caffe例程-RCNN-detectio.ipynb
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下文简述caffe example下的detectio.ipynb的流程和几个错误及其解决办法：
1.在命令行中打开ipython notebook，而不要使用PyChram。在编译这个例程时，后者会有莫名其妙的错误：
Traceback (most recent call last): File "../python/detect.py", line 173, in <module> main(sys.argv) File "../python/detect.py", line 144, in main detections = detector.detect_selective_search(inputs) File "/home/ljj/deep_learning/caffe-master/python/caffe/detector.py", line 120, in detect_selective_search cmd='selective_search_rcnn' File "/home/ljj/deep_learning/caffe-master/python/selective_search_ijcv_with_python/selective_search.py", line 36, in get_windows shlex.split(mc), stdout=open('/dev/null', 'w'), cwd=script_dirname) File "/usr/lib/python2.7/subprocess.py", line 711, in __init__ errread, errwrite) File "/usr/lib/python2.7/subprocess.py", line 1343, in _execute_child raise child_exceptionOSError: [Errno 2] No such file or directory2.将https://github.com/sergeyk/selective_search_ijcv_with_python下载并解压到/home/ljj/deep_learning/caffe-master/python，并重命名为selective_search_ijcv_with_python。
3.打开matlab，运行其中的demo.m
4.为matlab添加PATH，使得可以在terminal中输入matlab，就能打开matlab，具体方法见我的另一篇博客。
5.使用python2.7-anaconda
6.在完成上述以后，在ipython botebook中运行时，应当还是会报出几个错误，此时根据错误信息修改/home/ljj/deep_learning/caffe-master/python/caffe/detector.py如下（其实就是加了几个int()，使得slice中float转换为int）
#!/usr/bin/env python"""Do windowed detection by classifying a number of images/crops at once,optionally using the selective search window proposal method.This implementation follows ideas in Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. http://arxiv.org/abs/1311.2524The selective_search_ijcv_with_python code required for the selective searchproposal mode is available at https://github.com/sergeyk/selective_search_ijcv_with_python"""import numpy as npimport osimport caffeclass Detector(caffe.Net): """ Detector extends Net for windowed detection by a list of crops or selective search proposals. Parameters ---------- mean, input_scale, raw_scale, channel_swap : params for preprocessing options. context_pad : amount of surrounding context to take s.t. a `context_pad` sized border of pixels in the network input image is context, as in R-CNN feature extraction. """ def __init__(self, model_file, pretrained_file, mean=None, input_scale=None, raw_scale=None, channel_swap=None, context_pad=None): caffe.Net.__init__(self, model_file, pretrained_file, caffe.TEST) # configure pre-processing in_ = self.inputs[0] self.transformer = caffe.io.Transformer( {in_: self.blobs[in_].data.shape}) self.transformer.set_transpose(in_, (2, 0, 1)) if mean is not None: self.transformer.set_mean(in_, mean) if input_scale is not None: self.transformer.set_input_scale(in_, input_scale) if raw_scale is not None: self.transformer.set_raw_scale(in_, raw_scale) if channel_swap is not None: self.transformer.set_channel_swap(in_, channel_swap) self.configure_crop(context_pad) def detect_windows(self, images_windows): """ Do windowed detection over given images and windows. Windows are extracted then warped to the input dimensions of the net. Parameters ---------- images_windows: (image filename, window list) iterable. context_crop: size of context border to crop in pixels. Returns ------- detections: list of {filename: image filename, window: crop coordinates, predictions: prediction vector} dicts. """ # Extract windows. window_inputs = [] for image_fname, windows in images_windows: image = caffe.io.load_image(image_fname).astype(np.float32) for