def run_model(which_set, classifier, batch_size, model_axes, data_axes=('b', 0, 1, 'c')):
dset = SeqDataset(which_set=which_set)
#indices = dset.get_filtered_indices(perturbations=['0'], flips=[False])
indices = range(dset.numSamples)
#indices = [2,3,4]
#print indices
targets_ints = []
stats = []
fileName = []
for n in indices:
features, targets, fname = dset.get_clip(n)
misclass = []
frame_misclass = []
feature = features.reshape(len(features), 48, 48, 1)
target = None
if targets is not None:
target = basic_7emotion_names.index(targets.lower().replace("angry", "anger"))
#feature = feature / 255.
#feature = feature.astype('float32')
if data_axes != model_axes:
feature = feature.transpose(*[data_axes.index(axis) for axis in model_axes])
num_samples = feature.shape[3]
predictions = []
for i in range(num_samples / batch_size):
# TODO FIX ME, after grayscale
predictions.append(classifier(feature[0,:,:,i*batch_size:(i+1)*batch_size][numpy.newaxis,:,:,:])) #XXX:numpy.newaxis
# for modulo we pad with garbage
if batch_size > num_samples:
modulo = batch_size - num_samples
else:
modulo = num_samples % batch_size
if modulo != 0:
# TODO FIX ME, after grayscale
shape = [1, feature.shape[1], feature.shape[2], modulo]
padding = numpy.ones((shape)).astype('float32')
# TODO FIX ME, after grayscale
feature = numpy.concatenate((feature[0,:,:,(num_samples/batch_size) * batch_size:][numpy.newaxis,:,:,:], padding), axis = 3) #XXX:axis,numpy.newaxis
predictions.append(classifier(feature)[:batch_size - modulo])
targets_ints.append(target)
predictions = numpy.concatenate(predictions, axis=0)
misclass.append(accuracy(predictions, target))
frame_misclass.append(framewise_accuracy(predictions, target))
stats.append(get_stats(predictions, target))
fileName.append(fname)
# error = numpy.sum(misclass) / float(len(features))
# print "clip wise: ", error, 1-error
# frame_misclass = numpy.concatenate(frame_misclass)
# error = frame_misclass.sum() / float(len(frame_misclass))
# print "frame wise: ", error, 1-error
return numpy.vstack(stats), targets_ints, fileName
def get_data(which_set, clip):
data = SeqDataset(which_set=which_set)
# organize axis
data_axes = data.get_data_specs()[0].components[0].axes
if 't' in data_axes:
data_axes = [axis for axis in data_axes if axis not in ['t']]
#targets = numpy.argmax(data.targets, axis=1)
return data.get_clip(clip) + (data_axes,)
def get_data(which_set, clip):
data = SeqDataset(which_set=which_set)
# organize axis
data_axes = data.get_data_specs()[0].components[0].axes
if 't' in data_axes:
data_axes = [axis for axis in data_axes if axis not in ['t']]
#targets = numpy.argmax(data.targets, axis=1)
return data.get_clip(clip) + (data_axes, )
def run_model(which_set,
classifier,
batch_size,
model_axes,
data_axes=('b', 0, 1, 'c')):
dset = SeqDataset(which_set=which_set)
#indices = dset.get_filtered_indices(perturbations=['0'], flips=[False])
indices = range(dset.numSamples)
#indices = [2,3,4]
#print indices
targets_ints = []
stats = []
fileName = []
for n in indices:
features, targets, fname = dset.get_clip(n)
misclass = []
frame_misclass = []
feature = features.reshape(len(features), 48, 48, 1)
target = None
if targets is not None:
target = basic_7emotion_names.index(targets.lower().replace(
"angry", "anger"))
#feature = feature / 255.
#feature = feature.astype('float32')
if data_axes != model_axes:
feature = feature.transpose(
*[data_axes.index(axis) for axis in model_axes])
num_samples = feature.shape[3]
predictions = []
for i in range(num_samples / batch_size):
# TODO FIX ME, after grayscale
predictions.append(
classifier(feature[0, :, :,
i * batch_size:(i + 1) * batch_size]
[numpy.newaxis, :, :, :])) #XXX:numpy.newaxis
# for modulo we pad with garbage
if batch_size > num_samples:
modulo = batch_size - num_samples
else:
modulo = num_samples % batch_size
if modulo != 0:
# TODO FIX ME, after grayscale
shape = [1, feature.shape[1], feature.shape[2], modulo]
padding = numpy.ones((shape)).astype('float32')
# TODO FIX ME, after grayscale
feature = numpy.concatenate(
(feature[0, :, :, (num_samples / batch_size) *
batch_size:][numpy.newaxis, :, :, :], padding),
axis=3) #XXX:axis,numpy.newaxis
predictions.append(classifier(feature)[:batch_size - modulo])
targets_ints.append(target)
predictions = numpy.concatenate(predictions, axis=0)
misclass.append(accuracy(predictions, target))
frame_misclass.append(framewise_accuracy(predictions, target))
stats.append(get_stats(predictions, target))
fileName.append(fname)
# error = numpy.sum(misclass) / float(len(features))
# print "clip wise: ", error, 1-error
# frame_misclass = numpy.concatenate(frame_misclass)
# error = frame_misclass.sum() / float(len(frame_misclass))
# print "frame wise: ", error, 1-error
return numpy.vstack(stats), targets_ints, fileName