def process_batch(nets, optim, optim2, image_size, args):
global it, mean_loss, mean_rec
net, net_ctc = nets
net = net.net
net_ctc = net_ctc.net
net.blobs['data'].reshape(args.batch_size,1,image_size[1],image_size[0])
net.reshape()
it += 1
optim2.step(1)
im = net.blobs['data'].data[...]
draw = np.swapaxes(im,2,3)
draw = np.swapaxes(draw,1,3)
im_ctc = np.copy(draw)
draw += 1
draw *= 128
draw = np.array(draw, dtype="uint8").copy()
if args.debug:
grid_step = 16
line = 0
while line < image_size[0]:
cv2.line(draw[0], (0, line), (image_size[1], line), (128, 128, 128))
line += grid_step
boxes = net.blobs['boxes'].data[...]
word_gtob = net.blobs['gt_boxes'].data[...]
word_txt = net.blobs['gt_labels'].data[...]
lines_gtob = net.blobs['line_boxes'].data[...]
lines_txt = net.blobs['line_labels'].data[...]
#nms = boxeso[:, 0, 0, 8] == 0
#boxes = boxes[:, :, nms, :]
boxes[:, 0, :, 0] *= image_size[0]
boxes[:, 0, :, 1] *= image_size[1]
normFactor = math.sqrt(image_size[1] * image_size[1] + image_size[0] * image_size[0])
boxes[:, 0, :, 2] *= normFactor
boxes[:, 0, :, 3] *= normFactor
sum_cost = 0
count = 0
labels_gt = []
labels_det = []
gt_to_detection = {}
net_ctc.clear_param_diffs()
batch_buckets = []
dummy = {}
matched_detections = 0
for bid in range(im.shape[0]):
o_image = net.layers[0].get_image_file_name(bid)
o_image = cv2.imread(o_image, cv2.IMREAD_GRAYSCALE)
cx = net.layers[0].get_crop(bid, 0)
cy = net.layers[0].get_crop(bid, 1)
cmx = net.layers[0].get_crop(bid, 2)
cmy = net.layers[0].get_crop(bid, 3)
o_image = o_image[cy:cmy, cx:cmx]
boxes_count = 0
for i in range(0, boxes.shape[2]):
det_word = boxes[bid, 0, i]
if (det_word[0] == 0 and det_word[1] == 0) or det_word[5] < 0.01:
break
boxes_count += 1
x = [i for i in range(boxes_count)]
#random.shuffle(x)
bucket_images = {}
batch_buckets.append(bucket_images)
word_gto = word_gtob[bid]
word_gto_txt = word_txt[bid]
gt_count = 0
for gt_no in range(word_gto.shape[0]):
gt = word_gto[gt_no, :]
gt = gt.reshape(6)
gtnum = 1000 * bid + gt_no
if gt[5] == -1:
#print("ignore gt!")
continue
gt_count += 1
txt = word_gto_txt[gt_no, :]
gtbox = ((gt[0] * image_size[0], gt[1] * image_size[1]), (gt[2] * normFactor, gt[3] * normFactor), gt[4] * 180 / 3.14)
gtbox = cv2.boxPoints(gtbox)
gtbox = np.array(gtbox, dtype="int")
rect_gt = cv2.boundingRect(gtbox)
if rect_gt[0] == 0 or rect_gt[1] == 0 or rect_gt[0] + rect_gt[2] >= image_size[0] or rect_gt[1] + rect_gt[3] >= image_size[1]:
continue
if gt[3] * normFactor < 3:
if args.debug:
#print('too small gt!')
