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import errno
import numpy as np
from torch.nn import init
import torch
import torch.nn as nn
import arabic_reshaper
from bidi.algorithm import get_display
from PIL import Image, ImageDraw, ImageFont
from copy import deepcopy
import skimage.transform
from miscc.config import cfg
# For visualization ################################################
COLOR_DIC = {0:[128,64,128], 1:[244, 35,232],
2:[70, 70, 70], 3:[102,102,156],
4:[190,153,153], 5:[153,153,153],
6:[250,170, 30], 7:[220, 220, 0],
8:[107,142, 35], 9:[152,251,152],
10:[70,130,180], 11:[220,20, 60],
12:[255, 0, 0], 13:[0, 0, 142],
14:[119,11, 32], 15:[0, 60,100],
16:[0, 80, 100], 17:[0, 0, 230],
18:[0, 0, 70], 19:[0, 0, 0]}
FONT_MAX = 50
def drawCaption(convas, captions, ixtoword, vis_size, off1=2, off2=2):
num = captions.size(0)
img_txt = Image.fromarray(convas)
# get a font
# fnt = None # ImageFont.truetype('Pillow/Tests/fonts/FreeMono.ttf', 50)
# fnt = ImageFont.truetype('Pillow/Tests/fonts/FreeMono.ttf', 50)
fnt = ImageFont.truetype('/content/drive/MyDrive/Graduation Project/GANS/AD-GAN/code/miscc/sahel-font/dist/Sahel-Light.ttf', 18)
# get a drawing context
d = ImageDraw.Draw(img_txt)
sentence_list = []
for i in range(num):
cap = captions[i].data.cpu().numpy()
sentence = []
for j in range(len(cap)):
if cap[j] == 0:
break
word = ixtoword[cap[j]].encode('cp1256', 'ignore').decode('cp1256')
drawed_word = arabic_reshaper.reshape(word) # correct its shape
drawed_word = get_display(drawed_word) # correct its direction
d.text(((j + off1) * (vis_size + off2), i * FONT_MAX), '%d:%s' % (j, drawed_word[:6]),
font=fnt, fill=(255, 255, 255, 255))
sentence.append(word)
sentence_list.append(sentence)
return img_txt, sentence_list
def build_super_images(real_imgs, captions, ixtoword,
attn_maps, att_sze, lr_imgs=None,
batch_size=cfg.TRAIN.BATCH_SIZE,
max_word_num=cfg.TEXT.WORDS_NUM):
nvis = 8
real_imgs = real_imgs[:nvis]
if lr_imgs is not None:
lr_imgs = lr_imgs[:nvis]
if att_sze == 17:
vis_size = att_sze * 16
else:
vis_size = real_imgs.size(2)
text_convas = \
np.ones([batch_size * FONT_MAX,
(max_word_num + 2) * (vis_size + 2), 3],
dtype=np.uint8)
for i in range(max_word_num):
istart = (i + 2) * (vis_size + 2)
iend = (i + 3) * (vis_size + 2)
text_convas[:, istart:iend, :] = COLOR_DIC[i]
real_imgs = \
nn.functional.interpolate(real_imgs,size=(vis_size, vis_size),
mode='bilinear', align_corners=False)
# [-1, 1] --> [0, 1]
real_imgs.add_(1).div_(2).mul_(255)
real_imgs = real_imgs.data.numpy()
# b x c x h x w --> b x h x w x c
real_imgs = np.transpose(real_imgs, (0, 2, 3, 1))
pad_sze = real_imgs.shape
middle_pad = np.zeros([pad_sze[2], 2, 3])
post_pad = np.zeros([pad_sze[1], pad_sze[2], 3])
if lr_imgs is not None:
lr_imgs = \
nn.functional.interpolate(lr_imgs,size=(vis_size, vis_size),
mode='bilinear', align_corners=False)
# [-1, 1] --> [0, 1]
lr_imgs.add_(1).div_(2).mul_(255)
lr_imgs = lr_imgs.data.numpy()
# b x c x h x w --> b x h x w x c
lr_imgs = np.transpose(lr_imgs, (0, 2, 3, 1))
# batch x seq_len x 17 x 17 --> batch x 1 x 17 x 17
seq_len = max_word_num
img_set = []
num = nvis # len(attn_maps)
text_map, sentences = \
drawCaption(text_convas, captions, ixtoword, vis_size)
text_map = np.asarray(text_map).astype(np.uint8)
bUpdate = 1
for i in range(num):
attn = attn_maps[i].cpu().view(1, -1, att_sze, att_sze)
# --> 1 x 1 x 17 x 17
attn_max = attn.max(dim=1, keepdim=True)
attn = torch.cat([attn_max[0], attn], 1)
#
attn = attn.view(-1, 1, att_sze, att_sze)
attn = attn.repeat(1, 3, 1, 1).data.numpy()
# n x c x h x w --> n x h x w x c
attn = np.transpose(attn, (0, 2, 3, 1))
num_attn = attn.shape[0]
#
img = real_imgs[i]
if lr_imgs is None:
lrI = img
else:
lrI = lr_imgs[i]
row = [lrI, middle_pad]
row_merge = [img, middle_pad]
row_beforeNorm = []
minVglobal, maxVglobal = 1, 0
for j in range(num_attn):
one_map = attn[j]
if (vis_size // att_sze) > 1:
one_map = \
skimage.transform.pyramid_expand(one_map, sigma=20,
upscale=vis_size // att_sze,
multichannel=True)
row_beforeNorm.append(one_map)
minV = one_map.min()
maxV = one_map.max()
if minVglobal > minV:
minVglobal = minV
if maxVglobal < maxV:
maxVglobal = maxV
for j in range(seq_len + 1):
if j < num_attn:
one_map = row_beforeNorm[j]
one_map = (one_map - minVglobal) / (maxVglobal - minVglobal)
one_map *= 255
#
PIL_im = Image.fromarray(np.uint8(img))
PIL_att = Image.fromarray(np.uint8(one_map))
merged = \
Image.new('RGBA', (vis_size, vis_size), (0, 0, 0, 0))
mask = Image.new('L', (vis_size, vis_size), (210))
merged.paste(PIL_im, (0, 0))
merged.paste(PIL_att, (0, 0), mask)
merged = np.array(merged)[:, :, :3]
else:
one_map = post_pad
merged = post_pad
row.append(one_map)
row.append(middle_pad)
#
row_merge.append(merged)
row_merge.append(middle_pad)
row = np.concatenate(row, 1)
row_merge = np.concatenate(row_merge, 1)
txt = text_map[i * FONT_MAX: (i + 1) * FONT_MAX]
if txt.shape[1] != row.shape[1]:
print('txt', txt.shape, 'row', row.shape)
bUpdate = 0
break
row = np.concatenate([txt, row, row_merge], 0)
img_set.append(row)
if bUpdate:
img_set = np.concatenate(img_set, 0)
img_set = img_set.astype(np.uint8)
return img_set, sentences
else:
return None
def build_super_images2(real_imgs, captions, cap_lens, ixtoword,
attn_maps, att_sze, vis_size=256, topK=5):
batch_size = real_imgs.size(0)
max_word_num = np.max(cap_lens)
text_convas = np.ones([batch_size * FONT_MAX,
max_word_num * (vis_size + 2), 3],
dtype=np.uint8)
real_imgs = \
nn.functional.interpolate(real_imgs,size=(vis_size, vis_size),
mode='bilinear', align_corners=False)
# [-1, 1] --> [0, 1]
real_imgs.add_(1).div_(2).mul_(255)
real_imgs = real_imgs.data.numpy()
# b x c x h x w --> b x h x w x c
real_imgs = np.transpose(real_imgs, (0, 2, 3, 1))
pad_sze = real_imgs.shape
middle_pad = np.zeros([pad_sze[2], 2, 3])
# batch x seq_len x 17 x 17 --> batch x 1 x 17 x 17
img_set = []
num = len(attn_maps)
text_map, sentences = \
drawCaption(text_convas, captions, ixtoword, vis_size, off1=0)
text_map = np.asarray(text_map).astype(np.uint8)
bUpdate = 1
for i in range(num):
attn = attn_maps[i].cpu().view(1, -1, att_sze, att_sze)
#
attn = attn.view(-1, 1, att_sze, att_sze)
attn = attn.repeat(1, 3, 1, 1).data.numpy()
# n x c x h x w --> n x h x w x c
attn = np.transpose(attn, (0, 2, 3, 1))
num_attn = cap_lens[i]
thresh = 2./float(num_attn)
#
img = real_imgs[i]
row = []
row_merge = []
row_txt = []
row_beforeNorm = []
conf_score = []
for j in range(num_attn):
one_map = attn[j]
mask0 = one_map > (2. * thresh)
conf_score.append(np.sum(one_map * mask0))
mask = one_map > thresh
one_map = one_map * mask
if (vis_size // att_sze) > 1:
one_map = \
skimage.transform.pyramid_expand(one_map, sigma=20,
upscale=vis_size // att_sze,
multichannel=True)
minV = one_map.min()
maxV = one_map.max()
one_map = (one_map - minV) / (maxV - minV)
row_beforeNorm.append(one_map)
sorted_indices = np.argsort(conf_score)[::-1]
for j in range(num_attn):
one_map = row_beforeNorm[j]
one_map *= 255
#
PIL_im = Image.fromarray(np.uint8(img))
PIL_att = Image.fromarray(np.uint8(one_map))
merged = \
Image.new('RGBA', (vis_size, vis_size), (0, 0, 0, 0))
mask = Image.new('L', (vis_size, vis_size), (180)) # (210)
merged.paste(PIL_im, (0, 0))
merged.paste(PIL_att, (0, 0), mask)
merged = np.array(merged)[:, :, :3]
row.append(np.concatenate([one_map, middle_pad], 1))
#
row_merge.append(np.concatenate([merged, middle_pad], 1))
#
txt = text_map[i * FONT_MAX:(i + 1) * FONT_MAX,
j * (vis_size + 2):(j + 1) * (vis_size + 2), :]
row_txt.append(txt)
# reorder
row_new = []
row_merge_new = []
txt_new = []
for j in range(num_attn):
idx = sorted_indices[j]
row_new.append(row[idx])
row_merge_new.append(row_merge[idx])
txt_new.append(row_txt[idx])
row = np.concatenate(row_new[:topK], 1)
row_merge = np.concatenate(row_merge_new[:topK], 1)
txt = np.concatenate(txt_new[:topK], 1)
if txt.shape[1] != row.shape[1]:
print('Warnings: txt', txt.shape, 'row', row.shape,
'row_merge_new', row_merge_new.shape)
bUpdate = 0
break
row = np.concatenate([txt, row_merge], 0)
img_set.append(row)
if bUpdate:
img_set = np.concatenate(img_set, 0)
img_set = img_set.astype(np.uint8)
return img_set, sentences
else:
return None
####################################################################
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
nn.init.orthogonal_(m.weight.data, 1.0)
elif classname.find('BatchNorm') != -1:
m.weight.data.normal_(1.0, 0.02)
m.bias.data.fill_(0)
elif classname.find('Linear') != -1:
nn.init.orthogonal_(m.weight.data, 1.0)
if m.bias is not None:
m.bias.data.fill_(0.0)
def load_params(model, new_param):
for p, new_p in zip(model.parameters(), new_param):
p.data.copy_(new_p)
def copy_G_params(model):
flatten = deepcopy(list(p.data for p in model.parameters()))
return flatten
def mkdir_p(path):
try:
os.makedirs(path)
except OSError as exc: # Python >2.5
if exc.errno == errno.EEXIST and os.path.isdir(path):
pass
else:
raise
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