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import torch
import wandb
from dataset import BreastMRI_ABMIL_NII, MammographyDataset, BreastMRI, BreastMRI_ABMIL
#from src.models_all.models import create_soat_cnn_models
import torch.optim as optim
import pandas as pd
import argparse
import torch.nn as nn
from pathlib import Path
from torch.utils.data import WeightedRandomSampler
from torchinfo import summary
from src.trainer_tester.supervised_trainer import FullySupervisedMultiViewTrainer, MultiInstanceTrainer
from src.utils.utils_functions import compute_sample_weights, log_scalar_values, set_seed
from src.data_loader.mammo_transforms import BasicTransforms, TrainTransform, TrainTransformBaseline
from src.utils.plotter import display_confusion_matrix
from sklearn.metrics import classification_report
import numpy as np
import pandas as pd
from pathlib import Path
from src.utils.dataset_utils import extract_target_names
#import utils
import torch
import torch.nn as nn
import timm
from focal_loss.focal_loss import FocalLoss
class MultiViewSwinModel(nn.Module):
def __init__(self, model_name="swin_tiny_patch4_window7_224.ms_in22k",
pretrained=True, num_classes=3, attn_dim=1024, num_heads=4):
super(MultiViewSwinModel, self).__init__()
# Load backbone without classification head
self.backbone = timm.create_model(model_name, pretrained=pretrained, num_classes=0)
self.feature_dim = self.backbone.num_features # should be 1024 for swin_base
# Self-attention across views (3 views → sequence length 3)
self.attn = nn.MultiheadAttention(embed_dim=self.feature_dim, num_heads=num_heads, batch_first=True)
# Final classifier
self.classifier = nn.Linear(self.feature_dim, num_classes)
def extract_feat(self, x):
# Get unpooled features (B, H*W, C) and then pooled vector (B, C)
features = self.backbone.forward_features(x) # (B, H*W, C)
pooled = self.backbone.forward_head(features, pre_logits=True) # (B, C)
return pooled
def forward(self, img1, img2, img3):
# Per-view embeddings
f1 = self.extract_feat(img1) # [B, C]
f2 = self.extract_feat(img2)
f3 = self.extract_feat(img3)
# Stack views to form a sequence [B, 3, C]
views = torch.stack([f1, f2, f3], dim=1)
# Self-attention across views
attn_out, _ = self.attn(views, views, views) # [B, 3, C]
# Aggregate (mean pooling across 3 views)
fused = attn_out.mean(dim=1) # [B, C]
return self.classifier(fused)
class MultiViewSwinCrossAttn(nn.Module):
"""
Cross‑attention Swin for 3‑view breast‑MRI (pre / post / subtraction).
"""
def __init__(
self,
model_name: str = "swin_tiny_patch4_window7_224.ms_in22k",
pretrained: bool = True,
num_classes: int = 3,
num_xattn_layers: int = 2,
num_heads: int = 4,
dropout: float = 0.1,
):
super().__init__()
# 1) Swin backbone WITHOUT final linear head
self.backbone = timm.create_model(model_name, pretrained=pretrained, num_classes=0)
self.embed_dim = self.backbone.num_features # 1024 for swin‑base
# 2) Learnable CLS token (like ViT) and view‑type embeddings
self.cls_token = nn.Parameter(torch.zeros(1, 1, self.embed_dim))
self.view_embed = nn.Parameter(torch.zeros(3, 1, self.embed_dim)) # pre/post/sub
# 3) Cross‑attention encoder (TransformerEncoder)
encoder_layer = nn.TransformerEncoderLayer(
d_model=self.embed_dim,
nhead=num_heads,
dim_feedforward=self.embed_dim * 4,
dropout=dropout,
activation="gelu",
batch_first=True,
norm_first=True,
)
self.xattn = nn.TransformerEncoder(encoder_layer, num_layers=num_xattn_layers)
# 4) Classification head
self.norm = nn.LayerNorm(self.embed_dim)
self.classifier = nn.Linear(self.embed_dim, num_classes)
# init
nn.init.trunc_normal_(self.cls_token, std=0.02)
nn.init.trunc_normal_(self.view_embed, std=0.02)
# ------------------------------------------------------------------ #
def _img_embed(self, x: torch.Tensor) -> torch.Tensor:
"""
Forward a single image through Swin and return pooled vector (B, C).
