Tensorflow选择性初始化图中的变量

import tensorflow as tf

def initialize_uninitialized(sess):
    global_vars = tf.global_variables()
    is_not_initialized = sess.run([tf.is_variable_initialized(var) for var in global_vars])
    not_initialized_vars = [v for (v, f) in zip(global_vars, is_not_initialized) if not f]

    print [str(i.name) for i in not_initialized_vars] # only for testing
    if len(not_initialized_vars):
        sess.run(tf.variables_initializer(not_initialized_vars))

# stage_merged.py
# transform from single frame into multi-frame enhanced single raw
from __future__ import division
import os, time, scipy.io
import tensorflow as tf
import numpy as np
import rawpy
import glob
from model_sid_latest import network_stages_merged, network_my_unet, network_enhance_raw
import platform
from PIL import Image

if platform.system() == ‘Windows‘:
    data_dir = ‘D:/data/Sony/dataset/bbf-raw-selected/‘
elif platform.system() == ‘Linux‘:
    data_dir = ‘./dataset/bbf-raw-selected/‘
else:
    print(‘platform not supported!‘)
    assert False

os.environ["CUDA_VISIBLE_DEVICES"] = "6"
checkpoint_dir = ‘./model_stage_merged/‘
result_dir = ‘./out_stage_merged/‘
log_dir = ‘./log_stage_merged/‘
learning_rate = 1e-4
epoch_bound = 20000
save_model_every_n_epoch = 10

if platform.system() == ‘Windows‘:
    output_every_n_steps = 1
else:
    output_every_n_steps = 100

if platform.system() == ‘Windows‘:
    ckpt_enhance_raw = ‘D:/model/enhance_raw/‘
    ckpt_raw2rgb = ‘D:/model/raw2rgb-c1/‘
else:
    ckpt_enhance_raw = ‘./model/enhance_raw/‘
    ckpt_raw2rgb = ‘./model/raw2rgb-c1/‘

# BBF100-2
bbf_w = 4032
bbf_h = 3024

patch_w = 512
patch_h = 512

max_level = 1023
black_level = 64

patch_w = 512
patch_h = 512

# set up dataset
input_files = glob.glob(data_dir + ‘/*.dng‘)
input_files.sort()


def preprocess(raw, bl, wl):
    im = raw.raw_image_visible.astype(np.float32)
    im = np.maximum(im - bl, 0)
    return im / (wl - bl)


def pack_raw_bbf(path):
    raw = rawpy.imread(path)
    bl = 64
    wl = 1023
    im = preprocess(raw, bl, wl)
    im = np.expand_dims(im, axis=2)
    H = im.shape[0]
    W = im.shape[1]
    if raw.raw_pattern[0, 0] == 0: # CFA=RGGB
        out = np.concatenate((im[0:H:2, 0:W:2, :],
                              im[0:H:2, 1:W:2, :],
                              im[1:H:2, 1:W:2, :],
                              im[1:H:2, 0:W:2, :]), axis=2)
    elif raw.raw_pattern[0,0] == 2: # BGGR
        out = np.concatenate((im[1:H:2, 1:W:2, :],
                              im[0:H:2, 1:W:2, :],
                              im[0:H:2, 0:W:2, :],
                              im[1:H:2, 0:W:2, :]), axis=2)
    elif raw.raw_pattern[0,0] == 1 and raw.raw_pattern[0,1] == 0: # GRBG
        out = np.concatenate((im[0:H:2, 1:W:2, :],
                              im[0:H:2, 0:W:2, :],
                              im[1:H:2, 0:W:2, :],
                              im[1:H:2, 1:W:2, :]), axis=2)
    elif raw.raw_pattern[0,0] == 1 and raw.raw_pattern[0,1] == 2: # GBRG
        out = np.concatenate((im[1:H:2, 0:W:2, :],
                              im[0:H:2, 0:W:2, :],
                              im[0:H:2, 1:W:2, :],
                              im[1:H:2, 1:W:2, :]), axis=2)
    else:
        assert False
    wb = np.array(raw.camera_whitebalance)
    wb[3] = wb[1]
    wb = wb / wb[1]
    out = np.minimum(out * wb, 1.0)

    h_, w_ = im.shape[0]//2, im.shape[1]//2
    out_16bit_ = np.zeros([h_, w_, 4], dtype=np.uint16)
    out_16bit_[:, :, :] = np.uint16(out[:, :, :] * (wl - bl))
    del out
    return out_16bit_


tf.reset_default_graph()
gpu_options = tf.GPUOptions(allow_growth=True)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
in_im = tf.placeholder(tf.float32, [1, patch_h, patch_w, 4], name=‘input‘)

with tf.variable_scope(‘enhance_raw‘, reuse=tf.AUTO_REUSE):
    enhanced_raw = network_enhance_raw(in_im, patch_h, patch_w)
with tf.variable_scope(‘raw2rgb‘, reuse=tf.AUTO_REUSE):
    gt_im = network_my_unet(enhanced_raw, patch_h, patch_w)
with tf.variable_scope(‘stage_merged‘, reuse=tf.AUTO_REUSE):
    out_im = network_stages_merged(in_im, patch_h, patch_w)

gt_im_cut = tf.minimum(tf.maximum(gt_im, 0.0), 1.0)
out_im_cut = tf.minimum(tf.maximum(out_im, 0.0), 1.0)
ssim_loss = 1 - tf.image.ssim_multiscale(gt_im_cut[0], out_im_cut[0], 1.0)
l1_loss = tf.reduce_mean(tf.reduce_sum(tf.abs(gt_im_cut - out_im_cut), axis=-1))
l2_loss = tf.reduce_mean(tf.reduce_sum(tf.square(gt_im_cut - out_im_cut), axis=-1))
G_loss = ssim_loss
# G_loss = l1_loss + l2_loss

tf.summary.scalar(‘G_loss‘, G_loss)
tf.summary.scalar(‘L1 Loss‘, l1_loss)
tf.summary.scalar(‘L2 Loss‘, l2_loss)

