gray_norm = NormLayer(out[i][:, 0:1],
method="lcn",
use_divisor=use.norm_div).output
gray_norm = std_norm(gray_norm, axis=[-3, -2, -1])
depth_norm = var_norm(out[i][:, 1:])
out[i] = T.concatenate([gray_norm, depth_norm], axis=1)
elif use.norm:
out[i] = NormLayer(out[i], method="lcn",
use_divisor=use.norm_div).output
out[i] = std_norm(out[i], axis=[-3, -2, -1])
# convolutions
out[i] *= net.scaler[stage][i]
layers.append(
ConvLayer(
out[i],
**conv_args(stage, i, batch, net, use, tr.rng,
tr.video_shapes)))
out[i] = layers[-1].output
out[i] = PoolLayer(out[i], net.pools[stage],
method=net.pool_method).output
if tr.inspect: insp.append(T.mean(out[i]))
# flatten all convnets outputs
for i in xrange(len(out)):
out[i] = std_norm(out[i], axis=[-3, -2, -1])
out = [out[i].flatten(2) for i in range(len(out))]
vid_ = T.concatenate(out, axis=1)
# dropout
if use.drop:
drop.p_vid = shared(float32(drop.p_vid_val))
drop.p_hidden = shared(float32(drop.p_hidden_val))
def build():
use.load = True # we load the CNN parameteres here
x = ndtensor(len(tr.in_shape))(name='x') # video input
x_ = _shared(empty(tr.in_shape))
conv_shapes = []
for i in xrange(net.n_stages):
k, p, v = array(net.kernels[i]), array(net.pools[i]), array(
tr.video_shapes[i])
conv_s = tuple(v - k + 1)
conv_shapes.append(conv_s)
tr.video_shapes.append(tuple((v - k + 1) / p))
print "stage", i
print " conv", tr.video_shapes[i], "->", conv_s
print " pool", conv_s, "->", tr.video_shapes[i + 1], "x", net.maps[i +
1]
# number of inputs for MLP = (# maps last stage)*(# convnets)*(resulting video shape) + trajectory size
n_in_MLP = net.maps[-1] * net.n_convnets * prod(tr.video_shapes[-1])
print 'MLP:', n_in_MLP, "->", net.hidden, "->", net.n_class, ""
if use.depth:
if net.n_convnets == 2:
out = [x[:, :, 0, :, :, :], x[:, :,
1, :, :, :]] # 2 nets: body and hand
# build 3D ConvNet
layers = [] # all architecture layers
insp = []
for stage in xrange(net.n_stages):
for i in xrange(len(out)): # for body and hand
# normalization
if use.norm and stage == 0:
gray_norm = NormLayer(out[i][:, 0:1],
method="lcn",
use_divisor=use.norm_div).output
gray_norm = std_norm(gray_norm, axis=[-3, -2, -1])
depth_norm = var_norm(out[i][:, 1:])
out[i] = T.concatenate([gray_norm, depth_norm], axis=1)
elif use.norm:
out[i] = NormLayer(out[i],
method="lcn",
use_divisor=use.norm_div).output
out[i] = std_norm(out[i], axis=[-3, -2, -1])
# convolutions
out[i] *= net.scaler[stage][i]
layers.append(
ConvLayer(
out[i],
**conv_args(stage, i, batch, net, use, tr.rng,
tr.video_shapes)))
out[i] = layers[-1].output
out[i] = PoolLayer(out[i],
net.pools[stage],
method=net.pool_method).output
if tr.inspect: insp.append(T.mean(out[i]))
# flatten all convnets outputs
for i in xrange(len(out)):
out[i] = std_norm(out[i], axis=[-3, -2, -1])
out = [out[i].flatten(2) for i in range(len(out))]
vid_ = T.concatenate(out, axis=1)
# dropout
if use.drop:
drop.p_vid = shared(float32(drop.p_vid_val))
drop.p_hidden = shared(float32(drop.p_hidden_val))
drop.p_vid.set_value(float32(0.)) # dont use dropout when testing
drop.p_hidden.set_value(float32(0.)) # dont use dropout when testing
vid_ = DropoutLayer(vid_, rng=tr.rng, p=drop.p_vid).output
# MLP
# ------------------------------------------------------------------------------
# fusion
if net.fusion == "early":
out = vid_
# hidden layer
Wh, bh = load_params(use) # This is test, wudi added this!
