Spaces:
Runtime error
Runtime error
geekyrakshit
commited on
Commit
•
c8d52e7
1
Parent(s):
6fd61b9
added mirnet class for training and inference
Browse files- enhance_me/commons.py +30 -0
- enhance_me/mirnet/__init__.py +1 -0
- enhance_me/mirnet/mirnet.py +155 -0
- enhance_me/mirnet/models/mirnet_model.py +1 -2
enhance_me/commons.py
CHANGED
@@ -1,4 +1,7 @@
|
|
|
|
|
|
1 |
import tensorflow as tf
|
|
|
2 |
|
3 |
|
4 |
def read_image(image_path):
|
@@ -11,3 +14,30 @@ def read_image(image_path):
|
|
11 |
|
12 |
def peak_signal_noise_ratio(y_true, y_pred):
|
13 |
return tf.image.psnr(y_pred, y_true, max_val=255.0)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import os
|
2 |
+
import wandb
|
3 |
import tensorflow as tf
|
4 |
+
import matplotlib.pyplot as plt
|
5 |
|
6 |
|
7 |
def read_image(image_path):
|
|
|
14 |
|
15 |
def peak_signal_noise_ratio(y_true, y_pred):
|
16 |
return tf.image.psnr(y_pred, y_true, max_val=255.0)
|
17 |
+
|
18 |
+
|
19 |
+
def plot_results(images, titles, figure_size=(12, 12)):
|
20 |
+
fig = plt.figure(figsize=figure_size)
|
21 |
+
for i in range(len(images)):
|
22 |
+
fig.add_subplot(1, len(images), i + 1).set_title(titles[i])
|
23 |
+
_ = plt.imshow(images[i])
|
24 |
+
plt.axis("off")
|
25 |
+
plt.show()
|
26 |
+
|
27 |
+
|
28 |
+
def closest_number(n, m):
|
29 |
+
q = int(n / m)
|
30 |
+
n1 = m * q
|
31 |
+
if (n * m) > 0:
|
32 |
+
n2 = m * (q + 1)
|
33 |
+
else:
|
34 |
+
n2 = m * (q - 1)
|
35 |
+
if abs(n - n1) < abs(n - n2):
|
36 |
+
return n1
|
37 |
+
return n2
|
38 |
+
|
39 |
+
|
40 |
+
def init_wandb(project_name, experiment_name, wandb_api_key):
|
41 |
+
if project_name is not None and experiment_name is not None:
|
42 |
+
os.environ['WANDB_API_KEY'] = wandb_api_key
|
43 |
+
wandb.init(project=project_name, name=experiment_name)
|
enhance_me/mirnet/__init__.py
CHANGED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
from .mirnet import MIRNet
|
enhance_me/mirnet/mirnet.py
ADDED
@@ -0,0 +1,155 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import os
|
2 |
+
import numpy as np
|
3 |
+
from PIL import Image
|
4 |
+
from typing import List
|
5 |
+
from datetime import datetime
|
6 |
+
|
7 |
+
from tensorflow import keras
|
8 |
+
from tensorflow.keras import optimizers
|
9 |
+
|
10 |
+
from wandb.keras import WandbCallback
|
11 |
+
|
12 |
+
from .dataloader import LowLightDataset
|
13 |
+
from .models import build_mirnet_model
|
14 |
+
from .losses import CharbonnierLoss
|
15 |
+
from ..commons import peak_signal_noise_ratio, closest_number, init_wandb
|
16 |
+
|
17 |
+
|
18 |
+
class MIRNet:
|
19 |
+
def __init__(
|
20 |
+
self,
|
21 |
+
experiment_name: str,
|
22 |
+
image_size: int = 256,
|
23 |
+
apply_random_horizontal_flip: bool = True,
|
24 |
+
apply_random_vertical_flip: bool = True,
|
25 |
+
apply_random_rotation: bool = True,
|
26 |
+
wandb_api_key=None,
|
27 |
+
) -> None:
|
28 |
+
self.experiment_name = experiment_name
|
29 |
+
self.data_loader = LowLightDataset(
|
30 |
+
image_size=image_size,
|
31 |
+
apply_random_horizontal_flip=apply_random_horizontal_flip,
|
32 |
+
apply_random_vertical_flip=apply_random_vertical_flip,
|
33 |
+
apply_random_rotation=apply_random_rotation,
|
34 |
+
)
|
35 |
+
if wandb_api_key is not None:
|
36 |
+
init_wandb("mirnet", experiment_name, wandb_api_key)
|
37 |
+
self.using_wandb = True
|
38 |
+
else:
|
39 |
+
self.using_wandb = False
|
40 |
+
|
41 |
+
def build_datasets(
|
42 |
+
self,
|
43 |
+
low_light_images: List[str],
|
44 |
+
enhanced_images: List[str],
|
45 |
+
val_split: float = 0.2,
|
46 |
+
batch_size: int = 16,
|
47 |
+
):
|
48 |
+
(self.train_dataset, self.val_dataset) = self.data_loader.get_datasets(
|
49 |
+
low_light_images=low_light_images,
|
50 |
+
enhanced_images=enhanced_images,
|
51 |
+
val_split=val_split,
|
52 |
+
batch_size=batch_size,
|
53 |
+
)
|
54 |
+
|
55 |
+
def build_model(
|
56 |
+
self,
|
57 |
+
num_recursive_residual_groups: int = 3,
|
58 |
+
num_multi_scale_residual_blocks: int = 2,
|
59 |
+
channels: int = 64,
|
60 |
+
learning_rate: float = 1e-4,
|
61 |
+
epsilon: float = 1e-3,
|
62 |
+
):
|
63 |
+
self.model = build_mirnet_model(
|
64 |
+
num_rrg=num_recursive_residual_groups,
|
65 |
+
num_mrb=num_multi_scale_residual_blocks,
|
66 |
+
channels=channels,
|
67 |
+
)
|
68 |
+
self.model.compile(
|
69 |
+
optimizer=optimizers.Adam(learning_rate=learning_rate),
|
70 |
+
loss=CharbonnierLoss(epsilon=epsilon),
|
71 |
+
metrics=[peak_signal_noise_ratio],
|
72 |
+
)
|
73 |
+
|
74 |
+
def save_weights(self, filepath, overwrite=True, save_format=None, options=None):
|
75 |
+
self.model.save_weights(
|
76 |
+
filepath, overwrite=overwrite, save_format=save_format, options=options
|
77 |
+
)
|
78 |
+
|
79 |
+
def load_weights(self, filepath, by_name=False, skip_mismatch=False, options=None):
|
80 |
+
self.model.load_weights(
|
81 |
+
filepath, by_name=by_name, skip_mismatch=skip_mismatch, options=options
|
82 |
+
)
|
83 |
+
|
84 |
+
def train(self, epochs: int):
|
85 |
+
log_dir = os.path.join(
|
86 |
+
self.experiment_name,
|
87 |
+
"logs",
|
88 |
+
datetime.datetime.now().strftime("%Y%m%d-%H%M%S"),
|
89 |
+
)
|
90 |
+
tensorboard_callback = keras.callbacks.TensorBoard(log_dir, histogram_freq=1)
|
91 |
+
model_checkpoint_callback = keras.callbacks.ModelCheckpoint(
|
92 |
+
os.path.join(self.experiment_name, "weights.h5"),
|
93 |
+
save_best_only=True,
|
94 |
+
save_weights_only=True,
|
95 |
+
)
|
96 |
+
reduce_lr_callback = keras.callbacks.ReduceLROnPlateau(
|
97 |
+
monitor="val_peak_signal_noise_ratio",
|
98 |
+
factor=0.5,
|
99 |
+
patience=5,
|
100 |
+
verbose=1,
|
101 |
+
min_delta=1e-7,
|
102 |
+
mode="max",
|
103 |
+
)
|
104 |
+
callbacks = [
|
105 |
+
tensorboard_callback,
|
106 |
+
model_checkpoint_callback,
|
107 |
+
reduce_lr_callback,
|
108 |
+
]
|
109 |
+
if self.using_wandb:
|
110 |
+
callbacks += [WandbCallback()]
|
111 |
+
history = self.model.fit(
|
112 |
+
self.train_dataset,
|
113 |
+
validation_data=self.val_dataset,
|
114 |
+
epochs=epochs,
|
115 |
+
callbacks=callbacks,
|
116 |
+
)
|
117 |
+
return history
|
118 |
+
|
119 |
+
def infer(
|
120 |
+
self,
|
121 |
+
original_image,
|
122 |
+
image_resize_factor: float = 1.0,
|
123 |
+
resize_output: bool = False,
|
124 |
+
):
|
125 |
+
width, height = original_image.size
|
126 |
+
target_width, target_height = (
|
127 |
+
closest_number(width // image_resize_factor, 4),
|
128 |
+
closest_number(height // image_resize_factor, 4),
|
129 |
+
)
|
130 |
+
original_image = original_image.resize(
|
131 |
+
(target_width, target_height), Image.ANTIALIAS
|
132 |
+
)
|
133 |
+
image = keras.preprocessing.image.img_to_array(original_image)
|
134 |
+
image = image.astype("float32") / 255.0
|
135 |
+
image = np.expand_dims(image, axis=0)
|
136 |
+
output = self.model.predict(image)
|
137 |
+
output_image = output[0] * 255.0
|
138 |
+
output_image = output_image.clip(0, 255)
|
139 |
+
output_image = output_image.reshape(
|
140 |
+
(np.shape(output_image)[0], np.shape(output_image)[1], 3)
|
141 |
+
)
|
142 |
+
output_image = Image.fromarray(np.uint8(output_image))
|
143 |
+
original_image = Image.fromarray(np.uint8(original_image))
|
144 |
+
if resize_output:
|
145 |
+
output_image = output_image.resize((width, height), Image.ANTIALIAS)
|
146 |
+
return output_image
|
147 |
+
|
148 |
+
def infer_from_file(
|
149 |
+
self,
|
150 |
+
original_image_file: str,
|
151 |
+
image_resize_factor: float = 1.0,
|
152 |
+
resize_output: bool = False,
|
153 |
+
):
|
154 |
+
original_image = Image.open(original_image_file)
|
155 |
+
return self.infer(original_image, image_resize_factor, resize_output)
|
enhance_me/mirnet/models/mirnet_model.py
CHANGED
@@ -1,10 +1,9 @@
|
|
1 |
-
import tensorflow as tf
|
2 |
from tensorflow.keras import layers, Input, Model
|
3 |
|
4 |
from .recursive_residual_blocks import recursive_residual_group
|
5 |
|
6 |
|
7 |
-
def
|
8 |
input_tensor = Input(shape=[None, None, 3])
|
9 |
x1 = layers.Conv2D(channels, kernel_size=(3, 3), padding="same")(input_tensor)
|
10 |
for _ in range(num_rrg):
|
|
|
|
|
1 |
from tensorflow.keras import layers, Input, Model
|
2 |
|
3 |
from .recursive_residual_blocks import recursive_residual_group
|
4 |
|
5 |
|
6 |
+
def build_mirnet_model(num_rrg, num_mrb, channels):
|
7 |
input_tensor = Input(shape=[None, None, 3])
|
8 |
x1 = layers.Conv2D(channels, kernel_size=(3, 3), padding="same")(input_tensor)
|
9 |
for _ in range(num_rrg):
|