def sg_mae(tensor, opt):
r"""Returns absolute error between `tensor` and `target`.
Args:
tensor: A `Tensor`.
target: A `Tensor` with the same shape and dtype as `tensor`.
Returns:
A `Tensor` of the same shape and dtype as `tensor`
For example,
```
tensor = [[34, 11, 40], [13, 30, 42]]
target = [[34, 10, 41], [14, 31, 40]]
tensor.sg_mse(target=target) => [[ 0. 1. 1.]
[ 1. 1. 2.]]
```
"""
assert opt.target is not None, 'target is mandatory.'
# absolute error
out = tf.identity(tf.abs(tensor - opt.target), 'mae')
# add summary
tf.sg_summary_loss(out)
return out
def sg_quasi_conv1d(tensor, opt):
opt += tf.sg_opt(is_enc=False)
# Split into H and H_zfo
H = tensor[:Hp.bs]
H_z = tensor[Hp.bs:2 * Hp.bs]
H_f = tensor[2 * Hp.bs:3 * Hp.bs]
H_o = tensor[3 * Hp.bs:]
if opt.is_enc:
H_z, H_f, H_o = 0, 0, 0
# Convolution and merging
with tf.sg_context(act="linear",
causal=(not opt.is_enc),
bn=opt.is_enc,
ln=(not opt.is_enc)):
Z = H.sg_aconv1d() + H_z # (16, 300, 320)
F = H.sg_aconv1d() + H_f # (16, 300, 320)
O = H.sg_aconv1d() + H_o # (16, 300, 320)
# Activation
Z = Z.sg_bypass(act="tanh") # (16, 300, 320)
F = F.sg_bypass(act="sigmoid") # (16, 300, 320)
O = O.sg_bypass(act="sigmoid") # (16, 300, 320)
# Masking
M = tf.sign(tf.abs(H))[:, :, :1] # (16, 300, 1) float32. 0 or 1
Z *= M # broadcasting
F *= M # broadcasting
O *= M # broadcasting
# Concat
ZFO = tf.concat(axis=0, values=[Z, F, O])
return ZFO # (16*3, 150, 320)
def rnn_classify(x, num_classes, is_test=False):
with tf.sg_context(name='rnn_classify'):
fw_cell = tf.nn.rnn_cell.MultiRNNCell(
[lstm_cell(is_test) for _ in range(num_blocks)],
state_is_tuple=True)
bw_cell = tf.nn.rnn_cell.MultiRNNCell(
[lstm_cell(is_test) for _ in range(num_blocks)],
state_is_tuple=True)
words_used_in_sent = tf.sign(
tf.reduce_max(tf.abs(x), reduction_indices=2))
length = tf.cast(
tf.reduce_sum(words_used_in_sent, reduction_indices=1), tf.int32)
outputs, _ = tf.nn.bidirectional_dynamic_rnn(fw_cell,
bw_cell,
x,
dtype=tf.float32,
sequence_length=length)
output = tf.concat(outputs, 2).sg_reshape(shape=[-1, 2 * latent_dim])
prediction = output.sg_dense(dim=num_classes, name='dense')
res = tf.reshape(prediction,
[x.get_shape().as_list()[0], -1, num_classes])
return res
def sg_quasi_rnn(tensor, opt):
# Split
if opt.att:
H, Z, F, O = tf.split(tensor, 4, axis=0) # (16, 150, 320) for all
else:
Z, F, O = tf.split(tensor, 3, axis=0) # (16, 150, 320) for all
# step func
def step(z, f, o, c):
'''
Runs fo-pooling at each time step
'''
c = f * c + (1 - f) * z
if opt.att: # attention
a = tf.nn.softmax(tf.einsum("ijk,ik->ij", H,
c)) # alpha. (16, 150)
k = (a.sg_expand_dims() * H).sg_sum(
axis=1) # attentional sum. (16, 320)
h = o * (k.sg_dense(act="linear") + \
c.sg_dense(act="linear"))
else:
h = o * c
return h, c # hidden states, (new) cell memories
# Do rnn loop
c, hs = 0, []
timesteps = tensor.get_shape().as_list()[1]
for t in range(timesteps):
z = Z[:, t, :] # (16, 320)
f = F[:, t, :] # (16, 320)
o = O[:, t, :] # (16, 320)
# apply step function
h, c = step(z, f, o, c) # (16, 320), (16, 320)
# save result
hs.append(h.sg_expand_dims(axis=1))
# Concat to return
H = tf.concat(hs, 1) # (16, 150, 320)
seqlen = tf.to_int32(
tf.reduce_sum(tf.sign(tf.abs(tf.reduce_sum(H, axis=-1))),
1)) # (16,) float32
h = tf.reverse_sequence(input=H, seq_lengths=seqlen,
seq_dim=1)[:, 0, :] # last hidden state vector
