forked from KluvaDa/Chromosomes
-
Notifications
You must be signed in to change notification settings - Fork 0
/
instance_segmentation_evaluation.py
349 lines (295 loc) · 15.1 KB
/
instance_segmentation_evaluation.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
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
import torch
import pytorch_lightning as pl
from pytorch_lightning import loggers as pl_loggers
from matplotlib.colors import hsv_to_rgb
import pandas as pd
import os
import numpy as np
import matplotlib.pyplot as plt
from clustering import Clustering
from math import pi
from typing import Optional
from instance_segmentation import InstanceSegmentationModule, InstanceSegmentationDataModule, da_vector_2_angle
def load_module(dirpath: str, cross_validation: int):
"""
Loads a pytorch lightning module. Always loads version_0
:param dirpath: String path
:param cross_validation: Which cross validation run to ue.
:return:
"""
# find the first checkpoint for best
checkpoints_dir = os.path.join(dirpath, f'cv{cross_validation}', 'version_0', 'checkpoints')
checkpoints = os.listdir(checkpoints_dir)
checkpoint_name = ''
for checkpoint_name in checkpoints:
if checkpoint_name[0:4] == 'best' and checkpoint_name[-5:] == '.ckpt':
break
checkpoint = os.path.join(checkpoints_dir, checkpoint_name)
# load module
instance_segmentation_module = InstanceSegmentationModule.load_from_checkpoint(checkpoint)
return instance_segmentation_module
def evaluate(root_path: str, dirname: str, i_cv: int, clustering=None):
"""
Runs validation and testing and returns the metrics dictionary
:param root_path: Path to where the runs are saved
:param dirname: Name of the run
:param i_cv: The cross validation run in (0, 1, 2, 3) to evaluate
:param clustering: a clustering object that overrides the default clustering parameters
"""
dirpath = os.path.join(root_path, dirname)
data_module = InstanceSegmentationDataModule(i_cv)
module = load_module(dirpath, i_cv)
if clustering is not None:
module.clustering = clustering
trainer = pl.Trainer(gpus=1)
test_metrics = trainer.test(module, datamodule=data_module)
test_metrics = pd.DataFrame(test_metrics)
test_metrics = test_metrics.mean() # no repetition, should merely flatten
return test_metrics
def evaluate_average_cv(root_path: str, run_name: str):
"""
Finds the metrics averaged over the cross-validation runs
:param root_path: Path to where the runs are saved
:param run_name: Name of the run
"""
all_cv_metrics = []
for i_cv in (0, 1, 2, 3):
test_metrics = evaluate(root_path, run_name, i_cv)
all_cv_metrics.append(test_metrics)
average_metrics = pd.concat(all_cv_metrics)
average_metrics = average_metrics.groupby(average_metrics.index).mean()
return average_metrics
def evaluate_all():
"""
Evaluates all runs and saves the results as as csv file in results/instance_segmentation_test_metrics.csv
"""
root_path = 'results/instance_segmentation'
run_names = os.listdir(root_path)
all_metrics = dict()
for run_name in run_names:
if os.path.isdir(os.path.join(root_path, run_name)):
metrics = evaluate_average_cv(root_path, run_name)
all_metrics[run_name] = metrics
all_metrics = pd.DataFrame(all_metrics)
all_metrics.to_csv('results/instance_segmentation_test_metrics.csv')
def visualise(root_path, run_name, n_images, i_cv=0):
"""
Saves images of the test and validation in the root_path/run_name directory
:param root_path: Path to where the runs are saved
:param run_name: Name of the run
:param n_images: How many images to save from each dataset
:param i_cv: Which cross_validation run to use
"""
with torch.no_grad():
dirpath = os.path.join(root_path, run_name)
data_module = InstanceSegmentationDataModule(i_cv)
data_module.num_workers = 2
module = load_module(dirpath, i_cv)
data_module.setup()
dataloaders = data_module.test_dataloader()
dataloader_synthetic_val = dataloaders[0]
dataloader_synthetic_test = dataloaders[1]
dataloader_real_val = dataloaders[2]
dataloader_real_test = dataloaders[3]
dataloader_original_val = dataloaders[4]
dataloader_original_test = dataloaders[5]
iterator_synthetic_val = iter(dataloader_synthetic_val)
iterator_synthetic_test = iter(dataloader_synthetic_test)
iterator_real_val = iter(dataloader_real_val)
iterator_real_test = iter(dataloader_real_test)
iterator_original_val = iter(dataloader_original_val)
iterator_original_test = iter(dataloader_original_test)
data_iterators = {
'synthetic_val': iterator_synthetic_val,
'synthetic_test': iterator_synthetic_test,
'real_val': iterator_real_val,
'real_test': iterator_real_test,
'original_val': iterator_original_val,
'original_test': iterator_original_test
}
for dataset_name, dataset_iterator in data_iterators.items():
image_i = 0
while image_i < n_images:
try:
batch = next(dataset_iterator)
except StopIteration:
break
batch_in = batch[:, 0:1, ...]
