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documentation.txt
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MoNuSAC 2020 related modification documentation
# Classes
# Total Classes for Classification - 5 including Background (class 0)
self.nr_types = 5
# Nuclear Instance binary segmentation classes
self.nr_classes = 2 # Nuclei Pixels vs Background
# Dictionary for Nucleus Type
self.nuclei_type_dict = {
'Epithelial': 1, # ! Please ensure the matching ID is unique
'Lymphocyte': 2,
'Macrophage': 3,
'Neutrophil': 4,
}
#
DATA Changes:
Ignoring file - TCGA-P4-AAVK-01Z-00-DX1_6 (173, 74, 3) as it's size is even less than patch size. Losing about 5 (*4 = 20) annotations.
DATASET SELECTION:
MoNuSAC specifies WS are taken from 4 organs: Breast, kidney, lung and prostate, but does not specify the distribution.
Train - Holdout Split
Holdout -
TCGA-55-1594-01Z-00-DX1_005, TCGA-73-4668-01Z-00-DX1_004, TCGA-78-7220-01Z-00-DX1_002, TCGA-A2-A0CV-01Z-00-DX1_2, TCGA-A2-A0CV-01Z-00-DX1_5, TCGA-E2-A154-01Z-00-DX1_1, TCGA-E2-A154-01Z-00-DX1_5, TCGA-E2-A154-01Z-00-DX1_6, TCGA-E9-A22G-01Z-00-DX1_4, TCGA-EJ-5505-01Z-00-DX1-6, TCGA-EV-5903-01Z-00-DX1_5, TCGA-G7-A8LD-01Z-00-DX1_7, TCGA-KK-A6E0-01Z-00-DX1-2, TCGA-KK-A59X-01Z-00-DX1-2, TCGA-V1-A8WL-01Z-00-DX1-2
[cp TCGA-55-1594-01Z-00-DX1_005* TCGA-73-4668-01Z-00-DX1_004* TCGA-78-7220-01Z-00-DX1_002* TCGA-A2-A0CV-01Z-00-DX1_2* TCGA-A2-A0CV-01Z-00-DX1_5* TCGA-E2-A154-01Z-00-DX1_1* TCGA-E2-A154-01Z-00-DX1_5* TCGA-E2-A154-01Z-00-DX1_6* TCGA-E9-A22G-01Z-00-DX1_4* TCGA-EJ-5505-01Z-00-DX1-6* TCGA-EV-5903-01Z-00-DX1_5* TCGA-G7-A8LD-01Z-00-DX1_7* TCGA-KK-A6E0-01Z-00-DX1-2* TCGA-KK-A59X-01Z-00-DX1-2* TCGA-V1-A8WL-01Z-00-DX1-2* ../Valid_Labels/
]
Train - everything else
### FINAL REPORTING
## MODEL v1_multitask
# Files: v1_multitask/np_hv/07/model-33198.data-00000-of-00001, v1_multitask/np_hv/07/model-33198.index
# REPORTING THIS
#Performance '/home/dm1/shikhar/hover_net_modified/src/..//v1_multitask/np_hv/07/model-33198': [ 0.79046 0.68914 0.76172 0.56644 0.60720 0.60457]
metrics[0].append(get_dice_1(true, pred))
metrics[1].append(pq_info[0]) # dq
metrics[2].append(pq_info[1]) # sq
metrics[3].append(pq_info[2]) # pq
metrics[4].append(get_fast_aji_plus(true, pred))
metrics[5].append(get_fast_aji(true, pred))
MULTI-TASK DATASET
== Seg Only ==
== CPM15
img_ext = '.png'
img_dir = ROOT_DIR + '/../data/cpm15/Images/'
ann_dir = ROOT_DIR + '/../data/cpm15/Labels/'
####
print(img_dir)
out_dir = ROOT_DIR + "/../data/cpm15/train/%dx%d_%dx%d" % \
(win_size[0], win_size[1], step_size[0], step_size[1])
== CPM17 -Train & Test patch extraction done separately
Train
img_ext = '.png'
img_dir = ROOT_DIR + '/../data/cpm17/train/Images/'
ann_dir = ROOT_DIR + '/../data/cpm17/train/Labels/'
####
print(img_dir)
out_dir = ROOT_DIR + "/../data/cpm17/train/%dx%d_%dx%d" % \
(win_size[0], win_size[1], step_size[0], step_size[1])
Test
img_ext = '.png'
img_dir = ROOT_DIR + '/../data/cpm17/test/Images/'
ann_dir = ROOT_DIR + '/../data/cpm17/test/Labels/'
####
print(img_dir)
out_dir = ROOT_DIR + "/../data/cpm17/test/%dx%d_%dx%d" % \
(win_size[0], win_size[1], step_size[0], step_size[1])
== Kumar
.TIF format
-Train & Test patch extraction done separately
Train
img_ext = '.tif'
img_dir = ROOT_DIR + '/../data/kumar/train/Images/'
ann_dir = ROOT_DIR + '/../data/kumar/train/Labels/'
####
print(img_dir)
out_dir = ROOT_DIR + "/../data/kumar/train/%dx%d_%dx%d" % \
(win_size[0], win_size[1], step_size[0], step_size[1])
Test
img_ext = '.tif'
img_dir = ROOT_DIR + '/../data/kumar/test_diff/Images/'
ann_dir = ROOT_DIR + '/../data/kumar/test_diff/Labels/'
####
print(img_dir)
out_dir = ROOT_DIR + "/../data/kumar/test_diff/%dx%d_%dx%d" % \
(win_size[0], win_size[1], step_size[0], step_size[1])
== CoNSeP
Combine classes as per HoverNet paper
Train
img_ext = '.png'
img_dir = ROOT_DIR + '/../data/CoNSeP/Train/Images/'
ann_dir = ROOT_DIR + '/../data/CoNSeP/Train/Labels/'
####
print(img_dir)
out_dir = ROOT_DIR + "/../data/CoNSeP/Train/%dx%d_%dx%d" % \
(win_size[0], win_size[1], step_size[0], step_size[1])
@ TO DOs:
Loss Function Changes
In the Cross Entropy (& Jaccard component) Loss for NC & NP, weight the classes according to organizer class weights. Minority class gets 10 times the weight. Or something like that. Or maybe just Panoptic Quality itself....?
