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test_acc_dense.py
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test_acc_dense.py
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import os
import os.path as osp
import torch
import numpy as np
import sys
from csrnet import CSRNet
from dataset import *
import math
import tqdm
rand_seed = 64678
if rand_seed is not None:
random.seed(rand_seed)
np.random.seed(rand_seed)
torch.manual_seed(rand_seed)
torch.cuda.manual_seed_all(rand_seed)
EVAL_MODE = 'Rank' # None | Rank | NoCompare
#pretrained_model = '../results/eval_1_ShanghaiTechA_Combine_CSRNet/809_model_best.pth.tar'
#pretrained_model = '../results/eval_2_ShanghaiTechA_Reg_CSRNet/688_model_best.pth.tar'
#pretrained_model = '../results/eval_5_ShanghaiTechA_AllCombine_CSRNet/188_model_best.pth.tar'
#pretrained_model = '../results/eval_2_1_ShanghaiTechA_Reg_CSRNet/model_best.pth.tar'
#pretrained_model = '../results/22_1_UCF-QNRF_Combine_CSRNet/model_best.pth.tar'
pretrained_model = '../results/22_2_UCF-QNRF_AllCombine_CSRNet/model_best.pth.tar'
down_sample = 8
os.environ['CUDA_VISIBLE_DEVICES'] = "3"
net = CSRNet(add_mode=False)
net.cuda()
print("=> loading checkpoint '{}'".format(pretrained_model))
checkpoint = torch.load(pretrained_model)
net.load_state_dict(checkpoint['state_dict'])
#data_val = ShanghaiTech_eval(down_sample=down_sample, is_A=True)
#data_compare = ShanghaiTech_compare(down_sample=down_sample, is_A=True)
data_compare = UCF_compare(down_sample=down_sample)
data_val = UCF_eval(down_sample=down_sample)
if EVAL_MODE == 'None':
is_rank = False
elif EVAL_MODE == 'Rank':
is_rank = True
elif EVAL_MODE == 'NoCompare':
is_rank = None
else:
raise AssertionError("Invalid Evaluation Mode!")
data_loader_val = torch.utils.data.DataLoader(data_val,batch_size=1,shuffle=False)
data_loader_compare = torch.utils.data.DataLoader(data_compare,batch_size=1,shuffle=False)
def evaluate_model(net, data_loader_val, data_loader_compare, rank, scaling_rate=100):
net.eval()
mae_accs = []
comp_nums = []
comp_outs = []
with torch.no_grad():
if rank is None:
for blob in tqdm.tqdm(data_loader_val):
im_data = blob['im'].cuda()
num = blob['num'][0].numpy()
out, _ = net(im_data)
out = out[0].cpu().numpy()
pred = out * scaling_rate
mae_accs.append((num, np.abs(min(num, pred))/max(num, pred)))
else:
for blob in data_loader_compare:
im_data = blob['im'].cuda()
num = blob['num'][0].numpy()
out, _ = net(im_data)
out = out[0].cpu().numpy()
comp_nums.append(num)
comp_outs.append(out)
X = np.array(comp_outs)
y = np.array(comp_nums)
if rank:
arg_X = np.argsort(X.squeeze())
rank_X = arg_X.copy()
for i,e in enumerate(arg_X):
rank_X[e] = i
arg_y = np.argsort(y.squeeze())
rank_y = arg_y.copy()
for i,e in enumerate(arg_y):
rank_y[e] = i
X_b = np.c_[np.ones((len(rank_X), 1)),rank_X.reshape(-1,1)]
linalg = np.linalg.inv(X_b.T.dot(X_b)).dot(X_b.T).dot(rank_y.reshape(-1,1))
print(linalg[0], linalg[1])
for blob in tqdm.tqdm(data_loader_val):
im_data = blob['im'].cuda()
num = blob['num'][0].numpy()
out, _ = net(im_data)
out = out[0].cpu().numpy()
if out < min(X.squeeze()):
out = min(X.squeeze())
if out > max(X.squeeze()):
out = max(X.squeeze())
min_idx = 0
max_idx = 0
min_diff = 10000
max_diff = 10000
for i,e in enumerate(X.squeeze()):
if e < out:
if out - e < min_diff:
min_diff = out - e
min_idx = i
elif e > out:
if e - out < max_diff:
max_diff = e - out
max_idx = i
else:
min_idx = max_idx = i
min_diff = max_diff = 0
break
min_ = out - min_diff
max_ = out + max_diff
min_rank = rank_X[min_idx]
max_rank = rank_X[max_idx]
if min_idx != max_idx:
p = (out - min_)/(max_ - min_)
out_rank = p * min_rank + (1 - p) * max_rank
else:
out_rank = min_rank
pred_rank = linalg[1] * out_rank + linalg[0]
y.sort()
if pred_rank < min(rank_y):
pred = y[np.argmin(rank_y)]
elif pred_rank > max(rank_y):
pred = y[np.argmax(rank_y)]
else:
min_y_idx = math.floor(pred_rank)
max_y_idx = math.ceil(pred_rank)
pred = y[min_y_idx] * (max_y_idx - pred_rank)+ y[max_y_idx] * (pred_rank - min_y_idx)
#print(pred_rank, y[min_y_idx], y[max_y_idx], pred, num)
mae_accs.append((num, np.abs(min(num, pred))/max(num, pred)))
else:
X_b = np.c_[np.ones((len(X), 1)),X]
linalg = np.linalg.inv(X_b.T.dot(X_b)).dot(X_b.T).dot(y)
print(linalg[0],linalg[1])
for blob in tqdm.tqdm(data_loader_val):
im_data = blob['im'].cuda()
num = blob['num'][0].numpy()
out, _ = net(im_data)
out = out[0].cpu().numpy()
pred = linalg[1] * out + linalg[0]
mae_accs.append((num, np.abs(min(num, pred))/max(num, pred)))
acc_0_200 = []
acc_200_400 = []
acc_400_700 = []
acc_700_1000 = []
acc_1000_1500 = []
acc_1500_3000 = []
acc_3000_ = []
for mae in mae_accs:
if mae[0] >= 0 and mae[0] < 200:
acc_0_200.append(mae[1])
elif mae[0] >= 200 and mae[0] < 400:
acc_200_400.append(mae[1])
elif mae[0] >= 400 and mae[0] < 700:
acc_400_700.append(mae[1])
elif mae[0] >= 700 and mae[0] < 1000:
acc_700_1000.append(mae[1])
elif mae[0] >= 1000 and mae[0] < 1500:
acc_1000_1500.append(mae[1])
elif mae[0] >= 1500 and mae[0] < 3000:
acc_1500_3000.append(mae[1])
else:
acc_3000_.append(mae[1])
print(len(acc_0_200), len(acc_200_400), len(acc_400_700), len(acc_700_1000), len(acc_1000_1500), len(acc_1500_3000), len(acc_3000_))
acc_0_200 = np.average(np.array(acc_0_200))
acc_200_400 = np.average(np.array(acc_200_400))
acc_400_700 = np.average(np.array(acc_400_700))
acc_700_1000 = np.average(np.array(acc_700_1000))
acc_1000_1500 = np.average(np.array(acc_1000_1500))
acc_1500_3000 = np.average(np.array(acc_1500_3000))
acc_3000_ = np.average(np.array(acc_3000_))
print(acc_0_200, acc_200_400, acc_400_700, acc_700_1000, acc_1000_1500, acc_1500_3000, acc_3000_)
evaluate_model(net, data_loader_val, data_loader_compare, is_rank)