first upload

first upload

README.md

@@ -1,3 +1,31 @@
-# ssl_for_MFR
 
-TODO
+
+```
+python single_train.py --arch=MsvNetLite --num_train=1 --pretrain=pretrain\PPLCNet_x1_0_ssld_pretrained.pth --data_aug
+
+python single_train.py --arch=MsvNetLite --num_train=1 --simsiam_pretrain=pretrain\MsvNetLite1.0_simsiam_pretrain_BS32_6000\checkpoint_0099.pth.tar --lr_sch=cos --data_aug --base_lr=30
+
+python single_train.py --arch=MsvNetLite --num_train=1 --simsiam_pretrain=pretrain\MsvNetLite1.0_simsiam_pretrain_BS32_6000\checkpoint_0099.pth.tar --base_lr=30 --optim=sgdm --weight_decay=1e-6 --lr_sch=cos --freeze
+
+python single_train.py --arch=FeatureNetLite --num_train=1 --simsiam_pretrain=pretrain\FeatureNetLite1.0_simsiam_pretrain_BS32_6000_40e\checkpoint_0039.pth.tar  --weight_decay=1e-4 --lr_sch=cos --data_aug --base_lr=20
+
+####
+
+python single_train.py --arch=MsvNetLite --simsiam_pretrain=pretrain\MsvNetLite1.0_simsiam_pretrain_BS32_6000_200e\checkpoint_0199.pth.tar --program_type=draw_TSNE
+
+python single_train.py --arch=MsvNetLite --program_type=draw_TSNE   
+
+python single_train.py --arch=MsvNetLite --pretrain=pretrain\PPLCNet_x1_0_ssld_pretrained.pth --program_type=draw_TSNE   
+
+python single_train.py --arch=MsvNetLite --model_path=output\2022_03_18_10_50_08\best_model.pth --program_type=draw_TSNE
+
+# supervised 2022_03_09_16_31_21
+
+
+####
+# MsvNetLite
+python single_train.py --arch=MsvNetLite --model_path=output\2022_03_17_12_00_03\best_model.pth --program_type=draw_ROC_CM
+
+# FeatureNetLite
+
+```

core/__init__.py

@@ -0,0 +1,9 @@
+'''
+Author: whj
+Date: 2022-02-15 15:53:55
+LastEditors: whj
+LastEditTime: 2022-02-17 11:52:47
+Description: file content
+'''
+
+# coding: utf-8

core/dataset/__init__.py

@@ -0,0 +1,12 @@
+'''
+Author: whj
+Date: 2022-02-15 15:53:55
+LastEditors: whj
+LastEditTime: 2022-03-04 13:24:46
+Description: file content
+'''
+
+# coding: utf-8
+
+from .datasets import FeatureDataset, SimsiamDataset, createPartition
+

core/dataset/augmentations.py

@@ -0,0 +1,240 @@
+
+# coding: utf-8
+
+import os
+from pathlib import Path
+import random
+
+import torch
+import torchvision
+import torchvision.transforms as transforms
+from torch.utils.data.dataset import Dataset
+import torch.backends.cudnn as cudnn
+
+import numpy as np
+import cupy as cp
+import cupyx.scipy
+import cupyx.scipy.ndimage
+import cupyx
+from PIL import Image
+from scipy import ndimage
+
+
+def randomRotation(sample):
+    rotation = random.randint(0, 23)
+    if rotation == 1:
+        sample = cp.rot90(sample, 1, (1, 2))
+    elif rotation == 2:
+        sample = cp.rot90(sample, 2, (1, 2))
+    elif rotation == 3:
+        sample = cp.rot90(sample, 1, (2, 1))
+    elif rotation == 4:
+        sample = cp.rot90(sample, 1, (0, 1))
+    elif rotation == 5:
+        sample = cp.rot90(sample, 1, (0, 1))
+        sample = cp.rot90(sample, 1, (1, 2))
+    elif rotation == 6:
+        sample = cp.rot90(sample, 1, (0, 1))
+        sample = cp.rot90(sample, 2, (1, 2))
+    elif rotation == 7:
+        sample = cp.rot90(sample, 1, (0, 1))
+        sample = cp.rot90(sample, 1, (2, 1))
+    elif rotation == 8:
+        sample = cp.rot90(sample, 1, (1, 0))
+    elif rotation == 9:
+        sample = cp.rot90(sample, 1, (1, 0))
+        sample = cp.rot90(sample, 1, (1, 2))
+    elif rotation == 10:
+        sample = cp.rot90(sample, 1, (1, 0))
+        sample = cp.rot90(sample, 2, (1, 2))
+    elif rotation == 11:
+        sample = cp.rot90(sample, 1, (1, 0))
+        sample = cp.rot90(sample, 1, (2, 1))
+    elif rotation == 12:
+        sample = cp.rot90(sample, 2, (1, 0))
+    elif rotation == 13:
+        sample = cp.rot90(sample, 2, (1, 0))
+        sample = cp.rot90(sample, 1, (1, 2))
+    elif rotation == 14:
+        sample = cp.rot90(sample, 2, (1, 0))
+        sample = cp.rot90(sample, 2, (1, 2))
+    elif rotation == 15:
+        sample = cp.rot90(sample, 2, (1, 0))
+        sample = cp.rot90(sample, 1, (2, 1))
+    elif rotation == 16:
+        sample = cp.rot90(sample, 1, (0, 2))
+    elif rotation == 17:
+        sample = cp.rot90(sample, 1, (0, 2))
+        sample = cp.rot90(sample, 1, (1, 2))
+    elif rotation == 18:
+        sample = cp.rot90(sample, 1, (0, 2))
+        sample = cp.rot90(sample, 2, (1, 2))
+    elif rotation == 19:
+        sample = cp.rot90(sample, 1, (0, 2))
+        sample = cp.rot90(sample, 1, (2, 1))
+    elif rotation == 20:
+        sample = cp.rot90(sample, 1, (2, 0))
+    elif rotation == 21:
+        sample = cp.rot90(sample, 1, (2, 0))
+        sample = cp.rot90(sample, 1, (1, 2))
+    elif rotation == 22:
+        sample = cp.rot90(sample, 1, (2, 0))
+        sample = cp.rot90(sample, 2, (1, 2))
+    elif rotation == 23:
+        sample = cp.rot90(sample, 1, (2, 0))
+        sample = cp.rot90(sample, 1, (2, 1))
+
+    return sample
+
+
+def randomScaleCrop(sample):
+    resolution = int(sample.shape[0])
+    strategy = random.randint(0, 9)
+    if strategy == 0:
+        factor = random.uniform(1.0625, 1.25)
+        sample = ndimage.zoom(sample, factor, order=0)
+        startx = random.randint(0, sample.shape[0] - resolution)
+        starty = random.randint(0, sample.shape[1] - resolution)
+        startz = random.randint(0, sample.shape[2] - resolution)
+        sample = sample[startx:startx+resolution,
+                        starty:starty+resolution, startz:startz+resolution]
+    elif strategy == 1:
+        factor = random.uniform(0.9375, 0.75)
+        sample = ndimage.zoom(sample, factor, order=0)
+        padxwl = random.randint(0, resolution - sample.shape[0])
+        padxwr = resolution - padxwl - sample.shape[0]
+        padywl = random.randint(0, resolution - sample.shape[1])
+        padywr = resolution - padywl - sample.shape[1]
+        padzwl = random.randint(0, resolution - sample.shape[2])
+        padzwr = resolution - padzwl - sample.shape[2]
+        sample = np.pad(sample, ((padxwl, padxwr),
+                                 (padywl, padywr), (padzwl, padzwr)), mode='edge')
+    elif strategy == 2:
+        padr = int(resolution/8)
+        loc = 2*padr
+        startx = random.randint(0, loc)
+        starty = padr
+        startz = padr
+        sample = np.pad(sample, ((padr, padr), (padr, padr),
+                                 (padr, padr)), mode='edge')
+        sample = sample[startx:startx+resolution,
+                        starty:starty+resolution, startz:startz+resolution]
+    elif strategy == 3:
+        padr = int(resolution/8)
+        loc = 2*padr
+        startx = padr
+        starty = random.randint(0, loc)
+        startz = padr
+        sample = np.pad(sample, ((padr, padr), (padr, padr),
+                                 (padr, padr)), mode='edge')
+        sample = sample[startx:startx+resolution,
+                        starty:starty+resolution, startz:startz+resolution]
+    elif strategy == 4:
+        padr = int(resolution/8)
+        loc = 2*padr
+        startx = padr
+        starty = padr
+        startz = random.randint(0, loc)
+        sample = np.pad(sample, ((padr, padr), (padr, padr),
+                                 (padr, padr)), mode='edge')
+        sample = sample[startx:startx+resolution,
+                        starty:starty+resolution, startz:startz+resolution]
+
+    return sample
+
+
+def randomScale(sample):
+    resolution = int(sample.shape[0])
+    strategy = random.randint(0, 2)
+    if strategy == 0:
+        factor = random.uniform(1.0625, 1.1)
+        sample = ndimage.zoom(sample, factor, order=0)
+        startx = random.randint(0, sample.shape[0] - resolution)
+        starty = random.randint(0, sample.shape[1] - resolution)
+        startz = random.randint(0, sample.shape[2] - resolution)
+        sample = sample[startx:startx+resolution,
+                        starty:starty+resolution, startz:startz+resolution]
+    elif strategy == 1:
+        factor = random.uniform(0.9375, 0.75)
+        sample = ndimage.zoom(sample, factor, order=0)
+        padxwl = random.randint(0, resolution - sample.shape[0])
+        padxwr = resolution - padxwl - sample.shape[0]
+        padywl = random.randint(0, resolution - sample.shape[1])
+        padywr = resolution - padywl - sample.shape[1]
+        padzwl = random.randint(0, resolution - sample.shape[2])
+        padzwr = resolution - padzwl - sample.shape[2]
+        sample = np.pad(sample, ((padxwl, padxwr),
+                                 (padywl, padywr), (padzwl, padzwr)), mode='edge')
+
+    return sample
+
+
+def randomPadCrop(sample):
+    resolution = int(sample.shape[0])
+    strategy = random.randint(0, 3)
+    if strategy == 0:
+        padr = int(resolution/8)
+        loc = 2*padr
+        startx = random.randint(0, loc)
+        starty = padr
+        startz = padr
+        sample = np.pad(sample, ((padr, padr), (padr, padr),
+                                 (padr, padr)), mode='edge')
+        sample = sample[startx:startx+resolution,
+                        starty:starty+resolution, startz:startz+resolution]
+    elif strategy == 1:
+        padr = int(resolution/8)
+        loc = 2*padr
+        startx = padr
+        starty = random.randint(0, loc)
+        startz = padr
+        sample = np.pad(sample, ((padr, padr), (padr, padr),
+                                 (padr, padr)), mode='edge')
+        sample = sample[startx:startx+resolution,
+                        starty:starty+resolution, startz:startz+resolution]
+    elif strategy == 2:
+        padr = int(resolution/8)
+        loc = 2*padr
+        startx = padr
+        starty = padr
+        startz = random.randint(0, loc)
+        sample = np.pad(sample, ((padr, padr), (padr, padr),
+                                 (padr, padr)), mode='edge')
+        sample = sample[startx:startx+resolution,
+                        starty:starty+resolution, startz:startz+resolution]
+
+    return sample
+
+
+
+def cutout3D(sample):
+    # parameters
+    max_holes = 3
+    max_cutout_size = 12
+    # the random number of holes
+    holes = random.randint(0, max_holes)
+    if holes == 0:
+        return sample
+    # cutout
+    resolution = int(sample.shape[0])
+    for n in range(max_holes):
+        y = np.random.randint(resolution)
+        x = np.random.randint(resolution)
+        z = np.random.randint(resolution)
+
+        sizey = np.random.randint(4, max_cutout_size)
+        sizex = np.random.randint(4, max_cutout_size)
+        sizez = np.random.randint(4, max_cutout_size)
+
+        y1 = np.clip(y - sizey // 2, 0, resolution)
+        y2 = np.clip(y + sizey // 2, 0, resolution)
+        x1 = np.clip(x - sizex // 2, 0, resolution)
+        x2 = np.clip(x + sizex // 2, 0, resolution)
+        z1 = np.clip(z - sizez // 2, 0, resolution)
+        z2 = np.clip(z + sizez // 2, 0, resolution)
+
+        sample[y1: y2, x1: x2, z1: z2] = 0
+
+    return sample
+
+