New train profile argument for loggers (#2862)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

docker/Dockerfile-arm64

@@ -24,7 +24,7 @@ ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt /u
 
 # Install pip packages
 RUN python3 -m pip install --upgrade pip wheel
-RUN pip install --no-cache -e .
+RUN pip install --no-cache -e . thop
 
 
 # Usage Examples -------------------------------------------------------------------------------------------------------

docker/Dockerfile-cpu

@@ -25,7 +25,7 @@ ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt /u
 
 # Install pip packages
 RUN python3 -m pip install --upgrade pip wheel
-RUN pip install --no-cache -e . --extra-index-url https://download.pytorch.org/whl/cpu
+RUN pip install --no-cache -e . thop --extra-index-url https://download.pytorch.org/whl/cpu
 
 
 # Usage Examples -------------------------------------------------------------------------------------------------------

docker/Dockerfile-jetson

@@ -25,7 +25,7 @@ ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt /u
 
 # Install pip packages manually for TensorRT compatibility https://github.com/NVIDIA/TensorRT/issues/2567
 RUN python3 -m pip install --upgrade pip wheel
-RUN pip install --no-cache tqdm matplotlib pyyaml psutil pandas onnx "numpy==1.23"
+RUN pip install --no-cache tqdm matplotlib pyyaml psutil pandas onnx thop "numpy==1.23"
 RUN pip install --no-cache -e .
 
 # Set environment variables

docs/modes/train.md

@@ -83,6 +83,7 @@ task.
 | `resume`          | `False`  | resume training from last checkpoint                                        |
 | `amp`             | `True`   | Automatic Mixed Precision (AMP) training, choices=[True, False]             |
 | `fraction`        | `1.0`    | dataset fraction to train on (default is 1.0, all images in train set)      |
+| `profile`         | `False`  | profile ONNX and TensorRT speeds during training for loggers                |
 | `lr0`             | `0.01`   | initial learning rate (i.e. SGD=1E-2, Adam=1E-3)                            |
 | `lrf`             | `0.01`   | final learning rate (lr0 * lrf)                                             |
 | `momentum`        | `0.937`  | SGD momentum/Adam beta1                                                     |

docs/usage/cfg.md

@@ -105,6 +105,7 @@ The training settings for YOLO models encompass various hyperparameters and conf
 | `resume`          | `False`  | resume training from last checkpoint                                        |
 | `amp`             | `True`   | Automatic Mixed Precision (AMP) training, choices=[True, False]             |
 | `fraction`        | `1.0`    | dataset fraction to train on (default is 1.0, all images in train set)      |
+| `profile`         | `False`  | profile ONNX and TensorRT speeds during training for loggers                |
 | `lr0`             | `0.01`   | initial learning rate (i.e. SGD=1E-2, Adam=1E-3)                            |
 | `lrf`             | `0.01`   | final learning rate (lr0 * lrf)                                             |
 | `momentum`        | `0.937`  | SGD momentum/Adam beta1                                                     |

ultralytics/yolo/cfg/__init__.py

@@ -1,4 +1,5 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
+
 import contextlib
 import re
 import shutil
@@ -72,7 +73,7 @@ CFG_INT_KEYS = ('epochs', 'patience', 'batch', 'workers', 'seed', 'close_mosaic'
 CFG_BOOL_KEYS = ('save', 'exist_ok', 'verbose', 'deterministic', 'single_cls', 'rect', 'cos_lr', 'overlap_mask', 'val',
                  'save_json', 'save_hybrid', 'half', 'dnn', 'plots', 'show', 'save_txt', 'save_conf', 'save_crop',
                  'show_labels', 'show_conf', 'visualize', 'augment', 'agnostic_nms', 'retina_masks', 'boxes', 'keras',
-                 'optimize', 'int8', 'dynamic', 'simplify', 'nms', 'v5loader')
+                 'optimize', 'int8', 'dynamic', 'simplify', 'nms', 'v5loader', 'profile')
 
 
 def cfg2dict(cfg):

ultralytics/yolo/cfg/default.yaml

@@ -31,6 +31,7 @@ close_mosaic: 0  # (int) disable mosaic augmentation for final epochs
 resume: False  # resume training from last checkpoint
 amp: True  # Automatic Mixed Precision (AMP) training, choices=[True, False], True runs AMP check
 fraction: 1.0  # dataset fraction to train on (default is 1.0, all images in train set)
+profile: False  # profile ONNX and TensorRT speeds during training for loggers
 # Segmentation
 overlap_mask: True  # masks should overlap during training (segment train only)
 mask_ratio: 4  # mask downsample ratio (segment train only)

ultralytics/yolo/utils/benchmarks.py

@@ -4,7 +4,7 @@ Benchmark a YOLO model formats for speed and accuracy
 
 Usage:
     from ultralytics.yolo.utils.benchmarks import ProfileModels, benchmark
-    ProfileModels(['yolov8n.yaml', 'yolov8s.yaml'])
+    ProfileModels(['yolov8n.yaml', 'yolov8s.yaml']).profile()
     run_benchmarks(model='yolov8n.pt', imgsz=160)
 
