Fix bugs in instances

README.md

@@ -34,6 +34,12 @@ Install requirement.
 ``` {.sourceCode .bash}
 pip install -r requirements.txt
 ```
+
+### From pip
+``` {.sourceCode .bash}
+pip install jdit
+```
+
 ### From source
 This method is recommended, because you can keep the newest version.
 1.  Clone from github
@@ -50,13 +56,10 @@ This method is recommended, because you can keep the newest version.
 3.  Install
     You will find packages in `jdit/dist/`. Use pip to install.
     ``` {.sourceCode .bash}
-    pip install dist/jdit-0.x.0-py3-none-any.whl
+    pip install dist/jdit-x.y.z-py3-none-any.whl
     ```
 
-### From pip
-``` {.sourceCode .bash}
-pip install jdit
-```
+
 
 ## Quick start
 
@@ -109,23 +112,15 @@ class FashingClassTrainer(ClassificationTrainer):
 
     def compute_loss(self):
         var_dic = {}
-        var_dic["CEP"] = loss = nn.CrossEntropyLoss()(self.output, self.ground_truth.squeeze().long())
-
-        _, predict = torch.max(self.output.detach(), 1)  # 0100=>1  0010=>2
-        total = predict.size(0) * 1.0
         labels = self.ground_truth.squeeze().long()
-        correct = predict.eq(labels).cpu().sum().float()
-        acc = correct / total
-        var_dic["ACC"] = acc
+        var_dic["CEP"] = loss = nn.CrossEntropyLoss()(self.output, labels)
         return loss, var_dic
 
     def compute_valid(self):
-        var_dic = {}
-        var_dic["CEP"] = nn.CrossEntropyLoss()(self.output, self.labels.squeeze().long())
-
+        _, var_dic = self.compute_loss()
+        labels = self.ground_truth.squeeze().long()
         _, predict = torch.max(self.output.detach(), 1)  # 0100=>1  0010=>2
-        total = predict.size(0) * 1.0
-        labels = self.labels.squeeze().long()
+        total = predict.size(0)
         correct = predict.eq(labels).cpu().sum().float()
         acc = correct / total
         var_dic["ACC"] = acc
@@ -134,6 +129,7 @@ class FashingClassTrainer(ClassificationTrainer):
 