vis.draw_box_points(draw[bid], gtbox, color = (255, 255, 0))
cv2.imshow('draw', draw[bid])
continue
if args.debug:
vis.draw_box_points(draw[bid], gtbox, color = (0, 0, 0), thickness=2)
#vis.draw_box_points(draw[bid], gtbox, color = (255, 255, 255))
#cv2.imshow('draw', draw[bid])
rect_gt = [rect_gt[0], rect_gt[1], rect_gt[2], rect_gt[3]]
rect_gt[2] += rect_gt[0]
rect_gt[3] += rect_gt[1]
for i in range(0, min(100, boxes_count)):
if math.fabs(gt[4] - det_word[4]) > math.pi / 16:
continue
det_word = boxes[bid, 0, x[i], :]
if (det_word[0] == 0 and det_word[1] == 0) or det_word[5] < 0.01:
break
box = ((det_word[0], det_word[1]), (det_word[2], det_word[3]), det_word[4] * 180 / 3.14)
box = cv2.boxPoints(box)
if args.debug:
boxp = np.array(box, dtype="int")
vis.draw_box_points(draw[bid], boxp, color = (0, 255, 0))
box = np.array(box, dtype="int")
bbox = cv2.boundingRect(box)
bbox = [bbox[0], bbox[1], bbox[2], bbox[3]]
bbox[2] += bbox[0]
bbox[3] += bbox[1]
#rectangle intersection ...
inter = intersect(bbox, rect_gt)
uni = union(bbox, rect_gt)
ratio = area(inter) / float(area(uni))
ratio_gt = area(inter) / float(area(rect_gt))
if ratio_gt < 0.95:
continue
if ratio < 0.5:
continue
if not gt_to_detection.has_key(gtnum):
gt_to_detection[gtnum] = [0, 0, 0]
tupl = gt_to_detection[gtnum]
if tupl[0] < ratio:
tupl[0] = ratio
tupl[1] = x[i]
tupl[2] = ratio_gt
det_word = boxes[bid, 0, x[i], :]
box = ([det_word[0], det_word[1]], [det_word[2], det_word[3]], det_word[4] * 180 / 3.14)
boxO = get_obox(im_ctc[bid], o_image, box)
boxO = ((boxO[0][0], boxO[0][1]), (boxO[1][0], boxO[1][1]), boxO[2])
norm2, rot_mat = get_normalized_image(o_image, boxO)
#norm3, rot_mat = get_normalized_image(im_ctc[bid], ([det_word[0], det_word[1]], [det_word[2] * 1.2, det_word[3] * 1.1], det_word[4] * 180 / 3.14))
if norm2 is None:
continue
#if norm3 is None:
# continue
#continue
#cv2.imshow('ts', norm2)
#cv2.imshow('ts3', norm3)
#cv2.waitKey(1)
width_scale = 32.0 / norm2.shape[0]
width = norm2.shape[1] * width_scale
best_diff = width
bestb = 0
for b in range(0, len(buckets)):
if best_diff > abs(width * 1.3 - buckets[b]):
best_diff = abs(width * 1.3 - buckets[b])
bestb = b
scaled = cv2.resize(norm2, (buckets[bestb], 32))
scaled = np.asarray(scaled, dtype=np.float)
delta = scaled.max() - scaled.min()
scaled = (scaled) / (delta / 2)
scaled -= scaled.mean()
if not bucket_images.has_key(bestb):
bucket_images[bestb] = {}
bucket_images[bestb]['img'] = []
bucket_images[bestb]['sizes'] = []
bucket_images[bestb]['txt'] = []
bucket_images[bestb]['gt_enc'] = []
dummy[bestb] = 1
else:
if args.debug and len(bucket_images[bestb]) > 4:
continue
elif len(bucket_images[bestb]) > 32:
continue
gt_labels = []
txt_enc = ''
for k in range(txt.shape[1]):
if txt[0, k] > 0:
if codec_rev.has_key(txt[0, k]):
gt_labels.append( codec_rev[txt[0, k]] )
else:
gt_labels.append( 3 )
txt_enc += unichr(txt[0, k])
else:
gt_labels.append( 0 )
if scaled.ndim == 3:
scaled = cv2.cvtColor(scaled, cv2.COLOR_BGR2GRAY)
if args.debug:
cv2.imshow('scaled', scaled)
bucket_images[bestb]['sizes'].append(len(gt_labels))
bucket_images[bestb]['gt_enc'].append(gt_labels)
bucket_images[bestb]['txt'].append(txt_enc)
bucket_images[bestb]['img'].append(scaled)
matched_detections += 1
#and learn OCR
for bucket in bucket_images.keys():
imtf = np.asarray(bucket_images[bucket]['img'], dtype=np.float)
imtf = np.reshape(imtf, (imtf.shape[0], -1, imtf.shape[1], imtf.shape[2]))
#imtf = imtf.reshape((imtf.shape[0], imtf.shape[1], imtf.shape[2], 1))
#imtf = np.swapaxes(imtf,1,3)
net_ctc.blobs['data'].reshape(imtf.shape[0],imtf.shape[1],imtf.shape[2], imtf.shape[3])
net_ctc.blobs['data'].data[...] = imtf
labels = bucket_images[bucket]['gt_enc']
txt = bucket_images[bucket]['txt']
max_len = 0
for l in range(0, len(labels)):
max_len = max(max_len, len(labels[l]))
for l in range(0, len(labels)):
while len(labels[l]) < max_len:
labels[l].append(0)
labels = np.asarray(labels, np.float)
net_ctc.blobs['label'].reshape(labels.shape[0], labels.shape[1])
net_ctc.blobs['label'].data[...] = labels
if args.debug:
vis.vis_square(imtf[0])
cv2.imshow('draw', draw[0])
cv2.waitKey(5)
optim.step(1)
sum_cost += net_ctc.blobs['loss'].data[...]
if net_ctc.blobs['loss'].data[...] > 10:
vis.vis_square(imtf[0])
cv2.imshow('draw', draw[0])
sf = net_ctc.blobs['transpose'].data[...]
labels2 = sf.argmax(3)
out = utils.print_seq(labels2[:, 0, :])
print(u'{0} - {1}'.format(out, txt[0]) )
cv2.waitKey(5)
count += imtf.shape[0]
correct_cout = 0
for i in range(len(labels_gt)):
det_text = labels_det[i]
gt_text = labels_gt[i]
if it % 100 == 0:
print( u"{0} - {1}".format(det_text, gt_text).encode('utf8') )
if det_text == gt_text:
correct_cout += 1
count = max(count, 1)
mean_loss = 0.99 * mean_loss + 0.01 * sum_cost / count
mean_rec = mean_rec * 0.99 + 0.01 * correct_cout / float(max(1, len(labels_gt)))
#count detection ratio
tp = 0
for bid in range(im.shape[0]):
word_gto = word_gtob[bid]
for gt_no in range(len(word_gto)):
gt = word_gto[gt_no]
gtnum = 1000 * bid + gt_no
if gt_to_detection.has_key(gtnum):
tupl = gt_to_detection[gtnum]
if tupl[0] > 0.5:
tp += 1
loc_recall = tp / float(max(1, gt_count))
if args.debug:
cv2.imshow('draw', draw[0])
if im.shape[0] > 1:
cv2.imshow('draw2', draw[1])
cv2.waitKey(10)
if it % 10 == 0:
print('{0} - lr:{1:.3e} ctc:{2:.4f}/{3:.4f} wr:{4:.2f}/{5:.2f}, loc:{6:.2f} {7}'.format(it, 0.0001, sum_cost / count, mean_loss, correct_cout / float(max(1, len(labels_gt))), mean_rec, loc_recall, matched_detections))
if it % 1000 == 0:
optim.snapshot()
optim2.snapshot()
def process_batch(nets, optim, optim2, image_size, args):
global it, mean_loss, mean_rec
it += 1 # 迭代次数加一
net, net_ctc = nets
net = net.net
net_ctc = net_ctc.net
net.blobs['data'].reshape(args.batch_size, 1, image_size[1],
image_size[0]) # 把一个batch的输入图片reshape
net.reshape()
optim2.step(1)
im = net.blobs['data'].data[...] # shape [batch_size,1,416,416]
draw = np.swapaxes(im, 2, 3)
draw = np.swapaxes(draw, 1, 3)
im_ctc = np.copy(draw)
draw += 1
draw *= 128
draw = np.array(draw, dtype="uint8").copy()
if args.debug:
grid_step = 16
line = 0
while line < image_size[0]:
cv2.line(draw[0], (0, line), (image_size[1], line),
(128, 128, 128))
line += grid_step
boxes = net.blobs['boxes'].data[...] # shape (4, 1, 500, 15)
word_gtob = net.blobs['gt_boxes'].data[...] # shape (4, 6, 1, 6)
word_txt = net.blobs['gt_labels'].data[...] # shape (4, 6, 1, 14)
lines_gtob = net.blobs['line_boxes'].data[...] # shape (4, 1, 1, 5)
lines_txt = net.blobs['line_labels'].data[...] # shape (4, 1, 1, 7)
#nms = boxeso[:, 0, 0, 8] == 0
#boxes = boxes[:, :, nms, :]
boxes[:, 0, :, 0] *= image_size[0]
boxes[:, 0, :, 1] *= image_size[1]
normFactor = math.sqrt(image_size[1] * image_size[1] +
image_size[0] * image_size[0])
boxes[:, 0, :, 2] *= normFactor
boxes[:, 0, :, 3] *= normFactor
sum_cost = 0
count = 0
labels_gt = []
labels_det = []
gt_to_detection = {}
net_ctc.clear_param_diffs()
batch_buckets = []
dummy = {}
matched_detections = 0
for bid in range(im.shape[0]): # 遍历batchsize下的每一个样本
o_image = net.layers[0].get_image_file_name(bid)
o_image = cv2.imread(o_image, cv2.IMREAD_GRAYSCALE)
cx = net.layers[0].get_crop(bid, 0)
cy = net.layers[0].get_crop(bid, 1)
cmx = net.layers[0].get_crop(bid, 2)
cmy = net.layers[0].get_crop(bid, 3)
o_image = o_image[cy:cmy, cx:cmx]
boxes_count = 0
for i in range(0, boxes.shape[2]):
det_word = boxes[bid, 0, i]
if (det_word[0] == 0 and det_word[1] == 0) or det_word[5] < 0.01:
break
boxes_count += 1
x = [i for i in range(boxes_count)]
#random.shuffle(x)
bucket_images = {}
batch_buckets.append(bucket_images)
word_gto = word_gtob[bid]
word_gto_txt = word_txt[bid]
gt_count = 0
for gt_no in range(word_gto.shape[0]):
gt = word_gto[gt_no, :]
gt = gt.reshape(6)
gtnum = 1000 * bid + gt_no
if gt[5] == -1:
#print("ignore gt!")
continue
gt_count += 1
txt = word_gto_txt[gt_no, :]
gtbox = ((gt[0] * image_size[0], gt[1] * image_size[1]),
(gt[2] * normFactor,
gt[3] * normFactor), gt[4] * 180 / 3.14)
gtbox = cv2.boxPoints(gtbox)
gtbox = np.array(gtbox, dtype="int")
rect_gt = cv2.boundingRect(gtbox)
if rect_gt[0] == 0 or rect_gt[
1] == 0 or rect_gt[0] + rect_gt[2] >= image_size[
0] or rect_gt[1] + rect_gt[3] >= image_size[1]:
continue
if gt[3] * normFactor < 3:
if args.debug:
pass
print('too small gt!')
