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FCN-AlexNet Fully Convolutional Semantic Segmentation on PASCAL
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readme.md
@rahman-mdatiqur
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@mansirankawat, could you please tell me the protoc version, NVIDIA driver, cuda version and cuDNN version you are using?

@mansirankawat
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@atique81 Protobuf version 2.5.0, NVIDIA K20, CUDA 7.0.28 and am not using cuDNN

@rahman-mdatiqur
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thanks @mansirankawat...
however, I have now created the lmdb files according to your instruction and using the @Eranpaz provided python script. But, now when I go to train, it generates the following error.

Any idea about what is going wrong?

I0720 20:42:18.998431 20463 layer_factory.hpp:74] Creating layer data
I0720 20:42:18.998450 20463 net.cpp:85] Creating Layer data
I0720 20:42:18.998457 20463 net.cpp:339] data -> data
I0720 20:42:18.998486 20463 net.cpp:114] Setting up data
I0720 20:42:18.998569 20463 db.cpp:34] Opened lmdb /home/atique/caffe-future/sem_seg/data/lmdb_files/voc2011_train_img_lmdb
I0720 20:42:18.999052 20463 data_layer.cpp:67] output data size: 1,3,281,500
I0720 20:42:18.999843 20463 net.cpp:121] Top shape: 1 3 281 500 (421500)
I0720 20:42:18.999852 20463 layer_factory.hpp:74] Creating layer data_data_0_split
I0720 20:42:18.999862 20463 net.cpp:85] Creating Layer data_data_0_split
I0720 20:42:18.999866 20463 net.cpp:381] data_data_0_split <- data
I0720 20:42:18.999873 20463 net.cpp:339] data_data_0_split -> data_data_0_split_0
I0720 20:42:18.999881 20463 net.cpp:339] data_data_0_split -> data_data_0_split_1
I0720 20:42:18.999886 20463 net.cpp:114] Setting up data_data_0_split
I0720 20:42:18.999894 20463 net.cpp:121] Top shape: 1 3 281 500 (421500)
I0720 20:42:18.999898 20463 net.cpp:121] Top shape: 1 3 281 500 (421500)
I0720 20:42:18.999902 20463 layer_factory.hpp:74] Creating layer label
I0720 20:42:18.999909 20463 net.cpp:85] Creating Layer label
I0720 20:42:18.999914 20463 net.cpp:339] label -> label
I0720 20:42:18.999922 20463 net.cpp:114] Setting up label
F0720 20:42:18.999960 20463 db.hpp:109] Check failed: mdb_status == 0 (2 vs. 0) No such file or directory
*** Check failure stack trace: ***
@ 0x7f566d083a5d google::LogMessage::Fail()
@ 0x7f566d087ef7 google::LogMessage::SendToLog()
@ 0x7f566d085d59 google::LogMessage::Flush()
@ 0x7f566d08605d google::LogMessageFatal::~LogMessageFatal()
@ 0x7f5672ada21c caffe::db::LMDB::Open()
@ 0x7f5672a4f9f0 caffe::DataLayer<>::DataLayerSetUp()
@ 0x7f56729f59da caffe::BaseDataLayer<>::LayerSetUp()
@ 0x7f56729f5ae9 caffe::BasePrefetchingDataLayer<>::LayerSetUp()
@ 0x7f5672aa8124 caffe::Net<>::Init()
@ 0x7f5672aaa882 caffe::Net<>::Net()
@ 0x7f5672ab37cf caffe::Solver<>::InitTrainNet()
@ 0x7f5672ab3d3f caffe::Solver<>::Init()
@ 0x7f5672ab4175 caffe::Solver<>::Solver()
@ 0x40d7a8 caffe::GetSolver<>()
@ 0x4077b2 train()
@ 0x405cee main
@ 0x38ee21ed5d (unknown)
@ 0x4053b9 (unknown)
Aborted

@mansirankawat
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@atique81 you might want to ask this at caffe users google groups. I do not have any idea why this problem, maybe its not able to locate the file. I have seen similar questions being asked at the caffe users group.

@rahman-mdatiqur
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@mansirankawat..
you are right in saying that people have already raised the same issue on caffe user group. I am actually following those posts....

Thank you so much for being so cooperative..... Hope to receive your cooperation in future....

