def main(): data = PetsDataset("/home/helmuth/dlvc/cifar-10-batches-py", Subset.TRAINING) # ops chain op = ops.chain([ ops.vectorize(), ops.type_cast(np.float32), ops.add(-127.5), ops.mul(1/127.5), ]) # batch generator #1 bg1 = BatchGenerator(data, len(data), False) assert(len(bg1) == 1) # batch generator #2 bg2 = BatchGenerator(data, 500, False, op) assert(len(bg2) == 16) # first batch cnt = 0 for batch in bg2: cnt += 1 if cnt < 16: assert(batch.data.shape == (500, 3072)) assert(batch.labels.shape == (500,)) assert(batch.data.dtype == np.float32) assert(np.issubdtype(batch.labels.dtype, np.integer)) if cnt == 1: print("First batch, first sample, not shuffled") print(batch.data[0]) # batch generator #3 bg3 = BatchGenerator(data, 500, True, op) # run 5 times through first sample of shuffled batch generator for i in range(5): it = iter(bg3) print("First batch, first sample, shuffled") print(next(it).data[0])
def load_dataset(subset: Subset) -> batches.BatchGenerator: dataset = PetsDataset('../data/cifar-10-batches-py', subset) op = ops.chain([ ops.hwc2chw(), ops.add(-127.5), ops.mul(1 / 127.5), ops.type_cast(np.float32) ]) return batches.BatchGenerator(dataset, 128, True, op)
def load_dataset(subset: Subset, augment=False) -> batches.BatchGenerator: dataset = PetsDataset('../data/cifar-10-batches-py', subset) ops_list = [] if augment: ops_list += [ops.hflip(), ops.rcrop(32, 12, 'constant')] ops_list += [ ops.mul(1 / 255), ops.type_cast(np.float32), # Imagenet: # ops.normalize( mean=np.array([0.485, 0.456, 0.406]), # std=np.array([0.229, 0.224, 0.225])), # Cifar-10: ops.normalize(mean=np.array([0.41477802, 0.45935813, 0.49693552]), std=np.array([0.25241926, 0.24699265, 0.25279155])), ops.hwc2chw() ] op = ops.chain(ops_list) return batches.BatchGenerator(dataset, 128, True, op)
def set_parameter(model, freeze_parameters): if freeze_parameters: for param in model.parameters(): param.requires_grad = False if USE_TRANSFER_LEARNING: # there are two networks to use in transfer learning "resnet" and "alexnet" net = initialize_transfer_learning_model("resnet", NUM_CLASSES, FREEZE_CNN_PARAMETERS) net, input_size = net pad_mode_for_resizing = 'constant' op_chain = chain([ type_cast(dtype=np.float32), add(-127.5), mul(1 / 127.5), rcrop(25, 2, 'median'), resize(input_size, pad_mode_for_resizing), hwc2chw() ]) else: net = CatDogNet() op_chain = chain([ type_cast(dtype=np.float32), add(-127.5), mul(1 / 127.5), rcrop(25, 2, 'median'), hwc2chw() ]) batchGenerator_training = BatchGenerator(pets_training,
from dlvc.datasets.pets import PetsDataset from dlvc.dataset import Subset import dlvc.ops as ops np.random.seed(0) torch.manual_seed(0) DATA_PATH = "../cifar-10-batches-py/" MODEL_PATH = "best_model.pt" train_data = PetsDataset(DATA_PATH, Subset.TRAINING) val_data = PetsDataset(DATA_PATH, Subset.VALIDATION) op = ops.chain([ ops.type_cast(np.float32), ops.add(-127.5), ops.mul(1 / 127.5), ops.hflip(), ops.rcrop(32, 4, 'constant'), ops.add_noise(), ops.hwc2chw() ]) train_batches = BatchGenerator(train_data, 128, False, op) val_batches = BatchGenerator(val_data, 128, False, op) class Net(nn.Module): def __init__(self, img_size, num_classes): super(Net, self).__init__() self.img_size = img_size
import numpy as np from dlvc.dataset import Subset from dlvc.datasets.pets import PetsDataset from dlvc import ops, batches dataset = PetsDataset('../data/cifar-10-batches-py', Subset.TRAINING) op = ops.chain([ops.mul(1 / 255), ops.type_cast(np.float32)]) batch_generator = batches.BatchGenerator(dataset, 7959, True, op) training_images = [] for batch in batch_generator: training_images.append(batch.data) training_images = np.array(training_images, dtype=np.float32) training_images = training_images.reshape(training_images.shape[1:]) train_mean = np.mean(training_images, axis=(0, 1, 2)) train_std = np.std(training_images, axis=(0, 1, 2)) print(train_mean, train_std)