├── README.md ├── custom_dataset.py ├── custom_models.py ├── custom_scheduler.py ├── flickr_images ├── htc_m7 │ ├── urls │ └── urls_final ├── iphone_4s │ ├── urls │ └── urls_final ├── iphone_6 │ ├── urls │ └── urls_final ├── moto_maxx │ ├── urls │ └── urls_final ├── moto_x │ ├── urls │ ├── urls_final │ └── urls_firsov ├── nexus_5x │ ├── urls │ └── urls_final ├── nexus_6 │ ├── urls │ └── urls_final ├── samsung_note3 │ ├── urls │ └── urls_final ├── samsung_s4 │ ├── urls │ └── urls_final └── sony_nex7 │ ├── urls │ └── urls_final ├── imgs_train ├── imgs_val ├── train.py ├── train_utils.py ├── utils.py └── val_images ├── htc_m7 └── urls_dpreview ├── iphone_4s └── urls_anandtech ├── iphone_6 └── urls_dpreview ├── moto_maxx └── urls_phonearena ├── moto_x └── urls_dpreview ├── nexus_5x └── urls_engadget ├── nexus_6 └── urls_dpreview ├── samsung_note3 └── urls_dpreview ├── samsung_s4 └── urls_dpreview └── sony_nex7 └── urls_dpreview /README.md: -------------------------------------------------------------------------------- 1 | # IEEE's Signal Processing Society - Camera Model Identification 2 | 3 | The code is based on [Andres solution](https://github.com/antorsae/sp-society-camera-model-identification) for [Kaggle competition](https://www.kaggle.com/c/sp-society-camera-model-identification) with changes made to improve performance and readability. 4 | 5 | This repository is no longer supported. I recommend using [the following repository](https://github.com/PavelOstyakov/camera_identification/). 6 | -------------------------------------------------------------------------------- /custom_dataset.py: -------------------------------------------------------------------------------- 1 | import random 2 | from io import BytesIO 3 | import math 4 | 5 | import skimage 6 | from PIL import Image 7 | import jpeg4py as jpeg 8 | import cv2 9 | 10 | from torch.utils.data import Dataset 11 | 12 | from utils import * 13 | 14 | class IEEECameraDataset(Dataset): 15 | def __init__(self, items, crop_size, verbose=False, training=True): 16 | self.training = training 17 | self.items = items 18 | self.crop_size = crop_size 19 | self.verbose = verbose 20 | validation = not training 21 | self.transforms = VALIDATION_TRANSFORMS if validation else [[]] 22 | 23 | def __len__(self): 24 | return len(self.items) 25 | 26 | def __getitem__(self, idx): 27 | sample = process_item(self.items[idx], self.crop_size, self.verbose, training=self.training, transforms=self.transforms) 28 | if sample is None: 29 | print(self.items[idx]) 30 | 31 | X, O, y = sample 32 | return X, O, y 33 | 34 | 35 | RESOLUTIONS = { 36 | 0: [[1520,2688]], # flips 37 | 1: [[3264,2448]], # no flips 38 | 2: [[2432,4320]], # flips 39 | 3: [[3120,4160]], # flips 40 | 4: [[4128,2322]], # no flips 41 | 5: [[3264,2448]], # no flips 42 | 6: [[3024,4032]], # flips 43 | 7: [[1040,780], # Motorola-Nexus-6 no flips 44 | [3088,4130], [3120,4160]], # Motorola-Nexus-6 flips 45 | 8: [[4128,2322]], # no flips 46 | 9: [[6000,4000]], # no flips 47 | } 48 | 49 | 50 | ORIENTATION_FLIP_ALLOWED = [ 51 | True, 52 | False, 53 | True, 54 | True, 55 | False, 56 | False, 57 | True, 58 | True, 59 | False, 60 | False 61 | ] 62 | 63 | 64 | for class_id, resolutions in RESOLUTIONS.copy().items(): 65 | resolutions.extend([resolution[::-1] for resolution in resolutions]) 66 | RESOLUTIONS[class_id] = resolutions 67 | 68 | 69 | MANIPULATIONS = ['jpg70', 'jpg90', 'gamma0.8', 'gamma1.2', 'bicubic0.5', 'bicubic0.8', 'bicubic1.5', 'bicubic2.0'] 70 | 71 | 72 | load_img_fast_jpg = lambda img_path: jpeg.JPEG(img_path).decode() 73 | load_img = lambda img_path: np.array(Image.open(img_path)) 74 | 75 | 76 | def load_img_fast_jpg(img_path): 77 | try: 78 | x = jpeg.JPEG(img_path).decode() 79 | return