def visualize(args, mode, appcfg):
"""Load and display given image_ids
"""
log.debug("-------------------------------->")
log.debug("visualizing annotations...")
from falcon.utils import compute
from falcon.utils import visualize as _visualize
subset = args.eval_on
log.debug("subset: {}".format(subset))
datacfg = apputil.get_datacfg(appcfg)
dbcfg = apputil.get_dbcfg(appcfg)
dataset, num_classes, num_images, class_names, total_stats, total_verify = apputil.get_dataset_instance(appcfg, dbcfg, datacfg, subset)
colors = viz.random_colors(len(class_names))
log.debug("class_names: {}".format(class_names))
log.debug("len(class_names): {}".format(len(class_names)))
log.debug("len(colors), colors: {},{}".format(len(colors), colors))
log.debug("num_classes: {}".format(num_classes))
log.debug("num_images: {}".format(num_images))
name = dataset.name
datacfg.name = name
datacfg.classes = class_names
datacfg.num_classes = num_classes
image_ids = dataset.image_ids
# log.debug("dataset: {}".format(vars(dataset)))
# log.debug("len(dataset.image_info): {}".format(len(dataset.image_info)))
class_names = dataset.class_names
log.debug("dataset: len(image_ids): {}\nimage_ids: {}".format(len(image_ids), image_ids))
log.debug("dataset: len(class_names): {}\nclass_names: {}".format(len(class_names), class_names))
for image_id in image_ids:
image = dataset.load_image(image_id, datacfg)
if image is not None:
mask, class_ids, keys, values = dataset.load_mask(image_id, datacfg)
log.debug("keys: {}".format(keys))
log.debug("values: {}".format(values))
log.debug("class_ids: {}".format(class_ids))
## Display image and instances
# _visualize.display_top_masks(image, mask, class_ids, class_names)
## Compute Bounding box
bbox = compute.extract_bboxes(mask)
log.debug("bbox: {}".format(bbox))
# _visualize.display_instances(image, bbox, mask, class_ids, class_names, show_bbox=False)
_visualize.display_instances(image, bbox, mask, class_ids, class_names)
# return image, bbox, mask, class_ids, class_names
else:
log.error("error reading image with image_id: {}".format(image_id))
def get_data(subset, _appcfg):
## DONE: to be passed through cfg
CMD = hmd_detectron_config['AIDS']['CMD']
DBNAME = hmd_detectron_config['AIDS']['DBNAME']
EXP_ID = hmd_detectron_config['AIDS']['EXP_ID']
EVAL_ON = subset
# log.debug(_appcfg)
# log.info(_appcfg['APP']['DBCFG']['PXLCFG'])
# log.info(_appcfg['PATHS']['AI_ANNON_DATA_HOME_LOCAL'])
## datacfg and dbcfg
_cfg_.load_datacfg(CMD, _appcfg, DBNAME, EXP_ID, EVAL_ON)
datacfg = apputil.get_datacfg(_appcfg)
dbcfg = apputil.get_dbcfg(_appcfg)
# log.info("datacfg: {}".format(datacfg))
# log.info("dbcfg: {}".format(dbcfg))
## archcfg, cmdcfg
_cfg_.load_archcfg(CMD, _appcfg, DBNAME, EXP_ID, EVAL_ON)
archcfg = apputil.get_archcfg(_appcfg)
# log.debug("archcfg: {}".format(archcfg))
cmdcfg = archcfg
dataset, num_classes, num_images, class_names, total_stats, total_verify = apputil.get_dataset_instance(
_appcfg, dbcfg, datacfg, subset)
# log.debug("class_names: {}".format(class_names))
# log.debug("len(class_names): {}".format(len(class_names)))
# log.debug("num_classes: {}".format(num_classes))
# log.debug("num_images: {}".format(num_images))
name = dataset.name
datacfg.name = name
datacfg.classes = class_names
datacfg.num_classes = num_classes
cmdcfg.name = name
cmdcfg.config.NAME = name
cmdcfg.config.NUM_CLASSES = num_classes
annon = ANNON(dbcfg, datacfg, subset=subset)
class_ids = datacfg.class_ids if 'class_ids' in datacfg and datacfg[
'class_ids'] else []
class_ids = annon.getCatIds(catIds=class_ids) ## cat_ids
classinfo = annon.loadCats(class_ids) ## cats
id_map = {v: i for i, v in enumerate(class_ids)}
img_ids = sorted(list(annon.imgs.keys()))
imgs = annon.loadImgs(img_ids)
anns = [annon.imgToAnns[img_id] for img_id in img_ids]
return class_ids, id_map, imgs, anns
def inspect_annon(args, mode, appcfg):
"""inspection of data from command line for quick verification of data sanity
"""
log.debug("---------------------------->")
log.debug("Inspecting annotations...")
