MMdnn
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microsoft
AttributeError: 'NoneType' object has no attribute 'name' in FusedBatchNorm
Platform (like ubuntu 16.04/win10): ubuntu 16.04
Python version: 3.7.3
Source framework with version (like Tensorflow 1.4.1 with GPU): Tensorflow 1.4.0
Destination framework with version (like CNTK 2.3 with GPU): PyTorch 1.7.1
Pre-trained model path (webpath or webdisk path): https://drive.google.com/file/d/1awFM8y4A9jWcfUFaVs6S3CBGZramLBkY/view?usp=sharing
Running scripts: mmconvert -sf tensorflow -in model-50.meta -iw model-50 --dstNode resnet10/logits/BiasAdd -df pytorch -om model-50.pth
It seems that this is a bug that caused by Fused Batch Normalization layer, but I did not use that layer.
I only use tf.layers.batch_normalization as the BN layer in ResNet-10, but I didn't pass fused argument, which is set to None by default. None means it uses a faster, fused implementation if possible
Hi @machanic , I looked into the network. Here is a temporal solution:
You might want to replace these two liners with
mean = self.get_parent(source_node.name, [3], True)
var = self.get_parent(source_node.name, [4], True)
also, replace these two liners with
self.set_weight(source_node.name, 'mean', self.ckpt_data[mean.name.replace('Const', 'moving_mean')])
self.set_weight(source_node.name, 'var', self.ckpt_data[var.name.replace('Const_1', 'moving_variance')])
You can find your src path via import mmdnn; print(mmdnn.__path__)
Most likely, after the temporal hotfixes, you can get
def forward(self, x):
resnet10_Pad = F.pad(x, (3, 3, 3, 3), mode = 'constant', value = 0)
resnet10_conv1_Conv2D = self.resnet10_conv1_Conv2D(resnet10_Pad)
resnet10_pool1_MaxPool, resnet10_pool1_MaxPool_idx = F.max_pool2d(resnet10_conv1_Conv2D, kernel_size=(3, 3), stride=(2, 2), padding=0, ceil_mode=False, return_indices=True)
resnet10_block1_unit_1_bottleneck_v2_batch_normalization_FusedBatchNorm = self.resnet10_block1_unit_1_bottleneck_v2_batch_normalization_FusedBatchNorm(resnet10_pool1_MaxPool)
resnet10_block1_unit_1_bottleneck_v2_Relu = F.relu(resnet10_block1_unit_1_bottleneck_v2_batch_normalization_FusedBatchNorm)
resnet10_block1_unit_1_bottleneck_v2_shortcut_Conv2D = self.resnet10_block1_unit_1_bottleneck_v2_shortcut_Conv2D(resnet10_block1_unit_1_bottleneck_v2_Relu)
resnet10_block1_unit_1_bottleneck_v2_conv1_Conv2D = self.resnet10_block1_unit_1_bottleneck_v2_conv1_Conv2D(resnet10_block1_unit_1_bottleneck_v2_Relu)
resnet10_block1_unit_1_bottleneck_v2_conv1_Relu = F.relu(resnet10_block1_unit_1_bottleneck_v2_conv1_Conv2D)
resnet10_block1_unit_1_bottleneck_v2_Pad = F.pad(resnet10_block1_unit_1_bottleneck_v2_conv1_Relu, (1, 1, 1, 1), mode = 'constant', value = 0)
resnet10_block1_unit_1_bottleneck_v2_conv2_Conv2D = self.resnet10_block1_unit_1_bottleneck_v2_conv2_Conv2D(resnet10_block1_unit_1_bottleneck_v2_Pad)
resnet10_block1_unit_1_bottleneck_v2_conv2_Relu = F.relu(resnet10_block1_unit_1_bottleneck_v2_conv2_Conv2D)
resnet10_block1_unit_1_bottleneck_v2_conv3_Conv2D = self.resnet10_block1_unit_1_bottleneck_v2_conv3_Conv2D(resnet10_block1_unit_1_bottleneck_v2_conv2_Relu)
resnet10_block1_unit_1_bottleneck_v2_add = resnet10_block1_unit_1_bottleneck_v2_shortcut_Conv2D + resnet10_block1_unit_1_bottleneck_v2_conv3_Conv2D
resnet10_block2_unit_1_bottleneck_v2_batch_normalization_FusedBatchNorm = self.resnet10_block2_unit_1_bottleneck_v2_batch_normalization_FusedBatchNorm(resnet10_block1_unit_1_bottleneck_v2_add)
resnet10_block2_unit_1_bottleneck_v2_Relu = F.relu(resnet10_block2_unit_1_bottleneck_v2_batch_normalization_FusedBatchNorm)
resnet10_block2_unit_1_bottleneck_v2_shortcut_Conv2D = self.resnet10_block2_unit_1_bottleneck_v2_shortcut_Conv2D(resnet10_block2_unit_1_bottleneck_v2_Relu)
resnet10_block2_unit_1_bottleneck_v2_conv1_Conv2D = self.resnet10_block2_unit_1_bottleneck_v2_conv1_Conv2D(resnet10_block2_unit_1_bottleneck_v2_Relu)
resnet10_block2_unit_1_bottleneck_v2_conv1_Relu = F.relu(resnet10_block2_unit_1_bottleneck_v2_conv1_Conv2D)
