1""" 2/* Copyright (c) 2023 Amazon 3 Written by Jan Buethe */ 4/* 5 Redistribution and use in source and binary forms, with or without 6 modification, are permitted provided that the following conditions 7 are met: 8 9 - Redistributions of source code must retain the above copyright 10 notice, this list of conditions and the following disclaimer. 11 12 - Redistributions in binary form must reproduce the above copyright 13 notice, this list of conditions and the following disclaimer in the 14 documentation and/or other materials provided with the distribution. 15 16 THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS 17 ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT 18 LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR 19 A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER 20 OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, 21 EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, 22 PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR 23 PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF 24 LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING 25 NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS 26 SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 27*/ 28""" 29 30import torch 31from tqdm import tqdm 32import sys 33 34def train_one_epoch(model, criterion, optimizer, dataloader, device, scheduler, log_interval=10): 35 36 model.to(device) 37 model.train() 38 39 running_loss = 0 40 previous_running_loss = 0 41 42 # gru states 43 gru_a_state = torch.zeros(1, dataloader.batch_size, model.gru_a_units, device=device).to(device) 44 gru_b_state = torch.zeros(1, dataloader.batch_size, model.gru_b_units, device=device).to(device) 45 gru_states = [gru_a_state, gru_b_state] 46 47 with tqdm(dataloader, unit='batch', file=sys.stdout) as tepoch: 48 49 for i, batch in enumerate(tepoch): 50 51 # set gradients to zero 52 optimizer.zero_grad() 53 54 # zero out initial gru states 55 gru_a_state.zero_() 56 gru_b_state.zero_() 57 58 # push batch to device 59 for key in batch: 60 batch[key] = batch[key].to(device) 61 62 target = batch['target'] 63 64 # calculate model output 65 output = model(batch['features'], batch['periods'], batch['signals'], gru_states) 66 67 # calculate loss 68 loss = criterion(output.permute(0, 2, 1), target) 69 70 # calculate gradients 71 loss.backward() 72 73 # update weights 74 optimizer.step() 75 76 # update learning rate 77 scheduler.step() 78 79 # call sparsifier 80 model.sparsify() 81 82 # update running loss 83 running_loss += float(loss.cpu()) 84 85 # update status bar 86 if i % log_interval == 0: 87 tepoch.set_postfix(running_loss=f"{running_loss/(i + 1):8.7f}", current_loss=f"{(running_loss - previous_running_loss)/log_interval:8.7f}") 88 previous_running_loss = running_loss 89 90 91 running_loss /= len(dataloader) 92 93 return running_loss 94 95def evaluate(model, criterion, dataloader, device, log_interval=10): 96 97 model.to(device) 98 model.eval() 99 100 running_loss = 0 101 previous_running_loss = 0 102 103 # gru states 104 gru_a_state = torch.zeros(1, dataloader.batch_size, model.gru_a_units, device=device).to(device) 105 gru_b_state = torch.zeros(1, dataloader.batch_size, model.gru_b_units, device=device).to(device) 106 gru_states = [gru_a_state, gru_b_state] 107 108 with torch.no_grad(): 109 with tqdm(dataloader, unit='batch', file=sys.stdout) as tepoch: 110 111 for i, batch in enumerate(tepoch): 112 113 114 # zero out initial gru states 115 gru_a_state.zero_() 116 gru_b_state.zero_() 117 118 # push batch to device 119 for key in batch: 120 batch[key] = batch[key].to(device) 121 122 target = batch['target'] 123 124 # calculate model output 125 output = model(batch['features'], batch['periods'], batch['signals'], gru_states) 126 127 # calculate loss 128 loss = criterion(output.permute(0, 2, 1), target) 129 130 # update running loss 131 running_loss += float(loss.cpu()) 132 133 # update status bar 134 if i % log_interval == 0: 135 tepoch.set_postfix(running_loss=f"{running_loss/(i + 1):8.7f}", current_loss=f"{(running_loss - previous_running_loss)/log_interval:8.7f}") 136 previous_running_loss = running_loss 137 138 139 running_loss /= len(dataloader) 140 141 return running_loss