def make_net(ims, samples, pr, reuse = True, train = True):
if pr.net_type == 'i3d':
import i3d_kinetics
keep_prob = 0.5 if train else 1.
if pr.use_i3d_logits:
with tf.variable_scope('RGB', reuse = reuse):
net = tfu.normalize_ims(ims)
i3d_net = i3d_kinetics.InceptionI3d(pr.num_classes, spatial_squeeze = True, final_endpoint = 'Logits')
logits, _ = i3d_net(net, is_training = train, dropout_keep_prob = keep_prob)
return ut.Struct(logits = logits, prob = tf.nn.softmax(logits), last_conv = logits)
else:
with tf.variable_scope('RGB', reuse = reuse):
i3d_net = i3d_kinetics.InceptionI3d(pr.num_classes, final_endpoint = 'Mixed_5c')
net = tfu.normalize_ims(ims)
net, _ = i3d_net(net, is_training = train, dropout_keep_prob = keep_prob)
last_conv = net
net = tf.reduce_mean(last_conv, [1, 2, 3], keep_dims = True)
with slim.arg_scope(shift.arg_scope(pr, reuse = reuse, train = train)):
logits = shift.conv3d(
net, pr.num_classes, [1, 1, 1], scope = 'lb/logits',
activation_fn = None, normalizer_fn = None)[:, 0, 0, 0, :]
return ut.Struct(logits = logits,
prob = tf.nn.softmax(logits),
last_conv = net)
elif pr.net_type == 'shift':
with slim.arg_scope(shift.arg_scope(pr, reuse = reuse, train = train)):
# To train the network without audio, you can set samples to be an all-zero array, and
# set pr.use_sound = False.
shift_net = shift.make_net(ims, samples, pr, reuse = reuse, train = train)
if pr.use_dropout:
shift_net.last_conv = slim.dropout(shift_net.last_conv, is_training = train)
net = shift_net.last_conv
net = tf.reduce_mean(net, [1, 2, 3], keep_dims = True)
logits = shift.conv3d(
net, pr.num_classes, [1, 1, 1], scope = 'lb/logits',
activation_fn = None, normalizer_fn = None)[:, 0, 0, 0, :]
return ut.Struct(logits = logits, prob = tf.nn.softmax(logits), last_conv = net)
elif pr.net_type == 'c3d':
import c3d
with slim.arg_scope(shift.arg_scope(reuse = reuse, train = train)):
net = c3d.make_net(ims, samples, pr, reuse = reuse, train = train)
net = net.last_conv
net = tf.reduce_mean(net, [1, 2, 3], keep_dims = True)
logits = c3d.conv3d(
net, pr.num_classes, [1, 1, 1], scope = 'lb/logits',
activation_fn = None, normalizer_fn = None)[:, 0, 0, 0, :]
return ut.Struct(logits = logits, prob = tf.nn.softmax(logits), last_conv = net)
else:
raise RuntimeError()
def make_discrim_spec(spec_in,
spec_out,
phase_in,
phase_out,
pr,
reuse=True,
train=True):
with slim.arg_scope(unet_arg_scope(pr, reuse=reuse, train=train)):
spec_in = normalize_spec(spec_in, pr)
spec_out = normalize_spec(spec_out, pr)
spec_in = ed(spec_in, 3)
spec_out = ed(spec_out, 3)
phase_in = ed(phase_in, 3)
phase_out = ed(phase_out, 3)
net = tf.concat([spec_in, phase_in, spec_out, phase_out], 3)
net = conv2d_same(net, 32, 4, scope='discrim/spec/conv1', stride=2)
net = conv2d_same(net, 64, 4, scope='discrim/spec/conv2', stride=2)
net = conv2d(net, 128, 4, scope='discrim/spec/conv3', stride=2)
net = conv2d(net, 128, 4, scope='discrim/spec/conv4', stride=2)
# net = conv2d(net, 256, 4, scope = 'discrim/spec/conv5', stride = 2)
# net = conv2d(net, 256, 4, scope = 'discrim/spec/conv6', stride = [1, 2])
logits = conv2d(net,
1,
1,
scope='discrim/spec/logits',
stride=1,
normalizer_fn=None,
activation_fn=None)
return ut.Struct(logits=logits)
def triangulate_search(Ps, projs, im_shapes):
