def eddl_load_data_cifar(path=None, download=False, load_subset=False, subset_size=4096): """ :param path: Path to CIFAR10 dataset :param download: Default-False - download the dataset to current directory. :param load_subset: Default-False - use subset of the dataset for testing purpose. :param subset_size: Default-4096, the test size is subset_size/4. :return: two tuples of (x_train, y_train), (x_test, y_test) . """ (x_train, y_train), (x_test, y_test) = resnet.load_cifar(path, download) if load_subset: x_subset_train = Tensor([subset_size, 3, 32, 32]) y_subset_train = Tensor([subset_size, 10]) eddl.next_batch([x_train], [x_subset_train]) eddl.next_batch([y_train], [y_subset_train]) x_subset_test = Tensor([math.ceil(subset_size / 4), 3, 32, 32]) y_subset_test = Tensor([math.ceil(subset_size / 4), 10]) eddl.next_batch([x_test], [x_subset_test]) eddl.next_batch([y_test], [y_subset_test]) return (x_subset_train, y_subset_train), (x_subset_test, y_subset_test) return (x_train, y_train), (x_test, y_test)
def main(args): img = ecvl.ImRead(args.in_img) augs = ecvl.SequentialAugmentationContainer([ ecvl.AugRotate([-5, 5]), ecvl.AugMirror(.5), ecvl.AugFlip(.5), ecvl.AugGammaContrast([3, 5]), ecvl.AugAdditiveLaplaceNoise([0, 0.2 * 255]), ecvl.AugCoarseDropout([0, 0.55], [0.02, 0.1], 0.5), ecvl.AugAdditivePoissonNoise([0, 40]), ecvl.AugResizeDim([500, 500]), ]) ecvl.AugmentationParam.SetSeed(0) augs.Apply(img) print("Executing ImageToTensor") t = ecvl.ImageToTensor(img) t.div_(128) t.mult_(128) print("Executing TensorToImage") img = ecvl.TensorToImage(t) print("Executing TensorToView") ecvl.TensorToView(t) _ = ecvl.AugmentationFactory.create(AUG_TXT) training_augs = ecvl.SequentialAugmentationContainer([ ecvl.AugRotate([-5, 5]), ecvl.AugAdditiveLaplaceNoise([0, 0.2 * 255]), ecvl.AugCoarseDropout([0, 0.55], [0.02, 0.1], 0), ecvl.AugAdditivePoissonNoise([0, 40]), ecvl.AugResizeDim([30, 30]), ]) test_augs = ecvl.SequentialAugmentationContainer([ ecvl.AugResizeDim([30, 30]), ]) ds_augs = ecvl.DatasetAugmentations([training_augs, None, test_augs]) batch_size = 64 print("Creating a DLDataset") d = ecvl.DLDataset(args.in_ds, batch_size, ds_augs, ecvl.ColorType.GRAY) print("Create x and y") x = Tensor( [batch_size, d.n_channels_, d.resize_dims_[0], d.resize_dims_[1]]) y = Tensor([batch_size, len(d.classes_)]) # Load a batch of d.batch_size_ images into x and corresponding labels # into y. Images are resized to the dimensions specified in the # augmentations chain print("Executing LoadBatch on training set") d.LoadBatch(x, y) # Change colortype and channels img = ecvl.TensorToImage(x) img.colortype_ = ecvl.ColorType.GRAY img.channels_ = "xyc" # Switch to Test split and load a batch of images print("Executing LoadBatch on test set") d.SetSplit(ecvl.SplitType.test) d.LoadBatch(x, y)
def test_py_loss(): shape = [8, 10] a = np.random.random(shape).astype(np.float32) b = np.random.random(shape).astype(np.float32) t, y = Tensor.fromarray(a), Tensor.fromarray(b) z = Tensor(shape) exp_z = Tensor(shape) py_mse_loss = MSELoss() mse_loss = eddl.getLoss("mse") mse_loss.delta(t, y, exp_z) py_mse_loss.delta(t, y, z) c = np.array(z, copy=False) exp_c = np.array(exp_z, copy=False) assert np.array_equal(c, exp_c) v = py_mse_loss.value(t, y) exp_v = mse_loss.value(t, y) assert v == pytest.approx(exp_v)
def main(args): eddl.download_mnist() in_ = eddl.Input([784]) target = eddl.Reshape(in_, [1, 28, 28]) layer = in_ layer = eddl.Reshape(layer, [1, 28, 28]) layer = eddl.ReLu(eddl.Conv(layer, 8, [3, 3])) layer = eddl.ReLu(eddl.Conv(layer, 16, [3, 3])) layer = eddl.ReLu(eddl.Conv(layer, 8, [3, 3])) out = eddl.Sigmoid(eddl.Conv(layer, 1, [3, 3])) net = eddl.Model([in_], []) eddl.build( net, eddl.adam(0.001), [], [], eddl.CS_GPU(mem=args.mem) if args.gpu else eddl.CS_CPU(mem=args.mem) ) eddl.summary(net) x_train = Tensor.load("mnist_trX.bin") if args.small: x_train = x_train.select([":6000"]) x_train.div_(255.0) mse = eddl.newloss(mse_loss, [out, target], "mse_loss") dicei = eddl.newloss(dice_loss_img, [out, target], "dice_loss_img") dicep = eddl.newloss(dice_loss_pixel, [out, target], "dice_loss_pixel") batch = Tensor([args.batch_size, 784]) num_batches = x_train.shape[0] // args.batch_size for i in range(args.epochs): print("Epoch %d/%d (%d batches)" % (i + 1, args.epochs, num_batches)) diceploss = 0.0 diceiloss = 0.0 mseloss = 0 for j in range(num_batches): print("Batch %d " % j, end="", flush=True) eddl.next_batch([x_train], [batch]) eddl.zeroGrads(net) eddl.forward(net, [batch]) diceploss += eddl.compute_loss(dicep) / args.batch_size print("diceploss = %.6f " % (diceploss / (j + 1)), end="") diceiloss += eddl.compute_loss(dicei) / args.batch_size print("diceiloss = %.6f " % (diceiloss / (j + 1)), end="") mseloss += eddl.compute_loss(mse) / args.batch_size print("mseloss = %.6f\r" % (mseloss / (j + 1)), end="") eddl.optimize(dicep) eddl.update(net) print() print("All done")
def fun(rows): assert (len(rows) == 1) item = rows[0] feat, lab = self._get_img(item) with self.lock: self.feats.append(feat) self.labels.append(lab) self.perm.append(idx) self.cow += 1 self.onair -= 1 if (self.cow == self.tot): # last patch # recover original order of images sh = [] for (i, x) in enumerate(self.perm): sh.append([x, i]) sh = np.array(sorted(sh))[:, 1] # reorder data and conclude feats = np.array(self.feats)[sh] labels = np.array(self.labels)[sh] self.bb = (Tensor(feats.transpose(0, 3, 1, 2)), Tensor(labels)) self.finished_event.set()
def main(args): if not ecvl.ECVL_EDDL: print("No EDDL support - quitting") sys.exit(0) img = ecvl.ImRead(args.in_img) augs = ecvl.SequentialAugmentationContainer([ ecvl.AugCenterCrop(), # Make image square ecvl.AugRotate([-5, 5]), ecvl.AugMirror(.5), ecvl.AugFlip(.5), ecvl.AugGammaContrast([3, 5]), ecvl.AugAdditiveLaplaceNoise([0, 0.2 * 255]), ecvl.AugCoarseDropout([0, 0.55], [0.02, 0.1], 0.5), ecvl.AugAdditivePoissonNoise([0, 40]), ecvl.AugResizeDim([500, 500]), ecvl.AugCenterCrop([224, 224]), ecvl.AugToFloat32(255), ecvl.AugNormalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), ]) ecvl.AugmentationParam.SetSeed(0) print("Applying augmentations") augs.Apply(img) print("Executing ImageToTensor") t = ecvl.ImageToTensor(img) t.div_(128) t.mult_(128) print("Executing TensorToImage") img = ecvl.TensorToImage(t) print("Executing TensorToView") ecvl.TensorToView(t) print("Applying augmentations (from text)") newdeal_augs = ecvl.AugmentationFactory.create(AUG_TXT) newdeal_augs.Apply(img) training_augs = ecvl.SequentialAugmentationContainer([ ecvl.AugRotate([-5, 5]), ecvl.AugAdditiveLaplaceNoise([0, 0.2 * 255]), ecvl.AugCoarseDropout([0, 0.55], [0.02, 0.1], 0), ecvl.AugAdditivePoissonNoise([0, 40]), ecvl.AugResizeDim([30, 30]), ecvl.AugToFloat32(255), ecvl.AugNormalize(0.449, 0.226), # mean of imagenet stats ]) test_augs = ecvl.SequentialAugmentationContainer([ ecvl.AugResizeDim([30, 30]), ecvl.AugToFloat32(255), ecvl.AugNormalize(0.449, 0.226), # mean of imagenet stats ]) # number of augmentation containers must match number of dataset splits ds_augs = ecvl.DatasetAugmentations([training_augs, test_augs]) batch_size = 64 print("Creating a DLDataset") d = ecvl.DLDataset(args.in_ds, batch_size, ds_augs, ecvl.ColorType.GRAY) print("Create x and y") x = Tensor( [batch_size, d.n_channels_, d.resize_dims_[0], d.resize_dims_[1]]) y = Tensor([batch_size, len(d.classes_)]) # Load a batch of d.batch_size_ images into x and corresponding labels # into y. Images are resized to the dimensions specified in the # augmentations chain print("Executing LoadBatch on training set") d.LoadBatch(x, y) # Change colortype and channels img = ecvl.TensorToImage(x) img.colortype_ = ecvl.ColorType.GRAY img.channels_ = "xyc" # Switch to Test split and load a batch of images print("Executing LoadBatch on test set") d.SetSplit(ecvl.SplitType.test) d.LoadBatch(x, y) # Save some input images ecvl.ImWrite("mnist_batch.png", ecvl.MakeGrid(x, 8, False)) ecvl.ImWrite("mnist_batch_normalized.png", ecvl.MakeGrid(x, 8, True))
