σ ΎχXc@@sMdZddlmZddlmZddlZddlmZddlmZddlm Z dd lm Z dd lm Z dd lm Z dd lm Z dd lmZddlmZddlmZddlmZddlmZddlmZddlmZddlmZdZdZedddddd„Z dS(sŽVGG19 model for Keras. # Reference - [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://arxiv.org/abs/1409.1556) i(tprint_function(tabsolute_importNi(tModel(tFlatten(tDense(tInput(tConv2D(t MaxPooling2D(tGlobalAveragePooling2D(tGlobalMaxPooling2D(tget_source_inputs(t layer_utils(tget_file(tbackendi(tdecode_predictions(tpreprocess_input(t_obtain_input_shapesshttps://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg19_weights_tf_dim_ordering_tf_kernels.h5syhttps://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5timagenetiθc C@s¬|dd@hkr!tdƒ‚n|dkrN|rN|dkrNtdƒ‚nt|ddddd tjƒd |ƒ}|d@kr–td |ƒ}n-tj|ƒs½td |d |ƒ}n|}td dAddddddƒ|ƒ}td dBddddddƒ|ƒ}tdCddDddƒ|ƒ}tddEddddddƒ|ƒ}tddFddddddƒ|ƒ}tdGddHddƒ|ƒ}tddIddddddƒ|ƒ}tddJddddddƒ|ƒ}tddKdddddd ƒ|ƒ}tddLdddddd!ƒ|ƒ}tdMddNdd"ƒ|ƒ}td#dOdddddd$ƒ|ƒ}td#dPdddddd%ƒ|ƒ}td#dQdddddd&ƒ|ƒ}td#dRdddddd'ƒ|ƒ}tdSddTdd(ƒ|ƒ}td#dUdddddd)ƒ|ƒ}td#dVdddddd*ƒ|ƒ}td#dWdddddd+ƒ|ƒ}td#dXdddddd,ƒ|ƒ}tdYddZdd-ƒ|ƒ}|rAt dd.ƒ|ƒ}t d/dddd0ƒ|ƒ}t d/dddd1ƒ|ƒ}t |dd2dd3ƒ|ƒ}n<|d4kr_t ƒ|ƒ}n|d5kr}t ƒ|ƒ}n|d@k r˜t |ƒ}n|}t||dd6ƒ} |dkr¨|rέtd7td8d9ƒ} ntd:td8d9ƒ} | j| ƒtjƒd;kr!tj| ƒntjƒd<kr¨|r€| jdd-ƒ} | jd=} | jdd0ƒ} tj| | d<ƒntjƒd>kr₯tjd?ƒq₯q¨n| S([siInstantiates the VGG19 architecture. Optionally loads weights pre-trained on ImageNet. Note that when using TensorFlow, for best performance you should set `image_data_format="channels_last"` in your Keras config at ~/.keras/keras.json. The model and the weights are compatible with both TensorFlow and Theano. The data format convention used by the model is the one specified in your Keras config file. # Arguments include_top: whether to include the 3 fully-connected layers at the top of the network. weights: one of `None` (random initialization) or "imagenet" (pre-training on ImageNet). input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) to use as image input for the model. input_shape: optional shape tuple, only to be specified if `include_top` is False (otherwise the input shape has to be `(224, 224, 3)` (with `channels_last` data format) or `(3, 224, 244)` (with `channels_first` data format). It should have exactly 3 inputs channels, and width and height should be no smaller than 48. E.g. `(200, 200, 3)` would be one valid value. pooling: Optional pooling mode for feature extraction when `include_top` is `False`. - `None` means that the output of the model will be the 4D tensor output of the last convolutional layer. - `avg` means that global average pooling will be applied to the output of the last convolutional layer, and thus the output of the model will be a 2D tensor. - `max` means that global max pooling will be applied. classes: optional number of classes to classify images into, only to be specified if `include_top` is True, and if no `weights` argument is specified. # Returns A Keras model instance. # Raises ValueError: in case of invalid argument for `weights`, or invalid input shape. RspThe `weights` argument should be either `None` (random initialization) or `imagenet` (pre-training on ImageNet).iθsSIf using `weights` as imagenet with `include_top` as true, `classes` should be 1000t default_sizeiΰtmin_sizei0t data_formatt include_toptshapettensori@it activationtrelutpaddingtsametnamet block1_conv1t block1_conv2itstridest block1_pooli€t block2_conv1t block2_conv2t block2_poolit block3_conv1t block3_conv2t block3_conv3t block3_conv4t block3_poolit block4_conv1t block4_conv2t block4_conv3t block4_conv4t block4_poolt block5_conv1t block5_conv2t block5_conv3t block5_conv4t block5_pooltflattenitfc1tfc2tsoftmaxt predictionstavgtmaxtvgg19s+vgg19_weights_tf_dim_ordering_tf_kernels.h5t cache_subdirtmodelss1vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5ttheanotchannels_firstit tensorflowsςYou are using the TensorFlow backend, yet you are using the Theano image data format convention (`image_data_format="channels_first"`). For best performance, set `image_data_format="channels_last"` in your Keras config at ~/.keras/keras.json.N(ii(ii(ii(ii(ii(ii(ii(ii(ii(ii(ii(ii(ii(ii(ii(ii(ii(ii(ii(ii(ii(ii(ii(ii(ii(ii(tNonet ValueErrorRtKtimage_data_formatRtis_keras_tensorRRRRRR R RR t WEIGHTS_PATHtWEIGHTS_PATH_NO_TOPt load_weightsR R tconvert_all_kernels_in_modelt get_layert output_shapet!convert_dense_weights_data_formattwarningstwarn(Rtweightst input_tensort input_shapetpoolingtclassest img_inputtxtinputstmodelt weights_pathtmaxpoolRtdense((s7/tmp/pip-build-isqEY4/keras/keras/applications/vgg19.pytVGG19#sˆ5    ''''''''''''''''!        (!t__doc__t __future__RRRLR<RtlayersRRRRRRR tengine.topologyR tutilsR tutils.data_utilsR tR RBtimagenet_utilsRRRRERFtTrueR@RZ(((s7/tmp/pip-build-isqEY4/keras/keras/applications/vgg19.pyts0