prajwalkr
commented
4 years ago Could you please tell us your TensorFlow and Keras versions? Please ensure they are the same as the ones mentioned in requirements.txt.
Closed chetancc closed 4 years ago
prajwalkr
commented
4 years ago Could you please tell us your TensorFlow and Keras versions? Please ensure they are the same as the ones mentioned in requirements.txt.
chetancc
commented
4 years ago Sure sir, Based on Google Colab (https://colab.research.google.com/drive/1NLUwupCBsB1HrpEmOIHeMgU63sus2LxP#scrollTo=27x3sF9grFsH) I was using tensorflow-gpu==1.14.0 Keras==2.2.4
As per requirement.txt I will use tensorflow-gpu==1.8.0 Keras==2.2.4
And I will keep you posted on the progress
chetancc
commented
4 years ago Hello Sir, Hope you are doing fine. I used existing colab notebook and changed !pip install tensorflow-gpu==1.8.0 from !pip install tensorflow-gpu==1.14.0 which is under # Fixing tf version issue. and run all steps. When it executes import tensorflow as tf I receive below log Please help or provide me updated Google Colab notebook.
ImportError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/pywrap_tensorflow.py in
7 frames ImportError: libcublas.so.9.0: cannot open shared object file: No such file or directory
During handling of the above exception, another exception occurred:
ImportError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/pywrap_tensorflow.py in
ImportError: Traceback (most recent call last):
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/pywrap_tensorflow.py", line 58, in
Failed to load the native TensorFlow runtime.
See https://www.tensorflow.org/install/install_sources#common_installation_problems
for some common reasons and solutions. Include the entire stack trace above this error message when asking for help.
NOTE: If your import is failing due to a missing package, you can manually install dependencies using either !pip or !apt.
To view examples of installing some common dependencies, click the "Open Examples" button below.
Rudrabha
commented
4 years ago This is a CUDA error. Please check your CUDA version. You will need CUDA 9.0 for running the TensorFlow version
chetancc
commented
4 years ago Thx Sir, I will get back to you
Hello Sir,
I like your project very much and I am trying it on Google Colab by referring this link (https://colab.research.google.com/drive/1NLUwupCBsB1HrpEmOIHeMgU63sus2LxP). Attaching video (output_00006.mp4) and audio (taunt.wav) files for your reference. After executing all steps successfully while running last step I get below log. Please let me know if I am missing something as I did not see output file generated in /content directory even after refreshing folder in Google Colab.
/content/LipGAN /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'. _np_qint8 = np.dtype([("qint8", np.int8, 1)]) /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'. _np_quint8 = np.dtype([("quint8", np.uint8, 1)]) /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'. _np_qint16 = np.dtype([("qint16", np.int16, 1)]) /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'. _np_quint16 = np.dtype([("quint16", np.uint16, 1)]) /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'. _np_qint32 = np.dtype([("qint32", np.int32, 1)]) /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'. np_resource = np.dtype([("resource", np.ubyte, 1)]) /usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'. _np_qint8 = np.dtype([("qint8", np.int8, 1)]) /usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'. _np_quint8 = np.dtype([("quint8", np.uint8, 1)]) /usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'. _np_qint16 = np.dtype([("qint16", np.int16, 1)]) /usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'. _np_quint16 = np.dtype([("quint16", np.uint16, 1)]) /usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'. _np_qint32 = np.dtype([("qint32", np.int32, 1)]) /usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'. np_resource = np.dtype([("resource", np.ubyte, 1)]) Using TensorFlow backend. 