added config_sintel.py

config_example.py

@@ -1,6 +1,5 @@
 from artist import ArtistDataProvider
 from fivek import FiveKDataProvider
-from folder_data_provider import FolderDataProvider
 from critics import critic
 from agent import agent_generator
 from util import Dict
@@ -178,26 +177,22 @@ def c_lr_callback(t):
 ###########################################################################
 
 # Input dataset (train)
-cfg.fake_data_provider = lambda: FolderDataProvider(folder='data/sintel/inputs', default_batch_size=cfg.batch_size)
-'''
 cfg.fake_data_provider = lambda: FiveKDataProvider(
     raw=True,
     bnw=cfg.bnw,
     output_size=64,
     default_batch_size=cfg.batch_size,
     augmentation=0.3,
     set_name='2k_train',)
-'''
 
 # Input dataset (test)
-'''
 cfg.fake_data_provider_test = lambda: FiveKDataProvider(set_name='u_test', raw=True, bnw=cfg.bnw,
                                                         output_size=64,
                                                         default_batch_size=cfg.batch_size,
                                                         augmentation=0.0)
-'''
-cfg.fake_data_provider_test = lambda: FolderDataProvider(folder='data/sintel/inputs', default_batch_size=cfg.batch_size)
 
 # Target dataset
-cfg.real_data_provider = lambda: FolderDataProvider(folder='data/sintel/outputs', default_batch_size=cfg.batch_size)
-
+cfg.real_data_provider = lambda: ArtistDataProvider(augmentation=1.0, name='FiveK_C',
+                                                    output_size=64, bnw=cfg.bnw,
+                                                    default_batch_size=cfg.batch_size, target=None,
+                                                    set_name='2k_target')

config_sintel.py

@@ -0,0 +1,191 @@
+from artist import ArtistDataProvider
+from fivek import FiveKDataProvider
+from folder_data_provider import FolderDataProvider
+from critics import critic
+from agent import agent_generator
+from util import Dict
+from filters import *
+
+cfg = Dict()
+
+###########################################################################
+###########################################################################
+# Here is a list of parameters. Instead of hard coding them in the script, I summarize them here.
+# You do not need to modify most of them except the dataset part (see bottom), unless for good reasons.
+###########################################################################
+###########################################################################
+
+#--------------------------------------------------------------------------
+
+###########################################################################
+# Filter Parameters
+###########################################################################
+cfg.filters = [
+    ExposureFilter, GammaFilter, ImprovedWhiteBalanceFilter,
+    SaturationPlusFilter, ToneFilter, ContrastFilter, WNBFilter, ColorFilter
+]
+# Gamma = 1/x ~ x
+cfg.curve_steps = 8
+cfg.gamma_range = 3
+cfg.exposure_range = 3.5
+cfg.wb_range = 1.1
+cfg.color_curve_range = (0.90, 1.10)
+cfg.lab_curve_range = (0.90, 1.10)
+cfg.tone_curve_range = (0.5, 2)
+
+# Masking is DISABLED
+cfg.masking = False
+cfg.minimum_strength = 0.3
+cfg.maximum_sharpness = 1
+cfg.clamp = False
+
+###########################################################################
+# RL Parameters
+###########################################################################
+cfg.critic_logit_multiplier = 0.05
+cfg.discount_factor = 1.0
+# Each time the agent reuse a filter, a penalty is subtracted from the reward. Set to 0 to disable.
+cfg.filter_usage_penalty = 1.0
+# Use temporal difference error (thereby the value network is used) or directly a single step award (greedy)?
+cfg.use_TD = True
+# During test, do we use random walk or pick the action with maximized prob.?
+cfg.test_random_walk = False
+# Replay memory
+cfg.replay_memory_size = 128
+# Note, a trajectory will be killed either after achieving this value (by chance) or submission
+# Thus exploration will leads to kills as well.
+cfg.maximum_trajectory_length = 7
+cfg.over_length_keep_prob = 0.5
+cfg.all_reward = 1.0
+# Append input image with states?
+cfg.img_include_states = True
+# with prob. cfg.exploration, we randomly pick one action during training
+cfg.exploration = 0.05
+# Action entropy penalization
+cfg.exploration_penalty = 0.05
+cfg.early_stop_penalty = 1.0
+
+###########################################################################
+# CNN Parameters
+###########################################################################
+cfg.source_img_size = 64
+cfg.base_channels = 32
+cfg.dropout_keep_prob = 0.5
+# G and C use the same feed dict?
+cfg.share_feed_dict = True
+cfg.shared_feature_extractor = True
+cfg.fc1_size = 128
+cfg.bnw = False
+# number of filters for the first convolutional layers for all networks
+#                      (stochastic/deterministic policy, critic, value)
+cfg.feature_extractor_dims = 4096
+
+###########################################################################
+# GAN Parameters
+###########################################################################
+# For WGAN only
+cfg.use_penalty = True
+# LSGAN or WGAN? (LSGAN is not supported now, so please do not change this)
+cfg.gan = 'w'
+
+##################################
+# Generator
+##################################
+cfg.generator = agent_generator
+cfg.giters = 1
+
+##################################
+# Critic & Value Networks
+##################################
+cfg.critic = critic
+cfg.value = critic
+cfg.gradient_penalty_lambda = 10
+# max iter step, note the one step indicates that a Citers updates of critic and one update of generator
+cfg.citers = 5
+cfg.critic_initialization = 10
+# the upper bound and lower bound of parameters in critic
+# when using gradient penalty, clamping is disabled
+cfg.clamp_critic = 0.01
+
+# EMD output filter size
+cfg.median_filter_size = 101
+
+# Noise defined here is not actually used
+cfg.z_type = 'uniform'
+cfg.z_dim_per_filter = 16
+
+cfg.num_state_dim = 3 + len(cfg.filters)
+cfg.z_dim = 3 + len(cfg.filters) * cfg.z_dim_per_filter
+cfg.test_steps = 5
+
+cfg.real_img_size = 64
+cfg.real_img_channels = 1 if cfg.bnw else 3
+
+###########################################################################
+# Training
+###########################################################################
+cfg.supervised = False
+cfg.batch_size = 64
+multiplier = 2
+cfg.max_iter_step = int(10000 * multiplier)
+
+##################################
+# Learning Rates
+##################################
+lr_decay = 0.1
+base_lr = 5e-5
+
+segments = 3
+
+generator_lr_mul = 0.3
+cfg.parameter_lr_mul = 1
+cfg.value_lr_mul = 10
+critic_lr_mul = 1
+
+
+def g_lr_callback(t):
+  return generator_lr_mul * base_lr * lr_decay**(
+      1.0 * t * segments / cfg.max_iter_step)
+
+
+def c_lr_callback(t):
+  return critic_lr_mul * base_lr * lr_decay**(
+      1.0 * t * segments / cfg.max_iter_step)
+
+
+cfg.lr_g = g_lr_callback
+cfg.lr_c = c_lr_callback
+
+optimizer = lambda lr: tf.train.AdamOptimizer(learning_rate=lr, beta1=0.5, beta2=0.9)
+
+cfg.generator_optimizer = optimizer
+cfg.critic_optimizer = optimizer
+
+cfg.num_samples = 64
+cfg.img_channels = 1 if cfg.bnw else 3
+cfg.summary_freq = 100
+
+##################################
+# Debugging Outputs
+##################################
+cfg.vis_draw_critic_scores = True
+cfg.vis_step_test = False
+cfg.realtime_vis = False
+cfg.write_image_interval = int(200 * multiplier)
+
+###########################################################################
+# Dataset Parameters
+###########################################################################
+
+# Note: this is just demonstrating using custom data for training
+# and there is no train/test separation! Make sure you set fake_data_provider
+# and fake_data_provider_test to be two different parts!
+
+
+# Input dataset (train)
+cfg.fake_data_provider = lambda: FolderDataProvider(folder='models/sintel/inputs', default_batch_size=cfg.batch_size)
+# Input dataset (test)
+cfg.fake_data_provider_test = lambda: FolderDataProvider(folder='models/sintel/inputs', default_batch_size=cfg.batch_size)
+# Target dataset
+cfg.real_data_provider = lambda: FolderDataProvider(folder='models/sintel/outputs', default_batch_size=cfg.batch_size)
+