tuning with selected images from ade20k

[AyaMohamedS]

i have selected some training images from ade20k dataset with their color codded _seg.png mask images i set certain parameters at first as the train.py

INPUT_SIZE = '473,473'                                  #"Comma-separated string with height and width of images."
LEARNING_RATE = 1e-3                                    #"Base learning rate for training with polynomial decay."
MOMENTUM = 0.9                                          #"Momentum component of the optimiser."
NUM_CLASSES = 150                                       #"Number of classes to predict (including background)."
NUM_STEPS = 60001                                       #"Number of training steps."
POWER = 0.9                                             #"Decay parameter to compute the learning rate."
RANDOM_SEED = 1234                                      #"Random seed to have reproducible results."
WEIGHT_DECAY = 0.0001                                   #"Regularisation parameter for L2-loss."
RESTORE_FROM = './model/ade20k/x/'                       #"Where restore model parameters from."
SNAPSHOT_DIR = './model/ade20k/x/'                     #"Where to save snapshots of the model."
SAVE_NUM_IMAGES = 4                                     #"How many images to save."
SAVE_PRED_EVERY = 50                                    #"Save summaries and checkpoint every often."
tf.reset_default_graph() 

but when i tried training code on them they give me the following error.

---

INFO:tensorflow:Restoring parameters from ./model/ade20k/x/model.ckpt-0
Restored model parameters from ./model/ade20k/x/model.ckpt-0
INFO:tensorflow:Error reported to Coordinator: <class 'tensorflow.python.framework.errors_impl.ResourceExhaustedError'>, OOM when allocating tensor with shape[1333,2000,3] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
         [[Node: create_inputs/Cast = Cast[DstT=DT_FLOAT, SrcT=DT_UINT8, _device="/job:localhost/replica:0/task:0/device:GPU:0"](create_inputs/concat)]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.

Traceback (most recent call last):

  File "<ipython-input-7-78358ffb08d0>", line 1, in <module>
    runfile('D:/mobileNetPSPNet/tuning.py', wdir='D:/mobileNetPSPNet')

  File "C:\Users\BioHelwan\Anaconda3\lib\site-packages\spyder\utils\site\sitecustomize.py", line 866, in runfile
    execfile(filename, namespace)

  File "C:\Users\BioHelwan\Anaconda3\lib\site-packages\spyder\utils\site\sitecustomize.py", line 102, in execfile
    exec(compile(f.read(), filename, 'exec'), namespace)

  File "D:/mobileNetPSPNet/tuning.py", line 176, in <module>
    main()

  File "D:/mobileNetPSPNet/tuning.py", line 165, in main
    loss_value, _ = sess.run([reduced_loss, train_op], feed_dict=feed_dict)

  File "C:\Users\BioHelwan\Anaconda3\envs\tensorflow\Lib\site-packages\tensorflow\python\client\session.py", line 900, in run
    run_metadata_ptr)

  File "C:\Users\BioHelwan\Anaconda3\envs\tensorflow\Lib\site-packages\tensorflow\python\client\session.py", line 1135, in _run
    feed_dict_tensor, options, run_metadata)

  File "C:\Users\BioHelwan\Anaconda3\envs\tensorflow\Lib\site-packages\tensorflow\python\client\session.py", line 1316, in _do_run
    run_metadata)

  File "C:\Users\BioHelwan\Anaconda3\envs\tensorflow\Lib\site-packages\tensorflow\python\client\session.py", line 1335, in _do_call
    raise type(e)(node_def, op, message)

ResourceExhaustedError: OOM when allocating tensor with shape[3,3,256,256] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
     [[Node: gradients/L2Loss_31_grad/mul = Mul[T=DT_FLOAT, _class=["loc:@gradients/AddN_37"], _device="/job:localhost/replica:0/task:0/device:GPU:0"](conv4_2_3x3/weights/read, mul/x)]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.

     [[Node: conv4_3_3x3/weights/read/_741 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_321_conv4_3_3x3/weights/read", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.

