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lstm_inference.py
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lstm_inference.py
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# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
''' Imports '''
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import random
import sklearn.model_selection as ms
import sklearn.preprocessing as pr
import numpy as np
import time
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import argparse
import rospy
import signal
import sys
from std_msgs.msg import String
from std_msgs.msg import Float64MultiArray
from std_msgs.msg import Float64
from std_msgs.msg import MultiArrayLayout
import operator
from lstm import Model
from Var import Var
import operator
import json
plt.switch_backend('agg')
use_cuda = torch.cuda.is_available()
def callNoses(data):
''' Get positions of noses '''
global noses
noses = data
def callXPose(data):
global x_pose_arr
x_pose_arr = data
def callYPose(data):
global y_pose_arr
y_pose_arr = data
def callDab(data):
global dab_arr
dab_arr = data
def callData(data):
global lstm_data
lstm_data = data.data
def inference(model, data, states, classes):
output, states = model(data, states)
max_idx, val = max(enumerate(output[0]), key=operator.itemgetter(1))
states = (states[0].detach(), states[1].detach())
return classes[max_idx], states
def sigint_handler(sig, iteration):
''' Handles Ctrl + C. '''
sys.exit(0)
if __name__ == "__main__":
''' Initialize sigint handler '''
signal.signal(signal.SIGINT, sigint_handler)
parser = argparse.ArgumentParser()
parser.add_argument('--debug', dest='debug', action='store_true')
parser.add_argument('--only-arm', '-o', dest='use_arm', action='store_true')
parser.set_defaults(use_arm=False)
parser.add_argument('--ckpt_name', '-c', dest='ckpt_name',
default='lstm307.ckpt', type=str)
parser.add_argument('--multiply-by-score', '-m', dest='m_score', action='store_true')
parser.set_defaults(m_score=False)
parser.set_defaults(debug=False)
args = parser.parse_args()
debug = args.debug
use_arm = args.use_arm
m_score = args.m_score
ckpt_name = "lstmpts/" + args.ckpt_name
rospy.init_node('lstm_inference', anonymous=True)
v = Var(use_arm)
rate = rospy.Rate(v.get_rate())
''' Subscribers '''
nose_sub = rospy.Subscriber('nose', Float64MultiArray, callNoses)
x_pose_sub = rospy.Subscriber('xPose', Float64MultiArray, callXPose)
y_pose_sub = rospy.Subscriber('yPose', Float64MultiArray, callYPose)
dab_sub = rospy.Subscriber('dab', Float64MultiArray, callDab)
data_sub = rospy.Subscriber('data', String, callData)
''' Publishers '''
pubX = rospy.Publisher('distPOP', String, queue_size=10)
pubScore = rospy.Publisher('scorePOP', String, queue_size=10)
pubNoses = rospy.Publisher('nosePOP', Float64MultiArray, queue_size=10)
pubRes = rospy.Publisher('popnn', String, queue_size=10)
pubXPose = rospy.Publisher('xPose', Float64MultiArray, queue_size = 10)
pubYPose = rospy.Publisher('yPose', Float64MultiArray, queue_size = 10)
pubDab = rospy.Publisher('dab', Float64MultiArray, queue_size = 10)
lstm_vars = v.get_LSTM()
input_size = v.get_size()
num_layers = lstm_vars['num_layers']
hidden_size = lstm_vars['hidden_size']
seq_len = lstm_vars['seq_len']
num_features = v.get_num_features() - 1 if m_score else v.get_num_features()
dropout = lstm_vars['dropout']
classes = v.get_classes()
model = Model(input_size=input_size, num_layers=num_layers, hidden_size=hidden_size, seq_len=seq_len, num_features=num_features, dropout=dropout, mode="TEST")
model = model.cuda() if use_cuda else model
ckpt_fn = args.ckpt_name
model.load_state_dict(torch.load("lstmpts/%s" % ckpt_fn))
states = model.init_states()
lastCombined = np.zeros(0)
while True:
global noses
global x_pose_arr
global y_pose_arr
global dab_arr
global lstm_data
inference_data = json.loads(lstm_data)
x = np.array(inference_data['0']).reshape(1, 1, -1)
y = np.array(inference_data['1']).reshape(1, 1, -1)
xDist = np.array(inference_data['2']).reshape(1, 1, -1)
yDist = np.array(inference_data['3']).reshape(1, 1, -1)
scores = np.array(inference_data['4']).reshape(1, 1, -1)
scoreString = scores.tostring()
xString = x.tostring()
numHumans = x.shape[-1] / input_size
in_size = input_size*numHumans
if m_score:
x = np.stack(x[0][0][i]*scores[0][0][i] for i in range(x.shape[-1])).reshape(1, in_size)
y = np.stack(y[0][0][i]*scores[0][0][i] for i in range(y.shape[-1])).reshape(1, in_size)
# xDist = np.stack(xDist[0][0][i]*scores[0][0][i] for i in range(xDist.shape[-1])).reshape(1, input_size)
# yDist = np.stack(yDist[0][0][i]*scores[0][0][i] for i in range(yDist.shape[-1])).reshape(1, input_size)
# combined = np.concatenate(
# (newX.reshape(numHumans, 1, -1), newY.reshape(numHumans, 1, -1)), axis=1)
combined = np.concatenate((x, y), axis=1) if m_score else np.concatenate((x, y, scores), axis=1)
combined = combined.reshape(numHumans, -1)
combined = pr.normalize(combined).reshape(numHumans, num_features, input_size)
outs = []
if not np.array_equal(combined, lastCombined):
for human in combined:
datum = torch.from_numpy(human).cuda().float().view(1, num_features, input_size)
out, states = inference(model, datum, states, classes)
outs.append(out)
else:
outs = lastOuts
s = ", ".join(outs)
# xPoses = np.array([int(element) for element in list(x_pose_arr)[1:-1] if (element!='' and element!=' ' and element!='.')])
# yPoses = np.array([int(element) for element in list(y_pose_arr)[1:-1] if (element!='' and element!=' ' and element!='.')])
# dabs = ([int(element) for element in list(dab_arr)[1:-1] if (element!='' and element!=' ' and element!='.')])
xPoses = x_pose_arr.data
yPoses = y_pose_arr.data
dabs = dab_arr.data
print "-" * 25 + "HUMAN REPORT" + "-" * 25
for idx in range(numHumans):
try:
pose_string = "Person %d: Action: " % idx
pose_string += outs[idx]
pose_string += ", X-Pose : Yes" if xPoses[idx] == 1 else ", X-Pose : No"
pose_string += ", Y-Pose : Yes" if yPoses[idx] == 1 else ", Y-Pose : No"
pose_string += ", Dab : "
if dabs[idx] == 1:
pose_string += "Right Dab"
elif dabs[idx] == 2:
pose_string += "Left Dab"
else:
pose_string += "No Dab"
print (pose_string)
except:
pose_string = "No Humans in Frame"
print (pose_string)
break
lastCombined = combined
lastOuts = outs
outs = []
pubRes.publish(s)
pubNoses.publish(noses)
pubScore.publish(scoreString)
pubX.publish(xString)
pubXPose.publish(x_pose_arr)
pubYPose.publish(y_pose_arr)
pubDab.publish(dab_arr)
rate.sleep()