import h5py
import numpy as np
from glob import glob
import os
import cv2
# hdf5ファイルの作成
def hdf5_generate(file_path):
f = h5py.File('file.hdf5', mode='w')
group = f.create_group('/images')
file_list = glob(file_path)
for file in file_list:
arr = cv2.imread(file)
dataset = group.create_dataset(name = os.path.basename(file), shape=arr.shape, dtype=np.uint8)
dataset[...] = arr
f.close()
file_path = '/content/drive/MyDrive/x_ray_chest/images'
hdf5_generate(file_path)
次に、画像データ、Bboxの位置座標、ラベルの情報を画像ごとに整理していきます。
import torch
from torch.utils.data import Dataset
import torchvision
from torchvision import datasets
from torchvision import transforms
import h5py
# 画像データをimagesに格納
file_path = '/content/drive/MyDrive/x_ray_chest/file.hdf5'
images = []
with h5py.File(file_path, "r") as f:
group = f["/images"]
for i in range(len(file_name_list)):
file_name = file_name_list[i]
if file_name in group:
dataset = group[file_name]
img_data = np.array(dataset)
img_tensor = transforms.Compose([transforms.ToTensor()])(img_data)
images.append(img_tensor)
# Bbox情報をboxesに格納
boxes = []
for file_name in data_list['img_name'].unique():
box = []
row = data_list['img_name'] == file_name
data = data_list[row]
for i in range(len(data)):
lx = data['bbox_x'].iloc[i]
ly = data['bbox_y'].iloc[i]
rx = data['bbox_x'].iloc[i] + data['bbox_w'].iloc[i]
ry = data['bbox_y'].iloc[i] + data['bbox_h'].iloc[i]
bbox = [lx, ly, rx, ry]
box.append(bbox)
box_tensor = torch.tensor(box, dtype = torch.float)
boxes.append(box_tensor)
# ラベル情報をlabelsに格納
labels = []
label_kind = ['Atelectasis', 'Cardiomegaly', 'Effusion', 'Infiltrate', 'Mass', 'Nodule', 'Pneumonia', 'Pneumothorax']
for file_name in data_list['img_name'].unique():
label = []
row = data_list['img_name'] == file_name
data = data_list[row]
for i in range(len(data)):
label_name = data['label'].iloc[i]
label_index = label_kind.index(label_name)
label.append(label_index)
label_tensor = torch.tensor(label, dtype = torch.int64)
labels.append(label_tensor)
print(len(images))
print(len(boxes))
print(len(labels))
880
880
880
datasetに辞書型でそれぞれのデータをまとめていきます。
dataset = []
for i in range(len(images)):
data = (images[i], {'boxes':boxes[i], 'labels':labels[i]}, data_list['img_name'].unique()[i])
dataset.append(data)
train_list = []
val_list = []
test_list = []
[train_list.append(data[2]) for data in train_loader]
[val_list.append(data[2]) for data in val_loader]
[test_list.append(data[2]) for data in test_loader]
train_txt_path = os.path.join(train_folder_path, 'train_list.txt')
val_txt_path = os.path.join(train_folder_path, 'val_list.txt')
test_txt_path = os.path.join(train_folder_path, 'test_list.txt')
with open(train_txt_path, 'w') as f:
for item in train_list:
f.write(str(item) + '\n')
with open(val_txt_path, 'w') as f:
for item in val_list:
f.write(str(item) + '\n')
with open(test_txt_path, 'w') as f:
for item in test_list:
f.write(str(item) + '\n')
4. モデルの作成
今回はpytorchで使用できるFaster-R-CNNモデルを使用していきます。
pretrainedをTrueにすることで転移学習を行なっています。
また、classの数を変更し、予測の構造の部分に変更の値を返しています。
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)
num_classes = (len(label_kind)) + 1
in_features = model.roi_heads.box_predictor.cls_score.in_features
model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)
model
import csv
params = [p for p in model.parameters() if p.requires_grad]
optimizer = torch.optim.Adam(params, lr=0.0001)
epochs = 200
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
model.to(device)
loss_train_list=[]
loss_val_list = []
loss_outdir = os.path.join(train_folder_path, 'train_loss.csv')
with open(loss_outdir, 'w', newline='') as f:
writer = csv.writer(f)
writer.writerow(['epoch', 'train loss', 'val loss'])
for epoch in range(epochs):
model.train()
loss_train_epo=[]
for batch in train_loader:
images, targets, name = batch
images = list(image.to(device) for image in images)
boxes = list(box.to(device) for box in targets['boxes'])
labels = list(label.to(device) for label in targets['labels'])
targets = list([{'boxes':boxes[i], 'labels':labels[i]} for i in range(len(images))])
loss_dict = model(images, targets)
loss = sum(loss for loss in loss_dict.values())
loss_value = loss.item()
loss_train_epo.append(loss_value)
#パラメータを更新
optimizer.zero_grad()
loss.backward()
optimizer.step()
#Epochごとのlossの保存
loss_train_ave = sum(loss_train_epo)/len(loss_train_epo)
loss_train_list.append(loss_train_ave)
weight_num = str(epoch+1).zfill(4)
weight_name = f'weight_{weight_num}.pt'
torch.save(model.state_dict(), os.path.join(weight_folder_path, weight_name))
with torch.no_grad():
model.eval()
loss_val_epo=[]
for batch in val_loader:
images, targets, name = batch
images = list(image.to(device) for image in images)
boxes = list(box.to(device) for box in targets['boxes'])
labels = list(label.to(device) for label in targets['labels'])
targets = list([{'boxes':boxes[i], 'labels':labels[i]} for i in range(len(images))])
loss_dict = model(images, targets)
score_list = []
for i in range(len(loss_dict)):
score = loss_dict[i]['scores'].sum()
score_value = score.item()
score_list.append(score_value)
#lossの保存
score_ave = sum(score_list)/len(score_list)
loss_val_epo.append(score_ave)
#Epochごとのlossの保存
loss_val_ave = sum(loss_val_epo)/len(loss_val_epo)
loss_val_list.append(loss_val_ave)
print(f"epoch: {epoch+1}, loss: {np.mean(loss_train_epo)}, val loss: {loss_val_ave}")
with open(loss_outdir, 'a', newline='') as f:
writer = csv.writer(f)
writer.writerow([epoch+1, loss_train_ave, loss_val_ave])
epoch: 1, loss: 0.24110975912923965, val loss: 0.3246306985513204
epoch: 2, loss: 0.2282996491336709, val loss: 1.4351968351337645
epoch: 3, loss: 0.22264097455089263, val loss: 1.4306978616449568
epoch: 4, loss: 0.20749079919946584, val loss: 5.093314859602186
epoch: 5, loss: 0.20251873579463395, val loss: 2.666413033174144
epoch: 6, loss: 0.19240049850296878, val loss: 3.341125517669651
epoch: 7, loss: 0.1835352772867064, val loss: 2.689231221874555
epoch: 8, loss: 0.17658887369401308, val loss: 4.5536469121774035
epoch: 9, loss: 0.1688745028918872, val loss: 2.3997186314728527
epoch: 10, loss: 0.1469138625417026, val loss: 1.6564890493949254
epoch: 11, loss: 0.13697218944688086, val loss: 1.600402367197805
epoch: 12, loss: 0.12709340538181688, val loss: 5.514973554346296
epoch: 13, loss: 0.11845372894173994, val loss: 1.5726642373742328
epoch: 14, loss: 0.10922473976224074, val loss: 2.916120736135377
epoch: 15, loss: 0.09690021435475817, val loss: 0.9303635937265224
epoch: 16, loss: 0.09325711836288025, val loss: 2.534821828827262
epoch: 17, loss: 0.08580996685399757, val loss: 0.8161833409944342
epoch: 18, loss: 0.08081575220144853, val loss: 1.0927526873225968
epoch: 19, loss: 0.08133552688330224, val loss: 1.3887748159468174
epoch: 20, loss: 0.0727521351126716, val loss: 1.0132182172189157
epoch: 21, loss: 0.07121367414741975, val loss: 0.6771124113971988
epoch: 22, loss: 0.06907023071147231, val loss: 0.8857596593184603
epoch: 23, loss: 0.06628901709400056, val loss: 0.9108521235692832
epoch: 24, loss: 0.06673509317007974, val loss: 0.7442856341496937
epoch: 25, loss: 0.062146506343829656, val loss: 0.6246414736637639
epoch: 26, loss: 0.061015041457029955, val loss: 0.8104739433878826
epoch: 27, loss: 0.05744573669898787, val loss: 0.6706496070449551
epoch: 28, loss: 0.05474606130531395, val loss: 1.1524773749212425
epoch: 29, loss: 0.055251803001145, val loss: 0.6280674000995027
epoch: 30, loss: 0.05273347761818593, val loss: 0.5754670017502375
epoch: 31, loss: 0.050008386024669366, val loss: 0.32528595057212645
epoch: 32, loss: 0.04966567055548101, val loss: 0.33050697633168763
epoch: 33, loss: 0.04912358499397036, val loss: 0.6783891286080083
epoch: 34, loss: 0.04859020433116751, val loss: 0.6152269225050178
epoch: 35, loss: 0.04452142692258823, val loss: 0.43346481651274693
epoch: 36, loss: 0.04439374794684108, val loss: 0.5061396388337016
epoch: 37, loss: 0.04699440649489153, val loss: 0.25037881680246854
epoch: 38, loss: 0.04393274197362822, val loss: 0.19273884480612147
epoch: 39, loss: 0.0412014624346383, val loss: 0.33793715734241736
epoch: 40, loss: 0.042301838821162084, val loss: 0.37770448345690966
epoch: 41, loss: 0.03826545933112436, val loss: 0.3075255771788458
epoch: 42, loss: 0.03726111880393754, val loss: 0.3168445967344774
epoch: 43, loss: 0.040553005828631125, val loss: 0.289791036500699
epoch: 44, loss: 0.03873104128914075, val loss: 0.15397230784098306
epoch: 45, loss: 0.03735997831830426, val loss: 0.3136507644214564
epoch: 46, loss: 0.040122704197647574, val loss: 0.16717039897210068
epoch: 47, loss: 0.036537003315763046, val loss: 0.1613647932600644
epoch: 48, loss: 0.033273397550542344, val loss: 0.9169100125113295
epoch: 49, loss: 0.032753672763370247, val loss: 0.1807647309679952
epoch: 50, loss: 0.03304693002030984, val loss: 0.26329267945968443
epoch: 51, loss: 0.03421700729768418, val loss: 0.22143234483276805
epoch: 52, loss: 0.03436298223811679, val loss: 0.17814058189590773
epoch: 53, loss: 0.03525825669009425, val loss: 0.1409107974419991
epoch: 54, loss: 0.03350234282104635, val loss: 0.11430964044605692
epoch: 55, loss: 0.03423632765061089, val loss: 0.10250198634134398
epoch: 56, loss: 0.03222063158434297, val loss: 0.1298580646721853
epoch: 57, loss: 0.030284645334631932, val loss: 0.18020476235283744
epoch: 58, loss: 0.030352800964692506, val loss: 0.1549433045503166
epoch: 59, loss: 0.030562790613400823, val loss: 0.2312512820483082
epoch: 60, loss: 0.030917203308095137, val loss: 0.23355705296206805
epoch: 61, loss: 0.03158575625170472, val loss: 0.19695975269294447
epoch: 62, loss: 0.030637340262629827, val loss: 0.4018483566534188
epoch: 63, loss: 0.03038886107491556, val loss: 0.1631715577095747
epoch: 64, loss: 0.029974979020686948, val loss: 0.11120111500430438
epoch: 65, loss: 0.030613049891154213, val loss: 0.17864933537526262
epoch: 66, loss: 0.03159083963053332, val loss: 0.139477732591331
epoch: 67, loss: 0.03271760230856434, val loss: 0.1599722879214419
epoch: 68, loss: 0.029625480446117326, val loss: 0.3108530937073131
epoch: 69, loss: 0.02876559113926068, val loss: 0.15876602800562978
epoch: 70, loss: 0.030224516301359472, val loss: 0.10369957486788432
epoch: 71, loss: 0.028860015232235917, val loss: 0.3006380044130815
epoch: 72, loss: 0.028128108247924305, val loss: 0.20257677784603503
epoch: 73, loss: 0.025224404149293663, val loss: 0.12164105402512683
epoch: 74, loss: 0.02306718988983369, val loss: 0.08393369884126717
epoch: 75, loss: 0.023504642101680354, val loss: 0.222478655529105
epoch: 76, loss: 0.026592813241894993, val loss: 0.23118829049376977
epoch: 77, loss: 0.027799449206292553, val loss: 0.12991651333868504
epoch: 78, loss: 0.0246156811292325, val loss: 0.12436481911896004
epoch: 79, loss: 0.02376350378300112, val loss: 0.16421864135190845
epoch: 80, loss: 0.02365450924107988, val loss: 0.47107588758485186
epoch: 81, loss: 0.024729895744416273, val loss: 0.1490647716758152
epoch: 82, loss: 0.023687794335501167, val loss: 0.1587101083455814
epoch: 83, loss: 0.023237756310248624, val loss: 0.099386107073062
epoch: 84, loss: 0.021971292128653105, val loss: 0.20810616456179154
epoch: 85, loss: 0.02278891849900704, val loss: 0.12188714313217336
epoch: 86, loss: 0.02504808868678518, val loss: 0.10159987966633505
epoch: 87, loss: 0.02462780592839448, val loss: 0.07811070802725023
epoch: 88, loss: 0.023621539326274178, val loss: 0.21318252560579115
epoch: 89, loss: 0.023184740303191655, val loss: 0.15177055360335442
epoch: 90, loss: 0.02191986730014892, val loss: 0.16432732861075136
epoch: 91, loss: 0.024063879810959723, val loss: 0.1003557765442464
epoch: 92, loss: 0.023978719674529282, val loss: 0.2154051615960068
epoch: 93, loss: 0.023383827642686338, val loss: 0.12893925441635978
epoch: 94, loss: 0.023065722899010998, val loss: 0.13664892936746278
epoch: 95, loss: 0.023316614966456258, val loss: 0.17552660571204293
epoch: 96, loss: 0.023419570080032707, val loss: 0.2663915485350622
epoch: 97, loss: 0.021505099566134642, val loss: 0.06453627999871969
epoch: 98, loss: 0.019993429047051748, val loss: 0.1580528343717257
epoch: 99, loss: 0.020646440756219974, val loss: 0.09689174654583137
epoch: 100, loss: 0.020709944858624665, val loss: 0.11231084085173076
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