@rahman-mdatiqur
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Hi, @mansirankawat.....
I am now able to run fine-tuning, though couldn't make cudnn work, which is showing some error code 8 (CUDNN_STATUS_EXECUTION_FAILED)...

But, now the main problem is, the loss is not decreasing, its reporting the same every after 20 iterations (Train net output #0: loss = 3.04452 (* 1 = 3.04452 loss) ) for all iterations from 1 to 2200. I didn't proceed any further as the loss was not going down at all. I am training it on pascal_voc 2011 trian set (1112 images) and testing it on the pascal_voc 2011 validation set (1111 images).

could you please advise and share your experience regarding this?

Thanks.

@rahman-mdatiqur
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Hi @mansirankawat.....@Eranpaz...

could you guys please provide any pointer to the above mentioned issue?

Thanks.

@shelhamer
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Author

I only now noticed this comment thread, but I suggest looking at this model zoo example https://gist.github.com/shelhamer/80667189b218ad570e82#file-readme-md that includes further details on working with FCNs.

@ShravanTata
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I have the python file for preparing the database. Hope this is helpful to all of you:

import caffe
import lmdb
from PIL import Image
import numpy as np
import glob
from random import shuffle

Initialize the Image set:

NumberTrain = 1464#572 # Number of Training Images

NumberTest = 1449#143 # Number of Testing Images

Rheight = 380 # Required Height

Rwidth = 500 # Required Width

RheightLabel = 380 # Height for the label

RwidthLabel = 500 # Width for the label

LabelWidth = 118 # Downscaled width of the label

LabelHeight = 88 # Downscaled height of the label

Read the files in the Data Folder

inputs_data_train = sorted(glob.glob("/home/rcar/cnn/caffe/examples/SemanticSegmentation/VOC2012/SegmentationTrainingData/.jpg"))
inputs_data_valid = sorted(glob.glob("/home/rcar/cnn/caffe/examples/SemanticSegmentation/VOC2012/SegmentationValidationData/.jpg"))
inputs_label = sorted(glob.glob("/home/rcar/cnn/caffe/examples/SemanticSegmentation/VOC2012/SegmentationClass/*.png"))

shuffle(inputs_data_train) # Shuffle the DataSet
shuffle(inputs_data_valid) # Shuffle the DataSet

inputs_Train = inputs_data_train[:NumberTrain] # Extract the training data from the complete set

inputs_Test = inputs_data_valid[NumberTrain:NumberTrain+NumberTest] # Extract the testing data from the complete set

Creating LMDB for Training Data

print("Creating Training Data LMDB File ..... ")

in_db = lmdb.open('TrainVOC_Data_lmdb')

with in_db.begin(write=True) as in_txn:

for in_idx, in_ in enumerate(inputs_Train):
    print in_idx
    im = np.array(Image.open(in_)) # or load whatever ndarray you need
    Dtype = im.dtype
    im = im[:,:,::-1]
    im = Image.fromarray(im)
    im = im.resize([Rheight, Rwidth], Image.ANTIALIAS)
    im = np.array(im,Dtype)     

    im = im.transpose((2,0,1))
    im_dat = caffe.io.array_to_datum(im)
    in_txn.put('{:0>10d}'.format(in_idx),im_dat.SerializeToString())

in_db.close()

Creating LMDB for Training Labels

print("Creating Training Label LMDB File ..... ")

in_db = lmdb.open('TrainVOC_Label_lmdb',map_size=int(1e14))

with in_db.begin(write=True) as in_txn:

for in_idx, in_ in enumerate(inputs_Train):
    print in_idx
    in_label = in_[:-40]+'SegmentationClass/'+in_[-15:-3]+'png'
    L = np.array(Image.open(in_)) # or load whatever ndarray you need
    Dtype = L.dtype
    Limg = Image.fromarray(L)
    Limg = Limg.resize([LabelHeight, LabelWidth],Image.NEAREST) # To resize the Label file to the required size 

    L = np.array(Limg,Dtype)

    L = L.reshape(L.shape[0],L.shape[1],1)

    L = L.transpose((2,0,1))

    L_dat = caffe.io.array_to_datum(L)
    in_txn.put('{:0>10d}'.format(in_idx),L_dat.SerializeToString())

in_db.close()