subset = args.eval_on
log.debug("subset: {}".format(subset))
datacfg = apputil.get_datacfg(appcfg)
dbcfg = apputil.get_dbcfg(appcfg)
dataset, num_classes, num_images, class_names, total_stats, total_verify = apputil.get_dataset_instance(appcfg, dbcfg, datacfg, subset)
colors = viz.random_colors(len(class_names))
log.debug("class_names: {}".format(class_names))
log.debug("len(class_names): {}".format(len(class_names)))
log.debug("len(colors), colors: {},{}".format(len(colors), colors))
log.debug("num_classes: {}".format(num_classes))
log.debug("num_images: {}".format(num_images))
name = dataset.name
datacfg.name = name
datacfg.classes = class_names
datacfg.num_classes = num_classes
# log.debug("dataset: {}".format(vars(dataset)))
log.debug("len(dataset.image_info): {}".format(len(dataset.image_info)))
log.debug("len(dataset.image_ids): {}".format(len(dataset.image_ids)))
mod = apputil.get_module('inspect_annon')
archcfg = apputil.get_archcfg(appcfg)
log.debug("archcfg: {}".format(archcfg))
cmdcfg = archcfg
cmdcfg.name = name
cmdcfg.config.NAME = name
cmdcfg.config.NUM_CLASSES = num_classes
dnnmod = apputil.get_module(cmdcfg.dnnarch)
get_dnncfg = apputil.get_module_fn(dnnmod, "get_dnncfg")
dnncfg = get_dnncfg(cmdcfg.config)
log.debug("config.MINI_MASK_SHAPE: {}".format(dnncfg.MINI_MASK_SHAPE))
log.debug("type(dnncfg.MINI_MASK_SHAPE): {}".format(type(dnncfg.MINI_MASK_SHAPE)))
mod.all_steps(dataset, datacfg, dnncfg)
return
def get_data(subset, _appcfg):
## TODO: to be passed through cfg
cmd = "train"
# dbname = "PXL-291119_180404"
# dbname = "PXL-301219_174758"
# dbname = "PXL-310120_175129"
dbname = "PXL-100220_192533"
exp_id = "train-eee128cb-d7a1-493a-9819-95531f507092"
# exp_id = "train-422d30b0-f518-4203-9c4d-b36bd8796c62"
# exp_id = "train-d79fe253-60c8-43f7-a3f5-42a4abf97b6c"
# exp_id = "train-887c2e82-1faa-4353-91d4-2f4cdc9285c1"
eval_on = subset
# log.debug(_appcfg)
# log.info(_appcfg['APP']['DBCFG']['PXLCFG'])
# log.info(_appcfg['PATHS']['AI_ANNON_DATA_HOME_LOCAL'])
## datacfg and dbcfg
_cfg_.load_datacfg(cmd, _appcfg, dbname, exp_id, eval_on)
datacfg = apputil.get_datacfg(_appcfg)
dbcfg = apputil.get_dbcfg(_appcfg)
# log.info("datacfg: {}".format(datacfg))
# log.info("dbcfg: {}".format(dbcfg))
## archcfg, cmdcfg
_cfg_.load_archcfg(cmd, _appcfg, dbname, exp_id, eval_on)
archcfg = apputil.get_archcfg(_appcfg)
# log.debug("archcfg: {}".format(archcfg))
cmdcfg = archcfg
dataset, num_classes, num_images, class_names, total_stats, total_verify = apputil.get_dataset_instance(
_appcfg, dbcfg, datacfg, subset)
# log.debug("class_names: {}".format(class_names))
# log.debug("len(class_names): {}".format(len(class_names)))
# log.debug("num_classes: {}".format(num_classes))
# log.debug("num_images: {}".format(num_images))
name = dataset.name
datacfg.name = name
datacfg.classes = class_names
datacfg.num_classes = num_classes
cmdcfg.name = name
cmdcfg.config.NAME = name
cmdcfg.config.NUM_CLASSES = num_classes
annon = ANNON(dbcfg, datacfg, subset=subset)
class_ids = datacfg.class_ids if 'class_ids' in datacfg and datacfg[
'class_ids'] else []
class_ids = annon.getCatIds(catIds=class_ids) ## cat_ids
classinfo = annon.loadCats(class_ids) ## cats
id_map = {v: i for i, v in enumerate(class_ids)}
img_ids = sorted(list(annon.imgs.keys()))
imgs = annon.loadImgs(img_ids)
anns = [annon.imgToAnns[img_id] for img_id in img_ids]
return class_ids, id_map, imgs, anns
def evaluate(args, mode, appcfg):
