win_caffe_py_fast_rcnn训练报错问题。 10C

layer {
name: "rpn_bbox_pred"
type: "Convolution"
bottom: "rpn_conv1Process Process-1:
Traceback (most recent call last):
File "D:\Anaconda\Anaconda\lib\multiprocessing\process.py", line 267, in bootstrap
self.run()
File "D:\Anaconda\Anaconda\lib\multiprocessing\process.py", line 114, in run
self._target(*self._args, **self._kwargs)
File "D:\py-faster-rcnn\tools\train_faster_rcnn_alt_opt.py", line 129, in train_rpn
max_iters=max_iters)
File "D:\py-faster-rcnn\tools..\lib\fast_rcnn\train.py", line 160, in train_net
pretrained_model=pretrained_model)
File "D:\py-faster-rcnn\tools..\lib\fast_rcnn\train.py", line 46, in __init
_
self.solver = caffe.SGDSolver(solver_prototxt)
File "D:\py-faster-rcnn\tools..\lib\roi_data_layer\layer.py", line 128, in setup
top[idx].reshape(1, self._num_classes * 4)
IndexError: Index out of range

I0415 19:38:05.625026 12668 layer_factory.cpp:58] Creating layer input-data
I0415 19:38:05.682178 12668 net.cpp:84] Creating Layer input-data
I0415 19:38:05.682178 12668 net.cpp:380] input-data -> data
I0415 19:38:05.682178 12668 net.cpp:380] input-data -> im_info
I0415 19:38:05.682178 12668 net.cpp:380] input-data -> gt_boxes
然后就卡在这里了。

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我最近刚接触openCV的内容,在win10的pycharm里面试着去运行相关的程序,但是遇到了报错,可能问题很小白,希望各位大牛不吝赐教。其内容是:deep-learning-object-detection.py: error: the following arguments are required: 全篇代码如下 ``` # USAGE # python deep_learning_object_detection.py --image images/example_01.jpg \ # --prototxt MobileNetSSD_deploy.prototxt.txt --model MobileNetSSD_deploy.caffemodel # import the necessary packages import numpy as np import argparse import cv2 ap = argparse.ArgumentParser() ap.add_argument("-i", r"--C:\Users\52314\Desktop\deep\images\example_01.jpg", required=True, help="path to input image") ap.add_argument("-p", r"--C:\Users\52314\Desktop\deepMobileNetSSD_deploy.prototxt.txt", required=True, help="path to Caffe 'deploy' prototxt file") ap.add_argument("-m", r"--C:\Users\52314\Desktop\deep\deep_learning_object_detection.py", required=True, help="path to Caffe pre-trained model") ap.add_argument("-c", "--confidence", type=float, default=0.2, help="minimum probability to filter weak detections") args = vars(ap.parse_args()) # initialize the list of class labels MobileNet SSD was trained to # detect, then generate a set of bounding box colors for each class CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"] COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3)) # load our serialized model from disk print("[INFO] loading model...") net = cv2.dnn.readNetFromCaffe(Args[prototxt], Args[model]) # load the input image and construct an input blob for the image # by resizing to a fixed 300x300 pixels and then normalizing it # (note: normalization is done via the authors of the MobileNet SSD # implementation) image = cv2.imread(Args["image"]) (h, w) = image.shape[:2] blob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 0.007843, (300, 300), 127.5) # pass the blob through the network and obtain the detections and # predictions print("[INFO] computing object detections...") net.setInput(blob) detections = net.forward() # loop over the detections for i in np.arange(0, detections.shape[2]): # extract the confidence (i.e., probability) associated with the # prediction confidence = detections[0, 0, i, 2] # filter out weak detections by ensuring the `confidence` is # greater than the minimum confidence if confidence > Args["confidence"]: # extract the index of the class label from the `detections`, # then compute the (x, y)-coordinates of the bounding box for # the object idx = int(detections[0, 0, i, 1]) box = detections[0, 0, i, 3:7] * np.array([w, h, w, h]) (startX, startY, endX, endY) = box.astype("int") # display the prediction label = "{}: {:.2f}%".format(CLASSES[idx], confidence * 100) print("[INFO] {}".format(label)) cv2.rectangle(image, (startX, startY), (endX, endY), COLORS[idx], 2) y = startY - 15 if startY - 15 > 15 else startY + 15 cv2.putText(image, label, (startX, y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2) # show the output image cv2.imshow("Output", image) cv2.waitKey(0) ``` 我在网上看到说argparse在win10上面兼容不好所以换了个表达方式,那个也不行,那么到底是什么问题呢?要如何解决这个问题呢?非常感谢!

