First commit.

tests/data/meta_info_gt.txt

+baboon.png
+comic.png

tests/data/meta_info_pair.txt

+gt/baboon.png, lq/baboon.png
+gt/comic.png, lq/comic.png

tests/data/test_realesrgan_dataset.yml

+name: Demo
+type: RealESRGANDataset
+dataroot_gt: tests/data/gt
+meta_info: tests/data/meta_info_gt.txt
+io_backend:
+  type: disk
+
+blur_kernel_size: 21
+kernel_list: ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso']
+kernel_prob: [0.45, 0.25, 0.12, 0.03, 0.12, 0.03]
+sinc_prob: 1
+blur_sigma: [0.2, 3]
+betag_range: [0.5, 4]
+betap_range: [1, 2]
+
+blur_kernel_size2: 21
+kernel_list2: ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso']
+kernel_prob2: [0.45, 0.25, 0.12, 0.03, 0.12, 0.03]
+sinc_prob2: 1
+blur_sigma2: [0.2, 1.5]
+betag_range2: [0.5, 4]
+betap_range2: [1, 2]
+
+final_sinc_prob: 1
+
+gt_size: 128
+use_hflip: True
+use_rot: False

tests/data/test_realesrgan_model.yml

+scale: 4
+num_gpu: 1
+manual_seed: 0
+is_train: True
+dist: False
+
+# ----------------- options for synthesizing training data ----------------- #
+# USM the ground-truth
+l1_gt_usm: True
+percep_gt_usm: True
+gan_gt_usm: False
+
+# the first degradation process
+resize_prob: [0.2, 0.7, 0.1]  # up, down, keep
+resize_range: [0.15, 1.5]
+gaussian_noise_prob: 1
+noise_range: [1, 30]
+poisson_scale_range: [0.05, 3]
+gray_noise_prob: 1
+jpeg_range: [30, 95]
+
+# the second degradation process
+second_blur_prob: 1
+resize_prob2: [0.3, 0.4, 0.3]  # up, down, keep
+resize_range2: [0.3, 1.2]
+gaussian_noise_prob2: 1
+noise_range2: [1, 25]
+poisson_scale_range2: [0.05, 2.5]
+gray_noise_prob2: 1
+jpeg_range2: [30, 95]
+
+gt_size: 32
+queue_size: 1
+
+# network structures
+network_g:
+  type: RRDBNet
+  num_in_ch: 3
+  num_out_ch: 3
+  num_feat: 4
+  num_block: 1
+  num_grow_ch: 2
+
+network_d:
+  type: UNetDiscriminatorSN
+  num_in_ch: 3
+  num_feat: 2
+  skip_connection: True
+
+# path
+path:
+  pretrain_network_g: ~
+  param_key_g: params_ema
+  strict_load_g: true
+  resume_state: ~
+
+# training settings
+train:
+  ema_decay: 0.999
+  optim_g:
+    type: Adam
+    lr: !!float 1e-4
+    weight_decay: 0
+    betas: [0.9, 0.99]
+  optim_d:
+    type: Adam
+    lr: !!float 1e-4
+    weight_decay: 0
+    betas: [0.9, 0.99]
+
+  scheduler:
+    type: MultiStepLR
+    milestones: [400000]
+    gamma: 0.5
+
+  total_iter: 400000
+  warmup_iter: -1  # no warm up
+
+  # losses
+  pixel_opt:
+    type: L1Loss
+    loss_weight: 1.0
+    reduction: mean
+  # perceptual loss (content and style losses)
+  perceptual_opt:
+    type: PerceptualLoss
+    layer_weights:
+      # before relu
+      'conv1_2': 0.1
+      'conv2_2': 0.1
+      'conv3_4': 1
+      'conv4_4': 1
+      'conv5_4': 1
+    vgg_type: vgg19
+    use_input_norm: true
+    perceptual_weight: !!float 1.0
+    style_weight: 0
+    range_norm: false
+    criterion: l1
+  # gan loss
+  gan_opt:
+    type: GANLoss
+    gan_type: vanilla
+    real_label_val: 1.0
+    fake_label_val: 0.0
+    loss_weight: !!float 1e-1
+
+  net_d_iters: 1
+  net_d_init_iters: 0
+
+
+# validation settings
+val:
+  val_freq: !!float 5e3
+  save_img: False

tests/data/test_realesrgan_paired_dataset.yml

+name: Demo
+type: RealESRGANPairedDataset
+scale: 4
+dataroot_gt: tests/data
+dataroot_lq: tests/data
+meta_info: tests/data/meta_info_pair.txt
+io_backend:
+  type: disk
+
+phase: train
+gt_size: 128
+use_hflip: True
+use_rot: False

