From d473b9d1d79bf185b1811ce403f82fdd68fb366c Mon Sep 17 00:00:00 2001
From: Jordan Gong <jordan.gong@protonmail.com>
Date: Tue, 5 Jan 2021 21:57:07 +0800
Subject: Implement checkpoint mechanism

---
 models/model.py | 57 ++++++++++++++++++++++++++++++++++++++++++++++-----------
 1 file changed, 46 insertions(+), 11 deletions(-)

(limited to 'models')

diff --git a/models/model.py b/models/model.py
index 4354b35..9e52527 100644
--- a/models/model.py
+++ b/models/model.py
@@ -1,3 +1,4 @@
+import os
 from typing import Union, Optional
 
 import numpy as np
@@ -9,7 +10,8 @@ from torch.utils.data.dataloader import default_collate
 
 from models import RGBPartNet
 from utils.configuration import DataloaderConfiguration, \
-    HyperparameterConfiguration, DatasetConfiguration, ModelConfiguration
+    HyperparameterConfiguration, DatasetConfiguration, ModelConfiguration, \
+    SystemConfiguration
 from utils.dataset import CASIAB
 from utils.sampler import TripletSampler
 
@@ -17,12 +19,17 @@ from utils.sampler import TripletSampler
 class Model:
     def __init__(
             self,
+            system_config: SystemConfiguration,
             model_config: ModelConfiguration,
             hyperparameter_config: HyperparameterConfiguration
     ):
+        self.device = system_config['device']
+        self.save_dir = system_config['save_dir']
+
         self.meta = model_config
         self.hp = hyperparameter_config
         self.curr_iter = self.meta['restore_iter']
+        self.total_iter = self.meta['total_iter']
 
         self.is_train: bool = True
         self.train_size: int = 74
@@ -34,7 +41,7 @@ class Model:
         self._hp_sig: str = self._make_signature(self.hp)
         self._dataset_sig: str = 'undefined'
 
-        self.rbg_pn: Optional[RGBPartNet] = None
+        self.rgb_pn: Optional[RGBPartNet] = None
         self.optimizer: Optional[optim.Adam] = None
         self.scheduler: Optional[optim.lr_scheduler.StepLR] = None
 
@@ -63,19 +70,47 @@ class Model:
         # Prepare for model, optimizer and scheduler
         hp = self.hp.copy()
         lr, betas = hp.pop('lr', 1e-4), hp.pop('betas', (0.9, 0.999))
-        self.rbg_pn = RGBPartNet(self.train_size, self.in_channels, **hp)
-        self.optimizer = optim.Adam(self.rbg_pn.parameters(), lr, betas)
+        self.rgb_pn = RGBPartNet(self.train_size, self.in_channels, **hp)
+        self.optimizer = optim.Adam(self.rgb_pn.parameters(), lr, betas)
         self.scheduler = optim.lr_scheduler.StepLR(self.optimizer, 500, 0.9)
 
-        self.rbg_pn.train()
-        self.rbg_pn.apply(self.init_weights)
-        for iter_i, (x_c1, x_c2) in enumerate(dataloader):
-            loss = self.rbg_pn(x_c1['clip'], x_c2['clip'], x_c1['label'])
+        self.rgb_pn.train()
+        # Init weights at first iter
+        if self.curr_iter == 0:
+            self.rgb_pn.apply(self.init_weights)
+        else:  # Load saved state dicts
+            checkpoint = torch.load(os.path.join(self.save_dir, self.signature))
+            iter, loss = checkpoint['iter'], checkpoint['loss']
+            print('{0:5d} loss: {1:.3f}'.format(iter, loss))
+            self.rgb_pn.load_state_dict(checkpoint['model_state_dict'])
+            self.optimizer.load_state_dict(checkpoint['optim_state_dict'])
+        for (x_c1, x_c2) in dataloader:
+            self.curr_iter += 1
+            # Zero the parameter gradients
+            self.optimizer.zero_grad()
+            # forward + backward + optimize
+            loss, metrics = self.rgb_pn(x_c1['clip'], x_c2['clip'],
+                                        x_c1['label'])
             loss.backward()
             self.optimizer.step()
-            self.scheduler.step(iter_i)
-
-            if iter_i == self.meta['total_iter']:
+            # Step scheduler
+            self.scheduler.step(self.curr_iter)
+
+            if self.curr_iter % 100 == 0:
+                print('{0:5d} loss: {1:.3f}'.format(self.curr_iter, loss),
+                      '(xrecon = {:f}, pose_sim = {:f},'
+                      ' cano_cons = {:f}, ba_trip = {:f})'.format(*metrics),
+                      'lr:', self.scheduler.get_last_lr())
+
+            if self.curr_iter % 1000 == 0:
+                torch.save({
+                    'iter': self.curr_iter,
+                    'model_state_dict': self.rgb_pn.state_dict(),
+                    'optim_state_dict': self.optimizer.state_dict(),
+                    'loss': loss,
+                }, os.path.join(self.save_dir, self.signature))
+
+            if self.curr_iter == self.total_iter:
                 break
 
     @staticmethod
-- 
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