batchsize = 16 loadsize = 128 input_feature_layer_name = 'reshape1' metric_feature_layer_name = 'relu2' minlen = 5 maxlen = 5 weight_decay = np.float32(.2) gradient_clip_threshold = 1. train_data_provider = KTHDataProvider( batchsize=loadsize, minlen=minlen, maxlen=maxlen, numpy_rng=numpy_rng, frames_dir='../data_preparation/kth/frames', pkl_file='kth.pkl', bbox_file='../data_preparation/kth/KTHBoundingBoxInfo.txt', persons=train_persons, actions=('jogging', 'running', 'walking')) print 'fetching 100 samples from training set for approx of train IoUs...' train_data = train_data_provider.get_batch(return_all=True) perm = numpy_rng.permutation(train_data['inputs'].shape[0])[:100] train_data['inputs'] = train_data['inputs'][perm] train_data['targets'] = train_data['targets'][perm] train_data['masks'] = train_data['masks'][perm] print 'fetching validation set...' val_data_provider = KTHDataProvider(
batchsize = 16 loadsize = 128 input_feature_layer_name = 'reshape1' metric_feature_layer_name = 'relu2' plt.gray() weight_decay = np.float32(.2) numpy_rng = np.random.RandomState(1) print 'fetching test set...' test_data_provider = KTHDataProvider( numpy_rng=numpy_rng, frames_dir='/data/lisatmp3/michals/data/KTH/frames', pkl_file='kth.pkl', bbox_file='/data/lisatmp3/michals/data/KTH/KTHBoundingBoxInfo.txt', persons=[args.test_person], actions=('jogging', 'running', 'walking')) test_data = test_data_provider.get_batch() print 'loading pretrained CNN...' feature_network = HumanConvNet(name='Person CNN', nout=2, numpy_rng=numpy_rng, theano_rng=theano_rng, batchsize=batchsize) feature_network.load('convnet_eth_inria_data/human_convnet_val_best.h5') feature_network.mode.set_value(np.uint8(1)) print "instantiating model..."
nhid = 32 batchsize = 16 loadsize = 128 input_feature_layer_name = 'reshape1' metric_feature_layer_name = 'relu2' plt.gray() weight_decay = np.float32(.2) numpy_rng = np.random.RandomState(1) print 'fetching test set...' test_data_provider = KTHDataProvider( numpy_rng=numpy_rng, frames_dir='/data/lisatmp3/michals/data/KTH/frames', pkl_file='kth.pkl', bbox_file='/data/lisatmp3/michals/data/KTH/KTHBoundingBoxInfo.txt', persons=[args.test_person], actions=('jogging', 'running', 'walking')) test_data = test_data_provider.get_batch() print 'loading pretrained CNN...' feature_network = HumanConvNet( name='Person CNN', nout=2, numpy_rng=numpy_rng, theano_rng=theano_rng, batchsize=batchsize) feature_network.load('convnet_eth_inria_data/human_convnet_val_best.h5') feature_network.mode.set_value(np.uint8(1)) print "instantiating model..." model = RATM(name='RATM', imsize=imsize, patchsize=patchsize, nhid=nhid, numpy_rng=numpy_rng, eps=1e-4,
imsize = (120, 160) nhid = 32 batchsize = 16 loadsize = 128 input_feature_layer_name = 'reshape1' metric_feature_layer_name = 'relu2' minlen = 5 maxlen = 5 weight_decay = np.float32(.2) gradient_clip_threshold = 1. train_data_provider = KTHDataProvider( batchsize=loadsize, minlen=minlen, maxlen=maxlen, numpy_rng=numpy_rng, frames_dir='../data_preparation/kth/frames', pkl_file='kth.pkl', bbox_file='../data_preparation/kth/KTHBoundingBoxInfo.txt', persons=train_persons, actions=('jogging', 'running', 'walking')) print 'fetching 100 samples from training set for approx of train IoUs...' train_data = train_data_provider.get_batch(return_all=True) perm = numpy_rng.permutation(train_data['inputs'].shape[0])[:100] train_data['inputs'] = train_data['inputs'][perm] train_data['targets'] = train_data['targets'][perm] train_data['masks'] = train_data['masks'][perm] print 'fetching validation set...' val_data_provider = KTHDataProvider( numpy_rng=numpy_rng, frames_dir='../data_preparation/kth/frames',