Watch EasySeer turn a concept into a manuscript

"""MLRI-Net project hub (tkinter GUI).

* "Run Full Pipeline & Show Graphs" runs every stage for the selected dataset(s)
  on a background thread, then shows all graphs in the built-in gallery.
* Every stage can also be run on its own, as can the sample case study, the
  self-checks and the demo.
* Settings (data percentage, per-class cap, epochs, ...) are saved to run_settings.json.

Start:  python main.py
"""
import os
import queue
import random
import subprocess
import sys
import threading
import tkinter as tk
import traceback
from pathlib import Path
from tkinter import messagebox, scrolledtext, ttk

from PIL import Image, ImageTk

import run_config as cfg
from data_loader import discover_datasets
from feature_cache_builder import PipelineCancelled
from full_pipeline_runner import STAGE_ORDER, STAGE_TITLES, run_full_pipeline
from project_paths import (CODE_DIR, EXPERIMENTAL_OUTPUT_DIR, PARAMETERS_DIR, RESULTS_DIR, SAMPLE_CASE_STUDY_DIR,
                           ensure_output_folders)

ACCENT = "#882255"
PANEL_BG = "#F7F4F2"
THUMB = 230


def open_folder(path):
    path = Path(path)
    path.mkdir(parents=True, exist_ok=True)
    if sys.platform.startswith("win"):
        os.startfile(str(path))                                  # noqa: S606 (Windows only)
    elif sys.platform == "darwin":
        subprocess.Popen(["open", str(path)])
    else:
        subprocess.Popen(["xdg-open", str(path)])


class GraphGallery(ttk.Frame):
    """Scrollable grid of result graphs; click a thumbnail to enlarge it."""

    def __init__(self, master):
        super().__init__(master)
        top = ttk.Frame(self)
        top.pack(fill="x", pady=(4, 2))
        ttk.Label(top, text="Show:").pack(side="left", padx=(6, 2))
        self.filter = ttk.Combobox(top, state="readonly", width=34)
        self.filter.pack(side="left")
        self.filter.bind("<<ComboboxSelected>>", lambda _e: self.refresh())
        ttk.Button(top, text="Reload graphs", command=self.refresh).pack(side="left", padx=6)
        self.count_label = ttk.Label(top, text="")
        self.count_label.pack(side="left", padx=6)
        self.canvas = tk.Canvas(self, background="white", highlightthickness=0)
        scroll = ttk.Scrollbar(self, orient="vertical", command=self.canvas.yview)
        self.inner = tk.Frame(self.canvas, background="white")
        self.inner.bind("<Configure>", lambda _e: self.canvas.configure(scrollregion=self.canvas.bbox("all")))
        self.canvas.create_window((0, 0), window=self.inner, anchor="nw")
        self.canvas.configure(yscrollcommand=scroll.set)
        self.canvas.pack(side="left", fill="both", expand=True)
        scroll.pack(side="right", fill="y")
        self.canvas.bind_all("<MouseWheel>", lambda e: self.canvas.yview_scroll(int(-e.delta / 120), "units"))
        self.canvas.bind_all("<Button-4>", lambda _e: self.canvas.yview_scroll(-1, "units"))
…

"""Proposed method - MLRI-Net (Mean Squared LogCosh loss based Residual Inception Network).

Pipeline (context.docx Sec. 3): CIEWF -> DLNet -> CutMix/MixUp -> {f1 CS-LMP, f2 ResNeXt-101}
-> MLRI-Net -> fruit type + fruit grade -> XAI.

Architecture (Sec. 3.6):
  image branch : segmented image Sm (128x128) -> frozen ResNet18 conv1..layer3 (cached)
                 -> trainable layer4 -> SE channel attention (residual) -> inception (1024 ch) -> GAP
  fusion       : [1024 image | 256 from f1 CS-LMP | 256 from f2 ResNeXt-101] = 1536-D
  heads        : FC1 (1024) -> FC2 (128) -> fruit-type head and grade head
  loss         : L_Tot = 0.5 RMSLE + 0.5 LogCosh on softmax outputs, summed over both heads
  optimiser    : Adam (beta1 0.9, beta2 0.999, eps 1e-8), lr 1e-3, ReduceLROnPlateau, early stopping

