CaffeNet - a variant of AlexNet
Ref: Classification: Instant Recognition with Caffe
This is caffeNet
区别：https://github.com/BVLC/caffe/issues/4202
This is AlexNet.
单机版：http://www.cs.toronto.edu/~guerzhoy/tf_alexnet/myalexnet_forward_newtf.py
################################################################################ #Michael Guerzhoy and Davi Frossard, 2016 #AlexNet implementation in TensorFlow, with weights #Details: #http://www.cs.toronto.edu/~guerzhoy/tf_alexnet/ # #With code from https://github.com/ethereon/caffe-tensorflow #Model from https://github.com/BVLC/caffe/tree/master/models/bvlc_alexnet #Weights from Caffe converted using https://github.com/ethereon/caffe-tensorflow # # ################################################################################ from numpy import * import os #from pylab import * import numpy as np #import matplotlib.pyplot as plt #import matplotlib.cbook as cbook import time from scipy.misc import imread from scipy.misc import imresize import matplotlib.image as mpimg from scipy.ndimage import filters import urllib from numpy import random import tensorflow as tf from caffe_classes import class_names train_x = zeros((1, 227,227,3)).astype(float32) train_y = zeros((1, 1000)) xdim = train_x.shape[1:] ydim = train_y.shape[1] ################################################################################ #Read Image, and change to BGR im1 = (imread("laska.png")[:,:,:3]).astype(float32) im1 = im1 - mean(im1) im1[:, :, 0], im1[:, :, 2] = im1[:, :, 2], im1[:, :, 0] im2 = (imread("poodle.png")[:,:,:3]).astype(float32) im2[:, :, 0], im2[:, :, 2] = im2[:, :, 2], im2[:, :, 0] ################################################################################ # (self.feed('data') # .conv(11, 11, 96, 4, 4, padding='VALID', name='conv1') # .lrn(2, 2e-05, 0.75, name='norm1') # .max_pool(3, 3, 2, 2, padding='VALID', name='pool1') # .conv(5, 5, 256, 1, 1, group=2, name='conv2') # .lrn(2, 2e-05, 0.75, name='norm2') # .max_pool(3, 3, 2, 2, padding='VALID', name='pool2') # .conv(3, 3, 384, 1, 1, name='conv3') # .conv(3, 3, 384, 1, 1, group=2, name='conv4') # .conv(3, 3, 256, 1, 1, group=2, name='conv5') # .fc(4096, name='fc6') # .fc(4096, name='fc7') # .fc(1000, relu=False, name='fc8') # .softmax(name='prob')) #In Python 3.5, change this to: net_data = load(open("bvlc_alexnet.npy", "rb"), encoding="latin1").item() #net_data = load("bvlc_alexnet.npy").item() def conv(input, kernel, biases, k_h, k_w, c_o, s_h, s_w, padding="VALID", group=1): '''From https://github.com/ethereon/caffe-tensorflow ''' c_i = input.get_shape()[-1] assert c_i%group==0 assert c_o%group==0 convolve = lambda i, k: tf.nn.conv2d(i, k, [1, s_h, s_w, 1], padding=padding) if group==1: conv = convolve(input, kernel) else: input_groups = tf.split(input, group, 3) #tf.split(3, group, input) kernel_groups = tf.split(kernel, group, 3) #tf.split(3, group, kernel) output_groups = [convolve(i, k) for i,k in zip(input_groups, kernel_groups)] conv = tf.concat(output_groups, 3) #tf.concat(3, output_groups) return tf.reshape(tf.nn.bias_add(conv, biases), [-1]+conv.get_shape().as_list()[1:]) x = tf.placeholder(tf.float32, (None,) + xdim) #conv1 #conv(11, 11, 96, 4, 4, padding='VALID', name='conv1') k_h = 11; k_w = 11; c_o = 96; s_h = 4; s_w = 4 conv1W = tf.Variable(net_data["conv1"][0]) conv1b = tf.Variable(net_data["conv1"][1]) conv1_in = conv(x, conv1W, conv1b, k_h, k_w, c_o, s_h, s_w, padding="SAME", group=1) conv1 = tf.nn.relu(conv1_in) #lrn1 #lrn(2, 2e-05, 0.75, name='norm1') radius = 2; alpha = 2e-05; beta = 0.75; bias = 1.0 lrn1 = tf.nn.local_response_normalization(conv1, depth_radius=radius, alpha=alpha, beta=beta, bias=bias) #maxpool1 #max_pool(3, 3, 2, 2, padding='VALID', name='pool1') k_h = 3; k_w = 3; s_h = 2; s_w = 2; padding = 'VALID' maxpool1 = tf.nn.max_pool(lrn1, ksize=[1, k_h, k_w, 1], strides=[1, s_h, s_w, 1], padding=padding) #conv2 #conv(5, 5, 256, 1, 1, group=2, name='conv2') k_h = 5; k_w = 5; c_o = 256; s_h = 1; s_w = 1; group = 2 conv2W = tf.Variable(net_data["conv2"][0]) conv2b = tf.Variable(net_data["conv2"][1]) conv2_in = conv(maxpool1, conv2W, conv2b, k_h, k_w, c_o, s_h, s_w, padding="SAME", group=group) conv2 = tf.nn.relu(conv2_in) #lrn2 #lrn(2, 2e-05, 0.75, name='norm2') radius = 2; alpha = 2e-05; beta = 0.75; bias = 1.0 lrn2 = tf.nn.local_response_normalization(conv2, depth_radius=radius, alpha=alpha, beta=beta, bias=bias) #maxpool2 #max_pool(3, 3, 2, 2, padding='VALID', name='pool2') k_h = 3; k_w = 3; s_h = 2; s_w = 2; padding = 'VALID' maxpool2 = tf.nn.max_pool(lrn2, ksize=[1, k_h, k_w, 1], strides=[1, s_h, s_w, 1], padding=padding) #conv3 #conv(3, 3, 384, 1, 1, name='conv3') k_h = 3; k_w = 3; c_o = 384; s_h = 1; s_w = 1; group = 1 conv3W = tf.Variable(net_data["conv3"][0]) conv3b = tf.Variable(net_data["conv3"][1]) conv3_in = conv(maxpool2, conv3W, conv3b, k_h, k_w, c_o, s_h, s_w, padding="SAME", group=group) conv3 = tf.nn.relu(conv3_in) #conv4 #conv(3, 3, 384, 1, 1, group=2, name='conv4') k_h = 3; k_w = 3; c_o = 384; s_h = 1; s_w = 1; group = 2 conv4W = tf.Variable(net_data["conv4"][0]) conv4b = tf.Variable(net_data["conv4"][1]) conv4_in = conv(conv3, conv4W, conv4b, k_h, k_w, c_o, s_h, s_w, padding="SAME", group=group) conv4 = tf.nn.relu(conv4_in) #conv5 #conv(3, 3, 256, 1, 1, group=2, name='conv5') k_h = 3; k_w = 3; c_o = 256; s_h = 1; s_w = 1; group = 2 conv5W = tf.Variable(net_data["conv5"][0]) conv5b = tf.Variable(net_data["conv5"][1]) conv5_in = conv(conv4, conv5W, conv5b, k_h, k_w, c_o, s_h, s_w, padding="SAME", group=group) conv5 = tf.nn.relu(conv5_in) #maxpool5 #max_pool(3, 3, 2, 2, padding='VALID', name='pool5') k_h = 3; k_w = 3; s_h = 2; s_w = 2; padding = 'VALID' maxpool5 = tf.nn.max_pool(conv5, ksize=[1, k_h, k_w, 1], strides=[1, s_h, s_w, 1], padding=padding) #fc6 #fc(4096, name='fc6') fc6W = tf.Variable(net_data["fc6"][0]) fc6b = tf.Variable(net_data["fc6"][1]) fc6 = tf.nn.relu_layer(tf.reshape(maxpool5, [-1, int(prod(maxpool5.get_shape()[1:]))]), fc6W, fc6b) #fc7 #fc(4096, name='fc7') fc7W = tf.Variable(net_data["fc7"][0]) fc7b = tf.Variable(net_data["fc7"][1]) fc7 = tf.nn.relu_layer(fc6, fc7W, fc7b) #fc8 #fc(1000, relu=False, name='fc8') fc8W = tf.Variable(net_data["fc8"][0]) fc8b = tf.Variable(net_data["fc8"][1]) fc8 = tf.nn.xw_plus_b(fc7, fc8W, fc8b) #prob #softmax(name='prob')) prob = tf.nn.softmax(fc8) init = tf.initialize_all_variables() sess = tf.Session() sess.run(init) t = time.time() output = sess.run(prob, feed_dict = {x:[im1,im2]}) ################################################################################ #Output: for input_im_ind in range(output.shape[0]): inds = argsort(output)[input_im_ind,:] print("Image", input_im_ind) for i in range(5): print(class_names[inds[-1-i]], output[input_im_ind, inds[-1-i]]) print(time.time()-t)