Noise2Model

from fastai.basics import *
from fastai.vision.all import *
from fastai.vision.gan import *
from fastai.callback.all import *

from monai.networks.nets import UNet

from bioMONAI.data import *
from bioMONAI.transforms import *

/home/bm/miniforge3/envs/biomonai/lib/python3.11/site-packages/monai/utils/module.py:399: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.
  pkg = __import__(module)  # top level module
Disabling PyTorch because PyTorch >= 2.1 is required but found 2.0.1
None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won't be available and only tokenizers, configuration and file/data utilities can be used.
28-Jul-25 20:04:57 - bfio.backends - WARNING  - Java backend is not available. This could be due to a missing dependency (jpype).

bs = 128
size = 64

def get_gt(path, gt_file_name="avg50.png"): 
    def _fn(fn): return Path(path/"gt")/f"{parent_label(fn)}"/gt_file_name
    return _fn

path = '../_data/Confocal_BPAE_B/raw'

data_ops = {
    'blocks':       (BioImageBlock(cls=BioImage), BioImageBlock(cls=BioImage)),              # define a `TransformBlock` tailored for bioimaging data
    'get_items':    get_image_files,                                                               # get image files in path
    'get_x':        get_gt(Path('../_data/Confocal_BPAE_B')),                                                                  # Label item with the parent folder name
    'get_y':        lambda x: x,                                                # Label item with the grandparent folder name
    'valid_pct':    0.05,                     # split data with the grandparent folder name
    'item_tfms':    [RandCrop2D(size)], # list of item transforms
    'batch_tfms':   Normalize.from_stats(torch.tensor([0.5,0.5,0.5]), torch.tensor([0.5,0.5,0.5])),
    'bs':           bs,                                                                    # batch size
}

data = BioDataLoaders.from_source(
    path,                           # root directory for data
    show_summary=False,             # print summary of the data
    **data_ops,                     # rest of method arguments
    )

# print length of training and validation datasets
print('train images:', len(data.train_ds.items), '\nvalidation images:', len(data.valid_ds.items))

train images: 800 
validation images: 200

data.show_batch(max_n=8)

# generator = basic_generator(64, n_channels=1, n_extra_layers=1)
generator = UNet(2, 1, 1, channels=(4, 8, 16), strides=(2, 2),)

# critic    = basic_critic(64, n_channels=3, n_extra_layers=1, act_cls=partial(nn.LeakyReLU, negative_slope=0.2))
critic    = gan_critic(n_channels=1)

learn = GANLearner.wgan(data, generator, critic, opt_func = RMSProp)

learn.recorder.train_metrics=True
learn.recorder.valid_metrics=False

learn.fit_one_cycle(100, 1e-3, wd=0.)

/home/bm/miniforge3/envs/biomonai/lib/python3.11/site-packages/fastai/callback/core.py:71: UserWarning: You are shadowing an attribute (generator) that exists in the learner. Use `self.learn.generator` to avoid this
  warn(f"You are shadowing an attribute ({name}) that exists in the learner. Use `self.learn.{name}` to avoid this")
/home/bm/miniforge3/envs/biomonai/lib/python3.11/site-packages/fastai/callback/core.py:71: UserWarning: You are shadowing an attribute (critic) that exists in the learner. Use `self.learn.critic` to avoid this
  warn(f"You are shadowing an attribute ({name}) that exists in the learner. Use `self.learn.{name}` to avoid this")
/home/bm/miniforge3/envs/biomonai/lib/python3.11/site-packages/fastai/callback/core.py:71: UserWarning: You are shadowing an attribute (gen_mode) that exists in the learner. Use `self.learn.gen_mode` to avoid this
  warn(f"You are shadowing an attribute ({name}) that exists in the learner. Use `self.learn.{name}` to avoid this")

epoch	train_loss	gen_loss	crit_loss	time
0	-2555.725342	-8.855750	-3524.030029	00:05
1	-9171.391602	-44.314968	-15667.432617	00:05
2	-20479.978516	-245.935425	-44006.261719	00:05
3	-29629.142578	-499.743134	-58261.878906	00:05
4	-36985.750000	-641.925293	-70105.820312	00:05
5	-41806.429688	-745.183350	-70526.367188	00:05
6	-44930.250000	-562.076660	-67684.757812	00:05
7	-48039.218750	-867.235535	-74037.320312	00:05
8	-50323.011719	-1301.510132	-70982.398438	00:05
9	-51990.070312	-1011.221497	-72361.929688	00:05
10	-52694.460938	-1070.567871	-68849.398438	00:05
11	-54215.046875	-1278.330078	-70736.320312	00:05
12	-55039.480469	-1271.100220	-69847.812500	00:05
13	-56330.246094	-1383.572266	-75332.367188	00:05
14	-56714.425781	-1381.441528	-70363.679688	00:05
15	-56997.082031	-1558.157593	-71483.023438	00:05
16	-57394.003906	-1719.754883	-69988.320312	00:05
17	-57469.859375	-1838.876587	-70168.250000	00:05
18	-57449.664062	-2124.592529	-67045.140625	00:05
19	-57892.289062	-1979.348755	-76002.695312	00:05
20	-58208.968750	-2267.419678	-70614.460938	00:05
21	-58570.417969	-2560.939453	-72913.187500	00:05
22	-58912.085938	-2772.954346	-72357.554688	00:05
23	-58710.218750	-2990.201904	-68624.109375	00:05
24	-58385.925781	-3307.741455	-67303.929688	00:05
25	-58325.656250	-3320.586182	-68148.953125	00:05
26	-58451.281250	-3523.760498	-66745.437500	00:05
27	-58263.949219	-3617.496826	-67114.085938	00:05
28	-58247.828125	-3918.816650	-67373.429688	00:05
29	-58734.609375	-4046.113525	-71666.023438	00:05
30	-58417.695312	-4127.087891	-66409.718750	00:05
31	-58451.921875	-4379.448730	-68604.960938	00:05
32	-58119.710938	-4690.789551	-65217.980469	00:05
33	-57963.933594	-4833.750488	-66986.523438	00:05
34	-57998.644531	-4800.651855	-67332.304688	00:05
35	-58251.468750	-4936.716797	-71206.867188	00:05
36	-58448.957031	-4950.596191	-69872.226562	00:05
37	-58461.574219	-5231.733887	-68034.523438	00:05
38	-58602.554688	-5506.718262	-69505.117188	00:05
39	-59480.609375	-5747.910156	-75871.429688	00:05
40	-58995.625000	-5267.629395	-64849.859375	00:05
41	-58977.128906	-5611.252441	-65881.695312	00:05
42	-58801.718750	-5676.598145	-65844.492188	00:05
43	-58302.402344	-5836.274902	-64666.105469	00:05
44	-58347.101562	-6094.844238	-68369.851562	00:05
45	-58286.523438	-6109.770996	-67618.445312	00:05
46	-58374.449219	-6296.898438	-69571.625000	00:05
47	-58650.578125	-6659.979980	-72281.070312	00:05
48	-59117.601562	-6838.660645	-73362.617188	00:05
49	-59513.003906	-7379.285645	-73517.914062	00:05
50	-59659.074219	-6954.452637	-69732.445312	00:05
51	-59484.953125	-6999.523438	-67509.250000	00:05
52	-59320.210938	-7272.484863	-66735.820312	00:05
53	-59221.730469	-6849.975098	-70904.695312	00:05
54	-59152.105469	-7148.956543	-67752.710938	00:05
55	-58850.554688	-7220.743652	-65474.355469	00:05
56	-58561.949219	-7536.355957	-66434.351562	00:05
57	-58720.746094	-7684.017090	-70128.164062	00:05
58	-59213.441406	-7937.337402	-74009.953125	00:05
59	-59918.703125	-8084.930176	-74281.617188	00:05
60	-57586.945312	-3723.216797	-52365.136719	00:05
61	-58046.992188	-8134.827637	-70903.156250	00:05
62	-58271.789062	-9151.311523	-69963.273438	00:05
63	-58337.546875	-7649.717285	-67126.406250	00:05
64	-58684.027344	-8468.325195	-70284.281250	00:05
65	-59827.761719	-9247.046875	-78048.507812	00:05
66	-59587.492188	-7925.616699	-67480.078125	00:05
67	-59939.238281	-8443.958008	-72519.539062	00:05
68	-59456.238281	-8623.256836	-68618.023438	00:05
69	-59108.722656	-8669.090820	-64234.218750	00:05
70	-59420.562500	-8938.526367	-71713.085938	00:05
71	-59181.535156	-6871.854980	-66008.382812	00:05
72	-59059.433594	-8419.147461	-66976.046875	00:05
73	-58972.750000	-8657.824219	-68083.945312	00:05
74	-58900.992188	-8772.456055	-68560.007812	00:05
75	-58852.097656	-8931.586914	-68600.101562	00:05
76	-59197.417969	-8979.152344	-71580.406250	00:05
77	-59542.625000	-9254.100586	-73398.328125	00:05
78	-60583.347656	-9472.500977	-76918.570312	00:05
79	-59862.980469	-9991.527344	-66114.593750	00:05
80	-59912.042969	-9010.300781	-68012.812500	00:05
81	-60151.687500	-9152.114258	-71084.070312	00:05
82	-60573.488281	-9745.797852	-74115.554688	00:05
83	-61072.933594	-8934.475586	-73050.148438	00:05
84	-61386.250000	-12126.792969	-75693.921875	00:05
85	-60748.492188	-8996.679688	-64332.550781	00:05
86	-60856.929688	-9140.695312	-69207.898438	00:05
87	-61205.265625	-9348.092773	-75234.812500	00:05
88	-61060.812500	-9673.237305	-70567.179688	00:05
89	-60800.796875	-9457.715820	-68463.851562	00:05
90	-61188.296875	-9424.264648	-72567.539062	00:05
91	-60895.378906	-9552.094727	-68528.234375	00:05
92	-61207.582031	-9553.511719	-73301.593750	00:05
93	-61169.050781	-9681.491211	-69688.570312	00:05
94	-61118.429688	-9749.852539	-70681.375000	00:05
95	-62075.406250	-9744.019531	-78722.742188	00:05
96	-62198.058594	-9968.821289	-72208.546875	00:05
97	-62314.414062	-9765.531250	-72931.539062	00:05
98	-62202.933594	-9711.192383	-72671.250000	00:05
99	-62022.523438	-9778.769531	-71103.945312	00:05

learn.show_results(max_n=4, ds_idx=0)

/home/bm/miniforge3/envs/biomonai/lib/python3.11/site-packages/fastai/callback/core.py:71: UserWarning: You are shadowing an attribute (generator) that exists in the learner. Use `self.learn.generator` to avoid this
  warn(f"You are shadowing an attribute ({name}) that exists in the learner. Use `self.learn.{name}` to avoid this")
/home/bm/miniforge3/envs/biomonai/lib/python3.11/site-packages/fastai/callback/core.py:71: UserWarning: You are shadowing an attribute (critic) that exists in the learner. Use `self.learn.critic` to avoid this
  warn(f"You are shadowing an attribute ({name}) that exists in the learner. Use `self.learn.{name}` to avoid this")
/home/bm/miniforge3/envs/biomonai/lib/python3.11/site-packages/fastai/callback/core.py:71: UserWarning: You are shadowing an attribute (gen_mode) that exists in the learner. Use `self.learn.gen_mode` to avoid this
  warn(f"You are shadowing an attribute ({name}) that exists in the learner. Use `self.learn.{name}` to avoid this")