"""
feats = self.backbone.forward_features(x) # (B, H*W, C)
pooled = self.backbone.forward_head(feats, pre_logits=True) # (B, C)
return pooled
def forward(self, pre, post, sub):
"""
Args:
pre, post, sub: tensors (B, 3, 224, 224)
Returns:
logits (B, num_classes)
"""
# 1) Per‑view embeddings
z_pre = self._img_embed(pre) # (B, C)
z_post = self._img_embed(post)
z_sub = self._img_embed(sub)
# 2) Assemble sequence: [CLS] + three view tokens
B = z_pre.size(0)
cls = self.cls_token.expand(B, -1, -1) # (B, 1, C)
views = torch.stack([z_pre, z_post, z_sub], dim=1) # (B, 3, C)
# add view‑type embeddings (broadcast over batch)
views = views + self.view_embed.transpose(0, 1) # (B, 3, C)
tokens = torch.cat([cls, views], dim=1) # (B, 4, C)
# 3) Cross‑attention
tokens = self.xattn(tokens) # (B, 4, C)
# 4) CLS pooling → head
out = self.classifier(self.norm(tokens[:, 0])) # (B, num_classes)
return out
import torch
import torch.nn as nn
import timm
import torch
import torch.nn as nn
import timm
import math
import numpy as np
class CrossModalAttentionABMIL_Swin(nn.Module):
"""
Swin‑based Multiple‑Instance model with per‑slice cross‑modal attention
over (pre, post, sub) features, followed by ABMIL pooling.
Input : (B, 32, 3, 224, 224) # 3 modalities stacked in channel dim
Output : logits (B, num_classes), attention‑per‑slice (B, 32)
"""
def __init__(
self,
model_name: str = "swin_tiny_patch4_window7_224.ms_in22k",
pretrained: bool = True,
num_classes: int = 3,
hidden_dim: int = 256,
cross_attn_heads: int = 4,
dropout: float = 0.1,
):
super().__init__()
# ------------------------------------------------------------
# 1) Shared Swin backbone (no classifier head)
# ------------------------------------------------------------
self.backbone = timm.create_model(model_name, pretrained=pretrained, num_classes=0)
self.embed_dim = self.backbone.num_features # 768 for swin_tiny
# ------------------------------------------------------------
# 2) Per‑slice cross‑modal fusion
# Input: three embeddings (pre, post, sub) -> fused embedding
# ------------------------------------------------------------
encoder_layer = nn.TransformerEncoderLayer(
d_model=self.embed_dim,
nhead=cross_attn_heads,
dim_feedforward=self.embed_dim * 4,
dropout=dropout,
activation="gelu",
batch_first=True,
norm_first=True,
)
# One small transformer (2 layers) that attends across 3 tokens
self.slice_fuser = nn.TransformerEncoder(encoder_layer, num_layers=2)
# Token type embedding to differentiate modalities
self.mod_embed = nn.Parameter(torch.randn(3, 1, self.embed_dim))
# ------------------------------------------------------------
# 3) ABMIL Attention across 32 fused slices
# ------------------------------------------------------------
self.attn_V = nn.Linear(self.embed_dim, hidden_dim)
self.attn_U = nn.Linear(hidden_dim, 1)
# ------------------------------------------------------------
# 4) Classifier head
# ------------------------------------------------------------
self.norm = nn.LayerNorm(self.embed_dim)
self.dropout = nn.Dropout(dropout)
self.classifier = nn.Linear(self.embed_dim, num_classes)
# Init
nn.init.trunc_normal_(self.mod_embed, std=0.02)
nn.init.trunc_normal_(self.attn_V.weight, std=0.02)
nn.init.trunc_normal_(self.attn_U.weight, std=0.02)
# ------------------------------------------------------------
def _encode_slices(self, x: torch.Tensor) -> torch.Tensor:
"""
x : (B*N, 1, 224, 224) -> Swin pooled feats (B*N, embed_dim)
"""
feats = self.backbone.forward_features(x)
