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| import tensorflow as tf | |
| import gdown | |
| from pathlib import Path | |
| import os | |
| tf_version = int(tf.__version__.split(".")[0]) | |
| if tf_version == 1: | |
| from keras.models import Model | |
| from keras.layers import Input, BatchNormalization, ZeroPadding2D, Conv2D, ReLU, MaxPool2D, Add, UpSampling2D, concatenate, Softmax | |
| else: | |
| from tensorflow.keras.models import Model | |
| from tensorflow.keras.layers import Input, BatchNormalization, ZeroPadding2D, Conv2D, ReLU, MaxPool2D, Add, UpSampling2D, concatenate, Softmax | |
| def load_weights(model): | |
| exact_file = RetinaFace_Face_Detector_Extractor_Recognizer/retinaface.h5 | |
| model.load_weights(exact_file) | |
| return model | |
| def build_model(): | |
| data = Input(dtype=tf.float32, shape=(None, None, 3), name='data') | |
| bn_data = BatchNormalization(epsilon=1.9999999494757503e-05, name='bn_data', trainable=False)(data) | |
| conv0_pad = ZeroPadding2D(padding=tuple([3, 3]))(bn_data) | |
| conv0 = Conv2D(filters = 64, kernel_size = (7, 7), name = 'conv0', strides = [2, 2], padding = 'VALID', use_bias = False)(conv0_pad) | |
| bn0 = BatchNormalization(epsilon=1.9999999494757503e-05, name='bn0', trainable=False)(conv0) | |
| relu0 = ReLU(name='relu0')(bn0) | |
| pooling0_pad = ZeroPadding2D(padding=tuple([1, 1]))(relu0) | |
| pooling0 = MaxPool2D((3, 3), (2, 2), padding='VALID', name='pooling0')(pooling0_pad) | |
| stage1_unit1_bn1 = BatchNormalization(epsilon=1.9999999494757503e-05, name='stage1_unit1_bn1', trainable=False)(pooling0) | |
| stage1_unit1_relu1 = ReLU(name='stage1_unit1_relu1')(stage1_unit1_bn1) | |
| stage1_unit1_conv1 = Conv2D(filters = 64, kernel_size = (1, 1), name = 'stage1_unit1_conv1', strides = [1, 1], padding = 'VALID', use_bias = False)(stage1_unit1_relu1) | |
| stage1_unit1_sc = Conv2D(filters = 256, kernel_size = (1, 1), name = 'stage1_unit1_sc', strides = [1, 1], padding = 'VALID', use_bias = False)(stage1_unit1_relu1) | |
| stage1_unit1_bn2 = BatchNormalization(epsilon=1.9999999494757503e-05, name='stage1_unit1_bn2', trainable=False)(stage1_unit1_conv1) | |
| stage1_unit1_relu2 = ReLU(name='stage1_unit1_relu2')(stage1_unit1_bn2) | |
| stage1_unit1_conv2_pad = ZeroPadding2D(padding=tuple([1, 1]))(stage1_unit1_relu2) | |
| stage1_unit1_conv2 = Conv2D(filters = 64, kernel_size = (3, 3), name = 'stage1_unit1_conv2', strides = [1, 1], padding = 'VALID', use_bias = False)(stage1_unit1_conv2_pad) | |
| stage1_unit1_bn3 = BatchNormalization(epsilon=1.9999999494757503e-05, name='stage1_unit1_bn3', trainable=False)(stage1_unit1_conv2) | |
| stage1_unit1_relu3 = ReLU(name='stage1_unit1_relu3')(stage1_unit1_bn3) | |
| stage1_unit1_conv3 = Conv2D(filters = 256, kernel_size = (1, 1), name = 'stage1_unit1_conv3', strides = [1, 1], padding = 'VALID', use_bias = False)(stage1_unit1_relu3) | |
| plus0_v1 = Add()([stage1_unit1_conv3 , stage1_unit1_sc]) | |
| stage1_unit2_bn1 = BatchNormalization(epsilon=1.9999999494757503e-05, name='stage1_unit2_bn1', trainable=False)(plus0_v1) | |
| stage1_unit2_relu1 = ReLU(name='stage1_unit2_relu1')(stage1_unit2_bn1) | |
| stage1_unit2_conv1 = Conv2D(filters = 64, kernel_size = (1, 1), name = 'stage1_unit2_conv1', strides = [1, 1], padding = 'VALID', use_bias = False)(stage1_unit2_relu1) | |
| stage1_unit2_bn2 = BatchNormalization(epsilon=1.9999999494757503e-05, name='stage1_unit2_bn2', trainable=False)(stage1_unit2_conv1) | |
| stage1_unit2_relu2 = ReLU(name='stage1_unit2_relu2')(stage1_unit2_bn2) | |
| stage1_unit2_conv2_pad = ZeroPadding2D(padding=tuple([1, 1]))(stage1_unit2_relu2) | |
| stage1_unit2_conv2 = Conv2D(filters = 64, kernel_size = (3, 3), name = 'stage1_unit2_conv2', strides = [1, 1], padding = 'VALID', use_bias = False)(stage1_unit2_conv2_pad) | |
| stage1_unit2_bn3 = BatchNormalization(epsilon=1.9999999494757503e-05, name='stage1_unit2_bn3', trainable=False)(stage1_unit2_conv2) | |
| stage1_unit2_relu3 = ReLU(name='stage1_unit2_relu3')(stage1_unit2_bn3) | |
| stage1_unit2_conv3 = Conv2D(filters = 256, kernel_size = (1, 1), name = 'stage1_unit2_conv3', strides = [1, 1], padding = 'VALID', use_bias = False)(stage1_unit2_relu3) | |
| plus1_v2 = Add()([stage1_unit2_conv3 , plus0_v1]) | |
| stage1_unit3_bn1 = BatchNormalization(epsilon=1.9999999494757503e-05, name='stage1_unit3_bn1', trainable=False)(plus1_v2) | |
| stage1_unit3_relu1 = ReLU(name='stage1_unit3_relu1')(stage1_unit3_bn1) | |
| stage1_unit3_conv1 = Conv2D(filters = 64, kernel_size = (1, 1), name = 'stage1_unit3_conv1', strides = [1, 1], padding = 'VALID', use_bias = False)(stage1_unit3_relu1) | |
| stage1_unit3_bn2 = BatchNormalization(epsilon=1.9999999494757503e-05, name='stage1_unit3_bn2', trainable=False)(stage1_unit3_conv1) | |
| stage1_unit3_relu2 = ReLU(name='stage1_unit3_relu2')(stage1_unit3_bn2) | |
| stage1_unit3_conv2_pad = ZeroPadding2D(padding=tuple([1, 1]))(stage1_unit3_relu2) | |
