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',