├── .gitignore
├── LICENSE
├── README.md
├── python
├── ddff
│ ├── __init__.py
│ ├── dataproviders
│ │ ├── __init__.py
│ │ └── datareaders
│ │ │ ├── FocalStackDDFFH5Reader.py
│ │ │ └── __init__.py
│ ├── metricseval
│ │ ├── BaseDDFFEval.py
│ │ ├── DDFFEval.py
│ │ ├── DDFFTFLearnEval.py
│ │ └── __init__.py
│ ├── models
│ │ ├── DDFFNet.py
│ │ └── __init__.py
│ └── trainers
│ │ ├── BaseTrainer.py
│ │ ├── DDFFTrainer.py
│ │ └── __init__.py
├── eval_ddff.py
├── eval_ddff_tflearn.py
└── run_ddff.py
└── requirements.txt
/.gitignore:
--------------------------------------------------------------------------------
1 | # Byte-compiled / optimized / DLL files
2 | __pycache__/
3 | *.py[cod]
4 | *$py.class
5 |
6 | # C extensions
7 | *.so
8 |
9 | # Distribution / packaging
10 | .Python
11 | env/
12 | build/
13 | develop-eggs/
14 | dist/
15 | downloads/
16 | eggs/
17 | .eggs/
18 | lib/
19 | lib64/
20 | parts/
21 | sdist/
22 | var/
23 | wheels/
24 | *.egg-info/
25 | .installed.cfg
26 | *.egg
27 |
28 | # PyInstaller
29 | # Usually these files are written by a python script from a template
30 | # before PyInstaller builds the exe, so as to inject date/other infos into it.
31 | *.manifest
32 | *.spec
33 |
34 | # Installer logs
35 | pip-log.txt
36 | pip-delete-this-directory.txt
37 |
38 | # Unit test / coverage reports
39 | htmlcov/
40 | .tox/
41 | .coverage
42 | .coverage.*
43 | .cache
44 | nosetests.xml
45 | coverage.xml
46 | *.cover
47 | .hypothesis/
48 |
49 | # Translations
50 | *.mo
51 | *.pot
52 |
53 | # Django stuff:
54 | *.log
55 | local_settings.py
56 |
57 | # Flask stuff:
58 | instance/
59 | .webassets-cache
60 |
61 | # Scrapy stuff:
62 | .scrapy
63 |
64 | # Sphinx documentation
65 | docs/_build/
66 |
67 | # PyBuilder
68 | target/
69 |
70 | # Jupyter Notebook
71 | .ipynb_checkpoints
72 |
73 | # pyenv
74 | .python-version
75 |
76 | # celery beat schedule file
77 | celerybeat-schedule
78 |
79 | # SageMath parsed files
80 | *.sage.py
81 |
82 | # dotenv
83 | .env
84 |
85 | # virtualenv
86 | .venv
87 | venv/
88 | ENV/
89 |
90 | # Spyder project settings
91 | .spyderproject
92 | .spyproject
93 |
94 | # Rope project settings
95 | .ropeproject
96 |
97 | # mkdocs documentation
98 | /site
99 |
100 | # mypy
101 | .mypy_cache/
102 |
103 | #Pytorch checkpoints
104 | *.pt
105 |
106 | #Datasets
107 | *.h5
108 |
109 | #Weights
110 | *.npz
111 | *.npy
112 |
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--------------------------------------------------------------------------------
/README.md:
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1 | # Deep Depth From Focus
2 | [Deep Depth From Focus](http://hazirbas.com/projects/ddff/) implementation in PyTorch. Please check the [ddff-toolbox](https://github.com/hazirbas/ddff-toolbox) for refocusing and camera parameters.
3 |
4 | ## Usage
5 | ### Installation
6 | To run the project a Python 3.7.0 environment and a number of packages are required. The easiest way to fetch all dependencies is to install via pip.
7 | ```
8 | pip install -r requirements.txt
9 | ```
10 |
11 | ### Training and Testing
12 | This implementation contains the [Deep Depth from Focus model](python/ddff/models/DDFFNet.py) and a class to run the [training and prediction](python/ddff/trainers/DDFFTrainer.py) on a provided dataset. Furthermore a [datareader](python/ddff/dataproviders/datareaders/FocalStackDDFFH5Reader.py) class is provided to read hdf5 files containing focal stacks and their corresponding disparity maps.
13 |
14 | In order to evaluate the model, an [evaluation class](python/ddff/metricseval/DDFFEval.py) is provided. It takes a model checkpoint and a path to the test data (h5 file) and features a method to calculate the errors described in the Deep Depth From Focus paper.
15 |
16 | ince the original implementation of Deep Depth From Focus was created in TensorFlow and TFLearn the class [DDFFTFLearnEval](python/ddff/metricseval/DDFFTFLearnEval.py) loads the checkpoint exported from the original model in order to perform the error evlauation. [eval_ddff_tflearn.py](python/eval_ddff_tflearn.py) shows an example of how to use the class.
17 |
18 | The pretrained weights exported from the TensorFlow/TFLearn model and converted to a PyTorch compatible dict is available [here](https://vision.in.tum.de/webarchive/hazirbas/ddff12scene/ddffnet-cc3-snapshot-121256.npz)(159.3MB).
19 |
20 | The training process can be started by running [run_ddff.py](python/run_ddff.py) which can be provided with a training dataset passing the parameter ```--dataset```. To evaulate the results the generated checkpoint file can be loaded as shown in [eval_ddff.py](python/eval_ddff.py) which calculates the error metrics on a test dataset.
21 |
22 | #### Initiazation
23 | To train the network on the dataset introduced in the Deep Depth From Focus paper [run_ddff.py](python/run_ddff.py) has to be run with respective arguments specifying where the dataset is located and other hyper parameters that can be inspected by passing the argument ```-h```.
24 | The [datareader](python/ddff/dataproviders/datareaders/FocalStackDDFFH5Reader.py) class requires the provided h5 file to contain a key for the focal stacks (default: "stack_train") and a key for the corresponding disparity maps (default: "disp_train") that can be passed during initialization of the reader.
25 |
26 | #### Data preparation
27 | The focal stacks in the hdf5 file have to be of shape [stacksize, height, width, channels] containing values in the range [0,255].
28 |
29 | The disparity maps have to be of shape [1, height, width] containing the disparity in pixels. The dataset introduced in the Deep Depth From Focus paper contains disparities in the range [0.0202, 0.2825]
30 |
31 | Please download the [trainval](https://vision.in.tum.de/webarchive/hazirbas/ddff12scene/ddff-dataset-trainval.h5) (12.6GB) and [test](https://vision.in.tum.de/webarchive/hazirbas/ddff12scene/ddff-dataset-test.h5) (761.1MB) hdf5 datasets. Focal stacks can be read as:
32 | ~~~~
33 | import h5py
34 |
35 | dataset = h5py.File("ddff-dataset-trainval.h5", "r")
36 | focal_stacks = dataset["stacks_train"]
37 | disparities = dataset["disp_train"]
38 | ~~~~
39 |
40 | Please submit your results to the [Competition](https://competitions.codalab.org/competitions/17807) to evaluate on the test set.
41 |
42 | **Note that** test scores are a slightly worse by a margin of 0.0001 (MSE) than the results presented on the paper due to the framework switch.
43 |
44 | ## Citation
45 | If you use this code or the publicly shared model, please cite the following paper.
46 |
47 | Caner Hazirbas, Sebastian Georg Soyer, Maximilian Christian Staab, Laura Leal-Taixé and Daniel Cremers, _"Deep Depth From Focus"_, ACCV, 2018. ([arXiv](https://arxiv.org/abs/1704.01085))
48 |
49 | @InProceedings{hazirbas18ddff,
50 | author = {C. Hazirbas and S. G. Soyer and M. C. Staab and L. Leal-Taixé and D. Cremers},
51 | title = {Deep Depth From Focus},
52 | booktitle = {Asian Conference on Computer Vision (ACCV)},
53 | year = {2018},
54 | month = {December},
55 | eprint = {1704.01085},
56 | url = {https://hazirbas.com/projects/ddff/},
57 | }
58 |
59 | ## License
60 | The code is released under [GNU General Public License Version 3 (GPLv3)](http://www.gnu.org/licenses/gpl.html).
61 |
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/python/ddff/dataproviders/datareaders/FocalStackDDFFH5Reader.py:
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1 | #! /usr/bin/python3
2 |
3 | import os
4 | import numpy as np
5 | from torch.utils.data import Dataset
6 | import torchvision
7 | import torch
8 | import h5py
9 |
10 | class FocalStackDDFFH5Reader(Dataset):
11 |
12 | def __init__(self, hdf5_filename, transform=None, stack_key="stack_train", disp_key="disp_train"):
13 | """
14 | Args:
15 | root_dir_fs (string): Directory with all focal stacks of all image datasets.
16 | root_dir_depth (string): Directory with all depth images of all image datasets.