2000 Number of frames available for inference: 3841 (80, 328) Length of mel chunks: 95 0% 0/1 [00:00<?, ?it/s] 0% 0/61 [00:00<?, ?it/s] 2% 1/61 [00:02<02:19, 2.33s/it] 3% 2/61 [00:02<01:21, 1.37s/it] 5% 3/61 [00:03<01:01, 1.05s/it] 7% 4/61 [00:03<00:50, 1.12it/s] 8% 5/61 [00:03<00:44, 1.26it/s] 10% 6/61 [00:04<00:40, 1.37it/s] 11% 7/61 [00:04<00:36, 1.46it/s] 13% 8/61 [00:05<00:34, 1.54it/s] 15% 9/61 [00:05<00:32, 1.61it/s] 16% 10/61 [00:06<00:30, 1.66it/s] 18% 11/61 [00:06<00:29, 1.71it/s] 20% 12/61 [00:06<00:27, 1.76it/s] 21% 13/61 [00:07<00:26, 1.80it/s] 23% 14/61 [00:07<00:25, 1.83it/s] 25% 15/61 [00:08<00:24, 1.86it/s] 26% 16/61 [00:08<00:23, 1.89it/s] 28% 17/61 [00:08<00:22, 1.92it/s] 30% 18/61 [00:09<00:22, 1.94it/s] 31% 19/61 [00:09<00:21, 1.96it/s] 33% 20/61 [00:10<00:20, 1.98it/s] 34% 21/61 [00:10<00:20, 2.00it/s] 36% 22/61 [00:10<00:19, 2.01it/s] 38% 23/61 [00:11<00:18, 2.03it/s] 39% 24/61 [00:11<00:18, 2.05it/s] 41% 25/61 [00:12<00:17, 2.06it/s] 43% 26/61 [00:12<00:16, 2.07it/s] 44% 27/61 [00:12<00:16, 2.08it/s] 46% 28/61 [00:13<00:15, 2.09it/s] 48% 29/61 [00:13<00:15, 2.11it/s] 49% 30/61 [00:14<00:14, 2.12it/s] 51% 31/61 [00:14<00:14, 2.12it/s] 52% 32/61 [00:15<00:13, 2.13it/s] 54% 33/61 [00:15<00:13, 2.14it/s] 56% 34/61 [00:15<00:12, 2.15it/s] 57% 35/61 [00:16<00:12, 2.16it/s] 59% 36/61 [00:16<00:11, 2.16it/s] 61% 37/61 [00:17<00:11, 2.17it/s] 62% 38/61 [00:17<00:10, 2.18it/s] 64% 39/61 [00:17<00:10, 2.18it/s] 66% 40/61 [00:18<00:09, 2.19it/s] 67% 41/61 [00:18<00:09, 2.19it/s] 69% 42/61 [00:19<00:08, 2.20it/s] 70% 43/61 [00:19<00:08, 2.20it/s] 72% 44/61 [00:19<00:07, 2.21it/s] 74% 45/61 [00:20<00:07, 2.21it/s] 75% 46/61 [00:20<00:06, 2.22it/s] 77% 47/61 [00:21<00:06, 2.22it/s] 79% 48/61 [00:21<00:05, 2.23it/s] 80% 49/61 [00:21<00:05, 2.23it/s] 82% 50/61 [00:22<00:04, 2.24it/s] 84% 51/61 [00:22<00:04, 2.24it/s] 85% 52/61 [00:23<00:04, 2.24it/s] 87% 53/61 [00:23<00:03, 2.25it/s] 89% 54/61 [00:23<00:03, 2.25it/s] 90% 55/61 [00:24<00:02, 2.25it/s] 92% 56/61 [00:24<00:02, 2.26it/s] 93% 57/61 [00:25<00:01, 2.26it/s] 95% 58/61 [00:25<00:01, 2.26it/s] 97% 59/61 [00:26<00:00, 2.26it/s] 98% 60/61 [00:26<00:00, 2.27it/s] 100% 61/61 [00:26<00:00, 2.30it/s]WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:174: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:181: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.
2020-07-05 15:01:10.670458: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 AVX512F FMA 2020-07-05 15:01:10.674180: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcuda.so.1 2020-07-05 15:01:10.674808: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2020-07-05 15:01:10.675569: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0xcd015b80 executing computations on platform CUDA. Devices: 2020-07-05 15:01:10.675601: I tensorflow/compiler/xla/service/service.cc:175] StreamExecutor device (0): Tesla P100-PCIE-16GB, Compute Capability 6.0 2020-07-05 15:01:10.677270: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2000185000 Hz 2020-07-05 15:01:10.677454: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0xcd015800 executing computations on platform Host. Devices: 2020-07-05 15:01:10.677481: I tensorflow/compiler/xla/service/service.cc:175] StreamExecutor device (0):,
2020-07-05 15:01:10.677688: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-05 15:01:10.678238: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties:
name: Tesla P100-PCIE-16GB major: 6 minor: 0 memoryClockRate(GHz): 1.3285
pciBusID: 0000:00:04.0
2020-07-05 15:01:10.678764: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0
2020-07-05 15:01:10.682118: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0
2020-07-05 15:01:10.684604: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10.0
2020-07-05 15:01:10.685235: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10.0
2020-07-05 15:01:10.688999: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10.0
2020-07-05 15:01:10.690899: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10.0
2020-07-05 15:01:10.691003: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7
2020-07-05 15:01:10.691109: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-05 15:01:10.691678: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-05 15:01:10.692183: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0
2020-07-05 15:01:10.692252: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0
2020-07-05 15:01:10.693576: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-07-05 15:01:10.693601: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187] 0
2020-07-05 15:01:10.693612: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0: N
2020-07-05 15:01:10.693725: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-05 15:01:10.694282: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-05 15:01:10.694774: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:40] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.
2020-07-05 15:01:10.694810: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 15059 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0)
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1834: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead.
