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evaluation_model7_track_based.py
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# from Model_0 import resnet101
from Model_7 import resnet101
# from hierarchy_cls_train import model_save_path,train_loader,valid_loader,DEVICE,NUM_CLASSES, GRAYSCALE
import torch
import torch.nn as nn
import os
from util import compute_accuracy_model0,calculate_num_class,hierarchy_dict, \
calculate_num_class_model0, compute_accuracy_model12, \
compute_accuracy_model7_track_based, track_based_accuracy,\
track_based_accuracy_majority_vote,Otsu_Threshold
from IPython import embed
from torchvision import transforms
from fish_rail_dataloader_track_based import Fish_Rail_Dataset
from torch.utils.data import DataLoader
GRAYSCALE = False
# NUM_CLASSES = calculate_num_class(hierarchy_dict) #37
# NUM_CLASSES = calculate_num_class_model0(hierarchy_dict) # model0 31
NUM_level_1_CLASSES, NUM_level_2_CLASSES= calculate_num_class(hierarchy_dict)
# model_save_path = './checkpoints-model7-track_based-Eq loss 0.8 shark'
model_save_path = './checkpoints_plus_sleeper_shark_nonfish'
# model_save_path = './checkpoints-model6-track_based'
DEVICE = 'cuda:0'
BATCH_SIZE=1024 +512+256+256
# model-7
# save_path_val = './per img predictions val model7-track_based-Eq loss 0.8 shark'
# save_path_tr = './per img predictions tr model7-track_based-Eq loss 0.8 shark'
save_path_val = './per img predictions val plus_sleeper_shark_nonfish'
save_path_tr = './per img predictions tr plus_sleeper_shark_nonfish'
#model-6
# save_path_val = './per img predictions val model6-track_based 2nd'
# save_path_tr = './per img predictions tr model6-track_based 2nd'
custom_transform = transforms.Compose([transforms.Resize((128, 128)),
# transforms.CenterCrop((178, 178)),
#transforms.Grayscale(),
#transforms.Lambda(lambda x: x/255.),
transforms.ToTensor()])
valid_gt_path = 'Z:/Jie Mei/rail data/hierarchy_data_for_Transformer-SVM/labels_track_based/fish-rail-valid-plus_sleeper_shark_nonfish.csv'
train_gt_path = 'Z:/Jie Mei/rail data/hierarchy_data_for_Transformer-SVM/labels_track_based/fish-rail-train-plus_sleeper_shark_nonfish.csv'
img_dir = 'Z:/Jie Mei/rail data/hierarchy_data_for_Transformer-SVM/cropped_box_with_sleeper_shark_non_fish'
train_dataset = Fish_Rail_Dataset(csv_path=train_gt_path,
img_dir=img_dir,
transform=custom_transform,
hierarchy = hierarchy_dict)
valid_dataset = Fish_Rail_Dataset(csv_path=valid_gt_path,
img_dir=img_dir,
transform=custom_transform,
hierarchy = hierarchy_dict)
train_loader = DataLoader(dataset=train_dataset,
batch_size=BATCH_SIZE,
shuffle=False,
num_workers=0)
valid_loader = DataLoader(dataset=valid_dataset,
batch_size=BATCH_SIZE,
shuffle=False,
num_workers=0)
### load model
# best_epoch=138 #model-7
# best_epoch=135 #model-7 more
# best_epoch=90 #model-6
# best_epoch=126 #model-7 EQ
# best_epoch=87 #model-7 EQ 0.8
# best_epoch=86 #model-7 EQ 0.8 sleeper sharks
best_epoch=65 # nonfish + sleeper shark
stop_at_level_1_threshold=0.85
model = resnet101(NUM_level_1_CLASSES, NUM_level_2_CLASSES, GRAYSCALE)
PATH = os.path.join(model_save_path,'parameters_epoch_'+str(best_epoch)+'.pkl')
model.load_state_dict(torch.load(PATH))
model.to(DEVICE)
### 最后测试一下 for model7
model.eval()
# for model7
with torch.set_grad_enabled(False): # save memory during inference
### evaluate train data
# print('evaluating train data...')
# avg_level_1_acc_tr, avg_level_2_acc_tr, avg_level_2_acc_p1p2_31_tr, avg_level_2_acc_p1p2_maxmax_tr, \
# acc_1_tr, acc_2_tr, acc_2_p1p2_31_tr, acc_2_p1p2_maxmax_tr, avg_acc_can_stop_level_1_tr, all_num_level_1_tr, all_num_level_2_tr, species_stop_at_level_1_tr = compute_accuracy_model7_track_based(
# model, train_loader, best_epoch, DEVICE, save_path_tr, stop_at_level_1_threshold)
#
# ##根据记录下来的confidence，计算tarck-based的accuracy
# avg_level_1_acc_tr_track, avg_level_2_acc_tr_track, avg_level_2_acc_p1p2_31_tr_track, avg_level_2_acc_p1p2_maxmax_tr_track, \
# acc_1_tr_track, acc_2_tr_track, acc_2_p1p2_31_tr_track, acc_2_p1p2_maxmax_tr_track, avg_acc_can_stop_level_1_tr_track, all_num_level_1_tr_track, all_num_level_2_tr_track, species_stop_at_level_1_tr_track = \
# track_based_accuracy(save_path_tr, best_epoch)
#
# print(
# 'Track-based Epoch: %03d | Train: Level-1 Avg: %.3f%%, Level-2 Avg: %.3f%%, Level-2 Avg p1p2 max out of 31: %.3f%%, Level-2 Avg p1p2 maxmax: %.3f%%, , Level-2 Avg can stop at level-1: %.3f%%, num level-1: %d, num level-2: %d' % (
# best_epoch,
# # avg_level_1_acc_tr * 100,
# # avg_level_2_acc_tr * 100,
# # avg_level_2_acc_p1p2_31_tr * 100,
# # avg_level_2_acc_p1p2_maxmax_tr * 100,
# # avg_acc_can_stop_level_1_tr * 100,
# # all_num_level_1_tr,
# # all_num_level_2_tr,
# avg_level_1_acc_tr_track * 100,
# avg_level_2_acc_tr_track * 100,
# avg_level_2_acc_p1p2_31_tr_track * 100,
# avg_level_2_acc_p1p2_maxmax_tr_track * 100,
# avg_acc_can_stop_level_1_tr_track * 100,
# all_num_level_1_tr_track,
# all_num_level_2_tr_track
# ))
#
# print('Track-based Individual accuracy: Train: '
# 'Level-1:', acc_1_tr_track,
# 'Level-2:', acc_2_tr_track,
# 'Level-2 p1p2 max out of 31:', acc_2_p1p2_31_tr_track,
# 'Level-2 p1p2 maxmax:', acc_2_p1p2_maxmax_tr_track,
# 'species stop at level1:', species_stop_at_level_1_tr_track)
#
# print(
# 'Image-based Epoch: %03d | Train: Level-1 Avg: %.3f%%, Level-2 Avg: %.3f%%, Level-2 Avg p1p2 max out of 31: %.3f%%, Level-2 Avg p1p2 maxmax: %.3f%%, , Level-2 Avg can stop at level-1: %.3f%%, num level-1: %d, num level-2: %d' % (
# best_epoch,
# # avg_level_1_acc_tr * 100,
# # avg_level_2_acc_tr * 100,
# # avg_level_2_acc_p1p2_31_tr * 100,
# # avg_level_2_acc_p1p2_maxmax_tr * 100,
# # avg_acc_can_stop_level_1_tr * 100,
# # all_num_level_1_tr,
# # all_num_level_2_tr,
# avg_level_1_acc_tr * 100,
# avg_level_2_acc_tr * 100,
# avg_level_2_acc_p1p2_31_tr * 100,
# avg_level_2_acc_p1p2_maxmax_tr * 100,
# avg_acc_can_stop_level_1_tr * 100,
# all_num_level_1_tr,
# all_num_level_2_tr
# ))
#
# print('Image-based Individual accuracy: Train: '
# 'Level-1:', acc_1_tr,
# 'Level-2:', acc_2_tr,
# 'Level-2 p1p2 max out of 31:', acc_2_p1p2_31_tr,
# 'Level-2 p1p2 maxmax:', acc_2_p1p2_maxmax_tr,
# 'species stop at level1:', species_stop_at_level_1_tr)
### Otsu Threshold by using training data,读取记录下来的confidence，计算threshold
# Otsu_Threshold_level_1 = Otsu_Threshold(save_path_tr, best_epoch)
### evaluate valid data
print('evaluating valid data...')
avg_level_1_acc_val, avg_level_2_acc_val, avg_level_2_acc_p1p2_31_val, avg_level_2_acc_p1p2_maxmax_val, \
acc_1_val, acc_2_val, acc_2_p1p2_31_val, acc_2_p1p2_maxmax_val, avg_acc_can_stop_level_1_val, all_num_level_1_val, all_num_level_2_val, species_stop_at_level_1_val = compute_accuracy_model7_track_based(
model, valid_loader, best_epoch, DEVICE, save_path_val, stop_at_level_1_threshold)
##根据记录下来的confidence，计算tarck-based的accuracy
print('avg conf video-based method: ')
avg_level_1_acc_val_track, avg_level_2_acc_val_track, avg_level_2_acc_p1p2_31_val_track, avg_level_2_acc_p1p2_maxmax_val_track, \
acc_1_val_track, acc_2_val_track, acc_2_p1p2_31_val_track, acc_2_p1p2_maxmax_val_track, avg_acc_can_stop_level_1_val_track, all_num_level_1_val_track, all_num_level_2_val_track, species_stop_at_level_1_val_track= \
track_based_accuracy(save_path_val, best_epoch, stop_at_level_1_threshold)
print(
'Track-based Epoch: %03d | Valid: Level-1 Avg: %.3f%%, Level-2 Avg: %.3f%%, Level-2 Avg p1p2 max out of 31: %.3f%%, Level-2 Avg p1p2 maxmax: %.3f%%, Level-2 can stop at level-1: %.3f%%, num level-1: %d, num level-2: %d' % (
best_epoch ,
# avg_level_1_acc_tr * 100,
# avg_level_2_acc_tr * 100,
# avg_level_2_acc_p1p2_31_tr * 100,
# avg_level_2_acc_p1p2_maxmax_tr * 100,
# avg_acc_can_stop_level_1_tr * 100,
# all_num_level_1_tr,
# all_num_level_2_tr,
avg_level_1_acc_val_track * 100,
avg_level_2_acc_val_track * 100,
avg_level_2_acc_p1p2_31_val_track * 100,
avg_level_2_acc_p1p2_maxmax_val_track * 100,
avg_acc_can_stop_level_1_val_track * 100,
all_num_level_1_val_track,
all_num_level_2_val_track
))
print('Track-based Individual accuracy: Valid: '
'Level-1:', acc_1_val_track,
'Level-2:', acc_2_val_track,
'Level-2 p1p2 max out of 31:', acc_2_p1p2_31_val_track,
'Level-2 p1p2 maxmax:', acc_2_p1p2_maxmax_val_track,
'species stop at level-1(avg conf):', species_stop_at_level_1_val_track)
print('Majority vote video-based method: ')
avg_level_1_acc_val_track, avg_level_2_acc_val_track, avg_level_2_acc_p1p2_31_val_track, avg_level_2_acc_p1p2_maxmax_val_track, \
acc_1_val_track, acc_2_val_track, acc_2_p1p2_31_val_track, acc_2_p1p2_maxmax_val_track, avg_acc_can_stop_level_1_val_track, all_num_level_1_val_track, all_num_level_2_val_track, species_stop_at_level_1_val_track = \
track_based_accuracy_majority_vote(save_path_val, best_epoch)
print('Track-based Epoch: %03d | Valid: Level-1 Avg: %.3f%%, Level-2 Avg: %.3f%%, Level-2 Avg p1p2 max out of 31: %.3f%%, Level-2 Avg p1p2 maxmax: %.3f%%, Level-2 can stop at level-1: %.3f%%, num level-1: %d, num level-2: %d' % (
best_epoch,
# avg_level_1_acc_tr * 100,
# avg_level_2_acc_tr * 100,
# avg_level_2_acc_p1p2_31_tr * 100,
# avg_level_2_acc_p1p2_maxmax_tr * 100,
# avg_acc_can_stop_level_1_tr * 100,
# all_num_level_1_tr,
# all_num_level_2_tr,
avg_level_1_acc_val_track * 100,
avg_level_2_acc_val_track * 100,
avg_level_2_acc_p1p2_31_val_track * 100,
avg_level_2_acc_p1p2_maxmax_val_track * 100,
avg_acc_can_stop_level_1_val_track * 100,
all_num_level_1_val_track,
all_num_level_2_val_track
))
print('Track-based Individual accuracy: Valid: '
'Level-1:', acc_1_val_track,
'Level-2:', acc_2_val_track,
'Level-2 p1p2 max out of 31:', acc_2_p1p2_31_val_track,
'Level-2 p1p2 maxmax:', acc_2_p1p2_maxmax_val_track,
'species stop at level-1:', species_stop_at_level_1_val_track)
print(
'Image-based Epoch: %03d | Valid: Level-1 Avg: %.3f%%, Level-2 Avg: %.3f%%, Level-2 Avg p1p2 max out of 31: %.3f%%, Level-2 Avg p1p2 maxmax: %.3f%%, , Level-2 Avg can stop at level-1: %.3f%%, num level-1: %d, num level-2: %d' % (
best_epoch,
# avg_level_1_acc_tr * 100,
# avg_level_2_acc_tr * 100,
# avg_level_2_acc_p1p2_31_tr * 100,
# avg_level_2_acc_p1p2_maxmax_tr * 100,
# avg_acc_can_stop_level_1_tr * 100,
# all_num_level_1_tr,
# all_num_level_2_tr,
avg_level_1_acc_val * 100,
avg_level_2_acc_val * 100,
avg_level_2_acc_p1p2_31_val * 100,
avg_level_2_acc_p1p2_maxmax_val * 100,
avg_acc_can_stop_level_1_val * 100,
all_num_level_1_val,
all_num_level_2_val
))
print('Image-based Individual accuracy: Valid: '
'Level-1:', acc_1_val,
'Level-2:', acc_2_val,
'Level-2 p1p2 max out of 31:', acc_2_p1p2_31_val,
'Level-2 p1p2 maxmax:', acc_2_p1p2_maxmax_val,
'species stop at level1:', species_stop_at_level_1_val)
embed()