Creating LMDB for Testing Data

print("Creating Testing Data LMDB File ..... ")

in_db = lmdb.open('TestVOC_Data_lmdb',map_size=int(1e14))

with in_db.begin(write=True) as in_txn:

for in_idx, in_ in enumerate(inputs_Test):
    print in_idx    
    im = np.array(Image.open(in_)) # or load whatever ndarray you need
    Dtype = im.dtype
    im = im[:,:,::-1]
    im = Image.fromarray(im)
    im = im.resize([Rheight, Rwidth], Image.ANTIALIAS)
    im = np.array(im,Dtype)     

    im = im.transpose((2,0,1))
    im_dat = caffe.io.array_to_datum(im)
    in_txn.put('{:0>10d}'.format(in_idx),im_dat.SerializeToString())

in_db.close()

Creating LMDB for Testing Labels

print("Creating Testing Label LMDB File ..... ")

in_db = lmdb.open('TestVOC_Label_lmdb',map_size=int(1e14))

with in_db.begin(write=True) as in_txn:

for in_idx, in_ in enumerate(inputs_Test):
    print in_idx    
    in_label = in_[:-40]+'SegmentationClass/'+in_[-15:-3]+'png'
    L = np.array(Image.open(in_)) # or load whatever ndarray you need
    Dtype = L.dtype
    Limg = Image.fromarray(L)
    Limg = Limg.resize([LabelHeight, LabelWidth],Image.NEAREST) # To resize the Label file to the required size 

    L = np.array(Limg,Dtype)

    L = L.reshape(L.shape[0],L.shape[1],1)

    L = L.transpose((2,0,1))

    L_dat = caffe.io.array_to_datum(L)
    in_txn.put('{:0>10d}'.format(in_idx),L_dat.SerializeToString())

in_db.close()

@paritosh0908
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I am having trouble making a deploy.prototxt file for the same. Can anybody help me with this?

@chriss2401
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@paritosh0908 just copy your train_val prototxt, remove the loss function and all the label and data layers and replace it with:

input: "data"
input_dim: 1
input_dim: 3
input_dim: 500
input_dim: 500

@paritosh0908
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Should I do net surgery of this to get better parameters?

@CarrieHui
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Using the given caffemodel, I only obtains 39.075 mean I/U on PASCAL VOC11 segval (the subset that does not intersect with SBD train), not 48.0. Did anybody test that caffemodel ? If yes, what score did you get?
Thanks in advance.

@jpsquare
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jpsquare commented Apr 23, 2016 •
edited

Hi

I am trying to perform semantic segmentation on the PASCAL VOC 2012 dataset using the train_val prototxt files provided in FCN-AlexNet PASCAL and create a caffemodel. I did prepare the lmdb files for the dataset, but when I run the files using the Caffe-future branch, I get this error associated with the loss layer.

I0428 20:38:35.996023 10452 layer_factory.hpp:76] Creating layer prob
F0428 20:38:36.005141 10452 softmax_loss_layer.cpp:42] Check failed: outer_num_ * inner_num_ == bottom[1]->count() (190000 vs. 10384) Number of labels must match number of predictions; e.g., if softmax axis == 1 and prediction shape is (N, C, H, W), label count (number of labels) must be N_H_W, with integer values in {0, 1, ..., C-1}.
*** Check failure stack trace: ***
@ 0x7f7c17556778 (unknown)
@ 0x7f7c175566b2 (unknown)
@ 0x7f7c175560b4 (unknown)
@ 0x7f7c17559055 (unknown)
@ 0x7f7c179573c8 caffe::SoftmaxWithLossLayer<>::Reshape()
@ 0x7f7c178d61db caffe::Net<>::Init()
@ 0x7f7c178d7948 caffe::Net<>::Net()
@ 0x7f7c17912b22 caffe::Solver<>::InitTrainNet()
@ 0x7f7c17913e2a caffe::Solver<>::Init()
@ 0x7f7c17914159 caffe::Solver<>::Solver()
@ 0x4117a5 caffe::GetSolver<>()
@ 0x408e13 train()
@ 0x4067b7 main
@ 0x7f7c135b7b45 (unknown)
@ 0x406fb4 (unknown)
@ (nil) (unknown)

Has the error got to do with the way the labels have been assigned?, if yes how should I modify the dimensions of the label layer?