if opt.is_enc:
H_z = tf.tile((h.sg_dense(act="linear").sg_expand_dims(axis=1)),
[1, timesteps, 1])
H_f = tf.tile((h.sg_dense(act="linear").sg_expand_dims(axis=1)),
[1, timesteps, 1])
H_o = tf.tile((h.sg_dense(act="linear").sg_expand_dims(axis=1)),
[1, timesteps, 1])
concatenated = tf.concat([H, H_z, H_f, H_o], 0) # (16*4, 150, 320)
return concatenated
else:
return H # (16, 150, 320)
def sg_summary_param(tensor, prefix=None, name=None):
r"""Register `tensor` to summary report as `parameters`
Args:
tensor: A `Tensor` to log as parameters
prefix: A `string`. A prefix to display in the tensor board web UI.
name: A `string`. A name to display in the tensor board web UI.
Returns:
None
"""
# defaults
prefix = '' if prefix is None else prefix + '/'
# summary name
name = prefix + _pretty_name(tensor) if name is None else prefix + name
# summary statistics
_scalar(name + '/abs', tf.reduce_mean(tf.abs(tensor)))
_histogram(name + '/abs-h', tf.abs(tensor))
def sg_mae(tensor, opt):
assert opt.target is not None, 'target is mandatory.'
# absolute error
out = tf.identity(tf.abs(tensor - opt.target), 'mae')
# add summary
tf.sg_summary_loss(out)
return out
def sg_summary_gradient(tensor, gradient, prefix=None, name=None):
r"""Register `tensor` to summary report as `gradient`
Args:
tensor: A `Tensor` to log as gradient
gradient: A 0-D `Tensor`. A gradient to log
prefix: A `string`. A prefix to display in the tensor board web UI.
name: A `string`. A name to display in the tensor board web UI.
Returns:
None
"""
# defaults
prefix = '' if prefix is None else prefix + '/'
# summary name
name = prefix + _pretty_name(tensor) if name is None else prefix + name
# summary statistics
# noinspection PyBroadException
_scalar(name + '/grad', tf.reduce_mean(tf.abs(gradient)))
_histogram(name + '/grad-h', tf.abs(gradient))
def rnn_classify(x, num_classes, is_test=False):
with tf.sg_context(name='rnn_classify'):
fw_cell = tf.nn.rnn_cell.MultiRNNCell([lstm_cell(is_test) for _ in range(num_blocks)], state_is_tuple=True)
bw_cell = tf.nn.rnn_cell.MultiRNNCell([lstm_cell(is_test) for _ in range(num_blocks)], state_is_tuple=True)
words_used_in_sent = tf.sign(tf.reduce_max(tf.abs(x), reduction_indices=2))
length = tf.cast(tf.reduce_sum(words_used_in_sent, reduction_indices=1), tf.int32)
outputs, _ = tf.nn.bidirectional_dynamic_rnn(fw_cell, bw_cell, x, dtype=tf.float32, sequence_length=length)
output = tf.concat(outputs, 2).sg_reshape(shape=[-1, 2 * latent_dim])
prediction = output.sg_dense(dim=num_classes, name='dense')
res = tf.reshape(prediction, [x.get_shape().as_list()[0], -1, num_classes])
return res
def ner_cost(tensor, opt):
one_hot_labels = tf.one_hot(opt.target - 1, opt.num_classes, dtype=tf.float32)
cross_entropy = one_hot_labels * tf.log(tensor)
cross_entropy = -tf.reduce_sum(cross_entropy, reduction_indices=2)
mask = tf.sign(tf.abs(opt.target))
cross_entropy *= tf.cast(mask, tf.float32)
cross_entropy = tf.reduce_sum(cross_entropy, reduction_indices=1)
length = tf.cast(tf.reduce_sum(tf.sign(opt.target), reduction_indices=1), tf.int32)
cross_entropy /= tf.cast(length, tf.float32)
out = tf.reduce_mean(cross_entropy, name='ner_cost')
# add summary
tf.sg_summary_loss(out, name=opt.name)
return out
def ner_cost(tensor, opt):
one_hot_labels = tf.one_hot(opt.target - 1, opt.num_classes, dtype=tf.float32)
cross_entropy = one_hot_labels * tf.log(tensor)