projs = [p[0] for p in projs]
ts = np.array([center_from_P(P) for P in Ps])
(max_angle, X_best, tri) = (None, None, ut.Struct(inliers=[]))
for inds in ransac_sample(len(Ps), 2, 300):
Ps_s, projs_s, shapes_s = ut.take_inds_each([Ps, projs, im_shapes],
inds)
# # does not seem to help
# F = F_from_Ps(Ps_s[0], Ps_s[1], center_from_P(Ps_s[1]))
# if not F_test_matches(F, projs_s[0][na, :], projs_s[1][na, :])[0]:
# continue
X = triangulate_nonlinear_pt(Ps_s, projs_s, shapes_s)
angle = ut.angle_between(-X + ts[inds[0]], -X + ts[inds[1]])
pt_ok = (in_front_of(Ps_s[0], X) and in_front_of(Ps_s[1], X)) \
and (max(reproj_error(P, X, x) for P, x in zip(Ps_s, projs_s)) <= 100)
#pt_ok = (in_front_of(Ps_s[0], X) and in_front_of(Ps_s[1], X))
# F = F_from_Ps(Ps_s[0], Ps_s[1], center_from_P(Ps_s[1]))
# if max(reproj_error(P, X, x) for P, x in zip(Ps_s, projs_s)) > 100 and F_test_matches(F, projs_s[0][na, :], projs_s[1][na, :])[0]:
# asdf
if pt_ok:
if max_angle <= angle:
max_angle = angle
X_best = X
tri = ut.Struct(inliers=inds,
angle_deg=np.degrees(angle),
in_front=(in_front_of(Ps_s[0], X),
in_front_of(Ps_s[1], X)),
cam_dist=pylab.dist(ts[inds[0]], ts[inds[1]]),
reproj_errors=[
reproj_error(P, X, x)
for P, x in zip(Ps_s, projs_s)
])
return X_best, tri
def mix_sounds(samples0, pr, quiet_thresh_db=40., samples1=None):
if pr.normalize_rms:
samples0 = mu.normalize_rms(samples0)
if samples1 is not None:
samples1 = mu.normalize_rms(samples1)
if samples1 is None:
n = shape(samples0, 0) / 2
samples0 = samples0[:, :pr.sample_len]
if pr.both_videos_in_batch:
print 'Using both videos'
samples1 = tf.concat(
[samples0[n:, :pr.sample_len], samples0[:n, :pr.sample_len]],
axis=0)
else:
print 'Only using first videos'
samples1 = samples0[n:]
samples0 = samples0[:n]
else:
samples0 = samples0[:, :pr.sample_len]
samples1 = samples1[:, :pr.sample_len]
if pr.augment_rms:
print 'Augmenting rms'
# scale0 = tf.random_uniform((shape(samples0, 0), 1, 1), 0.9, 1.1)
# scale1 = tf.random_uniform((shape(samples1, 0), 1, 1), 0.9, 1.1)
db = 0.25
scale0 = 2.**tf.random_uniform((shape(samples0, 0), 1, 1), -db, db)
scale1 = 2.**tf.random_uniform((shape(samples1, 0), 1, 1), -db, db)
samples0 = scale0 * samples0
samples1 = scale1 * samples1
samples_mix = samples0 + samples1
spec_mix, phase_mix = stft(make_mono(samples_mix), pr)
spec0, phase0 = stft(make_mono(samples0), pr)
spec1, phase1 = stft(make_mono(samples1), pr)
print 'Before truncating specgram:', shape(spec_mix)
spec_mix = spec_mix[:, :pr.spec_len]
print 'After truncating specgram:', shape(spec_mix)
phase_mix = phase_mix[:, :pr.spec_len]
spec0 = spec0[:, :pr.spec_len]
spec1 = spec1[:, :pr.spec_len]
phase0 = phase0[:, :pr.spec_len]
phase1 = phase1[:, :pr.spec_len]
return ut.Struct(samples=samples_mix,
phase=phase_mix,
spec=spec_mix,
sample_parts=[samples0, samples1],
spec_parts=[spec0, spec1],
phase_parts=[phase0, phase1])
def test(pr, gpu, test_on_train=False, center_crop=True):
[gpu] = set_gpus([gpu])
if pr.inputs == ['press']:
net = PressClf(pr)
else:
#check_path = tf.train.latest_checkpoint(pr.train_dir)
check_path = pj(pr.train_dir, 'net.tf-%d' % pr.model_iter)
print 'Restoring from:', check_path
net = NetClf(pr, check_path, gpu)
if test_on_train:
print 'Testing on train!'