x 80 | except: 81 | return load_img(img_path) 82 | 83 | 84 | def random_manipulation(img, manipulation=None): 85 | if manipulation == None: 86 | manipulation = random.choice(MANIPULATIONS) 87 | 88 | if manipulation.startswith('jpg'): 89 | quality = int(manipulation[3:]) 90 | out = BytesIO() 91 | im = Image.fromarray(img) 92 | im.save(out, format='jpeg', quality=quality) 93 | im_decoded = jpeg.JPEG(np.frombuffer(out.getvalue(), dtype=np.uint8)).decode() 94 | del out 95 | del im 96 | elif manipulation.startswith('gamma'): 97 | gamma = float(manipulation[5:]) 98 | # alternatively use skimage.exposure.adjust_gamma 99 | # img = skimage.exposure.adjust_gamma(img, gamma) 100 | im_decoded = np.uint8(cv2.pow(img / 255., gamma)*255.) 101 | elif manipulation.startswith('bicubic'): 102 | scale = float(manipulation[7:]) 103 | im_decoded = cv2.resize(img,(0,0), fx=scale, fy=scale, interpolation = cv2.INTER_CUBIC) 104 | else: 105 | assert False 106 | return im_decoded 107 | 108 | 109 | def get_crop(img, crop_size, random_crop=False): 110 | center_x, center_y = img.shape[1] // 2, img.shape[0] // 2 111 | half_crop = crop_size // 2 112 | pad_x = max(0, crop_size - img.shape[1]) 113 | pad_y = max(0, crop_size - img.shape[0]) 114 | if (pad_x > 0) or (pad_y > 0): 115 | img = np.pad(img, ((pad_y//2, pad_y - pad_y//2), (pad_x//2, pad_x - pad_x//2), (0,0)), mode='wrap') 116 | center_x, center_y = img.shape[1] // 2, img.shape[0] // 2 117 | if random_crop: 118 | freedom_x, freedom_y = img.shape[1] - crop_size, img.shape[0] - crop_size 119 | if freedom_x > 0: 120 | center_x += np.random.randint(math.ceil(-freedom_x/2), freedom_x - math.floor(freedom_x/2) ) 121 | if freedom_y > 0: 122 | center_y += np.random.randint(math.ceil(-freedom_y/2), freedom_y - math.floor(freedom_y/2) ) 123 | 124 | return img[center_y - half_crop : center_y + crop_size - half_crop, center_x - half_crop : center_x + crop_size - half_crop] 125 | 126 | 127 | def process_item(item, crop_size, verbose, training, transforms=[[]]): 128 | class_name = item.split('/')[-2] 129 | class_idx = get_class(class_name) 130 | img = load_img_fast_jpg(item) 131 | 132 | if len(transforms) == 1: 133 | _img = img 134 | else: 135 | _img = np.copy(img) 136 | 137 | img_s = [ ] 138 | manipulated_s = [ ] 139 | class_idx_s = [ ] 140 | 141 | for transform in transforms: 142 | force_manipulation = 'manipulation' in transform 143 | 144 | if ('orientation' in transform) and (ORIENTATION_FLIP_ALLOWED[class_idx] is False): 145 | continue 146 | 147 | force_orientation = ('orientation' in transform) and ORIENTATION_FLIP_ALLOWED[class_idx] 148 | 149 | # some images are landscape, others are portrait, so augment training by randomly changing orientation 150 | if ((np.random.rand() < 0.5) and training and ORIENTATION_FLIP_ALLOWED[class_idx]) or force_orientation: 151 | img = np.rot90(_img, 1, (0,1)) 152 | # is it rot90(..3..), rot90(..1..) or both? 153 | # for phones with landscape mode pics could be taken upside down too, although less likely 154 | # most of the test images that are flipped are 1 155 | # however,eg. img_4d7be4c_unalt looks 3 156 | # and img_4df3673_manip img_6a31fd7_unalt looks 2! 157 | else: 158 | img = _img 159 | 160 | img = get_crop(img, crop_size * 2, random_crop=True if training else False) 161 | # * 2 bc may need to scale by 0.5x and still get a 512px crop 162 | 163 | if verbose: 164 | print("om: ", img.shape, item) 165 | 166 | manipulated = 0. 167 | if ((np.random.rand() < 0.5) and training) or force_manipulation: 168 | img = random_manipulation(img) 169 | manipulated = 1. 