Zacks_VGG_RNN_EventLayer_Inference.py

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf
from tensorflow.python.tools import inspect_checkpoint as chkp
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import nn_ops
from tensorflow.python.ops import init_ops
from tensorflow.python.ops import variable_scope as vs
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes

import numpy as np
import os
from os import listdir
from os.path import isfile, join, isdir
from random import shuffle, choice
from PIL import Image
import sys
import json
import collections, cv2


input_width = 224
input_height = 224
num_channels = 3
slim = tf.contrib.slim
n_hidden1 = 4096
n_hidden2 = 4096
feature_size = 4096
learnError = 0
n_epochs = 1
batch_size = 2
min_steps = batch_size

lr = 1e-10


def loadData(jsonData, inPath):
	batchPaths = []
	for vid in jsonData.keys():
		# VIRAT format
		# dirName = '_'.join(vid.split('.')[0].split('_')[2:])
		
		# Other dataset file name format
		dirName = ''.join(vid.split('.')[0])
		# print(dirName)
		vidPath = join(inPath,dirName)
		#print(vidPath)
		#batchPaths = batchPaths + sorted([str(join(vidPath, f) + '/') for f in listdir(vidPath) if isdir(join(vidPath, f))])
		
		# Breakfast Actions
		batchPaths = batchPaths + [vidPath]
		#print(batchPaths)
	return batchPaths

def loadMiniBatch(vidFilePath):
	vidName = vidFilePath.split('/')[-3]
	frameList = sorted([join(vidFilePath, f) for f in listdir(vidFilePath) if isfile(join(vidFilePath, f)) and f.endswith('.jpg')])
	frameList = sorted(frameList, key=lambda x: int(x.split('/')[-1].split('.')[0].split('_')[1]))
	its = [iter(frameList), iter(frameList[1:])]
	segments = zip(*its)
	minibatch = []
	for segment in segments:
		# print(segment)
		im = []
		numFrames = 0
		for j, imFile in enumerate(segment):
			img = Image.open(imFile)
			img = img.resize((input_width, input_height), Image.ANTIALIAS)
			img = np.array(img)
			img = img[:, :, ::-1].copy() 
			img = cv2.GaussianBlur(img,(5,5),0)
			im.append(img)
			numFrames += 1
		minibatch.append(np.stack(im))
	return vidFilePath, minibatch

def broadcast(tensor, shape):
    return tensor + tf.zeros(shape, dtype=tensor.dtype)

def RNNCell(W, B, inputs, state):
	"""Most basic RNN: output = new_state = act(W * input + U * state + B)."""
	one = constant_op.constant(1, dtype=dtypes.int32)
	add = math_ops.add
	multiply = math_ops.multiply
	sigmoid = math_ops.sigmoid
	activation = math_ops.tanh

	gate_inputs = math_ops.matmul(array_ops.concat([inputs, state], 1), W)
	gate_inputs = nn_ops.bias_add(gate_inputs, B)
	output = sigmoid(gate_inputs)
	return output, output

def lstm_cell(W, b, forget_bias, inputs, state):
	one = constant_op.constant(1, dtype=dtypes.int32)
	add = math_ops.add
	multiply = math_ops.multiply
	sigmoid = math_ops.sigmoid
	activation = math_ops.sigmoid
	# activation = math_ops.tanh

	c, h = array_ops.split(value=state, num_or_size_splits=2, axis=one)

	gate_inputs = math_ops.matmul(array_ops.concat([inputs, h], 1), W)
	gate_inputs = nn_ops.bias_add(gate_inputs, b)
	# i = input_gate, j = new_input, f = forget_gate, o = output_gate
	i, j, f, o = array_ops.split(value=gate_inputs, num_or_size_splits=4, axis=one)

	forget_bias_tensor = constant_op.constant(forget_bias, dtype=f.dtype)

	new_c = add(multiply(c, sigmoid(add(f, forget_bias_tensor))), multiply(sigmoid(i), activation(j)))
	new_h = multiply(activation(new_c), sigmoid(o))
	new_state = array_ops.concat([new_c, new_h], 1)

	return new_h, new_state

# def lstm_cell_New(W, b, forget_bias, inputs, state):
# 	one = constant_op.constant(1, dtype=dtypes.int32)
# 	add = math_ops.add
# 	multiply = math_ops.multiply
# 	sigmoid = math_ops.sigmoid
# 	activation = math_ops.sigmoid
# 	# activation = math_ops.tanh