| stage1_unit3_conv2 = Conv2D(filters = 64, kernel_size = (3, 3), name = 'stage1_unit3_conv2', strides = [1, 1], padding = 'VALID', use_bias = False)(stage1_unit3_conv2_pad) | |
| stage1_unit3_bn3 = BatchNormalization(epsilon=1.9999999494757503e-05, name='stage1_unit3_bn3', trainable=False)(stage1_unit3_conv2) | |
| stage1_unit3_relu3 = ReLU(name='stage1_unit3_relu3')(stage1_unit3_bn3) | |
| stage1_unit3_conv3 = Conv2D(filters = 256, kernel_size = (1, 1), name = 'stage1_unit3_conv3', strides = [1, 1], padding = 'VALID', use_bias = False)(stage1_unit3_relu3) | |
| plus2 = Add()([stage1_unit3_conv3 , plus1_v2]) | |
| stage2_unit1_bn1 = BatchNormalization(epsilon=1.9999999494757503e-05, name='stage2_unit1_bn1', trainable=False)(plus2) | |
| stage2_unit1_relu1 = ReLU(name='stage2_unit1_relu1')(stage2_unit1_bn1) | |
| stage2_unit1_conv1 = Conv2D(filters = 128, kernel_size = (1, 1), name = 'stage2_unit1_conv1', strides = [1, 1], padding = 'VALID', use_bias = False)(stage2_unit1_relu1) | |
| stage2_unit1_sc = Conv2D(filters = 512, kernel_size = (1, 1), name = 'stage2_unit1_sc', strides = [2, 2], padding = 'VALID', use_bias = False)(stage2_unit1_relu1) | |
| stage2_unit1_bn2 = BatchNormalization(epsilon=1.9999999494757503e-05, name='stage2_unit1_bn2', trainable=False)(stage2_unit1_conv1) | |
| stage2_unit1_relu2 = ReLU(name='stage2_unit1_relu2')(stage2_unit1_bn2) | |
| stage2_unit1_conv2_pad = ZeroPadding2D(padding=tuple([1, 1]))(stage2_unit1_relu2) | |
| stage2_unit1_conv2 = Conv2D(filters = 128, kernel_size = (3, 3), name = 'stage2_unit1_conv2', strides = [2, 2], padding = 'VALID', use_bias = False)(stage2_unit1_conv2_pad) | |
| stage2_unit1_bn3 = BatchNormalization(epsilon=1.9999999494757503e-05, name='stage2_unit1_bn3', trainable=False)(stage2_unit1_conv2) | |
| stage2_unit1_relu3 = ReLU(name='stage2_unit1_relu3')(stage2_unit1_bn3) | |
| stage2_unit1_conv3 = Conv2D(filters = 512, kernel_size = (1, 1), name = 'stage2_unit1_conv3', strides = [1, 1], padding = 'VALID', use_bias = False)(stage2_unit1_relu3) | |
| plus3 = Add()([stage2_unit1_conv3 , stage2_unit1_sc]) | |
| stage2_unit2_bn1 = BatchNormalization(epsilon=1.9999999494757503e-05, name='stage2_unit2_bn1', trainable=False)(plus3) | |
| stage2_unit2_relu1 = ReLU(name='stage2_unit2_relu1')(stage2_unit2_bn1) | |
| stage2_unit2_conv1 = Conv2D(filters = 128, kernel_size = (1, 1), name = 'stage2_unit2_conv1', strides = [1, 1], padding = 'VALID', use_bias = False)(stage2_unit2_relu1) | |
| stage2_unit2_bn2 = BatchNormalization(epsilon=1.9999999494757503e-05, name='stage2_unit2_bn2', trainable=False)(stage2_unit2_conv1) | |
| stage2_unit2_relu2 = ReLU(name='stage2_unit2_relu2')(stage2_unit2_bn2) | |
| stage2_unit2_conv2_pad = ZeroPadding2D(padding=tuple([1, 1]))(stage2_unit2_relu2) | |
| stage2_unit2_conv2 = Conv2D(filters = 128, kernel_size = (3, 3), name = 'stage2_unit2_conv2', strides = [1, 1], padding = 'VALID', use_bias = False)(stage2_unit2_conv2_pad) | |
| stage2_unit2_bn3 = BatchNormalization(epsilon=1.9999999494757503e-05, name='stage2_unit2_bn3', trainable=False)(stage2_unit2_conv2) | |
| stage2_unit2_relu3 = ReLU(name='stage2_unit2_relu3')(stage2_unit2_bn3) | |
| stage2_unit2_conv3 = Conv2D(filters = 512, kernel_size = (1, 1), name = 'stage2_unit2_conv3', strides = [1, 1], padding = 'VALID', use_bias = False)(stage2_unit2_relu3) | |
| plus4 = Add()([stage2_unit2_conv3 , plus3]) | |
| stage2_unit3_bn1 = BatchNormalization(epsilon=1.9999999494757503e-05, name='stage2_unit3_bn1', trainable=False)(plus4) | |
| stage2_unit3_relu1 = ReLU(name='stage2_unit3_relu1')(stage2_unit3_bn1) | |
| stage2_unit3_conv1 = Conv2D(filters = 128, kernel_size = (1, 1), name = 'stage2_unit3_conv1', strides = [1, 1], padding = 'VALID', use_bias = False)(stage2_unit3_relu1) | |
| stage2_unit3_bn2 = BatchNormalization(epsilon=1.9999999494757503e-05, name='stage2_unit3_bn2', trainable=False)(stage2_unit3_conv1) | |
| stage2_unit3_relu2 = ReLU(name='stage2_unit3_relu2')(stage2_unit3_bn2) | |
| stage2_unit3_conv2_pad = ZeroPadding2D(padding=tuple([1, 1]))(stage2_unit3_relu2) | |
| stage2_unit3_conv2 = Conv2D(filters = 128, kernel_size = (3, 3), name = 'stage2_unit3_conv2', strides = [1, 1], padding = 'VALID', use_bias = False)(stage2_unit3_conv2_pad) | |
| stage2_unit3_bn3 = BatchNormalization(epsilon=1.9999999494757503e-05, name='stage2_unit3_bn3', trainable=False)(stage2_unit3_conv2) | |
| stage2_unit3_relu3 = ReLU(name='stage2_unit3_relu3')(stage2_unit3_bn3) | |
| stage2_unit3_conv3 = Conv2D(filters = 512, kernel_size = (1, 1), name = 'stage2_unit3_conv3', strides = [1, 1], padding = 'VALID', use_bias = False)(stage2_unit3_relu3) | |
| plus5 = Add()([stage2_unit3_conv3 , plus4]) | |
| stage2_unit4_bn1 = BatchNormalization(epsilon=1.9999999494757503e-05, name='stage2_unit4_bn1', trainable=False)(plus5) | |
| stage2_unit4_relu1 = ReLU(name='stage2_unit4_relu1')(stage2_unit4_bn1) | |