return self.backbone.forward_head(feats, pre_logits=True)
# ------------------------------------------------------------
def forward(self, volume: torch.Tensor):
"""
volume : (B, 32, 3, 224, 224)
"""
B, N, C_img, H, W = volume.shape # C_img = 3 modalities
assert C_img == 3, "Expect channel dim = 3 (pre, post, sub)"
# --------------------------------------------------------
# 1) Split modalities, flatten, and encode with Swin
# --------------------------------------------------------
# Each is (B*N, 1, H, W)
def expand3(x): return x.expand(-1, 3, -1, -1)
pre = expand3(volume[:, :, 0, :, :].contiguous().view(B * N, 1, H, W))
post = expand3(volume[:, :, 1, :, :].contiguous().view(B * N, 1, H, W))
sub = expand3(volume[:, :, 2, :, :].contiguous().view(B * N, 1, H, W))
feat_pre = self._encode_slices(pre).view(B, N, -1) # (B, N, C)
feat_post = self._encode_slices(post).view(B, N, -1)
feat_sub = self._encode_slices(sub).view(B, N, -1)
# --------------------------------------------------------
# 2) Cross‑modal attention fusion (per slice)
# --------------------------------------------------------
# Build token sequence [pre, post, sub] for each slice
# Shape before fuser: (B, N, 3, C) -> we fuse along dim=2
slice_tokens = torch.stack([feat_pre, feat_post, feat_sub], dim=2)
# Add modality embeddings
slice_tokens = slice_tokens + self.mod_embed.transpose(0, 1) # (B, N, 3, C)
slice_tokens = slice_tokens.view(B * N, 3, self.embed_dim) # (B*N, 3, C)
# Transformer encoder attends across the 3 tokens
fused = self.slice_fuser(slice_tokens)[:, 0] # take CLS‑like first token
fused = fused.view(B, N, self.embed_dim) # (B, 32, C)
# --------------------------------------------------------
# 3) ABMIL attention over 32 fused slices
# --------------------------------------------------------
A = torch.tanh(self.attn_V(fused)) # (B, N, hidden)
A = self.attn_U(A) # (B, N, 1)
A = torch.softmax(A, dim=1) # (B, N, 1)
patient_feat = (A * fused).sum(dim=1) # (B, C)
# --------------------------------------------------------
# 4) Head
# --------------------------------------------------------
logits = self.classifier(self.dropout(self.norm(patient_feat)))
return logits, A.squeeze(-1) # (B, num_classes), (B, 32)
class ABMIL_Swin(nn.Module):
"""
Attention‑based Multiple‑Instance Learning (ABMIL) model
for 3‑channel breast MRI slice triplets (pre / post / sub).
Input shape : (B, 32, 3, 224, 224)
Output : logits (B, num_classes) + attention weights (B, 32)
"""
def __init__(
self,
model_name: str = "swin_tiny_patch4_window7_224.ms_in22k",
pretrained: bool = True,
num_classes: int = 2,
hidden_dim: int = 256,
dropout: float = 0.1,
):
super().__init__()
# 1) Swin backbone WITHOUT final linear head
self.backbone = timm.create_model(model_name, pretrained=pretrained, num_classes=0)
self.embed_dim = self.backbone.num_features # e.g. 768 (tiny), 1024 (base)
# 2) Attention network (ABMIL)
self.attn_V = nn.Linear(self.embed_dim, hidden_dim)
self.attn_U = nn.Linear(hidden_dim, 1)
# 3) Patient‑level classifier
self.norm = nn.LayerNorm(self.embed_dim)
self.dropout = nn.Dropout(dropout)
self.classifier = nn.Linear(self.embed_dim, num_classes)
# Optional init
nn.init.trunc_normal_(self.attn_V.weight, std=0.02)
nn.init.trunc_normal_(self.attn_U.weight, std=0.02)
# ------------------------------------------------------------------ #
def _slice_embed(self, x: torch.Tensor) -> torch.Tensor:
"""
Forward one or more images through Swin and return pooled features.
