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base_workflow.py
1722 lines (1471 loc) · 82 KB
/
base_workflow.py
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import math
import os
import datetime
import time
import json
import torch
import h5py
import zarr
import numpy as np
from tqdm import tqdm
from abc import ABCMeta, abstractmethod
from sklearn.model_selection import StratifiedKFold
import torch.multiprocessing as mp
import torch.distributed as dist
from biapy.models import build_model, build_torchvision_model
from biapy.engine import prepare_optimizer, build_callbacks
from biapy.data.generators import create_train_val_augmentors, create_test_augmentor, check_generator_consistence
from biapy.utils.misc import (get_world_size, get_rank, is_main_process, save_model, time_text, load_model_checkpoint, TensorboardLogger,
to_pytorch_format, to_numpy_format, is_dist_avail_and_initialized, setup_for_distributed)
from biapy.utils.util import (load_data_from_dir, load_3d_images_from_dir, create_plots, pad_and_reflect, save_tif, check_downsample_division,
read_chunked_data, order_dimensions)
from biapy.engine.train_engine import train_one_epoch, evaluate
from biapy.data.data_2D_manipulation import crop_data_with_overlap, merge_data_with_overlap, load_and_prepare_2D_train_data
from biapy.data.data_3D_manipulation import (crop_3D_data_with_overlap, merge_3D_data_with_overlap, load_and_prepare_3D_data,
extract_3D_patch_with_overlap_yield)
from biapy.data.post_processing.post_processing import ensemble8_2d_predictions, ensemble16_3d_predictions, apply_binary_mask
from biapy.engine.metrics import jaccard_index_numpy, voc_calculation
from biapy.data.post_processing import apply_post_processing
from biapy.data.pre_processing import preprocess_data
class Base_Workflow(metaclass=ABCMeta):
"""
Base workflow class. A new workflow should extend this class.
Parameters
----------
cfg : YACS configuration
Running configuration.
Job_identifier : str
Complete name of the running job.
device : Torch device
Device used.
args : argpase class
Arguments used in BiaPy's call.
"""
def __init__(self, cfg, job_identifier, device, args):
self.cfg = cfg
self.args = args
self.job_identifier = job_identifier
self.device = device
self.original_test_path = None
self.original_test_mask_path = None
self.test_mask_filenames = None
self.cross_val_samples_ids = None
self.post_processing = {}
self.post_processing['per_image'] = False
self.post_processing['all_images'] = False
self.test_filenames = None
self.metrics = []
self.data_norm = None
self.model = None
self.optimizer = None
self.loss_scaler = None
self.model_prepared = False
self.dtype = np.float32 if not self.cfg.TEST.REDUCE_MEMORY else np.float16
self.dtype_str = "float32" if not self.cfg.TEST.REDUCE_MEMORY else "float16"
self.loss_dtype = torch.float32
# Save paths in case we need them in a future
self.orig_train_path = self.cfg.DATA.TRAIN.PATH
self.orig_train_mask_path = self.cfg.DATA.TRAIN.GT_PATH
self.orig_val_path = self.cfg.DATA.VAL.PATH
self.orig_val_mask_path = self.cfg.DATA.VAL.GT_PATH
self.all_pred = []
self.all_gt = []
self.stats = {}
# Per crop
self.stats['loss_per_crop'] = 0
self.stats['iou_per_crop'] = 0
self.stats['patch_counter'] = 0
# Merging the image
self.stats['iou_per_image'] = 0
self.stats['ov_iou_per_image'] = 0
# Full image
self.stats['loss'] = 0
self.stats['iou'] = 0
self.stats['ov_iou'] = 0
# Post processing
self.stats['iou_post'] = 0
self.stats['ov_iou_post'] = 0
self.world_size = get_world_size()
self.global_rank = get_rank()
if self.cfg.TEST.BY_CHUNKS.ENABLE and self.cfg.PROBLEM.NDIM == '3D':
maxsize = min(10,self.cfg.SYSTEM.NUM_GPUS*10)
self.output_queue = mp.Queue(maxsize=maxsize)
self.input_queue = mp.Queue(maxsize=maxsize)
self.extract_info_queue = mp.Queue()
# Test variables
if self.cfg.TEST.ANALIZE_2D_IMGS_AS_3D_STACK and self.cfg.PROBLEM.NDIM == "2D":
if self.cfg.TEST.POST_PROCESSING.YZ_FILTERING or self.cfg.TEST.POST_PROCESSING.Z_FILTERING:
self.post_processing['all_images'] = True
elif self.cfg.PROBLEM.NDIM == "3D":
if self.cfg.TEST.POST_PROCESSING.YZ_FILTERING or self.cfg.TEST.POST_PROCESSING.Z_FILTERING:
self.post_processing['per_image'] = True
# Define permute shapes to pass from Numpy axis order (Y,X,C) to Pytorch's (C,Y,X)
self.axis_order = (0,3,1,2) if self.cfg.PROBLEM.NDIM == "2D" else (0,4,1,2,3)
self.axis_order_back = (0,2,3,1) if self.cfg.PROBLEM.NDIM == "2D" else (0,2,3,4,1)
# Define metrics
self.define_metrics()
# Load Bioimage model Zoo pretrained model information
self.torchvision_preprocessing = None
self.bmz_test_input = None
self.bmz_test_output = None
self.bmz_model_resource = None
if self.cfg.MODEL.SOURCE == "bmz":
import bioimageio.core
import xarray as xr
print("Loading Bioimage Model Zoo pretrained model . . .")
self.bmz_model_resource = bioimageio.core.load_resource_description(self.cfg.MODEL.BMZ.SOURCE_MODEL_DOI)
# Change PATCH_SIZE with the one stored in the RDF
input_image = np.load(self.bmz_model_resource.test_inputs[0])
opts = ["DATA.PATCH_SIZE", input_image.shape[2:]+(input_image.shape[1],)]
print("[BMZ] Changed 'DATA.PATCH_SIZE' from {} to {} as defined in the RDF"
.format(self.cfg.DATA.PATCH_SIZE,opts[1]))
self.cfg.merge_from_list(opts)
@abstractmethod
def define_metrics(self):
"""
This function must define the following variables:
self.metrics : List of functions
Metrics to be calculated during model's training and inference.
self.metric_names : List of str
Names of the metrics calculated.
self.loss : Function
Loss function used during training.
