#!/usr/bin/env python # coding: utf-8 # In[40]: # Import packages and setup gpu configuration. # This code block shouldnt need to be adjusted! import os import sys import json import yaml import numpy as np import copy import math import time import random from tqdm.auto import tqdm import webdataset as wds import matplotlib.pyplot as plt import torch import torch.nn as nn from torchvision import transforms import utils from mae_utils.flat_models import * import h5py from mae_utils import flat_models import pandas as pd from sklearn.preprocessing import StandardScaler from typing import List, Dict, Any, Tuple import argparse # tf32 data type is faster than standard float32 torch.backends.cuda.matmul.allow_tf32 = True # following fixes a Conv3D CUDNN_NOT_SUPPORTED error torch.backends.cudnn.benchmark = True # In[48]: # if running this interactively, can specify jupyter_args here for argparser to use if utils.is_interactive(): model_name_suffix = "testing" print("model_name_suffix:", model_name_suffix) # global_batch_size and batch_size should already be defined in the 2nd cell block jupyter_args = f"--found_model_name=HCPflat_large_gsrFalse_ --epoch_checkpoint epoch99.pth \ --hcp_flat_path=/weka/proj-medarc/shared/HCP-Flat \ --target=sex \ --model_suffix={model_name_suffix} \ --batch_size={batch_size} \ --max_lr=3e-4 --num_epochs=20 --no-save_ckpt --no-wandb_log --num_workers=10 \ --weight_decay=1e-5 \ --global_pool" # --multisubject_ckpt=../train_logs/multisubject_subj01_1024_24bs_nolow print(jupyter_args) jupyter_args = jupyter_args.split() from IPython.display import clear_output # function to clear print outputs in cell get_ipython().run_line_magic('load_ext', 'autoreload') # this allows you to change functions in models.py or utils.py and have this notebook automatically update with your revisions get_ipython().run_line_magic('autoreload', '2') # In[49]: parser = argparse.ArgumentParser(description="Model Training Configuration") parser.add_argument( "--found_model_name", type=str, default="Testing_flat", help="name of model, used for ckpt saving and wandb logging (if enabled)", ) parser.add_argument( "--epoch_checkpoint", type=str, default="epoch99.pth", help="the epoch number of the found_model_name checkpoint", ) parser.add_argument( "--model_suffix", type=str, default="Testing_flat", help="name of model, used for ckpt saving and wandb logging (if enabled)", ) parser.add_argument( "--hcp_flat_path", type=str, default=os.getcwd(), help="Path to where NSD data is stored / where to download it to", ) parser.add_argument( "--batch_size", type=int, default=128, help="Batch size can be increased by 10x if only training retreival submodule and not diffusion prior", ) parser.add_argument( "--wandb_log",action=argparse.BooleanOptionalAction,default=False, help="whether to log to wandb", ) parser.add_argument( "--num_epochs",type=int,default=150, help="number of epochs of training", ) parser.add_argument( "--lr_scheduler_type",type=str,default='cycle',choices=['cycle','linear'], ) parser.add_argument( "--save_ckpt",action=argparse.BooleanOptionalAction,default=True, ) parser.add_argument( "--seed",type=int,default=42, ) parser.add_argument( "--max_lr",type=float,default=3e-4, ) parser.add_argument( "--target",type=str,default='trial_type',choices=['trial_type','sex','age'], ) parser.add_argument( "--num_workers",type=int,default=10, ) parser.add_argument( "--weight_decay",type=float,default=1e-5, ) parser.add_argument( "--global_pool",action=argparse.BooleanOptionalAction,default=False, help="not implemented yet", ) if utils.is_interactive(): args = parser.parse_args(jupyter_args) else: args = parser.parse_args() print(f"------ ARGS ------- \n {args}") # create global variables without the args prefix for attribute_name in vars(args).keys(): globals()[attribute_name] = getattr(args, attribute_name) # seed