170 | if verbose: 171 | print("am: ", img.shape, item) 172 | 173 | img = get_crop(img, crop_size, random_crop=True if training else False) 174 | if verbose: 175 | print("ac: ", img.shape, item) 176 | 177 | img = preprocess_image(img)# TODO: 178 | if verbose: 179 | print("ap: ", img.shape, item) 180 | 181 | if len(transforms) > 1: 182 | img_s.append(img) 183 | manipulated_s.append(manipulated) 184 | class_idx_s.append(class_idx) 185 | 186 | if len(transforms) == 1: 187 | return img, np.array([manipulated], dtype=np.float32), class_idx 188 | else: 189 | return img_s, manipulated_s, class_idx_s 190 | 191 | 192 | VALIDATION_TRANSFORMS = [ [], ['orientation'], ['manipulation'], ['orientation','manipulation']] 193 | 194 | 195 | def preprocess_image(img): 196 | return img.astype(np.float32) 197 | -------------------------------------------------------------------------------- /custom_models.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | import torch.nn.init as init 4 | import torch.nn.functional as F 5 | from torchvision import models 6 | 7 | 8 | class AvgPool(nn.Module): 9 | def forward(self, x): 10 | return torch.nn.functional.avg_pool2d(x, (x.size(2), x.size(3))) 11 | 12 | 13 | class ResNet(nn.Module): 14 | def __init__(self, num_classes, net_cls=models.resnet50, pretrained=False): 15 | super().__init__() 16 | self.net = net_cls(pretrained=pretrained) 17 | self.net.avgpool = AvgPool() 18 | 19 | self.fc = nn.Sequential( 20 | nn.Linear(self.net.fc.in_features+1, 512), 21 | nn.Dropout(0.3), 22 | nn.Linear(512, 128), 23 | nn.Dropout(0.3), 24 | nn.Linear(128, num_classes) 25 | ) 26 | self.net.fc = nn.Dropout(0.0) 27 | 28 | def fresh_params(self): 29 | return self.net.fc.parameters() 30 | 31 | def forward(self, x, O): #0, 1, 2, 3 -> (0, 3, 1, 2) 32 | out = torch.transpose(x, 1, 3) #0, 3, 2, 1 33 | out = torch.transpose(out, 2, 3) #0, 3, 1, 2 34 | out = self.net(out) 35 | out = out.view(out.size(0), -1) 36 | out = torch.cat([out, O], 1) 37 | return self.fc(out) 38 | -------------------------------------------------------------------------------- /custom_scheduler.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import warnings 3 | from torch.optim.optimizer import Optimizer 4 | 5 | 6 | class ReduceLROnPlateau(object): 7 | """Reduce learning rate when a metric has stopped improving. 8 | Models often benefit from reducing the learning rate by a factor 9 | of 2-10 once learning stagnates. This scheduler reads a metrics 10 | quantity and if no improvement is seen for a 'patience' number 11 | of epochs, the learning rate is reduced. 12 | 13 | Args: 14 | factor: factor by which the learning rate will 15 | be reduced. new_lr = lr * factor 16 | patience: number of epochs with no improvement 17 | after which learning rate will be reduced. 18 | verbose: int. 0: quiet, 1: update messages. 19 | mode: one of {min, max}. In `min` mode, 20 | lr will be reduced when the quantity 21 | monitored has stopped decreasing; in `max` 22 | mode it will be reduced when the quantity 23 | monitored has stopped increasing. 24 | epsilon: threshold for measuring the new optimum, 25 | to only focus on significant changes. 26 | cooldown: number of epochs to wait before resuming 27 | normal operation after lr has been reduced. 28 | min_lr: lower bound on the learning rate. 29 | 30 | 31 | Example: 32 | >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9) 33 | >>> scheduler = ReduceLROnPlateau(optimizer, 'min') 34 | >>> for epoch in range(10): 35 | >>> train(...) 36 | >>> val_acc, val_loss = validate(...) 37 | >>> scheduler.step(val_loss, epoch) 38 | """ 39 | 40 | def __init__(self, optimizer, mode='min', factor=0.1, patience=10, 41 | verbose=0, epsilon=1e-4, cooldown=0, min_lr=0): 42 | super(ReduceLROnPlateau, self).__init__() 43 | 44 | if factor >= 1.0: 45 | raise ValueError('ReduceLROnPlateau ' 46 | 'does not support a factor >= 1.0.') 