Layer (type) Output Shape Param # Connected to
input_face (InputLayer) (None, 96, 96, 6) 0
conv2d_1 (Conv2D) (None, 96, 96, 32) 9440 input_face[0][0]
batch_normalization_1 (BatchNor (None, 96, 96, 32) 128 conv2d_1[0][0]
activation_1 (Activation) (None, 96, 96, 32) 0 batch_normalization_1[0][0]
conv2d_2 (Conv2D) (None, 48, 48, 64) 51264 activation_1[0][0]
batch_normalization_2 (BatchNor (None, 48, 48, 64) 256 conv2d_2[0][0]
activation_2 (Activation) (None, 48, 48, 64) 0 batch_normalization_2[0][0]
conv2d_3 (Conv2D) (None, 48, 48, 64) 36928 activation_2[0][0]
batch_normalization_3 (BatchNor (None, 48, 48, 64) 256 conv2d_3[0][0]
activation_3 (Activation) (None, 48, 48, 64) 0 batch_normalization_3[0][0]
conv2d_4 (Conv2D) (None, 48, 48, 64) 36928 activation_3[0][0]
batch_normalization_4 (BatchNor (None, 48, 48, 64) 256 conv2d_4[0][0]
activation_4 (Activation) (None, 48, 48, 64) 0 batch_normalization_4[0][0]
add_1 (Add) (None, 48, 48, 64) 0 activation_4[0][0]
activation_2[0][0]
activation_5 (Activation) (None, 48, 48, 64) 0 add_1[0][0]
input_audio (InputLayer) (None, 80, 27, 1) 0
conv2d_5 (Conv2D) (None, 48, 48, 64) 36928 activation_5[0][0]
conv2d_27 (Conv2D) (None, 80, 27, 32) 320 input_audio[0][0]
batch_normalization_5 (BatchNor (None, 48, 48, 64) 256 conv2d_5[0][0]
batch_normalization_27 (BatchNo (None, 80, 27, 32) 128 conv2d_27[0][0]
activation_6 (Activation) (None, 48, 48, 64) 0 batch_normalization_5[0][0]
activation_36 (Activation) (None, 80, 27, 32) 0 batch_normalization_27[0][0]
conv2d_6 (Conv2D) (None, 48, 48, 64) 36928 activation_6[0][0]
conv2d_28 (Conv2D) (None, 80, 27, 32) 9248 activation_36[0][0]
batch_normalization_6 (BatchNor (None, 48, 48, 64) 256 conv2d_6[0][0]
batch_normalization_28 (BatchNo (None, 80, 27, 32) 128 conv2d_28[0][0]
activation_7 (Activation) (None, 48, 48, 64) 0 batch_normalization_6[0][0]
activation_37 (Activation) (None, 80, 27, 32) 0 batch_normalization_28[0][0]
add_2 (Add) (None, 48, 48, 64) 0 activation_7[0][0]
activation_5[0][0]
conv2d_29 (Conv2D) (None, 80, 27, 32) 9248 activation_37[0][0]
activation_8 (Activation) (None, 48, 48, 64) 0 add_2[0][0]
batch_normalization_29 (BatchNo (None, 80, 27, 32) 128 conv2d_29[0][0]
conv2d_7 (Conv2D) (None, 24, 24, 128) 73856 activation_8[0][0]
activation_38 (Activation) (None, 80, 27, 32) 0 batch_normalization_29[0][0]
batch_normalization_7 (BatchNor (None, 24, 24, 128) 512 conv2d_7[0][0]
add_10 (Add) (None, 80, 27, 32) 0 activation_38[0][0]
activation_36[0][0]
activation_9 (Activation) (None, 24, 24, 128) 0 batch_normalization_7[0][0]
activation_39 (Activation) (None, 80, 27, 32) 0 add_10[0][0]
conv2d_8 (Conv2D) (None, 24, 24, 128) 147584 activation_9[0][0]
conv2d_30 (Conv2D) (None, 80, 27, 32) 9248 activation_39[0][0]
batch_normalization_8 (BatchNor (None, 24, 24, 128) 512 conv2d_8[0][0]
batch_normalization_30 (BatchNo (None, 80, 27, 32) 128 conv2d_30[0][0]
activation_10 (Activation) (None, 24, 24, 128) 0 batch_normalization_8[0][0]
activation_40 (Activation) (None, 80, 27, 32) 0 batch_normalization_30[0][0]
conv2d_9 (Conv2D) (None, 24, 24, 128) 147584 activation_10[0][0]
conv2d_31 (Conv2D) (None, 80, 27, 32) 9248 activation_40[0][0]
batch_normalization_9 (BatchNor (None, 24, 24, 128) 512 conv2d_9[0][0]
batch_normalization_31 (BatchNo (None, 80, 27, 32) 128 conv2d_31[0][0]
activation_11 (Activation) (None, 24, 24, 128) 0 batch_normalization_9[0][0]
