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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
from .abs_model import abs_model
from .Loss.Loss import norm_loss
from .blocks import *
from .SSN_Model import SSN_Model
class SSN(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']
self.ncols = opt['hyper_params']['n_cols']
self.model = SSN_Model(in_channels=in_channels, out_channels=out_channels, mid_act=mid_act, out_act=out_act)
self.optimizer = get_optimizer(opt, self.model)
self.visualization = {}
self.norm_loss_ = norm_loss(norm=1)
def setup_input(self, x):
return x
def forward(self, x):
keys = ['mask', 'ibl']
for k in keys:
assert k in x.keys(), '{} not in input'.format(k)
mask = x['mask']
ibl = x['ibl']
return self.model(mask, ibl)
def compute_loss(self, y, pred):
total_loss = self.norm_loss_.loss(y, pred)
return total_loss
def supervise(self, input_x, y, is_training:bool)->float:
optimizer = self.optimizer
model = self.model
optimizer.zero_grad()
pred = self.forward(input_x)
loss = self.compute_loss(y, pred)
# logging.info('Pred/Target: {}, {}/{}, {}'.format(pred.min().item(), pred.max().item(), y.min().item(), y.max().item()))
if is_training:
loss.backward()
optimizer.step()
self.visualization['mask'] = input_x['mask'].detach()
self.visualization['ibl'] = input_x['ibl'].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]
plot_v = (plot_v - plot_v.min())/(plot_v.max() - plot_v.min())
ret_vis[k] = np.clip(utils.make_grid(plot_v.cpu(), nrow=nrows).numpy().transpose(1,2,0), 0.0, 1.0)
return ret_vis
def get_logs(self):
pass
def inference(self, x):
keys = ['mask', 'ibl']
for k in keys:
assert k in x.keys(), '{} not in input'.format(k)
assert len(x[k].shape) == 2, '{} should be 2D tensor'.format(k)
# device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
device = torch.device('cpu')
mask = torch.tensor(x['mask'])[None, None, ...].float().to(device)
ibl = torch.tensor(x['ibl'])[None, None, ...].float().to(device)
input_x = {'mask': mask, 'ibl': ibl}
pred = self.forward(input_x)
pred = np.clip(pred[0, 0].detach().cpu().numpy() / 30.0, 0.0, 1.0)
return pred
def batch_inference(self, x):
# TODO
pass
""" 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 #
####################
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