all random functions utils.seed_everything(seed) # In[ ]: # ## MODEL TO LOAD ## # if utils.is_interactive(): # model_name = "HCPflat_large_gsrFalse_" # else: # model_name = sys.argv[1] # target = 'sex' # This can be 'trial_type' 'age' 'sex' # In[50]: # outdir = os.path.abspath(f'checkpoints/{model_name}') outdir = os.path.abspath(f'checkpoints/{found_model_name}') print("outdir", outdir) # Load previous config.yaml if available if os.path.exists(f"{outdir}/config.yaml"): config = yaml.load(open(f"{outdir}/config.yaml", 'r'), Loader=yaml.FullLoader) print(f"Loaded config.yaml from ckpt folder {outdir}") # create global variables from the config print("\n__CONFIG__") for attribute_name in config.keys(): print(f"{attribute_name} = {config[attribute_name]}") globals()[attribute_name] = config[f'{attribute_name}'] print("\n") world_size = os.getenv('WORLD_SIZE') if world_size is None: world_size = 1 else: world_size = int(world_size) print(f"WORLD_SIZE={world_size}") if utils.is_interactive(): # Following allows you to change functions in models.py or utils.py and # have this notebook automatically update with your revisions get_ipython().run_line_magic('load_ext', 'autoreload') get_ipython().run_line_magic('autoreload', '2') # batch_size = probe_batch_size # num_epochs = probe_num_epochs data_type = torch.float32 # change depending on your mixed_precision global_batch_size = batch_size * world_size device = torch.device('cuda') # hcp_flat_path = "/weka/proj-medarc/shared/HCP-Flat" # seed = 42 # num_frames = 16 # gsr = False # num_workers = 10 # batch_size = 128 # save_ckpt = True # wandb_log = True print("PID of this process =",os.getpid()) utils.seed_everything(seed) # In[55]: # if os.getenv('global_pool') == "False": # global_pool = False # else: # global_pool = True print(f"global_pool = {global_pool}") try: gsr except: gsr = True print("set gsr to True") print(f"gsr = {gsr}") for attribute_name in vars(args).keys(): globals()[attribute_name] = getattr(args, attribute_name) # In[3]: #### UNCOMMENT THIS TO SAVE THE HCP-FLAT IN HDF5 FORMAT # from torch.utils.data import default_collate # from mae_utils.flat import load_hcp_flat_mask # from mae_utils.flat import create_hcp_flat # from mae_utils.flat import batch_unmask # import mae_utils.visualize as vis # batch_size = 26 # print(f"changed batch_size to {batch_size}") # ## Test ## # datasets_to_include = "HCP" # assert "HCP" in datasets_to_include # test_dataset = create_hcp_flat(root=hcp_flat_path, # clip_mode="event", frames=num_frames, shuffle=False, gsr=gsr, sub_list = 'test') # test_dl = wds.WebLoader( # test_dataset.batched(batch_size, partial=False, collation_fn=default_collate), # batch_size=None, # shuffle=False, # num_workers=num_workers, # pin_memory=True, # ) # ## Train ## # assert "HCP" in datasets_to_include # train_dataset = create_hcp_flat(root=hcp_flat_path, # clip_mode="event", frames=num_frames, shuffle=False, gsr=gsr, sub_list = 'train') # train_dl = wds.WebLoader( # train_dataset.batched(batch_size, partial=False, collation_fn=default_collate), # batch_size=None, # shuffle=False, # num_workers=num_workers, # pin_memory=True, # ) # def flatten_meta(meta_dict): # """ # Flatten the meta dictionary by: # - Replacing single-item lists with the item itself. # - Converting tensors to scalar numbers. # """ # flattened = {} # for key, value in meta_dict.items(): # if isinstance(value, list): # if len(value) == 1: # flattened[key] = value[0] # Replace list with its single item # else: # flattened[key] = value # Keep as is if multiple items # elif isinstance(value, torch.Tensor): # # Convert tensor to scalar # if value.numel() == 1: # flattened[key] = value.item() # else: # flattened[key] = value.tolist() # Convert multi-element tensor to list # else: # flattened[key] = value # Keep the value as is # return flattened # import h5py # meta_array = np.array([], dtype=object) # # Open an HDF5 file in write mode # with h5py.File('train_hcp.hdf5', 'w') as h5f: # flatmaps_dset = None # total_samples = 0 # for i, batch in tqdm(enumerate(train_dl), total = 120000): # images = batch['image'][0] # meta = batch['meta'] # batch_size = images.shape[0] # meta_serializable = meta.copy() # # Step 2: Serialize the dictionary to a JSON string # meta_str = json.dumps(flatten_meta(meta_serializable), indent=4) # meta_array = np.append(meta_array, meta_str) # if flatmaps_dset is None: # # Initialize datasets with unlimited (None) maxshape along the first axis # flatmaps_shape = (0,) + images.shape[1:] # flatmaps_maxshape = (None,) + images.shape[1:] # flatmaps_dset = h5f.create_dataset( # 'flatmaps', # shape=flatmaps_shape, # maxshape=flatmaps_maxshape, # dtype=np.float16, # chunks=True # Enable chunking for efficient resizing # ) # # Resize datasets to accommodate new data # flatmaps_dset.resize(total_samples + batch_size, axis=0) # # Write data to the datasets # flatmaps_dset[total_samples:total_samples + batch_size] = images.numpy().astype(np.float16) # total_samples += batch_size # print(f"Processed {total_samples} samples") # np.save('metadata_test_HCP.npy', meta_array) # import h5py # meta_array = np.array([], dtype=object) # # Open an HDF5 file in write mode # with h5py.File('test_hcp.hdf5', 'w') as h5f: # flatmaps_dset = None # total_samples = 0 # for i, batch in tqdm(enumerate(test_dl), total = 12000): # images = batch['image'][0] # meta = batch['meta'] # batch_size = images.shape[0] # meta_serializable = meta.copy() # # Step 2: Serialize the dictionary to a JSON string # meta_str = json.dumps(flatten_meta(meta_serializable), indent=4) # meta_array = np.append(meta_array, meta_str) # if flatmaps_dset is None: # # Initialize datasets with unlimited (None) maxshape along the first axis # flatmaps_shape = (0,) + images.shape[1:] # flatmaps_maxshape = (None,) + images.shape[1:] # flatmaps_dset = h5f.create_dataset( # 'flatmaps', # shape=flatmaps_shape, # maxshape=flatmaps_maxshape, # dtype=np.float16, # chunks=True # Enable chunking for efficient resizing # ) # # Resize datasets to accommodate new data # flatmaps_dset.resize(total_samples + batch_size, axis=0) # # Write data to the datasets # flatmaps_dset[total_samples:total_samples + batch_size] = images.numpy().astype(np.float16) # total_samples += batch_size # print(f"Processed {total_samples} samples") # np.save('metadata_train_HCP.npy', meta_array) # ### Preparing data # In[56]: from sklearn.preprocessing import LabelEncoder if target == "trial_type": INCLUDE_CONDS = { "fear", "neut", "math", "story", "lf", "lh", "rf", "rh", "t", "match", "relation", "mental", "rnd", "0bk_body", "2bk_body", "0bk_faces", "2bk_faces", "0bk_places", "2bk_places", "0bk_tools", "2bk_tools", } # test_data = [] # # Iterate over the DataLoader with a progress bar # for sample in tqdm(train_dl, desc="Processing samples"): # x = sample['image'] # y = sample['meta']['trial_type'] # key = sample['meta']['key'] # print(x.shape, y, key) # break # Initialize the label encoder label_encoder = LabelEncoder() label_encoder.fit(sorted(INCLUDE_CONDS)) # Ensure consistent ordering num_classes = len(label_encoder.classes_) print(f"Number of classes: {num_classes}") # In[57]: f_train = h5py.File('/weka/proj-fmri/ckadirt/fMRI-foundation-model/src/train_hcp.hdf5', 'r') flatmaps_train = f_train['flatmaps'] f_test = h5py.File('/weka/proj-fmri/ckadirt/fMRI-foundation-model/src/test_hcp.hdf5', 'r') flatmaps_test = f_test['flatmaps'] metadata_train = np.load('/weka/proj-fmri/ckadirt/fMRI-foundation-model/src/metadata_train_HCP.npy', allow_pickle=True) metadata_test = np.load('/weka/proj-fmri/ckadirt/fMRI-foundation-model/src/metadata_test_HCP.npy', allow_pickle=True) # In[58]: from torch.utils.data import