remove the reduce fusion config of resnet18.
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@ -492,8 +492,8 @@ result: {'top_5_accuracy': 0.9342589628681178, 'top_1_accuracy': 0.7680657810499
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| Loss Function | Softmax Cross Entropy |
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| outputs | probability |
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| Loss | 0.0002519517 |
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| Speed | 10 ms/step（8pcs） |
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| Total time | 3 mins |
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| Speed | 13 ms/step（8pcs） |
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| Total time | 4 mins |
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| Parameters (M) | 11.2 |
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| Checkpoint for Fine tuning | 86M (.ckpt file) |
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| Scripts | [Link](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/resnet) |
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@ -512,8 +512,8 @@ result: {'top_5_accuracy': 0.9342589628681178, 'top_1_accuracy': 0.7680657810499
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| Loss Function | Softmax Cross Entropy |
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| outputs | probability |
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| Loss | 2.15702 |
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| Speed | 140ms/step（8pcs） |
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| Total time | 131 mins |
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| Speed | 110ms/step（8pcs） (may need to set_numa_enbale in dataset.py) |
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| Total time | 110 mins |
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| Parameters (M) | 11.7 |
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| Checkpoint for Fine tuning | 90M (.ckpt file) |
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| Scripts | [Link](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/resnet) |
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@ -459,8 +459,8 @@ result:{'top_5_accuracy':0.9342589628681178, 'top_1_accuracy':0.768065781049936}
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| 损失函数 | Softmax交叉熵 |
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| 输出 | 概率 |
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| 损失 | 0.0002519517 |
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| 速度 | 10毫秒/步（8卡） |
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| 总时长 | 3分钟 |
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| 速度 | 13毫秒/步（8卡） |
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| 总时长 | 4分钟 |
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| 参数(M) | 11.2 |
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| 微调检查点 | 86（.ckpt文件） |
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| 脚本 | [链接](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/resnet) |
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@ -479,8 +479,8 @@ result:{'top_5_accuracy':0.9342589628681178, 'top_1_accuracy':0.768065781049936}
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| 损失函数 | Softmax交叉熵 |
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| 输出 | 概率 |
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| 损失 | 2.15702 |
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| 速度 | 140毫秒/步（8卡） |
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| 总时长 | 131分钟 |
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| 速度 | 110毫秒/步（8卡） (可能需要在datasetpy中增加set_numa_enbale绑核操作) |
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| 总时长 | 110分钟 |
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| 参数(M) | 11.7 |
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| 微调检查点| 90M（.ckpt文件） |
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| 脚本 | [链接](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/resnet) |
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@ -176,8 +176,8 @@ class ResidualBlock(nn.Cell):
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in_channel (int): Input channel.
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out_channel (int): Output channel.
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stride (int): Stride size for the first convolutional layer. Default: 1.
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use_se (bool): enable SE-ResNet50 net. Default: False.
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se_block(bool): use se block in SE-ResNet50 net. Default: False.
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use_se (bool): Enable SE-ResNet50 net. Default: False.
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se_block(bool): Use se block in SE-ResNet50 net. Default: False.
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Returns:
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Tensor, output tensor.
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@ -276,8 +276,9 @@ class ResidualBlockBase(nn.Cell):
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in_channel (int): Input channel.
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out_channel (int): Output channel.
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stride (int): Stride size for the first convolutional layer. Default: 1.
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use_se (bool): enable SE-ResNet50 net. Default: False.
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se_block(bool): use se block in SE-ResNet50 net. Default: False.
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use_se (bool): Enable SE-ResNet50 net. Default: False.
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se_block(bool): Use se block in SE-ResNet50 net. Default: False.
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res_base (bool): Enable parameter setting of resnet18. Default: True.
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Returns:
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Tensor, output tensor.
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@ -290,9 +291,9 @@ class ResidualBlockBase(nn.Cell):
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in_channel,
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out_channel,
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stride=1,
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res_base=True,
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use_se=False,
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se_block=False):
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se_block=False,
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res_base=True):
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super(ResidualBlockBase, self).__init__()
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self.res_base = res_base
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self.conv1 = _conv3x3(in_channel, out_channel, stride=stride, res_base=self.res_base)
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@ -341,8 +342,10 @@ class ResNet(nn.Cell):
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out_channels (list): Output channel in each layer.
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strides (list): Stride size in each layer.
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num_classes (int): The number of classes that the training images are belonging to.
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use_se (bool): enable SE-ResNet50 net. Default: False.
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se_block(bool): use se block in SE-ResNet50 net in layer 3 and layer 4. Default: False.
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use_se (bool): Enable SE-ResNet50 net. Default: False.
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se_block(bool): Use se block in SE-ResNet50 net in layer 3 and layer 4. Default: False.
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res_base (bool): Enable parameter setting of resnet18. Default: True.
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Returns:
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Tensor, output tensor.
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@ -432,7 +435,7 @@ class ResNet(nn.Cell):
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in_channel (int): Input channel.
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out_channel (int): Output channel.
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stride (int): Stride size for the first convolutional layer.
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se_block(bool): use se block in SE-ResNet50 net. Default: False.
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se_block(bool): Use se block in SE-ResNet50 net. Default: False.
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Returns:
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SequentialCell, the output layer.
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@ -110,9 +110,7 @@ if __name__ == '__main__':
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set_algo_parameters(elementwise_op_strategy_follow=True)
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if args_opt.net == "resnet50" or args_opt.net == "se-resnet50":
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context.set_auto_parallel_context(all_reduce_fusion_config=[85, 160])
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elif args_opt.net == "resnet18":
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context.set_auto_parallel_context(all_reduce_fusion_config=[40, 61])
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else:
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elif args_opt.net == "resnet101":
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context.set_auto_parallel_context(all_reduce_fusion_config=[180, 313])
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init()
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# GPU target
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