47 | self.factor = factor 48 | self.min_lr = min_lr 49 | self.epsilon = epsilon 50 | self.patience = patience 51 | self.verbose = verbose 52 | self.cooldown = cooldown 53 | self.cooldown_counter = 0 # Cooldown counter. 54 | self.monitor_op = None 55 | self.wait = 0 56 | self.best = 0 57 | self.mode = mode 58 | assert isinstance(optimizer, Optimizer) 59 | self.optimizer = optimizer 60 | self._reset() 61 | 62 | def _reset(self): 63 | """Resets wait counter and cooldown counter. 64 | """ 65 | if self.mode not in ['min', 'max']: 66 | raise RuntimeError('Learning Rate Plateau Reducing mode %s is unknown!') 67 | if self.mode == 'min' : 68 | self.monitor_op = lambda a, b: np.less(a, b - self.epsilon) 69 | self.best = np.Inf 70 | else: 71 | self.monitor_op = lambda a, b: np.greater(a, b + self.epsilon) 72 | self.best = -np.Inf 73 | self.cooldown_counter = 0 74 | self.wait = 0 75 | self.lr_epsilon = self.min_lr * 1e-4 76 | 77 | def reset(self): 78 | self._reset() 79 | 80 | def step(self, metrics, epoch): 81 | current = metrics 82 | if current is None: 83 | warnings.warn('Learning Rate Plateau Reducing requires metrics available!', RuntimeWarning) 84 | else: 85 | if self.in_cooldown(): 86 | self.cooldown_counter -= 1 87 | self.wait = 0 88 | 89 | if self.monitor_op(current, self.best): 90 | self.best = current 91 | self.wait = 0 92 | elif not self.in_cooldown(): 93 | if self.wait >= self.patience: 94 | for param_group in self.optimizer.param_groups: 95 | old_lr = float(param_group['lr']) 96 | if old_lr > self.min_lr + self.lr_epsilon: 97 | new_lr = old_lr * self.factor 98 | new_lr = max(new_lr, self.min_lr) 99 | param_group['lr'] = new_lr 100 | if self.verbose > 0: 101 | print('\nEpoch %05d: reducing learning rate to %s.' % (epoch, new_lr)) 102 | self.cooldown_counter = self.cooldown 103 | self.wait = 0 104 | self.wait += 1 105 | 106 | def in_cooldown(self): 107 | return self.cooldown_counter > 0 108 | 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val_images/htc_m7/4209010853.jpg 183 | val_images/htc_m7/1867981988.jpg 184 | val_images/samsung_s4/7780308031.jpg 185 | val_images/htc_m7/6116267652.jpg 186 | val_images/moto_x/4094275209.jpg 187 | val_images/sony_nex7/8671492354.jpg 188 | val_images/htc_m7/2189320696.jpg 189 | val_images/iphone_6/8068224892.jpg 190 | val_images/htc_m7/3769653152.jpg 191 | val_images/iphone_4s/6-4S.jpg 192 | val_images/htc_m7/9445874168.jpg 193 | val_images/samsung_s4/7037445193.jpg 194 | val_images/iphone_6/7468753403.jpg 195 | val_images/htc_m7/3892818962.jpg 196 | val_images/sony_nex7/4569488900.jpg 197 | val_images/iphone_6/4267333809.jpg 198 | val_images/iphone_6/9204605187.jpg 199 | val_images/samsung_s4/2739347212.jpg 200 | val_images/htc_m7/8949800199.jpg 201 | val_images/sony_nex7/1414443578.jpg 202 | val_images/nexus_6/3273116215.jpg 203 | val_images/iphone_4s/10-4S.jpg 204 | val_images/htc_m7/5772588192.jpg 205 | val_images/sony_nex7/1477771810.jpg 206 | val_images/sony_nex7/6856121001.jpg 207 | val_images/iphone_4s/25-4S.jpg 208 | val_images/samsung_s4/5842423075.jpg 209 | val_images/moto_x/1030911813.jpg 210 | val_images/htc_m7/7243326986.jpg 211 | val_images/samsung_s4/0001050095.jpg 212 | val_images/samsung_s4/5317083150.jpg 213 | val_images/samsung_s4/7375413841.jpg 214 | val_images/htc_m7/0092477189.jpg 215 | val_images/sony_nex7/7471068583.jpg 216 | val_images/nexus_6/8822143348.jpg 217 | val_images/iphone_6/0422107871.jpg 218 | val_images/moto_x/8031841214.jpg 219 | val_images/htc_m7/3868594816.jpg 220 | val_images/htc_m7/0032376087.jpg 221 | val_images/iphone_6/0408919859.jpg 222 | val_images/samsung_s4/7774008813.jpg 223 | val_images/htc_m7/1927060896.jpg 224 | val_images/samsung_s4/1357827618.jpg 225 | val_images/moto_x/5167650138.jpg 226 | val_images/sony_nex7/7223369608.jpg 227 | val_images/htc_m7/7298334898.jpg 228 | val_images/htc_m7/7500729361.jpg 229 | val_images/htc_m7/3581003618.jpg 230 | val_images/iphone_4s/16-4S-HDR.jpg 231 | val_images/moto_x/6835896390.jpg 232 | val_images/samsung_s4/1199999617.jpg 233 | val_images/iphone_4s/18-4S-HDR.jpg 234 | val_images/sony_nex7/9901777145.jpg 235 | val_images/moto_x/0262835376.jpg 236 | val_images/sony_nex7/2598678952.jpg -------------------------------------------------------------------------------- /train.py: -------------------------------------------------------------------------------- 1 | import random 2 | import argparse 3 | import glob 4 | import re 5 | import csv 6 | from os.path import join 7 | import numpy as np 8 | import pandas as pd 9 | 10 | from sklearn.model_selection import train_test_split 11 | from sklearn.utils import class_weight 12 | from tqdm import tqdm 13 | from PIL import Image 14 | from conditional import conditional 15 | 16 | import torch 17 | import torch.nn as nn 18 | import torch.optim as optim 19 | import torch.nn.functional as F 20 | from torch.utils.data import Dataset, DataLoader, sampler 21 | 22 | from utils import * 23 | from train_utils import * 24 | from custom_dataset import IEEECameraDataset, preprocess_image 25 | from custom_scheduler import ReduceLROnPlateau 26 | from custom_models import ResNet 27 | 28 | import logging 29 | logging.basicConfig(filename='training.log', level=logging.INFO, format='%(asctime)s %(message)s') 30 | 31 | SEED = 42 32 | 33 | np.random.seed(SEED) 34 | random.seed(SEED) 35 | 36 | parser = argparse.ArgumentParser() 37 | parser.add_argument('--max-epoch', type=int, default=200, help='Epoch to run') 38 | parser.add_argument('-b', '--batch-size', type=int, default=16, help='Batch Size during training, e.g. -b 64') 39 | parser.add_argument('-l', '--learning_rate', type=float, default=1e-4, help='Initial learning rate') 40 | parser.add_argument('-m', '--model', help='load hdf5 model including weights (and continue training)') 41 | parser.add_argument('-t', '--test', action='store_true', help='Test model and generate CSV submission file') 42 | parser.add_argument('-tt', '--test-train', action='store_true', help='Test model on the training set') 43 | parser.add_argument('-cs', '--crop-size', type=int, default=512, help='Crop size') 44 | parser.add_argument('-w', '--workers', type=int, default=8, help='Num workers') 45 | parser.add_argument('-uiw', '--use-imagenet-weights', action='store_true', help='Use imagenet weights (transfer learning)') 46 | parser.add_argument('-x', '--extra-dataset', action='store_true', help='Use dataset from https://www.kaggle.com/c/sp-society-camera-model-identification/discussion/47235') 47 | parser.add_argument('-e', '--ensembling', type=str, default='arithmetic', help='Type of ensembling: arithmetic|geometric for TTA') 48 | parser.add_argument('-tta', action='store_true', help='Enable test time augmentation') 49 | 50 | args = parser.parse_args() 51 | 52 | num_workers = args.workers 53 | 54 | TRAIN_FOLDER = 'train' 55 | TEST_FOLDER = 'test' 56 | 57 | CROP_SIZE = args.crop_size 58 | 59 | experiment_name = 'resnet50_antorsaegen' 60 | 61 | # MAIN 62 | model = ResNet(len(CLASSES), pretrained=args.use_imagenet_weights) 63 | if cuda_is_available: 64 | model = nn.DataParallel(model).cuda() 65 | if args.model: 66 | print("Loading model " + args.model) 67 | state_dict = torch.load(args.model)['state_dict'] 68 | model.load_state_dict(state_dict) 69 | 70 | if not (args.test or args.test_train): 71 | # TRAINING 72 | ids = glob.glob(join(TRAIN_FOLDER, '*/*.jpg')) 73 | ids.sort() 74 | 75 | if not args.extra_dataset: 76 | ids_train, ids_val = train_test_split(ids, test_size=0.1, random_state=SEED) 77 | else: 78 | ids_train = [line.rstrip('\n') for