def save_labeled(self, batch, src2tgt, tgt2src):
"""Save a batch of labelled examples."""
src, src2tgt = un_normalize(batch['src']), un_normalize(src2tgt)
src_paths = batch['src_path']
tgt, tgt2src = un_normalize(batch['tgt']), un_normalize(tgt2src)
tgt_paths = batch['tgt_path']
# Make grid of images
i = 0
for s, s2t, sp, t, t2s, tp in zip(src, src2tgt, src_paths, tgt,
tgt2src, tgt_paths):
# Save image
if self.summary_writer is None:
self._write(s2t, os.path.basename(sp), subdir='src2tgt')
self._write(t2s, os.path.basename(tp), subdir='tgt2src')
else:
images_concat = make_grid([s, s2t],
nrow=2,
padding=4,
pad_value=(243, 124, 42))
self.summary_writer.add_image('src/src_src2tgt_{}'.format(i),
images_concat, self.global_step)
images_concat = make_grid([t, t2s],
nrow=2,
padding=4,
pad_value=(243, 124, 42))
self.summary_writer.add_image('tgt/tgt_tgt2src_{}'.format(i),
images_concat, self.global_step)
i += 1
self.global_step += 1
def save(self, src, src2tgt, tgt, tgt2src):
# Un-normalize
src, src2tgt = un_normalize(src), un_normalize(src2tgt)
tgt, tgt2src = un_normalize(tgt), un_normalize(tgt2src)
# Make grid of images
i = 0
for s, s2t, t, t2s in zip(src, src2tgt, tgt, tgt2src):
# Save image
if self.summary_writer is None:
images_concat = make_grid([s, s2t], nrow=2, padding=0)
file_name = 'src2tgt_{:04d}.png'.format(self.global_step)
self._write(images_concat, file_name)
images_concat = make_grid([t, t2s], nrow=2, padding=0)
file_name = 'tgt2src_{:04d}.png'.format(self.global_step)
self._write(images_concat, file_name)
else:
images_concat = make_grid([s, s2t],
nrow=2,
padding=4,
pad_value=(243, 124, 42))
self.summary_writer.add_image('src/src_src2tgt_{}'.format(i),
images_concat, self.global_step)
images_concat = make_grid([t, t2s],
nrow=2,
padding=4,
pad_value=(243, 124, 42))
self.summary_writer.add_image('tgt/tgt_tgt2src_{}'.format(i),
images_concat, self.global_step)
i += 1
self.global_step += 1
def test():
screen = util.make_grid(3, 7, fill='.')
screen = run(screen, 'rect 3x2')
assert util.grid_to_text(screen) == r"""
###....
###....
.......""".strip()
screen = run(screen, 'rotate column x=1 by 1')
assert util.grid_to_text(screen) == r"""
#.#....
###....
.#.....""".strip()
screen = run(screen, 'rotate row y=0 by 4')
assert util.grid_to_text(screen) == r"""
....#.#
###....
.#.....""".strip()
screen = run(screen, 'rotate column x=1 by 1')
assert util.grid_to_text(screen) == r"""
.#..#.#
#.#....
.#.....""".strip()
def render(self, fname):
self.batch_imgs = np.stack(self.batch_imgs)
img_width = self.batch_imgs.shape[2]
self.batch_imgs = util.make_grid(self.batch_imgs, len(self.batch_imgs), 2)
print(self.uncs,'\n')
tick_labels = [f'{angle:.02f}\n{unc:.04f}\n{label_hat}'
for (angle, unc, label_hat)
in zip(self.del_angles, self.uncs, self.label_hats)]
util.save_batch_fig(fname, self.batch_imgs, img_width, tick_labels)
def render(self, fname):
self.batch_imgs = np.stack(self.batch_imgs)
img_width = self.batch_imgs.shape[2]
self.batch_imgs = util.make_grid(self.batch_imgs, len(self.batch_imgs),
2)
# print(self.uncs, '\n')
# print(self.accs, '\n')
if self.action_size == 1:
tick_labels = [
str([float(f'{p:.01f}') for p in param]) +
f'\n{unc:.04f}\n{label_hat}\n{reward:.04f}'
for (param, unc, label_hat, reward)
# in zip(self.del_params, self.uncs, self.label_hats, self.rewards)]
in zip(self.del_params, self.accs, self.label_hats,
self.rewards)
]
elif self.action_size == 3:
tick_labels = [
str([float(f'{p:.01f}') for p in param[:1]]) + '\n' +
str([float(f'{p:.03f}') for p in param[1:3]]) +
f'\n{unc:.04f}\n{label_hat}\n{reward:.04f}'
for (param, unc, label_hat, reward)
# in zip(self.del_params, self.uncs, self.label_hats, self.rewards)]
in zip(self.del_params, self.accs, self.label_hats,
self.rewards)
]
elif self.action_size == 5:
tick_labels = [
str([float(f'{p:.01f}') for p in param[:1]]) + '\n' +
str([float(f'{p:.03f}') for p in param[1:3]]) + '\n' +
str([float(f'{p:.03f}') for p in param[3:5]]) +
f'\n{unc:.04f}\n{label_hat}\n{reward:.04f}'
for (param, unc, label_hat, reward)
# in zip(self.del_params, self.uncs, self.label_hats, self.rewards)]
in zip(self.del_params, self.accs, self.label_hats,