Thanks, any help is appreciated

@snakehaihai
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I0427 21:44:38.665948 16601 layer_factory.hpp:76] Creating layer data
I0427 21:44:38.666298 16601 net.cpp:111] Creating Layer data
I0427 21:44:38.666328 16601 net.cpp:434] data -> data
I0427 21:44:38.666980 16604 db_lmdb.cpp:22] Opened lmdb /home/snake/caffe-FCN/segnet/FCN-AlexNet/create_data/Train_Data_lmdb
I0427 21:44:38.667425 16601 data_layer.cpp:44] output data size: 1,3,380,500
I0427 21:44:38.676369 16601 net.cpp:156] Setting up data
I0427 21:44:38.676419 16601 net.cpp:164] Top shape: 1 3 380 500 (570000)
I0427 21:44:38.676439 16601 layer_factory.hpp:76] Creating layer data_data_0_split
I0427 21:44:38.676473 16601 net.cpp:111] Creating Layer data_data_0_split
I0427 21:44:38.676488 16601 net.cpp:478] data_data_0_split <- data
I0427 21:44:38.676507 16601 net.cpp:434] data_data_0_split -> data_data_0_split_0
I0427 21:44:38.676540 16601 net.cpp:434] data_data_0_split -> data_data_0_split_1
I0427 21:44:38.676568 16601 net.cpp:156] Setting up data_data_0_split
I0427 21:44:38.676584 16601 net.cpp:164] Top shape: 1 3 380 500 (570000)
I0427 21:44:38.676594 16601 net.cpp:164] Top shape: 1 3 380 500 (570000)
I0427 21:44:38.676604 16601 layer_factory.hpp:76] Creating layer label
I0427 21:44:38.676654 16601 net.cpp:111] Creating Layer label
I0427 21:44:38.676682 16601 net.cpp:434] label -> label
I0427 21:44:38.677767 16606 db_lmdb.cpp:22] Opened lmdb /home/snake/caffe-FCN/segnet/FCN-AlexNet/create_data/Train_Label_lmdb
I0427 21:44:38.678103 16601 data_layer.cpp:44] output data size: 1,1,88,118
I0427 21:44:38.678669 16601 net.cpp:156] Setting up label
I0427 21:44:38.678704 16601 net.cpp:164] Top shape: 1 1 88 118 (10384)
I0427 21:44:38.678715 16601 layer_factory.hpp:76] Creating layer conv1
I0427 21:44:38.678735 16601 net.cpp:111] Creating Layer conv1
I0427 21:44:38.678766 16601 net.cpp:478] conv1 <- data_data_0_split_0
I0427 21:44:38.678787 16601 net.cpp:434] conv1 -> conv1
I0427 21:44:38.680908 16601 net.cpp:156] Setting up conv1
I0427 21:44:38.680953 16601 net.cpp:164] Top shape: 1 96 143 173 (2374944)
I0427 21:44:38.680979 16601 layer_factory.hpp:76] Creating layer relu1
I0427 21:44:38.680996 16601 net.cpp:111] Creating Layer relu1
I0427 21:44:38.681008 16601 net.cpp:478] relu1 <- conv1
I0427 21:44:38.681021 16601 net.cpp:420] relu1 -> conv1 (in-place)
I0427 21:44:38.681241 16601 net.cpp:156] Setting up relu1
I0427 21:44:38.681254 16601 net.cpp:164] Top shape: 1 96 143 173 (2374944)
I0427 21:44:38.681293 16601 layer_factory.hpp:76] Creating layer pool1
I0427 21:44:38.681308 16601 net.cpp:111] Creating Layer pool1
I0427 21:44:38.681316 16601 net.cpp:478] pool1 <- conv1
I0427 21:44:38.681329 16601 net.cpp:434] pool1 -> pool1
I0427 21:44:38.681351 16601 net.cpp:156] Setting up pool1
I0427 21:44:38.681365 16601 net.cpp:164] Top shape: 1 96 71 86 (586176)
I0427 21:44:38.681375 16601 layer_factory.hpp:76] Creating layer norm1
I0427 21:44:38.681391 16601 net.cpp:111] Creating Layer norm1
I0427 21:44:38.681401 16601 net.cpp:478] norm1 <- pool1
I0427 21:44:38.681411 16601 net.cpp:434] norm1 -> norm1
