File size: 8,201 Bytes
352cafd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 |
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
import torch.nn.functional as F
import progressbar
import cv2
from models.network.crm_transferCoord_transferFeat import CRMNet
from dataset import OfflineDataset_crm_pad32 as OfflineDataset
from dataset import SplitTransformDataset
from util.image_saver_crm import tensor_to_im, tensor_to_gray_im, tensor_to_seg
from util.hyper_para import HyperParameters
from eval_helper_crm import process_high_res_im, process_im_single_pass
import os
from os import path
from argparse import ArgumentParser
import time
def make_coord(shape, ranges=None, flatten=True, device=None): #
""" Make coordinates at grid centers.
"""
coord_seqs = []
for i, n in enumerate(shape):
if ranges is None:
v0, v1 = -1, 1
else:
v0, v1 = ranges[i]
r = (v1 - v0) / (2 * n)
seq = v0 + r + (2 * r) * torch.arange(n, device=device).float() # ,
coord_seqs.append(seq)
ret = torch.stack(torch.meshgrid(*coord_seqs), dim=-1)
if flatten:
ret = ret.view(-1, ret.shape[-1])
return ret
class Parser():
def parse(self):
self.default = HyperParameters()
self.default.parse(unknown_arg_ok=True)
parser = ArgumentParser()
parser.add_argument('--dir', help='Directory with testing images')
parser.add_argument('--model', help='Pretrained model')
parser.add_argument('--output', help='Output directory')
parser.add_argument('--global_only', help='Global step only', action='store_true')
parser.add_argument('--L', help='Parameter L used in the paper', type=int, default=900)
parser.add_argument('--stride', help='stride', type=int, default=450)
parser.add_argument('--clear', help='Clear pytorch cache?', action='store_true')
parser.add_argument('--ade', help='Test on ADE dataset?', action='store_true')
args, _ = parser.parse_known_args()
self.args = vars(args)
def __getitem__(self, key):
if key in self.args:
return self.args[key]
else:
return self.default[key]
def __str__(self):
return str(self.args)
before_Parser_time = time.time()
print("\n before_Parser_time:", before_Parser_time)
# Parse command line arguments
para = Parser()
para.parse()
print('Hyperparameters: ', para)
# Construct model
model = nn.DataParallel(CRMNet(backend='resnet50').cuda())
model.load_state_dict(torch.load(para['model']))
batch_size = 1
memory_chunk = 50176*16
if para['ade']:
val_dataset = SplitTransformDataset(para['dir'], need_name=True, perturb=False, img_suffix='_im.jpg')
else:
val_dataset = OfflineDataset(para['dir'], need_name=True, resize=False, do_crop=False, padding=True)
val_loader = DataLoader(val_dataset, batch_size, shuffle=False, num_workers=2)
os.makedirs(para['output'], exist_ok=True)
epoch_start_time = time.time()
model = model.eval()
before_for_time = time.time()
print("\n before_for_time:", before_for_time, "; before_for_time - before_Parser_time:", before_for_time-before_Parser_time)
counting = 0
s_list = [0.125, 0.25, 0.5, 1.0]
with torch.no_grad():
# for s in [1.0]:
for im, seg, gt, name, crm_data in progressbar.progressbar(val_loader):
counting += 1
im, seg, gt = im, seg, gt
for k, v in crm_data.items():
crm_data[k] = v.cuda()
if para['global_only']:
images = {}
if para['ade']:
# GTs of small objects in ADE are too coarse -- less upsampling is better
images = process_im_single_pass(model, im, seg, 224, para)
else:
images = process_im_single_pass(model, im, seg, para['L'], para)
else:
torch.cuda.synchronize()
start_batch_time = torch.cuda.Event(enable_timing=True)
end_batch_time = torch.cuda.Event(enable_timing=True)
start_batch_time.record()
turns = len(s_list)
for turn in range(turns):
s = s_list[turn]
print(seg.shape, s)
torch.cuda.synchronize()
start_turn_time = torch.cuda.Event(enable_timing=True)
end_turn_time = torch.cuda.Event(enable_timing=True)
start_turn_time.record()
images = {}
im_ = F.interpolate(im, size=(round(im.shape[-2] * s), round(im.shape[-1] * s)), mode='bilinear', align_corners=True).cuda()
seg_ = F.interpolate(seg, size=(round(im.shape[-2] * s), round(im.shape[-1] * s)), mode='bilinear', align_corners=True).cuda()
transferFeat = None
transferCoord = None
for i in range(0, gt.shape[-2]*gt.shape[-1], memory_chunk):
print('batch_%s' % counting, 'chunk_%s' % (i//memory_chunk))
torch.cuda.synchronize()
start_chunk_time = torch.cuda.Event(enable_timing=True)
end_chunk_time = torch.cuda.Event(enable_timing=True)
start_chunk_time.record()
if transferFeat is None:
chunk_images, transferCoord, transferFeat = model(im_, seg_, coord=crm_data['coord'][:, i:i+memory_chunk, :], cell=crm_data['cell'][:, i:i+memory_chunk, :], transferCoord=transferCoord, transferFeat=transferFeat)
else:
chunk_images = model(im_, seg_, coord=crm_data['coord'][:, i:i+memory_chunk, :], cell=crm_data['cell'][:, i:i+memory_chunk, :], transferCoord=transferCoord, transferFeat=transferFeat)
if 'pred_224' not in images.keys():
images = chunk_images
else:
for key in images.keys():
images[key] = torch.cat((images[key], chunk_images[key]), axis=1)
if para['clear']:
torch.cuda.empty_cache()
end_chunk_time.record()
torch.cuda.synchronize()
print("chunk_time:", start_chunk_time.elapsed_time(end_chunk_time))
for key in images.keys():
images[key] = images[key].view(images[key].shape[0], images[key].shape[1]//(gt.shape[-2]*gt.shape[-1]), *gt.shape[-2:])
images['im'] = im
images['seg_'+str(turn)] = seg
images['gt'] = gt
# Suppress close-to-zero segmentation input
for b in range(seg.shape[0]):
if (seg[b]+1).sum() < 2:
images['pred_224'][b] = 0
# Save output images
for i in range(im.shape[0]):
if turn == 0:
cv2.imwrite(path.join(para['output'], '%s_im.png' % (name[i]))
,cv2.cvtColor(tensor_to_im(im[i])[32:-32, 32:-32], cv2.COLOR_RGB2BGR))
cv2.imwrite(path.join(para['output'], '%s_seg.png' % (name[i]))
,tensor_to_seg(images['seg_'+str(turn)][i])[32:-32, 32:-32])
cv2.imwrite(path.join(para['output'], '%s_gt.png' % (name[i]))
,tensor_to_gray_im(gt[i])[32:-32, 32:-32])
cv2.imwrite(path.join(para['output'], (str(s) + ('_%s_mask.png' % (name[i]))))
,tensor_to_gray_im(images['pred_224'][i])[32:-32, 32:-32])
cv2.imwrite(path.join(para['output'], (str(s) + ('_%s_01mask.png' % (name[i]))))
,tensor_to_gray_im(images['pred_224'][i]>0.5)[32:-32, 32:-32]) # 0 1
seg = (((images['pred_224'][0]).float()-0.5)*2).unsqueeze(0)
end_turn_time.record()
torch.cuda.synchronize()
print("Turn ", s, "; turn_time:", start_turn_time.elapsed_time(end_turn_time))
end_batch_time.record()
torch.cuda.synchronize()
print("batch_time:", start_batch_time.elapsed_time(end_batch_time))
print('Time taken: %.1f s' % (time.time() - epoch_start_time))
|