cross_entropy = -tf.reduce_sum(cross_entropy, reduction_indices=2)
mask = tf.sign(tf.reduce_max(tf.abs(one_hot_labels), reduction_indices=2))
cross_entropy *= tf.cast(mask, tf.float32)
cross_entropy = tf.reduce_sum(cross_entropy, reduction_indices=1)
length = tf.cast(tf.reduce_sum(tf.sign(opt.target), reduction_indices=1), tf.int32)
cross_entropy /= tf.cast(length, tf.float32)
out = tf.reduce_mean(cross_entropy, name='ner_cost')
# add summary
tf.sg_summary_loss(out, name=opt.name)
return out
def sg_quasi_conv1d(tensor, opt):
'''
Args:
tensor: A 3-D tensor of either [batch size, time steps, embedding size] for original
X or [batch size * 4, time steps, embedding size] for the others.
'''
opt += tf.sg_opt(is_enc=False)
# Split into H and H_zfo
H = tensor[:Hp.batch_size]
H_z = tensor[Hp.batch_size:2 * Hp.batch_size]
H_f = tensor[2 * Hp.batch_size:3 * Hp.batch_size]
H_o = tensor[3 * Hp.batch_size:]
if opt.is_enc:
H_z, H_f, H_o = 0, 0, 0
# Convolution and merging
with tf.sg_context(size=opt.size, act="linear", causal=(not opt.is_enc)):
Z = H.sg_aconv1d() + H_z # (16, 150, 320)
F = H.sg_aconv1d() + H_f # (16, 150, 320)
O = H.sg_aconv1d() + H_o # (16, 150, 320)
# Activation
with tf.sg_context(ln=True):
Z = Z.sg_bypass(act="tanh") # (16, 150, 320)
F = F.sg_bypass(act="sigmoid") # (16, 150, 320)
O = O.sg_bypass(act="sigmoid") # (16, 150, 320)
# Masking
M = tf.sign(tf.abs(tf.reduce_sum(
H, axis=-1, keep_dims=True))) # (16, 150, 1) float32. 0 or 1
Z *= M # broadcasting
F *= M # broadcasting
O *= M # broadcasting
# Concat
ZFO = tf.concat([Z, F, O], 0)
return ZFO # (16*3, 150, 320)
def wrapper(tensor, **kwargs):
r"""Manages arguments of `tf.sg_opt`.
Args:
tensor: A `tensor` (automatically passed by decorator).
kwargs:
shape: A list of integers. The shape of `tensor`. Inferred if not specified.
in_dim: An integer. The size of input dimension, which is set to the last one by default.
dim: An integer. The size of output dimension. Has the same value as in_dim by default.
bn: Boolean. If True, batch normalization is applied.
ln: Boolean. If True, layer normalization is applied.
dout: A float of range [0, 100). A dropout rate. Set to 0 by default.
bias: Boolean. If True, biases are added. As a default, it is set to True
name: A name for the layer. As a default, the function name is assigned.
act: A name of activation function. e.g., `sigmoid`, `tanh`, etc.
reuse: `True` or `None`; if `True`, we go into reuse mode for this `layer` scope
as well as all sub-scopes; if `None`, we just inherit the parent scope reuse.
regularizer: A string. None, 'l1' or 'l2'. The default is None
summary: If True, summaries are added. The default is True.
"""
from . import sg_initializer as init
from . import sg_activation
# kwargs parsing
opt = tf.sg_opt(kwargs) + sg_get_context()
# set default argument
try:
shape = tensor.get_shape().as_list()
# batch normalization off, layer normalization off, dropout off
opt += tf.sg_opt(shape=shape,
in_dim=shape[-1],
dim=shape[-1],
bn=False,
ln=False,
dout=0,
summary=True)
if opt.regularizer == 'l1':
opt.regularizer = lambda x: tf.reduce_mean(tf.abs(x))
elif opt.regularizer == 'l2':
opt.regularizer = lambda x: tf.square(
tf.reduce_mean(tf.square(x)))
else:
opt.regularizer = None
assert not (
opt.bn and opt.ln
), 'one of batch normalization and layer normalization is available.'
