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predict.py
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predict.py
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#!/usr/bin/env python3
import argparse
import os
import pickle
import numpy as np
import torch
import torch.nn.functional as F
import tqdm
import wandb
from data import CAAMLRawFrameDataset, SpacedSequentialSampler, SessionBatchSampler
from models import PASEEncodedModel, LSTMHead, CNNHead, GRUHead
from utils import get_channel_progression
PACE_EMB_DIM = 256
MEL_DIM = 40
PROSODY_DIM = 3
def get_args():
parser = argparse.ArgumentParser()
###############################
# GENERAL PARAMETERS
parser.add_argument('--device', type=str, default='cuda:0' if torch.cuda.is_available() else 'cpu',
help="Device to run model on")
parser.add_argument('--out', default='./predictions.raw.pkl')
parser.add_argument('--split_id')
parser.add_argument('--run')
parser.add_argument('--ckpt_type', default='loss', choices=['err', 'loss', 'f1', 'mAP'])
parser.add_argument('--mb', type=int, default=8, help='mini-batch size')
###############################
# DATA PARAMETERS
data_group = parser.add_argument_group('DATA PARAMETERS')
data_group.add_argument('--datapath', type=str, default='/research/hutchinson/workspace/slymane/pase/caaml_norm/')
data_group.add_argument('--split', type=str, default='/research/hutchinson/data/2019_ml_teaching/split.csv')
data_group.add_argument('--warmup', type=int, default=0)
data_group.add_argument('--nworkers', type=int, default=0)
args = parser.parse_args()
args.warmup *= 2
return args
def main():
# Setup
args = get_args()
ckpt_dir = os.path.join('checkpoints', args.run.split('/')[-1])
ckpt = f'model_best_{args.ckpt_type}.pkl'
run = wandb.Api().run(args.run)
config = run.config
if not os.path.isdir(ckpt_dir):
os.mkdir(ckpt_dir)
ckpt_path = os.path.join(ckpt_dir, ckpt)
if not os.path.exists(ckpt_path):
wandb.restore(ckpt, run_path=args.run, root=ckpt_dir)
input_dimension = 0
precomputed_dim = 0
if 'pase' in config['features']:
input_dimension += PACE_EMB_DIM
if 'mels' in config['features']:
input_dimension += MEL_DIM
precomputed_dim += MEL_DIM
if 'prosody' in config['features']:
input_dimension += PROSODY_DIM
precomputed_dim += PROSODY_DIM
# Model
if config['head'] == 'mlp':
cls_head = CNNHead(input_dimension, config['classes'], 1,
[config['hidden_size']] * (config['hidden_layers'] - 1),
config['smooth'], config['context_size'], 1, config['norm'], config['drop_hid'])
elif config['head'] == 'dtcnn':
cls_head = CNNHead(input_dimension, config['classes'], config['dilation_factor'],
get_channel_progression(config['hidden_size'], config['hidden_layers'], update_rules=[1, 2]),
config['smooth'], config['context_size'], config['context_size'], config['norm'], config['drop_hid'])
elif config['head'] == 'lstm':
cls_head = LSTMHead(input_dimension, config['classes'], config['hidden_size'], config['hidden_layers'],
config['smooth'], False, config['drop_hid'])
elif config['head'] == 'bilstm':
cls_head = LSTMHead(input_dimension, config['classes'], config['hidden_size']//2, config['hidden_layers'],
config['smooth'], True, config['drop_hid'])
elif config['head'] == 'gru':
cls_head = GRUHead(input_dimension, config['classes'], config['hidden_size'], config['hidden_layers'],
config['smooth'], False, config['drop_hid'])
elif config['head'] == 'bigru':
