File size: 5,484 Bytes
9b046d8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 |
"""
Baseline 4 Description :
--------------------------------
Temporal model with image features level
be representation per clip use 9 frames
per image then we have sequence for each clip of 9 steps
train an LSTM on these sequences.
"""
import os
import sys
import torch
import argparse
import torch.nn as nn
import albumentations as A
import torchvision.models as models
from albumentations.pytorch import ToTensorV2
from torch.utils.data import DataLoader
from torchinfo import summary
class Group_Activity_Temporal_Classifer(nn.Module):
def __init__(self, num_classes, input_size, hidden_size, num_layers):
super(Group_Activity_Temporal_Classifer, self).__init__()
resnet50 = models.resnet50(weights=models.ResNet50_Weights.DEFAULT)
self.feature_extraction = nn.Sequential(
*list(resnet50.children())[:-1], # remove fc layer
nn.Dropout(0.5)
)
self.lstm = nn.LSTM(
input_size=input_size,
hidden_size=hidden_size,
num_layers=num_layers,
batch_first=True,
)
self.fc = nn.Sequential(
nn.Linear(hidden_size, 32),
nn.ReLU(),
nn.Dropout(0.5),
nn.Linear(32, 32),
nn.ReLU(),
nn.Dropout(0.2),
nn.Linear(32, num_classes)
)
def forward(self, x):
# Input shape: (batch, 9, 3, 244, 244)
b, seq, c, h, w = x.shape
x = x.view(b * seq, c, h, w) # (batch * 9, 3, 244, 244)
x = self.feature_extraction(x) # (batch * 9, 2048, 1, 1)
x = x.view(b, seq, -1) # (batch, 9, 2048)
x, (h, c) = self.lstm(x) # x: (batch, 9 , hidden_size)
x = x[:, -1, :] # (64, hidden_size)
x = self.fc(x) # (64, num_classes)
return x
def collate_fn(batch):
clips, labels = zip(*batch)
clips = torch.stack(clips, dim=0)
labels = torch.stack(labels, dim=0)
labels = labels[:, -1, :] # utile the label of last frame
return clips, labels
def model_summary(args):
sys.path.append(os.path.abspath(args.project_root))
from helper_utils import load_config
config = load_config(args.config_path)
model = Group_Activity_Temporal_Classifer(
num_classes=config.model["num_classes"],
input_size=config.model["input_size"],
hidden_size=config.model["hidden_size"],
num_layers=config.model["num_layers"]
)
summary(model)
def eval(args, checkpoint_path):
sys.path.append(os.path.abspath(args.project_root))
import pickle
from helper_utils import load_config, load_checkpoint
from eval_utils import model_eval
from data_utils import Group_Activity_DataSet, group_activity_labels
config = load_config(args.config_path)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = Group_Activity_Temporal_Classifer(
num_classes=config.model['num_classes'],
input_size=config.model['input_size'],
hidden_size=config.model['hidden_size'],
num_layers=config.model['num_layers']
)
model = load_checkpoint(model=model, checkpoint_path=checkpoint_path, device=device, optimizer=None)
model = model.to(device)
test_transforms = A.Compose([
A.Resize(224, 224),
A.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]
),
ToTensorV2()
])
test_dataset = Group_Activity_DataSet(
videos_path=f"{args.project_root}/{config.data['videos_path']}",
annot_path=f"{args.project_root}/{config.data['annot_path']}",
split=config.data['video_splits']['test'],
labels=group_activity_labels,
transform=test_transforms,
crops=True,
seq=False,
)
test_loader = DataLoader(
test_dataset,
batch_size=64,
shuffle=True,
collate_fn=collate_fn,
num_workers=4,
pin_memory=True
)
criterion = nn.CrossEntropyLoss()
path = f"{args.project_root}/modeling/baseline 3/outputs"
prefix = "Group Activity Baseline 4 eval on testset"
metrics = model_eval(model=model, data_loader=test_loader, criterion=criterion, device=device , path=path, prefix=prefix, class_names=config.model["num_clases_label"]['group_activity'])
return metrics
if __name__ == "__main__":
ROOT = "/teamspace/studios/this_studio/Group-Activity-Recognition"
MODEL_CONFIG = "/teamspace/studios/this_studio/Group-Activity-Recognition/modeling/configs/Baseline B4.yml"
CHECKPOINT_PATH = "/teamspace/studios/this_studio/Group-Activity-Recognition/modeling/baseline 3/outputs/Baseline_B3_step_B_V1_20241127_192620/checkpoint_epoch_4.pkl"
parser = argparse.ArgumentParser(description="Group Activity Recognition Model Configuration")
parser.add_argument("--project_root", type=str, default=ROOT,
help="Path to the root directory of the project")
parser.add_argument("--config_path", type=str, default=MODEL_CONFIG,
help="Path to the YAML configuration file")
args = parser.parse_args()
model_summary(args) # Show model details
# eval(args, PERSON_ACTIVITY_CHECKPOINT_PATH, CHECKPOINT_PATH) # eval model against testset
|