Use Jac card Index in Loss Function for Classification, and not DICE
Post Processing of Output
Marker M generation for thr3shokding function - choose thresholds b and k again.
Pre Processing of Annotations
Annotations
Computing the Distance to Centre of Mass of each Nuclear Annotation Boundary will be important
Axes will have zero distance in complementary maps.
*Range is - 1 to 1 for distances.
*Consider using Gaussian Distribution rather than a rigid - 1 to 1.
Neural Network Architecture
Patch Size, Downsampling/Upsampling degree
Nuclear Classification Evaluation
??? CHECK
Network Training Experiments:
1. # v1.0, v1_multitask, v2_multitask, v2_multitask_short,
## Network Training Results
1. v1.0 (Stored at v1.0/np_hv) [LOGS - slurm-121403.out]
Training Config
'training_phase' : [
{
'nr_epochs': 50,
'manual_parameters' : {
# tuple(initial value, schedule)
'learning_rate': (1.0e-4, [('25', 1.0e-5)]),
},
'pretrained_path' : ROOT_DIR + '/ImageNet-ResNet50-Preact.npz',
'train_batch_size' : 8,
'infer_batch_size' : 16,
'model_flags' : {
'freeze' : True
}
},
{
'nr_epochs': 50,
'manual_parameters' : {
# tuple(initial value, schedule)
'learning_rate': (1.0e-4, [('25', 1.0e-5)]),
},
# path to load, -1 to auto load checkpoint from previous phase,
# None to start from scratch
'pretrained_path' : -1,
'train_batch_size' : 4, # unfreezing everything will
'infer_batch_size' : 16,
'model_flags' : {
'freeze' : False
}
}
~ Hover-net modified (Single Task) - Job 121403 [LOGS - slurm-121403.out]
[0507 06:38:16 @base.py:282] Epoch 50 (global_step 39150) finished, time:4 minutes 51 seconds.
[0507 06:38:17 @saver.py:77] Model saved to /tmp//v1.0//np_hv/01/model-39150.
100%|##########|41/41[00:02<00:00,13.86it/s]
[0507 06:38:20 @monitor.py:459] DataParallelInferenceRunner/QueueInput/queue_size: 49.826
[0507 06:38:20 @monitor.py:459] QueueInput/queue_size: 48.992
[0507 06:38:20 @monitor.py:459] learning_rate: 1e-05
[0507 06:38:20 @monitor.py:459] loss-bce: 0.13565
[0507 06:38:20 @monitor.py:459] loss-dice: 0.23893
[0507 06:38:20 @monitor.py:459] loss-dice-class: 1.9144
[0507 06:38:20 @monitor.py:459] loss-mse: 0.017225
[0507 06:38:20 @monitor.py:459] loss-msge: 0.20009
[0507 06:38:20 @monitor.py:459] loss-xentropy-class: 0.23285
[0507 06:38:20 @monitor.py:459] overall-loss: 2.7563
[0507 06:38:20 @monitor.py:459] valid_acc: 0.94164
[0507 06:38:20 @monitor.py:459] valid_dice: 0.82388
[0507 06:38:20 @monitor.py:459] valid_dice_Epithelial: 0.82992
[0507 06:38:20 @monitor.py:459] valid_dice_Lymphocyte: 0.77181
[0507 06:38:20 @monitor.py:459] valid_dice_Macrophage: 0.80882
[0507 06:38:20 @monitor.py:459] valid_dice_Neutrophil: 0.84185
[0507 06:38:20 @monitor.py:459] valid_mse: 0.037594
[0507 06:38:20 @group.py:48] Callbacks took 4.361 sec in total. DataParallelInferenceRunner: 3.59 seconds
[0507 06:38:20 @base.py:286] Training has finished!