data = ut.load(pj(pr.dsdir, 'train.pk'))
else:
data = ut.load(pj(pr.dsdir, 'test.pk'))
labels, probs, accs, vals = [], [], [], []
for i in xrange(len(data)):
ex = data[i]
label = ex['is_gripping']
def load_im(k, v):
if k.startswith('gel') or k.startswith('im'):
im = ig.uncompress(v)
elif k.startswith('depth'):
#v = np.tile(v, (1, 1, 3))
im = v.astype('float32')
else:
raise RuntimeError()
if center_crop:
im = ut.crop_center(im, 224)
return im
inputs = {k: load_im(k, ex[k]) for k in im_names}
inputs['initial_press_prob'] = ex['initial_press_prob']
inputs['ee'] = ex['end_effector']
pred, prob = net.predict(**inputs)
#print prob, pred, label
labels.append(label)
probs.append(prob)
accs.append(pred == label)
if i % 50 == 0:
print 'running average acc:', ut.f3(np.mean(accs))
vals.append(
ut.Struct(label=label,
prob=prob,
acc=accs[-1],
idx=i,
db_file=ex['db_file'],
object_name=ex['object_name']))
labels = np.array(labels, 'bool')
probs = np.array(probs, 'float32')
accs = np.array(accs)
acc = np.mean(accs)
ap = sklearn.metrics.average_precision_score(labels, probs)
print 'Accuracy:', acc
print 'mAP:', ap
print 'Base rate:', ut.f3(
np.array(ut.mapattr(vals).label).astype('float32').mean())
ut.save(pj(pr.resdir, 'eval_results.pk'),
dict(acc=acc, ap=ap, results=(labels, probs)))
ut.save(pj(pr.resdir, 'eval.pk'), vals)
return acc
def make_net(ims,
sfs,
spec,
phase,
pr,
reuse=True,
train=True,
vid_net_full=None):
if pr.mono:
print 'Using mono!'