# disable bias when normalization on
opt += tf.sg_opt(bias=not (opt.bn or opt.ln))
finally:
pass
# automatic layer naming
if opt.name is None:
# layer function name will be used as layer name
opt.name = func.__name__.replace('sg_', '')
# find existing layer names
exist_layers = []
for t in tf.global_variables():
scope_name = tf.get_variable_scope().name
prefix = scope_name + '/' if len(scope_name) > 0 else ''
i = t.name.rfind(prefix + opt.name)
if i >= 0:
exist_layers.append(t.name[i:].split('/')[-2])
exist_layers = list(set(exist_layers))
# layer name numbering
if len(exist_layers) == 0:
opt.name += '_1'
else:
opt.name += '_%d' % (
max([int(n.split('_')[-1]) for n in exist_layers]) + 1)
with tf.variable_scope(opt.name, reuse=opt.reuse) as scope:
# call layer function
out = func(tensor, opt)
# apply batch normalization
if opt.bn:
# offset, scale parameter
beta = init.constant('beta', opt.dim)
gamma = init.constant('gamma', opt.dim, value=1)
# calc batch mean, variance
mean, variance = tf.nn.moments(
out, axes=list(range(len(out.get_shape()) - 1)))
# offset, scale parameter ( for inference )
mean_running = init.constant('mean', opt.dim, trainable=False)
variance_running = init.constant('variance',
opt.dim,
value=1,
trainable=False)
# add running mean, variance to UPDATE_OP collection
decay = 0.99
tf.add_to_collection(
tf.GraphKeys.UPDATE_OPS,
mean_running.assign(mean_running * decay + mean *
(1 - decay)))
tf.add_to_collection(
tf.GraphKeys.UPDATE_OPS,
variance_running.assign(variance_running * decay +
variance * (1 - decay)))
# select mean, variance by training phase
m, v = tf.cond(
_phase,
lambda: (mean, variance), # batch mean, variance
lambda:
(mean_running, variance_running)) # saved mean, variance
# apply batch normalization
out = tf.nn.batch_normalization(out, m, v, beta, gamma,
tf.sg_eps)
# apply layer normalization
if opt.ln:
# offset, scale parameter
beta = init.constant('beta', opt.dim)
gamma = init.constant('gamma', opt.dim, value=1)
# calc layer mean, variance for final axis
mean, variance = tf.nn.moments(out,
axes=[len(out.get_shape()) - 1],
keep_dims=True)
# apply normalization
out = (out - mean) / tf.sqrt(variance + tf.sg_eps)
# apply parameter
out = gamma * out + beta
# apply activation
if opt.act:
out = getattr(sg_activation, 'sg_' + opt.act.lower())(out)
# apply dropout
if opt.dout:
out = tf.cond(_phase, lambda: tf.nn.dropout(out, 1 - opt.dout),
lambda: out)
# rename tensor
out = tf.identity(out, 'out')
# add final output summary
tf.sg_summary_activation(out)
# save node info for reuse
out._sugar = tf.sg_opt(func=func,
arg=tf.sg_opt(kwargs) + sg_get_context(),
prev=tensor,
is_layer=True,
name=opt.name)
# inject reuse function
out.sg_reuse = types.MethodType(sg_reuse, out)
return out
def train(self):
''' Network '''
batch_pred_feats, batch_pred_coords, batch_pred_confs, self.final_state = self.LSTM(
'lstm', self.x)
iou_predict_truth, intersection = self.iou(batch_pred_coords,
self.y[:, 0:4])
should_exist = I = tf.cast(
tf.reduce_sum(self.y[:, 0:4], axis=1) > 0., tf.float32)
no_I = tf.ones_like(I, dtype=tf.float32) - I
object_loss = tf.nn.l2_loss(
I * (batch_pred_confs - iou_predict_truth)) * self.object_scale
noobject_loss = tf.nn.l2_loss(
no_I *
(batch_pred_confs - iou_predict_truth)) * self.noobject_scale
