ilyukhin
/
model-v
1
1
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

fix: correct auto split sampling, fix non-TIF memory leak, update README

Browse Source 
**Bug fix**

1. Fixed an error in dataset sampling during automatic splitting.
2. Fixed a memory leak when loading images in formats other than `.tif`.

**Changes:**

1. `shuffle` can now be used with both `pre-split` and `split` methods.
2. `offsets` can be specified as floating-point values for dynamic computation.
3. `size` and `offsets` now support mixed formats (i.e., both `int` and `float`).
4. F1 and mAP metrics are now computed by default in `micro`, `macro`, and `per_class` variations.
5. Classes `BoundaryExclusion` and `IntensityDiversification` have been renamed to follow the MONAI naming style.
6. Minor updates to the README for clarity and consistency.
master
laynholt 3 weeks ago
parent f728464a52
commit bf90288a95
8 changed files with 857 additions and 814 deletions
Show Stats Download Patch File Download Diff File

13
README.md
Unescape View File

@ -160,6 +160,8 @@ A brief overview of the key parameters you can adjust in your JSON config:
* `is_split` (bool): Whether your data is already split (`true`) or needs splitting (`false`, default). * `is_split` (bool): Whether your data is already split (`true`) or needs splitting (`false`, default).
* `split` / `pre_split`: Directories for data when pre-split or unsplit. * `split` / `pre_split`: Directories for data when pre-split or unsplit.
* `train_size`, `valid_size`, `test_size` (int/float): Size or ratio of your splits (e.g., `0.7`, `0.1`, `0.2`). * `train_size`, `valid_size`, `test_size` (int/float): Size or ratio of your splits (e.g., `0.7`, `0.1`, `0.2`).
* `train_offset`, `valid_offset`, `test_offset` (int/float): The offset by which to take samples. When the data is not split, the samples are formed in the following order: `train`, `valid`, `test` (default: `0`, `0`, `0`).
* `shuffle` (bool): Flag for shuffling data when creating samples (default: `false`).
* `batch_size` (int): Number of samples per training batch (default: `1`). * `batch_size` (int): Number of samples per training batch (default: `1`).
* `num_epochs` (int): Total training epochs (default: `100`). * `num_epochs` (int): Total training epochs (default: `100`).
* `val_freq` (int): Frequency (in epochs) to run validation (default: `1`). * `val_freq` (int): Frequency (in epochs) to run validation (default: `1`).
@ -168,6 +170,7 @@ A brief overview of the key parameters you can adjust in your JSON config:
* `test_dir` (str): Directory containing test data (default: `"."`). * `test_dir` (str): Directory containing test data (default: `"."`).
* `test_size` (int/float): Portion or count of data for testing (default: `1.0`). * `test_size` (int/float): Portion or count of data for testing (default: `1.0`).
* `test_offset` (int/float): The amount of data by which the sample will be shifted before forming (default: `0`).
* `shuffle` (bool): Shuffle test data before evaluation (default: `true`). * `shuffle` (bool): Shuffle test data before evaluation (default: `true`).
> **Batch size note:** Validation, testing, and prediction runs always use a batch size of `1`, regardless of the `batch_size` setting in the training configuration. > **Batch size note:** Validation, testing, and prediction runs always use a batch size of `1`, regardless of the `batch_size` setting in the training configuration.
@ -190,12 +193,22 @@ python generate_config.py
python main.py -c config/templates/train/YourConfig.json -m train python main.py -c config/templates/train/YourConfig.json -m train
``` ```
> After training, the model will automatically attempt to perform testing if the directory for the test data was specified in the configuration file.
### Test a model
```bash
python main.py -c config/templates/predict/YourConfig.json -m test
```
### Predict on new data ### Predict on new data
```bash ```bash
python main.py -c config/templates/predict/YourConfig.json -m predict python main.py -c config/templates/predict/YourConfig.json -m predict
``` ```
> Unlike prediction testing, it is not necessary that the specified test directory contains a folder with true masks.
