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G2D19_P2OF_ResHB_1LSTM.py
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G2D19_P2OF_ResHB_1LSTM.py
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import tensorflow as tf
from src.net.NetBase import *
from src.layers.LayerHelper import *
from src.layers.BasicLayers import *
from src.layers.ResidualLayers import *
from src.layers.RNN import *
import settings.LayerSettings as layerSettings
import settings.DataSettings as dataSettings
import numpy as np
DARKNET19_MODEL_PATH = 'data/pretrainModels/darknet19/darknet19.pb'
class Net(NetworkBase):
def __init__(self, inputImage_, batchSize_, unrolledSize_, isTraining_, trainingStep_):
self._inputImage = inputImage_
self._batchSize = batchSize_
self._unrolledSize = unrolledSize_
self._isTraining = isTraining_
self._trainingStep = trainingStep_
self._DROPOUT_PROB = 0.5
self._NUMBER_OF_NEURONS_IN_LSTM = 1024
self._dictOfInterestedActivations = {}
if dataSettings.GROUPED_SIZE != 2:
errorMessage = __name__ + " only take GROUPED_SIZE = 2;\n"
errorMessage += "However, DataSettings.GROUPED_SIZE = " + str(dataSettings.GROUPED_SIZE)
raise ValueError(errorMessage)
def Build(self):
darknet19_GraphDef = tf.GraphDef()
'''
The CNN only take input shape [..., w, h, c]. Thus, move the UNROLLED_SIZE dimension
to merged with BATCH_SIZE, and form the shape: [b*u, w, h, c].
'''
convInput = tf.reshape(self._inputImage, [-1,
dataSettings.IMAGE_SIZE, dataSettings.IMAGE_SIZE, dataSettings.IMAGE_CHANNELS])
with tf.name_scope("DarkNet19"):
with open(DARKNET19_MODEL_PATH, 'rb') as modelFile:
darknet19_GraphDef.ParseFromString(modelFile.read())
listOfOperations = tf.import_graph_def(darknet19_GraphDef,
input_map={"input": convInput},
# return_elements=["2-maxpool", "32-leaky"])
return_elements=["2-maxpool", "36-leaky"])
pool2 = listOfOperations[0].outputs[0]
lastOp = listOfOperations[-1]
out = lastOp.outputs[0]
out, updateOp0 = BatchNormalization('BN_0', out, isConvLayer_=True,
isTraining_=self._isTraining, currentStep_=self._trainingStep)
opticalFlowOut, updateOF = self._buildOpticalFlowNet(pool2)
print("opticalFlowOut.shape = ", opticalFlowOut.shape) # shape = [b*u, 1024]
with tf.name_scope("CNN"):
'''
The input shape = [b, u, g, w, h, c]
after Conv, shape = [b*u*g, w', h', c']
here, decouple the Group dimension, shape = [b*u, g * w' * h' * c']
'''
print("In CNN:")
print("\t darknetOutput.shape = ", out.shape) # shape = [b*u*g, 7, 7, 1024]
w, h, c = out.shape[1:] # 7, 7, 1024
out = tf.reshape( out,
[self._batchSize * self._unrolledSize, dataSettings.GROUPED_SIZE,
w, h, c]) # [b*u, g, 7, 7, 1024]
out = tf.transpose(out, perm=[0, 2, 3, 4, 1]) # [b*u, 7, 7, 1024, g]
print("\t after transpose, out.shape = ", out.shape)
out = tf.reshape( out,
[self._batchSize * self._unrolledSize,
w, h, c * dataSettings.GROUPED_SIZE])
print("\t before ConcatConv, out.shape = ", out.shape) # shape = [b*u, 7, 7, 1024*g]
out, updateOp1 = ResidualHeadBlock('ResHeadBlock', out, [128, 64, 128], isTraining_=self._isTraining,
trainingStep_=self._trainingStep, activationType_="LEAKY_RELU", isTrainable_=True)
print("\t before Fc, out.shape = ", out.shape)
out = tf.cond(self._isTraining, lambda: tf.nn.dropout(out, self._DROPOUT_PROB), lambda: out)
out = FullyConnectedLayer('Fc1', out, numberOfOutputs_=1024)
out, updateOp2 = BatchNormalization('BN_4', out, isConvLayer_=False,
isTraining_=self._isTraining, currentStep_=self._trainingStep)
self._dictOfInterestedActivations['CNN'] = out
print("\t Fc out.shape = ", out.shape)
with tf.name_scope("Concat"):
out = tf.concat([out, opticalFlowOut], axis=1)
print("In Concat:")
print("\t before Fc, out.shape = ", out.shape)
out = FullyConnectedLayer('Fc', out, numberOfOutputs_=1024)
out, updateOp3 = BatchNormalization('BN_5', out, isConvLayer_=False,
isTraining_=self._isTraining, currentStep_=self._trainingStep)
'''
Note: For tf.nn.rnn_cell.dynamic_rnn(), the input shape of [1:] must be explicit.
i.e., one Can't Reshape the out by:
out = tf.reshape(out, [BATCH_SIZE, UNROLLED_SIZE, -1])
since '-1' is implicit dimension.
'''
featuresShapeInOneBatch = out.shape[1:].as_list()
targetShape = [self._batchSize, self._unrolledSize] + featuresShapeInOneBatch
out = tf.reshape(out, targetShape)
self._dictOfInterestedActivations['Concat'] = out
print("before LSTM, shape = ", out.shape)
out, self._stateTensorOfLSTM_1, self._statePlaceHolderOfLSTM_1 = LSTM( "LSTM_1",
out,
self._NUMBER_OF_NEURONS_IN_LSTM,
isTraining_=self._isTraining,
dropoutProb_=self._DROPOUT_PROB)
self._dictOfInterestedActivations['LSTM'] = out
with tf.name_scope("Fc_Final"):
featuresShapeInOneBatch = out.shape[2:].as_list()
targetShape = [self._batchSize * self._unrolledSize] + featuresShapeInOneBatch
out = tf.reshape(out, targetShape)
out = FullyConnectedLayer('Fc3', out, numberOfOutputs_=dataSettings.NUMBER_OF_CATEGORIES)
self._logits = tf.reshape(out, [self._batchSize, self._unrolledSize, -1])
self._updateOp = tf.group(updateOF, updateOp0, updateOp1, updateOp2, updateOp3)
print()
@property
def logitsOp(self):
return self._logits
@property
def updateOp(self):
return self._updateOp
def GetListOfStatesTensorInLSTMs(self):
'''
You should Not Only sess.run() the net.logits, but also this listOfTensors
to get the States of LSTM. And assign it to PlaceHolder next time.
ex:
>> tupleOfResults = sess.run( [out] + net.GetListOfStatesTensorInLSTMs(), ...)
>> listOfResults = list(tupleOfResults)
>> output = listOfResults.pop(0)
>> listOfStates = listOfResults
See GetFeedDictOfLSTM() method as well
'''
return [self._stateTensorOfLSTM_1]
def GetFeedDictOfLSTM(self, BATCH_SIZE_, listOfPreviousStateValues_=None):
'''
This function will return a dictionary that contained the PlaceHolder-Value map
of the LSTM states.
You can use this function as follows:
>> feed_dict = { netInput : batchOfImages }
>> feedDictOFLSTM = net.GetLSTM_Feed_Dict(BATCH_SIZE, listOfPreviousStateValues)
>> tupleOfOutputs = sess.run( [out] + net.GetListOfStatesTensorInLSTMs(),
feed_dict = feed_dict.update(feedDictOFLSTM) )
>> listOfOutputs = list(tupleOfOutputs)
>> output = listOfOutputs.pop(0)
>> listOfPreviousStateValues = listOfOutputs.pop(0)
'''
if listOfPreviousStateValues_ == None:
'''
For the first time (or, the first of Unrolls), there's no previous state,
return zeros state.
'''
initialCellState = tuple( [np.zeros([BATCH_SIZE_, self._NUMBER_OF_NEURONS_IN_LSTM])] * 2 )
initialCellState = tf.nn.rnn_cell.LSTMStateTuple(initialCellState[0], initialCellState[1])
return {self._statePlaceHolderOfLSTM_1 : initialCellState }
else:
if len(listOfPreviousStateValues_) != 1:
errorMessage = "len(listOfPreviousStateValues_) = " + str( len(listOfPreviousStateValues_) )
errorMessage += "; However, the expected lenght is 1.\n"
errorMessage += "\t Do you change the Network Structure, such as Add New LSTM?\n"
errorMessage += "\t Or, do you add more tensor to session.run()?\n"
return { self._statePlaceHolderOfLSTM_1 : listOfPreviousStateValues_[0] }
def _buildOpticalFlowNet(self, inputTensor_):
'''
The input shape = [b, u, g, w, h, c]
after Conv, shape = [b*u*g, w', h', c']
here, decouple the Group dimension, shape = [b*u, g * w' * h' * c']
'''
with tf.name_scope("OpticalFLow"):
print("In OpticalFlow:")
print("\t pool2.shape = ", inputTensor_.shape) # shape = [b*u*g, 112, 112, 32]
w, h, c = inputTensor_.shape[1:] # 112, 112, 32
out = tf.reshape( inputTensor_,
[self._batchSize * self._unrolledSize, dataSettings.GROUPED_SIZE,
w, h, c]) # [b*u, g, 112, 112, 32]
out = tf.transpose(out, perm=[0, 2, 3, 4, 1]) # [b*u, 112, 112, 32, g]
print("\t after transpose, out.shape = ", out.shape)
out = tf.reshape( out,
[self._batchSize * self._unrolledSize,
w, h, c * dataSettings.GROUPED_SIZE])
print("\t before Conv2, out.shape = ", out.shape) # shape = [b*u, 112, 112, 32*g]
out = ConvLayer('Conv2', out, filterSize_=3, numberOfFilters_=64, stride_=1, padding_='SAME', isTrainable_=True)
out, updateOp1 = BatchNormalization('BN2', out, isConvLayer_=True, isTraining_=self._isTraining,
currentStep_=self._trainingStep, isTrainable_=True)
out = LeakyRELU('RELU2', out)
out = MaxPoolLayer('Pool2', out, kernelSize_=2, stride_=2, padding_='SAME')
out, updateOp2 = ResidualBlock('ResBlock', out, [128, 128, 64], isTraining_=self._isTraining,
trainingStep_=self._trainingStep, activationType_="LEAKY_RELU", isTrainable_=True)
out = MaxPoolLayer('Pool5', out, kernelSize_=2, stride_=2, padding_='SAME')
print("\t before Fc, out.shape = ", out.shape) # shape = [b*u, 28, 28, 128]
out = tf.cond(self._isTraining, lambda: tf.nn.dropout(out, self._DROPOUT_PROB), lambda: out)
out = FullyConnectedLayer('Fc_of', out, numberOfOutputs_=1024)
out, updateOp3 = BatchNormalization('BN_of', out, isConvLayer_=False,
isTraining_=self._isTraining, currentStep_=self._trainingStep)
self._dictOfInterestedActivations['OpticalFlow'] = out
print("\t Fc final.shape = ", out.shape)
updateOp = tf.group(updateOp1, updateOp2, updateOp3)
return out, updateOp