activation_41 (Activation) (None, 80, 27, 32) 0 batch_normalization_31[0][0]
add_3 (Add) (None, 24, 24, 128) 0 activation_11[0][0]
activation_9[0][0]
add_11 (Add) (None, 80, 27, 32) 0 activation_41[0][0]
activation_39[0][0]
activation_12 (Activation) (None, 24, 24, 128) 0 add_3[0][0]
activation_42 (Activation) (None, 80, 27, 32) 0 add_11[0][0]
conv2d_10 (Conv2D) (None, 24, 24, 128) 147584 activation_12[0][0]
conv2d_32 (Conv2D) (None, 27, 9, 64) 18496 activation_42[0][0]
batch_normalization_10 (BatchNo (None, 24, 24, 128) 512 conv2d_10[0][0]
batch_normalization_32 (BatchNo (None, 27, 9, 64) 256 conv2d_32[0][0]
activation_13 (Activation) (None, 24, 24, 128) 0 batch_normalization_10[0][0]
activation_43 (Activation) (None, 27, 9, 64) 0 batch_normalization_32[0][0]
conv2d_11 (Conv2D) (None, 24, 24, 128) 147584 activation_13[0][0]
conv2d_33 (Conv2D) (None, 27, 9, 64) 36928 activation_43[0][0]
batch_normalization_11 (BatchNo (None, 24, 24, 128) 512 conv2d_11[0][0]
batch_normalization_33 (BatchNo (None, 27, 9, 64) 256 conv2d_33[0][0]
activation_14 (Activation) (None, 24, 24, 128) 0 batch_normalization_11[0][0]
activation_44 (Activation) (None, 27, 9, 64) 0 batch_normalization_33[0][0]
add_4 (Add) (None, 24, 24, 128) 0 activation_14[0][0]
activation_12[0][0]
conv2d_34 (Conv2D) (None, 27, 9, 64) 36928 activation_44[0][0]
activation_15 (Activation) (None, 24, 24, 128) 0 add_4[0][0]
batch_normalization_34 (BatchNo (None, 27, 9, 64) 256 conv2d_34[0][0]
conv2d_12 (Conv2D) (None, 24, 24, 128) 147584 activation_15[0][0]
activation_45 (Activation) (None, 27, 9, 64) 0 batch_normalization_34[0][0]
batch_normalization_12 (BatchNo (None, 24, 24, 128) 512 conv2d_12[0][0]
add_12 (Add) (None, 27, 9, 64) 0 activation_45[0][0]
activation_43[0][0]
activation_16 (Activation) (None, 24, 24, 128) 0 batch_normalization_12[0][0]
activation_46 (Activation) (None, 27, 9, 64) 0 add_12[0][0]
conv2d_13 (Conv2D) (None, 24, 24, 128) 147584 activation_16[0][0]
conv2d_35 (Conv2D) (None, 27, 9, 64) 36928 activation_46[0][0]
batch_normalization_13 (BatchNo (None, 24, 24, 128) 512 conv2d_13[0][0]
batch_normalization_35 (BatchNo (None, 27, 9, 64) 256 conv2d_35[0][0]
activation_17 (Activation) (None, 24, 24, 128) 0 batch_normalization_13[0][0]
activation_47 (Activation) (None, 27, 9, 64) 0 batch_normalization_35[0][0]
add_5 (Add) (None, 24, 24, 128) 0 activation_17[0][0]
activation_15[0][0]
conv2d_36 (Conv2D) (None, 27, 9, 64) 36928 activation_47[0][0]
activation_18 (Activation) (None, 24, 24, 128) 0 add_5[0][0]
batch_normalization_36 (BatchNo (None, 27, 9, 64) 256 conv2d_36[0][0]
conv2d_14 (Conv2D) (None, 12, 12, 256) 295168 activation_18[0][0]
activation_48 (Activation) (None, 27, 9, 64) 0 batch_normalization_36[0][0]
batch_normalization_14 (BatchNo (None, 12, 12, 256) 1024 conv2d_14[0][0]
add_13 (Add) (None, 27, 9, 64) 0 activation_48[0][0]
activation_46[0][0]
activation_19 (Activation) (None, 12, 12, 256) 0 batch_normalization_14[0][0]
activation_49 (Activation) (None, 27, 9, 64) 0 add_13[0][0]
conv2d_15 (Conv2D) (None, 12, 12, 256) 590080 activation_19[0][0]
conv2d_37 (Conv2D) (None, 9, 9, 128) 73856 activation_49[0][0]
batch_normalization_15 (BatchNo (None, 12, 12, 256) 1024 conv2d_15[0][0]
batch_normalization_37 (BatchNo (None, 9, 9, 128) 512 conv2d_37[0][0]
activation_20 (Activation) (None, 12, 12, 256) 0 batch_normalization_15[0][0]
activation_50 (Activation) (None, 9, 9, 128) 0 batch_normalization_37[0][0]