--- ---
## Acknowledgments ## Acknowledgments

57
config/dataset_config.py
Unescape View File

@ -9,7 +9,7 @@ class DatasetCommonConfig(BaseModel):
""" """
seed: int | None = 0 # Seed for splitting if data is not pre-split (and all random operations) seed: int | None = 0 # Seed for splitting if data is not pre-split (and all random operations)
device: str = "cuda:0" # Device used for training/testing (e.g., 'cpu' or 'cuda') device: str = "cuda:0" # Device used for training/testing (e.g., 'cpu' or 'cuda')
use_amp: bool = False # Flag to use Automatic Mixed Precision (AMP) use_amp: bool = True # Flag to use Automatic Mixed Precision (AMP)
roi_size: int = 512 # The size of the square window for cropping roi_size: int = 512 # The size of the square window for cropping
remove_boundary_objects: bool = True # Flag to remove boundary objects when testing remove_boundary_objects: bool = True # Flag to remove boundary objects when testing
masks_subdir: str = "" # Subdirectory where the required masks are located, e.g. 'masks/cars' masks_subdir: str = "" # Subdirectory where the required masks are located, e.g. 'masks/cars'
@ -34,7 +34,6 @@ class TrainingSplitInfo(BaseModel):
Contains: Contains:
- all_data_dir: Directory containing all data. - all_data_dir: Directory containing all data.
""" """
shuffle: bool = True # Shuffle data before splitting
all_data_dir: str = "." # Directory containing all data if not pre-split all_data_dir: str = "." # Directory containing all data if not pre-split
class TrainingPreSplitInfo(BaseModel): class TrainingPreSplitInfo(BaseModel):
@ -69,30 +68,31 @@ class DatasetTrainingConfig(BaseModel):
train_size: int | float = 0.7 # Training data size (int for static, float in (0,1] for dynamic) train_size: int | float = 0.7 # Training data size (int for static, float in (0,1] for dynamic)
valid_size: int | float = 0.1 # Validation data size (int for static, float in (0,1] for dynamic) valid_size: int | float = 0.1 # Validation data size (int for static, float in (0,1] for dynamic)
test_size: int | float = 0.2 # Testing data size (int for static, float in (0,1] for dynamic) test_size: int | float = 0.2 # Testing data size (int for static, float in (0,1] for dynamic)
train_offset: int = 0 # Offset for training data train_offset: int | float = 0 # Offset for training data (int for static, float in (0,1] for dynamic)
valid_offset: int = 0 # Offset for validation data valid_offset: int | float = 0 # Offset for validation data (int for static, float in (0,1] for dynamic)
test_offset: int = 0 # Offset for testing data test_offset: int | float = 0 # Offset for testing data (int for static, float in (0,1] for dynamic)
shuffle: bool = False # Shuffle data
batch_size: int = 1 # Batch size for training batch_size: int = 1 # Batch size for training
num_epochs: int = 100 # Number of training epochs num_epochs: int = 100 # Number of training epochs
val_freq: int = 1 # Frequency of validation during training val_freq: int = 1 # Frequency of validation during training
@field_validator("train_size", "valid_size", "test_size", mode="before") @field_validator("train_size", "valid_size", "test_size", "train_offset", "valid_offset", "test_offset", mode="before")
def validate_sizes(cls, v: int | float) -> int | float: def validate_sizes_and_offsets(cls, v: int | float) -> int | float:
""" """
Validates size values: Validates size and offset values:
- If provided as a float, must be in the range (0, 1]. - If provided as a float, must be in the range (0, 1].
- If provided as an int, must be non-negative. - If provided as an int, must be non-negative.
""" """
if isinstance(v, float): if isinstance(v, float):
if not (0 <= v <= 1): if not (0 <= v <= 1):
raise ValueError("When provided as a float, size must be in the range (0, 1]") raise ValueError("When provided as a float, size and offset must be in the range [0, 1]")
elif isinstance(v, int): elif isinstance(v, int):
if v < 0: if v < 0:
raise ValueError("When provided as an int, size must be non-negative") raise ValueError("When provided as an int, size and offset must be non-negative")
else: else:
raise ValueError("Size must be either an int or a float") raise ValueError("Size and offset must be either an int or a float")
return v return v
@model_validator(mode="after") @model_validator(mode="after")
@ -102,10 +102,6 @@ class DatasetTrainingConfig(BaseModel):
- If is_split is True, validates pre_split (train_dir must be non-empty and exist; if provided, valid_dir and test_dir must exist). - If is_split is True, validates pre_split (train_dir must be non-empty and exist; if provided, valid_dir and test_dir must exist).