resnet10_block2_unit_1_bottleneck_v2_Pad = F.pad(resnet10_block2_unit_1_bottleneck_v2_conv1_Relu, (1, 1, 1, 1), mode = 'constant', value = 0)
resnet10_block2_unit_1_bottleneck_v2_conv2_Conv2D = self.resnet10_block2_unit_1_bottleneck_v2_conv2_Conv2D(resnet10_block2_unit_1_bottleneck_v2_Pad)
resnet10_block2_unit_1_bottleneck_v2_conv2_Relu = F.relu(resnet10_block2_unit_1_bottleneck_v2_conv2_Conv2D)
resnet10_block2_unit_1_bottleneck_v2_conv3_Conv2D = self.resnet10_block2_unit_1_bottleneck_v2_conv3_Conv2D(resnet10_block2_unit_1_bottleneck_v2_conv2_Relu)
resnet10_block2_unit_1_bottleneck_v2_add = resnet10_block2_unit_1_bottleneck_v2_shortcut_Conv2D + resnet10_block2_unit_1_bottleneck_v2_conv3_Conv2D
resnet10_block3_unit_1_bottleneck_v2_batch_normalization_FusedBatchNorm = self.resnet10_block3_unit_1_bottleneck_v2_batch_normalization_FusedBatchNorm(resnet10_block2_unit_1_bottleneck_v2_add)
resnet10_block3_unit_1_bottleneck_v2_Relu = F.relu(resnet10_block3_unit_1_bottleneck_v2_batch_normalization_FusedBatchNorm)
resnet10_block3_unit_1_bottleneck_v2_shortcut_Conv2D = self.resnet10_block3_unit_1_bottleneck_v2_shortcut_Conv2D(resnet10_block3_unit_1_bottleneck_v2_Relu)
resnet10_block3_unit_1_bottleneck_v2_conv1_Conv2D = self.resnet10_block3_unit_1_bottleneck_v2_conv1_Conv2D(resnet10_block3_unit_1_bottleneck_v2_Relu)
resnet10_block3_unit_1_bottleneck_v2_conv1_Relu = F.relu(resnet10_block3_unit_1_bottleneck_v2_conv1_Conv2D)
resnet10_block3_unit_1_bottleneck_v2_Pad = F.pad(resnet10_block3_unit_1_bottleneck_v2_conv1_Relu, (1, 1, 1, 1), mode = 'constant', value = 0)
resnet10_block3_unit_1_bottleneck_v2_conv2_Conv2D = self.resnet10_block3_unit_1_bottleneck_v2_conv2_Conv2D(resnet10_block3_unit_1_bottleneck_v2_Pad)
resnet10_block3_unit_1_bottleneck_v2_conv2_Relu = F.relu(resnet10_block3_unit_1_bottleneck_v2_conv2_Conv2D)
resnet10_block3_unit_1_bottleneck_v2_conv3_Conv2D = self.resnet10_block3_unit_1_bottleneck_v2_conv3_Conv2D(resnet10_block3_unit_1_bottleneck_v2_conv2_Relu)
resnet10_block3_unit_1_bottleneck_v2_add = resnet10_block3_unit_1_bottleneck_v2_shortcut_Conv2D + resnet10_block3_unit_1_bottleneck_v2_conv3_Conv2D
resnet10_block4_unit_1_bottleneck_v2_batch_normalization_FusedBatchNorm = self.resnet10_block4_unit_1_bottleneck_v2_batch_normalization_FusedBatchNorm(resnet10_block3_unit_1_bottleneck_v2_add)
resnet10_block4_unit_1_bottleneck_v2_Relu = F.relu(resnet10_block4_unit_1_bottleneck_v2_batch_normalization_FusedBatchNorm)
resnet10_block4_unit_1_bottleneck_v2_shortcut_Conv2D = self.resnet10_block4_unit_1_bottleneck_v2_shortcut_Conv2D(resnet10_block4_unit_1_bottleneck_v2_Relu)
resnet10_block4_unit_1_bottleneck_v2_conv1_Conv2D = self.resnet10_block4_unit_1_bottleneck_v2_conv1_Conv2D(resnet10_block4_unit_1_bottleneck_v2_Relu)
resnet10_block4_unit_1_bottleneck_v2_conv1_Relu = F.relu(resnet10_block4_unit_1_bottleneck_v2_conv1_Conv2D)
resnet10_block4_unit_1_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet10_block4_unit_1_bottleneck_v2_conv1_Relu, (1, 1, 1, 1))
resnet10_block4_unit_1_bottleneck_v2_conv2_Conv2D = self.resnet10_block4_unit_1_bottleneck_v2_conv2_Conv2D(resnet10_block4_unit_1_bottleneck_v2_conv2_Conv2D_pad)
resnet10_block4_unit_1_bottleneck_v2_conv2_Relu = F.relu(resnet10_block4_unit_1_bottleneck_v2_conv2_Conv2D)
resnet10_block4_unit_1_bottleneck_v2_conv3_Conv2D = self.resnet10_block4_unit_1_bottleneck_v2_conv3_Conv2D(resnet10_block4_unit_1_bottleneck_v2_conv2_Relu)
resnet10_block4_unit_1_bottleneck_v2_add = resnet10_block4_unit_1_bottleneck_v2_shortcut_Conv2D + resnet10_block4_unit_1_bottleneck_v2_conv3_Conv2D
resnet10_batch_normalization_FusedBatchNorm = self.resnet10_batch_normalization_FusedBatchNorm(resnet10_block4_unit_1_bottleneck_v2_add)
resnet10_Relu = F.relu(resnet10_batch_normalization_FusedBatchNorm)
resnet10_pool5 = torch.mean(resnet10_Relu, 3, True)
resnet10_pool5 = torch.mean(resnet10_pool5, 2, True)
resnet10_logits_Conv2D = self.resnet10_logits_Conv2D(resnet10_pool5)
return resnet10_logits_Conv2D
Good Luck!