Dataset, DataLoader class HCPFlatDataset(Dataset): def __init__(self, flatmaps, metadata): self.flatmaps = flatmaps self.metadata = metadata def __len__(self): return len(self.metadata) def __getitem__(self, idx): return self.flatmaps[idx], json.loads(self.metadata[idx]) print("Creating datasets") # Loading to cpu for faster training, this can take several minutes. Remove this [:] if you want to move one at the time. train_dataset = HCPFlatDataset(flatmaps_train, metadata_train) train_dl = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers) test_dataset = HCPFlatDataset(flatmaps_test, metadata_test) test_dl = DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=0) print("Datasets ready") # In[59]: # open the file containing subject information if target == "age" or target == "sex": subject_information_HCP_path = os.path.join(hcp_flat_path, "subjects_data_restricted.csv") try: subject_information_HCP = pd.read_csv(subject_information_HCP_path) except: try: subject_information_HCP = pd.read_csv('./unrestricted_clane9_4_23_2024_13_28_14.csv') except: assert False, "Subject information file not found" ###### This is for unrestricted # age_related_columns = [ # 'Age', 'PicSeq_AgeAdj', 'CardSort_AgeAdj', 'Flanker_AgeAdj', # 'ReadEng_AgeAdj', 'PicVocab_AgeAdj', 'ProcSpeed_AgeAdj', # 'CogFluidComp_AgeAdj', 'CogEarlyComp_AgeAdj', 'CogTotalComp_AgeAdj', # 'CogCrystalComp_AgeAdj', 'Endurance_AgeAdj', 'Dexterity_AgeAdj', # 'Strength_AgeAdj', 'Odor_AgeAdj', 'Taste_AgeAdj' # ] # sex_related_columns = [ # 'Gender' # ] ###### This is for restricted gender_related_columns = [ 'Gender' ] age_related_columns = [ 'Age_in_Yrs', 'Menstrual_AgeBegan', 'Menstrual_AgeIrreg', 'Menstrual_AgeStop', 'SSAGA_Alc_Age_1st_Use', 'SSAGA_TB_Age_1st_Cig', 'SSAGA_Mj_Age_1st_Use', 'Endurance_AgeAdj', 'Dexterity_AgeAdj', 'Strength_AgeAdj', 'PicSeq_AgeAdj', 'CardSort_AgeAdj', 'Flanker_AgeAdj', 'ReadEng_AgeAdj', 'PicVocab_AgeAdj', 'ProcSpeed_AgeAdj', 'Odor_AgeAdj', 'Taste_AgeAdj' ] # # show the first few rows of the subject information # subject_information_HCP[age_related_columns + sex_related_columns].head() # Handle missing values (e.g., impute with mean) mean_age = subject_information_HCP['Age_in_Yrs'].mean() # Initialize the scaler scaler = StandardScaler() # Perform z-score normalization subject_information_HCP['Age_in_Yrs_z'] = scaler.fit_transform(subject_information_HCP[['Age_in_Yrs']]) def get_label_unrestricted(subject_id: List[str], target: str, method_for_age: str = 'mean') -> List: """ Get the label for the given subject id and target. For sex 0 is F and 1 is M """ # convert to list of ints subject_id = [int(x) for x in subject_id] if target == "age": age_array = [] for subject in subject_id: c_age = subject_information_HCP[subject_information_HCP['Subject'] == subject]['Age'].values # if the subject is not in the subject information file trigger an error if len(c_age) == 0: assert False, f"Subject {subject} not found in subject information file" if len(c_age) > 1: print(f"Warning: Multiple entries for subject {subject}") c_age = c_age[0].split('-') if len(c_age) < 2: c_age = c_age[0].split('+') age_array.append(int(c_age[0])) else: if method_for_age == 'mean': age_array.append(np.mean([int(x) for x in c_age])) elif method_for_age == 'min': age_array.append(np.min([int(x) for x in c_age])) elif method_for_age == 'max': age_array.append(np.max([int(x) for x in c_age])) else: assert False, f"Method {method_for_age} not recognized" return np.array(age_array) elif target == 'sex': sex_array = [] for subject in subject_id: c_sex = subject_information_HCP[subject_information_HCP['Subject'] == subject]['Gender'].values # if the subject is not in the subject information file trigger an error if len(c_sex) == 0: assert False, f"Subject {subject} not found in subject information file" if len(c_sex) > 1: print(f"Warning: Multiple entries for subject {subject}") sex_array.append(int(c_sex[0] == 'M')) return sex_array def get_label_restricted(subject_id: List[str], target: str, normalized: bool = True) -> List: """ Get the label for the given subject id and target. For sex 0 is F and 1 is M """ # convert to list of ints subject_id = [int(x) for x in subject_id] if target == "age": age_array = [] for subject in subject_id: c_age = subject_information_HCP[subject_information_HCP['Subject'] == subject]['Age_in_Yrs' if not normalized else 'Age_in_Yrs_z'].values # if the subject is not in the subject information file trigger an error if len(c_age) == 0: assert False, f"Subject {subject} not found in subject information file" if len(c_age) > 1: print(f"Warning: Multiple entries for subject {subject}") age_array.append(np.int8(c_age[0])) return np.array(age_array) elif target == 'sex': sex_array = [] for subject in subject_id: c_sex = subject_information_HCP[subject_information_HCP['Subject'] == subject]['Gender'].values # if the subject is not in the subject information file trigger an error if len(c_sex) == 0: assert False, f"Subject {subject} not found in subject information file" if len(c_sex) > 1: print(f"Warning: Multiple entries for subject {subject}") sex_array.append(int(c_sex[0] == 'M')) return sex_array # ### Creating and loading Model # In[60]: from mae_utils.flat import load_hcp_flat_mask from mae_utils.flat import create_hcp_flat from mae_utils.flat import batch_unmask import mae_utils.visualize as vis flat_mask = load_hcp_flat_mask(hcp_flat_path) mae_model = flat_models.mae_vit_large_fmri( patch_size=patch_size, decoder_embed_dim=decoder_embed_dim, t_patch_size=t_patch_size, pred_t_dim=pred_t_dim, decoder_depth=4, cls_embed=cls_embed, norm_pix_loss=norm_pix_loss, no_qkv_bias=no_qkv_bias, sep_pos_embed=sep_pos_embed, trunc_init=trunc_init, pct_masks_to_decode=pct_masks_to_decode, img_mask=flat_mask, ) # In[61]: checkpoint_files = [f for f in os.listdir(outdir) if f.endswith('.pth')] if utils.is_interactive(): latest_checkpoint = "epoch99.pth" else: latest_checkpoint = epoch_checkpoint print(f"latest_checkpoint: {latest_checkpoint}") # Load the checkpoint checkpoint_path = os.path.join(outdir, latest_checkpoint) state = torch.load(checkpoint_path) mae_model.load_state_dict(state["model_state_dict"], strict=False) mae_model.to(device) print(f"\nLoaded checkpoint {latest_checkpoint} from {outdir}\n") # In[62]: class LinearClassifier(nn.Module): def __init__(self, input_dim, num_classes): super(LinearClassifier, self).__init__() self.linear = nn.Linear(input_dim, num_classes) def forward(self, x): # Flatten the input except for the batch dimension x = x.view(x.size(0), -1) out = self.linear(x) return out # Raw logits # Determine the input dimension from a single sample # Assuming images are of shape [1, 16, 144, 320] input_dim = np.prod(mae_model(torch.randn(1,1,16,144,320).to(device),global_pool=global_pool, forward_features = True).shape[1:]) print(f"Input dimension: {input_dim}") # In[63]: class FullModel(nn.Module): def __init__(self, lc_model, mae_model): super(FullModel, self).__init__() self.lc_model = lc_model self.mae_model = mae_model def forward(self, x, gsr): x = self.mae_model(x, global_pool=global_pool, forward_features = True) x = self.lc_model(x) return x # In[64]: # Initialize the model if target == "trial_type": lc_model = LinearClassifier(input_dim=input_dim, num_classes=num_classes) criterion = nn.CrossEntropyLoss() elif target == "age": lc_model = LinearClassifier(input_dim=input_dim, num_classes=1) criterion = nn.MSELoss() elif target == "sex": lc_model = LinearClassifier(input_dim=input_dim, num_classes=1) criterion = nn.BCEWithLogitsLoss() # lc_model = LinearClassifier(input_dim=input_dim, num_classes=num_classes) model = FullModel(lc_model, mae_model) # Move the model to the