def get_features(path, split, standardize):
if path.find(',') != -1:
paths = path.split(',')
Xs = [ get_features(subpath, split, standardize) for subpath in paths ]
X = np.concatenate( Xs, axis = 1)
return X
if path.endswith('.npy'):
topo_view = np.load(path)
else:
topo_view = serial.load(path)
if str(type(topo_view)).find('h5py') != -1:
name ,= topo_view.keys()
topo_view = topo_view[name].value.T
if len(topo_view.shape) == 2:
X = topo_view
else:
view_converter = DefaultViewConverter(topo_view.shape[1:])
print 'converting data'
X = view_converter.topo_view_to_design_mat(topo_view)
if split:
X = np.concatenate( (np.abs(X),np.abs(-X)), axis=1)
if standardize:
assert False #bug: if X is test set, we need to subtract train mean, divide by train std
X -= X.mean(axis=0)
X /= np.sqrt(.01+np.var(X,axis=0))
return X
def make_viewer(mat, grid_shape=None, patch_shape=None, activation=None, pad=None, is_color = False, rescale = True):
""" Given filters in rows, guesses dimensions of patches
and nice dimensions for the PatchViewer and returns a PatchViewer
containing visualizations of the filters"""
num_channels = 1
if is_color:
num_channels = 3
if grid_shape is None:
grid_shape = PatchViewer.pick_shape(mat.shape[0] )
if patch_shape is None:
assert mat.shape[1] % num_channels == 0
patch_shape = PatchViewer.pick_shape(mat.shape[1] / num_channels, exact = True)
assert patch_shape[0] * patch_shape[1] * num_channels == mat.shape[1]
rval = PatchViewer(grid_shape, patch_shape, pad=pad, is_color = is_color)
topo_shape = (patch_shape[0], patch_shape[1], num_channels)
view_converter = DefaultViewConverter(topo_shape)
topo_view = view_converter.design_mat_to_topo_view(mat)
for i in xrange(mat.shape[0]):
if activation is not None:
if hasattr(activation[0], '__iter__'):
act = [a[i] for a in activation]
else:
act = activation[i]
else:
act = None
patch = topo_view[i, :]
rval.add_patch(patch, rescale=rescale,
activation=act)
return rval
def plot(w):
nblocks = int(model.n_g / model.sparse_gmask.bw_g)
filters_per_block = model.sparse_gmask.bw_g * model.sparse_hmask.bw_h
block_viewer = PatchViewer((model.sparse_gmask.bw_g, model.sparse_hmask.bw_h),
(opts.height, opts.width),
is_color = opts.color,
pad=(2,2))
chan_viewer = PatchViewer(get_dims(nblocks),
(block_viewer.image.shape[0],
block_viewer.image.shape[1]),
is_color = opts.color,
pad=(5,5))
main_viewer = PatchViewer(get_dims(nplots),
(chan_viewer.image.shape[0],
chan_viewer.image.shape[1]),
is_color = opts.color,
pad=(10,10))
topo_shape = [opts.height, opts.width, opts.chans]
view_converter = DefaultViewConverter(topo_shape)
if opts.splitblocks:
os.makedirs('filters/')
for chan_i in xrange(nplots):
viewer_dims = slice(0, None) if opts.color else chan_i
for bidx in xrange(nblocks):
for fidx in xrange(filters_per_block):
fi = bidx * filters_per_block + fidx
topo_view = view_converter.design_mat_to_topo_view(w[fi:fi+1,:])
try:
block_viewer.add_patch(topo_view[0,:,:,viewer_dims])
except:
import pdb; pdb.set_trace()
if opts.splitblocks:
pl.imshow(block_viewer.image, interpolation='nearest')
pl.axis('off')
pl.title('Wv - block %i, chan %i' % (bidx, chan_i))
pl.savefig('filters/filters_chan%i_block%i.png' % (bidx, chan_i))
chan_viewer.add_patch(block_viewer.image[:,:,viewer_dims] - 0.5)
block_viewer.clear()
main_viewer.add_patch(chan_viewer.image[:,:,viewer_dims] - 0.5)
chan_viewer.clear()
return copy.copy(main_viewer.image)
def set_topological_view(self, V, axes=('b', 0, 1, 'c')):
"""
Sets the dataset to represent V, where V is a batch
of topological views of examples.
.. todo::
Why is this parameter named 'V'?
Parameters
----------
V : ndarray
An array containing a design matrix representation of
training examples.
axes : WRITEME
"""
assert not contains_nan(V)
rows = V.shape[axes.index(0)]
cols = V.shape[axes.index(1)]
channels = V.shape[axes.index('c')]
self.view_converter = DefaultViewConverter([rows, cols, channels],
axes=axes)
self.X = self.view_converter.topo_view_to_design_mat(V)
# self.X_topo_space stores a "default" topological space that
# will be used only when self.iterator is called without a
# data_specs, and with "topo=True", which is deprecated.
self.X_topo_space = self.view_converter.topo_space
assert not contains_nan(self.X)
# Update data specs
X_space = VectorSpace(dim=self.X.shape[1])
X_source = 'features'
if self.y is None:
space = X_space
source = X_source
else:
if self.y.ndim == 1:
dim = 1
else:
dim = self.y.shape[-1]
# This is to support old pickled models
if getattr(self, 'y_labels', None) is not None:
y_space = IndexSpace(dim=dim, max_labels=self.y_labels)
elif getattr(self, 'max_labels', None) is not None:
y_space = IndexSpace(dim=dim, max_labels=self.max_labels)
else:
y_space = VectorSpace(dim=dim)
y_source = 'targets'
Latent_space = VectorSpace(dim=self.latent.shape[-1])
Latent_source = 'latents'
space = CompositeSpace((X_space, y_space,Latent_space))
source = (X_source, y_source,Latent_source)
self.data_specs = (space, source)
self.X_space = X_space
self._iter_data_specs = (X_space, X_source)
def _transform_multi_channel_data(self, X, y):
# Data partitioning
parted_X, parted_y = self._partition_data(X=X, y=y, partition_size=self.window_size)
transposed_X = np.transpose(parted_X, [0, 2, 1])
converted_X = np.reshape(transposed_X, (transposed_X.shape[0],
transposed_X.shape[1],
1,
transposed_X.shape[2]))
# Create view converter
view_converter = DefaultViewConverter(shape=self.sample_shape,
axes=('b', 0, 1, 'c'))
# Convert data into a design matrix
view_converted_X = view_converter.topo_view_to_design_mat(converted_X)
assert np.all(converted_X == view_converter.design_mat_to_topo_view(view_converted_X))
# Format the target into proper format
sum_y = np.sum(parted_y, axis=1)
sum_y[sum_y > 0] = 1
one_hot_formatter = OneHotFormatter(max_labels=self.n_classes)
hot_y = one_hot_formatter.format(sum_y)
return view_converted_X, hot_y, view_converter
def set_topological_view(self, topo_view, axes=('b', 0, 1, 'c')):
'''
Sets the dataset to represent topo_view, where topo_view is a batch
of topological views of examples.
Parameters
----------
topo_view : ndarray
An array containing a design matrix representation of training
examples.
'''
assert not np.any(np.isnan(topo_view))
frames = topo_view.shape[axes.index('b')] # pretend frames come in as batch dim
rows = topo_view.shape[axes.index(0)]
cols = topo_view.shape[axes.index(1)]
channels = topo_view.shape[axes.index('c')]
# leave out frames...
self.view_converter = DefaultViewConverter([rows, cols, channels], axes=axes)
self.X = self.view_converter.topo_view_to_design_mat(topo_view)
# self.X_topo_space stores a "default" topological space that
# will be used only when self.iterator is called without a
# data_specs, and with "topo=True", which is deprecated.
self.X_topo_space = self.view_converter.topo_space
assert not np.any(np.isnan(self.X))
# Update data specs
X_space = VectorSpace(dim = frames * rows * cols * channels)
X_source = 'features'
assert self.y is None, 'y not supported now'
space = X_space
source = X_source
self.data_specs = (space, source)
self.X_space = X_space
self._iter_data_specs = (X_space, X_source)
def set_topological_view(self, V, axes=('b', 0, 1, 'c'), start=0):
"""
Sets the dataset to represent V, where V is a batch
of topological views of examples.
Parameters
----------
V : ndarray
An array containing a design matrix representation of training
examples. If unspecified, the entire dataset (`self.X`) is used
instead.
TODO: why is this parameter named 'V'?
"""
assert not numpy.any(numpy.isnan(V))
rows = V.shape[axes.index(0)]
cols = V.shape[axes.index(1)]
channels = V.shape[axes.index('c')]
self.view_converter = DefaultViewConverter([rows, cols, channels], axes=axes)
X = self.view_converter.topo_view_to_design_mat(V)
assert not numpy.any(numpy.isnan(X))
FaceBBoxDDMPytables.fill_hdf5(h5file = self.h5file,
data_x = X,
start = start)
def find_adversary(model,
X0,
label,
P0=None,
mu=.1,
epsilon=.25,
maxits=10,
stop_thresh=0.5,
griffin_lim=False):
'''
Solves:
y* = argmin_y f(y; label)
s.t. y >= 0 and ||y-X0|| < e
where f(y) is the cost associated the network associates with the pair (y,label)
This can be solved using the projected gradient method:
min_y f(y)
s.t. y >= 0 and ||y-X0|| < e
z = max(0, y^k - mu.f'(y^k))
y^k+1 = P(z)
P(z) = min_u ||u-z|| s.t. {u | ||u-X0|| < e }
Lagrangian(u,l) = L(u,l) = ||u-z|| + nu*(||u-X0|| - e)
dL/du = u-z + nu*(u-X0) = 0
u = (1+nu)^-1 (z + nu*X0)
KKT:
||u-x|| = e
||(1/(1+nu))(z + nu*x) - x|| = e
||(1/(1+nu))z + ((nu/(1+nu))-1)x|| = e
||(1/(1+nu))z - (1/(1+nu))x|| = e
(1/(1+nu))||z-x|| = e
nu = max(0,||z-x||/e - 1)
function inputs:
model - pylearn2 dnn model (implements fprop, cost)
X0 - an example that the model classifies correctly
label - an incorrect label
'''
# convert integer label into one-hot vector
n_classes, n_examples = model.get_output_space().dim, X0.shape[0]
nfft = 2 * (X0.shape[1] - 1)
nhop = nfft // 2
# Set-up gradient computation w/ Theano
in_batch = model.get_input_space().make_theano_batch()
out_batch = model.get_output_space().make_theano_batch()
#cost = model.cost(one_hot, model.fprop(in_batch))
cost = model.cost(out_batch, model.fprop(in_batch))
#cost = model.layers[-1].cost(one_hot, model.fprop(in_batch))
dCost = T.grad(cost * n_examples, in_batch)
grad_theano = theano.function([in_batch, out_batch], dCost)
fprop_theano = theano.function([in_batch], model.fprop(in_batch))
fcost_theano = theano.function([in_batch, out_batch], cost)
input_space = model.get_input_space()
if isinstance(input_space, Conv2DSpace):
tframes, dim = input_space.shape
view_converter = DefaultViewConverter((tframes, dim, 1))
else:
dim = input_space.dim
tframes = 1
view_converter = None
nframes = X0.shape[0]
thop = 1.