faster rcnn训练的时候应该是哪个层出了问题

+ echo Logging output to experiments/logs/faster_rcnn_alt_opt_ZF_.txt.2017-04-19_01-16-47 Logging output to experiments/logs/faster_rcnn_alt_opt_ZF_.txt.2017-04-19_01-16-47 + ./tools/train_faster_rcnn_alt_opt.py --gpu 0 --net_name ZF --weights data/imagenet_models/CaffeNet.v2.caffemodel --imdb voc_2007_trainval --cfg experiments/cfgs/faster_rcnn_alt_opt.yml Called with args: Namespace(cfg_file='experiments/cfgs/faster_rcnn_alt_opt.yml', gpu_id=0, imdb_name='voc_2007_trainval', net_name='ZF', pretrained_model='data/imagenet_models/CaffeNet.v2.caffemodel', set_cfgs=None) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Stage 1 RPN, init from ImageNet model ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Init model: data/imagenet_models/CaffeNet.v2.caffemodel Using config: {'DATA_DIR': 'E:\\caffe-frcnn\\py-faster-rcnn-master\\data', 'DEDUP_BOXES': 0.0625, 'EPS': 1e-14, 'EXP_DIR': 'default', 'GPU_ID': 0, 'MATLAB': 'matlab', 'MODELS_DIR': 'E:\\caffe-frcnn\\py-faster-rcnn-master\\models\\pascal_voc', 'PIXEL_MEANS': array([[[ 102.9801, 115.9465, 122.7717]]]), 'RNG_SEED': 3, 'ROOT_DIR': 'E:\\caffe-frcnn\\py-faster-rcnn-master', 'TEST': {'BBOX_REG': True, 'HAS_RPN': False, 'MAX_SIZE': 1000, 'NMS': 0.3, 'PROPOSAL_METHOD': 'selective_search', 'RPN_MIN_SIZE': 16, 'RPN_NMS_THRESH': 0.7, 'RPN_POST_NMS_TOP_N': 300, 'RPN_PRE_NMS_TOP_N': 6000, 'SCALES': [600], 'SVM': False}, 'TRAIN': {'ASPECT_GROUPING': True, 'BATCH_SIZE': 128, 'BBOX_INSIDE_WEIGHTS': [1.0, 1.0, 1.0, 1.0], 'BBOX_NORMALIZE_MEANS': [0.0, 0.0, 0.0, 0.0], 'BBOX_NORMALIZE_STDS': [0.1, 0.1, 0.2, 0.2], 'BBOX_NORMALIZE_TARGETS': True, 'BBOX_NORMALIZE_TARGETS_PRECOMPUTED': False, 'BBOX_REG': False, 'BBOX_THRESH': 0.5, 'BG_THRESH_HI': 0.5, 'BG_THRESH_LO': 0.1, 'FG_FRACTION': 0.25, 'FG_THRESH': 0.5, 'HAS_RPN': True, 'IMS_PER_BATCH': 1, 'MAX_SIZE': 1000, 'PROPOSAL_METHOD': 'gt', 'RPN_BATCHSIZE': 256, 'RPN_BBOX_INSIDE_WEIGHTS': [1.0, 1.0, 1.0, 1.0], 'RPN_CLOBBER_POSITIVES': False, 'RPN_FG_FRACTION': 0.5, 'RPN_MIN_SIZE': 16, 'RPN_NEGATIVE_OVERLAP': 0.3, 'RPN_NMS_THRESH': 0.7, 'RPN_POSITIVE_OVERLAP': 0.7, 'RPN_POSITIVE_WEIGHT': -1.0, 'RPN_POST_NMS_TOP_N': 2000, 'RPN_PRE_NMS_TOP_N': 12000, 'SCALES': [600], 'SNAPSHOT_INFIX': '', 'SNAPSHOT_ITERS': 10000, 'USE_FLIPPED': True, 'USE_PREFETCH': False}, 'USE_GPU_NMS': True} Loaded dataset `voc_2007_trainval` for training Set proposal method: gt Appending horizontally-flipped training examples... voc_2007_trainval gt roidb loaded from E:\caffe-frcnn\py-faster-rcnn-master\data\cache\voc_2007_trainval_gt_roidb.pkl done Preparing training data... done roidb len: 100 Output will be saved to `E:\caffe-frcnn\py-faster-rcnn-master\output\default\voc_2007_trainval` Filtered 0 roidb entries: 100 -> 100 WARNING: Logging before InitGoogleLogging() is written to STDERR I0419 01:16:54.964942 25240 common.cpp:36] System entropy source not available, using fallback algorithm to generate seed instead. I0419 01:16:55.073168 25240 solver.cpp:44] Initializing solver from parameters: train_net: "models/pascal_voc/ZF/faster_rcnn_alt_opt/stage1_rpn_train.pt" base_lr: 0.001 display: 20 lr_policy: "step" gamma: 0.1 momentum: 0.9 weight_decay: 