GPU model.to(device) # Define optimizer with L2 regularization (weight_decay) # learning_rate = 1e-4 # weight_decay = 1e-5 # Adjust based on your needs optimizer = torch.optim.AdamW(model.parameters(), lr=max_lr, weight_decay=weight_decay) num_iterations_per_epoch = math.ceil(flatmaps_train.shape[0]/batch_size) if lr_scheduler_type == 'linear': lr_scheduler = torch.optim.lr_scheduler.LinearLR( optimizer, total_iters=int(np.floor(num_epochs*num_iterations_per_epoch)), last_epoch=-1 ) elif lr_scheduler_type == 'cycle': total_steps=int(np.floor(num_epochs*num_iterations_per_epoch)) print("total_steps", total_steps) lr_scheduler = torch.optim.lr_scheduler.OneCycleLR( optimizer, max_lr=max_lr, total_steps=total_steps, final_div_factor=1000, last_epoch=-1, pct_start=2/num_epochs ) # num_epochs = 20 # Adjust as needed # In[29]: # criterion # ### Data # In[65]: import uuid myuuid = uuid.uuid4() str(myuuid) # In[66]: import wandb if utils.is_interactive(): print("Running in interactive notebook. Disabling W&B and ckpt saving.") wandb_log = False save_ckpt = False if wandb_log: wandb_project = 'fMRI-foundation-model' wandb_config = { "model_name": f'{found_model_name}_HCP_FT_{target}', "batch_size": batch_size, "weight_decay": weight_decay, "num_epochs": num_epochs, "seed": seed, "lr_scheduler_type": lr_scheduler_type, "save_ckpt": save_ckpt, "seed": seed, "max_lr": max_lr, "target": target, "num_workers": num_workers, "weight_decay": weight_decay } print("wandb_config:\n", wandb_config) random_id = random.randint(0, 100000) wandb_id = f"{found_model_name}_{model_suffix}_{target}_HCPFT_{myuuid}" print("wandb_id:", wandb_id) wandb.init( id=wandb_id, project=wandb_project, name=f"{found_model_name}_{model_suffix}_{target}_HCPFT", config=wandb_config, resume="allow", ) # In[ ]: for epoch in range(num_epochs): running_train_loss = 0.0 correct_train = 0 mse_age_train = 0.0 total_train = 0 step = 0 # with torch.amp.autocast(device_type='cuda'): # Training Phase model.train() for batch in tqdm(train_dl, desc=f"Epoch {epoch+1}/{num_epochs} - Training"): optimizer.zero_grad() images = batch[0].to(device).float() # Shape: [batch_size, 1, 16, 144, 320] # Prepare labels based on target type if target == "trial_type": labels = batch[1]['trial_type'] # List of labels labels = label_encoder.transform(labels) labels = torch.tensor(labels, dtype=torch.long).to(device) # Shape: [batch_size] elif target == "age": labels = get_label_restricted(batch[1]['sub'], 'age') labels = torch.tensor(labels, dtype=torch.float).to(device) # Shape: [batch_size] elif target == "sex": labels = get_label_restricted(batch[1]['sub'], 'sex') labels = torch.tensor(labels, dtype=torch.float).to(device) # Shape: [batch_size] # labels = labels.unsqueeze(1) # Forward pass outputs = model(images, gsr=gsr) # Shape: [num_train_samples, num_classes] # Compute loss if target in ["trial_type", "sex"]: # For classification, ensure outputs are logits loss = criterion(outputs.squeeze(), labels.squeeze()) elif target == "age": # For regression, ensure outputs are single values loss = criterion(outputs.squeeze(), labels.squeeze()) # Backward pass and optimization loss.backward() optimizer.step() # Accumulate loss running_train_loss += loss.item() * images.size(0) # Calculate and accumulate metrics if target == "trial_type": _, predicted = torch.max(outputs, 1) correct_train += (predicted == labels).sum().item() elif target == "age": mse_age_train += (torch.sum((outputs.squeeze() - labels) ** 2).item()) / outputs.shape[0] elif target == "sex": threshold = 0.5 predicted = (torch.sigmoid(outputs) > threshold).float() correct_train += (predicted == labels).sum().item() total_train += labels.size(0) step += 1 # Print intermediate metrics every 100 steps if step % 100 == 0: if target in ["trial_type", "sex"]: current_accuracy = 100 * correct_train / total_train