# 	c, h = array_ops.split(value=state, num_or_size_splits=2, axis=one)

# 	gate_inputs = math_ops.matmul(array_ops.concat([inputs, h], 1), W)
# 	gate_inputs = nn_ops.bias_add(gate_inputs, b)
# 	# i = input_gate, j = new_input, f = forget_gate, o = output_gate
# 	i, j = array_ops.split(value=gate_inputs, num_or_size_splits=2, axis=one)

# 	new_c = add(c, multiply(i, activation(j)))
# 	new_h = activation(new_c)
# 	new_state = array_ops.concat([new_c, new_h], 1)

# 	return new_h, new_state

jsonData = json.load(open(sys.argv[1]))
vidPath = sys.argv[2]
modelPath = sys.argv[3]
outFileName = sys.argv[4] #'temporalSegmentation.txt'

batch = loadData(jsonData, vidPath)

#sys.exit(1)

inputs = tf.placeholder(tf.float32, (None, 224, 224, 3), name='inputs')
learning_rate = tf.placeholder(tf.float32, [])
is_training = tf.placeholder(tf.bool)

# Setup RNN
# init_state1 = tf.placeholder(tf.float32, [1, n_hidden1])
# W_RNN1 = vs.get_variable("W1", shape=[feature_size+n_hidden1, n_hidden1])
# b_RNN1 = vs.get_variable("b1", shape=[n_hidden1], initializer=init_ops.zeros_initializer(dtype=tf.float32))
# curr_state1 = init_state1

# Setup LSTM
init_state1 = tf.placeholder(tf.float32, [1, 2*n_hidden1], name="State")
W_lstm1 = vs.get_variable("W1", shape=[feature_size + n_hidden1, 4*n_hidden1])
b_lstm1 = vs.get_variable("b1", shape=[4*n_hidden1], initializer=init_ops.zeros_initializer(dtype=tf.float32))
# curr_state1 = broadcast(init_state1, [tf.shape(xs)[0], 2*n_hidden1])
curr_state1 = init_state1


W_fc1 = tf.Variable(tf.truncated_normal([n_hidden1, feature_size], stddev=0.1))
b_fc1 = tf.Variable(tf.constant(0.1, shape=[feature_size]))

scope = 'vgg_16'
fc_conv_padding = 'VALID'
dropout_keep_prob=0.8

r, g, b = tf.split(axis=3, num_or_size_splits=3, value=inputs * 255.0)
VGG_MEAN = [103.939, 116.779, 123.68]
VGG_inputs = tf.concat(values=[b - VGG_MEAN[0], g - VGG_MEAN[1], r - VGG_MEAN[2]], axis=3)

with tf.variable_scope(scope, 'vgg_16', [VGG_inputs]) as sc:
	end_points_collection = sc.original_name_scope + '_end_points'
	# Collect outputs for conv2d, fully_connected and max_pool2d.
	with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d],
											outputs_collections=end_points_collection):
		net = slim.repeat(inputs, 2, slim.conv2d, 64, [3, 3], scope='conv1')
		net = slim.max_pool2d(net, [2, 2], scope='pool1')
		net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], scope='conv2')
		net = slim.max_pool2d(net, [2, 2], scope='pool2')
		net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3], scope='conv3')
		net = slim.max_pool2d(net, [2, 2], scope='pool3')
		net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv4')
		net = slim.max_pool2d(net, [2, 2], scope='pool4')
		net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv5')
		net = slim.max_pool2d(net, [2, 2], scope='pool5')