 def start_fashingClassTrainer(gpus=(), nepochs=10, run_type="train"):
     """" An example of fashing-mnist classification
+
     """
     num_class = 10
     depth = 32
@@ -165,8 +161,10 @@ def start_fashingClassTrainer(gpus=(), nepochs=10, run_type="train"):
         Trainer.train()
     elif run_type == "debug":
         Trainer.debug()
+
 if __name__ == '__main__':
     start_fashingClassTrainer()
+
 ```
 
 Then you will see something like this as following.

jdit/trainer/instances/__init__.py

@@ -2,7 +2,7 @@
 from .fashingGenerateGan import FashingGenerateGenerateGanTrainer, start_fashingGenerateGanTrainer
 from .cifarPix2pixGan import start_cifarPix2pixGanTrainer
 from .fashionClassParallelTrainer import start_fashingClassPrarallelTrainer
-from .fashingAutoencoder import start_fashingAotoencoderTrainer, FashingAutoEncoderTrainer
+from .fashingAutoencoder import FashingAutoEncoderTrainer, start_fashingAotoencoderTrainer
 __all__ = ['FashingClassTrainer', 'start_fashingClassTrainer',
            'FashingGenerateGenerateGanTrainer', 'start_fashingGenerateGanTrainer',
            'cifarPix2pixGan', 'start_cifarPix2pixGanTrainer', 'start_fashingClassPrarallelTrainer',

jdit/trainer/instances/fashingAutoencoder.py

@@ -9,9 +9,8 @@
 
 
 class SimpleModel(nn.Module):
-    def __init__(self, depth=64, num_class=10):
+    def __init__(self, depth=32):
         super(SimpleModel, self).__init__()
-        self.num_class = num_class
         self.layer1 = nn.Conv2d(1, depth, 3, 1, 1)
         self.layer2 = nn.Conv2d(depth, depth * 2, 4, 2, 1)
         self.layer3 = nn.Conv2d(depth * 2, depth * 4, 4, 2, 1)

jdit/trainer/instances/fashingClassification.py

@@ -36,23 +36,15 @@ def __init__(self, logdir, nepochs, gpu_ids, net, opt, datasets, num_class):
 
     def compute_loss(self):
         var_dic = {}
-        var_dic["CEP"] = loss = nn.CrossEntropyLoss()(self.output, self.ground_truth.squeeze().long())
-
-        _, predict = torch.max(self.output.detach(), 1)  # 0100=>1  0010=>2
-        total = predict.size(0) * 1.0
         labels = self.ground_truth.squeeze().long()
-        correct = predict.eq(labels).cpu().sum().float()
-        acc = correct / total
-        var_dic["ACC"] = acc
+        var_dic["CEP"] = loss = nn.CrossEntropyLoss()(self.output, labels)
         return loss, var_dic
 
     def compute_valid(self):
-        var_dic = {}
-        var_dic["CEP"] = nn.CrossEntropyLoss()(self.output, self.labels.squeeze().long())
-
+        _, var_dic = self.compute_loss()
+        labels = self.ground_truth.squeeze().long()
         _, predict = torch.max(self.output.detach(), 1)  # 0100=>1  0010=>2
-        total = predict.size(0) * 1.0
-        labels = self.labels.squeeze().long()
+        total = predict.size(0)
         correct = predict.eq(labels).cpu().sum().float()
         acc = correct / total
         var_dic["ACC"] = acc

jdit/trainer/instances/fashionClassParallelTrainer.py

@@ -46,15 +46,7 @@ def compute_loss(self):
         return loss, var_dic
 
     def compute_valid(self):
-        var_dic = {}
-        var_dic["CEP"] = nn.CrossEntropyLoss()(self.output, self.labels.squeeze().long())
-
-        _, predict = torch.max(self.output.detach(), 1)  # 0100=>1  0010=>2
-        total = predict.size(0) * 1.0
-        labels = self.labels.squeeze().long()
-        correct = predict.eq(labels).cpu().sum().float()
-        acc = correct / total
-        var_dic["ACC"] = acc
+        _,var_dic =  self.compute_loss()
         return var_dic
 
 
@@ -102,7 +94,7 @@ def trainerParallel():
     return tp
 
 
-def start_fashingClassPrarallelTrainer():
+def start_fashingClassPrarallelTrainer(run_type="debug"):
     tp = trainerParallel()
     tp.train()
 

jdit/trainer/single/sup_single.py

@@ -21,9 +21,10 @@ def __init__(self, logdir, nepochs, gpu_ids_abs, net: Model, opt: Optimizer, dat
         self.net = net
         self.opt = opt
         self.datasets = datasets
-        self.fake = None
         self.fixed_input = None
+        self.input = None
         self.output = None
+        self.ground_truth = None
 
     def train_epoch(self, subbar_disable=False):
         for iteration, batch in tqdm(enumerate(self.datasets.loader_train, 1), unit="step", disable=subbar_disable):
@@ -108,7 +109,7 @@ def _watch_images(self, tag: str, grid_size: tuple = (3, 3), shuffle=False, save
 
     def compute_loss(self) -> (torch.Tensor, dict):
         """ Rewrite this method to compute your own loss Discriminator.
-
+        Use self.input, self.output and self.ground_truth to compute loss.
         You should return a **loss** for the first position.
         You can return a ``dict`` of loss that you want to visualize on the second position.like
 
@@ -126,7 +127,7 @@ def compute_loss(self) -> (torch.Tensor, dict):
 
     def compute_valid(self) -> dict:
         """ Rewrite this method to compute your validation values.
-
+        Use self.input, self.output and self.ground_truth to compute valid loss.
         You can return a ``dict`` of validation values that you want to visualize.
 
         Example::

setup.py

@@ -2,7 +2,7 @@
 
 setup(
     name="jdit",  # pypi中的名称，pip或者easy_install安装时使用的名称，或生成egg文件的名称
-    version="0.1.1",
+    version="0.1.3",
     author="Guanglei Ding",
     author_email="dingguanglei.bupt@qq.com",
     maintainer='Guanglei Ding',

unittests/test_instences.py

@@ -1,6 +1,6 @@
 from unittest import TestCase
 from jdit.trainer.instances import start_fashingClassTrainer, start_fashingGenerateGanTrainer, \
-    start_cifarPix2pixGanTrainer
+    start_cifarPix2pixGanTrainer, start_fashingAotoencoderTrainer, start_fashingClassPrarallelTrainer
 import shutil
 import os
 
@@ -15,6 +15,12 @@ def test_start_fashingGenerateGanTrainer(self):
     def test_start_cifarPix2pixGanTrainer(self):
         start_cifarPix2pixGanTrainer(run_type="debug")
 
+    def test_start_fashingAotoencoderTrainer(self):
+        start_fashingAotoencoderTrainer(run_type="debug")
+
+    def test_start_fashingClassPrarallelTrainer(self):
+        start_fashingClassPrarallelTrainer()
+
     def setUp(self):
         dir = "log_debug/"
         if os._exists(dir):