Searched refs:MaxPool2d (Results 1 – 25 of 86) sorted by relevance
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| /third_party/mindspore/mindspore/lite/examples/export_models/models/ |
| D | NetworkInNetwork.py | 37 nn.MaxPool2d(kernel_size=3, stride=2, pad_mode='same'),
48 nn.MaxPool2d(kernel_size=3, stride=2, pad_mode='same'),
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| D | mini_alexnet.py | 39 self.max_pool2d = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode='valid')
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| /third_party/mindspore/tests/st/pynative/ms_function/ |
| D | test_pynative_lenet_ms_function.py | 46 self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
63 self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
113 self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
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| /third_party/mindspore/tests/ut/python/exec/ |
| D | test_pooling.py | 28 self.maxpool = nn.MaxPool2d(kernel_size,
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| /third_party/mindspore/tests/ut/python/parallel/ |
| D | test_batchnorm_batch_parallel.py | 25 from mindspore.nn.layer.pooling import MaxPool2d
102 self.maxpool = MaxPool2d(kernel_size=3, stride=2, pad_mode="same")
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| /third_party/mindspore/tests/st/ops/cpu/ |
| D | test_maxpool_op.py | 31 self.maxpool_fun = nn.MaxPool2d(kernel_size=2, stride=2, pad_mode="VALID")
40 self.maxpool_fun2 = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode="SAME")
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| /third_party/mindspore/tests/st/ops/gpu/ |
| D | test_maxpool_gpu_op.py | 29 self.maxpool_fun = nn.MaxPool2d(kernel_size=2, stride=2, pad_mode="VALID")
38 self.maxpool_fun = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode="SAME")
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| D | test_time_distributed_op.py | 56 pool = nn.MaxPool2d(kernel_size=3, stride=1)
146 pool = nn.MaxPool2d(kernel_size=3, stride=1)
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| /third_party/mindspore/tests/ut/python/pynative_mode/nn/ |
| D | test_pooling.py | 51 max_pool = nn.MaxPool2d(kernel_size, stride, pad_mode='SAME')
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| /third_party/mindspore/tests/ut/python/pynative_mode/ge/ops/ |
| D | test_pooling.py | 46 max_pool = nn.MaxPool2d(kernel_size, stride)
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| /third_party/mindspore/tests/ut/python/optimizer/ |
| D | test_recompute.py | 27 self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
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| /third_party/mindspore/tests/st/quantization/lenet_quant/ |
| D | lenet_fusion.py | 46 self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
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| /third_party/mindspore/mindspore/nn/layer/ |
| D | pooling.py | 71 class MaxPool2d(_PoolNd): class
135 super(MaxPool2d, self).__init__(kernel_size, stride, pad_mode, data_format)
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| /third_party/mindspore/tests/st/networks/models/resnet50/src_thor/ |
| D | resnet.py | 123 … nn.ReLU(), nn.MaxPool2d(kernel_size=2, stride=2, pad_mode='same')])
150 …self.down_sample_layer = nn.SequentialCell([nn.MaxPool2d(kernel_size=2, stride=2, pad_mode='same'),
243 self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode="same")
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| /third_party/mindspore/tests/ut/python/ops/ |
| D | test_ops_check.py | 48 self.max_pool2d = nn.MaxPool2d(kernel_size=2)
161 'block': nn.MaxPool2d(5, pad_mode='same'),
164 'block': nn.MaxPool2d(5, pad_mode='valid'),
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| /third_party/mindspore/tests/st/networks/models/ |
| D | lenet.py | 30 self.pool = nn.MaxPool2d(kernel_size=2, stride=2, pad_mode="valid")
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| D | alexnet.py | 29 self.max_pool2d = nn.MaxPool2d(kernel_size=3, stride=2)
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| /third_party/mindspore/tests/ut/python/nn/ |
| D | test_pooling.py | 51 self.maxpool = nn.MaxPool2d(kernel_size,
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| /third_party/mindspore/tests/st/fl/mobile/src/ |
| D | model.py | 56 self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
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| /third_party/mindspore/tests/perf_test/ |
| D | lenet.py | 62 self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
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| /third_party/mindspore/tests/ut/python/model/ |
| D | test_lenet_core_after_exception.py | 37 self.max_pool2d = nn.MaxPool2d(kernel_size=2)
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| D | res18_example.py | 109 self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode='same')
178 self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2)
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| D | test_lenet.py | 39 self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
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| /third_party/mindspore/tests/ut/python/utils/ |
| D | test_checkparam.py | 36 self.max_pool2d = nn.MaxPool2d(kernel_size=2)
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| /third_party/mindspore/tests/st/fl/hybrid_lenet/src/ |
| D | model.py | 57 self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
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