conv2d_16 (Conv2D) (None, 12, 12, 256) 590080 activation_20[0][0]
conv2d_38 (Conv2D) (None, 9, 9, 128) 147584 activation_50[0][0]
batch_normalization_16 (BatchNo (None, 12, 12, 256) 1024 conv2d_16[0][0]
batch_normalization_38 (BatchNo (None, 9, 9, 128) 512 conv2d_38[0][0]
activation_21 (Activation) (None, 12, 12, 256) 0 batch_normalization_16[0][0]
activation_51 (Activation) (None, 9, 9, 128) 0 batch_normalization_38[0][0]
add_6 (Add) (None, 12, 12, 256) 0 activation_21[0][0]
activation_19[0][0]
conv2d_39 (Conv2D) (None, 9, 9, 128) 147584 activation_51[0][0]
activation_22 (Activation) (None, 12, 12, 256) 0 add_6[0][0]
batch_normalization_39 (BatchNo (None, 9, 9, 128) 512 conv2d_39[0][0]
conv2d_17 (Conv2D) (None, 12, 12, 256) 590080 activation_22[0][0]
activation_52 (Activation) (None, 9, 9, 128) 0 batch_normalization_39[0][0]
batch_normalization_17 (BatchNo (None, 12, 12, 256) 1024 conv2d_17[0][0]
add_14 (Add) (None, 9, 9, 128) 0 activation_52[0][0]
activation_50[0][0]
activation_23 (Activation) (None, 12, 12, 256) 0 batch_normalization_17[0][0]
activation_53 (Activation) (None, 9, 9, 128) 0 add_14[0][0]
conv2d_18 (Conv2D) (None, 12, 12, 256) 590080 activation_23[0][0]
conv2d_40 (Conv2D) (None, 9, 9, 128) 147584 activation_53[0][0]
batch_normalization_18 (BatchNo (None, 12, 12, 256) 1024 conv2d_18[0][0]
batch_normalization_40 (BatchNo (None, 9, 9, 128) 512 conv2d_40[0][0]
activation_24 (Activation) (None, 12, 12, 256) 0 batch_normalization_18[0][0]
activation_54 (Activation) (None, 9, 9, 128) 0 batch_normalization_40[0][0]
add_7 (Add) (None, 12, 12, 256) 0 activation_24[0][0]
activation_22[0][0]
conv2d_41 (Conv2D) (None, 9, 9, 128) 147584 activation_54[0][0]
activation_25 (Activation) (None, 12, 12, 256) 0 add_7[0][0]
batch_normalization_41 (BatchNo (None, 9, 9, 128) 512 conv2d_41[0][0]
conv2d_19 (Conv2D) (None, 6, 6, 512) 1180160 activation_25[0][0]
activation_55 (Activation) (None, 9, 9, 128) 0 batch_normalization_41[0][0]
batch_normalization_19 (BatchNo (None, 6, 6, 512) 2048 conv2d_19[0][0]
add_15 (Add) (None, 9, 9, 128) 0 activation_55[0][0]
activation_53[0][0]
activation_26 (Activation) (None, 6, 6, 512) 0 batch_normalization_19[0][0]
activation_56 (Activation) (None, 9, 9, 128) 0 add_15[0][0]
conv2d_20 (Conv2D) (None, 6, 6, 512) 2359808 activation_26[0][0]
conv2d_42 (Conv2D) (None, 3, 3, 256) 295168 activation_56[0][0]
batch_normalization_20 (BatchNo (None, 6, 6, 512) 2048 conv2d_20[0][0]
batch_normalization_42 (BatchNo (None, 3, 3, 256) 1024 conv2d_42[0][0]
activation_27 (Activation) (None, 6, 6, 512) 0 batch_normalization_20[0][0]
activation_57 (Activation) (None, 3, 3, 256) 0 batch_normalization_42[0][0]
conv2d_21 (Conv2D) (None, 6, 6, 512) 2359808 activation_27[0][0]
conv2d_43 (Conv2D) (None, 3, 3, 256) 590080 activation_57[0][0]
batch_normalization_21 (BatchNo (None, 6, 6, 512) 2048 conv2d_21[0][0]
batch_normalization_43 (BatchNo (None, 3, 3, 256) 1024 conv2d_43[0][0]
activation_28 (Activation) (None, 6, 6, 512) 0 batch_normalization_21[0][0]
activation_58 (Activation) (None, 3, 3, 256) 0 batch_normalization_43[0][0]
add_8 (Add) (None, 6, 6, 512) 0 activation_28[0][0]
activation_26[0][0]
conv2d_44 (Conv2D) (None, 3, 3, 256) 590080 activation_58[0][0]
activation_29 (Activation) (None, 6, 6, 512) 0 add_8[0][0]
batch_normalization_44 (BatchNo (None, 3, 3, 256) 1024 conv2d_44[0][0]
conv2d_22 (Conv2D) (None, 6, 6, 512) 2359808 activation_29[0][0]
