webdnn
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Published
20 hours ago •
mil-tokyo
mil-tokyo
Convert Dense layer for Keras and with backend fallback failed.
66 <keras.layers.core.Dense object at 0x0000018C985A6D68>
Using TensorFlow backend.
[convert_keras.py] Generating feedforward graph
2017-12-04 14:14:46.043447: W C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2017-12-04 14:14:46.043620: W C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2017-12-04 14:14:46.456242: I C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\gpu\gpu_device.cc:955] Found device 0 with properties:
name: GeForce GTX 860M
major: 5 minor: 0 memoryClockRate (GHz) 0.54
pciBusID 0000:01:00.0
Total memory: 2.00GiB
Free memory: 1.65GiB
2017-12-04 14:14:46.456353: I C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\gpu\gpu_device.cc:976] DMA: 0
2017-12-04 14:14:46.457797: I C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\gpu\gpu_device.cc:986] 0: Y
2017-12-04 14:14:46.458198: I C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\gpu\gpu_device.cc:1045] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 860M, pci bus id: 0000:01:00.0)
E:\CI-Cor-Ready\ai\face-demo-work\webdnn\webdnn\src\graph_transpiler\webdnn\util\console.py:30: Warning: [Pooling2D] Performing pooling with parameters which causes edge is ignored. Which edge (left / right) is ignored is different on frameworks, so slightly different result will be generated.
warnings.warn(message, category)
Traceback (most recent call last):
File "./webdnn/bin/convert_keras.py", line 117, in <module>
main()
File "./webdnn/bin/convert_keras.py", line 67, in main
graph = converter.convert(model)
File "E:\CI-Cor-Ready\ai\face-demo-work\webdnn\webdnn\src\graph_transpiler\webdnn\frontend\keras\converter.py", line 112, in convert
return self._convert_fallback(model)
File "E:\CI-Cor-Ready\ai\face-demo-work\webdnn\webdnn\src\graph_transpiler\webdnn\frontend\keras\converter.py", line 126, in _convert_fallback
self._convert_operator(node.outbound_layer)
File "E:\CI-Cor-Ready\ai\face-demo-work\webdnn\webdnn\src\graph_transpiler\webdnn\frontend\keras\converter.py", line 159, in _convert_operator
return super(KerasConverter, self)._convert_operator(k_op)
File "E:\CI-Cor-Ready\ai\face-demo-work\webdnn\webdnn\src\graph_transpiler\webdnn\frontend\converter.py", line 117, in _convert_operator
self._handler_map[self.__class__.__name__][operator_key](self, operator)
File "E:\CI-Cor-Ready\ai\face-demo-work\webdnn\webdnn\src\graph_transpiler\webdnn\frontend\keras\layers\core.py", line 37, in _convert_dense
y, = Tensordot(None, axes=[x.order.axes[-1], w.order.axes[0]])(x, w)
File "E:\CI-Cor-Ready\ai\face-demo-work\webdnn\webdnn\src\graph_transpiler\webdnn\graph\operators\tensordot.py", line 119, in __call__
"""
AssertionError:
[Tensordot] Reduction size of "A" and "B" must be same:
(A) = <Variable Variable106 shape=(1, 2048), order=[N, C]>
(B) = <ConstantVariable ConstantVariable182 shape=(512, 10574), order=[?175, ?176]>
(axes) = ((<Axis C>,), (<Axis ?175>,))
(reduction size of A) = 2048
(reduction size of B) = 512
Error message said that invalid size matrix multiplication ( [1, 2048] * [512 10574] ) was performed.
Could you give me your model structure information (ex. model.summary() in keras) ?
Using TensorFlow backend.