| stage2_unit4_conv1 = Conv2D(filters = 128, kernel_size = (1, 1), name = 'stage2_unit4_conv1', strides = [1, 1], padding = 'VALID', use_bias = False)(stage2_unit4_relu1) | |
| stage2_unit4_bn2 = BatchNormalization(epsilon=1.9999999494757503e-05, name='stage2_unit4_bn2', trainable=False)(stage2_unit4_conv1) | |
| stage2_unit4_relu2 = ReLU(name='stage2_unit4_relu2')(stage2_unit4_bn2) | |
| stage2_unit4_conv2_pad = ZeroPadding2D(padding=tuple([1, 1]))(stage2_unit4_relu2) | |
| stage2_unit4_conv2 = Conv2D(filters = 128, kernel_size = (3, 3), name = 'stage2_unit4_conv2', strides = [1, 1], padding = 'VALID', use_bias = False)(stage2_unit4_conv2_pad) | |
| stage2_unit4_bn3 = BatchNormalization(epsilon=1.9999999494757503e-05, name='stage2_unit4_bn3', trainable=False)(stage2_unit4_conv2) | |
| stage2_unit4_relu3 = ReLU(name='stage2_unit4_relu3')(stage2_unit4_bn3) | |
| stage2_unit4_conv3 = Conv2D(filters = 512, kernel_size = (1, 1), name = 'stage2_unit4_conv3', strides = [1, 1], padding = 'VALID', use_bias = False)(stage2_unit4_relu3) | |
| plus6 = Add()([stage2_unit4_conv3 , plus5]) | |
| stage3_unit1_bn1 = BatchNormalization(epsilon=1.9999999494757503e-05, name='stage3_unit1_bn1', trainable=False)(plus6) | |
| stage3_unit1_relu1 = ReLU(name='stage3_unit1_relu1')(stage3_unit1_bn1) | |
| stage3_unit1_conv1 = Conv2D(filters = 256, kernel_size = (1, 1), name = 'stage3_unit1_conv1', strides = [1, 1], padding = 'VALID', use_bias = False)(stage3_unit1_relu1) | |
| stage3_unit1_sc = Conv2D(filters = 1024, kernel_size = (1, 1), name = 'stage3_unit1_sc', strides = [2, 2], padding = 'VALID', use_bias = False)(stage3_unit1_relu1) | |
| stage3_unit1_bn2 = BatchNormalization(epsilon=1.9999999494757503e-05, name='stage3_unit1_bn2', trainable=False)(stage3_unit1_conv1) | |
| stage3_unit1_relu2 = ReLU(name='stage3_unit1_relu2')(stage3_unit1_bn2) | |
| stage3_unit1_conv2_pad = ZeroPadding2D(padding=tuple([1, 1]))(stage3_unit1_relu2) | |
| stage3_unit1_conv2 = Conv2D(filters = 256, kernel_size = (3, 3), name = 'stage3_unit1_conv2', strides = [2, 2], padding = 'VALID', use_bias = False)(stage3_unit1_conv2_pad) | |
| ssh_m1_red_conv = Conv2D(filters = 256, kernel_size = (1, 1), name = 'ssh_m1_red_conv', strides = [1, 1], padding = 'VALID', use_bias = True)(stage3_unit1_relu2) | |
| stage3_unit1_bn3 = BatchNormalization(epsilon=1.9999999494757503e-05, name='stage3_unit1_bn3', trainable=False)(stage3_unit1_conv2) | |
| ssh_m1_red_conv_bn = BatchNormalization(epsilon=1.9999999494757503e-05, name='ssh_m1_red_conv_bn', trainable=False)(ssh_m1_red_conv) | |
| stage3_unit1_relu3 = ReLU(name='stage3_unit1_relu3')(stage3_unit1_bn3) | |
| ssh_m1_red_conv_relu = ReLU(name='ssh_m1_red_conv_relu')(ssh_m1_red_conv_bn) | |
| stage3_unit1_conv3 = Conv2D(filters = 1024, kernel_size = (1, 1), name = 'stage3_unit1_conv3', strides = [1, 1], padding = 'VALID', use_bias = False)(stage3_unit1_relu3) | |
| plus7 = Add()([stage3_unit1_conv3 , stage3_unit1_sc]) | |
| stage3_unit2_bn1 = BatchNormalization(epsilon=1.9999999494757503e-05, name='stage3_unit2_bn1', trainable=False)(plus7) | |
| stage3_unit2_relu1 = ReLU(name='stage3_unit2_relu1')(stage3_unit2_bn1) | |
| stage3_unit2_conv1 = Conv2D(filters = 256, kernel_size = (1, 1), name = 'stage3_unit2_conv1', strides = [1, 1], padding = 'VALID', use_bias = False)(stage3_unit2_relu1) | |
| stage3_unit2_bn2 = BatchNormalization(epsilon=1.9999999494757503e-05, name='stage3_unit2_bn2', trainable=False)(stage3_unit2_conv1) | |
| stage3_unit2_relu2 = ReLU(name='stage3_unit2_relu2')(stage3_unit2_bn2) | |
| stage3_unit2_conv2_pad = ZeroPadding2D(padding=tuple([1, 1]))(stage3_unit2_relu2) | |
| stage3_unit2_conv2 = Conv2D(filters = 256, kernel_size = (3, 3), name = 'stage3_unit2_conv2', strides = [1, 1], padding = 'VALID', use_bias = False)(stage3_unit2_conv2_pad) | |
| stage3_unit2_bn3 = BatchNormalization(epsilon=1.9999999494757503e-05, name='stage3_unit2_bn3', trainable=False)(stage3_unit2_conv2) | |
| stage3_unit2_relu3 = ReLU(name='stage3_unit2_relu3')(stage3_unit2_bn3) | |
| stage3_unit2_conv3 = Conv2D(filters = 1024, kernel_size = (1, 1), name = 'stage3_unit2_conv3', strides = [1, 1], padding = 'VALID', use_bias = False)(stage3_unit2_relu3) | |
| plus8 = Add()([stage3_unit2_conv3 , plus7]) | |
| stage3_unit3_bn1 = BatchNormalization(epsilon=1.9999999494757503e-05, name='stage3_unit3_bn1', trainable=False)(plus8) | |
| stage3_unit3_relu1 = ReLU(name='stage3_unit3_relu1')(stage3_unit3_bn1) | |
| stage3_unit3_conv1 = Conv2D(filters = 256, kernel_size = (1, 1), name = 'stage3_unit3_conv1', strides = [1, 1], padding = 'VALID', use_bias = False)(stage3_unit3_relu1) | |
| stage3_unit3_bn2 = BatchNormalization(epsilon=1.9999999494757503e-05, name='stage3_unit3_bn2', trainable=False)(stage3_unit3_conv1) | |
| stage3_unit3_relu2 = ReLU(name='stage3_unit3_relu2')(stage3_unit3_bn2) | |
| stage3_unit3_conv2_pad = ZeroPadding2D(padding=tuple([1, 1]))(stage3_unit3_relu2) | |
| stage3_unit3_conv2 = Conv2D(filters = 256, kernel_size = (3, 3), name = 'stage3_unit3_conv2', strides = [1, 1], padding = 'VALID', use_bias = False)(stage3_unit3_conv2_pad) | |