activation_59 (Activation) (None, 3, 3, 256) 0 batch_normalization_44[0][0]
batch_normalization_22 (BatchNo (None, 6, 6, 512) 2048 conv2d_22[0][0]
add_16 (Add) (None, 3, 3, 256) 0 activation_59[0][0]
activation_57[0][0]
activation_30 (Activation) (None, 6, 6, 512) 0 batch_normalization_22[0][0]
activation_60 (Activation) (None, 3, 3, 256) 0 add_16[0][0]
conv2d_23 (Conv2D) (None, 6, 6, 512) 2359808 activation_30[0][0]
conv2d_45 (Conv2D) (None, 3, 3, 256) 590080 activation_60[0][0]
batch_normalization_23 (BatchNo (None, 6, 6, 512) 2048 conv2d_23[0][0]
batch_normalization_45 (BatchNo (None, 3, 3, 256) 1024 conv2d_45[0][0]
activation_31 (Activation) (None, 6, 6, 512) 0 batch_normalization_23[0][0]
activation_61 (Activation) (None, 3, 3, 256) 0 batch_normalization_45[0][0]
add_9 (Add) (None, 6, 6, 512) 0 activation_31[0][0]
activation_29[0][0]
conv2d_46 (Conv2D) (None, 3, 3, 256) 590080 activation_61[0][0]
activation_32 (Activation) (None, 6, 6, 512) 0 add_9[0][0]
batch_normalization_46 (BatchNo (None, 3, 3, 256) 1024 conv2d_46[0][0]
conv2d_24 (Conv2D) (None, 3, 3, 512) 2359808 activation_32[0][0]
activation_62 (Activation) (None, 3, 3, 256) 0 batch_normalization_46[0][0]
batch_normalization_24 (BatchNo (None, 3, 3, 512) 2048 conv2d_24[0][0]
add_17 (Add) (None, 3, 3, 256) 0 activation_62[0][0]
activation_60[0][0]
activation_33 (Activation) (None, 3, 3, 512) 0 batch_normalization_24[0][0]
activation_63 (Activation) (None, 3, 3, 256) 0 add_17[0][0]
conv2d_25 (Conv2D) (None, 1, 1, 512) 2359808 activation_33[0][0]
conv2d_47 (Conv2D) (None, 1, 1, 512) 1180160 activation_63[0][0]
batch_normalization_25 (BatchNo (None, 1, 1, 512) 2048 conv2d_25[0][0]
batch_normalization_47 (BatchNo (None, 1, 1, 512) 2048 conv2d_47[0][0]
activation_34 (Activation) (None, 1, 1, 512) 0 batch_normalization_25[0][0]
activation_64 (Activation) (None, 1, 1, 512) 0 batch_normalization_47[0][0]
conv2d_26 (Conv2D) (None, 1, 1, 512) 262656 activation_34[0][0]
conv2d_48 (Conv2D) (None, 1, 1, 512) 262656 activation_64[0][0]
batch_normalization_26 (BatchNo (None, 1, 1, 512) 2048 conv2d_26[0][0]
batch_normalization_48 (BatchNo (None, 1, 1, 512) 2048 conv2d_48[0][0]
activation_35 (Activation) (None, 1, 1, 512) 0 batch_normalization_26[0][0]
activation_65 (Activation) (None, 1, 1, 512) 0 batch_normalization_48[0][0]
concatenate_1 (Concatenate) (None, 1, 1, 1024) 0 activation_35[0][0]
activation_65[0][0]
conv2d_transpose_1 (Conv2DTrans (None, 3, 3, 512) 4719104 concatenate_1[0][0]
batch_normalization_49 (BatchNo (None, 3, 3, 512) 2048 conv2d_transpose_1[0][0]
activation_66 (Activation) (None, 3, 3, 512) 0 batch_normalization_49[0][0]
concatenate_2 (Concatenate) (None, 3, 3, 1024) 0 activation_33[0][0]
activation_66[0][0]
conv2d_transpose_2 (Conv2DTrans (None, 6, 6, 512) 4719104 concatenate_2[0][0]
batch_normalization_50 (BatchNo (None, 6, 6, 512) 2048 conv2d_transpose_2[0][0]
activation_67 (Activation) (None, 6, 6, 512) 0 batch_normalization_50[0][0]
conv2d_49 (Conv2D) (None, 6, 6, 512) 2359808 activation_67[0][0]
batch_normalization_51 (BatchNo (None, 6, 6, 512) 2048 conv2d_49[0][0]
activation_68 (Activation) (None, 6, 6, 512) 0 batch_normalization_51[0][0]
conv2d_50 (Conv2D) (None, 6, 6, 512) 2359808 activation_68[0][0]
batch_normalization_52 (BatchNo (None, 6, 6, 512) 2048 conv2d_50[0][0]
activation_69 (Activation) (None, 6, 6, 512) 0 batch_normalization_52[0][0]
