def inception_block_3b(X): X_3x3 = fr_utils.conv2d_bn(X, layer='inception_5b_3x3', cv1_out=96, cv1_filter=(1, 1), cv2_out=384, cv2_filter=(3, 3), cv2_strides=(1, 1), padding=(1, 1)) X_pool = MaxPooling2D(pool_size=3, strides=2, data_format='channels_first')(X) X_pool = fr_utils.conv2d_bn(X_pool, layer='inception_5b_pool', cv1_out=96, cv1_filter=(1, 1)) X_pool = ZeroPadding2D(padding=(1, 1), data_format='channels_first')(X_pool) X_1x1 = fr_utils.conv2d_bn(X, layer='inception_5b_1x1', cv1_out=256, cv1_filter=(1, 1)) inception = concatenate([X_3x3, X_pool, X_1x1], axis=1) return inception
def inception_block_2b(X): #inception4e X_3x3 = fr_utils.conv2d_bn(X, layer='inception_4e_3x3', cv1_out=160, cv1_filter=(1, 1), cv2_out=256, cv2_filter=(3, 3), cv2_strides=(2, 2), padding=(1, 1)) X_5x5 = fr_utils.conv2d_bn(X, layer='inception_4e_5x5', cv1_out=64, cv1_filter=(1, 1), cv2_out=128, cv2_filter=(5, 5), cv2_strides=(2, 2), padding=(2, 2)) X_pool = MaxPooling2D(pool_size=3, strides=2, data_format='channels_first')(X) X_pool = ZeroPadding2D(padding=((0, 1), (0, 1)), data_format='channels_first')(X_pool) inception = concatenate([X_3x3, X_5x5, X_pool], axis=1) return inception
def inception_block_2a(X): X_3x3 = fr_utils.conv2d_bn(X, layer='inception_4a_3x3', cv1_out=96, cv1_filter=(1, 1), cv2_out=192, cv2_filter=(3, 3), cv2_strides=(1, 1), padding=(1, 1)) X_5x5 = fr_utils.conv2d_bn(X, layer='inception_4a_5x5', cv1_out=32, cv1_filter=(1, 1), cv2_out=64, cv2_filter=(5, 5), cv2_strides=(1, 1), padding=(2, 2)) X_pool = AveragePooling2D(pool_size=(3, 3), strides=(3, 3), data_format='channels_first')(X) X_pool = fr_utils.conv2d_bn(X_pool, layer='inception_4a_pool', cv1_out=128, cv1_filter=(1, 1), padding=(2, 2)) X_1x1 = fr_utils.conv2d_bn(X, layer='inception_4a_1x1', cv1_out=256, cv1_filter=(1, 1)) inception = concatenate([X_3x3, X_5x5, X_pool, X_1x1], axis=1) return inception
def inception_block_1c(X): X_3x3 = fr_utils.conv2d_bn(X, layer='inception_3c_3x3', cv1_out=128, cv1_filter=(1, 1), cv2_out=256, cv2_filter=(3, 3), cv2_strides=(2, 2), padding=(1, 1)) X_5x5 = fr_utils.conv2d_bn(X, layer='inception_3c_5x5', cv1_out=32, cv1_filter=(1, 1), cv2_out=64, cv2_filter=(5, 5), cv2_strides=(2, 2), padding=(2, 2)) X_pool = MaxPooling2D(pool_size=3, strides=2, data_format='channels_first')(X) X_pool = ZeroPadding2D(padding=((0, 1), (0, 1)), data_format='channels_first')(X_pool) inception = concatenate([X_3x3, X_5x5, X_pool], axis=1) return inception
def inception_block_2a(X): X_3x3 = fr_utils.conv2d_bn(X, layer='inception_4a_3x3', cv1_out=96, cv1_filter=(1, 1), cv2_out=192, cv2_filter=(3, 3), cv2_strides=(1, 1), padding=(1, 1)) X_5x5 = fr_utils.conv2d_bn(X, layer='inception_4a_5x5', cv1_out=32, cv1_filter=(1, 1), cv2_out=64, cv2_filter=(5, 5), cv2_strides=(1, 1), padding=(2, 2)) X_pool = AveragePooling2D(pool_size=(3, 3), strides=(3, 3), data_format='channels_first')(X) X_pool = fr_utils.conv2d_bn(X_pool, layer='inception_4a_pool', cv1_out=128, cv1_filter=(1, 1), padding=(2, 2)) X_1x1 = fr_utils.conv2d_bn(X, layer='inception_4a_1x1', cv1_out=256, cv1_filter=(1, 1)) inception = concatenate([X_3x3, X_5x5, X_pool, X_1x1], axis=1) return inception
def inception_block_3b(X): X_3x3 = fr_utils.conv2d_bn(X, layer='inception_5b_3x3', cv1_out=96, cv1_filter=(1, 1), cv2_out=384, cv2_filter=(3, 3), cv2_strides=(1, 1), padding=(1, 1)) X_pool = MaxPooling2D(pool_size=3, strides=2, data_format='channels_first')(X) X_pool = fr_utils.conv2d_bn(X_pool, layer='inception_5b_pool', cv1_out=96, cv1_filter=(1, 1)) X_pool = ZeroPadding2D(padding=(1, 1), data_format='channels_first')(X_pool) X_1x1 = fr_utils.conv2d_bn(X, layer='inception_5b_1x1', cv1_out=256, cv1_filter=(1, 1)) inception = concatenate([X_3x3, X_pool, X_1x1], axis=1) return inception