0.0005 stepsize: 60000 snapshot: 0 snapshot_prefix: "zf_rpn" average_loss: 100 I0419 01:16:55.073168 25240 solver.cpp:77] Creating training net from train_net file: models/pascal_voc/ZF/faster_rcnn_alt_opt/stage1_rpn_train.pt I0419 01:16:55.074168 25240 net.cpp:51] Initializing net from parameters: name: "ZF" state { phase: TRAIN } layer { name: "input-data" type: "Python" top: "data" top: "im_info" top: "gt_boxes" python_param { module: "roi_data_layer.layer" layer: "RoIDataLayer" param_str: "\'num_classes\': 2" } } layer { name: "conv1" type: "Convolution" bottom: "data" top: "conv1" param { lr_mult: 1 } param { lr_mult: 2 } convolution_param { num_output: 96 pad: 3 kernel_size: 7 stride: 2 } } layer { name: "relu1" type: "ReLU" bottom: "conv1" top: "conv1" } layer { name: "norm1" type: "LRN" bottom: "conv1" top: "norm1" lrn_param { local_size: 3 alpha: 5e-05 beta: 0.75 norm_region: WITHIN_CHANNEL engine: CAFFE } } layer { name: "pool1" type: "Pooling" bottom: "norm1" top: "pool1" pooling_param { pool: MAX kernel_size: 3 stride: 2 pad: 1 } } layer { name: "conv2" type: "Convolution" bottom: "pool1" top: "conv2" param { lr_mult: 1 } param { lr_mult: 2 } convolution_param { num_output: 256 pad: 2 kernel_size: 5 stride: 2 } } layer { name: "relu2" type: "ReLU" bottom: "conv2" top: "conv2" } layer { name: "norm2" type: "LRN" bottom: "conv2" top: "norm2" lrn_param { local_size: 3 alpha: 5e-05 beta: 0.75 norm_region: WITHIN_CHANNEL engine: CAFFE } } layer { name: "pool2" type: "Pooling" bottom: "norm2" top: "pool2" pooling_param { pool: MAX kernel_size: 3 stride: 2 pad: 1 } } layer { name: "conv3" type: "Convolution" bottom: "pool2" top: "conv3" param { lr_mult: 1 } param { lr_mult: 2 } convolution_param { num_output: 384 pad: 1 kernel_size: 3 stride: 1 } } layer { name: "relu3" type: "ReLU" bottom: "conv3" top: "conv3" } layer { name: "conv4" type: "Convolution" bottom: "conv3" top: "conv4" param { lr_mult: 1 } param { lr_mult: 2 } convolution_param { num_output: 384 pad: 1 kernel_size: 3 stride: 1 } } layer { name: "relu4" type: "ReLU" bottom: "conv4" top: "conv4" } layer { name: "conv5" type: "Convolution" bottom: "conv4" top: "conv5" param { lr_mult: 1 } param { lr_mult: 2 } convolution_param { num_output: 256 pad: 1 kernel_size: 3 stride: 1 } } layer { name: "relu5" type: "ReLU" bottom: "conv5" top: "conv5" } layer { name: "rpn_conv1" type: "Convolution" bottom: "conv5" top: "rpn_conv1" param { lr_mult: 1 } param { lr_mult: 2 } convolution_param { num_output: 256 pad: 1 kernel_size: 3 stride: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } } layer { name: "rpn_relu1" type: "ReLU" bottom: "rpn_conv1" top: "rpn_conv1" } layer { name: "rpn_cls_score" type: "Convolution" bottom: "rpn_conv1" top: "rpn_cls_score" param { lr_mult: 1 } param { lr_mult: 2 } convolution_param { num_output: 18 pad: 0 kernel_size: 1 stride: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } } layer { name: "rpn_bbox_pred" type: "Convolution" bottom: "rpn_conv1"RoiDataLayer: name_to_top: {'gt_boxes': 2, 'data': 0, 'im_info': 1} top: "rpn_bbox_pred" param { lr_mult: 1 } param { lr_mult: 2 } convolution_param { num_output: 36 pad: 0 kernel_size: 