| stage3_unit3_bn3 = BatchNormalization(epsilon=1.9999999494757503e-05, name='stage3_unit3_bn3', trainable=False)(stage3_unit3_conv2) | |
| stage3_unit3_relu3 = ReLU(name='stage3_unit3_relu3')(stage3_unit3_bn3) | |
| stage3_unit3_conv3 = Conv2D(filters = 1024, kernel_size = (1, 1), name = 'stage3_unit3_conv3', strides = [1, 1], padding = 'VALID', use_bias = False)(stage3_unit3_relu3) | |
| plus9 = Add()([stage3_unit3_conv3 , plus8]) | |
| stage3_unit4_bn1 = BatchNormalization(epsilon=1.9999999494757503e-05, name='stage3_unit4_bn1', trainable=False)(plus9) | |
| stage3_unit4_relu1 = ReLU(name='stage3_unit4_relu1')(stage3_unit4_bn1) | |
| stage3_unit4_conv1 = Conv2D(filters = 256, kernel_size = (1, 1), name = 'stage3_unit4_conv1', strides = [1, 1], padding = 'VALID', use_bias = False)(stage3_unit4_relu1) | |
| stage3_unit4_bn2 = BatchNormalization(epsilon=1.9999999494757503e-05, name='stage3_unit4_bn2', trainable=False)(stage3_unit4_conv1) | |
| stage3_unit4_relu2 = ReLU(name='stage3_unit4_relu2')(stage3_unit4_bn2) | |
| stage3_unit4_conv2_pad = ZeroPadding2D(padding=tuple([1, 1]))(stage3_unit4_relu2) | |
| stage3_unit4_conv2 = Conv2D(filters = 256, kernel_size = (3, 3), name = 'stage3_unit4_conv2', strides = [1, 1], padding = 'VALID', use_bias = False)(stage3_unit4_conv2_pad) | |
| stage3_unit4_bn3 = BatchNormalization(epsilon=1.9999999494757503e-05, name='stage3_unit4_bn3', trainable=False)(stage3_unit4_conv2) | |
| stage3_unit4_relu3 = ReLU(name='stage3_unit4_relu3')(stage3_unit4_bn3) | |
| stage3_unit4_conv3 = Conv2D(filters = 1024, kernel_size = (1, 1), name = 'stage3_unit4_conv3', strides = [1, 1], padding = 'VALID', use_bias = False)(stage3_unit4_relu3) | |
| plus10 = Add()([stage3_unit4_conv3 , plus9]) | |
| stage3_unit5_bn1 = BatchNormalization(epsilon=1.9999999494757503e-05, name='stage3_unit5_bn1', trainable=False)(plus10) | |
| stage3_unit5_relu1 = ReLU(name='stage3_unit5_relu1')(stage3_unit5_bn1) | |
| stage3_unit5_conv1 = Conv2D(filters = 256, kernel_size = (1, 1), name = 'stage3_unit5_conv1', strides = [1, 1], padding = 'VALID', use_bias = False)(stage3_unit5_relu1) | |
| stage3_unit5_bn2 = BatchNormalization(epsilon=1.9999999494757503e-05, name='stage3_unit5_bn2', trainable=False)(stage3_unit5_conv1) | |
| stage3_unit5_relu2 = ReLU(name='stage3_unit5_relu2')(stage3_unit5_bn2) | |
| stage3_unit5_conv2_pad = ZeroPadding2D(padding=tuple([1, 1]))(stage3_unit5_relu2) | |
| stage3_unit5_conv2 = Conv2D(filters = 256, kernel_size = (3, 3), name = 'stage3_unit5_conv2', strides = [1, 1], padding = 'VALID', use_bias = False)(stage3_unit5_conv2_pad) | |
| stage3_unit5_bn3 = BatchNormalization(epsilon=1.9999999494757503e-05, name='stage3_unit5_bn3', trainable=False)(stage3_unit5_conv2) | |
| stage3_unit5_relu3 = ReLU(name='stage3_unit5_relu3')(stage3_unit5_bn3) | |
| stage3_unit5_conv3 = Conv2D(filters = 1024, kernel_size = (1, 1), name = 'stage3_unit5_conv3', strides = [1, 1], padding = 'VALID', use_bias = False)(stage3_unit5_relu3) | |
| plus11 = Add()([stage3_unit5_conv3 , plus10]) | |
| stage3_unit6_bn1 = BatchNormalization(epsilon=1.9999999494757503e-05, name='stage3_unit6_bn1', trainable=False)(plus11) | |
| stage3_unit6_relu1 = ReLU(name='stage3_unit6_relu1')(stage3_unit6_bn1) | |
| stage3_unit6_conv1 = Conv2D(filters = 256, kernel_size = (1, 1), name = 'stage3_unit6_conv1', strides = [1, 1], padding = 'VALID', use_bias = False)(stage3_unit6_relu1) | |
| stage3_unit6_bn2 = BatchNormalization(epsilon=1.9999999494757503e-05, name='stage3_unit6_bn2', trainable=False)(stage3_unit6_conv1) | |
| stage3_unit6_relu2 = ReLU(name='stage3_unit6_relu2')(stage3_unit6_bn2) | |
| stage3_unit6_conv2_pad = ZeroPadding2D(padding=tuple([1, 1]))(stage3_unit6_relu2) | |
| stage3_unit6_conv2 = Conv2D(filters = 256, kernel_size = (3, 3), name = 'stage3_unit6_conv2', strides = [1, 1], padding = 'VALID', use_bias = False)(stage3_unit6_conv2_pad) | |
| stage3_unit6_bn3 = BatchNormalization(epsilon=1.9999999494757503e-05, name='stage3_unit6_bn3', trainable=False)(stage3_unit6_conv2) | |
| stage3_unit6_relu3 = ReLU(name='stage3_unit6_relu3')(stage3_unit6_bn3) | |
| stage3_unit6_conv3 = Conv2D(filters = 1024, kernel_size = (1, 1), name = 'stage3_unit6_conv3', strides = [1, 1], padding = 'VALID', use_bias = False)(stage3_unit6_relu3) | |
| plus12 = Add()([stage3_unit6_conv3 , plus11]) | |
| stage4_unit1_bn1 = BatchNormalization(epsilon=1.9999999494757503e-05, name='stage4_unit1_bn1', trainable=False)(plus12) | |
| stage4_unit1_relu1 = ReLU(name='stage4_unit1_relu1')(stage4_unit1_bn1) | |
| stage4_unit1_conv1 = Conv2D(filters = 512, kernel_size = (1, 1), name = 'stage4_unit1_conv1', strides = [1, 1], padding = 'VALID', use_bias = False)(stage4_unit1_relu1) | |
| stage4_unit1_sc = Conv2D(filters = 2048, kernel_size = (1, 1), name = 'stage4_unit1_sc', strides = [2, 2], padding = 'VALID', use_bias = False)(stage4_unit1_relu1) | |