batch_label = batch[:, 1:, ...]
batch_prediction, all_separate_chromosomes = module(batch_in)
batch_prediction_category = torch.argmax(batch_prediction[:, 0:3, ...], dim=1, keepdim=True)
batch_prediction_dilated_intersection = (batch_prediction[:, 3:4, ...] > 0).type(module.dtype)
batch_prediction_angle = da_vector_2_angle(batch_prediction[:, 4:6, ...])
for batch_elem_i in range(batch.shape[0]):
if image_i >= n_images:
break
if 'synthetic' in dataset_name:
label_category = batch_label[batch_elem_i, 0:1, ...].long()
label_dilated_intersection = batch_label[batch_elem_i, 1:2, ...]
label_angle = batch_label[batch_elem_i, 2:3, ...]
label_chromosomes = batch_label[batch_elem_i, 3:5, ...]
whole_image = return_visualisation_raw(
batch_in[batch_elem_i, :, ...].cpu().numpy(),
batch_prediction_category[batch_elem_i, :, ...].cpu().numpy(),
batch_prediction_dilated_intersection[batch_elem_i, :, ...].cpu().numpy(),
batch_prediction_angle[batch_elem_i, :, ...].cpu().numpy(),
label_category.cpu().numpy(),
label_dilated_intersection.cpu().numpy(),
label_angle.cpu().numpy()
)
plt.clf()
plt.imshow(whole_image)
plt.axis('off')
plt.savefig(os.path.join(root_path, run_name, f"{dataset_name}_cv{i_cv}_{image_i:03}_raw.png"),
bbox_inches='tight',
dpi=200)
whole_image = return_visualisation_chromosomes(
batch_in[batch_elem_i, :, ...].cpu().numpy(),
all_separate_chromosomes[batch_elem_i],
label_chromosomes.cpu().numpy()
)
plt.clf()
plt.imshow(whole_image)
plt.axis('off')
plt.savefig(os.path.join(root_path, run_name, f"{dataset_name}_cv{i_cv}_{image_i:03}.png"),
bbox_inches='tight',
dpi=200)
if 'real' in dataset_name:
label_chromosomes = batch_label[batch_elem_i, :, ...]
whole_image = return_visualisation_raw(
batch_in[batch_elem_i, :, ...].cpu().numpy(),
batch_prediction_category[batch_elem_i, :, ...].cpu().numpy(),
batch_prediction_dilated_intersection[batch_elem_i, :, ...].cpu().numpy(),
batch_prediction_angle[batch_elem_i, :, ...].cpu().numpy(),
None,
None,
None
)
plt.clf()
plt.imshow(whole_image)
plt.axis('off')
plt.savefig(os.path.join(root_path, run_name, f"{dataset_name}_cv{i_cv}_{image_i:03}_raw.png"),
bbox_inches='tight',
dpi=200)
whole_image = return_visualisation_chromosomes(
batch_in[batch_elem_i, :, ...].cpu().numpy(),
all_separate_chromosomes[batch_elem_i],
label_chromosomes.cpu().numpy()
)
plt.clf()
plt.imshow(whole_image)
plt.axis('off')
plt.savefig(os.path.join(root_path, run_name, f"{dataset_name}_cv{i_cv}_{image_i:03}.png"),
bbox_inches='tight',
dpi=200)
if 'original' in dataset_name:
label_category = batch_label[batch_elem_i, 0:1, ...].long()
label_chromosomes = torch.cat([
torch.logical_or(torch.eq(label_category, 1), torch.eq(label_category, 3)),
torch.logical_or(torch.eq(label_category, 2), torch.eq(label_category, 3))
])
whole_image = return_visualisation_raw(
batch_in[batch_elem_i, :, ...].cpu().numpy(),
batch_prediction_category[batch_elem_i, :, ...].cpu().numpy(),
batch_prediction_dilated_intersection[batch_elem_i, :, ...].cpu().numpy(),
batch_prediction_angle[batch_elem_i, :, ...].cpu().numpy(),
None,
None,
None
)
plt.clf()
plt.imshow(whole_image)
plt.axis('off')
plt.savefig(os.path.join(root_path, run_name, f"{dataset_name}_cv{i_cv}_{image_i:03}_raw.png"),
bbox_inches='tight',
dpi=200)
whole_image = return_visualisation_chromosomes(
batch_in[batch_elem_i, :, ...].cpu().numpy(),
all_separate_chromosomes[batch_elem_i],
label_chromosomes.cpu().numpy()
)
plt.clf()
plt.imshow(whole_image)
plt.axis('off')