Args:
x : (N, 3, 224, 224)
Returns:
pooled : (N, C)
"""
feats = self.backbone.forward_features(x) # (N, H*W, C)
pooled = self.backbone.forward_head(feats, pre_logits=True) # (N, C)
return pooled
# ------------------------------------------------------------------ #
def forward(self, volume: torch.Tensor):
"""
Args:
volume : (B, 32, 3, 224, 224)
Returns:
logits : (B, num_classes)
attn_w : (B, 32) -- attention per slice (sums to 1)
"""
B, N, C, H, W = volume.shape
x = volume.view(B * N, C, H, W) # flatten slices
# (B*N, 3, 224, 224) → (B*N, embed_dim) → reshape
slice_feats = self._slice_embed(x).view(B, N, -1) # (B, 32, embed_dim)
# ---------------- ABMIL attention ---------------- #
A = torch.tanh(self.attn_V(slice_feats)) # (B, 32, hidden)
A = self.attn_U(A) # (B, 32, 1)
A = torch.softmax(A, dim=1) # attention weights
# Weighted sum → patient embedding
patient_feat = (A * slice_feats).sum(dim=1) # (B, embed_dim)
# ---------------- Head ---------------- #
out = self.classifier(self.dropout(self.norm(patient_feat))) # (B, num_classes)
return out, A.squeeze(-1) # logits, attention weights
def main(args):
read_excel = pd.read_csv(args.configuration_file, skip_blank_lines=True, na_values=['NaN'])
max_f1_scores = []
model_files = []
for index, row in read_excel.iterrows():
if args.log_wandb:
wandb.login()
run = wandb.init(project=args.project_name, reinit=True, entity="adarshbhandary")
with run:
print('Experiment Index', index)
torch.cuda.empty_cache()
print("Reading Configuration File..")
config = {
"cv_run": row['no'],
"task": row['task'],
"model": row['model'],
"method": row['method'],
"view": row['view'],
"pretrained": row['pretrained'],
"loss": row['loss'],
"optimizer": row['optimizer'],
"use_sampler": row['use_sampler'],
"height": int(row['height']),
"width": int(row['width']),
"background_crop": row['background_crop'],
"use_clahe": row['use_clahe'],
"batch_size": int(row['batch_size']),
"multi_gpu": bool(row['multi_gpu']),
"num_worker": row['num_worker'],
"learning_rate": row['lr'],
"weight_decay": row['weight_decay'],
"drop_out": row['drop_out'],
"num_epochs": int(row['epochs']),
"patience": row['patience'],
"probability": row['probability'],
"aug_mix_p":row['aug_mix_p'],
"erasing":row['erasing'],
"attention_head": row["attention_head"],
"rage":row["rage"],
"get_gradcam": bool(row['get_gradcam']),
"out_folder": row['out_folder'],
"folder_name": row['folder_name'],
"excel_file_train": row['train'],
"excel_file_validation": row['valid'],
"excel_file_test": row['test'],
"data_folder": row['data_folder'],
"pretrained_weights": row["pretrained_weights"],
"results_path": row['csv_results']
}
set_seed(int(config["cv_run"]))
wandb.config.update(config, allow_val_change=True)
os.makedirs(os.path.join(config["out_folder"], str(config["folder_name"])), exist_ok=True)
save_out_folder = config["out_folder"] + str(config["folder_name"])
## config method is MRI
target_names = extract_target_names(method = config["method"])
num_classes = len(target_names)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print('Device : ', device)
weight, samples_weight = compute_sample_weights(config["excel_file_train"], class_type = config["method"], view = config["view"])
if config["use_sampler"]:
sampler = WeightedRandomSampler(samples_weight, num_samples=len(samples_weight), replacement=True)
shuffle = False
else:
sampler = None
shuffle = True
if config["model"] == "vit_small":
train_transforms = TrainTransform(aug=True, aug_mix_p = config["aug_mix_p"])
else:
train_transforms = TrainTransformBaseline(aug=True, aug_mix_p = config["aug_mix_p"], erasing = config["erasing"])
valid_transforms = BasicTransforms()
if config["method"] == "MRI_MIL":
data_train = BreastMRI_ABMIL_NII(
split='train'
)
data_valid = BreastMRI_ABMIL_NII(
split='val'
)
data_test = BreastMRI_ABMIL_NII(
split='test'
)
else:
data_train = BreastMRI(csv_file = config["excel_file_train"],
root_dir=None,
split='train',
transform = valid_transforms)
data_valid = BreastMRI(csv_file = config["excel_file_train"],
root_dir=None,
split='val',
transform = valid_transforms)