self.rewards)
]
else: # self.action_size == 7
tick_labels = [
str([float(f'{p:.01f}') for p in param[:1]]) + '\n' +
str([float(f'{p:.03f}') for p in param[1:3]]) + '\n' +
str([float(f'{p:.03f}') for p in param[3:5]]) + '\n' +
str([float(f'{p:.01f}') for p in param[5:7]]) +
f'\n{unc:.04f}\n{label_hat}\n{reward:.04f}'
for (param, unc, label_hat, reward)
# in zip(self.del_params, self.uncs, self.label_hats, self.rewards)]
in zip(self.del_params, self.accs, self.label_hats,
self.rewards)
]
util.save_batch_fig(fname, self.batch_imgs, img_width, tick_labels)
pred_map_batch > 0.5) # visualize the first in the batch
vis_cands.append(res)
if ti % 10 == 0:
print(
'TEST [iter %d/%d, epoch %.3f]: loss=%f, mean_IU=%f' %
(ti, test_iter, test_epoch, loss, score))
sys.stdout.flush()
vis.compute_scores(suffix=it)
tot_save = 0
if opt.eval:
tot_save = len(vis_cands[0])
for tt in range(tot_save):
save_list = [a[tt] for a in vis_cands]
vis_grid = make_grid(save_list, ncols=6)
vis_grid = misc.imresize(vis_grid, 0.5)
misc.imsave(
os.path.join(
opt.checkpoint_path,
'img/test_results_{}_{}.png'.format(
str(test_epoch), str(tt))), vis_grid)
vis.reset()
epoch_iter = 0
if opt.eval: break
start = time.time()
# 1.进入data_gen的imerge函数
# 2.调用image中Iterator类的iter函数
# 3.
assert util.grid_to_text(screen) == r"""
#.#....
###....
.#.....""".strip()
screen = run(screen, 'rotate row y=0 by 4')
assert util.grid_to_text(screen) == r"""
....#.#
###....
.#.....""".strip()
screen = run(screen, 'rotate column x=1 by 1')
assert util.grid_to_text(screen) == r"""
.#..#.#
#.#....
.#.....""".strip()
test()
with open('input.txt', 'r') as f:
input = f.read()
screen = util.make_grid(6, 50, fill='.')
screen = run(screen, input)
cnt = util.grid_count(screen, '#')
print('Part 1 count:', cnt)
render = util.grid_to_text(screen, {'.': util.PIXEL_WHITE})
#FIXME - PIXEL_BLACK render size looks bad
print(render)
def end_iter(self):
"""Log info for end of an iteration."""
self.iter += self.batch_size
self.global_step += self.batch_size
# Periodically write to the log and TensorBoard
if self.global_step % self.iters_per_print == 0:
# Write a header for the log entry
avg_time = (time() - self.iter_start_time) / self.batch_size
message = '[epoch: {}, iter: {} / {}, time: {:.2f}] '\
.format(self.epoch, self.iter, self.dataset_len, avg_time)
# Write the current error report
loss_dict = self.model.get_loss_dict()
loss_keys = ['loss_g', 'loss_d'
] # Can add other losses here (e.g., 'loss_g_l1').
loss_strings = [
'{}: {:.3g}'.format(k, loss_dict[k]) for k in loss_keys
]
message += ', '.join(loss_strings)
# Write all errors as scalars to the graph
for k, v in loss_dict.items():
# Group generator and discriminator losses
if k.startswith('loss_d'):
k = 'd/' + k
else:
k = 'g/' + k
self.summary_writer.add_scalar(k, v, self.global_step)
self.write(message)
# Periodically visualize training examples
if self.global_step % self.iters_per_visual == 0:
image_dict = self.model.get_image_dict()
if len(image_dict) % 3 == 0:
# Image dict contains full cycle (e.g., in CycleGAN)
plot_groups = ('src', 'tgt')
image_keys = {
'src': ('src', 'src2tgt', 'src2tgt2src'),
'tgt': ('tgt', 'tgt2src', 'tgt2src2tgt')
}
nrow = 3
elif len(image_dict) > 2:
# Image dict contains src2tgt, tgt2src (e.g., in CycleFlow/Flow2Flow)
plot_groups = ('src', 'tgt')
image_keys = {
'src': ('src', 'src2tgt'),
'tgt': ('tgt', 'tgt2src')
}
nrow = 2
else:
# Image dict just contains src -> tgt (e.g., in Pix2Pix)
plot_groups = ('example', )
image_keys = {'example': ('src', 'src2tgt')}
nrow = 2
# Concatenate images into triples
for plot_group in plot_groups:
image_batches = [image_dict[k] for k in image_keys[plot_group]]
for i in range(self.num_visuals):
images = []
for image_batch in image_batches:
if len(image_batch) > i:
images.append(image_batch[i])
if len(images) == 0:
continue
images_title = '{}/{}_{}'.format(
plot_group, '_'.join(image_keys[plot_group]), i + 1)
images_concat = util.make_grid(images,
nrow=nrow,
padding=4,
pad_value=(243, 124, 42))
self.summary_writer.add_image(images_title, images_concat,
self.global_step)