-
Notifications
You must be signed in to change notification settings - Fork 18
/
Copy pathpatch_extraction_utils.py
244 lines (200 loc) · 11.4 KB
/
patch_extraction_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
### Dependencies
# Base Dependencies
import os
import pickle
import sys
# LinAlg / Stats / Plotting Dependencies
import h5py
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from PIL import Image
import umap
import umap.plot
from tqdm import tqdm
# Torch Dependencies
import torch
import torch.multiprocessing
import torchvision
from torch.utils.data.dataset import Dataset
from torchvision import transforms
from pl_bolts.models.self_supervised import resnets
from pl_bolts.utils.semi_supervised import Identity
device = torch.device('cuda:0')
torch.multiprocessing.set_sharing_strategy('file_system')
# Model Architectures
from nn_encoder_arch.vision_transformer import vit_small
from nn_encoder_arch.resnet_trunc import resnet50_trunc_baseline
### Helper Functions for Normalization + Loading in pytorch_lightning SSL encoder (for SimCLR)
def eval_transforms(pretrained=False):
if pretrained:
mean, std = (0.485, 0.456, 0.406), (0.229, 0.224, 0.225)
else:
mean, std = (0.5,0.5,0.5), (0.5,0.5,0.5)
trnsfrms_val = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean = mean, std = std)])
return trnsfrms_val
def torchvision_ssl_encoder(name: str, pretrained: bool = False, return_all_feature_maps: bool = False):
pretrained_model = getattr(resnets, name)(pretrained=pretrained, return_all_feature_maps=return_all_feature_maps)
pretrained_model.fc = Identity()
return pretrained_model
### Wrapper Classes for loading in patch datasets for BreastPathQ + BCSS (CRC100K uses the ImageFolder Dataset Class)
class CSVDataset_BreastPathQ(Dataset):
def __init__(self, dataroot, csv_path, transforms_eval=eval_transforms()):
self.csv = pd.read_csv(csv_path)
self.csv['img_path'] = dataroot+self.csv['slide'].astype(str) + "_" + self.csv['rid'].astype(str) + '.tif'
self.transforms = transforms_eval
def __getitem__(self, index):
img = Image.open(self.csv['img_path'][index])
return self.transforms(img), self.csv['y'][index]
def __len__(self):
return self.csv.shape[0]
class CSVDataset_BCSS(Dataset):
def __init__(self, dataset_csv, is_train=1, transforms_eval=eval_transforms()):
self.csv = dataset_csv
self.csv = self.csv[self.csv['train']==is_train]
self.transforms = transforms_eval
def __getitem__(self, index):
img = Image.open(self.csv.index[index])
return self.transforms(img), self.csv.iloc[index]['label']
def __len__(self):
return self.csv.shape[0]
### Functions for Loading + Saving + Visualizing Patch Embeddings
def save_embeddings(model, fname, dataloader, dataset=None, is_imagefolder=False,
save_patches=False, sprite_dim=128, overwrite=False):
if os.path.isfile('%s.pkl' % fname) and (overwrite == False):
return None
embeddings, labels = [], []
patches = []
for batch, target in tqdm(dataloader):
if save_patches:
for img in batch:
patches.append(tensor2im(input_image=img).resize(sprite_dim))
with torch.no_grad():
batch = batch.to(device)
embeddings.append(model(batch).detach().cpu().numpy())
labels.append(target.numpy())
embeddings = np.vstack(embeddings)
labels = np.vstack(labels).squeeze()
if is_imagefolder:
id2label = dict(map(reversed, dataset.class_to_idx.items()))
labels = np.array(list(map(id2label.get, labels.ravel())))
asset_dict = {'embeddings': embeddings, 'labels': labels}