2020-05-07 06:38:20.971934: W tensorflow/core/kernels/queue_base.cc:277] _2_QueueInput/input_queue: Skipping cancelled enqueue attempt with queue not closed
2020-05-07 06:38:20.972031: W tensorflow/core/kernels/queue_base.cc:277] _3_DataParallelInferenceRunner/QueueInput/input_queue: Skipping cancelled enqueue attempt with queue not closed
[0507 06:38:21 @input_source.py:174] EnqueueThread QueueInput/input_queue Exited.
[0507 06:38:22 @input_source.py:174] EnqueueThread DataParallelInferenceRunner/QueueInput/input_queue Exited.
PrefetchDataZMQ successfully cleaned-up.
PrefetchDataZMQ successfully cleaned-up.
Computed Statistics on Holdout Dataset are:
[ 0.81694 0.74608 0.80668 0.61129 0.64900 0.64629]
metrics[0].append(get_dice_1(true, pred))
metrics[1].append(pq_info[0]) # dq
metrics[2].append(pq_info[1]) # sq
metrics[3].append(pq_info[2]) # pq
metrics[4].append(get_fast_aji_plus(true, pred))
metrics[5].append(get_fast_aji(true, pred))
#MODEL_DIR = '/home/dm1/shikhar/hover_net_modified/v2_multitask/np_hv/07'
#Performance [ 0.79046 0.68914 0.76172 0.56644 0.60720 0.60457]
metrics[0].append(get_dice_1(true, pred))
metrics[1].append(pq_info[0]) # dq
metrics[2].append(pq_info[1]) # sq
metrics[3].append(pq_info[2]) # pq
metrics[4].append(get_fast_aji_plus(true, pred))
metrics[5].append(get_fast_aji(true, pred))
2. v1_multitask (cat slurm-121433.out)
Config:
PRETASK_FROZEN_EPOCHS = 2
PRETASK_UNFROZEN_EPOCHS = 2
TASK_EPOCHS = 50
np_hv = {
'train_input_shape' : [270, 270],
'train_mask_shape' : [ 80, 80],
'infer_input_shape' : [270, 270],
'infer_mask_shape' : [ 80, 80],
'training_phase' : [
# CoNSeP
{ # Learn Decoder on Task
# == START: Dataset specific arguments ==
'train_dir' : [ROOT_DIR + '/../data/CoNSeP/Train/540x540_80x80/'],
'valid_dir' : [ROOT_DIR + '/../data/CoNSeP/Test/540x540_80x80/'],
# == END: Dataset specific arguments ==
'nr_epochs': PRETASK_FROZEN_EPOCHS,
'manual_parameters' : {
# tuple(initial value, schedule)
'learning_rate': (1.0e-4, [('25', 1.0e-5)]),
},
'pretrained_path' : ROOT_DIR + '/../ImageNet-ResNet50-Preact.npz',
'train_batch_size' : 8,
'infer_batch_size' : 16,
'model_flags' : {
'freeze' : True,
'type_classification':True,
'nr_types': 5,
'nr_classes': 2,
'nuclei_type_dict': {
'other': 1,
'inflammatory': 2,
'epithelial': 3,
'spindle-shaped': 4,
}
}
},
{ # Learn Encoder for the Task
# == START: Dataset specific arguments ==
'train_dir' : [ROOT_DIR + '/../data/CoNSeP/Train/540x540_80x80/'],
'valid_dir' : [ROOT_DIR + '/../data/CoNSeP/Test/540x540_80x80/'],
# == END: Dataset specific arguments ==
'nr_epochs': PRETASK_UNFROZEN_EPOCHS,
'manual_parameters' : {
# tuple(initial value, schedule)
'learning_rate': (1.0e-4, [('25', 1.0e-5)]),
},
# path to load, -1 to auto load checkpoint from previous phase,
# None to start from scratch
'pretrained_path' : -1,
'train_batch_size' : 4, # unfreezing everything will
'infer_batch_size' : 16,
'model_flags' : {
'freeze' : False,
'type_classification':True,
'nr_types': 5,
# ! nr_types will replace nr_classes if type_classification=True
'nr_classes': 2, # Nuclei Pixels vs Background
'nuclei_type_dict': {
'other': 1, # ! Please ensure the matching ID is unique
'inflammatory': 2,
'epithelial': 3,
'spindle-shaped': 4,
}
}
},
# CPM17
{ # Learn Decoder on Task
# == START: Dataset specific arguments ==
'train_dir' : [ROOT_DIR + '/../data/cpm17/train/540x540_80x80/'],
'valid_dir' : [ROOT_DIR + '/../data/cpm17/test/540x540_80x80/'],
# == END: Dataset specific arguments ==
'nr_epochs': PRETASK_FROZEN_EPOCHS,
'manual_parameters' : {
# tuple(initial value, schedule)
'learning_rate': (1.0e-4, [('25', 1.0e-5)]),
},
'pretrained_path' : -1,
'train_batch_size' : 8,
'infer_batch_size' : 16,
'model_flags' : {
'freeze' : True,
'type_classification': False,
'nr_classes': 2, # Nuclei Pixels vs Background,
'nuclei_type_dict': {},
'nr_types': -1
}
},
{ # Learn Encoder for the Task
# == START: Dataset specific arguments ==
'train_dir' : [ROOT_DIR + '/../data/cpm17/train/540x540_80x80/'],
'valid_dir' : [ROOT_DIR + '/../data/cpm17/test/540x540_80x80/'],
