Cycle learning rate for unet

86d39706 · 王肇一 · 515970c7 · 86d39706 · 86d39706 · 86d39706
Commit 86d39706 authored Feb 11, 2020 by 王肇一
6 changed files
--- a/data/voc/train.txt
+++ b/data/voc/train.txt
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\ No newline at end of file
--- a/mrnet/mrnet_module.py
+++ b/mrnet/mrnet_module.py
@@ -42,7 +42,8 @@ class MultiUnet(nn.Module):
        self.pool = nn.MaxPool2d(2)
        self.outconv = nn.Sequential(
            nn.Conv2d(self.res9.outc, n_classes, kernel_size = 1),
-            nn.Sigmoid()
+            nn.Softmax()
+            #nn.Sigmoid()
        )
        # self.outconv = nn.Conv2d(self.res9.outc, n_classes,kernel_size = 1)

--- a/mrnet/train.py
+++ b/mrnet/train.py
@@ -28,8 +28,8 @@ def train_net(net, device, epochs = 5, batch_size = 1, lr = 0.1):
    val_loader = DataLoader(evalset, batch_size = batch_size, shuffle = False, num_workers = 8, pin_memory = True)
    optimizer = optim.Adam(net.parameters(), lr = lr)
-    criterion = nn.BCELoss()#nn.BCEWithLogitsLoss()
+    criterion = nn.BCELoss()# nn.BCEWithLogitsLoss()
-    scheduler = lr_scheduler.StepLR(optimizer,30,0.5)#lr_scheduler.ReduceLROnPlateau(optimizer, 'min')
+    scheduler = lr_scheduler.StepLR(optimizer, 30, 0.5)# lr_scheduler.ReduceLROnPlateau(optimizer, 'min')
    for epoch in range(epochs):
        net.train()

--- a/train_ignite.py
+++ b/train_ignite.py
@@ -7,7 +7,7 @@ from torch.utils.data import DataLoader
 from torch.optim import lr_scheduler
 from ignite.contrib.handlers.param_scheduler import LRScheduler
 from ignite.engine import Events, create_supervised_trainer, create_supervised_evaluator
-from ignite.metrics import Accuracy, Loss, DiceCoefficient, ConfusionMatrix, RunningAverage
+from ignite.metrics import Accuracy, Loss, DiceCoefficient, ConfusionMatrix, RunningAverage,mIoU
 from ignite.contrib.handlers import ProgressBar
 from argparse import ArgumentParser
@@ -34,11 +34,13 @@ def run(train_batch_size, val_batch_size, epochs, lr):
    optimizer = optim.Adam(model.parameters(), lr = lr)
    cm = ConfusionMatrix(num_classes = 1)
    dice = DiceCoefficient(cm)
+    iou = mIoU(cm)
    loss = torch.nn.BCELoss()  # torch.nn.NLLLoss()
-    scheduler = LRScheduler(lr_scheduler.ReduceLROnPlateau(optimizer))
+    scheduler = LRScheduler(lr_scheduler.StepLR(optimizer, 30, 0.5))
    trainer = create_supervised_trainer(model, optimizer, loss, device = device)
-    evaluator = create_supervised_evaluator(model, metrics = {'accuracy': Accuracy(), 'dice': dice, 'nll': Loss(loss)},
+    evaluator = create_supervised_evaluator(model,
+                                            metrics = {'accuracy': Accuracy(), 'dice': dice, 'nll': Loss(loss)},
                                            device = device)
    RunningAverage(output_transform = lambda x: x).attach(trainer, 'loss')
    trainer.add_event_handler(Events.EPOCH_COMPLETED, scheduler)

--- a/unet/train.py
+++ b/unet/train.py
@@ -47,8 +47,8 @@ def train_net(net, device, epochs = 5, batch_size = 1, lr = 0.1, save_cp = True)
    # optimizer = optim.Adam(net.parameters(), lr=lr, weight_decay = 1e-8)
    optimizer = optim.RMSprop(net.parameters(), lr = lr, weight_decay = 1e-8)
-    scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, 'min')
+    #scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, 'min')
-    # criterion = nn.BCEWithLogitsLoss()
+    scheduler = lr_scheduler.CyclicLR(optimizer, base_lr = 1e-10, max_lr = 0.01)
    if net.n_classes > 1:
        criterion = nn.CrossEntropyLoss()
    else:
@@ -59,13 +59,6 @@ def train_net(net, device, epochs = 5, batch_size = 1, lr = 0.1, save_cp = True)
        epoch_loss = 0
        with tqdm(total = n_train, desc = f'Epoch {epoch + 1}/{epochs}', unit = 'img') as pbar:
            for imgs,true_masks in train_loader:
-                # imgs = batch['image']
-                # true_masks = batch['mask']
-                # assert imgs.shape[1] == net.n_channels, \
-                #     f'Network has been defined with {net.n_channels} input channels, ' \
-                #     f'but loaded images have {imgs.shape[1]} channels. Please check that ' \
-                #     'the images are loaded correctly.'
                imgs = imgs.to(device = device, dtype = torch.float32)
                mask_type = torch.float32 if net.n_classes == 1 else torch.long
                true_masks = true_masks.to(device = device, dtype = mask_type)
@@ -80,11 +73,11 @@ def train_net(net, device, epochs = 5, batch_size = 1, lr = 0.1, save_cp = True)
                optimizer.zero_grad()
                loss.backward()
                optimizer.step()
+                scheduler.step()
                pbar.update(imgs.shape[0])
                global_step += 1  # if global_step % (len(dataset) // (10 * batch_size)) == 0:
        val_score = eval_net(net, val_loader, device, n_val)
-        scheduler.step(val_score)
+        #scheduler.step(val_score)
        if net.n_classes > 1:
            logging.info('Validation cross entropy: {}'.format(val_score))
            writer.add_scalar('Loss/test', val_score, global_step)

--- a/utils/eval.py
+++ b/utils/eval.py
@@ -2,6 +2,7 @@ import torch
 import torch.nn.functional as F
 from tqdm import tqdm
 from sklearn.metrics import jaccard_score
+import numpy as np
 from utils.dice_loss import dice_coeff, dice_coef
@@ -42,6 +43,7 @@ def eval_jac(net, loader, device, n_val):
            pred_masks = torch.round(pred_masks).cpu().detach().numpy()
            true_masks = torch.round(true_masks).cpu().numpy()
+            pred_masks = np.array([1 if x>0 else 0 for x in pred_masks])
            jac += jaccard_score(true_masks.flatten(), pred_masks.flatten())
            pbar.update(imgs.shape[0])