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val.py
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val.py
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import argparse
import datetime
import json
import numpy as np
import os
import time
from pathlib import Path
from PIL import Image
import matplotlib.pyplot as plt
import scipy.ndimage as ndimage
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
from torch.utils.data import Dataset
import torchvision
from torchvision import transforms
import torchvision.transforms.functional as TF
import timm
assert timm.__version__ == "0.3.2" # version check
import util.misc as misc
os.environ["CUDA_LAUNCH_BLOCKING"] = '1'
# load data from FSC147
data_path = '/data2/wangzhicheng/Dataset/FSC147/'
anno_file = data_path + 'annotation_FSC147_384.json'
data_split_file = data_path + 'Train_Test_Val_FSC_147.json'
im_dir = data_path + 'images_384_VarV2'
gt_dir = data_path + 'gt_density_map_adaptive_384_VarV2'
with open(anno_file) as f:
annotations = json.load(f)
with open(data_split_file) as f:
data_split = json.load(f)
class TestData(Dataset):
def __init__(self, dataset='val'):
self.img = data_split[dataset]
self.img_dir = im_dir
def __len__(self):
return len(self.img)
def __getitem__(self, idx):
im_id = self.img[idx]
anno = annotations[im_id]
bboxes = anno['box_examples_coordinates']
dots = np.array(anno['points'])
image = Image.open('{}/{}'.format(im_dir, im_id))
image.load()
W, H = image.size
new_H = 16*int(H/16)
new_W = 16*int(W/16)
scale_factor = float(new_W)/ W
image = transforms.Resize((new_H, new_W))(image)
Normalize = transforms.Compose([transforms.ToTensor()])
image = Normalize(image)
rects = list()
for bbox in bboxes:
x1 = int(bbox[0][0]*scale_factor)
y1 = bbox[0][1]
x2 = int(bbox[2][0]*scale_factor)
y2 = bbox[2][1]
rects.append([y1, x1, y2, x2])
boxes = list()
scale_x = []
scale_y = []
cnt = 0
for box in rects:
cnt+=1
if cnt>3:
break
box2 = [int(k) for k in box]
y1, x1, y2, x2 = box2[0], box2[1], box2[2], box2[3]
scale_x1 = torch.tensor((x2-x1+1)/384)
scale_x.append(scale_x1)
scale_y1 = torch.tensor((y2-y1+1)/384)
scale_y.append(scale_y1)
bbox = image[:,y1:y2+1,x1:x2+1]
bbox = transforms.Resize((64, 64))(bbox)
boxes.append(bbox.numpy())
scale_xx = torch.stack(scale_x).unsqueeze(-1)
scale_yy = torch.stack(scale_y).unsqueeze(-1)
scale = torch.cat((scale_xx,scale_yy),dim=1)
boxes = np.array(boxes)
boxes = torch.Tensor(boxes)
# Only for visualisation purpose, no need for ground truth density map indeed.
gt_map = np.zeros((image.shape[1], image.shape[2]),dtype='float32')
for i in range(dots.shape[0]):
gt_map[min(new_H-1,int(dots[i][1]))][min(new_W-1,int(dots[i][0]*scale_factor))]=1
gt_map = ndimage.gaussian_filter(gt_map, sigma=(1, 1), order=0)
gt_map = torch.from_numpy(gt_map)
gt_map = gt_map *60
sample = {'image':image,'dots':dots, 'boxes':boxes, 'pos':rects, 'gt_map':gt_map, 'scale':scale}
return sample['image'], sample['dots'], sample['boxes'], sample['pos'] ,sample['gt_map'],im_id,sample['scale']
def val_func(model,device,dataset='val'):
dataset_test = TestData(dataset)
print(dataset_test)
if True: # args.distributed:
num_tasks = misc.get_world_size()
global_rank = misc.get_rank()
sampler_test = torch.utils.data.DistributedSampler(
dataset_test, num_replicas=num_tasks, rank=global_rank, shuffle=False
)
print("Sampler_test = %s" % str(sampler_test))
else:
sampler_test = torch.utils.data.RandomSampler(dataset_test)
data_loader_test = torch.utils.data.DataLoader(
dataset_test, sampler=sampler_test,
batch_size=1,
num_workers=10,
pin_memory=True,
drop_last=False,
)
# test
epoch = 0
model.eval()
metric_logger = misc.MetricLogger(delimiter=" ")
header = 'Epoch: [{}]'.format(epoch)
print_freq = 20
# some parameters in training
train_mae = 0
train_rmse = 0
pred_cnt = 0
gt_cnt = 0
loss_array = []
gt_array = []
wrong_id = []
model.eval()
# device = model.device
for data_iter_step, (samples, gt_dots, boxes, pos, gt_map,im_id,scale) in enumerate(metric_logger.log_every(data_loader_test, print_freq, header)):
samples = samples.to(device, non_blocking=True)
gt_dots = gt_dots.to(device, non_blocking=True).half()
boxes = boxes.to(device, non_blocking=True)
scale = scale.to(device, non_blocking=True)
pos = pos
gt_map = gt_map.to(device, non_blocking=True)
_,_,h,w = samples.shape
r_cnt = 0
s_cnt = 0
for rect in pos:
r_cnt+=1
if r_cnt>3:
break
if rect[2]-rect[0]<10 and rect[3] - rect[1]<10:
s_cnt +=1
if s_cnt >= 1:
r_images = []
r_images.append(TF.crop(samples[0], 0, 0, int(h/3), int(w/3)))
r_images.append(TF.crop(samples[0], int(h/3), 0, int(h/3), int(w/3)))
r_images.append(TF.crop(samples[0], 0, int(w/3), int(h/3), int(w/3)))
r_images.append(TF.crop(samples[0], int(h/3), int(w/3), int(h/3), int(w/3)))
r_images.append(TF.crop(samples[0], int(h*2/3), 0, int(h/3), int(w/3)))
r_images.append(TF.crop(samples[0], int(h*2/3), int(w/3), int(h/3), int(w/3)))
r_images.append(TF.crop(samples[0], 0, int(w*2/3), int(h/3), int(w/3)))
r_images.append(TF.crop(samples[0], int(h/3), int(w*2/3), int(h/3), int(w/3)))
r_images.append(TF.crop(samples[0], int(h*2/3), int(w*2/3), int(h/3), int(w/3)))
pred_cnt = 0
for r_image in r_images:
r_image = transforms.Resize((h, w))(r_image).unsqueeze(0)
density_map = torch.zeros([h,w])
density_map = density_map.to(device, non_blocking=True)
start = 0
prev = -1
with torch.no_grad():
while start + 383 < w:
input_x = [r_image[:,:,:,start:start+384], boxes,scale]
output = model(input_x)
output=output.squeeze(0)
b1 = nn.ZeroPad2d(padding=(start, w-prev-1, 0, 0))
d1 = b1(output[:,0:prev-start+1])
b2 = nn.ZeroPad2d(padding=(prev+1, w-start-384, 0, 0))
d2 = b2(output[:,prev-start+1:384])
b3 = nn.ZeroPad2d(padding=(0, w-start, 0, 0))
density_map_l = b3(density_map[:,0:start])
density_map_m = b1(density_map[:,start:prev+1])
b4 = nn.ZeroPad2d(padding=(prev+1, 0, 0, 0))
density_map_r = b4(density_map[:,prev+1:w])
density_map = density_map_l + density_map_r + density_map_m/2 + d1/2 +d2
prev = start + 383
start = start + 128
if start+383 >= w:
if start == w - 384 + 128: break
else: start = w - 384
pred_cnt += torch.sum(density_map/60).item()
pred_cnt_new = 0
density_map = torch.zeros([h,w])
density_map = density_map.to(device, non_blocking=True)
start = 0
prev = -1
with torch.no_grad():
while start + 383 < w:
input_x = []
a = samples[:,:,:,start:start+384]
input_x.append(a)
input_x.append(boxes)
input_x.append(scale)
output = model(input_x)
output=output.squeeze(0)
b1 = nn.ZeroPad2d(padding=(start, w-prev-1, 0, 0))
d1 = b1(output[:,0:prev-start+1])
b2 = nn.ZeroPad2d(padding=(prev+1, w-start-384, 0, 0))
d2 = b2(output[:,prev-start+1:384])
b3 = nn.ZeroPad2d(padding=(0, w-start, 0, 0))
density_map_l = b3(density_map[:,0:start])
density_map_m = b1(density_map[:,start:prev+1])
b4 = nn.ZeroPad2d(padding=(prev+1, 0, 0, 0))
density_map_r = b4(density_map[:,prev+1:w])
density_map = density_map_l + density_map_r + density_map_m/2 + d1/2 +d2
prev = start + 383
start = start + 128
if start+383 >= w:
if start == w - 384 + 128: break
else: start = w - 384
pred_cnt_new = torch.sum(density_map/60).item()
e_cnt = 0
cnt = 0
for rect in pos:
cnt+=1
if cnt>3:
break
e_cnt += torch.sum(density_map[rect[0]:rect[2]+1,rect[1]:rect[3]+1]/60).item()
e_cnt = e_cnt / 3
if e_cnt > 1.8:
pred_cnt_new /= e_cnt
if pred_cnt>pred_cnt_new * 9:
pred_cnt = pred_cnt_new
else:
density_map = torch.zeros([h,w])
density_map = density_map.to(device, non_blocking=True)
start = 0
prev = -1
with torch.no_grad():
while start + 383 < w:
input_x = []
a = samples[:,:,:,start:start+384]
input_x.append(a)
input_x.append(boxes)
input_x.append(scale)
output = model(input_x)
output=output.squeeze(0)
b1 = nn.ZeroPad2d(padding=(start, w-prev-1, 0, 0))
d1 = b1(output[:,0:prev-start+1])
b2 = nn.ZeroPad2d(padding=(prev+1, w-start-384, 0, 0))
d2 = b2(output[:,prev-start+1:384])
b3 = nn.ZeroPad2d(padding=(0, w-start, 0, 0))
density_map_l = b3(density_map[:,0:start])
density_map_m = b1(density_map[:,start:prev+1])
b4 = nn.ZeroPad2d(padding=(prev+1, 0, 0, 0))
density_map_r = b4(density_map[:,prev+1:w])
density_map = density_map_l + density_map_r + density_map_m/2 + d1/2 +d2
prev = start + 383
start = start + 128
if start+383 >= w:
if start == w - 384 + 128: break
else: start = w - 384
pred_cnt = torch.sum(density_map/60).item()
e_cnt = 0
cnt = 0
for rect in pos:
cnt+=1
if cnt>3:
break
e_cnt += torch.sum(density_map[rect[0]:rect[2]+1,rect[1]:rect[3]+1]/60).item()
e_cnt = e_cnt / 3
if e_cnt > 1.8:
pred_cnt /= e_cnt
gt_cnt = gt_dots.shape[1]
cnt_err = abs(pred_cnt - gt_cnt)
if cnt_err > 200:
wrong_id.append(im_id)
print(im_id)
train_mae += cnt_err
train_rmse += cnt_err ** 2
print(f'{data_iter_step}/{len(data_loader_test)}: pred_cnt: {pred_cnt}, gt_cnt: {gt_cnt}, error: {cnt_err}, AE: {cnt_err}, SE: {cnt_err ** 2} ')
loss_array.append(cnt_err)
gt_array.append(gt_cnt)
torch.cuda.synchronize(device=0)
metric_logger.synchronize_between_processes()
print("Averaged stats:", metric_logger)
log_stats = {'MAE': train_mae/(len(data_loader_test)),
'RMSE': (train_rmse/(len(data_loader_test)))**0.5}
mae = train_mae/(len(data_loader_test))
mse = (train_rmse/(len(data_loader_test)))**0.5
print('Current MAE: {:5.2f}, RMSE: {:5.2f} '.format( train_mae/(len(data_loader_test)), (train_rmse/(len(data_loader_test)))**0.5))
return mae, mse