164 lines
23 KiB
Plaintext
164 lines
23 KiB
Plaintext
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d d g g e d d d d d d g d e d d d d g d e d d d g d d d d g d d! d" d# e d$ <00> Z d S(( sf
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A Keras port of the original Caffe SSD300 network.
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Copyright (C) 2018 Pierluigi Ferrari
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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i<EFBFBD><EFBFBD><EFBFBD><EFBFBD>( t divisionN( t Model( t Inputt Lambdat
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Build a Keras model with SSD300 architecture, see references.
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The base network is a reduced atrous VGG-16, extended by the SSD architecture,
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as described in the paper.
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Most of the arguments that this function takes are only needed for the anchor
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box layers. In case you're training the network, the parameters passed here must
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be the same as the ones used to set up `SSDBoxEncoder`. In case you're loading
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trained weights, the parameters passed here must be the same as the ones used
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to produce the trained weights.
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Some of these arguments are explained in more detail in the documentation of the
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`SSDBoxEncoder` class.
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Note: Requires Keras v2.0 or later. Currently works only with the
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TensorFlow backend (v1.0 or later).
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Arguments:
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image_size (tuple): The input image size in the format `(height, width, channels)`.
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n_classes (int): The number of positive classes, e.g. 20 for Pascal VOC, 80 for MS COCO.
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mode (str, optional): One of 'training', 'inference' and 'inference_fast'. In 'training' mode,
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the model outputs the raw prediction tensor, while in 'inference' and 'inference_fast' modes,
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the raw predictions are decoded into absolute coordinates and filtered via confidence thresholding,
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non-maximum suppression, and top-k filtering. The difference between latter two modes is that
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'inference' follows the exact procedure of the original Caffe implementation, while
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'inference_fast' uses a faster prediction decoding procedure.
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l2_regularization (float, optional): The L2-regularization rate. Applies to all convolutional layers.
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Set to zero to deactivate L2-regularization.
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min_scale (float, optional): The smallest scaling factor for the size of the anchor boxes as a fraction
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of the shorter side of the input images.
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max_scale (float, optional): The largest scaling factor for the size of the anchor boxes as a fraction
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of the shorter side of the input images. All scaling factors between the smallest and the
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largest will be linearly interpolated. Note that the second to last of the linearly interpolated
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scaling factors will actually be the scaling factor for the last predictor layer, while the last
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scaling factor is used for the second box for aspect ratio 1 in the last predictor layer
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if `two_boxes_for_ar1` is `True`.
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scales (list, optional): A list of floats containing scaling factors per convolutional predictor layer.
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This list must be one element longer than the number of predictor layers. The first `k` elements are the
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scaling factors for the `k` predictor layers, while the last element is used for the second box
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for aspect ratio 1 in the last predictor layer if `two_boxes_for_ar1` is `True`. This additional
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last scaling factor must be passed either way, even if it is not being used. If a list is passed,
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this argument overrides `min_scale` and `max_scale`. All scaling factors must be greater than zero.
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aspect_ratios_global (list, optional): The list of aspect ratios for which anchor boxes are to be
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generated. This list is valid for all prediction layers.
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aspect_ratios_per_layer (list, optional): A list containing one aspect ratio list for each prediction layer.
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This allows you to set the aspect ratios for each predictor layer individually, which is the case for the
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original SSD300 implementation. If a list is passed, it overrides `aspect_ratios_global`.
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two_boxes_for_ar1 (bool, optional): Only relevant for aspect ratio lists that contain 1. Will be ignored otherwise.
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If `True`, two anchor boxes will be generated for aspect ratio 1. The first will be generated
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using the scaling factor for the respective layer, the second one will be generated using
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geometric mean of said scaling factor and next bigger scaling factor.
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steps (list, optional): `None` or a list with as many elements as there are predictor layers. The elements can be
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either ints/floats or tuples of two ints/floats. These numbers represent for each predictor layer how many
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pixels apart the anchor box center points should be vertically and horizontally along the spatial grid over
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the image. If the list contains ints/floats, then that value will be used for both spatial dimensions.
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If the list contains tuples of two ints/floats, then they represent `(step_height, step_width)`.
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If no steps are provided, then they will be computed such that the anchor box center points will form an
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equidistant grid within the image dimensions.
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offsets (list, optional): `None` or a list with as many elements as there are predictor layers. The elements can be
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either floats or tuples of two floats. These numbers represent for each predictor layer how many
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pixels from the top and left boarders of the image the top-most and left-most anchor box center points should be
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as a fraction of `steps`. The last bit is important: The offsets are not absolute pixel values, but fractions
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of the step size specified in the `steps` argument. If the list contains floats, then that value will
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be used for both spatial dimensions. If the list contains tuples of two floats, then they represent
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`(vertical_offset, horizontal_offset)`. If no offsets are provided, then they will default to 0.5 of the step size.
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clip_boxes (bool, optional): If `True`, clips the anchor box coordinates to stay within image boundaries.
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variances (list, optional): A list of 4 floats >0. The anchor box offset for each coordinate will be divided by
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its respective variance value.
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coords (str, optional): The box coordinate format to be used internally by the model (i.e. this is not the input format
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of the ground truth labels). Can be either 'centroids' for the format `(cx, cy, w, h)` (box center coordinates, width,
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and height), 'minmax' for the format `(xmin, xmax, ymin, ymax)`, or 'corners' for the format `(xmin, ymin, xmax, ymax)`.
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normalize_coords (bool, optional): Set to `True` if the model is supposed to use relative instead of absolute coordinates,
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i.e. if the model predicts box coordinates within [0,1] instead of absolute coordinates.
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subtract_mean (array-like, optional): `None` or an array-like object of integers or floating point values
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of any shape that is broadcast-compatible with the image shape. The elements of this array will be
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subtracted from the image pixel intensity values. For example, pass a list of three integers
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to perform per-channel mean normalization for color images.
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divide_by_stddev (array-like, optional): `None` or an array-like object of non-zero integers or
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floating point values of any shape that is broadcast-compatible with the image shape. The image pixel
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intensity values will be divided by the elements of this array. For example, pass a list
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of three integers to perform per-channel standard deviation normalization for color images.
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swap_channels (list, optional): Either `False` or a list of integers representing the desired order in which the input
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image channels should be swapped.
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confidence_thresh (float, optional): A float in [0,1), the minimum classification confidence in a specific
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positive class in order to be considered for the non-maximum suppression stage for the respective class.
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A lower value will result in a larger part of the selection process being done by the non-maximum suppression
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stage, while a larger value will result in a larger part of the selection process happening in the confidence
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thresholding stage.
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iou_threshold (float, optional): A float in [0,1]. All boxes that have a Jaccard similarity of greater than `iou_threshold`
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with a locally maximal box will be removed from the set of predictions for a given class, where 'maximal' refers
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to the box's confidence score.
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top_k (int, optional): The number of highest scoring predictions to be kept for each batch item after the
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non-maximum suppression stage.
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nms_max_output_size (int, optional): The maximal number of predictions that will be left over after the NMS stage.
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return_predictor_sizes (bool, optional): If `True`, this function not only returns the model, but also
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a list containing the spatial dimensions of the predictor layers. This isn't strictly necessary since
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you can always get their sizes easily via the Keras API, but it's convenient and less error-prone
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to get them this way. They are only relevant for training anyway (SSDBoxEncoder needs to know the
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spatial dimensions of the predictor layers), for inference you don't need them.
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Returns:
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model: The Keras SSD300 model.
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predictor_sizes (optional): A Numpy array containing the `(height, width)` portion
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of the output tensor shape for each convolutional predictor layer. During
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training, the generator function needs this in order to transform
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the ground truth labels into tensors of identical structure as the
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output tensors of the model, which is in turn needed for the cost
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function.
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References:
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https://arxiv.org/abs/1512.02325v5
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i i i i sm `aspect_ratios_global` and `aspect_ratios_per_layer` cannot both be None. At least one needs to be specified.s<> It must be either aspect_ratios_per_layer is None or len(aspect_ratios_per_layer) == {}, but len(aspect_ratios_per_layer) == {}.sD Either `min_scale` and `max_scale` or `scales` need to be specified.sM It must be either scales is None or len(scales) == {}, but len(scales) == {}.i s= 4 variance values must be pased, but {} values were received.s8 All variances must be >0, but the variances given are {}s= You must provide at least one step value per predictor layer.s? You must provide at least one offset value per predictor layer.c S s | S( N( ( t tensor( ( sZ /home/dl-desktop/Desktop/Tesis/8.-Object_Detection/keras-ssd-master/models/keras_ssd300.pyt identity_layer<65> s c s | t j <00> <00> S( N( t npt array( R ( t
subtract_mean( sZ /home/dl-desktop/Desktop/Tesis/8.-Object_Detection/keras-ssd-master/models/keras_ssd300.pyt input_mean_normalization<6F> s c s | t j <00> <00> S( N( R R ( R ( t divide_by_stddev( sZ /home/dl-desktop/Desktop/Tesis/8.-Object_Detection/keras-ssd-master/models/keras_ssd300.pyt input_stddev_normalization<6F> s c s<> t <00> <00> d k rX t j | d <00> d f | d <00> d f | d <00> d f g d d <00>St <00> <00> d k r<> t j | d <00> d f | d <00> d f | d <00> d f | d <00> d f g d d <00>Sd S( Ni .i i i t axisi<73><69><EFBFBD><EFBFBD>i ( t lent Kt stack( R ( t
swap_channels( sZ /home/dl-desktop/Desktop/Tesis/8.-Object_Detection/keras-ssd-master/models/keras_ssd300.pyt input_channel_swap<61> s Ft shapet output_shapet nameR R R R i@ i t
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activationt relut paddingt samet kernel_initializert he_normalt kernel_regularizert conv1_1t conv1_2t pool_sizet stridest pool1i<31> t conv2_1t conv2_2t pool2i t conv3_1t conv3_2t conv3_3t pool3i t conv4_1t conv4_2t conv4_3t pool4t conv5_1t conv5_2t conv5_3t pool5i t
dilation_ratet fc6t fc7t conv6_1t
conv6_paddingt validt conv6_2t conv7_1t
conv7_paddingt conv7_2t conv8_1t conv8_2t conv9_1t conv9_2t
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gamma_initi t conv4_3_normt conv4_3_norm_mbox_conft
fc7_mbox_conft conv6_2_mbox_conft conv7_2_mbox_conft conv8_2_mbox_confi t conv9_2_mbox_conft conv4_3_norm_mbox_loct fc7_mbox_loct conv6_2_mbox_loct conv7_2_mbox_loct conv8_2_mbox_loct conv9_2_mbox_loct
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this_scalet
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next_scalet
aspect_ratiost two_boxes_for_ar1t
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this_stepst this_offsetst
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clip_boxest variancest coordst normalize_coordst conv4_3_norm_mbox_priorboxt fc7_mbox_priorboxt conv6_2_mbox_priorboxt conv7_2_mbox_priorboxt conv8_2_mbox_priorboxt conv9_2_mbox_priorboxi<78><69><EFBFBD><EFBFBD>t conv4_3_norm_mbox_conf_reshapet fc7_mbox_conf_reshapet conv6_2_mbox_conf_reshapet conv7_2_mbox_conf_reshapet conv8_2_mbox_conf_reshapet conv9_2_mbox_conf_reshapet conv4_3_norm_mbox_loc_reshapet fc7_mbox_loc_reshapet conv6_2_mbox_loc_reshapet conv7_2_mbox_loc_reshapet conv8_2_mbox_loc_reshapet conv9_2_mbox_loc_reshapei t" conv4_3_norm_mbox_priorbox_reshapet fc7_mbox_priorbox_reshapet conv6_2_mbox_priorbox_reshapet conv7_2_mbox_priorbox_reshapet conv8_2_mbox_priorbox_reshapet conv9_2_mbox_priorbox_reshapeR t mbox_conft mbox_loct
mbox_priorboxt softmaxt mbox_conf_softmaxt predictionsR t inputst outputst inferencet confidence_thresht
iou_thresholdt top_kt nms_max_output_sizet
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img_heightt img_widtht decoded_predictionst inference_fastsU `mode` must be one of 'training', 'inference' or 'inference_fast', but received '{}'.N( i i ( i i ( i i ( i i ( i i ( i i ( i i ( i i ( i i ( i i ( i i ( i i ( i i ( i i ( i i ( i i ( i i ( i i ( i i ( i i ( i i ( i i ( i i ( i i ( i i ( i i ( i i ( i i ( i i ( i i ( i i ( i i ( i i ( i i ( i i ( i i ( i i ( i i ( i i ( i i ( i i ( i i ( i i ( i i ( i i ( i i ( i i ( i i ( i i ( i i ( i i ( i i ( i i ( i i ( i<><69><EFBFBD><EFBFBD>i ( i<><69><EFBFBD><EFBFBD>i ( i<><69><EFBFBD><EFBFBD>i ( i<><69><EFBFBD><EFBFBD>i ( i<><69><EFBFBD><EFBFBD>i ( i<><69><EFBFBD><EFBFBD>i ( i<><69><EFBFBD><EFBFBD>i ( i<><69><EFBFBD><EFBFBD>i ( i<><69><EFBFBD><EFBFBD>i ( i<><69><EFBFBD><EFBFBD>i ( i<><69><EFBFBD><EFBFBD>i ( i<><69><EFBFBD><EFBFBD>i ( t Nonet
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ValueErrorR t formatR t linspaceR t anyt appendR R R R
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R R R R R R R R R
R t _keras_shape(o t
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image_sizet n_classest modet l2_regularizationt min_scalet max_scalet scalest aspect_ratios_globalt aspect_ratios_per_layerR\ t stepst offsetsR_ R` Ra Rb R R R R<> R<> R<> R<> t return_predictor_sizest n_predictor_layerst l2_regR<67> R<> t img_channelsR[ t n_boxest arR R R R t xt x1R) R* R- R. R/ R0 R1 R2 R3 R4 R5 R6 R7 R8 R9 R: R; R< R> R? R@ RC RD RF RG RH RI RJ RL RM RN RO RP RQ RR RS RT RU RV RW RX Rc Rd Re Rf Rg Rh Ri Rj Rk Rl Rm Rn Ro Rp Rq Rr Rs Rt Ru Rv Rw Rx Ry Rz R{ R| R} R R<> t modelR<6C> t predictor_sizes( ( R R R sZ /home/dl-desktop/Desktop/Tesis/8.-Object_Detection/keras-ssd-master/models/keras_ssd300.pyt ssd_300 st <00>
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__future__R t numpyR t keras.modelsR t keras.layersR R R R R R R R t keras.regularizersR
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t
keras.backendt backendR t$ keras_layers.keras_layer_AnchorBoxesR t( keras_layers.keras_layer_L2NormalizationR t) keras_layers.keras_layer_DecodeDetectionsR
t- keras_layers.keras_layer_DecodeDetectionsFastR R<> t Truet FalseR<65> ( ( ( sZ /home/dl-desktop/Desktop/Tesis/8.-Object_Detection/keras-ssd-master/models/keras_ssd300.pyt <module> sJ :
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