########## LOADING MODELS #############
scope_ = ‘enhance_raw‘
enhance_raw_var_list = tf.global_variables(scope_)
enhance_raw_var_names = [v.name.replace(scope_+‘/‘, ‘‘).replace(‘:0‘, ‘‘) for v in enhance_raw_var_list]
enhance_raw_map = dict()
for i in range(len(enhance_raw_var_names)):
    enhance_raw_map[enhance_raw_var_names[i]] = enhance_raw_var_list[i]

saver_enhance_raw = tf.train.Saver(var_list=enhance_raw_map)
ckpt = tf.train.get_checkpoint_state(ckpt_enhance_raw)
if ckpt:
    saver_enhance_raw.restore(sess, ckpt.model_checkpoint_path)
    print(‘loaded enhance_raw model: ‘ + ckpt.model_checkpoint_path)
else:
    print(‘Error: failed to load enhance_raw model!‘)
#----------------------------------------
scope_ = ‘raw2rgb‘
raw2rgb_var_list = tf.global_variables(scope_)
raw2rgb_var_names = [v.name.replace(scope_+‘/‘, ‘‘).replace(‘:0‘, ‘‘) for v in raw2rgb_var_list]
raw2rgb_map = dict()
for i in range(len(raw2rgb_var_names)):
    raw2rgb_map[raw2rgb_var_names[i]] = raw2rgb_var_list[i]

saver_raw2rgb = tf.train.Saver(var_list=raw2rgb_map)
ckpt = tf.train.get_checkpoint_state(ckpt_raw2rgb)
if ckpt:
    saver_raw2rgb.restore(sess, ckpt.model_checkpoint_path)
    print(‘loaded raw2rgb model: ‘ + ckpt.model_checkpoint_path)
else:
    print(‘Error: failed to load raw2rgb model!‘)
    assert False
#----------------------------------------


def initialize_uninitialized(sess):
    global_vars = tf.global_variables()
    bool_inits = sess.run([tf.is_variable_initialized(var) for var in global_vars])
    uninit_vars = [v for (v, b) in zip(global_vars, bool_inits) if not b]
    for v in uninit_vars:
        print(str(v.name))
    if len(uninit_vars):
        sess.run(tf.variables_initializer(uninit_vars))

t_vars = tf.trainable_variables(scope=‘stage_merged‘)
lr = tf.placeholder(tf.float32)
G_opt = tf.train.AdamOptimizer(learning_rate=lr).minimize(G_loss, var_list=t_vars)

saver = tf.train.Saver(var_list=t_vars)
ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
if ckpt:
    saver.restore(sess, ckpt.model_checkpoint_path)
    print(‘loaded ‘ + ckpt.model_checkpoint_path)
else:
    sess.run(tf.variables_initializer(var_list=t_vars))
    initialize_uninitialized(sess)
#######################################
if not os.path.isdir(result_dir):
    os.mkdir(result_dir)

input_images = [None] * len(input_files)
g_loss = np.zeros([500, 1])

merged = tf.summary.merge_all()
writer = tf.summary.FileWriter(log_dir, sess.graph)

steps = 0
st = time.time()

for epoch in range(0, epoch_bound):
    for ind in np.random.permutation(len(input_images)):
        steps += 1
        if input_images[ind] is None:
            input_images[ind] = np.expand_dims(pack_raw_bbf(input_files[ind]), axis=0)

        # random cropping
        xx = np.random.randint(0, bbf_w // 2 - patch_w)
        yy = np.random.randint(0, bbf_h // 2 - patch_h)
        input_patch = np.float32(input_images[ind][:, yy:yy + patch_h, xx:xx + patch_w, :]) / (
                    max_level - black_level)

        # random flipping
        if np.random.randint(2, size=1)[0] == 1:  # random flip
            input_patch = np.flip(input_patch, axis=1)
        if np.random.randint(2, size=1)[0] == 1:
            input_patch = np.flip(input_patch, axis=0)
        if np.random.randint(2, size=1)[0] == 1:  # random transpose
            input_patch = np.transpose(input_patch, (0, 2, 1, 3))

        summary, _, G_current, output, gt_im_ = sess.run(
            [merged, G_opt, G_loss, out_im_cut, gt_im_cut],
            feed_dict={
                in_im: input_patch,
                lr: learning_rate})
        g_loss[steps % len(g_loss)] = G_current

        if steps % output_every_n_steps == 0:
            loss_ = np.mean(g_loss[np.where(g_loss)])
            cost_ = (time.time() - st) / output_every_n_steps
            st = time.time()
            print("%d %d Loss=%.6f Speed=%.6f" % (epoch, steps, loss_, cost_))
            writer.add_summary(summary, global_step=steps)
            temp = np.concatenate(
                (input_patch[0, :, :, :3],
                 gt_im_[0, 0:patch_h*2:2, 0:patch_w*2:2, :3],
                 output[0, 0:patch_h*2:2, 0:patch_w*2:2, :3]), axis=1)
            scipy.misc.toimage(temp * 255, high=255, low=0, cmin=0, cmax=255)                 .save(result_dir + ‘/%d_%d.jpg‘ % (epoch, steps))

        # clean up the memory if necessary
        if platform.system() == ‘Windows‘:
            input_images[ind] = None

    if epoch % save_model_every_n_epoch == 0:
        saver.save(sess, checkpoint_dir + ‘%d.ckpt‘ % epoch)
        print(‘model saved.‘)