"""
NotImplementedError
@abstractmethod
def metric_calculation(self, output, targets, metric_logger=None):
"""
Execution of the metrics defined in :func:`~define_metrics` function.
Parameters
----------
output : Torch Tensor
Prediction of the model.
targets : Torch Tensor
Ground truth to compare the prediction with.
metric_logger : MetricLogger, optional
Class to be updated with the new metric(s) value(s) calculated.
Returns
-------
value : float
Value of the metric for the given prediction.
"""
NotImplementedError
def prepare_targets(self, targets, batch):
"""
Location to perform any necessary data transformations to ``targets``
before calculating the loss.
Parameters
----------
targets : Torch Tensor
Ground truth to compare the prediction with.
batch : Torch Tensor
Prediction of the model. Only used in SSL workflow.
Returns
-------
targets : Torch tensor
Resulting targets.
"""
# We do not use 'batch' input but in SSL workflow
return to_pytorch_format(targets, self.axis_order, self.device)
def load_train_data(self):
"""
Load training and validation data.
"""
if self.cfg.TRAIN.ENABLE:
print("##########################")
print("# LOAD TRAINING DATA #")
print("##########################")
if self.cfg.DATA.TRAIN.IN_MEMORY:
val_split = self.cfg.DATA.VAL.SPLIT_TRAIN if self.cfg.DATA.VAL.FROM_TRAIN else 0.
f_name = load_and_prepare_2D_train_data if self.cfg.PROBLEM.NDIM == '2D' else load_and_prepare_3D_data
preprocess_cfg = self.cfg.DATA.PREPROCESS if self.cfg.DATA.PREPROCESS.TRAIN else None
preprocess_fn = preprocess_data if self.cfg.DATA.PREPROCESS.TRAIN else None
is_y_mask = self.cfg.PROBLEM.TYPE in ['SEMANTIC_SEG', 'INSTANCE_SEG']
objs = f_name(self.cfg.DATA.TRAIN.PATH, self.mask_path, cross_val=self.cfg.DATA.VAL.CROSS_VAL,
cross_val_nsplits=self.cfg.DATA.VAL.CROSS_VAL_NFOLD, cross_val_fold=self.cfg.DATA.VAL.CROSS_VAL_FOLD,
val_split=val_split, seed=self.cfg.SYSTEM.SEED, shuffle_val=self.cfg.DATA.VAL.RANDOM,
random_crops_in_DA=self.cfg.DATA.EXTRACT_RANDOM_PATCH, crop_shape=self.cfg.DATA.PATCH_SIZE,
y_upscaling=self.cfg.PROBLEM.SUPER_RESOLUTION.UPSCALING, ov=self.cfg.DATA.TRAIN.OVERLAP,
padding=self.cfg.DATA.TRAIN.PADDING, minimum_foreground_perc=self.cfg.DATA.TRAIN.MINIMUM_FOREGROUND_PER,
reflect_to_complete_shape=self.cfg.DATA.REFLECT_TO_COMPLETE_SHAPE, convert_to_rgb=self.cfg.DATA.FORCE_RGB,
preprocess_cfg = preprocess_cfg, is_y_mask = is_y_mask, preprocess_f=preprocess_fn)
if self.cfg.DATA.VAL.FROM_TRAIN:
if self.cfg.DATA.VAL.CROSS_VAL:
self.X_train, self.Y_train, self.X_val, self.Y_val, self.train_filenames, self.cross_val_samples_ids = objs
else:
self.X_train, self.Y_train, self.X_val, self.Y_val, self.train_filenames = objs
else:
self.X_train, self.Y_train, self.train_filenames = objs
del objs
else:
self.X_train, self.Y_train = None, None
##################
### VALIDATION ###
##################
if not self.cfg.DATA.VAL.FROM_TRAIN:
if self.cfg.DATA.VAL.IN_MEMORY:
print("1) Loading validation images . . .")