- If is_split is False, validates split (all_data_dir must be non-empty and exist). - If is_split is False, validates split (all_data_dir must be non-empty and exist).
""" """
if any(isinstance(s, float) for s in (self.train_size, self.valid_size, self.test_size)):
if (self.train_size + self.valid_size + self.test_size) > 1 and not self.is_split:
raise ValueError("The total sample size with dynamically defined sizes must be <= 1")
if not self.is_split: if not self.is_split:
if not self.split.all_data_dir: if not self.split.all_data_dir:
raise ValueError("When is_split is False, all_data_dir must be provided and non-empty in pre_split") raise ValueError("When is_split is False, all_data_dir must be provided and non-empty in pre_split")
@ -128,7 +124,6 @@ class DatasetTrainingConfig(BaseModel):
Validates numeric fields: Validates numeric fields:
- batch_size and num_epochs must be > 0. - batch_size and num_epochs must be > 0.
- val_freq must be >= 0. - val_freq must be >= 0.
- offsets must be >= 0.
""" """
if self.batch_size <= 0: if self.batch_size <= 0:
raise ValueError("batch_size must be > 0") raise ValueError("batch_size must be > 0")
@ -136,8 +131,6 @@ class DatasetTrainingConfig(BaseModel):
raise ValueError("num_epochs must be > 0") raise ValueError("num_epochs must be > 0")
if self.val_freq < 0: if self.val_freq < 0:
raise ValueError("val_freq must be >= 0") raise ValueError("val_freq must be >= 0")
if self.train_offset < 0 or self.valid_offset < 0 or self.test_offset < 0:
raise ValueError("offsets must be >= 0")
return self return self
@ -145,35 +138,25 @@ class DatasetTestingConfig(BaseModel):
""" """
Configuration fields used only in testing mode. Configuration fields used only in testing mode.
""" """
test_dir: str = "." # Test data directory; must be non-empty test_dir: str = "." # Test data directory; must be non-empty
test_size: int | float = 1.0 # Testing data size (int for static, float in (0,1] for dynamic) test_size: int | float = 1.0 # Testing data size (int for static, float in (0,1] for dynamic)
test_offset: int = 0 # Offset for testing data test_offset: int | float = 0 # Offset for testing data
shuffle: bool = True # Shuffle data shuffle: bool = True # Shuffle data
@field_validator("test_size", mode="before") @field_validator("test_size", "test_offset", mode="before")
def validate_test_size(cls, v: int | float) -> int | float: def validate_test_size_and_offset(cls, v: int | float) -> int | float:
""" """
Validates the test_size value. Validates the test_size value.
""" """
if isinstance(v, float): if isinstance(v, float):
if not (0 < v <= 1): if not (0 < v <= 1):
raise ValueError("When provided as a float, test_size must be in the range (0, 1]") raise ValueError("When provided as a float, test_size and test_offset must be in the range (0, 1]")
elif isinstance(v, int): elif isinstance(v, int):
if v < 0: if v < 0:
raise ValueError("When provided as an int, test_size must be non-negative") raise ValueError("When provided as an int, test_size and test_offset must be non-negative")
else: else:
raise ValueError("test_size must be either an int or a float") raise ValueError("test_size and test_offset must be either an int or a float")
return v return v
@model_validator(mode="after")
def validate_numeric_fields(self) -> "DatasetTestingConfig":
"""
Validates numeric fields:
- test_offset must be >= 0.
"""
if self.test_offset < 0:
raise ValueError("test_offset must be >= 0")
return self
@model_validator(mode="after") @model_validator(mode="after")
def validate_testing(self) -> "DatasetTestingConfig": def validate_testing(self) -> "DatasetTestingConfig":
@ -185,8 +168,6 @@ class DatasetTestingConfig(BaseModel):
raise ValueError("In testing configuration, test_dir must be provided and non-empty") raise ValueError("In testing configuration, test_dir must be provided and non-empty")
if not os.path.exists(self.test_dir): if not os.path.exists(self.test_dir):
raise ValueError(f"Path for test_dir does not exist: {self.test_dir}") raise ValueError(f"Path for test_dir does not exist: {self.test_dir}")
if self.test_offset < 0:
raise ValueError("test_offset must be >= 0")
return self return self

4
core/data/transforms/__init__.py
Unescape View File

@ -1,4 +1,4 @@
from .cell_aware import IntensityDiversification from .cell_aware import IntensityDiversificationd
from .load_image import CustomLoadImaged from .load_image import CustomLoadImaged
from .normalize_image import CustomNormalizeImaged from .normalize_image import CustomNormalizeImaged
@ -66,7 +66,7 @@ def get_train_transforms(roi_size: int = 512):
# Randomly rotate the image and label by 90 degrees. # Randomly rotate the image and label by 90 degrees.
RandRotate90d(keys=["image", "mask"], prob=0.5, spatial_axes=(0, 1)), RandRotate90d(keys=["image", "mask"], prob=0.5, spatial_axes=(0, 1)),
# Diversify intensities for selected cell regions. # Diversify intensities for selected cell regions.
IntensityDiversification(keys=["image", "mask"], allow_missing_keys=True), IntensityDiversificationd(keys=["image", "mask"], allow_missing_keys=True),
# Apply random Gaussian noise to the image. # Apply random Gaussian noise to the image.
RandGaussianNoised(keys=["image"], prob=0.25, mean=0, std=0.1), RandGaussianNoised(keys=["image"], prob=0.25, mean=0, std=0.1),
# Randomly adjust the contrast of the image. # Randomly adjust the contrast of the image.

17
core/data/transforms/cell_aware.py
Unescape View File

@ -7,13 +7,20 @@ from monai.transforms import RandScaleIntensity, Compose, MapTransform # type: i
from core.logger import get_logger from core.logger import get_logger
__all__ = ["BoundaryExclusion", "IntensityDiversification"] __all__ = [
"BoundaryExclusiond",
"BoundaryExclusionD",
"BoundaryExclusionDict",
"IntensityDiversificationd",
"IntensityDiversificationD",
"IntensityDiversificationDict"
]
logger = get_logger(__name__) logger = get_logger(__name__)
class BoundaryExclusion(MapTransform): class BoundaryExclusiond(MapTransform):
""" """
Map the cell boundary pixel labels to the background class (0). Map the cell boundary pixel labels to the background class (0).
@ -87,7 +94,7 @@ class BoundaryExclusion(MapTransform):
return data return data
class IntensityDiversification(MapTransform): class IntensityDiversificationd(MapTransform):
""" """
Randomly rescale the intensity of cell pixels. Randomly rescale the intensity of cell pixels.
@ -202,3 +209,7 @@ class IntensityDiversification(MapTransform):
data["image"][c] = img_orig + img_changed data["image"][c] = img_orig + img_changed
return data return data
BoundaryExclusionD = BoundaryExclusionDict = BoundaryExclusiond
IntensityDiversificationD = IntensityDiversificationDict = IntensityDiversificationd

35
core/data/transforms/load_image.py
Unescape View File

@ -1,10 +1,8 @@
import numpy as np import numpy as np
import tifffile as tif import imageio.v3 as iio
import skimage.io as io
from typing import Final, Sequence, Type from typing import Final, Sequence, Type
from monai.utils.enums import PostFix from monai.utils.enums import PostFix
from monai.utils.module import optional_import
from monai.utils.misc import ensure_tuple, ensure_tuple_rep from monai.utils.misc import ensure_tuple, ensure_tuple_rep
from monai.data.utils import is_supported_format from monai.data.utils import is_supported_format
from monai.data.image_reader import ImageReader, NumpyReader from monai.data.image_reader import ImageReader, NumpyReader
@ -15,9 +13,6 @@ from monai.config.type_definitions import DtypeLike, PathLike, KeysCollection
# Default value for metadata postfix # Default value for metadata postfix
DEFAULT_POST_FIX = PostFix.meta() DEFAULT_POST_FIX = PostFix.meta()
# Try to import ITK library; if not available, has_itk will be False
itk, has_itk = optional_import("itk", allow_namespace_pkg=True)
__all__ = [ __all__ = [
"CustomLoadImage", # Basic image loader "CustomLoadImage", # Basic image loader
@ -142,9 +137,7 @@ class UniversalImageReader(NumpyReader):
Universal image reader for TIFF, PNG, JPG, BMP, etc. Universal image reader for TIFF, PNG, JPG, BMP, etc.