add_18 (Add) (None, 6, 6, 512) 0 activation_69[0][0]
activation_67[0][0]
activation_70 (Activation) (None, 6, 6, 512) 0 add_18[0][0]
conv2d_51 (Conv2D) (None, 6, 6, 512) 2359808 activation_70[0][0]
batch_normalization_53 (BatchNo (None, 6, 6, 512) 2048 conv2d_51[0][0]
activation_71 (Activation) (None, 6, 6, 512) 0 batch_normalization_53[0][0]
conv2d_52 (Conv2D) (None, 6, 6, 512) 2359808 activation_71[0][0]
batch_normalization_54 (BatchNo (None, 6, 6, 512) 2048 conv2d_52[0][0]
activation_72 (Activation) (None, 6, 6, 512) 0 batch_normalization_54[0][0]
add_19 (Add) (None, 6, 6, 512) 0 activation_72[0][0]
activation_70[0][0]
activation_73 (Activation) (None, 6, 6, 512) 0 add_19[0][0]
concatenate_3 (Concatenate) (None, 6, 6, 1024) 0 activation_32[0][0]
activation_73[0][0]
conv2d_transpose_3 (Conv2DTrans (None, 12, 12, 256) 2359552 concatenate_3[0][0]
batch_normalization_55 (BatchNo (None, 12, 12, 256) 1024 conv2d_transpose_3[0][0]
activation_74 (Activation) (None, 12, 12, 256) 0 batch_normalization_55[0][0]
conv2d_53 (Conv2D) (None, 12, 12, 256) 590080 activation_74[0][0]
batch_normalization_56 (BatchNo (None, 12, 12, 256) 1024 conv2d_53[0][0]
activation_75 (Activation) (None, 12, 12, 256) 0 batch_normalization_56[0][0]
conv2d_54 (Conv2D) (None, 12, 12, 256) 590080 activation_75[0][0]
batch_normalization_57 (BatchNo (None, 12, 12, 256) 1024 conv2d_54[0][0]
activation_76 (Activation) (None, 12, 12, 256) 0 batch_normalization_57[0][0]
add_20 (Add) (None, 12, 12, 256) 0 activation_76[0][0]
activation_74[0][0]
activation_77 (Activation) (None, 12, 12, 256) 0 add_20[0][0]
conv2d_55 (Conv2D) (None, 12, 12, 256) 590080 activation_77[0][0]
batch_normalization_58 (BatchNo (None, 12, 12, 256) 1024 conv2d_55[0][0]
activation_78 (Activation) (None, 12, 12, 256) 0 batch_normalization_58[0][0]
conv2d_56 (Conv2D) (None, 12, 12, 256) 590080 activation_78[0][0]
batch_normalization_59 (BatchNo (None, 12, 12, 256) 1024 conv2d_56[0][0]
activation_79 (Activation) (None, 12, 12, 256) 0 batch_normalization_59[0][0]
add_21 (Add) (None, 12, 12, 256) 0 activation_79[0][0]
activation_77[0][0]
activation_80 (Activation) (None, 12, 12, 256) 0 add_21[0][0]
concatenate_4 (Concatenate) (None, 12, 12, 512) 0 activation_25[0][0]
activation_80[0][0]
conv2d_transpose_4 (Conv2DTrans (None, 24, 24, 128) 589952 concatenate_4[0][0]
batch_normalization_60 (BatchNo (None, 24, 24, 128) 512 conv2d_transpose_4[0][0]
activation_81 (Activation) (None, 24, 24, 128) 0 batch_normalization_60[0][0]
conv2d_57 (Conv2D) (None, 24, 24, 128) 147584 activation_81[0][0]
batch_normalization_61 (BatchNo (None, 24, 24, 128) 512 conv2d_57[0][0]
activation_82 (Activation) (None, 24, 24, 128) 0 batch_normalization_61[0][0]
conv2d_58 (Conv2D) (None, 24, 24, 128) 147584 activation_82[0][0]
batch_normalization_62 (BatchNo (None, 24, 24, 128) 512 conv2d_58[0][0]
activation_83 (Activation) (None, 24, 24, 128) 0 batch_normalization_62[0][0]
add_22 (Add) (None, 24, 24, 128) 0 activation_83[0][0]
activation_81[0][0]
activation_84 (Activation) (None, 24, 24, 128) 0 add_22[0][0]
conv2d_59 (Conv2D) (None, 24, 24, 128) 147584 activation_84[0][0]
batch_normalization_63 (BatchNo (None, 24, 24, 128) 512 conv2d_59[0][0]
activation_85 (Activation) (None, 24, 24, 128) 0 batch_normalization_63[0][0]
conv2d_60 (Conv2D) (None, 24, 24, 128) 147584 activation_85[0][0]
batch_normalization_64 (BatchNo (None, 24, 24, 128) 512 conv2d_60[0][0]