f_name = load_data_from_dir if self.cfg.PROBLEM.NDIM == '2D' else load_3d_images_from_dir
preprocess_cfg = self.cfg.DATA.PREPROCESS if self.cfg.DATA.PREPROCESS.VAL else None
preprocess_fn = preprocess_data if self.cfg.DATA.PREPROCESS.VAL else None
is_y_mask = self.cfg.PROBLEM.TYPE in ['SEMANTIC_SEG', 'INSTANCE_SEG']
self.X_val, _, _ = f_name(self.cfg.DATA.VAL.PATH, crop=True, crop_shape=self.cfg.DATA.PATCH_SIZE,
overlap=self.cfg.DATA.VAL.OVERLAP, padding=self.cfg.DATA.VAL.PADDING,
reflect_to_complete_shape=self.cfg.DATA.REFLECT_TO_COMPLETE_SHAPE,
convert_to_rgb=self.cfg.DATA.FORCE_RGB, preprocess_cfg = preprocess_cfg,
is_mask = False, preprocess_f=preprocess_fn)
if self.cfg.PROBLEM.NDIM == '2D':
crop_shape = (self.cfg.DATA.PATCH_SIZE[0]*self.cfg.PROBLEM.SUPER_RESOLUTION.UPSCALING,
self.cfg.DATA.PATCH_SIZE[1]*self.cfg.PROBLEM.SUPER_RESOLUTION.UPSCALING, self.cfg.DATA.PATCH_SIZE[2])
else:
crop_shape = (self.cfg.DATA.PATCH_SIZE[0], self.cfg.DATA.PATCH_SIZE[1]*self.cfg.PROBLEM.SUPER_RESOLUTION.UPSCALING,
self.cfg.DATA.PATCH_SIZE[2]*self.cfg.PROBLEM.SUPER_RESOLUTION.UPSCALING, self.cfg.DATA.PATCH_SIZE[3])
if self.load_Y_val:
self.Y_val, _, _ = f_name(self.cfg.DATA.VAL.GT_PATH, crop=True, crop_shape=crop_shape,
overlap=self.cfg.DATA.VAL.OVERLAP, padding=self.cfg.DATA.VAL.PADDING,
reflect_to_complete_shape=self.cfg.DATA.REFLECT_TO_COMPLETE_SHAPE,
check_channel=False, check_drange=False,convert_to_rgb=self.cfg.DATA.FORCE_RGB,
preprocess_cfg = preprocess_cfg, is_mask = is_y_mask, preprocess_f=preprocess_fn)
else:
self.Y_val = None
if self.Y_val is not None and len(self.X_val) != len(self.Y_val):
raise ValueError("Different number of raw and ground truth items ({} vs {}). "
"Please check the data!".format(len(self.X_val), len(self.Y_val)))
else:
self.X_val, self.Y_val = None, None
def destroy_train_data(self):
"""
Delete training variable to release memory.
"""
print("Releasing memory . . .")
if 'X_train' in locals() or 'X_train' in globals():
del self.X_train
if 'Y_train' in locals() or 'Y_train' in globals():
del self.Y_train
if 'X_val' in locals() or 'X_val' in globals():
del self.X_val
if 'Y_val' in locals() or 'Y_val' in globals():
del self.Y_val
if 'train_generator' in locals() or 'train_generator' in globals():
del self.train_generator
if 'val_generator' in locals() or 'val_generator' in globals():
del self.val_generator
def prepare_train_generators(self):
"""
Build train and val generators.
"""
if self.cfg.TRAIN.ENABLE:
print("##############################")
print("# PREPARE TRAIN GENERATORS #")
print("##############################")
self.train_generator, \
self.val_generator, \
self.data_norm, \
self.num_training_steps_per_epoch = create_train_val_augmentors(self.cfg, self.X_train, self.Y_train,
self.X_val, self.Y_val, self.world_size, self.global_rank, self.args.distributed)
if self.cfg.DATA.CHECK_GENERATORS and self.cfg.PROBLEM.TYPE != 'CLASSIFICATION':
check_generator_consistence(
self.train_generator, self.cfg.PATHS.GEN_CHECKS+"_train", self.cfg.PATHS.GEN_MASK_CHECKS+"_train")
check_generator_consistence(
self.val_generator, self.cfg.PATHS.GEN_CHECKS+"_val", self.cfg.PATHS.GEN_MASK_CHECKS+"_val")
def bmz_model_call(self, in_img, is_train=False):
"""
Call Bioimage model zoo model.
Parameters
----------
in_img : Tensor
Input image to pass through the model.
is_train : bool, optional
Whether if the call is during training or inference.
Returns
-------
prediction : Tensor
Image prediction.
"""
# Convert from Numpy to xarray.DataArray
if self.cfg.PROBLEM.NDIM == '2D':
self.bmz_axes = ('b', 'c', 'y', 'x')
else:
self.bmz_axes = ('b', 'c', 'z', 'y', 'x')
in_img = xr.DataArray(in_img.cpu().numpy(), dims=tuple(self.bmz_axes))
# Apply pre-processing
in_img = dict(zip([ipt.name for ipt in self.model.input_specs], (in_img,)))
self.bmz_computed_measures = {}
self.model.apply_preprocessing(in_img, self.bmz_computed_measures)
# Predict
prediction_tensors = self.model.predict(*list(in_img.values()))
# Apply post-processing
prediction = dict(zip([out.name for out in self.model.output_specs], prediction_tensors))
self.model.apply_postprocessing(prediction, self.bmz_computed_measures)
# Convert back to Tensor
prediction = torch.from_numpy(prediction['output0'].to_numpy())
return prediction
@abstractmethod
def torchvision_model_call(self, in_img, is_train=False):
"""
Call a regular Pytorch model.
Parameters
----------
in_img : Tensor
Input image to pass through the model.
is_train : bool, optional
Whether if the call is during training or inference.
Returns
-------
prediction : Tensor
Image prediction.
"""
raise NotImplementedError
def model_call_func(self, in_img, to_pytorch=True, is_train=False):
"""
Call a regular Pytorch model.
Parameters
----------
in_img : Tensor
Input image to pass through the model.
to_pytorch : bool, optional
Whether if the input image needs to be converted into pytorch format or not.
is_train : bool, optional
Whether if the call is during training or inference.
Returns
-------
prediction : Tensor
Image prediction.