layers.append(
HiddenLayer(out,
W=Wh,
b=bh,
n_in=n_in_MLP,
n_out=net.hidden,
rng=tr.rng,
W_scale=net.W_scale[-2],
b_scale=net.b_scale[-2],
activation=relu))
out = layers[-1].output
if tr.inspect:
insp = T.stack(insp[0], insp[1], insp[2], insp[3], insp[4], insp[5],
T.mean(out))
else:
insp = T.stack(0, 0)
if use.drop: out = DropoutLayer(out, rng=tr.rng, p=drop.p_hidden).output
#maxout
# softmax layer
Ws, bs = load_params(use) # This is test, wudi added this!
layers.append(
LogRegr(out,
W=Ws,
b=bs,
rng=tr.rng,
activation=lin,
n_in=net.hidden,
W_scale=net.W_scale[-1],
b_scale=net.b_scale[-1],
n_out=net.n_class))
"""
layers[-1] : softmax layer
layers[-2] : hidden layer (video if late fusion)
layers[-3] : hidden layer (trajectory, only if late fusion)
"""
# prediction
y_pred = layers[-1].y_pred
p_y_given_x = layers[-1].p_y_given_x
####################################################################
####################################################################
print "\n%s\n\tcompiling\n%s" % (('-' * 30, ) * 2)
####################################################################
####################################################################
# compile functions
# ------------------------------------------------------------------------------
print 'compiling test_model'
eval_model = function([], [y_pred, p_y_given_x],
givens={x: x_},
on_unused_input='ignore')
return eval_model, x_
def __init__(self,
x,
use,
lr,
batch,
net,
reg,
drop,
mom,
tr,
res_dir,
load_path=""):
self.out = []
self.layers = []
self.insp_mean = []
self.insp_std = []
for c in (use, lr, batch, net, reg, drop, mom, tr):
write(c.__name__ + ":", res_dir)
_s = c.__dict__
del _s['__module__'], _s['__doc__']
for key in _s.keys():
val = str(_s[key])
if val.startswith("<static"):
val = str(_s[key].__func__.__name__)
if val.startswith("<Cuda"): continue
if val.startswith("<Tensor"): continue
write(" " + key + ": " + val, res_dir)
####################################################################
####################################################################
print "\n%s\n\tbuilding\n%s" % (('-' * 30, ) * 2)
####################################################################
####################################################################
# ConvNet
# ------------------------------------------------------------------------------
# calculate resulting video shapes for all stages
print net.n_stages
conv_shapes = []
for i in xrange(net.n_stages):
k, p, v = array(net.kernels[i]), array(net.pools[i]), array(
tr.video_shapes[i])
conv_s = tuple(v - k + 1)
conv_shapes.append(conv_s)
tr.video_shapes.append(tuple((v - k + 1) / p))
print "stage", i
if use.depth and i == 0:
print " conv", tr.video_shapes[
i], "x 2 ->", conv_s #for body and hand
else:
print " conv", tr.video_shapes[i], "->", conv_s
print " pool", conv_s, "->", tr.video_shapes[i +
1], "x", net.maps[i +
1]
# number of inputs for MLP = (# maps last stage)*(# convnets)*(resulting video shape) + trajectory size
n_in_MLP = net.maps[-1] * net.n_convnets * prod(tr.video_shapes[-1])
print 'debug1'
if use.depth:
if net.n_convnets == 2:
out = [x[:, :, 0, :, :, :],
x[:, :, 1, :, :, :]] # 2 nets: body and hand
# build 3D ConvNet
for stage in xrange(net.n_stages):
for i in xrange(len(out)): # for body and hand
# normalization
if use.norm and stage == 0:
gray_norm = NormLayer(out[i][:, 0:1],
method="lcn",
use_divisor=use.norm_div).output
gray_norm = std_norm(gray_norm, axis=[-3, -2, -1])
depth_norm = var_norm(out[i][:, 1:])
out[i] = T.concatenate([gray_norm, depth_norm], axis=1)
elif use.norm:
out[i] = NormLayer(out[i],
method="lcn",
use_divisor=use.norm_div).output
out[i] = std_norm(out[i], axis=[-3, -2, -1])
# convolutions
out[i] *= net.scaler[stage][i]
print 'debug2'
self.layers.append(
ConvLayer(
out[i],
**conv_args(stage, i, batch, net, use, tr.rng,
tr.video_shapes, load_path)))
out[i] = self.layers[-1].output
out[i] = PoolLayer(out[i],
net.pools[stage],
method=net.pool_method).output
if tr.inspect:
self.insp_mean.append(T.mean(out[i]))