cls_head = GRUHead(input_dimension, config['classes'], config['hidden_size']//2, config['hidden_layers'],
config['smooth'], True, config['drop_hid'])
model = PASEEncodedModel(cls_head, config['cfg'], config['ckpt'], drop_inp=config['drop_inp'],
drop_emb=config['drop_emb'], freeze_bn=config['freeze_bn'], tune=False).to(args.device)
model.load_state_dict(torch.load(ckpt_path))
model.eval()
if args.device == 'cpu':
model.encoder.rnn.layers[0].use_cuda = False
# Data
with torch.no_grad():
x = torch.rand(1, 1, 160*config['seqlen']).to(args.device) if 'pase' in config['features'] else None
p = torch.rand(1, precomputed_dim, config['seqlen']).to(args.device)
spacing = model(x, precomputed=p).detach().cpu().size(2)
data = CAAMLRawFrameDataset(args.split_id, args.datapath, classes=config['classes'],
seq_len=config['seqlen']+args.warmup, split_csv=args.split,
return_session=True, spacing=spacing, features=config['features'])
sampler = SpacedSequentialSampler(data, spacing=spacing)
batch_sampler = SessionBatchSampler(data, sampler, args.mb, False)
loader = torch.utils.data.DataLoader(data, batch_sampler=batch_sampler, pin_memory=True, num_workers=args.nworkers)
# Evaluate
probs_lst, labs_lst, sessions = [], [], []
logits_list, labs_list = [], []
_, _, _, cur_session = data[0]
with torch.no_grad():
for idx, (sigs, prec, labs, session) in enumerate(tqdm.tqdm(loader)):
sigs = sigs.to(args.device).float() if sigs.nelement() != 0 else None
prec = prec.to(args.device).float() if prec.nelement() != 0 else None
labs = labs.to(args.device)
# Forward
logits = model(sigs, precomputed=prec)
# Get prediction window for logits
offset = (logits.size(2) - spacing) // 2
logits = logits.narrow(2, offset, spacing)
# Get prediction window for labels
offset = (labs.size(1) - spacing) // 2
labs = labs.narrow(1, offset, spacing)
# Reshape into one long sequence
logits = logits.transpose(0, 1).reshape(config['classes'], -1) # N,C,S -> C,N,S -> C,N*S
labs = labs.reshape(-1) # N,S -> N*S
ignore = (labs == -1).squeeze()
labs = labs[~ignore]
logits = logits[:, ~ignore]
if labs.size(0) == 0:
continue
if session[0] == cur_session:
logits_list.append(logits.squeeze().detach().cpu())
labs_list.append(labs.squeeze().detach().cpu())
else:
logits_list = torch.cat(logits_list, dim=1)
labs_list = torch.cat(labs_list, dim=0)
sessions.append(cur_session)
# Calculate metrics
probs = F.softmax(logits_list, dim=0).cpu().numpy()
tqdm.tqdm.write(f'Finished {cur_session}')
# Store metrics
probs_lst.append(probs)
labs_lst.append(labs_list.numpy())
logits_list = [logits.squeeze().detach().cpu()]
labs_list = [labs.squeeze().detach().cpu()]
cur_session = session[0]
logits_list = torch.cat(logits_list, dim=1)
labs_list = torch.cat(labs_list, dim=0)
sessions.append(cur_session)
# Calculate metrics
probs = F.softmax(logits_list, dim=0).cpu().numpy()
tqdm.tqdm.write(f'Finished {cur_session}')
# Store metrics
probs_lst.append(probs)
labs_lst.append(labs_list.numpy())
logits_list = [logits.squeeze().detach().cpu()]
labs_list = [labs.squeeze().detach().cpu()]
cur_session = session[0]
# Collect all predictions
probs = np.concatenate(probs_lst, axis=1).transpose(1, 0)
# Save the outputted sequences
res = {session: {
'probs': probs_lst[i], # ([CxS] ndarray) Raw Probabilites
'labs': labs_lst[i] # ([S] ndarray) Ground Truth Labels
} for i, session in enumerate(sessions)}
with open(args.out, 'wb') as f:
pickle.dump(res, f)
if __name__ == "__main__":
main()