sup = np.arange(0, nframes - tframes + 1, np.int(tframes / thop))
if view_converter:
def grad(batch, labels):
data = np.vstack([
np.reshape(batch[i:i + tframes, :], (tframes * dim, ))
for i in sup
])
topo_view = grad_theano(
view_converter.get_formatted_batch(data, input_space), labels)
design_mat = view_converter.topo_view_to_design_mat(topo_view)
return np.vstack(
[np.reshape(r, (tframes, dim)) for r in design_mat])
def fprop(batch):
data = np.vstack([
np.reshape(batch[i:i + tframes, :], (tframes * dim, ))
for i in sup
])
return fprop_theano(
view_converter.get_formatted_batch(data, input_space))
def fcost(batch, labels):
data = np.vstack([
np.reshape(batch[i:i + tframes, :], (tframes * dim, ))
for i in sup
])
return fcost_theano(
view_converter.get_formatted_batch(data, input_space), labels)
else:
grad = grad_theano
fprop = fprop_theano
fcost = fcost_theano
one_hot = np.zeros((len(sup), n_classes), dtype=np.float32)
one_hot[:, label] = 1
X0 = X0[:len(sup) * tframes, :]
if P0 is not None: P0 = P0[:len(sup) * tframes, :]
# projected gradient:
last_pred = 0
#Y = np.array(np.random.rand(*X0.shape), dtype=np.float32)
Y = np.copy(X0)
Y_old = np.copy(Y)
t_old = 1
#print 'cost(X0,y): ', fcost(X0, one_hot)
for i in xrange(maxits):
# gradient step
g = grad(Y, one_hot)
Z = Y - mu * np.sign(g)
#print 'cost(X{},y): {}'.format(i+1, fcost(Z, one_hot))
# non-negative projection
Z = Z * (Z > 0)
if griffin_lim:
Z, P0 = griffin_lim_proj(np.hstack((Z, Z[:, -2:-nfft / 2 - 1:-1])),
P0,
its=0)
# maximum allowable signal-to-noise projection
nu = np.linalg.norm(
(Z - X0)) / n_examples / epsilon - 1 # lagrange multiplier
nu = nu * (nu >= 0)
Y = (Z + nu * X0) / (1 + nu)
# FISTA momentum
# t = .5 + np.sqrt(1+4*t_old**2)/2.
# alpha = (t_old - 1)/t
# Y += alpha * (Y - Y_old)
# Y_old = np.copy(Y)
# t_old = t
#'''
# stopping condition
# pred = np.sum(fprop(Y), axis=0)
# pred /= np.sum(pred)
#print 'iteration: {}, pred[label]: {}, nu: {}, snr: {}'.format(i, pred[label], nu, 20*np.log10(np.linalg.norm(X0)/np.linalg.norm(Y-X0)))
# if pred[label] > stop_thresh:
# break
# elif pred[label] < last_pred - 1e-4:
# pass#break
# last_pred = pred[label]
return Y, P0
def _execute(self):
batch_size = self.batch_size
feature_type = self.feature_type
pooling_region_counts = self.pooling_region_counts
dataset_family = self.dataset_family
which_set = self.which_set
model = self.model
size = self.size
nan = 0
dataset_descriptor = dataset_family[which_set][size]
dataset = dataset_descriptor.dataset_maker()
expected_num_examples = dataset_descriptor.num_examples
full_X = dataset.get_design_matrix()
assert full_X.dtype == 'float32'
num_examples = full_X.shape[0]
assert num_examples == expected_num_examples
print 'restricting to examples from classes 0 and 1'
full_X = full_X[dataset.y_fine < 2, :]
#update for after restriction
num_examples = full_X.shape[0]
assert num_examples > 0
dataset.X = None
dataset.design_loc = None
dataset.compress = False
patchifier = ExtractGridPatches(patch_shape=(size, size),
patch_stride=(1, 1))
pipeline = serial.load(dataset_descriptor.pipeline_path)
assert isinstance(pipeline.items[0], ExtractPatches)
pipeline.items[0] = patchifier
print 'defining features'
V = T.matrix('V')
assert V.type.dtype == 'float32'
model.make_pseudoparams()
d = model.infer(V=V)
H = d['H_hat']
Mu1 = d['S_hat']
G = d['G_hat']
if len(G) != 1:
raise NotImplementedError(
"only supports two layer pd-dbms for now")
G, = G
assert H.dtype == 'float32'
assert Mu1.dtype == 'float32'
nfeat = model.s3c.nhid + model.dbm.rbms[0].nhid
if self.feature_type == 'map_hs':
feat = (H > 0.5) * Mu1
raise NotImplementedError("doesn't support layer 2")
elif self.feature_type == 'map_h':
feat = T.cast(H > 0.5, dtype='float32')
raise NotImplementedError("doesn't support layer 2")
elif self.feature_type == 'exp_hs':
feat = H * Mu1
raise NotImplementedError("doesn't support layer 2")
elif self.feature_type == 'exp_hs_split':
Z = H * Mu1
pos = T.clip(Z, 0., 1e32)
neg = T.clip(-Z, 0, 1e32)
feat = T.concatenate((pos, neg), axis=1)
nfeat *= 2
raise NotImplementedError("doesn't support layer 2")
elif self.feature_type == 'exp_h,exp_g':
feat = T.concatenate((H, G), axis=1)
elif self.feature_type == 'exp_h_thresh':
feat = H * (H > .01)
raise NotImplementedError("doesn't support layer 2")
else:
raise NotImplementedError()
assert feat.dtype == 'float32'
print 'compiling theano function'
f = function([V], feat)
if config.device.startswith('gpu') and nfeat >= 4000:
f = halver(f, nfeat)
topo_feat_var = T.TensorType(broadcastable=(False, False, False,
False),
dtype='float32')()
if self.pool_mode == 'mean':
region_feat_var = topo_feat_var.mean(axis=(1, 2))
elif self.pool_mode == 'max':
region_feat_var = topo_feat_var.max(axis=(1, 2))
else:
raise ValueError("Unknown pool mode: " + self.pool_mode)
region_features = function([topo_feat_var], region_feat_var)
def average_pool(stride):
def point(p):
return p * ns / stride
rval = np.zeros(
(topo_feat.shape[0], stride, stride, topo_feat.shape[3]),
dtype='float32')
for i in xrange(stride):
for j in xrange(stride):
rval[:, i, j, :] = region_features(
topo_feat[:,
point(i):point(i + 1),
point(j):point(j + 1), :])
return rval
outputs = [
np.zeros((num_examples, count, count, nfeat), dtype='float32')
for count in pooling_region_counts
]
assert len(outputs) > 0
fd = DenseDesignMatrix(X=np.zeros((1, 1), dtype='float32'),
view_converter=DefaultViewConverter(
[1, 1, nfeat]))
ns = 32 - size + 1
depatchifier = ReassembleGridPatches(orig_shape=(ns, ns),
patch_shape=(1, 1))
if len(range(0, num_examples - batch_size + 1, batch_size)) <= 0:
print num_examples
print batch_size
for i in xrange(0, num_examples - batch_size + 1, batch_size):
print i
t1 = time.time()
d = copy.copy(dataset)
d.set_design_matrix(full_X[i:i + batch_size, :])
t2 = time.time()
#print '\tapplying preprocessor'
d.apply_preprocessor(pipeline, can_fit=False)
X2 = np.cast['float32'](d.get_design_matrix())
t3 = time.time()
#print '\trunning theano function'
feat = f(X2)
t4 = time.time()
assert feat.dtype == 'float32'
feat_dataset = copy.copy(fd)
if np.any(np.isnan(feat)):
nan += np.isnan(feat).sum()
feat[np.isnan(feat)] = 0
feat_dataset.set_design_matrix(feat)
#print '\treassembling features'
feat_dataset.apply_preprocessor(depatchifier)
#print '\tmaking topological view'
topo_feat = feat_dataset.get_topological_view()
assert topo_feat.shape[0] == batch_size
t5 = time.time()
#average pooling
for output, count in zip(outputs, pooling_region_counts):
output[i:i + batch_size, ...] = average_pool(count)
t6 = time.time()
print(t6 - t1, t2 - t1, t3 - t2, t4 - t3, t5 - t4, t6 - t5)
for output, save_path in zip(outputs, self.save_paths):
np.save(save_path, output)
if nan > 0:
warnings.warn(str(nan) + ' features were nan')
parser.add_option('--channels', action='store', type='int', dest='chans')
parser.add_option('--color', action='store_true', dest='color', default=False)
parser.add_option('--layer', action='store', type='int', dest='layer', default=0)
(opts, args) = parser.parse_args()
nplots = opts.chans
if opts.color:
assert opts.chans == 3
nplots = 1
def get_dims(nf):
num_rows = numpy.floor(numpy.sqrt(nf))
return (int(num_rows), int(numpy.ceil(nf / num_rows)))
topo_shape = [opts.height, opts.width, opts.chans]
viewconv = DefaultViewConverter(topo_shape)
viewdims = slice(0, None) if opts.color else 0
# load model and retrieve parameters
model = serial.load(opts.path)
if isinstance(model, TemperedDBN):
rbm = model.rbms[opts.layer]
else:
rbm = model
wv = rbm.Wv.get_value().T
wv_viewer = PatchViewer(get_dims(len(wv)), (opts.height, opts.width),
is_color = opts.color, pad=(2,2))
for i in xrange(len(wv)):
topo_wvi = viewconv.design_mat_to_topo_view(wv[i:i+1])
wv_viewer.add_patch(topo_wvi[0])
parser.add_option('--top', action='store', type='int', dest='top', default=5)
parser.add_option('--mu', action='store_true', dest='mu', default=False)
parser.add_option('--wv_only', action='store_true', dest='wv_only', default=False)
(opts, args) = parser.parse_args()
nplots = opts.chans
if opts.color:
assert opts.chans == 3
nplots = 1
def get_dims(nf):
num_rows = numpy.floor(numpy.sqrt(nf))