1 stride: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } } layer { name: "rpn_cls_score_reshape" type: "Reshape" bottom: "rpn_cls_score" top: "rpn_cls_score_reshape" reshape_param { shape { dim: 0 dim: 2 dim: -1 dim: 0 } } } layer { name: "rpn-data" type: "Python" bottom: "rpn_cls_score" bottom: "gt_boxes" bottom: "im_info" bottom: "data" top: "rpn_labels" top: "rpn_bbox_targets" top: "rpn_bbox_inside_weights" top: "rpn_bbox_outside_weights" python_param { module: "rpn.anchor_target_layer" layer: "AnchorTargetLayer" param_str: "\'feat_stride\': 16" } } layer { name: "rpn_loss_cls" type: "SoftmaxWithLoss" bottom: "rpn_cls_score_reshape" bottom: "rpn_labels" top: "rpn_cls_loss" loss_weight: 1 propagate_down: true propagate_down: false loss_param { ignore_label: -1 normalize: true } } layer { name: "rpn_loss_bbox" type: "SmoothL1Loss" bottom: "rpn_bbox_pred" bottom: "rpn_bbox_targets" bottom: "rpn_bbox_inside_weights" bottom: "rpn_bbox_outside_weights" top: "rpn_loss_bbox" loss_weight: 1 smooth_l1_loss_param { sigma: 3 } } layer { name: "dummy_roi_pool_conv5" type: "DummyData" top: "dummy_roi_pool_conv5" dummy_data_param { data_filler { type: "gaussian" std: 0.01 } shape { dim: 1 dim: 9216 } } } layer { name: "fc6" type: "InnerProduct" bottom: "dummy_roi_pool_conv5" top: "fc6" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } inner_product_param { num_output: 4096 } } layer { name: "relu6" type: "ReLU" bottom: "fc6" top: "fc6" } layer { name: "fc7" type: "InnerProduct" bottom: "fc6" top: "fc7" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } inner_product_param { num_output: 4096 } } layer { name: "silence_fc7" type: "Silence" bottom: "fc7" } I0419 01:16:55.074668 25240 layer_factory.cpp:58] Creating layer input-data I0419 01:16:55.109673 25240 net.cpp:84] Creating Layer input-data I0419 01:16:55.109673 25240 net.cpp:380] input-data -> data I0419 01:16:55.109673 25240 net.cpp:380] input-data -> im_info I0419 01:16:55.109673 25240 net.cpp:380] input-data -> gt_boxes I0419 01:16:55.111171 25240 net.cpp:122] Setting up input-data I0419 01:16:55.111171 25240 net.cpp:129] Top shape: 1 3 600 1000 (1800000) I0419 01:16:55.111171 25240 net.cpp:129] Top shape: 1 3 (3) I0419 01:16:55.111668 25240 net.cpp:129] Top shape: 1 4 (4) I0419 01:16:55.111668 25240 net.cpp:137] Memory required for data: 7200028 I0419 01:16:55.111668 25240 layer_factory.cpp:58] Creating layer data_input-data_0_split I0419 01:16:55.111668 25240 net.cpp:84] Creating Layer data_input-data_0_split I0419 01:16:55.111668 25240 net.cpp:406] data_input-data_0_split <- data I0419 01:16:55.111668 25240 net.cpp:380] data_input-data_0_split -> data_input-data_0_split_0 I0419 01:16:55.111668 25240 net.cpp:380] data_input-data_0_split -> data_input-data_0_split_1 I0419 01:16:55.111668 25240 net.cpp:122] Setting up data_input-data_0_split I0419 01:16:55.111668 25240 net.cpp:129] Top shape: 1 3 600 1000 (1800000) I0419 01:16:55.111668 25240 net.cpp:129] Top shape: 1 3 600 1000 (1800000) I0419 01:16:55.111668 25240 net.cpp:137] Memory required for data: 21600028 I0419 01:16:55.111668 25240 layer_factory.cpp:58] Creating layer conv1 I0419 01:16:55.111668 25240 