstruct.py b/tests/ut/python/pynative_mode/test_framstruct.py index bd4b625caa3..17db2f5b5d4 100644 --- a/tests/ut/python/pynative_mode/test_framstruct.py +++ b/tests/ut/python/pynative_mode/test_framstruct.py @@ -982,7 +982,7 @@ def test_bprop_with_wrong_output_shape(): @bprop_getters.register(BpropWithWrongOutputShape) def get_bprop_with_wrong_output_shape(self): """Generate bprop for BpropWithWrongOutputShape""" - ones = Tensor(np.ones([2, ]).astype(np.int32)) + ones = Tensor(np.ones([2,]).astype(np.int32)) def bprop(x, out, dout): return (ones,)