Caused by op 'gradients/L2Loss_31_grad/mul', defined at:
  File "C:\Users\BioHelwan\Anaconda3\lib\site-packages\spyder\utils\ipython\start_kernel.py", line 223, in <module>
    main()
  File "C:\Users\BioHelwan\Anaconda3\lib\site-packages\spyder\utils\ipython\start_kernel.py", line 219, in main
    kernel.start()
  File "C:\Users\BioHelwan\Anaconda3\lib\site-packages\ipykernel\kernelapp.py", line 474, in start
    ioloop.IOLoop.instance().start()
  File "C:\Users\BioHelwan\Anaconda3\lib\site-packages\zmq\eventloop\ioloop.py", line 162, in start
    super(ZMQIOLoop, self).start()
  File "C:\Users\BioHelwan\Anaconda3\lib\site-packages\tornado\ioloop.py", line 887, in start
    handler_func(fd_obj, events)
  File "C:\Users\BioHelwan\Anaconda3\lib\site-packages\tornado\stack_context.py", line 275, in null_wrapper
    return fn(*args, **kwargs)
  File "C:\Users\BioHelwan\Anaconda3\lib\site-packages\zmq\eventloop\zmqstream.py", line 440, in _handle_events
    self._handle_recv()
  File "C:\Users\BioHelwan\Anaconda3\lib\site-packages\zmq\eventloop\zmqstream.py", line 472, in _handle_recv
    self._run_callback(callback, msg)
  File "C:\Users\BioHelwan\Anaconda3\lib\site-packages\zmq\eventloop\zmqstream.py", line 414, in _run_callback
    callback(*args, **kwargs)
  File "C:\Users\BioHelwan\Anaconda3\lib\site-packages\tornado\stack_context.py", line 275, in null_wrapper
    return fn(*args, **kwargs)
  File "C:\Users\BioHelwan\Anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 276, in dispatcher
    return self.dispatch_shell(stream, msg)
  File "C:\Users\BioHelwan\Anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 228, in dispatch_shell
    handler(stream, idents, msg)
  File "C:\Users\BioHelwan\Anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 390, in execute_request
    user_expressions, allow_stdin)
  File "C:\Users\BioHelwan\Anaconda3\lib\site-packages\ipykernel\ipkernel.py", line 196, in do_execute
    res = shell.run_cell(code, store_history=store_history, silent=silent)
  File "C:\Users\BioHelwan\Anaconda3\lib\site-packages\ipykernel\zmqshell.py", line 501, in run_cell
    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
  File "C:\Users\BioHelwan\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2717, in run_cell
    interactivity=interactivity, compiler=compiler, result=result)
  File "C:\Users\BioHelwan\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2827, in run_ast_nodes
    if self.run_code(code, result):
  File "C:\Users\BioHelwan\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2881, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "<ipython-input-7-78358ffb08d0>", line 1, in <module>
    runfile('D:/mobileNetPSPNet/tuning.py', wdir='D:/mobileNetPSPNet')
  File "C:\Users\BioHelwan\Anaconda3\lib\site-packages\spyder\utils\site\sitecustomize.py", line 866, in runfile
    execfile(filename, namespace)
  File "C:\Users\BioHelwan\Anaconda3\lib\site-packages\spyder\utils\site\sitecustomize.py", line 102, in execfile
    exec(compile(f.read(), filename, 'exec'), namespace)
  File "D:/mobileNetPSPNet/tuning.py", line 176, in <module>
    main()
  File "D:/mobileNetPSPNet/tuning.py", line 125, in main
    grads = tf.gradients(reduced_loss, conv_trainable + fc_w_trainable + fc_b_trainable)
  File "C:\Users\BioHelwan\Anaconda3\envs\tensorflow\Lib\site-packages\tensorflow\python\ops\gradients_impl.py", line 532, in gradients
    gate_gradients, aggregation_method, stop_gradients)
  File "C:\Users\BioHelwan\Anaconda3\envs\tensorflow\Lib\site-packages\tensorflow\python\ops\gradients_impl.py", line 701, in _GradientsHelper
    lambda: grad_fn(op, *out_grads))
  File "C:\Users\BioHelwan\Anaconda3\envs\tensorflow\Lib\site-packages\tensorflow\python\ops\gradients_impl.py", line 396, in _MaybeCompile
    return grad_fn()  # Exit early
  File "C:\Users\BioHelwan\Anaconda3\envs\tensorflow\Lib\site-packages\tensorflow\python\ops\gradients_impl.py", line 701, in <lambda>
    lambda: grad_fn(op, *out_grads))
  File "C:\Users\BioHelwan\Anaconda3\envs\tensorflow\Lib\site-packages\tensorflow\python\ops\nn_grad.py", line 963, in _L2LossGrad
    return op.inputs[0] * grad
  File "C:\Users\BioHelwan\Anaconda3\envs\tensorflow\Lib\site-packages\tensorflow\python\ops\math_ops.py", line 847, in binary_op_wrapper
    return func(x, y, name=name)
  File "C:\Users\BioHelwan\Anaconda3\envs\tensorflow\Lib\site-packages\tensorflow\python\ops\math_ops.py", line 1091, in _mul_dispatch
    return gen_math_ops.mul(x, y, name=name)
  File "C:\Users\BioHelwan\Anaconda3\envs\tensorflow\Lib\site-packages\tensorflow\python\ops\gen_math_ops.py", line 5066, in mul
    "Mul", x=x, y=y, name=name)
  File "C:\Users\BioHelwan\Anaconda3\envs\tensorflow\Lib\site-packages\tensorflow\python\framework\op_def_library.py", line 787, in _apply_op_helper
    op_def=op_def)
  File "C:\Users\BioHelwan\Anaconda3\envs\tensorflow\Lib\site-packages\tensorflow\python\framework\ops.py", line 3414, in create_op
    op_def=op_def)
  File "C:\Users\BioHelwan\Anaconda3\envs\tensorflow\Lib\site-packages\tensorflow\python\framework\ops.py", line 1740, in __init__
    self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access