I0427 21:44:38.681426 16601 net.cpp:156] Setting up norm1
I0427 21:44:38.681437 16601 net.cpp:164] Top shape: 1 96 71 86 (586176)
I0427 21:44:38.681447 16601 layer_factory.hpp:76] Creating layer conv2
I0427 21:44:38.681459 16601 net.cpp:111] Creating Layer conv2
I0427 21:44:38.681469 16601 net.cpp:478] conv2 <- norm1
I0427 21:44:38.681483 16601 net.cpp:434] conv2 -> conv2
I0427 21:44:38.689705 16601 net.cpp:156] Setting up conv2
I0427 21:44:38.689739 16601 net.cpp:164] Top shape: 1 256 71 86 (1563136)
I0427 21:44:38.689757 16601 layer_factory.hpp:76] Creating layer relu2
I0427 21:44:38.689774 16601 net.cpp:111] Creating Layer relu2
I0427 21:44:38.689784 16601 net.cpp:478] relu2 <- conv2
I0427 21:44:38.689795 16601 net.cpp:420] relu2 -> conv2 (in-place)
I0427 21:44:38.689807 16601 net.cpp:156] Setting up relu2
I0427 21:44:38.689818 16601 net.cpp:164] Top shape: 1 256 71 86 (1563136)
I0427 21:44:38.689827 16601 layer_factory.hpp:76] Creating layer pool2
I0427 21:44:38.689839 16601 net.cpp:111] Creating Layer pool2
I0427 21:44:38.689848 16601 net.cpp:478] pool2 <- conv2
I0427 21:44:38.689859 16601 net.cpp:434] pool2 -> pool2
I0427 21:44:38.689874 16601 net.cpp:156] Setting up pool2
I0427 21:44:38.689885 16601 net.cpp:164] Top shape: 1 256 35 43 (385280)
I0427 21:44:38.689895 16601 layer_factory.hpp:76] Creating layer norm2
I0427 21:44:38.689908 16601 net.cpp:111] Creating Layer norm2
I0427 21:44:38.689918 16601 net.cpp:478] norm2 <- pool2
I0427 21:44:38.689929 16601 net.cpp:434] norm2 -> norm2
I0427 21:44:38.689941 16601 net.cpp:156] Setting up norm2
I0427 21:44:38.689951 16601 net.cpp:164] Top shape: 1 256 35 43 (385280)
I0427 21:44:38.689961 16601 layer_factory.hpp:76] Creating layer conv3
I0427 21:44:38.689973 16601 net.cpp:111] Creating Layer conv3
I0427 21:44:38.689982 16601 net.cpp:478] conv3 <- norm2
I0427 21:44:38.689993 16601 net.cpp:434] conv3 -> conv3
I0427 21:44:38.713974 16601 net.cpp:156] Setting up conv3
I0427 21:44:38.714025 16601 net.cpp:164] Top shape: 1 384 35 43 (577920)
I0427 21:44:38.714046 16601 layer_factory.hpp:76] Creating layer relu3
I0427 21:44:38.714061 16601 net.cpp:111] Creating Layer relu3
I0427 21:44:38.714072 16601 net.cpp:478] relu3 <- conv3
I0427 21:44:38.714084 16601 net.cpp:420] relu3 -> conv3 (in-place)
I0427 21:44:38.714099 16601 net.cpp:156] Setting up relu3
I0427 21:44:38.714109 16601 net.cpp:164] Top shape: 1 384 35 43 (577920)
I0427 21:44:38.714119 16601 layer_factory.hpp:76] Creating layer conv4
I0427 21:44:38.714133 16601 net.cpp:111] Creating Layer conv4
I0427 21:44:38.714143 16601 net.cpp:478] conv4 <- conv3
I0427 21:44:38.714155 16601 net.cpp:434] conv4 -> conv4
I0427 21:44:38.731659 16601 net.cpp:156] Setting up conv4
I0427 21:44:38.731683 16601 net.cpp:164] Top shape: 1 384 35 43 (577920)
I0427 21:44:38.731709 16601 layer_factory.hpp:76] Creating layer relu4
I0427 21:44:38.731739 16601 net.cpp:111] Creating Layer relu4
I0427 21:44:38.731750 16601 net.cpp:478] relu4 <- conv4
I0427 21:44:38.731761 16601 net.cpp:420] relu4 -> conv4 (in-place)
I0427 21:44:38.731775 16601 net.cpp:156] Setting up relu4