continue
rect_gt = [rect_gt[0], rect_gt[1], rect_gt[2], rect_gt[3]]
rect_gt[2] += rect_gt[0]
rect_gt[3] += rect_gt[1]
for i in range(0, min(100, boxes_count)):
if math.fabs(gt[4] - det_word[4]) > math.pi / 16:
continue
det_word = boxes[bid, 0, x[i], :]
if (det_word[0] == 0
and det_word[1] == 0) or det_word[5] < 0.01:
break
box = ((det_word[0], det_word[1]), (det_word[2], det_word[3]),
det_word[4] * 180 / 3.14)
box = cv2.boxPoints(box)
if args.debug:
boxp = np.array(box, dtype="int")
vis.draw_box_points(draw[bid], boxp, color=(0, 255, 0))
box = np.array(box, dtype="int")
bbox = cv2.boundingRect(box)
bbox = [bbox[0], bbox[1], bbox[2], bbox[3]]
bbox[2] += bbox[0]
bbox[3] += bbox[1]
#rectangle intersection ...
inter = intersect(bbox, rect_gt)
uni = union(bbox, rect_gt)
ratio = area(inter) / float(area(uni))
ratio_gt = area(inter) / float(area(rect_gt))
if ratio_gt < 0.95:
continue
if ratio < 0.5:
continue
if not gt_to_detection.has_key(gtnum):
gt_to_detection[gtnum] = [0, 0, 0]
tupl = gt_to_detection[gtnum]
if tupl[0] < ratio:
tupl[0] = ratio
tupl[1] = x[i]
tupl[2] = ratio_gt
det_word = boxes[bid, 0, x[i], :]
box = ([det_word[0],
det_word[1]], [det_word[2],
det_word[3]], det_word[4] * 180 / 3.14)
boxO = get_obox(im_ctc[bid], o_image, box)
boxO = ((boxO[0][0], boxO[0][1]), (boxO[1][0], boxO[1][1]),
boxO[2])
norm2, rot_mat = get_normalized_image(o_image, boxO)
#norm3, rot_mat = get_normalized_image(im_ctc[bid], ([det_word[0], det_word[1]], [det_word[2] * 1.2, det_word[3] * 1.1], det_word[4] * 180 / 3.14))
if norm2 is None:
continue
#if norm3 is None:
# continue
#continue
#cv2.imshow('ts', norm2)
#cv2.imshow('ts3', norm3)
#cv2.waitKey(1)
width_scale = 32.0 / norm2.shape[0]
width = norm2.shape[1] * width_scale
best_diff = width
bestb = 0
for b in range(0, len(buckets)):
if best_diff > abs(width * 1.3 - buckets[b]):
best_diff = abs(width * 1.3 - buckets[b])
bestb = b
scaled = cv2.resize(norm2, (buckets[bestb], 32))
scaled = np.asarray(scaled, dtype=np.float)
delta = scaled.max() - scaled.min()
scaled = (scaled) / (delta / 2)
scaled -= scaled.mean()
if not bucket_images.has_key(bestb):
bucket_images[bestb] = {}
bucket_images[bestb]['img'] = []
bucket_images[bestb]['sizes'] = []
bucket_images[bestb]['txt'] = []
bucket_images[bestb]['gt_enc'] = []
dummy[bestb] = 1
else:
if args.debug and len(bucket_images[bestb]) > 4:
continue
elif len(bucket_images[bestb]) > 32:
continue
gt_labels = []
txt_enc = ''
for k in range(txt.shape[1]):
if txt[0, k] > 0:
if codec_rev.has_key(txt[0, k]):
gt_labels.append(codec_rev[txt[0, k]])
else:
gt_labels.append(3)
txt_enc += unichr(txt[0, k])
else:
gt_labels.append(0)
if scaled.ndim == 3:
scaled = cv2.cvtColor(scaled, cv2.COLOR_BGR2GRAY)
if args.debug:
cv2.imshow('scaled', scaled)
bucket_images[bestb]['sizes'].append(len(gt_labels))
bucket_images[bestb]['gt_enc'].append(gt_labels)
bucket_images[bestb]['txt'].append(txt_enc)
bucket_images[bestb]['img'].append(scaled)
matched_detections += 1
#and learn OCR
for bucket in bucket_images.keys():
imtf = np.asarray(bucket_images[bucket]['img'], dtype=np.float)
imtf = np.reshape(imtf,
(imtf.shape[0], -1, imtf.shape[1], imtf.shape[2]))
#imtf = imtf.reshape((imtf.shape[0], imtf.shape[1], imtf.shape[2], 1))
#imtf = np.swapaxes(imtf,1,3)
net_ctc.blobs['data'].reshape(imtf.shape[0], imtf.shape[1],
imtf.shape[2], imtf.shape[3])
net_ctc.blobs['data'].data[...] = imtf
labels = bucket_images[bucket]['gt_enc']
txt = bucket_images[bucket]['txt']
max_len = 0
for l in range(0, len(labels)):
max_len = max(max_len, len(labels[l]))
for l in range(0, len(labels)):
while len(labels[l]) < max_len:
labels[l].append(0)
labels = np.asarray(labels, np.float)
net_ctc.blobs['label'].reshape(labels.shape[0], labels.shape[1])
net_ctc.blobs['label'].data[...] = labels
if args.debug:
vis.vis_square(imtf[0])
cv2.imshow('draw', draw[0])
cv2.waitKey(5)
#optim.step(1)
sum_cost += net_ctc.blobs['loss'].data[...]
if net_ctc.blobs['loss'].data[...] > 10:
#vis.vis_square(imtf[0])
#cv2.imshow('draw', draw[0])
sf = net_ctc.blobs['transpose'].data[...]
labels2 = sf.argmax(3)
out = utils.print_seq(labels2[:, 0, :])
print(u'{0} --- {1}'.format(out, txt[0]))
#cv2.waitKey(5)
count += imtf.shape[0]
correct_cout = 0
for i in range(len(labels_gt)):
det_text = labels_det[i]
gt_text = labels_gt[i]
if it % 100 == 0:
pass
#print( u"{0} -- {1}".format(det_text, gt_text).encode('utf8') )
if det_text == gt_text:
correct_cout += 1
count = max(count, 1)
mean_loss = 0.99 * mean_loss + 0.01 * sum_cost / count
mean_rec = mean_rec * 0.99 + 0.01 * correct_cout / float(
max(1, len(labels_gt)))
#count detection ratio
tp = 0
for bid in range(im.shape[0]):
word_gto = word_gtob[bid]
for gt_no in range(len(word_gto)):
gt = word_gto[gt_no]
gtnum = 1000 * bid + gt_no
if gt_to_detection.has_key(gtnum):
tupl = gt_to_detection[gtnum]
if tupl[0] > 0.5:
tp += 1
loc_recall = tp / float(max(1, gt_count))
if it % 10 == 0:
print(
'{0} - lr:{1:.3e} ctc:{2:.4f}/{3:.4f} wr:{4:.2f}/{5:.2f}, loc:{6:.2f} {7}'
.format(it, 0.0001, sum_cost / count, mean_loss,
correct_cout / float(max(1, len(labels_gt))), mean_rec,
loc_recall, matched_detections))
if it % snapshot_interval == 0:
#optim.snapshot()
optim2.snapshot()
def process_batch( net , optim , args ):
global it,data_batch,data_total,data_index,f_list,codec_rev,codec
text = []
W = []
H = []
bucket_images = {}
dummy={}
net_ctc = net.net
#print( len(codec))
#1 Read images and gts ( circular buffer form for small samples)
for img_ind in range( min(data_batch,data_total) ):
circ_ind = (data_index + img_ind ) % data_total
img_path = os.path.join( args.data_dir, f_list[circ_ind].replace('../','') )
#print('img_ind=%d,img_path=%s' %(data_index,img_path))
img=cv2.imread( img_path.strip() )
if img.shape[2]==3:
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
if img == None:
print("im read error")
print("img_path:%s" %img_path)
W = img.shape[1]
H = img.shape[0]
with open( img_path.strip().replace('.jpg','.txt')) as f:
item = f.readlines()
anns = item[0].split()
text = anns[6].decode('utf8')
data_index+=1
#2 Adjust image sizes to fixed height and variable width
width_scale = 32.0 / H
width = W * width_scale
best_diff = width
bestb = 0
for b in range(0, len(buckets)):
if best_diff > abs(width * 1.3 - buckets[b]):
best_diff = abs(width * 1.3 - buckets[b])
bestb = b
scaled = cv2.resize(img, (buckets[bestb], 32))
scaled = np.asarray(scaled, dtype=np.float)
delta = scaled.max() - scaled.min()
#print('scaled.max=%d scaled.min=%d delta=%d mean=%d' %(scaled.max(),scaled.min(),delta, scaled.mean()))
scaled = (scaled) / (delta / 2.0)
scaled -= scaled.mean()
#print( 'scaled')
#print(scaled.shape )
if not bucket_images.has_key(bestb):
bucket_images[bestb] = {}
bucket_images[bestb]['img'] = []
bucket_images[bestb]['sizes'] = []
bucket_images[bestb]['txt'] = []
bucket_images[bestb]['gt_enc'] = []
dummy[bestb] = 1
gt_labels = []
txt_enc = ''
for k in range( len(text) ):
t_unicode = text[k]
if t_unicode > 0:
if codec_rev.has_key( t_unicode ):
gt_labels.append( codec_rev[ t_unicode ] )
else:
gt_labels.append( 3 )
else:
gt_labels.append( 0 )
if scaled.ndim==3:
print( scaled.shape )
scaled = cv2.cvtColor(scaled, cv2.COLOR_RGB2GRAY)
if args.debug:
cv2.imshow('scaled', scaled)
bucket_images[bestb]['sizes'].append(len(gt_labels))
bucket_images[bestb]['gt_enc'].append(gt_labels)
bucket_images[bestb]['txt'].append(text)
bucket_images[bestb]['img'].append(scaled)
data_index = data_index % data_total
#3 Transfer the data into the net
for bucket in bucket_images.keys():
#print(bucket)
imtf = np.asarray(bucket_images[bucket]['img'], dtype=np.float)
#print('imtf.shape')
#print( imtf.shape )
imtf = np.reshape(imtf, (imtf.shape[0], -1, imtf.shape[1], imtf.shape[2]))
#print('imtf reshape')
#print( 'imtf.shape[0]=%d imtf.shape[1]=%d imtf.shape[2]=%d imtf.shape[3]=%d' %(imtf.shape[0],imtf.shape[1],imtf.shape[2],imtf.shape[3]) )
net_ctc.blobs['data'].reshape(imtf.shape[0],imtf.shape[1],imtf.shape[2], imtf.shape[3])
net_ctc.blobs['data'].data[...] = imtf
labels = bucket_images[bucket]['gt_enc']
txt = bucket_images[bucket]['txt']
# indentical length needed
max_len = 0
for l in range(0, len(labels)):
max_len = max(max_len, len(labels[l]))
for l in range(0, len(labels)):
while len(labels[l]) < max_len:
labels[l].append(0)
labels = np.asarray(labels, np.float)
net_ctc.blobs['label'].reshape(labels.shape[0], labels.shape[1])
net_ctc.blobs['label'].data[...] = labels
#4 Compute forward-backward
#optim.step(1)
net.step(1)
it +=1
#5 If loss is large, print it
#if net_ctc.blobs['loss'].data[...] > 10:
sf = net_ctc.blobs['transpose'].data[...]
#print( 'sf.shape' )
#print(sf.shape)
labels2 = sf.argmax(3)
out = utils.print_seq(labels2[:,0, :])
print(u'{0} <--> {1}'.format(out, txt[0]) )
if it%snapshot_interval == 0:
#optim.snapshot()
net.snapshot()
print ( 'it is %d, and snapshot_interval is %d' %(it,snapshot_interval) )
print( 'snapshot saved')