self.insp_std.append(T.std(out[i]))
print 'debug2'
# flatten all convnets outputs
for i in xrange(len(out)):
out[i] = std_norm(out[i], axis=[-3, -2, -1])
out = [out[i].flatten(2) for i in range(len(out))]
vid_ = T.concatenate(out, axis=1)
print 'debug3'
# dropout
if use.drop:
drop.p_vid = shared(float32(drop.p_vid_val))
drop.p_hidden = shared(float32(drop.p_hidden_val))
vid_ = DropoutLayer(vid_, rng=tr.rng, p=drop.p_hidden).output
#maxout
if use.maxout:
vid_ = maxout(vid_, (batch.micro, n_in_MLP))
net.activation = lin
n_in_MLP /= 2
# net.hidden *= 2
# MLP
# ------------------------------------------------------------------------------
# fusion
if net.fusion == "early":
out = vid_
# hidden layer
if use.load:
W, b = load_params(use, load_path)
self.layers.append(
HiddenLayer(out,
n_in=n_in_MLP,
n_out=net.hidden_vid,
rng=tr.rng,
W=W,
b=b,
W_scale=net.W_scale[-2],
b_scale=net.b_scale[-2],
activation=net.activation))
else:
self.layers.append(
HiddenLayer(out,
n_in=n_in_MLP,
n_out=net.hidden_vid,
rng=tr.rng,
W_scale=net.W_scale[-2],
b_scale=net.b_scale[-2],
activation=net.activation))
out = self.layers[-1].output
#if tr.inspect:
#self.insp_mean = T.stack(self.insp_mean)
#self.insp_std = T.stack(self.insp_std)
#self.insp = T.stack(self.insp[0],self.insp[1],self.insp[2],self.insp[3],self.insp[4],self.insp[5], T.mean(out))
#else: self.insp = T.stack(0,0)
# out = normalize(out)
if use.drop:
out = DropoutLayer(out, rng=tr.rng, p=drop.p_hidden).output
#maxout
if use.maxout:
out = maxout(out, (batch.micro, net.hidden))
net.hidden /= 2
print 'debug3'
# now assembly all the output
self.out = out
self.n_in_MLP = n_in_MLP
def build():
use.load = True # we load the CNN parameteres here
x = ndtensor(len(tr.in_shape))(name = 'x') # video input
x_ = _shared(empty(tr.in_shape))
conv_shapes = []
for i in xrange(net.n_stages):
k,p,v = array(net.kernels[i]), array(net.pools[i]), array(tr.video_shapes[i])
conv_s = tuple(v-k+1)
conv_shapes.append(conv_s)
tr.video_shapes.append(tuple((v-k+1)/p))
print "stage", i
print " conv",tr.video_shapes[i],"->",conv_s
print " pool",conv_s,"->",tr.video_shapes[i+1],"x",net.maps[i+1]
# number of inputs for MLP = (# maps last stage)*(# convnets)*(resulting video shape) + trajectory size
n_in_MLP = net.maps[-1]*net.n_convnets*prod(tr.video_shapes[-1])
print 'MLP:', n_in_MLP, "->", net.hidden, "->", net.n_class, ""
if use.depth:
if net.n_convnets==2:
out = [x[:,:,0,:,:,:], x[:,:,1,:,:,:]] # 2 nets: body and hand
# build 3D ConvNet
layers = [] # all architecture layers
insp = []
for stage in xrange(net.n_stages):
for i in xrange(len(out)): # for body and hand
# normalization
if use.norm and stage==0:
gray_norm = NormLayer(out[i][:,0:1], method="lcn",
use_divisor=use.norm_div).output
gray_norm = std_norm(gray_norm,axis=[-3,-2,-1])
depth_norm = var_norm(out[i][:,1:])
out[i] = T.concatenate([gray_norm,depth_norm],axis=1)
elif use.norm:
out[i] = NormLayer(out[i], method="lcn",use_divisor=use.norm_div).output
out[i] = std_norm(out[i],axis=[-3,-2,-1])
# convolutions
out[i] *= net.scaler[stage][i]
layers.append(ConvLayer(out[i], **conv_args(stage, i, batch, net, use, tr.rng, tr.video_shapes)))
out[i] = layers[-1].output
out[i] = PoolLayer(out[i], net.pools[stage], method=net.pool_method).output
if tr.inspect: insp.append(T.mean(out[i]))
# flatten all convnets outputs
for i in xrange(len(out)): out[i] = std_norm(out[i],axis=[-3,-2,-1])
out = [out[i].flatten(2) for i in range(len(out))]
vid_ = T.concatenate(out, axis=1)
# dropout
if use.drop:
drop.p_vid = shared(float32(drop.p_vid_val) )
drop.p_hidden = shared(float32(drop.p_hidden_val))
drop.p_vid.set_value(float32(0.)) # dont use dropout when testing
drop.p_hidden.set_value(float32(0.)) # dont use dropout when testing
vid_ = DropoutLayer(vid_, rng=tr.rng, p=drop.p_vid).output
# MLP
# ------------------------------------------------------------------------------
# fusion
if net.fusion == "early":
out = vid_
# hidden layer
Wh, bh = load_params(use) # This is test, wudi added this!