def main(args): num_classes = 1 size = [512, 512] # size of images thresh = 0.5 best_dice = -1 if args.out_dir: os.makedirs(args.out_dir, exist_ok=True) in_ = eddl.Input([1, size[0], size[1]]) out = SegNetBN(in_, num_classes) out_sigm = eddl.Sigmoid(out) net = eddl.Model([in_], [out_sigm]) eddl.build(net, eddl.adam(0.0001), ["cross_entropy"], ["mean_squared_error"], eddl.CS_GPU([1], mem='low_mem') if args.gpu else eddl.CS_CPU()) eddl.summary(net) eddl.setlogfile(net, "pneumothorax_segmentation_training") if args.ckpts and os.path.exists(args.ckpts): print("Loading checkpoints '{}'".format(args.ckpts)) eddl.load(net, args.ckpts, 'bin') training_augs = ecvl.SequentialAugmentationContainer([ ecvl.AugResizeDim(size), ecvl.AugMirror(0.5), ecvl.AugRotate([-10, 10]), ecvl.AugBrightness([0, 30]), ecvl.AugGammaContrast([0, 3]), ]) validation_augs = ecvl.SequentialAugmentationContainer( [ecvl.AugResizeDim(size)]) dataset_augs = ecvl.DatasetAugmentations( [training_augs, validation_augs, None]) print("Reading dataset") d = ecvl.DLDataset(args.in_ds, args.batch_size, dataset_augs, ecvl.ColorType.GRAY) # Prepare tensors which store batch x = Tensor([args.batch_size, d.n_channels_, size[0], size[1]]) y = Tensor([args.batch_size, d.n_channels_gt_, size[0], size[1]]) # Retrieve indices of images with a black ground truth # which are not include in a split train_split = d.GetSplit(ecvl.SplitType.training) val_split = d.GetSplit(ecvl.SplitType.validation) test_split = d.GetSplit(ecvl.SplitType.test) all_split = set(train_split + val_split + test_split) images_list = set(range(len(d.samples_))) # Obtain images with black ground truth black_images = images_list - all_split # Add a 25% of training samples with black ground truth. num_samples_train = math.floor(len(train_split) * 1.25) num_batches_train = num_samples_train // args.batch_size # Add a 25% of validation samples with black ground truth. num_samples_validation = math.floor(len(val_split) * 1.25) num_batches_validation = num_samples_validation // args.batch_size black_images = list(black_images) black_training = black_images[0:-(num_samples_validation - len(val_split))] black_validation = black_images[-(num_samples_validation - len(val_split)):] indices = list(range(args.batch_size)) evaluator = utils.Evaluator() print("Starting training") for e in range(args.epochs): print("Epoch {:d}/{:d} - Training".format(e + 1, args.epochs), flush=True) d.SetSplit(ecvl.SplitType.training) eddl.reset_loss(net) s = d.GetSplit() random.shuffle(s) d.split_.training_ = s random.shuffle(black_training) d.ResetAllBatches() # Indices to track mask and black vector in PneumothoraxLoadBatch m_i = 0 b_i = 0 for i, b in enumerate(range(num_batches_train)): d, images, labels, _, m_i, b_i = PneumothoraxLoadBatch( d, black_training, m_i, b_i) x, y = fill_tensors(images, labels, x, y) x.div_(255.0) y.div_(255.0) eddl.train_batch(net, [x], [y], indices) if i % args.log_interval == 0: print("Epoch {:d}/{:d} (batch {:d}/{:d}) - ".format( e + 1, args.epochs, b + 1, num_batches_train), end="", flush=True) eddl.print_loss(net, b) print() d.SetSplit(ecvl.SplitType.validation) evaluator.ResetEval() print("Epoch %d/%d - Evaluation" % (e + 1, args.epochs), flush=True) m_i = 0 b_i = 0 for b in range(num_batches_validation): n = 0 print("Epoch {:d}/{:d} (batch {:d}/{:d}) ".format( e + 1, args.epochs, b + 1, num_batches_validation), end="", flush=True) d, images, labels, names, m_i, b_i = PneumothoraxLoadBatch( d, black_validation, m_i, b_i) x, y = fill_tensors(images, labels, x, y) x.div_(255.0) y.div_(255.0) eddl.forward(net, [x]) output = eddl.getOutput(out_sigm) # Compute Dice metric and optionally save the output images for k in range(args.batch_size): pred = output.select([str(k)]) gt = y.select([str(k)]) pred_np = np.array(pred, copy=False) gt_np = np.array(gt, copy=False) # DiceCoefficient modifies image as a side effect dice = evaluator.DiceCoefficient(pred_np, gt_np, thresh=thresh) print("- Dice: {:.6f} ".format(dice), end="", flush=True) if args.out_dir: # Save original image fused together with prediction and # ground truth pred_np *= 255 pred_ecvl = ecvl.TensorToImage(pred) pred_ecvl.colortype_ = ecvl.ColorType.GRAY pred_ecvl.channels_ = "xyc" ecvl.ResizeDim(pred_ecvl, pred_ecvl, (1024, 1024), ecvl.InterpolationType.nearest) filename_gt = names[n + 1] gt_ecvl = ecvl.ImRead(filename_gt, ecvl.ImReadMode.GRAYSCALE) filename = names[n] # Image as BGR img_ecvl = ecvl.ImRead(filename) ecvl.Stack([img_ecvl, img_ecvl, img_ecvl], img_ecvl) img_ecvl.channels_ = "xyc" img_ecvl.colortype_ = ecvl.ColorType.BGR image_np = np.array(img_ecvl, copy=False) pred_np = np.array(pred_ecvl, copy=False) gt_np = np.array(gt_ecvl, copy=False) pred_np = pred_np.squeeze() gt_np = gt_np.squeeze() # Prediction summed in R channel image_np[:, :, -1] = np.where(pred_np == 255, pred_np, image_np[:, :, -1]) # Ground truth summed in G channel image_np[:, :, 1] = np.where(gt_np == 255, gt_np, image_np[:, :, 1]) n += 2 head, tail = os.path.splitext(os.path.basename(filename)) bname = "{}.png".format(head) filepath = os.path.join(args.out_dir, bname) ecvl.ImWrite(filepath, img_ecvl) print() mean_dice = evaluator.MeanMetric() if mean_dice > best_dice: print("Saving weights") eddl.save( net, "pneumothorax_segnetBN_adam_lr_0.0001_" "loss_ce_size_512_{}.bin".format(e + 1), "bin") best_dice = mean_dice print("Mean Dice Coefficient: {:.6g}".format(mean_dice))
def main(args): num_classes = 1 size = [512, 512] # size of images thresh = 0.5 if args.out_dir: os.makedirs(args.out_dir, exist_ok=True) in_ = eddl.Input([1, size[0], size[1]]) out = SegNetBN(in_, num_classes) out_sigm = eddl.Sigmoid(out) net = eddl.Model([in_], [out_sigm]) eddl.build( net, eddl.adam(0.0001), ["cross_entropy"], ["mean_squared_error"], eddl.CS_GPU([1]) if args.gpu else eddl.CS_CPU() ) eddl.summary(net) eddl.setlogfile(net, "pneumothorax_segmentation_inference") if not os.path.exists(args.ckpts): raise RuntimeError('Checkpoint "{}" not found'.format(args.ckpts)) eddl.load(net, args.ckpts, "bin") training_augs = ecvl.SequentialAugmentationContainer([ ecvl.AugResizeDim(size), ]) test_augs = ecvl.SequentialAugmentationContainer([ ecvl.AugResizeDim(size), ]) dataset_augs = ecvl.DatasetAugmentations([training_augs, None, test_augs]) print("Reading dataset") d = ecvl.DLDataset(args.in_ds, args.batch_size, dataset_augs, ecvl.ColorType.GRAY) x = Tensor([args.batch_size, d.n_channels_, size[0], size[1]]) print("Testing") d.SetSplit(ecvl.SplitType.test) num_samples_test = len(d.GetSplit()) num_batches_test = num_samples_test // args.batch_size evaluator = utils.Evaluator() evaluator.ResetEval() for b in range(num_batches_test): n = 0 print("Batch {:d}/{:d} ".format( b + 1, num_batches_test), end="", flush=True) d.LoadBatch(x) x.div_(255.0) eddl.forward(net, [x]) if args.out_dir: output = eddl.getOutput(out_sigm) for k in range(args.batch_size): img = output.select([str(k)]) img_I = ecvl.TensorToImage(img) img_I.colortype_ = ecvl.ColorType.GRAY img_I.channels_ = "xyc" ecvl.Threshold(img_I, img_I, thresh, 255) filename = d.samples_[d.GetSplit()[n]].location_[0] head, tail = os.path.splitext(os.path.basename(filename)) bname = "{}.png".format(head) output_fn = os.path.join(args.out_dir, bname) ecvl.ImWrite(output_fn, img_I) n += 1 print()
def main(args): num_classes = 8 size = [224, 224] # size of images in_ = eddl.Input([3, size[0], size[1]]) out = VGG16(in_, num_classes) net = eddl.Model([in_], [out]) eddl.build(net, eddl.sgd(0.001, 0.9), ["soft_cross_entropy"], ["categorical_accuracy"], eddl.CS_GPU([1]) if args.gpu else eddl.CS_CPU()) eddl.summary(net) eddl.setlogfile(net, "skin_lesion_classification") training_augs = ecvl.SequentialAugmentationContainer([ ecvl.AugResizeDim(size), ecvl.AugMirror(.5), ecvl.AugFlip(.5), ecvl.AugRotate([-180, 180]), ecvl.AugAdditivePoissonNoise([0, 10]), ecvl.AugGammaContrast([0.5, 1.5]), ecvl.AugGaussianBlur([0, 0.8]), ecvl.AugCoarseDropout([0, 0.3], [0.02, 0.05], 0.5) ]) validation_augs = ecvl.SequentialAugmentationContainer([ ecvl.AugResizeDim(size), ]) dataset_augs = ecvl.DatasetAugmentations( [training_augs, validation_augs, None]) print("Reading dataset") d = ecvl.DLDataset(args.in_ds, args.batch_size, dataset_augs) x = Tensor([args.batch_size, d.n_channels_, size[0], size[1]]) y = Tensor([args.batch_size, len(d.classes_)]) num_samples_train = len(d.GetSplit()) num_batches_train = num_samples_train // args.batch_size d.SetSplit(ecvl.SplitType.validation) num_samples_val = len(d.GetSplit()) num_batches_val = num_samples_val // args.batch_size indices = list(range(args.batch_size)) metric = eddl.getMetric("categorical_accuracy") print("Starting training") for e in range(args.epochs): print("Epoch {:d}/{:d} - Training".format(e + 1, args.epochs), flush=True) if args.out_dir: current_path = os.path.join(args.out_dir, "Epoch_%d" % e) for c in d.classes_: c_dir = os.path.join(current_path, c) os.makedirs(c_dir, exist_ok=True) d.SetSplit(ecvl.SplitType.training) eddl.reset_loss(net) total_metric = [] s = d.GetSplit() random.shuffle(s) d.split_.training_ = s d.ResetAllBatches() for b in range(num_batches_train): print("Epoch {:d}/{:d} (batch {:d}/{:d}) - ".format( e + 1, args.epochs, b + 1, num_batches_train), end="", flush=True) d.LoadBatch(x, y) x.div_(255.0) tx, ty = [x], [y] eddl.train_batch(net, tx, ty, indices) eddl.print_loss(net, b) print() print("Saving weights") eddl.save(net, "isic_classification_checkpoint_epoch_%s.bin" % e, "bin") print("Epoch %d/%d - Evaluation" % (e + 1, args.epochs), flush=True) d.SetSplit(ecvl.SplitType.validation) for b in range(num_batches_val): n = 0 print("Epoch {:d}/{:d} (batch {:d}/{:d}) - ".format( e + 1, args.epochs, b + 1, num_batches_val), end="", flush=True) d.LoadBatch(x, y) x.div_(255.0) eddl.forward(net, [x]) output = eddl.getOutput(out) sum_ = 0.0 for k in range(args.batch_size): result = output.select([str(k)]) target = y.select([str(k)]) ca = metric.value(target, result) total_metric.append(ca) sum_ += ca if args.out_dir: result_a = np.array(result, copy=False) target_a = np.array(target, copy=False) classe = np.argmax(result_a).item() gt_class = np.argmax(target_a).item() single_image = x.select([str(k)]) img_t = ecvl.TensorToView(single_image) img_t.colortype_ = ecvl.ColorType.BGR single_image.mult_(255.) filename = d.samples_[d.GetSplit()[n]].location_[0] head, tail = os.path.splitext(os.path.basename(filename)) bname = "%s_gt_class_%s.png" % (head, gt_class) cur_path = os.path.join(current_path, d.classes_[classe], bname) ecvl.ImWrite(cur_path, img_t) n += 1 print("categorical_accuracy:", sum_ / args.batch_size) total_avg = sum(total_metric) / len(total_metric) print("Total categorical accuracy:", total_avg)
def main(args): num_classes = 10 size = [28, 28] # size of images ctype = ecvl.ColorType.GRAY in_ = eddl.Input([1, size[0], size[1]]) out = LeNet(in_, num_classes) net = eddl.Model([in_], [out]) eddl.build(net, eddl.sgd(0.001, 0.9), ["soft_cross_entropy"], ["categorical_accuracy"], eddl.CS_GPU([1]) if args.gpu else eddl.CS_CPU()) eddl.summary(net) eddl.setlogfile(net, "mnist") training_augs = ecvl.SequentialAugmentationContainer([ ecvl.AugRotate([-5, 5]), ecvl.AugAdditivePoissonNoise([0, 10]), ecvl.AugGaussianBlur([0, 0.8]), ecvl.AugCoarseDropout([0, 0.3], [0.02, 0.05], 0), ]) dataset_augs = ecvl.DatasetAugmentations([training_augs, None, None]) print("Reading dataset") d = ecvl.DLDataset(args.in_ds, args.batch_size, dataset_augs, ctype) x_train = Tensor([args.batch_size, d.n_channels_, size[0], size[1]]) y_train = Tensor([args.batch_size, len(d.classes_)]) num_samples = len(d.GetSplit()) num_batches = num_samples // args.batch_size indices = list(range(args.batch_size)) print("Training") for i in range(args.epochs): eddl.reset_loss(net) s = d.GetSplit() random.shuffle(s) d.split_.training_ = s d.ResetCurrentBatch() for j in range(num_batches): print("Epoch %d/%d (batch %d/%d) - " % (i + 1, args.epochs, j + 1, num_batches), end="", flush=True) d.LoadBatch(x_train, y_train) x_train.div_(255.0) tx, ty = [x_train], [y_train] eddl.train_batch(net, tx, ty, indices) eddl.print_loss(net, j) print() print("Saving weights") eddl.save(net, "mnist_checkpoint.bin", "bin") print("Evaluation") d.SetSplit(ecvl.SplitType.test) num_samples = len(d.GetSplit()) num_batches = num_samples // args.batch_size for i in range(num_batches): print("batch %d / %d - " % (i, num_batches), end="", flush=True) d.LoadBatch(x_train, y_train) x_train.div_(255.0) eddl.evaluate(net, [x_train], [y_train])