"""prepare the report configuration like paths, report names etc. and calls the report generation function
"""
log.debug("evaluate---------------------------->")
subset = args.eval_on
iou_threshold = args.iou
log.debug("subset: {}".format(subset))
log.debug("iou_threshold: {}".format(iou_threshold))
get_mask = True
auto_show = False
datacfg = apputil.get_datacfg(appcfg)
dbcfg = apputil.get_dbcfg(appcfg)
log.debug("appcfg: {}".format(appcfg))
log.debug("datacfg: {}".format(datacfg))
dataset, num_classes, num_images, class_names, total_stats, total_verify = apputil.get_dataset_instance(appcfg, dbcfg, datacfg, subset)
colors = viz.random_colors(len(class_names))
log.debug("-------")
log.debug("len(colors), colors: {},{}".format(len(colors), colors))
log.debug("class_names: {}".format(class_names))
log.debug("len(class_names): {}".format(len(class_names)))
log.debug("num_classes: {}".format(num_classes))
log.debug("num_images: {}".format(num_images))
log.debug("len(dataset.image_info): {}".format(len(dataset.image_info)))
log.debug("len(dataset.image_ids): {}".format(len(dataset.image_ids)))
# log.debug("dataset: {}".format(vars(dataset)))
log.debug("-------")
# log.debug("TODO: color: cc")
# cc = dict(zip(class_names,colors))
name = dataset.name
datacfg.name = name
datacfg.classes = class_names
datacfg.num_classes = num_classes
archcfg = apputil.get_archcfg(appcfg)
log.debug("archcfg: {}".format(archcfg))
cmdcfg = archcfg
if 'save_viz_and_json' not in cmdcfg:
cmdcfg.save_viz_and_json = False
save_viz = args.save_viz
log.debug("save_viz: {}".format(save_viz))
cmdcfg.save_viz_and_json = save_viz
modelcfg_path = os.path.join(appcfg.PATHS.AI_MODEL_CFG_PATH, cmdcfg.model_info)
log.info("modelcfg_path: {}".format(modelcfg_path))
modelcfg = apputil.get_modelcfg(modelcfg_path)
## for prediction, get the label information from the model information
class_names_model = apputil.get_class_names(modelcfg)
log.debug("class_names_model: {}".format(class_names_model))
cmdcfg.name = name
cmdcfg.config.NAME = modelcfg.name
cmdcfg.config.NUM_CLASSES = len(class_names_model)
# class_names = apputil.get_class_names(datacfg)
# log.debug("class_names: {}".format(class_names))
weights_path = apputil.get_abs_path(appcfg, modelcfg, 'AI_WEIGHTS_PATH')
cmdcfg['weights_path'] = weights_path
## Prepare directory structure and filenames for reporting the evluation results
now = datetime.datetime.now()
## create log directory based on timestamp for evaluation reporting
timestamp = "{:%d%m%y_%H%M%S}".format(now)
datacfg_ts = datacfg.timestamp if 'TIMESTAMP' in datacfg else timestamp
save_viz_and_json = cmdcfg.save_viz_and_json
# iou_threshold = cmdcfg.iou_threshold
if 'evaluate_no_of_result' not in cmdcfg:
evaluate_no_of_result = -1
else:
evaluate_no_of_result = cmdcfg.evaluate_no_of_result
def clean_iou(iou):
return str("{:f}".format(iou)).replace('.','')[:3]
path = appcfg['PATHS']['AI_LOGS']
# evaluate_dir = datacfg_ts+"-evaluate_"+clean_iou(iou_threshold)+"-"+name+"-"+subset+"-"+timestamp
evaluate_dir = "evaluate_"+clean_iou(iou_threshold)+"-"+name+"-"+subset+"-"+timestamp
filepath = os.path.join(path, cmdcfg.dnnarch, evaluate_dir)
log.debug("filepath: {}".format(filepath))
common.mkdir_p(filepath)
for d in ['splash', 'mask', 'annotations', 'viz']:
common.mkdir_p(os.path.join(filepath,d))
## gt - ground truth
## pr/pred - prediction
def get_cfgfilename(cfg_filepath):
return cfg_filepath.split(os.path.sep)[-1]
## generate the summary on the evaluation run
evaluate_run_summary = defaultdict(list)
