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Commit daf2287e authored by tomrink's avatar tomrink
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parent a8566ff6
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import glob import glob
import tensorflow as tf import tensorflow as tf
from util.setup import logdir, modeldir, cachepath, now, ancillary_path from util.setup import logdir, modeldir, cachepath, now, ancillary_path
from util.util import EarlyStop, normalize, denormalize, resample, resample_2d_linear, resample_one, resample_2d_linear_one, get_grid_values_all from util.util import EarlyStop, normalize, denormalize, resample, resample_2d_linear, resample_one,\
resample_2d_linear_one, get_grid_values_all, add_noise
import os, datetime import os, datetime
import numpy as np import numpy as np
import pickle import pickle
...@@ -29,7 +30,7 @@ TRACK_MOVING_AVERAGE = False ...@@ -29,7 +30,7 @@ TRACK_MOVING_AVERAGE = False
EARLY_STOP = True EARLY_STOP = True
NOISE_TRAINING = True NOISE_TRAINING = True
NOISE_STDDEV = 0.01 NOISE_STDDEV = 0.001
DO_AUGMENT = True DO_AUGMENT = True
DO_ZERO_OUT = False DO_ZERO_OUT = False
...@@ -101,7 +102,7 @@ y_2 = y_67 ...@@ -101,7 +102,7 @@ y_2 = y_67
def build_residual_conv2d_block(conv, num_filters, block_name, activation=tf.nn.relu, padding='SAME', def build_residual_conv2d_block(conv, num_filters, block_name, activation=tf.nn.relu, padding='SAME',
kernel_initializer='he_uniform', scale=None, kernel_size=3, kernel_initializer='he_uniform', scale=None, kernel_size=3,
do_drop_out=True, drop_rate=0.5, do_batch_norm=False): do_drop_out=True, drop_rate=0.5, do_batch_norm=True):
with tf.name_scope(block_name): with tf.name_scope(block_name):
skip = tf.keras.layers.Conv2D(num_filters, kernel_size=kernel_size, padding=padding, kernel_initializer=kernel_initializer, activation=activation)(conv) skip = tf.keras.layers.Conv2D(num_filters, kernel_size=kernel_size, padding=padding, kernel_initializer=kernel_initializer, activation=activation)(conv)
...@@ -246,18 +247,18 @@ class SRCNN: ...@@ -246,18 +247,18 @@ class SRCNN:
data_s.append(nda) data_s.append(nda)
input_data = np.concatenate(data_s) input_data = np.concatenate(data_s)
add_noise = None DO_ADD_NOISE = False
noise_scale = None
if is_training and NOISE_TRAINING: if is_training and NOISE_TRAINING:
add_noise = True DO_ADD_NOISE = True
noise_scale = NOISE_STDDEV
data_norm = [] data_norm = []
for param in data_params: for param in data_params:
idx = params.index(param) idx = params.index(param)
# tmp = input_data[:, idx, slc_y_2, slc_x_2] # tmp = input_data[:, idx, slc_y_2, slc_x_2]
tmp = input_data[:, idx, y_130, x_130] tmp = input_data[:, idx, y_130, x_130]
tmp = normalize(tmp, param, mean_std_dct, add_noise=add_noise, noise_scale=noise_scale) tmp = normalize(tmp, param, mean_std_dct)
if DO_ADD_NOISE:
tmp = add_noise(tmp, noise_scale=NOISE_STDDEV)
# tmp = resample_2d_linear(x_2, y_2, tmp, t, s) # tmp = resample_2d_linear(x_2, y_2, tmp, t, s)
data_norm.append(tmp) data_norm.append(tmp)
# -------------------------- # --------------------------
...@@ -265,15 +266,23 @@ class SRCNN: ...@@ -265,15 +266,23 @@ class SRCNN:
idx = params.index(param) idx = params.index(param)
# tmp = input_data[:, idx, slc_y_2, slc_x_2] # tmp = input_data[:, idx, slc_y_2, slc_x_2]
tmp = input_data[:, idx, y_130, x_130] tmp = input_data[:, idx, y_130, x_130]
tmp = normalize(tmp, param, mean_std_dct, add_noise=add_noise, noise_scale=noise_scale) tmp = normalize(tmp, param, mean_std_dct)
if DO_ADD_NOISE:
tmp = add_noise(tmp, noise_scale=NOISE_STDDEV)
# tmp = resample_2d_linear(x_2, y_2, tmp, t, s) # tmp = resample_2d_linear(x_2, y_2, tmp, t, s)
data_norm.append(tmp) data_norm.append(tmp)
# -------- # --------
tmp = input_data[:, label_idx, slc_y_2, slc_x_2] tmp = input_data[:, label_idx, slc_y_2, slc_x_2]
if label_param != 'cloud_probability': if label_param != 'cloud_probability':
tmp = normalize(tmp, label_param, mean_std_dct, add_noise=add_noise, noise_scale=noise_scale) tmp = normalize(tmp, label_param, mean_std_dct)
if DO_ADD_NOISE:
tmp = add_noise(tmp, noise_scale=NOISE_STDDEV)
else: else:
if DO_ADD_NOISE:
tmp = add_noise(tmp, noise_scale=NOISE_STDDEV)
tmp = np.where(np.isnan(tmp), 0, tmp) tmp = np.where(np.isnan(tmp), 0, tmp)
tmp = np.where(tmp < 0.0, 0.0, tmp)
tmp = np.where(tmp > 1.0, 1.0, tmp)
tmp = resample_2d_linear(x_2, y_2, tmp, t, s) tmp = resample_2d_linear(x_2, y_2, tmp, t, s)
data_norm.append(tmp) data_norm.append(tmp)
# --------- # ---------
...@@ -417,8 +426,8 @@ class SRCNN: ...@@ -417,8 +426,8 @@ class SRCNN:
conv = conv_b = tf.keras.layers.Conv2D(num_filters, kernel_size=3, kernel_initializer='he_uniform', activation=activation, padding='VALID')(input_2d) conv = conv_b = tf.keras.layers.Conv2D(num_filters, kernel_size=3, kernel_initializer='he_uniform', activation=activation, padding='VALID')(input_2d)
print(conv.shape) print(conv.shape)
if NOISE_TRAINING: # if NOISE_TRAINING:
conv = conv_b = tf.keras.layers.GaussianNoise(stddev=NOISE_STDDEV)(conv) # conv = conv_b = tf.keras.layers.GaussianNoise(stddev=NOISE_STDDEV)(conv)
scale = 0.2 scale = 0.2
...@@ -426,11 +435,11 @@ class SRCNN: ...@@ -426,11 +435,11 @@ class SRCNN:
conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_2', scale=scale) conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_2', scale=scale)
conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_3', scale=scale) #conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_3', scale=scale)
conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_4', scale=scale) #conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_4', scale=scale)
conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_5', scale=scale) #conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_5', scale=scale)
conv_b = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, activation=activation, kernel_initializer='he_uniform', padding=padding)(conv_b) conv_b = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, activation=activation, kernel_initializer='he_uniform', padding=padding)(conv_b)
......
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