		# Use conv2d instead of fully_connected layers.
		net = slim.conv2d(net, 4096, [7, 7], padding=fc_conv_padding, scope='fc6')
		net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
											 scope='dropout6')
		net = slim.conv2d(net, 4096, [1, 1], scope='fc7')
		vgg16_Features = tf.reshape(net, (-1,4096))
		# Convert end_points_collection into a end_point dict.
		end_points = slim.utils.convert_collection_to_dict(end_points_collection)

RNN_inputs = tf.reshape(vgg16_Features[0,:], (-1, feature_size))

# RNN
# h_1, curr_state1 = RNNCell(W_RNN1, b_RNN1, RNN_inputs, curr_state1)

# LSTM
h_1, curr_state1 = lstm_cell(W_lstm1, b_lstm1, 1.0, RNN_inputs, curr_state1)

# fc1 = tf.matmul(h_1, W_fc1) + b_fc1
fc1 = h_1
print(fc1[0,:].shape, vgg16_Features[1,:].shape)
sseLoss1 = tf.square(tf.subtract(fc1[0,:], vgg16_Features[1,:]))
mask = tf.greater(sseLoss1, learnError * tf.ones_like(sseLoss1))
sseLoss1 = tf.multiply(sseLoss1, tf.cast(mask, tf.float32))
sseLoss = tf.reduce_mean(sseLoss1)

# Optimization
train_op = tf.train.GradientDescentOptimizer(learning_rate).minimize(sseLoss)


#####################
### Training loop ###
#####################

init = tf.global_variables_initializer()

#saver = tf.train.Saver(tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope="vgg_16"))
with tf.Session() as sess:
	# Initialize parameters
	sess.run(init)
	saver = tf.train.Saver(max_to_keep=0)
	saver.restore(sess, modelPath)

	avgPredError = 1.0

	### In case of interruption, load parameters from the last iteration (ex: 29)
	#saver.restore(sess, './model_stacked_lstm_29')
	### And update the loop to account for the previous iterations
	#for i in range(29,n_epochs):
	step = 0

	# RNN
	# new_state = np.random.uniform(-0.5,high=0.5,size=(1,n_hidden1))

	# LSTM
	new_state = np.random.uniform(-0.5,high=0.5,size=(1,2*n_hidden1))
	loss = []

	for miniBatchPath in batch:
		
		# RNN
		# new_state = np.random.uniform(-0.5,high=0.5,size=(1,n_hidden1))

		# LSTM
		new_state = np.random.uniform(-0.5,high=0.5,size=(1,2*n_hidden1))

		avgPredError = 0
		vidName, minibatches = loadMiniBatch(miniBatchPath)
		try:
			os.remove(vidName+outFileName)
		except OSError:
			pass
		#break
		segCount = 0
		predError = collections.deque(maxlen=20)
		numSegs = 0
		print('Video:', vidName+outFileName)
		prevSeg = 0
		for x_train in minibatches:
			segCount += 1
			ret = sess.run([sseLoss, sseLoss1, curr_state1, fc1], feed_dict = {inputs: x_train, is_training: False, init_state1: new_state, learning_rate:lr})
			new_state = ret[2]
			with open(vidName+outFileName, 'a') as outFile:
				outFile.write('%d\t%2f\n'%(segCount, ret[0]))
			# if ret[0]/avgPredError > 1.5:#and segCount - prevSeg > 30:
			# 	with open(vidName+outFileName, 'a') as outFile:
			# 		outFile.write('%d\t%d\n'%(prevSeg, segCount))
			# 	prevSeg = segCount
			# 	numSegs += 1
			# 	predError.clear()
			# predError.append(ret[0])
			# avgPredError = np.mean(predError)
		# with open(vidName+outFileName, 'a') as outFile:
		# 	outFile.write('%d\t%d\n'%(prevSeg, segCount))