17 | transform (callable, optional): Optional transform to be applied
18 | on a sample.
19 | """
20 | #Disable opencv threading since it leads to deadlocks in PyTorch DataLoader
21 | self.hdf5 = h5py.File(hdf5_filename, 'r')
22 | self.stack_key = stack_key
23 | self.disp_key = disp_key
24 | self.transform = transform
25 |
26 | def __len__(self):
27 | return self.hdf5[self.stack_key].shape[0]
28 |
29 | def __getitem__(self, idx):
30 | #Create sample dict
31 | sample = {'input': self.hdf5[self.stack_key][idx].astype(float), 'output': self.hdf5[self.disp_key][idx]}
32 |
33 | #Transform sample with data augmentation transformers
34 | if self.transform:
35 | sample = self.transform(sample)
36 |
37 | return sample
38 |
39 | def get_stack_size(self):
40 | return self.__getitem__(0)['input'].shape[0]
41 |
42 | class ToTensor(object):
43 | """Convert ndarrays in sample to Tensors."""
44 | def __call__(self, sample):
45 | #Add color dimension to depth map
46 | sample['output'] = np.expand_dims(sample['output'], axis=0)
47 | # swap color axis because
48 | # numpy image: H x W x C
49 | # torch image: C X H X W
50 | sample['input'] = torch.from_numpy(sample['input'].transpose((0,3,1,2))).float()
51 | sample['output'] = torch.from_numpy(sample['output']).float()
52 | return sample
53 |
54 | class Normalize(object):
55 | def __init__(self, mean_input, std_input, mean_output=None, std_output=None):
56 | self.mean_input = mean_input
57 | self.std_input = std_input
58 | self.mean_output = mean_output
59 | self.std_output = std_output
60 |
61 | def __call__(self, sample):
62 | input_images = torch.stack([torchvision.transforms.functional.normalize(sample_input, mean=self.mean_input, std=self.std_input) for sample_input in sample['input']])
63 | if self.mean_output is None or self.std_output is None:
64 | output_image = sample['output']
65 | else:
66 | output_image = torchvision.transforms.functional.normalize(sample['output'], mean=self.mean_output, std=self.std_output)
67 | return {'input': input_images, 'output': output_image}
68 |
69 | class ClipGroundTruth(object):
70 | def __init__(self, lower_bound, upper_bound):
71 | self.lower_bound = lower_bound
72 | self.upper_bound = upper_bound
73 |
74 | def __call__(self, sample):
75 | sample['output'][sample['output'] < self.lower_bound] = 0.0
76 | sample['output'][sample['output'] > self.upper_bound] = 0.0
77 | return sample
78 |
79 | class RandomCrop(object):
80 | def __init__(self, output_size, valid_crop_threshold=0.8):
81 | assert isinstance(output_size, (int, tuple))
82 | if isinstance(output_size, int):
83 | self.output_size = (output_size, output_size)
84 | else:
85 | assert len(output_size) == 2
86 | self.output_size = output_size
87 | self.valid_crop_threshold = valid_crop_threshold
88 |
89 | def __is_valid_crop(self, output_image, valid_pixel_cond=lambda x : x >= 0.01):
90 | valid_occurrances = valid_pixel_cond(output_image).sum()
91 | all_occurances = np.prod(output_image.shape)
92 | return (float(valid_occurrances) / float(all_occurances)) >= self.valid_crop_threshold
93 |
94 | def __call__(self, sample):
95 | h, w = sample['input'].shape[2:4]
96 | new_h, new_w = self.output_size
97 |
98 | #Generate list of possible random crops
99 | candidates = np.asarray([(x,y) for y in range(h - new_h) for x in range(w - new_w)])
100 | np.random.shuffle(candidates)
101 |
102 | #Iterate through candidates and choose forst valid crop
103 | for x,y in candidates:
104 | output_image = sample['output'][:,y:(y + new_h),x:(x + new_w)]
105 | if self.__is_valid_crop(output_image):
106 | input_images = torch.stack([sample_input[:,y:(y + new_h),x:(x + new_w)] for sample_input in sample['input']])
107 | return {'input': input_images, 'output': output_image}
108 |
109 | #No valid crop found. Return any crop
110 | top = np.random.randint(0, h - new_h)
111 | left = np.random.randint(0, w - new_w)
112 | input_images = torch.stack([sample_input[:,top:(top + new_h),left:(left + new_w)] for sample_input in sample['input']])
113 | output_image = sample['output'][:,top:(top + new_h),left:(left + new_w)]
114 | return {'input': input_images, 'output': output_image}
115 |
116 | class PadSamples(object):
117 | def __init__(self, output_size, ground_truth_pad_value=0.0):
118 | assert isinstance(output_size, (int, tuple))
119 | if isinstance(output_size, int):
120 | self.output_size = (output_size, output_size)
121 | else:
122 | assert len(output_size) == 2
123 | self.output_size = output_size
124 | self.ground_truth_pad_value = ground_truth_pad_value
125 |
126 | def __call__(self, sample):
127 | h, w = sample['input'].shape[2:4]
128 | new_h, new_w = self.output_size
129 | padh = np.int32(new_h - h)
130 | padw = np.int32(new_w - w)
131 | sample['input'] = torch.stack([torch.from_numpy(np.pad(sample_input.numpy(), ((0,0),(0,padh),(0,padw)), mode="reflect")).float() for sample_input in sample['input']])
132 | sample['output'] = torch.from_numpy(np.pad(sample['output'].numpy(), ((0,0),(0,padh),(0,padw)), mode="constant", constant_values=self.ground_truth_pad_value)).float()
133 |
134 | return sample
135 |
136 | class RandomSubStack(object):
137 | def __init__(self, output_size):
138 | self.output_size = output_size
139 |
140 | def __call__(self, sample):
141 | sample['input'] = torch.stack([sample['input'][i] for i in np.random.choice(sample['input'].shape[0], self.output_size, replace=False)])
142 | return sample
143 |
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/python/ddff/metricseval/BaseDDFFEval.py:
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1 | #! /usr/bin/python3
2 |
3 | import numpy as np
4 | import torch
5 | import skimage.filters as skf
6 |
7 | class BaseDDFFEval:
8 | def __init__(self, trainer):
9 | self.trainer = trainer
10 |
11 | def evaluate(self, dataloader, accthrs = [1.25, 1.25**2, 1.25**3], image_size=(383,552)):
12 | avgmetrics = np.zeros((1, 7+len(accthrs)), dtype=float)
13 | for i, data in enumerate(dataloader):
14 | inputs, output = data["input"], data["output"]
15 | if torch.cuda.is_available():
16 | inputs = inputs.cuda()
17 | output_approx = self.trainer.evaluate(inputs)
18 | metrics = self.__calmetrics(output_approx.permute(0,2,3,1).squeeze().data.cpu().numpy()[:image_size[0],:image_size[1]], output.permute(0,2,3,1).squeeze().numpy()[:image_size[0],:image_size[1]], 1.0, accthrs, bumpinessclip=0.05, ignore_zero=True)
19 | avgmetrics += metrics
20 | return avgmetrics/len(dataloader)
21 |
22 | # Metrics calculation provided by Caner Hazirbas
23 | def __calmetrics(self, pred, target, mse_factor, accthrs, bumpinessclip=0.05, ignore_zero=True):
24 | metrics = np.zeros((1, 7+len(accthrs)), dtype=float)
25 |
26 | if target.sum() == 0:
27 | return metrics
28 |
29 | pred_ = np.copy(pred)
30 | if ignore_zero:
31 | pred_[target==0.0] = 0.0
32 | numPixels = (target>0.0).sum() # number of valid pixels
33 | else:
34 | numPixels = target.size
35 |
36 | #euclidean norm
37 | metrics[0,0] = np.square(pred_-target).sum() / numPixels * mse_factor
38 |
39 | # RMS
40 | metrics[0,1] = np.sqrt(metrics[0,0])
41 |
42 | # log RMS
43 | logrms = (np.ma.log(pred_)-np.ma.log(target))
44 | metrics[0,2] = np.sqrt(np.square(logrms).sum() / numPixels)
45 |
46 | # absolute relative
47 | metrics[0,3] = np.ma.divide(np.abs(pred_-target), target).sum() / numPixels
48 |
49 | #square relative
50 | metrics[0,4] = np.ma.divide(np.square(pred_-target), target).sum() / numPixels
51 |
52 | # accuracies
53 | acc = np.ma.maximum(np.ma.divide(pred_,target), np.ma.divide(target, pred_))
54 | for i, thr in enumerate(accthrs):
55 | metrics[0, 5+i] = (acc < thr).sum() / numPixels * 100.
56 |
57 | # badpix
58 | metrics[0, 8]= (np.abs(pred_-target) > 0.07).sum() / numPixels * 100.