# == END: Dataset specific arguments ==
'nr_epochs': PRETASK_UNFROZEN_EPOCHS,
'manual_parameters' : {
# tuple(initial value, schedule)
'learning_rate': (1.0e-4, [('25', 1.0e-5)]),
},
# path to load, -1 to auto load checkpoint from previous phase,
# None to start from scratch
'pretrained_path' : -1,
'train_batch_size' : 4, # unfreezing everything will
'infer_batch_size' : 16,
'model_flags' : {
'freeze' : False,
'type_classification': False,
'nr_classes': 2, # Nuclei Pixels vs Background,
'nuclei_type_dict': {},
'nr_types': -1
}
},
# Kumar
{ # Learn Decoder on Task
# == START: Dataset specific arguments ==
'train_dir' : [ROOT_DIR + '/../data/kumar/train/540x540_80x80/'],
'valid_dir' : [ROOT_DIR + '/../data/kumar/test_diff/540x540_80x80/'],
# == END: Dataset specific arguments ==
'nr_epochs': PRETASK_FROZEN_EPOCHS,
'manual_parameters' : {
# tuple(initial value, schedule)
'learning_rate': (1.0e-4, [('25', 1.0e-5)]),
},
'pretrained_path' : -1,
'train_batch_size' : 8,
'infer_batch_size' : 16,
'model_flags' : {
'freeze' : True,
'type_classification': False,
'nr_classes': 2, # Nuclei Pixels vs Background
'nuclei_type_dict': {},
'nr_types': -1
}
},
{ # Learn Encoder for the Task
# == START: Dataset specific arguments ==
'train_dir' : [ROOT_DIR + '/../data/kumar/train/540x540_80x80/'],
'valid_dir' : [ROOT_DIR + '/../data/kumar/test_diff/540x540_80x80/'],
# == END: Dataset specific arguments ==
'nr_epochs': PRETASK_UNFROZEN_EPOCHS,
'manual_parameters' : {
# tuple(initial value, schedule)
'learning_rate': (1.0e-4, [('25', 1.0e-5)]),
},
# path to load, -1 to auto load checkpoint from previous phase,
# None to start from scratch
'pretrained_path' : -1,
'train_batch_size' : 4, # unfreezing everything will
'infer_batch_size' : 16,
'model_flags' : {
'freeze' : False,
'type_classification': False,
'nr_classes': 2, # Nuclei Pixels vs Background
'nuclei_type_dict': {},
'nr_types': -1
}
},
# MoNuSAC
{ # Train decoder on MoNuSAC based on multi-task encoded representations
# == START: Dataset specific arguments ==
'train_dir' : [ROOT_DIR + '/../MoNuSAC_processed/train/540x540_80x80/'],
'valid_dir' : [ROOT_DIR + '/../MoNuSAC_processed/valid/540x540_80x80/'],
# == END: Dataset specific arguments ==
'nr_epochs': TASK_EPOCHS,
'manual_parameters' : {
# tuple(initial value, schedule)
'learning_rate': (1.0e-4, [('25', 1.0e-5)]),
},
'pretrained_path' : -1, #ROOT_DIR + '/../ImageNet-ResNet50-Preact.npz'
'train_batch_size' : 8,
'infer_batch_size' : 16,
'model_flags' : {
'freeze' : True,
'type_classification': True,
'nr_types': 5,
# ! nr_types will replace nr_classes if type_classification=True
'nr_classes': 2, # Nuclei Pixels vs Background
'nuclei_type_dict': {
'Epithelial': 1, # ! Please ensure the matching ID is unique
'Lymphocyte': 2,
'Macrophage': 3,
'Neutrophil': 4,
}
}
},
{ # Train Encoder as fine-tuning on MoNuSAC
# == START: Dataset specific arguments ==
'train_dir' : [ROOT_DIR + '/../MoNuSAC_processed/train/540x540_80x80/'],
'valid_dir' : [ROOT_DIR + '/../MoNuSAC_processed/valid/540x540_80x80/'],
# == END: Dataset specific arguments ==
'nr_epochs': TASK_EPOCHS,
'manual_parameters' : {
# tuple(initial value, schedule)
'learning_rate': (1.0e-4, [('25', 1.0e-5)]),
},
# path to load, -1 to auto load checkpoint from previous phase,
# None to start from scratch
'pretrained_path' : -1,
'train_batch_size' : 4, # unfreezing everything will
'infer_batch_size' : 16,
'model_flags' : {
'freeze' : False,
'type_classification': True,
'nr_types': 5,
# ! nr_types will replace nr_classes if type_classification=True
'nr_classes': 2, # Nuclei Pixels vs Background
'nuclei_type_dict': {
'Epithelial': 1, # ! Please ensure the matching ID is unique
'Lymphocyte': 2,
'Macrophage': 3,
'Neutrophil': 4,
}
}
}
],
'loss_term' : {'bce' : 1, 'dice' : 1, 'mse' : 2, 'msge' : 1},
'optimizer' : tf.train.AdamOptimizer,
'inf_auto_metric' : 'valid_dice',
'inf_auto_comparator' : '>',
'inf_batch_size' : 16,
}
Results and Computed Statistics
===============================
[0507 21:23:35 @saver.py:77] Model saved to /home/dm1/shikhar/hover_net_modified/src/..//v1_multitask/np_hv/07/model-39984.