activation_86 (Activation) (None, 24, 24, 128) 0 batch_normalization_64[0][0]
add_23 (Add) (None, 24, 24, 128) 0 activation_86[0][0]
activation_84[0][0]
activation_87 (Activation) (None, 24, 24, 128) 0 add_23[0][0]
concatenate_5 (Concatenate) (None, 24, 24, 256) 0 activation_18[0][0]
activation_87[0][0]
conv2d_transpose_5 (Conv2DTrans (None, 48, 48, 64) 147520 concatenate_5[0][0]
batch_normalization_65 (BatchNo (None, 48, 48, 64) 256 conv2d_transpose_5[0][0]
activation_88 (Activation) (None, 48, 48, 64) 0 batch_normalization_65[0][0]
conv2d_61 (Conv2D) (None, 48, 48, 64) 36928 activation_88[0][0]
batch_normalization_66 (BatchNo (None, 48, 48, 64) 256 conv2d_61[0][0]
activation_89 (Activation) (None, 48, 48, 64) 0 batch_normalization_66[0][0]
conv2d_62 (Conv2D) (None, 48, 48, 64) 36928 activation_89[0][0]
batch_normalization_67 (BatchNo (None, 48, 48, 64) 256 conv2d_62[0][0]
activation_90 (Activation) (None, 48, 48, 64) 0 batch_normalization_67[0][0]
add_24 (Add) (None, 48, 48, 64) 0 activation_90[0][0]
activation_88[0][0]
activation_91 (Activation) (None, 48, 48, 64) 0 add_24[0][0]
conv2d_63 (Conv2D) (None, 48, 48, 64) 36928 activation_91[0][0]
batch_normalization_68 (BatchNo (None, 48, 48, 64) 256 conv2d_63[0][0]
activation_92 (Activation) (None, 48, 48, 64) 0 batch_normalization_68[0][0]
conv2d_64 (Conv2D) (None, 48, 48, 64) 36928 activation_92[0][0]
batch_normalization_69 (BatchNo (None, 48, 48, 64) 256 conv2d_64[0][0]
activation_93 (Activation) (None, 48, 48, 64) 0 batch_normalization_69[0][0]
add_25 (Add) (None, 48, 48, 64) 0 activation_93[0][0]
activation_91[0][0]
activation_94 (Activation) (None, 48, 48, 64) 0 add_25[0][0]
concatenate_6 (Concatenate) (None, 48, 48, 128) 0 activation_8[0][0]
activation_94[0][0]
conv2d_transpose_6 (Conv2DTrans (None, 96, 96, 32) 36896 concatenate_6[0][0]
batch_normalization_70 (BatchNo (None, 96, 96, 32) 128 conv2d_transpose_6[0][0]
activation_95 (Activation) (None, 96, 96, 32) 0 batch_normalization_70[0][0]
concatenate_7 (Concatenate) (None, 96, 96, 64) 0 activation_1[0][0]
activation_95[0][0]
conv2d_65 (Conv2D) (None, 96, 96, 16) 9232 concatenate_7[0][0]
batch_normalization_71 (BatchNo (None, 96, 96, 16) 64 conv2d_65[0][0]
activation_96 (Activation) (None, 96, 96, 16) 0 batch_normalization_71[0][0]
conv2d_66 (Conv2D) (None, 96, 96, 16) 2320 activation_96[0][0]
batch_normalization_72 (BatchNo (None, 96, 96, 16) 64 conv2d_66[0][0]
activation_97 (Activation) (None, 96, 96, 16) 0 batch_normalization_72[0][0]
conv2d_67 (Conv2D) (None, 96, 96, 3) 51 activation_97[0][0]
prediction (Activation) (None, 96, 96, 3) 0 conv2d_67[0][0]
Total params: 49,573,971 Trainable params: 49,543,123 Non-trainable params: 30,848
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.
Model Created Traceback (most recent call last): File "batch_inference.py", line 217, in
main()
File "batch_inference.py", line 193, in main
model.load_weights(args.checkpoint_path)
File "/usr/local/lib/python3.6/dist-packages/keras/engine/network.py", line 1166, in load_weights
f, self.layers, reshape=reshape)
File "/usr/local/lib/python3.6/dist-packages/keras/engine/saving.py", line 1056, in load_weights_from_hdf5_group
' elements.')
ValueError: Layer #37 (named "batch_normalization_34" in the current model) was found to correspond to layer conv2d_35 in the save file. However the new layer batch_normalization_34 expects 4 weights, but the saved weights have 2 elements.