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recurrent_att.py
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recurrent_att.py
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import theano as T
import theano.tensor as TT
import theano.tensor.nnet as NN
import theano.tensor.signal as SIG
import numpy as NP
import numpy.random as RNG
from collections import OrderedDict
#####################################################################
# Usage: #
# python -u recurrent_plain_base.py [opts] [model_name] #
# #
# Options: #
# --batch_size=INTEGER #
# --conv1_nr_filters=INTEGER #
# --conv1_filter_size=INTEGER #
# --conv1_stride=INTEGER #
# --img_size=INTEGER #
# --gru_dim=INTEGER #
# --seq_len=INTEGER #
# --use_cudnn (Set floatX to float32 if you use this) #
# --zero_tail_fc (Recommended) #
#####################################################################
### Utility functions begin
def get_fans(shape):
'''
Borrowed from keras
'''
fan_in = shape[0] if len(shape) == 2 else NP.prod(shape[1:])
fan_out = shape[1] if len(shape) == 2 else shape[0]
return fan_in, fan_out
def glorot_uniform(shape):
'''
Borrowed from keras
'''
fan_in, fan_out = get_fans(shape)
s = NP.sqrt(6. / (fan_in + fan_out))
return NP.cast[T.config.floatX](RNG.uniform(low=-s, high=s, size=shape))
def orthogonal(shape, scale=1.1):
'''
Borrowed from keras
'''
flat_shape = (shape[0], NP.prod(shape[1:]))
a = RNG.normal(0, 1, flat_shape)
u, _, v = NP.linalg.svd(a, full_matrices=False)
q = u if u.shape == flat_shape else v
q = q.reshape(shape)
return NP.cast[T.config.floatX](q)
def tensor5(name=None, dtype=None):
if dtype == None:
dtype = T.config.floatX
return TT.TensorType(dtype, [False] * 5, name=name)()
conv2d = NN.conv2d
### Utility functions end
### CONFIGURATION BEGIN
batch_size = 32
conv1_nr_filters = 32
conv1_filter_row = 10
conv1_filter_col = 10
conv1_stride = 5
img_row = 100
img_col = 100
# attentions are unused yet
attention_row = 25
attention_col = 25
gru_dim = 200
seq_len = 20
model_name = 'model.pkl'
zero_tail_fc = False
variadic_length = False
test = False
acc_scale = 0
zoom_scale = 0
double_mnist = False
NUM_N = 5
dataset_name = "train"
filename = "mnist.h5"
nr_objs = 1
clutter_move = 1
with_clutters = 1
att_params = 3 # fraction, scale, amplifier
### CONFIGURATION END
### getopt begin
from getopt import *
import sys
try:
opts, args = getopt(sys.argv[1:], "", ["batch_size=", "conv1_nr_filters=", "conv1_filter_size=", "conv1_stride=", "img_size=", "gru_dim=", "seq_len=", "use_cudnn", "zero_tail_fc", "var_len", "test", "acc_scale=",
"zoom_scale=", "dataset=", "double_mnist", "nr_objs=", "clutter_static", "without_clutters", "grid_size=", "filename="])
for opt in opts:
if opt[0] == "--batch_size":
batch_size = int(opt[1])
elif opt[0] == "--conv1_nr_filters":
conv1_nr_filters = int(opt[1])
elif opt[0] == "--conv1_filter_size":
conv1_filter_row = conv1_filter_col = int(opt[1])
elif opt[0] == "--conv1_stride":
conv1_stride = int(opt[1])
elif opt[0] == "--img_size":
img_row = img_col = int(opt[1])
elif opt[0] == "--gru_dim":
gru_dim = int(opt[1])
elif opt[0] == "--seq_len":
seq_len = int(opt[1])
elif opt[0] == "--use_cudnn":
if T.config.device[:3] == 'gpu':
import theano.sandbox.cuda.dnn as CUDNN
if CUDNN.dnn_available():
print 'Using CUDNN instead of Theano conv2d'
conv2d = CUDNN.dnn_conv
elif opt[0] == "--zero_tail_fc":
zero_tail_fc = True
elif opt[0] == "--var_len":
variadic_length = True
elif opt[0] == "--test":
test = True
elif opt[0] == "--acc_scale":
acc_scale = float(opt[1])
elif opt[0] == "--zoom_scale":
zoom_scale = float(opt[1])
elif opt[0] == "--double_mnist":
double_mnist = True
elif opt[0] == "--dataset":
dataset_name = opt[1]
elif opt[0] == "--nr_objs":
nr_objs = int(opt[1])
