################################################################################ #Michael Guerzhoy, 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 x_dummy = (random.random((1,)+ xdim)/255.).astype(float32) im = x_dummy.copy() im[0,:,:,:] = (imread("quail227.png")[:,:,:3]).astype(float32) im = im-mean(im) im[:, :, 0], im[:, :, 2] = im[:, :, 2], im[:, :, 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')) 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(3, group, input) kernel_groups = tf.split(3, group, kernel) output_groups = [convolve(i, k) for i,k in zip(input_groups, kernel_groups)] conv = tf.concat(3, output_groups) return tf.reshape(tf.nn.bias_add(conv, biases), conv.get_shape().as_list()) x = tf.placeholder(tf.float32, im.shape) #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: im}) print (time.time()-t), "sec elapsed on avg" ################################################################################ #Output: inds = argsort(output)[0,:] for i in range(5): print class_names[inds[-1-i]], output[0, inds[-1-i]]