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callbacks = [ | |
# Each criterion is calculated separately. | |
CriterionCallback( | |
input_key="mask", | |
prefix="loss_dice", | |
criterion_key="dice" | |
), | |
CriterionCallback( | |
input_key="mask", | |
prefix="loss_bce", |
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import torch | |
import segmentation_models_pytorch as smp | |
import numpy as np | |
import matplotlib.pyplot as plt | |
from catalyst import dl, metrics, core, contrib, utils | |
import torch.nn as nn | |
from skimage.io import imread | |
import os | |
from sklearn.model_selection import train_test_split | |
from catalyst.dl import CriterionCallback, MetricAggregationCallback |
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import albumentations as A | |
from torch.utils.data import Dataset, DataLoader | |
from collections import OrderedDict | |
class ChestXRayDataset(Dataset): | |
def __init__( | |
self, | |
images, | |
masks, | |
transforms): |
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import torch | |
import torch.nn as nn | |
import torch.nn.functional as F | |
import torch.optim as optim | |
from torch.autograd import Variable | |
from torchvision import datasets, transforms | |
from torch.optim import Optimizer | |
from torch.utils import data | |
import pretrainedmodels |
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import torch | |
import torch.nn as nn | |
import torch.nn.functional as F | |
import torch.optim as optim | |
from torch.autograd import Variable | |
from torchvision import datasets, transforms | |
from torch.optim import Optimizer | |
from torch.utils import data | |
class DataGenerator(data.Dataset): |
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#basically like at https://scikit-learn.org/stable/auto_examples/cluster/plot_cluster_comparison.html, but our data is reall | |
#prepre paramets | |
params = {'quantile': .3, | |
'eps': .3, | |
'damping': .9, | |
'preference': -200, | |
'n_neighbors': 10, | |
'n_clusters': 5} | |
bandwidth = estimate_bandwidth(embedding, quantile=params['quantile']) | |
connectivity = kneighbors_graph( |
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pca = PCA(n_components=100) | |
pca.fit(mat) | |
mat_reduce = pca.transform(mat) | |
embedding = umap.UMAP(n_neighbors=5, | |
min_dist=0.5, | |
metric='euclidean').fit_transform(mat_reduce) | |
plt.figure(figsize=(15,15)) | |
plt.scatter(embedding[:,0],embedding[:,1],s=0.2); | |
plt.title('Naive clustering'); |
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cells_expression = mat.sum(axis=1) | |
mat = mat[cells_expression>=100,:] | |
mat = np.log(mat+1) |
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mat = mat[:,CV>=10] | |
f, ax = plt.subplots(1,2,figsize=(15,5)) | |
per_cell_sum = mat.sum(axis=1) | |
ax[0].hist(np.log10(per_cell_sum+1)); | |
ax[0].set_title('Distribtion of #UMIs per cell\n min {}, max {}, mean {} +- {}'.format(min(per_cell_sum), | |
max(per_cell_sum), np.mean(per_cell_sum), | |
np.sqrt(np.std(per_cell_sum)))); | |
per_gene_sum = mat.sum(axis=0) | |
ax[1].hist(np.log10(per_gene_sum+1)); | |
ax[1].set_title('Distribtion of #UMIs per gene\n min {}, max {}, mean {} +- {}'.format(min(per_gene_sum), |
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low_expr_thr = 100 | |
high_expr_thr = 100000 | |
mat = mat[:,(per_gene_sum>=low_expr_thr) & (per_gene_sum<=high_expr_thr)] #just remove extreme outliers | |
mean_exp = mat.mean(axis=0) | |
std_exp = np.sqrt(mat.std(axis=0)) | |
CV = std_exp/mean_exp | |
plt.hist(CV); | |
plt.title('Distribution of CV, mean {} sd {}'.format(np.mean(CV), np.std(CV)**0.5)); |
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