I0427 21:44:38.731784 16601 net.cpp:164] Top shape: 1 384 35 43 (577920)
I0427 21:44:38.731794 16601 layer_factory.hpp:76] Creating layer conv5
I0427 21:44:38.731808 16601 net.cpp:111] Creating Layer conv5
I0427 21:44:38.731818 16601 net.cpp:478] conv5 <- conv4
I0427 21:44:38.731830 16601 net.cpp:434] conv5 -> conv5
I0427 21:44:38.743054 16601 net.cpp:156] Setting up conv5
I0427 21:44:38.743074 16601 net.cpp:164] Top shape: 1 256 35 43 (385280)
I0427 21:44:38.743101 16601 layer_factory.hpp:76] Creating layer relu5
I0427 21:44:38.743119 16601 net.cpp:111] Creating Layer relu5
I0427 21:44:38.743129 16601 net.cpp:478] relu5 <- conv5
I0427 21:44:38.743139 16601 net.cpp:420] relu5 -> conv5 (in-place)
I0427 21:44:38.743152 16601 net.cpp:156] Setting up relu5
I0427 21:44:38.743162 16601 net.cpp:164] Top shape: 1 256 35 43 (385280)
I0427 21:44:38.743172 16601 layer_factory.hpp:76] Creating layer pool5
I0427 21:44:38.743185 16601 net.cpp:111] Creating Layer pool5
I0427 21:44:38.743193 16601 net.cpp:478] pool5 <- conv5
I0427 21:44:38.743204 16601 net.cpp:434] pool5 -> pool5
I0427 21:44:38.743221 16601 net.cpp:156] Setting up pool5
I0427 21:44:38.743235 16601 net.cpp:164] Top shape: 1 256 17 21 (91392)
I0427 21:44:38.743245 16601 layer_factory.hpp:76] Creating layer fc6
I0427 21:44:38.743262 16601 net.cpp:111] Creating Layer fc6
I0427 21:44:38.743271 16601 net.cpp:478] fc6 <- pool5
I0427 21:44:38.743283 16601 net.cpp:434] fc6 -> fc6
I0427 21:44:39.708747 16601 net.cpp:156] Setting up fc6
I0427 21:44:39.708806 16601 net.cpp:164] Top shape: 1 4096 12 16 (786432)
I0427 21:44:39.708823 16601 layer_factory.hpp:76] Creating layer relu6
I0427 21:44:39.708839 16601 net.cpp:111] Creating Layer relu6
I0427 21:44:39.708850 16601 net.cpp:478] relu6 <- fc6
I0427 21:44:39.708863 16601 net.cpp:420] relu6 -> fc6 (in-place)
I0427 21:44:39.708878 16601 net.cpp:156] Setting up relu6
I0427 21:44:39.708889 16601 net.cpp:164] Top shape: 1 4096 12 16 (786432)
I0427 21:44:39.708897 16601 layer_factory.hpp:76] Creating layer drop6
I0427 21:44:39.708910 16601 net.cpp:111] Creating Layer drop6
I0427 21:44:39.708930 16601 net.cpp:478] drop6 <- fc6
I0427 21:44:39.708942 16601 net.cpp:420] drop6 -> fc6 (in-place)
I0427 21:44:39.708961 16601 net.cpp:156] Setting up drop6
I0427 21:44:39.708974 16601 net.cpp:164] Top shape: 1 4096 12 16 (786432)
I0427 21:44:39.708983 16601 layer_factory.hpp:76] Creating layer fc7
I0427 21:44:39.709010 16601 net.cpp:111] Creating Layer fc7
I0427 21:44:39.709022 16601 net.cpp:478] fc7 <- fc6
I0427 21:44:39.709033 16601 net.cpp:434] fc7 -> fc7
I0427 21:44:40.133029 16601 net.cpp:156] Setting up fc7
I0427 21:44:40.133085 16601 net.cpp:164] Top shape: 1 4096 12 16 (786432)
I0427 21:44:40.133101 16601 layer_factory.hpp:76] Creating layer relu7
I0427 21:44:40.133118 16601 net.cpp:111] Creating Layer relu7
I0427 21:44:40.133129 16601 net.cpp:478] relu7 <- fc7
I0427 21:44:40.133157 16601 net.cpp:420] relu7 -> fc7 (in-place)
I0427 21:44:40.133172 16601 net.cpp:156] Setting up relu7
I0427 21:44:40.133183 16601 net.cpp:164] Top shape: 1 4096 12 16 (786432)