sfs = make_mono(sfs, tile=True)
if vid_net_full is None:
if pr.net_style == 'static':
n = shape(ims, 1)
if 0:
ims_tile = tf.tile(ims[:, n / 2:n / 2 + 1], (1, n, 1, 1, 1))
else:
ims = tf.cast(ims, tf.float32)
ims_tile = tf.tile(ims[:, n / 2:n / 2 + 1], (1, n, 1, 1, 1))
vid_net_full = shift_net.make_net(ims_tile, sfs, pr, None, reuse,
train)
elif pr.net_style == 'no-im':
vid_net_full = None
elif pr.net_style == 'full':
vid_net_full = shift_net.make_net(ims, sfs, pr, None, reuse, train)
elif pr.net_style == 'i3d':
with tf.variable_scope('RGB', reuse=reuse):
import sep_i3d
i3d_net = sep_i3d.InceptionI3d(1)
vid_net_full = ut.Struct(
scales=i3d_net(ims, is_training=train))
with slim.arg_scope(unet_arg_scope(pr, reuse=reuse, train=train)): # test
acts = []
def conv(*args, **kwargs):
out = conv2d(*args, activation_fn=None, **kwargs)
acts.append(out)
out = mu.lrelu(out, 0.2)
return out
def deconv(*args, **kwargs):
args = list(args)
if kwargs.get('do_pop', True):
skip_layer = acts.pop()
else:
skip_layer = acts[-1]
if 'do_pop' in kwargs:
del kwargs['do_pop']
x = args[0]
if kwargs.get('concat', True):
x = tf.concat([x, skip_layer], 3)
if 'concat' in kwargs:
del kwargs['concat']
args[0] = tf.nn.relu(x)
return deconv2d(*args, activation_fn=None, **kwargs)
def merge_level(net, n):
if vid_net_full is None:
return net
vid_net = tf.reduce_mean(vid_net_full.scales[n], [2, 3],
keep_dims=True)
vid_net = vid_net[:, :, 0, :, :]
s = shape(vid_net)
if shape(net, 1) != s[1]:
vid_net = tf.image.resize_images(vid_net, [shape(net, 1), 1])
print 'Video net before merge:', s, 'After:', shape(vid_net)
else:
print 'No need to resize:', s, shape(net)
vid_net = tf.tile(vid_net, (1, 1, shape(net, 2), 1))
net = tf.concat([net, vid_net], 3)
acts[-1] = net
return net
num_freq = shape(spec, 2)
net = tf.concat([
ed(normalize_spec(spec, pr), 3),
ed(normalize_phase(phase, pr), 3)
], 3)
net = net[:, :, :pr.freq_len, :]
net = conv(net, 64, 4, scope='gen/conv1', stride=[1, 2])
net = conv(net, 128, 4, scope='gen/conv2', stride=[1, 2])
net = conv(net, 256, 4, scope='gen/conv3', stride=2)
net = merge_level(net, 0)
net = conv(net, 512, 4, scope='gen/conv4', stride=2)
net = merge_level(net, 1)
net = conv(net, 512, 4, scope='gen/conv5', stride=2)
net = merge_level(net, 2)
net = conv(net, 512, 4, scope='gen/conv6', stride=2)
net = conv(net, 512, 4, scope='gen/conv7', stride=2)
net = conv(net, 512, 4, scope='gen/conv8', stride=2)
net = conv(net, 512, 4, scope='gen/conv9', stride=2)
net = deconv(net, 512, 4, scope='gen/deconv1', stride=2, concat=False)
net = deconv(net, 512, 4, scope='gen/deconv2', stride=2)
net = deconv(net, 512, 4, scope='gen/deconv3', stride=2)
net = deconv(net, 512, 4, scope='gen/deconv4', stride=2)
net = deconv(net, 512, 4, scope='gen/deconv5', stride=2)
net = deconv(net, 256, 4, scope='gen/deconv6', stride=2)
net = deconv(net, 128, 4, scope='gen/deconv7', stride=2)
net = deconv(net, 64, 4, scope='gen/deconv8', stride=[1, 2])
out_fg = deconv(net,
2,
4,
scope='gen/fg',
stride=[1, 2],
normalizer_fn=None,
do_pop=False)
out_bg = deconv(net,
2,
4,
scope='gen/bg',
stride=[1, 2],
normalizer_fn=None,
do_pop=False)
def process(out):
pred_spec = out[..., 0]
pred_spec = tf.tanh(pred_spec)
pred_spec = unnormalize_spec(pred_spec, pr)
pred_phase = out[..., 1]
pred_phase = tf.tanh(pred_phase)
pred_phase = unnormalize_phase(pred_phase, pr)
val = soundrep.db_from_amp(0.) if pr.log_spec else 0.
