forked from mindspore-Ecosystem/mindspore
commit
2e684e89e7
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@ -167,7 +167,7 @@ class BertAttentionMask(nn.Cell):
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super(BertAttentionMask, self).__init__()
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self.has_attention_mask = has_attention_mask
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self.multiply_data = Tensor([-1000.0, ], dtype=dtype)
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self.multiply_data = Tensor([-1000.0,], dtype=dtype)
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self.multiply = P.Mul()
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if self.has_attention_mask:
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@ -198,7 +198,7 @@ class BertAttentionMaskBackward(nn.Cell):
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dtype=mstype.float32):
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super(BertAttentionMaskBackward, self).__init__()
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self.has_attention_mask = has_attention_mask
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self.multiply_data = Tensor([-1000.0, ], dtype=dtype)
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self.multiply_data = Tensor([-1000.0,], dtype=dtype)
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self.multiply = P.Mul()
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self.attention_mask = Tensor(np.ones(shape=attention_mask_shape).astype(np.float32))
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if self.has_attention_mask:
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@ -136,7 +136,7 @@ def test_LSTM():
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train_network.set_train()
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train_features = Tensor(np.ones([64, max_len]).astype(np.int32))
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train_labels = Tensor(np.ones([64, ]).astype(np.int32)[0:64])
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train_labels = Tensor(np.ones([64,]).astype(np.int32)[0:64])
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losses = []
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for epoch in range(num_epochs):
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loss = train_network(train_features, train_labels)
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@ -45,7 +45,6 @@ class Net(nn.Cell):
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@non_graph_engine
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def test_AssignAdd_1():
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"""test AssignAdd 1"""
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import mindspore.context as context
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context.set_context(mode=context.GRAPH_MODE)
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net = Net()
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x = Tensor(np.ones([1]).astype(np.int64) * 100)
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@ -65,7 +64,6 @@ def test_AssignAdd_1():
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@non_graph_engine
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def test_AssignAdd_2():
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"""test AssignAdd 2"""
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import mindspore.context as context
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context.set_context(mode=context.GRAPH_MODE)
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net = Net()
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x = Tensor(np.ones([1]).astype(np.int64) * 102)
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@ -13,9 +13,9 @@
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# limitations under the License.
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# ============================================================================
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"""test_dtype"""
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from dataclasses import dataclass
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import numpy as np
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import pytest
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from dataclasses import dataclass
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import mindspore as ms
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from mindspore.common import dtype
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@ -34,7 +34,7 @@ ndarr = np.ones((2, 3))
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def test_tensor_flatten():
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with pytest.raises(AttributeError):
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lst = [1, 2, 3, 4, ]
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lst = [1, 2, 3, 4,]
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tensor_list = ms.Tensor(lst, ms.float32)
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tensor_list = tensor_list.Flatten()
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print(tensor_list)
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@ -107,7 +107,7 @@ class TrainStepWrapForAdamDynamicLr(nn.Cell):
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class TempC2Wrap(nn.Cell):
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def __init__(self, op, c1=None, c2=None, ):
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def __init__(self, op, c1=None, c2=None,):
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super(TempC2Wrap, self).__init__()
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self.op = op
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self.c1 = c1
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@ -387,7 +387,7 @@ test_case_cell_ops = [
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'block': set_train(nn.Dense(in_channels=768,
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out_channels=3072,
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activation='gelu',
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weight_init=TruncatedNormal(0.02), )),
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weight_init=TruncatedNormal(0.02),)),
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'desc_inputs': [[3, 768]],
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'desc_bprop': [[3, 3072]]}),
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('GetNextSentenceOutput', {
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@ -12,8 +12,8 @@
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import numpy as np
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from collections import Counter
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import numpy as np
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import mindspore.nn as nn
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from mindspore import Tensor, Parameter
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@ -13,9 +13,9 @@
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# limitations under the License.
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# ============================================================================
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""" test container """
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from collections import OrderedDict
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import numpy as np
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import pytest
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from collections import OrderedDict
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import mindspore.nn as nn
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from mindspore import Tensor
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@ -60,5 +60,5 @@ def test_SoftmaxCrossEntropyExpand():
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loss = nn.SoftmaxCrossEntropyExpand()
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logits = Tensor(np.random.randint(0, 9, [100, 10]).astype(np.float32))
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labels = Tensor(np.random.randint(0, 9, [10, ]).astype(np.float32))
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labels = Tensor(np.random.randint(0, 9, [10,]).astype(np.float32))
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_executor.compile(loss, logits, labels)
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@ -54,7 +54,6 @@ def test_parameter_tuple_illegal():
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def test_parameter_init_illegal():
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import numpy as np
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dat = np.array([[1, 2, 3], [2, 3, 4]])
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tensor = Tensor(dat)
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data_none = None
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@ -13,13 +13,11 @@
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# limitations under the License.