if total_train > 0 else 0.0 print(f"Step [{step}/{len(train_dl)}] - Training Loss: {loss.item():.4f} - Training Accuracy: {current_accuracy:.2f}%") elif target == "age": current_mse = mse_age_train / total_train if total_train > 0 else 0.0 print(f"Step [{step}/{len(train_dl)}] - Training Loss: {loss.item():.4f} - Training MSE: {current_mse:.4f}") if lr_scheduler_type is not None: lr_scheduler.step() # Calculate epoch-level metrics epoch_train_loss = running_train_loss / total_train if total_train > 0 else 0.0 if target in ["trial_type", "sex"]: train_accuracy = 100 * correct_train / total_train if total_train > 0 else 0.0 elif target == "age": train_mse = mse_age_train / total_train if total_train > 0 else 0.0 # Validation Phase model.eval() running_val_loss = 0.0 correct_val = 0 mse_age_val = 0.0 total_val = 0 with torch.no_grad(): for batch in tqdm(test_dl, desc=f"Epoch {epoch+1}/{num_epochs} - Validation"): images = batch[0].to(device).float() labels = batch[1]['trial_type'] # Prepare labels based on target type if target == "trial_type": labels = batch[1]['trial_type'] # List of labels labels = label_encoder.transform(labels) labels = torch.tensor(labels, dtype=torch.long).to(device) # Shape: [batch_size] elif target == "age": labels = get_label_restricted(batch[1]['sub'], 'age') labels = torch.tensor(labels, dtype=torch.float).to(device) # Shape: [batch_size] elif target == "sex": labels = get_label_restricted(batch[1]['sub'], 'sex') labels = torch.tensor(labels, dtype=torch.float).to(device) # Shape: [batch_size] # labels = labels.unsqueeze(1) # Forward pass outputs = model(images, gsr=gsr) # Compute loss if target in ["trial_type", "sex"]: loss = criterion(outputs.squeeze(), labels.squeeze()) elif target == "age": loss = criterion(outputs.squeeze(), labels.squeeze()) # Accumulate loss running_val_loss += loss.item() * images.size(0) # Calculate and accumulate metrics if target == "trial_type": _, predicted = torch.max(outputs, 1) correct_val += (predicted == labels).sum().item() elif target == "age": mse_age_val += (torch.sum((outputs.squeeze() - labels) ** 2).item()) / outputs.shape[0] elif target == "sex": threshold = 0.5 predicted = (torch.sigmoid(outputs) > threshold).float() correct_val += (predicted == labels).sum().item() total_val += labels.size(0) # Calculate epoch-level validation metrics epoch_val_loss = running_val_loss / total_val if total_val > 0 else 0.0 if target in ["trial_type", "sex"]: val_accuracy = 100 * correct_val / total_val if total_val > 0 else 0.0 elif target == "age": val_mse = mse_age_val / total_val if total_val > 0 else 0.0 # Print epoch-level metrics if target in ["trial_type", "sex"]: print(f"Epoch [{epoch+1}/{num_epochs}] " f"- Training Loss: {epoch_train_loss:.4f}, Training Accuracy: {train_accuracy:.2f}% " f"- Validation Loss: {epoch_val_loss:.4f}, Validation Accuracy: {val_accuracy:.2f}%") elif target == "age": print(f"Epoch [{epoch+1}/{num_epochs}] " f"- Training Loss: {epoch_train_loss:.4f}, Training MSE: {train_mse:.4f} " f"- Validation Loss: {epoch_val_loss:.4f}, Validation MSE: {val_mse:.4f}") # Log metrics with wandb if wandb_log: log_dict = { "epoch_train_loss": epoch_train_loss, "epoch_val_loss": epoch_val_loss, } if target in ["trial_type", "sex"]: log_dict.update({ f"train_accuracy_{target}": train_accuracy, f"val_accuracy_{target}": val_accuracy, }) elif target == "age": log_dict.update({ f"train_mse_{target}": train_mse, f"val_mse_{target}": val_mse, }) wandb.log(log_dict) if save_ckpt: outdir = os.path.abspath(f'checkpoints/{f"{found_model_name}_{model_suffix}_{target}_HCPFT"}') os.makedirs(outdir, exist_ok=True) print("outdir", outdir) # Save model and config torch.save(model.state_dict(), f"{outdir}/model.pth") with open(f"{outdir}/config.yaml", 'w') as f: yaml.dump(wandb_config, f) print(f"Saved model and config to {outdir}")