Delete duplicate/old file: \/joint_model.py

///joint_model.py

@@ -1,217 +0,0 @@
-#!/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()