def main(save_id, gen_p, train_p, eval_p, backbone_id, return_eval=False, use_bottleneck=True, file_id=""):
print("save_id: {0}, train_p : {1}, eval_p: {2}, backbone_id: {3}, ".format(save_id, train_p, eval_p, backbone_id))
from model_def import define_model
model_dict = define_model(backbone_id)
num_segments = model_dict["num_segments"]
bottleneck_size = model_dict["bottleneck_size"]
dense_sample = model_dict["dense_sample"]
dense_rate = model_dict["dense_rate"]
dir_name = os.path.join("saved_models", save_id) # lfd_params
if not os.path.exists(dir_name):
os.makedirs(dir_name)
filename = os.path.join(dir_name, "../model")
lfd_params = default_model_args(save_id=save_id, log_dir=dir_name,
num_segments=num_segments, bottleneck_size=bottleneck_size,
dense_sample=dense_sample, dense_rate=dense_rate) # parse_model_args()
if gen_p:
print("Generating ITR Files")
model = Classifier(lfd_params, filename, backbone_id, use_feature_extractor=True, use_spatial_lstm=False,
spatial_train=False, use_bottleneck=use_bottleneck)
generate_iad_files(lfd_params, model, "train", backbone=backbone_id)
generate_iad_files(lfd_params, model, "evaluation", backbone=backbone_id)
if train_p:
model = Classifier(lfd_params, filename, backbone_id, use_feature_extractor=False, use_spatial_lstm=True,
spatial_train=True, use_bottleneck=use_bottleneck)
model = train(lfd_params, model, verbose=True, input_dtype="iad")
model.save_model()
if eval_p:
model = Classifier(lfd_params, filename, backbone_id, use_feature_extractor=False, use_spatial_lstm=True,
spatial_train=False, use_bottleneck=use_bottleneck)
train_df = evaluate(lfd_params, model, mode="train", input_dtype="iad")
train_df["mode"] = ["train"]*len(train_df)
eval_df = evaluate(lfd_params, model, mode="evaluation", verbose=True, input_dtype="iad")
eval_df["mode"] = ["evaluation"] * len(eval_df)
df = pd.concat([train_df, eval_df])
if return_eval:
return df
df["repeat"] = ["1"]*len(df)
out_filename = os.path.join(lfd_params.args.output_dir, "output_" + save_id + file_id+".csv")
df.to_csv(out_filename)
print("Output placed in: " + out_filename)
def exec_classifier_backbone(args):
# Train
if args.eval_only:
model = Classifier(lfd_params,
filename,
backbone_id,
use_feature_extractor=False,
use_spatial=True,
spatial_train=True,
use_bottleneck=use_bottleneck)
model = train(lfd_params, model, verbose=True)
model.save_model()
# Evaluate
model = Classifier(lfd_params,
filename,
backbone_id,
use_feature_extractor=False,
use_spatial=True,
spatial_train=False,
use_bottleneck=use_bottleneck)
train_df = evaluate(lfd_params, model, mode="train")
train_df["mode"] = ["train"] * len(train_df)
eval_df = evaluate(lfd_params, model, mode="evaluation", verbose=True)
eval_df["mode"] = ["evaluation"] * len(eval_df)
df = pd.concat([train_df, eval_df])
df["repeat"] = ["1"] * len(df)
out_filename = os.path.join(lfd_params.args.output_dir,
"output_" + save_id + "_spatial.csv")
df.to_csv(out_filename)
print("Output placed in: " + out_filename)
return 0
def main(save_id, gen_p, train_p, eval_p, backbone_id, use_bottleneck=True):
from model_def import define_model
model_dict = define_model(backbone_id)
num_segments = model_dict["num_segments"]
bottleneck_size = model_dict["bottleneck_size"]
dense_sample = model_dict["dense_sample"]
dense_rate = model_dict["dense_rate"]
dir_name = os.path.join("saved_models", save_id) # lfd_params
if not os.path.exists(dir_name):
os.makedirs(dir_name)
filename = os.path.join(dir_name, "../model")
lfd_params = default_model_args(save_id=save_id,
log_dir=dir_name,
num_segments=num_segments,
bottleneck_size=bottleneck_size,
dense_sample=dense_sample,
dense_rate=dense_rate)
if gen_p:
# Generate IADs
print("Generating ITR Files")
model = Classifier(lfd_params,
filename,
backbone_id,
use_feature_extractor=True,
use_spatial_lstm=False,
spatial_train=False,
use_bottleneck=use_bottleneck)
generate_iad_files(lfd_params, model, "train", backbone=backbone_id)
generate_iad_files(lfd_params,
model,
"evaluation",
backbone=backbone_id)
if train_p:
print("Training Policy")
model = PolicyLearner(lfd_params,
filename,
backbone_id,
use_feature_extractor=False,
use_spatial_lstm=True,
spatial_train=True,
policy_train=True,
use_bottleneck=use_bottleneck)
# Train policy learner
model = train(lfd_params,
model,
verbose=False,
input_dtype="iad",
ablation=False)
model.save_model()
if eval_p:
model = PolicyLearner(lfd_params,
filename,
backbone_id,
use_feature_extractor=False,
use_spatial_lstm=True,
policy_train=False,
use_bottleneck=use_bottleneck)
df = evaluate_action_trace(lfd_params,
model,
verbose=True,
input_dtype="iad")
out_filename = os.path.join(lfd_params.args.output_dir,
"output_" + save_id + "_action_trace.csv")
df.to_csv(out_filename)
print("Output placed in: " + out_filename)
df = evaluate_action_trace(lfd_params,
model,
input_dtype="iad",
ablation=True,
verbose=True,
mode="train")
out_filename = os.path.join(
lfd_params.args.output_dir,
"output_" + save_id + "_action_trace_ablation_train.csv")
df.to_csv(out_filename)
print("Output placed in: " + out_filename)
df = evaluate_action_trace(lfd_params,
model,
input_dtype="iad",
ablation=True,
verbose=True,
mode="evaluation")
out_filename = os.path.join(
lfd_params.args.output_dir,
"output_" + save_id + "_action_trace_ablation_eval.csv")
df.to_csv(out_filename)
print("Output placed in: " + out_filename)