100%|##########|41/41[00:02<00:00,14.05it/s]
[0507 21:23:39 @monitor.py:459] DataParallelInferenceRunner/QueueInput/queue_size: 49.875
[0507 21:23:39 @monitor.py:459] QueueInput/queue_size: 49.497
[0507 21:23:39 @monitor.py:459] learning_rate: 1e-05
[0507 21:23:39 @monitor.py:459] loss-bce: 0.15457
[0507 21:23:39 @monitor.py:459] loss-dice: 0.24664
[0507 21:23:39 @monitor.py:459] loss-dice-class: 1.7818
[0507 21:23:39 @monitor.py:459] loss-mse: 0.0184
[0507 21:23:39 @monitor.py:459] loss-msge: 0.20342
[0507 21:23:39 @monitor.py:459] loss-xentropy-class: 0.22228
[0507 21:23:39 @monitor.py:459] overall-loss: 2.6455
[0507 21:23:39 @monitor.py:459] valid_acc: 0.94072
[0507 21:23:39 @monitor.py:459] valid_dice: 0.82658
[0507 21:23:39 @monitor.py:459] valid_dice_Epithelial: 0.82894
[0507 21:23:39 @monitor.py:459] valid_dice_Lymphocyte: 0.74935
[0507 21:23:39 @monitor.py:459] valid_dice_Macrophage: 0.78966
[0507 21:23:39 @monitor.py:459] valid_dice_Neutrophil: 0.83614
[0507 21:23:39 @monitor.py:459] valid_mse: 0.039196
[0507 21:23:39 @group.py:48] Callbacks took 5.018 sec in total. DataParallelInferenceRunner: 3.58 seconds
[0507 21:23:39 @base.py:286] Training has finished!
3. v2_multitask (cat slurm-121439.out)
Configuration
ROOT_DIR = '/home/dm1/shikhar/hover_net_modified/src'
PRETASK_FROZEN_EPOCHS = 10
PRETASK_UNFROZEN_EPOCHS = 10
TASK_EPOCHS = 50
PRETASK_LEARNING_RATE = 1.0e-3
np_hv = {
'train_input_shape' : [270, 270],
'train_mask_shape' : [ 80, 80],
'infer_input_shape' : [270, 270],
'infer_mask_shape' : [ 80, 80],
'training_phase' : [
# CPM17
{ # Learn Decoder on Task
# == START: Dataset specific arguments ==
'train_dir' : [ROOT_DIR + '/../data/cpm17/train/540x540_80x80/'],
'valid_dir' : [ROOT_DIR + '/../data/cpm17/test/540x540_80x80/'],
# == END: Dataset specific arguments ==
'nr_epochs': PRETASK_FROZEN_EPOCHS,
'manual_parameters' : {
# tuple(initial value, schedule)
'learning_rate': (PRETASK_LEARNING_RATE, [('10', PRETASK_LEARNING_RATE)]),
},
'pretrained_path' : ROOT_DIR + '/../ImageNet-ResNet50-Preact.npz',
'train_batch_size' : 8,
'infer_batch_size' : 16,
'model_flags' : {
'freeze' : True,
'type_classification': False,
'nr_classes': 2, # Nuclei Pixels vs Background,
'nuclei_type_dict': {},
'nr_types': -1
}
},
{ # Learn Encoder for the Task
# == START: Dataset specific arguments ==
'train_dir' : [ROOT_DIR + '/../data/cpm17/train/540x540_80x80/'],
'valid_dir' : [ROOT_DIR + '/../data/cpm17/test/540x540_80x80/'],
# == END: Dataset specific arguments ==
'nr_epochs': PRETASK_UNFROZEN_EPOCHS,
'manual_parameters' : {
# tuple(initial value, schedule)
'learning_rate': (PRETASK_LEARNING_RATE, [('10', PRETASK_LEARNING_RATE)]),
},
# path to load, -1 to auto load checkpoint from previous phase,
# None to start from scratch
'pretrained_path' : -1,
'train_batch_size' : 8, # unfreezing everything will
'infer_batch_size' : 16,
'model_flags' : {
'freeze' : False,
'type_classification': False,
'nr_classes': 2, # Nuclei Pixels vs Background,
'nuclei_type_dict': {},
'nr_types': -1
}
},
# Kumar
{ # Learn Decoder on Task
# == START: Dataset specific arguments ==
'train_dir' : [ROOT_DIR + '/../data/kumar/train/540x540_80x80/'],
'valid_dir' : [ROOT_DIR + '/../data/kumar/test_diff/540x540_80x80/'],
# == END: Dataset specific arguments ==
'nr_epochs': PRETASK_FROZEN_EPOCHS,
'manual_parameters' : {
# tuple(initial value, schedule)
'learning_rate': (PRETASK_LEARNING_RATE, [('10', PRETASK_LEARNING_RATE)]),
},
'pretrained_path' : -1,
'train_batch_size' : 8,
'infer_batch_size' : 16,
'model_flags' : {
'freeze' : True,
'type_classification': False,
'nr_classes': 2, # Nuclei Pixels vs Background
'nuclei_type_dict': {},
'nr_types': -1
}
},
{ # Learn Encoder for the Task
# == START: Dataset specific arguments ==
'train_dir' : [ROOT_DIR + '/../data/kumar/train/540x540_80x80/'],
'valid_dir' : [ROOT_DIR + '/../data/kumar/test_diff/540x540_80x80/'],
# == END: Dataset specific arguments ==
'nr_epochs': PRETASK_UNFROZEN_EPOCHS,
'manual_parameters' : {
# tuple(initial value, schedule)
'learning_rate': (PRETASK_LEARNING_RATE, [('10', PRETASK_LEARNING_RATE)]),
},
# path to load, -1 to auto load checkpoint from previous phase,
# None to start from scratch
'pretrained_path' : -1,
'train_batch_size' : 8, # unfreezing everything will
'infer_batch_size' : 16,
'model_flags' : {
'freeze' : False,
'type_classification': False,
'nr_classes': 2, # Nuclei Pixels vs Background
'nuclei_type_dict': {},
'nr_types': -1
}
},
# CoNSeP
{ # Learn Decoder on Task
# == START: Dataset specific arguments ==
'train_dir' : [ROOT_DIR + '/../data/CoNSeP/Train/540x540_80x80/'],
'valid_dir' : [ROOT_DIR + '/../data/CoNSeP/Test/540x540_80x80/'],
# == END: Dataset specific arguments ==
'nr_epochs': 15,
'manual_parameters' : {
# tuple(initial value, schedule)
'learning_rate': (PRETASK_LEARNING_RATE, [('10', PRETASK_LEARNING_RATE)]),
},
'pretrained_path' : -1,
'train_batch_size' : 8,
'infer_batch_size' : 16,
'model_flags' : {
'freeze' : True,
'type_classification':True,
'nr_types': 5,
'nr_classes': 2,
'nuclei_type_dict': {
'other': 1,
'inflammatory': 2,
'epithelial': 3,
'spindle-shaped': 4,
}
}
},
{ # Learn Encoder for the Task
# == START: Dataset specific arguments ==
'train_dir' : [ROOT_DIR + '/../data/CoNSeP/Train/540x540_80x80/'],
'valid_dir' : [ROOT_DIR + '/../data/CoNSeP/Test/540x540_80x80/'],
# == END: Dataset specific arguments ==
'nr_epochs': 15,
'manual_parameters' : {
# tuple(initial value, schedule)
'learning_rate': (PRETASK_LEARNING_RATE, [('10', PRETASK_LEARNING_RATE)]),
},
# path to load, -1 to auto load checkpoint from previous phase,
# None to start from scratch
'pretrained_path' : -1,
'train_batch_size' : 8, # unfreezing everything will
'infer_batch_size' : 16,
'model_flags' : {
'freeze' : False,
'type_classification':True,
'nr_types': 5,
# ! nr_types will replace nr_classes if type_classification=True
'nr_classes': 2, # Nuclei Pixels vs Background
'nuclei_type_dict': {
'other': 1, # ! Please ensure the matching ID is unique
'inflammatory': 2,
'epithelial': 3,
'spindle-shaped': 4,
}
}
},
# MoNuSAC
{ # Train decoder on MoNuSAC based on multi-task encoded representations
# == START: Dataset specific arguments ==
'train_dir' : [ROOT_DIR + '/../MoNuSAC_processed/train/540x540_80x80/'],
'valid_dir' : [ROOT_DIR + '/../MoNuSAC_processed/valid/540x540_80x80/'],
# == END: Dataset specific arguments ==
'nr_epochs': TASK_EPOCHS,
'manual_parameters' : {
# tuple(initial value, schedule)
'learning_rate': (1.0e-4, [('25', 1.0e-5)]),
},
'pretrained_path' : -1, #ROOT_DIR + '/../ImageNet-ResNet50-Preact.npz'
'train_batch_size' : 8,
'infer_batch_size' : 16,
'model_flags' : {
'freeze' : True,
'type_classification': True,
'nr_types': 5,
# ! nr_types will replace nr_classes if type_classification=True
'nr_classes': 2, # Nuclei Pixels vs Background
'nuclei_type_dict': {
'Epithelial': 1, # ! Please ensure the matching ID is unique
'Lymphocyte': 2,
'Macrophage': 3,
'Neutrophil': 4,
}
}
},
{ # Train Encoder as fine-tuning on MoNuSAC
# == START: Dataset specific arguments ==
'train_dir' : [ROOT_DIR + '/../MoNuSAC_processed/train/540x540_80x80/'],
'valid_dir' : [ROOT_DIR + '/../MoNuSAC_processed/valid/540x540_80x80/'],
# == END: Dataset specific arguments ==
'nr_epochs': TASK_EPOCHS,
'manual_parameters' : {
# tuple(initial value, schedule)
'learning_rate': (1.0e-4, [('25', 1.0e-5)]),
},
# path to load, -1 to auto load checkpoint from previous phase,
# None to start from scratch
'pretrained_path' : -1,
'train_batch_size' : 4, # unfreezing everything will
'infer_batch_size' : 16,
'model_flags' : {
'freeze' : False,
'type_classification': True,
'nr_types': 5,
# ! nr_types will replace nr_classes if type_classification=True
'nr_classes': 2, # Nuclei Pixels vs Background
'nuclei_type_dict': {
'Epithelial': 1, # ! Please ensure the matching ID is unique
'Lymphocyte': 2,
'Macrophage': 3,
'Neutrophil': 4,
}
}
}
],
'loss_term' : {'bce' : 1, 'dice' : 1, 'mse' : 2, 'msge' : 1},
'optimizer' : tf.train.AdamOptimizer,
'inf_auto_metric' : 'valid_dice',
'inf_auto_comparator' : '>',
'inf_batch_size' : 16,
}
Results and Computed Statistics
===============================
[0507 23:46:34 @saver.py:77] Model saved to /home/dm1/shikhar/hover_net_modified/src/..//v2_multitask/np_hv/07/model-42118.
100%|##########|41/41[00:02<00:00,13.82it/s]
[0507 23:46:38 @monitor.py:459] DataParallelInferenceRunner/QueueInput/queue_size: 49.873
[0507 23:46:38 @monitor.py:459] QueueInput/queue_size: 48.004
[0507 23:46:38 @monitor.py:459] learning_rate: 1e-05
[0507 23:46:38 @monitor.py:459] loss-bce: 0.14295
[0507 23:46:38 @monitor.py:459] loss-dice: 0.22765
[0507 23:46:38 @monitor.py:459] loss-dice-class: 2.4458
[0507 23:46:38 @monitor.py:459] loss-mse: 0.018726
[0507 23:46:38 @monitor.py:459] loss-msge: 0.22298
[0507 23:46:38 @monitor.py:459] loss-xentropy-class: 0.17471
[0507 23:46:38 @monitor.py:459] overall-loss: 3.2515
[0507 23:46:38 @monitor.py:459] valid_acc: 0.93249
[0507 23:46:38 @monitor.py:459] valid_dice: 0.79559
[0507 23:46:38 @monitor.py:459] valid_dice_Epithelial: 0.83496
[0507 23:46:38 @monitor.py:459] valid_dice_Lymphocyte: 0.77113
[0507 23:46:38 @monitor.py:459] valid_dice_Macrophage: 0.7902
[0507 23:46:38 @monitor.py:459] valid_dice_Neutrophil: 0.83246
[0507 23:46:38 @monitor.py:459] valid_mse: 0.039461
[0507 23:46:38 @group.py:48] Callbacks took 4.851 sec in total. DataParallelInferenceRunner: 3.47 seconds
4. v2_multitask_short ( cat slurm-121440.out )
Config
ROOT_DIR = '/home/dm1/shikhar/hover_net_modified/src'
PRETASK_FROZEN_EPOCHS = 5
PRETASK_UNFROZEN_EPOCHS = 5
TASK_EPOCHS = 55
PRETASK_LEARNING_RATE = 1.0e-3
np_hv = {
'train_input_shape' : [270, 270],
'train_mask_shape' : [ 80, 80],
'infer_input_shape' : [270, 270],
'infer_mask_shape' : [ 80, 80],
'training_phase' : [
# CPM17
{ # Learn Decoder on Task
# == START: Dataset specific arguments ==
'train_dir' : [ROOT_DIR + '/../data/cpm17/train/540x540_80x80/'],
'valid_dir' : [ROOT_DIR + '/../data/cpm17/test/540x540_80x80/'],
# == END: Dataset specific arguments ==
'nr_epochs': PRETASK_FROZEN_EPOCHS,
'manual_parameters' : {
# tuple(initial value, schedule)
'learning_rate': (PRETASK_LEARNING_RATE, [('5', PRETASK_LEARNING_RATE)]),
},
'pretrained_path' : ROOT_DIR + '/../ImageNet-ResNet50-Preact.npz',
'train_batch_size' : 8,
'infer_batch_size' : 16,
'model_flags' : {
'freeze' : True,
'type_classification': False,
'nr_classes': 2, # Nuclei Pixels vs Background,
'nuclei_type_dict': {},
'nr_types': -1
}
},
{ # Learn Encoder for the Task
# == START: Dataset specific arguments ==
'train_dir' : [ROOT_DIR + '/../data/cpm17/train/540x540_80x80/'],
'valid_dir' : [ROOT_DIR + '/../data/cpm17/test/540x540_80x80/'],
# == END: Dataset specific arguments ==
'nr_epochs': PRETASK_UNFROZEN_EPOCHS,
'manual_parameters' : {
# tuple(initial value, schedule)
'learning_rate': (PRETASK_LEARNING_RATE, [('5', PRETASK_LEARNING_RATE)]),
},
# path to load, -1 to auto load checkpoint from previous phase,
# None to start from scratch
'pretrained_path' : -1,
'train_batch_size' : 8, # unfreezing everything will
'infer_batch_size' : 16,
'model_flags' : {
'freeze' : False,
'type_classification': False,
'nr_classes': 2, # Nuclei Pixels vs Background,
'nuclei_type_dict': {},
'nr_types': -1
}
},
# Kumar
{ # Learn Decoder on Task
# == START: Dataset specific arguments ==
'train_dir' : [ROOT_DIR + '/../data/kumar/train/540x540_80x80/'],
'valid_dir' : [ROOT_DIR + '/../data/kumar/test_diff/540x540_80x80/'],
# == END: Dataset specific arguments ==
'nr_epochs': PRETASK_FROZEN_EPOCHS,
'manual_parameters' : {
# tuple(initial value, schedule)
'learning_rate': (PRETASK_LEARNING_RATE, [('5', PRETASK_LEARNING_RATE)]),
},
'pretrained_path' : -1,
'train_batch_size' : 8,
'infer_batch_size' : 16,
'model_flags' : {
'freeze' : True,
'type_classification': False,
'nr_classes': 2, # Nuclei Pixels vs Background
'nuclei_type_dict': {},
'nr_types': -1
}
},
{ # Learn Encoder for the Task
# == START: Dataset specific arguments ==
'train_dir' : [ROOT_DIR + '/../data/kumar/train/540x540_80x80/'],
'valid_dir' : [ROOT_DIR + '/../data/kumar/test_diff/540x540_80x80/'],
# == END: Dataset specific arguments ==
'nr_epochs': PRETASK_UNFROZEN_EPOCHS,
'manual_parameters' : {
# tuple(initial value, schedule)
'learning_rate': (PRETASK_LEARNING_RATE, [('5', PRETASK_LEARNING_RATE)]),
},
# path to load, -1 to auto load checkpoint from previous phase,
# None to start from scratch
'pretrained_path' : -1,
'train_batch_size' : 8, # unfreezing everything will
'infer_batch_size' : 16,
'model_flags' : {
'freeze' : False,
'type_classification': False,
'nr_classes': 2, # Nuclei Pixels vs Background
'nuclei_type_dict': {},
'nr_types': -1
}
},
# CoNSeP
{ # Learn Decoder on Task
# == START: Dataset specific arguments ==
'train_dir' : [ROOT_DIR + '/../data/CoNSeP/Train/540x540_80x80/'],
'valid_dir' : [ROOT_DIR + '/../data/CoNSeP/Test/540x540_80x80/'],
# == END: Dataset specific arguments ==
'nr_epochs': 20,
'manual_parameters' : {
# tuple(initial value, schedule)
'learning_rate': (PRETASK_LEARNING_RATE, [('5', 1.0e-4)]),
},
'pretrained_path' : -1,
'train_batch_size' : 8,
'infer_batch_size' : 16,
'model_flags' : {
'freeze' : True,
'type_classification':True,
'nr_types': 5,
'nr_classes': 2,
'nuclei_type_dict': {
'other': 1,
'inflammatory': 2,
'epithelial': 3,
'spindle-shaped': 4,
}
}
},
{ # Learn Encoder for the Task
# == START: Dataset specific arguments ==
'train_dir' : [ROOT_DIR + '/../data/CoNSeP/Train/540x540_80x80/'],
'valid_dir' : [ROOT_DIR + '/../data/CoNSeP/Test/540x540_80x80/'],
# == END: Dataset specific arguments ==
'nr_epochs': 20,
'manual_parameters' : {
# tuple(initial value, schedule)
'learning_rate': (PRETASK_LEARNING_RATE, [('5', 1.0e-4)]),
},
# path to load, -1 to auto load checkpoint from previous phase,
# None to start from scratch
'pretrained_path' : -1,
'train_batch_size' : 8, # unfreezing everything will
'infer_batch_size' : 16,
'model_flags' : {
'freeze' : False,
'type_classification':True,
'nr_types': 5,
# ! nr_types will replace nr_classes if type_classification=True
'nr_classes': 2, # Nuclei Pixels vs Background