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specs/nico/training/em_encoder/train/01_m3_m3_encoder.py
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from __future__ import annotations | ||
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import json | ||
import os | ||
from functools import partial | ||
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import torch | ||
from imgaug import augmenters as iaa | ||
from torch.utils.data import DataLoader as TorchDataLoader | ||
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from zetta_utils.api.v0 import * | ||
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POST_WEIGHT = 1.6 | ||
FIELD_MAGN_THR = 0.8 | ||
LR = 1e-4 | ||
EQUI_WEIGHT = 0.5 | ||
EXP_NAME = "general_encoder_debug" | ||
TRAINING_ROOT = "gs://zetta-research-nico/training_artifacts" | ||
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EXP_VERSION = f"0.0.0_M3_M3_lr{LR}_equi{EQUI_WEIGHT}_fmt{FIELD_MAGN_THR}" | ||
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START_EXP_VERSION = None | ||
MODEL_CKPT = None # f"gs://zetta-research-nico/training_artifacts/base_enc_zfish/{#START_EXP_VERSION}/last.ckpt" | ||
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VALIDATION_SRC_PATH = "gs://zetta-research-nico/pairs_dsets/zfish_x1/img_pairwise/-1" | ||
VALIDATION_TGT_PATH = "gs://zetta-research-nico/pairs_dsets/zfish_x1/img_aligned" | ||
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SOURCE_PATHS = { | ||
"microns_pinky": {"contiguous": True, "resolution": [32, 32, 40]}, | ||
"microns_basil": {"contiguous": True, "resolution": [32, 32, 40]}, | ||
"microns_minnie": {"contiguous": False, "resolution": [32, 32, 40]}, | ||
"microns_interneuron": {"contiguous": False, "resolution": [32, 32, 40]}, | ||
"aibs_v1dd": {"contiguous": False, "resolution": [38.8, 38.8, 45]}, | ||
"kim_n2da": {"contiguous": True, "resolution": [32, 32, 50]}, | ||
"kim_pfc2022": {"contiguous": True, "resolution": [32, 32, 40]}, | ||
"kronauer_cra9": {"contiguous": True, "resolution": [32, 32, 42]}, | ||
"kubota_001": {"contiguous": True, "resolution": [40, 40, 40]}, | ||
"lee_fanc": {"contiguous": False, "resolution": [34.4, 34.4, 45]}, | ||
"lee_banc": {"contiguous": False, "resolution": [32, 32, 45]}, | ||
"lee_ppc": {"contiguous": True, "resolution": [32, 32, 40]}, | ||
"lee_mosquito": {"contiguous": False, "resolution": [32, 32, 40]}, | ||
"lichtman_zebrafish": {"contiguous": False, "resolution": [32, 32, 30]}, | ||
"prieto_godino_larva": {"contiguous": True, "resolution": [32, 32, 32]}, | ||
"fafb_v15": {"contiguous": False, "resolution": [32, 32, 40]}, | ||
"lichtman_h01": {"contiguous": False, "resolution": [32, 32, 33]}, | ||
"janelia_hemibrain": {"contiguous": True, "resolution": [32, 32, 32]}, | ||
"janelia_manc": {"contiguous": False, "resolution": [32, 32, 32]}, | ||
"nguyen_thomas_2022": {"contiguous": True, "resolution": [32, 32, 40]}, | ||
"mulcahy_2022_16h": {"contiguous": True, "resolution": [32, 32, 30]}, | ||
"wildenberg_2021_vta_dat12a": {"contiguous": True, "resolution": [32, 32, 40]}, | ||
"bumbarber_2013": {"contiguous": True, "resolution": [31.2, 31.2, 50]}, | ||
"wilson_2019_p3": {"contiguous": True, "resolution": [32, 32, 30]}, | ||
"ishibashi_2021_em1": {"contiguous": True, "resolution": [32, 32, 32]}, | ||
"ishibashi_2021_em2": {"contiguous": True, "resolution": [32, 32, 32]}, | ||
"templier_2019_wafer1": {"contiguous": True, "resolution": [32, 32, 50]}, | ||
"templier_2019_wafer3": {"contiguous": True, "resolution": [32, 32, 50]}, | ||
"lichtman_octopus2022": {"contiguous": True, "resolution": [32, 32, 30]}, | ||
} | ||
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BASE_PATH = "gs://zetta-research-nico/encoder/" | ||
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val_img_aug = [ | ||
partial(rearrange, pattern="c x y 1 -> c x y"), | ||
partial(divide, value=255.0), | ||
] | ||
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train_img_aug = [ | ||
partial(divide, value=255.0), | ||
partial( | ||
square_tile_pattern_aug, | ||
prob=0.5, | ||
tile_size=uniform_distr(64, 1024), | ||
tile_stride=uniform_distr(64, 1024), | ||
max_brightness_change=uniform_distr(0.0, 0.3), | ||
rotation_degree=uniform_distr(0, 90), | ||
preserve_data_val=0.0, | ||
repeats=1, | ||
device="cpu", | ||
), | ||
partial(torch.clamp, min=0.0, max=1.0), | ||
partial(multiply, value=255.0), | ||
partial(to_uint8), | ||
partial( | ||
imgaug_readproc, | ||
augmenters=[ | ||
iaa.SomeOf( | ||
n=2, | ||
children=[ | ||
iaa.OneOf( | ||
children=[ | ||
iaa.OneOf( | ||
children=[ | ||
iaa.GammaContrast(gamma=(0.5, 2.0)), | ||
iaa.SigmoidContrast(gain=(4, 6), cutoff=(0.3, 0.7)), | ||
iaa.LogContrast(gain=(0.7, 1.3)), | ||
iaa.LinearContrast(alpha=(0.4, 1.6)), | ||
] | ||
), | ||
iaa.AllChannelsCLAHE(clip_limit=(0.1, 8.0), tile_grid_size_px=(3, 64)), | ||
] | ||
), | ||
iaa.Add((-40, 40)), | ||
iaa.imgcorruptlike.DefocusBlur(severity=(1, 2)), | ||
iaa.Cutout( | ||
squared=False, nb_iterations=1, size=(0.05, 0.8), cval=(0, 255) | ||
), | ||
iaa.JpegCompression(compression=(0, 35)), | ||
], | ||
random_order=True, | ||
) | ||
], | ||
), | ||
] | ||
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shared_train_img_aug = [ | ||
partial( | ||
imgaug_readproc, | ||
augmenters=[ | ||
iaa.Sequential( | ||
children=[ | ||
iaa.Rot90(k=[0, 1, 2, 3]), | ||
iaa.Fliplr(p=0.25), | ||
iaa.Flipud(p=0.25), | ||
], | ||
random_order=True, | ||
), | ||
], | ||
), | ||
partial(rearrange, pattern="c x y 1 -> c x y"), | ||
partial(divide, value=255.0), | ||
] | ||
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training = JointDataset( | ||
mode="horizontal", | ||
datasets={ | ||
"images": JointDataset( | ||
mode="vertical", | ||
datasets={ | ||
name: LayerDataset( | ||
layer=build_layer_set( | ||
{ | ||
"src_img": build_cv_layer( | ||
BASE_PATH + "datasets/" + name, read_procs=train_img_aug | ||
), | ||
"tgt_img": build_cv_layer( | ||
BASE_PATH + "pairwise_aligned/" + name + "/warped_img", | ||
read_procs=train_img_aug, | ||
), | ||
}, | ||
readonly=True, | ||
read_procs=shared_train_img_aug, | ||
), | ||
sample_indexer=VolumetricNGLIndexer( | ||
path="zetta-research-nico/encoder/pairwise_aligned/" + name, | ||
resolution=settings["resolution"], | ||
chunk_size=[1024, 1024, 1], | ||
), | ||
) | ||
for name, settings in SOURCE_PATHS.items() | ||
}, | ||
), | ||
"field": LayerDataset( | ||
layer=build_cv_layer( | ||
"gs://zetta-research-nico/perlin_noise_fields/1px", | ||
read_procs=[ | ||
partial(rearrange, pattern="c x y 1 -> c x y"), | ||
], | ||
), | ||
sample_indexer=RandomIndexer( | ||
VolumetricStridedIndexer( | ||
bbox=BBox3D.from_coords( | ||
start_coord=[0, 0, 0], end_coord=[2048, 2048, 2040], resolution=[4, 4, 45] | ||
), | ||
stride=[128, 128, 1], | ||
chunk_size=[1024, 1024, 1], | ||
resolution=[4, 4, 45], | ||
) | ||
), | ||
), | ||
}, | ||
) | ||
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validation = JointDataset( | ||
mode="horizontal", | ||
datasets={ | ||
"images": LayerDataset( | ||
layer=build_layer_set( | ||
{ | ||
"src_img": build_cv_layer(VALIDATION_SRC_PATH, read_procs=val_img_aug), | ||
"tgt_img": build_cv_layer(VALIDATION_TGT_PATH, read_procs=val_img_aug), | ||
}, | ||
readonly=True, | ||
), | ||
sample_indexer=VolumetricNGLIndexer( | ||
resolution=[32, 32, 30], chunk_size=[1024, 1024, 1], path="nkem/zfish/val" | ||
), | ||
), | ||
"field": LayerDataset( | ||
layer=build_cv_layer( | ||
"gs://zetta-research-nico/perlin_noise_fields/1px", | ||
read_procs=[ | ||
partial(rearrange, pattern="c x y 1 -> c x y"), | ||
], | ||
), | ||
sample_indexer=RandomIndexer( | ||
VolumetricStridedIndexer( | ||
bbox=BBox3D.from_coords( | ||
start_coord=[0, 0, 0], end_coord=[2048, 2048, 2040], resolution=[4, 4, 45] | ||
), | ||
stride=[512, 512, 1], | ||
chunk_size=[1024, 1024, 1], | ||
resolution=[4, 4, 45], | ||
) | ||
), | ||
), | ||
}, | ||
) | ||
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target = partial( | ||
lightning_train, | ||
regime=BaseEncoderRegime( | ||
field_magn_thr=FIELD_MAGN_THR, | ||
post_weight=POST_WEIGHT, | ||
val_log_row_interval=4, | ||
train_log_row_interval=500, | ||
lr=LR, | ||
equivar_weight=EQUI_WEIGHT, | ||
model=load_weights_file( | ||
model=torch.nn.Sequential( | ||
ConvBlock( | ||
num_channels=[1, 32], | ||
kernel_sizes=[1, 1], | ||
activate_last=True, | ||
), | ||
UNet( | ||
list_num_channels=[ | ||
[32, 32, 32], | ||
[32, 32, 32], | ||
[32, 32, 32], | ||
[32, 32, 32], | ||
[32, 32, 32], | ||
[32, 32, 32], | ||
[32, 32, 32], | ||
], | ||
downsample=partial(torch.nn.MaxPool2d, kernel_size=2), | ||
upsample=partial( | ||
UpConv, | ||
kernel_size=3, | ||
upsampler=partial( | ||
torch.nn.Upsample, | ||
scale_factor=2, | ||
mode="nearest", | ||
align_corners=None, | ||
), | ||
conv=partial(torch.nn.Conv2d, padding=1), | ||
), | ||
activate_last=True, | ||
kernel_sizes=[3, 3], | ||
padding_modes="reflect", | ||
unet_skip_mode="sum", | ||
skips={"0": 2}, | ||
), | ||
torch.nn.Conv2d(in_channels=32, out_channels=1, kernel_size=1), | ||
torch.nn.Tanh(), | ||
) | ||
), | ||
), | ||
trainer=ZettaDefaultTrainer( | ||
accelerator="gpu", | ||
devices=1, | ||
max_epochs=10, | ||
default_root_dir=TRAINING_ROOT, | ||
experiment_name=EXP_NAME, | ||
experiment_version=EXP_VERSION, | ||
log_every_n_steps=100, | ||
val_check_interval=500, | ||
# track_grad_norm=2, | ||
# gradient_clip_algorithm="norm", | ||
# gradient_clip_val=CLIP, | ||
# detect_anomaly=True, | ||
# overfit_batches=100, | ||
checkpointing_kwargs={"update_every_n_secs": 600, "backup_every_n_secs": 900}, | ||
), | ||
train_dataloader=TorchDataLoader( | ||
batch_size=1, | ||
shuffle=True, | ||
num_workers=8, | ||
dataset=training, | ||
), | ||
val_dataloader=TorchDataLoader( | ||
batch_size=1, | ||
shuffle=False, | ||
num_workers=8, | ||
dataset=validation, | ||
), | ||
) | ||
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os.environ["ZETTA_RUN_SPEC"] = json.dumps("") | ||
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execute_on_gcp_with_sqs( | ||
target=target, | ||
worker_image="us.gcr.io/zetta-research/zetta_utils:nico_py3.9_20230905", | ||
worker_resources={"memory": "25560Mi", "nvidia.com/gpu": "1"}, | ||
worker_replicas=1, | ||
local_test=False, | ||
) |
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@@ -1,6 +1,3 @@ | ||
from . import encoding_coarsener | ||
from . import encoding_coarsener_highres | ||
from . import encoding_coarsener_gen_x1 | ||
from . import base_encoder | ||
from . import misalignment_detector | ||
from . import base_coarsener | ||
from . import misalignment_detector_aced |
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