Uses: Uses:
- tifffile for reading TIFF files. - imageio.v3 for reading files.
- ITK (if available) for reading other formats.
- skimage.io for reading if the previous methods fail.
The image is loaded with its original number of channels (layers) without forced modifications The image is loaded with its original number of channels (layers) without forced modifications
(e.g., repeating or cropping channels). (e.g., repeating or cropping channels).
@ -162,7 +155,7 @@ class UniversalImageReader(NumpyReader):
Supported extensions: tif, tiff, png, jpg, bmp, jpeg. Supported extensions: tif, tiff, png, jpg, bmp, jpeg.
""" """
return has_itk or is_supported_format(filename, SUPPORTED_IMAGE_FORMATS) return is_supported_format(filename, SUPPORTED_IMAGE_FORMATS)
def read(self, data: Sequence[PathLike] | PathLike, **kwargs): def read(self, data: Sequence[PathLike] | PathLike, **kwargs):
""" """
@ -186,17 +179,17 @@ class UniversalImageReader(NumpyReader):
for name in filenames: for name in filenames:
# Convert file name to string # Convert file name to string
name = f"{name}" name = f"{name}"
# If the file has a .tif or .tiff extension (case-insensitive), use tifffile for reading
if name.lower().endswith((".tif", ".tiff")): img_array = iio.imread(name, **kwargs)
img_array = tif.imread(name) # copy only if needed: not contiguous, not writeable, or is a view
else: needs_copy = (
# Attempt to read the image using ITK (if available) not img_array.flags["C_CONTIGUOUS"]
try: or not img_array.flags["WRITEABLE"]
img_itk = itk.imread(name, **kwargs_) or (img_array.base is not None) # likely a view on another object/buffer
img_array = itk.array_view_from_image(img_itk, keep_axes=False) )
except Exception: if needs_copy:
# If ITK fails, use skimage.io for reading # Ensure NumPy owns the buffer to avoid backend-held memory.
img_array = io.imread(name) img_array = np.array(img_array, copy=True, order="C")
# Check the number of dimensions (axes) of the loaded image # Check the number of dimensions (axes) of the loaded image
if img_array.ndim == 2: if img_array.ndim == 2:

75
core/segmentator.py
Unescape View File

@ -126,12 +126,28 @@ class CellSegmentator:
train_dir = self._dataset_setup.training.pre_split.train_dir train_dir = self._dataset_setup.training.pre_split.train_dir
valid_dir = self._dataset_setup.training.pre_split.valid_dir valid_dir = self._dataset_setup.training.pre_split.valid_dir
test_dir = self._dataset_setup.training.pre_split.test_dir test_dir = self._dataset_setup.training.pre_split.test_dir
train_number_of_images = len(os.listdir(os.path.join(train_dir, 'images')))
valid_number_of_images = len(os.listdir(os.path.join(valid_dir, 'images')))
test_number_of_images = len(os.listdir(os.path.join(test_dir, 'images')))
train_offset = (
self._dataset_setup.training.train_offset
if isinstance(self._dataset_setup.training.train_offset, int)
else int(train_number_of_images * self._dataset_setup.training.train_offset)
)
valid_offset = (
self._dataset_setup.training.valid_offset
if isinstance(self._dataset_setup.training.valid_offset, int)
else int(valid_number_of_images * self._dataset_setup.training.valid_offset)
)
test_offset = (
self._dataset_setup.training.test_offset
if isinstance(self._dataset_setup.training.test_offset, int)
else int(test_number_of_images * self._dataset_setup.training.test_offset)
)
train_offset = self._dataset_setup.training.train_offset shuffle = self._dataset_setup.training.shuffle
valid_offset = self._dataset_setup.training.valid_offset