tests/data/test_realesrnet_model.yml

+scale: 4
+num_gpu: 1
+manual_seed: 0
+is_train: True
+dist: False
+
+# ----------------- options for synthesizing training data ----------------- #
+gt_usm: True  # USM the ground-truth
+
+# the first degradation process
+resize_prob: [0.2, 0.7, 0.1]  # up, down, keep
+resize_range: [0.15, 1.5]
+gaussian_noise_prob: 1
+noise_range: [1, 30]
+poisson_scale_range: [0.05, 3]
+gray_noise_prob: 1
+jpeg_range: [30, 95]
+
+# the second degradation process
+second_blur_prob: 1
+resize_prob2: [0.3, 0.4, 0.3]  # up, down, keep
+resize_range2: [0.3, 1.2]
+gaussian_noise_prob2: 1
+noise_range2: [1, 25]
+poisson_scale_range2: [0.05, 2.5]
+gray_noise_prob2: 1
+jpeg_range2: [30, 95]
+
+gt_size: 32
+queue_size: 1
+
+# network structures
+network_g:
+  type: RRDBNet
+  num_in_ch: 3
+  num_out_ch: 3
+  num_feat: 4
+  num_block: 1
+  num_grow_ch: 2
+
+# path
+path:
+  pretrain_network_g: ~
+  param_key_g: params_ema
+  strict_load_g: true
+  resume_state: ~
+
+# training settings
+train:
+  ema_decay: 0.999
+  optim_g:
+    type: Adam
+    lr: !!float 2e-4
+    weight_decay: 0
+    betas: [0.9, 0.99]
+
+  scheduler:
+    type: MultiStepLR
+    milestones: [1000000]
+    gamma: 0.5
+
+  total_iter: 1000000
+  warmup_iter: -1  # no warm up
+
+  # losses
+  pixel_opt:
+    type: L1Loss
+    loss_weight: 1.0
+    reduction: mean
+
+
+# validation settings
+val:
+  val_freq: !!float 5e3
+  save_img: False

