add

.gitattributes

@@ -2,27 +2,20 @@
 *.arrow filter=lfs diff=lfs merge=lfs -text
 *.bin filter=lfs diff=lfs merge=lfs -text
 *.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ckpt filter=lfs diff=lfs merge=lfs -text
 *.ftz filter=lfs diff=lfs merge=lfs -text
 *.gz filter=lfs diff=lfs merge=lfs -text
 *.h5 filter=lfs diff=lfs merge=lfs -text
 *.joblib filter=lfs diff=lfs merge=lfs -text
 *.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
 *.model filter=lfs diff=lfs merge=lfs -text
 *.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
 *.onnx filter=lfs diff=lfs merge=lfs -text
 *.ot filter=lfs diff=lfs merge=lfs -text
 *.parquet filter=lfs diff=lfs merge=lfs -text
 *.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
 *.pt filter=lfs diff=lfs merge=lfs -text
 *.pth filter=lfs diff=lfs merge=lfs -text
 *.rar filter=lfs diff=lfs merge=lfs -text
-*.safetensors filter=lfs diff=lfs merge=lfs -text
 saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.tar.* filter=lfs diff=lfs merge=lfs -text
 *.tflite filter=lfs diff=lfs merge=lfs -text
@@ -30,5 +23,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.wasm filter=lfs diff=lfs merge=lfs -text
 *.xz filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
-*.zst filter=lfs diff=lfs merge=lfs -text
+*.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.jpg filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,9 @@
+__pycache__
+data/
+.idea
+checkpoints/
+results/
+pretrained/
+.DS_Store
+gradio_cached_examples/
+pretrain/

App_main.py

@@ -0,0 +1,114 @@
+import numpy as np
+
+import models
+import gradio as gr
+
+# os.system('nvidia-smi')
+# os.system('ls /usr/local')
+# os.system('pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/cu113')
+import torch
+from torchvision import transforms
+from torchvision.transforms import InterpolationMode
+
+device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
+
+
+def construct_sample(img, mean=0.5, std=0.5):
+    img = transforms.ToTensor()(img)
+    img = transforms.Resize(48, InterpolationMode.BICUBIC)(img)
+    img = transforms.Normalize(mean, std)(img)
+    return img
+
+def build_model(cp):
+    model_spec = torch.load(cp, map_location='cpu')['model']
+    print(model_spec['args'])
+    model = models.make(model_spec, load_sd=True).to(device)
+    return model
+
+
+# Function for building extraction
+def sr_func(img, cp, scale):
+    if cp == 'UC':
+        checkpoint = 'pretrain/UC_FunSR_RDN.pth'
+    elif cp == 'AID':
+        checkpoint = 'pretrain/AID_FunSR_RDN.pth'
+    else:
+        raise NotImplementedError
+    sample = construct_sample(img)
+    print('Use: ', device)
+    model = build_model(checkpoint)
+    model.eval()
+    sample = sample.to(device)
+    sample = sample.unsqueeze(0)
+
+    ori_size = torch.tensor(sample.shape[2:])  # BCHW
+    target_size = ori_size * scale
+    target_size = target_size.long()
+    lr_target_size_img = torch.nn.functional.interpolate(sample, scale_factor=scale, mode='nearest')
+    with torch.no_grad():
+        pred = model(sample, target_size.tolist())
+
+    if isinstance(pred, list):
+        pred = pred[-1]
+    pred = pred * 0.5 + 0.5
+
+    pred *= 255
+    pred = pred[0].detach().cpu()
+    lr_target_size_img = lr_target_size_img * 0.5 + 0.5
+    lr_target_size_img = 255 * lr_target_size_img[0].detach().cpu()
+
+    lr_target_size_img = torch.clamp(lr_target_size_img, 0, 255).permute(1,2,0).numpy().astype(np.uint8)
+    pred = torch.clamp(pred, 0, 255).permute(1,2,0).numpy().astype(np.uint8)
+
+    line = np.ones((pred.shape[0], 5, 3), dtype=np.uint8) * 255
+    pred = np.concatenate((lr_target_size_img, line, pred), axis=1)
+    return pred
+
+title = "FunSR"
+description = "Gradio demo for continuous remote sensing image super-resolution. Upload image from UCMerced or AID Dataset or click any one of the examples, " \
+              "Then change the upscaling magnification, and click \"Submit\" and wait for the super-resolved result. \n" \
+              "Paper: Continuous Remote Sensing Image Super-Resolution based on Context Interaction in Implicit Function Space"
+
+article = "<p style='text-align: center'><a href='https://kyanchen.github.io/FunSR/' target='_blank'>FunSR Project " \
+          "Page</a></p> "
+
+default_scale = 4.0
+examples = [
+    ['examples/AID_school_161_LR.png', 'AID', default_scale],
+    ['examples/AID_bridge_19_LR.png', 'AID', default_scale],
+    ['examples/AID_parking_60_LR.png', 'AID', default_scale],
+    ['examples/AID_commercial_32_LR.png', 'AID', default_scale],
+
+    ['examples/UC_airplane95_LR.png', 'UC', default_scale],
+    ['examples/UC_freeway35_LR.png', 'UC', default_scale],
+    ['examples/UC_storagetanks54_LR.png', 'UC', default_scale],
+    ['examples/UC_airplane00_LR.png', 'UC', default_scale],
+]
+
+with gr.Blocks() as demo:
+    image_input = gr.inputs.Image(type='pil', label='Input Img')
+    # with gr.Row().style(equal_height=True):
+    # image_LR_output = gr.outputs.Image(label='LR Img', type='numpy')
+    image_output = gr.outputs.Image(label='SR Result', type='numpy')
+    with gr.Row():
+        checkpoint = gr.inputs.Radio(['WHU', 'INRIA'], label='Checkpoint')
+        scale = gr.Slider(1, 12, value=4.0, step=0.1, label='scale')
+
+io = gr.Interface(fn=sr_func,
+                  inputs=[image_input,
+                          checkpoint,
+                          scale
+                          ],
+                  outputs=[
+                      # image_LR_output,
+                      image_output
+                  ],
+                  title=title,
+                  description=description,
+                  article=article,
+                  allow_flagging='auto',
+                  examples=examples,
+                  cache_examples=True,
+                  layout="grid"
+                  )
+io.launch()

README.md

@@ -1,12 +1,11 @@
 ---
 title: FunSR
-emoji: 👀
-colorFrom: blue
+emoji: 🐢
+colorFrom: red
 colorTo: yellow
 sdk: gradio
-sdk_version: 3.19.1
-app_file: app.py
+sdk_version: 3.18.0
+app_file: App_main.py
 pinned: false
 ---
-
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

configs/baselines/test_INR_liif_metasr_aliif.yaml

@@ -0,0 +1,24 @@
+test_dataset:
+  dataset:
+    name: hr_data_loader
+    args:
+#      root_path: /data/kyanchen/datasets/UC/256
+#      split_file: data_split/UC_split.json
+      root_path: /data/kyanchen/datasets/AID
+      split_file: data_split/AID_split.json
+      split_key: test
+      cache: none
+  wrapper:
+    name: inr_fixed_scale_sr_warp
+    args:
+      scale_ratio: 4
+      patch_size: 48
+      augment: false
+      val_mode: false
+      test_mode: true
+      return_cell: true
+  batch_size: 1
+eval_type: psnr+ssim
+data_norm:
+  inp: {sub: [0.5], div: [0.5]}
+  gt: {sub: [0.5], div: [0.5]}

configs/baselines/train_1x-5x_INR_aliif.yaml

@@ -0,0 +1,98 @@
+train_dataset:
+  dataset:
+    name: hr_data_loader
+    args:
+#      root_path: /data/kyanchen/datasets/UC/256
+#      split_file: data_split/UC_split.json
+      root_path: /data/kyanchen/datasets/AID
+      split_file: data_split/AID_split.json
+      split_key: train
+      cache: none
+  wrapper:
+    name: inr_range_scale_sr_warp
+    args:
+      max_scale_ratio: 5
+      patch_size: 48
+      augment: true
+      sample_q: 1024
+      val_mode: false
+      test_mode: false
+      return_cell: true
+  batch_size: 48
+  num_workers: 4
+
+val_dataset:
+  dataset:
+    name: hr_data_loader
+    args:
+#      root_path: /data/kyanchen/datasets/UC/256
+#      split_file: data_split/UC_split.json
+      root_path: /data/kyanchen/datasets/AID
+      split_file: data_split/AID_split.json
+      split_key: val
+      first_k: 128
+      cache: none
+  wrapper:
+    name: inr_range_scale_sr_warp
+    args:
+      max_scale_ratio: 5
+      patch_size: 48
+      augment: false
+      sample_q: 1024
+      val_mode: true
+      test_mode: false
+      return_cell: true
+  batch_size: 48
+  num_workers: 4
+
+eval_type: psnr+ssim
+
+data_norm:
+  inp: {sub: [0.5], div: [0.5]}
+  gt: {sub: [0.5], div: [0.5]}
+
+model:
+  name: aliif
+  args:
+    encoder_spec:
+#      name: edsr-baseline
+#      name: rdn
+      name: rcan
+      args:
+        no_upsampling: true
+    imnet_spec:
+      name: mlp_pw
+      args:
+        out_dim: 3
+        hidden_list: [16, 16, 16, 16]
+    pdn_spec:
+      name: ExpansionNet
+      args:
+        in_dim: 580
+        out_dim: 10
+        hidden_list: [ 256, 256, 256, 256 ]
+    basis_spec:
+      name: basis
+      args:
+        basis_num: 10
+        hidden: 16
+        state: train
+        path: ./save/test/epoch-last.pth
+
+optimizer:
+  name: adamw
+  args:
+    lr: 1.e-4
+
+epoch_max: 3000
+
+lr_scheduler:
+#  name: CosineAnnealingLR
+#  T_max: 1500
+#  eta_min: 1.e-7
+  name: MultiStepLR
+  milestones: [2000]
+  gamma: 0.1
+
+epoch_val_interval: 40
+epoch_save_interval: 300