data_test = BreastMRI(csv_file = config["excel_file_train"],
root_dir=None,
split='test',
transform = valid_transforms)
print("Train dataset size:", len(data_train))
print("Val dataset size:", len(data_valid))
print("Test dataset size:", len(data_test))
dataloader = {
'train': torch.utils.data.DataLoader(data_train,
batch_size=config["batch_size"],
shuffle=shuffle,
sampler=sampler,
num_workers=config["num_worker"],
pin_memory=True,
prefetch_factor=2,
drop_last=True
),
'valid': torch.utils.data.DataLoader(data_valid,
batch_size=1,
shuffle=False,
sampler=None,
num_workers=config["num_worker"],
pin_memory=True,
prefetch_factor=2,
drop_last=False
),
'test': torch.utils.data.DataLoader(data_test,
batch_size=1,
shuffle=False,
sampler=None,
num_workers=config["num_worker"],
pin_memory=True,
prefetch_factor=2,
drop_last=False
)
}
if config["method"]=="MRI":
if config["model"] == "swin_cross":
model = MultiViewSwinCrossAttn(num_classes=num_classes)
else:
model = MultiViewSwinModel(num_classes=num_classes)
elif config["method"] == "MRI_MIL":
if config["model"] == "swin_cross":
model = CrossModalAttentionABMIL_Swin(num_classes=num_classes)
else:
model = ABMIL_Swin(num_classes=num_classes)
model = model.to(device)
#summary(model, (config["batch_size"], 3, config["height"], config["width"]))
if config["loss"] == "F":
criterion = FocalLoss(gamma=config["erasing"], reduction='mean').to(device)
elif config["loss"] == "CE":
criterion = nn.CrossEntropyLoss().to(device)
optimizer_ft = optim.Adam(model.parameters(),
lr=config["learning_rate"],
weight_decay=config["weight_decay"])
if config["method"] == 'MRI':
train = FullySupervisedMultiViewTrainer(device,
config["method"],
model,
criterion,
optimizer_ft,
dataloader,
target_names,
save_out_folder,
config["num_epochs"],
config["patience"])
train.main_loop()
model.load_state_dict(torch.load(save_out_folder + 'max_metrics_epoch.pth'))
train = FullySupervisedMultiViewTrainer(device,
config["method"],
model,
criterion,
optimizer_ft,
dataloader,
target_names,
save_out_folder,
config["num_epochs"],
config["patience"])
test_accuracy, test_f1, true, pred, test_auc, averaged_results = train.test_loop()
elif config["method"] == 'MRI_MIL':
#model.load_state_dict(torch.load(save_out_folder + 'max_metrics_epoch.pth'))
train = MultiInstanceTrainer(device,
config["method"],
model,
criterion,
config["loss"],
optimizer_ft,
dataloader,
target_names,
save_out_folder,
config["num_epochs"],
config["patience"])
train.main_loop()
model.load_state_dict(torch.load(save_out_folder + 'max_metrics_epoch.pth'))
train = MultiInstanceTrainer(device,
config["method"],
model,
criterion,
config["loss"],
optimizer_ft,
dataloader,
target_names,
save_out_folder,
config["num_epochs"],
config["patience"])
test_accuracy, test_f1, true, pred, test_auc, averaged_results = train.test_loop()
print('Test Results')
print(classification_report(true, pred, target_names=target_names, digits=4))
display_confusion_matrix(true,
pred,
target_names,
save_out_folder+'confusion_matrix.png',
use_tta=False,
label=config["task"],
normalize=False)
config["model location"] = save_out_folder + 'least_validation_loss.pth'
config["test accuracy"] = test_accuracy
config["test f1"] = test_f1
config["test auc"] = test_auc
#log_scalar_values(config["results_path"], config)
max_f1_scores.append(test_f1)
model_files.append(save_out_folder + 'least_validation_loss.pth')
wandb.log({
"test_accuracy" : test_accuracy,
"test_f1" : test_f1,
"test_auc" : test_auc,
"test_metrics" : averaged_results,
})
#log_scalar_values(config["results_path"], config)
wandb.config.update(config, allow_val_change=True)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--configuration_file", type=Path, default='/cluster/panambur/DINO/config/transfer_baseline.csv',
help="""Change hyperparameters and input the filepath of CSV file""")
parser.add_argument("--log_wandb", type=bool, default=True,
help="""Use FALSE for Siemens Code.""")
parser.add_argument("--project_name", type=str, default='BreastMRI Tiny',
help="""For Wandb logging""")
args = parser.parse_args()
main(args) |