59 |
60 | # bumpiness -- Frobenius norm of the Hessian matrix
61 | diff = np.asarray(pred-target, dtype='float64') # PRED or PRED_
62 | chn = diff.shape[2] if len(diff.shape) > 2 else 1
63 | bumpiness = np.zeros_like(pred_).astype('float')
64 | for c in range(0,chn):
65 | if chn > 1:
66 | diff_ = diff[:, :, c]
67 | else:
68 | diff_ = diff
69 | dx = skf.scharr_v(diff_)
70 | dy = skf.scharr_h(diff_)
71 | dxx = skf.scharr_v(dx)
72 | dxy = skf.scharr_h(dx)
73 | dyy = skf.scharr_h(dy)
74 | dyx = skf.scharr_v(dy)
75 | hessiannorm = np.sqrt(np.square(dxx) + np.square(dxy) + np.square(dyy) + np.square(dyx))
76 | bumpiness += np.clip(hessiannorm, 0, bumpinessclip)
77 | bumpiness = bumpiness[target>0].sum() if ignore_zero else bumpiness.sum()
78 | metrics[0, 9] = bumpiness / chn / numPixels * 100.
79 |
80 | return metrics
81 |
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/python/ddff/metricseval/DDFFEval.py:
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1 | #! /usr/bin/python3
2 |
3 | import numpy as np
4 | import ddff.dataproviders.datareaders.FocalStackDDFFH5Reader as FocalStackDDFFH5Reader
5 | import ddff.trainers.DDFFTrainer as DDFFTrainer
6 | from ddff.metricseval.BaseDDFFEval import BaseDDFFEval
7 | import torchvision
8 | from torch.utils.data import DataLoader
9 |
10 | class DDFFEval(BaseDDFFEval):
11 | def __init__(self, checkpoint, focal_stack_size=10):
12 | self.trainer = DDFFTrainer.DDFFTrainer.from_checkpoint(checkpoint, focal_stack_size)
13 | super(DDFFEval, self).__init__(self.trainer)
14 |
15 | def evaluate(self, filename_testset, stack_key="stack_val", disp_key="disp_val", image_size=(383,552)):
16 | #Calculate pad size for images
17 | test_pad_size = (np.ceil((image_size[0] / 32)) * 32, np.ceil((image_size[1] / 32)) * 32) #32=2**numPoolings(=5)
18 | #Create test set transforms
19 | transform_test = [FocalStackDDFFH5Reader.FocalStackDDFFH5Reader.ToTensor(),
20 | FocalStackDDFFH5Reader.FocalStackDDFFH5Reader.ClipGroundTruth(0.0202, 0.2825),
21 | FocalStackDDFFH5Reader.FocalStackDDFFH5Reader.PadSamples(test_pad_size),
22 | FocalStackDDFFH5Reader.FocalStackDDFFH5Reader.Normalize(mean_input=[0.485, 0.456, 0.406], std_input=[0.229, 0.224, 0.225])]
23 | transform_test = torchvision.transforms.Compose(transform_test)
24 | #Create dataloader
25 | datareader = FocalStackDDFFH5Reader.FocalStackDDFFH5Reader(filename_testset, transform=transform_test, stack_key=stack_key, disp_key=disp_key)
26 | dataloader = DataLoader(datareader, batch_size=1, shuffle=False, num_workers=0)
27 | return super(DDFFEval, self).evaluate(dataloader)
28 |
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/python/ddff/metricseval/DDFFTFLearnEval.py:
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1 | #! /usr/bin/python3
2 |
3 | import numpy as np
4 | import ddff.dataproviders.datareaders.FocalStackDDFFH5Reader as FocalStackDDFFH5Reader
5 | import ddff.trainers.DDFFTrainer as DDFFTrainer
6 | from ddff.metricseval.BaseDDFFEval import BaseDDFFEval
7 | import torchvision
8 | from torch.utils.data import DataLoader
9 |
10 | class DDFFTFLearnEval(BaseDDFFEval):
11 | def __init__(self, checkpoint, focal_stack_size=10, norm_mean=None, norm_std=None):
12 | trainer = DDFFTrainer.DDFFTrainer.from_tflearn(checkpoint, focal_stack_size)
13 | self.norm_mean = norm_mean
14 | self.norm_std = norm_std
15 | super(DDFFTFLearnEval, self).__init__(trainer)
16 |
17 | def evaluate(self, filename_testset, stack_key="stack_val", disp_key="disp_val", image_size=(383,552)):
18 | #Calculate pad size for images
19 | test_pad_size = (np.ceil((image_size[0] / 32)) * 32, np.ceil((image_size[1] / 32)) * 32) #32=2**numPoolings(=5)
20 | #Create test set transforms
21 | transform_test = [FocalStackDDFFH5Reader.FocalStackDDFFH5Reader.ToTensor(),
22 | FocalStackDDFFH5Reader.FocalStackDDFFH5Reader.PadSamples(test_pad_size)]
23 | if self.norm_mean is not None and self.norm_std is not None:
24 | transform_test += [FocalStackDDFFH5Reader.FocalStackDDFFH5Reader.Normalize(mean_input=self.norm_mean, std_input=self.norm_std)]
25 | transform_test = torchvision.transforms.Compose(transform_test)
26 | #Create dataloader
27 | datareader = FocalStackDDFFH5Reader.FocalStackDDFFH5Reader(filename_testset, transform=transform_test, stack_key=stack_key, disp_key=disp_key)
28 | dataloader = DataLoader(datareader, batch_size=1, shuffle=False, num_workers=0)
29 | return super(DDFFTFLearnEval, self).evaluate(dataloader)
30 |
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/python/ddff/metricseval/__init__.py:
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https://raw.githubusercontent.com/soyers/ddff-pytorch/78a6c5b5118dd0404f97072d51ca68db0eb79990/python/ddff/metricseval/__init__.py
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/python/ddff/models/DDFFNet.py:
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1 | #! /usr/bin/python3
2 |
3 | import torch.nn as nn
4 | import torchvision
5 | import torch
6 | import numpy as np
7 |
8 | class DDFFNet(nn.Module):
9 | def __init__(self, focal_stack_size, output_dims=1, cc1_enabled=False, cc2_enabled=False, cc3_enabled=True, cc4_enabled=False, cc5_enabled=False, bias=False, pretrained='no_bn'):
10 | super(DDFFNet, self).__init__()
11 | self.autoencoder = DDFFAutoEncoder(output_dims, cc1_enabled, cc2_enabled, cc3_enabled, cc4_enabled, cc5_enabled, bias=bias)
12 | self.scoring = nn.Conv2d(focal_stack_size*output_dims, output_dims, 1, bias=False)
13 | #Init weights
14 | self.apply(self.weights_init)
15 | #Update pretrained weights
16 | if pretrained == 'no_bn':
17 | autoencoder_state_dict = self.autoencoder.state_dict()
18 | #Load pretrained dict
19 | pretrained_dict = torchvision.models.vgg16(pretrained=True).features.state_dict()
20 | #Filter and map pretrained dict
21 | pretrained_dict = self.__map_state_dict(pretrained_dict, bias=bias)
22 | #Update model dict
23 | autoencoder_state_dict.update(pretrained_dict)
24 | #Load updated state dict
25 | self.autoencoder.load_state_dict(autoencoder_state_dict)
26 | elif pretrained == 'bn':
27 | autoencoder_state_dict = self.autoencoder.state_dict()
28 | #Load pretrained dict
29 | pretrained_dict = torchvision.models.vgg16_bn(pretrained=True).features.state_dict()
30 | #Filter and map pretrained dict
31 | pretrained_dict = self.__map_state_dict_bn(pretrained_dict, bias=bias)
32 | #Update model dict
33 | autoencoder_state_dict.update(pretrained_dict)
34 | #Load updated state dict
35 | self.autoencoder.load_state_dict(autoencoder_state_dict)
36 | elif pretrained is not None:
37 | autoencoder_state_dict = self.autoencoder.state_dict()
38 | #Load pretrained dict
39 | pretrained_weights = np.load(pretrained, encoding="latin1").item()
40 | #Filter and map pretrained dict
41 | pretrained_dict = self.__map_state_dict_tf(pretrained_weights, bias=bias)
42 | #Update model dict
43 | autoencoder_state_dict.update(pretrained_dict)
44 | #Load updated state dict
45 | self.autoencoder.load_state_dict(autoencoder_state_dict)
46 |
47 | def forward(self, images):
48 | #Encode stacks in batch dimension and calculate features
49 | image_features = self.autoencoder(images.view(-1, *images.shape[2:]))
50 | #Encode stacks in feature dimension again
51 | image_features = image_features.view(images.shape[0], -1, *image_features.shape[2:])
52 | #Score extracted features
53 | result = self.scoring(image_features)
54 |
55 | return result
56 |
57 | def weights_init(self, m):
58 | classname = m.__class__.__name__
