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"""
Copyright 2021, Dana-Farber Cancer Institute and Weill Cornell Medicine
License: GNU GPL 2.0
"""
import importlib.util
import numpy as np
import pytest
import torch.nn as nn
import torch.nn.functional as F
from skimage.draw import ellipse
from skimage.measure import label, regionprops
from pathml.datasets.utils import DeepPatchFeatureExtractor
def requires_torchvision(func):
"""Decorator to skip tests that require torchvision."""
torchvision_installed = importlib.util.find_spec("torchvision") is not None
reason = "torchvision is required"
return pytest.mark.skipif(not torchvision_installed, reason=reason)(func)
class SimpleCNN(nn.Module):
def __init__(self, input_shape):
super(SimpleCNN, self).__init__()
self.conv1 = nn.Conv2d(
in_channels=input_shape[0],
out_channels=32,
kernel_size=3,
stride=1,
padding=1,
)
self.conv2 = nn.Conv2d(
in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1
)
self.pool = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
fc_input_size = (input_shape[1] // 4) * (input_shape[2] // 4) * 64
self.fc1 = nn.Linear(fc_input_size, 128)
self.fc2 = nn.Linear(128, 10)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
# Flatten the output for the fully connected layer
x = x.view(x.size(0), -1)
x = F.relu(self.fc1(x))
x = self.fc2(x)
return x
def make_fake_instance_maps(num, image_size, ellipse_height, ellipse_width):
img = np.zeros(image_size)
# Draw n ellipses
for i in range(num):
# Random center for each ellipse
center_x = np.random.randint(ellipse_width, image_size[1] - ellipse_width)
center_y = np.random.randint(ellipse_height, image_size[0] - ellipse_height)
# Coordinates for the ellipse
rr, cc = ellipse(
center_y, center_x, ellipse_height, ellipse_width, shape=image_size
)
# Draw the ellipse
img[rr, cc] = 1
label_img = label(img.astype(int))
return label_img
def make_fake_image(instance_map):
image = instance_map[:, :, None]
image[image > 0] = 1
noised_image = (
np.random.rand(instance_map.shape[0], instance_map.shape[1], 3) * 0.15 + image
) * 255
return noised_image.astype("uint8")
@pytest.mark.parametrize("patch_size", [1, 64, 128])
@pytest.mark.parametrize("entity", ["cell", "tissue"])
@pytest.mark.parametrize("threshold", [0, 0.1, 0.8])
@pytest.mark.parametrize("with_instance_masking", [True, False])
@pytest.mark.parametrize("extraction_layer", [None, "fc1"])
def test_feature_extractor(
entity, patch_size, threshold, with_instance_masking, extraction_layer
):
image_size = (256, 256)
instance_map = make_fake_instance_maps(
num=20, image_size=image_size, ellipse_height=20, ellipse_width=8
)
image = make_fake_image(instance_map.copy())
regions = regionprops(instance_map)
model = SimpleCNN(input_shape=(3, 224, 224))
extractor = DeepPatchFeatureExtractor(
patch_size=patch_size,
batch_size=1,
entity=entity,
architecture=model,
fill_value=255,
resize_size=224,
threshold=threshold,
with_instance_masking=with_instance_masking,
extraction_layer=extraction_layer,
)
features = extractor.process(image, instance_map)
if threshold == 0:
assert features.shape[0] == len(regions)
else:
assert features.shape[0] <= len(regions)
@requires_torchvision
@pytest.mark.parametrize("patch_size", [1, 64, 128])
@pytest.mark.parametrize("entity", ["cell", "tissue"])
@pytest.mark.parametrize("threshold", [0, 0.1, 0.8])
@pytest.mark.parametrize("extraction_layer", [None, "fc"])
def test_feature_extractor_torchvision(entity, patch_size, threshold, extraction_layer):
# pytest.importorskip("torchvision")
image_size = (256, 256)
instance_map = make_fake_instance_maps(
num=20, image_size=image_size, ellipse_height=20, ellipse_width=8
)
image = make_fake_image(instance_map.copy())
regions = regionprops(instance_map)
extractor = DeepPatchFeatureExtractor(
patch_size=patch_size,
batch_size=1,
entity=entity,
architecture="resnet34",
fill_value=255,
resize_size=224,
threshold=threshold,
extraction_layer=extraction_layer,
)
features = extractor.process(image, instance_map)
if threshold == 0:
assert features.shape[0] == len(regions)
else:
assert features.shape[0] <= len(regions)
@requires_torchvision
@pytest.mark.parametrize("patch_size", [64, 128])
@pytest.mark.parametrize("entity", ["cell", "tissue"])
@pytest.mark.parametrize("threshold", [0.8])
@pytest.mark.parametrize("extraction_layer", [None, "12"])
def test_feature_extractor_torchvision_no_resnet(
entity, patch_size, threshold, extraction_layer
):
# pytest.importorskip("torchvision")
image_size = (256, 256)
instance_map = make_fake_instance_maps(
num=20, image_size=image_size, ellipse_height=20, ellipse_width=8
)
image = make_fake_image(instance_map.copy())
regions = regionprops(instance_map)
extractor = DeepPatchFeatureExtractor(
patch_size=patch_size,
batch_size=1,
entity=entity,
architecture="mobilenet_v3_small",
fill_value=255,
resize_size=224,
threshold=threshold,
extraction_layer=extraction_layer,
)
features = extractor.process(image, instance_map)
if threshold == 0:
assert features.shape[0] == len(regions)
else:
assert features.shape[0] <= len(regions)
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