return (int(num_rows), int(numpy.ceil(nf / num_rows)))
topo_shape = [opts.height, opts.width, opts.chans]
viewconv = DefaultViewConverter(topo_shape)
viewdims = slice(0, None) if opts.color else 0
# load model and retrieve parameters
model = serial.load(opts.path)
wv = model.Wv.get_value().T
if opts.mu:
wv = wv * model.mu.get_value()[:, None]
view1 = PatchViewer(get_dims(len(wv)), (opts.height, opts.width), is_color = opts.color, pad=(2,2))
for i in xrange(len(wv)):
topo_wvi = viewconv.design_mat_to_topo_view(wv[i:i+1, :48*48])
view1.add_patch(topo_wvi[0])
view2 = PatchViewer(get_dims(len(wv)), (opts.height, opts.width), is_color = opts.color, pad=(2,2))
for i in xrange(len(wv)):
max_filters = max([len(Wi) for Wi in W])
print 'max_filters = ', max_filters
block_viewer = PatchViewer(get_dims(max_filters),
(opts.height, opts.width),
is_color = opts.color,
pad=(2,2))
main_viewer = PatchViewer(get_dims(nblocks),
(block_viewer.image.shape[0],
block_viewer.image.shape[1]),
is_color = opts.color,
pad=(5,5))
topo_shape = [opts.height, opts.width, opts.chans]
view_converter = DefaultViewConverter(topo_shape)
for di, w_di in enumerate(W):
if opts.k == -1:
# build "new_w" as linear combination of all previous filters
if di > 0:
new_w = numpy.dot(w_di, prev_w)
else:
new_w = w_di
else:
new_w = numpy.zeros((len(w_di), opts.height * opts.width)) if di else w_di
for fi in xrange(len(w_di)):
if opts.k != -1:
seglen = 30
x = x[:seglen * fs]
# make sure format agrees with training data
if len(x.shape) != 1:
print 'making mono:'
x = np.sum(x, axis=1) / 2. # mono
if fs != 22050:
print 'resampling to 22050 hz:'
import scikits.samplerate as samplerate
x = samplerate.resample(x, 22050. / fs, 'sinc_best')
fs = 22050
if isinstance(input_space, Conv2DSpace):
tframes, dim = input_space.shape
view_converter = DefaultViewConverter((tframes, dim, 1))
else:
dim = input_space.dim
tframes = 1
view_converter = None
nfft = 2 * (dim - 1)
nhop = nfft // 2
nframes = (len(x) - nfft) / nhop
x = x[:(nframes - 1) * nhop +
nfft] # truncate input to multiple of hopsize
# format batches for 1d/2d nets
thop = 1.
sup = np.arange(0, nframes - tframes + 1, np.int(tframes / thop))
if view_converter:
def __init__(self, config, adv_model, which_set='train'):
keys = ['train', 'test', 'valid']
assert which_set in keys
# load hdf5 metadata
self.hdf5 = tables.open_file(config['hdf5'], mode='r')
data = self.hdf5.get_node('/', 'Data')
param = self.hdf5.get_node('/', 'Param')
self.file_index = param.file_index[0]
self.file_dict = param.file_dict[0]
self.label_list = param.label_list[0]
self.targets = param.targets[0]
self.nfft = param.fft[0]['nfft']
# load parition information
self.support = config[which_set]
self.file_list = config[which_set + '_files']
self.mean = config['mean']
self.mean = self.mean.reshape((np.prod(self.mean.shape), ))
self.var = config['var']
self.var = self.var.reshape((np.prod(self.var.shape), ))
self.istd = np.reciprocal(np.sqrt(self.var))
self.mask = (self.istd < 20)
self.tframes = config['tframes']
# setup adversary
self.adv_model = adv_model
in_batch = adv_model.get_input_space().make_theano_batch()
out_batch = adv_model.get_output_space().make_theano_batch()
cost = adv_model.cost(out_batch, adv_model.fprop(in_batch))
dCost = T.grad(cost, in_batch)
grad_theano = theano.function([in_batch, out_batch], dCost)
fprop_theano = theano.function([in_batch], adv_model.fprop(in_batch))
fcost_theano = theano.function([in_batch, out_batch], cost)
self.input_space = adv_model.get_input_space()
if isinstance(self.input_space, Conv2DSpace):
tframes, dim = self.input_space.shape
view_converter = DefaultViewConverter((tframes, dim, 1))
def grad(batch, labels):
topo_view = grad_theano(
view_converter.get_formatted_batch(batch,
self.input_space),
labels)
return view_converter.topo_view_to_design_mat(topo_view)
def fprop(batch):
return fprop_theano(
view_converter.get_formatted_batch(batch,
self.input_space))
def fcost(batch, labels):
return fcost_theano(
view_converter.get_formatted_batch(batch,
self.input_space),
labels)
self.grad = grad
self.fprop = fprop
self.fcost = fcost
super(AdversaryDataset,
self).__init__(X=data.X,
y=data.y,
view_converter=view_converter)
else:
dim = self.input_space.dim
tframes = 1
view_converter = None
self.grad = grad_theano
self.fprop = fprop_theano
self.fcost = fcost_theano
super(AdversaryDataset, self).__init__(X=data.X, y=data.y)
parser.add_option('--top', action='store', type='int', dest='top', default=5)
parser.add_option('--mu', action='store_true', dest='mu', default=False)
parser.add_option('--wv_only', action='store_true', dest='wv_only', default=False)
(opts, args) = parser.parse_args()
nplots = opts.chans
if opts.color:
assert opts.chans == 3
nplots = 1
def get_dims(nf):
num_rows = numpy.floor(numpy.sqrt(nf))
return (int(num_rows), int(numpy.ceil(nf / num_rows)))
topo_shape = [opts.height, opts.width, opts.chans]
viewconv = DefaultViewConverter(topo_shape)
viewdims = slice(0, None) if opts.color else 0
# load model and retrieve parameters
model = serial.load(opts.path)
wv = model.Wv.get_value().T
if opts.mu:
wv = wv * model.mu.get_value()[:, None]
wv_viewer = PatchViewer(get_dims(len(wv)), (opts.height, opts.width),
is_color = opts.color, pad=(2,2))
for i in xrange(len(wv)):
topo_wvi = viewconv.design_mat_to_topo_view(wv[i:i+1])
wv_viewer.add_patch(topo_wvi[0])
if opts.wv_only:
wv_viewer.show()
class myDenseDesignMatrix(dense_design_matrix.DenseDesignMatrix):
_default_seed = (17, 2, 946)
def __init__(self, X=None, topo_view=None, y=None, latent = None,
view_converter=None, axes=('b', 0, 1, 'c'),
rng=_default_seed, preprocessor=None, fit_preprocessor=False,
X_labels=None, y_labels=None):
self.latent = latent
self.X = X
self.y = y
self.view_converter = view_converter
self.X_labels = X_labels
self.y_labels = y_labels
self._check_labels()
if topo_view is not None:
assert view_converter is None
self.set_topological_view(topo_view, axes)
else:
assert X is not None, ("DenseDesignMatrix needs to be provided "
"with either topo_view, or X")
if view_converter is not None:
# Get the topo_space (usually Conv2DSpace) from the
# view_converter
if not hasattr(view_converter, 'topo_space'):
raise NotImplementedError("Not able to get a topo_space "
"from this converter: %s"
% view_converter)
# self.X_topo_space stores a "default" topological space that
# will be used only when self.iterator is called without a
# data_specs, and with "topo=True", which is deprecated.
self.X_topo_space = view_converter.topo_space
else:
self.X_topo_space = None
# Update data specs, if not done in set_topological_view
X_source = 'features'
if X_labels is None:
X_space = VectorSpace(dim=X.shape[1])
else:
if X.ndim == 1:
dim = 1
else:
dim = X.shape[-1]
X_space = IndexSpace(dim=dim, max_labels=X_labels)
if y is None:
space = X_space
source = X_source
else:
if y.ndim == 1:
dim = 1
else:
dim = y.shape[-1]
if y_labels is not None:
y_space = IndexSpace(dim=dim, max_labels=y_labels)
else:
y_space = VectorSpace(dim=dim)
y_source = 'targets'
Latent_space = VectorSpace(dim=latent.shape[-1])
Latent_source = 'latents'
space = CompositeSpace((X_space, y_space, Latent_space))
source = (X_source, y_source, Latent_source)
self.data_specs = (space, source)
self.X_space = X_space
self.compress = False
self.design_loc = None
self.rng = make_np_rng(rng, which_method="random_integers")
# Defaults for iterators
self._iter_mode = resolve_iterator_class('sequential')
self._iter_topo = False
self._iter_targets = False
self._iter_data_specs = (self.X_space, 'features')
if preprocessor:
preprocessor.apply(self, can_fit=fit_preprocessor)
self.preprocessor = preprocessor
def get_data(self):
"""
Returns all the data, as it is internally stored.
The definition and format of these data are described in
`self.get_data_specs()`.
Returns
-------
data : numpy matrix or 2-tuple of matrices
The data
"""
if self.y is None:
return self.X
else:
return (self.X, self.y, self.latent)
def set_topological_view(self, V, axes=('b', 0, 1, 'c')):
"""
Sets the dataset to represent V, where V is a batch
of topological views of examples.
.. todo::
Why is this parameter named 'V'?