 Format                  | `format=argument`         | Model
@@ -163,13 +163,13 @@ class ProfileModels:
         profile(): Profiles the models and prints the result.
     """
 
-    def __init__(self, paths: list, num_timed_runs=100, num_warmup_runs=10, imgsz=640, trt=True):
+    def __init__(self, paths: list, num_timed_runs=100, num_warmup_runs=10, imgsz=640, trt=True, device=None):
         self.paths = paths
         self.num_timed_runs = num_timed_runs
         self.num_warmup_runs = num_warmup_runs
         self.imgsz = imgsz
         self.trt = trt  # run TensorRT profiling
-        self.profile()  # run profiling
+        self.device = device or torch.device(0 if torch.cuda.is_available() else 'cpu')
 
     def profile(self):
         files = self.get_files()
@@ -179,15 +179,16 @@ class ProfileModels:
             return
 
         table_rows = []
-        device = 0 if torch.cuda.is_available() else 'cpu'
+        output = []
         for file in files:
             engine_file = file.with_suffix('.engine')
             if file.suffix in ('.pt', '.yaml'):
                 model = YOLO(str(file))
+                model.fuse()  # to report correct params and GFLOPs in model.info()
                 model_info = model.info()
-                if self.trt and device == 0 and not engine_file.is_file():
+                if self.trt and self.device.type != 'cpu' and not engine_file.is_file():
-                    engine_file = model.export(format='engine', half=True, imgsz=self.imgsz, device=device)
+                    engine_file = model.export(format='engine', half=True, imgsz=self.imgsz, device=self.device)
-                onnx_file = model.export(format='onnx', half=True, imgsz=self.imgsz, simplify=True, device=device)
+                onnx_file = model.export(format='onnx', half=True, imgsz=self.imgsz, simplify=True, device=self.device)
             elif file.suffix == '.onnx':
                 model_info = self.get_onnx_model_info(file)
                 onnx_file = file
@@ -197,8 +198,10 @@ class ProfileModels:
             t_engine = self.profile_tensorrt_model(str(engine_file))
             t_onnx = self.profile_onnx_model(str(onnx_file))
             table_rows.append(self.generate_table_row(file.stem, t_onnx, t_engine, model_info))
+            output.append(self.generate_results_dict(file.stem, t_onnx, t_engine, model_info))
 
         self.print_table(table_rows)
+        return output
 
     def get_files(self):
         files = []
@@ -219,7 +222,7 @@ class ProfileModels:
         # return (num_layers, num_params, num_gradients, num_flops)
         return 0.0, 0.0, 0.0, 0.0
 
-    def iterative_sigma_clipping(self, data, sigma=2, max_iters=5):
+    def iterative_sigma_clipping(self, data, sigma=2, max_iters=3):
         data = np.array(data)
         for _ in range(max_iters):
             mean, std = np.mean(data), np.std(data)
@@ -235,13 +238,13 @@ class ProfileModels:
 
         # Warmup runs
         model = YOLO(engine_file)
-        input_data = np.random.rand(self.imgsz, self.imgsz, 3).astype(np.float32)
+        input_data = np.random.rand(self.imgsz, self.imgsz, 3).astype(np.float32)  # must be FP32
         for _ in range(self.num_warmup_runs):
             model(input_data, verbose=False)
 
         # Timed runs
         run_times = []
-        for _ in tqdm(range(self.num_timed_runs * 30), desc=engine_file):
+        for _ in tqdm(range(self.num_timed_runs * 50), desc=engine_file):
             results = model(input_data, verbose=False)
             run_times.append(results[0].speed['inference'])  # Convert to milliseconds
 
@@ -255,6 +258,7 @@ class ProfileModels:
         # Session with either 'TensorrtExecutionProvider', 'CUDAExecutionProvider', 'CPUExecutionProvider'
         sess_options = ort.SessionOptions()
         sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
+        sess_options.intra_op_num_threads = 8  # Limit the number of threads
         sess = ort.InferenceSession(onnx_file, sess_options, providers=['CPUExecutionProvider'])
 
         input_tensor = sess.get_inputs()[0]
@@ -289,13 +293,22 @@ class ProfileModels:
             sess.run([output_name], {input_name: input_data})
             run_times.append((time.time() - start_time) * 1000)  # Convert to milliseconds
 