layers.append(HiddenLayer(out, W = Wh, b =bh, n_in=n_in_MLP, n_out=net.hidden, rng=tr.rng,
W_scale=net.W_scale[-2], b_scale=net.b_scale[-2], activation=relu))
out = layers[-1].output
if tr.inspect: insp = T.stack(insp[0],insp[1],insp[2],insp[3],insp[4],insp[5], T.mean(out))
else: insp = T.stack(0,0)
if use.drop: out = DropoutLayer(out, rng=tr.rng, p=drop.p_hidden).output
#maxout
# softmax layer
Ws, bs = load_params(use) # This is test, wudi added this!
layers.append(LogRegr(out, W = Ws, b = bs, rng=tr.rng, activation=lin, n_in=net.hidden,
W_scale=net.W_scale[-1], b_scale=net.b_scale[-1], n_out=net.n_class))
"""
layers[-1] : softmax layer
layers[-2] : hidden layer (video if late fusion)
layers[-3] : hidden layer (trajectory, only if late fusion)
"""
# prediction
y_pred = layers[-1].y_pred
p_y_given_x = layers[-1].p_y_given_x
####################################################################
####################################################################
print "\n%s\n\tcompiling\n%s"%(('-'*30,)*2)
####################################################################
####################################################################
# compile functions
# ------------------------------------------------------------------------------
print 'compiling test_model'
eval_model = function([], [y_pred, p_y_given_x],
givens={x:x_},
on_unused_input='ignore')
return eval_model, x_
def __init__(self, x, use, lr, batch, net, reg, drop, mom, tr, res_dir, load_path=""):
self.out = []
self.layers = []
self.insp_mean = []
self.insp_std = []
for c in (use, lr, batch, net, reg, drop, mom, tr):
write(c.__name__+":", res_dir)
_s = c.__dict__
del _s['__module__'], _s['__doc__']
for key in _s.keys():
val = str(_s[key])
if val.startswith("<static"): val = str(_s[key].__func__.__name__)
if val.startswith("<Cuda"): continue
if val.startswith("<Tensor"): continue
write(" "+key+": "+val, res_dir)
####################################################################
####################################################################
print "\n%s\n\tbuilding\n%s"%(('-'*30,)*2)
####################################################################
####################################################################
# ConvNet
# ------------------------------------------------------------------------------
# calculate resulting video shapes for all stages
conv_shapes = []
for i in xrange(net.n_stages):
k,p,v = array(net.kernels[i]), array(net.pools[i]), array(tr.video_shapes[i])
conv_s = tuple(v-k+1)
conv_shapes.append(conv_s)
tr.video_shapes.append(tuple((v-k+1)/p))
print "stage", i
if use.depth and i==0:
print " conv",tr.video_shapes[i],"x 2 ->",conv_s #for body and hand
else:
print " conv",tr.video_shapes[i],"->",conv_s
print " pool",conv_s,"->",tr.video_shapes[i+1],"x",net.maps[i+1]
# number of inputs for MLP = (# maps last stage)*(# convnets)*(resulting video shape) + trajectory size
n_in_MLP = net.maps[-1]*net.n_convnets*prod(tr.video_shapes[-1])
if use.depth:
if net.n_convnets==2:
out = [x[:,:,0,:,:,:], x[:,:,1,:,:,:]] # 2 nets: body and hand
# build 3D ConvNet
for stage in xrange(net.n_stages):
for i in xrange(len(out)): # for body and hand
# normalization
if use.norm and stage==0:
gray_norm = NormLayer(out[i][:,0:1], method="lcn",