p_sqrt_w = tf.sqrt(
tf.minimum(1.0, tf.maximum(0.0, batch_pred_coords[:, 2])))
p_sqrt_h = tf.sqrt(
tf.minimum(1.0, tf.maximum(0.0, batch_pred_coords[:, 3])))
sqrt_w = tf.sqrt(tf.abs(self.y[:, 2]))
sqrt_h = tf.sqrt(tf.abs(self.y[:, 3]))
loss = (tf.nn.l2_loss(I * (batch_pred_coords[:, 0] - self.y[:, 0])) +
tf.nn.l2_loss(I * (batch_pred_coords[:, 1] - self.y[:, 1])) +
tf.nn.l2_loss(I * (p_sqrt_w - sqrt_w)) +
tf.nn.l2_loss(I * (p_sqrt_h - sqrt_h))) * self.coord_scale
#max_iou = tf.nn.l2_loss(I*(tf.ones_like(iou_predict_truth, dtype=tf.float32) - iou_predict_truth))
total_loss = loss + object_loss + noobject_loss #+ max_iou
''' Optimizer '''
optimizer = tf.train.AdamOptimizer(
learning_rate=self.learning_rate).minimize(
total_loss) # Adam Optimizer
''' Summary for tensorboard analysis '''
dataset_loss = -1
dataset_loss_best = 100
test_writer = tf.summary.FileWriter('summary/test')
tf.summary.scalar('dataset_loss', dataset_loss)
summary_op = tf.summary.merge_all()
''' Initializing the variables '''
self.saver = tf.train.Saver()
batch_states = np.zeros((self.batchsize, 2 * self.len_vec))
# TODO: make this a command line argument, etc.
# training set loader
batch_loader = BatchLoader(
"./DATA/TRAINING/",
seq_len=self.nsteps,
batch_size=self.batchsize,
step_size=1,
folders_to_use=[
"GOPR0005", "GOPR0006", "GOPR0008", "GOPR0008_2", "GOPR0009",
"GOPR0009_2", "GOPR0010", "GOPR0011", "GOPR0012", "GOPR0013",
"GOPR0014", "GOPR0015", "GOPR0016", "MVI_8607", "MVI_8609",
"MVI_8610", "MVI_8612", "MVI_8614", "MVI_8615", "MVI_8616"
])
validation_set_loader = BatchLoader(
"./DATA/VALID/",
seq_len=self.nsteps,
batch_size=self.batchsize,
step_size=1,
folders_to_use=[
"bbd_2017__2017-01-09-21-40-02_cam_flimage_raw",
"bbd_2017__2017-01-09-21-44-31_cam_flimage_raw",
"bbd_2017__2017-01-09-21-48-46_cam_flimage_raw",
"bbd_2017__2017-01-10-16-07-49_cam_flimage_raw",
"bbd_2017__2017-01-10-16-21-01_cam_flimage_raw",
"bbd_2017__2017-01-10-16-31-57_cam_flimage_raw",
"bbd_2017__2017-01-10-21-43-03_cam_flimage_raw",
"bbd_2017__2017-01-11-20-21-32_cam_flimage_raw",
"bbd_2017__2017-01-11-21-02-37_cam_flimage_raw"
])
print("%d available training batches" % len(batch_loader.batches))
print("%d available validation batches" %
len(validation_set_loader.batches))
''' Launch the graph '''
with tf.Session() as sess:
if self.restore_weights == True and os.path.isfile(
self.rolo_current_save + ".index"):
# sess.run(init)
tf.sg_init(sess)
self.saver.restore(sess, self.rolo_current_save)
print("Weight loaded, finetuning")
else:
# sess.run(init)
tf.sg_init(sess)
print("Training from scratch")
epoch_loss = []
for self.iter_id in range(self.n_iters):
''' Load training data & ground truth '''
batch_id = self.iter_id - self.batch_offset
batch_xs, batch_ys, _ = batch_loader.load_batch(batch_id)
''' Update weights by back-propagation '''
sess.run(optimizer,
feed_dict={
self.x: batch_xs,
self.y: batch_ys
})
if self.iter_id % self.display_step == 0:
''' Calculate batch loss '''
batch_loss = sess.run(total_loss,
feed_dict={
self.x: batch_xs,
self.y: batch_ys
})
epoch_loss.append(batch_loss)
print("Total Batch loss for iteration %d: %.9f" %
(self.iter_id, batch_loss))
if self.iter_id % self.display_step == 0:
''' Calculate batch loss '''