| stage4_unit1_bn2 = BatchNormalization(epsilon=1.9999999494757503e-05, name='stage4_unit1_bn2', trainable=False)(stage4_unit1_conv1) | |
| stage4_unit1_relu2 = ReLU(name='stage4_unit1_relu2')(stage4_unit1_bn2) | |
| stage4_unit1_conv2_pad = ZeroPadding2D(padding=tuple([1, 1]))(stage4_unit1_relu2) | |
| stage4_unit1_conv2 = Conv2D(filters = 512, kernel_size = (3, 3), name = 'stage4_unit1_conv2', strides = [2, 2], padding = 'VALID', use_bias = False)(stage4_unit1_conv2_pad) | |
| ssh_c2_lateral = Conv2D(filters = 256, kernel_size = (1, 1), name = 'ssh_c2_lateral', strides = [1, 1], padding = 'VALID', use_bias = True)(stage4_unit1_relu2) | |
| stage4_unit1_bn3 = BatchNormalization(epsilon=1.9999999494757503e-05, name='stage4_unit1_bn3', trainable=False)(stage4_unit1_conv2) | |
| ssh_c2_lateral_bn = BatchNormalization(epsilon=1.9999999494757503e-05, name='ssh_c2_lateral_bn', trainable=False)(ssh_c2_lateral) | |
| stage4_unit1_relu3 = ReLU(name='stage4_unit1_relu3')(stage4_unit1_bn3) | |
| ssh_c2_lateral_relu = ReLU(name='ssh_c2_lateral_relu')(ssh_c2_lateral_bn) | |
| stage4_unit1_conv3 = Conv2D(filters = 2048, kernel_size = (1, 1), name = 'stage4_unit1_conv3', strides = [1, 1], padding = 'VALID', use_bias = False)(stage4_unit1_relu3) | |
| plus13 = Add()([stage4_unit1_conv3 , stage4_unit1_sc]) | |
| stage4_unit2_bn1 = BatchNormalization(epsilon=1.9999999494757503e-05, name='stage4_unit2_bn1', trainable=False)(plus13) | |
| stage4_unit2_relu1 = ReLU(name='stage4_unit2_relu1')(stage4_unit2_bn1) | |
| stage4_unit2_conv1 = Conv2D(filters = 512, kernel_size = (1, 1), name = 'stage4_unit2_conv1', strides = [1, 1], padding = 'VALID', use_bias = False)(stage4_unit2_relu1) | |
| stage4_unit2_bn2 = BatchNormalization(epsilon=1.9999999494757503e-05, name='stage4_unit2_bn2', trainable=False)(stage4_unit2_conv1) | |
| stage4_unit2_relu2 = ReLU(name='stage4_unit2_relu2')(stage4_unit2_bn2) | |
| stage4_unit2_conv2_pad = ZeroPadding2D(padding=tuple([1, 1]))(stage4_unit2_relu2) | |
| stage4_unit2_conv2 = Conv2D(filters = 512, kernel_size = (3, 3), name = 'stage4_unit2_conv2', strides = [1, 1], padding = 'VALID', use_bias = False)(stage4_unit2_conv2_pad) | |
| stage4_unit2_bn3 = BatchNormalization(epsilon=1.9999999494757503e-05, name='stage4_unit2_bn3', trainable=False)(stage4_unit2_conv2) | |
| stage4_unit2_relu3 = ReLU(name='stage4_unit2_relu3')(stage4_unit2_bn3) | |
| stage4_unit2_conv3 = Conv2D(filters = 2048, kernel_size = (1, 1), name = 'stage4_unit2_conv3', strides = [1, 1], padding = 'VALID', use_bias = False)(stage4_unit2_relu3) | |
| plus14 = Add()([stage4_unit2_conv3 , plus13]) | |
| stage4_unit3_bn1 = BatchNormalization(epsilon=1.9999999494757503e-05, name='stage4_unit3_bn1', trainable=False)(plus14) | |
| stage4_unit3_relu1 = ReLU(name='stage4_unit3_relu1')(stage4_unit3_bn1) | |
| stage4_unit3_conv1 = Conv2D(filters = 512, kernel_size = (1, 1), name = 'stage4_unit3_conv1', strides = [1, 1], padding = 'VALID', use_bias = False)(stage4_unit3_relu1) | |
| stage4_unit3_bn2 = BatchNormalization(epsilon=1.9999999494757503e-05, name='stage4_unit3_bn2', trainable=False)(stage4_unit3_conv1) | |
| stage4_unit3_relu2 = ReLU(name='stage4_unit3_relu2')(stage4_unit3_bn2) | |
| stage4_unit3_conv2_pad = ZeroPadding2D(padding=tuple([1, 1]))(stage4_unit3_relu2) | |
| stage4_unit3_conv2 = Conv2D(filters = 512, kernel_size = (3, 3), name = 'stage4_unit3_conv2', strides = [1, 1], padding = 'VALID', use_bias = False)(stage4_unit3_conv2_pad) | |
| stage4_unit3_bn3 = BatchNormalization(epsilon=1.9999999494757503e-05, name='stage4_unit3_bn3', trainable=False)(stage4_unit3_conv2) | |
| stage4_unit3_relu3 = ReLU(name='stage4_unit3_relu3')(stage4_unit3_bn3) | |
| stage4_unit3_conv3 = Conv2D(filters = 2048, kernel_size = (1, 1), name = 'stage4_unit3_conv3', strides = [1, 1], padding = 'VALID', use_bias = False)(stage4_unit3_relu3) | |
| plus15 = Add()([stage4_unit3_conv3 , plus14]) | |
| bn1 = BatchNormalization(epsilon=1.9999999494757503e-05, name='bn1', trainable=False)(plus15) | |
| relu1 = ReLU(name='relu1')(bn1) | |
| ssh_c3_lateral = Conv2D(filters = 256, kernel_size = (1, 1), name = 'ssh_c3_lateral', strides = [1, 1], padding = 'VALID', use_bias = True)(relu1) | |
| ssh_c3_lateral_bn = BatchNormalization(epsilon=1.9999999494757503e-05, name='ssh_c3_lateral_bn', trainable=False)(ssh_c3_lateral) | |
| ssh_c3_lateral_relu = ReLU(name='ssh_c3_lateral_relu')(ssh_c3_lateral_bn) | |
| ssh_m3_det_conv1_pad = ZeroPadding2D(padding=tuple([1, 1]))(ssh_c3_lateral_relu) | |
| ssh_m3_det_conv1 = Conv2D(filters = 256, kernel_size = (3, 3), name = 'ssh_m3_det_conv1', strides = [1, 1], padding = 'VALID', use_bias = True)(ssh_m3_det_conv1_pad) | |
| ssh_m3_det_context_conv1_pad = ZeroPadding2D(padding=tuple([1, 1]))(ssh_c3_lateral_relu) | |
| ssh_m3_det_context_conv1 = Conv2D(filters = 128, kernel_size = (3, 3), name = 'ssh_m3_det_context_conv1', strides = [1, 1], padding = 'VALID', use_bias = True)(ssh_m3_det_context_conv1_pad) | |