...which was originally created as op 'L2Loss_31', defined at:
  File "C:\Users\BioHelwan\Anaconda3\lib\site-packages\spyder\utils\ipython\start_kernel.py", line 223, in <module>
    main()
[elided 20 identical lines from previous traceback]
  File "D:/mobileNetPSPNet/tuning.py", line 176, in <module>
    main()
  File "D:/mobileNetPSPNet/tuning.py", line 106, in main
    l2_losses = [WEIGHT_DECAY * tf.nn.l2_loss(v) for v in tf.trainable_variables() if 'weights' in v.name]
  File "D:/mobileNetPSPNet/tuning.py", line 106, in <listcomp>
    l2_losses = [WEIGHT_DECAY * tf.nn.l2_loss(v) for v in tf.trainable_variables() if 'weights' in v.name]
  File "C:\Users\BioHelwan\Anaconda3\envs\tensorflow\Lib\site-packages\tensorflow\python\ops\gen_nn_ops.py", line 4715, in l2_loss
    "L2Loss", t=t, name=name)
  File "C:\Users\BioHelwan\Anaconda3\envs\tensorflow\Lib\site-packages\tensorflow\python\framework\op_def_library.py", line 787, in _apply_op_helper
    op_def=op_def)
  File "C:\Users\BioHelwan\Anaconda3\envs\tensorflow\Lib\site-packages\tensorflow\python\framework\ops.py", line 3414, in create_op
    op_def=op_def)
  File "C:\Users\BioHelwan\Anaconda3\envs\tensorflow\Lib\site-packages\tensorflow\python\framework\ops.py", line 1740, in __init__
    self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access

ResourceExhaustedError (see above for traceback): OOM when allocating tensor with shape[3,3,256,256] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
     [[Node: gradients/L2Loss_31_grad/mul = Mul[T=DT_FLOAT, _class=["loc:@gradients/AddN_37"], _device="/job:localhost/replica:0/task:0/device:GPU:0"](conv4_2_3x3/weights/read, mul/x)]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.

     [[Node: conv4_3_3x3/weights/read/_741 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_321_conv4_3_3x3/weights/read", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.

how can i get rid of this error? and how can the train be done on the colored masks of ade20k dataset not gray masks? Thanks

[hellochick]

The important message is here: ResourceExhaustedError: OOM when allocating tensor with shape[3,3,256,256]

This message indicates that you GPU memory is not big enough. You should check the batch size you use.

[AyaMohamedS]

Really thanks you made my day ^_^ i made batch size =1 and it works i 'm little confused about this batch size and the number of training steps 1/ is that true "each step will take 1 training image"? 2/ shall i set the number of steps = no. of training images to make 1 epoch over the dataset?

can you help me in understanding the training steps w.r.t the training dataset?

[hellochick]

I'm glad that I can make you day haha ^_^.

1. Each step will run a batch of images, for example, if we have batch size = 8, every step will feed 8 images into the network.
2. As I mentioned above, if you want to make 1 epoch through the dataset, you need to just calculate by following formula, where num_images can obtained from the image list.

   num_steps_of_epoch = num_images / batch_size