line in open('imgs_train')] 79 | ids_val = [line.rstrip('\n') for line in open('imgs_val')] 80 | 81 | classes_val = [get_class(idx.split('/')[-2]) for idx in ids_val] 82 | classes_val_count = np.bincount(classes_val) 83 | max_classes_val_count = max(classes_val_count) 84 | 85 | # Balance validation dataset by filling up classes with less items from training set (and removing those from there) 86 | for class_idx in range(N_CLASSES): 87 | idx_to_transfer = [idx for idx in ids_train \ 88 | if get_class(idx.split('/')[-2]) == class_idx][:max_classes_val_count-classes_val_count[class_idx]] 89 | 90 | ids_train = list(set(ids_train).difference(set(idx_to_transfer))) 91 | 92 | ids_val.extend(idx_to_transfer) 93 | 94 | print("Training set distribution:") 95 | print_distribution(ids_train) 96 | 97 | print("Validation set distribution:") 98 | print_distribution(ids_val) 99 | 100 | classes_train = [get_class(idx.split('/')[-2]) for idx in ids_train] 101 | class_weight = class_weight.compute_class_weight('balanced', np.unique(classes_train), classes_train) 102 | classes_val = [get_class(idx.split('/')[-2]) for idx in ids_val] 103 | 104 | weights = [class_weight[i_class] for i_class in classes_train] 105 | weights = torch.DoubleTensor(weights) 106 | train_sampler = sampler.WeightedRandomSampler(weights, len(weights)) 107 | 108 | weights = [class_weight[i_class] for i_class in classes_val] 109 | weights = torch.DoubleTensor(weights) 110 | val_sampler = sampler.WeightedRandomSampler(weights, len(weights)) 111 | 112 | train_dataset = IEEECameraDataset(ids_train, crop_size=CROP_SIZE, training=True) 113 | val_dataset = IEEECameraDataset(ids_val, crop_size=CROP_SIZE, training=False) 114 | 115 | train_loader = DataLoader(train_dataset, batch_size=args.batch_size, sampler=train_sampler, num_workers=num_workers, pin_memory=True) 116 | valid_loader = DataLoader(val_dataset, batch_size=args.batch_size // 4, sampler=val_sampler, num_workers=num_workers, pin_memory=True, collate_fn=default_collate_unsqueeze) 117 | 118 | optimizer = optim.Adam(model.parameters(), lr=args.learning_rate) 119 | scheduler = ReduceLROnPlateau(optimizer, factor=0.5, patience=10, min_lr=1e-9, epsilon=1e-5, verbose=1, mode='min') 120 | 121 | criterion = nn.CrossEntropyLoss() 122 | 123 | best_val_loss = None 124 | train_and_validate( 125 | train_loader, 126 | valid_loader, 127 | model, 128 | optimizer, 129 | scheduler, 130 | criterion, 131 | args.max_epoch, 132 | 1, 133 | best_val_loss, 134 | experiment_name 135 | ) 136 | else: 137 | # TEST 138 | if args.test: 139 | ids = glob.glob(join(TEST_FOLDER, '*.tif')) 140 | elif args.test_train: 141 | ids = glob.glob(join(TRAIN_FOLDER, '*/*.jpg')) 142 | else: 143 | assert False 144 | 145 | ids.sort() 146 | 147 | match = re.search(r'([^/]*)\.pth', args.model) 148 | model_name = match.group(1) + ('_tta_' + args.ensembling if args.tta else '') 149 | csv_name = 'submission_' + model_name + '.csv' 150 | 151 | model.eval() 152 | with conditional(args.test, open(csv_name, 'w')) as csvfile: 153 | 154 | if args.test: 155 | csv_writer = csv.writer(csvfile, delimiter=',',quotechar='|', quoting=csv.QUOTE_MINIMAL) 156 | csv_writer.writerow(['fname', 'camera']) 157 | classes = [] 158 | else: 159 | correct_predictions = 0 160 | 161 | for i, idx in enumerate(tqdm(ids)): 162 | 163 | img = np.array(Image.open(idx)) 164 | 165 | if args.test_train: 166 | img = get_crop(img, 512*2, random_crop=False) 167 | 168 | original_img = img 169 | 170 | original_manipulated = np.float32([1. if idx.find('manip') != -1 else 0.]) 