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# ============================================================================
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"""ut for model serialize(save/load)"""
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import numpy as np
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import os
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import pytest
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import stat
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import time
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import numpy as np
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import pytest
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import mindspore.common.dtype as mstype
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import mindspore.nn as nn
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from mindspore import context
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from mindspore.common.parameter import Parameter
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@ -20,8 +20,8 @@ Usage:
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"""
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import argparse
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import numpy as np
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import os
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import numpy as np
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import mindspore.context as context
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import mindspore.nn as nn
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@ -17,7 +17,6 @@ import numpy as np
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from mobilenetv2_combined import MobileNetV2
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import mindspore.context as context
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import mindspore.ops.operations as P
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from mindspore import Tensor
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from mindspore import nn
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from mindspore.nn.layer import combined
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@ -14,8 +14,8 @@
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# ============================================================================
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""" test_graph_summary """
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import logging
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import numpy as np
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import os
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import numpy as np
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import mindspore.nn as nn
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from mindspore import Model, context
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@ -15,9 +15,9 @@
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"""Test histogram summary."""
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import logging
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import numpy as np
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import os
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import tempfile
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import numpy as np
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from mindspore.common.tensor import Tensor
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from mindspore.train.summary._summary_adapter import _calc_histogram_bins
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@ -19,8 +19,8 @@
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@Desc : test summary function
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"""
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import logging
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import numpy as np
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import os
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import numpy as np
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import mindspore.nn as nn
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from mindspore import Model, context
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@ -19,10 +19,11 @@
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@Desc : test summary function
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"""
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import logging
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import numpy as np
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import os
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import pytest
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import random
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import numpy as np
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import pytest
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import mindspore.nn as nn
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from mindspore.common.tensor import Tensor
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@ -127,7 +128,7 @@ def test_scalar_summary_sample_with_shape_1():
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class SummaryDemo(nn.Cell):
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""" SummaryDemo definition """
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def __init__(self, ):
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def __init__(self,):
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super(SummaryDemo, self).__init__()
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self.s = P.ScalarSummary()
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self.histogram_summary = P.HistogramSummary()
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@ -19,8 +19,8 @@
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@Desc : test summary function of abnormal input
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"""
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import logging
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import numpy as np
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import os
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import numpy as np
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from mindspore.common.tensor import Tensor
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from mindspore.train.summary.summary_record import SummaryRecord
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@ -19,8 +19,8 @@
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@Desc : test summary function
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"""
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import logging
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import numpy as np
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import os
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import numpy as np
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import mindspore.nn as nn
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from mindspore.common.tensor import Tensor
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class SummaryDemo(nn.Cell):
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""" SummaryDemo definition """
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def __init__(self, ):
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def __init__(self,):
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super(SummaryDemo, self).__init__()
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self.s = P.TensorSummary()
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self.add = P.TensorAdd()
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@ -13,10 +13,10 @@
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# limitations under the License.
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# ============================================================================
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"""test callback function."""
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import numpy as np
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import os
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import pytest
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import stat
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import numpy as np
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import pytest
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import mindspore.common.dtype as mstype
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import mindspore.nn as nn
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@ -14,9 +14,9 @@
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# ============================================================================
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""" test_initializer """
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import math
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from functools import reduce
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import numpy as np
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import pytest as py
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from functools import reduce
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from scipy import stats
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import mindspore as ms
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@ -13,11 +13,11 @@
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# limitations under the License.
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# ============================================================================
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"""ut for model serialize(save/load)"""
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import numpy as np
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import os
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import pytest
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import stat
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import time
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import pytest
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import numpy as np
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import mindspore.common.dtype as mstype
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import mindspore.nn as nn
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@ -31,7 +31,7 @@ from mindspore.ops import operations as P
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from mindspore.train.callback import _CheckpointManager
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from mindspore.train.serialization import save_checkpoint, load_checkpoint, load_param_into_net, \
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_exec_save_checkpoint, export, _save_graph
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from ..ut_filter import run_on_onnxruntime, non_graph_engine
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from ..ut_filter import non_graph_engine
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context.set_context(mode=context.GRAPH_MODE)
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