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myalexnet_forward.py
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myalexnet_forward.py
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################################################################################
#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
#
#
################################################################################
import numpy as np
import tensorflow as tf
train_x = np.zeros((1, 227,227,3)).astype(np.float32)
train_y = np.zeros((1, 1000))
xdim = train_x.shape[1:]
ydim = train_y.shape[1]
################################################################################
# (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'))
def extract_features(drive):
net_data = np.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), [-1]+conv.get_shape().as_list()[1:])
x = tf.placeholder(tf.float32, (None,) + xdim)
#summary_op = tf.image_summary('conv1/features', x, max_images=1)
#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, name='pool5')
#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(np.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)
# Visualize conv5 features
#summary_op = tf.image_summary('conv1/features', features, max_images=10)
#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)"""
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
#cross_entropy = -tf.reduce_sum([1.0,1.0]*tf.log([1.0,1.0]))
#tf.scalar_summary("cross_entropy", cross_entropy)
#merged = tf.merge_all_summaries()
#writer = tf.train.SummaryWriter('log/', sess.graph)
init = tf.initialize_all_variables()
sess.run(init)
features = []
labels = []
print "starting extraction"
for i in range(0, drive.train.size):
im, label = drive.train.next_batch()
#summary_str, output = sess.run([merged, prob], feed_dict = {x:[im]})
#writer.add_summary(summary_str, 0)
#tf.transpose(fc7,(1,0))
features.append(fc7.eval(session=sess,feed_dict={x:[im]}))
labels.append(label)
print "{} of {}".format(i, drive.train.size)
sess.close()
return features, labels