inpainting update

gimpml/tools/EnlightenGAN/enlighten_models/models.py

@@ -1,7 +1,7 @@
 
 def create_model(opt):
     model = None
-    print(opt.model)
+    # print(opt.model)
     if opt.model == 'cycle_gan':
         assert(opt.dataset_mode == 'unaligned')
         from .cycle_gan_model import CycleGANModel
@@ -34,5 +34,5 @@ def create_model(opt):
     else:
         raise ValueError("Model [%s] not recognized." % opt.model)
     model.initialize(opt)
-    print("model [%s] was created" % (model.name()))
+    # print("model [%s] was created" % (model.name()))
     return model

gimpml/tools/EnlightenGAN/enlighten_models/single_model.py

@@ -98,8 +98,8 @@ class SingleModel(BaseModel):
             if self.opt.patchD:
                 self.optimizer_D_P = torch.optim.Adam(self.netD_P.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999))
 
-        print('---------- Networks initialized -------------')
-        networks.print_network(self.netG_A)
+        # print('---------- Networks initialized -------------')
+        # networks.print_network(self.netG_A)
         # networks.print_network(self.netG_B)
         if self.isTrain:
             networks.print_network(self.netD_A)
@@ -112,7 +112,7 @@ class SingleModel(BaseModel):
         else:
             self.netG_A.eval()
             # self.netG_B.eval()
-        print('-----------------------------------------------')
+        # print('-----------------------------------------------')
 
     def set_input(self, input):
         AtoB = self.opt.which_direction == 'AtoB'

gimpml/tools/edge-connect/LICENSE.md

@@ -0,0 +1,161 @@
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gimpml/tools/edge-connect/src/__init__.py

@@ -0,0 +1 @@
+# empty

gimpml/tools/edge-connect/src/canny_opencv.py

@@ -0,0 +1,164 @@
+import numpy as np
+import cv2
+
+def smooth_with_function_and_mask(image, function, mask, sigma):
+    bleed_over = function(mask.astype(float), sigma)
+    masked_image = np.zeros(image.shape, image.dtype)
+    masked_image[mask] = image[mask]
+    smoothed_image = function(masked_image, sigma)
+    output_image = smoothed_image / (bleed_over + np.finfo(float).eps)
+    return output_image
+
+def generate_binary_structure(rank, connectivity):
+    if connectivity < 1:
+        connectivity = 1
+    if rank < 1:
+        return np.array(True, dtype=bool)
+    output = np.fabs(np.indices([3] * rank) - 1)
+    output = np.add.reduce(output, 0)
+    return output <= connectivity
+
+
+
+def canny(image, sigma=1., low_threshold=None, high_threshold=None, mask=None,
+          use_quantiles=False):
+    if low_threshold is None:
+        low_threshold = 0.1
+    elif use_quantiles:
+        if not(0.0 <= low_threshold <= 1.0):
+            raise ValueError("Quantile thresholds must be between 0 and 1.")
+
+    if high_threshold is None:
+        high_threshold = 0.2
+    elif use_quantiles:
+        if not(0.0 <= high_threshold <= 1.0):
+            raise ValueError("Quantile thresholds must be between 0 and 1.")
+
+    if mask is None:
+        mask = np.ones(image.shape, dtype=bool)
+
+    def fsmooth(x, sigma):
+        return cv2.GaussianBlur(x, (0,0), sigma)
+        # return img_as_float(gaussian(x, sigma, mode='constant'))
+
+    smoothed = smooth_with_function_and_mask(image, fsmooth, mask, sigma)
+    jsobel = cv2.Sobel(np.float32(smoothed), cv2.CV_64F, 1, 0, 3)
+    isobel = cv2.Sobel(np.float32(smoothed), cv2.CV_64F, 0, 1, 3)
+    # jsobel = ndi.sobel(smoothed, axis=1)
+    # isobel = ndi.sobel(smoothed, axis=0)
+    abs_isobel = np.abs(isobel)
+    abs_jsobel = np.abs(jsobel)
+    magnitude = np.hypot(isobel, jsobel)
+
+    #
+    # Make the eroded mask. Setting the border value to zero will wipe
+    # out the image edges for us.
+    #
+    kernel = np.ones((5, 5), np.uint8)
+    image = cv2.erode(image, kernel)
+    s = generate_binary_structure(2, 2)
+    s = np.array(s, dtype=np.uint8)
+    eroded_mask = cv2.erode(mask.astype(np.uint8), s)
+    # eroded_mask = binary_erosion(mask, s, border_value=0)
+    eroded_mask = eroded_mask & (magnitude > 0)
+    #
+    #--------- Find local maxima --------------
+    #
+    # Assign each point to have a normal of 0-45 degrees, 45-90 degrees,
+    # 90-135 degrees and 135-180 degrees.
+    #
+    local_maxima = np.zeros(image.shape, bool)
+    #----- 0 to 45 degrees ------
+    pts_plus = (isobel >= 0) & (jsobel >= 0) & (abs_isobel >= abs_jsobel)
+    pts_minus = (isobel <= 0) & (jsobel <= 0) & (abs_isobel >= abs_jsobel)
+    pts = pts_plus | pts_minus
+    pts = eroded_mask & pts
+    # Get the magnitudes shifted left to make a matrix of the points to the
+    # right of pts. Similarly, shift left and down to get the points to the
+    # top right of pts.
+    c1 = magnitude[:, :][pts[:, :]]
+    c2 = magnitude[:, :][pts[:, :]]
+    m = magnitude[pts]
+    w = abs_jsobel[pts] / abs_isobel[pts]
+    c_plus = c2 * w + c1 * (1 - w) <= m
+    c1 = magnitude[:, :][pts[:, :]]
+    c2 = magnitude[:, :][pts[:, :]]
+    c_minus = c2 * w + c1 * (1 - w) <= m
+    local_maxima[pts] = c_plus & c_minus
+    #----- 45 to 90 degrees ------
+    # Mix diagonal and vertical
+    #
+    pts_plus = (isobel >= 0) & (jsobel >= 0) & (abs_isobel <= abs_jsobel)
+    pts_minus = (isobel <= 0) & (jsobel <= 0) & (abs_isobel <= abs_jsobel)
+    pts = pts_plus | pts_minus
+    pts = eroded_mask & pts
+    c1 = magnitude[:, :][pts[:, :]]
+    c2 = magnitude[:, :][pts[:, :]]
+    m = magnitude[pts]
+    w = abs_isobel[pts] / abs_jsobel[pts]
+    c_plus = c2 * w + c1 * (1 - w) <= m
+    c1 = magnitude[:, :][pts[:, :]]
+    c2 = magnitude[:, :][pts[:, :]]
+    c_minus = c2 * w + c1 * (1 - w) <= m
+    local_maxima[pts] = c_plus & c_minus
+    #----- 90 to 135 degrees ------
+    # Mix anti-diagonal and vertical
+    #
+    pts_plus = (isobel <= 0) & (jsobel >= 0) & (abs_isobel <= abs_jsobel)
+    pts_minus = (isobel >= 0) & (jsobel <= 0) & (abs_isobel <= abs_jsobel)
+    pts = pts_plus | pts_minus
+    pts = eroded_mask & pts
+    c1a = magnitude[:, :][pts[:, :]]
+    c2a = magnitude[:, :][pts[:, :]]
+    m = magnitude[pts]
+    w = abs_isobel[pts] / abs_jsobel[pts]
+    c_plus = c2a * w + c1a * (1.0 - w) <= m
+    c1 = magnitude[:, :][pts[:, :]]
+    c2 = magnitude[:, :][pts[:, :]]
+    c_minus = c2 * w + c1 * (1.0 - w) <= m
+    local_maxima[pts] = c_plus & c_minus
+    #----- 135 to 180 degrees ------
+    # Mix anti-diagonal and anti-horizontal
+    #
+    pts_plus = (isobel <= 0) & (jsobel >= 0) & (abs_isobel >= abs_jsobel)
+    pts_minus = (isobel >= 0) & (jsobel <= 0) & (abs_isobel >= abs_jsobel)
+    pts = pts_plus | pts_minus
+    pts = eroded_mask & pts
+    c1 = magnitude[:, :][pts[:, :]]
+    c2 = magnitude[:, :][pts[:, :]]
+    m = magnitude[pts]
+    w = abs_jsobel[pts] / abs_isobel[pts]
+    c_plus = c2 * w + c1 * (1 - w) <= m
+    c1 = magnitude[:, :][pts[:, :]]
+    c2 = magnitude[:, :][pts[:, :]]
+    c_minus = c2 * w + c1 * (1 - w) <= m
+    local_maxima[pts] = c_plus & c_minus
+
+    #
+    #---- If use_quantiles is set then calculate the thresholds to use
+    #
+    if use_quantiles:
+        high_threshold = np.percentile(magnitude, 100.0 * high_threshold)
+        low_threshold = np.percentile(magnitude, 100.0 * low_threshold)
+
+    #
+    #---- Create two masks at the two thresholds.
+    #
+    high_mask = local_maxima & (magnitude >= high_threshold)
+    low_mask = local_maxima & (magnitude >= low_threshold)
+
+    #
+    # Segment the low-mask, then only keep low-segments that have
+    # some high_mask component in them
+    #
+    count, labels = cv2.connectedComponents(low_mask.astype(np.uint8))
+    # strel = np.ones((3, 3), bool)
+    # labels, count = label(low_mask, strel)
+    if count == 0:
+        return low_mask
+
+    sums = (np.array(np.sum(high_mask, labels, np.arange(count, dtype=np.int32) + 1), copy=False, ndmin=1))
+    good_label = np.zeros((count + 1,), bool)
+    good_label[1:] = sums > 0
+    output_mask = good_label[labels]
+    return output_mask

gimpml/tools/edge-connect/src/config.py

@@ -0,0 +1,64 @@
+import os
+# import yaml
+
+class Config(dict):
+    def __init__(self, config_path=None):
+        # with open(config_path, 'r') as f:
+            # self._yaml = f.read()
+        self._dict = DEFAULT_CONFIG  # yaml.load(self._yaml)
+        # self._dict['PATH'] = os.path.dirname(config_path)
+
+    def __getattr__(self, name):
+        if self._dict.get(name) is not None:
+            return self._dict[name]
+
+        if DEFAULT_CONFIG.get(name) is not None:
+            return DEFAULT_CONFIG[name]
+
+        return None
+
+    def print(self):
+        print('Model configurations:')
+        print('---------------------------------')
+        print(self._yaml)
+        print('')
+        print('---------------------------------')
+        print('')
+
+
+DEFAULT_CONFIG = {
+    'MODE': 1,                      # 1: train, 2: test, 3: eval
+    'MODEL': 1,                     # 1: edge model, 2: inpaint model, 3: edge-inpaint model, 4: joint model
+    'MASK': 3,                      # 1: random block, 2: half, 3: external, 4: (external, random block), 5: (external, random block, half)
+    'EDGE': 1,                      # 1: canny, 2: external
+    'NMS': 1,                       # 0: no non-max-suppression, 1: applies non-max-suppression on the external edges by multiplying by Canny
+    'SEED': 10,                     # random seed
+    'GPU': [0],                     # list of gpu ids
+    'DEBUG': 0,                     # turns on debugging mode
+    'VERBOSE': 0,                   # turns on verbose mode in the output console
+
+    'LR': 0.0001,                   # learning rate
+    'D2G_LR': 0.1,                  # discriminator/generator learning rate ratio
+    'BETA1': 0.0,                   # adam optimizer beta1
+    'BETA2': 0.9,                   # adam optimizer beta2
+    'BATCH_SIZE': 8,                # input batch size for training
+    'INPUT_SIZE': 256,              # input image size for training 0 for original size
+    'SIGMA': 2,                     # standard deviation of the Gaussian filter used in Canny edge detector (0: random, -1: no edge)
+    'MAX_ITERS': 2e6,               # maximum number of iterations to train the model
+
+    'EDGE_THRESHOLD': 0.5,          # edge detection threshold
+    'L1_LOSS_WEIGHT': 1,            # l1 loss weight
+    'FM_LOSS_WEIGHT': 10,           # feature-matching loss weight
+    'STYLE_LOSS_WEIGHT': 1,         # style loss weight
+    'CONTENT_LOSS_WEIGHT': 1,       # perceptual loss weight
+    'INPAINT_ADV_LOSS_WEIGHT': 0.01,# adversarial loss weight
+
+    'GAN_LOSS': 'nsgan',            # nsgan | lsgan | hinge
+    'GAN_POOL_SIZE': 0,             # fake images pool size
+
+    'SAVE_INTERVAL': 1000,          # how many iterations to wait before saving model (0: never)
+    'SAMPLE_INTERVAL': 1000,        # how many iterations to wait before sampling (0: never)
+    'SAMPLE_SIZE': 12,              # number of images to sample
+    'EVAL_INTERVAL': 0,             # how many iterations to wait before model evaluation (0: never)
+    'LOG_INTERVAL': 10,             # how many iterations to wait before logging training status (0: never)
+}

gimpml/tools/edge-connect/src/dataset.py

@@ -0,0 +1,210 @@
+import os
+import glob
+import scipy
+import torch
+import random
+import numpy as np
+import torchvision.transforms.functional as F
+from torch.utils.data import DataLoader
+from PIL import Image
+# from scipy.misc import imread
+import cv2
+# from skimage.feature import canny
+# from skimage.color import rgb2gray, gray2rgb
+from .utils import create_mask
+# from .canny_opencv import canny
+
+class Dataset(torch.utils.data.Dataset):
+    def __init__(self, config, flist, edge_flist, mask_flist, augment=True, training=True):
+        super(Dataset, self).__init__()
+        self.augment = augment
+        self.training = training
+        self.data = self.load_flist(flist)
+        self.edge_data = self.load_flist(edge_flist)
+        self.mask_data = self.load_flist(mask_flist)
+
+        self.input_size = config.INPUT_SIZE
+        self.sigma = config.SIGMA
+        self.edge = config.EDGE
+        self.mask = config.MASK
+        self.nms = config.NMS
+
+        # in test mode, there's a one-to-one relationship between mask and image
+        # masks are loaded non random
+        if config.MODE == 2:
+            self.mask = 6
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, index):
+        try:
+            item = self.load_item(index)
+        except:
+            print('loading error: ' + self.data[index])
+            item = self.load_item(0)
+
+        return item
+
+    def load_name(self, index):
+        name = self.data[index]
+        return os.path.basename(name)
+
+    def load_item(self, index):
+
+        size = self.input_size
+
+        # load image
+        img = cv2.imread(self.data[index])[:, :, ::-1]
+
+        # gray to rgb
+        if len(img.shape) < 3:
+            img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
+
+        # resize/crop if needed
+        if size != 0:
+            img = self.resize(img, size, size)
+
+        # create grayscale image
+        img_gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
+
+        # load mask
+        mask = self.load_mask(img, index)
+
+        # load edge
+        edge = self.load_edge(img_gray, index, mask)
+
+        # augment data
+        if self.augment and np.random.binomial(1, 0.5) > 0:
+            img = img[:, ::-1, ...]
+            img_gray = img_gray[:, ::-1, ...]
+            edge = edge[:, ::-1, ...]
+            mask = mask[:, ::-1, ...]
+
+        return self.to_tensor(img), self.to_tensor(img_gray), self.to_tensor(edge), self.to_tensor(mask)
+
+    def load_edge(self, img, index, mask):
+        sigma = self.sigma
+
+        # in test mode images are masked (with masked regions),
+        # using 'mask' parameter prevents canny to detect edges for the masked regions
+        mask = None if self.training else (1 - mask / 255).astype(np.bool)
+
+        # canny
+        # if self.edge == 1:
+        # no edge
+        if sigma == -1:
+            return np.zeros(img.shape).astype(np.float)
+
+        # random sigma
+        if sigma == 0:
+            sigma = random.randint(1, 4)
+
+        # TODO: fix canny edge
+        if not sigma % 2:
+            sigma += 1
+        max_val = np.max(img)
+        img = cv2.GaussianBlur(img, (sigma*3, sigma*3), sigma)
+        img = cv2.Canny(img, 0.1*max_val, 0.2*max_val)
+        return img*mask.astype(np.float)
+        # return canny(img, sigma=sigma, mask=mask).astype(np.float)
+
+
+        # # external
+        # else:
+        #     imgh, imgw = img.shape[0:2]
+        #     edge = cv2.imread(self.edge_data[index])[:, :, ::-1]
+        #     edge = self.resize(edge, imgh, imgw)
+        #
+        #     # non-max suppression
+        #     if self.nms == 1:
+        #         edge = edge * canny(img, sigma=sigma, mask=mask)
+        #
+        #     return edge
+
+    def load_mask(self, img, index):
+        imgh, imgw = img.shape[0:2]
+        mask_type = self.mask
+
+        # external + random block
+        if mask_type == 4:
+            mask_type = 1 if np.random.binomial(1, 0.5) == 1 else 3
+
+        # external + random block + half
+        elif mask_type == 5:
+            mask_type = np.random.randint(1, 4)
+
+        # random block
+        if mask_type == 1:
+            return create_mask(imgw, imgh, imgw // 2, imgh // 2)
+
+        # half
+        if mask_type == 2:
+            # randomly choose right or left
+            return create_mask(imgw, imgh, imgw // 2, imgh, 0 if random.random() < 0.5 else imgw // 2, 0)
+
+        # external
+        if mask_type == 3:
+            mask_index = random.randint(0, len(self.mask_data) - 1)
+            mask = cv2.imread(self.mask_data[mask_index])[:, :, ::-1]
+            mask = self.resize(mask, imgh, imgw)
+            mask = (mask > 0).astype(np.uint8) * 255       # threshold due to interpolation
+            return mask
+
+        # test mode: load mask non random
+        if mask_type == 6:
+            mask = cv2.imread(self.mask_data[index])[:, :, ::-1]
+            mask = self.resize(mask, imgh, imgw, centerCrop=False)
+            mask = cv2.cvtColor(mask, cv2.COLOR_RGB2GRAY)
+            mask = (mask > 0).astype(np.uint8) * 255
+            return mask
+
+    def to_tensor(self, img):
+        img = Image.fromarray(img)
+        img_t = F.to_tensor(img).float()
+        return img_t
+
+    def resize(self, img, height, width, centerCrop=True):
+        imgh, imgw = img.shape[0:2]
+
+        if centerCrop and imgh != imgw:
+            # center crop
+            side = np.minimum(imgh, imgw)
+            j = (imgh - side) // 2
+            i = (imgw - side) // 2
+            img = img[j:j + side, i:i + side, ...]
+
+        img = cv2.resize(img, (height, width))
+
+        return img
+
+    def load_flist(self, flist):
+        if isinstance(flist, list):
+            return flist
+
+        # flist: image file path, image directory path, text file flist path
+        if isinstance(flist, str):
+            if os.path.isdir(flist):
+                flist = list(glob.glob(flist + '/*.jpg')) + list(glob.glob(flist + '/*.png'))
+                flist.sort()
+                return flist
+
+            if os.path.isfile(flist):
+                try:
+                    return np.genfromtxt(flist, dtype=np.str, encoding='utf-8')
+                except:
+                    return [flist]
+
+        return []
+
+    def create_iterator(self, batch_size):
+        while True:
+            sample_loader = DataLoader(
+                dataset=self,
+                batch_size=batch_size,
+                drop_last=True
+            )
+
+            for item in sample_loader:
+                yield item
+

gimpml/tools/edge-connect/src/edge_connect.py

@@ -0,0 +1,414 @@
+import os
+import numpy as np
+import torch
+from torch.utils.data import DataLoader
+from .dataset import Dataset
+from .models import EdgeModel, InpaintingModel
+from .utils import Progbar, create_dir, stitch_images, imsave
+from .metrics import PSNR, EdgeAccuracy
+
+
+class EdgeConnect():
+    def __init__(self, config):
+        self.config = config
+        if config.MODEL == 1:
+            model_name = 'edge'
+        elif config.MODEL == 2:
+            model_name = 'inpaint'
+        elif config.MODEL == 3:
+            model_name = 'edge_inpaint'
+        elif config.MODEL == 4:
+            model_name = 'joint'
+
+        self.debug = False
+        self.model_name = model_name
+        self.edge_model = EdgeModel(config).to(config.DEVICE)
+        self.inpaint_model = InpaintingModel(config).to(config.DEVICE)
+
+        self.psnr = PSNR(255.0).to(config.DEVICE)
+        self.edgeacc = EdgeAccuracy(config.EDGE_THRESHOLD).to(config.DEVICE)
+
+        # test mode
+        if self.config.MODE == 2:
+            self.test_dataset = Dataset(config, config.TEST_FLIST, config.TEST_EDGE_FLIST, config.TEST_MASK_FLIST, augment=False, training=False)
+        else:
+            self.train_dataset = Dataset(config, config.TRAIN_FLIST, config.TRAIN_EDGE_FLIST, config.TRAIN_MASK_FLIST, augment=True, training=True)
+            self.val_dataset = Dataset(config, config.VAL_FLIST, config.VAL_EDGE_FLIST, config.VAL_MASK_FLIST, augment=False, training=True)
+            self.sample_iterator = self.val_dataset.create_iterator(config.SAMPLE_SIZE)
+
+        self.samples_path = os.path.join(config.PATH, 'samples')
+        self.results_path = os.path.join(config.PATH, 'results')
+
+        if config.RESULTS is not None:
+            self.results_path = os.path.join(config.RESULTS)
+
+        if config.DEBUG is not None and config.DEBUG != 0:
+            self.debug = True
+
+        self.log_file = os.path.join(config.PATH, 'log_' + model_name + '.dat')
+
+    def load(self):
+        if self.config.MODEL == 1:
+            self.edge_model.load()
+
+        elif self.config.MODEL == 2:
+            self.inpaint_model.load()
+
+        else:
+            self.edge_model.load()
+            self.inpaint_model.load()
+
+    def save(self):
+        if self.config.MODEL == 1:
+            self.edge_model.save()
+
+        elif self.config.MODEL == 2 or self.config.MODEL == 3:
+            self.inpaint_model.save()
+
+        else:
+            self.edge_model.save()
+            self.inpaint_model.save()
+
+    def train(self):
+        train_loader = DataLoader(
+            dataset=self.train_dataset,
+            batch_size=self.config.BATCH_SIZE,
+            num_workers=4,
+            drop_last=True,
+            shuffle=True
+        )
+
+        epoch = 0
+        keep_training = True
+        model = self.config.MODEL
+        max_iteration = int(float((self.config.MAX_ITERS)))
+        total = len(self.train_dataset)
+
+        if total == 0:
+            print('No training data was provided! Check \'TRAIN_FLIST\' value in the configuration file.')
+            return
+
+        while(keep_training):
+            epoch += 1
+            print('\n\nTraining epoch: %d' % epoch)
+
+            progbar = Progbar(total, width=20, stateful_metrics=['epoch', 'iter'])
+
+            for items in train_loader:
+                self.edge_model.train()
+                self.inpaint_model.train()
+
+                images, images_gray, edges, masks = self.cuda(*items)
+
+                # edge model
+                if model == 1:
+                    # train
+                    outputs, gen_loss, dis_loss, logs = self.edge_model.process(images_gray, edges, masks)
+
+                    # metrics
+                    precision, recall = self.edgeacc(edges * masks, outputs * masks)
+                    logs.append(('precision', precision.item()))
+                    logs.append(('recall', recall.item()))
+
+                    # backward
+                    self.edge_model.backward(gen_loss, dis_loss)
+                    iteration = self.edge_model.iteration
+
+
+                # inpaint model
+                elif model == 2:
+                    # train
+                    outputs, gen_loss, dis_loss, logs = self.inpaint_model.process(images, edges, masks)
+                    outputs_merged = (outputs * masks) + (images * (1 - masks))
+
+                    # metrics
+                    psnr = self.psnr(self.postprocess(images), self.postprocess(outputs_merged))
+                    mae = (torch.sum(torch.abs(images - outputs_merged)) / torch.sum(images)).float()
+                    logs.append(('psnr', psnr.item()))
+                    logs.append(('mae', mae.item()))
+
+                    # backward
+                    self.inpaint_model.backward(gen_loss, dis_loss)
+                    iteration = self.inpaint_model.iteration
+
+
+                # inpaint with edge model
+                elif model == 3:
+                    # train
+                    if True or np.random.binomial(1, 0.5) > 0:
+                        outputs = self.edge_model(images_gray, edges, masks)
+                        outputs = outputs * masks + edges * (1 - masks)
+                    else:
+                        outputs = edges
+
+                    outputs, gen_loss, dis_loss, logs = self.inpaint_model.process(images, outputs.detach(), masks)
+                    outputs_merged = (outputs * masks) + (images * (1 - masks))
+
+                    # metrics
+                    psnr = self.psnr(self.postprocess(images), self.postprocess(outputs_merged))
+                    mae = (torch.sum(torch.abs(images - outputs_merged)) / torch.sum(images)).float()
+                    logs.append(('psnr', psnr.item()))
+                    logs.append(('mae', mae.item()))
+
+                    # backward
+                    self.inpaint_model.backward(gen_loss, dis_loss)
+                    iteration = self.inpaint_model.iteration
+
+
+                # joint model
+                else:
+                    # train
+                    e_outputs, e_gen_loss, e_dis_loss, e_logs = self.edge_model.process(images_gray, edges, masks)
+                    e_outputs = e_outputs * masks + edges * (1 - masks)
+                    i_outputs, i_gen_loss, i_dis_loss, i_logs = self.inpaint_model.process(images, e_outputs, masks)
+                    outputs_merged = (i_outputs * masks) + (images * (1 - masks))
+
+                    # metrics
+                    psnr = self.psnr(self.postprocess(images), self.postprocess(outputs_merged))
+                    mae = (torch.sum(torch.abs(images - outputs_merged)) / torch.sum(images)).float()
+                    precision, recall = self.edgeacc(edges * masks, e_outputs * masks)
+                    e_logs.append(('pre', precision.item()))
+                    e_logs.append(('rec', recall.item()))
+                    i_logs.append(('psnr', psnr.item()))
+                    i_logs.append(('mae', mae.item()))
+                    logs = e_logs + i_logs
+
+                    # backward
+                    self.inpaint_model.backward(i_gen_loss, i_dis_loss)
+                    self.edge_model.backward(e_gen_loss, e_dis_loss)
+                    iteration = self.inpaint_model.iteration
+
+
+                if iteration >= max_iteration:
+                    keep_training = False
+                    break
+
+                logs = [
+                    ("epoch", epoch),
+                    ("iter", iteration),
+                ] + logs
+
+                progbar.add(len(images), values=logs if self.config.VERBOSE else [x for x in logs if not x[0].startswith('l_')])
+
+                # log model at checkpoints
+                if self.config.LOG_INTERVAL and iteration % self.config.LOG_INTERVAL == 0:
+                    self.log(logs)
+
+                # sample model at checkpoints
+                if self.config.SAMPLE_INTERVAL and iteration % self.config.SAMPLE_INTERVAL == 0:
+                    self.sample()
+
+                # evaluate model at checkpoints
+                if self.config.EVAL_INTERVAL and iteration % self.config.EVAL_INTERVAL == 0:
+                    print('\nstart eval...\n')
+                    self.eval()
+
+                # save model at checkpoints
+                if self.config.SAVE_INTERVAL and iteration % self.config.SAVE_INTERVAL == 0:
+                    self.save()
+
+        print('\nEnd training....')
+
+    def eval(self):
+        val_loader = DataLoader(
+            dataset=self.val_dataset,
+            batch_size=self.config.BATCH_SIZE,
+            drop_last=True,
+            shuffle=True
+        )
+
+        model = self.config.MODEL
+        total = len(self.val_dataset)
+
+        self.edge_model.eval()
+        self.inpaint_model.eval()
+
+        progbar = Progbar(total, width=20, stateful_metrics=['it'])
+        iteration = 0
+
+        for items in val_loader:
+            iteration += 1
+            images, images_gray, edges, masks = self.cuda(*items)
+
+            # edge model
+            if model == 1:
+                # eval
+                outputs, gen_loss, dis_loss, logs = self.edge_model.process(images_gray, edges, masks)
+
+                # metrics
+                precision, recall = self.edgeacc(edges * masks, outputs * masks)
+                logs.append(('precision', precision.item()))
+                logs.append(('recall', recall.item()))
+
+
+            # inpaint model
+            elif model == 2:
+                # eval
+                outputs, gen_loss, dis_loss, logs = self.inpaint_model.process(images, edges, masks)
+                outputs_merged = (outputs * masks) + (images * (1 - masks))
+
+                # metrics
+                psnr = self.psnr(self.postprocess(images), self.postprocess(outputs_merged))
+                mae = (torch.sum(torch.abs(images - outputs_merged)) / torch.sum(images)).float()
+                logs.append(('psnr', psnr.item()))
+                logs.append(('mae', mae.item()))
+
+
+            # inpaint with edge model
+            elif model == 3:
+                # eval
+                outputs = self.edge_model(images_gray, edges, masks)
+                outputs = outputs * masks + edges * (1 - masks)
+
+                outputs, gen_loss, dis_loss, logs = self.inpaint_model.process(images, outputs.detach(), masks)
+                outputs_merged = (outputs * masks) + (images * (1 - masks))
+
+                # metrics
+                psnr = self.psnr(self.postprocess(images), self.postprocess(outputs_merged))
+                mae = (torch.sum(torch.abs(images - outputs_merged)) / torch.sum(images)).float()
+                logs.append(('psnr', psnr.item()))
+                logs.append(('mae', mae.item()))
+
+
+            # joint model
+            else:
+                # eval
+                e_outputs, e_gen_loss, e_dis_loss, e_logs = self.edge_model.process(images_gray, edges, masks)
+                e_outputs = e_outputs * masks + edges * (1 - masks)
+                i_outputs, i_gen_loss, i_dis_loss, i_logs = self.inpaint_model.process(images, e_outputs, masks)
+                outputs_merged = (i_outputs * masks) + (images * (1 - masks))
+
+                # metrics
+                psnr = self.psnr(self.postprocess(images), self.postprocess(outputs_merged))
+                mae = (torch.sum(torch.abs(images - outputs_merged)) / torch.sum(images)).float()
+                precision, recall = self.edgeacc(edges * masks, e_outputs * masks)
+                e_logs.append(('pre', precision.item()))
+                e_logs.append(('rec', recall.item()))
+                i_logs.append(('psnr', psnr.item()))
+                i_logs.append(('mae', mae.item()))
+                logs = e_logs + i_logs
+
+
+            logs = [("it", iteration), ] + logs
+            progbar.add(len(images), values=logs)
+
+    def test(self):
+        self.edge_model.eval()
+        self.inpaint_model.eval()
+
+        model = self.config.MODEL
+        create_dir(self.results_path)
+
+        test_loader = DataLoader(
+            dataset=self.test_dataset,
+            batch_size=1,
+        )
+
+        index = 0
+        for items in test_loader:
+            name = self.test_dataset.load_name(index)
+            images, images_gray, edges, masks = self.cuda(*items)
+            index += 1
+
+            # edge model
+            if model == 1:
+                outputs = self.edge_model(images_gray, edges, masks)
+                outputs_merged = (outputs * masks) + (edges * (1 - masks))
+
+            # inpaint model
+            elif model == 2:
+                outputs = self.inpaint_model(images, edges, masks)
+                outputs_merged = (outputs * masks) + (images * (1 - masks))
+
+            # inpaint with edge model / joint model
+            else:
+                edges = self.edge_model(images_gray, edges, masks).detach()
+                outputs = self.inpaint_model(images, edges, masks)
+                outputs_merged = (outputs * masks) + (images * (1 - masks))
+
+            output = self.postprocess(outputs_merged)[0]
+            path = os.path.join(self.results_path, name)
+            print(index, name)
+
+            imsave(output, path)
+
+            if self.debug:
+                edges = self.postprocess(1 - edges)[0]
+                masked = self.postprocess(images * (1 - masks) + masks)[0]
+                fname, fext = name.split('.')
+
+                imsave(edges, os.path.join(self.results_path, fname + '_edge.' + fext))
+                imsave(masked, os.path.join(self.results_path, fname + '_masked.' + fext))
+
+        print('\nEnd test....')
+
+    def sample(self, it=None):
+        # do not sample when validation set is empty
+        if len(self.val_dataset) == 0:
+            return
+
+        self.edge_model.eval()
+        self.inpaint_model.eval()
+
+        model = self.config.MODEL
+        items = next(self.sample_iterator)
+        images, images_gray, edges, masks = self.cuda(*items)
+
+        # edge model
+        if model == 1:
+            iteration = self.edge_model.iteration
+            inputs = (images_gray * (1 - masks)) + masks
+            outputs = self.edge_model(images_gray, edges, masks)
+            outputs_merged = (outputs * masks) + (edges * (1 - masks))
+
+        # inpaint model
+        elif model == 2:
+            iteration = self.inpaint_model.iteration
+            inputs = (images * (1 - masks)) + masks
+            outputs = self.inpaint_model(images, edges, masks)
+            outputs_merged = (outputs * masks) + (images * (1 - masks))
+
+        # inpaint with edge model / joint model
+        else:
+            iteration = self.inpaint_model.iteration
+            inputs = (images * (1 - masks)) + masks
+            outputs = self.edge_model(images_gray, edges, masks).detach()
+            edges = (outputs * masks + edges * (1 - masks)).detach()
+            outputs = self.inpaint_model(images, edges, masks)
+            outputs_merged = (outputs * masks) + (images * (1 - masks))
+
+        if it is not None:
+            iteration = it
+
+        image_per_row = 2
+        if self.config.SAMPLE_SIZE <= 6:
+            image_per_row = 1
+
+        images = stitch_images(
+            self.postprocess(images),
+            self.postprocess(inputs),
+            self.postprocess(edges),
+            self.postprocess(outputs),
+            self.postprocess(outputs_merged),
+            img_per_row = image_per_row
+        )
+
+
+        path = os.path.join(self.samples_path, self.model_name)
+        name = os.path.join(path, str(iteration).zfill(5) + ".png")
+        create_dir(path)
+        print('\nsaving sample ' + name)
+        images.save(name)
+
+    def log(self, logs):
+        with open(self.log_file, 'a') as f:
+            f.write('%s\n' % ' '.join([str(item[1]) for item in logs]))
+
+    def cuda(self, *args):
+        return (item.to(self.config.DEVICE) for item in args)
+
+    def postprocess(self, img):
+        # [0, 1] => [0, 255]
+        img = img * 255.0
+        img = img.permute(0, 2, 3, 1)
+        return img.int()

gimpml/tools/edge-connect/src/loss.py

@@ -0,0 +1,231 @@
+import torch
+import torch.nn as nn
+import torchvision.models as models
+
+
+class AdversarialLoss(nn.Module):
+    r"""
+    Adversarial loss
+    https://arxiv.org/abs/1711.10337
+    """
+
+    def __init__(self, type='nsgan', target_real_label=1.0, target_fake_label=0.0):
+        r"""
+        type = nsgan | lsgan | hinge
+        """
+        super(AdversarialLoss, self).__init__()
+
+        self.type = type
+        self.register_buffer('real_label', torch.tensor(target_real_label))
+        self.register_buffer('fake_label', torch.tensor(target_fake_label))
+
+        if type == 'nsgan':
+            self.criterion = nn.BCELoss()
+
+        elif type == 'lsgan':
+            self.criterion = nn.MSELoss()
+
+        elif type == 'hinge':
+            self.criterion = nn.ReLU()
+
+    def __call__(self, outputs, is_real, is_disc=None):
+        if self.type == 'hinge':
+            if is_disc:
+                if is_real:
+                    outputs = -outputs
+                return self.criterion(1 + outputs).mean()
+            else:
+                return (-outputs).mean()
+
+        else:
+            labels = (self.real_label if is_real else self.fake_label).expand_as(outputs)
+            loss = self.criterion(outputs, labels)
+            return loss
+
+
+class StyleLoss(nn.Module):
+    r"""
+    Perceptual loss, VGG-based
+    https://arxiv.org/abs/1603.08155
+    https://github.com/dxyang/StyleTransfer/blob/master/utils.py
+    """
+
+    def __init__(self):
+        super(StyleLoss, self).__init__()
+        self.add_module('vgg', VGG19())
+        self.criterion = torch.nn.L1Loss()
+
+    def compute_gram(self, x):
+        b, ch, h, w = x.size()
+        f = x.view(b, ch, w * h)
+        f_T = f.transpose(1, 2)
+        G = f.bmm(f_T) / (h * w * ch)
+
+        return G
+
+    def __call__(self, x, y):
+        # Compute features
+        x_vgg, y_vgg = self.vgg(x), self.vgg(y)
+
+        # Compute loss
+        style_loss = 0.0
+        style_loss += self.criterion(self.compute_gram(x_vgg['relu2_2']), self.compute_gram(y_vgg['relu2_2']))
+        style_loss += self.criterion(self.compute_gram(x_vgg['relu3_4']), self.compute_gram(y_vgg['relu3_4']))
+        style_loss += self.criterion(self.compute_gram(x_vgg['relu4_4']), self.compute_gram(y_vgg['relu4_4']))
+        style_loss += self.criterion(self.compute_gram(x_vgg['relu5_2']), self.compute_gram(y_vgg['relu5_2']))
+
+        return style_loss
+
+
+
+class PerceptualLoss(nn.Module):
+    r"""
+    Perceptual loss, VGG-based
+    https://arxiv.org/abs/1603.08155
+    https://github.com/dxyang/StyleTransfer/blob/master/utils.py
+    """
+
+    def __init__(self, weights=[1.0, 1.0, 1.0, 1.0, 1.0]):
+        super(PerceptualLoss, self).__init__()
+        self.add_module('vgg', VGG19())
+        self.criterion = torch.nn.L1Loss()
+        self.weights = weights
+
+    def __call__(self, x, y):
+        # Compute features
+        x_vgg, y_vgg = self.vgg(x), self.vgg(y)
+
+        content_loss = 0.0
+        content_loss += self.weights[0] * self.criterion(x_vgg['relu1_1'], y_vgg['relu1_1'])
+        content_loss += self.weights[1] * self.criterion(x_vgg['relu2_1'], y_vgg['relu2_1'])
+        content_loss += self.weights[2] * self.criterion(x_vgg['relu3_1'], y_vgg['relu3_1'])
+        content_loss += self.weights[3] * self.criterion(x_vgg['relu4_1'], y_vgg['relu4_1'])
+        content_loss += self.weights[4] * self.criterion(x_vgg['relu5_1'], y_vgg['relu5_1'])
+
+
+        return content_loss
+
+
+
+class VGG19(torch.nn.Module):
+    def __init__(self):
+        super(VGG19, self).__init__()
+        features = models.vgg19(pretrained=True).features
+        self.relu1_1 = torch.nn.Sequential()
+        self.relu1_2 = torch.nn.Sequential()
+
+        self.relu2_1 = torch.nn.Sequential()
+        self.relu2_2 = torch.nn.Sequential()
+
+        self.relu3_1 = torch.nn.Sequential()
+        self.relu3_2 = torch.nn.Sequential()
+        self.relu3_3 = torch.nn.Sequential()
+        self.relu3_4 = torch.nn.Sequential()
+
+        self.relu4_1 = torch.nn.Sequential()
+        self.relu4_2 = torch.nn.Sequential()
+        self.relu4_3 = torch.nn.Sequential()
+        self.relu4_4 = torch.nn.Sequential()
+
+        self.relu5_1 = torch.nn.Sequential()
+        self.relu5_2 = torch.nn.Sequential()
+        self.relu5_3 = torch.nn.Sequential()
+        self.relu5_4 = torch.nn.Sequential()
+
+        for x in range(2):
+            self.relu1_1.add_module(str(x), features[x])
+
+        for x in range(2, 4):
+            self.relu1_2.add_module(str(x), features[x])
+
+        for x in range(4, 7):
+            self.relu2_1.add_module(str(x), features[x])
+
+        for x in range(7, 9):
+            self.relu2_2.add_module(str(x), features[x])
+
+        for x in range(9, 12):
+            self.relu3_1.add_module(str(x), features[x])
+
+        for x in range(12, 14):
+            self.relu3_2.add_module(str(x), features[x])
+
+        for x in range(14, 16):
+            self.relu3_3.add_module(str(x), features[x])
+
+        for x in range(16, 18):
+            self.relu3_4.add_module(str(x), features[x])
+
+        for x in range(18, 21):
+            self.relu4_1.add_module(str(x), features[x])
+
+        for x in range(21, 23):
+            self.relu4_2.add_module(str(x), features[x])
+
+        for x in range(23, 25):
+            self.relu4_3.add_module(str(x), features[x])
+
+        for x in range(25, 27):
+            self.relu4_4.add_module(str(x), features[x])
+
+        for x in range(27, 30):
+            self.relu5_1.add_module(str(x), features[x])
+
+        for x in range(30, 32):
+            self.relu5_2.add_module(str(x), features[x])
+
+        for x in range(32, 34):
+            self.relu5_3.add_module(str(x), features[x])
+
+        for x in range(34, 36):
+            self.relu5_4.add_module(str(x), features[x])
+
+        # don't need the gradients, just want the features
+        for param in self.parameters():
+            param.requires_grad = False
+
+    def forward(self, x):
+        relu1_1 = self.relu1_1(x)
+        relu1_2 = self.relu1_2(relu1_1)
+
+        relu2_1 = self.relu2_1(relu1_2)
+        relu2_2 = self.relu2_2(relu2_1)
+
+        relu3_1 = self.relu3_1(relu2_2)
+        relu3_2 = self.relu3_2(relu3_1)
+        relu3_3 = self.relu3_3(relu3_2)
+        relu3_4 = self.relu3_4(relu3_3)
+
+        relu4_1 = self.relu4_1(relu3_4)
+        relu4_2 = self.relu4_2(relu4_1)
+        relu4_3 = self.relu4_3(relu4_2)
+        relu4_4 = self.relu4_4(relu4_3)
+
+        relu5_1 = self.relu5_1(relu4_4)
+        relu5_2 = self.relu5_2(relu5_1)
+        relu5_3 = self.relu5_3(relu5_2)
+        relu5_4 = self.relu5_4(relu5_3)
+
+        out = {
+            'relu1_1': relu1_1,
+            'relu1_2': relu1_2,
+
+            'relu2_1': relu2_1,
+            'relu2_2': relu2_2,
+
+            'relu3_1': relu3_1,
+            'relu3_2': relu3_2,
+            'relu3_3': relu3_3,
+            'relu3_4': relu3_4,
+
+            'relu4_1': relu4_1,
+            'relu4_2': relu4_2,
+            'relu4_3': relu4_3,
+            'relu4_4': relu4_4,
+
+            'relu5_1': relu5_1,
+            'relu5_2': relu5_2,
+            'relu5_3': relu5_3,
+            'relu5_4': relu5_4,
+        }
+        return out

gimpml/tools/edge-connect/src/metrics.py

@@ -0,0 +1,46 @@
+import torch
+import torch.nn as nn
+
+
+class EdgeAccuracy(nn.Module):
+    """
+    Measures the accuracy of the edge map
+    """
+    def __init__(self, threshold=0.5):
+        super(EdgeAccuracy, self).__init__()
+        self.threshold = threshold
+
+    def __call__(self, inputs, outputs):
+        labels = (inputs > self.threshold)
+        outputs = (outputs > self.threshold)
+
+        relevant = torch.sum(labels.float())
+        selected = torch.sum(outputs.float())
+
+        if relevant == 0 and selected == 0:
+            return torch.tensor(1), torch.tensor(1)
+
+        true_positive = ((outputs == labels) * labels).float()
+        recall = torch.sum(true_positive) / (relevant + 1e-8)
+        precision = torch.sum(true_positive) / (selected + 1e-8)
+
+        return precision, recall
+
+
+class PSNR(nn.Module):
+    def __init__(self, max_val):
+        super(PSNR, self).__init__()
+
+        base10 = torch.log(torch.tensor(10.0))
+        max_val = torch.tensor(max_val).float()
+
+        self.register_buffer('base10', base10)
+        self.register_buffer('max_val', 20 * torch.log(max_val) / base10)
+
+    def __call__(self, a, b):
+        mse = torch.mean((a.float() - b.float()) ** 2)
+
+        if mse == 0:
+            return torch.tensor(0)
+
+        return self.max_val - 10 * torch.log(mse) / self.base10

gimpml/tools/edge-connect/src/models.py

@@ -0,0 +1,260 @@
+import os
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from .networks import InpaintGenerator, EdgeGenerator, Discriminator
+from .loss import AdversarialLoss, PerceptualLoss, StyleLoss
+
+
+class BaseModel(nn.Module):
+    def __init__(self, name, config):
+        super(BaseModel, self).__init__()
+
+        self.name = name
+        self.config = config
+        self.iteration = 0
+
+        self.gen_weights_path = os.path.join(config.PATH, name + '_gen.pth')
+        self.dis_weights_path = os.path.join(config.PATH, name + '_dis.pth')
+
+    def load(self):
+        if os.path.exists(self.gen_weights_path):
+            # print('Loading %s generator...' % self.name)
+
+            if torch.cuda.is_available():
+                data = torch.load(self.gen_weights_path)
+            else:
+                data = torch.load(self.gen_weights_path, map_location=lambda storage, loc: storage)
+
+            self.generator.load_state_dict(data['generator'])
+            self.iteration = data['iteration']
+
+        # load discriminator only when training
+        if self.config.MODE == 1 and os.path.exists(self.dis_weights_path):
+            print('Loading %s discriminator...' % self.name)
+
+            if torch.cuda.is_available():
+                data = torch.load(self.dis_weights_path)
+            else:
+                data = torch.load(self.dis_weights_path, map_location=lambda storage, loc: storage)
+
+            self.discriminator.load_state_dict(data['discriminator'])
+
+    def save(self):
+        print('\nsaving %s...\n' % self.name)
+        torch.save({
+            'iteration': self.iteration,
+            'generator': self.generator.state_dict()
+        }, self.gen_weights_path)
+
+        torch.save({
+            'discriminator': self.discriminator.state_dict()
+        }, self.dis_weights_path)
+
+
+class EdgeModel(BaseModel):
+    def __init__(self, config):
+        super(EdgeModel, self).__init__('EdgeModel', config)
+
+        # generator input: [grayscale(1) + edge(1) + mask(1)]
+        # discriminator input: (grayscale(1) + edge(1))
+        generator = EdgeGenerator(use_spectral_norm=True)
+        discriminator = Discriminator(in_channels=2, use_sigmoid=config.GAN_LOSS != 'hinge')
+        if len(config.GPU) > 1:
+            generator = nn.DataParallel(generator, config.GPU)
+            discriminator = nn.DataParallel(discriminator, config.GPU)
+        l1_loss = nn.L1Loss()
+        adversarial_loss = AdversarialLoss(type=config.GAN_LOSS)
+
+        self.add_module('generator', generator)
+        self.add_module('discriminator', discriminator)
+
+        self.add_module('l1_loss', l1_loss)
+        self.add_module('adversarial_loss', adversarial_loss)
+
+        self.gen_optimizer = optim.Adam(
+            params=generator.parameters(),
+            lr=float(config.LR),
+            betas=(config.BETA1, config.BETA2)
+        )
+
+        self.dis_optimizer = optim.Adam(
+            params=discriminator.parameters(),
+            lr=float(config.LR) * float(config.D2G_LR),
+            betas=(config.BETA1, config.BETA2)
+        )
+
+    def process(self, images, edges, masks):
+        self.iteration += 1
+
+
+        # zero optimizers
+        self.gen_optimizer.zero_grad()
+        self.dis_optimizer.zero_grad()
+
+
+        # process outputs
+        outputs = self(images, edges, masks)
+        gen_loss = 0
+        dis_loss = 0
+
+
+        # discriminator loss
+        dis_input_real = torch.cat((images, edges), dim=1)
+        dis_input_fake = torch.cat((images, outputs.detach()), dim=1)
+        dis_real, dis_real_feat = self.discriminator(dis_input_real)        # in: (grayscale(1) + edge(1))
+        dis_fake, dis_fake_feat = self.discriminator(dis_input_fake)        # in: (grayscale(1) + edge(1))
+        dis_real_loss = self.adversarial_loss(dis_real, True, True)
+        dis_fake_loss = self.adversarial_loss(dis_fake, False, True)
+        dis_loss += (dis_real_loss + dis_fake_loss) / 2
+
+
+        # generator adversarial loss
+        gen_input_fake = torch.cat((images, outputs), dim=1)
+        gen_fake, gen_fake_feat = self.discriminator(gen_input_fake)        # in: (grayscale(1) + edge(1))
+        gen_gan_loss = self.adversarial_loss(gen_fake, True, False)
+        gen_loss += gen_gan_loss
+
+
+        # generator feature matching loss
+        gen_fm_loss = 0
+        for i in range(len(dis_real_feat)):
+            gen_fm_loss += self.l1_loss(gen_fake_feat[i], dis_real_feat[i].detach())
+        gen_fm_loss = gen_fm_loss * self.config.FM_LOSS_WEIGHT
+        gen_loss += gen_fm_loss
+
+
+        # create logs
+        logs = [
+            ("l_d1", dis_loss.item()),
+            ("l_g1", gen_gan_loss.item()),
+            ("l_fm", gen_fm_loss.item()),
+        ]
+
+        return outputs, gen_loss, dis_loss, logs
+
+    def forward(self, images, edges, masks):
+        edges_masked = (edges * (1 - masks))
+        images_masked = (images * (1 - masks)) + masks
+        inputs = torch.cat((images_masked, edges_masked, masks), dim=1)
+        outputs = self.generator(inputs)                                    # in: [grayscale(1) + edge(1) + mask(1)]
+        return outputs
+
+    def backward(self, gen_loss=None, dis_loss=None):
+        if dis_loss is not None:
+            dis_loss.backward()
+        self.dis_optimizer.step()
+
+        if gen_loss is not None:
+            gen_loss.backward()
+        self.gen_optimizer.step()
+
+
+class InpaintingModel(BaseModel):
+    def __init__(self, config):
+        super(InpaintingModel, self).__init__('InpaintingModel', config)
+
+        # generator input: [rgb(3) + edge(1)]
+        # discriminator input: [rgb(3)]
+        generator = InpaintGenerator()
+        discriminator = Discriminator(in_channels=3, use_sigmoid=config.GAN_LOSS != 'hinge')
+        if len(config.GPU) > 1:
+            generator = nn.DataParallel(generator, config.GPU)
+            discriminator = nn.DataParallel(discriminator , config.GPU)
+
+        l1_loss = nn.L1Loss()
+        perceptual_loss = PerceptualLoss()
+        style_loss = StyleLoss()
+        adversarial_loss = AdversarialLoss(type=config.GAN_LOSS)
+
+        self.add_module('generator', generator)
+        self.add_module('discriminator', discriminator)
+
+        self.add_module('l1_loss', l1_loss)
+        self.add_module('perceptual_loss', perceptual_loss)
+        self.add_module('style_loss', style_loss)
+        self.add_module('adversarial_loss', adversarial_loss)
+
+        self.gen_optimizer = optim.Adam(
+            params=generator.parameters(),
+            lr=float(config.LR),
+            betas=(config.BETA1, config.BETA2)
+        )
+
+        self.dis_optimizer = optim.Adam(
+            params=discriminator.parameters(),
+            lr=float(config.LR) * float(config.D2G_LR),
+            betas=(config.BETA1, config.BETA2)
+        )
+
+    def process(self, images, edges, masks):
+        self.iteration += 1
+
+        # zero optimizers
+        self.gen_optimizer.zero_grad()
+        self.dis_optimizer.zero_grad()
+
+
+        # process outputs
+        outputs = self(images, edges, masks)
+        gen_loss = 0
+        dis_loss = 0
+
+
+        # discriminator loss
+        dis_input_real = images
+        dis_input_fake = outputs.detach()
+        dis_real, _ = self.discriminator(dis_input_real)                    # in: [rgb(3)]
+        dis_fake, _ = self.discriminator(dis_input_fake)                    # in: [rgb(3)]
+        dis_real_loss = self.adversarial_loss(dis_real, True, True)
+        dis_fake_loss = self.adversarial_loss(dis_fake, False, True)
+        dis_loss += (dis_real_loss + dis_fake_loss) / 2
+
+
+        # generator adversarial loss
+        gen_input_fake = outputs
+        gen_fake, _ = self.discriminator(gen_input_fake)                    # in: [rgb(3)]
+        gen_gan_loss = self.adversarial_loss(gen_fake, True, False) * self.config.INPAINT_ADV_LOSS_WEIGHT
+        gen_loss += gen_gan_loss
+
+
+        # generator l1 loss
+        gen_l1_loss = self.l1_loss(outputs, images) * self.config.L1_LOSS_WEIGHT / torch.mean(masks)
+        gen_loss += gen_l1_loss
+
+
+        # generator perceptual loss
+        gen_content_loss = self.perceptual_loss(outputs, images)
+        gen_content_loss = gen_content_loss * self.config.CONTENT_LOSS_WEIGHT
+        gen_loss += gen_content_loss
+
+
+        # generator style loss
+        gen_style_loss = self.style_loss(outputs * masks, images * masks)
+        gen_style_loss = gen_style_loss * self.config.STYLE_LOSS_WEIGHT
+        gen_loss += gen_style_loss
+
+
+        # create logs
+        logs = [
+            ("l_d2", dis_loss.item()),
+            ("l_g2", gen_gan_loss.item()),
+            ("l_l1", gen_l1_loss.item()),
+            ("l_per", gen_content_loss.item()),
+            ("l_sty", gen_style_loss.item()),
+        ]
+
+        return outputs, gen_loss, dis_loss, logs
+
+    def forward(self, images, edges, masks):
+        images_masked = (images * (1 - masks).float()) + masks
+        inputs = torch.cat((images_masked, edges), dim=1)
+        outputs = self.generator(inputs)                                    # in: [rgb(3) + edge(1)]
+        return outputs
+
+    def backward(self, gen_loss=None, dis_loss=None):
+        dis_loss.backward()
+        self.dis_optimizer.step()
+
+        gen_loss.backward()
+        self.gen_optimizer.step()

gimpml/tools/edge-connect/src/networks.py

@@ -0,0 +1,212 @@
+import torch
+import torch.nn as nn
+
+
+class BaseNetwork(nn.Module):
+    def __init__(self):
+        super(BaseNetwork, self).__init__()
+
+    def init_weights(self, init_type='normal', gain=0.02):
+        '''
+        initialize network's weights
+        init_type: normal | xavier | kaiming | orthogonal
+        https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/9451e70673400885567d08a9e97ade2524c700d0/models/networks.py#L39
+        '''
+
+        def init_func(m):
+            classname = m.__class__.__name__
+            if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1):
+                if init_type == 'normal':
+                    nn.init.normal_(m.weight.data, 0.0, gain)
+                elif init_type == 'xavier':
+                    nn.init.xavier_normal_(m.weight.data, gain=gain)
+                elif init_type == 'kaiming':
+                    nn.init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')
+                elif init_type == 'orthogonal':
+                    nn.init.orthogonal_(m.weight.data, gain=gain)
+
+                if hasattr(m, 'bias') and m.bias is not None:
+                    nn.init.constant_(m.bias.data, 0.0)
+
+            elif classname.find('BatchNorm2d') != -1:
+                nn.init.normal_(m.weight.data, 1.0, gain)
+                nn.init.constant_(m.bias.data, 0.0)
+
+        self.apply(init_func)
+
+
+class InpaintGenerator(BaseNetwork):
+    def __init__(self, residual_blocks=8, init_weights=True):
+        super(InpaintGenerator, self).__init__()
+
+        self.encoder = nn.Sequential(
+            nn.ReflectionPad2d(3),
+            nn.Conv2d(in_channels=4, out_channels=64, kernel_size=7, padding=0),
+            nn.InstanceNorm2d(64, track_running_stats=False),
+            nn.ReLU(True),
+
+            nn.Conv2d(in_channels=64, out_channels=128, kernel_size=4, stride=2, padding=1),
+            nn.InstanceNorm2d(128, track_running_stats=False),
+            nn.ReLU(True),
+
+            nn.Conv2d(in_channels=128, out_channels=256, kernel_size=4, stride=2, padding=1),
+            nn.InstanceNorm2d(256, track_running_stats=False),
+            nn.ReLU(True)
+        )
+
+        blocks = []
+        for _ in range(residual_blocks):
+            block = ResnetBlock(256, 2)
+            blocks.append(block)
+
+        self.middle = nn.Sequential(*blocks)
+
+        self.decoder = nn.Sequential(
+            nn.ConvTranspose2d(in_channels=256, out_channels=128, kernel_size=4, stride=2, padding=1),
+            nn.InstanceNorm2d(128, track_running_stats=False),
+            nn.ReLU(True),
+
+            nn.ConvTranspose2d(in_channels=128, out_channels=64, kernel_size=4, stride=2, padding=1),
+            nn.InstanceNorm2d(64, track_running_stats=False),
+            nn.ReLU(True),
+
+            nn.ReflectionPad2d(3),
+            nn.Conv2d(in_channels=64, out_channels=3, kernel_size=7, padding=0),
+        )
+
+        if init_weights:
+            self.init_weights()
+
+    def forward(self, x):
+        x = self.encoder(x)
+        x = self.middle(x)
+        x = self.decoder(x)
+        x = (torch.tanh(x) + 1) / 2
+
+        return x
+
+
+class EdgeGenerator(BaseNetwork):
+    def __init__(self, residual_blocks=8, use_spectral_norm=True, init_weights=True):
+        super(EdgeGenerator, self).__init__()
+
+        self.encoder = nn.Sequential(
+            nn.ReflectionPad2d(3),
+            spectral_norm(nn.Conv2d(in_channels=3, out_channels=64, kernel_size=7, padding=0), use_spectral_norm),
+            nn.InstanceNorm2d(64, track_running_stats=False),
+            nn.ReLU(True),
+
+            spectral_norm(nn.Conv2d(in_channels=64, out_channels=128, kernel_size=4, stride=2, padding=1), use_spectral_norm),
+            nn.InstanceNorm2d(128, track_running_stats=False),
+            nn.ReLU(True),
+
+            spectral_norm(nn.Conv2d(in_channels=128, out_channels=256, kernel_size=4, stride=2, padding=1), use_spectral_norm),
+            nn.InstanceNorm2d(256, track_running_stats=False),
+            nn.ReLU(True)
+        )
+
+        blocks = []
+        for _ in range(residual_blocks):
+            block = ResnetBlock(256, 2, use_spectral_norm=use_spectral_norm)
+            blocks.append(block)
+
+        self.middle = nn.Sequential(*blocks)
+
+        self.decoder = nn.Sequential(
+            spectral_norm(nn.ConvTranspose2d(in_channels=256, out_channels=128, kernel_size=4, stride=2, padding=1), use_spectral_norm),
+            nn.InstanceNorm2d(128, track_running_stats=False),
+            nn.ReLU(True),
+
+            spectral_norm(nn.ConvTranspose2d(in_channels=128, out_channels=64, kernel_size=4, stride=2, padding=1), use_spectral_norm),
+            nn.InstanceNorm2d(64, track_running_stats=False),
+            nn.ReLU(True),
+
+            nn.ReflectionPad2d(3),
+            nn.Conv2d(in_channels=64, out_channels=1, kernel_size=7, padding=0),
+        )
+
+        if init_weights:
+            self.init_weights()
+
+    def forward(self, x):
+        x = self.encoder(x)
+        x = self.middle(x)
+        x = self.decoder(x)
+        x = torch.sigmoid(x)
+        return x
+
+
+class Discriminator(BaseNetwork):
+    def __init__(self, in_channels, use_sigmoid=True, use_spectral_norm=True, init_weights=True):
+        super(Discriminator, self).__init__()
+        self.use_sigmoid = use_sigmoid
+
+        self.conv1 = self.features = nn.Sequential(
+            spectral_norm(nn.Conv2d(in_channels=in_channels, out_channels=64, kernel_size=4, stride=2, padding=1, bias=not use_spectral_norm), use_spectral_norm),
+            nn.LeakyReLU(0.2, inplace=True),
+        )
+
+        self.conv2 = nn.Sequential(
+            spectral_norm(nn.Conv2d(in_channels=64, out_channels=128, kernel_size=4, stride=2, padding=1, bias=not use_spectral_norm), use_spectral_norm),
+            nn.LeakyReLU(0.2, inplace=True),
+        )
+
+        self.conv3 = nn.Sequential(
+            spectral_norm(nn.Conv2d(in_channels=128, out_channels=256, kernel_size=4, stride=2, padding=1, bias=not use_spectral_norm), use_spectral_norm),
+            nn.LeakyReLU(0.2, inplace=True),
+        )
+
+        self.conv4 = nn.Sequential(
+            spectral_norm(nn.Conv2d(in_channels=256, out_channels=512, kernel_size=4, stride=1, padding=1, bias=not use_spectral_norm), use_spectral_norm),
+            nn.LeakyReLU(0.2, inplace=True),
+        )
+
+        self.conv5 = nn.Sequential(
+            spectral_norm(nn.Conv2d(in_channels=512, out_channels=1, kernel_size=4, stride=1, padding=1, bias=not use_spectral_norm), use_spectral_norm),
+        )
+
+        if init_weights:
+            self.init_weights()
+
+    def forward(self, x):
+        conv1 = self.conv1(x)
+        conv2 = self.conv2(conv1)
+        conv3 = self.conv3(conv2)
+        conv4 = self.conv4(conv3)
+        conv5 = self.conv5(conv4)
+
+        outputs = conv5
+        if self.use_sigmoid:
+            outputs = torch.sigmoid(conv5)
+
+        return outputs, [conv1, conv2, conv3, conv4, conv5]
+
+
+class ResnetBlock(nn.Module):
+    def __init__(self, dim, dilation=1, use_spectral_norm=False):
+        super(ResnetBlock, self).__init__()
+        self.conv_block = nn.Sequential(
+            nn.ReflectionPad2d(dilation),
+            spectral_norm(nn.Conv2d(in_channels=dim, out_channels=dim, kernel_size=3, padding=0, dilation=dilation, bias=not use_spectral_norm), use_spectral_norm),
+            nn.InstanceNorm2d(dim, track_running_stats=False),
+            nn.ReLU(True),
+
+            nn.ReflectionPad2d(1),
+            spectral_norm(nn.Conv2d(in_channels=dim, out_channels=dim, kernel_size=3, padding=0, dilation=1, bias=not use_spectral_norm), use_spectral_norm),
+            nn.InstanceNorm2d(dim, track_running_stats=False),
+        )
+
+    def forward(self, x):
+        out = x + self.conv_block(x)
+
+        # Remove ReLU at the end of the residual block
+        # http://torch.ch/blog/2016/02/04/resnets.html
+
+        return out
+
+
+def spectral_norm(module, mode=True):
+    if mode:
+        return nn.utils.spectral_norm(module)
+
+    return module

gimpml/tools/edge-connect/src/utils.py

@@ -0,0 +1,216 @@
+import os
+import sys
+import time
+import random
+import numpy as np
+# import matplotlib.pyplot as plt
+from PIL import Image
+
+
+def create_dir(dir):
+    if not os.path.exists(dir):
+        os.makedirs(dir)
+
+
+def create_mask(width, height, mask_width, mask_height, x=None, y=None):
+    mask = np.zeros((height, width))
+    mask_x = x if x is not None else random.randint(0, width - mask_width)
+    mask_y = y if y is not None else random.randint(0, height - mask_height)
+    mask[mask_y:mask_y + mask_height, mask_x:mask_x + mask_width] = 1
+    return mask
+
+
+def stitch_images(inputs, *outputs, img_per_row=2):
+    gap = 5
+    columns = len(outputs) + 1
+
+    width, height = inputs[0][:, :, 0].shape
+    img = Image.new('RGB', (width * img_per_row * columns + gap * (img_per_row - 1), height * int(len(inputs) / img_per_row)))
+    images = [inputs, *outputs]
+
+    for ix in range(len(inputs)):
+        xoffset = int(ix % img_per_row) * width * columns + int(ix % img_per_row) * gap
+        yoffset = int(ix / img_per_row) * height
+
+        for cat in range(len(images)):
+            im = np.array((images[cat][ix]).cpu()).astype(np.uint8).squeeze()
+            im = Image.fromarray(im)
+            img.paste(im, (xoffset + cat * width, yoffset))
+
+    return img
+
+#
+# def imshow(img, title=''):
+#     fig = plt.gcf()
+#     fig.canvas.set_window_title(title)
+#     plt.axis('off')
+#     plt.imshow(img, interpolation='none')
+#     plt.show()
+
+
+def imsave(img, path):
+    im = Image.fromarray(img.cpu().numpy().astype(np.uint8).squeeze())
+    im.save(path)
+
+
+class Progbar(object):
+    """Displays a progress bar.
+
+    Arguments:
+        target: Total number of steps expected, None if unknown.
+        width: Progress bar width on screen.
+        verbose: Verbosity mode, 0 (silent), 1 (verbose), 2 (semi-verbose)
+        stateful_metrics: Iterable of string names of metrics that
+            should *not* be averaged over time. Metrics in this list
+            will be displayed as-is. All others will be averaged
+            by the progbar before display.
+        interval: Minimum visual progress update interval (in seconds).
+    """
+
+    def __init__(self, target, width=25, verbose=1, interval=0.05,
+                 stateful_metrics=None):
+        self.target = target
+        self.width = width
+        self.verbose = verbose
+        self.interval = interval
+        if stateful_metrics:
+            self.stateful_metrics = set(stateful_metrics)
+        else:
+            self.stateful_metrics = set()
+
+        self._dynamic_display = ((hasattr(sys.stdout, 'isatty') and
+                                  sys.stdout.isatty()) or
+                                 'ipykernel' in sys.modules or
+                                 'posix' in sys.modules)
+        self._total_width = 0
+        self._seen_so_far = 0
+        # We use a dict + list to avoid garbage collection
+        # issues found in OrderedDict
+        self._values = {}
+        self._values_order = []
+        self._start = time.time()
+        self._last_update = 0
+
+    def update(self, current, values=None):
+        """Updates the progress bar.
+
+        Arguments:
+            current: Index of current step.
+            values: List of tuples:
+                `(name, value_for_last_step)`.
+                If `name` is in `stateful_metrics`,
+                `value_for_last_step` will be displayed as-is.
+                Else, an average of the metric over time will be displayed.
+        """
+        values = values or []
+        for k, v in values:
+            if k not in self._values_order:
+                self._values_order.append(k)
+            if k not in self.stateful_metrics:
+                if k not in self._values:
+                    self._values[k] = [v * (current - self._seen_so_far),
+                                       current - self._seen_so_far]
+                else:
+                    self._values[k][0] += v * (current - self._seen_so_far)
+                    self._values[k][1] += (current - self._seen_so_far)
+            else:
+                self._values[k] = v
+        self._seen_so_far = current
+
+        now = time.time()
+        info = ' - %.0fs' % (now - self._start)
+        if self.verbose == 1:
+            if (now - self._last_update < self.interval and
+                    self.target is not None and current < self.target):
+                return
+
+            prev_total_width = self._total_width
+            if self._dynamic_display:
+                sys.stdout.write('\b' * prev_total_width)
+                sys.stdout.write('\r')
+            else:
+                sys.stdout.write('\n')
+
+            if self.target is not None:
+                numdigits = int(np.floor(np.log10(self.target))) + 1
+                barstr = '%%%dd/%d [' % (numdigits, self.target)
+                bar = barstr % current
+                prog = float(current) / self.target
+                prog_width = int(self.width * prog)
+                if prog_width > 0:
+                    bar += ('=' * (prog_width - 1))
+                    if current < self.target:
+                        bar += '>'
+                    else:
+                        bar += '='
+                bar += ('.' * (self.width - prog_width))
+                bar += ']'
+            else:
+                bar = '%7d/Unknown' % current
+
+            self._total_width = len(bar)
+            sys.stdout.write(bar)
+
+            if current:
+                time_per_unit = (now - self._start) / current
+            else:
+                time_per_unit = 0
+            if self.target is not None and current < self.target:
+                eta = time_per_unit * (self.target - current)
+                if eta > 3600:
+                    eta_format = '%d:%02d:%02d' % (eta // 3600,
+                                                   (eta % 3600) // 60,
+                                                   eta % 60)
+                elif eta > 60:
+                    eta_format = '%d:%02d' % (eta // 60, eta % 60)
+                else:
+                    eta_format = '%ds' % eta
+
+                info = ' - ETA: %s' % eta_format
+            else:
+                if time_per_unit >= 1:
+                    info += ' %.0fs/step' % time_per_unit
+                elif time_per_unit >= 1e-3:
+                    info += ' %.0fms/step' % (time_per_unit * 1e3)
+                else:
+                    info += ' %.0fus/step' % (time_per_unit * 1e6)
+
+            for k in self._values_order:
+                info += ' - %s:' % k
+                if isinstance(self._values[k], list):
+                    avg = np.mean(self._values[k][0] / max(1, self._values[k][1]))
+                    if abs(avg) > 1e-3:
+                        info += ' %.4f' % avg
+                    else:
+                        info += ' %.4e' % avg
+                else:
+                    info += ' %s' % self._values[k]
+
+            self._total_width += len(info)
+            if prev_total_width > self._total_width:
+                info += (' ' * (prev_total_width - self._total_width))
+
+            if self.target is not None and current >= self.target:
+                info += '\n'
+
+            sys.stdout.write(info)
+            sys.stdout.flush()
+
+        elif self.verbose == 2:
+            if self.target is None or current >= self.target:
+                for k in self._values_order:
+                    info += ' - %s:' % k
+                    avg = np.mean(self._values[k][0] / max(1, self._values[k][1]))
+                    if avg > 1e-3:
+                        info += ' %.4f' % avg
+                    else:
+                        info += ' %.4e' % avg
+                info += '\n'
+
+                sys.stdout.write(info)
+                sys.stdout.flush()
+
+        self._last_update = now
+
+    def add(self, n, values=None):
+        self.update(self._seen_so_far + n, values)

gimpml/tools/ideepcolor/data/colorize_image.py

@@ -24,7 +24,7 @@ def lab2rgb_transpose(img_l, img_ab):
             returned value is XxXx3 '''
     pred_lab = np.concatenate((img_l, img_ab), axis=0).transpose((1, 2, 0))
     # im = color.lab2rgb(pred_lab)
-    im = cv2.cvtColor(pred_lab.astype('float32'),cv2.COLOR_LAB2RGB)
+    im = cv2.cvtColor(pred_lab.astype('float32'), cv2.COLOR_LAB2RGB)
     pred_rgb = (np.clip(im, 0, 1) * 255).astype('uint8')
     return pred_rgb
 
@@ -204,7 +204,7 @@ class ColorizeImageBase():
 
 class ColorizeImageTorch(ColorizeImageBase):
     def __init__(self, Xd=256, maskcent=False):
-        print('ColorizeImageTorch instantiated')
+        # print('ColorizeImageTorch instantiated')
         ColorizeImageBase.__init__(self, Xd)
         self.l_norm = 1.
         self.ab_norm = 1.
@@ -220,8 +220,8 @@ class ColorizeImageTorch(ColorizeImageBase):
     def prep_net(self, gpu_id=None, path='', dist=False):
         import torch
         import pytorch.model as model
-        print('path = %s' % path)
-        print('Model set! dist mode? ', dist)
+        # print('path = %s' % path)
+        # print('Model set! dist mode? ', dist)
         self.net = model.SIGGRAPHGenerator(dist=dist)
         state_dict = torch.load(path)
         if hasattr(state_dict, '_metadata'):

gimpml/tools/inpainting.py

@@ -3,15 +3,16 @@ import os
 import sys
 
 plugin_loc = os.path.dirname(os.path.realpath(__file__)) + '/'
-sys.path.extend([plugin_loc + 'Inpainting'])
+sys.path.extend([plugin_loc + 'edge-connect'])
 
 import torch
-import numpy as np
-from torch import nn
-import scipy.ndimage
 import cv2
-from DFNet_core import DFNet
-from RefinementNet_core import RefinementNet
+import numpy as np
+from src.edge_connect import EdgeConnect
+import random
+import os
+from src.config import Config
+
 
 
 def get_weight_path():
@@ -21,135 +22,86 @@ def get_weight_path():
     weight_path = data_output["weight_path"]
     return weight_path
 
-
-def to_numpy(tensor):
-    tensor = tensor.mul(255).byte().data.cpu().numpy()
-    tensor = np.transpose(tensor, [0, 2, 3, 1])
-    return tensor
-
-
-def padding(img, height=512, width=512, channels=3):
-    channels = img.shape[2] if len(img.shape) > 2 else 1
-    interpolation = cv2.INTER_NEAREST
-
-    if channels == 1:
-        img_padded = np.zeros((height, width), dtype=img.dtype)
-    else:
-        img_padded = np.zeros((height, width, channels), dtype=img.dtype)
-
-    original_shape = img.shape
-    rows_rate = original_shape[0] / height
-    cols_rate = original_shape[1] / width
-    new_cols = width
-    new_rows = height
-    if rows_rate > cols_rate:
-        new_cols = (original_shape[1] * height) // original_shape[0]
-        img = cv2.resize(img, (new_cols, height), interpolation=interpolation)
-        if new_cols > width:
-            new_cols = width
-        img_padded[:, ((img_padded.shape[1] - new_cols) // 2):((img_padded.shape[1] - new_cols) // 2 + new_cols)] = img
-    else:
-        new_rows = (original_shape[0] * width) // original_shape[1]
-        img = cv2.resize(img, (width, new_rows), interpolation=interpolation)
-        if new_rows > height:
-            new_rows = height
-        img_padded[((img_padded.shape[0] - new_rows) // 2):((img_padded.shape[0] - new_rows) // 2 + new_rows), :] = img
-    return img_padded, new_cols, new_rows
-
-
-def preprocess_image_dfnet(image, mask, model, device):
-    image, new_cols, new_rows = padding(image, 512, 512)
-    mask, _, _ = padding(mask, 512, 512)
-    image = np.ascontiguousarray(image.transpose(2, 0, 1)).astype(np.uint8)
-    mask = np.ascontiguousarray(np.expand_dims(mask, 0)).astype(np.uint8)
-
-    image = torch.from_numpy(image).to(device).float().div(255)
-    mask = 1 - torch.from_numpy(mask).to(device).float().div(255)
-    image_miss = image * mask
-    DFNET_output = model(image_miss.unsqueeze(0), mask.unsqueeze(0))[0]
-    DFNET_output = image * mask + DFNET_output * (1 - mask)
-    DFNET_output = to_numpy(DFNET_output)[0]
-    DFNET_output = cv2.cvtColor(DFNET_output, cv2.COLOR_BGR2RGB)
-    DFNET_output = DFNET_output[
-                   (DFNET_output.shape[0] - new_rows) // 2: (DFNET_output.shape[0] - new_rows) // 2 + new_rows,
-                   (DFNET_output.shape[1] - new_cols) // 2: (DFNET_output.shape[1] - new_cols) // 2 + new_cols, ...]
-
-    return DFNET_output
-
-
-def preprocess_image(image, mask, image_before_resize, model, device):
-    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-
-    shift_val = (100 / 512) * image.shape[0]
-
-    image_resized = cv2.resize(image_before_resize, (image.shape[1], image.shape[0]))
-
-    mask = mask // 255
-    image_matched = image * (1 - mask) + image_resized * mask
-    mask = mask * 255
-
-    img_1 = scipy.ndimage.shift(image_matched, (-shift_val, 0, 0), order=0, mode='constant', cval=1)
-    mask_1 = scipy.ndimage.shift(mask, (-shift_val, 0, 0), order=0, mode='constant', cval=255)
-    img_2 = scipy.ndimage.shift(image_matched, (shift_val, 0, 0), order=0, mode='constant', cval=1)
-    mask_2 = scipy.ndimage.shift(mask, (shift_val, 0, 0), order=0, mode='constant', cval=255)
-    img_3 = scipy.ndimage.shift(image_matched, (0, shift_val, 0), order=0, mode='constant', cval=1)
-    mask_3 = scipy.ndimage.shift(mask, (0, shift_val, 0), order=0, mode='constant', cval=255)
-    img_4 = scipy.ndimage.shift(image_matched, (0, -shift_val, 0), order=0, mode='constant', cval=1)
-    mask_4 = scipy.ndimage.shift(mask, (0, -shift_val, 0), order=0, mode='constant', cval=255)
-    image_cat = np.dstack((mask, image_matched, img_1, mask_1, img_2, mask_2, img_3, mask_3, img_4, mask_4))
-
-    mask_patch = torch.from_numpy(image_cat).to(device).float().div(255).unsqueeze(0)
-    mask_patch = mask_patch.permute(0, -1, 1, 2)
-    inputs = mask_patch[:, 1:, ...]
-    mask = mask_patch[:, 0:1, ...]
-    out = model(inputs, mask)
-    out = out.mul(255).byte().data.cpu().numpy()
-    out = np.transpose(out, [0, 2, 3, 1])[0]
-
-    return out
-
-
-def pad_image(image):
-    x = ((image.shape[0] // 256) + (1 if image.shape[0] % 256 != 0 else 0)) * 256
-    y = ((image.shape[1] // 256) + (1 if image.shape[1] % 256 != 0 else 0)) * 256
-    padded = np.zeros((x, y, image.shape[2]), dtype='uint8')
-    padded[:image.shape[0], :image.shape[1], ...] = image
-    return padded
-
-
-def get_inpaint(img, mask, cpu_flag=False, weight_path=None):
+def get_inpaint(images, masks, cpu_flag=False, weight_path=None):
     if weight_path is None:
         weight_path = get_weight_path()
+    config = Config()
+    config._dict = {'MODE': 2, 'MODEL': 3, 'MASK': 3, 'EDGE': 1, 'NMS': 1, 'SEED': 10, 'GPU': [0], 'DEBUG': 0,
+                    'VERBOSE': 0,
+                    'LR': 0.0001, 'D2G_LR': 0.1, 'BETA1': 0.0,
+                    'BETA2': 0.9, 'BATCH_SIZE': 8, 'INPUT_SIZE': 256, 'SIGMA': 2, 'MAX_ITERS': '2e6',
+                    'EDGE_THRESHOLD': 0.5,
+                    'L1_LOSS_WEIGHT': 1, 'FM_LOSS_WEIGHT': 10, 'STYLE_LOSS_WEIGHT': 250, 'CONTENT_LOSS_WEIGHT': 0.1,
+                    'INPAINT_ADV_LOSS_WEIGHT': 0.1, 'GAN_LOSS': 'nsgan', 'GAN_POOL_SIZE': 0, 'SAVE_INTERVAL': 1000,
+                    'SAMPLE_INTERVAL': 1000, 'SAMPLE_SIZE': 12, 'EVAL_INTERVAL': 0, 'LOG_INTERVAL': 10,
+                    'PATH': os.path.join(weight_path, 'edgeconnect', 'places2')}
+
+    os.environ['CUDA_VISIBLE_DEVICES'] = ','.join(str(e) for e in config.GPU)
+
+    # init device
     if torch.cuda.is_available() and not cpu_flag:
-        device = torch.device('cuda')
+        config.DEVICE = torch.device("cuda")
+        torch.backends.cudnn.benchmark = True  # cudnn auto-tuner
+    else:
+        config.DEVICE = torch.device("cpu")
+
+    # set cv2 running threads to 1 (prevents deadlocks with pytorch dataloader)
+    cv2.setNumThreads(0)
+
+    # initialize random seed
+    torch.manual_seed(config.SEED)
+    torch.cuda.manual_seed_all(config.SEED)
+    np.random.seed(config.SEED)
+    random.seed(config.SEED)
+
+    # build the model and initialize
+    model = EdgeConnect(config)
+    model.load()
+    # model.test()
+
+    images_gray = cv2.cvtColor(images, cv2.COLOR_RGB2GRAY)
+
+    masks = masks/255
+    sigma = config.SIGMA
+    # TODO: fix canny edge
+    if not sigma % 2:
+        sigma += 1
+    max_val = np.max(images_gray)
+    img = cv2.GaussianBlur(images_gray, (sigma * 3, sigma * 3), sigma)
+    img = cv2.Canny(img, 0.1 * max_val, 0.2 * max_val)
+    edge = img * (1 - masks.astype(float))
+    images_gray = images_gray / 255
+    images = images / 255
+
+    images = torch.from_numpy(images.astype(np.float32).copy()).permute((2, 0, 1)).unsqueeze(0)
+    images_gray = torch.from_numpy(images_gray.astype(np.float32)).unsqueeze(0).unsqueeze(0)
+    masks = torch.from_numpy(masks.astype(np.float32)).unsqueeze(0).unsqueeze(0)
+    edges = torch.from_numpy(edge.astype(np.float32)).unsqueeze(0).unsqueeze(0)
+
+    model.edge_model.eval()
+    model.inpaint_model.eval()
+
+    if config.DEVICE.type == 'cuda':
+        images, images_gray, edges, masks = model.cuda(*(images, images_gray, edges, masks))
+
+    # edge model
+    if config.MODEL == 1:
+        outputs = model.edge_model(images_gray, edges, masks)
+        outputs_merged = (outputs * masks) + (edges * (1 - masks))
+
+    # inpaint model
+    elif config.MODEL == 2:
+        outputs = model.inpaint_model(images, edges, masks)
+        outputs_merged = (outputs * masks) + (images * (1 - masks))
+
+    # inpaint with edge model / joint model
     else:
-        device = torch.device('cpu')
-
-    mask = mask[..., :1]
-
-    image = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
-    shape = image.shape
-
-    image = pad_image(image)
-    mask = pad_image(mask)
-
-    DFNet_model = DFNet().to(device)
-    weight_file_path = os.path.join(weight_path, "inpainting", "model_places2.pth")
-    with open(r"C:\Users\Kritik Soman\GIMP-ML\output.txt", "a") as file:
-        file.write(str(weight_file_path))
-    DFNet_model.load_state_dict(torch.load(weight_file_path, map_location=device))
-    DFNet_model.eval()
-    DFNET_output = preprocess_image_dfnet(image, mask, DFNet_model, device)
-    del DFNet_model
-    Refinement_model = RefinementNet().to(device)
-    weight_file_path = os.path.join(weight_path, "inpainting", "refinement.pth")
-    Refinement_model.load_state_dict(
-        torch.load(weight_file_path, map_location=device)['state_dict'])
-    Refinement_model.eval()
-    out = preprocess_image(image, mask, DFNET_output, Refinement_model, device)
-    out = out[:shape[0], :shape[1], ...]
-    del Refinement_model
-    return out
+        edges = model.edge_model(images_gray, edges, masks).detach()
+        outputs = model.inpaint_model(images, edges, masks)
+        outputs_merged = (outputs * masks) + (images * (1 - masks))
+
+    output = model.postprocess(outputs_merged)[0]
+    return np.uint8(output)
 
 
 if __name__ == "__main__":
@@ -161,9 +113,9 @@ if __name__ == "__main__":
     force_cpu = data_output["force_cpu"]
     if (np.sum(image1 == [0, 0, 0]) + np.sum(image1 == [255, 255, 255])) / (
             image1.shape[0] * image1.shape[1] * 3) > 0.8:
-        output = get_inpaint(image2, image1, cpu_flag=force_cpu, weight_path=weight_path)
+        output = get_inpaint(image2, image1[:, :, 0], cpu_flag=force_cpu, weight_path=weight_path)
     else:
-        output = get_inpaint(image1, image2, cpu_flag=force_cpu, weight_path=weight_path)
+        output = get_inpaint(image1, image2[:, :, 0], cpu_flag=force_cpu, weight_path=weight_path)
     cv2.imwrite(os.path.join(weight_path, '..', 'cache.png'), output[:, :, ::-1])
     # with open(os.path.join(weight_path, 'gimp_ml_run.pkl'), 'wb') as file:
     #     pickle.dump({"run_success": True}, file)

gimpml/tools/inpaintingedge.py

@@ -0,0 +1,79 @@
+import pickle
+import os
+import sys
+
+plugin_loc = os.path.dirname(os.path.realpath(__file__)) + '/'
+sys.path.extend([plugin_loc + 'edge-connect'])
+
+
+import torch
+import cv2
+import numpy as np
+from src.edge_connect import EdgeConnect
+import random
+import os
+from src.config import Config
+
+
+def get_weight_path():
+    config_path = os.path.dirname(os.path.realpath(__file__))
+    with open(os.path.join(config_path, 'gimp_ml_config.pkl'), 'rb') as file:
+        data_output = pickle.load(file)
+    weight_path = data_output["weight_path"]
+    return weight_path
+
+
+def get_inpaint_edge(img, mask, cpu_flag=False, weight_path=None):
+    config = Config('./config.yml')
+    config._dict = {'MODE': 2, 'MODEL': 3, 'MASK': 3, 'EDGE': 1, 'NMS': 1, 'SEED': 10, 'GPU': [0], 'DEBUG': 0, 'VERBOSE': 0,
+                    'TRAIN_FLIST': './datasets/places2_train.flist', 'VAL_FLIST': './datasets/places2_val.flist',
+                    'TEST_FLIST': './examples/places2/images',
+                    'TRAIN_EDGE_FLIST': './datasets/places2_edges_train.flist',
+                    'VAL_EDGE_FLIST': './datasets/places2_edges_val.flist',
+                    'TEST_EDGE_FLIST': './datasets/places2_edges_test.flist',
+                    'TRAIN_MASK_FLIST': './datasets/masks_train.flist', 'VAL_MASK_FLIST': './datasets/masks_val.flist',
+                    'TEST_MASK_FLIST': './examples/places2/masks', 'LR': 0.0001, 'D2G_LR': 0.1, 'BETA1': 0.0,
+                    'BETA2': 0.9, 'BATCH_SIZE': 8, 'INPUT_SIZE': 256, 'SIGMA': 2, 'MAX_ITERS': '2e6', 'EDGE_THRESHOLD': 0.5,
+                    'L1_LOSS_WEIGHT': 1, 'FM_LOSS_WEIGHT': 10, 'STYLE_LOSS_WEIGHT': 250, 'CONTENT_LOSS_WEIGHT': 0.1,
+                    'INPAINT_ADV_LOSS_WEIGHT': 0.1, 'GAN_LOSS': 'nsgan', 'GAN_POOL_SIZE': 0, 'SAVE_INTERVAL': 1000,
+                    'SAMPLE_INTERVAL': 1000, 'SAMPLE_SIZE': 12, 'EVAL_INTERVAL': 0, 'LOG_INTERVAL': 10,
+                    'PATH': r'C:\Users\Kritik Soman\GIMP-ML\weights\edgeconnect\places2', 'RESULTS': r'C:\Users\Kritik Soman\GIMP-ML'}
+
+    os.environ['CUDA_VISIBLE_DEVICES'] = ','.join(str(e) for e in config.GPU)
+
+    # init device
+    if torch.cuda.is_available():
+        config.DEVICE = torch.device("cuda")
+        torch.backends.cudnn.benchmark = True  # cudnn auto-tuner
+    else:
+        config.DEVICE = torch.device("cpu")
+
+    # set cv2 running threads to 1 (prevents deadlocks with pytorch dataloader)
+    cv2.setNumThreads(0)
+
+    # initialize random seed
+    torch.manual_seed(config.SEED)
+    torch.cuda.manual_seed_all(config.SEED)
+    np.random.seed(config.SEED)
+    random.seed(config.SEED)
+
+    # build the model and initialize
+    model = EdgeConnect(config)
+    model.load()
+    model.test()
+
+if __name__ == "__main__":
+    weight_path = get_weight_path()
+    image1 = cv2.imread(os.path.join(weight_path, '..', "cache0.png"))[:, :, ::-1]
+    image2 = cv2.imread(os.path.join(weight_path, '..', "cache1.png"))[:, :, ::-1]
+    with open(os.path.join(weight_path, '..', 'gimp_ml_run.pkl'), 'rb') as file:
+        data_output = pickle.load(file)
+    force_cpu = data_output["force_cpu"]
+    if (np.sum(image1 == [0, 0, 0]) + np.sum(image1 == [255, 255, 255])) / (
+            image1.shape[0] * image1.shape[1] * 3) > 0.8:
+        output = get_inpaint_edge(image2, image1, cpu_flag=force_cpu, weight_path=weight_path)
+    else:
+        output = get_inpaint_edge(image1, image2, cpu_flag=force_cpu, weight_path=weight_path)
+    cv2.imwrite(os.path.join(weight_path, '..', 'cache.png'), output[:, :, ::-1])
+    # with open(os.path.join(weight_path, 'gimp_ml_run.pkl'), 'wb') as file:
+    #     pickle.dump({"run_success": True}, file)

gimpml/tools/superresolution.py

@@ -11,7 +11,8 @@ from torch.autograd import Variable
 import numpy as np
 from PIL import Image
 import cv2
-
+import warnings
+warnings.filterwarnings("ignore")
 
 def get_weight_path():
     config_path = os.path.dirname(os.path.realpath(__file__))