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3_test_mae_rmse_epochs_range.py
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3_test_mae_rmse_epochs_range.py
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#import matplotlib
#matplotlib.use('Agg')
#import matplotlib.pyplot as plt
#import matplotlib.cm as CM
import os
import numpy as np
from skimage import io;
import glob;
import cv2 ;
import sys;
#from scipy.misc import imresize
#from scipy.ndimage.filters import convolve
from skimage.measure import label
#import pickle;
from skimage import filters
import math
from tqdm import tqdm as tqdm
import time
import torch
import torch.nn as nn
#from scipy import ndimage
from unet_vgg4_cc import UnetVggCC
from my_dataset_test import CrowdDataset
'''
Generate calculate mae and rmse for a range of saved epochs
Output files:
out_.txt: stats per image and over all images.
In main set epochs range:
start_epoch
end_epoch
In main are some default configurations for the datasets: ShanghaTech Part A, ShanghaTech Part B, UCF-QNRF, JHU++, NWPU-Crowd.
Uncomment the approporiate configuration
'''
def run_eval_model(model_param_path, test_loader, out_dir, log_file, thresh_low=0.4, thresh_high=0.5, size_thresh=-1, model_name='topocount_best'):
global model
global device
criterion_sig = nn.Sigmoid() # initialize sigmoid layer
model.load_state_dict(torch.load(model_param_path), strict=True);
model.to(device)
mae = 0
rmse = 0
pos = 0
neg = 0
files_count = 0
#log_file.write("modelname {} \n".format(model_name))
#log_file.flush()
for i,(img,gt_dots,img_name) in enumerate(tqdm(test_loader, disable=True)):
sys.stdout.flush()
files_count += 1
img_name = img_name[0]
print('img', img_name, img.shape)
sys.stdout.flush();
if(img.shape[-1]<2048 and img.shape[-2]<2048):
img=img.to(device)
# forward propagation
et_dmap=model(img)[:,:,2:-2,2:-2]
et_sig = criterion_sig(et_dmap.squeeze(dim=1)).squeeze().detach().cpu().numpy()
et_dmap=et_dmap.squeeze().detach().cpu().numpy()
elif(img.shape[-2] * img.shape[-1] > 30000): # divide image into 3x3 overlapping to fit in GPU
et_dmap = 0
et_sig = np.zeros((img.shape[-2], img.shape[-1]))
et_dmap = np.zeros((img.shape[-2], img.shape[-1]))
y_part = img.shape[-2]//3
x_part = img.shape[-1]//3
overlap_y = 96
overlap_x = 96
max_scale = 16
ds_rows=int(y_part//max_scale)*max_scale
ds_cols=int(x_part//max_scale)*max_scale
overlap_y += (max_scale - (y_part - ds_rows))
overlap_x += (max_scale - (x_part - ds_cols))
#print('#quad 0,0')
#quad 0,0
img_sub = img[:,:,:y_part+overlap_y, :x_part+overlap_x].to(device)
#print('img_sub',img_sub.shape)
#et_dmap_sub=model(img_sub)[:,:,2:-2,2:-2]
et_dmap_sub=model(img_sub)
#print('et_dmap_sub',et_dmap_sub.shape)
et_sig_sub = criterion_sig(et_dmap_sub.squeeze(dim=1)).squeeze().detach().cpu().numpy()
et_dmap_sub=et_dmap_sub.squeeze().detach().cpu().numpy()