"""
if to_pytorch:
in_img = to_pytorch_format(in_img, self.axis_order, self.device)
if self.cfg.MODEL.SOURCE == "biapy":
p = self.model(in_img)
elif self.cfg.MODEL.SOURCE == "bmz":
p = self.bmz_model_call(in_img, is_train)
elif self.cfg.MODEL.SOURCE == "torchvision":
p = self.torchvision_model_call(in_img, is_train)
return p
def prepare_model(self):
"""
Build the model.
"""
if self.model_prepared:
print("Model already prepared!")
return
print("###############")
print("# Build model #")
print("###############")
if self.cfg.MODEL.SOURCE == "biapy":
self.model = build_model(self.cfg, self.job_identifier, self.device)
elif self.cfg.MODEL.SOURCE == "torchvision":
self.model, self.torchvision_preprocessing = build_torchvision_model(self.cfg, self.device)
# Bioimage Model Zoo pretrained models
elif self.cfg.MODEL.SOURCE == "bmz":
# Create a bioimage pipeline to create predictions
try:
self.model = bioimageio.core.create_prediction_pipeline(
self.bmz_model_resource, devices=None,
weight_format="torchscript",
)
except Exception as e:
print(f"The error thrown during the BMZ model load was:\n{e}")
raise ValueError("An error ocurred when creating the BMZ model (see above). "
"BiaPy only supports models prepared with Torchscript.")
if self.args.distributed:
raise ValueError("DDP can not be activated when loading a BMZ pretrained model")
self.model_without_ddp = self.model
if self.args.distributed:
self.model = torch.nn.parallel.DistributedDataParallel(self.model, device_ids=[self.args.gpu],
find_unused_parameters=False)
self.model_without_ddp = self.model.module
self.model_prepared = True
# Load checkpoint if necessary
if self.cfg.MODEL.SOURCE == "biapy" and self.cfg.MODEL.LOAD_CHECKPOINT:
self.start_epoch = load_model_checkpoint(cfg=self.cfg, jobname=self.job_identifier, model_without_ddp=self.model_without_ddp,
device=self.device, optimizer=self.optimizer, loss_scaler=self.loss_scaler)
else:
self.start_epoch = 0
def prepare_logging_tool(self):
"""
Prepare looging tool.
"""
print("#######################")
print("# Prepare loggin tool #")
print("#######################")
# To start the logging
now = datetime.datetime.now()
now = now.strftime("%Y_%m_%d_%H_%M_%S")
self.log_file = os.path.join(self.cfg.LOG.LOG_DIR, self.cfg.LOG.LOG_FILE_PREFIX + "_log_"+str(now)+".txt")
if self.global_rank == 0:
os.makedirs(self.cfg.LOG.LOG_DIR, exist_ok=True)
os.makedirs(self.cfg.PATHS.CHECKPOINT, exist_ok=True)
self.log_writer = TensorboardLogger(log_dir=self.cfg.LOG.TENSORBOARD_LOG_DIR)
else:
self.log_writer = None
self.plot_values = {}
self.plot_values['loss'] = []
self.plot_values['val_loss'] = []
for i in range(len(self.metric_names)):
self.plot_values[self.metric_names[i]] = []
self.plot_values['val_'+self.metric_names[i]] = []
def train(self):
"""
Training phase.
"""
self.load_train_data()
if not self.model_prepared:
self.prepare_model()
self.prepare_train_generators()
self.prepare_logging_tool()
self.early_stopping = build_callbacks(self.cfg)
self.optimizer, self.lr_scheduler, self.loss_scaler = prepare_optimizer(self.cfg, self.model_without_ddp,
len(self.train_generator))
print("#####################")
print("# TRAIN THE MODEL #")
print("#####################")
print(f"Start training in epoch {self.start_epoch+1} - Total: {self.cfg.TRAIN.EPOCHS}")
start_time = time.time()
val_best_metric = np.zeros(len(self.metric_names), dtype=np.float32)
val_best_loss = np.Inf
for epoch in range(self.start_epoch, self.cfg.TRAIN.EPOCHS):
print("~~~ Epoch {}/{} ~~~\n".format(epoch+1, self.cfg.TRAIN.EPOCHS))
e_start = time.time()
if self.args.distributed:
self.train_generator.sampler.set_epoch(epoch)
if self.log_writer is not None:
self.log_writer.set_step(epoch * self.num_training_steps_per_epoch)
# Train
train_stats = train_one_epoch(self.cfg, model=self.model, model_call_func=self.model_call_func, loss_function=self.loss,
activations=self.apply_model_activations, metric_function=self.metric_calculation, prepare_targets=self.prepare_targets,
data_loader=self.train_generator, optimizer=self.optimizer, device=self.device, loss_scaler=self.loss_scaler, epoch=epoch,
log_writer=self.log_writer, lr_scheduler=self.lr_scheduler, start_steps=epoch * self.num_training_steps_per_epoch)
# Save checkpoint
if self.cfg.MODEL.SAVE_CKPT_FREQ != -1:
if (epoch + 1) % self.cfg.MODEL.SAVE_CKPT_FREQ == 0 or epoch + 1 == self.cfg.TRAIN.EPOCHS and is_main_process():
save_model(cfg=self.cfg, jobname=self.job_identifier, model=self.model, model_without_ddp=self.model_without_ddp,