net.cpp:84] Creating Layer conv1 I0419 01:16:55.111668 25240 net.cpp:406] conv1 <- data_input-data_0_split_0 I0419 01:16:55.111668 25240 net.cpp:380] conv1 -> conv1 I0419 01:16:55.577394 25240 net.cpp:122] Setting up conv1 I0419 01:16:55.577394 25240 net.cpp:129] Top shape: 1 96 300 500 (14400000) I0419 01:16:55.577394 25240 net.cpp:137] Memory required for data: 79200028 I0419 01:16:55.577394 25240 layer_factory.cpp:58] Creating layer relu1 I0419 01:16:55.577394 25240 net.cpp:84] Creating Layer relu1 I0419 01:16:55.577394 25240 net.cpp:406] relu1 <- conv1 I0419 01:16:55.577394 25240 net.cpp:367] relu1 -> conv1 (in-place) I0419 01:16:55.577394 25240 net.cpp:122] Setting up relu1 I0419 01:16:55.577394 25240 net.cpp:129] Top shape: 1 96 300 500 (14400000) I0419 01:16:55.577394 25240 net.cpp:137] Memory required for data: 136800028 I0419 01:16:55.577394 25240 layer_factory.cpp:58] Creating layer norm1 I0419 01:16:55.577394 25240 net.cpp:84] Creating Layer norm1 I0419 01:16:55.577394 25240 net.cpp:406] norm1 <- conv1 I0419 01:16:55.577394 25240 net.cpp:380] norm1 -> norm1 I0419 01:16:55.577394 25240 net.cpp:122] Setting up norm1 I0419 01:16:55.577394 25240 net.cpp:129] Top shape: 1 96 300 500 (14400000) I0419 01:16:55.577394 25240 net.cpp:137] Memory required for data: 194400028 I0419 01:16:55.577394 25240 layer_factory.cpp:58] Creating layer pool1 I0419 01:16:55.577394 25240 net.cpp:84] Creating Layer pool1 I0419 01:16:55.577394 25240 net.cpp:406] pool1 <- norm1 I0419 01:16:55.577394 25240 net.cpp:380] pool1 -> pool1 I0419 01:16:55.577394 25240 net.cpp:122] Setting up pool1 I0419 01:16:55.577394 25240 net.cpp:129] Top shape: 1 96 151 251 (3638496) I0419 01:16:55.577394 25240 net.cpp:137] Memory required for data: 208954012 I0419 01:16:55.577394 25240 layer_factory.cpp:58] Creating layer conv2 I0419 01:16:55.577394 25240 net.cpp:84] Creating Layer conv2 I0419 01:16:55.577394 25240 net.cpp:406] conv2 <- pool1 I0419 01:16:55.577394 25240 net.cpp:380] conv2 -> conv2 I0419 01:16:55.593016 25240 net.cpp:122] Setting up conv2 I0419 01:16:55.593016 25240 net.cpp:129] Top shape: 1 256 76 126 (2451456) I0419 01:16:55.593016 25240 net.cpp:137] Memory required for data: 218759836 I0419 01:16:55.593016 25240 layer_factory.cpp:58] Creating layer relu2 I0419 01:16:55.593016 25240 net.cpp:84] Creating Layer relu2 I0419 01:16:55.593016 25240 net.cpp:406] relu2 <- conv2 I0419 01:16:55.593016 25240 net.cpp:367] relu2 -> conv2 (in-place) I0419 01:16:55.593016 25240 net.cpp:122] Setting up relu2 I0419 01:16:55.593016 25240 net.cpp:129] Top shape: 1 256 76 126 (2451456) I0419 01:16:55.593016 25240 net.cpp:137] Memory required for data: 228565660 I0419 01:16:55.593016 25240 layer_factory.cpp:58] Creating layer norm2 I0419 01:16:55.593016 25240 net.cpp:84] Creating Layer norm2 I0419 01:16:55.593016 25240 net.cpp:406] norm2 <- conv2 I0419 01:16:55.593016 25240 net.cpp:380] norm2 -> norm2 I0419 01:16:55.593016 25240 net.cpp:122] Setting up norm2 I0419 01:16:55.593016 25240 net.cpp:129] Top shape: 1 256 76 126 (2451456) I0419 01:16:55.593016 25240 net.cpp:137] Memory required for data: 238371484 I0419 01:16:55.593016 25240 layer_factory.cpp:58] Creating layer pool2 