test_offset = self._dataset_setup.training.test_offset
shuffle = False
else: else:
# Same validation for split mode with full data directory # Same validation for split mode with full data directory
if ( if (
@ -160,11 +176,23 @@ class CellSegmentator:
else int(number_of_images * self._dataset_setup.training.valid_size) else int(number_of_images * self._dataset_setup.training.valid_size)
) )
train_offset = self._dataset_setup.training.train_offset train_offset = (
valid_offset = self._dataset_setup.training.valid_offset + train_size self._dataset_setup.training.train_offset
test_offset = self._dataset_setup.training.test_offset + train_size + valid_size if isinstance(self._dataset_setup.training.train_offset, int)
else int(number_of_images * self._dataset_setup.training.train_offset)
shuffle = True )
valid_offset = (
self._dataset_setup.training.valid_offset
if isinstance(self._dataset_setup.training.valid_offset, int)
else int(number_of_images * self._dataset_setup.training.valid_offset)
) + train_size + train_offset
test_offset = (
self._dataset_setup.training.test_offset
if isinstance(self._dataset_setup.training.test_offset, int)
else int(number_of_images * self._dataset_setup.training.test_offset)
) + valid_offset + valid_size
shuffle = self._dataset_setup.training.shuffle
# Train dataloader # Train dataloader
train_dataset = self.__get_dataset( train_dataset = self.__get_dataset(
@ -370,7 +398,7 @@ class CellSegmentator:
self.__print_with_logging(valid_metrics, epoch) self.__print_with_logging(valid_metrics, epoch)
# Update best model on improved F1 # Update best model on improved F1
f1 = valid_metrics.get("valid_f1_score", 0.0) f1 = valid_metrics.get("valid_f1_score_micro", 0.0)
if f1 > best_f1_score: if f1 > best_f1_score:
best_f1_score = f1 best_f1_score = f1
# Deep copy weights to avoid reference issues # Deep copy weights to avoid reference issues
@ -426,7 +454,7 @@ class CellSegmentator:
): ):
# Disable gradient computation for inference # Disable gradient computation for inference
with torch.no_grad(): with torch.no_grad():
# Run the model’s forward pass in ‘predict’ mode # Run the model's forward pass in 'predict' mode
raw_output = self.__run_inference(inputs, mode="predict") raw_output = self.__run_inference(inputs, mode="predict")
# Convert logits/probabilities to discrete instance masks # Convert logits/probabilities to discrete instance masks
@ -451,7 +479,7 @@ class CellSegmentator:
- If training is enabled in the dataset setup, start training. - If training is enabled in the dataset setup, start training.
- Otherwise, if a test DataLoader is provided, run evaluation. - Otherwise, if a test DataLoader is provided, run evaluation.
- Else if a prediction DataLoader is provided, run inference/prediction. - Else if a prediction DataLoader is provided, run inference/prediction.
- If neither loader is available in non‐training mode, raise an error. - If neither loader is available in non-training mode, raise an error.
Args: Args:
save_results (bool): If True, the predicted masks and test metrics will be saved. save_results (bool): If True, the predicted masks and test metrics will be saved.
@ -481,7 +509,7 @@ class CellSegmentator:
else: else:
# 3) ERROR: no appropriate loader found # 3) ERROR: no appropriate loader found
raise RuntimeError( raise RuntimeError(
"Neither test nor predict DataLoader is set for non‐training mode." "Neither test nor predict DataLoader is set for non-training mode."