optimizer=self.optimizer, loss_scaler=self.loss_scaler, epoch=epoch+1)
# Validation
if self.val_generator is not None:
test_stats = evaluate(self.cfg, model=self.model, model_call_func=self.model_call_func, loss_function=self.loss,
activations=self.apply_model_activations, metric_function=self.metric_calculation, prepare_targets=self.prepare_targets,
epoch=epoch, data_loader=self.val_generator, lr_scheduler=self.lr_scheduler)
# Save checkpoint is val loss improved
if test_stats['loss'] < val_best_loss:
f = os.path.join(self.cfg.PATHS.CHECKPOINT,"{}-checkpoint-best.pth".format(self.job_identifier))
print("Val loss improved from {} to {}, saving model to {}".format(val_best_loss, test_stats['loss'], f))
m = " "
for i in range(len(val_best_metric)):
val_best_metric[i] = test_stats[self.metric_names[i]]
m += f"{self.metric_names[i]}: {val_best_metric[i]:.4f} "
val_best_loss = test_stats['loss']
if is_main_process():
save_model(cfg=self.cfg, jobname=self.job_identifier, model=self.model, model_without_ddp=self.model_without_ddp,
optimizer=self.optimizer, loss_scaler=self.loss_scaler, epoch="best")
print(f'[Val] best loss: {val_best_loss:.4f} best '+m)
# Store validation stats
if self.log_writer is not None:
self.log_writer.update(test_loss=test_stats['loss'], head="perf", step=epoch)
for i in range(len(self.metric_names)):
self.log_writer.update(test_iou=test_stats[self.metric_names[i]], head="perf", step=epoch)
log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
**{f'test_{k}': v for k, v in test_stats.items()},
'epoch': epoch}
else:
log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
'epoch': epoch}
# Write statistics in the logging file
if is_main_process():
# Log epoch stats
if self.log_writer is not None:
self.log_writer.flush()
with open(self.log_file, mode="a", encoding="utf-8") as f:
f.write(json.dumps(log_stats) + "\n")
# Create training plot
self.plot_values['loss'].append(train_stats['loss'])
if self.val_generator is not None:
self.plot_values['val_loss'].append(test_stats['loss'])
for i in range(len(self.metric_names)):
self.plot_values[self.metric_names[i]].append(train_stats[self.metric_names[i]])
if self.val_generator is not None:
self.plot_values['val_'+self.metric_names[i]].append(test_stats[self.metric_names[i]])
if (epoch+1) % self.cfg.LOG.CHART_CREATION_FREQ == 0:
create_plots(self.plot_values, self.metric_names, self.job_identifier, self.cfg.PATHS.CHARTS)
if self.val_generator is not None and self.early_stopping is not None:
self.early_stopping(test_stats['loss'])
if self.early_stopping.early_stop:
print("Early stopping")
break
e_end = time.time()
t_epoch = e_end - e_start
print("[Time] {} {}/{}\n".format(time_text(t_epoch), time_text(e_end - start_time),
time_text((e_end - start_time)+(t_epoch*(self.cfg.TRAIN.EPOCHS-epoch)))))
total_time = time.time() - start_time
self.total_training_time_str = str(datetime.timedelta(seconds=int(total_time)))
print('Training time {}'.format(self.total_training_time_str))
print("Train loss: {}".format(train_stats['loss']))
for i in range(len(self.metric_names)):
print("Train {}: {}".format(self.metric_names[i], train_stats[self.metric_names[i]]))
if self.val_generator is not None:
print("Val loss: {}".format(val_best_loss))
for i in range(len(self.metric_names)):
print("Val {}: {}".format(self.metric_names[i], val_best_metric))
print('Finished Training')
# Save two samples to export the model to BMZ
if self.bmz_test_input is None:
sample = next(enumerate(self.train_generator))
self.bmz_test_input = sample[1][0][0]
self.bmz_test_output = sample[1][1]
if not isinstance(self.bmz_test_output, int):
self.bmz_test_output = self.bmz_test_output[0]
self.destroy_train_data()
def load_test_data(self):
"""
Load test data.
"""
if self.cfg.TEST.ENABLE:
print("######################")
print("# LOAD TEST DATA #")
print("######################")
if not self.cfg.DATA.TEST.USE_VAL_AS_TEST:
if self.cfg.DATA.TEST.IN_MEMORY:
print("2) Loading test images . . .")
f_name = load_data_from_dir if self.cfg.PROBLEM.NDIM == '2D' else load_3d_images_from_dir
preprocess_cfg = self.cfg.DATA.PREPROCESS if self.cfg.DATA.PREPROCESS.TEST else None
preprocess_fn = preprocess_data if self.cfg.DATA.PREPROCESS.TEST else None
is_y_mask = self.cfg.PROBLEM.TYPE in ['SEMANTIC_SEG', 'INSTANCE_SEG']
self.X_test, _, _ = f_name(self.cfg.DATA.TEST.PATH, convert_to_rgb=self.cfg.DATA.FORCE_RGB,
preprocess_cfg=preprocess_cfg, is_mask=False, preprocess_f=preprocess_fn)
if self.cfg.DATA.TEST.LOAD_GT:
print("3) Loading test masks . . .")
