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evaluation_metrics.py
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evaluation_metrics.py
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import os
import sys
import argparse
import time
import numpy as np
import datetime
import sklearn.metrics
from model import GPNN
from instrument_dataset import SurgicalDataset18
# Torch
import torch
from torch.utils.data import Dataset, DataLoader
from torch.autograd import Variable
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
INSTRUMENT_CLASSES = (
'', 'kidney', 'bipolar_forceps', 'fenestrated_bipolar', 'prograsp_forceps', 'large_needle_driver', 'vessel_sealer',
'grasping_retractor', 'monopolar_curved_scissors', 'ultrasound_probe', 'suction', 'clip_applier', 'stapler')
ACTION_CLASSES = (
'Idle', 'Grasping', 'Retraction', 'Tissue_Manipulation', 'Tool_Manipulation', 'Cutting', 'Cauterization'
, 'Suction', 'Looping', 'Suturing', 'Clipping', 'Staple', 'Ultrasound_Sensing')
class Args:
resume = 'ckpt/parsing/'
visualize = False
vis_top_k = 1
# Optimization Options
batch_size = 1
no_cuda = False
epochs = 100
start_epoch = 0
link_weight = 100
lr = 1e-5
lr_decay = 0.6
momentum = 0.9
log_interval = 200
prefetch = 0
#others
ckpt_dir = 'ckpt/model/'
def evaluation(det_indices, pred_node_labels, node_labels, y_true, y_score, test=False):
np_pred_node_labels = pred_node_labels.data.cpu().numpy()
np_pred_node_labels_exp = np.exp(np_pred_node_labels)
np_pred_node_labels = np_pred_node_labels_exp/(np_pred_node_labels_exp+1) # overflows when x approaches np.inf
np_node_labels = node_labels.data.cpu().numpy()
new_y_true = np.empty((2 * len(det_indices), action_class_num))
new_y_score = np.empty((2 * len(det_indices), action_class_num))
for y_i, (batch_i, i, j) in enumerate(det_indices):
new_y_true[2*y_i, :] = np_node_labels[batch_i, i, :]
new_y_true[2*y_i+1, :] = np_node_labels[batch_i, j, :]
new_y_score[2*y_i, :] = np_pred_node_labels[batch_i, i, :]
new_y_score[2*y_i+1, :] = np_pred_node_labels[batch_i, j, :]
y_true = np.vstack((y_true, new_y_true))
y_score = np.vstack((y_score, new_y_score))
return y_true, y_score
def weighted_loss(output, target):
weight_mask = torch.autograd.Variable(torch.ones(target.size()))
if hasattr(args, 'cuda') and args.cuda:
weight_mask = weight_mask.cuda()
link_weight = args.link_weight if hasattr(args, 'link_weight') else 1.0
weight_mask += target * link_weight
return torch.nn.MultiLabelSoftMarginLoss(weight=weight_mask).cuda()(output, target)
def loss_fn(pred_adj_mat, adj_mat, pred_node_labels, node_labels, mse_loss, multi_label_loss, human_num=[], obj_num=[]):
np_pred_adj_mat = pred_adj_mat.data.cpu().numpy()
det_indices = list()
batch_size = pred_adj_mat.size()[0]
loss = 0
for batch_i in range(batch_size):
valid_node_num = human_num[batch_i] + obj_num[batch_i]
np_pred_adj_mat_batch = np_pred_adj_mat[batch_i, :, :]
if len(human_num) != 0:
human_interval = human_num[batch_i]
obj_interval = human_interval + obj_num[batch_i]
max_score = np.max([np.max(np_pred_adj_mat_batch), 0.01])
mean_score = np.mean(np_pred_adj_mat_batch)
batch_det_indices = np.where(np_pred_adj_mat_batch > 0.5)
for i, j in zip(batch_det_indices[0], batch_det_indices[1]):
# check validity for H-O interaction instead of O-O interaction
if len(human_num) != 0:
if i < human_interval and j < obj_interval:
if j >= human_interval:
det_indices.append((batch_i, i, j))
loss = loss + weighted_loss(pred_node_labels[batch_i, :valid_node_num].view(-1, action_class_num), node_labels[batch_i, :valid_node_num].view(-1, action_class_num))
return det_indices, loss
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.val, self.avg, self.sum, self.count = 0, 0, 0, 0
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
if __name__ == '__main__':
args = Args()
if not os.path.exists(args.ckpt_dir):
os.makedirs(args.ckpt_dir)
action_class_num = 13
hoi_class_num = 13
edge_feature_size = 200
node_feature_size = 200
np.random.seed(0)
torch.manual_seed(0)
start_time = time.time()
args.cuda = not args.no_cuda and torch.cuda.is_available()
dataset = SurgicalDataset18(seq_set=[2, 3, 4, 6, 7, 9, 10, 11, 12, 14, 15], is_train=True)
train_loader = DataLoader(dataset=dataset, batch_size=args.batch_size, shuffle=True, num_workers=2, drop_last=True)
dataset_valid = SurgicalDataset18(seq_set=[1, 5, 16], is_train=True)
valid_loader = DataLoader(dataset=dataset_valid, batch_size=args.batch_size, shuffle=False, num_workers=2, drop_last=True)
message_size = int(edge_feature_size/2)*2
model_args = {'model_path': args.resume, 'edge_feature_size': edge_feature_size, 'node_feature_size': node_feature_size,
'message_size': message_size, 'link_hidden_size': 512,
'link_hidden_layers': 2, 'link_relu': False, 'update_hidden_layers': 1, 'update_dropout': False,
'update_bias': True, 'propagate_layers': 3,
'hoi_classes': action_class_num, 'resize_feature_to_message_size': False}
model = GPNN(model_args)
mse_loss = torch.nn.MSELoss(size_average=True)
multi_label_loss = torch.nn.MultiLabelSoftMarginLoss(size_average=True)
if args.cuda:
model = model.cuda()
gpu_ids = range(1)
model = torch.nn.parallel.DataParallel(model, device_ids=gpu_ids)
mse_loss = mse_loss.cuda()
multi_label_loss = multi_label_loss.cuda()
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
epoch_errors = list()
avg_epoch_error = np.inf
best_epoch_error = np.inf
best_epoch = 0
best_mAP = 0
best_loss = 0
model.load_state_dict(torch.load("ckpt/model/epoch_14.pth.tar"))
batch_time = AverageMeter()
losses = AverageMeter()
y_true = np.empty((0, action_class_num))
y_score = np.empty((0, action_class_num))
# switch to evaluate mode
model.eval()
print('size of valid sample:', len(valid_loader))
end = time.time()
for i, (edge_features, node_features, adj_mat, node_labels, file_name, human_num, obj_num) in enumerate(
valid_loader):
edge_features = torch.from_numpy(np.asarray(edge_features, np.float32)).float()
node_features = torch.from_numpy(np.asarray(node_features, np.float32)).float()
adj_mat = torch.from_numpy(np.asarray(adj_mat, np.float32)).float()
node_labels = torch.from_numpy(np.asarray(node_labels, np.float32)).float()
edge_features, node_features = Variable(edge_features).cuda(), Variable(node_features).cuda()
adj_mat, node_labels = Variable(adj_mat).cuda(), Variable(node_labels).cuda()
pred_adj_mat, pred_node_labels = model(edge_features, node_features, adj_mat, node_labels, human_num, obj_num,
args)
det_indices, loss = loss_fn(pred_adj_mat, adj_mat, pred_node_labels, node_labels, mse_loss, multi_label_loss,
human_num, obj_num)
if len(det_indices) > 0:
losses.update(loss.data, len(det_indices))
y_true, y_score = evaluation(det_indices, pred_node_labels, node_labels, y_true, y_score, test=False)
# print(y_true.shape)
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
avg_prec = sklearn.metrics.average_precision_score(y_true, y_score, average=None)
mAP = np.nansum(avg_prec) / (len(avg_prec) - 3)
# auc_all = []
# for i in range(len(y_true)):
# if len(np.unique(y_true[i])) != 1:
# auc_all.append(sklearn.metrics.roc_auc_score(y_true[i], y_score[i]))
# auc_avg = np.mean(np.array(auc_all))
avg_auc = sklearn.metrics.roc_auc_score(np.array(y_true).flatten(), np.array(y_score).flatten())
precision, recall, threshold = sklearn.metrics.precision_recall_curve(y_true.flatten(), y_score.flatten())
avg_recall = np.mean(recall)
print('mAP:%.4f, AUC:%.4f, recall:%.4f'%(mAP, avg_auc, avg_recall))