def main(args): batch_size = args.batch_size image_size = args.size, args.size thresh = 0.5 if args.weights: os.makedirs(args.weights, exist_ok=True) training_augs = ecvl.SequentialAugmentationContainer([ ecvl.AugResizeDim(image_size, ecvl.InterpolationType.cubic, gt_interp=ecvl.InterpolationType.nearest), ecvl.AugMirror(.5), ecvl.AugFlip(.5), ecvl.AugRotate([-180, 180]), ecvl.AugAdditivePoissonNoise([0, 10]), ecvl.AugGammaContrast([0.5, 1.5]), ecvl.AugGaussianBlur([0, 0.8]), ecvl.AugCoarseDropout([0, 0.03], [0.02, 0.05], 0.25), ecvl.AugToFloat32(255, divisor_gt=255), ecvl.AugNormalize([0.6681, 0.5301, 0.5247], [0.1337, 0.1480, 0.1595]), # isic stats ]) validation_test_augs = ecvl.SequentialAugmentationContainer([ ecvl.AugResizeDim(image_size, ecvl.InterpolationType.cubic, gt_interp=ecvl.InterpolationType.nearest), ecvl.AugToFloat32(255, divisor_gt=255), ecvl.AugNormalize([0.6681, 0.5301, 0.5247], [0.1337, 0.1480, 0.1595]), # isic stats ]) dataset_augs = ecvl.DatasetAugmentations( [training_augs, validation_test_augs, validation_test_augs]) print('Reading dataset') d = ecvl.DLDataset(args.in_ds, args.batch_size, dataset_augs, ctype=ecvl.ColorType.RGB) num_classes = len(d.classes_) or d.n_channels_gt_ size = d.n_channels_, args.size, args.size if args.ckpts: net = eddl.import_net_from_onnx_file(args.ckpts, size) else: in_ = eddl.Input(size) out = Unet(in_, num_classes) out_sigm = eddl.Sigmoid(out) net = eddl.Model([in_], [out_sigm]) # model_path = utils.DownloadModel(segmentation_zoo[args.model]['url'], f'{args.model}.onnx', 'model_onnx') # net = eddl.import_net_from_onnx_file(model_path, size) # eddl.removeLayer(net, segmentation_zoo[args.model]['to_remove']) # top = eddl.getLayer(net, segmentation_zoo[args.model]['top']) # # out = eddl.Sigmoid(eddl.Conv(top, num_classes, [3, 3], name='last_layer')) # data_input = eddl.getLayer(net, segmentation_zoo[args.model]['input']) # input of the onnx # net = eddl.Model([data_input], [out]) loss_name = 'binary_cross_entropy' metric_name = 'mean_squared_error' eddl.build( net, eddl.adam(args.learning_rate), [loss_name], [metric_name], eddl.CS_GPU(args.gpu, mem="low_mem") if args.gpu else eddl.CS_CPU(), True) out = eddl.getOut(net)[0] # if not args.ckpts: # eddl.initializeLayer(net, "last_layer") eddl.summary(net) eddl.setlogfile(net, 'skin_lesion_segmentation') x = Tensor([args.batch_size, *size]) y = Tensor([args.batch_size, d.n_channels_gt_, size[1], size[2]]) miou = 0. if args.train: num_samples_train = len(d.GetSplit()) num_batches_train = num_samples_train // args.batch_size num_samples_val = len(d.GetSplit(ecvl.SplitType.validation)) num_batches_val = num_samples_val // args.batch_size evaluator = utils.Evaluator() print('Starting training') for e in range(args.epochs): d.SetSplit(ecvl.SplitType.training) eddl.reset_loss(net) s = d.GetSplit() random.shuffle(s) d.split_.training_ = s d.ResetAllBatches() for b in range(num_batches_train): d.LoadBatch(x, y) # x_ = x.select(["0"]) # x_.normalize_(0, 1) # x_.mult_(255.) # x_.save(f'images/train_{e}_{b}.png') # # y_ = y.select(["0"]) # # y_.mult_(255.) # y_.save(f'images/train_gt_{e}_{b}.png') eddl.train_batch(net, [x], [y]) losses = eddl.get_losses(net) metrics = eddl.get_metrics(net) print( f'Train - epoch [{e + 1}/{args.epochs}] - batch [{b + 1}/{num_batches_train}]' f' - {loss_name}={losses[0]:.3f} - {metric_name}={metrics[0]:.3f}', flush=True) d.SetSplit(ecvl.SplitType.validation) evaluator.ResetEval() eddl.reset_loss(net) for b in range(num_batches_val): n = 0 print( f'Validation - epoch [{e + 1}/{args.epochs}] - batch [{b + 1}/{num_batches_val}]' ) d.LoadBatch(x, y) eddl.forward(net, [x]) output = eddl.getOutput(out) for bs in range(args.batch_size): img = output.select([str(bs)]) gt = y.select([str(bs)]) img_np = np.array(img, copy=False) gt_np = np.array(gt, copy=False) iou = evaluator.BinaryIoU(img_np, gt_np, thresh=thresh) print(f' - IoU: {iou:.3f}', end="", flush=True) if args.out_dir: # C++ BinaryIoU modifies image as a side effect img_np[img_np >= thresh] = 1 img_np[img_np < thresh] = 0 img_t = ecvl.TensorToView(img) img_t.colortype_ = ecvl.ColorType.GRAY img_t.channels_ = "xyc" img.mult_(255.) # orig_img orig_img = x.select([str(bs)]) orig_img.mult_(255.) orig_img_t = ecvl.TensorToImage(orig_img) orig_img_t.colortype_ = ecvl.ColorType.BGR orig_img_t.channels_ = "xyc" tmp, labels = ecvl.Image.empty(), ecvl.Image.empty() ecvl.CopyImage(img_t, tmp, ecvl.DataType.uint8) ecvl.ConnectedComponentsLabeling(tmp, labels) ecvl.CopyImage(labels, tmp, ecvl.DataType.uint8) contours = ecvl.FindContours(tmp) ecvl.CopyImage(orig_img_t, tmp, ecvl.DataType.uint8) tmp_np = np.array(tmp, copy=False) for cseq in contours: for c in cseq: tmp_np[c[0], c[1], 0] = 0 tmp_np[c[0], c[1], 1] = 0 tmp_np[c[0], c[1], 2] = 255 filename = d.samples_[d.GetSplit()[n]].location_[0] head, tail = os.path.splitext( os.path.basename(filename)) bname = "%s.png" % head output_fn = os.path.join(args.out_dir, bname) ecvl.ImWrite(output_fn, tmp) if e == 0: gt_t = ecvl.TensorToView(gt) gt_t.colortype_ = ecvl.ColorType.GRAY gt_t.channels_ = "xyc" gt.mult_(255.) gt_filename = d.samples_[d.GetSplit() [n]].label_path_ gt_fn = os.path.join(args.out_dir, os.path.basename(gt_filename)) ecvl.ImWrite(gt_fn, gt_t) n += 1 print() last_miou = evaluator.MIoU() print( f'Validation - epoch [{e + 1}/{args.epochs}] - Total MIoU: {last_miou:.3f}' ) if last_miou > miou: miou = last_miou eddl.save_net_to_onnx_file( net, os.path.join(args.weights, f'isic_segm_{args.model}_epoch_{e + 1}.onnx')) print('Weights saved') elif args.test: evaluator = utils.Evaluator() evaluator.ResetEval() d.SetSplit(ecvl.SplitType.test) num_samples_test = len(d.GetSplit()) num_batches_test = num_samples_test // batch_size for b in range(num_batches_test): n = 0 print(f'Test - batch [{b + 1}/{num_batches_test}]') d.LoadBatch(x, y) eddl.forward(net, [x]) output = eddl.getOutput(out) for bs in range(args.batch_size): img = output.select([str(bs)]) gt = y.select([str(bs)]) img_np, gt_np = np.array(img, copy=False), np.array(gt, copy=False) iou = evaluator.BinaryIoU(img_np, gt_np, thresh=thresh) print(f' - IoU: {iou:.3f}', end="", flush=True) if args.out_dir: # C++ BinaryIoU modifies image as a side effect img_np[img_np >= thresh] = 1 img_np[img_np < thresh] = 0 img_t = ecvl.TensorToView(img) img_t.colortype_ = ecvl.ColorType.GRAY img_t.channels_ = "xyc" img.mult_(255.) # orig_img orig_img = x.select([str(bs)]) orig_img.mult_(255.) orig_img_t = ecvl.TensorToImage(orig_img) orig_img_t.colortype_ = ecvl.ColorType.BGR orig_img_t.channels_ = "xyc" tmp, labels = ecvl.Image.empty(), ecvl.Image.empty() ecvl.CopyImage(img_t, tmp, ecvl.DataType.uint8) ecvl.ConnectedComponentsLabeling(tmp, labels) ecvl.CopyImage(labels, tmp, ecvl.DataType.uint8) contours = ecvl.FindContours(tmp) ecvl.CopyImage(orig_img_t, tmp, ecvl.DataType.uint8) tmp_np = np.array(tmp, copy=False) for cseq in contours: for c in cseq: tmp_np[c[0], c[1], 0] = 0 tmp_np[c[0], c[1], 1] = 0 tmp_np[c[0], c[1], 2] = 255 filename = d.samples_[d.GetSplit()[n]].location_[0] head, tail = os.path.splitext(os.path.basename(filename)) bname = "%s.png" % head output_fn = os.path.join(args.out_dir, bname) ecvl.ImWrite(output_fn, tmp) gt_t = ecvl.TensorToView(gt) gt_t.colortype_ = ecvl.ColorType.GRAY gt_t.channels_ = "xyc" gt.mult_(255.) gt_filename = d.samples_[d.GetSplit()[n]].label_path_ gt_fn = os.path.join(args.out_dir, os.path.basename(gt_filename)) ecvl.ImWrite(gt_fn, gt_t) n += 1 miou = evaluator.MIoU() print(f'Test - Total MIoU: {miou:.3f}')
def main(args): eddl.download_drive() in_1 = eddl.Input([3, 584, 584]) in_2 = eddl.Input([1, 584, 584]) layer = eddl.Concat([in_1, in_2]) layer = eddl.RandomCropScale(layer, [0.9, 1.0]) layer = eddl.CenteredCrop(layer, [512, 512]) img = eddl.Select(layer, ["0:3"]) mask = eddl.Select(layer, ["3"]) # DA net danet = eddl.Model([in_1, in_2], []) eddl.build(danet) if args.gpu: eddl.toGPU(danet, mem="low_mem") eddl.summary(danet) # SegNet in_ = eddl.Input([3, 512, 512]) out = eddl.Sigmoid(UNetWithPadding(in_)) segnet = eddl.Model([in_], [out]) eddl.build( segnet, eddl.adam(0.00001), # Optimizer ["mse"], # Losses ["mse"], # Metrics eddl.CS_GPU(mem=args.mem) if args.gpu else eddl.CS_CPU(mem=args.mem) ) eddl.summary(segnet) print("Reading training data") # x_train_f = Tensor.fromarray(np.load("drive_trX.npy").astype(np.float32)) x_train_f = Tensor.load("drive_trX.bin") x_train = x_train_f.permute([0, 3, 1, 2]) x_train.info() x_train.div_(255.0) print("Reading test data") # y_train = Tensor.fromarray(np.load("drive_trY.npy").astype(np.float32)) y_train = Tensor.load("drive_trY.bin") y_train.info() y_train.reshape_([20, 1, 584, 584]) y_train.div_(255.0) xbatch = Tensor([args.batch_size, 3, 584, 584]) ybatch = Tensor([args.batch_size, 1, 584, 584]) print("Starting training") for i in range(args.epochs): print("\nEpoch %d/%d" % (i + 1, args.epochs)) eddl.reset_loss(segnet) for j in range(args.num_batches): eddl.next_batch([x_train, y_train], [xbatch, ybatch]) # DA net eddl.forward(danet, [xbatch, ybatch]) xbatch_da = eddl.getOutput(img) ybatch_da = eddl.getOutput(mask) # SegNet eddl.train_batch(segnet, [xbatch_da], [ybatch_da]) eddl.print_loss(segnet, j) if i == args.epochs - 1: yout = eddl.getOutput(out).select(["0"]) yout.save("./out_%d.jpg" % j) print() print("All done")
def main(args): eddl.download_flickr() epochs = 2 if args.small else 50 olength = 20 outvs = 2000 embdim = 32 # True: remove last layers and set new top = flatten # new input_size: [3, 256, 256] (from [224, 224, 3]) net = eddl.download_resnet18(True, [3, 256, 256]) lreshape = eddl.getLayer(net, "top") # create a new model from input output image_in = eddl.getLayer(net, "input") # Decoder ldecin = eddl.Input([outvs]) ldec = eddl.ReduceArgMax(ldecin, [0]) ldec = eddl.RandomUniform(eddl.Embedding(ldec, outvs, 1, embdim, True), -0.05, 0.05) ldec = eddl.Concat([ldec, lreshape]) layer = eddl.LSTM(ldec, 512, True) out = eddl.Softmax(eddl.Dense(layer, outvs)) eddl.setDecoder(ldecin) net = eddl.Model([image_in], [out]) # Build model eddl.build( net, eddl.adam(0.01), ["softmax_cross_entropy"], ["accuracy"], eddl.CS_GPU(mem=args.mem) if args.gpu else eddl.CS_CPU(mem=args.mem)) eddl.summary(net) # Load dataset x_train = Tensor.load("flickr_trX.bin", "bin") y_train = Tensor.load("flickr_trY.bin", "bin") if args.small: x_train = x_train.select([f"0:{2 * args.batch_size}", ":", ":", ":"]) y_train = y_train.select([f"0:{2 * args.batch_size}", ":"]) xtrain = Tensor.permute(x_train, [0, 3, 1, 2]) y_train = Tensor.onehot(y_train, outvs) # batch x timesteps x input_dim y_train.reshape_([y_train.shape[0], olength, outvs]) eddl.fit(net, [xtrain], [y_train], args.batch_size, epochs) eddl.save(net, "img2text.bin", "bin") print("\n === INFERENCE ===\n") # Get all the reshapes of the images. Only use the CNN timage = Tensor([x_train.shape[0], 512]) # images reshape cnn = eddl.Model([image_in], [lreshape]) eddl.build( cnn, eddl.adam(0.001), # not relevant ["mse"], # not relevant ["mse"], # not relevant eddl.CS_GPU(mem=args.mem) if args.gpu else eddl.CS_CPU(mem=args.mem)) eddl.summary(cnn) # forward images xbatch = Tensor([args.batch_size, 3, 256, 256]) # numbatches = x_train.shape[0] / args.batch_size j = 0 eddl.next_batch([x_train], [xbatch]) eddl.forward(cnn, [xbatch]) ybatch = eddl.getOutput(lreshape) sample = str(j * args.batch_size) + ":" + str((j + 1) * args.batch_size) timage.set_select([sample, ":"], ybatch) # Create Decoder non recurrent for n-best ldecin = eddl.Input([outvs]) image = eddl.Input([512]) lstate = eddl.States([2, 512]) ldec = eddl.ReduceArgMax(ldecin, [0]) ldec = eddl.RandomUniform(eddl.Embedding(ldec, outvs, 1, embdim), -0.05, 0.05) ldec = eddl.Concat([ldec, image]) lstm = eddl.LSTM([ldec, lstate], 512, True) lstm.isrecurrent = False # Important out = eddl.Softmax(eddl.Dense(lstm, outvs)) decoder = eddl.Model([ldecin, image, lstate], [out]) eddl.build( decoder, eddl.adam(0.001), # not relevant ["softmax_cross_entropy"], # not relevant ["accuracy"], # not relevant eddl.CS_GPU(mem=args.mem) if args.gpu else eddl.CS_CPU(mem=args.mem)) eddl.summary(decoder) # Copy params from trained net eddl.copyParam(eddl.getLayer(net, "LSTM1"), eddl.getLayer(decoder, "LSTM2")) eddl.copyParam(eddl.getLayer(net, "dense1"), eddl.getLayer(decoder, "dense2")) eddl.copyParam(eddl.getLayer(net, "embedding1"), eddl.getLayer(decoder, "embedding2")) # N-best for sample s s = 1 if args.small else 100 # sample 100 # three input tensors with batch_size = 1 (one sentence) treshape = timage.select([str(s), ":"]) text = y_train.select([str(s), ":", ":"]) # 1 x olength x outvs for j in range(olength): print(f"Word: {j}") word = None if j == 0: word = Tensor.zeros([1, outvs]) else: word = text.select(["0", str(j - 1), ":"]) word.reshape_([1, outvs]) # batch = 1 treshape.reshape_([1, 512]) # batch = 1 state = Tensor.zeros([1, 2, 512]) # batch = 1 input_ = [word, treshape, state] eddl.forward(decoder, input_) # outword = eddl.getOutput(out) vstates = eddl.getStates(lstm) for i in range(len(vstates)): vstates[i].reshape_([1, 1, 512]) state.set_select([":", str(i), ":"], vstates[i]) print("All done")
def main(args): eddl.download_eutrans() epochs = 1 if args.small else 5 ilength = 30 olength = 30 invs = 687 outvs = 514 embedding = 64 # Encoder in_ = eddl.Input([1]) # 1 word layer = in_ lE = eddl.RandomUniform( eddl.Embedding(layer, invs, 1, embedding, True), -0.05, 0.05 ) enc = eddl.LSTM(lE, 128, True) cps = eddl.GetStates(enc) # Decoder ldin = eddl.Input([outvs]) ld = eddl.ReduceArgMax(ldin, [0]) ld = eddl.RandomUniform( eddl.Embedding(ld, outvs, 1, embedding), -0.05, 0.05 ) layer = eddl.LSTM([ld, cps], 128) out = eddl.Softmax(eddl.Dense(layer, outvs)) eddl.setDecoder(ldin) net = eddl.Model([in_], [out]) # Build model eddl.build( net, eddl.adam(0.01), ["softmax_cross_entropy"], ["accuracy"], eddl.CS_GPU(mem=args.mem) if args.gpu else eddl.CS_CPU(mem=args.mem) ) eddl.summary(net) # Load dataset x_train = Tensor.load("eutrans_trX.bin") y_train = Tensor.load("eutrans_trY.bin") y_train = Tensor.onehot(y_train, outvs) # batch x timesteps x input_dim x_train.reshape_([x_train.shape[0], ilength, 1]) # batch x timesteps x ouput_dim y_train.reshape_([y_train.shape[0], olength, outvs]) x_test = Tensor.load("eutrans_tsX.bin") y_test = Tensor.load("eutrans_tsY.bin") y_test = Tensor.onehot(y_test, outvs) # batch x timesteps x input_dim x_test.reshape_([x_test.shape[0], ilength, 1]) # batch x timesteps x ouput_dim y_test.reshape_([y_test.shape[0], olength, outvs]) if args.small: sel = [f":{3 * args.batch_size}", ":", ":"] x_train = x_train.select(sel) y_train = y_train.select(sel) x_test = x_test.select(sel) y_test = y_test.select(sel) # Train model ybatch = Tensor([args.batch_size, olength, outvs]) eddl.next_batch([y_train], [ybatch]) for i in range(epochs): eddl.fit(net, [x_train], [y_train], args.batch_size, 1) print("All done")
def eddl_validate_DLDataset(model, out, d): batch_time = AverageMeter('BatchTime', ':6.3f') total_time = AverageMeter('TotalTime', ':6.3f') # Use the image resized dims defined by user or default image size for resnet [224,224] if hasattr(d, 'resize_dims_'): size = d.resize_dims_ else: size = [224, 224] x = Tensor([d.batch_size_, d.n_channels_, size[0], size[1]]) y = Tensor([d.batch_size_, len(d.classes_)]) d.SetSplit(ecvl.SplitType.validation) num_samples_val = len(d.GetSplit()) num_batches_val = num_samples_val // d.batch_size_ indices = list(range(d.batch_size_)) metric = eddl.getMetric("categorical_accuracy") print("Start Evaluation: ", flush=True) total_metric = [] print("Evaluation (validation set)", flush=True) d.ResetAllBatches() d.SetSplit(ecvl.SplitType.validation) end_total = time.time() batch_time.reset() end = time.time() for b in range(num_batches_val): n = 0 print("(batch {:d}/{:d}) - ".format(b + 1, num_batches_val), end="", flush=True) d.LoadBatch(x, y) x.div_(255.0) eddl.forward(model, [x]) output = eddl.getOutput(out) sum_ = 0.0 for k in range(d.batch_size_): result = output.select([str(k)]) target = y.select([str(k)]) ca = metric.value(target, result) total_metric.append(ca) sum_ += ca """ if args.out_dir: result_a = np.array(result, copy=False) target_a = np.array(target, copy=False) classe = np.argmax(result_a).item() gt_class = np.argmax(target_a).item() single_image = eddlT.select(x, k) img_t = ecvl.TensorToView(single_image) img_t.colortype_ = ecvl.ColorType.BGR single_image.mult_(255.) filename = d.samples_[d.GetSplit()[n]].location_[0] head, tail = os.path.splitext(os.path.basename(filename)) bname = "%s_gt_class_%s.png" % (head, gt_class) cur_path = os.path.join( current_path, d.classes_[classe], bname ) ecvl.ImWrite(cur_path, img_t) """ n += 1 batch_time.update(time.time() - end) end = time.time() print("categorical_accuracy:".format(sum_ / d.batch_size_), flush=True) print(batch_time) if (num_batches_val > 0): total_avg = sum(total_metric) / len(total_metric) print("Total categorical accuracy:{}".format(total_avg), flush=True) else: print( "Warning! \n " "Please check your validation set size, it might be smaller than the batch size,\n " "Validation test didn't execute as batch number is 0") #total_avg = sum(total_metric) / len(total_metric) #print("Total categorical accuracy:{}".format(total_avg), flush=True) total_time.update(time.time() - end_total) print(total_time, flush=True) return model
def eddl_train_DLDataset(model, out, d, learning_rate=1e-2, momentum=0.9, epochs=10, dynamic_lr=False): batch_time = AverageMeter('BatchTime', ':6.3f') total_time = AverageMeter('TotalTime', ':6.3f') # Use the image resized dims defined by user or default image size for resnet [224,224] if hasattr(d, 'resize_dims_'): size = d.resize_dims_ else: size = [224, 224] x = Tensor([d.batch_size_, d.n_channels_, size[0], size[1]]) y = Tensor([d.batch_size_, len(d.classes_)]) d.SetSplit(ecvl.SplitType.training) num_samples_train = len(d.GetSplit()) num_batches_train = num_samples_train // d.batch_size_ d.SetSplit(ecvl.SplitType.validation) num_samples_val = len(d.GetSplit()) num_batches_val = num_samples_val // d.batch_size_ indices = list(range(d.batch_size_)) metric = eddl.getMetric("categorical_accuracy") print("Starting training", flush=True) end_total = time.time() for e in range(epochs): if dynamic_lr and (e % 30 == 0) and e > 0: # every 30 epochs we want to decrease the learning rate value by 0.1 print( "every 30 epochs we want to decrease the learning rate value by 0.1" ) learning_rate = learning_rate * 0.1 eddl.setlr(model, [learning_rate, momentum]) print("Epoch {:d}/{:d} - Training".format(e + 1, epochs), flush=True) d.SetSplit(ecvl.SplitType.training) eddl.reset_loss(model) total_metric = [] s = d.GetSplit() random.shuffle(s) d.split_.training_ = s d.ResetAllBatches() batch_time.reset() end = time.time() for b in range(num_batches_train): print("Epoch {:d}/{:d} (batch {:d}/{:d}) - ".format( e + 1, epochs, b + 1, num_batches_train), end="", flush=True) d.LoadBatch(x, y) x.div_(255.0) tx, ty = [x], [y] eddl.train_batch(model, tx, ty, indices) eddl.print_loss(model, b) batch_time.update(time.time() - end) end = time.time() print(batch_time, flush=True) print() #print("Saving weights") #eddl.save( # net, "isic_classification_checkpoint_epoch_%s.bin" % e, "bin" #) print("Epoch %d/%d - Evaluation (validation set)" % (e + 1, epochs), flush=True) d.SetSplit(ecvl.SplitType.validation) batch_time.reset() end = time.time() for b in range(num_batches_val): n = 0 print("Epoch {:d}/{:d} (batch {:d}/{:d}) - ".format( e + 1, epochs, b + 1, num_batches_val), end="", flush=True) d.LoadBatch(x, y) x.div_(255.0) eddl.forward(model, [x]) output = eddl.getOutput(out) sum_ = 0.0 for k in range(d.batch_size_): result = output.select([str(k)]) target = y.select([str(k)]) ca = metric.value(target, result) total_metric.append(ca) sum_ += ca """ if args.out_dir: result_a = np.array(result, copy=False) target_a = np.array(target, copy=False) classe = np.argmax(result_a).item() gt_class = np.argmax(target_a).item() single_image = eddlT.select(x, k) img_t = ecvl.TensorToView(single_image) img_t.colortype_ = ecvl.ColorType.BGR single_image.mult_(255.) filename = d.samples_[d.GetSplit()[n]].location_[0] head, tail = os.path.splitext(os.path.basename(filename)) bname = "%s_gt_class_%s.png" % (head, gt_class) cur_path = os.path.join( current_path, d.classes_[classe], bname ) ecvl.ImWrite(cur_path, img_t) """ n += 1 batch_time.update(time.time() - end) end = time.time() print("batch categorical accuracy:{}".format(sum_ / d.batch_size_), flush=True) print(batch_time, flush=True) if (num_batches_val > 0): total_avg = sum(total_metric) / len(total_metric) print("Total categorical accuracy:{}".format(total_avg), flush=True) else: print( "Warning! \n " "Please check your validation set size, it might be smaller than the batch size,\n " "Validation test didn't execute as batch number is 0") total_time.update(time.time() - end_total) print(total_time, flush=True) return model
def main(args): num_classes = 8 size = [224, 224] # size of images in_ = eddl.Input([3, size[0], size[1]]) out = VGG16(in_, num_classes) net = eddl.Model([in_], [out]) eddl.build(net, eddl.sgd(0.001, 0.9), ["soft_cross_entropy"], ["categorical_accuracy"], eddl.CS_GPU([1]) if args.gpu else eddl.CS_CPU()) eddl.summary(net) eddl.setlogfile(net, "skin_lesion_classification_inference") training_augs = ecvl.SequentialAugmentationContainer([ ecvl.AugResizeDim(size), ]) test_augs = ecvl.SequentialAugmentationContainer([ ecvl.AugResizeDim(size), ]) dataset_augs = ecvl.DatasetAugmentations([training_augs, None, test_augs]) print("Reading dataset") d = ecvl.DLDataset(args.in_ds, args.batch_size, dataset_augs) if args.out_dir: for c in d.classes_: os.makedirs(os.path.join(args.out_dir, c), exist_ok=True) x = Tensor([args.batch_size, d.n_channels_, size[0], size[1]]) y = Tensor([args.batch_size, len(d.classes_)]) d.SetSplit(ecvl.SplitType.test) num_samples = len(d.GetSplit()) num_batches = num_samples // args.batch_size metric = eddl.getMetric("categorical_accuracy") total_metric = [] if not os.path.exists(args.ckpts): raise RuntimeError('Checkpoint "{}" not found'.format(args.ckpts)) eddl.load(net, args.ckpts, "bin") print("Testing") for b in range(num_batches): n = 0 print("Batch {:d}/{:d}".format(b + 1, num_batches)) d.LoadBatch(x, y) x.div_(255.0) eddl.forward(net, [x]) output = eddl.getOutput(out) sum_ = 0.0 for j in range(args.batch_size): result = output.select([str(j)]) target = y.select([str(j)]) ca = metric.value(target, result) total_metric.append(ca) sum_ += ca if args.out_dir: result_a = np.array(result, copy=False) target_a = np.array(target, copy=False) classe = np.argmax(result_a).item() gt_class = np.argmax(target_a).item() single_image = x.select([str(j)]) img_t = ecvl.TensorToView(single_image) img_t.colortype_ = ecvl.ColorType.BGR single_image.mult_(255.) filename = d.samples_[d.GetSplit()[n]].location_[0] head, tail = os.path.splitext(os.path.basename(filename)) bname = "%s_gt_class_%s.png" % (head, gt_class) cur_path = os.path.join(args.out_dir, d.classes_[classe], bname) ecvl.ImWrite(cur_path, img_t) n += 1 print("categorical_accuracy:", sum_ / args.batch_size) total_avg = sum(total_metric) / len(total_metric) print("Total categorical accuracy:", total_avg)
def main(args): num_classes = 1 size = [192, 192] # size of images thresh = 0.5 if args.out_dir: os.makedirs(args.out_dir, exist_ok=True) in_ = eddl.Input([3, size[0], size[1]]) out = SegNet(in_, num_classes) out_sigm = eddl.Sigmoid(out) net = eddl.Model([in_], [out_sigm]) eddl.build(net, eddl.adam(0.0001), ["cross_entropy"], ["mean_squared_error"], eddl.CS_GPU([1]) if args.gpu else eddl.CS_CPU()) eddl.summary(net) eddl.setlogfile(net, "skin_lesion_segmentation_inference") if not os.path.exists(args.ckpts): raise RuntimeError('Checkpoint "{}" not found'.format(args.ckpts)) eddl.load(net, args.ckpts, "bin") training_augs = ecvl.SequentialAugmentationContainer([ ecvl.AugResizeDim(size), ]) test_augs = ecvl.SequentialAugmentationContainer([ ecvl.AugResizeDim(size), ]) dataset_augs = ecvl.DatasetAugmentations([training_augs, None, test_augs]) print("Reading dataset") d = ecvl.DLDataset(args.in_ds, args.batch_size, dataset_augs) x = Tensor([args.batch_size, d.n_channels_, size[0], size[1]]) y = Tensor([args.batch_size, d.n_channels_gt_, size[0], size[1]]) print("Testing") d.SetSplit(ecvl.SplitType.test) num_samples_test = len(d.GetSplit()) num_batches_test = num_samples_test // args.batch_size evaluator = utils.Evaluator() evaluator.ResetEval() for b in range(num_batches_test): n = 0 print("Batch {:d}/{:d} ".format(b + 1, num_batches_test), end="", flush=True) d.LoadBatch(x, y) x.div_(255.0) y.div_(255.0) eddl.forward(net, [x]) output = eddl.getOutput(out_sigm) for k in range(args.batch_size): img = output.select([str(k)]) gt = y.select([str(k)]) img_np, gt_np = np.array(img, copy=False), np.array(gt, copy=False) iou = evaluator.BinaryIoU(img_np, gt_np, thresh=thresh) print("- IoU: %.6g " % iou, end="", flush=True) if args.out_dir: # C++ BinaryIoU modifies image as a side effect img_np[img_np >= thresh] = 1 img_np[img_np < thresh] = 0 img_t = ecvl.TensorToView(img) img_t.colortype_ = ecvl.ColorType.GRAY img_t.channels_ = "xyc" img.mult_(255.) # orig_img orig_img = x.select([str(k)]) orig_img.mult_(255.) orig_img_t = ecvl.TensorToImage(orig_img) orig_img_t.colortype_ = ecvl.ColorType.BGR orig_img_t.channels_ = "xyc" tmp, labels = ecvl.Image.empty(), ecvl.Image.empty() ecvl.CopyImage(img_t, tmp, ecvl.DataType.uint8) ecvl.ConnectedComponentsLabeling(tmp, labels) ecvl.CopyImage(labels, tmp, ecvl.DataType.uint8) contours = ecvl.FindContours(tmp) ecvl.CopyImage(orig_img_t, tmp, ecvl.DataType.uint8) tmp_np = np.array(tmp, copy=False) for cseq in contours: for c in cseq: tmp_np[c[0], c[1], 0] = 0 tmp_np[c[0], c[1], 1] = 0 tmp_np[c[0], c[1], 2] = 255 filename = d.samples_[d.GetSplit()[n]].location_[0] head, tail = os.path.splitext(os.path.basename(filename)) bname = "%s.png" % head output_fn = os.path.join(args.out_dir, bname) ecvl.ImWrite(output_fn, tmp) gt_t = ecvl.TensorToView(gt) gt_t.colortype_ = ecvl.ColorType.GRAY gt_t.channels_ = "xyc" gt.mult_(255.) gt_filename = d.samples_[d.GetSplit()[n]].label_path_ gt_fn = os.path.join(args.out_dir, os.path.basename(gt_filename)) ecvl.ImWrite(gt_fn, gt_t) n += 1 print() print("MIoU: %.6g" % evaluator.MeanMetric())
def main(args): num_classes = 1 size = [192, 192] # size of images thresh = 0.5 if args.out_dir: os.makedirs(args.out_dir, exist_ok=True) in_ = eddl.Input([3, size[0], size[1]]) out = SegNet(in_, num_classes) out_sigm = eddl.Sigmoid(out) net = eddl.Model([in_], [out_sigm]) eddl.build(net, eddl.adam(0.0001), ["cross_entropy"], ["mean_squared_error"], eddl.CS_GPU([1]) if args.gpu else eddl.CS_CPU()) eddl.summary(net) eddl.setlogfile(net, "skin_lesion_segmentation") training_augs = ecvl.SequentialAugmentationContainer([ ecvl.AugResizeDim(size), ecvl.AugMirror(0.5), ecvl.AugFlip(0.5), ecvl.AugRotate([-180, 180]), ecvl.AugAdditivePoissonNoise([0, 10]), ecvl.AugGammaContrast([0.5, 1.5]), ecvl.AugGaussianBlur([0, 0.8]), ecvl.AugCoarseDropout([0, 0.3], [0.02, 0.05], 0.5) ]) validation_augs = ecvl.SequentialAugmentationContainer( [ecvl.AugResizeDim(size)]) dataset_augs = ecvl.DatasetAugmentations( [training_augs, validation_augs, None]) print("Reading dataset") d = ecvl.DLDataset(args.in_ds, args.batch_size, dataset_augs) x = Tensor([args.batch_size, d.n_channels_, size[0], size[1]]) y = Tensor([args.batch_size, d.n_channels_gt_, size[0], size[1]]) num_samples_train = len(d.GetSplit()) num_batches_train = num_samples_train // args.batch_size d.SetSplit(ecvl.SplitType.validation) num_samples_validation = len(d.GetSplit()) num_batches_validation = num_samples_validation // args.batch_size indices = list(range(args.batch_size)) evaluator = utils.Evaluator() print("Starting training") for e in range(args.epochs): print("Epoch {:d}/{:d} - Training".format(e + 1, args.epochs), flush=True) d.SetSplit(ecvl.SplitType.training) eddl.reset_loss(net) s = d.GetSplit() random.shuffle(s) d.split_.training_ = s d.ResetAllBatches() for b in range(num_batches_train): print("Epoch {:d}/{:d} (batch {:d}/{:d}) - ".format( e + 1, args.epochs, b + 1, num_batches_train), end="", flush=True) d.LoadBatch(x, y) x.div_(255.0) y.div_(255.0) tx, ty = [x], [y] eddl.train_batch(net, tx, ty, indices) eddl.print_loss(net, b) print() print("Saving weights") eddl.save(net, "isic_segmentation_checkpoint_epoch_%s.bin" % e, "bin") d.SetSplit(ecvl.SplitType.validation) evaluator.ResetEval() print("Epoch %d/%d - Evaluation" % (e + 1, args.epochs), flush=True) for b in range(num_batches_validation): n = 0 print("Epoch {:d}/{:d} (batch {:d}/{:d}) ".format( e + 1, args.epochs, b + 1, num_batches_validation), end="", flush=True) d.LoadBatch(x, y) x.div_(255.0) y.div_(255.0) eddl.forward(net, [x]) output = eddl.getOutput(out_sigm) for k in range(args.batch_size): img = output.select([str(k)]) gt = y.select([str(k)]) img_np = np.array(img, copy=False) gt_np = np.array(gt, copy=False) iou = evaluator.BinaryIoU(img_np, gt_np, thresh=thresh) print("- IoU: %.6g " % iou, end="", flush=True) if args.out_dir: # C++ BinaryIoU modifies image as a side effect img_np[img_np >= thresh] = 1 img_np[img_np < thresh] = 0 img_t = ecvl.TensorToView(img) img_t.colortype_ = ecvl.ColorType.GRAY img_t.channels_ = "xyc" img.mult_(255.) # orig_img orig_img = x.select([str(k)]) orig_img.mult_(255.) orig_img_t = ecvl.TensorToImage(orig_img) orig_img_t.colortype_ = ecvl.ColorType.BGR orig_img_t.channels_ = "xyc" tmp, labels = ecvl.Image.empty(), ecvl.Image.empty() ecvl.CopyImage(img_t, tmp, ecvl.DataType.uint8) ecvl.ConnectedComponentsLabeling(tmp, labels) ecvl.CopyImage(labels, tmp, ecvl.DataType.uint8) contours = ecvl.FindContours(tmp) ecvl.CopyImage(orig_img_t, tmp, ecvl.DataType.uint8) tmp_np = np.array(tmp, copy=False) for cseq in contours: for c in cseq: tmp_np[c[0], c[1], 0] = 0 tmp_np[c[0], c[1], 1] = 0 tmp_np[c[0], c[1], 2] = 255 filename = d.samples_[d.GetSplit()[n]].location_[0] head, tail = os.path.splitext(os.path.basename(filename)) bname = "%s.png" % head output_fn = os.path.join(args.out_dir, bname) ecvl.ImWrite(output_fn, tmp) if e == 0: gt_t = ecvl.TensorToView(gt) gt_t.colortype_ = ecvl.ColorType.GRAY gt_t.channels_ = "xyc" gt.mult_(255.) gt_filename = d.samples_[d.GetSplit()[n]].label_path_ gt_fn = os.path.join(args.out_dir, os.path.basename(gt_filename)) ecvl.ImWrite(gt_fn, gt_t) n += 1 print() print("MIoU: %.6g" % evaluator.MeanMetric())
def main(args): batch_size = args.batch_size image_size = args.size, args.size if args.weights: os.makedirs(args.weights, exist_ok=True) training_augs = ecvl.SequentialAugmentationContainer([ ecvl.AugResizeDim(image_size, ecvl.InterpolationType.cubic), ecvl.AugMirror(.5), ecvl.AugFlip(.5), ecvl.AugRotate([-180, 180]), ecvl.AugAdditivePoissonNoise([0, 10]), ecvl.AugGammaContrast([0.5, 1.5]), ecvl.AugGaussianBlur([0, 0.8]), ecvl.AugCoarseDropout([0, 0.03], [0.02, 0.05], 0.25), ecvl.AugToFloat32(255), ]) validation_test_augs = ecvl.SequentialAugmentationContainer([ ecvl.AugResizeDim(image_size), ecvl.AugToFloat32(255), ]) dataset_augs = ecvl.DatasetAugmentations( [training_augs, validation_test_augs, validation_test_augs]) print('Reading dataset') d = ecvl.DLDataset(args.in_ds, args.batch_size, dataset_augs, ctype=ecvl.ColorType.RGB) num_classes = len(d.classes_) size = d.n_channels_, args.size, args.size if args.ckpts: net = eddl.import_net_from_onnx_file(args.ckpts, size) else: model_path = utils.DownloadModel(classification_zoo[args.model]['url'], f'{args.model}.onnx', 'model_onnx') net = eddl.import_net_from_onnx_file(model_path, size) eddl.removeLayer(net, classification_zoo[args.model] ['to_remove']) # remove last Linear of resnet top = eddl.getLayer( net, classification_zoo[args.model]['top']) # get flatten of resnet out = eddl.Softmax(eddl.Dense(top, num_classes, True, 'classifier')) # true is for the bias data_input = eddl.getLayer( net, classification_zoo[args.model]['input']) # input of the onnx net = eddl.Model([data_input], [out]) eddl.build( net, eddl.adam(args.learning_rate), ['softmax_cross_entropy'], ['accuracy'], eddl.CS_GPU(args.gpu, mem="low_mem") if args.gpu else eddl.CS_CPU(), False) out = eddl.getOut(net)[0] if not args.ckpts: eddl.initializeLayer(net, "classifier") eddl.summary(net) eddl.setlogfile(net, 'skin_lesion_classification') x = Tensor([batch_size, *size]) y = Tensor([batch_size, num_classes]) metric_fn = eddl.getMetric('accuracy') best_accuracy = 0. if args.train: num_samples_train = len(d.GetSplit()) num_batches_train = num_samples_train // args.batch_size num_samples_val = len(d.GetSplit(ecvl.SplitType.validation)) num_batches_val = num_samples_val // args.batch_size print('Starting training') for e in range(args.epochs): if args.out_dir: current_path = os.path.join(args.out_dir, f'Epoch_{e}') for c in d.classes_: c_dir = os.path.join(current_path, c) os.makedirs(c_dir, exist_ok=True) d.SetSplit(ecvl.SplitType.training) eddl.reset_loss(net) s = d.GetSplit() random.shuffle(s) d.split_.training_ = s d.ResetAllBatches() for b in range(num_batches_train): d.LoadBatch(x, y) eddl.train_batch(net, [x], [y]) losses = eddl.get_losses(net) metrics = eddl.get_metrics(net) print( f'Train - epoch [{e + 1}/{args.epochs}] - batch [{b + 1}/{num_batches_train}]' f' - loss={losses[0]:.3f} - accuracy={metrics[0]:.3f}', flush=True) d.SetSplit(ecvl.SplitType.validation) values = np.zeros(num_batches_val) eddl.reset_loss(net) for b in range(num_batches_val): n = 0 d.LoadBatch(x, y) eddl.forward(net, [x]) output = eddl.getOutput(out) value = metric_fn.value(y, output) values[b] = value if args.out_dir: for k in range(args.batch_size): result = output.select([str(k)]) target = y.select([str(k)]) result_a = np.array(result, copy=False) target_a = np.array(target, copy=False) classe = np.argmax(result_a).item() gt_class = np.argmax(target_a).item() single_image = x.select([str(k)]) img_t = ecvl.TensorToView(single_image) img_t.colortype_ = ecvl.ColorType.BGR single_image.mult_(255.) filename = d.samples_[d.GetSplit()[n]].location_[0] head, tail = os.path.splitext( os.path.basename(filename)) bname = '{}_gt_class_{}.png'.format(head, gt_class) cur_path = os.path.join(current_path, d.classes_[classe], bname) ecvl.ImWrite(cur_path, img_t) n += 1 print( f'Validation - epoch [{e + 1}/{args.epochs}] - batch [{b + 1}/{num_batches_val}] -' f' accuracy={np.mean(values[:b + 1] / batch_size):.3f}') last_accuracy = np.mean(values / batch_size) print( f'Validation - epoch [{e + 1}/{args.epochs}] - total accuracy={last_accuracy:.3f}' ) if last_accuracy > best_accuracy: best_accuracy = last_accuracy print('Saving weights') eddl.save_net_to_onnx_file( net, f'isic_classification_{args.model}_epoch_{e + 1}.onnx') elif args.test: d.SetSplit(ecvl.SplitType.test) num_samples_test = len(d.GetSplit()) num_batches_test = num_samples_test // batch_size values = np.zeros(num_batches_test) eddl.reset_loss(net) for b in range(num_batches_test): d.LoadBatch(x, y) eddl.forward(net, [x]) output = eddl.getOutput(out) value = metric_fn.value(y, output) values[b] = value if args.out_dir: n = 0 for k in range(args.batch_size): result = output.select([str(k)]) target = y.select([str(k)]) result_a = np.array(result, copy=False) target_a = np.array(target, copy=False) classe = np.argmax(result_a).item() gt_class = np.argmax(target_a).item() single_image = x.select([str(k)]) img_t = ecvl.TensorToView(single_image) img_t.colortype_ = ecvl.ColorType.BGR single_image.mult_(255.) filename = d.samples_[d.GetSplit()[n]].location_[0] head, tail = os.path.splitext(os.path.basename(filename)) bname = "%s_gt_class_%s.png" % (head, gt_class) cur_path = os.path.join(args.out_dir, d.classes_[classe], bname) ecvl.ImWrite(cur_path, img_t) n += 1 print( f'Test - batch [{b + 1}/{num_batches_test}] - accuracy={np.mean(values[:b + 1] / batch_size):.3f}' ) print(f'Test - total accuracy={np.mean(values / batch_size):.3f}')