test the deeplearning template and implement a test module

config.json

@@ -1,14 +1,13 @@
 {
     "name": "Mnist_LeNet",
-    "n_gpu": 1,
-
+    "n_gpu": 2,
     "arch": {
         "type": "MnistModel",
         "args": {}
     },
     "data_loader": {
         "type": "MnistDataLoader",
-        "args":{
+        "args": {
             "data_dir": "data/",
             "batch_size": 128,
             "shuffle": true,
@@ -18,15 +17,16 @@
     },
     "optimizer": {
         "type": "Adam",
-        "args":{
+        "args": {
             "lr": 0.001,
             "weight_decay": 0,
             "amsgrad": true
         }
     },
     "loss": "nll_loss",
     "metrics": [
-        "accuracy", "top_k_acc"
+        "accuracy",
+        "top_k_acc"
     ],
     "lr_scheduler": {
         "type": "StepLR",
@@ -37,14 +37,11 @@
     },
     "trainer": {
         "epochs": 100,
-
         "save_dir": "saved/",
         "save_period": 1,
         "verbosity": 2,
-
         "monitor": "min val_loss",
         "early_stop": 10,
-
         "tensorboard": true
     }
-}
+}

data_loader/data_loaders.py

@@ -1,16 +1,168 @@
 from torchvision import datasets, transforms
+import os
+import sys
+from skimage import io, transform
+import numpy as np
+import torch
+import pandas as pd
+
+sys.path.append(os.path.join(os.path.dirname(__file__), "../"))
 from base import BaseDataLoader
 
 
+# 1.####################################MnistDataLoader########################################
+
 class MnistDataLoader(BaseDataLoader):
     """
     MNIST data loading demo using BaseDataLoader
     """
-    def __init__(self, data_dir, batch_size, shuffle=True, validation_split=0.0, num_workers=1, training=True):
+
+    def __init__(self,
+                 data_dir,
+                 batch_size,
+                 shuffle=True,
+                 validation_split=0.0,
+                 num_workers=1,
+                 training=True):
         trsfm = transforms.Compose([
             transforms.ToTensor(),
-            transforms.Normalize((0.1307,), (0.3081,))
+            transforms.Normalize((0.1307, ), (0.3081, ))
         ])
         self.data_dir = data_dir
-        self.dataset = datasets.MNIST(self.data_dir, train=training, download=True, transform=trsfm)
-        super().__init__(self.dataset, batch_size, shuffle, validation_split, num_workers)
+        self.dataset = datasets.MNIST(self.data_dir,
+                                      train=training,
+                                      download=True,
+                                      transform=trsfm)
+        super().__init__(self.dataset, batch_size, shuffle, validation_split,
+                         num_workers)
+
+
+# 2.####################################FaceLandmarksDataset########################################
+
+class Rescale(object):
+    """Rescale the image in a sample to a given size.
+
+    Args:
+        output_size (tuple or int): Desired output size. If tuple, output is
+            matched to output_size. If int, smaller of image edges is matched
+            to output_size keeping aspect ratio the same.
+    """
+
+    def __init__(self, output_size):
+        assert isinstance(output_size, (int, tuple))
+        self.output_size = output_size
+
+    def __call__(self, sample):
+        image, landmarks = sample['image'], sample['landmarks']
+
+        h, w = image.shape[:2]
+        if isinstance(self.output_size, int):
+            if h > w:
+                new_h, new_w = self.output_size * h / w, self.output_size
+            else:
+                new_h, new_w = self.output_size, self.output_size * w / h
+        else:
+            new_h, new_w = self.output_size
+
+        new_h, new_w = int(new_h), int(new_w)
+
+        img = transform.resize(image, (new_h, new_w))
+
+        # h and w are swapped for landmarks because for images,
+        # x and y axes are axis 1 and 0 respectively
+        landmarks = landmarks * [new_w / w, new_h / h]
+        return {'image': img, 'landmarks': landmarks}
+
+
+class RandomCrop(object):
+    """Crop randomly the image in a sample.
+
+    Args:
+        output_size (tuple or int): Desired output size. If int, square crop
+            is made.
+    """
+
+    def __init__(self, output_size):
+        assert isinstance(output_size, (int, tuple))
+        if isinstance(output_size, int):
+            self.output_size = (output_size, output_size)
+        else:
+            assert len(output_size) == 2
+            self.output_size = output_size
+
+    def __call__(self, sample):
+        image, landmarks = sample['image'], sample['landmarks']
+
+        h, w = image.shape[:2]
+        new_h, new_w = self.output_size
+
+        top = np.random.randint(0, h - new_h)
+        left = np.random.randint(0, w - new_w)
+
+        image = image[top:top + new_h, left:left + new_w]
+        landmarks = landmarks - [left, top]
+        return {'image': image, 'landmarks': landmarks}
+
+
+class ToTensor(object):
+    """Convert ndarrays in sample to Tensors."""
+
+    def __call__(self, sample):
+        image, landmarks = sample['image'], sample['landmarks']
+
+        # swap color axis because
+        # numpy image: H x W x C
+        # torch image: C x H x W
+        image = image.transpose((2, 0, 1))
+        return {
+            'image': torch.from_numpy(image),
+            'landmarks': torch.from_numpy(landmarks)
+        }
+
+
+class FaceLandmarksDataset(BaseDataLoader):
+    """Face Landmarks dataset."""
+
+    def __init__(self, csv_file, root_dir, transfm=None):
+        """
+        Args:
+            csv_file (string): Path to the csv file with annotations.
+            root_dir (string): Directory with all the images.
+            transform (callable, optional): Optional transform to be applied
+                on a sample.
+        """
+        self.landmarks_frame = pd.read_csv(csv_file)
+        self.root_dir = root_dir
+        self.transform = transfm
+
+    def __len__(self):
+        return len(self.landmarks_frame)
+
+    def __getitem__(self, idx):
+        if torch.is_tensor(idx):
+            idx = idx.tolist()
+
+        img_name = os.path.join(self.root_dir, self.landmarks_frame.iloc[idx,
+                                                                         0])
+        image = io.imread(img_name)
+        landmarks = self.landmarks_frame.iloc[idx, 1:]
+        landmarks = np.array([landmarks])
+        landmarks = landmarks.astype('float').reshape(-1, 2)
+        sample = {'image': image, 'landmarks': landmarks}
+
+        if self.transform:
+            sample = self.transform(sample)
+
+        return sample
+
+
+if __name__ == "__main__":
+    composed = transforms.Compose([Rescale(256),
+                                   RandomCrop(224)])
+    face_data = FaceLandmarksDataset(csv_file='data/faces/face_landmarks.csv',
+                                     root_dir='data/faces/',
+                                     transfm=composed)
+    for i in range(len(face_data)):
+        sample = face_data[i]
+        print("sample['image'].shape: {}".format(sample['image'].shape))
+        print("sample['landmarks'].shape: {}".format(sample['landmarks'].shape))

parse_config.py

@@ -9,6 +9,7 @@
 
 
 class ConfigParser:
+
     def __init__(self, config, resume=None, modification=None, run_id=None):
         """
         class to parse configuration json file. Handles hyperparameters for training, initializations of modules, checkpoint saving
@@ -26,7 +27,7 @@ class to parse configuration json file. Handles hyperparameters for training, in
         save_dir = Path(self.config['trainer']['save_dir'])
 
         exper_name = self.config['name']
-        if run_id is None: # use timestamp as default run-id
+        if run_id is None:  # use timestamp as default run-id
             run_id = datetime.now().strftime(r'%m%d_%H%M%S')
         self._save_dir = save_dir / 'models' / exper_name / run_id
         self._log_dir = save_dir / 'log' / exper_name / run_id
@@ -67,14 +68,17 @@ def from_args(cls, args, options=''):
             assert args.config is not None, msg_no_cfg
             resume = None
             cfg_fname = Path(args.config)
-        
+
         config = read_json(cfg_fname)
         if args.config and resume:
             # update new config for fine-tuning
             config.update(read_json(args.config))
 
         # parse custom cli options into dictionary
-        modification = {opt.target : getattr(args, _get_opt_name(opt.flags)) for opt in options}
+        modification = {
+            opt.target: getattr(args, _get_opt_name(opt.flags))
+            for opt in options
+        }
         return cls(config, resume, modification)
 
     def init_obj(self, name, module, *args, **kwargs):
@@ -88,7 +92,9 @@ def init_obj(self, name, module, *args, **kwargs):
         """
         module_name = self[name]['type']
         module_args = dict(self[name]['args'])
-        assert all([k not in module_args for k in kwargs]), 'Overwriting kwargs given in config file is not allowed'
+        assert all([
+            k not in module_args for k in kwargs
+        ]), 'Overwriting kwargs given in config file is not allowed'
         module_args.update(kwargs)
         return getattr(module, module_name)(*args, **module_args)
 
@@ -103,16 +109,20 @@ def init_ftn(self, name, module, *args, **kwargs):
         """
         module_name = self[name]['type']
         module_args = dict(self[name]['args'])
-        assert all([k not in module_args for k in kwargs]), 'Overwriting kwargs given in config file is not allowed'
+        assert all([
+            k not in module_args for k in kwargs
+        ]), 'Overwriting kwargs given in config file is not allowed'
         module_args.update(kwargs)
         return partial(getattr(module, module_name), *args, **module_args)
 
     def __getitem__(self, name):
         """Access items like ordinary dict."""
         return self.config[name]
 
+    # verbosity 指的是详细等级
     def get_logger(self, name, verbosity=2):
-        msg_verbosity = 'verbosity option {} is invalid. Valid options are {}.'.format(verbosity, self.log_levels.keys())
+        msg_verbosity = 'verbosity option {} is invalid. Valid options are {}.'.format(
+            verbosity, self.log_levels.keys())
         assert verbosity in self.log_levels, msg_verbosity
         logger = logging.getLogger(name)
         logger.setLevel(self.log_levels[verbosity])
@@ -131,6 +141,7 @@ def save_dir(self):
     def log_dir(self):
         return self._log_dir
 
+
 # helper functions to update config dict with custom cli options
 def _update_config(config, modification):
     if modification is None:
@@ -141,17 +152,20 @@ def _update_config(config, modification):
             _set_by_path(config, k, v)
     return config
 
+
 def _get_opt_name(flags):
     for flg in flags:
         if flg.startswith('--'):
             return flg.replace('--', '')
     return flags[0].replace('--', '')
 
+
 def _set_by_path(tree, keys, value):
     """Set a value in a nested object in tree by sequence of keys."""
     keys = keys.split(';')
     _get_by_path(tree, keys[:-1])[keys[-1]] = value
 
+
 def _get_by_path(tree, keys):
     """Access a nested object in tree by sequence of keys."""
     return reduce(getitem, keys, tree)