def val_iter(self, count, recorder):
'''use the val_iter_fn compiled'''
if self.current_v==0 and self.subb_v == 0:
self.data.shuffle_data(mode='val')
self.data.shard_data(mode='val',rank=self.rank, size=self.size)
img= self.data.val_img_shard
labels = self.data.val_labels_shard
img_mean = self.data.rawdata[4]
mode='val'
function=self.val_iter_fn
if self.subb_v == 0: # load the whole file into shared_x when loading sub-batch 0 of each file.
arr = img[self.current_v] #- img_mean
arr = np.rollaxis(arr,0,4)
self.shared_x.set_value(arr)
self.shared_y.set_value(labels[self.current_v])
if self.current_v == self.data.n_batch_val - 1:
self.last_one_v = True
else:
self.last_one_v = False
from theanompi.models.layers2 import Dropout, Crop
Dropout.SetDropoutOff()
Crop.SetRandCropOff()
cost,error,error_top5 = function(self.subb_v)
Dropout.SetDropoutOn()
Crop.SetRandCropOn()
recorder.val_error(count, cost, error, error_top5)
if (self.subb_v+1)//self.n_subb == 1: # test if next sub-batch is in another file
if self.last_one_v == False:
self.current_v+=1
else:
self.current_v=0
self.subb_v=0
else:
self.subb_v+=1
def val_iter(self, count,recorder):
'''use the val_iter_fn compiled'''
if self.current_v==0: self.data.shard_data(file_batch_size, self.rank, self.size)
img= self.data.val_img_shard
labels = self.data.val_labels_shard
mode='val'
function=self.val_iter_fn
if self.subb_v == 0: # load the whole file into shared_x when loading sub-batch 0 of each file.
# parallel loading of shared_x
if self.data.para_load:
icomm = self.data.icomm
if self.current_v == 0:
# 3.0 give mode signal to adjust loading mode between train and val
icomm.isend('val',dest=0,tag=40)
# 3.1 give load signal to load the very first file
icomm.isend(img[self.current_v],dest=0,tag=40)
if self.current_v == self.data.n_batch_val - 1:
self.last_one_v = True
# Only to get the last copy_finished signal from load
icomm.isend(img[self.current_v],dest=0,tag=40)
else:
self.last_one_v = False
# 4. give preload signal to load next file
icomm.isend(img[self.current_v+1],dest=0,tag=40)
# 5. wait for the batch to be loaded into shared_x
msg = icomm.recv(source=0,tag=55) #
assert msg == 'copy_finished'
else:
arr = hkl.load(img[self.current_v]) #- img_mean
# arr = np.rollaxis(arr,0,4)
self.shared_x.set_value(arr)
# direct loading of shared_y
self.shared_y.set_value(labels[self.current_v])
if self.current_v == self.data.n_batch_val - 1:
self.last_one_v = True
else:
self.last_one_v = False
from theanompi.models.layers2 import Dropout, Crop
Dropout.SetDropoutOff()
Crop.SetRandCropOff()
cost,error,error_top5 = function(self.subb_v)
Dropout.SetDropoutOn()
Crop.SetRandCropOn()
recorder.val_error(count, cost, error, error_top5)
if (self.subb_v+1)//self.n_subb == 1: # test if next sub-batch is in another file
if self.last_one_v == False:
self.current_v+=1
else:
self.current_v=0
self.subb_v=0
else:
self.subb_v+=1