#print('et_sig_sub',et_sig_sub.shape)
#print(':y_half',y_half)
#print(':x_half',x_half)
et_sig[:y_part, :x_part] = et_sig_sub[:y_part, :x_part]
et_dmap[:y_part, :x_part] = et_dmap_sub[:y_part, :x_part]
del img_sub, et_dmap_sub, et_sig_sub
#quad 0,1
img_sub = img[:,:,:y_part+overlap_y, x_part-overlap_x//2:2*x_part+overlap_x//2 ].to(device)
#print('img_sub',img_sub.shape)
#et_dmap_sub=model(img_sub)[:,:,2:-2,2:-2]
et_dmap_sub=model(img_sub)
#print('et_dmap_sub',et_dmap_sub.shape)
et_sig_sub = criterion_sig(et_dmap_sub.squeeze(dim=1)).squeeze().detach().cpu().numpy()
et_dmap_sub=et_dmap_sub.squeeze().detach().cpu().numpy()
#print('et_sig_sub',et_sig_sub.shape)
#print(':y_half',y_half)
#print(':x_half',x_half)
et_sig[:y_part, x_part:2*x_part] = et_sig_sub[:y_part, overlap_x//2:overlap_x//2 + x_part]
et_dmap[:y_part, x_part:2*x_part] = et_dmap_sub[:y_part, overlap_x//2:overlap_x//2 + x_part]
del img_sub, et_dmap_sub, et_sig_sub
#quad 0,2
img_sub = img[:,:,:y_part+overlap_y, 2*x_part-overlap_x: ].to(device)
#print('img_sub',img_sub.shape)
#et_dmap_sub=model(img_sub)[:,:,2:-2,2:-2]
et_dmap_sub=model(img_sub)
#print('et_dmap_sub',et_dmap_sub.shape)
et_sig_sub = criterion_sig(et_dmap_sub.squeeze(dim=1)).squeeze().detach().cpu().numpy()
et_dmap_sub=et_dmap_sub.squeeze().detach().cpu().numpy()
#print('et_sig_sub',et_sig_sub.shape)
#print(':y_half',y_half)
#print(':x_half',x_half)
et_sig[:y_part, -x_part:] = et_sig_sub[:y_part, -x_part:]
et_dmap[:y_part, -x_part:] = et_dmap_sub[:y_part, -x_part:]
del img_sub, et_dmap_sub, et_sig_sub
#quad 1,0
img_sub = img[:,:,y_part-overlap_y//2:2*y_part+overlap_y//2, :x_part+overlap_x].to(device)
#print('img_sub',img_sub.shape)
#et_dmap_sub=model(img_sub)[:,:,2:-2,2:-2]
et_dmap_sub=model(img_sub)
#print('et_dmap_sub',et_dmap_sub.shape)
et_sig_sub = criterion_sig(et_dmap_sub.squeeze(dim=1)).squeeze().detach().cpu().numpy()
et_dmap_sub=et_dmap_sub.squeeze().detach().cpu().numpy()
#print('et_sig_sub',et_sig_sub.shape)
#print(':y_half',y_half)
#print(':x_half',x_half)
et_sig[y_part:2*y_part, :x_part] = et_sig_sub[overlap_y//2:overlap_y//2 + y_part, :x_part]
et_dmap[y_part:2*y_part, :x_part] = et_dmap_sub[overlap_y//2:overlap_y//2 + y_part, :x_part]
del img_sub, et_dmap_sub, et_sig_sub
#quad 1,1
img_sub = img[:,:,y_part-overlap_y//2:2*y_part+overlap_y//2, x_part-overlap_x//2:2*x_part+overlap_x//2 ].to(device)
#print('img_sub',img_sub.shape)
#et_dmap_sub=model(img_sub)[:,:,2:-2,2:-2]
et_dmap_sub=model(img_sub)
#print('et_dmap_sub',et_dmap_sub.shape)
et_sig_sub = criterion_sig(et_dmap_sub.squeeze(dim=1)).squeeze().detach().cpu().numpy()
et_dmap_sub=et_dmap_sub.squeeze().detach().cpu().numpy()
#print('et_sig_sub',et_sig_sub.shape)
#print(':y_half',y_half)
#print(':x_half',x_half)
et_sig[y_part:2*y_part, x_part:2*x_part] = et_sig_sub[overlap_y//2:overlap_y//2 + y_part, overlap_x//2:overlap_x//2 + x_part]
et_dmap[y_part:2*y_part, x_part:2*x_part] = et_dmap_sub[overlap_y//2:overlap_y//2 + y_part, overlap_x//2:overlap_x//2 + x_part]
del img_sub, et_dmap_sub, et_sig_sub
#quad 1,2
img_sub = img[:,:,y_part-overlap_y//2:2*y_part+overlap_y//2, 2*x_part-overlap_x: ].to(device)
#print('img_sub',img_sub.shape)
#et_dmap_sub=model(img_sub)[:,:,2:-2,2:-2]
et_dmap_sub=model(img_sub)
#print('et_dmap_sub',et_dmap_sub.shape)
et_sig_sub = criterion_sig(et_dmap_sub.squeeze(dim=1)).squeeze().detach().cpu().numpy()
et_dmap_sub=et_dmap_sub.squeeze().detach().cpu().numpy()
#print('et_sig_sub',et_sig_sub.shape)
#print(':y_half',y_half)
#print(':x_half',x_half)
et_sig[y_part:2*y_part, -x_part:] = et_sig_sub[overlap_y//2:overlap_y//2 + y_part, -x_part:]
et_dmap[y_part:2*y_part, -x_part:] = et_dmap_sub[overlap_y//2:overlap_y//2 + y_part, -x_part:]
del img_sub, et_dmap_sub, et_sig_sub
#quad 2,0
img_sub = img[:,:,2*y_part-overlap_y:, :x_part+overlap_x].to(device)
#print('img_sub',img_sub.shape)
#et_dmap_sub=model(img_sub)[:,:,2:-2,2:-2]
et_dmap_sub=model(img_sub)
#print('et_dmap_sub',et_dmap_sub.shape)
et_sig_sub = criterion_sig(et_dmap_sub.squeeze(dim=1)).squeeze().detach().cpu().numpy()
et_dmap_sub=et_dmap_sub.squeeze().detach().cpu().numpy()
#print('et_sig_sub',et_sig_sub.shape)
#print(':y_half',y_half)
#print(':x_half',x_half)
et_sig[-y_part:, :x_part] = et_sig_sub[-y_part:, :x_part]
et_dmap[-y_part:, :x_part] = et_dmap_sub[-y_part:, :x_part]
del img_sub, et_dmap_sub, et_sig_sub
#quad 2,1
img_sub = img[:,:,2*y_part-overlap_y:, x_part-overlap_x//2:2*x_part+overlap_x//2 ].to(device)
#print('img_sub',img_sub.shape)
#et_dmap_sub=model(img_sub)[:,:,2:-2,2:-2]
et_dmap_sub=model(img_sub)
#print('et_dmap_sub',et_dmap_sub.shape)
et_sig_sub = criterion_sig(et_dmap_sub.squeeze(dim=1)).squeeze().detach().cpu().numpy()
et_dmap_sub=et_dmap_sub.squeeze().detach().cpu().numpy()
#print('et_sig_sub',et_sig_sub.shape)
#print(':y_half',y_half)
#print(':x_half',x_half)
et_sig[-y_part:, x_part:2*x_part] = et_sig_sub[-y_part:, overlap_x//2:overlap_x//2 + x_part]
et_dmap[-y_part:, x_part:2*x_part] = et_dmap_sub[-y_part:, overlap_x//2:overlap_x//2 + x_part]
del img_sub, et_dmap_sub, et_sig_sub
#quad 2,2
img_sub = img[:,:,2*y_part-overlap_y:, 2*x_part-overlap_x: ].to(device)
#print('img_sub',img_sub.shape)
#et_dmap_sub=model(img_sub)[:,:,2:-2,2:-2]
et_dmap_sub=model(img_sub)
#print('et_dmap_sub',et_dmap_sub.shape)
et_sig_sub = criterion_sig(et_dmap_sub.squeeze(dim=1)).squeeze().detach().cpu().numpy()
et_dmap_sub=et_dmap_sub.squeeze().detach().cpu().numpy()
#print('et_sig_sub',et_sig_sub.shape)
#print(':y_half',y_half)
#print(':x_half',x_half)
et_sig[-y_part:, -x_part:] = et_sig_sub[-y_part:, -x_part:]
et_dmap[-y_part:, -x_part:] = et_dmap_sub[-y_part:, -x_part:]
del img_sub, et_dmap_sub, et_sig_sub
else: # divide image into 2x2 overlapping to fit in GPU
et_dmap = 0
et_sig = np.zeros((img.shape[-2], img.shape[-1]))
et_dmap = np.zeros((img.shape[-2], img.shape[-1]))
y_half = img.shape[-2]//2
x_half = img.shape[-1]//2
overlap_y = 96
overlap_x = 96
max_scale = 16
ds_rows=int(img.shape[-2]//max_scale)*max_scale
ds_cols=int(img.shape[-1]//max_scale)*max_scale
overlap_y += (max_scale - (img.shape[-2] - ds_rows))
overlap_x += (max_scale - (img.shape[-1] - ds_cols))
#print('#quad 0,0')
#print('mem',torch.cuda.memory_allocated(device))
#quad 0,0
img_sub = img[:,:,:y_half+overlap_y, :x_half+overlap_x].to(device)
#print('img_sub',img_sub.shape)
#et_dmap_sub=model(img_sub)[:,:,2:-2,2:-2]
et_dmap_sub=model(img_sub)
img_sub = img_sub.squeeze().detach().cpu().numpy()
#print('et_dmap_sub',et_dmap_sub.shape)
et_sig_sub = criterion_sig(et_dmap_sub.squeeze(dim=1)).squeeze().detach().cpu().numpy()
et_dmap_sub=et_dmap_sub.squeeze().detach().cpu().numpy()
#print('et_sig_sub',et_sig_sub.shape)
#print(':y_half',y_half)
#print(':x_half',x_half)
if(et_sig_sub.shape[0] > y_half + overlap_y):
et_sig_sub = et_sig_sub[ (et_sig_sub.shape[0]-y_half - overlap_y)//2:-(et_sig_sub.shape[0]-y_half - overlap_y)//2 ,:]
et_dmap_sub = et_dmap_sub[ (et_dmap_sub.shape[0]-y_half - overlap_y)//2:-(et_dmap_sub.shape[0]-y_half - overlap_y)//2 ,:]
if(et_sig_sub.shape[1] > x_half + overlap_x):
et_sig_sub = et_sig_sub[ :, (et_sig_sub.shape[1]-x_half - overlap_x)//2:-(et_sig_sub.shape[1]-x_half - overlap_x)//2]
et_dmap_sub = et_dmap_sub[ :, (et_dmap_sub.shape[1]-x_half - overlap_x)//2:-(et_dmap_sub.shape[1]-x_half - overlap_x)//2]
et_sig[:y_half, :x_half] = et_sig_sub[:y_half, :x_half]
et_dmap[:y_half, :x_half] = et_dmap_sub[:y_half, :x_half]
del img_sub, et_dmap_sub, et_sig_sub
torch.cuda.empty_cache()
#print('#quad 0,1')
#print('mem',torch.cuda.memory_allocated(device))
#quad 0,1
img_sub = img[:,:,:y_half+overlap_y, x_half-overlap_x:].to(device)
#print('img_sub',img_sub.shape)
#et_dmap_sub=model(img_sub)[:,:,2:-2,2:-2]
et_dmap_sub=model(img_sub)
#print('et_dmap_sub',et_dmap_sub.shape)
et_sig_sub = criterion_sig(et_dmap_sub.squeeze(dim=1)).squeeze().detach().cpu().numpy()
et_dmap_sub=et_dmap_sub.squeeze().detach().cpu().numpy()
#print('et_sig_sub',et_sig_sub.shape)
#print(':y_half',y_half)
#print('x_half:',x_half, overlap_x)
if(et_sig_sub.shape[0] > y_half + overlap_y):
et_sig_sub = et_sig_sub[ (et_sig_sub.shape[0]-y_half - overlap_y)//2:-(et_sig_sub.shape[0]-y_half - overlap_y)//2 ,:]
et_dmap_sub = et_dmap_sub[ (et_dmap_sub.shape[0]-y_half - overlap_y)//2:-(et_dmap_sub.shape[0]-y_half - overlap_y)//2 ,:]
if(et_sig_sub.shape[1] > x_half + overlap_x):
et_sig_sub = et_sig_sub[ :, (et_sig_sub.shape[1]-x_half - overlap_x)//2:-(et_sig_sub.shape[1]-x_half - overlap_x)//2]
et_dmap_sub = et_dmap_sub[ :, (et_dmap_sub.shape[1]-x_half - overlap_x)//2:-(et_dmap_sub.shape[1]-x_half - overlap_x)//2]
et_sig[:y_half, x_half:] = et_sig_sub[:y_half, overlap_y:]
et_dmap[:y_half, x_half:] = et_dmap_sub[:y_half, overlap_y:]
del img_sub, et_dmap_sub, et_sig_sub
torch.cuda.empty_cache()
#print('#quad 1,0')
#print('mem',torch.cuda.memory_allocated(device))
#quad 1,0
img_sub = img[:,:,y_half-overlap_y:, :x_half+overlap_x].to(device)
#print('img_sub',img_sub.shape)
#et_dmap_sub=model(img_sub)[:,:,2:-2,2:-2]
et_dmap_sub=model(img_sub)
#print('et_dmap_sub',et_dmap_sub.shape)
et_sig_sub = criterion_sig(et_dmap_sub.squeeze(dim=1)).squeeze().detach().cpu().numpy()
et_dmap_sub=et_dmap_sub.squeeze().detach().cpu().numpy()
#print('et_sig_sub',et_sig_sub.shape)
#print('y_half:',y_half, overlap_y)
#print(':x_half',x_half)
if(et_sig_sub.shape[0] > y_half + overlap_y):
et_sig_sub = et_sig_sub[ (et_sig_sub.shape[0]-y_half - overlap_y)//2:-(et_sig_sub.shape[0]-y_half - overlap_y)//2 ,:]
et_dmap_sub = et_dmap_sub[ (et_dmap_sub.shape[0]-y_half - overlap_y)//2:-(et_dmap_sub.shape[0]-y_half - overlap_y)//2 ,:]
if(et_sig_sub.shape[1] > x_half + overlap_x):
et_sig_sub = et_sig_sub[ :, (et_sig_sub.shape[1]-x_half - overlap_x)//2:-(et_sig_sub.shape[1]-x_half - overlap_x)//2]
et_dmap_sub = et_dmap_sub[ :, (et_dmap_sub.shape[1]-x_half - overlap_x)//2:-(et_dmap_sub.shape[1]-x_half - overlap_x)//2]
et_sig[y_half:, :x_half] = et_sig_sub[overlap_y:, :x_half]
et_dmap[y_half:, :x_half] = et_dmap_sub[overlap_y:, :x_half]
del img_sub, et_dmap_sub, et_sig_sub
torch.cuda.empty_cache()
#print('#quad 1,1')
#print('mem',torch.cuda.memory_allocated(device))
#quad 1,1
img_sub = img[:,:,y_half-overlap_y:, x_half-overlap_x:].to(device)
#print('img_sub',img_sub.shape)
#et_dmap_sub=model(img_sub)[:,:,2:-2,2:-2]
et_dmap_sub=model(img_sub)
#print('et_dmap_sub',et_dmap_sub.shape)
et_sig_sub = criterion_sig(et_dmap_sub.squeeze(dim=1)).squeeze().detach().cpu().numpy()
et_dmap_sub=et_dmap_sub.squeeze().detach().cpu().numpy()
#print('et_sig_sub',et_sig_sub.shape)
#print('y_half:',y_half, overlap_y)
#print('x_half:',x_half, overlap_x)
if(et_sig_sub.shape[0] > y_half + overlap_y):
et_sig_sub = et_sig_sub[ (et_sig_sub.shape[0]-y_half - overlap_y)//2:-(et_sig_sub.shape[0]-y_half - overlap_y)//2 ,:]
et_dmap_sub = et_dmap_sub[ (et_dmap_sub.shape[0]-y_half - overlap_y)//2:-(et_dmap_sub.shape[0]-y_half - overlap_y)//2 ,:]
if(et_sig_sub.shape[1] > x_half + overlap_x):
et_sig_sub = et_sig_sub[ :, (et_sig_sub.shape[1]-x_half - overlap_x)//2:-(et_sig_sub.shape[1]-x_half - overlap_x)//2]
et_dmap_sub = et_dmap_sub[ :, (et_dmap_sub.shape[1]-x_half - overlap_x)//2:-(et_dmap_sub.shape[1]-x_half - overlap_x)//2]
et_sig[y_half:, x_half:] = et_sig_sub[overlap_y:, overlap_x:]
et_dmap[y_half:, x_half:] = et_dmap_sub[overlap_y:, overlap_x:]
del img_sub, et_dmap_sub, et_sig_sub
torch.cuda.empty_cache()
#print('mem',torch.cuda.memory_allocated(device))
gt_dots = gt_dots.detach().cpu().numpy().squeeze()
g_count = gt_dots.sum()
img = img.detach().cpu().numpy()
#print('mem',torch.cuda.memory_allocated(device))
e_hard = filters.apply_hysteresis_threshold(et_sig, thresh_low, thresh_high)
e_hard2 = (e_hard > 0).astype(np.uint8)
comp_mask = label(e_hard2)
e_count = comp_mask.max()
s_count=0
if(size_thresh > 0):
for c in range(1,comp_mask.max()+1):
s = (comp_mask == c).sum()
if(s < size_thresh):
e_count -=1
s_count +=1
# get dot predictions from topological map (centers of connected components)
e_dot = np.zeros(gt_dots.shape)
e_dot_vis = np.zeros(gt_dots.shape)
contours, hierarchy = cv2.findContours(e_hard2, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
for idx in range(len(contours)):
contour_i = contours[idx]
M = cv2.moments(contour_i)
if(M['m00'] == 0):
continue;
cx = round(M['m10'] / M['m00'])
cy = round(M['m01'] / M['m00'])
e_dot_vis[cy-1:cy+1, cx-1:cx+1] = 1
e_dot[cy, cx] = 1
io.imsave(os.path.join(out_dir, img_name + '_e_centers_'+'.png'), (e_dot_vis*255).astype(np.uint8))
e_dot.astype(np.uint8).dump(os.path.join(out_dir, img_name + '_e_centers_'+'.npy'))
err= e_count - g_count
mae += abs(err)
rmse += err**2
if(err > 0):
pos += err
else:
neg -= err
#print(img_name, e_count, g_count, err)
log_file.write("image {} e_count {} g_count {} err {} \n".format(img_name, e_count, g_count, err))
log_file.flush()
sys.stdout.flush();
#io.imsave(os.path.join(out_dir, img_name), (img.squeeze().detach().cpu().numpy()*255).transpose(1,2,0).astype(np.uint8));
io.imsave(os.path.join(out_dir, img_name.replace('.jpg','_likelihood'+'.png')), (et_sig*255).astype(np.uint8));
io.imsave(os.path.join(out_dir, img_name.replace('.jpg','_hard_'+str(thresh_low)+'-'+str(thresh_high)+'_err'+str(err)+'.png')), (e_hard2*255).astype(np.uint8));
et_dmap.astype(np.float16).dump(os.path.join(out_dir, img_name.replace('.jpg','_raw'+'.npy')))
et_sig.astype(np.float16).dump(os.path.join(out_dir, img_name.replace('.jpg','_likelihood'+'.npy')))
e_hard2.astype(np.uint8).dump(os.path.join(out_dir, img_name.replace('.jpg','_hard_'+str(thresh_low)+'-'+str(thresh_high)+'_err'+str(err)+'.npy')))
del img,gt_dots, et_dmap, et_sig
torch.cuda.empty_cache()
mae /= files_count
rmse = math.sqrt(rmse/files_count)
print('modelname', model_name, 'mae', mae, 'rmse', rmse, '2rmse+mae',2*rmse+mae, 'pos_err', pos, 'neg_err', neg)
log_file.write("modelname {} mae {} rmse {} 2rmse+mae {} pos_err {} neg_err {} \n".format(model_name, mae, rmse, 2*rmse+mae, pos, neg))
log_file.flush()
sys.stdout.flush();
if __name__=="__main__":
# Below are some default configurations for the datasets: ShanghaTech Part A, ShanghaTech Part B, UCF-QNRF, JHU++, NWPU-Crowd.
# Uncomment the approporiate configuration
####################################################################################
## Configuration for ShanghaiTech Part A - Test
####################################################################################
'''
models_root_dir = './checkpoints/sh_parta_custom_topo1_patch50'
#model_filename = 'topocount_sh_parta.pth'
#out_dir = './eval/parta_custom_topo1_patch50_e48_test'
out_dir = './eval/sh_parta_custom_topo1_patch50_topocount_test_epochs'
out_filename = 'out.txt'
root = './datasets/ShanghaiTech/'
test_image_root = os.path.join(root,'part_A/test_data','images')
test_dots_root = os.path.join(root,'part_A/test_data','ground-truth_dots')
test_split_txt_filepath = None
start_epoch=0
end_epoch=5
'''
#####################################################################################
### Configuration for ShanghaiTech Part B - Test
#####################################################################################
#'''
models_root_dir = './checkpoints/sh_partb_custom_topo1_patch50'
#model_filename = 'topocount_sh_partb.pth'
#out_dir = './eval/parta_custom_topo1_patch50_e48_test'
out_dir = './eval/sh_partb_custom_topo1_patch50_topocount_test_epochs'
out_filename = 'out.txt'
root = './datasets/ShanghaiTech/'
test_image_root = os.path.join(root,'part_B/test_data','images')
test_dots_root = os.path.join(root,'part_B/test_data','ground-truth_dots')
test_split_txt_filepath = None
start_epoch=0
end_epoch=5
#'''
#####################################################################################
### Configuration for UCF-QNRF - Test
#####################################################################################
'''
models_root_dir = './checkpoints/qnrf_custom_topo1_patch100'
#model_filename = 'topocount_qnrf.pth'
#out_dir = './eval/qnrf_custom_topo1_patch100_e48_test'
out_dir = './eval/qnrf_custom_topo1_patch100_topocount_test_epochs'
out_filename = 'out.txt'
root = './datasets/UCF-QNRF_ECCV18/UCF-QNRF_ECCV18'
test_image_root = os.path.join(root,'Test','img_scaleshort2048')
test_dots_root = os.path.join(root,'Test','ground-truth_dots_scaleshort2048')
test_split_txt_filepath = None
start_epoch=0
end_epoch=5
'''
#####################################################################################
### Configuration for JHU++ - Validation
#####################################################################################
'''
models_root_dir = './checkpoints/jhu_custom_topo1_patch100'
#model_filename = 'topocount_jhu.pth'
#out_dir = './eval/jhu_custom_topo1_patch100_e48_val'
out_dir = './eval/jhu_custom_topo1_patch100_topocount_val_epochs'
out_filename = 'out.txt'
root = './datasets/jhu/jhu_crowd_v2.0'
test_image_root = os.path.join(root, 'val','images')
test_dots_root = os.path.join(root, 'val','ground-truth_dots')
test_split_txt_filepath = None
start_epoch=0
end_epoch=5
'''
#####################################################################################
### Configuration for JHU++ - Test
#####################################################################################
'''
models_root_dir = './checkpoints/jhu_custom_topo1_patch100'
#model_filename = 'topocount_jhu.pth'
#out_dir = './eval/jhu_custom_topo1_patch100_e48_test'
out_dir = './eval/jhu_custom_topo1_patch100_topocount_test_epochs'
out_filename = 'out.txt'
root = './datasets/jhu/jhu_crowd_v2.0'
test_image_root = os.path.join(root, 'test','images')
test_dots_root = os.path.join(root, 'test','ground-truth_dots')
test_split_txt_filepath = None
start_epoch=0
end_epoch=5
'''
#####################################################################################
### Configuration for NWPU-Crowd - Validation
#####################################################################################
'''
models_root_dir = './checkpoints/nwpu_custom_topo1_patch100'
#model_filename = 'topocount_nwpu.pth'
#out_dir = './eval/nwpu_custom_topo1_patch100_e48_val'
out_dir = './eval/nwpu_custom_topo1_patch100_topocount_val_epochs'
out_filename = 'out.txt'
root = './datasets/nwpu-crowd'
test_image_root = os.path.join(root,'images')
test_dots_root = os.path.join(root,'ground-truth_dots')
test_split_txt_filepath = os.path.join(root,'val.txt')
start_epoch=0
end_epoch=5
'''
#####################################################################################
### Configuration for NWPU-Crowd - Test
#####################################################################################
'''
models_root_dir = './checkpoints/nwpu_custom_topo1_patch100'
#model_filename = 'topocount_nwpu.pth'
#out_dir = './eval/nwpu_custom_topo1_patch100_e48_test'
out_dir = './eval/nwpu_custom_topo1_patch100_topocount_test_epochs'
out_filename = 'out.txt'
root = './datasets/nwpu-crowd'
test_image_root = os.path.join(root,'images')
test_dots_root = os.path.join(root,'ground-truth_dots')
test_split_txt_filepath = os.path.join(root,'test.txt')
start_epoch=0
end_epoch=5
'''
####################################################################################
if not os.path.exists(out_dir):
os.mkdir(out_dir)
#thresh_low = 0.4
#thresh_high = 0.5
#size_thresh = -1
gpu_or_cpu='cuda' # use cuda or cpu
dropout_keep_prob = 1.0
initial_pad = 126
interpolate = 'False'
conv_init = 'he'
n_classes = 1
n_channels = 1
#if not os.path.exists(out_dir):
# os.mkdir(out_dir)
device=torch.device(gpu_or_cpu)
model=UnetVggCC(kwargs={'dropout_keep_prob':dropout_keep_prob, 'initial_pad':initial_pad, 'interpolate':interpolate, 'conv_init':conv_init, 'n_classes':n_classes, 'n_channels':n_channels})
model.to(device)
test_dataset=CrowdDataset(test_image_root, test_dots_root, split_txt_filepath=test_split_txt_filepath, phase='test', normalize=False)
test_loader=torch.utils.data.DataLoader(test_dataset,batch_size=1,shuffle=False)
with open(os.path.join(out_dir, out_filename), 'a+') as log_file:
for epoch in range(start_epoch, end_epoch+1):
print('test epoch ' + str(epoch) )
model_param_path = os.path.join(models_root_dir, 'epoch_'+str(epoch)+'.pth');
if(not os.path.isfile(model_param_path )):
print('not found ', model_param_path )
model_param_path = os.path.join(models_root_dir, 'epoch_'+str(epoch)+'_tmp.pth');
if(not os.path.isfile(model_param_path )):
print('not found ', model_param_path )
continue;
run_eval_model(model_param_path, test_loader, out_dir, log_file)
log_file.flush()
print('Done.')
print('Check output in: ', os.path.join(out_dir, out_filename))