evaluate_run_summary['name'] =name
evaluate_run_summary['execution_start_time'] = timestamp
evaluate_run_summary['subset'] = subset
evaluate_run_summary['total_labels'] = num_classes
evaluate_run_summary['total_images'] = num_images
evaluate_run_summary['evaluate_no_of_result'] = evaluate_no_of_result
evaluate_run_summary['evaluate_dir'] = evaluate_dir
evaluate_run_summary['dataset'] = get_cfgfilename(appcfg.DATASET[appcfg.ACTIVE.DATASET].cfg_file)
evaluate_run_summary['arch'] = get_cfgfilename(appcfg.ARCH[appcfg.ACTIVE.ARCH].cfg_file)
evaluate_run_summary['model'] = cmdcfg['model_info']
## classification report and confusion matrix - json and csv
## generate the filenames for what reports to be generated
reportcfg = {
'filepath':filepath
,'evaluate_run_summary_reportfile':os.path.join(filepath, "evaluate_run_summary_rpt-"+subset)
,'classification_reportfile':os.path.join(filepath, "classification_rpt-"+subset)
,'confusionmatrix_reportfile':os.path.join(filepath, "confusionmatrix_rpt-"+subset)
,'iou_threshold':iou_threshold
,'evaluate_run_summary':evaluate_run_summary
,'save_viz_and_json':save_viz_and_json
,'evaluate_no_of_result':evaluate_no_of_result
}
log.debug("reportcfg: {}".format(reportcfg))
dnnmod = apputil.get_module(cmdcfg.dnnarch)
fn_evaluate = apputil.get_module_fn(dnnmod, "evaluate")
evaluate_run_summary = fn_evaluate(mode, cmdcfg, appcfg, modelcfg, dataset, datacfg, class_names, reportcfg, get_mask)
return evaluate_run_summary
def train(args, mode, appcfg):
log.debug("train---------------------------->")
datacfg = apputil.get_datacfg(appcfg)
## Training dataset.
subset = "train"
log.info("subset: {}".format(subset))
dbcfg = apputil.get_dbcfg(appcfg)
dataset_train, num_classes_train, num_images_train, class_names_train, total_stats_train, total_verify_train = apputil.get_dataset_instance(appcfg, dbcfg, datacfg, subset)
colors = viz.random_colors(len(class_names_train))
log.info("-------")
log.info("len(colors), colors: {},{}".format(len(colors), colors))
log.info("subset, class_names_train: {}, {}".format(subset, class_names_train))
log.info("subset, len(class_names_train): {}, {}".format(subset, len(class_names_train)))
log.info("subset, num_classes_train: {}, {}".format(subset, num_classes_train))
log.info("subset, num_images_train: {}, {}".format(subset, num_images_train))
log.info("subset, len(dataset_train.image_info): {}, {}".format(subset, len(dataset_train.image_info)))
log.info("subset, len(dataset_train.image_ids): {}, {}".format(subset, len(dataset_train.image_ids)))
## Validation dataset
subset = "val"
log.info("subset: {}".format(subset))
dataset_val, num_classes_val, num_images_val, class_names_val, total_stats_val, total_verify_val = apputil.get_dataset_instance(appcfg, dbcfg, datacfg, subset)
log.info("-------")
log.info("subset, class_names_val: {}, {}".format(subset, class_names_val))
log.info("subset, len(class_names_val): {}, {}".format(subset, len(class_names_val)))
log.info("subset, num_classes_val: {}, {}".format(subset, num_classes_val))
log.info("subset, num_images_val: {}, {}".format(subset, num_images_val))
log.info("subset, len(dataset_val.image_info): {}, {}".format(subset, len(dataset_val.image_info)))
log.info("subset, len(dataset_val.image_ids): {}, {}".format(subset, len(dataset_val.image_ids)))
log.info("-------")
## Ensure label sequence and class_names of train and val dataset are excatly same, if not abort training
assert class_names_train == class_names_val
archcfg = apputil.get_archcfg(appcfg)