Ablation variants (Sec. 4.7.2) reuse the class with a different configuration:
  without DLNet        -> image branch, f1 and f2 computed on the CIEWF image (no background isolation)
  without augmentation -> no CutMix / MixUp
"""
import torch
import torch.nn as nn

import run_config as cfg
from comparison_method_interface import NeuralTwoHeadMethod
from loss_rmsle_logcosh import MeanSquaredLogCoshLoss
from rai_net_blocks import RAIImageBranch, TwoHeadClassifier
from texture_features_cslmp import CSLMP_DIM


class MLRINet(nn.Module):
    def __init__(self, n_types, n_grades, cslmp_dim=CSLMP_DIM, resnext_dim=1000, proj=256, dropout=None):
        super().__init__()
        dropout = cfg.DROPOUT if dropout is None else dropout
        self.image_branch = RAIImageBranch()
        self.cslmp_proj = nn.Sequential(nn.Linear(cslmp_dim, proj), nn.ReLU(inplace=True), nn.Dropout(dropout))
        self.resnext_proj = nn.Sequential(nn.Linear(resnext_dim, proj), nn.ReLU(inplace=True), nn.Dropout(dropout))
        self.classifier = TwoHeadClassifier(RAIImageBranch.out_dim + 2 * proj, n_types, n_grades,
                                            fc1=1024, fc2=128, dropout=dropout)

    def forward(self, layer3_maps, vectors, return_maps=False):
        f1, f2 = vectors
        if return_maps:
            image_vec, maps = self.image_branch(layer3_maps, return_maps=True)
        else:
            image_vec = self.image_branch(layer3_maps)
        fused = torch.cat([image_vec, self.cslmp_proj(f1), self.resnext_proj(f2)], dim=1)
        type_logits, grade_logits, embedding = self.classifier(fused)
        if return_maps:
            return type_logits, grade_logits, embedding, maps
        return type_logits, grade_logits, embedding


class MLRINetMethod(NeuralTwoHeadMethod):
    key = "mlri_net"
    display_name = "MLRI-Net (proposed)"
    source = "Proposed method (context.docx Sec. 3)"
    map_key = "layer3_seg"
    vector_keys = ("cslmp_seg", "resnext_seg")
    augment = "mix"
    default_batch_size = None          # -> run_config.BATCH_SIZE at run time

    def build_model(self):
        return MLRINet(self.n_types, self.n_grades)

    def build_optimizer(self, model):
        params = [p for p in model.parameters() if p.requires_grad]
        return torch.optim.Adam(params, lr=cfg.LEARNING_RATE, betas=(cfg.ADAM_BETA1, cfg.ADAM_BETA2),
                                eps=cfg.ADAM_EPSILON)

    def loss(self):
        return MeanSquaredLogCoshLoss()

…

"""Building blocks of RAI-Net (Residual-Attention-Inception Network).

Source: Maurya et al., "RAI-Net: Tomato Plant Disease Classification Using
Residual-Attention-Inception Network" (IEEE Access 2025), Sec. IV, Fig. 2:
  ResNet18 (fine-tuned last layers) -> 512x4x4 maps
  -> channel attention (squeeze-and-excitation), applied residually:  y_CA = CA(x) + x   (Eq. 8)
  -> inception module, concat of 1x1 | 1x1-3x3 | 1x1-5x5 | maxpool-1x1   (Eq. 9) -> 1024x4x4
  -> global average pooling -> FC1 -> FC2 (128) -> classes
"""
import torch
import torch.nn as nn

from pretrained_backbones import pretrained_resnet18_layer4


class SqueezeExcitationAttention(nn.Module):
    """Eqs. (1)-(5): F_avg -> conv + ReLU -> conv -> sigmoid = M_c; F_hat = F * M_c."""

    def __init__(self, channels=512, reduction=16):
        super().__init__()
        self.pool = nn.AdaptiveAvgPool2d(1)
        self.reduce = nn.Conv2d(channels, channels // reduction, 1)
        self.expand = nn.Conv2d(channels // reduction, channels, 1)
        self.detach_gate = False          # True while computing LRP (gate treated as constant)

    def forward(self, x):
        gate = torch.sigmoid(self.expand(torch.relu(self.reduce(self.pool(x)))))
        if self.detach_gate:
            gate = gate.detach()
        return x * gate + x               # Eq. (8): y_CA = CA(x) + x


def _conv_bn_relu(cin, cout, k):
    return nn.Sequential(nn.Conv2d(cin, cout, k, padding=k // 2, bias=False),
                         nn.BatchNorm2d(cout), nn.ReLU(inplace=True))


class InceptionModule(nn.Module):
    """Eq. (9): multi-scale branches concatenated along channels (512 -> 1024)."""

