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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import numpy as np
import pickle
import torch
import tqdm
from fvcore.common.file_io import PathManager
import slowfast.datasets.utils as data_utils
import slowfast.utils.checkpoint as cu
import slowfast.utils.distributed as du
import slowfast.utils.logging as logging
import slowfast.utils.misc as misc
import slowfast.visualization.tensorboard_vis as tb
from slowfast.datasets import loader
from slowfast.models import build_model
from slowfast.visualization.gradcam_utils import GradCAM
from slowfast.visualization.prediction_vis import WrongPredictionVis
from slowfast.visualization.utils import (
GetWeightAndActivation,
process_layer_index_data,
)
from slowfast.visualization.video_visualizer import VideoVisualizer
logger = logging.get_logger(__name__)
def run_visualization(vis_loader, model, cfg, writer=None):
"""
Run model visualization (weights, activations and model inputs) and visualize
them on Tensorboard.
Args:
vis_loader (loader): video visualization loader.
model (model): the video model to visualize.
cfg (CfgNode): configs. Details can be found in
slowfast/config/defaults.py
writer (TensorboardWriter, optional): TensorboardWriter object
to writer Tensorboard log.
"""
n_devices = cfg.NUM_GPUS * cfg.NUM_SHARDS
prefix = "module/" if n_devices > 1 else ""
# Get a list of selected layer names and indexing.
layer_ls, indexing_dict = process_layer_index_data(
cfg.TENSORBOARD.MODEL_VIS.LAYER_LIST, layer_name_prefix=prefix
)
logger.info("Start Model Visualization.")
# Register hooks for activations.
model_vis = GetWeightAndActivation(model, layer_ls)
if writer is not None and cfg.TENSORBOARD.MODEL_VIS.MODEL_WEIGHTS:
layer_weights = model_vis.get_weights()
writer.plot_weights_and_activations(
layer_weights, tag="Layer Weights/", heat_map=False
)
video_vis = VideoVisualizer(
cfg.MODEL.NUM_CLASSES,
cfg.TENSORBOARD.CLASS_NAMES_PATH,
cfg.TENSORBOARD.MODEL_VIS.TOPK_PREDS,
cfg.TENSORBOARD.MODEL_VIS.COLORMAP,
)
if n_devices > 1:
grad_cam_layer_ls = [
"module/" + layer
for layer in cfg.TENSORBOARD.MODEL_VIS.GRAD_CAM.LAYER_LIST
]
else:
grad_cam_layer_ls = cfg.TENSORBOARD.MODEL_VIS.GRAD_CAM.LAYER_LIST
if cfg.TENSORBOARD.MODEL_VIS.GRAD_CAM.ENABLE:
gradcam = GradCAM(
model,
target_layers=grad_cam_layer_ls,
data_mean=cfg.DATA.MEAN,
data_std=cfg.DATA.STD,
colormap=cfg.TENSORBOARD.MODEL_VIS.GRAD_CAM.COLORMAP,
)
logger.info("Finish drawing weights.")
global_idx = -1
for inputs, labels, _, meta in tqdm.tqdm(vis_loader):
if cfg.NUM_GPUS:
# Transfer the data to the current GPU device.
if isinstance(inputs, (list,)):
for i in range(len(inputs)):
inputs[i] = inputs[i].cuda(non_blocking=True)
else:
inputs = inputs.cuda(non_blocking=True)
labels = labels.cuda()
for key, val in meta.items():
if isinstance(val, (list,)):
for i in range(len(val)):
val[i] = val[i].cuda(non_blocking=True)
else:
meta[key] = val.cuda(non_blocking=True)
if cfg.DETECTION.ENABLE:
activations, preds = model_vis.get_activations(
inputs, meta["boxes"]
)
else:
activations, preds = model_vis.get_activations(inputs)
if cfg.TENSORBOARD.MODEL_VIS.GRAD_CAM.ENABLE:
if cfg.TENSORBOARD.MODEL_VIS.GRAD_CAM.USE_TRUE_LABEL:
inputs, preds = gradcam(inputs, labels=labels)
else:
inputs, preds = gradcam(inputs)
if cfg.NUM_GPUS:
inputs = du.all_gather_unaligned(inputs)
activations = du.all_gather_unaligned(activations)
preds = du.all_gather_unaligned(preds)
if isinstance(inputs[0], list):
for i in range(len(inputs)):
for j in range(len(inputs[0])):
inputs[i][j] = inputs[i][j].cpu()
else:
inputs = [inp.cpu() for inp in inputs]
preds = [pred.cpu() for pred in preds]
else:
inputs, activations, preds = [inputs], [activations], [preds]
boxes = [None] * max(n_devices, 1)
if cfg.DETECTION.ENABLE and cfg.NUM_GPUS:
boxes = du.all_gather_unaligned(meta["boxes"])
boxes = [box.cpu() for box in boxes]
if writer is not None:
total_vids = 0
for i in range(max(n_devices, 1)):
cur_input = inputs[i]
cur_activations = activations[i]
cur_batch_size = cur_input[0].shape[0]
cur_preds = preds[i]
cur_boxes = boxes[i]
for cur_batch_idx in range(cur_batch_size):
global_idx += 1
total_vids += 1
if (
cfg.TENSORBOARD.MODEL_VIS.INPUT_VIDEO
or cfg.TENSORBOARD.MODEL_VIS.GRAD_CAM.ENABLE
):
for path_idx, input_pathway in enumerate(cur_input):
if cfg.TEST.DATASET == "ava" and cfg.AVA.BGR:
video = input_pathway[
cur_batch_idx, [2, 1, 0], ...
]
else:
video = input_pathway[cur_batch_idx]
if not cfg.TENSORBOARD.MODEL_VIS.GRAD_CAM.ENABLE:
# Permute to (T, H, W, C) from (C, T, H, W).
video = video.permute(1, 2, 3, 0)
video = data_utils.revert_tensor_normalize(
video, cfg.DATA.MEAN, cfg.DATA.STD
)
else:
# Permute from (T, C, H, W) to (T, H, W, C)
video = video.permute(0, 2, 3, 1)
bboxes = (
None if cur_boxes is None else cur_boxes[:, 1:]
)
cur_prediction = (
cur_preds
if cfg.DETECTION.ENABLE
else cur_preds[cur_batch_idx]
)
video = video_vis.draw_clip(
video, cur_prediction, bboxes=bboxes
)
video = (
torch.from_numpy(np.array(video))
.permute(0, 3, 1, 2)
.unsqueeze(0)
)
writer.add_video(
video,
tag="Input {}/Pathway {}".format(
global_idx, path_idx + 1
),
)
if cfg.TENSORBOARD.MODEL_VIS.ACTIVATIONS:
writer.plot_weights_and_activations(
cur_activations,
tag="Input {}/Activations: ".format(global_idx),
batch_idx=cur_batch_idx,
indexing_dict=indexing_dict,
)
def perform_wrong_prediction_vis(vis_loader, model, cfg):
"""
Visualize video inputs with wrong predictions on Tensorboard.
Args:
vis_loader (loader): video visualization loader.
model (model): the video model to visualize.
cfg (CfgNode): configs. Details can be found in
slowfast/config/defaults.py
"""
wrong_prediction_visualizer = WrongPredictionVis(cfg=cfg)
for batch_idx, (inputs, labels, _, _) in tqdm.tqdm(enumerate(vis_loader)):
if cfg.NUM_GPUS:
# Transfer the data to the current GPU device.
if isinstance(inputs, (list,)):
for i in range(len(inputs)):
inputs[i] = inputs[i].cuda(non_blocking=True)
else:
inputs = inputs.cuda(non_blocking=True)
labels = labels.cuda()
# Some model modify the original input.
inputs_clone = [inp.clone() for inp in inputs]
preds = model(inputs)
if cfg.NUM_GPUS > 1:
preds, labels = du.all_gather([preds, labels])
if isinstance(inputs_clone, (list,)):
inputs_clone = du.all_gather(inputs_clone)
else:
inputs_clone = du.all_gather([inputs_clone])[0]
if cfg.NUM_GPUS:
# Transfer the data to the current CPU device.
labels = labels.cpu()
preds = preds.cpu()
if isinstance(inputs_clone, (list,)):
for i in range(len(inputs_clone)):
inputs_clone[i] = inputs_clone[i].cpu()
else:
inputs_clone = inputs_clone.cpu()
# If using CPU (NUM_GPUS = 0), 1 represent 1 CPU.
n_devices = max(cfg.NUM_GPUS, 1)
for device_idx in range(1, n_devices + 1):
wrong_prediction_visualizer.visualize_vid(
video_input=inputs_clone,
labels=labels,
preds=preds.detach().clone(),
batch_idx=device_idx * batch_idx,
)
logger.info(
"Class indices with wrong predictions: {}".format(
sorted(wrong_prediction_visualizer.wrong_class_prediction)
)
)
wrong_prediction_visualizer.clean()
def visualize(cfg):
"""
Perform layer weights and activations visualization on the model.
