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import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import utils
from collections import OrderedDict
import numpy as np
import matplotlib.cm as cm
import matplotlib as mpl
from .abs_model import abs_model
from .blocks import *
from .SSN import SSN
from .SSN_v1 import SSN_v1
from .Loss.Loss import norm_loss
class GSSN(abs_model):
def __init__(self, opt):
mid_act = opt['model']['mid_act']
out_act = opt['model']['out_act']
in_channels = opt['model']['in_channels']
out_channels = opt['model']['out_channels']
resnet = opt['model']['resnet']
self.ncols = opt['hyper_params']['n_cols']
self.focal = opt['model']['focal']
if 'backbone' not in opt['model'].keys():
self.model = SSN(in_channels=in_channels,
out_channels=out_channels,
mid_act=mid_act,
out_act=out_act,
resnet=resnet)
else:
backbone = opt['model']['backbone']
if backbone == 'vanilla':
self.model = SSN(in_channels=in_channels,
out_channels=out_channels,
mid_act=mid_act,
out_act=out_act,
resnet=resnet)
elif backbone == 'SSN_v1':
self.model = SSN_v1(in_channels=in_channels,
out_channels=out_channels,
mid_act=mid_act,
out_act=out_act,
resnet=resnet)
else:
raise NotImplementedError('{} has not implemented yet'.format(backbone))
self.optimizer = get_optimizer(opt, self.model)
self.visualization = {}
self.norm_loss = norm_loss()
# inference related
BINs = 100
MAX_RAD = 20
self.size_interval = MAX_RAD / BINs
self.soft_distribution = [[np.exp(-0.2 * (i - j) ** 2) for i in np.arange(BINs)] for j in np.arange(BINs)]
def setup_input(self, x):
return x
def forward(self, x):
x, softness = x
return self.model(x, softness)
def compute_loss(self, y, pred):
b = y.shape[0]
total_loss = self.norm_loss.loss(y, pred)
if self.focal:
total_loss = torch.pow(total_loss, 3)
return total_loss
def supervise(self, input_x, y, is_training:bool)->float:
optimizer = self.optimizer
model = self.model
x, softness = input_x['x'], input_x['softness']
optimizer.zero_grad()
pred = model(x, softness)
loss = self.compute_loss(y, pred)
if is_training:
loss.backward()
optimizer.step()
xc = x.shape[1]
for i in range(xc):
self.visualization['x{}'.format(i)] = x[:, i:i+1].detach()
self.visualization['y'] = y.detach()
self.visualization['pred'] = pred.detach()
return loss.item()
def get_visualize(self) -> OrderedDict:
""" Convert to visualization numpy array
"""
nrows = self.ncols
visualizations = self.visualization
ret_vis = OrderedDict()
for k, v in visualizations.items():
batch = v.shape[0]
n = min(nrows, batch)
plot_v = v[:n]
ret_vis[k] = np.clip(utils.make_grid(plot_v.cpu(), nrow=nrows).numpy().transpose(1,2,0), 0.0, 1.0)
ret_vis[k] = self.plasma(ret_vis[k])
return ret_vis
def get_logs(self):
pass
def inference(self, x):
x, l, device = x['x'], x['l'], x['device']
x = torch.from_numpy(x.transpose((2,0,1))).unsqueeze(dim=0).to(device)
l = torch.from_numpy(np.array(self.soft_distribution[int(l/self.size_interval)]).astype(np.float32)).unsqueeze(dim=0).to(device)
pred = self.forward((x, l))
pred = pred[0].detach().cpu().numpy().transpose((1,2,0))
return pred
def batch_inference(self, x):
x, l = x['x'], x['softness']
pred = self.forward((x, l))
return pred
""" Getter & Setter
"""
def get_models(self) -> dict:
return {'model': self.model}
def get_optimizers(self) -> dict:
return {'optimizer': self.optimizer}
def set_models(self, models: dict) :
# input test
if 'model' not in models.keys():
raise ValueError('{} not in self.model'.format('model'))
self.model = models['model']
def set_optimizers(self, optimizer: dict):
self.optimizer = optimizer['optimizer']
####################
# Personal Methods #
####################
def plasma(self, x):
norm = mpl.colors.Normalize(vmin=0.0, vmax=1)
mapper = cm.ScalarMappable(norm=norm, cmap='plasma')
bimg = mapper.to_rgba(x[:,:,0])[:,:,:3]
return bimg
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