I0419 01:16:55.593016 25240 net.cpp:84] Creating Layer pool2 I0419 01:16:55.593016 25240 net.cpp:406] pool2 <- norm2 I0419 01:16:55.593016 25240 net.cpp:380] pool2 -> pool2 I0419 01:16:55.593016 25240 net.cpp:122] Setting up pool2 I0419 01:16:55.593016 25240 net.cpp:129] Top shape: 1 256 39 64 (638976) I0419 01:16:55.593016 25240 net.cpp:137] Memory required for data: 240927388 I0419 01:16:55.593016 25240 layer_factory.cpp:58] Creating layer conv3 I0419 01:16:55.593016 25240 net.cpp:84] Creating Layer conv3 I0419 01:16:55.593016 25240 net.cpp:406] conv3 <- pool2 I0419 01:16:55.593016 25240 net.cpp:380] conv3 -> conv3 I0419 01:16:55.593016 25240 net.cpp:122] Setting up conv3 I0419 01:16:55.593016 25240 net.cpp:129] Top shape: 1 384 39 64 (958464) I0419 01:16:55.593016 25240 net.cpp:137] Memory required for data: 244761244 I0419 01:16:55.593016 25240 layer_factory.cpp:58] Creating layer relu3 I0419 01:16:55.593016 25240 net.cpp:84] Creating Layer relu3 I0419 01:16:55.593016 25240 net.cpp:406] relu3 <- conv3 I0419 01:16:55.593016 25240 net.cpp:367] relu3 -> conv3 (in-place) I0419 01:16:55.593016 25240 net.cpp:122] Setting up relu3 I0419 01:16:55.593016 25240 net.cpp:129] Top shape: 1 384 39 64 (958464) I0419 01:16:55.593016 25240 net.cpp:137] Memory required for data: 248595100 I0419 01:16:55.593016 25240 layer_factory.cpp:58] Creating layer conv4 I0419 01:16:55.593016 25240 net.cpp:84] Creating Layer conv4 I0419 01:16:55.593016 25240 net.cpp:406] conv4 <- conv3 I0419 01:16:55.593016 25240 net.cpp:380] conv4 -> conv4 I0419 01:16:55.593016 25240 net.cpp:122] Setting up conv4 I0419 01:16:55.593016 25240 net.cpp:129] Top shape: 1 384 39 64 (958464) I0419 01:16:55.593016 25240 net.cpp:137] Memory required for data: 252428956 I0419 01:16:55.593016 25240 layer_factory.cpp:58] Creating layer relu4 I0419 01:16:55.593016 25240 net.cpp:84] Creating Layer relu4 I0419 01:16:55.593016 25240 net.cpp:406] relu4 <- conv4 I0419 01:16:55.593016 25240 net.cpp:367] relu4 -> conv4 (in-place) I0419 01:16:55.593016 25240 net.cpp:122] Setting up relu4 I0419 01:16:55.593016 25240 net.cpp:129] Top shape: 1 384 39 64 (958464) I0419 01:16:55.593016 25240 net.cpp:137] Memory required for data: 256262812 I0419 01:16:55.593016 25240 layer_factory.cpp:58] Creating layer conv5 I0419 01:16:55.593016 25240 net.cpp:84] Creating Layer conv5 I0419 01:16:55.593016 25240 net.cpp:406] conv5 <- conv4 I0419 01:16:55.593016 25240 net.cpp:380] conv5 -> conv5 I0419 01:16:55.608644 25240 net.cpp:122] Setting up conv5 I0419 01:16:55.608644 25240 net.cpp:129] Top shape: 1 256 39 64 (638976) I0419 01:16:55.608644 25240 net.cpp:137] Memory required for data: 258818716 I0419 01:16:55.608644 25240 layer_factory.cpp:58] Creating layer relu5 I0419 01:16:55.608644 25240 net.cpp:84] Creating Layer relu5 I0419 01:16:55.608644 25240 net.cpp:406] relu5 <- conv5 I0419 01:16:55.608644 25240 net.cpp:367] relu5 -> conv5 (in-place) I0419 01:16:55.608644 25240 net.cpp:122] Setting up relu5 I0419 01:16:55.608644 25240 net.cpp:129] Top shape: 1 256 39 64 (638976) I0419 01:16:55.608644 25240 net.cpp:137] Memory required for data: 261374620 I0419 01:16:55.608644 25240 layer_factory.cpp:58] Creating