app.py

import json
import os
import random
from functools import partial
from pathlib import Path
from typing import List

import deepinv as dinv
import gradio as gr
import torch
from PIL import Image
from torchvision import transforms

from evals import PhysicsWithGenerator, EvalModel, BaselineModel, EvalDataset, Metric


DEVICE_STR = 'cuda'


### Gradio Utils

def generate_imgs_from_dataset(dataset: EvalDataset, idx: int,
                               model: EvalModel, baseline: BaselineModel,
                               physics: PhysicsWithGenerator, use_gen: bool,
                               metrics: List[Metric]):
    ### Load 1 image
    x = dataset[idx]    # shape : (3, 256, 256)
    x = x.unsqueeze(0)  # shape : (1, 3, 256, 256)

    return generate_imgs(x, model, baseline, physics, use_gen, metrics)

def generate_imgs_from_user(image,
                            model: EvalModel, baseline: BaselineModel,
                            physics: PhysicsWithGenerator, use_gen: bool,
                            metrics: List[Metric]):
    if image is None:
        return None, None, None, None, None, None, None, None

    # PIL image -> torch.Tensor
    x = transforms.ToTensor()(image).unsqueeze(0).to('cuda')

    return generate_imgs(x, model, baseline, physics, use_gen, metrics)
    
def generate_imgs(x: torch.Tensor,
                  model: EvalModel, baseline: BaselineModel,
                  physics: PhysicsWithGenerator, use_gen: bool,
                  metrics: List[Metric]):

    with torch.no_grad():
        ### Compute y
        y = physics(x, use_gen)  # possible reduction in img shape due to Blurring

        ### Compute x_hat
        out = model(y=y, physics=physics.physics)
        out_baseline = baseline(y=y, physics=physics.physics)

        ### Process tensors before metric computation
        if "Blur" in physics.name:
            w_1, w_2 = (x.shape[2] - y.shape[2]) // 2, (x.shape[2] + y.shape[2]) // 2
            h_1, h_2 = (x.shape[3] - y.shape[3]) // 2, (x.shape[3] + y.shape[3]) // 2

            x = x[..., w_1:w_2, h_1:h_2]
            out = out[..., w_1:w_2, h_1:h_2]
            if out_baseline.shape != out.shape:
                out_baseline = out_baseline[..., w_1:w_2, h_1:h_2]

        ### Metrics
        metrics_y = ""
        metrics_out = ""
        metrics_out_baseline = ""
        for metric in metrics:
            if y.shape == x.shape:
                metrics_y += f"{metric.name} = {metric(y, x).item():.4f}" + "\n"
            metrics_out += f"{metric.name} = {metric(out, x).item():.4f}" + "\n"
            metrics_out_baseline += f"{metric.name} = {metric(out_baseline, x).item():.4f}" + "\n"

    ### Process y when y shape is different from x shape
    if physics.name == "MRI" or "SR" in physics.name:
        y_plot = physics.physics.prox_l2(physics.physics.A_adjoint(y), y, 1e4)
    else:
        y_plot = y.clone()

    ### Processing images for plotting :
    #     - clip value outside of [0,1]
    #     - shape (1, C, H, W) -> (C, H, W)
    #     - torch.Tensor object -> Pil object
    process_img = partial(dinv.utils.plotting.preprocess_img, rescale_mode="clip")
    to_pil = transforms.ToPILImage()
    x = to_pil(process_img(x)[0].to('cpu'))
    y = to_pil(process_img(y_plot)[0].to('cpu'))
    out = to_pil(process_img(out)[0].to('cpu'))
    out_baseline = to_pil(process_img(out_baseline)[0].to('cpu'))

    return x, y, out, out_baseline, physics.display_saved_params(), metrics_y, metrics_out, metrics_out_baseline