2017-12-04 17:38:51.004583: W C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2017-12-04 17:38:51.004718: W C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2017-12-04 17:38:51.927643: I C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\gpu\gpu_device.cc:955] Found device 0 with properties:
name: GeForce GTX 860M
major: 5 minor: 0 memoryClockRate (GHz) 0.54
pciBusID 0000:01:00.0
Total memory: 2.00GiB
Free memory: 1.65GiB
2017-12-04 17:38:51.927791: I C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\gpu\gpu_device.cc:976] DMA: 0
2017-12-04 17:38:51.928786: I C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\gpu\gpu_device.cc:986] 0: Y
2017-12-04 17:38:51.929183: I C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\gpu\gpu_device.cc:1045] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 860M, pci bus id: 0000:01:00.0)
____________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
====================================================================================================
input_1 (InputLayer) (None, 96, 96, 1) 0
____________________________________________________________________________________________________
conv2d_1 (Conv2D) (None, 48, 48, 64) 3200 input_1[0][0]
____________________________________________________________________________________________________
batch_normalization_1 (BatchNorm (None, 48, 48, 64) 256 conv2d_1[0][0]
____________________________________________________________________________________________________
activation_1 (Activation) (None, 48, 48, 64) 0 batch_normalization_1[0][0]
____________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D) (None, 24, 24, 64) 0 activation_1[0][0]
____________________________________________________________________________________________________
conv2d_2 (Conv2D) (None, 24, 24, 64) 36928 max_pooling2d_1[0][0]
____________________________________________________________________________________________________
batch_normalization_2 (BatchNorm (None, 24, 24, 64) 256 conv2d_2[0][0]
____________________________________________________________________________________________________
activation_2 (Activation) (None, 24, 24, 64) 0 batch_normalization_2[0][0]
____________________________________________________________________________________________________
conv2d_3 (Conv2D) (None, 24, 24, 64) 36928 activation_2[0][0]
____________________________________________________________________________________________________
add_1 (Add) (None, 24, 24, 64) 0 max_pooling2d_1[0][0]
conv2d_3[0][0]
____________________________________________________________________________________________________
batch_normalization_3 (BatchNorm (None, 24, 24, 64) 256 add_1[0][0]
____________________________________________________________________________________________________
activation_3 (Activation) (None, 24, 24, 64) 0 batch_normalization_3[0][0]
____________________________________________________________________________________________________
conv2d_4 (Conv2D) (None, 24, 24, 64) 36928 activation_3[0][0]
____________________________________________________________________________________________________
batch_normalization_4 (BatchNorm (None, 24, 24, 64) 256 conv2d_4[0][0]
____________________________________________________________________________________________________
activation_4 (Activation) (None, 24, 24, 64) 0 batch_normalization_4[0][0]
____________________________________________________________________________________________________
conv2d_5 (Conv2D) (None, 24, 24, 64) 36928 activation_4[0][0]
____________________________________________________________________________________________________
add_2 (Add) (None, 24, 24, 64) 0 add_1[0][0]
conv2d_5[0][0]
____________________________________________________________________________________________________
batch_normalization_5 (BatchNorm (None, 24, 24, 64) 256 add_2[0][0]
____________________________________________________________________________________________________
activation_5 (Activation) (None, 24, 24, 64) 0 batch_normalization_5[0][0]
____________________________________________________________________________________________________
conv2d_6 (Conv2D) (None, 12, 12, 128) 73856 activation_5[0][0]
____________________________________________________________________________________________________
batch_normalization_6 (BatchNorm (None, 12, 12, 128) 512 conv2d_6[0][0]
____________________________________________________________________________________________________
activation_6 (Activation) (None, 12, 12, 128) 0 batch_normalization_6[0][0]
____________________________________________________________________________________________________
conv2d_8 (Conv2D) (None, 12, 12, 128) 8320 add_2[0][0]
____________________________________________________________________________________________________
conv2d_7 (Conv2D) (None, 12, 12, 128) 147584 activation_6[0][0]
____________________________________________________________________________________________________
add_3 (Add) (None, 12, 12, 128) 0 conv2d_8[0][0]
conv2d_7[0][0]
____________________________________________________________________________________________________
batch_normalization_7 (BatchNorm (None, 12, 12, 128) 512 add_3[0][0]
____________________________________________________________________________________________________
activation_7 (Activation) (None, 12, 12, 128) 0 batch_normalization_7[0][0]
____________________________________________________________________________________________________
conv2d_9 (Conv2D) (None, 12, 12, 128) 147584 activation_7[0][0]
____________________________________________________________________________________________________
batch_normalization_8 (BatchNorm (None, 12, 12, 128) 512 conv2d_9[0][0]
____________________________________________________________________________________________________
activation_8 (Activation) (None, 12, 12, 128) 0 batch_normalization_8[0][0]
____________________________________________________________________________________________________
conv2d_10 (Conv2D) (None, 12, 12, 128) 147584 activation_8[0][0]
____________________________________________________________________________________________________
add_4 (Add) (None, 12, 12, 128) 0 add_3[0][0]