Parameters
----------
V : ndarray
An array containing a design matrix representation of
training examples.
axes : WRITEME
"""
assert not contains_nan(V)
rows = V.shape[axes.index(0)]
cols = V.shape[axes.index(1)]
channels = V.shape[axes.index('c')]
self.view_converter = DefaultViewConverter([rows, cols, channels],
axes=axes)
self.X = self.view_converter.topo_view_to_design_mat(V)
# self.X_topo_space stores a "default" topological space that
# will be used only when self.iterator is called without a
# data_specs, and with "topo=True", which is deprecated.
self.X_topo_space = self.view_converter.topo_space
assert not contains_nan(self.X)
# Update data specs
X_space = VectorSpace(dim=self.X.shape[1])
X_source = 'features'
if self.y is None:
space = X_space
source = X_source
else:
if self.y.ndim == 1:
dim = 1
else:
dim = self.y.shape[-1]
# This is to support old pickled models
if getattr(self, 'y_labels', None) is not None:
y_space = IndexSpace(dim=dim, max_labels=self.y_labels)
elif getattr(self, 'max_labels', None) is not None:
y_space = IndexSpace(dim=dim, max_labels=self.max_labels)
else:
y_space = VectorSpace(dim=dim)
y_source = 'targets'
Latent_space = VectorSpace(dim=self.latent.shape[-1])
Latent_source = 'latents'
space = CompositeSpace((X_space, y_space,Latent_space))
source = (X_source, y_source,Latent_source)
self.data_specs = (space, source)
self.X_space = X_space
self._iter_data_specs = (X_space, X_source)
def get_targets(self):
"""
.. todo::
WRITEME
"""
return self.y
def get_latents(self):
"""
.. todo::
WRITEME
"""
return self.latent
def get_batch_design(self, batch_size, include_labels=False):
try:
idx = self.rng.randint(self.X.shape[0] - batch_size + 1)
except ValueError:
if batch_size > self.X.shape[0]:
reraise_as(ValueError("Requested %d examples from a dataset "
"containing only %d." %
(batch_size, self.X.shape[0])))
raise
rx = self.X[idx:idx + batch_size, :]
if include_labels:
if self.y is None:
return rx, None
ry = self.y[idx:idx + batch_size]
rlatent = self.latent[idx:idx + batch_size]
return rx, ry,rlatent
rx = np.cast[config.floatX](rx)
return rx
def get_batch_topo(self, batch_size, include_labels=False):
"""
.. todo::
WRITEME
Parameters
----------
batch_size : int
WRITEME
include_labels : bool
WRITEME
"""
if include_labels:
batch_design, labels, latents= self.get_batch_design(batch_size, True)
else:
batch_design = self.get_batch_design(batch_size)
rval = self.view_converter.design_mat_to_topo_view(batch_design)
if include_labels:
return rval, labels, latents
return rval
def _execute(self):
batch_size = self.batch_size
pooling_region_counts = self.pooling_region_counts
dataset_family = self.dataset_family
which_set = self.which_set
size = self.size
nan = 0
dataset_descriptor = dataset_family[which_set][size]
dataset = dataset_descriptor.dataset_maker()
expected_num_examples = dataset_descriptor.num_examples
full_X = dataset.get_design_matrix()
num_examples = full_X.shape[0]
assert num_examples == expected_num_examples
if self.restrict is not None:
assert self.restrict[1] <= full_X.shape[0]
print 'restricting to examples ',self.restrict[0],' through ',self.restrict[1],' exclusive'
full_X = full_X[self.restrict[0]:self.restrict[1],:]
assert self.restrict[1] > self.restrict[0]
#update for after restriction
num_examples = full_X.shape[0]
assert num_examples > 0
dataset.X = None
dataset.design_loc = None
dataset.compress = False
patchifier = ExtractGridPatches( patch_shape = (size,size), patch_stride = (1,1) )
pipeline = serial.load(dataset_descriptor.pipeline_path)
assert isinstance(pipeline.items[0], ExtractPatches)
pipeline.items[0] = patchifier
print 'defining features'
V = T.matrix('V')
Z = T.dot(V, self.W)
alpha = self.alpha
if self.one_sided:
feat = T.clip(abs(Z),alpha,1e30)-alpha
else:
pos = T.clip(Z,alpha,1e30) - alpha
neg = T.clip(-Z,alpha,1e30) - alpha
feat = T.concatenate((pos, neg), axis=1)
assert feat.dtype == 'float32'
print 'compiling theano function'
f = function([V],feat)
nfeat = self.W.get_value().shape[1] * (2-self.one_sided)
if config.device.startswith('gpu') and nfeat >= 4000:
f = halver(f, nfeat)
topo_feat_var = T.TensorType(broadcastable = (False,False,False,False), dtype='float32')()
region_features = function([topo_feat_var],
topo_feat_var.mean(axis=(1,2)) )
def average_pool( stride ):
def point( p ):
return p * ns / stride
rval = np.zeros( (topo_feat.shape[0], stride, stride, topo_feat.shape[3] ) , dtype = 'float32')
for i in xrange(stride):
for j in xrange(stride):
rval[:,i,j,:] = region_features( topo_feat[:,point(i):point(i+1), point(j):point(j+1),:] )
return rval
outputs = [ np.zeros((num_examples,count,count,nfeat),dtype='float32') for count in pooling_region_counts ]
assert len(outputs) > 0
fd = DenseDesignMatrix(X = np.zeros((1,1),dtype='float32'), view_converter = DefaultViewConverter([1, 1, nfeat] ) )
ns = 32 - size + 1
depatchifier = ReassembleGridPatches( orig_shape = (ns, ns), patch_shape=(1,1) )
if len(range(0,num_examples-batch_size+1,batch_size)) <= 0:
print num_examples
print batch_size
for i in xrange(0,num_examples-batch_size+1,batch_size):
print i
t1 = time.time()
d = copy.copy(dataset)
d.set_design_matrix(full_X[i:i+batch_size,:])
t2 = time.time()
#print '\tapplying preprocessor'
d.apply_preprocessor(pipeline, can_fit = False)
X2 = d.get_design_matrix()
t3 = time.time()
#print '\trunning theano function'
feat = f(X2)
t4 = time.time()
assert feat.dtype == 'float32'
feat_dataset = copy.copy(fd)
if np.any(np.isnan(feat)):
nan += np.isnan(feat).sum()
feat[np.isnan(feat)] = 0
feat_dataset.set_design_matrix(feat)
#print '\treassembling features'
feat_dataset.apply_preprocessor(depatchifier)
#print '\tmaking topological view'
topo_feat = feat_dataset.get_topological_view()
assert topo_feat.shape[0] == batch_size
t5 = time.time()
#average pooling
for output, count in zip(outputs, pooling_region_counts):
output[i:i+batch_size,...] = average_pool(count)
t6 = time.time()
print (t6-t1, t2-t1, t3-t2, t4-t3, t5-t4, t6-t5)
for output, save_path in zip(outputs, self.save_paths):
if self.chunk_size is not None:
assert save_path.endswith('.npy')
save_path_pieces = save_path.split('.npy')
assert len(save_path_pieces) == 2
assert save_path_pieces[1] == ''
save_path = save_path_pieces[0] + '_' + chr(ord('A')+self.chunk_id)+'.npy'
np.save(save_path,output)
if nan > 0:
warnings.warn(str(nan)+' features were nan')
def make_viewer(mat,
grid_shape=None,
patch_shape=None,
activation=None,
pad=None,
is_color=False,
rescale=True):
"""
Given filters in rows, guesses dimensions of patches
and nice dimensions for the PatchViewer and returns a PatchViewer
containing visualizations of the filters.
Parameters
----------
mat : ndarray
Values should lie in [-1, 1] if `rescale` is False.
0. always indicates medium gray, with negative values drawn as
blacker and positive values drawn as whiter.
A matrix with each row being a different image patch, OR
a 4D tensor in ('b', 0, 1, 'c') format.
If matrix, we assume it was flattened using the same procedure as a
('b', 0, 1, 'c') DefaultViewConverter uses.
grid_shape : tuple, optional
A tuple of two ints specifying the shape of the grad in the
PatchViewer, in (rows, cols) format. If not specified, this
function does its best to choose an aesthetically pleasing
value.
patch_shape : tupe, optional
A tuple of two ints specifying the shape of the patch.
If `mat` is 4D, this function gets the patch shape from the shape of
`mat`. If `mat` is 2D and patch_shape is not specified, this function
assumes the patches are perfectly square.
activation : iterable
An iterable collection describing some kind of activation value
associated with each patch. This is indicated with a border around the
patch whose color intensity increases with activation value.
The individual activation values may be single floats to draw one
border or iterable collections of floats to draw multiple borders with
differing intensities around the patch.
pad : int, optional
The amount of padding to add between patches in the displayed image.
is_color : int
If True, assume the images are in color.
Note needed if `mat` is in ('b', 0, 1, 'c') format since we can just
look at its shape[-1].
rescale : bool
If True, rescale each patch so that its highest magnitude pixel
reaches a value of either 0 or 1 depending on the sign of that pixel.
Returns
-------
patch_viewer : PatchViewer
A PatchViewer containing the patches stored in `mat`.
"""
num_channels = 1
if is_color:
num_channels = 3
if grid_shape is None:
grid_shape = PatchViewer.pick_shape(mat.shape[0])
if mat.ndim > 2:
patch_shape = mat.shape[1:3]
topo_view = mat
num_channels = mat.shape[3]
is_color = num_channels > 1
else:
if patch_shape is None:
assert mat.shape[1] % num_channels == 0
patch_shape = PatchViewer.pick_shape(mat.shape[1] // num_channels,
exact=True)
assert mat.shape[1] == (patch_shape[0] * patch_shape[1] *
num_channels)
topo_shape = (patch_shape[0], patch_shape[1], num_channels)
view_converter = DefaultViewConverter(topo_shape)
topo_view = view_converter.design_mat_to_topo_view(mat)
rval = PatchViewer(grid_shape, patch_shape, pad=pad, is_color=is_color)
for i in xrange(mat.shape[0]):
if activation is not None:
if hasattr(activation[0], '__iter__'):
act = [a[i] for a in activation]
else:
act = activation[i]
else:
act = None
patch = topo_view[i, :]
rval.add_patch(patch, rescale=rescale, activation=act)
return rval
def test_init_with_vc():
rng = np.random.RandomState([4, 5, 6])
d = DenseDesignMatrix(X=rng.randn(12, 5),
view_converter=DefaultViewConverter([1, 2, 3]))
def __init__(self,
which_set,
base_path='${PYLEARN2_DATA_PATH}/icml_2013_emotions',
start=None,
stop=None,
preprocessor=None,
fit_preprocessor=False,
axes=('b', 0, 1, 'c'),
fit_test_preprocessor=False):
"""
which_set: A string specifying which portion of the dataset
to load. Valid values are 'train' or 'public_test'
base_path: The directory containing the .csv files from kaggle.com.
This directory should be writable; if the .csv files haven't
already been converted to npy, this class will convert them
to save memory the next time they are loaded.
fit_preprocessor: True if the preprocessor is allowed to fit the
data.
fit_test_preprocessor: If we construct a test set based on this
dataset, should it be allowed to fit the test set?
"""
self.test_args = locals()
self.test_args['which_set'] = 'public_test'
self.test_args['fit_preprocessor'] = fit_test_preprocessor
del self.test_args['start']
del self.test_args['stop']
del self.test_args['self']
files = {'train': 'train.csv', 'public_test': 'test.csv'}
try:
filename = files[which_set]
except KeyError:
raise ValueError("Unrecognized dataset name: " + which_set)
path = base_path + '/' + filename
path = preprocess(path)
X, y = self._load_data(path, which_set == 'train')
if start is not None:
assert which_set != 'test'
assert isinstance(start, int)
assert isinstance(stop, int)
assert start >= 0
assert start < stop
assert stop <= X.shape[0]
X = X[start:stop, :]
if y is not None:
y = y[start:stop, :]
view_converter = DefaultViewConverter(shape=[48, 48, 1], axes=axes)
super(EmotionsDataset, self).__init__(X=X,
y=y,
y_labels=7,
view_converter=view_converter)
if preprocessor:
preprocessor.apply(self, can_fit=fit_preprocessor)
# check for global scaling
if not opts.local:
samples = samples / numpy.abs(samples).max()
##############
# PLOT FILTERS
##############
import pdb; pdb.set_trace()
viewer = PatchViewer(get_dims(model.batch_size),
(opts.height, opts.width),
is_color = opts.color,
pad=(2,2))
topo_shape = [opts.height, opts.width, opts.chans]
view_converter = DefaultViewConverter(topo_shape)
topo_view = view_converter.design_mat_to_topo_view(samples)
for chan_i in xrange(nplots):
topo_chan = topo_view if opts.color else topo_view[..., chan_i:chan_i+1]
for bi in xrange(model.batch_size):
viewer.add_patch(topo_chan[bi])
#pl.subplot(1, nplots, chan_i+1)
#pl.imshow(viewer.image, interpolation=None)
#pl.axis('off'); pl.title('samples (channel %i)' % chan_i)
viewer.show()
quit(-1)
models = []
try:
for model in serial.load(model_path):
models.append(model)
except Exception as e:
usage()
print(model_path + "doesn't seem to be a valid model path, I got this error when trying to load it: ")
print(e)
# load the test set
with open('preprocessed_test_for_pylearn2.pkl') as f:
dataset = pkl.load(f)
dataset = DenseDesignMatrix(X=dataset, view_converter=DefaultViewConverter(shape=[32, 32, 1], axes=['b', 0, 1, 'c']))
print(models)
predictions = []
print(len(models))
for model in models:
print(model)
model.set_batch_size(dataset.X.shape[0])
X = model.get_input_space().make_batch_theano()
Y = model.fprop(X) # forward prop the test data
y = T.argmax(Y, axis=1)
class TemporalDenseDesignMatrix(DenseDesignMatrix):
'''
A class for representing datasets that can be stored as a dense design
matrix, but whose examples are slices of width >= 2 rows each.
'''
_default_seed = (17, 2, 946)
def __init__(self, X=None, topo_view=None, y=None,
view_converter=None, axes = ('b', 0, 1, 2, 'c'),
rng=_default_seed, preprocessor = None, fit_preprocessor=False):
'''
TODO: rewrite or just inherit...
same as DenseDesignMatrix...???
Parameters
----------
X : ndarray, 2-dimensional, optional
Should be supplied if `topo_view` is not. A design
matrix of shape (number examples, number features)
that defines the dataset.
XXXXXXXXXXX not allowed
topo_view : ndarray, optional
Should be supplied if X is not. An array whose first
dimension is of length number examples. The remaining
dimensions are xamples with topological significance,
e.g. for images the remaining axes are rows, columns,
and channels.
TODO: time is 0, ii is 1, jj is 2
y : ndarray, 1-dimensional(?), optional
Labels or targets for each example. The semantics here
are not quite nailed down for this yet.
view_converter : object, optional
An object for converting between the design matrix
stored internally and the data that will be returned
by iterators.
rng : object, optional
A random number generator used for picking random
indices into the design matrix when choosing minibatches.
'''
assert topo_view is not None, (
'For TemporalDenseDesignMatrix, must provide topo_view (not X)'
)
assert axes == ('b', 0, 1, 2, 'c')
reduced_axes = ('b', 0, 1, 'c')
super(TemporalDenseDesignMatrix, self).__init__(
X = X,
topo_view = topo_view,
y = y,
view_converter = view_converter,
axes = reduced_axes,
rng = rng,
preprocessor = preprocessor,
fit_preprocessor = fit_preprocessor
)
self._X = self.X
self.X = None # prevent other access
def set_topological_view(self, topo_view, axes=('b', 0, 1, 'c')):
'''
Sets the dataset to represent topo_view, where topo_view is a batch
of topological views of examples.
Parameters
----------
topo_view : ndarray
An array containing a design matrix representation of training
examples.
'''
assert not np.any(np.isnan(topo_view))
frames = topo_view.shape[axes.index('b')] # pretend frames come in as batch dim
rows = topo_view.shape[axes.index(0)]
cols = topo_view.shape[axes.index(1)]
channels = topo_view.shape[axes.index('c')]
# leave out frames...
self.view_converter = DefaultViewConverter([rows, cols, channels], axes=axes)
self.X = self.view_converter.topo_view_to_design_mat(topo_view)
# self.X_topo_space stores a "default" topological space that
# will be used only when self.iterator is called without a
# data_specs, and with "topo=True", which is deprecated.
self.X_topo_space = self.view_converter.topo_space
assert not np.any(np.isnan(self.X))
# Update data specs
X_space = VectorSpace(dim = frames * rows * cols * channels)
X_source = 'features'
assert self.y is None, 'y not supported now'
space = X_space
source = X_source
self.data_specs = (space, source)
self.X_space = X_space
self._iter_data_specs = (X_space, X_source)
@functools.wraps(Dataset.iterator)
def iterator(self, mode=None, batch_size=None, num_batches=None,
topo=None, targets=None, rng=None, data_specs=None,
return_tuple=False):
'''thin wrapper... TODO: doc'''
assert mode == 'shuffled_sequential', (
'Only shuffled_sequential mode is supported'
)
assert data_specs != None, 'Must provide data_specs'
assert len(data_specs) == 2, 'data_specs must include only one tuple for "features"'
assert type(data_specs[0]) is CompositeSpace, 'must be composite space...??'
assert data_specs[0].num_components == 1, 'must only have one component, features'
assert data_specs[1][0] == 'features', 'data_specs must include only one tuple for "features"'
output_space = data_specs[0].components[0]
num_frames = output_space.shape[0]
if num_batches is None:
num_batches = 10 # another hack... just determines how often new iterators will be created?
base_num_batches = num_batches * batch_size
# Iterates through ONE example at a time
# BEGIN HUGE HACK (enable self.X access just for this function)
self.X = self._X
base_iterator = super(TemporalDenseDesignMatrix, self).iterator(
mode='random_slice', # to return continguous bits
batch_size=num_frames,
num_batches=base_num_batches,
topo=topo,
targets=targets,
rng=rng,
data_specs=data_specs,
return_tuple=False)
self.X = None
# END HUGE HACK
return CopyingConcatenatingIterator(base_iterator, how_many = batch_size)
class FaceBBoxDDMPytables(dense_design_matrix.DenseDesignMatrix):
filters = tables.Filters(complib='blosc', complevel=1)
h5file = None
"""
DenseDesignMatrix based on PyTables for face bounding boxes.
"""
def __init__(self, X=None, h5file=None, topo_view=None, y=None,
view_converter=None, axes = ('b', 0, 1, 'c'),
image_shape=None, receptive_field_shape=None,
bbox_conversion_type=ConversionType.GUID,
area_ratio=None,
stride=None, use_output_map=True, rng=None):
"""
Parameters
----------
X : ndarray, 2-dimensional, optional
Should be supplied if `topo_view` is not. A design
matrix of shape (number examples, number features)
that defines the dataset.
topo_view : ndarray, optional
Should be supplied if X is not. An array whose first
dimension is of length number examples. The remaining
dimensions are xamples with topological significance,
e.g. for images the remaining axes are rows, columns,
and channels.
y : ndarray, 1-dimensional(?), optional
Labels or targets for each example. The semantics here
are not quite nailed down for this yet.
view_converter : object, optional
An object for converting between design matrices and
topological views. Currently DefaultViewConverter is
the only type available but later we may want to add
one that uses the retina encoding that the U of T group
uses.
image_shape: list
Shape of the images that we are processing.
receptive_field_size: list
Size of the receptive field of the convolutional neural network.
stride: integer
The stride that we have used for the convolution operation.
rng : object, optional
A random number generator used for picking random
indices into the design matrix when choosing minibatches.
"""
if rng is None:
rng = (17, 2, 946)
assert image_shape is not None
assert receptive_field_shape is not None
assert stride is not None
self.image_shape = image_shape
self.receptive_field_shape = receptive_field_shape
self.stride = stride
self.use_output_map = use_output_map
self.bbox_conversion_type = bbox_conversion_type
self.h5file = h5file
self.area_ratio = area_ratio
self._deprecated_interface = True
FaceBBoxDDMPytables.filters = tables.Filters(complib='blosc', complevel=1)
super(FaceBBoxDDMPytables, self).__init__(X = X,
topo_view = topo_view,
y = y,
view_converter = view_converter,
axes = axes,
rng = rng)
def set_design_matrix(self, X, start = 0):
"""
Parameters
----------
X: Images
"""
assert (len(X.shape) == 2)
assert self.h5file is not None
assert not numpy.any(numpy.isnan(X))
if self.h5file.isopen and (self.h5file.mode == "w" or self.h5file.mode == "r+"):
self.fill_hdf5(h5file=self.h5file,
data_x=X,
start=start)
else:
raise ValueError("H5File is not open or not in the writable mode!")
def set_topological_view(self, V, axes=('b', 0, 1, 'c'), start=0):
"""
Sets the dataset to represent V, where V is a batch
of topological views of examples.
Parameters
----------
V : ndarray
An array containing a design matrix representation of training
examples. If unspecified, the entire dataset (`self.X`) is used
instead.
TODO: why is this parameter named 'V'?
"""
assert not numpy.any(numpy.isnan(V))
rows = V.shape[axes.index(0)]
cols = V.shape[axes.index(1)]
channels = V.shape[axes.index('c')]
self.view_converter = DefaultViewConverter([rows, cols, channels], axes=axes)
X = self.view_converter.topo_view_to_design_mat(V)
assert not numpy.any(numpy.isnan(X))
FaceBBoxDDMPytables.fill_hdf5(h5file = self.h5file,
data_x = X,
start = start)
@functools.wraps(Dataset.iterator)
def iterator(self, mode=None, batch_size=None, num_batches=None,
topo=None, targets=None, rng=None, data_specs=None,
return_tuple=False):
# build data_specs from topo and targets if needed
if topo is None:
topo = getattr(self, '_iter_topo', False)
if data_specs[0] is not None:
if isinstance(data_specs[0], Conv2DSpace) or isinstance(data_specs[0].components[0],
Conv2DSpace):
topo = True
if topo:
# self.iterator is called without a data_specs, and with
# "topo=True", so we use the default topological space
# stored in self.X_topo_space
assert self.X_topo_space is not None
X_space = self.X_topo_space
else:
X_space = self.X_space
if targets is None:
if "targets" in data_specs[1]:
targets = True
else:
targets = False
if data_specs is None:
if targets:
assert self.y is not None
y_space = data_specs[0].components[1]
space = CompositeSpace(components=(X_space, y_space))
source = ('features', 'targets')
else:
space = X_space
source = 'features'
print space
data_specs = (space, source)
# TODO: Refactor
if mode is None:
if hasattr(self, '_iter_subset_class'):
mode = self._iter_subset_class
else:
raise ValueError('iteration mode not provided and no default '
'mode set for %s' % str(self))
else:
mode = resolve_iterator_class(mode)
if batch_size is None:
batch_size = getattr(self, '_iter_batch_size', None)
if num_batches is None:
num_batches = getattr(self, '_iter_num_batches', None)
if rng is None and mode.stochastic:
rng = self.rng
if data_specs is None:
data_specs = self._iter_data_specs
return FaceBBoxDDMIterator(self,
mode(self.X.shape[0], batch_size, num_batches, rng),
img_shape=self.image_shape,
receptive_field_shape=self.receptive_field_shape,
stride=self.stride,
bbox_conversion_type=self.bbox_conversion_type,
topo=topo,
targets=targets,
area_ratio=self.area_ratio,
use_output_map=self.use_output_map,
data_specs=data_specs,
return_tuple=return_tuple)
@staticmethod
def init_hdf5(path=None, shapes=None):
"""
Initialize hdf5 file to be used as a dataset
"""
assert shapes is not None
x_shape, y_shape = shapes
print "init_hdf5"
# make pytables
if path is None:
if FaceBBoxDDMPytables.h5file is None:
raise ValueError("path variable should not be empty.")
else:
h5file = FaceBBoxDDMPytables.h5file
else:
h5file = tables.openFile(path, mode = "w", title = "Google Face bounding boxes Dataset.")
gcolumns = h5file.createGroup(h5file.root, "Data", "Data")
atom = tables.Float32Atom() if config.floatX == 'float32' else tables.Float64Atom()
filters = FaceBBoxDDMPytables.filters
h5file.createCArray(gcolumns, 'X', atom = atom, shape = x_shape,
title = "Images", filters = filters)
h5file.createTable(gcolumns, 'bboxes', BoundingBox,
title = "Face bounding boxes", filters = filters)
return h5file, gcolumns
@staticmethod
def fill_hdf5(h5file, data_x, data_y = None, node = None, start = 0, batch_size = 5000):
"""
PyTables tends to crash if you write large data on them at once.
This function write data on file in batches
start: the start index to write data
"""
if node is None:
node = h5file.root.Data
if FaceBBoxDDMPytables.h5file is None:
FaceBBoxDDMPytables.h5file = h5file
data_size = data_x.shape[0]
last = numpy.floor(data_size / float(batch_size)) * batch_size
for i in xrange(0, data_size, batch_size):
stop = i + numpy.mod(data_size, batch_size) if i >= last else i + batch_size
assert len(range(start + i, start + stop)) == len(range(i, stop))
assert (start + stop) <= (node.X.shape[0])
node.X[start + i: start + stop, :] = data_x[i:stop, :]
if data_y is not None:
node.y[start + i: start + stop, :] = data_y[i:stop, :]
h5file.flush()
@staticmethod
def resize(h5file, start, stop, remove_old_node=False):
if h5file is None:
raise ValueError("h5file should not be None.")
data = h5file.root.Data
node_name = "Data_%s_%s" % (start, stop)
if remove_old_node:
try:
gcolumns = h5file.createGroup('/', node_name, "Data %s" % node_name)
except tables.exceptions.NodeError:
h5file.removeNode('/', node_name, 1)
gcolumns = h5file.createGroup('/', node_name, "Data %s" % node_name)
elif node_name in h5file.root:
return h5file, getattr(h5file.root, node_name)
else:
gcolumns = h5file.createGroup('/', node_name, "Data %s" % node_name)
if FaceBBoxDDMPytables.h5file is None:
FaceBBoxDDMPytables.h5file = h5file
start = 0 if start is None else start
stop = gcolumns.X.nrows if stop is None else stop
atom = tables.Float32Atom() if config.floatX == 'float32' else tables.Float64Atom()
filters = FaceBBoxDDMPytables.filters
x = h5file.createCArray(gcolumns, 'X', atom = atom, shape = ((stop - start, data.X.shape[1])),
title = "Images", filters = filters)
y = h5file.createTable(gcolumns, 'bboxes', BoundingBox,
title = "Face bounding boxes", filters = filters)
x[:] = data.X[start:stop]
bboxes = get_image_bboxes(slice(start, stop), data.bboxes)
y.append(bboxes)
if remove_old_node:
h5file.removeNode('/', "Data", 1)
h5file.renameNode('/', "Data", node_name)
h5file.flush()
return h5file, gcolumns
def analyze(config):
output_path = config.get('output_path');
# model_file = os.path.join(output_path, 'eeg', 'conv3', 'convolutional_network.pkl');
# model_file = os.path.join(output_path, 'eeg', 'conv10', 'epochs', 'cnn_epoch94.pkl');
model_file = '../../../debug/debug_run4/debug_network.pkl';
with log_timing(log, 'loading convnet model from {}'.format(model_file)):
model = serial.load(model_file);
input_shape = model.get_input_space().shape;
config = config.eeg;
hyper_params = {
'input_length':input_shape[0], #25+151-1+301-1, # this should leave a single value per channel after convolution
'hop_size':5, # reduce amount of data by factor 5
'dataset_root': config.get('dataset_root'),
'dataset_suffix': config.get('dataset_suffix'),
'save_path': config.get('save_path'),
}
dataset_yaml = '''
!obj:deepthought.datasets.rwanda2013rhythms.EEGDataset.EEGDataset {
name : 'testset',
path : %(dataset_root)s,
suffix : '_channels', # %(dataset_suffix)s,
subjects : [0],
resample : [400, 100],
start_sample : 2500,
stop_sample : 3200, # None (empty) = end of sequence
# FIXME:
# n_fft : 24,
# frame_size : 10, # %(input_length)i,
frame_size : %(input_length)i,
hop_size : %(hop_size)i,
label_mode : 'rhythm_type',
# save_matrix_path: '../../../debug/debug.pkl'
}
'''
dataset_yaml = dataset_yaml % hyper_params;
print dataset_yaml;
with log_timing(log, 'parsing yaml'):
testset = yaml_parse.load(dataset_yaml);
# print testset.subject_partitions;
# print testset.sequence_partitions;
seq_starts = testset.sequence_partitions;
# return;
# axes=['b', 0, 1, 'c']
# def dimshuffle(b01c):
# default = ('b', 0, 1, 'c')
# return b01c.transpose(*[default.index(axis) for axis in axes])
# data = dimshuffle(testset.X);
# design_matrix = model.get_design_matrix()
# view_converter = DefaultViewConverter([475, 1, 1]);
# data = view_converter.
# ## get the labels
# data_specs= (model.get_output_space(), "targets");
# it = testset.iterator(
# mode='sequential',
# batch_size=100,
# data_specs=data_specs);
# labels = np.hstack([np.argmax(minibatch, axis = 1) for minibatch in it])
# print labels[0:1000]
#
# ## get the predictions
# minibatch = model.get_input_space().make_theano_batch();
# output_fn = theano.function(inputs=[minibatch],
# outputs=T.argmax(model.fprop(minibatch), axis = 1));
# print "function compiled"
# # data_specs= (CompositeSpace((
# # model.get_input_space(),
# # model.get_output_space())),
# # ("features", "targets"));
#
# data_specs= (model.get_input_space(), "features");
# it = testset.iterator(
# mode='sequential',
# batch_size=100,
# data_specs=data_specs);
# print "iterator ready"
#
# y_pred = np.hstack([output_fn(minibatch) for minibatch in it])
#
# print y_pred[0:1000]
minibatch = model.get_input_space().make_theano_batch();
output_fn = theano.function(inputs=[minibatch],
outputs=T.argmax(model.fprop(minibatch), axis = 1));
print "function compiled"
data_specs= (CompositeSpace((
model.get_input_space(),
model.get_output_space())),
("features", "targets"));
it = testset.iterator('sequential',
batch_size=100,
data_specs=data_specs);
print "iterator ready"
y_pred = [];
y_real = [];
for minibatch, target in it:
y_pred.append(output_fn(minibatch));
y_real.append(np.argmax(target, axis = 1));
y_pred = np.hstack(y_pred);
y_real = np.hstack(y_real);
print y_pred[0:1000]
print classification_report(y_real, y_pred);
print confusion_matrix(y_real, y_pred);
misclass = (y_real != y_pred);
print misclass.mean();
correct = 0;
s_real = [];
s_pred = [];
s_pred_agg = [];
n_channels = 16;
channel_scores = np.zeros(n_channels, dtype=np.int);
for i in xrange(len(seq_starts)):
start = seq_starts[i];
if i < len(seq_starts) - 1:
stop = seq_starts[i+1];
else:
stop = None;
s_real.append(y_real[start]);
# print np.bincount(y_pred[start:stop]);
# print np.argmax(np.bincount(y_pred[start:stop]));
s_pred.append(np.argmax(np.bincount(y_pred[start:stop])));
s_pred_agg.append(np.mean(y_pred[start:stop])); # works only for binary classification
seq_misclass = misclass[start:stop].mean();
# print '{} [{}{}]: {}'.format(i, start, stop, seq_misclass);
if seq_misclass < 0.5: # more correct than incorrect
correct += 1;
channel_scores[i%n_channels] += 1;
s_real = np.hstack(s_real);
s_pred = np.hstack(s_pred);
print s_real;
print s_pred;
print s_pred_agg;
print 'aggregated'
print classification_report(s_real, s_pred);
print confusion_matrix(s_real, s_pred);
s_misclass = (s_real != s_pred);
print s_misclass.mean();
print channel_scores;
return;
input_shape = model.get_input_space().shape;
print input_shape
view_converter = DefaultViewConverter((input_shape[0], input_shape[1], 1));
data = view_converter.design_mat_to_topo_view(testset.X);
print data.shape;
X = model.get_input_space().make_theano_batch()
Y = model.fprop( X )
Y = T.argmax( Y, axis = 1 ) # needed - otherwise not single value
output_fn = theano.function( [X], Y );
# y_pred = output_fn( data );
batch_size = 1000;
y_pred = [];
batch_start = 0;
while batch_start < data.shape[0]:
batch_stop = min(data.shape[0], batch_start + batch_size);
y_pred.append(output_fn( data[batch_start:batch_stop] ));
# if batch_start == 0: print y_pred;
batch_start = batch_stop;
y_pred = np.hstack(y_pred);
print testset.labels[0:1000]
print y_pred[0:1000]
print classification_report(testset.labels, y_pred);
print confusion_matrix(testset.labels, y_pred);
labels = np.argmax(testset.y, axis=1)
print classification_report(labels, y_pred);
print confusion_matrix(labels, y_pred);
labels = np.argmax(testset.y, axis=1)
print classification_report(labels, y_pred);
print confusion_matrix(labels, y_pred);
misclass = (labels != y_pred).mean()
print misclass
# # alternative version from KeepBestParams
# minibatch = T.matrix('minibatch')
# output_fn = theano.function(inputs=[minibatch],outputs=T.argmax( model.fprop(minibatch), axis = 1 ));
# it = testset.iterator('sequential', batch_size=batch_size, targets=False);
# y_pred = [output_fn(mbatch) for mbatch in it];
# y_hat = T.argmax(state, axis=1)
# y = T.argmax(target, axis=1)
# misclass = T.neq(y, y_hat).mean()
# misclass = T.cast(misclass, config.floatX)
# rval['misclass'] = misclass
# rval['nll'] = self.cost(Y_hat=state, Y=target)
log.debug('done');
def make_viewer(mat, grid_shape=None, patch_shape=None,
activation=None, pad=None, is_color = False, rescale = True):
"""
Given filters in rows, guesses dimensions of patches
and nice dimensions for the PatchViewer and returns a PatchViewer
containing visualizations of the filters.
Parameters
----------
mat : ndarray
Values should lie in [-1, 1] if `rescale` is False.
0. always indicates medium gray, with negative values drawn as
blacker and positive values drawn as whiter.
A matrix with each row being a different image patch, OR
a 4D tensor in ('b', 0, 1, 'c') format.
If matrix, we assume it was flattened using the same procedure as a
('b', 0, 1, 'c') DefaultViewConverter uses.
grid_shape : tuple, optional
A tuple of two ints specifying the shape of the grad in the
PatchViewer, in (rows, cols) format. If not specified, this
function does its best to choose an aesthetically pleasing
value.
patch_shape : tupe, optional
A tuple of two ints specifying the shape of the patch.
If `mat` is 4D, this function gets the patch shape from the shape of
`mat`. If `mat` is 2D and patch_shape is not specified, this function
assumes the patches are perfectly square.
activation : iterable
An iterable collection describing some kind of activation value
associated with each patch. This is indicated with a border around the
patch whose color intensity increases with activation value.
The individual activation values may be single floats to draw one
border or iterable collections of floats to draw multiple borders with
differing intensities around the patch.
pad : int, optional
The amount of padding to add between patches in the displayed image.
is_color : int
If True, assume the images are in color.
Note needed if `mat` is in ('b', 0, 1, 'c') format since we can just
look at its shape[-1].
rescale : bool
If True, rescale each patch so that its highest magnitude pixel
reaches a value of either 0 or 1 depending on the sign of that pixel.
Returns
-------
patch_viewer : PatchViewer
A PatchViewer containing the patches stored in `mat`.
"""
num_channels = 1
if is_color:
num_channels = 3
if grid_shape is None:
grid_shape = PatchViewer.pick_shape(mat.shape[0] )
if mat.ndim > 2:
patch_shape = mat.shape[1:3]
topo_view = mat
num_channels = mat.shape[3]
is_color = num_channels > 1
else:
if patch_shape is None:
assert mat.shape[1] % num_channels == 0
patch_shape = PatchViewer.pick_shape(mat.shape[1] / num_channels,
exact = True)
assert mat.shape[1] == (patch_shape[0] *
patch_shape[1] *
num_channels)
topo_shape = (patch_shape[0], patch_shape[1], num_channels)
view_converter = DefaultViewConverter(topo_shape)
topo_view = view_converter.design_mat_to_topo_view(mat)
rval = PatchViewer(grid_shape, patch_shape, pad=pad, is_color = is_color)
for i in xrange(mat.shape[0]):
if activation is not None:
if hasattr(activation[0], '__iter__'):
act = [a[i] for a in activation]
else:
act = activation[i]
else:
act = None
patch = topo_view[i, :]
rval.add_patch(patch, rescale=rescale,
activation=act)
return rval
def _execute(self):
global num_superpixels
num_output_features = self.num_output_features
idxs = self.idxs
top = self.top
bottom = self.bottom
left = self.left
right = self.right
save_path = self.save_path
batch_size = self.batch_size
dataset_family = self.dataset_family
which_set = self.which_set
model = self.model
size = self.size
nan = 0
dataset_descriptor = dataset_family[which_set][size]
dataset = dataset_descriptor.dataset_maker()
expected_num_examples = dataset_descriptor.num_examples
full_X = dataset.get_design_matrix()
num_examples = full_X.shape[0]
assert num_examples == expected_num_examples
if self.restrict is not None:
assert self.restrict[1] <= full_X.shape[0]
print('restricting to examples ', self.restrict[0], ' through ',
self.restrict[1], ' exclusive')
full_X = full_X[self.restrict[0]:self.restrict[1], :]
assert self.restrict[1] > self.restrict[0]
#update for after restriction
num_examples = full_X.shape[0]
assert num_examples > 0
dataset.X = None
dataset.design_loc = None
dataset.compress = False
patchifier = ExtractGridPatches(patch_shape=(size, size),
patch_stride=(1, 1))
pipeline = serial.load(dataset_descriptor.pipeline_path)
assert isinstance(pipeline.items[0], ExtractPatches)
pipeline.items[0] = patchifier
print('defining features')
V = T.matrix('V')
mu = model.mu
feat = triangle_code(V, mu)
assert feat.dtype == 'float32'
print('compiling theano function')
f = function([V], feat)
nhid = model.mu.get_value().shape[0]
if config.device.startswith('gpu') and nhid >= 4000:
f = halver(f, model.nhid)
topo_feat_var = T.TensorType(broadcastable=(False, False, False,
False),
dtype='float32')()
if self.pool_mode == 'mean':
region_features = function([topo_feat_var],
topo_feat_var.mean(axis=(1, 2)))
elif self.pool_mode == 'max':
region_features = function([topo_feat_var],
topo_feat_var.max(axis=(1, 2)))
else:
assert False
def average_pool(stride):
def point(p):
return p * ns / stride
rval = np.zeros(
(topo_feat.shape[0], stride, stride, topo_feat.shape[3]),
dtype='float32')
for i in xrange(stride):
for j in xrange(stride):
rval[:, i, j, :] = region_features(
topo_feat[:,
point(i):point(i + 1),
point(j):point(j + 1), :])
return rval
output = np.zeros((num_examples, num_output_features), dtype='float32')
fd = DenseDesignMatrix(X=np.zeros((1, 1), dtype='float32'),
view_converter=DefaultViewConverter(
[1, 1, nhid]))
ns = 32 - size + 1
depatchifier = ReassembleGridPatches(orig_shape=(ns, ns),
patch_shape=(1, 1))
if len(range(0, num_examples - batch_size + 1, batch_size)) <= 0:
print(num_examples)
print(batch_size)
for i in xrange(0, num_examples - batch_size + 1, batch_size):
print(i)
t1 = time.time()
d = copy.copy(dataset)
d.set_design_matrix(full_X[i:i + batch_size, :])
t2 = time.time()
#print '\tapplying preprocessor'
d.apply_preprocessor(pipeline, can_fit=False)
X2 = d.get_design_matrix()
t3 = time.time()
#print '\trunning theano function'
feat = f(X2)
t4 = time.time()
assert feat.dtype == 'float32'
feat_dataset = copy.copy(fd)
if contains_nan(feat):
nan += np.isnan(feat).sum()
feat[np.isnan(feat)] = 0
feat_dataset.set_design_matrix(feat)
#print '\treassembling features'
feat_dataset.apply_preprocessor(depatchifier)
#print '\tmaking topological view'
topo_feat = feat_dataset.get_topological_view()
assert topo_feat.shape[0] == batch_size
t5 = time.time()
#average pooling
superpixels = average_pool(num_superpixels)
assert batch_size == 1
if self.pool_mode == 'mean':
for j in xrange(num_output_features):
output[i:i + batch_size,
j] = superpixels[:, top[j]:bottom[j] + 1,
left[j]:right[j] + 1,
idxs[j]].mean()
elif self.pool_mode == 'max':
for j in xrange(num_output_features):
output[i:i + batch_size,
j] = superpixels[:, top[j]:bottom[j] + 1,
left[j]:right[j] + 1,
idxs[j]].max()
else:
assert False
assert output[i:i + batch_size, :].max() < 1e20
t6 = time.time()
print((t6 - t1, t2 - t1, t3 - t2, t4 - t3, t5 - t4, t6 - t5))
if self.chunk_size is not None:
assert save_path.endswith('.npy')
save_path_pieces = save_path.split('.npy')
assert len(save_path_pieces) == 2
assert save_path_pieces[1] == ''
save_path = save_path_pieces[0] + '_' + chr(
ord('A') + self.chunk_id) + '.npy'
np.save(save_path, output)
if nan > 0:
warnings.warn(str(nan) + ' features were nan')
def _execute(self):
global num_superpixels
global num_output_features
global idxs
global top
global bottom
global left
global right
save_path = self.save_path
batch_size = self.batch_size
dataset_family = self.dataset_family
which_set = self.which_set
size = self.size
nan = 0
dataset_descriptor = dataset_family[which_set][size]
dataset = dataset_descriptor.dataset_maker()
expected_num_examples = dataset_descriptor.num_examples
full_X = dataset.get_design_matrix()
num_examples = full_X.shape[0]
assert num_examples == expected_num_examples
if self.restrict is not None:
assert self.restrict[1] <= full_X.shape[0]
print 'restricting to examples ',self.restrict[0],' through ',self.restrict[1],' exclusive'
full_X = full_X[self.restrict[0]:self.restrict[1],:]
assert self.restrict[1] > self.restrict[0]
#update for after restriction
num_examples = full_X.shape[0]
assert num_examples > 0
dataset.X = None
dataset.design_loc = None
dataset.compress = False
patchifier = ExtractGridPatches( patch_shape = (size,size), patch_stride = (1,1) )
pipeline = serial.load(dataset_descriptor.pipeline_path)
assert isinstance(pipeline.items[0], ExtractPatches)
pipeline.items[0] = patchifier
Z = T.matrix('Z')
pos = T.clip(Z,0.,1e30)
neg = T.clip(-Z,0.,1e30)
feat = T.concatenate((pos, neg), axis=1)
assert feat.dtype == 'float32'
print 'compiling theano function'
f = function([Z],feat)
nhid = 3200 # 2 * num dictionary elems
if config.device.startswith('gpu') and nhid >= 4000:
f = halver(f, nhid)
topo_feat_var = T.TensorType(broadcastable = (False,False,False,False), dtype='float32')()
region_features = function([topo_feat_var],
topo_feat_var.mean(axis=(1,2)) )
def average_pool( stride ):
def point( p ):
return p * ns / stride
rval = np.zeros( (topo_feat.shape[0], stride, stride, topo_feat.shape[3] ) , dtype = 'float32')
for i in xrange(stride):
for j in xrange(stride):
rval[:,i,j,:] = region_features( topo_feat[:,point(i):point(i+1), point(j):point(j+1),:] )
return rval
output = np.zeros((num_examples,num_output_features),dtype='float32')
fd = DenseDesignMatrix(X = np.zeros((1,1),dtype='float32'), view_converter = DefaultViewConverter([1, 1, nhid] ) )
ns = 32 - size + 1
depatchifier = ReassembleGridPatches( orig_shape = (ns, ns), patch_shape=(1,1) )
if len(range(0,num_examples-batch_size+1,batch_size)) <= 0:
print num_examples
print batch_size
for i in xrange(0,num_examples-batch_size+1,batch_size):
print i
t1 = time.time()
d = copy.copy(dataset)
d.set_design_matrix(full_X[i:i+batch_size,:])
t2 = time.time()
#print '\tapplying preprocessor'
d.apply_preprocessor(pipeline, can_fit = False)
X2 = d.get_design_matrix()
t3 = time.time()
#print '\trunning theano function'
M.put(s,'batch',X2)
M.eval(s, 'Z = sparse_codes(batch, dictionary, lambda)')
Z = M.get(s, 'Z')
feat = f(np.cast['float32'](Z))
t4 = time.time()
assert feat.dtype == 'float32'
feat_dataset = copy.copy(fd)
if np.any(np.isnan(feat)):
nan += np.isnan(feat).sum()
feat[np.isnan(feat)] = 0
feat_dataset.set_design_matrix(feat)
#print '\treassembling features'
feat_dataset.apply_preprocessor(depatchifier)
#print '\tmaking topological view'
topo_feat = feat_dataset.get_topological_view()
assert topo_feat.shape[0] == batch_size
t5 = time.time()
#average pooling
superpixels = average_pool(num_superpixels)
assert batch_size == 1
assert superpixels.shape[0] == batch_size
assert superpixels.shape[1] == num_superpixels
assert superpixels.shape[2] == num_superpixels
assert superpixels.shape[3] == 2 * num_filters
for j in xrange(num_output_features):
output[i:i+batch_size, j] = superpixels[:,top[j]:bottom[j]+1,
left[j]:right[j]+1, idxs[j]].mean()
t6 = time.time()
print (t6-t1, t2-t1, t3-t2, t4-t3, t5-t4, t6-t5)
if self.chunk_size is not None:
assert save_path.endswith('.npy')
save_path_pieces = save_path.split('.npy')
assert len(save_path_pieces) == 2
assert save_path_pieces[1] == ''
save_path = save_path_pieces[0] + '_' + chr(ord('A')+self.chunk_id)+'.npy'
np.save(save_path,output)
if nan > 0:
warnings.warn(str(nan)+' features were nan')
def __init__(self, patient_id, which_set, preprocessor_path, data_dir,
leave_one_out_seizure, sample_size_second, batch_size,
default_seed=0):
"""
The Epilepsiae dataset customized for leave-one-seizure-out cross validation.
Parameters
----------
patient_id : int
Patient ID.
which_set : string
Name used to specify which partition of the dataset to be loaded (e.g., 'train', 'valid', or 'test').
If not specified, all data will be loaded.
preprocessor_path : string
File path to store the scaler for pre-processing the EEG data.
data_dir : string
Directory that store the source EEG data.
leave_one_out_seizure : int
Index of the withheld seizure.
sample_size_second : int
Number of seconds used to specify sample size.
batch_size : int
Size of the batch, used to remove a few samples to make the the number samples dividable by the batch size.
default_seed : int, optional
Seed for random.
For preprocessing, see more in
https://github.com/lisa-lab/pylearn2/blob/master/pylearn2/datasets/preprocessing.py
For customizing dataset, see more in
https://github.com/lisa-lab/pylearn2/blob/master/pylearn2/scripts/icml_2013_wrepl/emotions/emotions_dataset.py
"""
# Load data
files = ['rec_26402102/26402102_0003.mat',
'rec_26402102/26402102_0007.mat',
'rec_26402102/26402102_0008.mat',
'rec_26402102/26402102_0017.mat']
scalp_channels = np.asarray([ u'FP1',
u'FP2',
u'F3',
u'F4',
u'C3',
u'C4',
u'P3',
u'P4',
u'O1',
u'O2',
u'F7',
u'F8',
u'T3',
u'T4',
u'T5',
u'T6',
u'FZ',
u'CZ',
u'PZ' ])
# Get seizure information
seizure_info = pd.read_table(os.path.join(data_dir, 'RECORDS-WITH-SEIZURES.txt'), sep='\t')
seizure_info['filename'] = seizure_info['filename'].str.replace('.data', '.mat', case=False)
self.data_dir = data_dir
self.files = files
self.seizure_info = seizure_info
self.filter_channels = scalp_channels
self.default_seed = default_seed
self.leave_one_out_seizure = leave_one_out_seizure
self.batch_size = batch_size
X, y, n_channels, sample_size = self.load_data(which_set, sample_size_second, batch_size, preprocessor_path)
self.n_channels = n_channels
self.sample_size = sample_size
view_converter = DefaultViewConverter((1, sample_size, 1))
view_converter.set_axes(axes=['b', 0, 1, 'c'])
DenseDesignMatrix.__init__(self, X=X, y=y,
view_converter=view_converter,
axes=['b', 0, 1, 'c'])
def __init__(self,
patient_id,
which_set,
preprocessor_path,
data_dir,
leave_one_out_seizure,
sample_size_second,
batch_size,
default_seed=0):
"""
The Epilepsiae dataset customized for leave-one-seizure-out cross validation.
Parameters
----------
patient_id : int
Patient ID.
which_set : string
Name used to specify which partition of the dataset to be loaded (e.g., 'train', 'valid', or 'test').
If not specified, all data will be loaded.
preprocessor_path : string
File path to store the scaler for pre-processing the EEG data.
data_dir : string
Directory that store the source EEG data.
leave_one_out_seizure : int
Index of the withheld seizure.
sample_size_second : int
Number of seconds used to specify sample size.
batch_size : int
Size of the batch, used to remove a few samples to make the the number samples dividable by the batch size.
default_seed : int, optional
Seed for random.
For preprocessing, see more in
https://github.com/lisa-lab/pylearn2/blob/master/pylearn2/datasets/preprocessing.py
For customizing dataset, see more in
https://github.com/lisa-lab/pylearn2/blob/master/pylearn2/scripts/icml_2013_wrepl/emotions/emotions_dataset.py
"""
# Load data
files = [
'rec_26402102/26402102_0003.mat', 'rec_26402102/26402102_0007.mat',
'rec_26402102/26402102_0008.mat', 'rec_26402102/26402102_0017.mat'
]
scalp_channels = np.asarray([
u'FP1', u'FP2', u'F3', u'F4', u'C3', u'C4', u'P3', u'P4', u'O1',
u'O2', u'F7', u'F8', u'T3', u'T4', u'T5', u'T6', u'FZ', u'CZ',
u'PZ'
])
# Get seizure information
seizure_info = pd.read_table(os.path.join(data_dir,
'RECORDS-WITH-SEIZURES.txt'),
sep='\t')
seizure_info['filename'] = seizure_info['filename'].str.replace(
'.data', '.mat', case=False)
self.data_dir = data_dir
self.files = files
self.seizure_info = seizure_info
self.filter_channels = scalp_channels
self.default_seed = default_seed
self.leave_one_out_seizure = leave_one_out_seizure
self.batch_size = batch_size
X, y, n_channels, sample_size = self.load_data(which_set,
sample_size_second,
batch_size,
preprocessor_path)
self.n_channels = n_channels
self.sample_size = sample_size
view_converter = DefaultViewConverter((1, sample_size, 1))
view_converter.set_axes(axes=['b', 0, 1, 'c'])
DenseDesignMatrix.__init__(self,
X=X,
y=y,
view_converter=view_converter,
axes=['b', 0, 1, 'c'])