pred_spec = tf.pad(pred_spec,
[(0, 0), (0, 0),
(0, num_freq - shape(pred_spec, 2))],
constant_values=val)
if pr.phase_type == 'pred':
pred_phase = tf.concat([pred_phase, phase[..., -1:]], 2)
elif pr.phase_type == 'orig':
pred_phase = phase
else:
raise RuntimeError()
# if ut.hastrue(pr, 'griffin_lim'):
# print 'using griffin-lim'
# pred_wav = griffin_lim(pred_spec, pred_phase, pr)
# else:
pred_wav = istft(pred_spec, pred_phase, pr)
return pred_spec, pred_phase, pred_wav
pred_spec_fg, pred_phase_fg, pred_wav_fg = process(out_fg)
pred_spec_bg, pred_phase_bg, pred_wav_bg = process(out_bg)
return ut.Struct(
pred_spec_fg=pred_spec_fg,
pred_wav_fg=pred_wav_fg,
pred_phase_fg=pred_phase_fg,
pred_spec_bg=pred_spec_bg,
pred_phase_bg=pred_phase_bg,
pred_wav_bg=pred_wav_bg,
vid_net=vid_net_full,
)
def make_net(ims, sfs, pr, im_net=None, reuse=True, train=True):
if pr.subsample_frames and ims is not None:
ims = ims[:, ::pr.subsample_frames]
im_scales = []
scales = []
with slim.arg_scope(arg_scope(pr=pr, reuse=reuse, train=train)):
sf_net = normalize_sfs(sfs)
sf_net = ed(sf_net, 2)
sf_net = conv2d(sf_net,
64, [65, 1],
scope='sf/conv1_1',
stride=4,
reuse=reuse)
sf_net = slim.max_pool2d(sf_net, [4, 1], [4, 1])
sf_net = block2(sf_net,
128, [15, 1],
'sf/conv2_1',
stride=[4, 1],
reuse=reuse)
sf_net = block2(sf_net,
128, [15, 1],
'sf/conv3_1',
stride=[4, 1],
reuse=reuse)
sf_net = block2(sf_net,
256, [15, 1],
'sf/conv4_1',
stride=[4, 1],
reuse=reuse)
if im_net is None:
im_net = mu.normalize_ims(ims)
im_net = conv3d(im_net, 64, [5, 7, 7], scope='im/conv1', stride=2)
im_net = pool3d(im_net, [1, 3, 3], [1, 2, 2])
im_net = block3(im_net,
64, [3, 3, 3],
'im/conv2_1',
stride=1,
reuse=reuse)
im_net = block3(im_net,
64, [3, 3, 3],
'im/conv2_2',
stride=2,
reuse=reuse)
scales.append(im_net)
im_scales.append(im_net)
net = merge(sf_net, im_net, pr, reuse, train=train)
net = block3(net, 128, [3, 3, 3], 'im/conv3_1', stride=1, reuse=reuse)
net = block3(net, 128, [3, 3, 3], 'im/conv3_2', stride=1, reuse=reuse)
scales.append(net)
im_scales.append(net)
#s = (2 if ut.hastrue(pr, 'cam2') else 1)
s = 2
net = block3(net,
256, [3, 3, 3],
'im/conv4_1',
stride=[2, s, s],
reuse=reuse)
net = block3(net, 256, [3, 3, 3], 'im/conv4_2', stride=1, reuse=reuse)
im_scales.append(net)
time_stride = (2 if ut.hastrue(pr, 'extra_stride') else 1)
print 'time_stride =', time_stride
s = (1 if pr.cam else 2)
net = block3(net,
512, [3, 3, 3],
'im/conv5_1',
stride=[time_stride, s, s],
reuse=reuse)
net = block3(net, 512, [3, 3, 3], 'im/conv5_2', stride=1, reuse=reuse)
scales.append(net)
im_scales.append(net)
last_conv = net
net = tf.reduce_mean(net, [1, 2, 3], keep_dims=True)
logits_name = 'joint/logits'
logits = conv3d(net,
1, [1, 1, 1],
scope=logits_name,
activation_fn=None,
normalizer_fn=None)[:, 0, 0, 0, :]
cam = conv3d(last_conv,
1, [1, 1, 1],
scope=logits_name,
activation_fn=None,
normalizer_fn=None,
reuse=True)
return ut.Struct(logits=logits,
cam=cam,
last_conv=last_conv,
im_net=im_net,
scales=scales,
im_scales=im_scales)
def make_model(self):
with tf.device(self.default_gpu):
pr = self.pr
# steps
self.step = tf.get_variable('global_step', [],
trainable=False,
initializer=tf.constant_initializer(0),
dtype=tf.int64)
self.lr = tf.constant(pr.base_lr)
# model
opt = make_opt(pr.opt_method, pr.base_lr, pr)
self.inputs = read_data(pr, self.gpus)
gpu_grads, gpu_losses = {}, {}
for i, gpu in enumerate(self.gpus):
with tf.device(gpu):
reuse = (i > 0)
with tf.device('/cpu:0'):
ims = self.inputs[i]['ims']
samples_ex = self.inputs[i]['samples']
assert pr.both_examples
assert not pr.small_augment
labels = tf.random_uniform([shape(ims, 0)],
0,
2,
dtype=tf.int64,
name='labels_sample')
samples0 = tf.where(tf.equal(labels, 1),
samples_ex[:, 1], samples_ex[:, 0])
samples1 = tf.where(tf.equal(labels, 0),
samples_ex[:, 1], samples_ex[:, 0])
labels1 = 1 - labels
net0 = make_net(ims,
samples0,
pr,
reuse=reuse,
train=self.is_training)
net1 = make_net(None,
samples1,
pr,
im_net=net0.im_net,
reuse=True,
train=self.is_training)
labels = tf.concat([labels, labels1], 0)
net = ut.Struct(
logits=tf.concat([net0.logits, net1.logits], 0),
cam=tf.concat([net0.cam, net1.cam], 0),
last_conv=tf.concat([net0.last_conv, net1.last_conv],
0))
loss = mu.Loss('loss')
loss.add_loss(slim_losses_with_prefix(None), 'reg')
loss.add_loss_acc(sigmoid_loss(net.logits, labels),
'label')
grads = opt.compute_gradients(loss.total_loss())
ut.add_dict_list(gpu_grads, loss.name, grads)
ut.add_dict_list(gpu_losses, loss.name, loss)
#self.loss = loss
if i == 0:
self.net = net
self.loss = mu.merge_losses(gpu_losses['loss'])
for name, val in zip(self.loss.get_loss_names(),
self.loss.get_losses()):
tf.summary.scalar(name, val)
if not self.is_training:
#pr_test = pr.copy()
pr_test = self.pr_test.copy()
pr_test.augment_ims = False
print 'pr_test ='
print pr_test
self.test_ims, self.test_samples, self.test_ytids = mu.on_cpu(
lambda: shift_dset.make_db_reader(
pr_test.test_list,
pr_test,
pr.test_batch, ['im', 'samples', 'ytid'],
one_pass=True))
if pr_test.do_shift:
self.test_labels = tf.random_uniform(
[shape(self.test_ims, 0)], 0, 2, dtype=tf.int64)
self.test_samples = tf.where(tf.equal(self.test_labels, 1),
self.test_samples[:, 1],
self.test_samples[:, 0])
else:
self.test_labels = tf.ones(shape(self.test_ims, 0),
dtype=tf.int64)
#self.test_samples = tf.where(tf.equal(self.test_labels, 1), self.test_samples[:, 1], self.test_samples[:, 0])
print 'sample shape:', shape(self.test_samples)
self.test_net = make_net(self.test_ims,
self.test_samples,
pr_test,
reuse=True,
train=self.is_training)
(gs, vs) = zip(*mu.average_grads(gpu_grads['loss']))
if pr.grad_clip is not None:
gs, _ = tf.clip_by_global_norm(gs, pr.grad_clip)
gs = [
mu.print_every(gs[0], 100,
['grad norm:', tf.global_norm(gs)])
] + list(gs[1:])
gvs = zip(gs, vs)
bn_ups = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
if pr.multipass:
ops = [