window in windows: window_inputs.append(self.crop(image, window)) # Run through the net (warping windows to input dimensions). in_ = self.inputs[0] caffe_in = np.zeros((len(window_inputs), window_inputs[0].shape[2]) + self.blobs[in_].data.shape[2:], dtype=np.float32) for ix, window_in in enumerate(window_inputs): caffe_in[ix] = self.transformer.preprocess(in_, window_in) out = self.forward_all(**{in_: caffe_in}) predictions = out[self.outputs[0]] #origin # Package predictions with images and windows. detections = [] ix = 0 for image_fname, windows in images_windows: for window in windows: detections.append({ 'window': window, 'prediction': predictions[ix], 'filename': image_fname }) ix += 1 return detections def detect_selective_search(self, image_fnames): """ Do windowed detection over Selective Search proposals by extracting the crop and warping to the input dimensions of the net. Parameters ---------- image_fnames: list Returns ------- detections: list of {filename: image filename, window: crop coordinates, predictions: prediction vector} dicts. """ import selective_search_ijcv_with_python as selective_search # Make absolute paths so MATLAB can find the files. image_fnames = [os.path.abspath(f) for f in image_fnames] windows_list = selective_search.get_windows( image_fnames, cmd='selective_search_rcnn' ) # Run windowed detection on the selective search list. return self.detect_windows(zip(image_fnames, windows_list)) def crop(self, im, window): """ Crop a window from the image for detection. Include surrounding context according to the `context_pad` configuration. Parameters ---------- im: H x W x K image ndarray to crop. window: bounding box coordinates as ymin, xmin, ymax, xmax. Returns ------- crop: cropped window. """ # Crop window from the image. crop = im[int(window[0]):int(window[2]), int(window[1]):int(window[3])] if self.context_pad: box = window.copy() crop_size = self.blobs[self.inputs[0]].width # assumes square scale = crop_size / (1. * crop_size - self.context_pad * 2) # Crop a box + surrounding context. half_h = (box[2] - box[0] + 1) / 2. half_w = (box[3] - box[1] + 1) / 2. center = (box[0] + half_h, box[1] + half_w) scaled_dims = scale * np.array((-half_h, -half_w, half_h, half_w)) box = np.round(np.tile(center, 2) + scaled_dims) full_h = box[2] - box[0] + 1 full_w = box[3] - box[1] + 1 scale_h = crop_size / full_h scale_w = crop_size / full_w pad_y = round(max(0, -box[0]) * scale_h) # amount out-of-bounds pad_x = round(max(0, -box[1]) * scale_w) # Clip box to image dimensions. im_h, im_w = im.shape[:2] box = np.clip(box, 0., [im_h, im_w, im_h, im_w]) clip_h = box[2] - box[0] + 1 clip_w = box[3] - box[1] + 1 assert(clip_h > 0 and clip_w > 0) crop_h = round(clip_h * scale_h) crop_w = round(clip_w * scale_w) if pad_y + crop_h > crop_size: crop_h = crop_size - pad_y if pad_x + crop_w > crop_size: crop_w = crop_size - pad_x # collect with context padding and place in input # with mean padding context_crop = im[int(box[0]):int(box[2]), int(box[1]):int(box[3])] context_crop = caffe.io.resize_image(context_crop, (crop_h, crop_w)) crop = np.ones(self.crop_dims, dtype=np.float32) * self.crop_mean crop[int(pad_y):int(pad_y + crop_h), int(pad_x):int(pad_x + crop_w)] = context_crop return crop def configure_crop(self, context_pad): """ Configure crop dimensions and amount of context for cropping. If context is included, make the special input mean for context padding. Parameters ---------- context_pad : amount of context for cropping. """ # crop dimensions in_ = self.inputs[0] tpose = self.transformer.transpose[in_] inv_tpose = [tpose[t] for t in tpose] self.crop_dims = np.array(self.blobs[in_].data.shape[1:])[inv_tpose] #.transpose(inv_tpose) # context padding self.context_pad = context_pad if self.context_pad: in_ = self.inputs[0] transpose = self.transformer.transpose.get(in_) channel_order = self.transformer.channel_swap.get(in_) raw_scale = self.transformer.raw_scale.get(in_) # Padding context crops needs the mean in unprocessed input space. mean = self.transformer.mean.get(in_) if mean is not None: inv_transpose = [transpose[t] for t in transpose] crop_mean = mean.copy().transpose(inv_transpose) if channel_order is not None: channel_order_inverse = [channel_order.index(i) for i in range(crop_mean.shape[2])] crop_mean = crop_mean[:, :, channel_order_inverse] if raw_scale is not None: crop_mean /= raw_scale self.crop_mean = crop_mean else: self.crop_mean = np.zeros(self.crop_dims, dtype=np.float32)7.此时，再运行detection.ipynb，应当一切正常，信息如下：GPU modeWARNING: Logging before InitGoogleLogging() is written to STDERRW0710 15:26:55.556707 9306 _caffe.cpp:135] DEPRECATION WARNING - deprecated use of Python interfaceW0710 15:26:55.556727 9306 _caffe.cpp:136] Use this instead (with the named "weights" parameter):W0710 15:26:55.556730 9306 _caffe.cpp:138] Net('../models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt', 1, weights='../models/bvlc_reference_rcnn_ilsvrc13/bvlc_reference_rcnn_ilsvrc13.caffemodel')I0710 15:26:55.557760 9306 net.cpp:51] Initializing net from parameters: name: "R-CNN-ilsvrc13"state { phase: TEST level: 0}layer { name: "data" type: "Input" top: "data" input_param { shape { dim: 10 dim: 3 dim: 227 dim: 227 } }}layer { name: "conv1" type: "Convolution" bottom: "data" top: "conv1" convolution_param { num_output: 96 kernel_size: 11 stride: 4 }}layer { name: "relu1" type: "ReLU" bottom: "conv1" top: "conv1"}layer { name: "pool1" type: "Pooling" bottom: "conv1" top: "pool1" pooling_param { pool: MAX kernel_size: 3 stride: 2 }}layer { name: "norm1" type: "LRN" bottom: "pool1" top: "norm1" lrn_param { local_size: 5 alpha: 0.0001 beta: 0.75 }}layer { name: "conv2" type: "Convolution" bottom: "norm1" top: "conv2" convolution_param { num_output: 256 pad: 2 kernel_size: 5 group: 2 }}layer { name: "relu2" type: "ReLU" bottom: "conv2" top: "conv2"}layer { name: "pool2" type: "Pooling" bottom: "conv2" top: "pool2" pooling_param { pool: MAX kernel_size: 3 stride: 2 }}layer { name: "norm2" type: "LRN" bottom: "pool2" top: "norm2" lrn_param { local_size: 5 alpha: 0.0001 beta: 0.75 }}layer { name: "conv3" type: "Convolution" bottom: "norm2" top: "conv3" convolution_param { num_output: 384 pad: 1 kernel_size: 3 }}layer { name: "relu3" type: "ReLU" bottom: "conv3" top: "conv3"}layer { name: "conv4" type: "Convolution" bottom: "conv3" top: "conv4" convolution_param { num_output: 384 pad: 1 kernel_size: 3 group: 2 }}layer { name: "relu4" type: "ReLU" bottom: "conv4" top: "conv4"}layer { name: "conv5" type: "Convolution" bottom: "conv4" top: "conv5" convolution_param { num_output: 256 pad: 1 kernel_size: 3 group: 2 }}layer { name: "relu5" type: "ReLU" bottom: "conv5" top: "conv5"}layer { name: "pool5" type: "Pooling" bottom: "conv5" top: "pool5" pooling_param { pool: MAX kernel_size: 3 stride: 2 }}layer { name: "fc6" type: "InnerProduct" bottom: "pool5" top: "fc6" inner_product_param { num_output: 4096 }}layer { name: "relu6" type: "ReLU" bottom: "fc6" top: "fc6"}layer { name: "drop6" type: "Dropout" bottom: "fc6" top: "fc6" dropout_param { dropout_ratio: 0.5 }}layer { name: "fc7" type: "InnerProduct" bottom: "fc6" top: "fc7" inner_product_param { num_output: 4096 }}layer { name: "relu7" type: "ReLU" bottom: "fc7" top: "fc7"}layer { name: "drop7" type: "Dropout" bottom: "fc7" top: "fc7" dropout_param { dropout_ratio: 0.5 }}layer { name: "fc-rcnn" type: "InnerProduct" bottom: "fc7" top: "fc-rcnn" inner_product_param { num_output: 200 }}I0710 15:26:55.557831 9306 layer_factory.hpp:77] Creating layer dataI0710 15:26:55.557837 9306 net.cpp:84] Creating Layer dataI0710 15:26:55.557840 9306 net.cpp:380] data -> dataI0710 15:26:55.564779 9306 net.cpp:122] Setting up dataI0710 15:26:55.564796 9306 net.cpp:129] Top shape: 10 3 227 227 (1545870)I0710 15:26:55.564800 9306 net.cpp:137] Memory required for data: 6183480I0710 15:26:55.564805 9306 layer_factory.hpp:77] Creating layer conv1I0710 15:26:55.564812 9306 net.cpp:84] Creating Layer conv1I0710 15:26:55.564815 9306 net.cpp:406] conv1 <- dataI0710 15:26:55.564820 9306 net.cpp:380] conv1 -> conv1I0710 15:26:55.565482 9306 net.cpp:122] Setting up conv1I0710 15:26:55.565491 9306 net.cpp:129] Top shape: 10 96 55 55 (2904000)I0710 15:26:55.565493 9306 net.cpp:137] Memory required for data: 17799480I0710 15:26:55.565501 9306 layer_factory.hpp:77] Creating layer relu1I0710 15:26:55.565506 9306 net.cpp:84] Creating Layer relu1I0710 15:26:55.565508 9306 net.cpp:406] relu1 <- conv1I0710 15:26:55.565512 9306 net.cpp:367] relu1 -> conv1 (in-place)I0710 15:26:55.565517 9306 net.cpp:122] Setting up relu1I0710 15:26:55.565521 9306 net.cpp:129] Top shape: 10 96 55 55 (2904000)I0710 15:26:55.565523 9306 net.cpp:137] Memory required for data: 29415480I0710 15:26:55.565526 9306 layer_factory.hpp:77] Creating layer pool1I0710 15:26:55.565531 9306 net.cpp:84] Creating Layer pool1I0710 15:26:55.565533 9306 net.cpp:406] pool1 <- conv1I0710 15:26:55.565536 9306 net.cpp:380] pool1 -> pool1I0710 15:26:55.565560 9306 net.cpp:122] Setting up pool1I0710 15:26:55.565565 9306 net.cpp:129] Top shape: 10 96 27 27 (699840)I0710 15:26:55.565567 9306 net.cpp:137] Memory required for data: 32214840I0710 15:26:55.565568 9306 layer_factory.hpp:77] Creating layer norm1I0710 15:26:55.565574 9306 net.cpp:84] Creating Layer norm1I0710 15:26:55.565577 9306 net.cpp:406] norm1 <- pool1I0710 15:26:55.565579 9306 net.cpp:380] norm1 -> norm1I0710 15:26:55.565599 9306 net.cpp:122] Setting up norm1I0710 15:26:55.565609 9306 net.cpp:129] Top shape: 10 96 27 27 (699840)I0710 15:26:55.565613 9306 net.cpp:137] Memory required for data: 35014200I0710 15:26:55.565614 9306 layer_factory.hpp:77] Creating layer conv2I0710 15:26:55.565619 9306 net.cpp:84] Creating Layer conv2I0710 15:26:55.565621 9306 net.cpp:406] conv2 <- norm1I0710 15:26:55.565624 9306 net.cpp:380] conv2 -> conv2I0710 15:26:55.566407 9306 net.cpp:122] Setting up conv2I0710 15:26:55.566416 9306 net.cpp:129] Top shape: 10 256 27 27 (1866240)I0710 15:26:55.566417 9306 net.cpp:137] Memory required for data: 42479160I0710 15:26:55.566423 9306 layer_factory.hpp:77] Creating layer relu2I0710 15:26:55.566428 9306 net.cpp:84] Creating Layer relu2I0710 15:26:55.566431 9306 net.cpp:406] relu2 <- conv2I0710 15:26:55.566433 9306 net.cpp:367] relu2 -> conv2 (in-place)I0710 15:26:55.566437 9306 net.cpp:122] Setting up relu2I0710 15:26:55.566440 9306 net.cpp:129] Top shape: 10 256 27 27 (1866240)I0710 15:26:55.566442 9306 net.cpp:137] Memory