) )
elapsed = time.time() - start_time elapsed = time.time() - start_time
@ -652,6 +680,7 @@ class CellSegmentator:
if config.dataset_config.is_training: if config.dataset_config.is_training:
training = config.dataset_config.training training = config.dataset_config.training
logger.info("[MODE] Training") logger.info("[MODE] Training")
logger.info(f"├─ Shuffle: {'yes' if training.shuffle else 'no'}")
logger.info(f"├─ Batch size: {training.batch_size}") logger.info(f"├─ Batch size: {training.batch_size}")
logger.info(f"├─ Epochs: {training.num_epochs}") logger.info(f"├─ Epochs: {training.num_epochs}")
logger.info(f"├─ Validation frequency: {training.val_freq}") logger.info(f"├─ Validation frequency: {training.val_freq}")
@ -664,7 +693,6 @@ class CellSegmentator:
else: else:
logger.info( "├─ Using unified dataset with splits:") logger.info( "├─ Using unified dataset with splits:")
logger.info( "│ ├─ All data dir: {training.split.all_data_dir}") logger.info( "│ ├─ All data dir: {training.split.all_data_dir}")
logger.info(f"│ └─ Shuffle: {'yes' if training.split.shuffle else 'no'}")
logger.info( "└─ Dataset split:") logger.info( "└─ Dataset split:")
logger.info(f" ├─ Train size: {training.train_size}, offset: {training.train_offset}") logger.info(f" ├─ Train size: {training.train_size}, offset: {training.train_offset}")
@ -780,6 +808,7 @@ class CellSegmentator:
# Shuffle image-mask pairs if requested # Shuffle image-mask pairs if requested
if shuffle: if shuffle:
self.__set_seed(self._dataset_setup.common.seed)
if masks_dir is not None: if masks_dir is not None:
combined = list(zip(images, masks)) # type: ignore combined = list(zip(images, masks)) # type: ignore
random.shuffle(combined) random.shuffle(combined)
@ -1001,15 +1030,27 @@ class CellSegmentator:
fp_array = np.vstack(all_fp) fp_array = np.vstack(all_fp)
fn_array = np.vstack(all_fn) fn_array = np.vstack(all_fn)
epoch_metrics[f"{mode}_f1_score"] = self.__compute_f1_metric( epoch_metrics[f"{mode}_f1_score_micro"] = self.__compute_f1_metric(
tp_array, fp_array, fn_array, reduction="micro" tp_array, fp_array, fn_array, reduction="micro"
) )
epoch_metrics[f"{mode}_f1_score_iw"] = self.__compute_f1_metric( epoch_metrics[f"{mode}_f1_score_iw"] = self.__compute_f1_metric(
tp_array, fp_array, fn_array, reduction="imagewise" tp_array, fp_array, fn_array, reduction="imagewise"
) )
epoch_metrics[f"{mode}_mAP"] = self.__compute_average_precision_metric( epoch_metrics[f"{mode}_f1_score_pc"] = self.__compute_f1_metric(
tp_array, fp_array, fn_array, reduction="per_class"
)
epoch_metrics[f"{mode}_f1_score_macro"] = self.__compute_f1_metric(
tp_array, fp_array, fn_array, reduction="macro"
)
epoch_metrics[f"{mode}_mAP_micro"] = self.__compute_average_precision_metric(
tp_array, fp_array, fn_array, reduction="micro"
)
epoch_metrics[f"{mode}_mAP_macro"] = self.__compute_average_precision_metric(
tp_array, fp_array, fn_array, reduction="macro" tp_array, fp_array, fn_array, reduction="macro"
) )
epoch_metrics[f"{mode}_mAP_pc"] = self.__compute_average_precision_metric(
tp_array, fp_array, fn_array, reduction="per_class"
)
return epoch_metrics return epoch_metrics

2
pyproject.toml
Unescape View File

@ -9,10 +9,12 @@ dependencies = [
"fastremap>=1.16.1", "fastremap>=1.16.1",
"fill-voids>=2.0.8", "fill-voids>=2.0.8",
"imagecodecs>=2025.3.30", "imagecodecs>=2025.3.30",
"imageio>=2.37.0",
"matplotlib>=3.10.3", "matplotlib>=3.10.3",
"monai>=1.4.0", "monai>=1.4.0",
"numba>=0.61.2", "numba>=0.61.2",
"numpy>=1.26.4", "numpy>=1.26.4",
"pillow>=11.2.1",
"pydantic>=2.11.4", "pydantic>=2.11.4",
"scikit-image>=0.25.2", "scikit-image>=0.25.2",
"scipy>=1.15.3", "scipy>=1.15.3",

1468
uv.lock
View File

File diff suppressed because it is too large Load Diff
Write Preview
Loading…
Cancel
Save