configs/baselines/train_1x-5x_INR_diinn_arbrcan_sadn_overnet.yaml

@@ -0,0 +1,81 @@
+train_dataset:
+  dataset:
+    name: hr_data_loader
+    args:
+#      root_path: /data/kyanchen/datasets/UC/256
+#      split_file: data_split/UC_split.json
+      root_path: /data/kyanchen/datasets/AID
+      split_file: data_split/AID_split.json
+      split_key: train
+      cache: none
+  wrapper:
+    name: inr_diinn_select_scale_sr_warp
+    args:
+      scales: [2, 2.5, 3, 3.5, 4]
+      patch_size: 48
+      augment: true
+      val_mode: false
+      test_mode: false
+  batch_size: 32
+  num_workers: 4
+
+val_dataset:
+  dataset:
+    name: hr_data_loader
+    args:
+#      root_path: /data/kyanchen/datasets/UC/256
+#      split_file: data_split/UC_split.json
+      root_path: /data/kyanchen/datasets/AID
+      split_file: data_split/AID_split.json
+      split_key: val
+      first_k: 128
+      cache: none
+  wrapper:
+    name: cnn_fixed_scale_sr_warp
+    args:
+      scale_ratio: 5
+      patch_size: 48
+      augment: false
+      val_mode: true
+      test_mode: false
+  batch_size: 8
+  num_workers: 4
+
+eval_type: psnr+ssim
+
+data_norm:
+  img: {sub: [0.5], div: [0.5]}
+  gt: {sub: [0.5], div: [0.5]}
+
+model:
+#  name: diinn
+#  name: arbrcan
+#  name: sadnarc
+#  name: edsr-sadn
+#  name: rdn-sadn
+  name: overnet
+  args:
+    encoder_spec:
+#      name: edsr-baseline
+#      name: rdn
+      name: rcan
+      args:
+        no_upsampling: true
+
+optimizer:
+  name: adamw
+  args:
+    lr: 1.e-4
+
+epoch_max: 3000
+
+lr_scheduler:
+#  name: CosineAnnealingLR
+#  T_max: 1500
+#  eta_min: 1.e-7
+  name: MultiStepLR
+  milestones: [2000]
+  gamma: 0.1
+
+epoch_val_interval: 40
+epoch_save_interval: 300

configs/baselines/train_1x-5x_INR_liif.yaml

@@ -0,0 +1,85 @@
+train_dataset:
+  dataset:
+    name: hr_data_loader
+    args:
+#      root_path: /data/kyanchen/datasets/UC/256
+#      split_file: data_split/UC_split.json
+      root_path: /data/kyanchen/datasets/AID
+      split_file: data_split/AID_split.json
+      split_key: train
+      cache: none
+  wrapper:
+    name: inr_range_scale_sr_warp
+    args:
+      max_scale_ratio: 5
+      patch_size: 48
+      augment: true
+      sample_q: 1024
+      val_mode: false
+      test_mode: false
+      return_cell: true
+  batch_size: 64
+  num_workers: 4
+
+val_dataset:
+  dataset:
+    name: hr_data_loader
+    args:
+#      root_path: /data/kyanchen/datasets/UC/256
+#      split_file: data_split/UC_split.json
+      root_path: /data/kyanchen/datasets/AID
+      split_file: data_split/AID_split.json
+      split_key: val
+      first_k: 128
+      cache: none
+  wrapper:
+    name: inr_range_scale_sr_warp
+    args:
+      max_scale_ratio: 5
+      patch_size: 48
+      augment: false
+      sample_q: 1024
+      val_mode: true
+      test_mode: false
+      return_cell: true
+  batch_size: 64
+  num_workers: 4
+
+eval_type: psnr+ssim
+
+data_norm:
+  inp: {sub: [0.5], div: [0.5]}
+  gt: {sub: [0.5], div: [0.5]}
+
+model:
+  name: liif
+  args:
+    encoder_spec:
+#      name: edsr-baseline
+#      name: rdn
+      name: rcan
+      args:
+        no_upsampling: true
+    imnet_spec:
+      name: mlp
+      args:
+        out_dim: 3
+        hidden_list: [256, 256, 256, 256]
+
+optimizer:
+  name: adamw
+  args:
+    lr: 1.e-4
+
+epoch_max: 4000
+
+lr_scheduler:
+#  name: CosineAnnealingLR
+#  T_max: 1500
+#  eta_min: 1.e-7
+  name: MultiStepLR
+  milestones: [2500]
+  gamma: 0.1
+
+epoch_val_interval: 40
+epoch_save_interval: 300

configs/baselines/train_1x-5x_INR_metasr.yaml

@@ -0,0 +1,80 @@
+train_dataset:
+  dataset:
+    name: hr_data_loader
+    args:
+#      root_path: /data/kyanchen/datasets/UC/256
+#      split_file: data_split/UC_split.json
+      root_path: /data/kyanchen/datasets/AID
+      split_file: data_split/AID_split.json
+      split_key: train
+      cache: none
+  wrapper:
+    name: inr_range_scale_sr_warp
+    args:
+      max_scale_ratio: 5
+      patch_size: 48
+      augment: true
+      sample_q: 1024
+      val_mode: false
+      test_mode: false
+      return_cell: true
+  batch_size: 64
+  num_workers: 4
+
+val_dataset:
+  dataset:
+    name: hr_data_loader
+    args:
+#      root_path: /data/kyanchen/datasets/UC/256
+#      split_file: data_split/UC_split.json
+      root_path: /data/kyanchen/datasets/AID
+      split_file: data_split/AID_split.json
+      split_key: val
+      first_k: 128
+      cache: none
+  wrapper:
+    name: inr_range_scale_sr_warp
+    args:
+      max_scale_ratio: 5
+      patch_size: 48
+      augment: false
+      sample_q: 1024
+      val_mode: true
+      test_mode: false
+      return_cell: true
+  batch_size: 64
+  num_workers: 4
+
+eval_type: psnr+ssim
+
+data_norm:
+  inp: {sub: [0.5], div: [0.5]}
+  gt: {sub: [0.5], div: [0.5]}
+
+model:
+  name: metasr
+  args:
+    encoder_spec:
+#      name: edsr-baseline
+#      name: rdn
+      name: rcan
+      args:
+        no_upsampling: true
+
+optimizer:
+  name: adamw
+  args:
+    lr: 1.e-4
+
+epoch_max: 3000
+
+lr_scheduler:
+#  name: CosineAnnealingLR
+#  T_max: 1500
+#  eta_min: 1.e-7
+  name: MultiStepLR
+  milestones: [2000]
+  gamma: 0.1
+
+epoch_val_interval: 40
+epoch_save_interval: 300

configs/test_CNN.yaml

@@ -0,0 +1,25 @@
+test_dataset:
+  dataset:
+    name: hr_data_loader
+    args:
+#      root_path: /data/kyanchen/datasets/UC/256
+#      split_file: data_split/UC_split.json
+      root_path: /data/kyanchen/datasets/AID
+      split_file: data_split/AID_split.json
+      split_key: test
+      cache: none
+  wrapper:
+    name: cnn_fixed_scale_sr_warp
+    args:
+      scale_ratio: 5
+      patch_size: 48
+      augment: false
+      val_mode: false
+      test_mode: true
+  batch_size: 8
+
+eval_type: psnr+ssim
+
+data_norm:
+  img: {sub: [0.5], div: [0.5]}
+  gt: {sub: [0.5], div: [0.5]}

configs/test_INR_diinn_arbrcan_funsr_overnet.yaml

@@ -0,0 +1,25 @@
+test_dataset:
+  dataset:
+    name: hr_data_loader
+    args:
+#      root_path: /data/kyanchen/datasets/UC/256
+#      split_file: data_split/UC_split.json
+      root_path: /data/kyanchen/datasets/AID
+      split_file: data_split/AID_split.json
+      split_key: test
+      cache: none
+  wrapper:
+    name: cnn_fixed_scale_sr_warp
+    args:
+      scale_ratio: 5
+      patch_size: 48
+      augment: false
+      val_mode: false
+      test_mode: true
+  batch_size: 8
+
+eval_type: psnr+ssim
+
+data_norm:
+  img: {sub: [0.5], div: [0.5]}
+  gt: {sub: [0.5], div: [0.5]}

configs/test_interpolate.yaml

@@ -0,0 +1,24 @@
+test_dataset:
+  dataset:
+    name: hr_data_loader
+    args:
+#      root_path: /data/kyanchen/datasets/UC/256
+#      split_file: data_split/UC_split.json
+      root_path: /data/kyanchen/datasets/AID
+      split_file: data_split/AID_split.json
+      split_key: test
+      cache: none
+  wrapper:
+    name: cnn_fixed_scale_sr_warp
+    args:
+      scale_ratio: 4
+      patch_size: 48
+      test_mode: true
+      augment: false
+  batch_size: 64
+
+eval_type: psnr+ssim
+
+data_norm:
+  img: {sub: [0.5], div: [0.5]}
+  gt: {sub: [0.5], div: [0.5]}

