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graspNet.py
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#!/usr/bin/env python
import numpy as np
import os
import time
import tensorflow as tf
class model:
def __init__(self):
self.BATCH_SIZE = 128
self.THETA_SIZE = 18
def initial_weights(self, weight_file='./models/weights/bvlc_alexnet.npy'):
if weight_file:
# Load what you want the initialisation to be
print('Loading weights from {0}'.format(weight_file))
net_data = np.load(weight_file).item()
conv1W_init = net_data["conv1"][0]
conv1b_init = net_data["conv1"][1]
conv2W_init = net_data["conv2"][0]
conv2b_init = net_data["conv2"][1]
conv3W_init = net_data["conv3"][0]
conv3b_init = net_data["conv3"][1]
conv4W_init = net_data["conv4"][0]
conv4b_init = net_data["conv4"][1]
conv5W_init = net_data["conv5"][0]
conv5b_init = net_data["conv5"][1]
fc6W_init = tf.truncated_normal([9216, 4096], stddev=0.01)
fc6b_init = tf.constant(0.1, shape=[4096])
fc7W_init = tf.truncated_normal([4096, 1024], stddev=0.01)
fc7b_init = tf.constant(0.1, shape=[1024])
fc8W_init = tf.truncated_normal([1024, 18], stddev=0.01)
fc8b_init = tf.constant(0.1, shape=[18])
else:
conv1W_init = tf.truncated_normal([11, 11, 3, 96], stddev=0.1)
conv1b_init = tf.constant(0.1, shape=[96])
conv2W_init = tf.truncated_normal([5, 5, 48, 256], stddev=0.1)
conv2b_init = tf.constant(0.1, shape=[256])
conv3W_init = tf.truncated_normal([3, 3, 256, 384], stddev=0.1)
conv3b_init = tf.constant(0.1, shape=[384])
conv4W_init = tf.truncated_normal([3, 3, 192, 384], stddev=0.1)
conv4b_init = tf.constant(0.1, shape=[384])
conv5W_init = tf.truncated_normal([3, 3, 192, 256], stddev=0.1)
conv5b_init = tf.constant(0.1, shape=[256])
fc6W_init = tf.truncated_normal([9216, 4096], stddev=0.1)
fc6b_init = tf.constant(0.1, shape=[4096])
fc7W_init = tf.truncated_normal([4096, 1024], stddev=0.1)
fc7b_init = tf.constant(0.1, shape=[1024])
fc8W_init = tf.truncated_normal(
[1024, self.THETA_SIZE], stddev=0.1)
fc8b_init = tf.constant(0.1, shape=[self.THETA_SIZE])
self.conv1W = tf.Variable(conv1W_init)
self.conv1b = tf.Variable(conv1b_init)
self.conv2W = tf.Variable(conv2W_init)
self.conv2b = tf.Variable(conv2b_init)
self.conv3W = tf.Variable(conv3W_init)
self.conv3b = tf.Variable(conv3b_init)
self.conv4W = tf.Variable(conv4W_init)
self.conv4b = tf.Variable(conv4b_init)
self.conv5W = tf.Variable(conv5W_init)
self.conv5b = tf.Variable(conv5b_init)
self.fc6W = tf.Variable(fc6W_init)
self.fc6b = tf.Variable(fc6b_init)
self.fc7W = tf.Variable(fc7W_init)
self.fc7b = tf.Variable(fc7b_init)
self.fc8b = tf.Variable(fc8b_init)
self.fc8W = tf.Variable(fc8W_init)
self.dropfc6 = tf.placeholder(tf.float32, name="dropoutfc6_keep_prob")
self.dropfc7 = tf.placeholder(tf.float32, name="dropoutfc7_keep_prob")
def gen_model(self, image_batch):
# 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
conv1_in = conv(image_batch, self.conv1W, self.conv1b,
k_h, k_w, c_o, s_h, s_w, padding="SAME", group=1)
self.conv1_in = conv1_in
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
conv2_in = conv(maxpool1, self.conv2W, self.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
conv3_in = conv(maxpool2, self.conv3W, self.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
conv4_in = conv(conv3, self.conv4W, self.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
conv5_in = conv(conv4, self.conv5W, self.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')
fc6 = tf.nn.relu_layer(tf.reshape(maxpool5, [-1, int(np.prod(maxpool5.get_shape()[1:]))]),
self.fc6W, self.fc6b)
# dropout
drop6 = tf.nn.dropout(fc6, self.dropfc6)
# fc7
#fc(4096, name='fc7')
fc7 = tf.nn.relu_layer(drop6, self.fc7W, self.fc7b)
# dropout
drop7 = tf.nn.dropout(fc7, self.dropfc7)
# fc8
#fc(1000, relu=False, name='fc8')
fc8 = tf.nn.xw_plus_b(drop7, self.fc8W, self.fc8b)
# Debug stuff
self.fc7 = fc7
self.fc6 = fc6
self.conv5 = conv5
# End Debug
return fc8
def gen_loss(self, fc8, theta_label_batch, grasp_label_batch):
fc8_shape = tf.shape(fc8)
# computiong batch size as an op from the output
input_batch_size = tf.gather(fc8_shape, 0)
theta_one_hot = tf.one_hot(
theta_label_batch, depth=18, on_value=1.0, off_value=0.0)
theta_acted = tf.reduce_sum(
fc8 * theta_one_hot, reduction_indices=1, name='theta_acted')
sig_op = tf.clip_by_value(tf.nn.sigmoid(
theta_acted), 0.001, 0.999, name='clipped_sigmoid')
sig_loss = tf.to_float(grasp_label_batch) * -tf.log(sig_op) + (1 -
tf.to_float(grasp_label_batch)) * -tf.log(1 - sig_op)
self.sig_op = sig_op
self.sig_loss = sig_loss
loss = tf.reduce_mean(sig_loss)
#loss = tf.clip_by_value(unclipped_loss, -2, 2, name='clipped_loss')
#sig_loss = tf.reduce_mean(tf.square(sig_op), name='sig_loss')
conf = tf.equal(tf.to_int32(tf.greater_equal(sig_op, 0.5)), tf.to_int32(tf.greater_equal(
grasp_label_batch, 0.1))) # the plus 0.5 is there because of the alpha added on because of shake
self.conf = conf
accuracy = tf.reduce_mean(tf.to_float(conf))
return loss, accuracy
def conv(input, kernel, biases, k_h, k_w, c_o, s_h, s_w, padding="VALID", group=1):
c_i = input.get_shape()[-1]
assert c_i % group == 0
assert c_o % group == 0
def convolve(i, k): return 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)
input_groups = tf.split(input, group, 3)
# kernel_groups = tf.split(3, group, kernel)
kernel_groups = tf.split(kernel, group, 3)
output_groups = [convolve(i, k)
for i, k in zip(input_groups, kernel_groups)]
conv = tf.concat(output_groups, 3)
return tf.reshape(tf.nn.bias_add(conv, biases), [-1] + conv.get_shape().as_list()[1:])