59 | if classname.find('Conv') != -1:
60 | nn.init.kaiming_normal_(m.weight, a=0, mode='fan_in')
61 | if m.bias is not None:
62 | m.bias.data.fill_(0)
63 | elif classname.find('BatchNorm') != -1:
64 | m.weight.data.normal_(0, 1.0)
65 | m.running_var.normal_(0, 1.0)
66 | m.running_mean.fill_(0)
67 | m.bias.data.fill_(0)
68 |
69 | def __map_state_dict(self, vgg16_features_dict, bias):
70 | layer_mappings = {'0.weight': 'conv1_1.weight',
71 | '2.weight': 'conv1_2.weight',
72 | '5.weight': 'conv2_1.weight',
73 | '7.weight': 'conv2_2.weight',
74 | '10.weight': 'conv3_1.weight',
75 | '12.weight': 'conv3_2.weight',
76 | '14.weight': 'conv3_3.weight',
77 | '17.weight': 'conv4_1.weight',
78 | '19.weight': 'conv4_2.weight',
79 | '21.weight': 'conv4_3.weight',
80 | '24.weight': 'conv5_1.weight',
81 | '26.weight': 'conv5_2.weight',
82 | '28.weight': 'conv5_3.weight'}
83 | if bias:
84 | layer_mappings.update({'0.bias': 'conv1_1.bias',
85 | '2.bias': 'conv1_2.bias',
86 | '5.bias': 'conv2_1.bias',
87 | '7.bias': 'conv2_2.bias',
88 | '10.bias': 'conv3_1.bias',
89 | '12.bias': 'conv3_2.bias',
90 | '14.bias': 'conv3_3.bias',
91 | '17.bias': 'conv4_1.bias',
92 | '19.bias': 'conv4_2.bias',
93 | '21.bias': 'conv4_3.bias',
94 | '24.bias': 'conv5_1.bias',
95 | '26.bias': 'conv5_2.bias',
96 | '28.bias': 'conv5_3.bias'})
97 | #Update according to generated mapping
98 | pretrained_dict = {layer_mappings[k]: v for k, v in vgg16_features_dict.items() if k in layer_mappings}
99 | return pretrained_dict
100 |
101 | def __map_state_dict_bn(self, vgg16_features_dict, bias):
102 | layer_mappings = {'0.weight': 'conv1_1.weight',
103 | '1.weight': 'conv1_1_bn.weight', '1.bias': 'conv1_1_bn.bias', '1.running_mean': 'conv1_1_bn.running_mean', '1.running_var': 'conv1_1_bn.running_var',
104 | '3.weight': 'conv1_2.weight',
105 | '4.weight': 'conv1_2_bn.weight', '4.bias': 'conv1_2_bn.bias', '4.running_mean': 'conv1_2_bn.running_mean', '4.running_var': 'conv1_2_bn.running_var',
106 | '7.weight': 'conv2_1.weight',
107 | '8.weight': 'conv2_1_bn.weight', '8.bias': 'conv2_1_bn.bias', '8.running_mean': 'conv2_1_bn.running_mean', '8.running_var': 'conv2_1_bn.running_var',
108 | '10.weight': 'conv2_2.weight',
109 | '11.weight': 'conv2_2_bn.weight', '11.bias': 'conv2_2_bn.bias', '11.running_mean': 'conv2_2_bn.running_mean', '11.running_var': 'conv2_2_bn.running_var',
110 | '14.weight': 'conv3_1.weight',
111 | '15.weight': 'conv3_1_bn.weight', '15.bias': 'conv3_1_bn.bias', '15.running_mean': 'conv3_1_bn.running_mean', '15.running_var': 'conv3_1_bn.running_var',
112 | '17.weight': 'conv3_2.weight',
113 | '18.weight': 'conv3_2_bn.weight', '18.bias': 'conv3_2_bn.bias', '18.running_mean': 'conv3_2_bn.running_mean', '18.running_var': 'conv3_2_bn.running_var',
114 | '20.weight': 'conv3_3.weight',
115 | '21.weight': 'conv3_3_bn.weight', '21.bias': 'conv3_3_bn.bias', '21.running_mean': 'conv3_3_bn.running_mean', '21.running_var': 'conv3_3_bn.running_var',
116 | '24.weight': 'conv4_1.weight',
117 | '25.weight': 'conv4_1_bn.weight', '25.bias': 'conv4_1_bn.bias', '25.running_mean': 'conv4_1_bn.running_mean', '25.running_var': 'conv4_1_bn.running_var',
118 | '27.weight': 'conv4_2.weight',
119 | '28.weight': 'conv4_2_bn.weight', '28.bias': 'conv4_2_bn.bias', '28.running_mean': 'conv4_2_bn.running_mean', '28.running_var': 'conv4_2_bn.running_var',
120 | '30.weight': 'conv4_3.weight',
121 | '31.weight': 'conv4_3_bn.weight', '31.bias': 'conv4_3_bn.bias', '31.running_mean': 'conv4_3_bn.running_mean', '31.running_var': 'conv4_3_bn.running_var',
122 | '34.weight': 'conv5_1.weight',
123 | '35.weight': 'conv5_1_bn.weight', '35.bias': 'conv5_1_bn.bias', '35.running_mean': 'conv5_1_bn.running_mean', '35.running_var': 'conv5_1_bn.running_var',
124 | '37.weight': 'conv5_2.weight',
125 | '38.weight': 'conv5_2_bn.weight', '38.bias': 'conv5_2_bn.bias', '38.running_mean': 'conv5_2_bn.running_mean', '38.running_var': 'conv5_2_bn.running_var',
126 | '40.weight': 'conv5_3.weight',
127 | '41.weight': 'conv5_3_bn.weight', '41.bias': 'conv5_3_bn.bias', '41.running_mean': 'conv5_3_bn.running_mean', '41.running_var': 'conv5_3_bn.running_var'}
128 | if bias:
129 | layer_mappings.update({'0.bias': 'conv1_1.bias',
130 | '3.bias': 'conv1_2.bias',
131 | '7.bias': 'conv2_1.bias',
132 | '10.bias': 'conv2_2.bias',
133 | '14.bias': 'conv3_1.bias',
134 | '17.bias': 'conv3_2.bias',
135 | '20.bias': 'conv3_3.bias',
136 | '24.bias': 'conv4_1.bias',
137 | '27.bias': 'conv4_2.bias',
138 | '30.bias': 'conv4_3.bias',
139 | '34.bias': 'conv5_1.bias',
140 | '37.bias': 'conv5_2.bias',
141 | '40.bias': 'conv5_3.bias'
142 | })
143 | #Update according to generated mapping
144 | pretrained_dict = {layer_mappings[k]: v for k, v in vgg16_features_dict.items() if k in layer_mappings}
145 | return pretrained_dict
146 |
147 | def __map_state_dict_tf(self, vgg16_features, bias):
148 | pretrained_dict = {
149 | 'conv1_1.weight': torch.from_numpy(vgg16_features['conv1_1'][0].transpose((3, 2, 0, 1))).float(),
150 | 'conv1_2.weight': torch.from_numpy(vgg16_features['conv1_2'][0].transpose((3, 2, 0, 1))).float(),
151 | 'conv2_1.weight': torch.from_numpy(vgg16_features['conv2_1'][0].transpose((3, 2, 0, 1))).float(),
152 | 'conv2_2.weight': torch.from_numpy(vgg16_features['conv2_2'][0].transpose((3, 2, 0, 1))).float(),
153 | 'conv3_1.weight': torch.from_numpy(vgg16_features['conv3_1'][0].transpose((3, 2, 0, 1))).float(),
154 | 'conv3_2.weight': torch.from_numpy(vgg16_features['conv3_2'][0].transpose((3, 2, 0, 1))).float(),
155 | 'conv3_3.weight': torch.from_numpy(vgg16_features['conv3_3'][0].transpose((3, 2, 0, 1))).float(),
156 | 'conv4_1.weight': torch.from_numpy(vgg16_features['conv4_1'][0].transpose((3, 2, 0, 1))).float(),
157 | 'conv4_2.weight': torch.from_numpy(vgg16_features['conv4_2'][0].transpose((3, 2, 0, 1))).float(),
158 | 'conv4_3.weight': torch.from_numpy(vgg16_features['conv4_3'][0].transpose((3, 2, 0, 1))).float(),
159 | 'conv5_1.weight': torch.from_numpy(vgg16_features['conv5_1'][0].transpose((3, 2, 0, 1))).float(),
160 | 'conv5_2.weight': torch.from_numpy(vgg16_features['conv5_2'][0].transpose((3, 2, 0, 1))).float(),
161 | 'conv5_3.weight': torch.from_numpy(vgg16_features['conv5_3'][0].transpose((3, 2, 0, 1))).float(),
162 | }
163 | if bias:
164 | pretrained_dict.update({
165 | 'conv1_1.bias': torch.from_numpy(vgg16_features['conv1_1'][1]).float(),
166 | 'conv1_2.bias': torch.from_numpy(vgg16_features['conv1_2'][1]).float(),
167 | 'conv2_1.bias': torch.from_numpy(vgg16_features['conv2_1'][1]).float(),
168 | 'conv2_2.bias': torch.from_numpy(vgg16_features['conv2_2'][1]).float(),
169 | 'conv3_1.bias': torch.from_numpy(vgg16_features['conv3_1'][1]).float(),
170 | 'conv3_2.bias': torch.from_numpy(vgg16_features['conv3_2'][1]).float(),
171 | 'conv3_3.bias': torch.from_numpy(vgg16_features['conv3_3'][1]).float(),
172 | 'conv4_1.bias': torch.from_numpy(vgg16_features['conv4_1'][1]).float(),
173 | 'conv4_2.bias': torch.from_numpy(vgg16_features['conv4_2'][1]).float(),
174 | 'conv4_3.bias': torch.from_numpy(vgg16_features['conv4_3'][1]).float(),
175 | 'conv5_1.bias': torch.from_numpy(vgg16_features['conv5_1'][1]).float(),
176 | 'conv5_2.bias': torch.from_numpy(vgg16_features['conv5_2'][1]).float(),
177 | 'conv5_3.bias': torch.from_numpy(vgg16_features['conv5_3'][1]).float()
178 | })
179 | return pretrained_dict
180 |
181 | class DDFFAutoEncoder(nn.Module):
182 | """Create model from VGG_16 by deleting the classifier layer."""