self.Y_test, _, _ = f_name(self.cfg.DATA.TEST.GT_PATH, check_channel=False, check_drange=False,
preprocess_cfg=preprocess_cfg, is_mask=is_y_mask, preprocess_f=preprocess_fn)
if len(self.X_test) != len(self.Y_test):
raise ValueError("Different number of raw and ground truth items ({} vs {}). "
"Please check the data!".format(len(self.X_test), len(self.Y_test)))
else:
self.Y_test = None
else:
self.X_test, self.Y_test = None, None
if self.original_test_path is None:
self.test_filenames = sorted(next(os.walk(self.cfg.DATA.TEST.PATH))[2])
else:
self.test_filenames = sorted(next(os.walk(self.original_test_path))[2])
else:
# The test is the validation, and as it is only available when validation is obtained from train and when
# cross validation is enabled, the test set files reside in the train folder
self.test_filenames = sorted(next(os.walk(self.cfg.DATA.TRAIN.PATH))[2])
self.X_test, self.Y_test = None, None
if self.cross_val_samples_ids is None:
# Split the test as it was the validation when train is not enabled
skf = StratifiedKFold(n_splits=self.cfg.DATA.VAL.CROSS_VAL_NFOLD, shuffle=self.cfg.DATA.VAL.RANDOM,
random_state=self.cfg.SYSTEM.SEED)
fold = 1
test_index = None
A = B = np.zeros(len(self.test_filenames))
for _, te_index in skf.split(A, B):
if self.cfg.DATA.VAL.CROSS_VAL_FOLD == fold:
self.cross_val_samples_ids = te_index.copy()
break
fold += 1
if len(self.cross_val_samples_ids) > 5:
print("Fold number {} used for test data. Printing the first 5 ids: {}".format(fold, self.cross_val_samples_ids[:5]))
else:
print("Fold number {}. Indexes used in cross validation: {}".format(fold, self.cross_val_samples_ids))
self.test_filenames = [x for i, x in enumerate(self.test_filenames) if i in self.cross_val_samples_ids]
self.original_test_path = self.orig_train_path
self.original_test_mask_path = self.orig_train_mask_path
def destroy_test_data(self):
"""
Delete test variable to release memory.
"""
print("Releasing memory . . .")
if 'X_test' in locals() or 'X_test' in globals():
del self.self.X_test
if 'Y_test' in locals() or 'Y_test' in globals():
del self.self.Y_test
if 'test_generator' in locals() or 'test_generator' in globals():
del self.test_generator
if '_X' in locals() or '_X' in globals():
del self._X
if '_Y' in locals() or '_Y' in globals():
del self._Y
def prepare_test_generators(self):
"""
Prepare test data generator.
"""
if self.cfg.TEST.ENABLE:
print("############################")
print("# PREPARE TEST GENERATOR #")
print("############################")
self.test_generator, self.data_norm = create_test_augmentor(self.cfg, self.X_test, self.Y_test, self.cross_val_samples_ids)
def apply_model_activations(self, pred, training=False):
"""
Function that apply the last activation (if any) to the model's output.
Parameters
----------
pred : Torch Tensor
Predictions of the model.
training : bool, optional
To advice the function if this is being applied during training of inference. During training,
``CE_Sigmoid`` activations will NOT be applied, as ``torch.nn.BCEWithLogitsLoss`` will apply
``Sigmoid`` automatically in a way that is more stable numerically
(`ref <https://pytorch.org/docs/stable/generated/torch.nn.BCEWithLogitsLoss.html>`_).
Returns
-------
pred : Torch tensor
Resulting predictions after applying last activation(s).
"""
for key, value in self.activations.items():
# Ignore CE_Sigmoid as torch.nn.BCEWithLogitsLoss will apply Sigmoid automatically in a way
# that is more stable numerically (ref: https://pytorch.org/docs/stable/generated/torch.nn.BCEWithLogitsLoss.html)
if (training and value not in ["Linear", "CE_Sigmoid"]) or (not training and value != "Linear"):
value = "Sigmoid" if value == "CE_Sigmoid" else value
act = getattr(torch.nn, value)()
if key == ':':
pred = act(pred)
else:
pred[:,int(key),...] = act(pred[:,int(key),...])
return pred
@torch.no_grad()
def test(self):
"""
Test/Inference step.
"""
self.load_test_data()
if not self.model_prepared:
self.prepare_model()
self.prepare_test_generators()
# Switch to evaluation mode
if self.cfg.MODEL.SOURCE != "bmz":
self.model_without_ddp.eval()
# Check possible checkpoint problems
if self.start_epoch == -1:
raise ValueError("There was a problem loading the checkpoint. Test phase aborted!")
image_counter = 0
print("###############")
print("# INFERENCE #")
print("###############")
print("Making predictions on test data . . .")
