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a6b4c35 | 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 | #!/usr/bin/python
'''
Defines a PyTorch graph for forward and backward propogation
within the network encoders and decoders.
'''
import torch
from utils import *
from Generators import *
from data_loader import *
from params import *
'''
Class Definition for 1 Encoder and 1 Decoder joint model
'''
class Enc_Dec_Network():
def initialize(self, opt, encoder, decoder, frozen_dec=False, frozen_enc=False, gpu_ids='1'):
self.opt = opt
self.isTrain = opt.isTrain
self.encoder = encoder
self.decoder = decoder
self.frozen_dec = frozen_dec
self.frozen_enc = frozen_enc
self.device = torch.device('cuda:{}'.format(gpu_ids[0])) # if self.gpu_ids else torch.device('cpu')
# self.encoder.net = encoder.net.to(self.device)
# self.decoder.net = decoder.net.to(self.device)
def set_input(self, input, target, convert_enc=True, shuffle_channel=True):
self.input = input.to(self.device)
self.target = target.to(self.device)
self.encoder.set_data(self.input, self.input, convert=convert_enc, shuffle_channel=shuffle_channel)
def save_networks(self, epoch):
self.encoder.save_networks(epoch)
self.decoder.save_networks(epoch)
def save_outputs(self):
self.encoder.save_outputs()
self.decoder.save_outputs()
def update_learning_rate(self):
self.encoder.update_learning_rate()
def forward(self):
self.encoder.forward()
self.decoder.set_data(self.encoder.output, self.target)
self.decoder.forward()
def test(self):
self.encoder.test()
self.decoder.set_data(self.encoder.output, self.target)
self.decoder.test()
def backward(self):
self.decoder.backward()
# self.encoder.backward()
def optimize_parameters(self):
self.forward()
self.backward()
if not self.frozen_enc:
self.encoder.optimizer.step()
if not self.frozen_dec:
self.decoder.optimizer.step()
self.encoder.optimizer.zero_grad()
self.decoder.optimizer.zero_grad()
def eval(self):
self.encoder.forward()
self.decoder.set_data(self.encoder.output, self.target)
self.decoder.forward()
'''
Class Definition for the final DLoc architecture with
1 Encoder and 2 Decoders joint model
'''
class Enc_2Dec_Network():
def initialize(self, opt , encoder, decoder, offset_decoder, frozen_dec=False, frozen_enc=False, gpu_ids='1'):
print('initializing Encoder and 2 Decoders Model')
self.opt = opt
self.isTrain = opt.isTrain
self.encoder = encoder
self.decoder = decoder
self.offset_decoder = offset_decoder
self.frozen_dec = frozen_dec
self.frozen_enc = frozen_enc
self.device = torch.device('cuda:{}'.format(gpu_ids[0])) # if self.gpu_ids else torch.device('cpu')
# self.encoder.net = encoder.net.to(self.device)
# self.decoder.net = decoder.net.to(self.device)
self.results_save_dir = opt.results_dir
def set_input(self, input, target ,offset_target ,convert_enc=True, shuffle_channel=True):
# features_w_offset, labels_gaussian_2d, features_wo_offset
# input, target, offset_target
self.input = input.to(self.device)
self.target = target.to(self.device)
self.offset_target = offset_target.to(self.device)
self.encoder.set_data(self.input, self.input, convert=convert_enc, shuffle_channel=shuffle_channel)
def save_networks(self, epoch):
self.encoder.save_networks(epoch)
self.decoder.save_networks(epoch)
self.offset_decoder.save_networks(epoch)
def save_outputs(self):
self.encoder.save_outputs()
self.decoder.save_outputs()
self.offset_decoder.save_outputs()
def update_learning_rate(self):
self.encoder.update_learning_rate()
self.decoder.update_learning_rate()
self.offset_decoder.update_learning_rate()
def forward(self):
self.encoder.forward()
self.decoder.set_data(self.encoder.output, self.target)
self.offset_decoder.set_data(self.encoder.output, self.offset_target)
self.decoder.forward()
self.offset_decoder.forward()
# Test the network once set into Evaluation mode!
def test(self):
self.encoder.test()
self.decoder.set_data(self.encoder.output, self.target)
self.offset_decoder.set_data(self.encoder.output, self.offset_target)
self.decoder.test()
self.offset_decoder.test()
def backward(self):
self.decoder.backward()
self.offset_decoder.backward()
# self.encoder.backward()
def optimize_parameters(self):
self.forward()
self.backward()
if not self.frozen_enc:
self.encoder.optimizer.step()
if not self.frozen_dec:
self.decoder.optimizer.step()
self.offset_decoder.optimizer.step()
self.encoder.optimizer.zero_grad()
self.decoder.optimizer.zero_grad()
self.offset_decoder.optimizer.zero_grad()
# set the models to evaluation mode
def eval(self):
self.encoder.eval()
self.decoder.eval()
self.offset_decoder.eval()
class _EncoderProxy:
"""Lightweight proxy that shares opt/optimizer but no-ops on LR update.
trainer.py calls update_learning_rate() on both model.encoder and
model.decoder. For the Mamba pipeline there is only one optimizer,
so this proxy prevents double-stepping the LR scheduler.
"""
def __init__(self, model_adt):
self.opt = model_adt.opt
self.optimizer = model_adt.optimizer
def update_learning_rate(self):
pass # decoder handles the single scheduler
def save_networks(self, epoch):
pass # decoder handles saving
class Mamba_Network():
"""Joint-model wrapper for the single end-to-end MambaDLocNet.
Exposes the same interface that trainer.py expects
(model.encoder, model.decoder, set_input, optimize_parameters, ...)
while internally using a single ModelADT with one optimizer.
"""
def initialize(self, opt, mamba_model_adt, gpu_ids='0'):
self.opt = opt
self.isTrain = opt.isTrain
self.decoder = mamba_model_adt # trainer reads .output, .loss, .model_name, .opt
self.encoder = _EncoderProxy(mamba_model_adt) # no-op LR update
self.device = torch.device('cuda:{}'.format(gpu_ids[0]))
def set_input(self, input, target, shuffle_channel=False):
self.input = input.to(self.device)
self.target = target.to(self.device)
self.decoder.set_data(self.input, self.target, shuffle_channel=shuffle_channel)
def save_networks(self, epoch):
self.decoder.save_networks(epoch)
def update_learning_rate(self):
self.decoder.update_learning_rate()
def forward(self):
self.decoder.forward()
def test(self):
self.decoder.test()
def backward(self):
self.decoder.backward()
def optimize_parameters(self):
self.forward()
self.backward()
self.decoder.optimizer.step()
self.decoder.optimizer.zero_grad()
def eval(self):
self.decoder.eval()
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