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Commit 689a58a

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author
xyliao
committed
update char rnn
1 parent 3575751 commit 689a58a

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3 files changed

+55
-21
lines changed

3 files changed

+55
-21
lines changed
Lines changed: 1 addition & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1 @@
1+
Subproject commit b679ba82feb3c50bfa763f2035a7c5cdfbe72952

‎chapter9_Computer-Vision/fine_tune/main.py‎

Lines changed: 22 additions & 8 deletions
Original file line numberDiff line numberDiff line change
@@ -38,12 +38,14 @@ def test_tf(img):
3838

3939
def get_train_data():
4040
train_set = ImageFolder(opt.train_data_path, train_tf)
41-
return DataLoader(train_set, opt.batch_size, True, num_workers=opt.num_workers)
41+
return DataLoader(
42+
train_set, opt.batch_size, True, num_workers=opt.num_workers)
4243

4344

4445
def get_test_data():
4546
test_set = ImageFolder(opt.test_data_path, test_tf)
46-
return DataLoader(test_set, opt.batch_size, True, num_workers=opt.num_workers)
47+
return DataLoader(
48+
test_set, opt.batch_size, True, num_workers=opt.num_workers)
4749

4850

4951
def get_model():
@@ -59,8 +61,11 @@ def get_loss(score, label):
5961

6062

6163
def get_optimizer(model):
62-
optimizer = torch.optim.SGD(model.parameters(), lr=opt.lr, momentum=opt.momentum,
63-
weight_decay=opt.weight_decay)
64+
optimizer = torch.optim.SGD(
65+
model.parameters(),
66+
lr=opt.lr,
67+
momentum=opt.momentum,
68+
weight_decay=opt.weight_decay)
6469
return ScheduledOptim(optimizer)
6570

6671

@@ -75,6 +80,7 @@ def __init__(self):
7580
self.metric_meter['acc'] = meter.AverageValueMeter()
7681

7782
def train(self, kwargs):
83+
self.reset_meter()
7884
self.model.train()
7985
train_data = kwargs['train_data']
8086
for data in tqdm(train_data):
@@ -101,8 +107,12 @@ def train(self, kwargs):
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102108
# Update to tensorboard.
103109
if (self.n_iter + 1) % opt.plot_freq == 0:
104-
self.writer.add_scalars('loss', {'train': self.metric_meter['loss'].value()[0]}, self.n_plot)
105-
self.writer.add_scalars('acc', {'train': self.metric_meter['acc'].value()[0]}, self.n_plot)
110+
self.writer.add_scalars(
111+
'loss', {'train': self.metric_meter['loss'].value()[0]},
112+
self.n_plot)
113+
self.writer.add_scalars(
114+
'acc', {'train': self.metric_meter['acc'].value()[0]},
115+
self.n_plot)
106116
self.n_plot += 1
107117
self.n_iter += 1
108118

@@ -111,6 +121,7 @@ def train(self, kwargs):
111121
self.metric_log['train acc'] = self.metric_meter['acc'].value()[0]
112122

113123
def test(self, kwargs):
124+
self.reset_meter()
114125
self.model.eval()
115126
test_data = kwargs['test_data']
116127
for data in tqdm(test_data):
@@ -129,8 +140,11 @@ def test(self, kwargs):
129140
self.metric_meter['acc'].add(acc.data[0])
130141

131142
# Update to tensorboard.
132-
self.writer.add_scalars('loss', {'test': self.metric_meter['loss'].value()[0]}, self.n_plot)
133-
self.writer.add_scalars('acc', {'test': self.metric_meter['acc'].value()[0]}, self.n_plot)
143+
self.writer.add_scalars('loss',
144+
{'test': self.metric_meter['loss'].value()[0]},
145+
self.n_plot)
146+
self.writer.add_scalars(
147+
'acc', {'test': self.metric_meter['acc'].value()[0]}, self.n_plot)
134148
self.n_plot += 1
135149

136150
# Log the test metric to dict.

‎chapter9_Computer-Vision/segmentation/main.py‎

Lines changed: 32 additions & 13 deletions
Original file line numberDiff line numberDiff line change
@@ -26,8 +26,10 @@
2626

2727

2828
def get_data(is_train):
29-
voc_data = VocSegDataset(opt.voc_root, is_train, opt.crop_size, img_transforms)
30-
return DataLoader(voc_data, opt.batch_size, True, num_workers=opt.num_workers)
29+
voc_data = VocSegDataset(opt.voc_root, is_train, opt.crop_size,
30+
img_transforms)
31+
return DataLoader(
32+
voc_data, opt.batch_size, True, num_workers=opt.num_workers)
3133

3234

3335
def get_model(num_classes):
@@ -38,7 +40,8 @@ def get_model(num_classes):
3840