conv2d_10[0][0]
____________________________________________________________________________________________________
batch_normalization_9 (BatchNorm (None, 12, 12, 128) 512 add_4[0][0]
____________________________________________________________________________________________________
activation_9 (Activation) (None, 12, 12, 128) 0 batch_normalization_9[0][0]
____________________________________________________________________________________________________
conv2d_11 (Conv2D) (None, 6, 6, 256) 295168 activation_9[0][0]
____________________________________________________________________________________________________
batch_normalization_10 (BatchNor (None, 6, 6, 256) 1024 conv2d_11[0][0]
____________________________________________________________________________________________________
activation_10 (Activation) (None, 6, 6, 256) 0 batch_normalization_10[0][0]
____________________________________________________________________________________________________
conv2d_13 (Conv2D) (None, 6, 6, 256) 33024 add_4[0][0]
____________________________________________________________________________________________________
conv2d_12 (Conv2D) (None, 6, 6, 256) 590080 activation_10[0][0]
____________________________________________________________________________________________________
add_5 (Add) (None, 6, 6, 256) 0 conv2d_13[0][0]
conv2d_12[0][0]
____________________________________________________________________________________________________
batch_normalization_11 (BatchNor (None, 6, 6, 256) 1024 add_5[0][0]
____________________________________________________________________________________________________
activation_11 (Activation) (None, 6, 6, 256) 0 batch_normalization_11[0][0]
____________________________________________________________________________________________________
conv2d_14 (Conv2D) (None, 6, 6, 256) 590080 activation_11[0][0]
____________________________________________________________________________________________________
batch_normalization_12 (BatchNor (None, 6, 6, 256) 1024 conv2d_14[0][0]
____________________________________________________________________________________________________
activation_12 (Activation) (None, 6, 6, 256) 0 batch_normalization_12[0][0]
____________________________________________________________________________________________________
conv2d_15 (Conv2D) (None, 6, 6, 256) 590080 activation_12[0][0]
____________________________________________________________________________________________________
add_6 (Add) (None, 6, 6, 256) 0 add_5[0][0]
conv2d_15[0][0]
____________________________________________________________________________________________________
batch_normalization_13 (BatchNor (None, 6, 6, 256) 1024 add_6[0][0]
____________________________________________________________________________________________________
activation_13 (Activation) (None, 6, 6, 256) 0 batch_normalization_13[0][0]
____________________________________________________________________________________________________
conv2d_16 (Conv2D) (None, 3, 3, 512) 1180160 activation_13[0][0]
____________________________________________________________________________________________________
batch_normalization_14 (BatchNor (None, 3, 3, 512) 2048 conv2d_16[0][0]
____________________________________________________________________________________________________
activation_14 (Activation) (None, 3, 3, 512) 0 batch_normalization_14[0][0]
____________________________________________________________________________________________________
conv2d_18 (Conv2D) (None, 3, 3, 512) 131584 add_6[0][0]
____________________________________________________________________________________________________
conv2d_17 (Conv2D) (None, 3, 3, 512) 2359808 activation_14[0][0]
____________________________________________________________________________________________________
add_7 (Add) (None, 3, 3, 512) 0 conv2d_18[0][0]
conv2d_17[0][0]
____________________________________________________________________________________________________
batch_normalization_15 (BatchNor (None, 3, 3, 512) 2048 add_7[0][0]
____________________________________________________________________________________________________
activation_15 (Activation) (None, 3, 3, 512) 0 batch_normalization_15[0][0]
____________________________________________________________________________________________________
conv2d_19 (Conv2D) (None, 3, 3, 512) 2359808 activation_15[0][0]
____________________________________________________________________________________________________
batch_normalization_16 (BatchNor (None, 3, 3, 512) 2048 conv2d_19[0][0]
____________________________________________________________________________________________________
activation_16 (Activation) (None, 3, 3, 512) 0 batch_normalization_16[0][0]
____________________________________________________________________________________________________
conv2d_20 (Conv2D) (None, 3, 3, 512) 2359808 activation_16[0][0]
____________________________________________________________________________________________________
add_8 (Add) (None, 3, 3, 512) 0 add_7[0][0]
conv2d_20[0][0]
____________________________________________________________________________________________________
batch_normalization_17 (BatchNor (None, 3, 3, 512) 2048 add_8[0][0]
____________________________________________________________________________________________________
activation_17 (Activation) (None, 3, 3, 512) 0 batch_normalization_17[0][0]
____________________________________________________________________________________________________
average_pooling2d_1 (AveragePool (None, 1, 1, 512) 0 activation_17[0][0]
____________________________________________________________________________________________________
flatten_1 (Flatten) (None, 512) 0 average_pooling2d_1[0][0]
____________________________________________________________________________________________________
dense_1 (Dense) (None, 10574) 5424462 flatten_1[0][0]
====================================================================================================
Total params: 16,605,518
Trainable params: 16,597,710
Non-trainable params: 7,808
____________________________________________________________________________________________________