171 | 172 | sx = img.shape[1] // CROP_SIZE 173 | sy = img.shape[0] // CROP_SIZE 174 | 175 | if args.test and args.tta: 176 | transforms = [[], ['orientation']] 177 | elif args.test_train: 178 | transforms = [[], ['orientation'], ['manipulation'], ['manipulation', 'orientation']] 179 | else: 180 | transforms = [[]] 181 | 182 | img_batch = np.zeros((len(transforms)* sx * sy, CROP_SIZE, CROP_SIZE, 3), dtype=np.float32) 183 | manipulated_batch = np.zeros((len(transforms)* sx * sy, 1), dtype=np.float32) 184 | 185 | i = 0 186 | for transform in transforms: 187 | img = np.copy(original_img) 188 | manipulated = np.copy(original_manipulated) 189 | 190 | if 'orientation' in transform: 191 | img = np.rot90(img, 1, (0,1)) 192 | if 'manipulation' in transform and not original_manipulated: 193 | img = random_manipulation(img) 194 | manipulated = np.float32([1.]) 195 | 196 | if args.test_train: 197 | img = get_crop(img, 512, random_crop=False) 198 | 199 | sx = img.shape[1] // CROP_SIZE 200 | sy = img.shape[0] // CROP_SIZE 201 | 202 | for x in range(sx): 203 | for y in range(sy): 204 | _img = np.copy(img[y*CROP_SIZE:(y+1)*CROP_SIZE, x*CROP_SIZE:(x+1)*CROP_SIZE]) 205 | img_batch[i] = preprocess_image(_img) 206 | manipulated_batch[i] = manipulated 207 | i += 1 208 | 209 | img_batch, manipulated_batch = variable(torch.from_numpy(img_batch)), variable(torch.from_numpy(manipulated_batch)) 210 | prediction = model(img_batch, manipulated_batch).data.cpu().numpy() 211 | if prediction.shape[0] != 1: # TTA 212 | if args.ensembling == 'geometric': 213 | predictions = np.log(prediction + K.epsilon()) # avoid numerical instability log(0) 214 | prediction = np.sum(prediction, axis=0) 215 | 216 | prediction_class_idx = np.argmax(prediction) 217 | 218 | if args.test_train: 219 | class_idx = get_class(idx.split('/')[-2]) 220 | if class_idx == prediction_class_idx: 221 | correct_predictions += 1 222 | 223 | if args.test: 224 | csv_writer.writerow([idx.split('/')[-1], CLASSES[prediction_class_idx]]) 225 | classes.append(prediction_class_idx) 226 | 227 | if args.test_train: 228 | print("Accuracy: " + str(correct_predictions / (len(transforms) * i))) 229 | 230 | if args.test: 231 | print("Test set predictions distribution:") 232 | print_distribution(None, classes=classes) 233 | print("Now you are ready to:") 234 | print("kg submit {}".format(csv_name)) 235 | -------------------------------------------------------------------------------- /train_utils.py: -------------------------------------------------------------------------------- 1 | import os 2 | import logging 3 | 4 | import random 5 | import numpy as np 6 | 7 | import torch 8 | import torch.nn.functional as F 9 | 10 | 11 | checkpoint_dir = os.getcwd() + '/models' 12 | cuda_is_available = torch.cuda.is_available() 13 | 14 | 15 | def variable(x, volatile=False): 16 | if isinstance(x, (list, tuple)): 17 | return [variable(y, volatile=volatile) for y in x] 18 | return cuda(torch.autograd.Variable(x.cuda(async=True), volatile=volatile)) 19 | 20 | 21 | def cuda(x): 22 | return x.cuda() if cuda_is_available else x 23 | 24 | 25 | def train_and_validate( 26 | train_data_loader, 27 | valid_data_loader, 28 | model, 29 | optimizer, 30 | scheduler, 31 | loss_fn, 32 | epochs, 33 | start_epoch, 34 | best_val_loss, 35 | experiment_name, 36 | ): 37 | if best_val_loss is None: 38 | best_val_loss = float('+inf') 39 | 40 | valid_losses = [] 41 | 42 | for epoch in range(start_epoch, epochs+1): 43 | train( 44 | train_data_loader, 45 | model, 46 | optimizer, 47 | loss_fn, 48 | epoch, 49 | ) 50 | val_loss = validate( 51 | valid_data_loader, 52 | model, 53 | loss_fn, 54 | ) 55 | valid_losses.append(val_loss) 56 | 57 | if val_loss < best_val_loss: 58 | save_checkpoint( 59 | { 60 | 'epoch': epoch, 61 | 'state_dict': model.state_dict(), 62 | 'optimizer': optimizer.state_dict(), 63 | 'val_loss': val_loss, 64 | }, 65 | '{experiment_name}_{epoch}_{val_loss}.pth'.format(experiment_name=experiment_name, epoch=epoch, val_loss=val_loss), 