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import os
import numpy as np
# import cv2
from PIL import Image
from utils import paramUtil
import math
import time
import matplotlib.pyplot as plt
# from scipy.ndimage import gaussian_filter
def mkdir(path):
if not os.path.exists(path):
os.makedirs(path)
COLORS = [[255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 255, 0], [170, 255, 0], [85, 255, 0], [0, 255, 0],
[0, 255, 85], [0, 255, 170], [0, 255, 255], [0, 170, 255], [0, 85, 255], [0, 0, 255], [85, 0, 255],
[170, 0, 255], [255, 0, 255], [255, 0, 170], [255, 0, 85]]
MISSING_VALUE = -1
def save_image(image_numpy, image_path):
img_pil = Image.fromarray(image_numpy)
img_pil.save(image_path)
def save_logfile(log_loss, save_path):
with open(save_path, 'wt') as f:
for k, v in log_loss.items():
w_line = k
for digit in v:
w_line += ' %.3f' % digit
f.write(w_line + '\n')
def print_current_loss(start_time, niter_state, total_niters, losses, epoch=None, sub_epoch=None,
inner_iter=None, tf_ratio=None, sl_steps=None):
def as_minutes(s):
m = math.floor(s / 60)
s -= m * 60
return '%dm %ds' % (m, s)
def time_since(since, percent):
now = time.time()
s = now - since
es = s / percent
rs = es - s
return '%s (- %s)' % (as_minutes(s), as_minutes(rs))
if epoch is not None:
print('ep/it:%2d-%4d niter:%6d' % (epoch, inner_iter, niter_state), end=" ")
message = ' %s completed:%3d%%)' % (time_since(start_time, niter_state / total_niters), niter_state / total_niters * 100)
# now = time.time()
# message += '%s'%(as_minutes(now - start_time))
for k, v in losses.items():
message += ' %s: %.4f ' % (k, v)
# message += ' sl_length:%2d tf_ratio:%.2f'%(sl_steps, tf_ratio)
print(message)
def print_current_loss_decomp(start_time, niter_state, total_niters, losses, epoch=None, inner_iter=None):
def as_minutes(s):
m = math.floor(s / 60)
s -= m * 60
return '%dm %ds' % (m, s)
def time_since(since, percent):
now = time.time()
s = now - since
es = s / percent
rs = es - s
return '%s (- %s)' % (as_minutes(s), as_minutes(rs))
print('epoch: %03d inner_iter: %5d' % (epoch, inner_iter), end=" ")
# now = time.time()
message = '%s niter: %07d completed: %3d%%)'%(time_since(start_time, niter_state / total_niters), niter_state, niter_state / total_niters * 100)
for k, v in losses.items():
message += ' %s: %.4f ' % (k, v)
print(message)
def compose_gif_img_list(img_list, fp_out, duration):
img, *imgs = [Image.fromarray(np.array(image)) for image in img_list]
img.save(fp=fp_out, format='GIF', append_images=imgs, optimize=False,
save_all=True, loop=0, duration=duration)
def save_images(visuals, image_path):
if not os.path.exists(image_path):
os.makedirs(image_path)
for i, (label, img_numpy) in enumerate(visuals.items()):
img_name = '%d_%s.jpg' % (i, label)
save_path = os.path.join(image_path, img_name)
save_image(img_numpy, save_path)
def save_images_test(visuals, image_path, from_name, to_name):
if not os.path.exists(image_path):
os.makedirs(image_path)
for i, (label, img_numpy) in enumerate(visuals.items()):
img_name = "%s_%s_%s" % (from_name, to_name, label)
save_path = os.path.join(image_path, img_name)
save_image(img_numpy, save_path)
def compose_and_save_img(img_list, save_dir, img_name, col=4, row=1, img_size=(256, 200)):
# print(col, row)
compose_img = compose_image(img_list, col, row, img_size)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
img_path = os.path.join(save_dir, img_name)
# print(img_path)
compose_img.save(img_path)
def compose_image(img_list, col, row, img_size):
to_image = Image.new('RGB', (col * img_size[0], row * img_size[1]))
for y in range(0, row):
for x in range(0, col):
from_img = Image.fromarray(img_list[y * col + x])
# print((x * img_size[0], y*img_size[1],
# (x + 1) * img_size[0], (y + 1) * img_size[1]))
paste_area = (x * img_size[0], y*img_size[1],
(x + 1) * img_size[0], (y + 1) * img_size[1])
to_image.paste(from_img, paste_area)
# to_image[y*img_size[1]:(y + 1) * img_size[1], x * img_size[0] :(x + 1) * img_size[0]] = from_img
return to_image
def plot_loss_curve(losses, save_path, intervals=500):
plt.figure(figsize=(10, 5))
plt.title("Loss During Training")
for key in losses.keys():
plt.plot(list_cut_average(losses[key], intervals), label=key)
plt.xlabel("Iterations/" + str(intervals))
plt.ylabel("Loss")
plt.legend()
plt.savefig(save_path)
plt.show()
def list_cut_average(ll, intervals):
if intervals == 1:
return ll
bins = math.ceil(len(ll) * 1.0 / intervals)
ll_new = []
for i in range(bins):
l_low = intervals * i
l_high = l_low + intervals
l_high = l_high if l_high < len(ll) else len(ll)
ll_new.append(np.mean(ll[l_low:l_high]))
return ll_new
# def motion_temporal_filter(motion, sigma=1):
# motion = motion.reshape(motion.shape[0], -1)
# # print(motion.shape)
# for i in range(motion.shape[1]):
# motion[:, i] = gaussian_filter(motion[:, i], sigma=sigma, mode="nearest")
# return motion.reshape(motion.shape[0], -1, 3)
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