File size: 10,159 Bytes
0870534 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 |
###########################################################################
# Referred to: https://github.com/zhanghang1989/PyTorch-Encoding
###########################################################################
import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parallel.data_parallel import DataParallel
from torch.nn.parallel.scatter_gather import scatter
import threading
import torch
from torch.cuda._utils import _get_device_index
from torch.cuda.amp import autocast
from torch._utils import ExceptionWrapper
up_kwargs = {'mode': 'bilinear', 'align_corners': True}
__all__ = ['LSeg_MultiEvalModule']
class LSeg_MultiEvalModule(DataParallel):
"""Multi-size Segmentation Eavluator"""
def __init__(self, module, device_ids=None, flip=True,
scales=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75]):
super(LSeg_MultiEvalModule, self).__init__(module, device_ids)
self.base_size = module.base_size
self.crop_size = module.crop_size
self.scales = scales
self.flip = flip
print('MultiEvalModule: base_size {}, crop_size {}'. \
format(self.base_size, self.crop_size))
def parallel_forward(self, inputs, label_set='', **kwargs):
"""Multi-GPU Mult-size Evaluation
Args:
inputs: list of Tensors
"""
if len(label_set) < 10:
print('** MultiEvalModule parallel_forward phase: {} **'.format(label_set))
self.nclass = len(label_set)
inputs = [(input.unsqueeze(0).cuda(device),)
for input, device in zip(inputs, self.device_ids)]
replicas = self.replicate(self, self.device_ids[:len(inputs)])
kwargs = scatter(kwargs, target_gpus, dim) if kwargs else []
if len(inputs) < len(kwargs):
inputs.extend([() for _ in range(len(kwargs) - len(inputs))])
elif len(kwargs) < len(inputs):
kwargs.extend([{} for _ in range(len(inputs) - len(kwargs))])
outputs = parallel_apply(replicas, inputs, label_set, kwargs)
return outputs
def forward(self, image, label_set=''):
"""Mult-size Evaluation"""
# only single image is supported for evaluation
if len(label_set) < 10:
print('** MultiEvalModule forward phase: {} **'.format(label_set))
batch, _, h, w = image.size()
assert(batch == 1)
self.nclass = len(label_set)
stride_rate = 2.0/3.0
crop_size = self.crop_size
stride = int(crop_size * stride_rate)
with torch.cuda.device_of(image):
scores = image.new().resize_(batch,self.nclass,h,w).zero_().cuda()
for scale in self.scales:
long_size = int(math.ceil(self.base_size * scale))
if h > w:
height = long_size
width = int(1.0 * w * long_size / h + 0.5)
short_size = width
else:
width = long_size
height = int(1.0 * h * long_size / w + 0.5)
short_size = height
"""
short_size = int(math.ceil(self.base_size * scale))
if h > w:
width = short_size
height = int(1.0 * h * short_size / w)
long_size = height
else:
height = short_size
width = int(1.0 * w * short_size / h)
long_size = width
"""
# resize image to current size
cur_img = resize_image(image, height, width, **self.module._up_kwargs)
if long_size <= crop_size:
pad_img = pad_image(cur_img, self.module.mean,
self.module.std, crop_size)
outputs = module_inference(self.module, pad_img, label_set, self.flip)
outputs = crop_image(outputs, 0, height, 0, width)
else:
if short_size < crop_size:
# pad if needed
pad_img = pad_image(cur_img, self.module.mean,
self.module.std, crop_size)
else:
pad_img = cur_img
_,_,ph,pw = pad_img.shape #.size()
assert(ph >= height and pw >= width)
# grid forward and normalize
h_grids = int(math.ceil(1.0 * (ph-crop_size)/stride)) + 1
w_grids = int(math.ceil(1.0 * (pw-crop_size)/stride)) + 1
with torch.cuda.device_of(image):
outputs = image.new().resize_(batch,self.nclass,ph,pw).zero_().cuda()
count_norm = image.new().resize_(batch,1,ph,pw).zero_().cuda()
# grid evaluation
for idh in range(h_grids):
for idw in range(w_grids):
h0 = idh * stride
w0 = idw * stride
h1 = min(h0 + crop_size, ph)
w1 = min(w0 + crop_size, pw)
crop_img = crop_image(pad_img, h0, h1, w0, w1)
# pad if needed
pad_crop_img = pad_image(crop_img, self.module.mean,
self.module.std, crop_size)
output = module_inference(self.module, pad_crop_img, label_set, self.flip)
outputs[:,:,h0:h1,w0:w1] += crop_image(output,
0, h1-h0, 0, w1-w0)
count_norm[:,:,h0:h1,w0:w1] += 1
assert((count_norm==0).sum()==0)
outputs = outputs / count_norm
outputs = outputs[:,:,:height,:width]
score = resize_image(outputs, h, w, **self.module._up_kwargs)
scores += score
return scores
def module_inference(module, image, label_set, flip=True):
output = module.evaluate_random(image, label_set)
if flip:
fimg = flip_image(image)
foutput = module.evaluate_random(fimg, label_set)
output += flip_image(foutput)
return output
def resize_image(img, h, w, **up_kwargs):
return F.interpolate(img, (h, w), **up_kwargs)
def pad_image(img, mean, std, crop_size):
b,c,h,w = img.shape #.size()
assert(c==3)
padh = crop_size - h if h < crop_size else 0
padw = crop_size - w if w < crop_size else 0
pad_values = -np.array(mean) / np.array(std)
img_pad = img.new().resize_(b,c,h+padh,w+padw)
for i in range(c):
# note that pytorch pad params is in reversed orders
img_pad[:,i,:,:] = F.pad(img[:,i,:,:], (0, padw, 0, padh), value=pad_values[i])
assert(img_pad.size(2)>=crop_size and img_pad.size(3)>=crop_size)
return img_pad
def crop_image(img, h0, h1, w0, w1):
return img[:,:,h0:h1,w0:w1]
def flip_image(img):
assert(img.dim()==4)
with torch.cuda.device_of(img):
idx = torch.arange(img.size(3)-1, -1, -1).type_as(img).long()
return img.index_select(3, idx)
def get_a_var(obj):
if isinstance(obj, torch.Tensor):
return obj
if isinstance(obj, list) or isinstance(obj, tuple):
for result in map(get_a_var, obj):
if isinstance(result, torch.Tensor):
return result
if isinstance(obj, dict):
for result in map(get_a_var, obj.items()):
if isinstance(result, torch.Tensor):
return result
return None
def parallel_apply(modules, inputs, label_set, kwargs_tup=None, devices=None):
r"""Applies each `module` in :attr:`modules` in parallel on arguments
contained in :attr:`inputs` (positional) and :attr:`kwargs_tup` (keyword)
on each of :attr:`devices`.
Args:
modules (Module): modules to be parallelized
inputs (tensor): inputs to the modules
devices (list of int or torch.device): CUDA devices
:attr:`modules`, :attr:`inputs`, :attr:`kwargs_tup` (if given), and
:attr:`devices` (if given) should all have same length. Moreover, each
element of :attr:`inputs` can either be a single object as the only argument
to a module, or a collection of positional arguments.
"""
assert len(modules) == len(inputs)
if kwargs_tup is not None:
assert len(modules) == len(kwargs_tup)
else:
kwargs_tup = ({},) * len(modules)
if devices is not None:
assert len(modules) == len(devices)
else:
devices = [None] * len(modules)
devices = [_get_device_index(x, True) for x in devices]
lock = threading.Lock()
results = {}
grad_enabled, autocast_enabled = torch.is_grad_enabled(), torch.is_autocast_enabled()
def _worker(i, module, input, label_set, kwargs, device=None):
torch.set_grad_enabled(grad_enabled)
if device is None:
device = get_a_var(input).get_device()
try:
with torch.cuda.device(device), autocast(enabled=autocast_enabled):
# this also avoids accidental slicing of `input` if it is a Tensor
if not isinstance(input, (list, tuple)):
input = (input,)
output = module(*input, label_set, **kwargs)
with lock:
results[i] = output
except Exception:
with lock:
results[i] = ExceptionWrapper(
where="in replica {} on device {}".format(i, device))
if len(modules) > 1:
threads = [threading.Thread(target=_worker,
args=(i, module, input, label_set, kwargs, device))
for i, (module, input, kwargs, device) in
enumerate(zip(modules, inputs, kwargs_tup, devices))]
for thread in threads:
thread.start()
for thread in threads:
thread.join()
else:
_worker(0, modules[0], inputs[0], label_set, kwargs_tup[0], devices[0])
outputs = []
for i in range(len(inputs)):
output = results[i]
if isinstance(output, ExceptionWrapper):
output.reraise()
outputs.append(output)
return outputs
|