configs/train_1x-5x_INR_funsr.yaml

@@ -0,0 +1,115 @@
+train_dataset:
+  dataset:
+    name: hr_data_loader
+    args:
+      root_path: /data/kyanchen/datasets/UC/256
+      split_file: data_split/UC_split.json
+#      root_path: /data/kyanchen/datasets/AID
+#      split_file: data_split/AID_split.json
+      split_key: train
+      cache: none
+  wrapper:
+    name: inr_diinn_select_scale_sr_warp
+    args:
+      scales: [2, 2.5, 3, 3.5, 4]
+#      scales: [1.5, 1.7, 2, 2.3, 2.5, 2.7, 3, 3.3, 3.5, 3.7, 4, 4.3, 4.5, 4.7, 5]
+      patch_size: 48
+      augment: true
+      val_mode: false
+      test_mode: false
+  batch_size: 8
+  num_workers: 4
+
+val_dataset:
+  dataset:
+    name: hr_data_loader
+    args:
+      root_path: /data/kyanchen/datasets/UC/256
+      split_file: data_split/UC_split.json
+#      root_path: /data/kyanchen/datasets/AID
+#      split_file: data_split/AID_split.json
+      split_key: test
+#      first_k: 128
+      cache: none
+  wrapper:
+    name: cnn_fixed_scale_sr_warp
+    args:
+      scale_ratio: 2
+      patch_size: 48
+      augment: false
+      val_mode: false
+      test_mode: true
+  batch_size: 4
+  num_workers: 4
+
+eval_type: psnr+ssim
+
+data_norm:
+  img: {sub: [0.5], div: [0.5]}
+  gt: {sub: [0.5], div: [0.5]}
+
+model:
+  name: funsr
+  args:
+    encoder_spec:
+      name: edsr-baseline
+#      name: rdn
+#      name: rcan
+      args:
+        no_upsampling: true
+    has_multiscale: true
+    neck:
+      name: transformer_neck
+      args:
+        d_dim: 256
+        downsample: true
+        has_pe: true
+        has_norm: true
+        class_token: true
+        num_encoder_layers: 3
+    decoder:
+      name: sirens
+      args:
+        num_inner_layers: 9
+        is_residual: true
+    global_decoder:
+      name: sirens
+      is_proj: true
+      args:
+        num_inner_layers: 9
+        is_residual: true
+    encoder_rgb: true
+    n_forward_times: 1
+    encode_hr_coord: true
+    has_bn: true
+    encode_scale_ratio: true
+    local_unfold: false
+    weight_gen_func: 'nearest-exact'  # bilinear, nearest-exact,bicubic
+
+optimizer:
+  name: adamw
+  args:
+    lr: 0.0001
+#optimizer:
+#  name: adam
+#  args:
+#    lr: 0.0001
+
+epoch_max: 4000
+
+lr_scheduler:
+#  name: CosineAnnealingLR
+#  T_max: 1500
+#  eta_min: 1.e-7
+  name: CosineAnnealingWarmUpLR
+  epochs: 4000
+  warm_up_epochs: 50
+  eta_min: 1.e-8
+
+#  name: MultiStepLR
+#  milestones: [2000, 3000]
+#  gamma: 0.1
+
+#resume: checkpoints/EXP20221215_00/epoch-last.pth
+epoch_val_interval: 50
+epoch_save_interval: 300

configs/train_CNN.yaml

@@ -0,0 +1,94 @@
+train_dataset:
+  dataset:
+    name: hr_data_loader
+    args:
+#      root_path: /data/kyanchen/datasets/UC/256
+#      split_file: data_split/UC_split.json
+      root_path: /data/kyanchen/datasets/AID
+      split_file: data_split/AID_split.json
+      split_key: train
+      cache: none
+  wrapper:
+    name: cnn_fixed_scale_sr_warp
+    args:
+      scale_ratio: 2
+      patch_size: 48
+      augment: true
+      val_mode: false
+      test_mode: false
+  batch_size: 96
+  num_workers: 8
+
+val_dataset:
+  dataset:
+    name: hr_data_loader
+    args:
+#      root_path: /data/kyanchen/datasets/UC/256
+#      split_file: data_split/UC_split.json
+      root_path: /data/kyanchen/datasets/AID
+      split_file: data_split/AID_split.json
+      split_key: val
+      cache: none
+  wrapper:
+    name: cnn_fixed_scale_sr_warp
+    args:
+      scale_ratio: 2
+      patch_size: 48
+      augment: false
+      val_mode: true
+      test_mode: false
+  batch_size: 32
+  num_workers: 4
+
+data_norm:
+  img: {sub: [0.5], div: [0.5]}
+  gt: {sub: [0.5], div: [0.5]}
+
+#model:
+#  name: TransENet
+#  args:
+#    scale_ratio: 2
+
+#model:
+#  name: SRCNN
+#  args:
+#    scale_ratio: 2
+
+#model:
+#  name: FSRCNN
+#  args:
+#    scale_ratio: 2
+
+#model:
+#  name: LGCNET
+#  args:
+#    scale_ratio: 2
+
+#model:
+#  name: DCM
+#  args:
+#    scale_ratio: 2
+
+model:
+  name: VDSR
+  args:
+    scale_ratio: 2
+
+optimizer:
+  name: adamw
+  args:
+    lr: 0.0001
+
+epoch_max: 1300
+#epoch_max: 2000
+
+lr_scheduler:
+#  name: CosineAnnealingLR
+#  T_max: 300
+#  eta_min: 1.e-6
+  name: MultiStepLR
+  milestones: [1000]
+  gamma: 0.5
+
+epoch_val_interval: 50
+epoch_save_interval: 300

data_split/UC_split.json

@@ -0,0 +1 @@
+{"train": ["agricultural/agricultural49.tif", "agricultural/agricultural58.tif", "agricultural/agricultural24.tif", "agricultural/agricultural44.tif", "agricultural/agricultural17.tif", "agricultural/agricultural21.tif", "agricultural/agricultural94.tif", "agricultural/agricultural50.tif", "agricultural/agricultural34.tif", "agricultural/agricultural16.tif", "agricultural/agricultural92.tif", "agricultural/agricultural62.tif", "agricultural/agricultural59.tif", "agricultural/agricultural87.tif", "agricultural/agricultural28.tif", "agricultural/agricultural79.tif", "agricultural/agricultural05.tif", "agricultural/agricultural43.tif", "agricultural/agricultural55.tif", "agricultural/agricultural51.tif", "agricultural/agricultural75.tif", "agricultural/agricultural57.tif", "agricultural/agricultural99.tif", "agricultural/agricultural83.tif", "agricultural/agricultural73.tif", "agricultural/agricultural04.tif", "agricultural/agricultural69.tif", "agricultural/agricultural42.tif", "agricultural/agricultural70.tif", "agricultural/agricultural78.tif", "agricultural/agricultural71.tif", "agricultural/agricultural54.tif", "agricultural/agricultural77.tif", "agricultural/agricultural39.tif", "agricultural/agricultural32.tif", "agricultural/agricultural81.tif", "agricultural/agricultural33.tif", "agricultural/agricultural84.tif", "agricultural/agricultural89.tif", "agricultural/agricultural13.tif", "agricultural/agricultural61.tif", "agricultural/agricultural41.tif", "agricultural/agricultural31.tif", "agricultural/agricultural65.tif", "agricultural/agricultural18.tif", "agricultural/agricultural80.tif", "agricultural/agricultural12.tif", "agricultural/agricultural37.tif", "agricultural/agricultural82.tif", "agricultural/agricultural06.tif", "agricultural/agricultural85.tif", "agricultural/agricultural95.tif", "agricultural/agricultural52.tif", "agricultural/agricultural14.tif", "agricultural/agricultural72.tif", "agricultural/agricultural86.tif", "agricultural/agricultural10.tif", "agricultural/agricultural56.tif", "agricultural/agricultural90.tif", "agricultural/agricultural63.tif", "airplane/airplane53.tif", "airplane/airplane22.tif", "airplane/airplane00.tif", "airplane/airplane99.tif", "airplane/airplane10.tif", "airplane/airplane31.tif", "airplane/airplane32.tif", "airplane/airplane90.tif", "airplane/airplane51.tif", "airplane/airplane07.tif", "airplane/airplane82.tif", "airplane/airplane48.tif", "airplane/airplane93.tif", "airplane/airplane39.tif", "airplane/airplane52.tif", "airplane/airplane49.tif", "airplane/airplane84.tif", "airplane/airplane73.tif", "airplane/airplane05.tif", "airplane/airplane01.tif", "airplane/airplane14.tif", "airplane/airplane50.tif", "airplane/airplane59.tif", "airplane/airplane57.tif", "airplane/airplane68.tif", "airplane/airplane72.tif", "airplane/airplane04.tif", "airplane/airplane78.tif", "airplane/airplane28.tif", "airplane/airplane26.tif", "airplane/airplane83.tif", "airplane/airplane55.tif", "airplane/airplane66.tif", "airplane/airplane92.tif", "airplane/airplane60.tif", "airplane/airplane58.tif", "airplane/airplane63.tif", "airplane/airplane81.tif", "airplane/airplane21.tif", "airplane/airplane69.tif", "airplane/airplane18.tif", "airplane/airplane54.tif", "airplane/airplane44.tif", "airplane/airplane87.tif", "airplane/airplane62.tif", "airplane/airplane30.tif", "airplane/airplane76.tif", "airplane/airplane24.tif", "airplane/airplane36.tif", "airplane/airplane56.tif", "airplane/airplane38.tif", "airplane/airplane75.tif", "airplane/airplane70.tif", "airplane/airplane79.tif", "airplane/airplane41.tif", "airplane/airplane89.tif", "airplane/airplane06.tif", "airplane/airplane67.tif", "airplane/airplane02.tif", "airplane/airplane45.tif", "baseballdiamond/baseballdiamond63.tif", "baseballdiamond/baseballdiamond39.tif", "baseballdiamond/baseballdiamond67.tif", "baseballdiamond/baseballdiamond10.tif", "baseballdiamond/baseballdiamond80.tif", "baseballdiamond/baseballdiamond11.tif", "baseballdiamond/baseballdiamond46.tif", "baseballdiamond/baseballdiamond77.tif", "baseballdiamond/baseballdiamond33.tif", "baseballdiamond/baseballdiamond76.tif", "baseballdiamond/baseballdiamond78.tif", "baseballdiamond/baseballdiamond96.tif", "baseballdiamond/baseballdiamond84.tif", "baseballdiamond/baseballdiamond42.tif", "baseballdiamond/baseballdiamond40.tif", "baseballdiamond/baseballdiamond02.tif", "baseballdiamond/baseballdiamond45.tif", "baseballdiamond/baseballdiamond86.tif", "baseballdiamond/baseballdiamond49.tif", "baseballdiamond/baseballdiamond43.tif", "baseballdiamond/baseballdiamond59.tif", "baseballdiamond/baseballdiamond03.tif", "baseballdiamond/baseballdiamond01.tif", "baseballdiamond/baseballdiamond26.tif", "baseballdiamond/baseballdiamond69.tif", "baseballdiamond/baseballdiamond18.tif", "baseballdiamond/baseballdiamond44.tif", "baseballdiamond/baseballdiamond68.tif", "baseballdiamond/baseballdiamond14.tif", "baseballdiamond/baseballdiamond94.tif", "baseballdiamond/baseballdiamond22.tif", "baseballdiamond/baseballdiamond00.tif", "baseballdiamond/baseballdiamond05.tif", "baseballdiamond/baseballdiamond60.tif", "baseballdiamond/baseballdiamond57.tif", "baseballdiamond/baseballdiamond51.tif", "baseballdiamond/baseballdiamond65.tif", "baseballdiamond/baseballdiamond06.tif", "baseballdiamond/baseballdiamond28.tif", "baseballdiamond/baseballdiamond20.tif", "baseballdiamond/baseballdiamond23.tif", "baseballdiamond/baseballdiamond97.tif", "baseballdiamond/baseballdiamond13.tif", "baseballdiamond/baseballdiamond87.tif", "baseballdiamond/baseballdiamond70.tif", "baseballdiamond/baseballdiamond66.tif", "baseballdiamond/baseballdiamond09.tif", "baseballdiamond/baseballdiamond19.tif", "baseballdiamond/baseballdiamond95.tif", "baseballdiamond/baseballdiamond12.tif", "baseballdiamond/baseballdiamond75.tif", "baseballdiamond/baseballdiamond07.tif", "baseballdiamond/baseballdiamond15.tif", "baseballdiamond/baseballdiamond54.tif", "baseballdiamond/baseballdiamond32.tif", "baseballdiamond/baseballdiamond48.tif", "baseballdiamond/baseballdiamond61.tif", "baseballdiamond/baseballdiamond16.tif", "baseballdiamond/baseballdiamond29.tif", "baseballdiamond/baseballdiamond47.tif", "beach/beach16.tif", "beach/beach73.tif", "beach/beach43.tif", "beach/beach64.tif", "beach/beach84.tif", "beach/beach34.tif", "beach/beach01.tif", "beach/beach44.tif", "beach/beach49.tif", "beach/beach21.tif", "beach/beach52.tif", "beach/beach86.tif", "beach/beach40.tif", "beach/beach00.tif", "beach/beach90.tif", "beach/beach57.tif", "beach/beach91.tif", "beach/beach22.tif", "beach/beach23.tif", "beach/beach28.tif", "beach/beach30.tif", "beach/beach26.tif", "beach/beach25.tif", "beach/beach09.tif", "beach/beach79.tif", "beach/beach48.tif", "beach/beach87.tif", "beach/beach03.tif", "beach/beach17.tif", "beach/beach60.tif", "beach/beach19.tif", "beach/beach78.tif", "beach/beach80.tif", "beach/beach37.tif", "beach/beach72.tif", "beach/beach24.tif", "beach/beach85.tif", "beach/beach59.tif", "beach/beach54.tif", "beach/beach07.tif", "beach/beach33.tif", "beach/beach71.tif", "beach/beach18.tif", "beach/beach65.tif", "beach/beach67.tif", "beach/beach38.tif", "beach/beach89.tif", "beach/beach74.tif", "beach/beach15.tif", "beach/beach20.tif", "beach/beach50.tif", "beach/beach76.tif", "beach/beach99.tif", "beach/beach69.tif", "beach/beach93.tif", "beach/beach06.tif", "beach/beach27.tif", "beach/beach83.tif", "beach/beach63.tif", "beach/beach62.tif", "buildings/buildings06.tif", "buildings/buildings94.tif", "buildings/buildings37.tif", "buildings/buildings08.tif", "buildings/buildings18.tif", "buildings/buildings33.tif", "buildings/buildings58.tif", "buildings/buildings95.tif", "buildings/buildings56.tif", "buildings/buildings29.tif", "buildings/buildings89.tif", "buildings/buildings20.tif", "buildings/buildings90.tif", "buildings/buildings83.tif", "buildings/buildings13.tif", "buildings/buildings03.tif", "buildings/buildings39.tif", "buildings/buildings31.tif", "buildings/buildings64.tif", "buildings/buildings05.tif", "buildings/buildings24.tif", "buildings/buildings71.tif", "buildings/buildings78.tif", "buildings/buildings76.tif", "buildings/buildings88.tif", "buildings/buildings02.tif", "buildings/buildings72.tif", "buildings/buildings92.tif", "buildings/buildings82.tif", "buildings/buildings07.tif", "buildings/buildings77.tif", "buildings/buildings87.tif", "buildings/buildings53.tif", "buildings/buildings17.tif", "buildings/buildings66.tif", "buildings/buildings81.tif", "buildings/buildings65.tif", "buildings/buildings68.tif", "buildings/buildings99.tif", "buildings/buildings42.tif", "buildings/buildings16.tif", "buildings/buildings85.tif", "buildings/buildings55.tif", "buildings/buildings14.tif", "buildings/buildings48.tif", "buildings/buildings69.tif", "buildings/buildings32.tif", "buildings/buildings67.tif", "buildings/buildings70.tif", "buildings/buildings40.tif", "buildings/buildings00.tif", "buildings/buildings57.tif", "buildings/buildings26.tif", "buildings/buildings04.tif", "buildings/buildings75.tif", "buildings/buildings80.tif", "buildings/buildings52.tif", "buildings/buildings10.tif", "buildings/buildings35.tif", "buildings/buildings30.tif", "chaparral/chaparral80.tif", "chaparral/chaparral16.tif", "chaparral/chaparral10.tif", "chaparral/chaparral33.tif", "chaparral/chaparral28.tif", "chaparral/chaparral32.tif", "chaparral/chaparral71.tif", "chaparral/chaparral97.tif", "chaparral/chaparral88.tif", "chaparral/chaparral17.tif", "chaparral/chaparral05.tif", "chaparral/chaparral09.tif", "chaparral/chaparral46.tif", "chaparral/chaparral49.tif", "chaparral/chaparral91.tif", "chaparral/chaparral92.tif", "chaparral/chaparral02.tif", "chaparral/chaparral68.tif", "chaparral/chaparral18.tif", "chaparral/chaparral25.tif", "chaparral/chaparral00.tif", "chaparral/chaparral11.tif", "chaparral/chaparral50.tif", "chaparral/chaparral24.tif", "chaparral/chaparral54.tif", "chaparral/chaparral81.tif", "chaparral/chaparral58.tif", "chaparral/chaparral85.tif", "chaparral/chaparral75.tif", "chaparral/chaparral41.tif", "chaparral/chaparral90.tif", "chaparral/chaparral62.tif", "chaparral/chaparral22.tif", "chaparral/chaparral76.tif", "chaparral/chaparral48.tif", "chaparral/chaparral65.tif", "chaparral/chaparral08.tif", "chaparral/chaparral53.tif", "chaparral/chaparral45.tif", "chaparral/chaparral01.tif", "chaparral/chaparral21.tif", "chaparral/chaparral63.tif", "chaparral/chaparral26.tif", "chaparral/chaparral64.tif", "chaparral/chaparral03.tif", "chaparral/chaparral31.tif", "chaparral/chaparral59.tif", "chaparral/chaparral82.tif", "chaparral/chaparral19.tif", "chaparral/chaparral51.tif", "chaparral/chaparral23.tif", "chaparral/chaparral07.tif", "chaparral/chaparral56.tif", "chaparral/chaparral39.tif", "chaparral/chaparral34.tif", "chaparral/chaparral36.tif", "chaparral/chaparral79.tif", "chaparral/chaparral94.tif", "chaparral/chaparral42.tif", "chaparral/chaparral35.tif", "denseresidential/denseresidential07.tif", "denseresidential/denseresidential01.tif", "denseresidential/denseresidential16.tif", "denseresidential/denseresidential30.tif", "denseresidential/denseresidential80.tif", "denseresidential/denseresidential56.tif", "denseresidential/denseresidential08.tif", "denseresidential/denseresidential95.tif", "denseresidential/denseresidential61.tif", "denseresidential/denseresidential77.tif", "denseresidential/denseresidential93.tif", "denseresidential/denseresidential04.tif", "denseresidential/denseresidential99.tif", "denseresidential/denseresidential86.tif", "denseresidential/denseresidential97.tif", "denseresidential/denseresidential82.tif", "denseresidential/denseresidential96.tif", "denseresidential/denseresidential87.tif", "denseresidential/denseresidential84.tif", "denseresidential/denseresidential90.tif", "denseresidential/denseresidential41.tif", 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"freeway/freeway60.tif", "freeway/freeway40.tif", "freeway/freeway23.tif", "freeway/freeway72.tif", "freeway/freeway38.tif", "freeway/freeway09.tif", "freeway/freeway30.tif", "freeway/freeway18.tif", "freeway/freeway12.tif", "freeway/freeway75.tif", "freeway/freeway42.tif", "freeway/freeway92.tif", "freeway/freeway34.tif", "freeway/freeway88.tif", "freeway/freeway43.tif", "freeway/freeway24.tif", "freeway/freeway79.tif", "freeway/freeway69.tif", "freeway/freeway81.tif", "freeway/freeway64.tif", "freeway/freeway32.tif", "freeway/freeway44.tif", "freeway/freeway90.tif", "freeway/freeway76.tif", "golfcourse/golfcourse68.tif", "golfcourse/golfcourse62.tif", "golfcourse/golfcourse17.tif", "golfcourse/golfcourse43.tif", "golfcourse/golfcourse79.tif", "golfcourse/golfcourse40.tif", "golfcourse/golfcourse13.tif", "golfcourse/golfcourse19.tif", "golfcourse/golfcourse22.tif", "golfcourse/golfcourse82.tif", "golfcourse/golfcourse05.tif", "golfcourse/golfcourse37.tif", 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"harbor/harbor95.tif", "harbor/harbor96.tif", "harbor/harbor24.tif", "harbor/harbor91.tif", "harbor/harbor94.tif", "harbor/harbor63.tif", "harbor/harbor34.tif", "harbor/harbor62.tif", "harbor/harbor32.tif", "harbor/harbor76.tif", "harbor/harbor40.tif", "harbor/harbor86.tif", "harbor/harbor17.tif", "harbor/harbor30.tif", "harbor/harbor38.tif", "harbor/harbor21.tif", "intersection/intersection82.tif", "intersection/intersection88.tif", "intersection/intersection35.tif", "intersection/intersection56.tif", "intersection/intersection03.tif", "intersection/intersection00.tif", "intersection/intersection29.tif", "intersection/intersection68.tif", "intersection/intersection25.tif", "intersection/intersection66.tif", "intersection/intersection61.tif", "intersection/intersection86.tif", "intersection/intersection93.tif", "intersection/intersection39.tif", "intersection/intersection76.tif", "intersection/intersection14.tif", "intersection/intersection26.tif", "intersection/intersection22.tif", "intersection/intersection97.tif", "intersection/intersection94.tif", "mediumresidential/mediumresidential32.tif", "mediumresidential/mediumresidential31.tif", "mediumresidential/mediumresidential92.tif", "mediumresidential/mediumresidential23.tif", "mediumresidential/mediumresidential08.tif", "mediumresidential/mediumresidential74.tif", "mediumresidential/mediumresidential18.tif", "mediumresidential/mediumresidential00.tif", "mediumresidential/mediumresidential50.tif", "mediumresidential/mediumresidential67.tif", "mediumresidential/mediumresidential72.tif", "mediumresidential/mediumresidential04.tif", "mediumresidential/mediumresidential40.tif", "mediumresidential/mediumresidential09.tif", "mediumresidential/mediumresidential69.tif", "mediumresidential/mediumresidential76.tif", "mediumresidential/mediumresidential34.tif", "mediumresidential/mediumresidential20.tif", "mediumresidential/mediumresidential83.tif", "mediumresidential/mediumresidential05.tif", "mobilehomepark/mobilehomepark52.tif", "mobilehomepark/mobilehomepark59.tif", "mobilehomepark/mobilehomepark28.tif", "mobilehomepark/mobilehomepark38.tif", "mobilehomepark/mobilehomepark29.tif", "mobilehomepark/mobilehomepark43.tif", "mobilehomepark/mobilehomepark68.tif", "mobilehomepark/mobilehomepark93.tif", "mobilehomepark/mobilehomepark66.tif", "mobilehomepark/mobilehomepark35.tif", "mobilehomepark/mobilehomepark51.tif", "mobilehomepark/mobilehomepark48.tif", "mobilehomepark/mobilehomepark07.tif", "mobilehomepark/mobilehomepark96.tif", "mobilehomepark/mobilehomepark24.tif", "mobilehomepark/mobilehomepark58.tif", "mobilehomepark/mobilehomepark63.tif", "mobilehomepark/mobilehomepark34.tif", "mobilehomepark/mobilehomepark08.tif", "mobilehomepark/mobilehomepark41.tif", "overpass/overpass23.tif", "overpass/overpass85.tif", "overpass/overpass25.tif", "overpass/overpass84.tif", "overpass/overpass46.tif", "overpass/overpass15.tif", "overpass/overpass87.tif", "overpass/overpass20.tif", "overpass/overpass32.tif", "overpass/overpass59.tif", "overpass/overpass13.tif", "overpass/overpass62.tif", "overpass/overpass55.tif", "overpass/overpass28.tif", "overpass/overpass17.tif", "overpass/overpass33.tif", "overpass/overpass69.tif", "overpass/overpass08.tif", "overpass/overpass21.tif", "overpass/overpass97.tif", "parkinglot/parkinglot28.tif", "parkinglot/parkinglot65.tif", "parkinglot/parkinglot09.tif", "parkinglot/parkinglot70.tif", "parkinglot/parkinglot82.tif", "parkinglot/parkinglot91.tif", "parkinglot/parkinglot88.tif", "parkinglot/parkinglot56.tif", "parkinglot/parkinglot38.tif", "parkinglot/parkinglot03.tif", "parkinglot/parkinglot66.tif", "parkinglot/parkinglot99.tif", "parkinglot/parkinglot77.tif", "parkinglot/parkinglot42.tif", "parkinglot/parkinglot17.tif", "parkinglot/parkinglot76.tif", "parkinglot/parkinglot23.tif", "parkinglot/parkinglot26.tif", "parkinglot/parkinglot90.tif", "parkinglot/parkinglot67.tif", "river/river14.tif", "river/river40.tif", "river/river67.tif", "river/river36.tif", "river/river53.tif", "river/river72.tif", "river/river89.tif", "river/river00.tif", "river/river97.tif", "river/river99.tif", "river/river93.tif", "river/river44.tif", "river/river20.tif", "river/river78.tif", "river/river43.tif", "river/river26.tif", "river/river07.tif", "river/river54.tif", "river/river85.tif", "river/river88.tif", "runway/runway08.tif", "runway/runway02.tif", "runway/runway97.tif", "runway/runway11.tif", "runway/runway66.tif", "runway/runway51.tif", "runway/runway58.tif", "runway/runway78.tif", "runway/runway95.tif", "runway/runway21.tif", "runway/runway38.tif", "runway/runway92.tif", "runway/runway47.tif", "runway/runway64.tif", "runway/runway69.tif", "runway/runway14.tif", "runway/runway76.tif", "runway/runway59.tif", "runway/runway42.tif", "runway/runway93.tif", "sparseresidential/sparseresidential25.tif", "sparseresidential/sparseresidential27.tif", "sparseresidential/sparseresidential87.tif", "sparseresidential/sparseresidential95.tif", "sparseresidential/sparseresidential35.tif", "sparseresidential/sparseresidential20.tif", "sparseresidential/sparseresidential46.tif", "sparseresidential/sparseresidential19.tif", "sparseresidential/sparseresidential40.tif", "sparseresidential/sparseresidential31.tif", "sparseresidential/sparseresidential49.tif", "sparseresidential/sparseresidential42.tif", "sparseresidential/sparseresidential08.tif", "sparseresidential/sparseresidential02.tif", "sparseresidential/sparseresidential12.tif", "sparseresidential/sparseresidential04.tif", "sparseresidential/sparseresidential92.tif", "sparseresidential/sparseresidential38.tif", "sparseresidential/sparseresidential26.tif", "sparseresidential/sparseresidential76.tif", "storagetanks/storagetanks79.tif", "storagetanks/storagetanks62.tif", "storagetanks/storagetanks63.tif", "storagetanks/storagetanks29.tif", "storagetanks/storagetanks87.tif", "storagetanks/storagetanks74.tif", "storagetanks/storagetanks50.tif", "storagetanks/storagetanks64.tif", "storagetanks/storagetanks10.tif", "storagetanks/storagetanks94.tif", "storagetanks/storagetanks22.tif", "storagetanks/storagetanks59.tif", "storagetanks/storagetanks47.tif", "storagetanks/storagetanks77.tif", "storagetanks/storagetanks04.tif", "storagetanks/storagetanks70.tif", "storagetanks/storagetanks02.tif", "storagetanks/storagetanks96.tif", "storagetanks/storagetanks09.tif", "storagetanks/storagetanks69.tif", "tenniscourt/tenniscourt81.tif", "tenniscourt/tenniscourt88.tif", "tenniscourt/tenniscourt32.tif", "tenniscourt/tenniscourt45.tif", "tenniscourt/tenniscourt87.tif", "tenniscourt/tenniscourt53.tif", "tenniscourt/tenniscourt86.tif", "tenniscourt/tenniscourt76.tif", "tenniscourt/tenniscourt99.tif", "tenniscourt/tenniscourt94.tif", "tenniscourt/tenniscourt12.tif", "tenniscourt/tenniscourt21.tif", "tenniscourt/tenniscourt98.tif", "tenniscourt/tenniscourt19.tif", "tenniscourt/tenniscourt44.tif", "tenniscourt/tenniscourt70.tif", "tenniscourt/tenniscourt13.tif", "tenniscourt/tenniscourt69.tif", "tenniscourt/tenniscourt91.tif", "tenniscourt/tenniscourt68.tif"]}