# Reactivate prints to see each rank progress
if self.cfg.TEST.BY_CHUNKS.ENABLE and self.cfg.PROBLEM.NDIM == '3D':
setup_for_distributed(True)
# Process all the images
for i, batch in tqdm(enumerate(self.test_generator), total=len(self.test_generator)):
if self.cfg.DATA.TEST.LOAD_GT and self.cfg.PROBLEM.TYPE not in ["SELF_SUPERVISED"]:
X, X_norm, Y, Y_norm = batch
else:
X, X_norm = batch
Y, Y_norm = None, None
del batch
if self.cfg.TEST.BY_CHUNKS.ENABLE and self.cfg.PROBLEM.NDIM == '3D':
if type(X) is tuple:
self._X = X[0]
if self.cfg.DATA.TEST.LOAD_GT and self.cfg.PROBLEM.TYPE not in ["SELF_SUPERVISED"]:
self._Y = Y[0]
else:
self._Y = None
else:
self._X = X
self._Y = Y if self.cfg.DATA.TEST.LOAD_GT else None
if len(self.test_filenames) == 0:
self.test_filenames = sorted(next(os.walk(self.cfg.DATA.TEST.PATH))[1])
self.processing_filenames = self.test_filenames[i]
if is_main_process():
print("Processing image: {}".format(self.processing_filenames))
# Process each image separately
self.f_numbers = [i]
self.process_sample_by_chunks(self.processing_filenames)
else:
# Process all the images in the batch, sample by sample
l_X = len(X)
for j in tqdm(range(l_X), leave=False):
self.processing_filenames = self.test_filenames[(i*l_X)+j:(i*l_X)+j+1]
if is_main_process():
print("Processing image(s): {}".format(self.processing_filenames))
if self.cfg.PROBLEM.TYPE != 'CLASSIFICATION':
if type(X) is tuple:
self._X = X[j]
if self.cfg.DATA.TEST.LOAD_GT and self.cfg.PROBLEM.TYPE not in ["SELF_SUPERVISED"]:
self._Y = Y[j]
else:
self._Y = None
else:
self._X = np.expand_dims(X[j],0)
if self.cfg.DATA.TEST.LOAD_GT and self.cfg.PROBLEM.TYPE not in ["SELF_SUPERVISED"]:
self._Y = np.expand_dims(Y[j],0)
else:
self._Y = None
else:
self._X = np.expand_dims(X[j], 0)
self._Y = np.expand_dims(Y, 0) if self.cfg.DATA.TEST.LOAD_GT else None
# Process each image separately
self.f_numbers = list(range((i*l_X)+j,(i*l_X)+j+1))
self.process_sample(norm=(X_norm, Y_norm))
image_counter += 1
# Deactivate again the print function
if self.cfg.TEST.BY_CHUNKS.ENABLE and self.cfg.PROBLEM.NDIM == '3D':
setup_for_distributed(is_main_process())
self.destroy_test_data()
if is_main_process():
self.after_all_images()
print("#############")
print("# RESULTS #")
print("#############")
if self.cfg.TRAIN.ENABLE:
print("Epoch number: {}".format(len(self.plot_values['val_loss'])))
print("Train time (s): {}".format(self.total_training_time_str))
print("Train loss: {}".format(np.min(self.plot_values['loss'])))
for i in range(len(self.metric_names)):
if self.metric_names[i] == "IoU":
print("Train Foreground {}: {}".format(self.metric_names[i], np.max(self.plot_values[self.metric_names[i]])))
else:
print("Train {}: {}".format(self.metric_names[i], np.max(self.plot_values[self.metric_names[i]])))
print("Validation loss: {}".format(np.min(self.plot_values['val_loss'])))
for i in range(len(self.metric_names)):
if self.metric_names[i] == "IoU":
print("Validation Foreground {}: {}".format(self.metric_names[i], np.max(self.plot_values['val_'+self.metric_names[i]])))
else:
print("Validation {}: {}".format(self.metric_names[i], np.max(self.plot_values['val_'+self.metric_names[i]])))
self.print_stats(image_counter)
def process_sample_by_chunks(self, filenames):
"""
Function to process a sample in the inference phase. A final H5/Zarr file is created in "TZCYX" or "TZYXC" order
depending on ``TEST.BY_CHUNKS.INPUT_IMG_AXES_ORDER`` ('T' is always included).
Parameters
----------
filenames : List of str
Filenames fo the samples to process.
"""
filename, file_extension = os.path.splitext(filenames)
if file_extension not in ['.hdf5', '.h5', ".zarr"]:
print("WARNING: you could have saved more memory by converting input test images into H5 file format (.h5) "
"or Zarr (.zarr) as with 'TEST.BY_CHUNKS.ENABLE' option enabled H5/Zarr files will be processed by chunks")
# Load data
if file_extension in ['.hdf5', '.h5', ".zarr"]:
self._X_file, self._X = read_chunked_data(self._X)
else: # Numpy array
if self._X.ndim == 3:
c_pos = -1 if self.cfg.TEST.BY_CHUNKS.INPUT_IMG_AXES_ORDER[-1] == 'C' else 1
self._X = np.expand_dims(self._X, c_pos)
print(f"Loaded image shape is {self._X.shape}")
data_shape = self._X.shape
if self._X.ndim < 3:
raise ValueError("Loaded image need to have at least 3 dimensions: {} (ndim: {})".format(self._X.shape, self._X.ndim))
if 'T' in self.cfg.TEST.BY_CHUNKS.INPUT_IMG_AXES_ORDER:
if len(self.cfg.TEST.BY_CHUNKS.INPUT_IMG_AXES_ORDER) > len(data_shape):
data_shape = (1,)+data_shape
else:
data_shape = (1,)+data_shape
if len(self.cfg.TEST.BY_CHUNKS.INPUT_IMG_AXES_ORDER) != self._X.ndim:
raise ValueError("'TEST.BY_CHUNKS.INPUT_IMG_AXES_ORDER' value {} does not match the number of dimensions of the loaded H5/Zarr "
"file {} (ndim: {})".format(self.cfg.TEST.BY_CHUNKS.INPUT_IMG_AXES_ORDER, self._X.shape, self._X.ndim))
# Data paths
os.makedirs(self.cfg.PATHS.RESULT_DIR.PER_IMAGE, exist_ok=True)