required for data: 49944120I0710 15:26:55.566445 9306 layer_factory.hpp:77] Creating layer pool2I0710 15:26:55.566449 9306 net.cpp:84] Creating Layer pool2I0710 15:26:55.566452 9306 net.cpp:406] pool2 <- conv2I0710 15:26:55.566455 9306 net.cpp:380] pool2 -> pool2I0710 15:26:55.566478 9306 net.cpp:122] Setting up pool2I0710 15:26:55.566483 9306 net.cpp:129] Top shape: 10 256 13 13 (432640)I0710 15:26:55.566485 9306 net.cpp:137] Memory required for data: 51674680I0710 15:26:55.566488 9306 layer_factory.hpp:77] Creating layer norm2I0710 15:26:55.566493 9306 net.cpp:84] Creating Layer norm2I0710 15:26:55.566496 9306 net.cpp:406] norm2 <- pool2I0710 15:26:55.566500 9306 net.cpp:380] norm2 -> norm2I0710 15:26:55.566519 9306 net.cpp:122] Setting up norm2I0710 15:26:55.566524 9306 net.cpp:129] Top shape: 10 256 13 13 (432640)I0710 15:26:55.566526 9306 net.cpp:137] Memory required for data: 53405240I0710 15:26:55.566529 9306 layer_factory.hpp:77] Creating layer conv3I0710 15:26:55.566535 9306 net.cpp:84] Creating Layer conv3I0710 15:26:55.566537 9306 net.cpp:406] conv3 <- norm2I0710 15:26:55.566542 9306 net.cpp:380] conv3 -> conv3I0710 15:26:55.567703 9306 net.cpp:122] Setting up conv3I0710 15:26:55.567713 9306 net.cpp:129] Top shape: 10 384 13 13 (648960)I0710 15:26:55.567716 9306 net.cpp:137] Memory required for data: 56001080I0710 15:26:55.567724 9306 layer_factory.hpp:77] Creating layer relu3I0710 15:26:55.567728 9306 net.cpp:84] Creating Layer relu3I0710 15:26:55.567731 9306 net.cpp:406] relu3 <- conv3I0710 15:26:55.567736 9306 net.cpp:367] relu3 -> conv3 (in-place)I0710 15:26:55.567741 9306 net.cpp:122] Setting up relu3I0710 15:26:55.567744 9306 net.cpp:129] Top shape: 10 384 13 13 (648960)I0710 15:26:55.567746 9306 net.cpp:137] Memory required for data: 58596920I0710 15:26:55.567749 9306 layer_factory.hpp:77] Creating layer conv4I0710 15:26:55.567754 9306 net.cpp:84] Creating Layer conv4I0710 15:26:55.567757 9306 net.cpp:406] conv4 <- conv3I0710 15:26:55.567761 9306 net.cpp:380] conv4 -> conv4I0710 15:26:55.568743 9306 net.cpp:122] Setting up conv4I0710 15:26:55.568755 9306 net.cpp:129] Top shape: 10 384 13 13 (648960)I0710 15:26:55.568758 9306 net.cpp:137] Memory required for data: 61192760I0710 15:26:55.568763 9306 layer_factory.hpp:77] Creating layer relu4I0710 15:26:55.568768 9306 net.cpp:84] Creating Layer relu4I0710 15:26:55.568770 9306 net.cpp:406] relu4 <- conv4I0710 15:26:55.568775 9306 net.cpp:367] relu4 -> conv4 (in-place)I0710 15:26:55.568780 9306 net.cpp:122] Setting up relu4I0710 15:26:55.568783 9306 net.cpp:129] Top shape: 10 384 13 13 (648960)I0710 15:26:55.568785 9306 net.cpp:137] Memory required for data: 63788600I0710 15:26:55.568787 9306 layer_factory.hpp:77] Creating layer conv5I0710 15:26:55.568792 9306 net.cpp:84] Creating Layer conv5I0710 15:26:55.568795 9306 net.cpp:406] conv5 <- conv4I0710 15:26:55.568799 9306 net.cpp:380] conv5 -> conv5I0710 15:26:55.569517 9306 net.cpp:122] Setting up conv5I0710 15:26:55.569525 9306 net.cpp:129] Top shape: 10 256 13 13 (432640)I0710 15:26:55.569528 9306 net.cpp:137] Memory required for data: 65519160I0710 15:26:55.569535 9306 layer_factory.hpp:77] Creating layer relu5I0710 15:26:55.569540 9306 net.cpp:84] Creating Layer relu5I0710 15:26:55.569543 9306 net.cpp:406] relu5 <- conv5I0710 15:26:55.569547 9306 net.cpp:367] relu5 -> conv5 (in-place)I0710 15:26:55.569552 9306 net.cpp:122] Setting up relu5I0710 15:26:55.569556 9306 net.cpp:129] Top shape: 10 256 13 13 (432640)I0710 15:26:55.569558 9306 net.cpp:137] Memory required for data: 67249720I0710 15:26:55.569561 9306 layer_factory.hpp:77] Creating layer pool5I0710 15:26:55.569566 9306 net.cpp:84] Creating Layer pool5I0710 15:26:55.569568 9306 net.cpp:406] pool5 <- conv5I0710 15:26:55.569572 9306 net.cpp:380] pool5 -> pool5I0710 15:26:55.569597 9306 net.cpp:122] Setting up pool5I0710 15:26:55.569602 9306 net.cpp:129] Top shape: 10 256 6 6 (92160)I0710 15:26:55.569604 9306 net.cpp:137] Memory required for data: 67618360I0710 15:26:55.569607 9306 layer_factory.hpp:77] Creating layer fc6I0710 15:26:55.569614 9306 net.cpp:84] Creating Layer fc6I0710 15:26:55.569617 9306 net.cpp:406] fc6 <- pool5I0710 15:26:55.569622 9306 net.cpp:380] fc6 -> fc6I0710 15:26:55.617084 9306 net.cpp:122] Setting up fc6I0710 15:26:55.617112 9306 net.cpp:129] Top shape: 10 4096 (40960)I0710 15:26:55.617117 9306 net.cpp:137] Memory required for data: 67782200I0710 15:26:55.617127 9306 layer_factory.hpp:77] Creating layer relu6I0710 15:26:55.617137 9306 net.cpp:84] Creating Layer relu6I0710 15:26:55.617143 9306 net.cpp:406] relu6 <- fc6I0710 15:26:55.617151 9306 net.cpp:367] relu6 -> fc6 (in-place)I0710 15:26:55.617159 9306 net.cpp:122] Setting up relu6I0710 15:26:55.617164 9306 net.cpp:129] Top shape: 10 4096 (40960)I0710 15:26:55.617167 9306 net.cpp:137] Memory required for data: 67946040I0710 15:26:55.617172 9306 layer_factory.hpp:77] Creating layer drop6I0710 15:26:55.617178 9306 net.cpp:84] Creating Layer drop6I0710 15:26:55.617182 9306 net.cpp:406] drop6 <- fc6I0710 15:26:55.617187 9306 net.cpp:367] drop6 -> fc6 (in-place)I0710 15:26:55.617215 9306 net.cpp:122] Setting up drop6I0710 15:26:55.617221 9306 net.cpp:129] Top shape: 10 4096 (40960)I0710 15:26:55.617225 9306 net.cpp:137] Memory required for data: 68109880I0710 15:26:55.617229 9306 layer_factory.hpp:77] Creating layer fc7I0710 15:26:55.617235 9306 net.cpp:84] Creating Layer fc7I0710 15:26:55.617239 9306 net.cpp:406] fc7 <- fc6I0710 15:26:55.617244 9306 net.cpp:380] fc7 -> fc7I0710 15:26:55.645105 9306 net.cpp:122] Setting up fc7I0710 15:26:55.645131 9306 net.cpp:129] Top shape: 10 4096 (40960)I0710 15:26:55.645136 9306 net.cpp:137] Memory required for data: 68273720I0710 15:26:55.645146 9306 layer_factory.hpp:77] Creating layer relu7I0710 15:26:55.645155 9306 net.cpp:84] Creating Layer relu7I0710 15:26:55.645159 9306 net.cpp:406] relu7 <- fc7I0710 15:26:55.645164 9306 net.cpp:367] relu7 -> fc7 (in-place)I0710 15:26:55.645170 9306 net.cpp:122] Setting up relu7I0710 15:26:55.645174 9306 net.cpp:129] Top shape: 10 4096 (40960)I0710 15:26:55.645176 9306 net.cpp:137] Memory required for data: 68437560I0710 15:26:55.645179 9306 layer_factory.hpp:77] Creating layer drop7I0710 15:26:55.645184 9306 net.cpp:84] Creating Layer drop7I0710 15:26:55.645186 9306 net.cpp:406] drop7 <- fc7I0710 15:26:55.645190 9306 net.cpp:367] drop7 -> fc7 (in-place)I0710 15:26:55.645210 9306 net.cpp:122] Setting up drop7I0710 15:26:55.645215 9306 net.cpp:129] Top shape: 10 4096 (40960)I0710 15:26:55.645217 9306 net.cpp:137] Memory required for data: 68601400I0710 15:26:55.645221 9306 layer_factory.hpp:77] Creating layer fc-rcnnI0710 15:26:55.645227 9306 net.cpp:84] Creating Layer fc-rcnnI0710 15:26:55.645231 9306 net.cpp:406] fc-rcnn <- fc7I0710 15:26:55.645236 9306 net.cpp:380] fc-rcnn -> fc-rcnnI0710 15:26:55.646569 9306 net.cpp:122] Setting up fc-rcnnI0710 15:26:55.646595 9306 net.cpp:129] Top shape: 10 200 (2000)I0710 15:26:55.646598 