Modernize codebase: fix deprecations, improve code quality

.gitignore

@@ -1,2 +1,108 @@
-*.pyc
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+Pipfile.lock
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# Project specific
+saved_models/
 *.npz
+*.pth
+*.pt
+*.pkl
+*.pickle
+test_images/
+VOCdevkit/
+
+# IDE
+.vscode/
+.idea/
+*.swp
+*.swo
+*~
+
+# OS
+.DS_Store
+Thumbs.db

Pipfile

@@ -14,4 +14,4 @@ ipython = "*"
 scipy = "*"
 
 [requires]
-python_version = "3.6"
+python_version = "3.8"

README.md

@@ -1,6 +1,6 @@
 # Approximate Convolutional Sparse Coding (ACSC)
 
-A pytorch implementation of a ACSC model based on **Lerned Convolutional Sparse Coding** model proposed [here](https://arxiv.org/abs/1711.00328) and or [here](https://ieeexplore.ieee.org/abstract/document/8462313).
+A PyTorch implementation of an ACSC model based on **Learned Convolutional Sparse Coding** model proposed [here](https://arxiv.org/abs/1711.00328) and [here](https://ieeexplore.ieee.org/abstract/document/8462313).
 
 
 ## ACSC block description

analyze_model.py

@@ -48,21 +48,21 @@ def plot_dict(model, save_path):
     my_subplot(cd, [kers_per_row, kers_per_col], 'conv-dictionary', save_path)
 
 
-def evaluate_thrshold(model, save_path, name):
-    thrshold_avg = [float(model.softthrsh0.thrshold.mean())]
+def evaluate_threshold(model, save_path, name):
+    threshold_avg = [float(model.softthrsh0.threshold.mean())]
 
     for thrsh in model.softthrsh1:
-        thrshold_avg.append(float(thrsh.thrshold.mean()))
+        threshold_avg.append(float(thrsh.threshold.mean()))
 
-    plt.plot(range(len(thrshold_avg)), thrshold_avg, '*')
+    plt.plot(range(len(threshold_avg)), threshold_avg, '*')
     plt.savefig(os.path.join(save_path, name))
     plt.clf()
 
 def evaluate_csc(model, img_n, save_path, im_name):
     """Plot CSC
     """
     sparse_code_delta = []
-    for csc, csc_res, lista_iter in model.forward_enc_generataor(img_n.unsqueeze(0)):
+    for csc, csc_res, lista_iter in model.forward_enc_generator(img_n.unsqueeze(0)):
         _, depth, rows, cols = csc.shape
         sc_per_col = int(np.sqrt(depth))
         sc_per_row = sc_per_col + (depth - sc_per_col**2)
@@ -94,7 +94,7 @@ def evaluate(args):
 
     plot_dict(model, log_dir)
     evaluate_csc(model, testset[7][0], log_dir, testset.image_filenames[7])
-    evaluate_thrshold(model, log_dir, 'thrshold')
+    evaluate_threshold(model, log_dir, 'threshold')
 
 def main():
     """Run test on trained model.

common.py

@@ -13,7 +13,7 @@ def to_np(_x): return _x.data.cpu().numpy()
 
 def I(_x): return _x
 
-def normilize(_x, _val=255, shift=0):
+def normalize(_x, _val=255, shift=0):
     return (_x - shift)/ _val
 
 def count_parameters(model):
@@ -48,21 +48,6 @@ def init_model_dir(path, name):
     os.mkdir(full_path)
     return full_path
 
-    '''
-        Either string defining an activation function or module (e.g. nn.ReLU)
-    '''
-    if isinstance(act_fun, str):
-        if act_fun == 'LeakyReLU':
-            return nn.LeakyReLU(0.2, inplace=True)
-        elif act_fun == 'ELU':
-            return nn.ELU()
-        elif act_fun == 'none':
-            return nn.Sequential()
-        else:
-            assert False
-    else:
-        return act_fun()
-
 
 def flip(x, dim):
     dim = x.dim() + dim if dim < 0 else dim
@@ -113,7 +98,7 @@ def delete_pixels(ins, is_training, sample_prob=0.3):
         return ins * mask  + (1 - mask)
     return ins
 
-def reconsturction_loss(distance='l1', use_cuda=True):
+def reconstruction_loss(distance='l1', use_cuda=True):
 
     if distance == 'l1':
         dist = nn.L1Loss()
@@ -124,8 +109,6 @@ def reconsturction_loss(distance='l1', use_cuda=True):
     else:
         raise ValueError(f"unidentified value {distance}")
 
-    #if use_cuda:
-    #    dist = dist.cuda()
     return dist
 
 def get_criterion(losses_types, factors, use_cuda=True):
@@ -138,13 +121,10 @@ def get_criterion(losses_types, factors, use_cuda=True):
     """
     losses = []
     for loss_type in losses_types:
-        losses.append(reconsturction_loss(loss_type))
-
-    #if use_cuda:
-    #   losses = [l.cuda() for l in losses]
+        losses.append(reconstruction_loss(loss_type))
 
     def total_loss(results, targets):
-        """Cacluate total loss
+        """Calculate total loss
             total_loss = sum_i losses_i(results_i, targets_i)
         Args:
             results(tensor): nn outputs.
@@ -181,20 +161,19 @@ def clean(save_path, save_count=10):
         print('removing', f)
         os.remove(f)
 
-def save_train(path, model, optimizer, schedular=None, epoch=None):
+def save_train(path, model, optimizer, scheduler=None, epoch=None):
     state = {
         'model': model.state_dict(),
         'optimizer': optimizer.state_dict(),
     }
-    #TODO(hillel): fix this so we can save schedular state
-    #if schedular is not None:
-    #    state['schedular'] = schedular.state_dict()
+    if scheduler is not None:
+        state['scheduler'] = scheduler.state_dict()
     if epoch is not None:
         state['epoch'] = epoch
     torch.save(state, os.path.join(path, 'epoch_{}'.format(epoch)))
     return os.path.join(path, 'epoch_{}'.format(epoch))
 
-def load_train(path, model, optimizer, schedular=None):
+def load_train(path, model, optimizer, scheduler=None):
     state = torch.load(path)
 
     pretrained = state['model']
@@ -205,12 +184,12 @@ def load_train(path, model, optimizer, schedular=None):
         except Exception as e:
             print(f'did not restore optimizer due to error {e}')
     else:
-        print('Optimizer not inilized since no data for it exists in supplied path')
-    if schedular is not None:
-        if 'schedular' in state:
-            schedular.load_state_dict(state['schedular'])
+        print('Optimizer not initialized since no data for it exists in supplied path')
+    if scheduler is not None:
+        if 'scheduler' in state:
+            scheduler.load_state_dict(state['scheduler'])
         else:
-            print('Schedular not inilized since no data for it exists in supplied path')
+            print('Scheduler not initialized since no data for it exists in supplied path')
     if 'epoch' in state:
         e = state['epoch']
     else:
@@ -224,10 +203,9 @@ def load_eval(path, model):
 
     state = torch.load(path, map_location='cpu')
     pretrained = state['model']
-    current = model.state_dict()
 
-    # very dangerous!!!
-    pretrained = {k:v for k, v in zip(current.keys(), pretrained.values())}
+    # Load state dict with strict=False to allow for model architecture changes
+    # This will warn about missing or unexpected keys
     model.load_state_dict(pretrained, strict=False)
     model.eval()
 

convsparse_net.py

@@ -97,9 +97,9 @@ def forward_enc(self, inputs):
             csc = self.softthrsh1[lyr](csc + sc_residual)
         return csc
 
-    def forward_enc_generataor(self, inputs):
-        """forwar encoder generator
-        Use for debug and anylize model.
+    def forward_enc_generator(self, inputs):
+        """forward encoder generator
+        Use for debug and analyze model.
         """
         csc = self.softthrsh0(self.encode_conv0(inputs))
 
@@ -135,9 +135,8 @@ def __init__(self, _lambd):
         self._lambd = _lambd
 
     @property
-    def thrshold(self):
+    def threshold(self):
         return self._lambd
-#        self._lambd.register_hook(print)
 
     def forward(self, inputs):
         """ sign(inputs) * (abs(inputs)  - thrshold)"""

datasets.py

@@ -1,5 +1,4 @@
 from __future__ import division
-from torch.autograd import Variable
 import torch.utils.data as data
 from functools import partial
 import torch
@@ -14,7 +13,7 @@ def is_image_file(filename):
 
 def load_img(filepath, convert='L'):
     img = np.array(Image.open(filepath).convert(convert))
-    img = Variable(torch.from_numpy(img[None,...]),requires_grad=False).float()
+    img = torch.from_numpy(img[None,...]).float()
     return img
 
 def find_file_in_folder(folder, file_name):
@@ -110,7 +109,7 @@ def __init__(self, npz_path, key, pre_transform, inputs_transform, use_cuda=True
         self._inputs_transform = inputs_transform
 
     def __getitem__(self, index):
-        _targets = Variable(torch.from_numpy(self._targets[index]).float(), requires_grad=False)
+        _targets = torch.from_numpy(self._targets[index]).float()
         _inputs = self._inputs_transform(_targets)
         if self._use_cuda:
             _targets = _targets.cuda()

requirements.txt

@@ -0,0 +1,11 @@
+# Core dependencies
+torch>=1.7.0
+torchvision>=0.8.0
+numpy>=1.19.0
+matplotlib>=3.3.0
+scipy>=1.5.0
+Pillow>=8.3.2
+ipython>=7.0.0
+
+# Development dependencies
+pybm3d>=3.0.0