3941

4042
def get_optimizer(model):
41-
optimizer = torch.optim.SGD(model.parameters(), lr=opt.lr, weight_decay=opt.weight_decay)
43+
optimizer = torch.optim.SGD(
44+
model.parameters(), lr=opt.lr, weight_decay=opt.weight_decay)
4245
return ScheduledOptim(optimizer)
4346

4447

@@ -64,6 +67,7 @@ def __init__(self):
6467
self.metric_meter[m] = meter.AverageValueMeter()
6568

6669
def train(self, kwargs):
70+
self.reset_meter()
6771
self.model.train()
6872
train_data = kwargs['train_data']
6973
for data in tqdm(train_data):
@@ -97,28 +101,37 @@ def train(self, kwargs):
97101

98102
if (self.n_iter + 1) % opt.plot_freq == 0:
99103
# Plot metrics curve in tensorboard.
100-
self.writer.add_scalars('loss', {'train': self.metric_meter['loss'].value()[0]}, self.n_plot)
101-
self.writer.add_scalars('acc', {'train': self.metric_meter['acc'].value()[0]}, self.n_plot)
102-
self.writer.add_scalars('iou', {'train': self.metric_meter['iou'].value()[0]}, self.n_plot)
104+
self.writer.add_scalars(
105+
'loss', {'train': self.metric_meter['loss'].value()[0]},
106+
self.n_plot)
107+
self.writer.add_scalars(
108+
'acc', {'train': self.metric_meter['acc'].value()[0]},
109+
self.n_plot)
110+
self.writer.add_scalars(
111+
'iou', {'train': self.metric_meter['iou'].value()[0]},
112+
self.n_plot)
103113

104114
# Show segmentation images.
105115
# Get prediction segmentation and ground truth segmentation.
106116
origin_image = inverse_normalization(imgs[0].cpu().data)
107117
pred_seg = cm[pred_labels[0]]
108118
gt_seg = cm[true_labels[0]]
109119

110-
self.writer.add_image('train ori_img', origin_image, self.n_plot)
120+
self.writer.add_image('train ori_img', origin_image,
121+
self.n_plot)
111122
self.writer.add_image('train gt', gt_seg, self.n_plot)
112123
self.writer.add_image('train pred', pred_seg, self.n_plot)
113124
self.n_plot += 1
114125

115126
self.n_iter += 1
116127

117128
self.metric_log['Train Loss'] = self.metric_meter['loss'].value()[0]
118-
self.metric_log['Train Mean Class Accuracy'] = self.metric_meter['acc'].value()[0]
129+
self.metric_log['Train Mean Class Accuracy'] = self.metric_meter[
130+
'acc'].value()[0]
119131
self.metric_log['Train Mean IoU'] = self.metric_meter['iou'].value()[0]
120132

121133
def test(self, kwargs):
134+
self.reset_meter()
122135
self.model.eval()
123136
test_data = kwargs['test_data']
124137
for data in tqdm(test_data):
@@ -146,9 +159,13 @@ def test(self, kwargs):
146159
self.metric_meter['iou'].add(eval_metrics['miou'])
147160

148161
# Plot metrics curve in tensorboard.
149-
self.writer.add_scalars('loss', {'test': self.metric_meter['loss'].value()[0]}, self.n_plot)
150-
self.writer.add_scalars('acc', {'test': self.metric_meter['acc'].value()[0]}, self.n_plot)
151-
self.writer.add_scalars('iou', {'test': self.metric_meter['iou'].value()[0]}, self.n_plot)
162+
self.writer.add_scalars('loss',
163+
{'test': self.metric_meter['loss'].value()[0]},
164+
self.n_plot)
165+
self.writer.add_scalars(
166+
'acc', {'test': self.metric_meter['acc'].value()[0]}, self.n_plot)
167+
self.writer.add_scalars(
168+
'iou', {'test': self.metric_meter['iou'].value()[0]}, self.n_plot)
152169

153170
origin_img = inverse_normalization(imgs[0].cpu().data)
154171
pred_seg = cm[pred_labels[0]]
@@ -160,7 +177,8 @@ def test(self, kwargs):
160177
self.n_plot += 1
161178

162179
self.metric_log['Test Loss'] = self.metric_meter['loss'].value()[0]
163-
self.metric_log['Test Mean Class Accuracy'] = self.metric_meter['acc'].value()[0]
180+
self.metric_log['Test Mean Class Accuracy'] = self.metric_meter[
181+
'acc'].value()[0]
164182
self.metric_log['Test Mean IoU'] = self.metric_meter['iou'].value()[0]
165183

166184
def get_best_model(self):
@@ -178,7 +196,8 @@ def train(**kwargs):
178196
fcn_trainer = FcnTrainer()
179197
train_data = get_data(is_train=True)
180198
test_data = get_data(is_train=False)
181-
fcn_trainer.fit(train_data=train_data, test_data=test_data, epochs=opt.max_epoch)
199+
fcn_trainer.fit(
200+
train_data=train_data, test_data=test_data, epochs=opt.max_epoch)
182201

183202

184203
if __name__ == '__main__':

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