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import torch.nn as nn
import pretrainedmodels
from torchvision.models import densenet121
from layers import Flatten
import torch
import torchvision.transforms as transforms
from pathlib import Path
from constant import IMAGENET_MEAN, IMAGENET_STD
import os
import sys
script_dir = os.path.dirname(os.path.abspath(__file__))
yolov9 = os.path.join(script_dir, '..', 'chestXray14')
sys.path.append(yolov9)
class ChexNet(nn.Module):
tfm = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(IMAGENET_MEAN, IMAGENET_STD)
])
def __init__(self, trained=False, model_name='20180525-222635'):
super().__init__()
# chexnet.parameters() is freezed except head
if trained:
self.load_model(model_name)
else:
self.load_pretrained()
def load_model(self, model_name):
self.backbone = densenet121(False).features
self.head = nn.Sequential(
nn.AdaptiveAvgPool2d(1),
Flatten(),
nn.Linear(1024, 14)
)
path = Path('chestX-ray-14')
state_dict = torch.load('chexnet.h5')
self.load_state_dict(state_dict)
def load_pretrained(self, torch=False):
if torch:
self.backbone = densenet121(True).features
else:
self.backbone = pretrainedmodels.__dict__['densenet121']().features
self.head = nn.Sequential(
nn.AdaptiveAvgPool2d(1),
Flatten(),
nn.Linear(1024, 14)
)
def forward(self, x):
return self.head(self.backbone(x))
def predict(self, image):
"""
input: PIL image (w, h, c)
output: prob np.array
"""
image_tensor = self.tfm(image).unsqueeze(0) # Add batch dimension
image_tensor = image_tensor.to(next(self.parameters()).device) # Move to the same device as the model
with torch.no_grad():
py = torch.sigmoid(self(image_tensor))
prob = py.cpu().numpy()[0]
return prob
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