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'module' object has no attribute 'core_rnn_cell' #50

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qclu opened this issue Aug 7, 2017 · 3 comments
Open

'module' object has no attribute 'core_rnn_cell' #50

qclu opened this issue Aug 7, 2017 · 3 comments

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@qclu
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qclu commented Aug 7, 2017

Hi,

I have tried sample train with following cmd as listed below, but got error as title descirbes.
Tensorflow version is 0.12.1.
python src/launcher.py --phase=train --data-path=sample/sample.txt --data-base-dir=sample --log-path=log.txt --no-load-model
Detailed log info is listed below.

2017-08-07 17:39:12.301997: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2017-08-07 17:39:12.302045: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2017-08-07 17:39:12.302056: W 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.
2017-08-07 17:39:12.302065: W 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.
2017-08-07 17:39:12.302073: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
2017-08-07 17:39:15.857019: I tensorflow/core/common_runtime/gpu/gpu_device.cc:940] Found device 0 with properties:
name: Tesla P40
major: 6 minor: 1 memoryClockRate (GHz) 1.531
pciBusID 0000:02:00.0
Total memory: 22.38GiB
Free memory: 22.22GiB
2017-08-07 17:39:16.239217: W tensorflow/stream_executor/cuda/cuda_driver.cc:523] A non-primary context 0x7bf2770 exists before initializing the StreamExecutor. We haven't verified StreamExecutor works with that.
2017-08-07 17:39:16.240932: I tensorflow/core/common_runtime/gpu/gpu_device.cc:940] Found device 1 with properties:
name: Tesla P40
major: 6 minor: 1 memoryClockRate (GHz) 1.531
pciBusID 0000:03:00.0
Total memory: 22.38GiB
Free memory: 22.22GiB
2017-08-07 17:39:16.626551: W tensorflow/stream_executor/cuda/cuda_driver.cc:523] A non-primary context 0x7bf65c0 exists before initializing the StreamExecutor. We haven't verified StreamExecutor works with that.
2017-08-07 17:39:16.628301: I tensorflow/core/common_runtime/gpu/gpu_device.cc:940] Found device 2 with properties:
name: Tesla P40
major: 6 minor: 1 memoryClockRate (GHz) 1.531
pciBusID 0000:83:00.0
Total memory: 22.38GiB
Free memory: 22.22GiB
2017-08-07 17:39:17.002842: W tensorflow/stream_executor/cuda/cuda_driver.cc:523] A non-primary context 0x7c1ad70 exists before initializing the StreamExecutor. We haven't verified StreamExecutor works with that.
2017-08-07 17:39:17.004574: I tensorflow/core/common_runtime/gpu/gpu_device.cc:940] Found device 3 with properties:
name: Tesla P40
major: 6 minor: 1 memoryClockRate (GHz) 1.531
pciBusID 0000:84:00.0
Total memory: 22.38GiB
Free memory: 22.22GiB
2017-08-07 17:39:17.006291: I tensorflow/core/common_runtime/gpu/gpu_device.cc:832] Peer access not supported between device ordinals 0 and 2
2017-08-07 17:39:17.006319: I tensorflow/core/common_runtime/gpu/gpu_device.cc:832] Peer access not supported between device ordinals 0 and 3
2017-08-07 17:39:17.006346: I tensorflow/core/common_runtime/gpu/gpu_device.cc:832] Peer access not supported between device ordinals 1 and 2
2017-08-07 17:39:17.006362: I tensorflow/core/common_runtime/gpu/gpu_device.cc:832] Peer access not supported between device ordinals 1 and 3
2017-08-07 17:39:17.006398: I tensorflow/core/common_runtime/gpu/gpu_device.cc:832] Peer access not supported between device ordinals 2 and 0
2017-08-07 17:39:17.006412: I tensorflow/core/common_runtime/gpu/gpu_device.cc:832] Peer access not supported between device ordinals 2 and 1
2017-08-07 17:39:17.007788: I tensorflow/core/common_runtime/gpu/gpu_device.cc:832] Peer access not supported between device ordinals 3 and 0
2017-08-07 17:39:17.007814: I tensorflow/core/common_runtime/gpu/gpu_device.cc:832] Peer access not supported between device ordinals 3 and 1
2017-08-07 17:39:17.007880: I tensorflow/core/common_runtime/gpu/gpu_device.cc:961] DMA: 0 1 2 3
2017-08-07 17:39:17.007895: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 0: Y Y N N
2017-08-07 17:39:17.007908: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 1: Y Y N N
2017-08-07 17:39:17.007920: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 2: N N Y Y
2017-08-07 17:39:17.007941: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 3: N N Y Y
2017-08-07 17:39:17.008000: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1030] Creating TensorFlow device (/gpu:0) -> (device: 0, name: Tesla P40, pci bus id: 0000:02:00.0)
2017-08-07 17:39:17.008017: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1030] Creating TensorFlow device (/gpu:1) -> (device: 1, name: Tesla P40, pci bus id: 0000:03:00.0)
2017-08-07 17:39:17.008030: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1030] Creating TensorFlow device (/gpu:2) -> (device: 2, name: Tesla P40, pci bus id: 0000:83:00.0)
2017-08-07 17:39:17.008039: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1030] Creating TensorFlow device (/gpu:3) -> (device: 3, name: Tesla P40, pci bus id: 0000:84:00.0)
2017-08-07 17:39:17,797 root INFO loading data
2017-08-07 17:39:17,806 root INFO phase: train
2017-08-07 17:39:17,806 root INFO model_dir: train
2017-08-07 17:39:17,807 root INFO load_model: False
2017-08-07 17:39:17,807 root INFO output_dir: results
2017-08-07 17:39:17,807 root INFO steps_per_checkpoint: 500
2017-08-07 17:39:17,807 root INFO batch_size: 64
2017-08-07 17:39:17,807 root INFO num_epoch: 1000
2017-08-07 17:39:17,808 root INFO learning_rate: 1
2017-08-07 17:39:17,808 root INFO reg_val: 0
2017-08-07 17:39:17,808 root INFO max_gradient_norm: 5.000000
2017-08-07 17:39:17,808 root INFO clip_gradients: True
2017-08-07 17:39:17,808 root INFO valid_target_length inf
2017-08-07 17:39:17,808 root INFO target_vocab_size: 39
2017-08-07 17:39:17,809 root INFO target_embedding_size: 10.000000
2017-08-07 17:39:17,809 root INFO attn_num_hidden: 128
2017-08-07 17:39:17,809 root INFO attn_num_layers: 2
2017-08-07 17:39:17,809 root INFO visualize: True
2017-08-07 17:39:17,809 root INFO buckets
2017-08-07 17:39:17,809 root INFO [(16, 11), (27, 17), (35, 19), (64, 22), (80, 32)]
input_tensor dim: (?, 1, 32, ?)
CNN outdim before squeeze: (?, 1, ?, 512)
CNN outdim: (?, ?, 512)
Traceback (most recent call last):
File "src/launcher.py", line 146, in
main(sys.argv[1:], exp_config.ExpConfig)
File "src/launcher.py", line 142, in main
session = sess)
File "/home/xxx/ocrbench/Attention-OCR/src/model/model.py", line 151, in init
use_gru = use_gru)
File "/home/xxx/ocrbench/Attention-OCR/src/model/seq2seq_model.py", line 87, in init
single_cell = tf.contrib.rnn.core_rnn_cell.BasicLSTMCell(attn_num_hidden, forget_bias=0.0, state_is_tuple=False)
AttributeError: 'module' object has no attribute 'core_rnn_cell'
@Fred-Erik
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Make sure you use TensorFlow 1.0.1.