| ssh_c3_up = UpSampling2D(size=(2, 2), interpolation="nearest", name="ssh_c3_up")(ssh_c3_lateral_relu) | |
| ssh_m3_det_conv1_bn = BatchNormalization(epsilon=1.9999999494757503e-05, name='ssh_m3_det_conv1_bn', trainable=False)(ssh_m3_det_conv1) | |
| ssh_m3_det_context_conv1_bn = BatchNormalization(epsilon=1.9999999494757503e-05, name='ssh_m3_det_context_conv1_bn', trainable=False)(ssh_m3_det_context_conv1) | |
| x1_shape = tf.shape(ssh_c3_up) | |
| x2_shape = tf.shape(ssh_c2_lateral_relu) | |
| offsets = [0, (x1_shape[1] - x2_shape[1]) // 2, (x1_shape[2] - x2_shape[2]) // 2, 0] | |
| size = [-1, x2_shape[1], x2_shape[2], -1] | |
| crop0 = tf.slice(ssh_c3_up, offsets, size, "crop0") | |
| ssh_m3_det_context_conv1_relu = ReLU(name='ssh_m3_det_context_conv1_relu')(ssh_m3_det_context_conv1_bn) | |
| plus0_v2 = Add()([ssh_c2_lateral_relu , crop0]) | |
| ssh_m3_det_context_conv2_pad = ZeroPadding2D(padding=tuple([1, 1]))(ssh_m3_det_context_conv1_relu) | |
| ssh_m3_det_context_conv2 = Conv2D(filters = 128, kernel_size = (3, 3), name = 'ssh_m3_det_context_conv2', strides = [1, 1], padding = 'VALID', use_bias = True)(ssh_m3_det_context_conv2_pad) | |
| ssh_m3_det_context_conv3_1_pad = ZeroPadding2D(padding=tuple([1, 1]))(ssh_m3_det_context_conv1_relu) | |
| ssh_m3_det_context_conv3_1 = Conv2D(filters = 128, kernel_size = (3, 3), name = 'ssh_m3_det_context_conv3_1', strides = [1, 1], padding = 'VALID', use_bias = True)(ssh_m3_det_context_conv3_1_pad) | |
| ssh_c2_aggr_pad = ZeroPadding2D(padding=tuple([1, 1]))(plus0_v2) | |
| ssh_c2_aggr = Conv2D(filters = 256, kernel_size = (3, 3), name = 'ssh_c2_aggr', strides = [1, 1], padding = 'VALID', use_bias = True)(ssh_c2_aggr_pad) | |
| ssh_m3_det_context_conv2_bn = BatchNormalization(epsilon=1.9999999494757503e-05, name='ssh_m3_det_context_conv2_bn', trainable=False)(ssh_m3_det_context_conv2) | |
| ssh_m3_det_context_conv3_1_bn = BatchNormalization(epsilon=1.9999999494757503e-05, name='ssh_m3_det_context_conv3_1_bn', trainable=False)(ssh_m3_det_context_conv3_1) | |
| ssh_c2_aggr_bn = BatchNormalization(epsilon=1.9999999494757503e-05, name='ssh_c2_aggr_bn', trainable=False)(ssh_c2_aggr) | |
| ssh_m3_det_context_conv3_1_relu = ReLU(name='ssh_m3_det_context_conv3_1_relu')(ssh_m3_det_context_conv3_1_bn) | |
| ssh_c2_aggr_relu = ReLU(name='ssh_c2_aggr_relu')(ssh_c2_aggr_bn) | |
| ssh_m3_det_context_conv3_2_pad = ZeroPadding2D(padding=tuple([1, 1]))(ssh_m3_det_context_conv3_1_relu) | |
| ssh_m3_det_context_conv3_2 = Conv2D(filters = 128, kernel_size = (3, 3), name = 'ssh_m3_det_context_conv3_2', strides = [1, 1], padding = 'VALID', use_bias = True)(ssh_m3_det_context_conv3_2_pad) | |
| ssh_m2_det_conv1_pad = ZeroPadding2D(padding=tuple([1, 1]))(ssh_c2_aggr_relu) | |
| ssh_m2_det_conv1 = Conv2D(filters = 256, kernel_size = (3, 3), name = 'ssh_m2_det_conv1', strides = [1, 1], padding = 'VALID', use_bias = True)(ssh_m2_det_conv1_pad) | |
| ssh_m2_det_context_conv1_pad = ZeroPadding2D(padding=tuple([1, 1]))(ssh_c2_aggr_relu) | |
| ssh_m2_det_context_conv1 = Conv2D(filters = 128, kernel_size = (3, 3), name = 'ssh_m2_det_context_conv1', strides = [1, 1], padding = 'VALID', use_bias = True)(ssh_m2_det_context_conv1_pad) | |
| ssh_m2_red_up = UpSampling2D(size=(2, 2), interpolation="nearest", name="ssh_m2_red_up")(ssh_c2_aggr_relu) | |
| ssh_m3_det_context_conv3_2_bn = BatchNormalization(epsilon=1.9999999494757503e-05, name='ssh_m3_det_context_conv3_2_bn', trainable=False)(ssh_m3_det_context_conv3_2) | |
| ssh_m2_det_conv1_bn = BatchNormalization(epsilon=1.9999999494757503e-05, name='ssh_m2_det_conv1_bn', trainable=False)(ssh_m2_det_conv1) | |
| ssh_m2_det_context_conv1_bn = BatchNormalization(epsilon=1.9999999494757503e-05, name='ssh_m2_det_context_conv1_bn', trainable=False)(ssh_m2_det_context_conv1) | |
| x1_shape = tf.shape(ssh_m2_red_up) | |
| x2_shape = tf.shape(ssh_m1_red_conv_relu) | |
| offsets = [0, (x1_shape[1] - x2_shape[1]) // 2, (x1_shape[2] - x2_shape[2]) // 2, 0] | |
| size = [-1, x2_shape[1], x2_shape[2], -1] | |
| crop1 = tf.slice(ssh_m2_red_up, offsets, size, "crop1") | |
| ssh_m3_det_concat = concatenate([ssh_m3_det_conv1_bn, ssh_m3_det_context_conv2_bn, ssh_m3_det_context_conv3_2_bn], 3, name='ssh_m3_det_concat') | |
| ssh_m2_det_context_conv1_relu = ReLU(name='ssh_m2_det_context_conv1_relu')(ssh_m2_det_context_conv1_bn) | |
| plus1_v1 = Add()([ssh_m1_red_conv_relu , crop1]) | |
| ssh_m3_det_concat_relu = ReLU(name='ssh_m3_det_concat_relu')(ssh_m3_det_concat) | |
| ssh_m2_det_context_conv2_pad = ZeroPadding2D(padding=tuple([1, 1]))(ssh_m2_det_context_conv1_relu) | |
| ssh_m2_det_context_conv2 = Conv2D(filters = 128, kernel_size = (3, 3), name = 'ssh_m2_det_context_conv2', strides = [1, 1], padding = 'VALID', use_bias = True)(ssh_m2_det_context_conv2_pad) | |
| ssh_m2_det_context_conv3_1_pad = ZeroPadding2D(padding=tuple([1, 1]))(ssh_m2_det_context_conv1_relu) | |
| ssh_m2_det_context_conv3_1 = Conv2D(filters = 128, kernel_size = (3, 3), name = 'ssh_m2_det_context_conv3_1', strides = [1, 1], padding = 'VALID', use_bias = True)(ssh_m2_det_context_conv3_1_pad) | |
| ssh_c1_aggr_pad = ZeroPadding2D(padding=tuple([1, 1]))(plus1_v1) | |
| ssh_c1_aggr = Conv2D(filters = 256, kernel_size = (3, 3), name = 'ssh_c1_aggr', strides = [1, 1], padding = 'VALID', use_bias = True)(ssh_c1_aggr_pad) | |
| face_rpn_cls_score_stride32 = Conv2D(filters = 4, kernel_size = (1, 1), name = 'face_rpn_cls_score_stride32', strides = [1, 1], padding = 'VALID', use_bias = True)(ssh_m3_det_concat_relu) | |
| inter_1 = concatenate([face_rpn_cls_score_stride32[:, :, :, 0], face_rpn_cls_score_stride32[:, :, :, 1]], axis=1) | |
| inter_2 = concatenate([face_rpn_cls_score_stride32[:, :, :, 2], face_rpn_cls_score_stride32[:, :, :, 3]], axis=1) | |
| final = tf.stack([inter_1, inter_2]) | |
| face_rpn_cls_score_reshape_stride32 = tf.transpose(final, (1, 2, 3, 0), name="face_rpn_cls_score_reshape_stride32") | |
| face_rpn_bbox_pred_stride32 = Conv2D(filters = 8, kernel_size = (1, 1), name = 'face_rpn_bbox_pred_stride32', strides = [1, 1], padding = 'VALID', use_bias = True)(ssh_m3_det_concat_relu) | |
| face_rpn_landmark_pred_stride32 = Conv2D(filters = 20, kernel_size = (1, 1), name = 'face_rpn_landmark_pred_stride32', strides = [1, 1], padding = 'VALID', use_bias = True)(ssh_m3_det_concat_relu) | |
| ssh_m2_det_context_conv2_bn = BatchNormalization(epsilon=1.9999999494757503e-05, name='ssh_m2_det_context_conv2_bn', trainable=False)(ssh_m2_det_context_conv2) | |
| ssh_m2_det_context_conv3_1_bn = BatchNormalization(epsilon=1.9999999494757503e-05, name='ssh_m2_det_context_conv3_1_bn', trainable=False)(ssh_m2_det_context_conv3_1) | |
| ssh_c1_aggr_bn = BatchNormalization(epsilon=1.9999999494757503e-05, name='ssh_c1_aggr_bn', trainable=False)(ssh_c1_aggr) | |
| ssh_m2_det_context_conv3_1_relu = ReLU(name='ssh_m2_det_context_conv3_1_relu')(ssh_m2_det_context_conv3_1_bn) | |
| ssh_c1_aggr_relu = ReLU(name='ssh_c1_aggr_relu')(ssh_c1_aggr_bn) | |
| face_rpn_cls_prob_stride32 = Softmax(name = 'face_rpn_cls_prob_stride32')(face_rpn_cls_score_reshape_stride32) | |
| input_shape = [tf.shape(face_rpn_cls_prob_stride32)[k] for k in range(4)] | |
| sz = tf.dtypes.cast(input_shape[1] / 2, dtype=tf.int32) | |
| inter_1 = face_rpn_cls_prob_stride32[:, 0:sz, :, 0] | |
| inter_2 = face_rpn_cls_prob_stride32[:, 0:sz, :, 1] | |
| inter_3 = face_rpn_cls_prob_stride32[:, sz:, :, 0] | |
| inter_4 = face_rpn_cls_prob_stride32[:, sz:, :, 1] | |
| final = tf.stack([inter_1, inter_3, inter_2, inter_4]) | |
| face_rpn_cls_prob_reshape_stride32 = tf.transpose(final, (1, 2, 3, 0), name="face_rpn_cls_prob_reshape_stride32") | |
| ssh_m2_det_context_conv3_2_pad = ZeroPadding2D(padding=tuple([1, 1]))(ssh_m2_det_context_conv3_1_relu) | |
| ssh_m2_det_context_conv3_2 = Conv2D(filters = 128, kernel_size = (3, 3), name = 'ssh_m2_det_context_conv3_2', strides = [1, 1], padding = 'VALID', use_bias = True)(ssh_m2_det_context_conv3_2_pad) | |
| ssh_m1_det_conv1_pad = ZeroPadding2D(padding=tuple([1, 1]))(ssh_c1_aggr_relu) | |
| ssh_m1_det_conv1 = Conv2D(filters = 256, kernel_size = (3, 3), name = 'ssh_m1_det_conv1', strides = [1, 1], padding = 'VALID', use_bias = True)(ssh_m1_det_conv1_pad) | |
| ssh_m1_det_context_conv1_pad = ZeroPadding2D(padding=tuple([1, 1]))(ssh_c1_aggr_relu) | |
| ssh_m1_det_context_conv1 = Conv2D(filters = 128, kernel_size = (3, 3), name = 'ssh_m1_det_context_conv1', strides = [1, 1], padding = 'VALID', use_bias = True)(ssh_m1_det_context_conv1_pad) | |
| ssh_m2_det_context_conv3_2_bn = BatchNormalization(epsilon=1.9999999494757503e-05, name='ssh_m2_det_context_conv3_2_bn', trainable=False)(ssh_m2_det_context_conv3_2) | |
| ssh_m1_det_conv1_bn = BatchNormalization(epsilon=1.9999999494757503e-05, name='ssh_m1_det_conv1_bn', trainable=False)(ssh_m1_det_conv1) | |
| ssh_m1_det_context_conv1_bn = BatchNormalization(epsilon=1.9999999494757503e-05, name='ssh_m1_det_context_conv1_bn', trainable=False)(ssh_m1_det_context_conv1) | |
| ssh_m2_det_concat = concatenate([ssh_m2_det_conv1_bn, ssh_m2_det_context_conv2_bn, ssh_m2_det_context_conv3_2_bn], 3, name='ssh_m2_det_concat') | |
| ssh_m1_det_context_conv1_relu = ReLU(name='ssh_m1_det_context_conv1_relu')(ssh_m1_det_context_conv1_bn) | |
| ssh_m2_det_concat_relu = ReLU(name='ssh_m2_det_concat_relu')(ssh_m2_det_concat) | |
| ssh_m1_det_context_conv2_pad = ZeroPadding2D(padding=tuple([1, 1]))(ssh_m1_det_context_conv1_relu) | |
| ssh_m1_det_context_conv2 = Conv2D(filters = 128, kernel_size = (3, 3), name = 'ssh_m1_det_context_conv2', strides = [1, 1], padding = 'VALID', use_bias = True)(ssh_m1_det_context_conv2_pad) | |
| ssh_m1_det_context_conv3_1_pad = ZeroPadding2D(padding=tuple([1, 1]))(ssh_m1_det_context_conv1_relu) | |
| ssh_m1_det_context_conv3_1 = Conv2D(filters = 128, kernel_size = (3, 3), name = 'ssh_m1_det_context_conv3_1', strides = [1, 1], padding = 'VALID', use_bias = True)(ssh_m1_det_context_conv3_1_pad) | |
