-
Notifications
You must be signed in to change notification settings - Fork 0
/
custom_utils.py
283 lines (255 loc) · 9.51 KB
/
custom_utils.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
import albumentations as A
import cv2
import numpy as np
import torch
import matplotlib.pyplot as plt
import os
from albumentations.pytorch import ToTensorV2
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
plt.style.use('ggplot')
# this class keeps track of the training and validation loss values...
# ... and helps to get the average for each epoch as well
class Averager:
def __init__(self):
self.current_total = 0.0
self.iterations = 0.0
def send(self, value):
self.current_total += value
self.iterations += 1
@property
def value(self):
if self.iterations == 0:
return 0
else:
return 1.0 * self.current_total / self.iterations
def reset(self):
self.current_total = 0.0
self.iterations = 0.0
class SaveBestModel:
"""
Class to save the best model while training. If the current epoch's
validation loss is less than the previous least less, then save the
model state.
"""
def __init__(
self, best_valid_loss=float('inf')
):
self.best_valid_loss = best_valid_loss
def __call__(
self, current_valid_loss,
epoch, model, optimizer
):
if current_valid_loss < self.best_valid_loss:
self.best_valid_loss = current_valid_loss
print(f"\nBest validation loss: {self.best_valid_loss}")
print(f"\nSaving best model for epoch: {epoch+1}\n")
torch.save({
'epoch': epoch+1,
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
}, 'outputs/best_model.pth')
def collate_fn(batch):
"""
To handle the data loading as different images may have different number
of objects and to handle varying size tensors as well.
"""
# batch = list(filter(lambda x: x is not None, batch))
return tuple(zip(*batch))
# define the training tranforms
def get_train_transform():
return A.Compose([
A.Flip(0.5),
A.RandomRotate90(0.5),
A.MotionBlur(p=0.2),
A.MedianBlur(blur_limit=3, p=0.1),
A.Blur(blur_limit=3, p=0.1),
A.Normalize(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
ToTensorV2(p=1.0),
], bbox_params={
'format': 'pascal_voc',
'label_fields': ['labels']
})
# define the validation transforms
def get_valid_transform():
return A.Compose([
A.Normalize(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
ToTensorV2(p=1.0),
], bbox_params={
'format': 'pascal_voc',
'label_fields': ['labels']
})
def show_transformed_image(train_loader, DEVICE, CLASSES):
"""
This function shows the transformed images from the `train_loader`.
Helps to check whether the tranformed images along with the corresponding
labels are correct or not.
Only runs if `VISUALIZE_TRANSFORMED_IMAGES = True` in config.py.
:param train_loader: Training data loader.
"""
if len(train_loader) > 0:
for i in range(1):
images, targets = next(iter(train_loader))
images = list(image.to(DEVICE) for image in images)
targets = [{k: v.to(DEVICE) for k, v in t.items()} for t in targets]
boxes = targets[i]['boxes'].cpu().numpy().astype(np.int32)
labels = targets[i]['labels'].cpu().numpy().astype(np.int32)
sample = images[i].permute(1, 2, 0).cpu().numpy()
for box_num, box in enumerate(boxes):
cv2.rectangle(sample,
(box[0], box[1]),
(box[2], box[3]),
(0, 0, 255), 2)
cv2.putText(sample, CLASSES[labels[box_num]],
(box[0], box[1]-10), cv2.FONT_HERSHEY_SIMPLEX,
1.0, (0, 0, 255), 2)
cv2.imshow('Transformed image', sample)
cv2.waitKey(0)
cv2.destroyAllWindows()
def save_model_state(epoch, model, optimizer, OUT_DIR):
"""
Function to save the trained model till current epoch, or whenever called.
:param epoch: The epoch number.
:param model: The neural network model.
:param optimizer: The optimizer.
"""
torch.save({
'epoch': epoch+1,
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
}, f'{OUT_DIR}/last_model_state.pth')
def save_loss_plot(OUT_DIR, train_loss_list, val_loss_list):
"""
Function to save both train and validation loss graphs.
:param OUT_DIR: Path to save the graphs.
:param train_loss_list: List containing the training loss values.
:param val_loss_list: List containing the validation loss values.
"""
figure_1, train_ax = plt.subplots()
figure_2, valid_ax = plt.subplots()
train_ax.plot(train_loss_list, color='tab:blue')
train_ax.set_xlabel('iterations')
train_ax.set_ylabel('train loss')
valid_ax.plot(val_loss_list, color='tab:red')
valid_ax.set_xlabel('iterations')
valid_ax.set_ylabel('validation loss')
figure_1.savefig(f"{OUT_DIR}/train_loss.png")
figure_2.savefig(f"{OUT_DIR}/valid_loss.png")
print('SAVING PLOTS COMPLETE...')
plt.close('all')
def save_train_loss_plot(OUT_DIR, train_loss_list):
"""
Function to save both train loss graph.
:param OUT_DIR: Path to save the graphs.
:param train_loss_list: List containing the training loss values.
"""
figure_1, train_ax = plt.subplots()
train_ax.plot(train_loss_list, color='tab:blue')
train_ax.set_xlabel('iterations')
train_ax.set_ylabel('train loss')
figure_1.savefig(f"{OUT_DIR}/train_loss.png")
print('SAVING PLOTS COMPLETE...')
plt.close('all')
def denormalize(x, mean=None, std=None):
# 3, H, W, B
# print(x.shape)
# ten = x.clone().permute(1, 2, 3, 0)
for t, m, s in zip(x, mean, std):
t.mul_(s).add_(m)
# B, 3, H, W
return torch.clamp(x, 0, 1)
def save_validation_results(images, detections, counter, OUT_DIR):
"""
Function to save validation results if provided in `config.py`.
:param images: All the images from the current batch.
:param detections: All the detection results.
:param counter: Step counter for saving with unique ID.
"""
IMG_MEAN = [0.485, 0.456, 0.406]
IMG_STD = [0.229, 0.224, 0.225]
for i, detection in enumerate(detections):
image_c = images[i].clone()
image_c = denormalize(image_c, IMG_MEAN, IMG_STD)
image_c = image_c.detach().cpu().numpy().astype(np.float32)
image = np.transpose(image_c, (1, 2, 0))
image = np.ascontiguousarray(image, dtype=np.float32)
scores = detection[:, 4].cpu()
labels = detection[:, 5]
bboxes = detection[:, :4].detach().cpu().numpy()
boxes = bboxes[scores >= 0.3]
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
for j, box in enumerate(boxes):
cv2.rectangle(
image,
(int(box[0]), int(box[1])),
(int(box[2]), int(box[3])),
(0, 0, 255), 2
)
cv2.imwrite(f"{OUT_DIR}/image_{i}_{counter}.jpg", image*255.)
def set_infer_dir():
"""
This functions counts the number of inference directories already present
and creates a new one in `outputs/inference/`.
And returns the directory path.
"""
if not os.path.exists('outputs/inference'):
os.makedirs('outputs/inference')
num_infer_dirs_present = len(os.listdir('outputs/inference/'))
next_dir_num = num_infer_dirs_present + 1
new_dir_name = f"outputs/inference/res_{next_dir_num}"
os.makedirs(new_dir_name, exist_ok=True)
return new_dir_name
def set_training_dir():
"""
This functions counts the number of training directories already present
and creates a new one in `outputs/training/`.
And returns the directory path.
"""
if not os.path.exists('outputs/training'):
os.makedirs('outputs/training')
num_train_dirs_present = len(os.listdir('outputs/training/'))
next_dir_num = num_train_dirs_present + 1
new_dir_name = f"outputs/training/res_{next_dir_num}"
os.makedirs(new_dir_name, exist_ok=True)
return new_dir_name
def draw_bboxes(
image,
outputs,
w, h,
detection_threshold,
colors,
classes
):
"""
Function draws bounding boxes around the `image` and returns
the result.
"""
orig_h, orig_w = image.shape[0], image.shape[1]
scores = outputs[:, 4].cpu()
labels = outputs[:, 5]
bboxes = outputs[:, :4].detach().cpu().numpy()
boxes = bboxes[scores >= detection_threshold]
# Notice the -1 in the color and class indices in the
# following annotations. The model predicts from [1, NUM_CLASSES],
# but class indices start from 0. So, to manage that we have to -1
# from the predicted label number.
for i, box in enumerate(boxes):
box_0 = ((box[0]/w)*orig_w)
box_1 = ((box[1]/h)*orig_h)
box_2 = ((box[2]/w)*orig_w)
box_3 = ((box[3]/h)*orig_h)
cv2.rectangle(
image,
(int(box_0), int(box_1)),
(int(box_2), int(box_3)),
colors[int(labels[i])-1], 2
)
cv2.putText(
image,
classes[int(labels[i])-1],
(int(box_0), int(box_1-10)),
cv2.FONT_HERSHEY_SIMPLEX,
0.8,
colors[int(labels[i])-1], 2,
lineType=cv2.LINE_AA
)
return image