Args:
cfg (CfgNode): configs. Details can be found in
slowfast/config/defaults.py
"""
if cfg.TENSORBOARD.ENABLE and (
cfg.TENSORBOARD.MODEL_VIS.ENABLE
or cfg.TENSORBOARD.WRONG_PRED_VIS.ENABLE
):
# Set up environment.
du.init_distributed_training(cfg)
# Set random seed from configs.
np.random.seed(cfg.RNG_SEED)
torch.manual_seed(cfg.RNG_SEED)
# Setup logging format.
logging.setup_logging(cfg.OUTPUT_DIR)
# Print config.
logger.info("Model Visualization with config:")
logger.info(cfg)
# Build the video model and print model statistics.
model = build_model(cfg)
model.eval()
if du.is_master_proc() and cfg.LOG_MODEL_INFO:
misc.log_model_info(model, cfg, use_train_input=False)
cu.load_test_checkpoint(cfg, model)
# Create video testing loaders.
vis_loader = loader.construct_loader(cfg, "test")
if cfg.DETECTION.ENABLE:
assert cfg.NUM_GPUS == cfg.TEST.BATCH_SIZE or cfg.NUM_GPUS == 0
# Set up writer for logging to Tensorboard format.
if du.is_master_proc(cfg.NUM_GPUS * cfg.NUM_SHARDS):
writer = tb.TensorboardWriter(cfg)
else:
writer = None
if cfg.TENSORBOARD.PREDICTIONS_PATH != "":
assert not cfg.DETECTION.ENABLE, "Detection is not supported."
logger.info(
"Visualizing class-level performance from saved results..."
)
if writer is not None:
with PathManager.open(
cfg.TENSORBOARD.PREDICTIONS_PATH, "rb"
) as f:
preds, labels = pickle.load(f, encoding="latin1")
writer.plot_eval(preds, labels)
if cfg.TENSORBOARD.MODEL_VIS.ENABLE:
if cfg.TENSORBOARD.MODEL_VIS.GRAD_CAM.ENABLE:
assert (
not cfg.DETECTION.ENABLE
), "Detection task is currently not supported for Grad-CAM visualization."
if cfg.MODEL.ARCH in cfg.MODEL.SINGLE_PATHWAY_ARCH:
assert (
len(cfg.TENSORBOARD.MODEL_VIS.GRAD_CAM.LAYER_LIST) == 1
), "The number of chosen CNN layers must be equal to the number of pathway(s), given {} layer(s).".format(
len(cfg.TENSORBOARD.MODEL_VIS.GRAD_CAM.LAYER_LIST)
)
elif cfg.MODEL.ARCH in cfg.MODEL.MULTI_PATHWAY_ARCH:
assert (
len(cfg.TENSORBOARD.MODEL_VIS.GRAD_CAM.LAYER_LIST) == 2
), "The number of chosen CNN layers must be equal to the number of pathway(s), given {} layer(s).".format(
len(cfg.TENSORBOARD.MODEL_VIS.GRAD_CAM.LAYER_LIST)
)
else:
raise NotImplementedError(
"Model arch {} is not in {}".format(
cfg.MODEL.ARCH,
cfg.MODEL.SINGLE_PATHWAY_ARCH
+ cfg.MODEL.MULTI_PATHWAY_ARCH,
)
)
logger.info(
"Visualize model analysis for {} iterations".format(
len(vis_loader)
)
)
# Run visualization on the model
run_visualization(vis_loader, model, cfg, writer)
if cfg.TENSORBOARD.WRONG_PRED_VIS.ENABLE:
logger.info(
"Visualize Wrong Predictions for {} iterations".format(
len(vis_loader)
)
)
perform_wrong_prediction_vis(vis_loader, model, cfg)
if writer is not None:
writer.close()
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