183 | def __init__(self, output_dims, cc1_enabled, cc2_enabled, cc3_enabled, cc4_enabled, cc5_enabled, bias=False):
184 | super(DDFFAutoEncoder, self).__init__()
185 | #Save parameters
186 | self.output_dims = output_dims
187 | self.cc1_enabled = cc1_enabled
188 | self.cc2_enabled = cc2_enabled
189 | self.cc3_enabled = cc3_enabled
190 | self.cc4_enabled = cc4_enabled
191 | self.cc5_enabled = cc5_enabled
192 |
193 | #Encoder
194 | self.conv1_1 = nn.Conv2d(3, 64, 3, padding=1, bias=bias)
195 | self.conv1_1_bn = nn.BatchNorm2d(64, eps=0.001)
196 | self.conv1_2 = nn.Conv2d(64, 64, 3, padding=1, bias=bias)
197 | self.conv1_2_bn = nn.BatchNorm2d(64, eps=0.001)
198 | self.pool1 = nn.MaxPool2d(2, stride=2)
199 | self.conv2_1 = nn.Conv2d(64, 128, 3, padding=1, bias=bias)
200 | self.conv2_1_bn = nn.BatchNorm2d(128, eps=0.001)
201 | self.conv2_2 = nn.Conv2d(128, 128 , 3, padding=1, bias=bias)
202 | self.conv2_2_bn = nn.BatchNorm2d(128, eps=0.001)
203 | self.pool2 = nn.MaxPool2d(2, stride=2)
204 | self.conv3_1 = nn.Conv2d(128, 256, 3, padding=1, bias=bias)
205 | self.conv3_1_bn = nn.BatchNorm2d(256, eps=0.001)
206 | self.conv3_2 = nn.Conv2d(256, 256, 3, padding=1, bias=bias)
207 | self.conv3_2_bn = nn.BatchNorm2d(256, eps=0.001)
208 | self.conv3_3 = nn.Conv2d(256, 256, 3, padding=1, bias=bias)
209 | self.conv3_3_bn = nn.BatchNorm2d(256, eps=0.001)
210 | self.pool3 = nn.MaxPool2d(2, stride=2)
211 | self.encdrop3 = nn.Dropout(p=0.5)
212 | self.conv4_1 = nn.Conv2d(256, 512, 3, padding=1, bias=bias)
213 | self.conv4_1_bn = nn.BatchNorm2d(512, eps=0.001)
214 | self.conv4_2 = nn.Conv2d(512, 512, 3, padding=1, bias=bias)
215 | self.conv4_2_bn = nn.BatchNorm2d(512, eps=0.001)
216 | self.conv4_3 = nn.Conv2d(512, 512, 3, padding=1, bias=bias)
217 | self.conv4_3_bn = nn.BatchNorm2d(512, eps=0.001)
218 | self.pool4 = nn.MaxPool2d(2, stride=2)
219 | self.encdrop4 = nn.Dropout(p=0.5)
220 | self.conv5_1 = nn.Conv2d(512, 512, 3, padding=1, bias=bias)
221 | self.conv5_1_bn = nn.BatchNorm2d(512, eps=0.001)
222 | self.conv5_2 = nn.Conv2d(512, 512, 3, padding=1, bias=bias)
223 | self.conv5_2_bn = nn.BatchNorm2d(512, eps=0.001)
224 | self.conv5_3 = nn.Conv2d(512, 512, 3, padding=1, bias=bias)
225 | self.conv5_3_bn = nn.BatchNorm2d(512, eps=0.001)
226 | self.pool5 = nn.MaxPool2d(2, stride=2)
227 | self.encdrop5 = nn.Dropout(p=0.5)
228 |
229 | #Decoder
230 | self.upconv5 = nn.ConvTranspose2d(512, 512, 4, padding=1, stride=2, bias=False)
231 | if self.cc5_enabled:
232 | self.conv5_3_D = nn.Conv2d(1024, 512, 3, padding=1, bias=bias)
233 | else:
234 | self.conv5_3_D = nn.Conv2d(512, 512, 3, padding=1, bias=bias)
235 | self.conv5_3_D_bn = nn.BatchNorm2d(512, eps=0.001)
236 | self.conv5_2_D = nn.Conv2d(512, 512, 3, padding=1, bias=bias)
237 | self.conv5_2_D_bn = nn.BatchNorm2d(512, eps=0.001)
238 | self.conv5_1_D = nn.Conv2d(512, 512, 3, padding=1, bias=bias)
239 | self.conv5_1_D_bn = nn.BatchNorm2d(512, eps=0.001)
240 | self.decdrop5 = nn.Dropout(p=0.5)
241 |
242 | self.upconv4 = nn.ConvTranspose2d(512, 512, 4, padding=1, stride=2, bias=False)
243 | if self.cc4_enabled:
244 | self.conv4_3_D = nn.Conv2d(1024, 512, 3, padding=1, bias=bias)
245 | else:
246 | self.conv4_3_D = nn.Conv2d(512, 512, 3, padding=1, bias=bias)
247 | self.conv4_3_D_bn = nn.BatchNorm2d(512, eps=0.001)
248 | self.conv4_2_D = nn.Conv2d(512, 512, 3, padding=1, bias=bias)
249 | self.conv4_2_D_bn = nn.BatchNorm2d(512, eps=0.001)
250 | self.conv4_1_D = nn.Conv2d(512, 256, 3, padding=1, bias=bias)
251 | self.conv4_1_D_bn = nn.BatchNorm2d(256, eps=0.001)
252 | self.decdrop4 = nn.Dropout(p=0.5)
253 |
254 | self.upconv3 = nn.ConvTranspose2d(256, 256, 4, padding=1, stride=2, bias=False)
255 | if self.cc3_enabled:
256 | self.conv3_3_D = nn.Conv2d(512, 256, 3, padding=1, bias=bias)
257 | else:
258 | self.conv3_3_D = nn.Conv2d(256, 256, 3, padding=1, bias=bias)
259 | self.conv3_3_D_bn = nn.BatchNorm2d(256, eps=0.001)
260 | self.conv3_2_D = nn.Conv2d(256, 256, 3, padding=1, bias=bias)
261 | self.conv3_2_D_bn = nn.BatchNorm2d(256, eps=0.001)
262 | self.conv3_1_D = nn.Conv2d(256, 128, 3, padding=1, bias=bias)
263 | self.conv3_1_D_bn = nn.BatchNorm2d(128, eps=0.001)
264 | self.decdrop3 = nn.Dropout(p=0.5)
265 |
266 | self.upconv2 = nn.ConvTranspose2d(128, 128, 4, padding=1, stride=2, bias=False)
267 | if self.cc2_enabled:
268 | self.conv2_2_D = nn.Conv2d(256, 128, 3, padding=1, bias=bias)
269 | else:
270 | self.conv2_2_D = nn.Conv2d(128, 128, 3, padding=1, bias=bias)
271 | self.conv2_2_D_bn = nn.BatchNorm2d(128, eps=0.001)
272 | self.conv2_1_D = nn.Conv2d(128, 64, 3, padding=1, bias=bias)
273 | self.conv2_1_D_bn = nn.BatchNorm2d(64, eps=0.001)
274 |
275 | self.upconv1 = nn.ConvTranspose2d(64, 64, 4, padding=1, stride=2, bias=False)
276 | if self.cc1_enabled:
277 | self.conv1_2_D = nn.Conv2d(128, 64, 3, padding=1, bias=bias)
278 | else:
279 | self.conv1_2_D = nn.Conv2d(64, 64, 3, padding=1, bias=bias)
280 | self.conv1_2_D_bn = nn.BatchNorm2d(64, eps=0.001)
281 | self.conv1_1_D = nn.Conv2d(64, self.output_dims, 3, padding=1, bias=bias)
282 | self.conv1_1_D_bn = nn.BatchNorm2d(self.output_dims, eps=0.001)
283 |
284 | def forward(self, x):
285 | #Encoder
286 | x = nn.functional.relu(self.conv1_1_bn(self.conv1_1(x)))
287 | cc1 = nn.functional.relu(self.conv1_2_bn(self.conv1_2(x)))
288 | x = self.pool1(cc1)
289 | x = nn.functional.relu(self.conv2_1_bn(self.conv2_1(x)))
290 | cc2 = nn.functional.relu(self.conv2_2_bn(self.conv2_2(x)))
291 | x = self.pool2(cc2)
292 | x = nn.functional.relu(self.conv3_1_bn(self.conv3_1(x)))
293 | x = nn.functional.relu(self.conv3_2_bn(self.conv3_2(x)))
294 | cc3 = nn.functional.relu(self.conv3_3_bn(self.conv3_3(x)))
295 | x = self.pool3(cc3)
296 | x = self.encdrop3(x)
297 | x = nn.functional.relu(self.conv4_1_bn(self.conv4_1(x)))
298 | x = nn.functional.relu(self.conv4_2_bn(self.conv4_2(x)))
299 | cc4 = nn.functional.relu(self.conv4_3_bn(self.conv4_3(x)))
300 | x = self.pool4(cc4)
301 | x = self.encdrop4(x)
302 | x = nn.functional.relu(self.conv5_1_bn(self.conv5_1(x)))
303 | x = nn.functional.relu(self.conv5_2_bn(self.conv5_2(x)))
304 | cc5 = nn.functional.relu(self.conv5_3_bn(self.conv5_3(x)))
305 | x = self.pool5(cc5)
306 | x = self.encdrop5(x)
307 |
308 | #Decoder
309 | x = self.upconv5(x)
310 | if self.cc5_enabled:
311 | x = torch.cat([x, cc5], 1)
312 | x = nn.functional.relu(self.conv5_3_D_bn(self.conv5_3_D(x)))
313 | x = nn.functional.relu(self.conv5_2_D_bn(self.conv5_2_D(x)))
314 | x = nn.functional.relu(self.conv5_1_D_bn(self.conv5_1_D(x)))
315 | x = self.decdrop5(x)
316 | x = self.upconv4(x)
317 | if self.cc4_enabled:
318 | x = torch.cat([x, cc4], 1)
319 | x = nn.functional.relu(self.conv4_3_D_bn(self.conv4_3_D(x)))
320 | x = nn.functional.relu(self.conv4_2_D_bn(self.conv4_2_D(x)))
321 | x = nn.functional.relu(self.conv4_1_D_bn(self.conv4_1_D(x)))
322 | x = self.decdrop4(x)
323 | x = self.upconv3(x)
324 | if self.cc3_enabled:
325 | x = torch.cat([x, cc3], 1)
326 | x = nn.functional.relu(self.conv3_3_D_bn(self.conv3_3_D(x)))
327 | x = nn.functional.relu(self.conv3_2_D_bn(self.conv3_2_D(x)))
328 | x = nn.functional.relu(self.conv3_1_D_bn(self.conv3_1_D(x)))
329 | x = self.decdrop3(x)
330 | x = self.upconv2(x)
331 | if self.cc2_enabled:
332 | x = torch.cat([x, cc2], 1)
333 | x = nn.functional.relu(self.conv2_2_D_bn(self.conv2_2_D(x)))
334 | x = nn.functional.relu(self.conv2_1_D_bn(self.conv2_1_D(x)))
335 | x = self.upconv1(x)
336 | if self.cc1_enabled:
337 | x = torch.cat([x, cc1], 1)
338 | x = nn.functional.relu(self.conv1_2_D_bn(self.conv1_2_D(x)))
339 | x = nn.functional.relu(self.conv1_1_D_bn(self.conv1_1_D(x)))
340 | return x
341 |
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/python/ddff/models/__init__.py:
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/python/ddff/trainers/BaseTrainer.py:
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1 | #! /usr/bin/python3
2 |
3 | import os
4 | import torch
5 | from torch import optim
6 |
7 | class BaseTrainer:
8 | def __init__(self, model, optimizer, training_loss, deterministic, scheduler=None, supervised=True):
9 | self.deterministic = deterministic
10 | if deterministic:
11 | self.__set_deterministic()
12 | self.model = model
13 | if torch.cuda.is_available():
14 | self.model.cuda()
15 | self.optimizer = optimizer
16 | self.training_loss = training_loss
17 | self.scheduler = scheduler
18 | self.supervised = supervised
19 |
20 | if not os.path.exists('checkpoints'):
21 | os.makedirs('checkpoints')
22 |
23 | def create_optimizer(self, net, optimizer_params):
24 | if optimizer_params["algorithm"] == 'sgd':
25 | return optim.SGD(
26 | filter(lambda p: p.requires_grad, net.parameters()),
27 | lr=optimizer_params["learning_rate"] if "learning_rate" in optimizer_params else 0.001,
28 | momentum=optimizer_params["momentum"] if "momentum" in optimizer_params else 0.9,
29 | weight_decay=optimizer_params["weight_decay"] if "weight_decay" in optimizer_params else 0.0005)
30 | elif optimizer_params["algorithm"] == 'adam':
31 | return optim.Adam(
32 | filter(lambda p: p.requires_grad, net.parameters()),
33 | lr=optimizer_params["learning_rate"] if "learning_rate" in optimizer_params else 0.001,
34 | weight_decay=optimizer_params["weight_decay"] if "weight_decay" in optimizer_params else 0.0005)
35 | else:
36 | return optim.SGD(
37 | filter(lambda p: p.requires_grad, net.parameters()),
38 | lr=0.001,
39 | momentum=0.9,
40 | weight_decay=0.0005)
41 |
42 |
43 | def __set_deterministic(self):
44 | import random
45 | import numpy as np
46 | #Set RNG seeds
47 | torch.manual_seed(42)
48 | torch.cuda.manual_seed_all(42)
49 | random.seed(42)
50 | np.random.seed(42)
51 | #Make results deterministic by disabling undeterministic functions in cuDNN
52 | torch.backends.cudnn.deterministic = True
53 |
54 | def set_supervised(self, supervised):
55 | self.supervised = supervised
56 |
57 | def set_training_loss(self, training_loss):
58 | self.training_loss = training_loss
59 |
60 | def train(self, dataloader, epochs, print_frequency=50, max_gradient=None, checkpoint_file=None, checkpoint_frequency=50):
61 | #Train model
62 | self.model.train()
63 | #Create list to keep track of losses foreach epoch
64 | epoch_losses = []
65 | #Run trainign loop
66 | for epoch in range(epochs):
67 | epoch_loss = 0.0
68 | running_loss = 0.0
69 | for i, data in enumerate(dataloader):
70 | #Zero the parameter gradients
71 | self.optimizer.zero_grad()
72 |
73 | #Get the inputs
74 | inputs = data['input']
75 | #Copy inputs to GPU
76 | if torch.cuda.is_available():
77 | if isinstance(inputs, list):
78 | inputs = [element.cuda() for element in inputs]
79 | else:
80 | inputs = inputs.cuda()
81 |
82 | #Forward
83 | if isinstance(inputs, list):
84 | output_approx = self.model(*inputs)
85 | else:
86 | output_approx = self.model(inputs)
87 |
88 | if self.supervised:
89 | #Get the inputs
90 | outputs = data['output']
91 | #Copy outputs to GPU
92 | if torch.cuda.is_available():
93 | if isinstance(outputs, list):
94 | outputs = [element.cuda() for element in outputs]
95 | else:
96 | outputs = outputs.cuda()
97 |
98 | if isinstance(outputs, list):
99 | loss = self.training_loss(*output_approx, *outputs)
100 | else:
101 | loss = self.training_loss(output_approx, outputs)
102 | else:
103 | #Calculate loss
104 | if isinstance(inputs, list):
105 | loss = self.training_loss(*output_approx, *inputs)
106 | else:
107 | loss = self.training_loss(output_approx, inputs)
108 |
109 | #Backward
110 | loss.backward()
111 |
112 | #Clip gradients
113 | if max_gradient is not None:
114 | torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_gradient, norm_type=2)
115 |
116 | #Optimize
117 | self.optimizer.step()
118 |
119 | #Store epoch loss
120 | epoch_loss += loss.item()
121 |
122 | #Print statistics
123 | running_loss += loss.item()
124 | if i % print_frequency == print_frequency-1: # print every print_frequency mini-batches
125 | print('[%d, %5d] loss: ' %
126 | (epoch + 1, i + 1) + str(running_loss / print_frequency))
127 | running_loss = 0.0
128 |
129 | #Save checkpoint
130 | if checkpoint_file is not None and epoch % checkpoint_frequency == checkpoint_frequency-1:
131 | self.save_checkpoint(checkpoint_file, epoch=(epoch+1), save_optimizer=True)
132 |
133 | #Save loss of epoch
134 | epoch_losses += [epoch_loss/len(dataloader)]
135 |
136 | #Update learning rate based on defined schedule
137 | if self.scheduler is not None:
138 | self.scheduler.step()
139 | #Save final checkpoint
140 | if checkpoint_file is not None:
141 | self.save_checkpoint(checkpoint_file, epoch=epochs, save_optimizer=True)
142 | print("Training finished")
143 | return epoch_losses
144 |
145 | def evaluate(self, inputs):
146 | #Set model to eval mode in order to disable dropout
147 | self.model.eval()
148 | inputs.requires_grad = False
149 | return self.model(inputs)
150 |
151 |
152 | def save_checkpoint(self, filename, epoch=None, save_optimizer=True):
153 | state = {'state_dict': self.model.state_dict()}
154 | if save_optimizer:
155 | state['optimizer'] = self.optimizer.state_dict()
156 | if epoch is not None:
157 | state['epoch'] = epoch
158 | torch.save(state, 'checkpoints/'+filename)
159 |
160 | def load_checkpoint(self, filename, load_optimizer=True, load_scheduler=True):
161 | #Load model to cpu
162 | checkpoint = torch.load(filename, map_location=lambda storage, location: storage)
163 | self.model.load_state_dict(checkpoint['state_dict'])
164 | #Upload model to GPU
165 | if torch.cuda.is_available():
166 | self.model.cuda()
167 | if load_optimizer:
168 | self.optimizer.load_state_dict(checkpoint['optimizer'])
169 | if load_scheduler and self.scheduler is not None and 'epoch' in checkpoint:
170 | self.scheduler.last_epoch = checkpoint['epoch']
171 | if 'epoch' in checkpoint:
172 | return checkpoint['epoch']
173 |
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/python/ddff/trainers/DDFFTrainer.py:
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1 | #! /usr/bin/python3
2 |
3 | import os
4 | import numpy as np
5 | import torch
6 | import torch.nn as nn
7 | import torchvision
8 | from torch import optim
9 | from torch.utils.data import DataLoader
10 | import ddff.models.DDFFNet as DDFFNet
11 | import ddff.dataproviders.datareaders.FocalStackDDFFH5Reader as FocalStackDDFFH5Reader
12 | from ddff.trainers.BaseTrainer import BaseTrainer
13 |
14 | class DDFFTrainer(BaseTrainer):
15 | def __init__(self, stack_size, learning_rate=0.001, cliprange=[0.0202, 0.2825],
16 | cc1_enabled=False,
17 | cc2_enabled=False,
18 | cc3_enabled=True,
19 | cc4_enabled=False,
20 | cc5_enabled=False,
21 | pretrained='no_bn',
22 | scheduler_step_size=4,
23 | scheduler_gama=0.9,
24 | deterministic=False,
25 | optimizer='sgd',
26 | normalize_loss=False):
27 | #Define model
28 | net = DDFFNet.DDFFNet(stack_size, cc1_enabled=cc1_enabled, cc2_enabled=cc2_enabled, cc3_enabled=cc3_enabled, cc4_enabled=cc4_enabled, cc5_enabled=cc5_enabled, pretrained=pretrained)
29 | #Define optimizer
30 | if optimizer == 'sgd':
31 | opt = self.create_optimizer(net, {"algorithm":'sgd', "learning_rate":learning_rate, "weight_decay": 0.0005, "momentum":0.9})
32 | else:
33 | opt = self.create_optimizer(net, {"algorithm":'adam', "learning_rate":learning_rate, "weight_decay": 0.0005})
34 | #Define scheduler
35 | scheduler = optim.lr_scheduler.StepLR(opt, step_size=scheduler_step_size, gamma=scheduler_gama)
36 | #Define training loss
37 | training_loss = self.MaskedLoss(nn.MSELoss(reduction="elementwise_mean" if normalize_loss else "sum"), valid_cond=lambda x : x >= cliprange[0])
38 |
39 | #Call parent constructor
40 | super(DDFFTrainer, self).__init__(net, opt, training_loss, deterministic, scheduler=scheduler)
41 |
42 | @classmethod
43 | def from_h5_data(cls,root_dir,
44 | learning_rate=0.001,
45 | cc1_enabled=False,
46 | cc2_enabled=False,
47 | cc3_enabled=True,
48 | cc4_enabled=False,
49 | cc5_enabled=False,
50 | training_crop_size=None,
51 | validation_crop_size=None,
52 | pretrained='no_bn',
53 | normalize_mean=[0.485, 0.456, 0.406],
54 | normalize_std=[0.229, 0.224, 0.225],
55 | scheduler_step_size=4,
56 | scheduler_gama=0.9,
57 | max_gradient=5.0,
58 | deterministic=False,
59 | optimizer='sgd',
60 | normalize_loss=False,
61 | epochs=20,
62 | batch_size=2,
63 | num_workers=4,
64 | checkpoint_file=None,
65 | checkpoint_frequency=50):
66 | #Create data loaders
67 | transform_train = cls.__create_preprocessing(cls, crop_size=training_crop_size, mean=normalize_mean, std=normalize_std)
68 | transform_validation = cls.__create_preprocessing(cls, crop_size=validation_crop_size, mean=normalize_mean, std=normalize_std)
69 | #Create h5 reader
70 | dataset_train = FocalStackDDFFH5Reader.FocalStackDDFFH5Reader(root_dir, transform=transform_train, stack_key="stack_train", disp_key="disp_train")
71 | dataset_validation = FocalStackDDFFH5Reader.FocalStackDDFFH5Reader(root_dir, transform=transform_validation, stack_key="stack_val", disp_key="disp_val")
72 | #Create data loader
73 | dataloader_train = DataLoader(dataset_train, batch_size=batch_size, shuffle=True, num_workers=num_workers)
74 | dataloader_validation = DataLoader(dataset_validation, batch_size=1, shuffle=True, num_workers=0)
75 | #Call constructor
76 | instance = cls(dataset_train.get_stack_size(), learning_rate=learning_rate,
77 | cc1_enabled=cc1_enabled,
78 | cc2_enabled=cc2_enabled,
79 | cc3_enabled=cc3_enabled,
80 | cc4_enabled=cc4_enabled,
81 | cc5_enabled=cc5_enabled,
82 | pretrained=pretrained,
83 | scheduler_step_size=scheduler_step_size,
84 | scheduler_gama=scheduler_gama,
85 | deterministic=deterministic,
86 | optimizer=optimizer,
87 | normalize_loss=normalize_loss)
88 |
89 | #Save instances
90 | instance.dataloader_validation = dataloader_validation
91 |
92 | #Load checkpoint if ther already exists a file
93 | if os.path.isfile(checkpoint_file):
94 | start_epoch = instance.load_checkpoint(checkpoint_file)
95 | if start_epoch is None:
96 | start_epoch = 0
97 | else:
98 | start_epoch = 0
99 |
100 | #Fit instance
101 | epoch_losses = instance.train(dataloader_train, epochs, checkpoint_file=checkpoint_file, checkpoint_frequency=checkpoint_frequency, max_gradient=max_gradient)
102 | print("Losses per epoch: " + str(epoch_losses))
103 |
104 | return instance
105 |
106 | @classmethod
107 | def from_checkpoint(cls, checkpoint_file, stack_size,
108 | cc1_enabled=False,
109 | cc2_enabled=False,
110 | cc3_enabled=True,
111 | cc4_enabled=False,
112 | cc5_enabled=False,
113 | deterministic=False,
114 | optimizer='sgd',
115 | normalize_loss=False):
116 | #Call constructor
117 | instance = cls(stack_size,
118 | cc1_enabled=cc1_enabled,
119 | cc2_enabled=cc2_enabled,
120 | cc3_enabled=cc3_enabled,
121 | cc4_enabled=cc4_enabled,
122 | cc5_enabled=cc5_enabled,
123 | deterministic=deterministic,
124 | optimizer=optimizer,
125 | normalize_loss=normalize_loss)
126 |
127 | #Load checkpoint
128 | instance.load_checkpoint(checkpoint_file)
129 |
130 | return instance
131 |
132 | @classmethod
133 | def from_tflearn(cls, checkpoint_file, stack_size,
134 | cc1_enabled=False,
135 | cc2_enabled=False,
136 | cc3_enabled=True,
137 | cc4_enabled=False,
138 | cc5_enabled=False,
139 | deterministic=False,
140 | optimizer='sgd'):
141 | #Call constructor
142 | instance = cls(stack_size,
143 | cc1_enabled=cc1_enabled,
144 | cc2_enabled=cc2_enabled,
145 | cc3_enabled=cc3_enabled,
146 | cc4_enabled=cc4_enabled,
147 | cc5_enabled=cc5_enabled,
148 | deterministic=deterministic,
149 | optimizer=optimizer,
150 | pretrained=None)
151 |
152 | #Load checkpoint
153 | instance.load_tflearn(checkpoint_file)
154 |
155 | return instance
156 |
157 | def load_tflearn(self, checkpoint_file):
158 | #Load dict
159 | pretrained_dict = np.load(checkpoint_file)
160 | #Update according to generated mapping
161 | pretrained_dict = {self.__translate_tflearn_key(k): v for k, v in pretrained_dict.items()}
162 | #Transpose all weight tensors since tflearn stores them transposed
163 | # Tensorflow 2D Conv layer: h * w * in_channels * out_channels
164 | # PyTorch 2D Conv layer: out_channels * in_channels * h * w
165 | #Same logic was also implemented in https://github.com/ruotianluo/pytorch-mobilenet-from-tf/blob/master/convert.py
166 | pretrained_dict = {k:(v.transpose((3, 2, 0, 1)) if (k.startswith("conv") or k.startswith("upconv")) and v.ndim == 4 else v) for k, v in pretrained_dict.items()}
167 | pretrained_dict = {("scoring" + k[len("conv_disp"):] if k.startswith("conv_disp") else "autoencoder." + k):v for k, v in pretrained_dict.items()}
168 | #Convert weight arrays to torch tensors