66 | checkpoint_dir, 67 | ) 68 | best_val_loss = val_loss 69 | scheduler.step(val_loss, epoch) 70 | return model 71 | 72 | 73 | def train(train_loader, model, optimizer, criterion, epoch): 74 | losses = [] 75 | 76 | model.train() 77 | 78 | logging.info('Epoch: {}'.format(epoch)) 79 | for i, (inputs, O, targets) in enumerate(train_loader): 80 | inputs, O, targets = variable(inputs), variable(O), variable(targets) 81 | outputs = model(inputs, O) 82 | loss = criterion(outputs, targets) 83 | optimizer.zero_grad() 84 | batch_size = inputs.size(0) 85 | (batch_size * loss).backward() 86 | optimizer.step() 87 | 88 | losses.append(loss.data[0]) 89 | 90 | if i % 100 == 0: 91 | logging.info('Step: {}, train_loss: {}'.format(i, np.mean(losses[-100:]))) 92 | 93 | train_loss = np.mean(losses) 94 | logging.info('train_loss: {}'.format(train_loss)) 95 | 96 | 97 | def validate(val_loader, model, criterion): 98 | accuracy_scores = [] 99 | losses = [] 100 | 101 | model.eval() 102 | 103 | for i, (inputs, O, targets) in enumerate(val_loader): 104 | inputs, O, targets = variable(inputs, volatile=True), variable(O), variable(targets) 105 | outputs = model(inputs, O) 106 | loss = criterion(outputs, targets) 107 | 108 | losses.append(loss.data[0]) 109 | 110 | accuracy_scores += list(targets.data.cpu().numpy() == np.argmax(outputs.data.cpu().numpy(), axis=1)) 111 | 112 | valid_loss, valid_accuracy = np.mean(losses), np.mean(accuracy_scores) 113 | logging.info('valid_loss: {}, valid_acc: {}'.format(valid_loss, valid_accuracy)) 114 | return valid_loss 115 | 116 | 117 | def save_checkpoint(state, filename, checkpoint_dir): 118 | filepath = os.path.join(checkpoint_dir, filename) 119 | torch.save(state, filepath) 120 | -------------------------------------------------------------------------------- /utils.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from torch.utils.data.dataloader import default_collate 3 | 4 | 5 | CLASSES = [ 6 | 'HTC-1-M7', 7 | 'iPhone-6', 8 | 'Motorola-Droid-Maxx', 9 | 'Motorola-X', 10 | 'Samsung-Galaxy-S4', 11 | 'iPhone-4s', 12 | 'LG-Nexus-5x', 13 | 'Motorola-Nexus-6', 14 | 'Samsung-Galaxy-Note3', 15 | 'Sony-NEX-7'] 16 | 17 | 18 | EXTRA_CLASSES = [ 19 | 'htc_m7', 20 | 'iphone_6', 21 | 'moto_maxx', 22 | 'moto_x', 23 | 'samsung_s4', 24 | 'iphone_4s', 25 | 'nexus_5x', 26 | 'nexus_6', 27 | 'samsung_note3', 28 | 'sony_nex7' 29 | ] 30 | 31 | 32 | N_CLASSES = len(CLASSES) 33 | 34 | 35 | def get_class(class_name): 36 | if class_name in CLASSES: 37 | class_idx = CLASSES.index(class_name) 38 | elif class_name in EXTRA_CLASSES: 39 | class_idx = EXTRA_CLASSES.index(class_name) 40 | else: 41 | assert False 42 | assert class_idx in range(N_CLASSES) 43 | return class_idx 44 | 45 | 46 | def print_distribution(ids, classes=None): 47 | if classes is None: 48 | classes = [get_class(idx.split('/')[-2]) for idx in ids] 49 | classes_count = np.bincount(classes) 50 | for class_name, class_count in zip(CLASSES, classes_count): 51 | print('{:>22}: {:5d} ({:04.1f}%)'.format(class_name, class_count, 100. * class_count / len(classes))) 52 | 53 | 54 | def default_collate_unsqueeze(batchs): 55 | new_batch = [] 56 | for batch in batchs: 57 | for X, O, y in zip(*batch): 58 | new_batch.append([X, np.float32([O]), y]) 59 | return default_collate(new_batch) 60 | -------------------------------------------------------------------------------- /val_images/htc_m7/urls_dpreview: -------------------------------------------------------------------------------- 1 | https://1.img-dpreview.com/files/p/sample_galleries/4279611148/3769653152.jpg 2 | https://1.img-dpreview.com/files/p/sample_galleries/4279611148/4658019505.jpg 3 | 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