ext = ".h5" if self.cfg.TEST.BY_CHUNKS.FORMAT == "h5" else ".zarr"
if self.cfg.SYSTEM.NUM_GPUS > 1:
out_data_filename = os.path.join(self.cfg.PATHS.RESULT_DIR.PER_IMAGE, filename+"_part"+str(get_rank())+ext)
out_data_mask_filename = os.path.join(self.cfg.PATHS.RESULT_DIR.PER_IMAGE, filename+"_part"+str(get_rank())+"_mask"+ext)
else:
out_data_filename = os.path.join(self.cfg.PATHS.RESULT_DIR.PER_IMAGE, filename+"_nodiv"+ext)
out_data_mask_filename = os.path.join(self.cfg.PATHS.RESULT_DIR.PER_IMAGE, filename+"_mask"+ext)
out_data_div_filename = os.path.join(self.cfg.PATHS.RESULT_DIR.PER_IMAGE, filename+ext)
in_data = self._X
# Process in charge of processing one predicted patch
output_handle_proc = mp.Process(target=insert_patch_into_dataset, args=(out_data_filename, out_data_mask_filename,
data_shape, self.output_queue, self.extract_info_queue, self.cfg, self.dtype_str, self.dtype,
self.cfg.TEST.BY_CHUNKS.FORMAT, self.cfg.TEST.VERBOSE))
output_handle_proc.daemon=True
output_handle_proc.start()
# Process in charge of loading part of the data
load_data_process = mp.Process(target=extract_patch_from_dataset, args=(in_data, self.cfg, self.input_queue,
self.extract_info_queue, self.cfg.TEST.VERBOSE))
load_data_process.daemon=True
load_data_process.start()
if '_X_file' in locals() and isinstance(self._X_file, h5py.File):
self._X_file.close()
del self._X, in_data
# Lock the thread inferring until no more patches
if self.cfg.TEST.VERBOSE and self.cfg.SYSTEM.NUM_GPUS > 1:
print(f"[Rank {get_rank()} ({os.getpid()})] Doing inference ")
while True:
obj = self.input_queue.get(timeout=60)
if obj == None: break
img, patch_coords = obj
img, _ = self.test_generator.norm_X(img)
if self.cfg.TEST.AUGMENTATION:
p = ensemble16_3d_predictions(img[0], batch_size_value=self.cfg.TRAIN.BATCH_SIZE,
pred_func=(
lambda img_batch_subdiv:
to_numpy_format(
self.apply_model_activations(
self.model_call_func(img_batch_subdiv),
),
self.axis_order_back
)
)
)
else:
with torch.cuda.amp.autocast():
p = self.model_call_func(img)
p = to_numpy_format(self.apply_model_activations(p), self.axis_order_back)
# Create a mask with the overlap. Calculate the exact part of the patch that will be inserted in the
# final H5/Zarr file
p = p[0, self.cfg.DATA.TEST.PADDING[0]:p.shape[1]-self.cfg.DATA.TEST.PADDING[0],
self.cfg.DATA.TEST.PADDING[1]:p.shape[2]-self.cfg.DATA.TEST.PADDING[1],
self.cfg.DATA.TEST.PADDING[2]:p.shape[3]-self.cfg.DATA.TEST.PADDING[2]]
m = np.ones(p.shape, dtype=np.uint8)
# Put the prediction into queue
self.output_queue.put([p, m, patch_coords])
# Get some auxiliar variables
self.stats['patch_counter'] = self.extract_info_queue.get(timeout=60)
if is_main_process():
z_vol_info = self.extract_info_queue.get(timeout=60)
list_of_vols_in_z = self.extract_info_queue.get(timeout=60)
load_data_process.join()
output_handle_proc.join()
# Wait until all threads are done so the main thread can create the full size image
if self.cfg.SYSTEM.NUM_GPUS > 1 :
if self.cfg.TEST.VERBOSE:
print(f"[Rank {get_rank()} ({os.getpid()})] Finish sample inference ")
if is_dist_avail_and_initialized():
dist.barrier()
t_axes = (0,1,3,4,2) if "ZCYX" in self.cfg.TEST.BY_CHUNKS.INPUT_IMG_AXES_ORDER else (0,1,2,3,4)
# Create the final H5/Zarr file that contains all the individual parts
if is_main_process():
if self.cfg.SYSTEM.NUM_GPUS > 1:
# Obtain parts of the data created by all GPUs
if self.cfg.TEST.BY_CHUNKS.FORMAT == "h5":
data_parts_filenames = sorted(next(os.walk(self.cfg.PATHS.RESULT_DIR.PER_IMAGE))[2])
else:
data_parts_filenames = sorted(next(os.walk(self.cfg.PATHS.RESULT_DIR.PER_IMAGE))[1])
parts = []
mask_parts = []
for x in data_parts_filenames:
if filename+"_part" in x and x.endswith(self.cfg.TEST.BY_CHUNKS.FORMAT):
if "_mask" not in x:
parts.append(x)
else:
mask_parts.append(x)
data_parts_filenames = parts
data_parts_mask_filenames = mask_parts
del parts, mask_parts
if max(1,self.cfg.SYSTEM.NUM_GPUS) != len(data_parts_filenames) != len(list_of_vols_in_z):
raise ValueError("Number of data parts is not the same as number of GPUs")
# Compose the large image
for i, data_part_fname in enumerate(data_parts_filenames):
print("Reading {}".format(os.path.join(self.cfg.PATHS.RESULT_DIR.PER_IMAGE, data_part_fname)))
data_part_file, data_part = read_chunked_data(os.path.join(self.cfg.PATHS.RESULT_DIR.PER_IMAGE, data_part_fname))
data_mask_part_file, data_mask_part = read_chunked_data(os.path.join(self.cfg.PATHS.RESULT_DIR.PER_IMAGE, data_parts_mask_filenames[i]))
if 'data' not in locals():
all_data_filename = os.path.join(self.cfg.PATHS.RESULT_DIR.PER_IMAGE, filename+ext)
if self.cfg.TEST.BY_CHUNKS.FORMAT == "h5":
allfile = h5py.File(all_data_filename,'w')
data = allfile.create_dataset("data", data_part.shape, dtype=self.dtype_str, compression="gzip")
else:
allfile = zarr.open_group(all_data_filename, mode="w")
data = allfile.create_dataset("data", shape=data_part.shape, dtype=self.dtype_str, compression="gzip")