if save_patches:
asset_dict.update({'patches': patches})
with open('%s.pkl' % (fname), 'wb') as handle:
pickle.dump(asset_dict, handle, protocol=pickle.HIGHEST_PROTOCOL)
def create_UMAP(library_path, enc_name, dataset, n=50, d=0.2):
path = os.path.join(library_path, '%s_%s.pkl' % (dataset, enc_name))
with open(path, 'rb') as handle:
asset_dict = pickle.load(handle)
embeddings, labels = asset_dict['embeddings'], asset_dict['labels']
mapper = umap.UMAP(n_neighbors=n, min_dist=d).fit(embeddings)
fig = plt.figure(figsize=(10, 10), dpi=100)
umap.plot.points(mapper, labels=labels, width=600, height=600)
plt.tight_layout()
plt.savefig(os.path.join(library_path, 'UMAPs', '%s_%s_umap_n%d_d%0.2f.jpg' % (dataset, enc_name, n, d)))
def create_embeddings(embeddings_dir, enc_name, dataset, save_patches=False, sprite_dim=128,
patch_datasets='path/to/patch/datasets', assets_dir ='./ckpts/',
disentangle=-1, stage=-1):
print("Extracting Features for '%s' via '%s'" % (dataset, enc_name))
if enc_name == 'resnet50_trunc':
model = resnet50_trunc_baseline(pretrained=True)
eval_t = eval_transforms(pretrained=True)
elif 'dino' in enc_name:
ckpt_path = os.path.join(assets_dir, enc_name+'.pt')
assert os.path.isfile(ckpt_path)
model = vit_small(patch_size=16)
state_dict = torch.load(ckpt_path, map_location="cpu")['teacher']
state_dict = {k.replace("module.", ""): v for k, v in state_dict.items()}
state_dict = {k.replace("backbone.", ""): v for k, v in state_dict.items()}
missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False)
#print("Missing Keys:", missing_keys)
#print("Unexpected Keys:", unexpected_keys)
eval_t = eval_transforms(pretrained=False)
elif 'simclr' in enc_name:
ckpt_path = os.path.join(assets_dir, enc_name+'.pt')
assert os.path.isfile(ckpt_path)
model = torchvision_ssl_encoder('resnet50', pretrained=True)
missing_keys, unexpected_keys = model.load_state_dict(torch.load(ckpt_path), strict=False)
#print("Missing Keys:", missing_keys)
#print("Unexpected Keys:", unexpected_keys)
eval_t = eval_transforms(pretrained=False)
else:
pass
model = model.to(device)
model.eval()
if 'simclr' in enc_name or 'simsiam' in enc_name:
_model = model
model = lambda x: _model.forward(x)[0]
elif 'dino' in enc_name:
_model = model
if stage == -1:
model = _model
else:
model = lambda x: torch.cat([x[:, 0] for x in _model.get_intermediate_layers(x, stage)], dim=-1)
if stage != -1:
_stage = '_s%d' % stage
else:
_stage = ''
if dataset == 'crc100k':
### Train
dataroot = os.path.join(patch_datasets, 'NCT-CRC-HE-100K/')
dataset = torchvision.datasets.ImageFolder(dataroot, transform=eval_t)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=10, shuffle=False, num_workers=4)
fname = os.path.join(embeddings_dir, 'crc100k_train_%s%s' % (enc_name, _stage))
save_embeddings(model=model, fname=fname, dataloader=dataloader, dataset=dataset,
save_patches=save_patches, sprite_dim=sprite_dim, is_imagefolder=True)
### Test
dataroot = os.path.join(patch_datasets, 'CRC-VAL-HE-7K/')
dataset = torchvision.datasets.ImageFolder(dataroot, transform=eval_t)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=False, num_workers=4)
fname = os.path.join(embeddings_dir, 'crc100k_val_%s%s' % (enc_name, _stage))
save_embeddings(model=model, fname=fname, dataloader=dataloader, dataset=dataset,
save_patches=save_patches, sprite_dim=sprite_dim, is_imagefolder=True)
elif dataset == 'crc100knonorm':