opt.apply_gradients(gvs, global_step=self.step)
for i in xrange(pr.multipass_count)
]
def op_helper(count=[0]):
op = ops[count[0] % len(ops)]
count[0] += 1
return op
self.train_op = op_helper
else:
op = tf.group(opt.apply_gradients(gvs, global_step=self.step),
*bn_ups)
self.train_op = lambda: op
self.coord = tf.train.Coordinator()
self.saver = tf.train.Saver()
self.init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
self.sess.run(self.init_op)
tf.train.start_queue_runners(sess=self.sess, coord=self.coord)
self.merged_summary = tf.summary.merge_all()
print 'Tensorboard command:'
summary_dir = ut.mkdir(pj(pr.summary_dir, ut.simple_timestamp()))
print 'tensorboard --logdir=%s' % summary_dir
self.sum_writer = tf.summary.FileWriter(summary_dir,
self.sess.graph)
def test(pr, gpu, test_on_train=False, crop_type='center'):
[gpu] = set_gpus([gpu])
if ut.hastrue(pr, 'use_clf'):
net = SVMClf(pr)
else:
#check_path = tf.train.latest_checkpoint(pr.train_dir)
check_path = pj(pr.train_dir, 'net.tf-%d' % pr.model_iter)
print 'Restoring from:', check_path
net = NetClf(pr, check_path, gpu)
if test_on_train:
print 'Testing on train!'
data = ut.load(pj(pr.dsdir, 'train.pk'))
else:
data = ut.load(pj(pr.dsdir, 'test.pk'))
labels, probs, accs, vals = [], [], [], []
for i in xrange(len(data)):
ex = data[i]
label = ex['is_gripping']
def load_im(k, v):
if k.startswith('gel') or k.startswith('im'):
im = ig.uncompress(v)
elif k.startswith('depth'):
#v = np.tile(v, (1, 1, 3))
im = v.astype('float32')
else:
raise RuntimeError()
if crop_type == 'center':
crops = [ut.crop_center(im, 224)]
elif crop_type == 'multi':
crops = []
dh = (im.shape[0] - crop_dim)
num_dim_samples = 3
for y in np.linspace(0, dh, num_dim_samples).astype('l'):
dw = (im.shape[1] - crop_dim)
for x in np.linspace(0, dw, num_dim_samples).astype('l'):
crops.append(im[y:y + crop_dim, x:x + crop_dim])
return ut.shuffled_with_seed(crops, k.split('_')[0] + str(i))
all_inputs = {k: load_im(k, ex[k]) for k in im_names}
ps = []
for j in xrange(len(all_inputs['gel0_pre'])):
inputs = {k: all_inputs[k][j] for k in im_names}
inputs['initial_press_prob'] = ex['initial_press_prob']
inputs['ee'] = ex['end_effector']
_, prob = net.predict(**inputs)
ps.append(prob)
prob = np.mean(ps)
pred = int(prob >= net.thresh)
print prob, pred, label
labels.append(label)
probs.append(prob)
accs.append(pred == label)
print 'running average acc:', np.mean(accs)
vals.append(
ut.Struct(label=label,
prob=prob,
acc=accs[-1],
idx=i,
db_file=ex['db_file'],
object_name=ex['object_name']))
labels = np.array(labels, 'bool')
probs = np.array(probs, 'float32')
accs = np.array(accs)
acc = np.mean(accs)
ap = sklearn.metrics.average_precision_score(labels, probs)
print 'Accuracy:', acc
print 'mAP:', ap
print 'Base rate:', ut.f3(
np.array(ut.mapattr(vals).label).astype('float32').mean())
ut.save(pj(pr.resdir, 'eval_results.pk'),
dict(acc=acc, ap=ap, results=(labels, probs)))
ut.save(pj(pr.resdir, 'eval.pk'), vals)