elif opt[0] == "--clutter_static":
clutter_move = 0
elif opt[0] == "--without_clutters":
with_clutters = 0
elif opt[0] == "--grid_size":
NUM_N = int(opt[1])
elif opt[0] == "--filename":
filename = opt[1]
if len(args) > 0:
model_name = args[0]
except:
pass
### getopt end
### Computed hyperparameters begin
conv1_output_dim = ((img_row - conv1_filter_row) / conv1_stride + 1) * \
((img_col - conv1_filter_col) / conv1_stride + 1) * \
conv1_nr_filters
print conv1_output_dim
gru_input_dim = conv1_output_dim + 4
### Computed hyperparameters end
print 'Initializing parameters'
### NETWORK PARAMETERS BEGIN
conv1_filters = T.shared(glorot_uniform((conv1_nr_filters, 1, conv1_filter_row, conv1_filter_col)), name='conv1_filters')
Wr = T.shared(glorot_uniform((gru_input_dim, gru_dim)), name='Wr')
Ur = T.shared(orthogonal((gru_dim, gru_dim)), name='Ur')
br = T.shared(NP.zeros((gru_dim,), dtype=T.config.floatX), name='br')
Wz = T.shared(glorot_uniform((gru_input_dim, gru_dim)), name='Wz')
Uz = T.shared(orthogonal((gru_dim, gru_dim)), name='Uz')
bz = T.shared(NP.zeros((gru_dim,), dtype=T.config.floatX), name='bz')
Wg = T.shared(glorot_uniform((gru_input_dim, gru_dim)), name='Wg')
Ug = T.shared(orthogonal((gru_dim, gru_dim)), name='Ug')
bg = T.shared(NP.zeros((gru_dim,), dtype=T.config.floatX), name='bg')
W_fc2 = T.shared(glorot_uniform((gru_dim, 4)) if not zero_tail_fc else NP.zeros((gru_dim, 4), dtype=T.config.floatX), name='W_fc2')
b_fc2 = T.shared(NP.zeros((4,), dtype=T.config.floatX), name='b_fc2')
W_fc3 = T.shared(glorot_uniform((gru_dim, att_params)), name='W_fc2')
b_fc3 = T.shared(NP.zeros((att_params,), dtype=T.config.floatX), name='b_fc2')
### NETWORK PARAMETERS END
print 'Building network'
A = TT.arange(img_col, dtype=T.config.floatX)
B = TT.arange(img_row, dtype=T.config.floatX)
A.name = 'a'
B.name = 'b'
def __filterbank(center_x, center_y, delta, sigma):
'''
From Bornschein's DRAW
cx, cy, delta, sigma are absolute and respective to the whole canvas (in pixels)
'''
muX = center_x.dimshuffle(0, 'x') + delta.dimshuffle(0, 'x') * (TT.arange(NUM_N, dtype=T.config.floatX) - (NUM_N - 1) / 2.)
muY = center_y.dimshuffle(0, 'x') + delta.dimshuffle(0, 'x') * (TT.arange(NUM_N, dtype=T.config.floatX) - (NUM_N - 1) / 2.)
eps = 1e-8
FX = TT.exp(-(A - muX.dimshuffle(0, 1, 'x')) ** 2 / 2. / (sigma.dimshuffle(0, 'x', 'x') ** 2 + eps))
FY = TT.exp(-(B - muY.dimshuffle(0, 1, 'x')) ** 2 / 2. / (sigma.dimshuffle(0, 'x', 'x') ** 2 + eps))
FX = FX / (FX.sum(axis=-1).dimshuffle(0, 1, 'x') + eps)
FY = FY / (FY.sum(axis=-1).dimshuffle(0, 1, 'x') + eps)
return FX, FY
### Recurrent step
# img: of shape (batch_size, nr_channels, img_rows, img_cols)
def __step(img, prev_bbox, prev_att, state):
cx = (prev_bbox[:, 2] + prev_bbox[:, 0]) / 2.
cy = (prev_bbox[:, 3] + prev_bbox[:, 1]) / 2.
sigma = TT.exp(prev_att[:, 0]) * (max(img_col, img_row) / 2)
fract = TT.exp(prev_att[:, 1])
amplifier = TT.exp(prev_att[:, 2])
eps = 1e-8
abs_cx = (cx + 1) / 2. * (img_col - 1)
abs_cy = (cy + 1) / 2. * (img_row - 1)
abs_stride = (fract * (max(img_col, img_row) - 1)) * ((1. / (NUM_N - 1.)) if NUM_N > 1 else 0)
FX, FY = __filterbank(abs_cx, abs_cy, abs_stride, sigma)
unnormalized_mask = (FX.dimshuffle(0, 'x', 1, 'x', 2) * FY.dimshuffle(0, 1, 'x', 2, 'x')).sum(axis=2).sum(axis=1)
mask = unnormalized_mask# / (unnormalized_mask.sum(axis=2).sum(axis=1) + eps).dimshuffle(0, 'x', 'x')
masked_img = (mask.dimshuffle(0, 'x', 1, 2) * img) * amplifier.dimshuffle(0, 'x', 'x', 'x')
conv1 = conv2d(masked_img, conv1_filters, subsample=(conv1_stride, conv1_stride))
act1 = TT.tanh(conv1)
flat1 = TT.reshape(act1, (batch_size, conv1_output_dim))
gru_in = TT.concatenate([flat1, prev_bbox], axis=1)
gru_z = NN.sigmoid(TT.dot(gru_in, Wz) + TT.dot(state, Uz) + bz)
gru_r = NN.sigmoid(TT.dot(gru_in, Wr) + TT.dot(state, Ur) + br)
gru_h_ = TT.tanh(TT.dot(gru_in, Wg) + TT.dot(gru_r * state, Ug) + bg)
gru_h = (1 - gru_z) * state + gru_z * gru_h_
bbox = TT.tanh(TT.dot(gru_h, W_fc2) + b_fc2)
att = TT.dot(gru_h, W_fc3) + b_fc3
return bbox, att, gru_h, mask
# imgs: of shape (batch_size, seq_len, nr_channels, img_rows, img_cols)
imgs = tensor5()
starts = TT.matrix()
startAtt = TT.matrix()
# Move the time axis to the top
_imgs = imgs.dimshuffle(1, 0, 2, 3, 4)
sc,_ = T.scan(__step, sequences=[imgs.dimshuffle(1, 0, 2, 3, 4)], outputs_info=[starts, startAtt, T.shared(NP.zeros((batch_size, gru_dim), dtype=T.config.floatX)), None])
bbox_seq = sc[0].dimshuffle(1, 0, 2)
att_seq = sc[1].dimshuffle(1, 0, 2)
mask_seq = sc[3].dimshuffle(1, 0, 2, 3)
# targets: of shape (batch_size, seq_len, 4)
targets = TT.tensor3()
seq_len_scalar = TT.scalar()
cost = ((targets - bbox_seq) ** 2).sum() / batch_size / seq_len_scalar
print 'Building optimizer'
params = [conv1_filters, Wr, Ur, br, Wz, Uz, bz, Wg, Ug, bg, W_fc2, b_fc2, W_fc3, b_fc3]
### RMSProp begin
def rmsprop(cost, params, lr=0.001, rho=0.9, epsilon=1e-6):
'''
Borrowed from keras, no constraints, though
'''
updates = OrderedDict()
grads = T.grad(cost, params)
acc = [T.shared(NP.zeros(p.get_value().shape, dtype=T.config.floatX)) for p in params]
for p, g, a in zip(params, grads, acc):
new_a = rho * a + (1 - rho) * g ** 2
updates[a] = new_a
new_p = p - lr * g / TT.sqrt(new_a + epsilon)
updates[p] = new_p
return updates
### RMSprop end
train = T.function([seq_len_scalar, imgs, starts, startAtt, targets], [cost, bbox_seq, att_seq, mask_seq], updates=rmsprop(cost, params) if not test else None, allow_input_downcast=True)
import cPickle
try:
f = open(model_name, "rb")
param_saved = cPickle.load(f)
for _p, p in zip(params, param_saved):
_p.set_value(p)
except IOError:
pass
print 'Generating dataset'
from data_handler import *
print 'START'
bmnist = BouncingMNIST(nr_objs, seq_len, batch_size, img_row, dataset_name+"/inputs", dataset_name+"/targets", acc=acc_scale, scale_range=zoom_scale, clutter_move = clutter_move, with_clutters = with_clutters, buff=True, filename=filename)
try:
for i in range(0, 60):
for j in range(0, 2000):
_len = seq_len
#_len = int(RNG.exponential(seq_len - 5) + 5) if variadic_length else seq_len
data, label = bmnist.GetBatch(count = 2 if double_mnist else 1)
data = data[:, :, NP.newaxis, :, :] / 255.0
label = label / (img_row / 2.) - 1.
att = np.zeros((batch_size, att_params))
att[:, 0] = 10
att[:, 1] = 1
att[:, 2] = 0
cost, bbox_seq, att_seq, mask = train(_len, data, label[:, 0, :], att, label)
print 'Mask ', NP.max(mask)
print 'Attention, sigma, strideH, strideW', NP.mean(NP.abs(att_seq), axis=1)
left = NP.max([bbox_seq[:, :, 0], label[:, :, 0]], axis=0)
top = NP.max([bbox_seq[:, :, 1], label[:, :, 1]], axis=0)
right = NP.min([bbox_seq[:, :, 2], label[:, :, 2]], axis=0)
bottom = NP.min([bbox_seq[:, :, 3], label[:, :, 3]], axis=0)
intersect = (right - left) * ((right - left) > 0) * (bottom - top) * ((bottom - top) > 0)
label_area = (label[:, :, 2] - label[:, :, 0]) * (label[:, :, 2] - label[:, :, 0] > 0) * (label[:, :, 3] - label[:, :, 1]) * (label[:, :, 3] - label[:, :, 1] > 0)
predict_area = (bbox_seq[:, :, 2] - bbox_seq[:, :, 0]) * (bbox_seq[:, :, 2] - bbox_seq[:, :, 0] > 0) * (bbox_seq[:, :, 3] - bbox_seq[:, :, 1]) * (bbox_seq[:, :, 3] - bbox_seq[:, :, 1] > 0)
union = label_area + predict_area - intersect
print i, j, cost
iou = intersect / union
print NP.average(iou, axis=1)
f = open(model_name + str(i), "wb")
cPickle.dump(map(lambda x: x.get_value(), params), f)
f.close()
finally:
if not test:
f = open(model_name, "wb")
cPickle.dump(map(lambda x: x.get_value(), params), f)
f.close()