datasets/__init__.py

@@ -0,0 +1,9 @@
+from .datasets import register, make
+from . import image_folder
+from . import wrappers
+from . import rs_super_warp
+from . import cnn_sr_wrappers
+from . import inr_sr_wrappers
+from . import datasets_loader
+
+from .inr_diinn_sr_wrappers import INRSelectScaleSRWarp

datasets/cnn_sr_wrappers.py

@@ -0,0 +1,75 @@
+import functools
+import os.path
+import random
+import math
+
+import torchvision.transforms
+from PIL import Image
+import numpy as np
+import torch
+from einops import rearrange
+from torch.utils.data import Dataset
+from torchvision import transforms
+from torchvision.transforms import InterpolationMode
+
+from datasets import register
+import torchvision.transforms
+from utils import to_pixel_samples, to_coordinates
+
+
+
+def resize_fn(img, size):
+    return transforms.ToTensor()(
+        transforms.Resize(size, Image.BICUBIC)(
+            transforms.ToPILImage()(img)))
+
+
+@register('cnn_fixed_scale_sr_warp')
+class CNNFixedScaleSRWarp(Dataset):
+    def __init__(self, dataset, scale_ratio, patch_size=48,
+                 augment=False, val_mode=False, test_mode=False,
+                 vis_continuous=False):
+        self.dataset = dataset
+        self.augment = augment
+        self.scale_ratio = scale_ratio
+        self.hr_size = int(patch_size * scale_ratio)
+        self.test_mode = test_mode
+        self.val_mode = val_mode
+        self.patch_size = patch_size
+        self.vis_continuous = vis_continuous
+
+    def __len__(self):
+        return len(self.dataset)
+
+    def __getitem__(self, idx):
+        img_hr, file_name = self.dataset[idx]
+        class_name = os.path.basename(os.path.dirname(file_name))
+        file_name = os.path.basename(file_name).split('.')[0]
+
+        if self.vis_continuous:
+            img_lr = transforms.Resize(self.patch_size, InterpolationMode.BICUBIC)(
+                transforms.CenterCrop(4*self.patch_size)(img_hr))
+
+        # img_hr: 3xHxW
+        if self.test_mode:
+            img_hr = transforms.CenterCrop(self.hr_size)(img_hr)
+        else:
+            img_hr = transforms.RandomCrop(self.hr_size)(img_hr)
+
+        if not self.vis_continuous:
+            img_lr = transforms.Resize(self.patch_size, InterpolationMode.BICUBIC)(img_hr)
+
+        if self.augment and not self.test_mode:
+            if random.random() < 0.5:
+                img_lr = img_lr.flip(-1)
+                img_hr = img_hr.flip(-1)
+            if random.random() < 0.5:
+                img_lr = img_lr.flip(-2)
+                img_hr = img_hr.flip(-2)
+
+        return {
+            'img': img_lr,
+            'gt': img_hr,
+            'class_name': class_name,
+            'filename': file_name
+        }

datasets/datasets.py

@@ -0,0 +1,21 @@
+import copy
+
+
+datasets = {}
+
+
+def register(name):
+    def decorator(cls):
+        datasets[name] = cls
+        return cls
+    return decorator
+
+
+def make(dataset_spec, args=None):
+    if args is not None:
+        dataset_args = copy.deepcopy(dataset_spec['args'])
+        dataset_args.update(args)
+    else:
+        dataset_args = dataset_spec['args']
+    dataset = datasets[dataset_spec['name']](**dataset_args)
+    return dataset

datasets/datasets_loader.py

@@ -0,0 +1,69 @@
+import os
+import json
+from PIL import Image
+
+import pickle
+import imageio
+import numpy as np
+import torch
+from torch.utils.data import Dataset
+from torchvision import transforms
+
+from datasets import register
+
+
+@register('hr_data_loader')
+class HRImgLoader(Dataset):
+    def __init__(self, root_path, split_file, split_key, first_k=None, cache='none'):
+        self.cache = cache
+        with open(split_file, 'r') as f:
+            filenames = json.load(f)[split_key]
+        if first_k is not None:
+            filenames = filenames[:first_k]
+
+        self.files = []
+        for filename in filenames:
+            file = os.path.join(root_path, filename)
+
+            if cache == 'none':
+                self.files.append(file)
+
+            elif cache == 'bin':
+                bin_root = os.path.join(os.path.dirname(root_path),
+                    '_bin_' + os.path.basename(root_path))
+                if not os.path.exists(bin_root):
+                    os.mkdir(bin_root)
+                    print('mkdir', bin_root)
+                bin_file = os.path.join(
+                    bin_root, filename.split('.')[0] + '.pkl')
+                if not os.path.exists(bin_file):
+                    with open(bin_file, 'wb') as f:
+                        pickle.dump(imageio.imread(file), f)
+                    print('dump', bin_file)
+                self.files.append(bin_file)
+
+            elif cache == 'in_memory':
+                self.files.append(transforms.ToTensor()(
+                    Image.open(file).convert('RGB')))
+
+    def __len__(self):
+        return len(self.files)
+
+    def __getitem__(self, idx):
+        x = self.files[idx]
+        file_name = x
+
+        if self.cache == 'none':
+            return transforms.ToTensor()(Image.open(x).convert('RGB')), file_name
+
+        elif self.cache == 'bin':
+            with open(x, 'rb') as f:
+                x = pickle.load(f)
+            x = np.ascontiguousarray(x.transpose(2, 0, 1))
+            x = torch.from_numpy(x).float() / 255
+            return x, file_name
+
+        elif self.cache == 'in_memory':
+            return x, file_name
+
+

datasets/image_folder.py

@@ -0,0 +1,87 @@
+import os
+import json
+from PIL import Image
+
+import pickle
+import imageio
+import numpy as np
+import torch
+from torch.utils.data import Dataset
+from torchvision import transforms
+
+from datasets import register
+
+
+@register('image-folder')
+class ImageFolder(Dataset):
+
+    def __init__(self, root_path, split_file=None, split_key=None, first_k=None,
+                 repeat=1, cache='none'):
+        self.repeat = repeat
+        self.cache = cache
+
+        if split_file is None:
+            filenames = sorted(os.listdir(root_path))
+        else:
+            with open(split_file, 'r') as f:
+                filenames = json.load(f)[split_key]
+        if first_k is not None:
+            filenames = filenames[:first_k]
+
+        self.files = []
+        for filename in filenames:
+            file = os.path.join(root_path, filename)
+
+            if cache == 'none':
+                self.files.append(file)
+
+            elif cache == 'bin':
+                bin_root = os.path.join(os.path.dirname(root_path),
+                    '_bin_' + os.path.basename(root_path))
+                if not os.path.exists(bin_root):
+                    os.mkdir(bin_root)
+                    print('mkdir', bin_root)
+                bin_file = os.path.join(
+                    bin_root, filename.split('.')[0] + '.pkl')
+                if not os.path.exists(bin_file):
+                    with open(bin_file, 'wb') as f:
+                        pickle.dump(imageio.imread(file), f)
+                    print('dump', bin_file)
+                self.files.append(bin_file)
+
+            elif cache == 'in_memory':
+                self.files.append(transforms.ToTensor()(
+                    Image.open(file).convert('RGB')))
+
+    def __len__(self):
+        return len(self.files) * self.repeat
+
+    def __getitem__(self, idx):
+        x = self.files[idx % len(self.files)]
+
+        if self.cache == 'none':
+            return transforms.ToTensor()(Image.open(x).convert('RGB'))
+
+        elif self.cache == 'bin':
+            with open(x, 'rb') as f:
+                x = pickle.load(f)
+            x = np.ascontiguousarray(x.transpose(2, 0, 1))
+            x = torch.from_numpy(x).float() / 255
+            return x
+
+        elif self.cache == 'in_memory':
+            return x
+
+
+@register('paired-image-folders')
+class PairedImageFolders(Dataset):
+
+    def __init__(self, root_path_1, root_path_2, **kwargs):
+        self.dataset_1 = ImageFolder(root_path_1, **kwargs)
+        self.dataset_2 = ImageFolder(root_path_2, **kwargs)
+
+    def __len__(self):
+        return len(self.dataset_1)
+
+    def __getitem__(self, idx):
+        return self.dataset_1[idx], self.dataset_2[idx]

datasets/inr_diinn_sr_wrappers.py

@@ -0,0 +1,76 @@
+import copy
+import functools
+import os
+import random
+import math
+from PIL import Image
+
+import numpy as np
+import torch
+from einops import rearrange
+from torch.utils.data import Dataset
+from torchvision import transforms
+
+from datasets import register
+from utils import to_pixel_samples, to_coordinates
+
+import torchvision.transforms.functional as TF
+import random
+from typing import Sequence
+
+
+class MyRotateTransform:
+    def __init__(self, angles: Sequence[int], p=0.5):
+        self.angles = angles
+        self.p = p
+
+    def __call__(self, x):
+        if torch.rand(1) < self.p:
+            return x
+        angle = random.choice(self.angles)
+        return TF.rotate(x, angle)
+
+
+@register('inr_diinn_select_scale_sr_warp')
+class INRSelectScaleSRWarp(Dataset):
+    def __init__(self,
+                 dataset, scales, patch_size=48,
+                 augment=False,
+                 val_mode=False, test_mode=False
+                 ):
+        super(INRSelectScaleSRWarp, self).__init__()
+        self.dataset = dataset
+        self.scales = scales
+        self.patch_size = patch_size
+        self.augment = augment
+        self.test_mode = test_mode
+        self.val_mode = val_mode
+
+    def __len__(self):
+        return len(self.dataset)
+
+    def __getitem__(self, idx):
+        # import pdb
+        # pdb.set_trace()
+        img_hr_ori, file_name = self.dataset[idx]
+        class_name = os.path.basename(os.path.dirname(file_name))
+
+        sample = {}
+        for scale in self.scales:
+            hr_size = self.patch_size * scale
+            hr_size = int(hr_size)
+
+            if self.test_mode or self.val_mode:
+                hr_size = int(self.patch_size * max(self.scales))
+                img_hr = transforms.CenterCrop(hr_size)(img_hr_ori)
+            else:
+                img_hr = transforms.RandomCrop(hr_size)(copy.deepcopy(img_hr_ori))
+                if self.augment:
+                    img_hr = transforms.RandomHorizontalFlip(p=0.5)(img_hr)
+                    img_hr = transforms.RandomVerticalFlip(p=0.5)(img_hr)
+                    img_hr = MyRotateTransform([90, 180, 270], p=0.5)(img_hr)
+
+            img_lr = transforms.Resize(self.patch_size, TF.InterpolationMode.BICUBIC)(img_hr)
+            sample[scale] = {'img': img_lr, 'gt': img_hr, 'class_name': class_name}
+
+        return sample

datasets/inr_sr_wrappers.py

@@ -0,0 +1,177 @@
+import functools
+import os
+import random
+import math
+from PIL import Image
+
+import numpy as np
+import torch
+from einops import rearrange
+from torch.utils.data import Dataset
+from torchvision import transforms
+
+from datasets import register
+from utils import to_pixel_samples, to_coordinates
+
+import torchvision.transforms.functional as TF
+import random
+from typing import Sequence
+
+
+class MyRotateTransform:
+    def __init__(self, angles: Sequence[int], p=0.5):
+        self.angles = angles
+        self.p = p
+
+    def __call__(self, x):
+        if torch.rand(1) < self.p:
+            return x
+        angle = random.choice(self.angles)
+        return TF.rotate(x, angle)
+
+@register('inr_fixed_scale_sr_warp')
+class INRFixedScaleSRWarp(Dataset):
+    def __init__(self,
+                 dataset, scale_ratio, patch_size=48,
+                 augment=False, sample_q=None,
+                 val_mode=False, test_mode=False,
+                 encode_scale_ratio=False,
+                 return_cell=False,  # for liff
+                 ):
+        super(INRFixedScaleSRWarp, self).__init__()
+        self.dataset = dataset
+        self.scale_ratio = scale_ratio
+        self.patch_size = patch_size
+        self.hr_size = int(patch_size * scale_ratio)
+        self.augment = augment
+        self.sample_q = sample_q
+        self.test_mode = test_mode
+        self.val_mode = val_mode
+        self.encode_scale_ratio = encode_scale_ratio
+        self.return_cell = return_cell
+
+    def __len__(self):
+        return len(self.dataset)
+
+    def __getitem__(self, idx):
+        # import pdb
+        # pdb.set_trace()
+        img_hr, file_name = self.dataset[idx]
+        class_name = os.path.basename(os.path.dirname(file_name))
+        file_name = os.path.basename(file_name).split('.')[0]
+        # img_hr: 3xHxW
+        h, w = img_hr.shape[-2:]
+        # if h < 256 or w < 256:
+        #     img_hr = transforms.Resize(256, Image.BICUBIC)(img_hr)
+
+        if self.test_mode or self.val_mode:
+            img_hr = transforms.CenterCrop(self.hr_size)(img_hr)
+        else:
+            img_hr = transforms.RandomCrop(self.hr_size)(img_hr)
+            if self.augment:
+                img_hr = transforms.RandomHorizontalFlip(p=0.5)(img_hr)
+                img_hr = transforms.RandomVerticalFlip(p=0.5)(img_hr)
+                img_hr = MyRotateTransform([90, 180, 270], p=0.5)(img_hr)
+
+        img_lr = transforms.Resize(self.patch_size, Image.BICUBIC)(img_hr)
+
+        hr_coord = to_coordinates(size=img_hr.shape[-2:], return_map=False)
+        hr_rgb = rearrange(img_hr, 'C H W -> (H W) C')
+
+        if self.sample_q is not None and not self.test_mode:
+            sample_lst = np.random.choice(
+                len(hr_coord), self.sample_q, replace=False)
+            hr_coord = hr_coord[sample_lst]
+            hr_rgb = hr_rgb[sample_lst]
+        return_dict = {
+            'inp': img_lr,
+            'coord': hr_coord,
+            'gt': hr_rgb,
+            'class_name': class_name,
+            'filename': file_name
+        }
+
+        if self.encode_scale_ratio:
+            scale_ratio = torch.ones_like(hr_coord) * self.patch_size / self.hr_size
+            return_dict['scale_ratio'] = scale_ratio
+
+        if self.return_cell:
+            cell = torch.ones_like(hr_coord)
+            cell[:, 0] *= 2 / img_hr.shape[-2]
+            cell[:, 1] *= 2 / img_hr.shape[-1]
+            return_dict['cell'] = cell
+
+        return return_dict
+
+
+@register('inr_range_scale_sr_warp')
+class INRRangeScaleSRWarp(Dataset):
+    def __init__(self,
+                 dataset, max_scale_ratio, patch_size=48,
+                 augment=False, sample_q=None,
+                 val_mode=False, test_mode=False,
+                 encode_scale_ratio=False,
+                 return_cell=False,  # for liff
+                 ):
+        super(INRRangeScaleSRWarp, self).__init__()
+        self.dataset = dataset
+        self.max_scale_ratio = max_scale_ratio
+        self.patch_size = patch_size
+        assert max_scale_ratio <= 8
+        self.augment = augment
+        self.sample_q = sample_q
+        self.test_mode = test_mode
+        self.val_mode = val_mode
+        self.encode_scale_ratio = encode_scale_ratio
+        self.return_cell = return_cell
+
+    def __len__(self):
+        return len(self.dataset)
+
+    def __getitem__(self, idx):
+        img_hr, file_name = self.dataset[idx]
+        class_name = os.path.basename(os.path.dirname(file_name))
+        h, w = img_hr.shape[-2:]
+        # if h < 256 or w < 256:
+        #     img_hr = transforms.Resize(256, Image.BICUBIC)(img_hr)
+
+        hr_size = self.patch_size + self.patch_size * torch.rand([]) * (self.max_scale_ratio - 1)
+        hr_size = int(hr_size)
+
+        if self.test_mode or self.val_mode:
+            hr_size = int(self.patch_size * self.max_scale_ratio)
+            img_hr = transforms.CenterCrop(hr_size)(img_hr)
+        else:
+            img_hr = transforms.RandomCrop(hr_size)(img_hr)
+            if self.augment:
+                img_hr = transforms.RandomHorizontalFlip(p=0.5)(img_hr)
+                img_hr = transforms.RandomVerticalFlip(p=0.5)(img_hr)
+                img_hr = MyRotateTransform([90, 180, 270], p=0.5)(img_hr)
+
+        img_lr = transforms.Resize(self.patch_size, Image.BICUBIC)(img_hr)
+
+        hr_coord = to_coordinates(size=img_hr.shape[-2:], return_map=False)
+        hr_rgb = rearrange(img_hr, 'C H W -> (H W) C')
+
+        if self.sample_q is not None and not self.test_mode:
+            sample_lst = np.random.choice(
+                len(hr_coord), self.sample_q, replace=False)
+            hr_coord = hr_coord[sample_lst]
+            hr_rgb = hr_rgb[sample_lst]
+        return_dict = {
+            'inp': img_lr,
+            'coord': hr_coord,
+            'gt': hr_rgb,
+            'class_name': class_name
+        }
+        if self.encode_scale_ratio:
+            scale_ratio = torch.ones_like(hr_coord) * self.patch_size / hr_size
+            return_dict['scale_ratio'] = scale_ratio
+
+        if self.return_cell:
+            cell = torch.ones_like(hr_coord)
+            cell[:, 0] *= 2 / img_hr.shape[-2]
+            cell[:, 1] *= 2 / img_hr.shape[-1]
+            return_dict['cell'] = cell
+
+        return return_dict

datasets/rs_super_warp.py

@@ -0,0 +1,75 @@
+import functools
+import random
+import math
+from PIL import Image
+import numpy as np
+import torch
+from einops import rearrange
+from torch.utils.data import Dataset
+from torchvision import transforms
+from datasets import register
+from utils import to_pixel_samples, to_coordinates
+
+
+def resize_fn(img, size):
+    return transforms.ToTensor()(
+        transforms.Resize(size, Image.BICUBIC)(
+            transforms.ToPILImage()(img)))
+
+
+@register('rs_sr_warp')
+class RSSRWarp(Dataset):
+    def __init__(self, dataset, size_min=None, size_max=None,
+                 augment=False, gt_resize=None, sample_q=None, val_mode=False):
+        self.dataset = dataset
+        self.size_min = size_min
+        if size_max is None:
+            size_max = size_min
+        self.size_max = size_max
+        self.augment = augment
+        self.gt_resize = gt_resize
+        self.sample_q = sample_q
+        self.val_mode = val_mode
+
+    def __len__(self):
+        return len(self.dataset)
+
+    def __getitem__(self, idx):
+        img_lr, img_hr = self.dataset[idx]
+        # p = idx / (len(self.dataset) - 1)
+        if not self.val_mode:
+            p = random.random()
+            w_hr = round(self.size_min + (self.size_max - self.size_min) * p)
+            img_hr = resize_fn(img_hr, w_hr)
+        else:
+            img_hr = resize_fn(img_hr, self.size_max)
+
+
+        if self.augment and not self.val_mode:
+            if random.random() < 0.5:
+                img_lr = img_lr.flip(-1)
+                img_hr = img_hr.flip(-1)
+            if random.random() < 0.5:
+                img_lr = img_lr.flip(-2)
+                img_hr = img_hr.flip(-2)
+
+        if self.gt_resize is not None:
+            img_hr = resize_fn(img_hr, self.gt_resize)
+
+        hr_coord = to_coordinates(size=img_hr.shape[-2:], return_map=False)
+        hr_rgb = rearrange(img_hr, 'C H W -> (H W) C')
+
+        if self.sample_q is not None:
+            sample_lst = np.random.choice(len(hr_coord), self.sample_q, replace=False)
+            hr_coord = hr_coord[sample_lst]
+            hr_rgb = hr_rgb[sample_lst]
+
+        # cell = torch.ones_like(hr_coord)
+        # cell[:, 0] *= 2 / img_hr.shape[-2]
+        # cell[:, 1] *= 2 / img_hr.shape[-1]
+
+        return {
+            'inp': img_lr,
+            'coord': hr_coord,
+            'gt': hr_rgb
+        }

datasets/wrappers.py

@@ -0,0 +1,248 @@
+import functools
+import random
+import math
+from PIL import Image
+
+import numpy as np
+import torch
+from torch.utils.data import Dataset
+from torchvision import transforms
+
+from datasets import register
+from utils import to_pixel_samples
+
+
+@register('liff_test_warp')
+class LIIFTestWarp(Dataset):
+    def __init__(self, dataset, scale_ratio, val_mode=False, sample_q=None):
+        self.dataset = dataset
+        self.scale_ratio = scale_ratio
+        self.val_mode = val_mode
+        self.sample_q = sample_q
+        print('hr_scale: ', int(scale_ratio*32))
+
+    def __len__(self):
+        return len(self.dataset)
+
+    def __getitem__(self, idx):
+        img_lr, img_hr = self.dataset[idx]
+        if img_hr.shape[-1] < 256:
+            img_hr = transforms.Resize([256, 256])(img_hr)
+
+        img_hr = transforms.Resize([self.scale_ratio*32, self.scale_ratio*32])(img_hr)
+
+        hr_coord, hr_rgb = to_pixel_samples(img_hr.contiguous())
+
+        if self.sample_q is not None:
+            sample_lst = np.random.choice(len(hr_coord), self.sample_q, replace=False)
+            hr_coord = hr_coord[sample_lst]
+            hr_rgb = hr_rgb[sample_lst]
+
+        cell = torch.ones_like(hr_coord)
+        cell[:, 0] *= 2 / img_hr.shape[-2]
+        cell[:, 1] *= 2 / img_hr.shape[-1]
+
+        return {
+            'inp': img_lr,
+            'coord': hr_coord,
+            'cell': cell,
+            'gt': hr_rgb
+        }
+
+@register('sr-implicit-paired')
+class SRImplicitPaired(Dataset):
+
+    def __init__(self, dataset, inp_size=None, augment=False, sample_q=None):
+        self.dataset = dataset
+        self.inp_size = inp_size
+        self.augment = augment
+        self.sample_q = sample_q
+
+    def __len__(self):
+        return len(self.dataset)
+
+    def __getitem__(self, idx):
+        img_lr, img_hr = self.dataset[idx]
+        if img_hr.shape[-1] < 256:
+            img_hr = transforms.Resize([256, 256])(img_hr)
+
+        s = img_hr.shape[-2] // img_lr.shape[-2]  # assume int scale
+        if self.inp_size is None:
+            h_lr, w_lr = img_lr.shape[-2:]
+            img_hr = img_hr[:, :h_lr * s, :w_lr * s]
+            crop_lr, crop_hr = img_lr, img_hr
+        else:
+            w_lr = self.inp_size
+            x0 = random.randint(0, img_lr.shape[-2] - w_lr)
+            y0 = random.randint(0, img_lr.shape[-1] - w_lr)
+            crop_lr = img_lr[:, x0: x0 + w_lr, y0: y0 + w_lr]
+            w_hr = w_lr * s
+            x1 = x0 * s
+            y1 = y0 * s
+            crop_hr = img_hr[:, x1: x1 + w_hr, y1: y1 + w_hr]
+
+        if self.augment:
+            hflip = random.random() < 0.5
+            vflip = random.random() < 0.5
+            dflip = random.random() < 0.5
+
+            def augment(x):
+                if hflip:
+                    x = x.flip(-2)
+                if vflip:
+                    x = x.flip(-1)
+                if dflip:
+                    x = x.transpose(-2, -1)
+                return x
+
+            crop_lr = augment(crop_lr)
+            crop_hr = augment(crop_hr)
+
+        hr_coord, hr_rgb = to_pixel_samples(crop_hr.contiguous())
+
+        if self.sample_q is not None:
+            sample_lst = np.random.choice(
+                len(hr_coord), self.sample_q, replace=False)
+            hr_coord = hr_coord[sample_lst]
+            hr_rgb = hr_rgb[sample_lst]
+
+        cell = torch.ones_like(hr_coord)
+        cell[:, 0] *= 2 / crop_hr.shape[-2]
+        cell[:, 1] *= 2 / crop_hr.shape[-1]
+
+        return {
+            'inp': crop_lr,
+            'coord': hr_coord,
+            'cell': cell,
+            'gt': hr_rgb
+        }
+
+
+def resize_fn(img, size):
+    return transforms.ToTensor()(
+        transforms.Resize(size, Image.BICUBIC)(
+            transforms.ToPILImage()(img)))
+
+
+@register('sr-implicit-downsampled')
+class SRImplicitDownsampled(Dataset):
+
+    def __init__(self, dataset, inp_size=None, scale_min=1, scale_max=None,
+                 augment=False, sample_q=None):
+        self.dataset = dataset
+        self.inp_size = inp_size
+        self.scale_min = scale_min
+        if scale_max is None:
+            scale_max = scale_min
+        self.scale_max = scale_max
+        self.augment = augment
+        self.sample_q = sample_q
+
+    def __len__(self):
+        return len(self.dataset)
+
+    def __getitem__(self, idx):
+        img = self.dataset[idx]
+        s = random.uniform(self.scale_min, self.scale_max)
+
+        if self.inp_size is None:
+            h_lr = math.floor(img.shape[-2] / s + 1e-9)
+            w_lr = math.floor(img.shape[-1] / s + 1e-9)
+            img = img[:, :round(h_lr * s), :round(w_lr * s)] # assume round int
+            img_down = resize_fn(img, (h_lr, w_lr))
+            crop_lr, crop_hr = img_down, img
+        else:
+            w_lr = self.inp_size
+            w_hr = round(w_lr * s)
+            x0 = random.randint(0, img.shape[-2] - w_hr)
+            y0 = random.randint(0, img.shape[-1] - w_hr)
+            crop_hr = img[:, x0: x0 + w_hr, y0: y0 + w_hr]
+            crop_lr = resize_fn(crop_hr, w_lr)
+
+        if self.augment:
+            hflip = random.random() < 0.5
+            vflip = random.random() < 0.5
+            dflip = random.random() < 0.5
+
+            def augment(x):
+                if hflip:
+                    x = x.flip(-2)
+                if vflip:
+                    x = x.flip(-1)
+                if dflip:
+                    x = x.transpose(-2, -1)
+                return x
+
+            crop_lr = augment(crop_lr)
+            crop_hr = augment(crop_hr)
+
+        hr_coord, hr_rgb = to_pixel_samples(crop_hr.contiguous())
+
+        if self.sample_q is not None:
+            sample_lst = np.random.choice(
+                len(hr_coord), self.sample_q, replace=False)
+            hr_coord = hr_coord[sample_lst]
+            hr_rgb = hr_rgb[sample_lst]
+
+        cell = torch.ones_like(hr_coord)
+        cell[:, 0] *= 2 / crop_hr.shape[-2]
+        cell[:, 1] *= 2 / crop_hr.shape[-1]
+
+        return {
+            'inp': crop_lr,
+            'coord': hr_coord,
+            'cell': cell,
+            'gt': hr_rgb
+        }
+
+
+@register('sr-implicit-uniform-varied')
+class SRImplicitUniformVaried(Dataset):
+
+    def __init__(self, dataset, size_min, size_max=None,
+                 augment=False, gt_resize=None, sample_q=None):
+        self.dataset = dataset
+        self.size_min = size_min
+        if size_max is None:
+            size_max = size_min
+        self.size_max = size_max
+        self.augment = augment
+        self.gt_resize = gt_resize
+        self.sample_q = sample_q
+
+    def __len__(self):
+        return len(self.dataset)
+
+    def __getitem__(self, idx):
+        img_lr, img_hr = self.dataset[idx]
+        # p = idx / (len(self.dataset) - 1)
+        p = random.random()
+        w_hr = round(self.size_min + (self.size_max - self.size_min) * p)
+        img_hr = resize_fn(img_hr, w_hr)
+
+        if self.augment:
+            if random.random() < 0.5:
+                img_lr = img_lr.flip(-1)
+                img_hr = img_hr.flip(-1)
+
+        if self.gt_resize is not None:
+            img_hr = resize_fn(img_hr, self.gt_resize)
+
+        hr_coord, hr_rgb = to_pixel_samples(img_hr)
+
+        if self.sample_q is not None:
+            sample_lst = np.random.choice(
+                len(hr_coord), self.sample_q, replace=False)
+            hr_coord = hr_coord[sample_lst]
+            hr_rgb = hr_rgb[sample_lst]
+
+        cell = torch.ones_like(hr_coord)
+        cell[:, 0] *= 2 / img_hr.shape[-2]
+        cell[:, 1] *= 2 / img_hr.shape[-1]
+
+        return {
+            'inp': img_lr,
+            'coord': hr_coord,
+            'cell': cell,
+            'gt': hr_rgb
+        }

examples/resize.py

@@ -0,0 +1,20 @@
+import glob
+
+from PIL import Image
+from torchvision import transforms
+import cv2
+from torchvision.transforms import InterpolationMode
+
+patch_size = 48
+
+for file in glob.glob("*.tif"):
+    img = transforms.ToTensor()(Image.open(file).convert('RGB')) * 255
+    img_lr = transforms.Resize(patch_size, InterpolationMode.BICUBIC)(
+        transforms.CenterCrop(4 * patch_size)(img))
+
+    img_hr = transforms.CenterCrop(4 * patch_size)(img)
+
+    cv2.imwrite(f'UC_{file.split(".")[0]}_LR.png', img_lr.permute((1, 2, 0)).numpy())
+    print(f'UC_{file.split(".")[0]}_LR.png')
+    cv2.imwrite(f'UC_{file.split(".")[0]}_HR.png', img_hr.permute((1, 2, 0)).numpy())
+