| face_rpn_cls_score_stride16 = Conv2D(filters = 4, kernel_size = (1, 1), name = 'face_rpn_cls_score_stride16', strides = [1, 1], padding = 'VALID', use_bias = True)(ssh_m2_det_concat_relu) | |
| inter_1 = concatenate([face_rpn_cls_score_stride16[:, :, :, 0], face_rpn_cls_score_stride16[:, :, :, 1]], axis=1) | |
| inter_2 = concatenate([face_rpn_cls_score_stride16[:, :, :, 2], face_rpn_cls_score_stride16[:, :, :, 3]], axis=1) | |
| final = tf.stack([inter_1, inter_2]) | |
| face_rpn_cls_score_reshape_stride16 = tf.transpose(final, (1, 2, 3, 0), name="face_rpn_cls_score_reshape_stride16") | |
| face_rpn_bbox_pred_stride16 = Conv2D(filters = 8, kernel_size = (1, 1), name = 'face_rpn_bbox_pred_stride16', strides = [1, 1], padding = 'VALID', use_bias = True)(ssh_m2_det_concat_relu) | |
| face_rpn_landmark_pred_stride16 = Conv2D(filters = 20, kernel_size = (1, 1), name = 'face_rpn_landmark_pred_stride16', strides = [1, 1], padding = 'VALID', use_bias = True)(ssh_m2_det_concat_relu) | |
| ssh_m1_det_context_conv2_bn = BatchNormalization(epsilon=1.9999999494757503e-05, name='ssh_m1_det_context_conv2_bn', trainable=False)(ssh_m1_det_context_conv2) | |
| ssh_m1_det_context_conv3_1_bn = BatchNormalization(epsilon=1.9999999494757503e-05, name='ssh_m1_det_context_conv3_1_bn', trainable=False)(ssh_m1_det_context_conv3_1) | |
| ssh_m1_det_context_conv3_1_relu = ReLU(name='ssh_m1_det_context_conv3_1_relu')(ssh_m1_det_context_conv3_1_bn) | |
| face_rpn_cls_prob_stride16 = Softmax(name = 'face_rpn_cls_prob_stride16')(face_rpn_cls_score_reshape_stride16) | |
| input_shape = [tf.shape(face_rpn_cls_prob_stride16)[k] for k in range(4)] | |
| sz = tf.dtypes.cast(input_shape[1] / 2, dtype=tf.int32) | |
| inter_1 = face_rpn_cls_prob_stride16[:, 0:sz, :, 0] | |
| inter_2 = face_rpn_cls_prob_stride16[:, 0:sz, :, 1] | |
| inter_3 = face_rpn_cls_prob_stride16[:, sz:, :, 0] | |
| inter_4 = face_rpn_cls_prob_stride16[:, sz:, :, 1] | |
| final = tf.stack([inter_1, inter_3, inter_2, inter_4]) | |
| face_rpn_cls_prob_reshape_stride16 = tf.transpose(final, (1, 2, 3, 0), name="face_rpn_cls_prob_reshape_stride16") | |
| ssh_m1_det_context_conv3_2_pad = ZeroPadding2D(padding=tuple([1, 1]))(ssh_m1_det_context_conv3_1_relu) | |
| ssh_m1_det_context_conv3_2 = Conv2D(filters = 128, kernel_size = (3, 3), name = 'ssh_m1_det_context_conv3_2', strides = [1, 1], padding = 'VALID', use_bias = True)(ssh_m1_det_context_conv3_2_pad) | |
| ssh_m1_det_context_conv3_2_bn = BatchNormalization(epsilon=1.9999999494757503e-05, name='ssh_m1_det_context_conv3_2_bn', trainable=False)(ssh_m1_det_context_conv3_2) | |
| ssh_m1_det_concat = concatenate([ssh_m1_det_conv1_bn, ssh_m1_det_context_conv2_bn, ssh_m1_det_context_conv3_2_bn], 3, name='ssh_m1_det_concat') | |
| ssh_m1_det_concat_relu = ReLU(name='ssh_m1_det_concat_relu')(ssh_m1_det_concat) | |
| face_rpn_cls_score_stride8 = Conv2D(filters = 4, kernel_size = (1, 1), name = 'face_rpn_cls_score_stride8', strides = [1, 1], padding = 'VALID', use_bias = True)(ssh_m1_det_concat_relu) | |
| inter_1 = concatenate([face_rpn_cls_score_stride8[:, :, :, 0], face_rpn_cls_score_stride8[:, :, :, 1]], axis=1) | |
| inter_2 = concatenate([face_rpn_cls_score_stride8[:, :, :, 2], face_rpn_cls_score_stride8[:, :, :, 3]], axis=1) | |
| final = tf.stack([inter_1, inter_2]) | |
| face_rpn_cls_score_reshape_stride8 = tf.transpose(final, (1, 2, 3, 0), name="face_rpn_cls_score_reshape_stride8") | |
| face_rpn_bbox_pred_stride8 = Conv2D(filters = 8, kernel_size = (1, 1), name = 'face_rpn_bbox_pred_stride8', strides = [1, 1], padding = 'VALID', use_bias = True)(ssh_m1_det_concat_relu) | |
| face_rpn_landmark_pred_stride8 = Conv2D(filters = 20, kernel_size = (1, 1), name = 'face_rpn_landmark_pred_stride8', strides = [1, 1], padding = 'VALID', use_bias = True)(ssh_m1_det_concat_relu) | |
| face_rpn_cls_prob_stride8 = Softmax(name = 'face_rpn_cls_prob_stride8')(face_rpn_cls_score_reshape_stride8) | |
| input_shape = [tf.shape(face_rpn_cls_prob_stride8)[k] for k in range(4)] | |
| sz = tf.dtypes.cast(input_shape[1] / 2, dtype=tf.int32) | |
| inter_1 = face_rpn_cls_prob_stride8[:, 0:sz, :, 0] | |
| inter_2 = face_rpn_cls_prob_stride8[:, 0:sz, :, 1] | |
| inter_3 = face_rpn_cls_prob_stride8[:, sz:, :, 0] | |
| inter_4 = face_rpn_cls_prob_stride8[:, sz:, :, 1] | |
| final = tf.stack([inter_1, inter_3, inter_2, inter_4]) | |
| face_rpn_cls_prob_reshape_stride8 = tf.transpose(final, (1, 2, 3, 0), name="face_rpn_cls_prob_reshape_stride8") | |
| model = Model(inputs=data, | |
| outputs=[face_rpn_cls_prob_reshape_stride32, | |
| face_rpn_bbox_pred_stride32, | |
| face_rpn_landmark_pred_stride32, | |
| face_rpn_cls_prob_reshape_stride16, | |
| face_rpn_bbox_pred_stride16, | |
| face_rpn_landmark_pred_stride16, | |
| face_rpn_cls_prob_reshape_stride8, | |
| face_rpn_bbox_pred_stride8, | |
| face_rpn_landmark_pred_stride8 | |
| ]) | |
| model = load_weights(model) | |
| return model | |