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main_cpu.py
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main_cpu.py
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import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable
import
import torchvision.transforms as transforms
import torch.optim as optim
import torch.nn.functional as tfunc
from torch.utils.data import DataLoader
from torch.optim.lr_scheduler import ReduceLROnPlateau
import torch.nn.functional as func
from DensenetModels import DenseNet121
from DensenetModels import DenseNet169
from DensenetModels import DenseNet201
from DatasetGenerator import DatasetGenerator
import cv2
import matplotlib.pyplot as plt
import re
from PIL import Image, ImageQt
class HeatmapGenerator():
# 初始化热图生成器
# 全连接神经网络 DenseNet121
def __init__(self, pathModel, nnArchitecture, nnClassCount, transCrop):
if nnArchitecture == 'DENSE-NET-121':
if use_gpu:
model = DenseNet121(nnClassCount, True).cuda()
else:
model = DenseNet121(nnClassCount, True)
elif nnArchitecture == 'DENSE-NET-169':
if use_gpu:
model = DenseNet169(nnClassCount, True).cuda()
else:
model = DenseNet169(nnClassCount, True)
elif nnArchitecture == 'DENSE-NET-201':
if use_gpu:
model = DenseNet201(nnClassCount, True).cuda()
else:
model = DenseNet201(nnClassCount, True)
if use_gpu:
model = torch.nn.DataParallel(model).cuda()
else:
model = torch.nn.DataParallel(model)
if use_gpu:
modelCheckpoint = torch.load(pathModel)
state_dict = modelCheckpoint['state_dict']
remove_data_parallel = False
pattern = re.compile(
r'^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$')
for key in list(state_dict.keys()):
match = pattern.match(key)
new_key = match.group(1) + match.group(2) if match else key
new_key = new_key[7:] if remove_data_parallel else new_key
state_dict[new_key] = state_dict[key]
if match or remove_data_parallel:
del state_dict[key]
model.load_state_dict(modelCheckpoint['state_dict'])
else:
modelCheckpoint = torch.load(pathModel, map_location='cpu')
state_dict = modelCheckpoint['state_dict']
remove_data_parallel = False
pattern = re.compile(
r'^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$')
for key in list(state_dict.keys()):
match = pattern.match(key)
new_key = match.group(1) + match.group(2) if match else key
new_key = new_key[7:] if remove_data_parallel else new_key
state_dict[new_key] = state_dict[key]
if match or remove_data_parallel:
del state_dict[key]
model.load_state_dict(modelCheckpoint['state_dict'])
self.model = model.module.densenet121.features
self.model.eval()
# 初始化模型权重
self.weights = list(self.model.parameters())[-2]
# 图像变换，下采样
normalize = transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
transformList = []
transformList.append(transforms.Resize(transCrop))
transformList.append(transforms.ToTensor())
transformList.append(normalize)
self.transformSequence = transforms.Compose(transformList)
# --------------------------------------------------------------------------------
def generate(self, pathImageFile, transCrop, probability):
imageData = Image.open(pathImageFile).convert('RGB')
imageData = self.transformSequence(imageData)
imageData = imageData.unsqueeze_(0)
input = torch.autograd.Variable(imageData)
if use_gpu:
self.model.cuda()
output = self.model(input.cuda())
else:
output = self.model(input)
heatmap = None
for i in range(0, len(self.weights)):
map = output[0, i, :, :]
if i == 0:
heatmap = self.weights[i] * map
else:
heatmap += self.weights[i] * map
npHeatmap = heatmap.cpu().data.numpy()
imgOriginal = cv2.imread(pathImageFile, 1)
imgOriginal = cv2.resize(imgOriginal, (transCrop, transCrop))
cam = npHeatmap / np.max(npHeatmap)
cam = cv2.resize(cam, (transCrop, transCrop))
heatmap = cv2.applyColorMap(np.uint8(255 * cam), cv2.COLORMAP_JET)
img = heatmap * 0.5 + imgOriginal
img3 = img / np.max(img)
img3 = 255 * img3
img3 = img3.astype(np.uint8)
img3 = cv2.cvtColor(img3, cv2.COLOR_BGR2RGB)
plt.figure()
plt.subplot(121)
plt.imshow(imgOriginal)
plt.subplot(122)
plt.title(probability)
plt.imshow(img3)
plt.show()
def predict_chest_xray(imagePath, pathModel, nnArchitecture, nnClassCount, nnIsTrained, trBatchSize, transResize, transCrop, launchTimeStamp):
CLASS_NAMES = ['Atelectasis', 'Cardiomegaly', 'Effusion', 'Infiltration', 'Mass', 'Nodule', 'Pneumonia',
'Pneumothorax', 'Consolidation', 'Edema', 'Emphysema', 'Fibrosis', 'Pleural_Thickening', 'Hernia']
if nnArchitecture == 'DENSE-NET-121':
if use_gpu:
model = DenseNet121(nnClassCount, True).cuda()
else:
model = DenseNet121(nnClassCount, True)
elif nnArchitecture == 'DENSE-NET-169':
if use_gpu:
model = DenseNet169(nnClassCount, True).cuda()
else:
model = DenseNet169(nnClassCount, True)
elif nnArchitecture == 'DENSE-NET-201':
if use_gpu:
model = DenseNet201(nnClassCount, True).cuda()
else:
model = DenseNet201(nnClassCount, True)
if use_gpu:
model = torch.nn.DataParallel(model).cuda()
else:
model = torch.nn.DataParallel(model)
if use_gpu:
modelCheckpoint = torch.load(pathModel)
state_dict = modelCheckpoint['state_dict']
remove_data_parallel = False
pattern = re.compile(
r'^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$')
for key in list(state_dict.keys()):
match = pattern.match(key)
new_key = match.group(1) + match.group(2) if match else key
new_key = new_key[7:] if remove_data_parallel else new_key
state_dict[new_key] = state_dict[key]
if match or remove_data_parallel:
del state_dict[key]
model.load_state_dict(modelCheckpoint['state_dict'])
else:
modelCheckpoint = torch.load(pathModel, map_location='cpu')
state_dict = modelCheckpoint['state_dict']
remove_data_parallel = False
pattern = re.compile(
r'^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$')
for key in list(state_dict.keys()):
match = pattern.match(key)
new_key = match.group(1) + match.group(2) if match else key
new_key = new_key[7:] if remove_data_parallel else new_key
state_dict[new_key] = state_dict[key]
if match or remove_data_parallel:
del state_dict[key]
model.load_state_dict(modelCheckpoint['state_dict'])
img = Image.open(imagePath).convert('RGB') # 读取图像
data_transforms = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
img2 = data_transforms(img) # 归一化
# 因为是一幅图，所以将维度更新为 [1,3,256,256]
input = img2[None, :, :, :]
model.eval()
if use_gpu:
input = Variable(input.cuda())
else:
input = Variable(input)
output = model(input)
output = output.cpu().data.numpy()
maxIndex = output[0].argsort()[-3:][::-1]
probability = str(CLASS_NAMES[maxIndex[0]] + ":" + format(output[0][maxIndex[0]], '.2%') + ", " +
CLASS_NAMES[maxIndex[1]] + ":" + format(output[0][maxIndex[1]], '.2%') + ", " +
CLASS_NAMES[maxIndex[2]] + ":" + format(output[0][maxIndex[2]], '.2%'))
h = HeatmapGenerator(pathModel, nnArchitecture, nnClassCount, imgtransCrop)
h.generate(imagePath, imgtransCrop, probability)
print(probability)
return probability
if __name__ == '__main__':
image_name = '00006821_002.png'
imagePath = './test/' + image_name
nnArchitecture = 'DENSE-NET-121'
nnIsTrained = True
nnClassCount = 14
trBatchSize = 2
imgtransResize = 256
imgtransCrop = 224
pathModel = './m-25012018-123527.pth.tar'
timestampLaunch = ''
predict_chest_xray(imagePath, pathModel, nnArchitecture, nnClassCount, nnIsTrained, trBatchSize, imgtransResize, imgtransCrop, timestampLaunch)
import pandas as pd
import matplotlib.patches as patches
labeldata = pd.read_csv('./test/BBox_List_2017.csv')
image_labeldata = labeldata[labeldata["Image Index"] == image_name]
im = np.array(Image.open(imagePath), dtype=np.uint8)
fig, ax = plt.subplots(1)
ax.imshow(im, cmap='gray')
for item in image_labeldata.values:
Bbox_x = int(item[2])
Bbox_y = int(item[3])
Bbox_w = int(item[4])
Bbox_h = int(item[5])
Finding_Label = item[1]
rect = patches.Rectangle((Bbox_x, Bbox_y), Bbox_w, Bbox_h, linewidth=1, edgecolor='r', facecolor='none')
ax.add_patch(rect)
plt.show()