    def __init__(self, cin=512, branch=256):
        super().__init__()
        self.b1 = _conv_bn_relu(cin, branch, 1)
        self.b3 = nn.Sequential(_conv_bn_relu(cin, branch // 2, 1), _conv_bn_relu(branch // 2, branch, 3))
        self.b5 = nn.Sequential(_conv_bn_relu(cin, branch // 4, 1), _conv_bn_relu(branch // 4, branch, 5))
        self.bp = nn.Sequential(nn.MaxPool2d(3, stride=1, padding=1), _conv_bn_relu(cin, branch, 1))

    def forward(self, x):
        return torch.cat([self.b1(x), self.b3(x), self.b5(x), self.bp(x)], dim=1)


class RAIImageBranch(nn.Module):
    """Trainable part of RAI-Net on top of the frozen layer3 maps (256x8x8 -> 1024-D)."""

    out_dim = 1024

    def __init__(self):
        super().__init__()
        self.layer4 = pretrained_resnet18_layer4()       # residual blocks, ImageNet initialised
        self.attention = SqueezeExcitationAttention(512)
        self.inception = InceptionModule(512, 256)
        self.pool = nn.AdaptiveAvgPool2d(1)

    def forward(self, layer3_maps, return_maps=False):
        l4 = self.layer4(layer3_maps)
        att = self.attention(l4)
        inc = self.inception(att)
        pooled = torch.flatten(self.pool(inc), 1)
        if return_maps:
            return pooled, {"layer4": l4, "attention": att, "inception": inc}
…

"""Stage 3.2 - Contextual Image Enhancement Wiener Filter (CIEWF).

Source: Sudhakar et al., "High-performance parallel multi-scale attention network
with explainable AI for intelligent diagnosis of leaf diseases in agricultural
systems", p. 5, Eqs. (1)-(3):

    CIEWF[I(x1,x2)] = alpha + (beta^2 - beta_n^2) / beta^2 * (I(x1,x2) - alpha)
    alpha  = local mean over the surrounding window s
    beta^2 = local variance over s,   beta_n^2 = noise variance

This is a locally adaptive (Lee / Wiener) filter. In flat regions the local
variance is close to the noise variance, so the pixel is pulled to the local
mean (noise removed). On edges the variance is large, so the pixel is kept
(detail preserved). The paper says CIEWF "selects the most suitable window"
but gives no size, so each image is filtered with every candidate window in
CIEWF_WINDOWS and the window that removes the most noise while keeping at
least half of the edge energy is kept.
"""
import cv2
import numpy as np
from skimage.metrics import peak_signal_noise_ratio, structural_similarity

import run_config as cfg


def wiener_filter_channel(channel, window, noise_variance=None):
    """Apply Eq. (1) to one float channel in [0, 1]."""
    alpha = cv2.blur(channel, (window, window))                        # local mean, Eq. (2)
    beta_sq = cv2.blur(channel * channel, (window, window)) - alpha ** 2  # local variance, Eq. (3)
    beta_sq = np.maximum(beta_sq, 0.0)
    if noise_variance is None:                                         # beta_n^2 estimate
        noise_variance = float(beta_sq.mean())
    gain = np.maximum(beta_sq - noise_variance, 0.0) / np.maximum(beta_sq, noise_variance + 1e-12)
    return alpha + gain * (channel - alpha)


def estimate_noise_sigma(gray):
    """Immerkaer's fast noise estimate (sigma) of a float grey image in [0, 1]."""
    kernel = np.array([[1, -2, 1], [-2, 4, -2], [1, -2, 1]], dtype=np.float32)
    conv = cv2.filter2D(gray.astype(np.float32), -1, kernel)
    h, w = gray.shape
    return float(np.sqrt(np.pi / 2.0) * np.abs(conv[1:-1, 1:-1]).sum() / (6.0 * (w - 2) * (h - 2)))


def _edge_energy(gray):
    return float(cv2.Laplacian(gray.astype(np.float32), cv2.CV_32F).var()) + 1e-12