169 | pretrained_dict = {k:torch.from_numpy(v).float() for k, v in pretrained_dict.items()}
170 | #Load weights
171 | model_state_dict = self.model.state_dict()
172 | model_state_dict.update(pretrained_dict)
173 | self.model.load_state_dict(model_state_dict)
174 |
175 | def __translate_tflearn_key(self, key):
176 | if key.endswith("/W:0"):
177 | return key[:-len("/W:0")] + ".weight"
178 | if key.endswith("/up_filter:0"):
179 | return key[:-len("/up_filter:0")] + ".weight"
180 | if key.endswith("/gamma:0"):
181 | return key[:-len("/gamma:0")] + ".weight"
182 | if key.endswith("/beta:0"):
183 | return key[:-len("/beta:0")] + ".bias"
184 | if key.endswith("/moving_mean:0"):
185 | return key[:-len("/moving_mean:0")] + ".running_mean"
186 | if key.endswith("/moving_variance:0"):
187 | return key[:-len("/moving_variance:0")] + ".running_var"
188 |
189 | def __create_preprocessing(self, crop_size=None, cliprange=[0.0202, 0.2825], mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]):
190 | transform = [FocalStackDDFFH5Reader.FocalStackDDFFH5Reader.ToTensor()]
191 | if cliprange is not None:
192 | transform += [FocalStackDDFFH5Reader.FocalStackDDFFH5Reader.ClipGroundTruth(cliprange[0], cliprange[1])]
193 | if crop_size is not None:
194 | transform += [FocalStackDDFFH5Reader.FocalStackDDFFH5Reader.RandomCrop(crop_size)]
195 | if mean is not None and std is not None:
196 | transform += [FocalStackDDFFH5Reader.FocalStackDDFFH5Reader.Normalize(mean_input=mean, std_input=std)]
197 | transform = torchvision.transforms.Compose(transform)
198 | return transform
199 |
200 | def create_validation_loader(self):
201 | try:
202 | return self.dataloader_validation
203 | except AttributeError:
204 | return None
205 |
206 | class MaskedLoss(nn.Module):
207 | def __init__(self, loss, valid_cond=lambda x : x > 0.0):
208 | super(DDFFTrainer.MaskedLoss, self).__init__()
209 | self.loss = loss
210 | self.valid_cond = valid_cond
211 |
212 | def forward(self, inputs, outputs):
213 | mask = self.valid_cond(outputs)
214 | return self.loss(inputs[mask], outputs[mask])
215 |
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/python/ddff/trainers/__init__.py:
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/python/eval_ddff.py:
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1 | #! /usr/bin/python3
2 |
3 | import ddff.dataproviders.datareaders.FocalStackDDFFH5Reader as FocalStackDDFFH5Reader
4 | import ddff.metricseval.DDFFEval as DDFFEval
5 |
6 | if __name__ == "__main__":
7 | #Set parameters
8 | image_size = (383,552)
9 | filename_testset = "ddff-dataset-trainval.h5"
10 | checkpoint_file = "ddff_cc3_checkpoint.pt"
11 |
12 | #Create validation reader
13 | tmp_datareader = FocalStackDDFFH5Reader.FocalStackDDFFH5Reader(filename_testset, transform=None, stack_key="stack_val", disp_key="disp_val")
14 |
15 | #Create PSPDDFF evaluator
16 | evaluator = DDFFEval.DDFFEval(checkpoint_file, focal_stack_size=tmp_datareader.get_stack_size())
17 | #Evaluate
18 | metrics = evaluator.evaluate(filename_testset, image_size=image_size)
19 | print(metrics)
20 |
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/python/eval_ddff_tflearn.py:
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1 | import ddff.dataproviders.datareaders.FocalStackDDFFH5Reader as FocalStackDDFFH5Reader
2 | import ddff.metricseval.DDFFTFLearnEval as DDFFTFLearnEval
3 |
4 | if __name__ == "__main__":
5 | #Set parameters
6 | image_size = (383,552)
7 | filename_testset = "ddff-dataset-trainval.h5"
8 | checkpoint_file = "ddffnet-cc3-snapshot-121256.npz"
9 | stack_key = "stack_val"
10 | disp_key="disp_val"
11 |
12 | #Create validation reader
13 | tmp_datareader = FocalStackDDFFH5Reader.FocalStackDDFFH5Reader(filename_testset, transform=None, stack_key=stack_key, disp_key=disp_key)
14 |
15 | #Create PSPDDFF evaluator
16 | evaluator = DDFFTFLearnEval.DDFFTFLearnEval(checkpoint_file, focal_stack_size=tmp_datareader.get_stack_size(), norm_mean=None, norm_std=None)
17 | #Evaluate
18 | metrics = evaluator.evaluate(filename_testset, stack_key=stack_key, disp_key=disp_key, image_size=image_size)
19 | print(metrics)
20 |
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/python/run_ddff.py:
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1 | #! /usr/bin/python3
2 |
3 | import argparse
4 | import torch
5 | import random
6 | import numpy as np
7 | import ddff.trainers.DDFFTrainer as DDFFTrainer
8 |
9 | if __name__ == "__main__":
10 | #Add command line parser arguments
11 | parser = argparse.ArgumentParser(description='Train ddff net on specified h5 dataset.')
12 | parser.add_argument('--dataset', default="ddff-dataset-trainval.h5", help='h5 file that contains the training and validation data (default: ddff-dataset-trainval.h5)')
13 | parser.add_argument('--epochs', default=200, type=int, help='number of training epochs (default: 200)')
14 | parser.add_argument('--checkpoint', default="ddff_cc3_checkpoint.pt", help='Checkpoint file to be created during training (default: ddff_cc3_checkpoint.pt)')
15 | parser.add_argument('--checkpoint_frequency', default=5, type=int, help='Checkpoint frequency to save intermediate models. (default: 5)')
16 | parser.add_argument('--workers', default=0, type=int, help='Number of threads reading the dataset. (default: 0)')
17 | parser.add_argument('--batchsize', default=2, type=int, help='batch size during training (default: 2)')
18 | parser.add_argument('--pretrained', default="bn", help='Either specify a npy file to load tensorflow weights or use "bn" or "no_bn" to use pretrained weights from torchvision package (default: bn)')
19 |
20 | #Parse arguments
21 | args = parser.parse_args()
22 |
23 | #Finetune tensorflow vgg16 model
24 | ddff_trainer = DDFFTrainer.DDFFTrainer.from_h5_data(args.dataset,
25 | learning_rate=0.001,
26 | max_gradient=5.0,
27 | cc1_enabled=False,
28 | cc2_enabled=False,
29 | cc3_enabled=True,
30 | cc4_enabled=False,
31 | cc5_enabled=False,
32 | training_crop_size=None,
33 | validation_crop_size=None,
34 | pretrained=args.pretrained,
35 | normalize_mean=None, normalize_std=None,
36 | epochs=args.epochs,
37 | checkpoint_file=args.checkpoint,
38 | checkpoint_frequency=args.checkpoint_frequency,
39 | batch_size=args.batchsize,
40 | num_workers=args.workers,
41 | deterministic=True)
42 |
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/requirements.txt:
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1 | backcall==0.1.0
2 | decorator==4.3.0
3 | h5py==2.8.0
4 | ipython==7.0.1
5 | ipython-genutils==0.2.0
6 | jedi==0.13.1
7 | numpy==1.15.2
8 | parso==0.3.1
9 | pexpect==4.6.0
10 | pickleshare==0.7.5
11 | Pillow==5.3.0
12 | prompt-toolkit==2.0.6
13 | ptyprocess==0.6.0
14 | Pygments==2.2.0
15 | simplegeneric==0.8.1
16 | six==1.11.0
17 | torch==0.4.1.post2
18 | torchvision==0.2.1
19 | traitlets==4.3.2
20 | wcwidth==0.1.7
21 |
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