-        run_times = self.iterative_sigma_clipping(np.array(run_times), sigma=2, max_iters=3)  # sigma clipping
+        run_times = self.iterative_sigma_clipping(np.array(run_times), sigma=2, max_iters=5)  # sigma clipping
         return np.mean(run_times), np.std(run_times)
 
     def generate_table_row(self, model_name, t_onnx, t_engine, model_info):
         layers, params, gradients, flops = model_info
         return f'| {model_name:18s} | {self.imgsz} | - | {t_onnx[0]:.2f} ± {t_onnx[1]:.2f} ms | {t_engine[0]:.2f} ± {t_engine[1]:.2f} ms | {params / 1e6:.1f} | {flops:.1f} |'
 
+    def generate_results_dict(self, model_name, t_onnx, t_engine, model_info):
+        layers, params, gradients, flops = model_info
+        return {
+            'model/name': model_name,
+            'model/parameters': params,
+            'model/GFLOPs': round(flops, 3),
+            'model/speed_ONNX(ms)': round(t_onnx[0], 3),
+            'model/speed_TensorRT(ms)': round(t_engine[0], 3)}
+
     def print_table(self, table_rows):
         gpu = torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'GPU'
         header = f'| Model | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>{gpu} TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |'

ultralytics/yolo/utils/callbacks/__init__.py

@@ -1,3 +1,5 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+
 from .base import add_integration_callbacks, default_callbacks, get_default_callbacks
 
 __all__ = 'add_integration_callbacks', 'default_callbacks', 'get_default_callbacks'

ultralytics/yolo/utils/callbacks/clearml.py

@@ -1,11 +1,12 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
+
 import re
 
 import matplotlib.image as mpimg
 import matplotlib.pyplot as plt
 
 from ultralytics.yolo.utils import LOGGER, TESTS_RUNNING
-from ultralytics.yolo.utils.torch_utils import get_flops, get_num_params
+from ultralytics.yolo.utils.torch_utils import model_info_for_loggers
 
 try:
     import clearml
@@ -105,11 +106,7 @@ def on_fit_epoch_end(trainer):
                                         value=trainer.epoch_time,
                                         iteration=trainer.epoch)
         if trainer.epoch == 0:
-            model_info = {
+            for k, v in model_info_for_loggers(trainer).items():
-                'model/parameters': get_num_params(trainer.model),
-                'model/GFLOPs': round(get_flops(trainer.model), 3),
-                'model/speed(ms)': round(trainer.validator.speed['inference'], 3)}
-            for k, v in model_info.items():
                 task.get_logger().report_single_value(k, v)
 
 

ultralytics/yolo/utils/callbacks/comet.py

@@ -1,9 +1,10 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
+
 import os
 from pathlib import Path
 
 from ultralytics.yolo.utils import LOGGER, RANK, TESTS_RUNNING, ops
-from ultralytics.yolo.utils.torch_utils import get_flops, get_num_params
+from ultralytics.yolo.utils.torch_utils import model_info_for_loggers
 
 try:
     import comet_ml
@@ -324,11 +325,7 @@ def on_fit_epoch_end(trainer):
     experiment.log_metrics(trainer.metrics, step=curr_step, epoch=curr_epoch)
     experiment.log_metrics(trainer.lr, step=curr_step, epoch=curr_epoch)
     if curr_epoch == 1:
-        model_info = {
+        experiment.log_metrics(model_info_for_loggers(trainer), step=curr_step, epoch=curr_epoch)
-            'model/parameters': get_num_params(trainer.model),
-            'model/GFLOPs': round(get_flops(trainer.model), 3),
-            'model/speed(ms)': round(trainer.validator.speed['inference'], 3), }
-        experiment.log_metrics(model_info, step=curr_step, epoch=curr_epoch)
 
     if not save_assets:
         return

ultralytics/yolo/utils/callbacks/hub.py

@@ -5,7 +5,7 @@ from time import time
 
 from ultralytics.hub.utils import PREFIX, events
 from ultralytics.yolo.utils import LOGGER
-from ultralytics.yolo.utils.torch_utils import get_flops, get_num_params
+from ultralytics.yolo.utils.torch_utils import model_info_for_loggers
 
 
 def on_pretrain_routine_end(trainer):
@@ -24,11 +24,7 @@ def on_fit_epoch_end(trainer):
         # Upload metrics after val end
         all_plots = {**trainer.label_loss_items(trainer.tloss, prefix='train'), **trainer.metrics}
         if trainer.epoch == 0:
-            model_info = {
+            all_plots = {**all_plots, **model_info_for_loggers(trainer)}
-                'model/parameters': get_num_params(trainer.model),
-                'model/GFLOPs': round(get_flops(trainer.model), 3),
-                'model/speed(ms)': round(trainer.validator.speed['inference'], 3)}
-            all_plots = {**all_plots, **model_info}
         session.metrics_queue[trainer.epoch] = json.dumps(all_plots)
         if time() - session.timers['metrics'] > session.rate_limits['metrics']:
             session.upload_metrics()