def enhance_image_ciewf(rgb_uint8, windows=None):
    """Enhance one RGB uint8 image. Returns (enhanced uint8, info dict)."""
    windows = windows or cfg.CIEWF_WINDOWS
    image = rgb_uint8.astype(np.float32) / 255.0
    gray_in = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
    edge_in = _edge_energy(gray_in)
    best = None
    for window in windows:
        out = np.stack([wiener_filter_channel(image[..., c], window) for c in range(3)], axis=-1)
        out = np.clip(out, 0.0, 1.0)
        gray_out = cv2.cvtColor(out, cv2.COLOR_RGB2GRAY)
        noise = estimate_noise_sigma(gray_out)
        edge_keep = _edge_energy(gray_out) / edge_in
        # Prefer the lowest residual noise among windows that keep >= 50 % edge energy.
        key = (edge_keep < 0.5, noise)
        if best is None or key < best[0]:
            best = (key, window, out, noise, edge_keep)
    _, window, out, noise, edge_keep = best
    enhanced = (out * 255.0 + 0.5).astype(np.uint8)
    info = {
        "ciewf_window": window,
        "noise_sigma_before": estimate_noise_sigma(gray_in),
…

"""
ShPCN-Net research control panel (tkinter GUI).

* "Run Full Pipeline & Show Graphs" runs every step for the chosen dataset(s) on a
  background thread and then shows all result graphs in the built-in viewer.
* Every pipeline step can also be run on its own from the left-hand menu.
* Settings: dataset selector (or "All datasets"), DATA_PERCENT (1-100), a toggle that
  draws a new random seed (a new set of results), and a quick smoke-test mode.

Run:  python main.py
"""
import os
import platform
import queue
import subprocess
import sys
import threading
import tkinter as tk
import traceback
from tkinter import messagebox, ttk

from PIL import Image, ImageTk

import full_pipeline_runner as runner
import run_config as cfg
from data_loader import discover_datasets

ALL_DATASETS = "All datasets"
ACCENT = "#D55E00"
INK = "#1B1B1B"
PANEL = "#F6F4F1"


def open_folder(path):
    path.mkdir(parents=True, exist_ok=True)
    if platform.system() == "Windows":
        os.startfile(str(path))
    elif platform.system() == "Darwin":
        subprocess.Popen(["open", str(path)])
    else:
        subprocess.Popen(["xdg-open", str(path)])


class ControlPanel(tk.Tk):
    def __init__(self):
        super().__init__()
        self.title("ShPCN-Net · Heart Disease Detection · Research Control Panel")
        # fit the window to the screen (laptops with 125-150 % scaling report ~1536 x 864)
        width = min(1360, self.winfo_screenwidth() - 40)
        height = min(860, self.winfo_screenheight() - 90)
        self.geometry(f"{width}x{height}+10+10")
        self.minsize(min(1000, width), min(600, height))
        self.configure(bg=PANEL)
        self.messages = queue.Queue()
        self.worker = None
        self.graph_paths = []
        self.preview_image = None
        self.datasets = list(discover_datasets())
        self._build_style()
        self._build_header()
        self._build_body()
        self._drain_job = self.after(150, self._drain_messages)
        self.refresh_gallery()
        if not self.datasets:
            messagebox.showerror("No dataset", "No non-empty code/dataset_*/ folder was found.\n"
                                               "Add the dataset before running the pipeline.")

    # ------------------------------------------------------------------ layout
    def _build_style(self):
        style = ttk.Style(self)
…

"""
Proposed method - ShPCN-Net (context.docx, Stage 3; concept handwritten diagram p.3):

    F_s --(3a embedding)--> I_p --[ShuffleNet-V2 + SE]--> O1
    F_s --(regression y1)--+
                           +--[Taylor fusion O2 = 2*y1 - O1]--> O2 --[PCNN 1/3/5/7]--> O3..O6
    O7 = softmax( fc( O3 || O4 || O5 || O6 ) )  ->  Disease / Normal
"""
import torch
from torch import nn

from network_building_blocks import (FeatureToMapEmbedding, HiddenDenseStack, ParallelMultiscaleConv,
                                     ShuffleNetSEBranch, TaylorRegressionFusion)
from torch_training_loop import TorchTabularClassifier


class ShPCNNetModule(nn.Module):
    def __init__(self, n_features, s):
        super().__init__()
        c, m = s["channels"], s["map_size"]
        self.embedding = FeatureToMapEmbedding(n_features, m)                       # 3a
        self.shufflenet_branch = ShuffleNetSEBranch(c, s["se_reduction"])           # 3b
        self.taylor_fusion = TaylorRegressionFusion(n_features, c, m)               # 3c
        self.pcnn = ParallelMultiscaleConv(c, s["pcnn_filters"], s["pcnn_kernel_sizes"])  # 3d
        self.head = HiddenDenseStack(s["pcnn_filters"] * len(s["pcnn_kernel_sizes"]),
                                     s["hidden_neurons"], s["hidden_layers"], s["dropout_rate"],
                                     s["activation"])

    def forward(self, features, return_intermediates=False):
        i_p = self.embedding(features)
        o1, se_weights, unit_map = self.shufflenet_branch(i_p)
        o2, y1 = self.taylor_fusion(features, o1)
        pooled, branch_maps = self.pcnn(o2)
        logits = self.head(pooled)
        if not return_intermediates:
            return logits
        return logits, {
            "I_p": i_p, "shufflenet_unit_map": unit_map, "se_weights": se_weights,
            "O1": o1, "y1": y1, "O2": o2,
            "O3": branch_maps[0], "O4": branch_maps[1], "O5": branch_maps[2], "O6": branch_maps[3],
            "O7": torch.softmax(logits, dim=1),
        }


class ShPCNNetClassifier(TorchTabularClassifier):
    display_name = "ShPCN-Net (proposed)"

    def build_module(self, n_features):
        return ShPCNNetModule(n_features, self.settings)

    def trace_intermediates(self, X):
        """Every intermediate tensor (as numpy) for visualisation of one or more samples."""
        self.module.eval()
        with torch.no_grad():
            _, inter = self.module(torch.as_tensor(X, dtype=torch.float32), return_intermediates=True)
        return {k: v.numpy() for k, v in inter.items()}

"""
Stage 2 - feature selection with the Gray Langurs Optimizer (GLO)  (N_d -> F_s).

Source: Barshandeh et al., "Gray langurs optimizer: a multi-group bio-inspired
optimization algorithm" (Artificial Intelligence Review, 2026). The paper is not
in basepapers/, so the equations below follow its published description (three
group types: one-male, multi-male and all-male groups; social hierarchy;
migration between groups after alpha death / mate finding / puberty; arbitrary
movement inside the territory). The exact coefficients are our documented
interpretation - see context.docx "Open questions".

Notation (per langur i, dimension d, iteration t of T):
  a      = a_start - (a_start - a_end) * t / (T - 1)       control parameter, 2 -> 0
  r1,r2,r3 ~ U(0,1)
  One-male group  (follow the single alpha A_g):
      X_new = A_g - a * (2*r1 - 1) * |r2 * A_g - X_i|
  Multi-male group (follow the mean M_g of the top-3 males + global best G):
      X_new = M_g - a * (2*r1 - 1) * |r2 * M_g - X_i| + r3 * (1 - a/a_start) * (G - X_i)
  All-male group  (bachelors roam: random partner X_j + pull to G for mate finding):
      X_new = X_i + a * (2*r1 - 1) * (X_j - X_i) + r2 * (1 - a/a_start) * (G - X_i)
  Territory movement (probability p_rand):
      X_new = X_i + a * N(0, 0.1 * (ub - lb))
  Greedy replacement: X_i <- X_new only if fitness improves.
  Alpha death: if a group's best fitness has not improved for L iterations, its
      worst member is re-born near the global best (puberty / new male joins).
  Migration: every M iterations the worst member of group g swaps with a random
      member of group g+1 (langurs move between groups).
  Boundaries: positions are clipped to [lb, ub].

Solution encoding (context.docx 3.3.1): position vector in [0,1]^N, feature d is
selected when X_d > 0.5 -> equivalent to the list of selected feature indices.
Fitness (Eq. 24): Fitness = alpha*(1 - Accuracy) + (1 - alpha) * R / K  (minimised),
Accuracy from an RBF-SVM wrapper with stratified inner CV on the training data only.
"""
import numpy as np
from sklearn.model_selection import StratifiedKFold, cross_val_score
from sklearn.svm import SVC

import run_config as cfg

GROUP_TYPES = ("one_male", "multi_male", "all_male")


def position_to_mask(position, threshold):
    """Continuous langur position -> boolean feature mask (at least one feature)."""
    mask = position > threshold
    if not mask.any():
        mask[int(np.argmax(position))] = True
    return mask


class FeatureSubsetFitness:
    """Eq. (24) wrapper fitness with a cache (identical masks are scored once)."""

    def __init__(self, X, y, alpha, cv_folds, svm_c, seed, threshold):
        self.X, self.y = X, y
        self.alpha, self.threshold = alpha, threshold
        n_splits = int(max(2, min(cv_folds, np.bincount(y).min())))
        self.cv = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=seed)
        self.svm_c = svm_c
        self.cache = {}

    def accuracy_of(self, mask):
        key = tuple(np.flatnonzero(mask))
        if key not in self.cache:
            model = SVC(C=self.svm_c, kernel="rbf", gamma="scale")
            self.cache[key] = float(cross_val_score(model, self.X[:, mask], self.y, cv=self.cv).mean())
        return self.cache[key]

    def __call__(self, position):
…

"""
Reusable PyTorch blocks for ShPCN-Net and the ablation baselines (context.docx, Stage 3).

FeatureToMapEmbedding   3a: k selected features -> learned 1 x 8 x 8 map I_p
ShuffleNetV2Unit        3b: channel split, 1x1 conv, 3x3 depthwise conv, 1x1 conv,
                            concatenation, channel shuffle
SqueezeExcitation       3b: Eq. 8-10   Z_c = 1/(H*W) sum_i sum_j I_p
                                       s   = sigma(W2 * delta(W1 * Z_c))
                                       O1  = s * I_p
TaylorRegressionFusion  3c: y1 = sum F_i * w_i (learned map to the shape of O1)
                            O2 = 2*y1 - O1   (first-order Taylor rule, k = 1)
ParallelMultiscaleConv  3d: O3..O6 = f_conv1..4(O2) with 1/3/5/7 kernels
HiddenDenseStack        FC hidden layers (depth = "hidden layers" setting)
"""
import torch
from torch import nn


def make_activation(name):
    return {"relu": nn.ReLU(), "gelu": nn.GELU(), "elu": nn.ELU(), "tanh": nn.Tanh()}.get(name, nn.ReLU())


class FeatureToMapEmbedding(nn.Module):
    """Dense k -> map_size^2, reshaped to (B, 1, H, W). This is the network input I_p."""

    def __init__(self, n_features, map_size):
        super().__init__()
        self.map_size = map_size
        self.linear = nn.Linear(n_features, map_size * map_size)

    def forward(self, x):
        return self.linear(x).view(-1, 1, self.map_size, self.map_size)


def channel_shuffle(x, groups):
    b, c, h, w = x.shape
    return x.view(b, groups, c // groups, h, w).transpose(1, 2).contiguous().view(b, c, h, w)


class ShuffleNetV2Unit(nn.Module):
    """Stride-1 ShuffleNet-V2 basic unit (Ma et al., 2018)."""

    def __init__(self, channels):
        super().__init__()
        half = channels // 2
        self.branch = nn.Sequential(
            nn.Conv2d(half, half, 1, bias=False), nn.BatchNorm2d(half), nn.ReLU(inplace=True),
            nn.Conv2d(half, half, 3, padding=1, groups=half, bias=False), nn.BatchNorm2d(half),
            nn.Conv2d(half, half, 1, bias=False), nn.BatchNorm2d(half), nn.ReLU(inplace=True),
        )

    def forward(self, x):
        left, right = x.chunk(2, dim=1)                      # channel split
        out = torch.cat([left, self.branch(right)], dim=1)   # concatenation
        return channel_shuffle(out, 2)                       # channel shuffle


class SqueezeExcitation(nn.Module):
    """Eq. 8-10. Returns (O1, s) so the attention weights can be visualised."""

    def __init__(self, channels, reduction):
        super().__init__()
        hidden = max(channels // reduction, 2)
        self.w1 = nn.Linear(channels, hidden)     # W1
        self.w2 = nn.Linear(hidden, channels)     # W2
        self.delta = nn.ReLU()                    # delta
        self.sigma = nn.Sigmoid()                 # sigma

    def forward(self, i_p):
        z_c = i_p.mean(dim=(2, 3))                              # Eq. 8: global average pooling
…