plt.savefig(os.path.join(root_path, run_name, f"{dataset_name}_cv{i_cv}_{image_i:03}.png"),
bbox_inches='tight',
dpi=200)
image_i += 1
def return_visualisation_raw(in_image: np.ndarray,
prediction_category: np.ndarray,
prediction_dilated_intersection: np.ndarray,
prediction_angle: np.ndarray,
label_category: Optional[np.ndarray],
label_dilated_intersection: Optional[np.ndarray],
label_angle: Optional[np.ndarray]):
padding = 2
if any((label_category is None, label_dilated_intersection is None, label_angle is None)):
n_rows = 1
else:
n_rows = 2
n_columns = 4
row_size = in_image.shape[1]
col_size = in_image.shape[2]
whole_row_size = row_size * n_rows + padding * (n_rows - 1)
whole_col_size = col_size * n_columns + padding * (n_columns - 1)
whole_image = np.ones((whole_row_size, whole_col_size, 3))
def grid_slice(ir, ic):
return slice((row_size+padding)*ir, row_size*(ir+1) + padding*ir),\
slice((col_size+padding)*ic, col_size*(ic+1) + padding*ic),\
slice(None)
whole_image[grid_slice(0, 0)] = np.clip(np.mean(in_image, axis=0), 0, 1)[:, :, None]
whole_image[grid_slice(0, 1)] = plt.get_cmap('tab10')(prediction_category[0])[:, :, 0:3]
whole_image[grid_slice(0, 2)] = prediction_dilated_intersection[0, :, :, None] > 0
prediction_rgb = angle_pi_to_rgb(prediction_angle[0])
prediction_rgb *= prediction_category[0, :, :, None] == 1
whole_image[grid_slice(0, 3)] = prediction_rgb
if n_rows == 2:
whole_image[grid_slice(1, 1)] = plt.get_cmap('tab10')(label_category[0])[:, :, 0:3]
whole_image[grid_slice(1, 2)] = label_dilated_intersection[0, :, :, None] > 0
label_rgb = angle_pi_to_rgb(label_angle[0])
label_rgb *= label_category[0, :, :, None] == 1
whole_image[grid_slice(1, 3)] = label_rgb
return whole_image
def return_visualisation_chromosomes(in_image,
prediction_chromosomes,
label_chromosomes):
padding = 2
n_rows = 2
n_columns = max(prediction_chromosomes.shape[0], label_chromosomes.shape[0]) + 1
row_size = in_image.shape[1]
col_size = in_image.shape[2]
whole_row_size = row_size * n_rows + padding * (n_rows - 1)
whole_col_size = col_size * n_columns + padding * (n_columns - 1)
whole_image = np.ones((whole_row_size, whole_col_size, 3))
def grid_slice(ir, ic):
return slice((row_size + padding) * ir, row_size * (ir + 1) + padding * ir), \
slice((col_size + padding) * ic, col_size * (ic + 1) + padding * ic), \
slice(None)
whole_image[grid_slice(0, 0)] = np.clip(np.mean(in_image, axis=0), 0, 1)[:, :, None]
for i_prediction_chromosome in range(prediction_chromosomes.shape[0]):
whole_image[grid_slice(0, i_prediction_chromosome + 1)] = \
prediction_chromosomes[i_prediction_chromosome, :, :, None] > 0
for i_label_chromosome in range(label_chromosomes.shape[0]):
whole_image[grid_slice(1, i_label_chromosome + 1)] = \
label_chromosomes[i_label_chromosome, :, :, None] > 0
return whole_image
def angle_pi_to_rgb(angle: np.ndarray) -> np.ndarray:
"""
Takes an np image with angles mod pi and creates an rgb visualisation.
Args:
param angle: 2D np array with angle values mod pi
Returns: rgb image, channel last
"""
angle = np.remainder(angle, pi)/pi # normalise to [0, 1)
rgb = hsv_to_rgb(np.stack([angle, np.ones_like(angle), np.ones_like(angle)], axis=2))
return rgb
def visualise_all(n_images):
root_path = 'results/instance_segmentation'
run_names = os.listdir(root_path)
for run_name in run_names:
print(run_name)
if os.path.isdir(os.path.join(root_path, run_name)):
visualise(root_path, run_name, n_images, 0)
if __name__ == '__main__':
evaluate_all()
visualise_all(10)