tests/test_dataset.py

+import pytest
+import yaml
+
+from realesrgan.data.realesrgan_dataset import RealESRGANDataset
+from realesrgan.data.realesrgan_paired_dataset import RealESRGANPairedDataset
+
+
+def test_realesrgan_dataset():
+
+    with open('tests/data/test_realesrgan_dataset.yml', mode='r') as f:
+        opt = yaml.load(f, Loader=yaml.FullLoader)
+
+    dataset = RealESRGANDataset(opt)
+    assert dataset.io_backend_opt['type'] == 'disk'  # io backend
+    assert len(dataset) == 2  # whether to read correct meta info
+    assert dataset.kernel_list == [
+        'iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso'
+    ]  # correct initialization the degradation configurations
+    assert dataset.betag_range2 == [0.5, 4]
+
+    # test __getitem__
+    result = dataset.__getitem__(0)
+    # check returned keys
+    expected_keys = ['gt', 'kernel1', 'kernel2', 'sinc_kernel', 'gt_path']
+    assert set(expected_keys).issubset(set(result.keys()))
+    # check shape and contents
+    assert result['gt'].shape == (3, 400, 400)
+    assert result['kernel1'].shape == (21, 21)
+    assert result['kernel2'].shape == (21, 21)
+    assert result['sinc_kernel'].shape == (21, 21)
+    assert result['gt_path'] == 'tests/data/gt/baboon.png'
+
+    # ------------------ test lmdb backend -------------------- #
+    opt['dataroot_gt'] = 'tests/data/gt.lmdb'
+    opt['io_backend']['type'] = 'lmdb'
+
+    dataset = RealESRGANDataset(opt)
+    assert dataset.io_backend_opt['type'] == 'lmdb'  # io backend
+    assert len(dataset.paths) == 2  # whether to read correct meta info
+    assert dataset.kernel_list == [
+        'iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso'
+    ]  # correct initialization the degradation configurations
+    assert dataset.betag_range2 == [0.5, 4]
+
+    # test __getitem__
+    result = dataset.__getitem__(1)
+    # check returned keys
+    expected_keys = ['gt', 'kernel1', 'kernel2', 'sinc_kernel', 'gt_path']
+    assert set(expected_keys).issubset(set(result.keys()))
+    # check shape and contents
+    assert result['gt'].shape == (3, 400, 400)
+    assert result['kernel1'].shape == (21, 21)
+    assert result['kernel2'].shape == (21, 21)
+    assert result['sinc_kernel'].shape == (21, 21)
+    assert result['gt_path'] == 'comic'
+
+    # ------------------ test with sinc_prob = 0 -------------------- #
+    opt['dataroot_gt'] = 'tests/data/gt.lmdb'
+    opt['io_backend']['type'] = 'lmdb'
+    opt['sinc_prob'] = 0
+    opt['sinc_prob2'] = 0
+    opt['final_sinc_prob'] = 0
+    dataset = RealESRGANDataset(opt)
+    result = dataset.__getitem__(0)
+    # check returned keys
+    expected_keys = ['gt', 'kernel1', 'kernel2', 'sinc_kernel', 'gt_path']
+    assert set(expected_keys).issubset(set(result.keys()))
+    # check shape and contents
+    assert result['gt'].shape == (3, 400, 400)
+    assert result['kernel1'].shape == (21, 21)
+    assert result['kernel2'].shape == (21, 21)
+    assert result['sinc_kernel'].shape == (21, 21)
+    assert result['gt_path'] == 'baboon'
+
+    # ------------------ lmdb backend should have paths ends with lmdb -------------------- #
+    with pytest.raises(ValueError):
+        opt['dataroot_gt'] = 'tests/data/gt'
+        opt['io_backend']['type'] = 'lmdb'
+        dataset = RealESRGANDataset(opt)
+
+
+def test_realesrgan_paired_dataset():
+
+    with open('tests/data/test_realesrgan_paired_dataset.yml', mode='r') as f:
+        opt = yaml.load(f, Loader=yaml.FullLoader)
+
+    dataset = RealESRGANPairedDataset(opt)
+    assert dataset.io_backend_opt['type'] == 'disk'  # io backend
+    assert len(dataset) == 2  # whether to read correct meta info
+
+    # test __getitem__
+    result = dataset.__getitem__(0)
+    # check returned keys
+    expected_keys = ['gt', 'lq', 'gt_path', 'lq_path']
+    assert set(expected_keys).issubset(set(result.keys()))
+    # check shape and contents
+    assert result['gt'].shape == (3, 128, 128)
+    assert result['lq'].shape == (3, 32, 32)
+    assert result['gt_path'] == 'tests/data/gt/baboon.png'
+    assert result['lq_path'] == 'tests/data/lq/baboon.png'
+
+    # ------------------ test lmdb backend -------------------- #
+    opt['dataroot_gt'] = 'tests/data/gt.lmdb'
+    opt['dataroot_lq'] = 'tests/data/lq.lmdb'
+    opt['io_backend']['type'] = 'lmdb'
+
+    dataset = RealESRGANPairedDataset(opt)
+    assert dataset.io_backend_opt['type'] == 'lmdb'  # io backend
+    assert len(dataset) == 2  # whether to read correct meta info
+
+    # test __getitem__
+    result = dataset.__getitem__(1)
+    # check returned keys
+    expected_keys = ['gt', 'lq', 'gt_path', 'lq_path']
+    assert set(expected_keys).issubset(set(result.keys()))
+    # check shape and contents
+    assert result['gt'].shape == (3, 128, 128)
+    assert result['lq'].shape == (3, 32, 32)
+    assert result['gt_path'] == 'comic'
+    assert result['lq_path'] == 'comic'
+
+    # ------------------ test paired_paths_from_folder -------------------- #
+    opt['dataroot_gt'] = 'tests/data/gt'
+    opt['dataroot_lq'] = 'tests/data/lq'
+    opt['io_backend'] = dict(type='disk')
+    opt['meta_info'] = None
+
+    dataset = RealESRGANPairedDataset(opt)
+    assert dataset.io_backend_opt['type'] == 'disk'  # io backend
+    assert len(dataset) == 2  # whether to read correct meta info
+
+    # test __getitem__
+    result = dataset.__getitem__(0)
+    # check returned keys
+    expected_keys = ['gt', 'lq', 'gt_path', 'lq_path']
+    assert set(expected_keys).issubset(set(result.keys()))
+    # check shape and contents
+    assert result['gt'].shape == (3, 128, 128)
+    assert result['lq'].shape == (3, 32, 32)
+
+    # ------------------ test normalization -------------------- #
+    dataset.mean = [0.5, 0.5, 0.5]
+    dataset.std = [0.5, 0.5, 0.5]
+    # test __getitem__
+    result = dataset.__getitem__(0)
+    # check returned keys
+    expected_keys = ['gt', 'lq', 'gt_path', 'lq_path']
+    assert set(expected_keys).issubset(set(result.keys()))
+    # check shape and contents
+    assert result['gt'].shape == (3, 128, 128)
+    assert result['lq'].shape == (3, 32, 32)

tests/test_discriminator_arch.py

+import torch
+
+from realesrgan.archs.discriminator_arch import UNetDiscriminatorSN
+
+
+def test_unetdiscriminatorsn():
+    """Test arch: UNetDiscriminatorSN."""
+
+    # model init and forward (cpu)
+    net = UNetDiscriminatorSN(num_in_ch=3, num_feat=4, skip_connection=True)
+    img = torch.rand((1, 3, 32, 32), dtype=torch.float32)
+    output = net(img)
+    assert output.shape == (1, 1, 32, 32)
+
+    # model init and forward (gpu)
+    if torch.cuda.is_available():
+        net.cuda()
+        output = net(img.cuda())
+        assert output.shape == (1, 1, 32, 32)