ultralytics/yolo/utils/callbacks/neptune.py

@@ -1,9 +1,10 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
+
 import matplotlib.image as mpimg
 import matplotlib.pyplot as plt
 
 from ultralytics.yolo.utils import LOGGER, TESTS_RUNNING
-from ultralytics.yolo.utils.torch_utils import get_flops, get_num_params
+from ultralytics.yolo.utils.torch_utils import model_info_for_loggers
 
 try:
     import neptune
@@ -68,11 +69,7 @@ def on_train_epoch_end(trainer):
 def on_fit_epoch_end(trainer):
     """Callback function called at end of each fit (train+val) epoch."""
     if run and trainer.epoch == 0:
-        model_info = {
+        run['Configuration/Model'] = model_info_for_loggers(trainer)
-            'parameters': get_num_params(trainer.model),
-            'GFLOPs': round(get_flops(trainer.model), 3),
-            'speed(ms)': round(trainer.validator.speed['inference'], 3)}
-        run['Configuration/Model'] = model_info
     _log_scalars(trainer.metrics, trainer.epoch + 1)
 
 

ultralytics/yolo/utils/callbacks/raytune.py

@@ -1,3 +1,5 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+
 try:
     import ray
     from ray import tune

ultralytics/yolo/utils/callbacks/tensorboard.py

@@ -1,4 +1,5 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
+
 from ultralytics.yolo.utils import LOGGER, TESTS_RUNNING, colorstr
 
 try:

ultralytics/yolo/utils/callbacks/wb.py

@@ -1,30 +1,27 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-from ultralytics.yolo.utils.torch_utils import get_flops, get_num_params
+from ultralytics.yolo.utils import TESTS_RUNNING
+from ultralytics.yolo.utils.torch_utils import model_info_for_loggers
 
 try:
     import wandb as wb
 
     assert hasattr(wb, '__version__')
+    assert not TESTS_RUNNING  # do not log pytest
 except (ImportError, AssertionError):
     wb = None
 
 
 def on_pretrain_routine_start(trainer):
     """Initiate and start project if module is present."""
-    wb.init(project=trainer.args.project or 'YOLOv8', name=trainer.args.name, config=vars(
+    wb.run or wb.init(project=trainer.args.project or 'YOLOv8', name=trainer.args.name, config=vars(trainer.args))
-        trainer.args)) if not wb.run else wb.run
 
 
 def on_fit_epoch_end(trainer):
     """Logs training metrics and model information at the end of an epoch."""
     wb.run.log(trainer.metrics, step=trainer.epoch + 1)
     if trainer.epoch == 0:
-        model_info = {
+        wb.run.log(model_info_for_loggers(trainer), step=trainer.epoch + 1)
-            'model/parameters': get_num_params(trainer.model),
-            'model/GFLOPs': round(get_flops(trainer.model), 3),
-            'model/speed(ms)': round(trainer.validator.speed['inference'], 3)}
-        wb.run.log(model_info, step=trainer.epoch + 1)
 
 
 def on_train_epoch_end(trainer):

ultralytics/yolo/utils/torch_utils.py

@@ -192,6 +192,29 @@ def get_num_gradients(model):
     return sum(x.numel() for x in model.parameters() if x.requires_grad)
 
 
+def model_info_for_loggers(trainer):
+    """
+    Return model info dict with useful model information.
+
+    Example for YOLOv8n:
+        {'model/parameters': 3151904,
+         'model/GFLOPs': 8.746,
+         'model/speed_ONNX(ms)': 41.244,
+         'model/speed_TensorRT(ms)': 3.211,
+         'model/speed_PyTorch(ms)': 18.755}
+    """
+    if trainer.args.profile:  # profile ONNX and TensorRT times
+        from ultralytics.yolo.utils.benchmarks import ProfileModels
+        results = ProfileModels([trainer.last], device=trainer.device).profile()[0]
+        results.pop('model/name')
+    else:  # only return PyTorch times from most recent validation
+        results = {
+            'model/parameters': get_num_params(trainer.model),
+            'model/GFLOPs': round(get_flops(trainer.model), 3)}
+    results['model/speed_PyTorch(ms)'] = round(trainer.validator.speed['inference'], 3)
+    return results
+
+
 def get_flops(model, imgsz=640):
     """Return a YOLO model's FLOPs."""
     try: