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hotel.yaml
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# Name of the experiment
name: "hotel"
# -----------------------------------------------------------------------------
# Optimization settings |
# -----------------------------------------------------------------------------
# Buffer size of the tf.data.Dataset.prefetch option
prefetchSize: 100
# Shuffle the dataset
shuffle: False
# Number of epochs
epochs: 50
# Use centered option in RMSProp
centered: true
# Learning rate
learningRate: 0.003
# Learning rate decay
learningRateDecay: 0.95
# Clipping ratio
clippingRatio: 5
# Optimizer decay rate
optimizerDecay: 0.95
# Optimizer momentum rate
optimizerMomentum: 0.2
# Weight regularization rate
l2Rate: 0.005
# -----------------------------------------------------------------------------
# Dataset parameters |
# -----------------------------------------------------------------------------
# Folder of the datasets
dataPath: "datasets"
# Name of the training files in the dataPath
trainDatasets:
- "train/biwi_eth_train.txt"
- "train/crowds_zara01_train.txt"
- "train/crowds_zara02_train.txt"
- "train/crowds_zara03_train.txt"
- "train/students001_train.txt"
- "train/students003_train.txt"
- "train/uni_examples_train.txt"
# Name of the validation files in the dataPath
validationDatasets:
- "val/biwi_eth_val.txt"
- "val/crowds_zara01_val.txt"
- "val/crowds_zara02_val.txt"
- "val/crowds_zara03_val.txt"
- "val/students001_val.txt"
- "val/students003_val.txt"
- "val/uni_examples_val.txt"
# Name of the test files in the dataPath
testDatasets:
- "test/biwi_hotel.txt"
# Name of the training navigation maps in the dataPath
trainMaps:
- "navMap/eth.npy"
- "navMap/zara1.npy"
- "navMap/zara2.npy"
- "navMap/zara3.npy"
- "navMap/univ1.npy"
- "navMap/univ2.npy"
- "navMap/univ3.npy"
# Name of the validation navigation maps in the dataPath
validationMaps:
- "navMap/eth.npy"
- "navMap/zara1.npy"
- "navMap/zara2.npy"
- "navMap/zara3.npy"
- "navMap/univ1.npy"
- "navMap/univ2.npy"
- "navMap/univ3.npy"
# Name of the test navigation maps in the dataPath
testMaps:
- "navMap/hotel.npy"
# Semantig grid size
semanticGridSize: 32
# Number of labels of the semantic map
numLabels: 8
# List of semantic maps
semanticMaps:
- "semMap/eth.npy"
- "semMap/hotel.npy"
- "semMap/univ.npy"
- "semMap/zara.npy"
# List of homography matrix
homography:
- "homography/eth.txt"
- "homography/hotel.txt"
- "homography/univ.txt"
- "homography/zara.txt"
# Name of the training navigation maps in the dataPath
trainMapping:
- "eth"
- "zara"
- "zara"
- "zara"
- "univ"
- "univ"
- "univ"
# Name of the validation navigation maps in the dataPath
validationMapping:
- "eth"
- "zara"
- "zara"
- "zara"
- "univ"
- "univ"
- "univ"
# Name of the test navigation maps in the dataPath
testMapping:
- "hotel"
# Delimiter used in the files of the dataset
delimiter: "tab"
# Number of frame observed
obsLen: 8
# Number of frame that the model has to predict
predLen: 12
# Number of frames to skip while making the dataset. If True, the number of
# frame to skip is random, between 1 and (obsLen + predLen). For e.g. if
# sequence_1 in the dataset is from Frame_1 - Frame_N and skip = 5 then the
# sequence_2 will be start from Frame_N+5
skip: 1
# -----------------------------------------------------------------------------
# Model parameters |
# -----------------------------------------------------------------------------
# Maximum number of pedestrian per frame
maxNumPed: 93
# Size of the sum pooling window
gridSize: 8
# Size of the neighborhood
neighborhoodSize: 2
# Number of hidden
rnnSize: 128
# Dimension of the embedding layer
embeddingSize: 64
# Type of pooling module to use. Option supported: social, occupancy,
# navigation, combined or null. Combined pooling is a list of the other pooling
# layers
poolingModule:
- "social"
- "semantic"
# Image width plus padding
imageWidth: 25
# Image height plus padding
imageHeight: 20
# Width of the navigation grid
navigationWidth: 400
# Height of the navigation grid
navigationHeight: 320
# Kernel size for the average pooling
kernelSize: 2
# Size of the neighborhood navigation grid
navigationGrid: 33
# Size of the neighborhood semantic grid
semanticGridSize: 50
# -----------------------------------------------------------------------------
# Logger settings |
# -----------------------------------------------------------------------------
# Folder where to save the logs
logFolder: "logs"
# Logger level
logLevel: "INFO"
# Folder where to save the model
modelFolder: "models"
# Save the sampled coordinates
saveCoordinates: true