layer rpn_conv1 I0419 01:16:55.608644 25240 net.cpp:84] Creating Layer rpn_conv1 I0419 01:16:55.608644 25240 net.cpp:406] rpn_conv1 <- conv5 I0419 01:16:55.608644 25240 net.cpp:380] rpn_conv1 -> rpn_conv1 I0419 01:16:55.624267 25240 net.cpp:122] Setting up rpn_conv1 I0419 01:16:55.624267 25240 net.cpp:129] Top shape: 1 256 39 64 (638976) I0419 01:16:55.624267 25240 net.cpp:137] Memory required for data: 263930524 I0419 01:16:55.624267 25240 layer_factory.cpp:58] Creating layer rpn_relu1 I0419 01:16:55.624267 25240 net.cpp:84] Creating Layer rpn_relu1 I0419 01:16:55.624267 25240 net.cpp:406] rpn_relu1 <- rpn_conv1 I0419 01:16:55.624267 25240 net.cpp:367] rpn_relu1 -> rpn_conv1 (in-place) I0419 01:16:55.624267 25240 net.cpp:122] Setting up rpn_relu1 I0419 01:16:55.624267 25240 net.cpp:129] Top shape: 1 256 39 64 (638976) I0419 01:16:55.624267 25240 net.cpp:137] Memory required for data: 266486428 I0419 01:16:55.624267 25240 layer_factory.cpp:58] Creating layer rpn_conv1_rpn_relu1_0_split I0419 01:16:55.624267 25240 net.cpp:84] Creating Layer rpn_conv1_rpn_relu1_0_split I0419 01:16:55.624267 25240 net.cpp:406] rpn_conv1_rpn_relu1_0_split <- rpn_conv1 I0419 01:16:55.624267 25240 net.cpp:380] rpn_conv1_rpn_relu1_0_split -> rpn_conv1_rpn_relu1_0_split_0 I0419 01:16:55.624267 25240 net.cpp:380] rpn_conv1_rpn_relu1_0_split -> rpn_conv1_rpn_relu1_0_split_1 I0419 01:16:55.624267 25240 net.cpp:122] Setting up rpn_conv1_rpn_relu1_0_split I0419 01:16:55.624267 25240 net.cpp:129] Top shape: 1 256 39 64 (638976) I0419 01:16:55.624267 25240 net.cpp:129] Top shape: 1 256 39 64 (638976) I0419 01:16:55.624267 25240 net.cpp:137] Memory required for data: 271598236 I0419 01:16:55.624267 25240 layer_factory.cpp:58] Creating layer rpn_cls_score I0419 01:16:55.624267 25240 net.cpp:84] Creating Layer rpn_cls_score I0419 01:16:55.624267 25240 net.cpp:406] rpn_cls_score <- rpn_conv1_rpn_relu1_0_split_0 I0419 01:16:55.624267 25240 net.cpp:380] rpn_cls_score -> rpn_cls_score I0419 01:16:55.624267 25240 net.cpp:122] Setting up rpn_cls_score I0419 01:16:55.624267 25240 net.cpp:129] Top shape: 1 18 39 64 (44928) I0419 01:16:55.624267 25240 net.cpp:137] Memory required for data: 271777948 I0419 01:16:55.624267 25240 layer_factory.cpp:58] Creating layer rpn_cls_score_rpn_cls_score_0_split I0419 01:16:55.624267 25240 net.cpp:84] Creating Layer rpn_cls_score_rpn_cls_score_0_split I0419 01:16:55.624267 25240 net.cpp:406] rpn_cls_score_rpn_cls_score_0_split <- rpn_cls_score I0419 01:16:55.624267 25240 net.cpp:380] rpn_cls_score_rpn_cls_score_0_split -> rpn_cls_score_rpn_cls_score_0_split_0 I0419 01:16:55.624267 25240 net.cpp:380] rpn_cls_score_rpn_cls_score_0_split -> rpn_cls_score_rpn_cls_score_0_split_1 I0419 01:16:55.624267 25240 net.cpp:122] Setting up rpn_cls_score_rpn_cls_score_0_split I0419 01:16:55.624267 25240 net.cpp:129] Top shape: 1 18 39 64 (44928) I0419 01:16:55.624267 25240 net.cpp:129] Top shape: 1 18 39 64 (44928) I0419 01:16:55.624267 25240 net.cpp:137] Memory required for data: 272137372 I0419 01:16:55.624267 25240 layer_factory.cpp:58] Creating layer rpn_bbox_pred I0419 01:16:55.624267 25240 net.cpp:84] Creating Layer rpn_bbox_pred I0419 01:16:55.624267 