log.debug("archcfg: {}".format(archcfg))
cmdcfg = archcfg
name = dataset_train.name
## generate the modelinfo template to be used for evaluate and prediction
modelinfocfg = {
'classes': class_names_train.copy()
,'classinfo': None
,'config': cmdcfg.config.copy()
,'dataset': cmdcfg.dbname
,'dbname': cmdcfg.dbname
,'dnnarch': cmdcfg.dnnarch
,'framework_type': cmdcfg.framework_type
,'id': None
,'load_weights': cmdcfg.load_weights.copy()
,'name': name
,'num_classes': num_classes_train
,'problem_id': None
,'rel_num': None
,'weights': None
,'weights_path': None
,'log_dir': None
,'checkpoint_path': None
,'model_info': None
,'timestamp': None
,'creator': None
}
datacfg.name = name
datacfg.classes = class_names_train
datacfg.num_classes = num_classes_train
cmdcfg.name = name
cmdcfg.config.NAME = name
cmdcfg.config.NUM_CLASSES = num_classes_train
modelcfg_path = os.path.join(appcfg.PATHS.AI_MODEL_CFG_PATH, cmdcfg.model_info)
log.info("modelcfg_path: {}".format(modelcfg_path))
modelcfg = apputil.get_modelcfg(modelcfg_path)
log_dir_path = apputil.get_abs_path(appcfg, cmdcfg, 'AI_LOGS')
cmdcfg['log_dir_path'] = log_dir_path
weights_path = apputil.get_abs_path(appcfg, modelcfg, 'AI_WEIGHTS_PATH')
cmdcfg['weights_path'] = weights_path
dnnmod = apputil.get_module(cmdcfg.dnnarch)
load_model_and_weights = apputil.get_module_fn(dnnmod, "load_model_and_weights")
model = load_model_and_weights(mode, cmdcfg, appcfg)
modelinfocfg['log_dir'] = model.log_dir
modelinfocfg['checkpoint_path'] = model.checkpoint_path
if 'creator' in cmdcfg:
modelinfocfg['creator'] = cmdcfg['creator']
log.info("modelinfocfg: {}".format(modelinfocfg))
fn_create_modelinfo = apputil.get_module_fn(dnnmod, "create_modelinfo")
modelinfo = fn_create_modelinfo(modelinfocfg)
create_modelinfo = args.create_modelinfo
try:
if not create_modelinfo:
log.info("Training...")
fn_train = apputil.get_module_fn(dnnmod, "train")
fn_train(model, dataset_train, dataset_val, cmdcfg)
log.info("Training Completed!!!")
finally:
## save modelinfo
## popolate the relative weights_path of the last model from the training if any model is generated otherwise None
logs_path = appcfg['PATHS']['AI_LOGS']
dnn = cmdcfg.dnnarch
##TODO
list_of_files = glob.glob(os.path.join(model.log_dir,dnn+'*')) # * means all if need specific format then *.h5
latest_file = max(list_of_files, key=os.path.getctime)
new_weights_path = re.sub('\{}'.format(logs_path+'/'), '', latest_file)
modelinfo['weights_path'] = new_weights_path
modelinfo_filepath = apputil.get_abs_path(appcfg, modelinfo, 'AI_MODEL_CFG_PATH')
common.yaml_safe_dump(modelinfo_filepath, modelinfo)
log.info("TRAIN:MODELINFO_FILEPATH: {}".format(modelinfo_filepath))
log.info("---x--x--x---")
return modelinfo_filepath
# Run one of the code blocks below to import and load the configurations to use.
# In[4]:
appcfg['APP']['DBCFG']['PXLCFG']['host'] = HOST
appcfg['PATHS']['AI_ANNON_DATA_HOME_LOCAL'] = AI_ANNON_DATA_HOME_LOCAL
# log.debug(appcfg)
# log.info(appcfg['APP']['DBCFG']['PXLCFG'])
# log.info(appcfg['PATHS']['AI_ANNON_DATA_HOME_LOCAL'])
# In[5]:
## datacfg and dbcfg
_cfg_.load_datacfg(cmd, appcfg, dbname, exp_id, eval_on)
datacfg = apputil.get_datacfg(appcfg)
dbcfg = apputil.get_dbcfg(appcfg)
# log.info("datacfg: {}".format(datacfg))
# log.info("dbcfg: {}".format(dbcfg))
# ## Dataset
# In[6]:
## archcfg, cmdcfg
_cfg_.load_archcfg(cmd, appcfg, dbname, exp_id, eval_on)
archcfg = apputil.get_archcfg(appcfg)
log.debug("archcfg: {}".format(archcfg))
cmdcfg = archcfg