I0427 21:44:40.133193 16601 layer_factory.hpp:76] Creating layer drop7
I0427 21:44:40.133219 16601 net.cpp:111] Creating Layer drop7
I0427 21:44:40.133229 16601 net.cpp:478] drop7 <- fc7
I0427 21:44:40.133240 16601 net.cpp:420] drop7 -> fc7 (in-place)
I0427 21:44:40.133255 16601 net.cpp:156] Setting up drop7
I0427 21:44:40.133265 16601 net.cpp:164] Top shape: 1 4096 12 16 (786432)
I0427 21:44:40.133275 16601 layer_factory.hpp:76] Creating layer score-fr
I0427 21:44:40.133288 16601 net.cpp:111] Creating Layer score-fr
I0427 21:44:40.133297 16601 net.cpp:478] score-fr <- fc7
I0427 21:44:40.133308 16601 net.cpp:434] score-fr -> score-fc7
I0427 21:44:40.133857 16601 net.cpp:156] Setting up score-fr
I0427 21:44:40.133888 16601 net.cpp:164] Top shape: 1 21 12 16 (4032)
I0427 21:44:40.133929 16601 layer_factory.hpp:76] Creating layer upsample
I0427 21:44:40.133965 16601 net.cpp:111] Creating Layer upsample
I0427 21:44:40.133975 16601 net.cpp:478] upsample <- score-fc7
I0427 21:44:40.133990 16601 net.cpp:434] upsample -> bigscore
I0427 21:44:40.134496 16601 net.cpp:156] Setting up upsample
I0427 21:44:40.134526 16601 net.cpp:164] Top shape: 1 21 415 543 (4732245)
I0427 21:44:40.134546 16601 layer_factory.hpp:76] Creating layer crop
I0427 21:44:40.134563 16601 net.cpp:111] Creating Layer crop
I0427 21:44:40.134574 16601 net.cpp:478] crop <- bigscore
I0427 21:44:40.134584 16601 net.cpp:478] crop <- data_data_0_split_1
I0427 21:44:40.134596 16601 net.cpp:434] crop -> score
I0427 21:44:40.134660 16601 net.cpp:156] Setting up crop
I0427 21:44:40.134673 16601 net.cpp:164] Top shape: 1 21 380 500 (3990000)
I0427 21:44:40.134683 16601 layer_factory.hpp:76] Creating layer prob
I0427 21:44:40.134699 16601 net.cpp:111] Creating Layer prob
I0427 21:44:40.134721 16601 net.cpp:478] prob <- score
I0427 21:44:40.134732 16601 net.cpp:478] prob <- label
I0427 21:44:40.134743 16601 net.cpp:434] prob -> loss
I0427 21:44:40.134763 16601 layer_factory.hpp:76] Creating layer prob
F0427 21:44:40.141227 16601 softmax_loss_layer.cpp:42] Check failed: outer_num_ * inner_num_ == bottom[1]->count() (190000 vs. 10384) Number of labels must match number of predictions; e.g., if softmax axis == 1 and prediction shape is (N, C, H, W), label count (number of labels) must be N_H_W, with integer values in {0, 1, ..., C-1}.
*** Check failure stack trace: ***
@ 0x7f867a93fdaa (unknown)
@ 0x7f867a93fce4 (unknown)
@ 0x7f867a93f6e6 (unknown)
@ 0x7f867a942687 (unknown)
@ 0x7f867ad7f7e0 caffe::SoftmaxWithLossLayer<>::Reshape()
@ 0x7f867acbd3be caffe::Net<>::Init()
@ 0x7f867acbe405 caffe::Net<>::Net()
@ 0x7f867acd28ba caffe::Solver<>::InitTrainNet()
@ 0x7f867acd39f4 caffe::Solver<>::Init()
@ 0x7f867acd3cf9 caffe::Solver<>::Solver()
@ 0x412915 caffe::GetSolver<>()
@ 0x40b61e train()
@ 0x4094a1 main
@ 0x7f8679443ec5 (unknown)
@ 0x409c3b (unknown)
@ (nil) (unknown)
Aborted (core dumped)

The size are different, in your sample you put it like 1X1X88X118. But here is 1 21 380 500. I`m a bit confused about the actual label size you are going to set. anyway. Its not working.

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