25240 net.cpp:406] rpn_bbox_pred <- rpn_conv1_rpn_relu1_0_split_1 I0419 01:16:55.624267 25240 net.cpp:380] rpn_bbox_pred -> rpn_bbox_pred I0419 01:16:55.624267 25240 net.cpp:122] Setting up rpn_bbox_pred I0419 01:16:55.624267 25240 net.cpp:129] Top shape: 1 36 39 64 (89856) I0419 01:16:55.624267 25240 net.cpp:137] Memory required for data: 272496796 I0419 01:16:55.624267 25240 layer_factory.cpp:58] Creating layer rpn_cls_score_reshape I0419 01:16:55.624267 25240 net.cpp:84] Creating Layer rpn_cls_score_reshape I0419 01:16:55.624267 25240 net.cpp:406] rpn_cls_score_reshape <- rpn_cls_score_rpn_cls_score_0_split_0 I0419 01:16:55.624267 25240 net.cpp:380] rpn_cls_score_reshape -> rpn_cls_score_reshape I0419 01:16:55.624267 25240 net.cpp:122] Setting up rpn_cls_score_reshape I0419 01:16:55.624267 25240 net.cpp:129] Top shape: 1 2 351 64 (44928) I0419 01:16:55.624267 25240 net.cpp:137] Memory required for data: 272676508 I0419 01:16:55.624267 25240 layer_factory.cpp:58] Creating layer rpn-data I0419 01:16:55.639891 25240 net.cpp:84] Creating Layer rpn-data I0419 01:16:55.639891 25240 net.cpp:406] rpn-data <- rpn_cls_score_rpn_cls_score_0_split_1 I0419 01:16:55.639891 25240 net.cpp:406] rpn-data <- gt_boxes I0419 01:16:55.639891 25240 net.cpp:406] rpn-data <- im_info I0419 01:16:55.639891 25240 net.cpp:406] rpn-data <- data_input-data_0_split_1 I0419 01:16:55.639891 25240 net.cpp:380] rpn-data -> rpn_labels I0419 01:16:55.639891 25240 net.cpp:380] rpn-data -> rpn_bbox_targets I0419 01:16:55.639891 25240 net.cpp:380] rpn-data -> rpn_bbox_inside_weights I0419 01:16:55.639891 25240 net.cpp:380] rpn-data -> rpn_bbox_outside_weights I0419 01:16:55.639891 25240 net.cpp:122] Setting up rpn-data I0419 01:16:55.639891 25240 net.cpp:129] Top shape: 1 1 351 64 (22464) I0419 01:16:55.639891 25240 net.cpp:129] Top shape: 1 36 39 64 (89856) I0419 01:16:55.639891 25240 net.cpp:129] Top shape: 1 36 39 64 (89856) I0419 01:16:55.639891 25240 net.cpp:129] Top shape: 1 36 39 64 (89856) I0419 01:16:55.639891 25240 net.cpp:137] Memory required for data: 273844636 I0419 01:16:55.639891 25240 layer_factory.cpp:58] Creating layer rpn_loss_cls I0419 01:16:55.639891 25240 net.cpp:84] Creating Layer rpn_loss_cls I0419 01:16:55.639891 25240 net.cpp:406] rpn_loss_cls <- rpn_cls_score_reshape I0419 01:16:55.639891 25240 net.cpp:406] rpn_loss_cls <- rpn_labels I0419 01:16:55.639891 25240 net.cpp:380] rpn_loss_cls -> rpn_cls_loss I0419 01:16:55.639891 25240 layer_factory.cpp:58] Creating layer rpn_loss_cls I0419 01:16:55.639891 25240 net.cpp:122] Setting up rpn_loss_cls I0419 01:16:55.639891 25240 net.cpp:129] Top shape: (1) I0419 01:16:55.639891 25240 net.cpp:132] with loss weight 1 I0419 01:16:55.639891 25240 net.cpp:137] Memory required for data: 273844640 I0419 01:16:55.639891 25240 layer_factory.cpp:58] Creating layer rpn_loss_bbox I0419 01:16:55.639891 25240 net.cpp:84] Creating Layer rpn_loss_bbox I0419 01:16:55.639891 25240 net.cpp:406] rpn_loss_bbox <- rpn_bbox_pred I0419 01:16:55.639891 25240 net.cpp:406] rpn_loss_bbox <- rpn_bbox_targets I0419 01:16:55.639891 2*** Check failure stack trace: ***

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