@shoaibahmed
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If you are using TF 1.2 or 1.3, you can rename the tf.contrib.rnn.core_rnn_cell to tf.contrib.rnn so tf.contrib.rnn.core_rnn_cell.BasicLSTMCell will become tf.contrib.rnn.BasicLSTMCell and tf.contrib.rnn.MultiRNNCell, tf.contrib.rnn.GRUCell will be changed in the same way. Hope it helps.

@anandvijay619
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Also you will face an issue of Kernel and Bias, so use this code after what Shoaib Suggested

OLD_CHECKPOINT_FILE = "translate.ckpt-60000"
NEW_CHECKPOINT_FILE = "translate2.ckpt-60000"

import tensorflow as tf
vars_to_rename = {
"weights": "kernel",
"biases": "bias",

}

var_names = ["weights", "biases"]
new_checkpoint_vars = {}
reader = tf.train.NewCheckpointReader(OLD_CHECKPOINT_FILE)
for old_name in reader.get_variable_to_shape_map():
print(old_name)
for name in var_names:
if name in old_name:
print(vars_to_rename[name])
new_name = old_name.replace( name ,vars_to_rename[name])
break
else:
new_name = old_name
new_checkpoint_vars[new_name] = tf.Variable(reader.get_tensor(old_name))
print(new_name)
init = tf.global_variables_initializer()
saver = tf.train.Saver(new_checkpoint_vars)

with tf.Session() as sess:
sess.run(init)
saver.save(sess, NEW_CHECKPOINT_FILE)

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@Fred-Erik @anandvijay619 @shoaibahmed @qclu