use_divisor=use.norm_div).output
gray_norm = std_norm(gray_norm,axis=[-3,-2,-1])
depth_norm = var_norm(out[i][:,1:])
out[i] = T.concatenate([gray_norm,depth_norm],axis=1)
elif use.norm:
out[i] = NormLayer(out[i], method="lcn",use_divisor=use.norm_div).output
out[i] = std_norm(out[i],axis=[-3,-2,-1])
# convolutions
out[i] *= net.scaler[stage][i]
self.layers.append(ConvLayer(out[i], **conv_args(stage, i, batch, net, use, tr.rng, tr.video_shapes, load_path)))
out[i] = self.layers[-1].output
out[i] = PoolLayer(out[i], net.pools[stage], method=net.pool_method).output
if tr.inspect:
self.insp_mean.append(T.mean(out[i]))
self.insp_std.append(T.std(out[i]))
# flatten all convnets outputs
for i in xrange(len(out)): out[i] = std_norm(out[i],axis=[-3,-2,-1])
out = [out[i].flatten(2) for i in range(len(out))]
vid_ = T.concatenate(out, axis=1)
# dropout
if use.drop:
drop.p_vid = shared(float32(drop.p_vid_val) )
drop.p_hidden = shared(float32(drop.p_hidden_val))
vid_ = DropoutLayer(vid_, rng=tr.rng, p=drop.p_hidden).output
#maxout
if use.maxout:
vid_ = maxout(vid_, (batch.micro,n_in_MLP))
net.activation = lin
n_in_MLP /= 2
# net.hidden *= 2
# MLP
# ------------------------------------------------------------------------------
# fusion
if net.fusion == "early":
out = vid_
# hidden layer
if use.load:
W,b = load_params(use, load_path)
self.layers.append(HiddenLayer(out, n_in=n_in_MLP, n_out=net.hidden_vid, rng=tr.rng, W=W, b=b,
W_scale=net.W_scale[-2], b_scale=net.b_scale[-2], activation=net.activation))
else:
self.layers.append(HiddenLayer(out, n_in=n_in_MLP, n_out=net.hidden_vid, rng=tr.rng,
W_scale=net.W_scale[-2], b_scale=net.b_scale[-2], activation=net.activation))
out = self.layers[-1].output
#if tr.inspect:
#self.insp_mean = T.stack(self.insp_mean)
#self.insp_std = T.stack(self.insp_std)
#self.insp = T.stack(self.insp[0],self.insp[1],self.insp[2],self.insp[3],self.insp[4],self.insp[5], T.mean(out))
#else: self.insp = T.stack(0,0)
# out = normalize(out)
if use.drop: out = DropoutLayer(out, rng=tr.rng, p=drop.p_hidden).output
#maxout
if use.maxout:
out = maxout(out, (batch.micro,net.hidden))
net.hidden /= 2
# now assembly all the output
self.out = out
self.n_in_MLP = n_in_MLP
insp = []
for stage in xrange(net.n_stages):
for i in xrange(len(out)): # for body and hand
# normalization
if use.norm and stage==0:
gray_norm = NormLayer(out[i][:,0:1], method="lcn",
use_divisor=use.norm_div).output
gray_norm = std_norm(gray_norm,axis=[-3,-2,-1])
depth_norm = var_norm(out[i][:,1:])
out[i] = T.concatenate([gray_norm,depth_norm],axis=1)
elif use.norm:
out[i] = NormLayer(out[i], method="lcn",use_divisor=use.norm_div).output
out[i] = std_norm(out[i],axis=[-3,-2,-1])
# convolutions
out[i] *= net.scaler[stage][i]
layers.append(ConvLayer(out[i], **conv_args(stage, i, batch, net, use, tr.rng, tr.video_shapes)))
out[i] = layers[-1].output
out[i] = PoolLayer(out[i], net.pools[stage], method=net.pool_method).output
if tr.inspect: insp.append(T.mean(out[i]))
# flatten all convnets outputs
for i in xrange(len(out)): out[i] = std_norm(out[i],axis=[-3,-2,-1])
out = [out[i].flatten(2) for i in range(len(out))]
vid_ = T.concatenate(out, axis=1)
# dropout
if use.drop:
vid_ = DropoutLayer(vid_, rng=tr.rng, p=drop.p_vid).output
#maxout
if use.maxout: