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denseNet_localization.py
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denseNet_localization.py
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import numpy as np
from os import listdir
import skimage.transform
import torch
from torch.utils.data import Dataset, DataLoader
from torch.nn import functional as F
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torchvision
import torchvision.transforms as transforms
from torch.autograd import Variable
import torch.optim as optim
from torch.autograd import Function
from torchvision import models
from torchvision import utils
import cv2
import sys
import os
import pickle
from collections import defaultdict
from collections import OrderedDict
import skimage
from skimage.io import *
from skimage.transform import *
import scipy
import scipy.ndimage as ndimage
import scipy.ndimage.filters as filters
from scipy.ndimage import binary_dilation
import matplotlib.patches as patches
os.environ['CUDA_VISIBLE_DEVICES'] = "0"
test_txt_path = sys.argv[1]
img_folder_path = sys.argv[2]
with open(test_txt_path, "r") as f:
test_list = [i.strip() for i in f.readlines()]
print("number of test examples:",len(test_list))
test_X = []
print("load and transform image")
for i in range(len(test_list)):
image_path = os.path.join(img_folder_path, test_list[i])
img = scipy.misc.imread(image_path)
if img.shape != (1024,1024):
img = img[:,:,0]
img_resized = skimage.transform.resize(img,(256,256))
test_X.append((np.array(img_resized)).reshape(256,256,1))
if i % 100==0:
print(i)
test_X = np.array(test_X)
# model archi
# construct model
class DenseNet121(nn.Module):
"""Model modified.
The architecture of our model is the same as standard DenseNet121
except the classifier layer which has an additional sigmoid function.
"""
def __init__(self, out_size):
super(DenseNet121, self).__init__()
self.densenet121 = torchvision.models.densenet121(pretrained=True)
num_ftrs = self.densenet121.classifier.in_features
self.densenet121.classifier = nn.Sequential(
nn.Linear(num_ftrs, out_size),
nn.Sigmoid()
)
def forward(self, x):
x = self.densenet121(x)
return x
model = DenseNet121(8).cuda()
model = torch.nn.DataParallel(model)
model.load_state_dict(torch.load("model/DenseNet121_aug4_pretrain_WeightBelow1_1_0.829766922537.pkl"))
print("model loaded")
# build test dataset
class ChestXrayDataSet_plot(Dataset):
def __init__(self, input_X = test_X, transform=None):
self.X = np.uint8(test_X*255)
self.transform = transform
def __getitem__(self, index):
"""
Args:
index: the index of item
Returns:
image
"""
current_X = np.tile(self.X[index],3)
image = self.transform(current_X)
return image
def __len__(self):
return len(self.X)
test_dataset = ChestXrayDataSet_plot(input_X = test_X,transform=transforms.Compose([
transforms.ToPILImage(),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],[0.229, 0.224, 0.225])
]))
thresholds = np.load("thresholds.npy")
print("activate threshold",thresholds)
print("generate heatmap ..........")
# ======= Grad CAM Function =========
class PropagationBase(object):
def __init__(self, model, cuda=False):
self.model = model
self.model.eval()
if cuda:
self.model.cuda()
self.cuda = cuda
self.all_fmaps = OrderedDict()
self.all_grads = OrderedDict()
self._set_hook_func()
self.image = None
def _set_hook_func(self):
raise NotImplementedError
def _encode_one_hot(self, idx):
one_hot = torch.FloatTensor(1, self.preds.size()[-1]).zero_()
one_hot[0][idx] = 1.0
return one_hot.cuda() if self.cuda else one_hot
def forward(self, image):
self.image = image
self.preds = self.model.forward(self.image)
# self.probs = F.softmax(self.preds)[0]
# self.prob, self.idx = self.preds[0].data.sort(0, True)
return self.preds.cpu().data.numpy()
def backward(self, idx):
self.model.zero_grad()
one_hot = self._encode_one_hot(idx)
self.preds.backward(gradient=one_hot, retain_graph=True)
class GradCAM(PropagationBase):
def _set_hook_func(self):
def func_f(module, input, output):
self.all_fmaps[id(module)] = output.data.cpu()
def func_b(module, grad_in, grad_out):
self.all_grads[id(module)] = grad_out[0].cpu()
for module in self.model.named_modules():
module[1].register_forward_hook(func_f)
module[1].register_backward_hook(func_b)
def _find(self, outputs, target_layer):
for key, value in outputs.items():
for module in self.model.named_modules():
if id(module[1]) == key:
if module[0] == target_layer:
return value
raise ValueError('Invalid layer name: {}'.format(target_layer))
def _normalize(self, grads):
l2_norm = torch.sqrt(torch.mean(torch.pow(grads, 2))) + 1e-5
return grads / l2_norm.data[0]
def _compute_grad_weights(self, grads):
grads = self._normalize(grads)
self.map_size = grads.size()[2:]
return nn.AvgPool2d(self.map_size)(grads)
def generate(self, target_layer):
fmaps = self._find(self.all_fmaps, target_layer)
grads = self._find(self.all_grads, target_layer)
weights = self._compute_grad_weights(grads)
gcam = torch.FloatTensor(self.map_size).zero_()
for fmap, weight in zip(fmaps[0], weights[0]):
gcam += fmap * weight.data
gcam = F.relu(Variable(gcam))
gcam = gcam.data.cpu().numpy()
gcam -= gcam.min()
gcam /= gcam.max()
gcam = cv2.resize(gcam, (self.image.size(3), self.image.size(2)))
return gcam
def save(self, filename, gcam, raw_image):
gcam = cv2.applyColorMap(np.uint8(gcam * 255.0), cv2.COLORMAP_JET)
gcam = gcam.astype(np.float) + raw_image.astype(np.float)
gcam = gcam / gcam.max() * 255.0
cv2.imwrite(filename, np.uint8(gcam))
# ======== Create heatmap ===========
heatmap_output = []
image_id = []
output_class = []
gcam = GradCAM(model=model, cuda=True)
for index in range(len(test_dataset)):
input_img = Variable((test_dataset[index]).unsqueeze(0).cuda(), requires_grad=True)
probs = gcam.forward(input_img)
activate_classes = np.where((probs > thresholds)[0]==True)[0] # get the activated class
for activate_class in activate_classes:
gcam.backward(idx=activate_class)
output = gcam.generate(target_layer="module.densenet121.features.denseblock4.denselayer16.conv.2")
#### this output is heatmap ####
if np.sum(np.isnan(output)) > 0:
print("fxxx nan")
heatmap_output.append(output)
image_id.append(index)
output_class.append(activate_class)
print("test ",str(index)," finished")
print("heatmap output done")
print("total number of heatmap: ",len(heatmap_output))
# ======= Plot bounding box =========
img_width, img_height = 224, 224
img_width_exp, img_height_exp = 1024, 1024
crop_del = 16
rescale_factor = 4
class_index = ['Atelectasis', 'Cardiomegaly', 'Effusion', 'Infiltration', 'Mass', 'Nodule', 'Pneumonia', 'Pneumothorax']
avg_size = np.array([[411.8, 512.5, 219.0, 139.1], [348.5, 392.3, 479.8, 381.1],
[396.5, 415.8, 221.6, 318.0], [394.5, 389.1, 294.0, 297.4],
[434.3, 366.7, 168.7, 189.8], [502.4, 458.7, 71.9, 70.4],
[378.7, 416.7, 276.5, 304.5], [369.3, 209.4, 198.9, 246.0]])
prediction_dict = {}
for i in range(len(test_list)):
prediction_dict[i] = []
for img_id, k, npy in zip(image_id, output_class, heatmap_output):
data = npy
img_fname = test_list[img_id]
# output avgerge
prediction_sent = '%s %.1f %.1f %.1f %.1f' % (class_index[k], avg_size[k][0], avg_size[k][1], avg_size[k][2], avg_size[k][3])
prediction_dict[img_id].append(prediction_sent)
if np.isnan(data).any():
continue
w_k, h_k = (avg_size[k][2:4] * (256 / 1024)).astype(np.int)
# Find local maxima
neighborhood_size = 100
threshold = .1
data_max = filters.maximum_filter(data, neighborhood_size)
maxima = (data == data_max)
data_min = filters.minimum_filter(data, neighborhood_size)
diff = ((data_max - data_min) > threshold)
maxima[diff == 0] = 0
for _ in range(5):
maxima = binary_dilation(maxima)
labeled, num_objects = ndimage.label(maxima)
slices = ndimage.find_objects(labeled)
xy = np.array(ndimage.center_of_mass(data, labeled, range(1, num_objects+1)))
for pt in xy:
if data[int(pt[0]), int(pt[1])] > np.max(data)*.9:
upper = int(max(pt[0]-(h_k/2), 0.))
left = int(max(pt[1]-(w_k/2), 0.))
right = int(min(left+w_k, img_width))
lower = int(min(upper+h_k, img_height))
prediction_sent = '%s %.1f %.1f %.1f %.1f' % (class_index[k], (left+crop_del)*rescale_factor, \
(upper+crop_del)*rescale_factor, \
(right-left)*rescale_factor, \
(lower-upper)*rescale_factor)
prediction_dict[img_id].append(prediction_sent)
with open("bounding_box.txt","w") as f:
for i in range(len(prediction_dict)):
fname = test_list[i]
prediction = prediction_dict[i]
print(os.path.join(img_folder_path, fname), len(prediction))
f.write('%s %d\n' % (os.path.join(img_folder_path, fname), len(prediction)))
for p in prediction:
print(p)
f.write(p+"\n")