def generate_random_imgs_from_dataset(dataset: EvalDataset,
                                        model: EvalModel,
                                        baseline: BaselineModel,
                                        physics: PhysicsWithGenerator,
                                        use_gen: bool,
                                        metrics: List[Metric]):
    idx = random.randint(0, len(dataset)-1)
    x, y, out, out_baseline, saved_params_str, metrics_y, metrics_out, metrics_out_baseline = generate_imgs_from_dataset(
        dataset, idx, model, baseline, physics, use_gen, metrics
        )
    return idx, x, y, out, out_baseline, saved_params_str, metrics_y, metrics_out, metrics_out_baseline


get_list_metrics_on_DEVICE_STR = partial(Metric.get_list_metrics, device_str=DEVICE_STR)
get_eval_model_on_DEVICE_STR = partial(EvalModel, device_str=DEVICE_STR)
get_baseline_model_on_DEVICE_STR = partial(BaselineModel, device_str=DEVICE_STR)
get_dataset_on_DEVICE_STR = partial(EvalDataset, device_str=DEVICE_STR)
get_physics_on_DEVICE_STR = partial(PhysicsWithGenerator, device_str=DEVICE_STR)

AVAILABLE_PHYSICS = PhysicsWithGenerator.all_physics
def get_dataset(dataset_name):
    global AVAILABLE_PHYSICS
    if dataset_name == 'MRI':
        AVAILABLE_PHYSICS = ['MRI']
        baseline_name = 'DPIR_MRI'
        physics_name = 'MRI'
    elif dataset_name == 'CT':
        AVAILABLE_PHYSICS = ['CT']
        baseline_name = 'DPIR_CT'
        physics_name = 'CT'
    else:
        AVAILABLE_PHYSICS = ['MotionBlur_easy', 'MotionBlur_medium', 'MotionBlur_hard', 'GaussianBlur_easy', 'GaussianBlur_medium', 'GaussianBlur_hard']
        baseline_name = 'DPIR'
        physics_name = 'MotionBlur_easy'
    return get_dataset_on_DEVICE_STR(dataset_name), get_physics_on_DEVICE_STR(physics_name), get_baseline_model_on_DEVICE_STR(baseline_name)


### Gradio Blocks interface

# Define custom CSS
custom_css = """
.fixed-textbox textarea {
    height: 90px !important;  /* Adjust height to fit exactly 4 lines */
    overflow: scroll;         /* Add a scroll bar if necessary */
    resize: none;             /* User can resize vertically the textbox */
}
"""

title = "Inverse problem playground"  # displayed on gradio tab and in the gradio page
with gr.Blocks(title=title, css=custom_css) as interface:
    gr.Markdown("## " + title)

    # Loading things
    model_a_placeholder = gr.State(lambda: get_eval_model_on_DEVICE_STR("unext_emb_physics_config_C", ""))  # lambda expression to instanciate a callable in a gr.State
    model_b_placeholder = gr.State(lambda: get_baseline_model_on_DEVICE_STR("DPIR"))  # lambda expression to instanciate a callable in a gr.State
    dataset_placeholder = gr.State(lambda: get_dataset_on_DEVICE_STR("Natural"))
    physics_placeholder = gr.State(lambda: get_physics_on_DEVICE_STR("MotionBlur_easy"))  # lambda expression to instanciate a callable in a gr.State
    metrics_placeholder = gr.State(get_list_metrics_on_DEVICE_STR(["PSNR"]))

    @gr.render(inputs=[dataset_placeholder, physics_placeholder, metrics_placeholder])
    def dynamic_layout(dataset, physics, metrics):
        ### LAYOUT
        dataset_name = dataset.name
        physics_name = physics.name
        metric_names = [metric.name for metric in metrics]

        # Components: Inputs/Outputs + Load EvalDataset/PhysicsWithGenerator/EvalModel/BaselineModel
        with gr.Row():
            with gr.Column():
                with gr.Row():
                    with gr.Column():
                        clean = gr.Image(label=f"{dataset_name} IMAGE", interactive=True)
                        physics_params = gr.Textbox(label="Physics parameters", elem_classes=["fixed-textbox"], value=physics.display_saved_params())
                    with gr.Column():
                        y_image = gr.Image(label=f"{physics_name} IMAGE", interactive=False)
                        y_metrics = gr.Textbox(label="Metrics(y, x)", elem_classes=["fixed-textbox"],)

                choose_physics = gr.Radio(choices=AVAILABLE_PHYSICS,
                                          label="List of PhysicsWithGenerator",
                                          value=physics_name)
                with gr.Row():
                    key_selector = gr.Dropdown(choices=list(physics.saved_params["updatable_params"].keys()),
                                               label="Updatable Parameter Key",
                                               scale=2)
                    value_text = gr.Textbox(label="Update Value", scale=2)
                    update_button = gr.Button("Manually update parameter value", scale=1)

            with gr.Column():
                with gr.Row():
                    with gr.Column():
                        model_a_out = gr.Image(label="RAM OUTPUT", interactive=False)
                        out_a_metric = gr.Textbox(label="Metrics(RAM(y, physics), x)", elem_classes=["fixed-textbox"])
                    with gr.Column():
                        model_b_out = gr.Image(label="DPIR OUTPUT", interactive=False)
                        out_b_metric = gr.Textbox(label="Metrics(DPIR(y, physics), x)", elem_classes=["fixed-textbox"])
                with gr.Row():
                    choose_dataset = gr.Radio(choices=EvalDataset.all_datasets,
                                              label="List of EvalDataset",
                                              value=dataset_name,
                                              scale=2)
                    idx_slider = gr.Slider(minimum=0, maximum=len(dataset)-1, step=1, label="Sample index", scale=1)

        # Components: Load Metric + Load image Buttons
        with gr.Row():
            with gr.Column(scale=3):
                choose_metrics = gr.CheckboxGroup(choices=Metric.all_metrics,
                                                  value=metric_names,
                                                  label="Choose metrics you are interested")
            use_generator_button = gr.Checkbox(label="Generate valid physics parameters", scale=1)
            run_button = gr.Button("Run current image", scale=1)
            with gr.Column(scale=1):
                load_button = gr.Button("Load images from dataset...")
                load_random_button = gr.Button("Load randomly from dataset...")

        ### Event listeners
        choose_dataset.change(fn=get_dataset,
                              inputs=choose_dataset,
                              outputs=[dataset_placeholder, physics_placeholder, model_b_placeholder])
        choose_physics.change(fn=get_physics_on_DEVICE_STR,
                              inputs=choose_physics,
                              outputs=[physics_placeholder])
        update_button.click(fn=physics.update_and_display_params, inputs=[key_selector, value_text], outputs=physics_params)
        choose_metrics.change(fn=get_list_metrics_on_DEVICE_STR,
                              inputs=choose_metrics,
                              outputs=metrics_placeholder)
        run_button.click(fn=generate_imgs_from_user,
                         inputs=[clean,
                                 model_a_placeholder,
                                 model_b_placeholder,
                                 physics_placeholder,
                                 use_generator_button,
                                 metrics_placeholder],
                         outputs=[clean, y_image, model_a_out, model_b_out, physics_params, y_metrics, out_a_metric, out_b_metric])
        load_button.click(fn=generate_imgs_from_dataset,
                          inputs=[dataset_placeholder,
                                  idx_slider,
                                  model_a_placeholder,
                                  model_b_placeholder,
                                  physics_placeholder,
                                  use_generator_button,
                                  metrics_placeholder],
                          outputs=[clean, y_image, model_a_out, model_b_out, physics_params, y_metrics, out_a_metric, out_b_metric])
        load_random_button.click(fn=generate_random_imgs_from_dataset,
                                 inputs=[dataset_placeholder,
                                         model_a_placeholder,
                                         model_b_placeholder,
                                         physics_placeholder,
                                         use_generator_button,
                                         metrics_placeholder],
                                 outputs=[idx_slider, clean, y_image, model_a_out, model_b_out, physics_params, y_metrics, out_a_metric, out_b_metric])

interface.launch()