9306 net.cpp:137] Memory required for data: 68609400I0710 15:26:55.646606 9306 net.cpp:200] fc-rcnn does not need backward computation.I0710 15:26:55.646610 9306 net.cpp:200] drop7 does not need backward computation.I0710 15:26:55.646613 9306 net.cpp:200] relu7 does not need backward computation.I0710 15:26:55.646615 9306 net.cpp:200] fc7 does not need backward computation.I0710 15:26:55.646618 9306 net.cpp:200] drop6 does not need backward computation.I0710 15:26:55.646621 9306 net.cpp:200] relu6 does not need backward computation.I0710 15:26:55.646623 9306 net.cpp:200] fc6 does not need backward computation.I0710 15:26:55.646626 9306 net.cpp:200] pool5 does not need backward computation.I0710 15:26:55.646628 9306 net.cpp:200] relu5 does not need backward computation.I0710 15:26:55.646631 9306 net.cpp:200] conv5 does not need backward computation.I0710 15:26:55.646634 9306 net.cpp:200] relu4 does not need backward computation.I0710 15:26:55.646637 9306 net.cpp:200] conv4 does not need backward computation.I0710 15:26:55.646639 9306 net.cpp:200] relu3 does not need backward computation.I0710 15:26:55.646642 9306 net.cpp:200] conv3 does not need backward computation.I0710 15:26:55.646646 9306 net.cpp:200] norm2 does not need backward computation.I0710 15:26:55.646648 9306 net.cpp:200] pool2 does not need backward computation.I0710 15:26:55.646651 9306 net.cpp:200] relu2 does not need backward computation.I0710 15:26:55.646656 9306 net.cpp:200] conv2 does not need backward computation.I0710 15:26:55.646661 9306 net.cpp:200] norm1 does not need backward computation.I0710 15:26:55.646663 9306 net.cpp:200] pool1 does not need backward computation.I0710 15:26:55.646667 9306 net.cpp:200] relu1 does not need backward computation.I0710 15:26:55.646672 9306 net.cpp:200] conv1 does not need backward computation.I0710 15:26:55.646674 9306 net.cpp:200] data does not need backward computation.I0710 15:26:55.646677 9306 net.cpp:242] This network produces output fc-rcnnI0710 15:26:55.646690 9306 net.cpp:255] Network initialization done.I0710 15:26:55.735093 9306 upgrade_proto.cpp:53] Attempting to upgrade input file specified using deprecated V1LayerParameter: ../models/bvlc_reference_rcnn_ilsvrc13/bvlc_reference_rcnn_ilsvrc13.caffemodelI0710 15:26:55.855332 9306 upgrade_proto.cpp:61] Successfully upgraded file specified using deprecated V1LayerParameterI0710 15:26:55.856111 9306 upgrade_proto.cpp:67] Attempting to upgrade input file specified using deprecated input fields: ../models/bvlc_reference_rcnn_ilsvrc13/bvlc_reference_rcnn_ilsvrc13.caffemodelI0710 15:26:55.856118 9306 upgrade_proto.cpp:70] Successfully upgraded file specified using deprecated input fields.W0710 15:26:55.856122 9306 upgrade_proto.cpp:72] Note that future Caffe releases will only support input layers and not input fields.Loading input...selective_search_rcnn({'/home/ljj/deep_learning/caffe-master/examples/images/fish-bike.jpg'}, '/tmp/tmp9O84If.mat')Processed 1565 windows in 47.733 s./home/ljj/.local/lib/python2.7/site-packages/pandas/core/generic.py:1138: PerformanceWarning: your performance may suffer as PyTables will pickle object types that it cannotmap directly to c-types [inferred_type->mixed,key->block1_values] [items->['prediction']] return pytables.to_hdf(path_or_buf, key, self, **kwargs)Saved to _temp/det_output.h5 in 0.035 s.8.目前只做到了这里，解决上述问题，花了很多时间，故做记录参考：
http://blog.csdn.net/thystar/article/details/50727830
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