batch_loss = sess.run(loss,
feed_dict={
self.x: batch_xs,
self.y: batch_ys
})
print(
"Bounding box coord error loss for iteration %d: %.9f"
% (self.iter_id, batch_loss))
if self.display_object_loss and self.iter_id % self.display_step == 0:
''' Calculate batch object loss '''
batch_o_loss = sess.run(object_loss,
feed_dict={
self.x: batch_xs,
self.y: batch_ys
})
print("Object loss for iteration %d: %.9f" %
(self.iter_id, batch_o_loss))
if self.display_object_loss and self.iter_id % self.display_step == 0:
''' Calculate batch object loss '''
batch_noo_loss = sess.run(noobject_loss,
feed_dict={
self.x: batch_xs,
self.y: batch_ys
})
print("No Object loss for iteration %d: %.9f" %
(self.iter_id, batch_noo_loss))
if self.iou_with_ground_truth and self.iter_id % self.display_step == 0:
''' Calculate batch object loss '''
batch_o_loss = sess.run(tf.reduce_mean(iou_predict_truth),
feed_dict={
self.x: batch_xs,
self.y: batch_ys
})
print("Average IOU with ground for iteration %d: %.9f" %
(self.iter_id, batch_o_loss))
if self.display_coords is True and self.iter_id % self.display_step == 0:
''' Caculate predicted coordinates '''
coords_predict = sess.run(batch_pred_coords,
feed_dict={
self.x: batch_xs,
self.y: batch_ys
})
print("predicted coords:" + str(coords_predict[0]))
print("ground truth coords:" + str(batch_ys[0]))
''' Save model '''
if self.iter_id % self.save_step == 1:
self.saver.save(sess, self.rolo_current_save)
print("\n Model saved in file: %s" %
self.rolo_current_save)
''' Validation '''
if self.validate == True and self.iter_id % self.validate_step == 0 and self.iter_id > 0:
# Run validation set
dataset_loss = self.test(sess, total_loss,
validation_set_loader,
batch_pred_feats,
batch_pred_coords,
batch_pred_confs,
self.final_state)
''' Early-stop regularization '''
if dataset_loss <= dataset_loss_best:
dataset_loss_best = dataset_loss
self.saver.save(sess, self.rolo_weights_file)
print("\n Better Model saved in file: %s" %
self.rolo_weights_file)
''' Write summary for tensorboard '''
summary = sess.run(summary_op,
feed_dict={
self.x: batch_xs,
self.y: batch_ys
})
test_writer.add_summary(summary, self.iter_id)
print("Average total loss %f" % np.mean(epoch_loss))
return
tf.sg_init(sess)
# restore parameters
saver = tf.train.Saver()
# saver.restore(sess, tf.train.latest_checkpoint('/home/vivo/Desktop/SuperResolution/SRGAN-master/asset/train/ckpt'))
saver.restore(
sess, "./asset/train/GAN_SR/model_cifar10/model.ckpt-627924")
# run generator
gt, small, low, bicubic, sr = sess.run(
[x, x_small, x_nearest, x_bicubic, gen])
bicubic = bicubic.clip(0.0, 1.0)
#diff = sess.run(tf.reduce_mean(tf.abs(tf.subtract(gt, sr)), axis = 3))
#diff2 = sess.run(tf.reduce_mean(tf.abs(tf.subtract(gt, bicubic)), axis = 3))
diff_SR = sess.run(tf.abs(tf.subtract(gt, sr)))
diff_BC = sess.run(tf.abs(tf.subtract(gt, bicubic)))
# MSE = sess.run(MSE(x,sr))
MSE_SR = np.zeros(gt.shape[0])
MSE_BC = np.zeros(gt.shape[0])
for i in range(gt.shape[0]):
MSE_SR[i] = np.mean(np.square(diff_SR[i, :, :, :]))
MSE_BC[i] = np.mean(np.square(diff_BC[i, :, :, :]))
PSNR_SR = 10 * np.log10(1.0 / (MSE_SR))
PSNR_BC = 10 * np.log10(1.0 / (MSE_BC))
print "SRGAN MSE = " + str(np.mean(MSE_SR))
print "Bicubic MSE = " + str(np.mean(MSE_BC))