### Train
dataroot = os.path.join(patch_datasets, 'NCT-CRC-HE-100K-NONORM/')
dataset = torchvision.datasets.ImageFolder(dataroot, transform=eval_t)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=10, shuffle=False, num_workers=4)
fname = os.path.join(embeddings_dir, 'crc100knonorm_train_%s%s' % (enc_name, _stage))
save_embeddings(model=model, fname=fname, dataloader=dataloader, dataset=dataset,
save_patches=save_patches, sprite_dim=sprite_dim, is_imagefolder=True)
### Test
dataroot = os.path.join(patch_datasets, 'CRC-VAL-HE-7K/')
dataset = torchvision.datasets.ImageFolder(dataroot, transform=eval_t)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=False, num_workers=4)
fname = os.path.join(embeddings_dir, 'crc100knonorm_val_%s%s' % (enc_name, _stage))
save_embeddings(model=model, fname=fname, dataloader=dataloader, dataset=dataset,
save_patches=save_patches, sprite_dim=sprite_dim, is_imagefolder=True)
elif dataset == 'breastpathq':
train_dataroot = os.path.join(patch_datasets, 'BreastPathQ/breastpathq/datasets/train/')
val_dataroot = os.path.join(patch_datasets, 'BreastPathQ/breastpathq/datasets/validation/')
train_csv = os.path.join(patch_datasets, 'BreastPathQ/breastpathq/datasets/train_labels.csv')
val_csv = os.path.join(patch_datasets, 'BreastPathQ/breastpathq/datasets/val_labels.csv')
train_dataset = CSVDataset_BreastPathQ(dataroot=train_dataroot, csv_path=train_csv, transforms_eval=eval_t)
train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=1, shuffle=False, num_workers=4)
val_dataset = CSVDataset_BreastPathQ(dataroot=val_dataroot, csv_path=val_csv, transforms_eval=eval_t)
val_dataloader = torch.utils.data.DataLoader(val_dataset, batch_size=1, shuffle=False, num_workers=4)
train_fname = os.path.join(embeddings_dir, 'breastq_train_%s%s' % (enc_name, _stage))
val_fname = os.path.join(embeddings_dir, 'breastq_val_%s%s' % (enc_name, _stage))
save_embeddings(model=model, fname=train_fname, dataloader=train_dataloader,
save_patches=save_patches, sprite_dim=sprite_dim)
save_embeddings(model=model, fname=val_fname, dataloader=val_dataloader,
save_patches=save_patches, sprite_dim=sprite_dim)
elif dataset == 'bcss':
dataroot = os.path.join(patch_datasets, 'BCSS/40x/patches/All/')
csv_path = os.path.join(patch_datasets, 'BCSS/40x/patches/summary.csv')
dataset_csv = pd.read_csv(csv_path, sep=' ')['filename,train'].str.split(',', expand=True).astype(int)
dataset_csv.columns = ['label', 'train']
dataset_csv = dataset_csv[dataset_csv['label'].isin([0,1,2,3])]
dataset_csv.index = [os.path.join(dataroot, fname+'.png') for fname in dataset_csv.index]
train_dataset = CSVDataset_BCSS(dataset_csv=dataset_csv, is_train=1, transforms_eval=eval_t)
train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=1, shuffle=False, num_workers=1)
val_dataset = CSVDataset_BCSS(dataset_csv=dataset_csv, is_train=0, transforms_eval=eval_t)
val_dataloader = torch.utils.data.DataLoader(val_dataset, batch_size=1, shuffle=False, num_workers=1)
train_fname = os.path.join(embeddings_dir, 'bcss_train_%s%s' % (enc_name, _stage))
val_fname = os.path.join(embeddings_dir, 'bcss_val_%s%s' % (enc_name, _stage))
save_embeddings(model=model, fname=train_fname, dataloader=train_dataloader,
save_patches=save_patches, sprite_dim=sprite_dim)
save_embeddings(model=model, fname=val_fname, dataloader=val_dataloader,
save_patches=save_patches, sprite_dim=sprite_dim)